Integrated Pelagic Ecosystem Survey on the Vancouver Island Continental Shelf, June 24 - July 23, 2024
Bibliographic record
Abstract
Fisheries and Oceans Canada’s mandated ecosystem based approach to fisheries management requires a comprehensive understanding of factors that affect species’ distribution, abundance, and interdependencies. The Integrated Pelagic Ecosystem Survey (IPES) is the only broad-scale survey in the Pacific Region that collects this information for the pelagic ecosystem. The goal of the IPES is to understand factors affecting the distribution, abundance, and food web linkages of pelagic fish species (e.g., Pacific Herring, juvenile Pacific Salmon). The IPES is an annual, random stratified survey on Vancouver Island’s continental shelf, 2017-present, with comprehensive biological, stomach content, and physical- and biological-oceanographic sampling. IPES data are required for ecosystem monitoring and modelling, support for SARA-listed species, DFO’s State of the Pacific Ocean reporting, and the State of the Salmon program. In 2024, Pacific Herring (Clupea pallasii), Lion’s Mane Jellyfish (Cyanea capillata), and North Pacific Spiny Dogfish (Squalus suckleyi) dominated catch weights. In 2024, the biomass estimate of Pacific Herring was the second highest since 2022. Juvenile Pacific Salmon (Oncorhynchus) were captured in 46% of the tows and the relative abundance (by count) was ranked highest to lowest for: Chum Salmon (O. keta), Coho Salmon (O. kitsutch), Sockeye Salmon (O. nerka), Pink Salmon (O. gorbusha) and Chinook Salmon (O. tschawytscha). The most common prey in fish diets were Euphausiids (Euphausiacea), true crabs, and teleosts (primarily Pacific Herring). The IPES supports research into 1) linkages between oceanographic conditions and fish abundance and distribution; 2) Pacific Salmon ocean ecology; 3) forecasted adult fish returns, and 4) food-web dynamics.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".