SURATLANT: a 1993–2017 surface sampling in the central part of theNorth Atlantic subpolar gyre
Bibliographic record
Abstract
Abstract. This paper presents the SURATLANT dataset (SURveillance ATLANTique), consisting of individual data of temperature, salinity, parameters of the carbonate system, nutrients and water stable isotopes (δ18O and δD) collected mostly from ships of opportunity since 1993 along transects between Iceland and Newfoundland (https://doi.org/10.17882/54517). We discuss how the data are validated, qualified, their accuracy and the overall characteristics of the data set. The data are used to reconstruct seasonal cycles and interannual anomalies, in particular of Sea Surface Salinity (SSS), inorganic nutrients, dissolved inorganic carbon (DIC) and its isotopic composition δ13CDIC, total alkalinity (At), and water isotope concentrations. Derived parameters, such as fCO2 and pH are also estimated. The relation between salinity and At is estimated in these data to investigate the possibility to replace missing At when estimating other parameters of the carbonate system. We compare the seasonal cycle derived from these data with other climatologies. We find a period of small seasonal change between January and late April, except on the Newfoundland shelf/continental slope, when changes related with spring-stratification and blooms occur earlier. The data were collected in a period of multi-decennial variability associated with the Atlantic meridional oscillation with warming between 1994 and 2004–2007, and the recent cooling having peaked in 2014–2016. We also observe strong salinification in 2004–2009 and fresher waters in 1994–1995 as well as since 2010 south of 54° N and in 2016–2017 north of 54° N. Indication of multi-decadal variability is also suggested by other variables, such as phosphate or DIC, but cannot be well resolved seasonally with the discrete sampling and in the presence of interannual variability. As a whole, over the 24 years ocean fCO2 trend (+1.9 µatm yr-1) is close to the atmospheric trend and associated with an increase in DIC (+0.70 μmol kg-1 yr-1). The data also revealed a "canonical" pH decrease of −0.002 yr-1. There is also a decrease in δ13CDIC between 2005 and 2017 (in winter, −0.015 ‰ yr-1, but larger in summer, −0.042 ‰ yr-1), suggesting significant anthropogenic carbon signal at play together with other processes (mixing, biological activity).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".