Multiple hypothesis situation analysis support system prototype
Bibliographic record
Abstract
Uncertainty makes the analysis of even simple situations difficult. It forces intelligence analysts to formulate and manage hypotheses during the construction of explicit representations of real world situations. This may quickly become overwhelming. To provide better support to the intelligence staff, the main concepts behind multiple hypothesis tracking have been revisited to develop a proof-of-concept prototype of a multiple hypothesis situation analysis (MHSA) support system. A key objective is to showcase the potential and utility of MHSA. It has thus been conceived to allow users, developers, and managers to better understand each and every aspect of the MHSA process, which isn't like a Bayesian Net. This paper discusses a situation modeling graphical language, the interdependency and uncertainty about the situation model components, the hypothesis tree data structure used to keep track of the uncertainty, different issues regarding the hypothesis tree, hypothesis scoring, and user interactions with the MHSA support system prototype.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".