Validating Star-Oddi heart rate and acceleration data storage tags for use in Atlantic salmon (Salmo salar)
Bibliographic record
Abstract
Abstract Background Data storage tags (DSTs) record and store information about animals and their environment, and can provide important data relevant to fish culture, ecology and conservation. A DST has recently been developed that records heart rate ( f H ), electrocardiograms (ECGs), tri-axial acceleration and temperature. However, at the time of this study, no research using these tags had been performed on fish or determined the quality of the data collected. Thus, our research asked: do these DSTs provide reliable and meaningful data? To examine this question, Atlantic salmon (1.4 ± 0.7 kg) were implanted with DSTs, then swam at increasing speeds in a swim tunnel after 1 week of recovery. Further, in two separate experiments, salmon (2.4 ± 0.1 kg) were implanted with DSTs and held in a large tank with conspecifics for 1 week at 11 °C or 6 weeks at 8–12 °C. Results External acceleration (EA) and variation in EA (VAR) increased exponentially with swimming speed and tail beat frequency. The quality index (QI) assigned to ECG recordings (where QI 0 means very good quality, and QI 1, QI 2 and QI 3 are of reduced quality) did not change significantly with increasing swimming speed (QI 0 ~ 60–80%). However, we found that the accuracy of the tag algorithm in estimating f H from ECGs was reduced when QI >0 . Diurnal patterns of f H and EA were evident from the time the salmon were placed in the tank. Heart rate appeared to stabilize by ~ 4 days post-surgery in the first experiment, but extended holding showed that f H declined for 2–3 weeks. During extended holding, the tag had difficulty recording low f H values < 30 bpm, and for this reason, in addition to the fact that the algorithm can miscalculate f H , it is highly recommended that ECGs be saved when possible for quality control and so that f H values with QI >0 can be manually calculated. Conclusions With these DSTs, parameters of acceleration can be used to monitor the activity of free-swimming salmon. Further, changes in f H and heart rate variability (HRV) due to diurnal rhythms, and in response to temperature, activity and stressors, can be recorded.
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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 | 0.005 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".