Muscle glycogen and blood lactate in yellowfin tuna, Thunnus albacares, and skipjack, Katsuwonus pelamis, following capture and tagging
Bibliographic record
Abstract
ENGLISH: Tagging and the recovery of tagged yellowfin (Thunnus albacares) and skipjack(Katsuwonus pelamis) tunas are important aspects of the investigations conducted by the Inter-American Tropical Tuna Commission in the Eastern Tropical Pacific Ocean. The results of the tagging program provide information on population structures, migrations, mortality rates and growth rates of these two species. The present experimental program was undertaken to study the relationship between muscular fatigue and high tagging mortalities in yellowfin and skipjack.SPANISH: La marcación del atún aleta amarilla (Thunnus albacares) y del barrilete (Katsuwonus pelamis), y el recobro de estos atunes marcados, son aspectos importantes de la investigación que efectúa la Comisión Interamericana del Atún Tropical en el Océano Pacífico Oriental Tropical. Los resultados del programa de marcación proporcionan información sobre la estructura de las poblaciones,migraciones, tasas de mortalidad y tasas de crecimiento de estas dos especies. El programa experimental presente fue emprendido para estudiar la relación entre la fatiga muscular y la alta mortalidad causada por la marcación en el atún aleta amarilla y el barrilete.(PDF contains 52 pages.)
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".