Social aspects of forest recreation
Bibliographic record
Abstract
We consider the social aspects of recreational use of forests, such as forest recreants attendance and attractiveness of different stands on the size and type of locality, age, gender, employment. It was estab-lished that recreants distributed fairly evenly by the number of visits to the forest in the summer. Most vacationers are in a forest 1–2 times and 3–4 times per month. Women are in forest considerably less than men. Working in the forest are less likely than senior citizens, students and pupils. Residents of ru-ral areas are more likely than residents of large citie s, visiting the forest. More than 3/4 of the respond-ents prefer to rest in mixed stands, and one in five in the pure stands. More than a quarter of the women surveyed, and almost one in four works in favor of rest in pure stands. More than half of the respond-ents chose the pine stands as a place of rest. A large proportion of respondents (22.1%) of the answers a few tree species, including two species – 15.3%. Dominated by a combination of pine and birch, pine, birch and spruce. Women choose for rest stands consisting of one or two tree species, mostly pine, birch or mixed of these breeds. Men are more varia tion in their choice. Working are characterized by a more flexible approach to the selection of stands for summer vacation. The study offered the best in terms of recreants composition recreational forests of the country.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".