Machine learning and hydrological sciences: A systematic overview of review papers
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
Water sciences have greatly contributed to the proliferation of machine learning in the twenty-first century, especially through engineering hydrology. This process has consequently necessitated transfer of core theory and knowledge of machine learning to the domain of hydrological sciences. In this regard, it is noteworthy that published academic literature played a substantial role in supporting development and learning of hydrologists. Specifically, research articles (and book sections) that review machine learning concepts and algorithms along with their applications in hydrology bolster progress of science by presenting encapsulated information (e.g, in the form of literature synthesis). Despite the rapid increase in the number and scope of such research articles, a systematic understanding of how this line of research publications has evolved with respect to their scientific context, objectives, and methods is still lacking. Hereby, we present an analysis of review papers in hydrology and machine learning based on a  systematic search strategy. The overview includes analysis of bibliographic information, review types (objective, focus theme, etc.), review methodologies (narrative, systematic, etc.) as well as thematic context (hydrology subjects and machine learning topics). We believe that our analysis can provide important insights into topics and discussions in hydrology and machine learning that need further exploration by hydrologists. Furthermore, the public online library on Zotero (https://www.zotero.org/groups/4828386/machine_learning_hydrology_review_papers/library) might encourage more participation towards sustainable literature search and active reading on this subject at the intersection of two fundamental disciplines, machine learning and hydrology.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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