Bibliographic record
Abstract
sign-enterprise-agreementJune 7, 2017 -Redlands, California-Esri, the global leader in spatial analytics, today announced that it entered into an enterprise agreement with the North Atlantic Treaty Organization (NATO) Communications and Information (NCI) Agency earlier this year.The enterprise agreement for the NATO Core Geographic Information System (GIS) includes several addendums to cover the use of Esri software by NATO Functional Area Services (FAS) and NATO Nations that use NATO FAS and/or the NATO Core GIS, as well as the support of all these systems."The potential of this agreement can be expected to reach far beyond today's use of the NATO Core Geographic Information System and will allow the NCI Agency to implement a true enterprise GIS platform for NATO in the future," said John Teurfert, Joint Intelligence Surveillance and Reconnaissance Services, Geospatial Branch Head."This new GIS platform will be able to provide much of today's stove-piped spatial analytical functionality as centralized GIS web services.This bears the potential for additional significant cost savings in the command and control and functional area domains."NATO has used Esri technology as part of its Core GIS for many years.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.429 | 0.356 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".