Geochemical fingerprinting of a radon anomaly: high-resolution PCA–ANOVA case study, Castleisland, SW Ireland
Bibliographic record
Abstract
Radon represents a global health risk, and so accurate delineation of radon-prone areas is a prerequisite for evidence-based radon mitigation and public health protection. National probabilistic radon models of Ireland, based on 1:1 M bedrock geological maps that group the Clare Shales with limestones achieve ∼74% accuracy. Despite the high accuracy of Ireland's national radon map, some discrepancies still exist. Using geochemical and geostatistical methods, we investigated a significant and persistent radon anomaly over the Clare Shale Formation at Castleisland, Co. Kerry, Ireland. Fifty-six topsoil samples collected from the A-horizon within a 6 km 2 grid, with samples spaced every 250–500 m apart, were included in the analysis and compared to co-located soil-gas radon measurements. Following centred and isometric log-ratio transformations of inductively coupled plasma mass spectroscopy (ICP-MS)/optical emission spectroscopy (OES) data for 37 elements, soils above the Clare Shales exhibited a median U concentration of 4 mg kg −1 (range 1.4–37 mg kg −1 ). Pearson correlations between log 10 soil-gas radon and individual elements peaked at a Pearson correlation coefficient ( r ) = 0.57 for Sr (coefficient of determination ( R 2 ) = 0.32), with similarly strong associations for V ( r = 0.54), Ag ( r = 0.52), P ( r = 0.47), Au ( r = 0.46), U ( r = 0.45) and Tl ( r = 0.43) (all P < 0.001). One-way ANOVA indicates radon class categories explain a median 43.8% of variance in 14 trace elements, while bedrock geology explains 27.8%. Shared tracers (U, V, P, Ag, Sb) underscore overlapping lithological and radiogenic controls. Principal components 1–3 (PC1–PC3) capture 62.9% of total variance (PC1 = 31.8%, PC2 = 17.2%, PC3 = 13.9%). PC1 is defined by strong positive loadings on Sc, Mg, Fe, Al, Th, Ni and Co, and negative loadings on Sr, Ag, U, P and V, neatly contrasting shale-derived, high-radon soils from carbonate terrains. This study demonstrates that local-scale lithogeochemical proxies, resolved at 250 m and calibrated against a 1:100 k geological framework, can effectively delineate elevated radon sources. This approach offers a systematic means to investigate map anomalies, refine national radon models, enhance spatial accuracy, and support evidence-based radon risk assessment and public health protection initiatives.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".