Virtual Research Org for South America USGS International Remote Sensing Research: Provides Basis for Developing Countries Internet/Web Based GIS and Remote Sensing Community Updates
Bibliographic record
Abstract
The U.S. Geological Survey, (USGS) serves the globe by providing reliable scientific information to describe and understand the Earth; minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect quality of life. Through GMI the USGS funded a comprehensive research study of the African International GIS and Remote Sensing market as follow-on to the NOAA, Satellite and Information Service Division, International GIS and Remote Sensing Study of U.S., Canada, Europe, and Asia in 2005, 2006 and 2007. These studies provided the basis for the development of a virtual research organization providing community-based inputs concerning GIS and Remote Sensing in developing countries. To promote community-based value-add-ons to the content of the research and in order for the research to remain up-to-date GMI created a web-enabled interactive Google Earth map allowing participants to click and view the study highlights by country in South America, or to complete a survey directly from the map site and/or provide updated information on their country. Over 450 participants from the user population have responded through these on-line maps with 25 in-country partners, research institutions and collaborators providing input as well.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.472 | 0.192 |
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".