Analysis of Ice Hockey Player Production and Team Location
Bibliographic record
Abstract
III regional production changes over time.All players who competed on teams in the top professional leagues of North America (the AHL, CHL, ECHL, IHL and NHL) are included in the analysis.Because previous studies covered only one year, they often included college and high school level players in analysis.Since the current study examines four individual years, only professional players are used to keep the amount of data manageable.A few important limitations must be discussed before any analysis is attempted.As with many studies that use hometown information, it must be noted that some discrepancies may exist.Methods for collecting roster information are not always consistent over time or between different leagues.In some occasions players are listed by birthplace as opposed to hometown and this can produce some irregularities.Recreational hockey participation numbers were desired to help explain variations in production in this analysis; however, the author could not acquire this information.Though attempts were made to contact USA Hockey, the governing body of organized leagues throughout the country, no response was received and no organized published reports could be located. Organization of the StudyPrevious studies of hockey in 1974 and 1988 created 'snapshots' of hockey production during the time of their respective studies.They
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".