Calculation of untrawlable areas within the boundaries of a bottom trawl survey
Bibliographic record
Abstract
A geographic information system (GIS) analysis of 5039 bottom trawl events from U.S. west coast bottom trawl surveys (19771998) estimated that the survey area was about 77% trawlable but five of the 30 strata were less than 50% trawlable. Untrawlable areas, by definition, cannot be surveyed with the bottom trawl; however, there has never been a means of identifying and excluding these areas from relative abundance estimates, which are calculated only from hauls completed in the trawlable portions of each stratum. Unknown amounts of untrawlable habitat are a problem for relative abundance estimation in many bottom trawl surveys. This manuscript describes one method of using the bottom trawling events of a survey, such as ripped-up hauls and abandoned stations, to calculate the amount of area that is untrawlable. A comparison of catch rates between undamaged tows and a limited number of damaged tows, which are normally discarded as faulty samples, showed that Sebastes catch rates were generally higher in damaged tows. Thus untrawlable areas may have substantial importance on relative abundance estimates of Sebastes, the original target species group for this survey.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".