Regressing Gas/Particle Partitioning Data for Polycyclic Aromatic Hydrocarbons
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) were measured in the rural atmosphere of Southern Ontario, Canada from October 2001 to November 2002. Sixty seven pairs of gaseous and particle-bound concentrations of PAHs were determined concurrently in a forest and a clearing. The gas/particle partitioning behavior of the PAHs was investigated by fitting the original Junge-Pankow equation to the fraction in the particle phase phi for each set of measured data, either allowing the slope m to deviate from -1 (two-parameter model) or not (one-parameter model). This fitting procedure was judged more robust than linear logarithmic regressions involving the gas/particle partition coefficient, because the latter is sensitive to the applied blank correction, tends to ignore a significant amount of analytical information, and gives undue weight to more uncertain data points. The experimental data fit was good for both nonlinear models, and discrepancies between experimental data and models and between models are mostly related to sampling/experimental artifacts. In particular, samples taken close to the freezing point appear to suffer from blow-off artifacts. Applying slopes m different from -1 appears only justified if it can be assured that a second parameter indeed provides a better fit and that this better fit is not due to experimental, analytical, or statistical artifacts. The magnitude of the differences in the model fitting parameters between sampling events is consistent with the reported variability in the nature and concentration of atmospheric particles. Statistical tests on the regression results indicate that the gas/particle partitioning was not significantly different between the forest and the clearing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".