animated videos to Figure 9, case A-E

SLAC. An improved upper limit for photoproduction of J/PSI near threshold.

Medicaid and the Children’s Health Insurance Program (CHIP) provide medical coverage to millions of Americans, and one of the main criteria to determine eligibility is income. In states with expanded coverage, the minimum eligibility threshold for adults is 138 percent of the federal poverty level (FPL).

The impact of the Affordable Care Act The Affordable Care Act (ACA) created the opportunity for states to expand Medicaid to cover nearly all low-income adults. Most states chose to extend coverage, meaning adults are eligible if their household income is at or below 138 percent of the FPL. Before the ACA, applicants had to fit into one of several categories in order to be eligible. Each group had its own income rules, and they all differed from state to state. Most low-income adults without children were not eligible.

Medicaid income rules simplified The ACA established a new methodology to determine income eligibility that helped to align rules that previously varied nationwide. In general, an individual’s eligibility is now determined by their Modified Adjusted Gross Income (MAGI) and where it falls in relation to the FPL. In 2021, the FPL for a one-person household was set at 12,880 U.S. dollars, which was the minimum income a person had to earn to qualify for Medicaid. In expansion states, an individual would still be eligible if they earned up to 138 percent of that figure.

Public contracts and concessions awarded,volume of contracts: Germany, quarters, customer level, upper threshold, order type, sustainability criteria, specifications

Dataset describes jurisdictions according to 8 measures which will be used to gauge RHNA performance. Each measure has been categorized into two groups, for most the top 25 cities in a category versus the remainder.

The core metrics mapping directly to CA HCD objective metrics include: Percent of RHNA as lower income units for jurisdictions with the highest housing costs. Measure: Housing costs Share of homeowners living in units valued above $750,000. Threshold grouping: upper third vs rest Percent of RHNA as lower income units for jurisdictions with highest percent of single-family homes. Measure: Percent single family Threshold grouping: upper third vs rest Total unit allocations for jurisdictions with the most jobs. Measure: Total Jobs Threshold grouping: upper third vs rest Allocations of lower income units for jurisdictions with the most low-wage jobs. Measure: Low wage jobs Threshold grouping: upper third vs rest Percent of RHNA as lower income units for jurisdictions with the highest ratio of low-wage jobs to housing units affordable to low-wage workers. Measure: jobs-housing fit Threshold grouping: upper third vs rest Percent of RHNA as lower income units for low-income jurisdictions. Measure: median household income. Low income threshold: bottom third Percent of RHNA as lower income units for high-income jurisdiction. Measure: median household income. High income threshold: upper third Percent of RHNA as lower income units for jurisdictions with the most households in High Resource/Highest Resource tracts. Measure: Share households in HRAs Threshold grouping: upper third vs rest

Model parameters and range of values for sensitivity analysis: Utilities scores, costs, and probabilities.

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are geography-specific; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% income threshold of Nova Scotian tax filers. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

Repository for the raw data for individual observers reported in a paper titled "Luminosity thresholds of colored surfaces are determined by their upper-limit luminances empirically internalized in the visual system".

Categorical classification of groundwater age based on concentrations of tritium (3H) in groundwater can provide useful information for the assessment and understanding of groundwater resources. These data present a three-part groundwater age classification system for the continental United States based on tritium thresholds that vary in space and time: modern (recharged after 1952), if the measured value is larger than an upper threshold; premodern (recharged prior to 1953) if the measured value is smaller than a lower threshold; or mixed if the measured value is between the two thresholds. Inclusion of spatially-varying that vary geographically on the basis of the location of the sample rather than a single threshold accounts for the observed systematic variation in 3H deposition across the U.S. Inclusion of time-varying thresholds rather than a single threshold accounts for the date of sampling given the radioactive decay of 3H. The efficacy of the three-part classification ...

The threshold to enter Milward Brown's BrandZ China Top 100 ranking rose to 815 million U.S. dollars in 2020, indicating a growth of 20 percent since the previous year. In 2020, the total value of the brands listed in the BrandZ China Top 100 increased significantly, too.

Base-case results (written to 2 decimal places).

The data needed to generate figures 5-18 of the article Particle-in-cell simulations of parametric decay instabilities at the upper hybrid layer of fusion plasmas to determine their primary threshold. / Senstius, Mads Givskov; Nielsen, Stefan Kragh; Vann, R. G.; Hansen, Søren Kjer.In: Plasma Physics and Controlled Fusion, 2019. https://doi.org/10.1088/1361-6587/ab49ca

As sea levels rise, wetlands can adapt to changing conditions through vertical development (that is, soil surface elevation gains via biophysical feedbacks) and horizontal migration into upslope areas. Elevation-based models of wetland transformation from sea-level rise are often hampered from a variety of sources of uncertainty, including contemporary elevation and water levels and future water levels from sea-level rise. This data release includes geospatial data products that utilize Monte Carlo simulations to address these sources of uncertainty and highlight potential wetland transformations under various relative sea-level rise scenarios along Texas' middle and upper coast. This data release includes the current extent of coastal wetlands and decadal maps of coastal wetland transformation from 2030–2100 for three relative sea-level rise scenarios — Intermediate-low, Intermediate, and Intermediate-high — from an interagency sea-level rise report published in 2022 (Sweet and others, 2022). Datasets in this release include the following classes: 1) Upslope (that is, areas that are above the National Oceanographic and Atmospheric Administration’s (NOAA) moderate high tide flooding threshold; Sweet and others, 2022); 2) Irregularly oceanic-flooded wetlands (that is, wetlands that are flooded by oceanic water less frequently than daily [that is, below the NOAA moderate high tide flooding threshold and above the mean high water datum]); 3) Regularly oceanic-flooded wetlands (that is, wetlands that are flooded by oceanic water daily [that is, below the mean high water datum and above the mean lower low water datum] and generally fell in the upper two-thirds of this wetland zone based on elevation); 4) Converting to open water (that is, wetlands that are flooded by oceanic water daily [that is, below the mean high water datum and above the mean lower low water datum] and generally fell in the lower third of this wetland zone based on elevation; 5) Converted to open water (that is, areas where the decade of initiation for coastal wetland drowning has passed and have been in the “converting to open water” class for at least 50 years); 6) Low-lying, developed (that is, areas that fall in elevation ranges for wetland classes [that is, regularly oceanic-flooded wetlands, regularly oceanic-flooded wetlands, and converting to open water], but are located within developed areas); 7) Low-lying, leveed (that is, areas that fall in elevation ranges for wetland classes [that is, regularly oceanic-flooded wetlands, regularly oceanic-flooded wetlands, and converting to open water], but are located within levees); and 8) Low-lying, developed and leveed (that is, areas that fall in elevation ranges for wetland classes [that is, regularly oceanic-flooded wetlands, regularly oceanic-flooded wetlands, and converting to open water], but are located within levees or developed areas). Incorporating soil elevation change processes into wetland transformation models can be complex because soil elevation change processes can vary over space and time. In the past decade, there has been growing consensus regarding critical sea-level rise rate thresholds for the onset of wetland drowning (Morris and others, 2016, Horton and others, 2018, Saintilan and others, 2020, Törnqvist and others, 2020, Buffington and others, 2021, Saintilan and others, 2022, Saintilan and others, 2023). Here, our products utilize information from an analysis of when and where sea-level rise rates could cross thresholds for initiating coastal wetland drowning across the conterminous United States. The thresholds included are 4 mm/year, 7 mm/year, and 10 mm/year (see discussion in Osland and others, 2024). For this approach, we determined the relative sea-level rise rate by decade for watersheds within the study area. The decade that these rates exceeded one of these thresholds (that is, 4 mm/year, 7 mm/year, and 10 mm/year) marked the initiation of coastal wetland drowning. In other words, the 4 mm/year threshold indicates that wetland drowning would be initiated when the decadal sea-level rise rate exceeds 4 mm/year. Because wetland conversion to open water is not immediate once crossing this threshold, we left areas in that fell within “Converting to open water” class until 50 years after the threshold was surpassed. For example, if the decade the 4 mm/year threshold was crossed was 2020, then no wetlands would be moved to the “Converted to open water” class until 2070. For the 2070 map, areas in the “Converted to open water” class would include areas that were in the “Converting to open water” class in current wetland map (i.e., 50 years after the threshold was crossed). Similarly, for this threshold, areas in the “Converted to open water” class would be those that were “Converting to open water” class on and before 2030 (that is, 2030 and the current wetland map). For more information on the decades for when watershed-level drowning thresholds were passed, see the shapefile titled “Wetland_Drown_Years.shp.” Natural resource managers can utilize this information to explore potential scenarios related to the ability of current wetlands to adapt to sea-level rise via in situ vertical adjustment. For our study area, there was a high level of redundancy when the watershed-level drowning thresholds were passed, especially between maps for 4 mm/yr and 7 mm/yr. If users are interested in seeing where/when redundancy may occur, see the shapefile titled “Wetland_Drown_Years.shp.” This metadata file is for the datasets for the wetland current condition and wetland transformation by decade for sea-level rise scenario and coastal wetland drowning threshold.

Estimates of excess deaths can provide information about the burden of mortality potentially related to the COVID-19 pandemic, including deaths that are directly or indirectly attributed to COVID-19. Excess deaths are typically defined as the difference between the observed numbers of deaths in specific time periods and expected numbers of deaths in the same time periods. This visualization provides weekly estimates of excess deaths by the jurisdiction in which the death occurred. Weekly counts of deaths are compared with historical trends to determine whether the number of deaths is significantly higher than expected.Counts of deaths from all causes of death, including COVID-19, are presented. As some deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not diagnosed or not mentioned on the death certificate), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted. Additionally, deaths from all causes excluding COVID-19 were also estimated. Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are reported as due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to the COVID-19 pandemic (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems).Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). A range of values for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound of the 95% prediction interval), by week and jurisdiction.Provisional death counts are weighted to account for incomplete data. However, data for the most recent week(s) are still likely to be incomplete. Weights are based on completeness of provisional data in prior years, but the timeliness of data may have changed in 2020 relative to prior years, so the resulting weighted estimates may be too high in some jurisdictions and too low in others. As more information about the accuracy of the weighted estimates is obtained, further refinements to the weights may be made, which will impact the estimates. Any changes to the methods or weighting algorithm will be noted in the Technical Notes when they occur. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.This visualization includes several different estimates:Number of excess deaths: A range of estimates for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound threshold), by week and jurisdiction. Negative values, where the observed count fell below the threshold, were set to zero.Percent excess: The percent excess was defined as the number of excess deaths divided by the threshold.Total number of excess deaths: The total number of excess deaths in each jurisdiction was calculated by summing the excess deaths in each week, from February 1, 2020 to present. Similarly, the total number of excess deaths for the US overall was computed as a sum of jurisdiction-specific numbers of excess deaths (with negative values set to zero), and not directly estimated using the Farrington surveillance algorithms.Select a dashboard from the menu, then click on “Update Dashboard” to navigate through the different graphics.The first dashboard shows the weekly predicted counts of deaths from all causes, and the threshold for the expected number of deaths. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.The second dashboard shows the weekly predicted counts of deaths from all causes and the weekly count of deaths from all causes excluding COVID-19. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.The th

DESY-PETRA, PLUTO COLLABORATION.

The statistic displays the minimum threshold of wealth owned by the population in selected European countries in order to be selected into the richest one percent as of 2014. For instance, in Luxembourg, the top richest one percent of the population started at 2.7 million euros in 2014. In comparison, in Spain the cut-off point was at 227.7 thousand euros in the same year.

This data release consists of rasters representing the probability of exceeding multiple thresholds of nitrate in shallow groundwater for the Mid-Atlantic region of the United States. Each raster represents the results from multiple spatial-probability models that were developed using U.S. Geological Survey water-quality data in conjunction with geographic data such as land cover, geology, and soils. There are 10 rasters of predictions (PRED_1 through PRED_10) and 10 rasters of the highest possible probability (UPPER_1 through UPPER_10). The highest probability is the upper limit of the prediction confidence interval calculated as part of each model. Geospatial data provided are in the North American Albers equal-area conic projection at 1500-meter resolution.
Please refer to the Supplemental Information and Process Steps elements of this metadata record for specific geographic data layers and statistical processes.

Extraction of ions and organic acids, with exception of pinic and pinonic acids, was done as follows: A quarter of the filter was broken into small pieces and placed in polyethylene tubes, using steel tweezers. Slotted filters were ultrasonically extracted for 30 min with 7 mL of ultra-pure water, while backup filters with 15 mL of ultra-pure water. Extracts were filtrated through a 0.45 μm PTFE filter to remove residues before the analysis. Determination and quantification of major ions and organic acids were performed using an IC-MS system.For the determination of pinic and pinonic acids a HPLC-MS/MS system was used.Values below the detection limit of the measurements are indicated (<0.xxxxx). Ions and organic acids were quantified in 16 aerosol samples collected during the Arctic campaign at Gruvebadet Laboratory in 2014. Aerosol samples were collected in quartz fiber filters. They were spiked with isotopically labelled standard solutions (2 - 3 µg/ml) prior extraction. Ion and organic acid content was measured using IC-MS and HPLC-MS.

Annual-resolved sulfur and non-sea-salt sulfur concentrations and inferred volcanic sulfate depoistion rates from the six ice cores NEEM-2011-S1 (Sigl et al., 2013), NGRIP1 (Plummer et al., 2012), NGRIP2 (McConnell et al., 2018), TUNU2013 (Sigl et al., 2015) and B19 between 699 and 1001 CE and annual-resolved non-sea-salt sulfur concentrations from a four ice-core stack (NEEM-2011-S1, NGRIP1, TUNU2013, B19) between 1731 and 1996 CE including volcanic samples and with volcanic samples replaced by a 11-year running median. Volcanic event detection is based a 91-year running median (RM) was used on the annually averaged nssS records on periods unaffected by strong changes in volcanic background emissions to estimate the natural background sulfate levels; a Median of Absolute Deviation (MAD), calculated from the RM, was used for volcanic peak detection over the background period. Between 700 and 1000 CE sulfur peaks were considered volcanic if they passed an upper threshold (K=3, estimated as RM plus 3* MAD). The duration of the event was determined when it passed the lower threshold (K=1, estimated as the RM plus 1 * MAD). These upper and lower thresholds were selected by validation on well-known historic eruptions; volcanic peaks were then removed to calculate the non-volcanic background (S RRMi). To further calculate the amount of sulfate deposited, S RRMi was subtracted from the average annual nss-S and then multiplied by the accumulation rate of the drill site; finally, volcanic flux was calculated for each event by summing the sulfate deposited across the total duration of the event. All ice cores are presented on the NS1-2011 chronology (Sigl et al., 2015) except for NGRIP2 which was on the NGRIP2-DRI chronology (McConnell et al., 2018).

The upper ocean pycnocline (UOP) monthly climatology is based on the ISAS20 ARGO dataset containing Argo and Deep-Argo temperature and salinity profiles on the period 2002-2020. Regardless of the season, the UOP is defined as the shallowest significant stratification peak captured by the method described in Sérazin et al. (2022), whose detection threshold is proportional to the standard deviation of the stratification profile. The three main characteristics of the UOP are provided -- intensity, depth and thickness -- along with hydrographic variables at the upper and lower edges of the pycnocline, the Turner angle and density ratio at the depth of the UOP. A stratification index (SI) that evaluates the amount of buoyancy required to destratify the upper ocean down to a certain depth, is also included. When evaluated at the bottom of the UOP, this gives the upper ocean stratification index (UOSI) as discussed in Sérazin et al. (2022). Three mixed layer depth variables are also included in this dataset, including the one using the classic density threshold of 0.03 kg.m-3, along with the minimum of these MLD variables. Several statistics of the UOP characteristics and the associated quantities are available in 2°×2° bins for each month of the year, whose results were smoothed using a diffusive gaussian filter with a 500 km scale. UOP characteristics are also available for each profile, with all the profiles sorted in one file per month.