Quantitative Assessment of Factors Contributing to Variations in Sea Surface <i>p</i>CO<sub>2</sub> in the Pacific Sector of the Arctic Ocean
Bibliographic record
Abstract
Abstract To quantitatively assess seasonal variations in the partial pressure of carbon dioxide (pCO2) in the Pacific sector of the Arctic Ocean (Canada Basin and Chukchi Sea) in 2021, water temperature, salinity, chlorophyll‐a, pCO2, dissolved inorganic carbon (DIC), total alkalinity (TA), and nutrients were measured. During summer 2021, surface water pCO2 in our study area (315 ± 41 μatm) was undersaturated with respect to the atmosphere (404 ± 4 μatm), and the ocean was a sink for atmospheric CO2 (−10.5 ± 11.0 mmol C m−2 day−1). Using DIC, TA, and nutrients in the temperature minimum layer, we estimated the under‐ice pCO2 in the water during the previous winter and calculated changes in pCO2 (δpCO2) due to temperature changes, freshwater inflow, biological activity, and other factors (gas exchange and advection) from winter to summer. In the Chukchi Sea, biological activity and temperature changes had significant impacts on pCO2, whereas in the Canada Basin, the influx of freshwater caused a significant decrease in pCO2. Our results suggested that different types of freshwaters had different effects on pCO2, with sea ice meltwater having a greater effect on reducing pCO2 than river water or snowmelt water. We therefore emphasize the importance of freshwater type and proportion, as well as freshwater supply, for prediction of future pCO2 changes.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".