Precautionary Approach In Development Of A Directed Horse Clam FisheryIn British Columbia, Canada
Bibliographic record
Abstract
A directed fishery on horse clams is being developed in British Columbia, Canada. The paper describes how the development follows the guidelines of a framework, which explicitly endorses the precautionary approach. At the first phase of the development, biological and fisheries information on horse clams, such as longevity, reproductive characteristics, growth, mortality, and management schemes, was synthesised based on scientific literature, technical reports, and surveys. Growth parameters and natural mortality rates were estimated in the literature with certain degrees of uncertainties. At the second phase of the development, alternative management strategies were evaluated and a precautionary approach for setting up biological reference points was provided through a simulation study. A stochastic spawning stock biomass per recruit model was found to be able to fully utilise the available information. Uncertainties about the estimates of population parameters were incorporated in the simulation to produce a probability distribution of possible consequences in terms of reproduction potentials. After setting up a limit reference point, the fisheries managers are able to find from the probability distribution such an exploitation rate that the risk of reducing the reproduction potential below the limit reference point is small (e.g. 5%).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".