Accelerometer-derived activity correlates with volitional swimming speed in lake sturgeon (<i>Acipenser</i> <i>fulvescens</i>)
Bibliographic record
Abstract
Quantifying fine-scale locomotor behaviours associated with different activities is challenging for free-swimming fish. Biologging and biotelemetry tools can help address this problem. An open channel flume was used to generate volitional swimming speed (Us) estimates of cultured lake sturgeon (Acipenser fulvescens Rafinesque, 1817) and these were paired with simultaneously recorded accelerometer-derived metrics of activity obtained from three types of data-storage tags. This study examined whether a predictive relationship could be established between four different activity metrics (tail-beat frequency (TBF), tail-beat acceleration amplitude (TBAA), overall dynamic body acceleration (ODBA), and vectorial dynamic body acceleration (VeDBA)) and the swimming speed of A. fulvescens. Volitional Us of sturgeon ranged from 0.48 to 2.70 m·s−1 (0.51–3.18 body lengths (BL)·s−1). Swimming speed increased linearly with all accelerometer-derived metrics, and when all tag types were combined, Us increased 0.46 BL·s−1 for every 1 Hz increase in TBF, and 0.94, 0.61, and 0.94 BL·s−1 for every 1g increase in TBAA, ODBA, and VeDBA, respectively. Predictive relationships varied among tag types and tag-specific parameter estimates of Us are presented for all metrics. This use of acceleration data-storage tags demonstrated their applicability for the field quantification of sturgeon swimming speed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".