Fisheries and Oceans Canada's ecosystem approach to fisheries management science methods toolbox : user guide
Bibliographic record
Abstract
Fisheries and Oceans Canada’s (DFO) national Ecosystem Approach to Fisheries Management (EAFM) Working Group developed a National EAFM Science Methods Toolbox (Toolbox); a compilation of Science methods used by DFO for incorporating ecosystem variables into stock assessments and other assessment-related research activities. This report is the user guide for the Toolbox, which can be downloaded from the Government of Canada’s Open Data portal (and found in an appendix of this report). The Toolbox is a starting point for researchers looking to incorporate ecosystem information in their stock assessment activities, and is not intended to provide an exhaustive list of available analytical tools. Researchers should assess the suitability of any tools in the Toolbox to particular research objectives, as well as investigate the possibility of tools not presently included in the Toolbox (pre-existing or new). It is expected that the Toolbox will remain ‘evergreen’ with periodic updates to reflect emerging best practices.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.070 |
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".