Evaluating methods for estimating mortality from acoustic telemetry data
Bibliographic record
Abstract
Mortality rates are major determinants of long-term sustainability of exploited fish populations, yet accurately estimating these rates can be challenging. We used simulations to evaluate nonspatial and spatial modeling approaches for estimating mortality rates from acoustic telemetry detection data. Data were generated assuming different receiver configurations (grids, lines), number of receivers, and mortality levels. Relative error rates for total mortality ranged from 0% to 83% for the nonspatial model and from 1% to 141% for the spatial model. Absolute error rates ranged from 0.00 to 0.11 for the nonspatial model and from 0.01 to 0.15 for the spatial model. Accuracy and precision in mortality estimates were sensitive to assumed mortality rates and receiver configurations; the high-density receiver grid resulted in the lowest error rates. Estimates were consistently positively biased. We recommend using grid receiver configurations for mortality rate estimation from acoustic telemetry studies. The potential for biased mortality estimation from acoustic telemetry detection data should be considered during study design, particularly for those species with behavior and ecology that may result in long periods without detection.
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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 | 0.037 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".