An Algorithm-Level Test Bed for Level-One Data Fusion Research (CASE-ATTI)
Bibliographic record
Abstract
This report summarizes part of the research conducted at the Center for Multisource Information Fusion (CMIF) at the State University of New York at Buffalo (SUNY at Buffalo) during the second year of a two-year Air Force Office of Scientific Research (AFOSR)-funded research grant. The overarching research objective of this grant is to provide understanding about the nature of multi-platform and distributed data fusion and the influence that such methods might have on flight-testing of future multi-platform systems at major range facilities such as, in particular, Edwards Air Force Base (the Air Force Flight Test Center, AFFTC), and also with a special focus on Electronic Warfare (EW) aspects and impacts. This particular report describes a simulation-based research tool called 'CASE-ATTI' (Concept Analysis and Simulation Environment for Automatic Target Tracking and Identification) that was used to conduct various other research projects within the overarching grant effort. This tool was graciously provided to CMIF by the Canadian Department of National Defense and the Defense Research Establishment, Valcartier (DREV, Quebec, Canada) in particular, for which we are very grateful. This tool is a state-of-the-art Level 1 data fusion research tool, focused on multisensor, fusion-based techniques for tracking and identification of single objects. It is typical of the type of tools that will be necessary at AFFTC for testing and evaluation of future data fusion-capable flight platforms. This report describes this advanced tool and an example of its application and use in a research task being conducted at CMIF.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".