A low-level control policy for data fusion
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
A procedure to regulate the feedback signals of multiple sensors, at different rates, inside of a low-level control loop using data fusion has been developed and tested in simulation. The procedure is independent of the fusion method, and applicable to sensors with widely different sampling rates. Thus, it permits the use of "secondary" sensors, which may be available to the system for other purposes, to monitor sensor fault or failure occurrence, provide smooth transition for the system on fault, and provide a way to dynamically reconfigure the sensing system based on sensor signals uncertainty. In this procedure, sensor signals are time-correlated using Kalman filters, before being fused together. To regulate the fused feedback signal when there is no data available from the slower sensors, a Kalman filter is used to observe and generate a prediction of the fused measurement signal in place of the slower sensors measurement. Since the slow sensor lag compensation and fused measurement stabilization are independent of the fusion process, any real-time data fusion process can be used with this procedure.
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.
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 | 0.002 | 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