Estimating the Absolute Salinity of China Offshore Seawater Using Nutrients and Inorganic Carbon Data
Bibliographic record
Abstract
Abstract. In June 2009, the Intergovernmental Oceanographic Commission of UNESCO released the international thermodynamic equation of seawater – 2010 (TEOS-10 for short) (IOC et al, 2010) to define, describe and calculate the thermodynamic properties of seawater. Compared to Equation of Ocean State-80 (EOS-80 for short), the most obvious change of TEOS-10 is taking Absolute Salinity as salinity argument, replacing the Practical Salinity used in ocean society for 30 years. Due to lack of observation data, Absolute Salinity algorithm the applicability in the offshore and semi-closed sea is not very clear to date. Based on the Marine Integrated Investigation and Evaluation Project of China Offshore, other relevant data together with Pa08 model, we obtain the magnitude, distribution characteristics and formation mechanism of Absolute Salinity in China offshore. As the main composition anomaly relative to SSW, calcium carbonate, originating from terrestrial input of high calcium carbonate content and re-dissolution of sediment of China offshore, raises the Absolute Salinity Anomaly δSA as high as 0.20 g kg-1 and increases the Practical Salinity about 0.025 at most comparing to the chlorinity-based salinity. Moreover, both of them show obvious seasonal variation. Finally, relevant suggestions are proposed for the accurate measurement and expression of Absolute Salinity of the China offshore.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".