The Ocean Tracking Network: Advancing frontiers in aquatic science and management
Bibliographic record
Abstract
Aquatic animals are integral to ocean and freshwater ecosystems and their resilience, are depended upon globally for food sustainability, and support coastal communities and Indigenous peoples. However, global aquatic environments are changing profoundly due to anthropogenic actions and environmental change. These changes are altering distributions, movements, and survival of aquatic animals in ways that are not well understood. The Ocean Tracking Network (OTN) is a global partnership that is filling this knowledge gap. OTN Canada, a pan-Canadian (and beyond) research network, was launched in 2010 with visionary funding by the Canadian government. In our introduction to this special issue, we briefly overview how this interdisciplinary network has used state-of-the-art technologies, infrastructure, electronic tags and sensors, and associated cutting-edge research and training programs to better understand changing marine and freshwater dynamics and their impact on ecosystems, resources, and animal ecology. These studies have provided unprecedented insights into animal ecology and resource management at a range of spatial and temporal scales and by interfacing animal movements with novel measures of environment, physiology, disease, genetics–genomics, and anthropogenic stressors.
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 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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".