Biological reference points in the management of North American sea urchin fisheries
Bibliographic record
Abstract
The precautionary approach calls for both target and limit reference points (TRPs and LRPs, respectively) and notes the vulnerability of developing fisheries to excess effort. LRPs should reflect a population's ability to continue to persist, which depends on lifetime egg production (LEP) and current abundance. The unique characteristics of sea urchin fisheries, such as (i) tight ecological coupling, (ii) their being roe fisheries, (iii) protection of juveniles under adult spine canopies, and (iv) broadcast spawning, can influence their management. Most North American sea urchin fisheries developed rapidly. Their reference points include (i) several LRPs and TRPs based on the logistic or surplus production model, (ii) a unique TRP involving direct monitoring of the bathymetric position of their macro phyte food, and (iii) an LRP based on the fraction of natural, unfished LEP. The dominant effect of fishing down and serial depletion on catch and effort data from developing sea urchin fisheries adversely affects fits to the logistic model. Reference points based on tight ecological coupling will be useful only where food webs are simple and one-dimensional. Sea urchin fisheries developed in the future should consider the fraction of natural LEP as an LRP and attempt to limit early excess fishing capacity.
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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 | 0.013 | 0.026 |
| 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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".