Intercalibration of Research Survey Vessels on Lake Erie
Bibliographic record
Abstract
Abstract Fish abundance indices obtained from annual research trawl surveys are an integral part of fisheries stock assessment and management in the Great Lakes. It is difficult, however, to administer trawl surveys using a single vessel−gear combination owing to the large size of these systems, the jurisdictional boundaries that bisect the Great Lakes, and changes in vessels as a result of fleet replacement. When trawl surveys are administered by multiple vessel−gear combinations, systematic error may be introduced in combining catch-per-unit-effort (CPUE) data across vessels. This bias is associated with relative differences in catchability among vessel−gear combinations. In Lake Erie, five different research vessels conduct seasonal trawl surveys in the western half of the lake. To eliminate this systematic bias, the Lake Erie agencies conducted a side-by-side trawling experiment in 2003 to develop correction factors for CPUE data associated with different vessel−gear combinations. Correcting for systematic bias in CPUE data should lead to more accurate and comparable estimates of species density and biomass. We estimated correction factors for the 10 most commonly collected species age-groups for each vessel during the experiment. Most of the correction factors (70%) ranged from 0.5 to 2.0, indicating that the systematic bias associated with different vessel−gear combinations was not large. Differences in CPUE were most evident for vessels using different sampling gears, although significant differences also existed for vessels using the same gears. These results suggest that standardizing gear is important for multiple-vessel surveys, but there will still be significant differences in catchability stemming from the vessel effects and agencies must correct for this. With standardized estimates of CPUE, the Lake Erie agencies will have the ability to directly compare and combine time series for species abundance.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".