Exploring the availability of Ontario's non-industrial private forest lands for recreation and forestry activities
Bibliographic record
Abstract
Privately owned forest lands contribute significant amounts of land for wood supply and recreational opportunities in various parts of Canada including areas within Ontario. The decisions that landowners make about permitting various activities on their lands can impact resource managers and current and potential users of forested environments. In this study, the willingness of Ontario's non-industrial private forest landowners to conduct forest harvesting and to permit hunting and wildlife recreational opportunities is examined. The study explores whether the willingness of landowners with large-sized landholdings (i.e., minimum 20 ha) is influenced by characteristics that describe the private lands and the owners of these private lands. The results show that trends towards land parcelization, afforestation and loss of agricultural lands may impact the availability of lands for forest harvesting and hunting. The models also suggest that northern Ontario landowners may make different decisions about conducting forest harvesting or permitting hunting on their lands than do southern Ontario landowners. Key words: non-industrial private forest landowners, forest harvesting, hunting, wildlife viewing, land parcelization
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".