Aggregated Salmon Gillnet Catch and Effort SCEAs 2001-2007
Bibliographic record
Abstract
The Aggregated Salmon Gillnet (GN) dataset has been assembled from source data provided by Fisheries and Oceans Canada, Pacific Biological Station. The dataset is comprised of an aggregate of five species (Chinook, chum, coho, pink, and sockeye) and specifically targets the 2001-2007 fishing seasons. Data has been binned by salmon catch estimate areas (SCEAs). This is one of thee shapefiles intended to represent the spatial distribution of commercial salmon fishing by different gear types. The salmon catch estimate areas (SCEA) have been created as a means of rolling up the catch and effort values from the many annual openings for commercial salmon fishing. DFO first started to use SCEAs to categorize salmon catch in 2001. Because different fleets fish at different times for different species, the SCEAs have been digitized to represent where all openings took place for the given geographic unit in a given year. When a certain geographic feature is consistently not opened for any gear type (e.g., ribbon boundary around a creek mouth, protected area etc.) that feature has been removed from the SCEA, hence SCEAs may change over time. When a SCEA changes, a new SCEA name is assigned.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".