Results of comparative fishing between the CCGS Teleost and CCGS Alfred Needler with the CCGS John Cabot and CCGS Capt. Jacques Cartier in the Newfoundland and Labrador Region in 2021 and 2022
Bibliographic record
Abstract
Multispecies bottom trawl surveys have been conducted annually in the spring and fall in the Newfoundland and Labrador Region using a Campelen 1800 survey trawl aboard the Canadian Coast Guard Ship (CCGS) Teleost and CCGS Alfred Needler since 1995. These surveys are used to estimate the distribution and abundance of many fish and invertebrate species, to determine species life history characteristics, and form the basis of a number of ecosystem indicators. The CCGS Alfred Needler and CCGS Teleost will no longer be used for these surveys after 2022 and 2023, respectively, and will be replaced by the CCGS John Cabot and CCGS Capt. Jacques Cartier. The Campelen 1800 survey trawl will continue to be used, with a few modifications. Comparative fishing has been ongoing since 2021 to determine differences in relative catchability between the outgoing vessels and the new vessels with the modified Campelen trawl. Data collected were sufficient to estimate conversion factors for the fall CCGS Teleost series, and for a portion of taxa within the fall CCGS Alfred Needler series. There were insufficient data to determine conversion factors for the spring CCGS Alfred Needler series, though a special case was made for Yellowtail Flounder (Myzopsetta ferruginea) where fall and spring data were combined to determine a conversion, given the species limited distribution across both seasons. Overall, for the CCGS Teleost fall time series, conversion factors were accepted for 14 taxa, with two species having a significant length effect, 18 taxa had no significant difference in catchability, and 9 taxa were deemed to have insufficient data to accurately determine a conversion. For the CCGS Alfred Needler fall time series, conversion factors were accepted for 15 taxa, including 6 taxa with a significant length effect, 17 taxa had no difference in catchability, and 12 were deemed to have insufficient data to accurately determine a conversion. Comparative fishing work will continue during 2023 to fill identified data gaps from the 2021–22 data set, including deep water (>1,000 m) and shallow water sets (<150 m).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".