The Development of a Learning Unit to Promote Biodiversity Utilization in Agricultural Ecosystems
Bibliographic record
Abstract
The objectives of this research were to 1) develop a learning unit to promote biodiversity use in agricultural ecosystems, 2) assess student learning outcomes related to their knowledge of biodiversity in agricultural ecosystems and to their skills of knowledge management, and 3) evaluate the impacts of learning to manage biodiversity in agricultural ecosystems on cultivators. Agriculture is a major occupation in Thailand. Knowledge about utilizing biodiversity in an agricultural ecosystem is essential for safe food consumption. Teachers play a vital role in developing and managing learning opportunities for cultivators, leading to participatory learning. Important components of learning management are teachers, learning sources in the local community, learning units about managing agricultural ecosystem biodiversity, and collaborative learning assessments for developing the learning units. This study develops learning outcomes at secondary schools to address the use of biodiversity in agricultural ecosystems along with agricultural methods and teaching skills related to sustainable agricultural learning management. Teaching skills have been improved through the guideline Encouraging Learning Outcomes for Biodiversity in Agricultural Ecosystems, in accordance with the sustainable development goals of the United Nations.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".