A Model for Prediction of the Buffalo and Cattle male Calves' live Weight
Bibliographic record
Abstract
The buffalo and cattle population in Egypt reached about 4.898,893 and 3,476,396 heads, respectively, in 2019. The buffalo and cattle meat production represented around 43% and 45% of the total meat production in Egypt, respectively, and the average carcass weight of buffalo and cattle reached 318 kg and 336Kg in 2019. About 50% of livestock numbers are slaughtered off-slaughter houses due to a shortage in the capacity of slaughterhouses and other financial and administrative reasons. The conducted field survey of this study showed that the majority of Egyptian livestock markets lack live weight scales, and if available, they are expensive and frightening for the animals. Therefore, numerous feedlot enterprises hire an agent who can visually forecast the expected live weight of the exhibited feeder calves for sale in the market at a high charge. Most of the models to predict the weight of live cattle-bulls knowing the chest circumference were lacking such models for buffalo-bulls and were linear models that fit only limited periods of cattle-fattening. The sample survey included 500 buffalo males and 340 cattle males of different live weights from 14 villages in 4 provinces in the Nile Delta. The data were used to estimate the best-fitted model for Egyptian livestock. This study showed that the best-fitted model was curved linear. It was a quadratic form. For empirical application, the study designed a table to predict the live weight of buffalo and cattle males at a range of 100-200 cm chest circumference. The results showed that a 1% increase in the chest circumference was associated with a 2.4% and 2.8% increase in the buffalo and cattle male's circumference, which implicitly reflects higher live weight gain of cattle than buffalo males and better meat quality. Therefore, the farm price of cattle feeder males surpassed the buffalo by $1000.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".