Tracking Methyl <i>tert</i> -Butyl Ether in Groundwater: Four Years Later
Bibliographic record
Abstract
Methyl tert-butyl ether (MtBE) has been used as a gasoline additive to increase the octane rating and reduce air pollution in the United States. Due to its high solubility in water, mobility, and low natural biodegradation potential in the subsurface environment, MtBE has become a significant groundwater contaminant. During the last decade, the public, scientific, and regulatory communities have gained a great deal of knowledge about MtBE behavior in the subsurface. This article presents a review of MtBE historical data and its trends over a four-year period at a number of gasoline impacted sites in Los Angeles, California. This article uses field data obtained in the last four years to compare the highest concentration of MtBE at a well with the current concentration at the same well, and with the current highest concentration at the site (may not be the same well as that with the highest historical MtBE value). Statistics of the MtBE data showed that the MtBE historical highest concentrations have decreased between one and two orders of magnitude over the last four years. This result may be explained by the active cleanup at the sites, natural attenuation, and a better management of MtBE. However, the correlation coefficient revealed that a site with a relatively high historical highest concentration (CHH) may not have a relatively high current concentration (CC) today at the same well, suggesting that once the source is removed, the center of the MtBE plume may migrate downgradient of groundwater flow. The correlation coefficient also showed that a site with the relatively high CHH relates to current highest concentrations (CCH) at the site.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".