Characterization of Buffalo Dairy Production Systems in Egypt Using Cluster Analysis Procedure
Bibliographic record
Abstract
The study objective was to characterize and classify buffalo dairy production systems in Egypt. Ten governorates having high buffalo population density were selected as the study area. The data were collected from 1811 dairy buffalo farms using survey. Buffalo holders were face to face interviewed by constructed questionnaire. The survey was applied in two years (2010 and 2011). Two-Step Cluster procedure (CA) was used and analysis was repeated several times until the cluster quality came good (average silhouette ≥0.5). The algorithm selected the number of clusters, after calculating the Akaike’s information criterion (AIC). Statistics of CA showed that the numbers of farm in each cluster were 43 (2.4%) in cluster1 (CL1), 1364 (75.3%) in cluster2 (CL2) and 404 (22.3%) in cluster3 (CL3). CL1 farms had a good availability of facilities. The management practices were the higher in comparison with the farms in the other clusters. Management and feeding systems practices in CL1 ranged from medium to high. CL2 was the largest, with 1364 farms located in all the ten governorates. The availability of facilities and equipment were low or lacking. The management practices were the lowest in comparison with farms in other clusters. CL3 facilities availability were low to medium. The management practices were medium when compared with the farms in the other clusters. The results of the current study demonstrate the existence of a large variability among buffalo dairy production systems in Egypt. These systems variability should be taken into consideration for sustainable system development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".