Harnessing Technologies for Monitoring Pacific Conservation Areas: From Sea Floor to Sky
Bibliographic record
Abstract
Following the announcement of the United Nation's Sustainable Development Goals, Canada has committed to increasing its marine conservation areas to cover 30% of their oceans by 2030. To meet this target, the Marine Spatial Ecology and Analysis program of Fisheries and Oceans Canada has implemented a mix of emerging technologies to inform the development and subsequent monitoring of marine conservation areas in British Columbia. Canadian Pacific conservation areas are diverse and complex in their survey needs. They span from the deep sea to the intertidal and have habitats ranging from seamounts and rocky reefs to eelgrass meadows and oyster beds. Three key research and monitoring technologies for these conservation areas include Remotely Operated Vehicles (ROVs), Remotely Piloted Aircraft Systems (RPAS), and environmental DNA (eDNA). These technologies allow us to expand the spatial scale of our research without exponentially increasing survey efforts and prioritize non-destructive sampling methods. Combining ROV, RPAS, and eDNA technologies with traditional survey techniques is crucial to effectively managing our ocean environment and achieving the Sustainable Development Goals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".