Bibliographic record
Abstract
The purpose of this study was to find out genetic potential for growth, and feed conversion coefficient in pigs. It was done by analysing the relationship between variables that are relevant to animals' development, namely the growth rate, feed conversion coefficient, and live weight. The study entails a hybrid dynamic mathematical model of the traits studied. The model is a species-specific concept, it was built for growing domestic pigs. Pigs are well-known model animals in human physiology. Some physiological factors are reportedly control both growth and ageing. Growth hormone and insulin-like growth factor-1 are reportedly the factors, which modulate growth, aging, and body size in mammals. The model does not entail growth hormone and insulin-like growth factor-1 as variables. However, the study demonstrates nonlinear dynamic of relevant variables in domestic pigs from 30kg up to 600kg. The model was constructed by considering functional relations between variables analysed in experiments and field observations. Theoretical notions about growth processes as a dynamic system are included in the model. An invariant of growth dynamic is introduced. The study suggests that growth, feed conversion, and life span are functionally related traits in domestic pigs. In the pig, longevity is a function of growth rate and feed conversion coefficient. The novelty of the study is a method for the investigation of genetic potential for growth, and longevity in pigs. In the model, genetic potential for growth rate and feed conversion were identified by analysing functional relations between relevant variables. The concept supports the opinion that growth, and ageing are interrelated processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".