Seawater Mg/Ca and Sr/Ca ratios worldwide from 2009 to 2017 collected in 79 cruises, including coupled measured data for temperature, salinity, total alkalinity, DIC, DO, and calculated pH and CO2 (NCEI Accession 0171017)
Bibliographic record
Abstract
This dataset consists of seawater Mg/Ca and Sr/Ca ratios, temperature, salinity, total alkalinity, DIC and DO measured from 79 cruises between 2009 and 2017. The long debate about the conservative behaviour of seawater Mg/Ca and Sr/Ca ratios becomes more complex by studying the ocean as a whole and not only open ocean settings or the deep sea. In this project we measured in 79 cruises around the world seawater Mg/Ca and Sr/Ca ratios to discover that the modern ocean ranges are well beyond the pre-assumed values used by modellers, paleoceanographers and experimentalists as the basis for their work (Mg/Ca = 4.5 to 6.5 mol:mol and Sr/CA = 7 to 9.8 mmol:mol). We find out that modern seawater ranges are similar to the last 20 million years (Neogene) increase in these ratios, challenging the pre-assumed knowledge and a good number of methods used that assume a constant seawater Mg/Ca or Sr/Ca ratio. We agree that open ocean, the deep sea and reconstructions of past seawater based on samples derived from these settings give a good representation, but by no means they represent the ocean as a whole, especially coastal areas, upwelling zones, polar waters, and even the euphotic zone.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.004 | 0.006 |
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".