Gas exchange of CO<sub>2</sub>and O<sub>2</sub>in partially ice-covered regions of the Arctic Ocean investigated using<i>in situ</i>sensors
Bibliographic record
Abstract
Sea surface CO 2 dynamics are not well characterized in the Arctic Ocean (AO). Most data are from ship-based studies during the low-ice period (May-September) and obtained from near shore areas because of accessibility. More CO 2 data are needed to improve models for predicting the future of the carbon cycle in the region and its relationship to ocean acidification. Air-sea gas exchange rates are complicated by the presence of ice. Consequently gas exchange rates have a larger uncertainty in the AO compared to other ocean regions. To provide more information about CO 2 dynamics and gas exchange in the AO, in situ time-series data have been collected from the Canada Basin during late summer to autumn of 2012. Partial pressure of CO 2 ( p CO 2 ), dissolved O 2 (DO) concentration, temperature, and salinity were measured at ∼6-m depth under little ice and multi-year ice on two ice-tethered profilers (ITPs) for 40-50 days. The p CO 2 levels were always below atmospheric saturation whereas DO was almost always slightly above saturation. Although the two ITPs were on an average only 222 km apart, one was further south and had 14 ± 12% ice cover; whereas the more northern ITP had 63 ± 16% ice cover. Consequently the two data sets differed significantly in external forcings. Modeled variability of CO 2 and DO estimate that gas exchange would significantly alter sea surface p CO 2 in the low ice cover record but minimally in the more extensively ice-covered region. If these conditions extended over the entire AO, the total uptake of atmospheric CO 2 would be 28.6 Tg C yr -1 and 15.4 Tg C yr -1 under low and high ice-covered conditions, respectively.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".