SURATLANT: a surface dataset in the central part of the North Atlantic subpolar gyre
Bibliographic record
Abstract
The SURATLANT dataset (SURveillance ATLANTique) consists of individual data of temperature, salinity, dissolved inorganic carbon (DIC) and its isotopic composition d13CDIC, total alkalinity (At), inorganic nutrients and water stable isotopes (δ18O and δD) collected mostly from ships of opportunity since 1993 along transects between Iceland and Newfoundland (shipping company EIMSKIP), as well as, since 2014, between west Greenland and Danemark (shipping company RAL). The data have been validated, qualified, and their accuracy and the overall characteristics of the data set are presented in a paper (Reverdin et al., 2018). The csv file provides a listing of the data with one line for each collection date. This includes collection date, position, temperature, salinity, and the measured, validated and in some cases adjusted variables, as well as a quality code following WOCE/GLODAP format. For water isotopes and isotopic composition of inroganic carbon, a code is also provided indicating the method of measurement used. An additional text-file provides a normalized average seasonal cycle of the 10 variables measured in 5 boxes between the vicinity of Newfoundland to the south-west of Iceland (corresponding to the figure 3 of the paper Reverdin et al in the References). Format and information is provided in the top 25 lines and the gridded seasonal cyle data start at line 26.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".