Bibliographic record
Abstract
Vast amounts of methane (CH4) stored in permafrost and submarine sediments are susceptible to release in a warming Arctic, further exacerbating climate change in a positive feedback. It is therefore critical to monitor CH4 over pan-regional scales to detect early signs of CH4 release. However, our ability to monitor CH4 is hampered in remote northern regions by sampling and logistical constraints and few good baseline data exist in many areas. To create a baseline study of current background levels of CH4 in North Atlantic waters, we collected continuous real-time atmospheric CH4 data, along with ambient air temperature and wind parameters over 22 days in summer 2021 on a roughly 5100 km voyage in the northern Labrador Sea and Baffin Bay up to 71° N. In addition, we measured CH4 concentrations in the water column using discrete water samples at selected stations. Measured atmospheric mixing ratios of CH4 ranged from 1944.7 ppb to 2012.0 ppb, with a mean of 1966.0±7.4 ppb and a baseline of 1954.2−1980.6 ppb. Dissolved CH4 concentrations in the near-surface water peaked at 56.58±0.05 nM within 1 km down-current of a known cold seep at Scott Inlet but were consistently super-saturated throughout the water column in Southwind Fjord, which is an area recently affected by submarine landslides. Local sea-air CH4 fluxes ranged from 0.1−14.1 µmol m-2 d-1 indicating that the ocean acted as a CH4 source to the atmosphere. Atmospheric CH4 levels were also driven by meteorological, spatial, and temporal variations. Highest atmospheric CH4 mixing ratios were detected in the Cumberland Sound in Nunavut, suggesting onshore sources from nearby waterbodies and wetlands, whereas ocean-based contributions at this location could not be ruled out. Coupled real-time measurements of marine and atmospheric CH4 data have the potential to provide ongoing monitoring in a region susceptible to CH4 releases, as well as critical validation data for global-scale measurements and modelling.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.170 | 0.132 |
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".