Bibliographic record
Abstract
Abstract. Mineral dust absorbs and scatters solar and infrared radiation, thereby affecting the radiance spectrum at the surface and top-of-atmosphere and the atmospheric heating rate. While half of the outgoing thermal radiation is emitted in the far infrared (FIR, 15–100 μm), knowledge of the optical properties and thermal radiative effects of dust is currently limited to the mid-infrared region (MIR, 3–15 μm). In this study we performed pellet spectroscopy measurements to evaluate the MIR and FIR contribution to dust absorbance and explore the variability and spectral diversity of the dust signature within the 2.5–25 μm range. Thirteen dust samples re-suspended from parent soils with contrasting mineralogy were investigated, including low and mid latitude dust (LMLD) sources in Africa, America, Asia, and Middle East, and high latitude dust (HLD) from Iceland. Results show that the absorbance of dust in the FIR up to 25 μm is comparable in intensity to that in the MIR. Also, spectrally different absorption (position and shape of the peaks) is observed for HLD compared to LMLD, due to differences in mineralogical composition. Corroborated with the few available literature data on absorption properties of natural dust and single minerals up to 100 μm wavelength, these data suggest the relevance of MIR and FIR interactions to the dust radiative effect for low to high latitude sources. Furthermore, the dust spectral signatures in the MIR and FIR could potentially be used to characterise the mineralogy and differentiate the origin of airborne particles based on infrared remote sensing observations.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.587 | 0.455 |
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".