Citizen Science Ferrara USAGE - Open-geo-data from citizens to fight local impacts of climate change
Bibliographic record
Abstract
Presentation held at csvconf.com 2025The presentation is about Citizen Science Ferrara (www.citizenscienceferrara.org), a movement born in 2023 within USAGE project (www.usage-project.eu) in Ferrara, Italy. It is focused on practical activities and results achieved in the last 2 years, from the involvement of more than 150 citizens (including high schools' students) for collecting data related to urban heat island effect and to flash flood during heavy rains. Citizen scientists used low-cost mobile sensors (meteotracker.com) and free/open-source apps (QField, iNaturalist) to collect data about air temperature, humidity, flooded areas, biodiversity. Data collected have been analysed, integrated and harmonized, to be made available with open licence within the open data portal of Ferrara Municipality (dati.comune.fe.it/dataset?q=citizen+science) and federated catalogues (dati.emilia-romagna.it, dati.gov.it and data.europa.eu)
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.208 | 0.128 |
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".