Comment on essd-2020-402
Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> Internally consistent, quality-controlled (QC) data products play an important role in promoting regional-to-global research efforts to understand societal vulnerabilities to ocean acidification (OA).<span id="page2778"/> However, there are currently no such data products for the coastal ocean, where most of the OA-susceptible commercial and recreational fisheries and aquaculture industries are located. In this collaborative effort, we compiled, quality-controlled, and synthesized 2 decades of discrete measurements of inorganic carbon system parameters, oxygen, and nutrient chemistry data from the North American continental shelves to generate a data product called the Coastal Ocean Data Analysis Product in North America (CODAP-NA). There are few deep-water (<span class="inline-formula">></span>â1500âm) sampling locations in the current data product. As a result, crossover analyses, which rely on comparisons between measurements on different cruises in the stable deep ocean, could not form the basis for cruise-to-cruise adjustments. For this reason, care was taken in the selection of data sets to include in this initial release of CODAP-NA, and only data sets from laboratories with known quality assurance practices were included. New consistency checks and outlier detections were used to QC the data. Future releases of this CODAP-NA product will use this core data product as the basis for cruise-to-cruise comparisons. We worked closely with the investigators who collected and measured these data during the QC process. This version (v2021) of the CODAP-NA is comprised of 3391 oceanographic profiles from 61 research cruises covering all continental shelves of North America, from Alaska to Mexico in the west and from Canada to the Caribbean in the east. Data for 14 variables (temperature; salinity; dissolved oxygen content; dissolved inorganic carbon content; total alkalinity; pH on total scale; carbonate ion content; fugacity of carbon dioxide; and substance contents of silicate, phosphate, nitrate, nitrite, nitrate plus nitrite, and ammonium) have been subjected to extensive QC. CODAP-NA is available as a merged data product (Excel, CSV, MATLAB, and NetCDF; <a href="https://doi.org/10.25921/531n-c230">https://doi.org/10.25921/531n-c230</a>, <span class="uri">https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0219960.html</span>, last access: 15 May 2021) (Jiang et al., 2021a). The original cruise data have also been updated with data providers' consent and summarized in a table with links to NOAA's National Centers for Environmental Information (NCEI) archives (<span class="uri">https://www.ncei.noaa.gov/access/ocean-acidification-data-stewardship-oads/synthesis/NAcruises.html</span>).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.064 | 0.003 |
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; both teacher heads agree on what is shown here.
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".