Land Suitability Assessment for Cassava var. Jarak Towo, Using Determinant Factors as the Strategy Fundament in Hilly Area Jatiyoso-Indonesia
Bibliographic record
Abstract
The increasing demand for various creative food industries requires cassava raw material supply which has quality and quantity. This research purpose is to identify land suitability, determining the factors, and the strategy of land management for Jarak Towo production in Jatiyoso District. This research using survey method with the land unit based on altitude as observation design which divided into six, namely 400 masl, 600 masl, 800 masl, 1000 masl, 1200 masl, and 1400 masl, and the sampling point is determined by purposive sampling which each land unit has four repetitions and obtain 24 sample points. The land suitability class assessment was carried out by matching the observation data with cassava-modified growth requirements for the Jarak Towo variety. The results in the area were classified into two classes, namely marginally suitable and not suitable. The land suitability determinant factors were temperature, organic carbon, Total-N, and slope. Land units 3 and 4 are land units which land suitability class can increase if these two locations are used as places for planting cassava var. Jarak Towo with the direction of land management strategies that have been given.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".