A Hybrid Non-Local Model of Ontogenetic Growth Revealed a Phase Transition and Two Bifurcations
Bibliographic record
Abstract
The problem this study deals with is the dynamics of growth of animals. In the study, some features of the growth of pigs were modelled. The research concerns the growth dynamics during a period of growth close to a bifurcation point. In the point, two bifurcations of the growth trajectory take place. The period of growth entails the weight in which an animal's growth stops when individual maximum weight is reached. In the study, methods of applied mathematics were used. The growth of animals was modelled by a hybrid and continuum methods as a dynamic system. In the hybrid model, time was considered as a discrete variable. In the study the factors, which control trajectories and the dynamics of growth were revealed. There are three results in this study. The first result suggests that in animals, the current weight M can be described by derivative of the average consumed feed. The second result gives the equation of the weight balance in an integral form. Third result implies that in ontogeny, growth of pigs has to be modelled as a dynamic system. The system has two bifurcations; one of the trajectory of the weight gain, and other of the trajectory of the growth invariant K. As a result, new growth trajectories emerge. In some instances, the findings can be translated to man in others they apply to animals.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".