Incorporating bottom type information into survey estimates of sea scallop (Placopecten magellanicus) abundance
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The sea scallop (Placopecten magellanicus) population off Southwest Nova Scotia in Scallop Fishing Area 29 has been monitored by an annual drag survey since the fishery started there in 2001. From 2002 to 2004, a stratified random survey design was used with management subareas used as strata. New strata based upon surficial geology maps from a recently completed multibeam bottom mapping and geology groundtruth project in this area were used for the 2005 survey. The results of this survey confirm that variations in scallop densities are related to surficial bottom type. As a result variations in annual trends in scallop abundance may be partly explained by annual variation in bottom type coverage by the survey. In this paper, we compare design-based, model-assisted design-based and model-based methods for estimating annual estimates of scallop abundance that incorporate bottom type information.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".