The brokering framework empowering WMO Hydrological Observing System (WHOS)
Bibliographic record
Abstract
The WMO Commission of Hydrology (CHy) is realizing the WMO Hydrological Observing System (WHOS), a software (and human) framework with the aim of improving sharing of hydrological data and knowledge worldwide. National Hydrological Services (NHS) are already sharing on the web (both archived and near real time) data collected in each country, using disparate publication services. WHOS is leveraging the Discovery and Access Broker (DAB) technology developed and operated in its cloud infrastructure by CNR-IIA to realize WHOS-broker, a key component of WHOS architecture. WHOS-broker is in charge of harmonizing the available and heterogeneous metadata, data and services making the already published information more accessible to scientists (e.g. modelers), decision makers and general public worldwide. WHOS-broker supports many service interfaces and API that hydrological application builders already can leverage, example given OGC SOS, OGC CSW, OGC WMS, ESRI Feature Service, CUAHSI WaterOneFlow, DAB REST API, USGS RDB, OAI-PMH/WIGOS, THREDDS. New API and service protocols are continuously added to support new applications, being WHOS-broker a modular and flexible framework with the aim of enabling interoperability and assuring it as the standards will change/evolve through time. Three target programmes have already benefited from WHOS: La Plata river basin: hydro and meteo data from Argentina, Bolivia, Brazil, Paraguay, Uruguay are harmonized and shared by WHOS-broker to the benefit of different applications, one of them is the Plata Basin Hydrometeorological Forecasting and Early Warning System (PROHMSAT-Plata model, developed by HRC), based on CUAHSI WaterOneFlow and experts from the five countries. Arctic-HYCOS: hydro data from Canada, Finland, Greenland, Iceland, Norway, Russia, United States are harmonized and shared by WHOS-broker to the benefit of different applications, one of them is the WMO HydroHub Arctic portal, based on ESRI technologies. Dominican Republic: hydro and meteo data of Dominican Republic published by different originators is being harmonized by WHOS-broker to the benefit of different applications, one of them is the Met data explorer application developed by BYU based on THREDDS catalog service. The three programmes should act as a driving force for more to follow, by demonstrating possible applications that can be built on top of WHOS. The public launch of WHOS official homepage at WMO is expected by mid 2021, will include: A dedicated web portal based on Water Data Explorer application developed by BYU Results from the three programs Detailed information on how to access WHOS data by using one of the many WHOS-broker service interfaces An online training course for data providers interested in WHOS The WHOS Hydro Ontology, leveraged by WHOS-broker in order to both semantically augment user queries and harmonize results (e.g. in case of synonyms of the same concept in different languages).
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".