The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3rd and 97th and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95th percentile. Extrapolating beyond the 97th percentile is not recommended and leads to compressed z-score values. This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95th percentile using an extended reference population. Data from youth at or above the 95th percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. The modelled distributions above the 95th percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

A sieve analysis (or gradation test) is a practice or procedure commonly used in civil engineering to assess the particle size distribution (also called gradation) of a granular material.

As part of the Sediment Analysis and Geo-App (SAGA) a series of data processing web services are available to assist in computing sediment statistics based on results of sieve analysis. The Calculate Percentile service returns one of the following percentiles: D5, D10, D16, D35, D50, D84, D90, D95.

Percentiles can also be computed for classification sub-groups: Overall (OVERALL), <62.5 um (DS_FINE), 62.5-250um (DS_MED), and > 250um (DS_COARSE)

Parameter #1: Input Sieve Size, Percent Passing, Sieve Units.

• Semi-colon separated. ex: 75000, 100, um; 50000, 100, um; 37500, 100, um; 25000,100,um; 19000,100,um
• A minimum of 4 sieve sizes must be used. Units supported: um, mm, inches, #, Mesh, phi
• All sieve sizes must be numeric

Parameter #2: Percentile

• Options: D5, D10, D16, D35, D50, D84, D90, D95

Parameter #3: Subgroup

• Options: OVERALL, DS_COARSE, DS_MED, DS_FINE
• The statistics are computed for the overall sample and into Coarse, Medium, and Fine sub-classes
• Coarse (> 250 um) DS_COARSE
• Medium (62.5 – 250 um) DS_MED
• Fine (< 62.5 um) DS_FINE
• OVERALL (all records)

Parameter #4: Outunits

• Options: phi, m, um

This service is part of the Sediment Analysis and Geo-App (SAGA) Toolkit.

Looking for a comprehensive user interface to run this tool?
Go to SAGA Online to view this geoprocessing service with data already stored in the SAGA database.

This service can be used independently of the SAGA application and user interface, or the tool can be directly accessed through http://navigation.usace.army.mil/SEM/Analysis/GSD

Official GMAT Focus Edition section scores (Quantitative, Verbal, and Data Insights) to percentile conversion tables for scores ranging from 60 to 90

The Risk-Targeted Maximum Considered Earthquake (MCER) spectral response accelerations of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each spectral period and site class, the MCER spectral response acceleration (S_aM) is calculated via the following equation: S_aM = min[ S_aRT , max( S_a84th , S_aDLL ) ] where S_aRT = risk-targeted spectral acceleration S_a84th = 84th-percentile spectral acceleration S_aDLL = deterministic lower limit spectral acceleration

The poorest five percent of the population in Brazil received a monthly income of merely *** reals in 2024, with their jobs as their only source of income. By contrast, the average income of workers who fall within the 40 percent to 50 percent percentile, and from 50 percent to 60 percent are **** and **** Brazilian reals, respectively.

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

In the analysis ensembles of 7-year SEM simulations were performed for 100 assessments for different scenarios and substances. For each assessment, an ensemble of 365 simulations was performed with varying dates of substance application, covering every day of the year. For each simulation the following postprocessing was performed on the daily substance emission (g.m-2.d-1) from the greenhouse and its 10-day moving average:

* Determine the of the annual maximum for each of the 7 simulation years.

* Calculate the 50th and 90th percentiles over the 7 annual maxima (referred to as PEC50 and the PEC90, respectively).

This results in four PEC values (PEC50--daily, PEC90--daily, PEC50--10-day-average, PEC90--10-day-average) for each of the 100x365 simulations. Next, for each of the 100 assessments, the results of the 365 simulations were processed as follows:

* Calculate the 90th percentile over 365 values for the four PEC values--this is referred to as the "true" 90th percentile.

* Remove 5 simulations for application dates 7-Feb, 21-Apr, 3-Jul, 14-Sep and 26-Nov, resulting in a set of 360 simulations. This is done because 360 has more divisors than 365.

Subsequently, processing was performed on subsamples of different sizes N, taken from the 360 simulations. The following subsample sizes were considered: 12, 15, 18, 20, 24, 30, 36, 40, 45, and 60. For each subsample size N, M_N = 360/N sets of subsamples were taken with application date evenly spread over the year. For example, for N=12, M_12=30 sets of application dates were selected, with each set one day offset to the next. This results in 10 sets of subsamples of varying size. For each set N, the following processing was performed:

* For each M_N values for the four PECs, calculate the relative difference compared to the true 90th percentile (based on the full 365 set of simulations; see above) as follows: RD = (PEC_est-PEC_365)/PEC_365.

* Calculate the 10th percentile over the M_N relative differences for each of the four PECs; this is referred to as the 90th percentile underestimation

* For each M_N values for the four PECs, calculate the multiplication factor relative to the true 90th percentile as follows: MF = PEC_est/PEC_365.

* Calculate the 90th percentile over the M_N multiplication factors for each of the four PECs.

This results in 4000 values for the relative difference and multiplication factor for each combination of assessment (100), subsample size N (10), and PEC quantity (4). The relative underestimations form the data underlying Figure 13.3 in Braakhekke et al. (2024). The multiplication factors for N=12 form the data underlying table 13.1 in Braakhekke et al. (2024).

In 2024, the average annual full-time earnings for the top ten percent of earners in the United Kingdom was 72,150 British pounds, compared with 22,763 for the bottom ten percent of earners. As of this year, the average annual earnings for all full-time employees was 37,430 pounds, up from 34,963 pounds in the previous year. Strong wage growth continues in 2025 As of February 2025, wages in the UK were growing by approximately 5.9 percent compared with the previous year, with this falling to 5.6 percent if bonus pay is included. When adjusted for inflation, regular pay without bonuses grew by 2.1 percent, with overall pay including bonus pay rising by 1.9 percent. While UK wages have now outpaced inflation for almost two years, there was a long period between 2021 and 2023 when high inflation in the UK was rising faster than wages, one of the leading reasons behind a severe cost of living crisis at the time. UK's gender pay gap falls in 2024 For several years, the difference between average hourly earnings for men and women has been falling, with the UK's gender pay gap dropping to 13.1 percent in 2024, down from 27.5 percent in 1997. When examined by specific industry sectors, however, the discrepancy between male and female earnings can be much starker. In the financial services sector, for example, the gender pay gap was almost 30 percent, with professional, scientific and technical professions also having a relatively high gender pay gap rate of 20 percent.

This paper demonstrates a quadratic polynomial proxy, to calculate quantile variance estimates of continuous distributions in the percentile scale, for the quantile estimating function (Koencker & Bassett). The empirical variance distribution function created by the backtransformation of the percentile scale calculations to the original measurement scale for the proxy estimator, has a step function morphology, in common with the empirical cumulative distribution function.

The use of the empirical variance distribution approach to calculating the variance of the quantile regression residuals becomes a new method for calculating confidence intervals for fitted quantile regression coefficients.

In March 2025, the top one percent of earners in the United Kingdom received an average pay of over 16,000 British pounds per month, compared with the bottom ten percent of earners who earned around 800 pounds a month.

The Risk-Targeted Maximum Considered Earthquake (MCER) spectral response accelerations of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each spectral period and site class, the MCER spectral response acceleration (S_aM) is calculated via the following equation: S_aM = min[ S_aRT , max( S_a84th , S_aDLL ) ] where S_aRT = risk-targeted spectral acceleration S_a84th = 84th-percentile spectral acceleration S_aDLL = deterministic lower limit spectral acceleration

In Mexico, as of 2022, the bottom 50 percent, which represents the population whose income lied below the median, earned on average 2,076 euros at purchasing power parity (PPP) before income taxes. Meanwhile, the top ten percent had an average earning of 111,484 euros, 53 times over than the average earning of the bottom half. Further, the bottom 50 percent accounted for -0.3 percent of the overall national wealth in Mexico, that is, they have on average more debts than assets.

The average pre-tax income of the top ten percent earners in Spain was over 95,500 euros at purchasing power parity (PPP) as of 2022, almost nine times more than the average income of the bottom half earners. Looking at the distribution of national income in Spain, the earnings of the least affluent half of the population equated to 21 percent of the total country income in 2022, 0.1 percentage points less than one decade earlier. Moreover, the top one percent of earners in Spain accounted for over ten percent of the overall national income.

The Risk-Targeted Maximum Considered Earthquake (MCER) spectral response accelerations of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each spectral period and site class, the MCER spectral response acceleration (S_aM) is calculated via the following equation: S_aM = min[ S_aRT , max( S_a84th , S_aDLL ) ] where S_aRT = risk-targeted spectral acceleration S_a84th = 84th-percentile spectral acceleration S_aDLL = deterministic lower limit spectral acceleration

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

The average personal wealth of the bottom 50 percent in Mexico was valued at -200 euros. That is, on average, people from this group had more debts than assets. On the other hand, the richest one percent held an average wealth of 2.91 million euros in this Latin American country. Similarly, Chilean's average personal wealth of the one percent reached 2.67 million euros that same year.

In 2023 in France, the average net monthly full-time equivalent salary was ***** euros. That year, ** percent of the poorest French employees earned less than ***** euros per month. On the other hand, ** percent of the richest French employees received more than ***** euros. The French people who were part of the richest one percent of the working population earned a salary over ***** euros per month.

Spatial predictions of plant available water capacity (PAWC), drained upper limit ((DUL) and crop lower limit (CLL) for grain-growing regions of NSW and Queensland, Australia, from Littleboy and Padarian Campusano pedotransfer functions and Soil and Landscape Grid of Australia datasets.

PAWC is the amount of water a soil can hold against gravity (i.e. water which does not freely drain) that is available to plants through their roots. This soil property is very important in dryland cropping areas which rely on rainfall. The maximum amount of water which can be held by a soil against gravity is called the DUL. The water that remains in a soil after plants have extracted all that is available to them is called the crop lower limit (CLL). PAWC is calculated as DUL minus CLL.

Digital soil mapping (DSM) allows the spatial prediction of soil properties across large areas using modelling techniques which combine point data measured in the field and continuous datasets related to soil forming processes such as climate, topography, land cover, existing soil mapping and lithology. Pedotransfer functions (PTFs) are equations which use the easier to measure soil attributes, e.g. sand, clay, bulk density, to model the harder to measure attributes like DUL and CLL. DSM techniques such as Latin Hypercube (LHC) sampling can be used to incorporate the uncertainties associated with the input datasets in the modelling, and to produce estimates of model output precision and reliability.

This data collection consists of spatially predicted PAWC, DUL and CLL for the grain-growing regions of New South Wales and Queensland, Australia, as defined by the boundary of the Grains Research and Development Corporation's Northern Region. PAWC was modelled using PTFs for DUL from Littleboy and CLL from Padarian Campusano, with LHC sampling to incorporate the uncertainties associated with the input datasets. The PAWC, DUL and CLL were modelled at the six Global Soil Map depths of 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. The top five depths have been aggregated to create a PAWC prediction for 0-100 cm.

Lineage: INPUT DATASETS 1. Soil attribute layers from the Soil and Landscape Grid of Australia (SLGA): clay (%), sand (%), bulk density (BD; g cm^-3), and organic carbon (OC; %). The estimated value (mean) and the confidence interval limits (5th and 95th percentiles) were used for all six Global Soil Map depths (0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). https://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html 2. The Northern Region boundary from the Grains Research and Development Corporation (GRDC)

PEDOTRANSFER FUNCTIONS DUL equation from Littleboy (1997) and CLL equation from Padarian Campusano (2014), which used a subset of 806 soil profiles from the APSoil database that included field measurements of DUL and CLL: 1. DUL = 0.3486 - 0.0018*sand + 0.0039*clay + 0.0228*OC – 0.0738*BD 2. CLL = 0.6151*DUL – 0.02192 3. PAWC = DUL – CLL

METHODS These methods are available from Austin et al. (2019), see Related Links section.

The SLGA input datasets were clipped to the study area boundary and divided into tiles of 200 x 200 grid cells prior to parallel processing in a supercomputer environment. Except for the LHC sampling and correlation matrices, all code was written in Python. Layer thickness for each of the six soil depths was calculated in mm from the depth layer upper and lower bounds (e.g. 5 to 15 cm).

A correlation matrix was generated in the R package for the SLGA clay, sand, BD and CEC input datasets for each of the six depths, with correlation values derived using data for the whole study area for each of the inputs.

Each of the six soil depth layers was modelled separately. For every grid cell in each depth layer, the following steps were used to calculate DUL, CLL and PAWC: 1. Standard deviation (SD) was calculated from the 5th and 95th percentiles for the clay, sand, CEC and BD input variables using the following equation from Malone et al. (2011): SDi = (UPLi – LPLi) / 2 x z where SDi is the variance associated with prediction i, UPL and LPL are the upper and lower prediction limits, and z is the z-value used for a confidence interval (CI) which in this case is 90% and z = 1.64. A normal distribution is assumed

1. LHC sampling with a correlation matrix (from the R pse library; Chalom and Prado, 2014), using means, SDs and a correlation matrix as inputs, produced fifty realisations of each input variable. Fifty realisations were chosen following the work of Malone et al. (2015) who found that there was little difference in outcome when using more than 50 samples

2. 50 DUL and CLL values were calculated from the 50 input variable realisations using the DUL and CLL equations from Padarian Campusano (2014)

3. 50 PAWC values were calculated from the DUL and CLL values, constrained by the depth layer thickness, with units of mm

4. From the 50 DUL, CLL and PAWC values for each grid cell, the mean, median, 5th and 95th percentiles, and SD were calculated and written to file as geotiffs

The tiled outputs were merged to form single rasters of the study area for DUL, CLL and PAWC at each of the six depths. Additionally, the 0-5, 5-15, 15-30, 30-60 and 60-100 cm soil depth layers were used to calculate 0-1 m versions of DUL, CLL and PAWC. The mean, median, 5th and 95th percentile values were summed to produce the 0-1 m DUL, CLL or PAWC prediction for each grid cell. This aggregation of depths assumes high correlation between layers – for example, the 95th percentile for the 0 – 1 m layer is the sum of the 95th percentiles for each contributing layer. If the layers were uncorrelated, the 95th percentile would end up closer to the mean. The SD for each of the 0-1 m DUL, CLL and PAWC layers was calculated from the summed 5th and 95th percentiles, as per the equation from Malone et al. (2011).