Designing acoustic arrays for estimation of mortality rates in riverine and estuarine systems
Bibliographic record
Abstract
Motivated by monitoring populations of threatened tropical euryhaline elasmobranchs, this study examines aspects of acoustic telemetry array design for estimating survival rates in riverine populations. Simulation models incorporating movement and survival were constructed whose outputs were input into an observation model mimicking an acoustic telemetry array and into a simplified survival model. Precision of mortality rate and observation probability parameter estimates were examined as a function of the study design. Coefficients of variation on survival rate and observation probability parameters indicated that the volume of detections was more strongly related to the number of receiver locations than the probability of detecting tagged individuals at each location. Observation probabilities approaching one had only minor effect on reducing uncertainty and also for characterizing the persistence of individuals in the system. Uncertainty in survival probability estimates was more strongly tied to number of tagged individuals. Conversely, uncertainty in observation probability was most strongly related to number of receivers. This approach provides guidelines for robust estimation of movement and mortality from studies utilizing acoustic telemetry on euryhaline elasmobranchs.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".