Estate Executives’ Perception Towards Participation in Cattle-Oil Palm Integration Projects
Bibliographic record
Abstract
Having Malaysia moving towards high income nation, agriculture sector still stands firm as the important pillars of Malaysian economy through vast amount of crude oil production. As part of Entry Point Project (EPPs) under the National Key Economic Areas (NKEAs), it is targeted additional 300,000 cattle will be reared in the oil palm plantation through cattle-oil palm plantation integration projects. At present there is a total of 5.6 million hectares of oil palm planted areas (MPIC, 2015). The vast area of oil palm plantation provides a large area for cattle integration projects and massive amount of feed. Through the symbiotic relationship known exist in cattle-oil palm plantation integration system; it is believed that the project will bring in positive return to the government, private sectors and the estate executive themselves. This study aims to investigate the participation of estate executives and their perception on the factors that influenced the decision. A data from 123 estate executives were collected through self-completion questionnaires throughout Malaysia using the cluster randomize propose sampling. The inclination factor in implementation of cattle-oil palm plantation integration system was grouped into economy, potential and costing factors while suggestions were proffered on all parties including government, private sectors and estate executives for an improved and efficient cattle-oil palm plantation integration system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".