Development of a Learning Model to Enhance the Buddhist Way of Temples and Urban Community as a Cremation Model
Bibliographic record
Abstract
The objectives of this paper were to 1) study the learning model of the smart crematorium system, 2) create a learning manual on smart cremation management, and 3) promote the development of learning for undertakers to use the smart crematorium. This was mixed method research with qualitative research and action research as parts of the conduct of quantitative research. The samples were from informants that consisted of 10 monks, 5 community leaders, 5 academicians, 17 seminars, 30 participants, a total of 67 people, and content analysis according to the study issues. The research instruments consisted of 1) an interview form, 2) a focus group meeting, 3) an activity participation form, and 4) an activity assessment form. The data collection was as follows: 1) secondary sources, documents, books, journals, and research reports related to concepts, and theories, 2) workshops, 3) in-depth interviews, 4) specific group discussions, and 5) collecting data from measurement reports and analysis of dioxin/furans compounds to categorize the data and analyzed according to the study issues. The findings revealed that 1. A learning model for using a smart crematorium system for the undertakers: 1) filling the fielder with the reaper into the storage tank 2) turning on the air compressor to fill the tank 3) opening the valve to let air into the system 4) checking the wind pressure and 5) checking the air flowing through the system along the main pipes which would pass the Vimutti substances into the crematorium and smoke furnace room continued to for about 30 minutes continuously. 2. Operations of creating a learning manual on smart cremation management that contained details in the book: 1) the problem of pollution from cremation 2) the smart crematorium with new options 3) the benefits of using the smart crematorium. This would introduce the features of a new smart crematorium, how to use and the benefits of using a smart crematorium. 3. To promote and develop knowledge for undertakers to use smart crematoriums and Vimutti substance sprayers by organizing training to educate about dioxins and furans, organized training and demonstrating how to use the smart crematorium and the Vimutti substance sprayers. This was the development of a learning model to enhance the Buddhist way of temples and urban communities as a cremation model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".