Data Fusion: Cumulative Effects of Discrete Fusion on Target Detection Probability
Bibliographic record
Abstract
This paper describes the culmination of a four-year research, application, and development program towards finding and quantifying a methodology for sensor and data fusion of remotely sensed targets. It builds on previous research reported in IGARSS'02 [1] and IGARSS'04 [2]. Here, we examine the effectiveness of the data fusion methodology, specifically, the impact of iterative data collection on the effective probability of detection of targets. Through statistical (Bayesian) combination of sensor iterations, low confidence sensors (those with moderate probability of detection and moderately low probability of false alarms) can provide high detection performance. This can be extended to multiple data source types. The goal is to make use of higher coverage data products which have only moderate detection performance in the detection and tracking of targets. This is made possible through a combination of discrete target data, along with the analysis of parameters from the respective remote sensing technologies. This process requires the existence of a reasonable maneuvering model or sufficient processing resources.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".