Publishing And Federating Global Water Data And Maps Via Web Services
Bibliographic record
Abstract
Finding and accessing data in most countries of the world about local, regional and national water resources (streamflow discharge, gauge depth, soil moisture, etc.) has been complicated by a number of issues, from concerns of local and national security, to lack of suitable conventions and standards for data exchange that could be reasonably implemented and enforced at the national and international levels. These issues are now starting to be addressed, thanks to recently adopted standards for hydrologic data exchange, and growing acceptance of community standards for web services to perform such data exchange. This presentation reviews recent work in this area, in particular from an international initiative for the Global Earth Observation System of Systems (GEOSS) to federate regional water data into national pictures for Italy, New Zealand, Canada, and a growing number of countries in Latin America. This builds on previous similar work by the Consortium of Universities for Advancement of Hydrologic Science (CUAHSI) with the U.S. Geological Survey and several other U.S. national agencies. The ability to discover and access such important data should improve the awareness and responsiveness of policy- and decision-makers in the event of natural disasters from storms, flooding and drought.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.029 |
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".