Analisis Faktor-faktor Penentu Keberhasilan Inseminasi Buatan (Ib) Ternak Sapi Potong di Kabupaten Tanjung Jabung Barat
Bibliographic record
Abstract
This study aims to determine the dominant factors, both direct and indirect, to the success rate of the IB program in West Tanjung Jabung Regency. This research was carried out from 12 December 2020 to 6 January 2021. The objects observed in this study were all inseminators and a sample of breeders from each inseminator. The data obtained from this study are primary data and secondary data. The data were analyzed using Stepwise Multiple Regression analysis and processed using SPSS. The results of the analysis show that the success of Artificial Insemination in West Tanjung Jabung Regency has been good, this can be seen from the S/C value per inseminator of 1.54 ± 0.46. The dominant factors directly and indirectly influence the success of AI in West Tanjung Jabung Regency such as the length of time of raising livestock, the skill factor of the inseminator, the work area factor, the age factor of the acceptor. Based on this research, it can be concluded that the success of the Insemination program (Artificial AI) in West Tanjung Jabung Regency is quite good. This can be seen from the Service Perception (S/C) value in Tanjung Jabung Barat Regency, which is 1.54 ± 0.63, which shows the fertility level of acceptor cattle is quite good, the dominant factors that influence the success of AI in West Tanjung Jabung Regency are length of breeding, inseminator factors, area factors and acceptor age factors, while the farmer's knowledge factor and BCS factor do not significantly affect the success of AI in Tanjung Regency. West Jabung, the BCS factor does not have a significant effect because livestock from the study in the West Tanjung Jabung Regency area have a homogeneous BCS score.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".