Leveraging Community Science to Address Significant Data, Management, and Policy Gaps for Beach Spawning Forage Fish Across the Salish Sea
Bibliographic record
Abstract
Community science is an important tool to address conservation challenges. WWF-Canada has facilitated the development of a ‘Forage Fish Monitoring Network’ within British Columbia’s Salish Sea. This network brings together community scientists, academics, professionals, and First Nation communities in mapping, monitoring, and identifying important spawning habitat for Pacific sand lance (PSL) and surf smelt (SS). PSL and SS are two ecologically important species that use intertidal (beach) habitat for spawning. These fish act as a trophic bridge between zooplankton and culturally and ecologically important predators, such as Marbled Murrelets, and Chinook salmon, which are the primary prey for northern and southern resident killer whales. The network has a community-based approach whereby community scientists conduct the field surveys for forage fish eggs to map PSL and SS habitat, become community advocates, contribute to local ecological knowledge, and manage and own the data themselves. The network practices open science; the data, methods, and results are publicly available on the Strait of Georgia Data Centre. The outcomes of the network have addressed significant knowledge gaps on the spatial and temporal distribution of beach spawning forage fishes. Without this collaborative, coordinated effort, significant gaps would exist in our knowledge of spawning beaches, leaving significant habitat at risk of degradation and loss in a time of rapid expansion of coastal developments and threat of coastal squeeze. This work contributes knowledge that can be better integrated into management decisions and restoration strategies for municipalities, regional districts, and First Nations and provides direction for habitat managers such as information for emergency spill response.
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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 | 0.092 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".