Preliminary Version 5 Unobserved Diversity, Depletion and Irreversibility The importance of subpopulations for Management of cod stocks
Bibliographic record
Abstract
Diversity is often associated with resilience but in this model, unobserved genetic or behavioral diversity can explain the collapse of supposedly regulated fish stocks such as cod. Recent studies have shown the existence of separate sub-stocks of cod even at a very fine geographical scale. We show that modeling a group of distinct stocks as if it were one large stock will tend to over-estimate the growth and harvest potential. If quotas are based on such over-estimates, the unobserved stock diversity can explain sudden stock collapses and unexpectedly slow recovery as observed for Canadian cod. The problem is a lack of information that leads to irreversibility. The differences between the various stocks may be behavioral or genetic but cannot be observed by the fishermen or regulators who believe there is a gradual decline in one big stock while in fact they are witnessing the successive disappearance of a series of sub-stocks.
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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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".