A Model of Phenotypes of Ontogenetic Growth in Animals and Its Translation into Modelling Growth of Humans
Bibliographic record
Abstract
The problem that this study deals with is ontogenetic growth of humans and animals. The novelty of this research is an approach to the problem how to recognise growth phenotypes in animals. The aim of this research was to analyse an analytical model of ontogenetic growth of animals with intention to recognise growth phenotypes. In this study we discuss possibility to extend results to the modelling of growth phenotypes in humans. In this study we not only analysed the model of animal growth but also offer an insight into the option to apply some methods known in mathematical physics and applied mathematics. In this research we concentrate on the modelling of growth of pigs. Pigs are known as a good model animal of humans in many aspects, including growth, obesity, digestion, and some others. In this model two aspects of ontogenetic growth were considered; the intention was to advance the biological understanding of the growth process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".