Quantifying uncertainty when extrapolating the relationship between snorkel counts and mark-recapture estimates of juvenile salmonids
Bibliographic record
Abstract
Snorkel surveys are frequently used to monitor stream-dwelling fish. Inferring local abundance from snorkel surveys is complicated by two primary factors: variable fish detection probabilities and the relative abundance of fish in habitat types below the recommended minimum depth for snorkeling. We examine these factors across three salmonid species ( Oncorhynchus spp .), 4 years, and 113 location-years in Oregon coastal streams. We calibrate snorkel counts to mark-recapture estimates and develop mathematically explicit expressions that convert a new snorkel count into a probability density of abundance for streams containing mixed habitat types that were and were not snorkeled. Snorkelers detected 63%, 47%, and 39% of juvenile coho salmon ( Oncorhynchus kitsutch), steelhead trout ( Oncorhynchus mykiss), and cutthroat trout ( Oncorhynchus clarkii clarkia) estimated by mark-recapture, respectively, but uncertainty within and among sampling units was high. Further, analytics developed here can be used to infer abundance and uncertainty for habitat types that were not snorkeled. Our quantification of uncertainty arising from using snorkel counts as a proxy for abundance will help managers balance biological risks with available resources.
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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 | 0.027 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".