An Algorithm-Level Test Bed for Level-One Data Fusion Research (CASE-ATTI)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 it