A systematic approach for evaluation of gas-phase filter model
Bibliographic record
Abstract
This article reports the development of a systematic methodology for the evaluation of gas-phase filtration models. In this approach, two sets of experiments are performed. For the first, all of the required model input parameters are quantified either experimentally or empirically. The second set of experiments is needed for the overall model validation process. The proposed methodology was applied to an existing gas-phase filter model that was developed for application to a single or a mixture of contaminants. The model was evaluated for two gases, namely n-hexane and methyl ethyl ketone, and four scenarios: (1) single methyl ethyl ketone at a dry air condition, (2) single n-hexane at a dry air condition, (3) a mixture of methyl ethyl ketone and n-hexane at a dry air condition, and (4) a mixture of methyl ethyl ketone and n-hexane at a humid air condition. The model was able to predict the lifetime of the filter for a single contaminant with less than 10% relative error. For the binary mixture, the model could not predict the lifetime of the heavier compound; however, it was able to predict the lifetime of the filter for the lighter compound with about 25% relative error. For the case of a mixture, the model underestimates the displacement phenomenon of a lighter compound. It was also noted that in the case of a heavier compound, there is good agreement between the model's prediction, when it was applied to a single gas, and the experimental data for the single and mixture gas. It was also concluded that humidity has little effect on the breakthrough profile.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".