Applying Ecologically and Biologically Significant Area (EBSA) criteria to freshwater habitats: a case study to identify potential Ecologically Significant Area (ESA) candidates in freshwaters of New Brunswick
Bibliographic record
Abstract
Ecologically Significant Areas (ESAs) under section 35.2 serve as a regulatory tool to protect sensitive, productive, or unique fish habitats in marine, estuarine, and freshwater environments. This study evaluates the suitability of Ecologically and Biologically Significant Areas (EBSAs) criteria—uniqueness, aggregation, fitness consequence, and a modified criterion of ecologically important physical features—for identifying ESA candidates in New Brunswick freshwater systems. Data from long-term (>30 years), georeferenced electrofishing surveys in four regions (Miramichi, Southeast NB, Northern NB, and Saint John/Bay of Fundy) were used to assess fish species richness and density, ranking habitats based on their ecological importance. Electrofishing surveys provide an objective basis for ESA identification but are limited to river riffles, primarily habitats for Atlantic Salmon (Salmo salar), and do not account for other habitat types or seasonal ecological changes such as overwintering. Despite these limitations, the Fisheries and Oceans Canada Gulf Region’s extensive fish population surveys enable robust comparisons of fish habitats and the ranking of ESA candidates. The application of EBSA criteria to electrofishing data demonstrates their utility in identifying freshwater ESAs, highlighting the importance of enhancing data availability and expanding habitat type coverage for comprehensive conservation planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".