Socio-Economic Analysis and Land Suitability Mapping in the Development of Medicinal Plants (Biopharmaca) During COVID-19 Situation in Tinombo District, Parigi Moutong Regency, Indonesia
Bibliographic record
Abstract
The research objectives were to analyze the socio-economic conditions of farmers while identifying the suitability level of the land and develop a mapping of high potential for medicinal plants (biopharmaca). The method used was purposive sampling carried out by conducting direct surveys, followed by sampling the soil at the research sites, and analyzing the socio-economic level of farmers in Tinombo District. The maps of slope class, soil, and land use were overlaid by using the ArcGIS 10.0 application. The observation revealed that in general, the socio-economic value of the farming community on the cultivation of medicinal plants was quite good. Farmers put a high level of interest, cultivation techniques, and land suitability, with an average of 2.22, 2.72, and 2.1, respectively. However, the level of knowledge on seedling and marketing parameters found low, with an average of 1.5 and 1.0, respectively. The analysis of soil samples seemed to determine the land suitability. The pH parameter H2O has a value ranging from 5.81 to 7.09, C-organic was 1.14 - 6.37%, total N-value was 0.28 to 0.49%, P- availability was 3.29 - 130.55 ppm, and cation exchange capacity was 0.08 - 1.46 cmol+/kg. In the parameters of the exchangeable bases of the land, including K about 0.07 - 1.46 cmol+/ kg, Ca about 0.13 - 8.88 cmol+/ kg, Mg about 0.18 - 8.66 cmol+/ kg, and Na about 0.10 - 0.18 cmol+/ kg. Then, the soil base saturation parameter valued of 1.34 - 56.63%. The characteristics of the cultivated land for medicinal plants, both chemical and physical, have been identified in order to create agricultural land with suitable characteristics of the cultivated plants.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".