Calibration of Permeation Passive Samplers with Silicone Membranes Based on Physicochemical Properties of the Analytes
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Bibliographic record
Abstract
Passive sampling is a very attractive alternative to active sampling due to its simplicity and low cost. Among the passive samplers used in air analysis, permeation passive samplers are the least affected by ambient conditions, including humidity, air currents, and temperature changes. The biggest drawback of permeation passive samplers is the need to calibrate them experimentally for each individual target analyte. The paper presents the results of research on the calibration of permeation passive samplers based on physicochemical properties of the analytes. Strong correlations were found between the calibration constants of the samplers and the number of carbon atoms among families of compounds (R2 ranging from 0.8507 for alcohols to 0.9995 for aromatic hydrocarbons), the molecular weights of the compounds (R2 = 0.8742), their boiling points (R2 = 0.8911), and linear temperature-programmed retention indexes (R2 = 0.9225). The last correlation makes it possible to estimate the calibration constants for unidentified analytes, which is impossible when the conventional procedure is used. This makes it possible to deploy permeation passive samplers in the same way in which active sampling is deployed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it