Hydrology Research Articles are Becoming More Topically Diverse
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
We used Natural Language Processing (NLP) to assess topic diversity in all research articles (∼75,000) from eighteen water science and hydrology journals published between 1991 and 2019. We found that individual water science and hydrology research articles are becoming increasingly interdisciplinary in the sense that, on average, the number of equally-common topics represented in individual articles is increasing. This is true even though the body of water science and hydrology literature as a whole is not becoming more topically diverse. These findings suggest that the National Research Council’s (1991) recommendation to increase multidisciplinarity of hydrological research has been followed. Topics with the largest increases in popularity were Climate Change Impacts, Water Policy & Planning, and Pollutant Removal. Topics with the largest decreases in popularity were Stochastic Models and Numerical Models. At a journal level, Water Resources Research, Journal of Hydrology, and Hydrological Processes are the three most topically diverse journals in the discipline. We also identified topics that are becoming increasingly isolated, and which could potentially benefit from integrating more with the wider hydrology discipline.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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