Comment on essd-2023-325
Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> As elsewhere in the global ocean, the Gulf of Alaska is experiencing the rapid onset of ocean acidification (OA) driven by oceanic absorption of anthropogenic emissions of carbon dioxide from the atmosphere. In support of OA research and monitoring, we present here a data product of marine inorganic carbon chemistry parameters measured from seawater samples taken during biannual cruises between 2008 and 2017 in the northern Gulf of Alaska. Samples were collected each May and September over the 10–year period using a conductivity, temperature, depth (CTD) profiler coupled with a Niskin bottle rosette at stations including a long–term hydrographic survey transect known as the Gulf of Alaska (GAK) Line. This dataset includes discrete seawater measurements such as dissolved inorganic carbon and total alkalinity, which allows the calculation of other marine carbon parameters, including carbonate mineral saturation states, carbon dioxide (CO<sub>2</sub>), and pH. Cumulative daily Bakun upwelling indices illustrate the pattern of downwelling in the northern Gulf of Alaska, with a period of relaxation spanning between the May and September cruises. The observed time and space variability impart challenges for disentangling the OA signal despite this dataset spanning a decade. However, this data product greatly enhances our understanding of seasonal and interannual variability on the marine inorganic carbon system parameters. The product can also aid in the ground truthing of biogeochemical models, refining estimates of sea–air CO<sub>2</sub> exchange, and determining appropriate CO<sub>2</sub> parameter ranges for experiments targeting potentially vulnerable species. Data are available at <a href="https://doi.org/10.25921/x9sg-9b08" target="_blank" rel="noopener">https://doi.org/10.25921/x9sg-9b08</a> (Monacci et al., 2023).
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 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 | 0.001 | 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) | 0.033 | 0.030 |
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".