Bibliographic record
Abstract
Forest visiting frequency level is likely to indirectly affect the preferences and needs of the program. The forest visiting frequency degree is an important factor which can affect the development and conduct of the program. Therefore, forest needs analysis was conducted for the development of forest therapy program in accordance with the frequency of visits. First, after examining the frequency of forest visits degree according to demographic characteristics, the group of 217 people visiting the forest at least once a week or a month was classified as a high-frequency group. The group of 403 people visiting the forest at least once a quarter or a year and almost no visit was classified as a low-frequency group. The need difference according to the frequency of visits was analyzed through descriptive statistics analysis, frequency analysis, crosstabs by use of SPSS 21.0 program. The most important factors of Implementation period, season, paid the participation fee made differences depending on the frequency of visit. This study, as an exploratory research, making us understand the needs related to forest healing program for the forest visit groups of high and low frequencies, will be an important basis for the development and operation of forest therapy program.
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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 | 0.001 | 0.000 |
| 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) | 0.006 | 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".