Eyes on the Water: Citizen Science in the Salish Sea
Bibliographic record
Abstract
The knowledge and expertise of local communities is often ignored or underused in planning for protected species. This is a success story of citizen science collaboration, although the catalyst for this project was driven by the lack of reliable year-round data on Southern Resident Killer Whales (SRKW) and how they use their designated critical habitat. This data, particularly in the high traffic areas of Boundary Pass, Active Pass and Strait of Georgia, is critical to make informed management decisions for this endangered species. Frustrated, community groups capitalized on their considerable local knowledge about the marine ecosystem, and the SRKW, to structure an evidenced-based monitoring program to observe the social associations, travelling and feeding strategies of cetaceans in the area, and formed the Southern Gulf Islands Sighting Network (SGISN). Since its conception, SGISN has closely collaborated with Saturna Island Marine Research Education Society (SIMRES) which has been collecting hydrophone data from Saturna since 2014. SGISN also collaborates with the BC Cetacean Sighting Network to use their interactive whale reporting platform on the web and an app for phones as well as with researchers from Simon Fraser University. SGISN also compiles reliable data on vessel infractions in the Saturna and Pender ISZs. Data is shared with Canadian federal agencies each month to augment the government’s remote technology to track vessels. Both these data sets will be presented to demonstrate that if conservation and protection of the SRKW are the goals, enforcement and education are essential management tools for protecting species at risk. As island residents in the Salish Sea we seek to advance local knowledge and commitment to marine conservation planning and management for species at risk. Future developments include making hydrophone audio available to sighters in near-real time to verify species/ecotypes and detect cetaceans at night.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".