Multivariate Analysis of Ground Water and Soil Data from a Waste Disposal Site
Bibliographic record
Abstract
Abstract Environmental site investigations often involve the collection and analysis of hundreds of samples producing data sets that contain thousands of data points, which are difficult and time consuming to analyze. Consequently, investigators often focus on key surrogate parameters for site characterization and remedial action planning and assessment, which results in a large portion of the data collected remaining unused. This study presents the application of principal component analysis (PCA) as an efficient statistical technique to examine large environmental data sets through highlighting patterns in a reduced‐variable space. In this work, PCA was applied to ground water and soil data collected from a National Priorities List Superfund site. Analysis of the soil sample data identified several samples with contaminant parameters that were more closely related to those of the waste material than the background samples, and provided both a measure and delineation of the overall soil contamination. Analysis of the ground water data identified elevated metal concentrations due to the corrosion of a carbon steel well screen, a potential hydraulic connection between upper and lower water bearing zones at one well location, and two potentially impacted well locations. These results demonstrate that PCA facilitates the efficient analysis of large environmental data sets, providing a measure of contamination based on multiple sample parameters and aiding in the definition of a remediation boundary. These advantages can expedite data interpretation, guide additional sampling efforts, and define more accurate remediation boundaries, ultimately reducing the total cost of site investigation.
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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 | 0.000 | 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 | 0.000 | 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".