Assessing the relative effects of fishing on the New Zealand marine environment through risk analysis
Bibliographic record
Abstract
Abstract Campbell, M. L. and Gallagher, C. 2007. Assessing the relative effects of fishing on the New Zealand marine environment through risk analysis – ICES Journal of Marine Science, 64: 256–270. Risk analysis is a tool often used by management to aid decision-making. We present a risk-analysis framework that was developed to facilitate managing New Zealand fisheries. Using catch-effort and observer data, the likelihood that a certain fishery will impact upon five effects of fishing (EoF) issues (non-target species, biodiversity, habitat, trophic interactions, and legislated protected species) is determined. The consequences (impact and/or change) of such events are then determined to determine a relative risk ranking across fisheries. Consequence matrices were developed to assess each of the five EoF categories. To illustrate the model, a 13-y data set of New Zealand fisheries catch-effort and observer data was analysed, using orange roughy (Hoplostethus atlanticus) as an example fishery. The New Zealand fisheries management framework follows a traditional model in which socio-political imperatives are determined (through risk assessment) after ecological impacts are assessed. By maintaining separation between ecological and socio-political imperatives, a transparent and objective framework is established.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".