The use and value of opportunistic sightings for cetacean conservation and management in Canada
Bibliographic record
Abstract
Twenty-one marine mammal species are designated under the Species at Risk Act (SARA) in Canada. When species are designated under SARA, management plans, recovery strategies, and action plans are outlined to prevent wildlife species from being extirpated or becoming extinct. In these plans, monitoring and outreach are often key recovery objectives for the species. Opportunistic sightings (OS) can help support the monitoring and outreach of species at-risk and can provide an important source of information on the presence of a species when systematic surveys are impractical or costly. To better understand the use and value of OS for cetacean conservation and management, marine mammal experts in Canada were interviewed (n= 15). A thematic analysis was used to examine the qualitative data of the interviews. Results suggested that OS are being used in a variety of different ways, from filling in data gaps, creating species distribution maps, informing management measures and being used as education and outreach tools. Experienced observers and reliability of a sighting were reported as key to being able to use the data. One main limitation of OS is the potential for poor data quality. Recommendations on how to improve OS for cetacean conservation and management include improving the quality of OS data by adding pictures or videos of cetaceans when reporting and using mobile applications to help record data, create a centralized database where open-source data is shared across the country, and improve education and outreach programs to increase cetacean identification training sessions for stakeholders on the water. Keywords: opportunistic sightings; citizen science; cetaceans; marine mammals; whales; conservation; management; species at risk; Canada; community-based monitoring.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".