Assessing the radiative impact of aerosol smoke using MODTRAN5
Bibliographic record
Abstract
Aerosols in the atmosphere affect the Earth's radiation budget in complicated ways, depending on their physical and optical characteristics and how they interact with solar and terrestrial radiation or affect cloud nucleation. While the Arctic atmosphere is generally very clean, spring incursions of haze and dust from Eurasia are known to perturb the surface radiation balance. Recent analyses (based on "Radiative impact of boreal smoke in the Arctic: Observed and modeled", Stone, et al., to be referred to throughout this ms as Stone2008) also reveal that smoke plumes from boreal forest fires can have significant effects during summer. Once aloft, upper-level winds can transport this smoke long distances. In late June and July 2004 fires raged across eastern Alaska and the Yukon and the resulting smoke was advected across the Arctic, reaching as far as Europe. The long-range transport was tracked using a dispersion model combined with various in situ measurements along its path, all showing enhancements in aerosol opacity. The measurements made at Barrow, Alaska, documented just a portion of the transport and the radiative impact of smoke. The comprehensive measuring systems in place near Barrow (NOAA/GMD and DoE/ARM) presented a unique opportunity to characterize the smoke aerosol both physically and optically, and therefore permit quantification of the upwelling radiance (outgoing shortwave radiance - OSR, 0.28 to 4.0 μm) as observed by NASA satellites: Clouds and the Earth's Radiant Energy System (CERES) 5, coupled with data from Moderate Resolution Imaging Spectroradiometer (MODIS).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".