Bibliographic record
Abstract
Lately, something called eco‐hydrology has blazed forth as the next big thing in hydrologic science. A series of four papers on the topic appeared in Advances in Water Research last year [Rodríguez‐Iturbe et al., 2001]. Before that, in 1996, the International Hydrology Programme (IHP) initiated a new project under the title of eco‐hydrology [Zalewski et al., 1997]; and the AGU Spring Meeting, also in 1996, included a special session on eco‐hydrology. Since then, a book of edited contributions has appeared [Baird and Wilby, 1999], and eco‐hydrology was the topic of both a “vision for the future” in Water Resources Research [Rodríguez‐Iturbe, 2000] and the Langbein Lecture at AGU's 2000 Spring Meeting. Another session on the topic will be featured at this year's AGU Spring Meeting, and a Chapman Conference on Eco‐hydrology of Semiarid Landscapes will be held in September 2002.
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.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".