Use of Logarithmic-Scale Correlation Plots to Represent Contaminant Ratios for Evaluation of Subsurface Environmental Data
Bibliographic record
Abstract
Contaminant concentration ratios are used commonly to distinguish between different sources of contamination and to evaluate contaminant attenuation in groundwater, soil air, soil, and sediment. Logarithmic-scale correlation (log-log) plots provide special capabilities in representing contaminant ratios. Log-log plots can reflect the ranges in concentrations over many orders of magnitude, the magnitude and ranges of concentration ratios, and whether ratios are constant or change with declining concentration. Declines in contaminant concentrations due to dilution and dispersion processes will not change contaminant ratios, and such data should plot along isoratio lines. If contaminant concentrations are reduced also by other attenuation processes such as sorption or biodegradation that affect one of the contaminants to a greater degree than the other, the ratio will change and the data will deviate from the isoratio line trends. Examples are given to illustrate the use of log-log plots in the interpretation of chemical data from sites of groundwater, soil air, and sediment contamination.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".