Memorial University Ocean Glider Deployments : 2005 – Present
Bibliographic record
Abstract
Memorial University has over the past 15 years been involved in various ocean glider activities with deployments focused primarily on the Newfoundland Shelf and the Labrador Sea. For example, there are four deployments with glider data in the Labrador Sea. Partnerships with Fisheries and Oceans Canada and Ocean Gliders Canada have also resulted in deployments of Memorial's gliders in the Pacific. The data contain 14’663 mission kms, 620 deployment days and 25’108 individual glider profiles. L1 NETCDF files for every deployment archived on the Memorial University’s Glider Data server are made available publicly for scientific research. Deployments vary in duration and region. All files were processed with the SOCIB glider toolbox (Troupin et al., 2015), modified by Nicolai von Oppeln-Bronikowski, 2019 for MUN glider deployments. Metadata: File creator(s), contact info, institution, applicable funding, responsible researcher(s), deployment region, deployment start, deployment end, longitude min, longitude max, latitude min, latitude max, glider type, glider configuration if known, sensors, sensor serial numbers, science data QC. Glider Data: - Minimum Data: Time, Depth, Position, Depth-Averaged Current, CTD. - Most Deployments: Oxy_umolL, Oxy_Calphase_DEG, Oxy_sat - Some Deployments: pCO2_uatm, pCO2_Calphase_DEG, pCO2_Dphase
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".