Estimation of short-term centers of activity from an array of omnidirectional hydrophones and its use in studying animal movements
Bibliographic record
Abstract
A method for studying animal movements based on data from independent data-logging acoustic receivers is described. The method takes presence or absence data from multiple receivers arranged in an array and converts them to position estimates based on weighted means of the number of signal receptions at each receiver during a specified time period. The method is equivalent to a short-term center of activity rather than a precise estimate of location at a single time. The utility of the method was assessed using data from a study of neonate blacktip sharks (Carcharhinus limbatus). Periods between 5 and 60 min were tested to find the most appropriate interval for estimating positions. The results from the method agreed closely with a simulated shark track and data from actively tracked sharks. The median distances between successive locations from the mean-position algorithm were between 28% and 42% of those from active tracking because of the center-of-activity nature of the method. The results presented demonstrate that the technique provides a useful method for investigating long-term movement patterns, space utilization patterns over broader areas, and home range.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".