Two decades of annual landed and discarded catches of three southern Gulf of St Lawrence skate species estimated under multiple sources of uncertainty
Bibliographic record
Abstract
Abstract Benoît, H. P. 2013. Two decades of annual landed and discarded catches of three southern Gulf of St Lawrence skate species estimated under multiple sources of uncertainty. – ICES Journal of Marine Science, 70: 554–563. Estimating fishery impacts on commercially unimportant species is often hindered by limited and possibly biased data for landed and discarded catch, and poor information on discard mortality. The three skate (family Rajidae) species occurring in southern Gulf of St Lawrence (Canada) exemplify this problem. Assessing the contribution of fishing to important declines in their adult abundance has been complicated by catch data that are not disaggregated by species, concerns about the reliability of discard amounts estimated from fisheries observer surveys, and unknown discard mortality rates. An approach is presented for producing annual estimates of landed and discarded catch, as well as discard mortality rates, for the three species for the period 1991–2011. The approach used data from landing statistics and from observer surveys, and models for disaggregating mixed fishery catches into their constituent species and for estimating minimum discard mortalities. Bootstrapping was used to propagate errors associated with different components of the estimation process. The estimation was partly validated by comparing recorded landings with landings estimated from fisheries observer surveys. This paper demonstrates how multiple sources of uncertainty in discard loss estimation can be addressed by dividing the estimation process into linked components that can be individually addressed and ideally validated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".