The Namibia Early Flood Warning System, a CEOS pilot project
Bibliographic record
Abstract
This paper describes a pilot project effort under the auspices of the Namibian Ministry of Agriculture Water and Forestry (MAWF)/Department of Water Affairs, the Committee on Earth Observing Satellites (CEOS) /Working Group on Information Systems and Services (WGISS) and originally moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort began by identifying and prototyping technologies which enabled the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management. This was followed by an international collaboration to build small portions of the identified system which was prototyped during the past few years during the flood seasons which occurred in the February through May timeframe of 2010 and 2011 with further prototyping to ongoing in 2012. The pilot effort has been fostered by CEOS to facilitate international efforts to promote satellite sensor data interoperability. In particular, the group has been making use of a technology effort call SensorWeb being developed at NASA which leverages Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to facilitate various satellite and ground sensor interoperability. The group has made use of such satellites such as Earth Observing 1, Terra/Aqua MODIS and the Canadian Space Agency (CSA) Radarsat together with various ground sensors such as river gauges in Namibia and models such as Global Disaster Alert and Coordination System (GDACS) from Joint Research Center (JRC) from the European Commission. Finally, the group has been experimenting with integrating a large Cloud Computing service provided by the Open Cloud Consortium (OCC) with the SensorWeb to provide management and distribution of the large data sets for emergency workers.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".