The impact of commercial fishing on the determination of habitat associations for sea scallops (Placopecten magellanicus, Gmelin)
Bibliographic record
Abstract
Abstract Smith, S. J., Black, J., Todd, B. J., Kostylev, V. E. and Lundy, M. J. 2009. The impact of commercial fishing on the determination of habitat associations for sea scallops (Placopecten magellanicus, Gmelin). – ICES Journal of Marine Science, 66: 2043–2051. The sea scallop (Placopecten magellanicus) population off southwestern Nova Scotia in Scallop Fishing Area 29 has been monitored by an annual drag survey since the fishery started there in 2001. A new stratification scheme based on surficial geology maps from a multibeam bottom mapping and geology ground-truth project completed in 2004 in the area have been used for survey design since 2005. Survey data from before 2005 have been post-stratified using the new strata. The efficiency of the design with respect to variance reduction appears to have diminished over time suggesting that the association between scallop abundance and bottom type may not have been as strong or constant as first assumed. Modelling of the association between scallop abundance and bottom type and depth using a Bayesian hierarchical approach confirms this diminishing relationship. Comparison of the results from the model with spatial measures of fishing effort based on satellite vessel monitoring data suggests that increasing exploitation may be masking the relationships as scallop beds are targeted and fished down. These results could have implications on the interpretation of species habitat associations from areas where data are only available from periods when the populations have been exploited over a long time. In these cases, the spatial distribution of fishing effort may be a better indicator of species habitat associations than the estimates from surveys.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".