Kinerja Inseminasi Buatan (IB) Pada Program Upaya Khusus Sapi Indukan Wajib Bunting (UPSUS SIWAB)
Bibliographic record
Abstract
Efforts to meet the increasing demand for beef in Indonesia require rapid cattle population growth. One of the steps to spur the growth of the cattle population is the eval_uation of the performance of Artificial Insemination (IB) in the Special Efforts for Pregnant Breeders (UPSUS SIWAB) in Purworejo Regency based on the Conception Rate and Service Per Conception and what factors affect the success of Insemination. Artificial (IB) in Onggole (PO) and simmental cattle. This research was conducted in Purworejo Regency, Central Java Province in 2021. The research method used was field research (field research in the form of case studies), data collection was carried out through the ISIKHNAS application. The basis in this study is acceptor data, IB reports, PKb reports and reports of cattle births in Purworejo Regency in 2018, 2019 and 2020. The results show that the success rate of Artificial Insemination (IB) is based on the Conception Rate (CR) and Service Per Conception ( S/C) will describe the success rate of IB implementation in Purworejo Regency within a period of 3 years and as material for eval_uation and improvement for various parties in the future.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.015 | 0.002 |
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".