Regular airborne surveys of Arctic sea ice and atmosphere
Bibliographic record
Abstract
The Arctic is undergoing rapid environmental change, manifested most dramatically by reductions in sea ice extent and thickness. The changes are attributed to anthropogenic effects related to greenhouse warming, with secondary contributions from changing ocean and wind currents as well as from pollutants, especially “absorbing” black carbon. The warmer Arctic air temperatures and new patterns of wind and ocean circulation have also contributed to a younger ice cover [Maslanik et al., 2011]. Specific factors that determine the temporal distribution of sea ice are poorly understood because few observations of key variables have been made in the central Arctic. For example, the planetary boundary layer (PBL), the lowest part of the atmosphere governed by interaction with Earth's surface, plays a critical role involving the exchange of momentum, heat, water vapor, trace gases, and aerosol particles. Satellites can provide limited observations of sea ice properties, but so far, accurate measurements of ice thickness or boundary layer properties have not been easily obtained. Although satellite retrievals of geophysical variables might be an essential source of information, their reliability remains questionable owing to inadequate spatial and/or temporal resolution and to a need for further validation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".