Bibliographic record
Abstract
Emerging image-based sensor systems can observe a relatively large area (e.g., the size of an urban neighborhood) for long time intervals either continually or with high revisit rates. This type of sensor data makes new types of exploitation possible, but only with the assistance of automated exploitation aids because of the massive volume of data that must be studied as a whole. Automated methods to extract the simplest events from image sequences are often fairly robust (e.g., change events derived from EO or SAR image sequences or from video-derived tracks). Massive numbers of such events can contain information with high intelligence value. This paper examines this general-purpose problem: How massive numbers of the simplest sensor-derived events can be exploited. We summarize the basic functionality an intelligence analyst needs for studying this type of event data, in short: to understand the spatial structure, temporal structure and event-pair structure within an area of regard. Then we present a number of algorithms for automated exploitation of such data, and some visualization tools to help analysts study such data. Experimental results using all those technologies are also presented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".