Effects of observer efficiency, arrival timing, and survey life on estimates of escapement for steelhead trout (<i>Oncorhynchus mykiss</i>) derived from repeat markrecapture experiments
Bibliographic record
Abstract
Estimation of escapement for steelhead trout (Oncorhynchus mykiss) from periodic visual counts of spawners is complicated by extreme changes in observer efficiency over the migration period, low numbers of fish, pulsed arrival timing, and variable survey life. We present a maximum likelihood method to compute escapement and uncertainty that accounts for these difficulties using markrecapture data from radiotelemetry and snorkel surveys. Estimates of escapement were highly sensitive to assumptions about arrival dynamics and survey life, moderately sensitive to the assumed ending date of the run, and insensitive to assumptions about the form of observation error. Discharge and diver visibility explained between 69 and 78% of the variation in observer efficiency. Simulations revealed that declines in observer efficiency over the duration of the run increased bias and variability in escapement estimates but that this can be mitigated to a limited extent by increasing the number of surveys. The simulations also provided evidence that our likelihood approach was superior to the standard area-under-the-curve method for computing escapement when estimates of the numbers present over time are affected by substantial sampling error.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".