Groundwater Data Analysis for MTBE Relative to Other Oxygenates at Gasoline-Impacted Sites
Bibliographic record
Abstract
Abstract This article describes the results of a statistical analysis on MTBE relative to other gasoline oxygenates based on groundwater monitoring data collected in Los Angeles County, CA. The other gasoline oxygenates in this study include di-isopropyl ether (DIPE), ethyl tertiary butyl ether (ETBE), tertiary amyl methyl ether (TAME), and tertiary butyl alcohol (TBA). Correlation coefficients were calculated between MTBE and other oxygenates, and between the gasoline oxygenates and depth to groundwater and geological material type of aquifer. The correlation coefficients indicated poor correlation among all components mentioned. Analysis of variance (ANOVA) was used to compare MTBE concentrations detected in aquifers comprised chiefly of coarse-grained material with those comprised of finegrained material. The ANOVA results indicated that the difference is not statistically significant between MTBE detected in the two types of aquifer materials. Similar results were also reached for TBA. The oxygenate concentration distributions were further studied by grouping the data according to different gasoline brands. There is not an overall statistically clear trend on whether certain brands of gasoline contain MTBE and TBA consistently higher than other brands, or whether TBA is consistently higher than MTBE among all brands of gasoline. However, TBA shows statistically higher than MTBE in the mean, the median, and the 95th percentile in the overall groundwater data studied. Therefore, further studies on the relationship between MTBE and TBA are warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".