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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".