An Approach for Dairy Buffalo Development through Investment in Genetic Improvement
Bibliographic record
Abstract
Dairy products are an essential source of animal protein, particularly for nutritional vulnerable groups and vegetarians in Asian countries. Therefore, the approach towards increasing the domestic supply of milk is to raise the buffalo milk yield via genetic improvement from the semen of the selected buffalo sire that possesses a high predicted milk difference. Data were collected from Artificial Insemination Centers in Egypt, as a case study, to apply a dynamic mathematical investment model for estimating the rate of return (IRR) to genetic investment. The effective variables in IRR, besides the economic variables, are the reproductive traits and feed efficiency. The estimated most probable level of IRR was feasible, i.e., 19.71%. A worse change in the reproductive efficiency variables, i.e., the aggregate of 10% increase in the number of conception services, age at the First Calving, and the service period, would decrease the IRR by 7.51%. A decrease in feed efficiency by 10% would decrease the IRR by 9%. A worse change by 10% in the feed costs, price of semen, and milk price would decrease IRR by 7%. To import buffalo sires' semen of high predicted milk difference at moderate prices till establishment, domestic genetic merit is required.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".