Citizen science in river monitoring: a systematic literature review of the whys and hows
Bibliographic record
Abstract
River monitoring is a prevalent focus within citizen science projects. Despite numerous reports and institutional manuals detailing the monitoring techniques employed in individual projects, there is a notable lack of comprehensive academic research on the diverse methods and objectives utilized by citizen scientists in river monitoring. This study conducts a systematic literature review to clarify the specific objectives of these citizen science projects and the primary methods used to achieve each objective. We followed the PSALSAR methodology for systematic reviews in environmental science to assess information on global citizen science initiatives in river monitoring available in both published and grey literature. We ultimately reviewed 97 documents from three databases: Web of Science, Google Scholar, and Google. These documents revealed a dominant focus among river-based citizen science projects on objectives related to water quality and river ecosystem health. Methods were varied, and many common methods are routinely applied to multiple objectives. The study provides a framework that links the main objectives to the primary methods, serving as both a practical guide for new initiatives and a valuable index for academic research.
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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 | 0.046 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".