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.
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.
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 | 0.000 |
| 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 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".