Quantification of the variability and penetration of per- and poly-fluoroalkyl substances through a concrete pad
Bibliographic record
Abstract
Historical use of aqueous film forming foams (AFFF) containing per- and poly-fluoroalkyl substances (PFAS) for fire-fighting activities has contributed to widespread contamination of infrastructure which can represent an ongoing source of PFAS to the surrounding environment. A concrete fire training pad with historical use of Ansulite and Lightwater AFFF formulations had PFAS concentrations measured to quantify spatial variability of PFAS within the pad. Surface chips and whole cores of concrete through to the underlying aggregate base were collected over the 24 × 9 m concrete pad and depth profiles of PFAS concentrations in nine cores were analysed. PFOS and PFHxS dominated the PFAS for surface samples, along the depth profile of cores and in the underlying plastic and aggregate material, with substantial variability in the concentrations of PFAS in the samples. Although there was variability of individual PFAS along the depth profile, higher surface concentrations of PFAS generally followed the designed movement of water across the pad. Total oxidisable precursor (TOP) assessments of one core indicated additional PFAS were present along the entire length of the core. This study highlights concentrations of PFAS (up to low μg/kg) from historical use of AFFF can occur throughout concrete, with the variable concentrations throughout the profile.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".