Using the ecosystem based management framework to support fisheries advice provision: case study for the inshore Lobster fishery in the Maritimes Region (LFA 27-38)
Bibliographic record
Abstract
The Ecosystem Based Management (EBM) Framework links legislation, policy, and mandates for Fisheries and Oceans Canada (DFO) to suites of objectives ecosystem sustainability. Here we apply the EBM Framework to four documents that support current single species fisheries management decisions for the Lobster fishery in the Maritimes Region: 1) Lobster research framework (LRF), 2) Indigenous Fisheries Management and Indigenous Management Plans and Protocols (IFM), 3) Integrated Fisheries Management Plan (IFMP), and 4) the Blue Economy Lobster Team (BELT) Project. Through qualitative categorization, the data and information within the documents were used to determine if the EBM Framework objectives could be met by the supporting documents. We found that the total objectives addressed by the four documents in this case study represented 51% (67) of the second-level EBM Framework objectives. We documented potential gaps in the EBM Framework where this case study could help to identify useful methods and scenarios to enhance the EBM Framework tool for more effective use in the fisheries context. The EBM Framework can be adopted as a useful checklist to ensure broader EBM Pillars are considered to meet legislative, policy, and mandate requirements for fisheries, and used as a basis for future research avenues for incorporating ecosystem components into stock assessments and fisheries management advice.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".