Learning From Human Responses to Deconditioning Environments: Improved Understanding of the “Use It or Lose It” Principle
Bibliographic record
Abstract
, and required for maintenance of health. Thus, systems such as the musculoskeletal and cardiovascular systems appear to require constant reinforcement or conditioning to maintain integrity. Loss of conditioning or development of chronic deconditioning can have multiple consequences. The study of different types of deconditioning and their prevention or reversal can offer a number of clues to the regulation of these systems and point to how deconditioning poses risk for disease development and progression. From the study of deconditioning associated with spaceflight, a condition not predicted by evolution, prolonged bedrest, protracted sedentary behavior, as well as menopause and obesity and their consequences, provide a background to better understand human heterogeneity and how physical fitness may impact the risks for chronic conditions subsequent to the deconditioning. The effectiveness of optimized physical activity and exercise protocols likely depend on the nature of the deconditioning, the sex and genetics of the individual, whether one is addressing prevention of deconditioning-associated disease or disease-associated progression, and whether it is focused on acute or chronic deconditioning associated with different forms of deconditioning. While considerable research effort has gone into preventing deconditioning, the study of the process of deconditioning and its endpoints can provide clues to the regulation of the affected systems and their contributions to human heterogeneity that have been framed by the boundary conditions of Earth during evolution and the "use it or lose it" principle of regulation. Such information regarding heterogeneity that is elaborated by the study of deconditioning environments could enhance the effectiveness of individualized interventions to prevent deconditions or rescue those who have become deconditioned.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".