The Ocean Tracking Network (OTN) Canada: a template for developing other integrated research networks within the global OTN
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. The Ocean Tracking Network (OTN) is the world’s aquatic animal tracking network: a global research and technology development platform and partnership that aims to revolutionize the ways that oceans and freshwater ecosystems are monitored and understood. Headquartered at Dalhousie University, Halifax, NS, OTN uses sonic and other telemetry technologies (satellite tags, archival data-storage tags) to document movements and survival of marine animals. OTN is creating a global network of acoustic receivers and oceanographic sensors (deployed in all the world’s oceans and connecting waters, spanning seven continents), which record animal detections, movements, and interactions, in addition to oceanographic observations; is establishing a global network of users with a common database; and is demonstrating technologies that link animal locations and movements to oceanographic/environmental conditions. OTN Canada is the 7-year Canada-wide integrative research network program designed to use and develop OTN technologies and infrastructure to better understand changing marine ecosystems across Canada, to demonstrate how we can learn about these ecosystems through cutting-edge collaborative research, and to contribute to global observation of coastal and offshore ecosystems. Through this integrative approach of a national network of researchers, with international links and outreach, OTN Canada serves as a prototype research hub for other regions worldwide
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.039 |
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".