Lessons Learned in Developing a Canadian Operational Glider Fleet
Bibliographic record
Abstract
Abstract Canada's expanding “Blue Economy” requires a major expansion of existing ocean monitoring if developments are to be sustainably managed. Dalhousie University, the Ocean Tracking Network, and the Marine Environmental Observation Prediction and Response Network of Centers of Excellence have operated a mixed fleet of gliders for 7 years on missions covering >50,000 km. The data from these missions are used by research programs, nongovernmental organizations, and government agencies. The gliders have proven to be reliable platforms for ocean observation, collecting data in inclement weather, and times of the year when it is difficult to get ships at sea. However, glider operations have a steep learning curve, and much of the expertise that resides within an operational glider group is gleaned through experience. Managing glider data also poses significant challenges. Planning, risk management, rapid adaptation to the unexpected, and dedicated highly qualified personnel are the keys to sustaining successful glider operations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".