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
The extremal harmonic active control of power consists to define an energetic criterion at the main harmonic and to extremalize it on-line by commanding a secondary source of power. The term extremal means that the optimal criterion value is reached with an on-line optimization algorithm. Without loss of originality, the context of rotating machines oscillation in synchronization with rotor position is used to illustrate this approach. A second commanded torque source, attached to the shaft, is used to control speed oscillation due to torque disturbance. Such configuration can be found in hybrid automotive for example. The energetic criterion can be the kinetic energy (for conventional active speed oscillation cancelling), the reactive power (to add a virtual flywheel on the shaft) or the active power (for energy harvesting to charge a battery). The optimization algorithm is the method of steepest descent implemented with implicit or explicit gradient computation. But, in the case of reactive power, the implicit controller is not practically achievable. The experimental results with a test bed show that the explicit controllers converge at the extremum of each criterion. To conclude, the presented explicit extremal controller is a good candidate to be used for energetic criterion control.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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