Guidelines for Natural Food Conservation for the Community around the Upstream Forest of the Chi River Basin
Bibliographic record
Abstract
The study of guideline for natural food conservation of communities around the upstream forest of the Chi river basin has aimed to find a way to cultivate the natural food plants of a community in the buffer zone between a national park and the community around and upstream forest in the Nongbuadang District, Chaiyaphum Province, Thailand. This study was phenomenological, with a, qualitative method used to collect data from four key informant (KI) groups; local wisdom (10 persons), government officials form Phukeaw National Park (two persons), village headmen (seven persons) and villagers using natural product in forest (40 persons). It was found that there are two patterns of natural food use for villagers from the forest; 1) consumption in the household; and 2) finding for sale in the local market. There are two levels of problems: 1) impact from government policy with national development relating to land use for increasing potential of agriculture production; and 2) behavior of villagers regarding resource use. However, the present government is mainly organized to conserve resources. Nevertheless, the guidelines of natural food conservation are created so the government must empower communities with villager participation, create cognizance in villagers around the forest, use local wisdom as a mechanism for transferring knowledge, set up a public network for learning and working including group responsibility, and create a pattern of demonstration plots.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".