Making The Ideal 2022-23 Winnipeg Jets Line-Up: The Struggle Bus Edition
Bibliographic record
Abstract
On tonight's episode, we attempt to build as an idealized line-up for the 2022-23 Winnipeg Jets as humanly possible. Where will Blake Wheeler end up in this new-look regime? Can Cole Perfetti seize a top-line spot alongside Scheifele? What can new addition Kevin Stenlund do for the bottom-6? What role best suits Adam Lowry, and should it change with future additions? Are Dylan Samberg and Ville Heinola ready to lead the charge from the blueline? What will this team even be capable of next season?Support Us By Supporting Our Sponsors!Built BarBuilt Bar is a protein bar that tastes like a candy bar. Go to builtbar.com and use promo code \\"LOCKED15,\\" and you'll get 15% off your next order.BetOnlineBetOnline.net has you covered this season with more props, odds and lines than ever before. BetOnline - Where The Game Starts!Learn more about your ad choices. Visit podcastchoices.com/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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.554 | 0.004 |
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".