Rationale for Monitoring Discharge on the Ground
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
Abstract The hydrological cycle is receiving increasing attention both as an essential natural resource for humans and ecosystems and as a critical component controlling the earth’s climate system. Better understanding of the water cycle and its interaction with changing climate will require improved monitoring of the various water fluxes and storages in hydrological processes. River discharge is a unique component reflecting an integrated hydrological signal over larger regions. Existing in situ monitoring solutions to monitor discharge are often considered too expensive and the difficulties in data sharing are viewed as insurmountable obstacles, which has led to growing interest in finding an alternative. This paper argues that in situ monitoring is far less expensive than claimed and the obstacles are not necessarily as insurmountable as often stated and a conscious effort to revitalize in situ monitoring will be needed. This paper demonstrates that there is no substitute for in situ discharge monitoring, but there should be a synergy between in situ monitoring and remote sensing since they are truly complementary. This paper primarily focuses on river discharge, but the conclusions are relevant for a host of other earth observations (particularly water quality) that would greatly benefit from a reconsidered balance between in situ and remote sensing observations.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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