Citizen science and international collaboration through environmental monitoring with simple chemical sensors
Bibliographic record
Abstract
Abstract Building capacity for carrying out and understanding responsible science that is relevant to local challenges is a key ingredient in the OPCW’s strategy for achieving and maintaining a world free of chemical weapons. Two important contexts for building that capacity for responsible science are (1) the global attention being drawn to the rapidly increasing human chemical footprint on our planet and (2) the pervasive use of digital technologies. We describe an effort coordinated by the Organisation for the Prohibition of Chemical Weapons to build capacity among young people around the world to harness the power of small mobile chemical sensors to develop data literacy in complex chemical analysis based on measuring analytes that are relevant to their lives and local contexts. This new type of data literacy is an emergent element in educational programs and is key to developing the capacity for decision-making on chemical measurement data. The project brings together student and faculty collaborators from the fields of chemistry, social sciences and informatics, to provide proof of concept in four areas that support the overall goal of building a collective effort for scientific analysis; the development of low cost environmental sensors for air and water samples; the collection of representative test data sets on priority contaminants; the assessment and visualization of data; and education about the effect of priority pollutants on human and environmental health. We report on the project goals and preliminary steps taken to achieve them.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".