Improving the precision of design-based scallop drag surveys using adaptive allocation methods
Bibliographic record
Abstract
Periodic scientific surveys of commercially exploited fish and invertebrate species are a major source of monitoring data for tracking population trends and evaluating fisheries management plans. The precision of the estimates is important for assessing their quality, as well as being used directly in population models and decision rules. For design-based surveys, precision is partly a function of the survey design and can be improved for the commonly used stratified random design through the judicious definition of strata boundaries and sample-to-strata allocation schemes. In this study, we used adaptive allocation schemes to improve the precision of sea scallop (Placopecten magellanicus) surveys in 1999 and 2004 over the standard stratified random design. The adaptive surveys for both years were more efficient (smaller variance of the mean) than the standard stratified random surveys that had been used. Greater gains in efficiency were obtained for the 2004 survey in which scallops were more abundant (stratified mean of 227 scallops per tow) than in 1999 (stratified mean of 73 scallops per tow). The 2004 survey also benefitted from having tows allocated proportionally to stratum size at the first phase of sampling. Adaptive allocation methods appear to work best for small area surveys with one or few target species.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".