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
High tempo battlefield requirements, rapidly evolving communications/information technologies and the need to include legacy components, call for a system-of- systems engineering approach in the development of data fusion enabled networks (DFEN). System-of- systems engineers are concerned with large scale interdisciplinary issues combining multiple, heterogeneous, distributed systems that are embedded in networks at multiple levels and multiple domains. These issues are also of concern to data fusion engineers because the networks being engineered to resolve the issues are the environment in which future data fusion systems must perform successfully. This paper is a description of a system-of-systems approach to the development of DFEN's. Applying system-of-systems techniques, a DFEN software and hardware infrastructure is being developed The infrastructure is being developed as a capability within a network enabled C4ISR infrastructure, using a service oriented architecture (SOA) and a DFEN specific ontology. Within this framework, important aspects of data fusion system development can be addressed in a process-driven, scalable and evolvable manner.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it