CO <sub>2</sub> and CH <sub>4</sub> in sea ice from a subarctic fjord under influence of riverine input
Bibliographic record
Abstract
Abstract. We present the CH4 concentration [CH4], the partial pressure of CO2 (pCO2) and the total gas content in bulk sea ice from subarctic, land-fast sea ice in the Kapisillit fjord, Greenland. Fjord systems are characterized by freshwater runoff and riverine input and based on δ18O data, we show that > 30% of the surface water originated from periodic river input during ice growth. This resulted in fresher sea-ice layers with higher gas content than is typical from marine sea ice. The bulk ice [CH4] ranged from 1.8 to 12.1 nmol L−1, which corresponds to a partial pressure ranging from 3 to 28 ppmv. This is markedly higher than the average atmospheric methane content of 1.9 ppmv. Evidently most of the trapped methane within the ice was contained inside bubbles, and only a minor portion was dissolved in the brines. The bulk ice pCO2 ranged from 60 to 330 ppmv indicating that sea ice at temperatures above −4 °C is undersaturated compared to the atmosphere (390 ppmv). This study adds to the few existing studies of CH4 and CO2 in sea ice, and we conclude that subarctic seawater can be a sink for atmospheric CO2, while being a net source of CH4.
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 | 0.001 | 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".