Demonstration of a Principal Component Analysis Trajectory Method to Assess Bioremediation Progress at a <scp>TCE</scp> ‐Impacted Site
Bibliographic record
Abstract
Abstract In‐situ bioremediation (ISB) is a popular remediation technology for the treatment of a range of compounds, including chlorinated solvents such as tetrachloroethene and trichloroethene (TCE). Large amounts of data are collected before, during, and after ISB applications to determine amendment approaches, monitor progress and evaluate success. The interpretation of these large datasets can be limited by the tools and techniques used for data analysis, and there is considerable potential in applying data reduction and multivariate techniques used elsewhere to performance monitoring during ISB. In this study, a principal component analysis (PCA) trajectory method was applied to a TCE‐impacted ISB site dataset, as an alternative to the inspection of time series data. The method connected each monitoring well's scores through PCA space to account for temporal changes in multiple analytes across the site. The method was used to separate monitoring well locations into categories that included On‐track and Unsuccessful based on their similarity to background wells in PCA space. The results agreed with those generated using traditional methods (e.g., time series plots) and were able to efficiently summarize large amounts of data to facilitate interpretation. It is expected that this PCA trajectory method could provide a useful screening tool to quickly identify site‐wide trends for the evaluation of ISB performance.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".