Obesity: a disease or a biological adaptation? An update
Bibliographic record
Abstract
Obesity is characterized by the accumulation of excess body fat and can be conceptualized as the physical manifestation of chronic energy excess. An important challenge of today's world is that our so-called obesogenic environment is conducive to the consumption of energy and unfavourable to the expenditure of energy. The modern, computer-dependent, sleep-deprived, physically inactive humans live chronically stressed in a society of food abundance. From a physiological standpoint, the excess weight gain observed in prone individuals is perceived as a normal consequence to a changed environment rather than a pathological process. In other words, weight gain is a sign of our contemporary way of living or a 'collateral damage' in the physiological struggle against modernity. Additionally, substantial body fat loss can complicate appetite control, decrease energy expenditure to a greater extent than predicted, increase the proneness to hypoglycaemia and its related risk towards depressive symptoms, increase the plasma and tissue levels of persistent organic pollutants that promote hormone disruption and metabolic complications, all of which are adaptations that can increase the risk of weight regain. In contrast, body fat gain generally provides the opposite adaptations, emphasizing that obesity may realistically be perceived as an a priori biological adaptation for most individuals. Accordingly, prevention and treatment strategies for obesity should ideally target the main drivers or root causes of body fat gain in order to be able to improve the health of the population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".