Fusing of binary correlated data with unknown statistics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We address the problem of distributed target detection with correlated observations, i.e., where local detectors transmit their binary decisions to a fusion center but these decisions are correlated in an unknown manner. We propose three Separating Function Estimation Tests (SFETs) and a Generalized Likelihood Ratio Test (GLRT) to fuse the binary data. SFETs convert the detection problem into a problem of estimating a separating function that is positive under the alternative (to the null) hypothesis. Detection decisions are achieved by comparing the estimate of the Separating Function (SF) with a threshold, where the threshold is set to satisfy a probability of false alarm constraint. The SFETs are derived based on the asymptotically optimal SF (AOSF) theorem (SFET <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ), the Euclidean distance (SFET <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) and Kullback-Leibler (K-L) divergence (SFET <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) of the probability mass function (pmf) of the observations under each hypothesis. Since the correlations are unknown, we formulate a linear optimization program to estimate the pmf. The simulation results show that the probability of detection of the SFETs using the AOSF and the Euclidean distance is greater than the GLRT and the SFET using K-L divergence. Interestingly, when the observations are independent SFET <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and SFET <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> provide optimal performance for the problem.
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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 | 0.001 | 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 it