Spectral discrimination of coarse and fine mode optical depth
Bibliographic record
Abstract
The recognition that the aerosol particle size distribution (PSD) is effectively bimodal permits the extraction of the fine and coarse mode optical depths (τ f and τ c ) from the spectral shape of the total aerosol optical depth (τ a = τ f + τ c ). This purely optical technique avoids intermediate computations of the PSD and yields a direct optical output that is commensurate in complexity with the spectral information content of τ a . The separation into τ f and τ c is a robust process and yields aerosol optical statistics, which are more intrinsic than those, obtained from a generic analysis of τ a . Partial (optical) validation is provided by (1) demonstrating the physical coherence of the simple model employed, (2) demonstrating that τ c variation is coherent with photographic evidence of thin cloud events and that τ f variation is coherent with photographic evidence of clear sky and haze events, and (3) showing that the retrieved values of τ f and τ c are well‐correlated, if weakly biased, relative to formal inversions of combined solar extinction and sky radiance data. The spectral inversion technique permitted a closer scrutiny of a standard (temporally based) cloud‐screening algorithm. Perturbations of monthly or longer‐term statistics associated with passive or active shortcomings of operational cloud screening were inferred to be small to occasionally moderate over a sampling of cases. Diurnal illustrations were given where it was clear that such shortcomings can have a significant impact on the interpretation of specific events; (1) commission errors in τ f due to the exclusion of excessively high‐frequency fine mode events and (2) omission errors in τ c due to the inclusion of insufficiently high‐frequency thin homogeneous cloud events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".