Quantitative passive soil vapor sampling for VOCs – part 4: flow-through cell
Bibliographic record
Abstract
This paper presents a controlled experiment comparing several quantitative passive samplers for monitoring concentrations of volatile organic compound (VOC) vapors in soil gas using a flow-through cell. This application is simpler than conventional active sampling using adsorptive tubes because the flow rate does not need to be precisely measured and controlled, which is advantageous because the permeability of subsurface materials affects the flow rate and the permeability of geologic materials is highly variable. Using passive samplers in a flow-through cell, the flow rate may not need to be known exactly, as long as it is sufficient to purge the cell in a reasonable time and minimize any negative bias attributable to the starvation effect. An experiment was performed in a 500 mL flow-through cell using a two-factor, one-half fraction fractional factorial test design with flow rates of 80, 670 and 930 mL min(-1) and sample durations of 10, 15 and 20 minutes for each of five different passive samplers (passive Automatic Thermal Desorption Tube, Radiello®, SKC Ultra, Waterloo Membrane Sampler™ and 3M™ OVM 3500). A Summa canister was collected coincident with each passive sampler and analyzed by EPA Method TO-15 to provide a baseline for comparison of the passive sampler concentrations. The passive sampler concentrations were within a factor of 2 of the Summa canister concentrations in 32 of 35 cases. Passive samples collected at the low flow rate and short duration showed low concentrations, which is likely attributable to insufficient purging of the cell after sampler placement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".