Robust optical features of fine mode size distributions: Application to the Québec smoke event of 2002
Bibliographic record
Abstract
Simple relationships involving the fundamental parameters of fine mode aerosol optical depth (τf), Angstrom exponent (αf) and its derivative (αf′) as near‐monotonic functions of the effective van de Hulst parameter (ρeff,f = 2 (2 π reff,f/λ) ∣m − 1∣) were derived for the conceptual case of a log‐translatable particle size distribution (LTPSD). This notion is useful in the interpretation of real sunphotometer data; the fine mode size distribution often approximates a LTPSD while departures from this behavior become more readily understood once one understands the first order optics. The near dependency of the fine mode optical parameters on ρeff,f was also exploited to obtain an explicit expression for ρeff,f as a function of αf and αf′. The relationships were applied to a representative case study to demonstrate their general applicability and then to the specific case of the July 2002 Québec smoke event. A number of illustrations were given where the coherency of the derived relations indicated that the LTPSD concept was often a good approximation to reality. The analysis of the Québec‐smoke extinction data showed the existence of a weak but systematic dependence of the Angstrom exponents on smoke trajectory time and by inference a steady growth in particle size with time. The variation of reff,f (derived from AERONET inversions) was however observed to be inconsistent with this dependence unless one redefined this parameter in terms of the clearly delineated peak of the asymmetric fine mode particle size distribution (PSD). This definition led to a re‐computed temporal rate of increase in reff,f which was coherent with the variation of the Angstrom parameters and which was coherent with a simple coagulative model based on conservation of volume. It was demonstrated that trajectory time was essentially a proxy variable for reff,f and more fundamentally ρeff,f (as predicted by the LTPSD relations). A similar proxy argument could be applied to the dependence of the Angstrom exponent on optical depth but such arguments are tempered by the relative variations of fine‐mode abundance (Af) and particle size (by the value of the parameter γ = dlogAf/dlogreff,f).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".