Bibliographic record
Abstract
Cam & Jim dive into potential trade targets for the Winnipeg Jets as the deadline approaches in March, with names like Ryan Donato, Yanni Gourde, and Scott Laughton surfacing. Are these the types of players the Jets should pursue?Don't miss the segment with top hockey insider and storyteller John Shannon, who weighs in on the 4 Nations Face-Off break. Is the extended break too long, and how will the tournament results impact the rest of the season?Plus, hear from three goaltenders on just how difficult it is to stop a shot from the one and only Alex Ovechkin.Could the Boston Bruins really trade Brad Marchand? They're reportedly fielding calls...And in an exclusive segment, only available on this podcast, Cam and Jim break down a potential playoff matchup: If the playoffs started today, the Jets would face the Canucks. Is this the best matchup for Winnipeg in the Western Conference?Learn more about your ad choices. Visit megaphone.fm/adchoices
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.005 |
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".