Traffic analytics, rankings, and competitive metrics for gemini.com as of October 2025

Artificial Intelligence (AI) has emerged as a critical challenge to the authenticity of journalistic content, raising concerns over the ease with which artificially generated articles can mimic human-written news. This study focuses on using machine learning to identify distinguishing features, or “stylistic fingerprints,” of AI-generated and human-authored journalism. By analyzing these unique characteristics, we aim to classify news pieces with high accuracy, enhancing our ability to verify the authenticity of digital news.To conduct this study, we gathered a balanced dataset of 150 original journalistic articles and their 150 AI-generated counterparts, sourced from popular news websites. A variety of lexical, syntactic, and readability features were extracted from each article to serve as input data for training machine learning models. Five classifiers were then trained to evaluate how accurately they could distinguish between authentic and artificial articles, with each model learning specific patterns and variations in writing style.In addition to model training, BERTopic, a topic modeling technique, was applied to extract salient keywords from the journalistic articles. These keywords were used to prompt Google’s Gemini, an AI text generation model, to create artificial articles on the same topics as the original human-written pieces. This ensured a high level of relevance between authentic and AI-generated articles, which added complexity to the classification task.Among the five classifiers tested, the Random Forest model delivered the best performance, achieving an accuracy of 98.3% along with high precision (0.984), recall (0.983), and F1-score (0.983). Feature importance analyses were conducted using methods like Random Forest Feature Importance, Analysis of Variance (ANOVA), Mutual Information, and Recursive Feature Elimination. This analysis revealed that the top five discriminative features were sentence length range, paragraph length coefficient of variation, verb ratio, sentence complexity tags, and paragraph length range. These features appeared to encapsulate subtle but meaningful stylistic differences between human and AI-generated content.This research makes a significant contribution to combating disinformation by offering a robust method for authenticating journalistic content. By employing machine learning to identify subtle linguistic patterns, this study not only advances our understanding of AI in journalism but also enhances the tools available to ensure the credibility of news in the digital age.

Real-time performance metrics and analytics data for gemini-2.5-pro AI model by gemini

For generated by gemini :

original_text- The prompt the essay was written in response to.

prompt - The prompt provided to Gemini to rewritten the text

rewritten_text - The output from Gemini.

For generated by Gemma :

original_text- The prompt the essay was written in response to.

rewritten_prompt - The prompt provided to Gemma to rewritten the text

rewritten_text - The output from Gemma.

AbstractBackground: Birth control methods (BCMs) are often underutilized or misunderstood, especially among young individuals entering their reproductive years. With the growing reliance on artificial intelligence (AI) platforms for health-related information, this study evaluates the performance of GPT-4 (OpenAI, San Francisco, CA, USA) and Google Gemini (Google, Mountain View, CA, USA) in addressing commonly asked questions about BCMs.Methods: Thirty questions, derived from the American College of Obstetrics and Gynecologists website, were posed to both AI platforms. Questions spanned four categories: general contraception, specific contraceptive types, emergency contraception, and other topics. Responses were evaluated using a 5-point rubric assessing accuracy, completeness, and lack of false information. Overall scores were calculated by averaging the rubric scores. Statistical analysis, including the Wilcoxon signed-rank and Kruskal-Wallis tests, was performed to compare performance metrics.Results: ChatGPT and Google Gemini both provided high-quality responses, with overall scores averaging 4.38 ± 0.58 and 4.37 ± 0.52, respectively, categorized as "excellent." ChatGPT outperformed in reducing false information (4.70 ± 0.60 vs. 4.47 ± 0.73), while Google Gemini excelled in accuracy (4.53 ± 0.57 vs. 4.30 ± 0.70). Completeness scores were comparable. No significant differences were found in overall performance (p = 0.548), though Google Gemini showed a significant edge in accuracy (p = 0.035). Both platforms scored consistently across question categories, with no statistically significant differences noted.Conclusions: GPT-4 and Google Gemini provide reliable and accurate responses to BCM-related queries, with slight differences in strengths. These findings underscore the potential of AI tools in addressing public health information needs, particularly for young individuals seeking guidance on contraception. Further studies with larger datasets may elucidate nuanced differences between AI platforms.

Real-time performance metrics and analytics data for gemini-2.5-flash AI model by gemini

Non-traditional data signals from social media and employment platforms for GMNI stock analysis

Non-traditional data signals from social media and employment platforms for GEMI stock analysis

Non-traditional data signals from social media and employment platforms for 0174.HK stock analysis

Non-traditional data signals from social media and employment platforms for GMTX stock analysis

This dataset contains full-text Bloomberg News articles published between 2006 and 2013 (source: Hugging Face mirror of Bloomberg Financial News). A few articles were annotated via a weak-supervision workflow using Gemini labelling, and then those were used to fine-tune DeBERTAv3-base model. The fine-tuned model was then used to score all the remaining articles. The primary motivation for assembling this dataset was to explore whether aggregated news sentiment can help predict movements in the S&P 500 (event studies, signal construction and backtesting).

The size of the Sports Analytics Market market was valued at USD 2.87 Million in 2023 and is projected to reach USD 18.05 Million by 2032, with an expected CAGR of 30.04% during the forecast period. Recent developments include: October 2023, Texas A&M Athletics Sports Science announced that it has entered into an arrangement with Gemini Sports Analytics to offer the Aggies' staff Gemini’s AI software platform built-for sports that is projected to empower the Aggies to access prognostic analytics in addition to metrics to aid support student-athletes. The Gemini application authorizes stakeholders by offering predictive data analytics to the end users, cumulative interdisciplinary professionals' efficiency, and permitting high-level decision-makers to make game-changing choices faster., February 2023: Gemini Sports Analytics is an AI and Automated Machine learning tool, and SIS (Sports Info Solutions) announced a partnership to pre-integrate SIS data into the Gemini app. Along with the data integration, the two companies would leverage their complementary offerings and develop solutions for their current and future clients. Gemini's mission is to make it faster and easier for sports organizations across the globe to use predictive analytics in their decision-making processes around recruitment, player development, personnel, health and performance, and other management choices.. Key drivers for this market are: Rising Adoption of Big Data Analytics, AI and ML Technologies, Increase in Investments in the Newer Technologies. Potential restraints include: Lack of Awareness About the Benefits of Sports Analytics Solutions. Notable trends are: Football Sport is Expected to Hold Significant Market Share.

Oncology-specific annotation files for use with GEMINI - with (upcoming) special support through Galaxy's GEMINI annotate tool wrapper.

The global sports analytics market, valued at $2.87 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 30.04% from 2025 to 2033. This explosive growth is fueled by several key factors. The increasing adoption of data-driven decision-making by sports teams and organizations is a primary driver. Teams are leveraging advanced analytics to improve player performance, optimize strategies, enhance scouting processes, and personalize fan experiences. Technological advancements, particularly in areas like AI, machine learning, and big data processing, are further accelerating market expansion. The rising availability of affordable and sophisticated analytics tools is making these technologies accessible to a wider range of teams and leagues, regardless of size or budget. Furthermore, the growing popularity of fantasy sports and esports is generating significant demand for detailed sports data and analytical insights, contributing to market growth. The market's segmentation reveals a diverse landscape of players. Established technology giants like IBM, SAP, and Oracle provide comprehensive data analytics solutions, while specialized firms like Opta Sports and Stats LLC cater to the specific needs of the sports industry. The emergence of innovative startups further underscores the dynamic nature of this sector. Geographic expansion also plays a crucial role, with North America and Europe currently dominating the market. However, growing interest in sports analytics in Asia-Pacific and other emerging regions presents significant opportunities for future growth. While challenges such as data security concerns and the need for skilled analytics professionals exist, the overall market outlook remains exceptionally positive, driven by the continued convergence of sports and technology. Recent developments include: October 2023, Texas A&M Athletics Sports Science announced that it has entered into an arrangement with Gemini Sports Analytics to offer the Aggies' staff Gemini’s AI software platform built-for sports that is projected to empower the Aggies to access prognostic analytics in addition to metrics to aid support student-athletes. The Gemini application authorizes stakeholders by offering predictive data analytics to the end users, cumulative interdisciplinary professionals' efficiency, and permitting high-level decision-makers to make game-changing choices faster., February 2023: Gemini Sports Analytics is an AI and Automated Machine learning tool, and SIS (Sports Info Solutions) announced a partnership to pre-integrate SIS data into the Gemini app. Along with the data integration, the two companies would leverage their complementary offerings and develop solutions for their current and future clients. Gemini's mission is to make it faster and easier for sports organizations across the globe to use predictive analytics in their decision-making processes around recruitment, player development, personnel, health and performance, and other management choices.. Key drivers for this market are: Rising Adoption of Big Data Analytics, AI and ML Technologies, Increase in Investments in the Newer Technologies. Potential restraints include: Rising Adoption of Big Data Analytics, AI and ML Technologies, Increase in Investments in the Newer Technologies. Notable trends are: Football Sport is Expected to Hold Significant Market Share.

The dataset contains the results of developing alternative text for images using chatbots based on large language models. The study was carried out in April-June 2024. Microsoft Copilot, Google Gemini, and YandexGPT chatbots were used to generate 108 text descriptions for 12 images. Descriptions were generated by chatbots using keywords specified by a person. The experts then rated the resulting descriptions on a Likert scale (from 1 to 5). The data set is presented in a Microsoft Excel table on the “Data” sheet with the following fields: record number; image number; chatbot; image type (photo, logo); request date; list of keywords; number of keywords; length of keywords; time of compilation of keywords; generated descriptions; required length of descriptions; actual length of descriptions; description generation time; usefulness; reliability; completeness; accuracy; literacy. The “Images” sheet contains links to the original images. Data set is presented in Russian.

Data science is a rapidly growing field in the tech industry, and LinkedIn is a popular platform for finding job opportunities in this domain.

This dataset provides valuable insights into data science job postings, including the required skills and software proficiency sought by employers.

If you find this dataset useful, don't forget to hit the upvote button! 😊💝

Checkout my top datasets

• Top Spotify Songs in 73 Countries
• Wages by Education in the USA
• Amazon Products Dataset (1.4M Products)
• TMDB 950K Movies
• Amazon Kindle Books (130K Books)

Interesting Task Ideas:

1. Analyze the most in-demand skills and software for data science positions.
2. Find common job titles in the data science field.
3. Explore the geographical distribution of data engineering job opportunities.
4. Identify the most sought-after skills

Photo by Shahadat Rahman on Unsplash

Detailed metrics and specifications for Gemini Dollar – Complete Payment Service Analysis and Information Guide

Interest is increasing among political scientists in leveraging the extensive information available in images. However, the challenge of interpreting these images lies in the need for specialized knowledge in computer vision and access to specialized hardware. As a result, image analysis has been limited to a relatively small group within the political science community. This landscape could potentially change thanks to the rise of large language models (LLMs). This paper aims to raise awareness of the feasibility of using Gemini for image content analysis. A retrospective analysis was conducted on a corpus of 688 images. Content reports were elicited from Gemini for each image and then manually evaluated by the authors. We find that Gemini is highly accurate in performing object detection, which is arguably the most common and fundamental task in image analysis for political scientists. Equally important, we show that it is easy to implement as the entire command consists of a single prompt in natural language; it is fast to run and should meet the time budget of most researchers; and it is free to use and does not require any specialized hardware. In addition, we illustrate how political scientists can leverage Gemini for other image understanding tasks, including face identification, sentiment analysis, and caption generation. Our findings suggest that Gemini and other similar LLMs have the potential to drastically stimulate and accelerate image research in political science and social sciences more broadly.

Based on the Desk based assessment of the project area KP15.5 + and the analysis of the available geophysical data a number of observation can be made concerning the Underwater Cultural Heritage (UCH) in the project area. The deskbased assessment showed that the project area was an area of intensive shipping traffic. One database entry of note was discovered in the Machu, and Archis databases with the designation Rottumeroog 1, a wooden wreck dated between 1850 and 1950 of which an exact position is not known. Furthermore, for the small portion within the Wadden Sea there is a possibility that traces of human habitation or land use have been preserved buried under the seabed. Due to lower sea-levels in the past, there is also a possibility of prehistoric sites being preserved on the now submerged Pleistocene layers and early-Holocene layers.The analysis of the geophysical data resulted in the retaining of a huge number of magnetic anomalies and some side scan targets of which 2 are considered to be of a ‘high risk’ for being UCH, 20 of medium risk, and 9 of low risk.

The data used in this tutorial are a subset of the data published previously in Training material for the course "Exome analysis with GALAXY". Credit for uploading the original data goes to Paolo Uva and Gianmauro Cuccuru!

Specifically, you may need the following datasets for following the tutorial:

Raw sequencing reads

• https://zenodo.org/record/3243160/files/father_R1.fq.gz
• https://zenodo.org/record/3243160/files/father_R2.fq.gz
• https://zenodo.org/record/3243160/files/mother_R1.fq.gz
• https://zenodo.org/record/3243160/files/mother_R2.fq.gz
• https://zenodo.org/record/3243160/files/proband_R1.fq.gz
• https://zenodo.org/record/3243160/files/proband_R2.fq.gz

Premapped sequencing reads

• https://zenodo.org/record/3243160/files/mapped_reads_father.bam
• https://zenodo.org/record/3243160/files/mapped_reads_mother.bam
• https://zenodo.org/record/3243160/files/mapped_reads_proband.bam

Reference sequence (human chromosome 8)

• https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz

If you would just like to play with GEMINI rather than work through the full tutorial, you'll find below a prebuilt GEMINI database (for GEMINI version 0.20.1) for the family trio. You can start exploring this database without having to run GEMINI load and, in fact, without having to install GEMINI's bundled annotation data.