Evaluating Rockfish Conservation Areas in Southern British Columbia, Canada using a Random Forest Model of Rocky Reef Habitat
Bibliographic record
Abstract
We developed a rockfish habitat model to evaluate a network of Rockfish Conservation Areas (RCAs) implemented by Fisheries and Oceans Canada to reverse population declines of inshore Pacific rockfishes (Sebastes spp.). We modeled rocky reef habitat in all nearshore waters of southern British Columbia (BC) using a supervised classification of variables derived from a bathymetry model with 20 m^2 resolution. We compared the results from models at intermediate (20 m^2) and fine (5 m^2) resolutions in five test areas where acoustic multibeam echosounder and backscatter data were available. The inclusion of backscatter variables did not substantially improve model accuracy. The intermediate-resolution model performed well with an accuracy of 75%, except in very steep habitats such as coastal inlets; it was used to estimate the total habitat area and the percent of rocky habitat in 144 RCAs in southern BC. We also compared the amount of habitat estimated by our 20 m^2 model to the 100 m^2 management model used to designate the RCAs and found that a slightly lower proportion of habitat (18% vs 20%) but a considerably smaller area (400 km^2 vs 1370 km^2) is protected in the RCAs, likely as a result of the poor resolution of the original model. Empirically derived maps of important habitats, such as rocky reefs, are necessary to support effective marine spatial planning and to design and evaluate the efficacy of management and conservation actions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".