Unsupervised Machine Learning as a Tool for Exploratory Analysis of Acoustic Telemetry Data: A Case Study With Northern Pike in Toronto Harbour
Bibliographic record
Abstract
Spatial ecology aims to further knowledge of an organism's relationship with its environment and guide decision-making related to conservation.The advancement of biotelemetry has facilitated this goal, however, data management, from its acquisition to its utilization, is central to its success.Standard analysis may include separating tagged individuals into predefined groups based on biometrics or capture location, and then comparing relationships among groups, environmental measures, and their seasonal habitat choices.While effective in that it informs on the relationship among variables, this approach is computationally intensive, and the insight provided is limited to behaviour among predefined groups.This study effectively and efficiently leverages machine learning methods -hierarchical clustering and principal component analysis -to explore animal behaviour, thus providing an efficient, alternative method to analyzing acoustic telemetry data.A by-product of this project is software development that can facilitate analysis of acoustic telemetry data.i
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".