Innovative solutions for global water quality challenges: insights from a collaborative hackathon event
Bibliographic record
Abstract
Addressing the global water quality challenges requires collaborative efforts, multidisciplinary approaches, and innovative solutions. Here we report on the success of a special collective intelligence “hackathon event,” organized by five United Nations agencies and the European Commission, with the aim of reinventing engagement with diverse experts and stakeholders to tackle real-world challenges in water quality monitoring and assessment. Participants from diverse backgrounds and regions convened to devise inventive solutions in four key challenge areas, including (1) transformation of water quality data into water stewardship action, (2) empowering citizen scientists to improve water quality, (3) incorporation of Indigenous communities and their water quality knowledge in global information systems, and (4) routine monitoring of antimicrobial resistance in water. The hackathon approach fosters collective intelligence in a safe, creative and collaborative environment, enabling participants to harness their collective knowledge, expertise and skills. Key outcomes were conceptualizing practical frameworks and tailored toolboxes for diverse water quality innovations to improve monitoring, empower communities, and support policy-making. Emphasis was placed on the purpose and value of interdisciplinary collaborations to address complex global challenges, showcasing synergies between technology, environmental science, and social engagement. Hackathons are catalysts for collaborative innovation which unlock future endeavors in harnessing collective intelligence to safeguard our most precious resource – water.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".