A Characterization of Arctic Aerosols as Derived from Airborne Observations and their Influence on the Surface Radiation Budget \n
Bibliographic record
Abstract
The Arctic is a key player in the climate system because of the strong modification of the surface energy budget through snow and ice cover, which is tightly coupled to the global circulation of the atmosphere and the ocean. AWI (Alfred Wegener Institute) initiated therefore together with EC (Environment Canada) a special airborne program, as the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP). The past two campaigns with POLAR 5 took place during April 2009 as well as April 2011. The Instrumentation, included a tethered electromagnetic (EM) sensor for sea ice\t thickness measurements [Haas et al, 2010], analyzers for ozone, gaseous elementary mercury, bromine monoxide, aerosol light scattering and aerosol light absorption and refractory black carbon, aerosol number concentration and aerosol size distribution, and aerosol optical depth (AOD). In addition, aerosol and ozone LIDAR were operated, and drop sondes were launched to characterize atmospheric state variables and to use it for combined LIDAR and aerosol data analysis [Hoffmann et al., 2012]. The traverses were completed within about a month, providing 3-D snapshots of aerosol, trace gases, atmospheric condition and sea ice thickness
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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 | 0.000 |
| 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 | 0.000 | 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".