Calibration of bottom trawl survey vessels : results of comparative fishing between the CCGS Teleost and CCGS John Cabot / CCGS Captain Jacques Cartier on the Scotian Shelf and Bay of Fundy in 2022 and 2023
Bibliographic record
Abstract
Bottom-trawl surveys provide key inputs to stock assessments for groundfish stocks and other taxa, for ecosystem monitoring and reporting, and for research. These surveys can produce annual indices of abundance that are proportional to stock size, provided that the proportionality constant, typically called catchability, does not change over time. This is typically achieved through the use of standardized survey design and procedures. In the Maritimes Region, the Canadian Coast Guard Ship (CCGS) Teleost fishing a Western IIA bottom-trawl conducting the annual Summer Ecosystem Research Vessel Survey of the Scotian Shelf will be replaced by the CCGS John Cabot and CCGS Captain Jacques Cartier fishing the Northeast Fisheries Science Centre Ecosystem Survey Trawl and such a change in protocols required calibration experiments to estimate adjustments for possible changes in catchability. Hence, a comparative fishing experiment was conducted in the summers of 2022 and 2023 involving fishing by paired vessels and gears at a large number of locations to obtain data for catch required to estimate their relative fishing efficiency for a large number of fish and invertebrate taxa that are routinely sampled in this survey. This document briefly describes the comparative fishing experiment and the resulting catch data, followed by detailed analyses of the data for 108 fish and invertebrate taxa routinely sampled by the RV survey for which there were sufficient data from the experiment. The analyses employed a suite of contemporary statistical models used previously in comparative fishing analyses in the eastern United States as well as the Atlantic regions in Canada. Recommendations for vessel calibrations of catch numbers based on the results of the analyses were provided for these taxa, where 22 taxa had length-dependent conversion factors using length-disaggregated analysis, 14 taxa had length-independent conversion factors using length-disaggregated analysis, 40 taxa had length-independent conversion factors using length aggregated analysis, and calibration for 21 taxa is recommended as not necessary. Complementary results were also provided for 59 taxa using length-aggregated analysis of catch biomass, of which 42 taxa appear to require a conversion factor, while 15 taxa do not.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".