Visualizing PFAS Trends at a South Dakota AFFF‐Impacted Site
Bibliographic record
Abstract
ABSTRACT Various visualization alternatives are demonstrated for evaluating per‐and polyfluoroalkyl substances (PFAS) trends at an aqueous film forming foam‐ (AFFF‐) impacted site in South Dakota, including the use of radial diagrams, stacked bar maps, and pie charts. The purpose of this study was to compare and contrast visualization methods which may be used for PFAS site characterization or forensic assessments. PFAS groundwater concentration trends are first visualized based on site‐wide wells with maximum perfluorosulfonic acid (PFOS) plus perfluorooctanoic acid (PFOA) concentrations in AFFF source areas. Then a more detailed analysis of trends, including the potential for precursor transformations to perfluoroalkyl acids (PFAAs), is presented for a smaller portion of the site where former fire training activities were conducted. The advantages of using radial diagram reference series, such as maximum source or background concentrations, to better illustrate changes along a flow path are discussed. The benefits of including symbols on radial diagram maps to illustrate where PFAS are non‐detect or are in exceedance of site cleanup criteria, particularly in support of a PFAS plume delineation, are demonstrated. Radial diagrams and stacked bar maps are used to illustrate the relative proportion of perfluoroalkyl sulfonates and carboxylates in groundwater, which may help to identify relative contributions of AFFF products derived from electrochemical fluorination versus telomerization manufacturing processes. The benefit of using select PFAS ratios on radial diagram axes to support a combined assessment of precursor transformation and PFAA production along a flow path is demonstrated. Stacked bar maps are shown to have significant advantages over pie charts for PFAS forensic analyses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".