Physical Environment Assessment Tools for the Performance of Recreational Farm
Bibliographic record
Abstract
The goal of this study is to achieve sustainability environment. According to our previous study in the nine Indicators of Taiwanese Green Building, and the Canada GBTools evaluation, combined with Taiwan’s leisure farms, we simulate evaluation factors of sustainability for the practical environments in Taiwan’s leisure farms. We then classify the evaluation factors according to its tendencies and create an expertise questionnaire using these tendencies. Through the results of the expertise questionnaires, we understood how much the experts valued ecology, energy, resource, society, and economy. We then further analyze the cause of the varying degree of attention paid to each category according to its attributes . SPSS statistic software is used as the analytic tool. Through factor analysis and the deletion of factor loading, this study attained four kinds and nine evaluations as well as 29 evaluations points: "perfect environment", "resources management ", "energy efficiency management". In addition, "complete plans and evaluations", "environment workload", "environment plans", the conclusion is that the most valued points for the business are: "environment plans", "environment workload", "perfect environment". However, the most valued points for the government are: "perfect environment", "resources management ", water resources and green materials. As for scholars, the most valued points are: "perfect environment", "environment workload", "using local renewable materials" as well as "environment plans". Analysis of Variance, the conclusion is that the most valued points for the government and scholars are: "natural sounding usage", "legality of base selection", "effectiveness in pollution process", "light efficiency control management systems", "simplified interior design". Finally, LSD analysis attained five evaluation points: "natural sounding usage", "legality of base selection", "effectiveness in pollution process", "light efficiency control management systems", "simplified interior design". The result of this study shows that implementations would be much easier if the necessary equipments and space were put into consideration during the planning stage of recreational farms. Lastly, this study suggests leisure farms choose their operating method carefully. The operating method should meet the nature of leisure agriculture; it should also consider utilizing the sustainable development assessment indicators and relevant decorations in the future. If a correct operating method is chosen, it will facilitate an environmentally friendly leisure farm. Keyword: Recreational Farm, Sustainability, Factor analysis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".