Bibliographic record
Abstract
PURPOSE: To determine how an individual optimizes muscular work. SOURCE: Several previous investigations by the author that explored the hedonicity of various sensations aroused during work and compared the results with the subjects' performances. PRINCIPAL FINDINGS: When a subject is given the task to climb 300 m elevation on a treadmill, at various combinations of speed and slope, if slope is imposed and speed self adjusted, or speed imposed and slope self adjusted, the subject spontaneously climbs the 300 m in a constant time. Thus, the subject worked his body at a constant power (work x duration(-1)). Thus, a person optimizes his own behaviour spontaneously. Systematic exploration of the hedonic dimension (pleasure/displeasure) of sensory experience from various parts of the body, over a broad range of muscular work showed that pleasure is experienced when a useful stimulus, as judged from the point of view of optimization of physiological function, is present. Displeasure occurs when a noxious stimulus is present. When a stimulus is neither useful nor noxious, the sensation aroused is indifferent. The relationship of hedonicity with physiology is so tight that these properties of sensation can be used as a tool to explore the body's physiological integrity. Hedonicity is also aroused during muscular exercise. Experimental evidence will be provided to demonstrate that the pleasures/displeasures of sensory inputs from the chest and from muscles are the signals that are the source of optimal muscular work. CONCLUSION: The experimental results confirmed that pleasure is the common currency that is used by the brain to compare sensations aroused by muscular work from various parts of the body. Maximization of the algeabraic sum of these hedonic sensations optimizes the resulting muscular performance.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".