Citizen science in river monitoring: a systematic literature review of the whys and hows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it