A North American Arctic Aerosol Climatology using Ground-based Sunphotometry
Bibliographic record
Abstract
The Arctic is known as a key area for the detection of climate changes and atmospheric pollution on a global scale. In this paper we describe a new Canadian sunphotometer network called AEROCAN, whose primary mandate is to establish a climatology of atmospheric aerosols. This network is part of AERONET, the worldwide federated sunphotometer network managed by the NASA Goddard Space Flight Center. The potential of sunphotometer data from the AERONET/AEROCAN network for monitoring of Arctic aerosols is illustrated, using examples of the multiyear variation of aerosol optical properties and atmospheric precipitable water vapour content at some stations, and in particular at Bonanza Creek, Alaska since 1994. Despite its sparse spatial density, the network represents an important tool for monitoring the spatio-temporal variation of Arctic aerosols. It also represents an important source of independent aerosol data, which we feel should be further developed in northern areas to improve our understanding of how atmospheric aerosols influence global climate.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".