A mathematical model on the effect of growth hormone on glucose homeostasis
Bibliographic record
Abstract
Extending an existing model devoted to the interaction between β-Cell Mass, Insulin, Glucose, Receptor Dynamics and Free Fatty Acids in glucose regulatory system simulation, this paper proposes a mathematical model introducing the effect of growth hormone on the glucose homeostasis alongside the other variables. Stability analysis is carried out and pragmatic explanation of the equilibrium points is emphasized. Finally, simulation illustrated how β-Cell Mass, Insulin, Glucose, Receptor Dynamics, Free Fatty Acids and Growth Hormone may vary with different values of some parameters in the model. Prolongeant un précédent model publié dédié à l'interaction entre les cellules beta, l'insuline, le glucose, les recpteurs d'insuline et les acides gras libres, cet article propose un modele mathématique introduisant l'effet de l'hrmone de croissance sur la homéostasie du glucose. L'analyse de stabilité a été suivie d'explication pratique des points d'équilibre. Enfin, la simulation a illustré comment les cellules beta, l'insuline, le glucose, les recpteurs d'insuline, les acides gras libres et l'hormone de croissance peuvent varier en fonction des différentes valeurs de certains paramètres.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".