Controlling factors analysis of pCO<sub>2</sub>distribution in the Western Arctic Ocean in summertime
Bibliographic record
Abstract
The uptake of carbon dioxide (CO2) by the Arctic Ocean has been changing because of the rapid sea-ice retreat with global warming. The Chukchi Sea is the only gateway of the warm and nutrient-rich Pacific Ocean water flowing into the North Pole, and the high productivity-water had great impact on the CO2 uptake by the Arctic Ocean. We used the in situ underway data of aquatic partial pressure of CO2 (pCO2), temperature and salinity, as well as the remote sensing data of sea ice concentration, chlorophyll concentration, sea surface temperature in August in 2008, 2011 and 2012 to analyze the major controlling factors of aquatic pCO2 in the Western Arctic Ocean. We analyzed the pCO2 variation under the effects of thermodynamic process (temperature), mixing of water mass (salinity), biological drawdown (chlorophyll), and sea ice concentration. The aquatic pCO2 was generally unsaturation relative to the atmospheric CO2 in most of the Western Arctic Ocean. According to different controlling mechanisms, the study area was divided into three parts: the area affected by the Pacific Ocean water (mainly in the Chukchi Sea), the area where sea ice mostly melted with weak biological production (the southern Canada Basin and the Western Beaufort Sea), and the area mostly covered by sea ice (the Northern Canada Basin). The aquatic pCO2 was low in the Chukchi Sea with the influence of the Pacific Ocean water. While, pCO2 in the area where sea ice melted was up to 360-380 μatm because of warming, CO2 invasion from the atmosphere, and a low biological production. For the Canada Basin, it was controlled by temperature change and sea ice cover. The remote sensing data in large spatial-temporal scale can help to understand the pCO2 variation and its response to global change; and it needs to develop satellite algorithm of pCO2 based on the quantification of controlling processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".