Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The impact of transcontinental travel and high-volume travel on athletes can result in physiologic disturbances and a complicated set of physical symptoms. Jet lag and travel fatigue have been identified by athletes, athletic trainers, coaches, and physicians as important but challenging problems that could benefit from practical solutions. Currently, there is a culture of disregard and lack of knowledge regarding the negative effects of jet lag and travel fatigue on the athlete's well-being and performance. In addition, the key physiologic metric (determination of the human circadian phase) that guides jet lag treatment interventions is elusive and thus limits evidence-based therapeutic advice. A better understanding of preflight, in-flight, and postflight management options, such as use of melatonin or the judicious application of sedatives, is important for the sports clinician to help athletes limit fatigue symptoms and maintain optimal performance. The purpose of this article was to provide a practical applied method of implementing a travel management program for athletic teams.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it