A scaling analysis of ozone photochemistry: I Model development
Bibliographic record
Abstract
Abstract. A scaling analysis has been used to capture the integrated behaviour of several photochemical mechanisms for a wide range of precursor concentrations and a variety of environmental conditions. The Buckingham Pi method of dimensional analysis was used to express the relevant variables in terms of dimensionless groups. These grouping show maximum ozone, initial NOx and initial VOC concentrations are made non-dimensional by the average NO2 photolysis rate (jav) and the rate constant for the NO-O3 titration reaction (kNO); temperature by the NO-O3 activation energy (ENO) and Boltzmann constant (k) and total irradiation time by the cumulative javΔt photolysis rate (π3). The analysis shows dimensionless maximum ozone concentration can be described by a product of powers of dimensionless initial NOx concentration, dimensionless temperature, and a similarity curve directly dependent on the ratio of initial VOC to NOx concentration and implicitly dependent on the cumulative NO2 photolysis rate. When Weibull transformed, the similarity relationship shows a scaling break with dimensionless model output clustering onto two straight line segments, parameterized using four variables: two describing the slopes of the line segments and two giving the location of their intersection. A fifth parameter is used to normalize the model output. The scaling analysis, similarity curve and parameterization appear to be independent of the details of the chemical mechanism, hold for a variety of VOC species and mixtures and a wide range of temperatures and actinic fluxes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".