TERIFIC Ocean Glider Deployments in the Labrador Sea
Bibliographic record
Abstract
As part of the TERIFIC (Targeted Experiment to Reconcile Increased Freshwater with Increased Convection) project funded by the European Research Council, two Kongsberg Seagliders (sg602 and sg638) were deployed offshore of Qaqortoq, southwest of Greenland, in December 2019 from the 30-m research vessel Adolf Jensen. Both gliders were retrieved in Trinity Bay on the Labrador coast of Canada in May 2020 from a fishing boat. Glider sg602 had a standard fairing and a standard hardware; glider sg638 had an ogive fairing and was equipped with a science controller. Unpumped CTDs (Conductivity, Temperature, and Depth), referred to as CT sails and provided by Sea-Bird Electronics (Bellevue, WA) were mounted on both Seagliders with a sampling frequency of 0.1 Hz. An Aanderaa oxygen optode was also fixed on both gliders but only sg638 returned measurements of dissolved oxygen concentration. Both gliders were equipped with WETLabs (Western Environmental Technologies Laboratories) BBFL2 ECO Puck that measures chlorophyll, colored dissolved organic matter and optical backscatter (700 nm). Initial processing with the University of Washington’s basestation corrects for the thermal-inertia effect of the CT sail. Both gliders were processed with the GliderTools toolbox (Gregor et al., 2019) to determine adjusted temperature and salinity variables.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".