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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".