Importance of Beef Cattle Farming Model and Appropriate Benefits for Small-Scale Farmers in Nakhon Sawan Province, Thailand
Bibliographic record
Abstract
The objective of this study was to investigate the feasibility and reasonable production costs for small cattle farmers in Nakhon Sawan Province of Thailand. Small-scale beef cattle are naturally reared by farmers without planning. Studying the primary data of beef cattle farming models and comparing the economic return costs of each form of beef cattle farming among smallholder farmers in Nakhon Sawan Province of Thailand shows that the yields are not worthwhile. By applying the specific method to 25% of the area of all farms in Nakhon Sawan Province and comparing the descriptive statistical yields, the results showed that two types of cattle with 4 characteristics predominate. The first is rearing pregnant mother cows for sale and buying mother cows. The second category is feeder cattle, release cattle and fattening cattle. A study of the costs and economic compensation of suitable small-scale beef cattle farmers in Nakhon Sawan Province of Thailand found that cattle farms should raise cattle because the yield from farming is valued and because of the economic returns and the time spent. The payback for this form is faster than other forms of investment.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".