Bibliographic record
Abstract
The aim of this study was to find out a formula for the feed conversion coefficient which is applicable solely to farm pigs. The study was performed by applying a hybrid model of growth of pigs. The model of growth of animals in this research was not advanced, it was published elsewhere. In this study only necessary equations of the model were used. Feed conversion coefficient is a complicated trait. In this research the usually used formula of feed conversion coefficient was revised and transformed. In the study three features of feed conversion were analysed. The reason to distinct the three case studies was that the feed conversion coefficient differs in the same weight pigs under condition that one is a growing animal but other reached its maximum weight. The first case study concerns pigs that reached their maximum weight. The second case study concerns growing animals in a limited weight range. Third one considers a general case; weight range from weaning up to maximum weight. There are three results in this study. The first one suggests a formula for the average feed conversion coefficient in pigs which reached their maximum weight. The second result suggests a formula of the average feed conversion coefficient for growing animals in a limited weight range. Third result suggests a formula of the average feed conversion coefficient for pigs in any weight range between 30 ± 6 kg, and maximum weight.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".