Estimating occupancy and detection probability of juvenile bull trout using backpack electrofishing gear in a west-central Alberta watershed
Bibliographic record
Abstract
Occupancy modeling is well suited to quantitative assessment of bull trout (Salvelinus confluentus) distribution at multiple scales. We used models to estimate occupancy of juvenile bull trout (≤150 mm fork length) in a west-central Alberta watershed. Based on a backpack electrofishing survey of 92 sites, we assessed the relative importance of stream habitat characteristics on detection probability (p) and potential for false absences to bias occupancy estimates. Median distance to first bull trout detection was 16 m (range 0–289 m). Models including ambient water conductivity as a covariate of detection probability were most supported with an 85 μS·cm−1 increase resulting in over a tenfold increase in detection. Conditional detection probability using backpack electrofishing gear approached 95% in the first 200 m of effort in streams with a conductivity around 200 μS·cm−1. The potential for false absences in our study area was relatively low. Modeled site ([Formula: see text] = 0.53; SE = 0.13) and patch-scale ([Formula: see text] = 0.47; SE = 0.12) occupancy closely corresponded to naive (i.e., assuming p = 1) estimates (0.47 and 0.43, respectively). Our results highlight the potential efficiencies of an occupancy modeling approach when assessing fish distribution, but careful consideration of model assumptions is necessary.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".