Bimodal size distribution influences on the variation of Angstrom derivatives in spectral and optical depth space
Bibliographic record
Abstract
The variation of the aerosol optical depth and its first and second spectral derivatives (α and α′) can be largely described in terms of the spectral interaction between the individual optical components of a bimodal size distribution. Simple analytical expressions involving the separate optical components of each mode explain virtually all the features seen in spectra of the aerosol optical depth and its derivatives. Illustrations are given for a variety of measured optical depth spectra; these include comparative simulations of the diurnal behavior of α and α′ spectra as well as the diurnal and general statistical behavior of α and α′ as a function of optical depth (optical depth space). Each mode acts as a fixed “basis vector” from which much of the behavior in spectral and optical depth space can be generated by varying the extensive (number density dependent) contributions of fine and coarse mode optical depths. Departures from these basis vectors are caused by changes in aerosol type (average size and refractive index) and thus are associated with differing synoptical air masses, source trajectories or humidity conditions. Spectral parameters are very sensitive to interband errors in measured optical depth data. Third‐order polynomial fits within the visible‐NIR spectral region effectively filter such errors while representing the limit of useful extractable information.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".