Realizing Expectations from Planting Trees on Private Land in Ontario, Canada
Bibliographic record
Abstract
This study explores the motivations behind participation in tree planting programs by private landowners in Ontario, Canada, as well as perceptions as to whether benefits were realized up to ten years after trees were planted. Forests Ontario, which has offered tree planting support programs in this province since 2007, provides up to 90% of the cost of seedlings for tree planting projects at least one hectare (ha) in size. This online survey of 570 former participants in tree planting programs indicated that a desire to create a habitat for wildlife (77.6%) was the most common motivation for taking part in a tree planting program. Concern with restoring native forest cover was also a reason for most participants (71.4%), as well as with improving soil, air and water quality (54.8%), and addressing climate change (54.3%). The most common benefit of planting trees was an increase in well-being and enjoyment of their property (67% of respondents). Overall, 27% of respondents with a desire to increase wildlife habitat, and 20% of those wishing to improve their local environment reported an improvement after tree planting. Reported improvements in the local environment and wildlife increased with time since tree planting, whereas enhanced well-being and enjoyment of the property were evident among participants even with newly planted trees.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".