Continuous-cover management and attractiveness of managed Scots pine forests
Bibliographic record
Abstract
Forest management, characterized in many northern countries by the predominance of clear cutting and growing even-aged and -sized trees, has simplified the structure of boreal forests. Consequences include alterations in cultural ecosystem services such as forest attractiveness, i.e., combined aesthetic and recreational values. Continuous-cover forestry might mitigate these effects through the use of selection and gap cutting, but these methods have been little studied, particularly from the attractiveness viewpoint. We used photo surveys to assess Finnish citizens’ perceptions of attractiveness of in-stand scenery of Scots pine (Pinus sylvestris L.) forests logged using different methods. (i) The attractiveness scores, given by respondents, declined steadily from unharvested forest through continuous-cover methods to seed-tree and clear-cut methods. (ii) Respondents with a negative attitude to forest management gave lower scores than respondents with a positive attitude, but the declining slopes of attractiveness against logging intensity were similar. (iii) In unharvested and less intensively managed stands, summer photos received higher scores than corresponding winter photos. (iv) Background variables (gender, education, living environment, memberships in recreational or nature NGOs, forestry profession, and forest ownership) had negligible effects on the scores. We recommend the use of continuous-cover logging methods in settlement and recreational areas.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".