Accelerometer tags: detecting and identifying activities in fish and the effect of sampling frequency
Bibliographic record
Abstract
Monitoring and measuring the behaviour and movement of aquatic animals in the wild is typically challenging, though micro-accelerometer (archival or telemetry) tags now provide the means to remotely identify and quantify behavioural states and rates such as resting, swimming and migrating, and to estimate activity and energy budgets. Most studies use low-frequency (≤32 Hz) accelerometer sampling because of battery and data-archiving constraints. In this study we assessed the effect of sampling frequency (aliasing) on activity detection probability using the great sculpin (Myoxocephalus polyacanthoceaphalus) as a model species. Feeding strikes and escape responses (fast-start activities) and spontaneous movements among seven different great sculpin were triggered, observed and recorded using video records and a tri-axial accelerometer sampling at 100 Hz. We demonstrate that multiple parameters in the time and probability domains can statistically differentiate between activities with high detection (90%) and identification (80%) probabilities. Detection probability for feeding and escape activities decreased by 50% when sampling at <10 Hz. Our analyses illustrate additional problems associated with aliasing and how activity and energy-budget estimates can be compromised and misinterpreted. We recommend that high-frequency (>30 Hz) accelerometer sampling be used in similar laboratory and field studies. If battery and/or data storage is limited, we also recommend archiving the events via an on-board algorithm that determines the highest likelihood and subsequent archiving of the various event classes of interest.
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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 | 0.001 | 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 | 0.000 | 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".