Rationale for Monitoring Discharge on the Ground
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.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".