Do people with food allergies benefit from food bans? Why are athletes still getting injured? & The stupidest baby names of 2021
Bibliographic record
Abstract
Bans on certain kinds of food are currently present in many schools in Ontario but there are some scientists who are looking into whether or not there's a better way to be going about this. Could blanket bans on food like peanuts not be the answer?Guest: Dr. Susan Waserman, Director of the Adverse Reactions Clinic, Firestone Institute of Respiratory Health, Professor of Medicine at the Division of Clinical Immunology & Allergy, Department of Medicine, McMaster University-Athletes are in the best shape they've ever been and yet they are still far from perfect. Injuries are still a rather common occurrence in sport despite all the supports being there. Are we maxing out the human body's potential?Guest: Steve Lidstone, Associate Director, Sports Performance, Brock University-The COVID-19 pandemic has driven people crazy! Or at least you'd think so after seeing this list of stupid names given to babies since the beginning of the year. So gather up your kids named Salad and Pandemica because Scott and Ben have quite the unique list lined up for you.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.461 | 0.001 |
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".