Estimating detection probability and detection range of radiotelemetry tags for migrating sockeye salmon (Oncorhynchus nerka) in the Harrison River, British Columbia
Bibliographic record
Abstract
Radiotelemetry is a commonly used tool for tracking migration rates, estimating mortality, and revealing fish behaviour. However, researchers risk misinterpreting tag detection data by not appropriately accounting for signal detection probability or detection range of fixed antennas. In this study, I use generalized linear mixed effects models to estimate signal detection probability and detection range of six radiotelemetry tags at four fixed antenna sites. Detection probability differed among the four telemetry fixed sites despite identical techniques and similar receiver site equipment in a relatively small geographic area. The interaction of depth and distance demonstrated the greatest impact on detections at all sites. I conclude that rigorous testing of detection probabilities and detection range of test tags at individual receiver sites should be standard protocol for telemetry studies to optimize study designs and to ensure that appropriate inferences are drawn when telemetry data are used to support management decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".