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
Lockheed Martin Advanced Technology Laboratories (LM ATL) has researched and developed Situation Understanding technologies to provide tailored, Actionable Intelligence to the individual warfighter. Situation Understanding (SU) is a core requirement of the Future Combat Systems and programs such as the Distributed Common Ground Station Army. LM ATL has developed an SU Engine to automatically fuse multiple intelligence reports with track data into a Common Relevant Operating Picture (CROP) of the battlespace. The SU Engine augments the CROP with hypotheses as to the relationships that may exist between entities, environment, and events within the battlespace. These relationships are then used as the basis for inferring the most likely and most dangerous courses of actions that the enemy may be pursuing. The Future Force is actively trading weight for intelligence, while at the same time supporting a broader range of missions, with fewer operators and greater volumes of information. The SU Engine maintains the context of the various warfighters that the system is supporting. A warfighter's context includes location of the warfighter, the warfighter's mission, and the state of the battlespace surrounding the warfighter. The SU Engine, based on any explicit information requests provided by the warfighter combined with needs inferred by the SU Engine, dynamically composes multi-level fusion services to convert raw sensor and report data into higher level relationships and ultimately into predictions of enemy courses of action. The SU Engine can access sensor and report data from a range of sources including service-enabled net-centric systems. Services within the SU Engine are described using industry open standards augmented with semantic definitions to support just-in-time service composition.
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 | 0.000 | 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 | 0.001 |
| 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