Bibliographic record
Abstract
Since the time of Descartes the machine-like control of movement in animals and the animal-like control of movement in automata has fascinated and inspired scientists, engineers and philosophers alike. In 1966, Drs. Rajko Tomovic and Robert McGhee proposed the concept of a "cybernetic actuator," a new type of control system which "possesses the property of producing continuous controlled motion from an input which may assume only four distinct states". The specific application at the time was an artificial limb prosthesis. Signals from sensors monitoring joint angle and ground contact were to be continuously compared to a set of threshold values corresponding to specific moments in the step cycle. The binary signals (above or below threshold) were listed in a look-up chart which associated sensory combinations with actuator states. It was proposed that this system would provide all of the known state transitions required of an above knee prosthesis. In this and later papers Tomovic was careful to point out the differences between such "artificial reflex control" systems and neural control systems in animals. Nonetheless in the last few years it has become commonplace to see the control of locomotion and other rhythmical behaviors described in terms of "sensory rules," that is in terms of finite state systems. With the advent of neural nets and fuzzy logic control robotic devices are taking on more and more of the features of biological control systems. In turn, neurophysiologists borrow more and more from the concepts and mechanisms of modern control theory. The influence of Tomovic's simple but powerful idea continues to spread.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".