Supplementary material to "An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021"
Bibliographic record
Abstract
The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical data, with an emphasis on seawater inorganic carbon chemistry and related 85 variables determined through chemical analysis of seawater samples.GLODAPv2.2021 is an update of the previous version, GLODAPv2.2020.The major changes are: data from 43 new cruises were added, data coverage extended until 2020, removal of all data with missing temperatures, and the inclusion of a digital object identifier (doi) for each cruise in the product files.In addition, a number of minor corrections to GLODAPv2.2020data were performed.GLODAPv2.2021includes measurements from more than 1.3 million water samples from the global oceans collected on 90 989 cruises.GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019.The major changes are: data from 106 new cruises added, extension of time coverage to 2019, and the inclusion of available (also for historical cruises) discrete fugacity of CO2 (fCO2) values in the merged product files.GLODAPv2.2020now includes measurements from more than 1.2 million water samples from the global oceans collected on 946 cruises.The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, 95 CFC-113, and CCl4) have undergone extensive quality control with a focus on systematic evaluation of bias.The data are available in two formats: (i) as submitted by the data originator but updated to WOCE exchange format and (ii) as a merged data product with adjustments applied to minimize bias. .For this annual update, adjustments for the 43 new cruises were derived by comparing those data with the data from the 946 quality-controlled cruises in the GLODAPv2.2020These adjustments were derived by comparing the data from the 106 new cruises with the data from 100 the 840 quality-controlled cruises of the GLODAPv2.2019data product using crossover analysis.Comparisons to empirical algorithm estimates provided additional context for adjustment decisions, this is new to this version.The adjustments are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated.The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in 105 silicate, 2 % in phosphate, 4 mol kg -1 in dissolved inorganic carbon, 4 mol kg -1 in total alkalinity, 0.01-0.02 in pH (depending on region), and 5 % in the halogenated transient tracers.The other variables included in the compilation, such as isotopic tracers and discrete fCO2, were not subjected to bias comparison or adjustments.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.645 | 0.332 |
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".