Radon Gas Detection via Vegetation Spectra Responses Using Space-borne Remote Sensing: A Tool for Uranium Exploration
Bibliographic record
Abstract
This research aims to determine if there is a discernable satellite-derived spectral signature within vegetation communities that can be linked to elevated occurrences of radon gas. Radon surveys, where the gas is measured directly on the ground, are a tool used in uranium exploration as statistically significant elevated radon values are known to occur in proximity to uranium mineralization. To-date, there has been little to no research into the use of optical remote sensing to quantify radon gas in uranium exploration. Through digitizing and geo-referencing historic survey data from Cluff Lake, Saskatchewan, the radon values were first explored along environmental gradients to understand its spatial distribution. The data were then linked with satellite imagery (Sentinel-2A) to explore spectral patterns and evaluate the potential of characterizing a spectral response that can highlight areas containing above background gas concentrations. Results show that there is strong potential for mapping radon gas via changing spectral characteristics within vegetation, interpreted to be attributed to the effects of radiogenic stress and metal contamination within plants coinciding with anomalous radon gas occurrences and/or elevated amounts of its progeny. It is shown that there are differences in spectral curves of natural-logarithmically transformed radon point-values that have been grouped based on standard deviation between what is considered background, moderate, and high values of radon. Furthermore, vegetation indices using Sentinel-2A bands, focusing in the red-edge and NIR portion of the electromagnetic spectrum, show a significant variation of means between grouped radon values allowing for trend detection and radon pseudo-survey map generation. Investigation into radon distribution at Cluff Lake has also shown a potentially significant relationship between radon gas and vegetation communities, specifically black spruce (Picea mariana), which was not hypothesized. The potential species specific relationship between radon gas and vegetation, along with the variation in spectral curves differentiating what is considered background and elevated occurrences of the gas, show strong potential for further refining radon pseudo-survey maps based on spectral characteristics of the tree-canopy. This research was designed as a tool in uranium exploration, to compliment geological, geophysical, and geochemical exploration methods. The research also has trans-disciplinary applications in biogeochemistry, ecology, and the environmental sector as an aid in mapping radiogenic 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".