Bibliographic record
Abstract
Historically, Groundfish surveys in the Canadian Salish Sea were focused on individual, commercially important species such as Lingcod, Spiny Dogfish, Pacific Hake and inshore rockfish species. These surveys date back to as early as 1948 and employed different protocols and diverse gear types including trawl, longline, and dive. Over the last several years there has been a departure from this single-species approach towards multi-species and ecosystem-based surveys. In particular, two survey series have recently been implemented in the Canadian Salish Sea that provide a synoptic approach, covering broad areas and including multiple species of fish and invertebrates. The first, initiated in 2003, employs longline gear and a random depth-stratified design. Each year this survey alternates between the northern and southern portions of the Canadian Salish Sea. The second, initiated in 2012, employs bottom trawl gear and a random depth-stratified design that covers an area spanning from north of Cortez Island to Saturna Island in the south. Each of these surveys targets different bottom types; the longline survey is directed on hard, high-relief bottom that is characteristically occupied by rockfish, while the bottom trawl survey targets softer and smoother bottom that is generally occupied by flatfish. In addition to collecting species composition and biological data, both surveys also collect environmental data such as temperature and salinity, using a variety of gear-mounted probes and CTDs. Taken together, the two surveys will provide a valuable time-series of the Salish Sea ecosystem into the future, giving us insight into fish population changes over time and how these changes relate to environmental factors.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".