Reconstructing ecosystem dynamics in the central Pacific Ocean, 19521998. I. Estimating population biomass and recruitment of tunas and billfishes
Bibliographic record
Abstract
Commercial yield of tunas in the central Pacific increased severalfold between 1952 and 1998. We developed age-structured production models that incorporate information from multiple fisheries to estimate population biomass and recruitment trends of tunas (Thunnus alalunga, Thunnus albacares, Thunnus obesus, and Katsuwonus pelamis) and billfish in the central north Pacific (0°N to 40°N and 130°E to 150°W). Our results suggest that all tuna stocks remain above 40% of 1950s levels, whereas blue marlin (Makaira nigricans) declined to 21% and swordfish (Xiphias gladius) to 56%. Estimated biomasses of juvenile bigeye (T. obsesus) and yellowfin (T. albacares) tuna increased to 112 and 129%, respectively, of 1950s levels. Juvenile albacore (T. alalunga) decreased during the 1970s and 1980s but recovered to historical highs (121%) in recent years. Skipjack (K. pelamis) remained relatively stable between 1952 and 1980, declined by 35% between 1981 and 1990, and then increased to 68% of 1950s levels. These changes generally represent decreases in top predators and increases in small tunas, which make up their prey. Application of stock assessment methods set in a food web context provides an important step toward developing a method that recognizes fishery exploitation as a component of ecosystem dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".