Expert opinion on the status and stressors of brook trout, <i>Salvelinus fontinalis</i>, in Ontario
Bibliographic record
Abstract
Abstract Ontario supports a vast fisheries resource with an abundance of lakes, rivers and streams. A landscape approach to management informed by a broad‐scale monitoring programme has been initiated to assess the status of fisheries within lakes. However, not all species are assessed by this programme, and there is no provincial monitoring of species inhabiting rivers and streams. As such, changes in the status of a species such as brook trout, Salvelinus fontinalis (Mitchill), could be entirely missed. Brook trout is a highly valued and sought after species by anglers within the province, but there are concerns the species is declining. Given the paucity of broad, empirical data, the status and trends of brook trout across the province have been based on expert opinion at multiple local scales. In 2016, a online questionnaire was sent to brook trout experts to determine status, stressors, management approaches and assess risks (magnitude and probability) to lake and river/stream populations in different geographic areas of Ontario. A Bayesian network was used to analyse responses and develop a risk assessment based on expert opinion for brook trout at multiple scales within the province.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".