Acoustic detection and biological data for Lake Trout, Salvelinus namaycush, in Lake Ontario
Bibliographic record
Abstract
Acoustic Telemetry is gaining popularity for use in fisheries research as a method to estimate survival and observe behaviors of native fish species. Methodology for capture and tagging of fish is typically context and species-specific, requiring a case by case basis for best practices to maximize survival of tagged individuals. This dataset includes acoustic detection data from 320 adult Lake Trout, Salvelinus namaycush, captured and acoustic-tagged in Lake Ontario during April-June of 2023. Biological data (total length), capture data (surface water temperature, capture depth), capture location, and capture gear (angling, bottom trawls, gillnets) are also included in the dataset as covariates that can be analyzed to determine if any of these factors affect post-release survival of tagged Lake Trout. Acoustic detection data is available from April 2023 to November 2024. Survival of acoustic-tagged Lake Trout was estimated through acoustic telemetry detections indicating the status of the Lake Trout (alive vs. dead).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".