Acoustic telemetry observation systems: challenges encountered and overcome in the Laurentian Great Lakes
Bibliographic record
Abstract
The Great Lakes Acoustic Telemetry Observation System (GLATOS), organized in 2012, aims to advance and improve conservation and management of Great Lakes fishes by providing information on behavior, habitat use, and population dynamics. GLATOS faced challenges during establishment, including a funding agency-imposed urgency to initiate projects, a lack of telemetry expertise, and managing a flood of data. GLATOS now connects 190+ investigators, provides project consultation, maintains a web-based data portal, contributes data to Ocean Tracking Network’s global database, loans equipment, and promotes science transfer to managers. The GLATOS database currently has 50+ projects, 39 species tagged, 8000+ fish released, and 150+ million tag detections. Lessons learned include (1) seek advice from others experienced in telemetry; (2) organize networks prior to when shared data is urgently needed; (3) establish a data management system so that all receivers can contribute to every project; (4) hold annual meetings to foster relationships; (5) involve fish managers to ensure relevancy; and (6) staff require full-time commitment to lead and coordinate projects and to analyze data and publish results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".