The Variety and Value of Grassroots, Ground-up Recovery Efforts
Bibliographic record
Abstract
The endangered status of the southern resident killer whale population and the likelihood of further human growth and development around the Salish Sea both indicate that the population’s recovery will be a daunting, long-term challenge to Canadian and U.S. governments and First Nation Tribes. Communities and non-governmental organizations presently have, and will continue to have, a vital role to play in contributing to the collective effort to provide the whales a quieter, less disruptive Salish Sea to successfully forage, communicate, socialize and raise their calves. For this panel, presenters will describe some of the activities currently underway to reach the recreational boating community and provide the necessary information to be responsible stewards as we all have a role to play in orca recovery. Questions we want them to answer: · What needs is your program addressing? · Who is your intended audience and how do you effectively reach them? · How are you measuring the success/impact of people’s behavior change? Programs we will hear about: Green Boating, Friends of the San Juans Be Whale Wise / Whale Warning Flag, San Juan County Gulf Islands Sighting Network, SIMRES BC Cetacean Sightings Network’s Whale Report Alert System (WRAS)- Ocean Wise Give Them Space, The Whale Trail Share the Water, Orca Network
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".