Bibliographic record
Abstract
Fans around the world will be able to watch the 50 games of the XVII WBSC Men’s Softball World Cup through the WBSC's exclusive online video platform, GameTime.The top 12 men’s national softball teams in the world will take to the diamond on Saturday, to open the XVII WBSC Men’s Softball World Cup at Rosedale Park in Auckland, New Zealand. A total of 50 games will be played in nine days on the two diamonds at the stunning complex, and each game will be available to watch around the world.\n\n\n\n\n\n\t\n\n\t\nCLICK HERE TO WATCH LIVE FREE\n\t\n\t\n\n\t\nCLICK HERE TO WATCH LIVE FREE\n\t\n\n\n\n\n \n\n\nThis dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gdf\n\n\nThe press conference for the tournament took place on Friday, on the eve of Saturday's start to the tournament. “After the disappointing defeat in the Prague final where we finished in second place, everyone was crying,” Japan head coach Hiroshi Yoshimura said. “It was a heart breaking loss for everyone. Since then, we’ve been working to win, so we’re going to do our best to lift the cup this time.”\n\n“We’re a bit out of season, we play in Canada until August, so that’s always a challenge for us,” said John Stuart, Canada’s head coach. “Our preparation started the first week of September as a team, a lot of communication via Zoom, phone calls, and training program just making sure the guys are prepared.”\n\n“Our goal, the same as all the teams that are here, is to win the title. Then, let’s see what happens, but we’re here to win the championship. Every team here is ready, we all want to win,” said Venezuela’s Luis Russo.\n\nCheck all the head coaches' quotes and team profiles here.\n\nWhere To Watch\n\nA total of 50 games will be played in nine days and all of them will be available to watch around the world.\n\nIn the New Zealand territory, indigenous broadcaster Whakaata Māori will livestream all games of the tournament on its digital platform MĀORI+, with all the games of New Zealand Black Sox broadcasted live on free-to-air television. Whakaata Māori (formerly known as Māori Television) is the official broadcaster of the XVII WBSC Men’s Softball World Cup.\n\n\nΤhe CORD-19 dataset released by the team of Semantic Scholar1 anddg\nΤhe curated data provided by the LitCovid hub2.gd\n\n\nThese data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:\n\n\nInfluence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.\nInfluence_alt: Citation-based measure reflecting the total impact of a\n\n\nThese data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdh\n\n\nInfluence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sdgd\nInfluence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sdgf\n\n\nsafs    Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdsg\n\n\nsf    Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfb\nSocial Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset.\n\n\nWe provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".