A review and tests of validation and sensitivity of geolocation models for marine fish tracking
Bibliographic record
Abstract
Abstract Uncertainties in fish tracking studies limit their integration into conservation and fisheries management plans. This is especially true for archival tagging studies that rely on geolocation models to infer fish tracks from recorded environmental variables. Hidden Markov Models (HMMs) are increasingly popular to geolocate marine fish equipped with archival tags; however, true errors and sensitivity of geolocation HMMs are seldom evaluated. In this study, we first review validation methods and implementations of geolocation HMMs to adapt to regional oceanography, fish species and tag data. We then use a case‐study to evaluate strengths and limitations of each validation approach and to illustrate the sensitivity of geolocation HMMs to implementation assumptions. Simulated and fixed tag locations are the most widely implemented methods, but less common methods relying on true fish tracking, that is double‐tagging or distance from recapture experiments, provide more informative estimates of model accuracy and precision. Results showed that model performance can be improved using simple assumptions when pre‐processing tag data rather than using a complex movement behaviour model. In addition, accelerometer show potential to further parameterise geolocation models. Overall, results from our case‐study and previous studies showed that current geolocation HMMs have average errors of ca. 30–50 and 120 km for demersal and large pelagic fish, respectively. We suggest that these errors are acceptable for investigations at the scale of fisheries management units.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".