Assessing the quality of groundfish population indices derived from the small-mesh multi-species bottom trawl survey
Bibliographic record
Abstract
Since 1973, Fisheries and Oceans Canada has conducted the Small-mesh Multi-species Bottom Trawl Survey (SMMS), the longest continuous fisheries-independent monitoring time series for groundfish off the west coast of Vancouver Island (WCVI). Initially designed to assess Pink shrimp (Pandalus jordani) populations, the survey also samples groundfish. The SMMS provides a valuable historical baseline for groundfish species, as current groundfish trawl surveys began in 2003 and are biennial across regions. However, changes to the SMMS, including changes to the sampling area, fishing gear, and catch recording procedures, may affect the quality of groundfish population indices. Here, we summarise these changes and examine their impact on relative biomass indices for groundfish. We compare spatiotemporal model-based population indices from the SMMS with other regional indices and examine the distribution of lengths and ages in comparison to the synoptic West Coast Vancouver Island trawl survey (SYN WCVI). Our analysis suggests that the modelled SMMS index is likely an appropriate index of relative biomass for many groundfish species. However, changes to the survey, such as the switch to comprehensive species sorting and identification, introduce uncertainties in the pre-2003 data. Additionally, the SMMS detected signs of rockfish recruitment earlier than the SYN WCVI for some species.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".