Long Term Monitoring of Hydrocarbon Contamination Using Multi-Level Vapor Phase Piezometers
Bibliographic record
Abstract
This study evaluated the feasibility of supplementing groundwater monitoring protocols by assessing the vadose zone for the extent of residual subsurface contamination. The study also characterized the response of the soil gas signatures with respect to different soil types and degrees of contamination. A field study was conducted at a former gasoline vending station located in Ottawa, Canada. The current state of contamination was determined by analysis of soil samples taken from boreholes. A series of 10 nested soil gas wells with monitoring depths of 0.75, 1.5, 2.25 and 3.0 m were then installed. Using these wells, soil gas surveys were performed at regular intervals over an extended period to quantify Gaseous TPH (TPH g ), oxygen and carbon dioxide concentrations in the soil gas. Results indicate that soil gas wells located near the source term exhibited characteristic soil gas signatures and significant fluctuations in TPH g , oxygen, and carbon dioxide concentrations with time. Soil gas wells located beyond the soil contamination demonstrated limited correlation between TPH g , oxygen and carbon dioxide concentrations and decreased seasonal variability.
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.
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 | 0.000 |
| 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 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".