The Need for Shifting Baselines to Guide Fisheries and Ocean Activities From Days to Decades
Bibliographic record
Abstract
ABSTRACT With novel ocean conditions rapidly appearing as the result of climate change, basing decisions about fisheries and other ocean activities on historical conditions is no longer tenable. There is instead a widespread need for shifting ecological baselines to more effectively guide decisions into the future. What has not been as widely recognised is that the relevant timescales differ substantially across ocean‐related decisions, from lead times of hours to decades depending on the decision being made, and that this range necessitates a matching range of ecological forecast products across similar timescales. At the moment, a predictability gap exists at intermediate timescales, from multi‐annual to multi‐decadal forecasts. Because most fisheries and many other ocean activities rely on biological conditions like fish abundance or distribution, the ecological inertia of organismal growth, generational turnover, movement, and food web dynamics can help push ecological forecasts further across this gap. To realise this potential for more effective and usable ecological forecasts, coordinated research and implementation at the intersection of biology, climate science, social science, and decision‐making is needed. These efforts will be critical for forecasting shifting ecosystem baselines and sustaining fisheries, ocean ecosystems, and the ocean economy in the coming decades of rapid change.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".