Management implications of shifting baselines in fish stock assessments
Bibliographic record
Abstract
Abstract To make sound decisions about the future of fisheries, managers need to have a good understanding of the amount of fish that have been caught over long periods. Unfortunately, current stock assessment processes are often flawed, as they are frequently based on data time series that do not represent the full range of change. The process of selecting a shortened (or truncated) time series may lead to misconceptions regarding the fishery's status and impact on management decisions. This study investigates shifting baseline effects in official stock assessments from the Ransom Myers Legacy Stock Assessment Database that used truncated catch time series. The findings suggest that truncated time series often fail to account for important features, such as historical biomass maxima, past recoveries, low abundance levels and biomass fluctuations, whose omission can bias reference points and perceptions of stock status. This study emphasises the importance of considering long‐term data wherever possible to improve historical scientific baselines and inform sustainable fisheries management.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".