When citizen science meets radon building diagnosis: Synthesis of a French pilot project developed in the framework of the European RadoNorm research project
Bibliographic record
Abstract
As part of the European RadoNorm research project, citizen science pilot projects focusing on the management of radon risk in houses have been implemented in four countries. This article describes the methodological basis, the development and the results of the French pilot project. Building on an initial review of existing literature, the pilot project aims to frame a 'participatory approach' aligned with the standards and recognized practices of citizen science. Particular attention was given to the management of data and the inclusion of ethical considerations. The focal point of the project was the process of radon building diagnosis which is supposed to be carried out whenever (high) radon concentrations are measured and should be prerequisite to mitigation works. As experience shows, however, this diagnosis is hardly implemented in France. To help remedy this situation, the pilot project recruited citizens already aware about radon from Pays Vesoul Val-de-Saône (East of France) to test an existing online self-evaluation guide for radon diagnosis, report on their operational experience and meet with radon/building experts. This enabled citizens to contribute to improvements in form and content to the guide and to ensure that it would be better fit for purpose. Comparison of the guide with experts' practices offered additional perspectives on what building diagnosis should entail. The pilot project produced rich and high-quality data that will nurture the evolution of the guide. The project demonstrated both the viability and the utility of applying the citizen science approach to radon post-measurement phases, with measurable benefits in bridging knowledge gaps and in encouraging behavioural changes. The results of using a citizen science approach in the field of radon management and research are encouraging, and they far outweigh the challenges involved in the implementation.
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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 | 0.135 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".