Aerosol optical depth approximation using an optimization-subdivision method
Bibliographic record
Abstract
Two methods to estimate aerosol optical depth (AOD) at a relatively low computational cost, using the data of the sun photometers from the Aerosol Robotic Network (AERONET) are presented and compared. One interpolates the data and the other approximates the data. The technique is based on a geometric approach. Assuming that AOD can be represented as a highly continuous surface function of time and position, a height field approximating AOD using the data from the sun photometers is obtained. Both methods use an optimization-subdivision iterative algorithm to create a function that interpolates or approximates the AOD values measured by the sun photometers. The methods begin by constructing a Delaunay triangulation of the location of the sun photometer sites over the region were the AOD value is to be approximated. The algorithm then alternatively optimizes and subdivides the triangular mesh. At each iteration, the optimization step first creates a data dependent triangulation which is then subdivided. The results obtained by the two methods are compared with those obtained from piecewise linear interpolation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".