Automated continuous monitoring and response to toxic subsurface vapors entering overlying buildings—Selected observations, implications and considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Vapor intrusion characterization and response efforts must consider four key interactive factors: background indoor air constituents, preferential vapor migration pathways, complex patterns of vapor distribution within buildings, and temporal concentration variability caused by pressure differentials within and exterior to structures. An additional challenge is found at sites contaminated by trichloroethylene (TCE), which in the United States has very low indoor air screening levels due to acute risk over short exposure durations for sensitive populations. Timely and accurate characterization of vapor intrusion has been constrained by traditional passive time‐averaging sampling methods. This article presents three case studies of a robust new methodology for vapor intrusion characterization particularly suited for sites where there is a critical need for rapid response to exposure exceedances to minimize health risks and liabilities. The new methodology comprises low‐detection‐level field analytical instrumentation with grab sample and continuous monitoring capabilities for key volatile constituents integrated with pressure differential measurements and web‐based reporting. The system also provides automated triggered alerts to project teams and capability for integration with engineered systems for vapor intrusion control. The three case studies illustrate key findings and lessons learned during system deployment at two sites undergoing characterization studies and one site undergoing thermal remediation of volatile contaminants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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