Validation of a new acoustic telemetry transmitter for the study of predation events in small fishes
Bibliographic record
Abstract
Acoustic telemetry has emerged as an important tool for studying the movement and behavior of aquatic animals. Predation-sensing acoustic transmitters combine the functions of typical acoustic transmitters with the added ability to identify the predation of tagged animals. The objective of this paper was to assess the performance of a newly miniaturized acid-based predation-sensing acoustic transmitter (Innovasea V3D; 0.33 g in air). We conducted staged predation events in the laboratory where acoustically tagged rainbow trout (Oncorhynchus mykiss) were fed to largemouth bass (Micropterus nigricans) at 3.3-7.0, 9.0-10.8, 16.0-20.0, and 22.0-25.8°C. We also conducted false-positive tests where tagged rainbow trout were held at 10.0 and 16.8°C without the risk of predation. Predation events were successfully identified in 92% of the staged predation trials. Signal lag (i.e., the time required for a predation tag to indicate that predation occurred) ranged from 0.11 to 6.29 days and decreased strongly with increasing water temperature and increased with increasing body mass of the tagged prey. Tag retention in the gut of the predator was much more variable than signal lag and was influenced by water temperature and individual predators but not by prey mass. No false positives were detected after 60 days at either temperature (n = 27 individuals). Although the relationships between water temperature, signal lag, and retention time are likely species-specific, the data reported here provide useful information for the use of these transmitters to study predation in wild fishes, especially for temperate, freshwater fish.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".