Interactions of productivity, predation risk, and fishing effort in the efficacy of marine protected areas for the central Pacific
Bibliographic record
Abstract
Ecospace biomass-dynamics models for the central North Pacific predict strong space-time variation in abundances of various trophic groups in relation to nutrient-driven patterns in primary production and circulation-driven concentration of production in convergence areas. The model predicts simple patterns in ecosystem organization and abundances along productivity gradients. Predicted patterns are robust to alternative assumptions about how mobile organisms may alter dispersal behavior in relation to local fitness (per capita gain from net food intake minus predation mortality). Large marine protected areas (MPAs) would be needed to rebuild endangered populations and counter impacts of growing fishing effort. We expected that increases in dispersal rate in response to lower food availability and higher predation risk would reduce efficacy of MPAs as a management tool. Instead, simulations indicated that this negative effect may generally be eliminated or even reversed by positive effects of fitness-maximizing behaviors. Interannual variability in ocean circulation can further reduce the efficacy of MPAs; therefore, the dynamic nature of pelagic environments should also be considered in MPA design. Anomalies in ocean circulation may shift productive areas relative to MPA location, resulting in increased fishing mortality and (or) misinterpretation of catch statistics.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".