Bibliographic record
Abstract
Based on the dynamics, structure and properties of imbedded multi-stack systems, certain functions of the central nervous system (CNS) can be modeled and computer programmed. These models imitate the natural system at some level and can be integrated in future more comprehensive models of the CNS in studies of human movement. Several CNS functions are discussed here. The involvement of the reticular formation (RF) in identifying objects and their properties by touch is addressed. The vision system is discussed in sensing and storage of planar images. The creation of periodic motions for dance or sport is modeled. Mental processing in is the writing or drawing of images is formulated. The touch problem addressed here is the mechanisms to explore, probe, grope and identify an object that is not visible. The object of enquiry may be part of another object, lie under another object or be part of a bigger thing. It may also be involved in the early dynamics of touch in the new-born where the vision dynamics have not yet fully developed. How the central nervous system (CNS) may pursue these tasks is one objective. One elementary attempt is to develop larger structures that can handle CNS functions, how thoughts are formulated, expanded, summarized or abandoned. Multiple Stack System seem to be convenient for handling information flowing from the sensory channels to the cerebrum and the cerebellum. By considering specific tasks, the paper focuses on several functions, connections, and structures that are involved in movement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".