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
In this work we present multi-sensor data fusion architecture. The objective of the architecture is to obtain fused measured data that represent the measured parameter as accurate as possible. The architecture is based on the use of adaptive Kalman filter formed by using Kalman filter and fuzzy logic techniques. Measurements generated from each sensor are fed into an adaptive Kalman filter. So there are n adaptive Kalman filters for n sensors working in parallel. A Correlation coefficient, produced as correlating the predicted output to measured data, is used as qualifying quantity for each adaptive Kalman filter. Based on the value of the correlation coefficient the measurement noise covariance matrix was adjusted using fuzzy logic techniques. Measurements produced from these adaptive Kalman filters were fused to form a single output. Results of testing showed notable improvement for each Kalman filter over a traditional Kalman filter. Fusing data coming from several sensors showed better results than using individual sensors.
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 | 0.000 |
| 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