Using Boolean truth tables to evaluate property acquisition for private forest investment : a case study in Montreal's rural-urban fringe
Bibliographic record
Abstract
This thesis offers a decision-making framework to select the best land near a city for private investment in multiple-use urban forestry. More specifically, this thesis postulates that privately financed forest restoration for multiple-use urban forestry can add green infrastructure to ecologically impoverished landscapes in a city's rural-urban fringe. A strategic framework simplifies complex decision-making in land selection problems, and here a multi-criteria Boolean algebraic truth table is used to create the framework. A truth table cell is set to either true (YES) or false (NO) by checking the land plot conditions against each land selection criteria. The selection criteria included biophysical factors, zoning types, and threat of takings. The approach is illustrated with a bottomland area along the St. Jacques River, in the City of La Prairie, in Montreal's rural-urban fringe. A major conclusion is that a Boolean decision-making framework may be useful to evaluate property acquisition before making private forest investments. Of the property selection criteria examined, stability of property rights may be the most important to consider first. While private investment in forest restoration for future uses could add new forests in urban areas, several important regulatory takings cases in the past 20 years in the Montreal region may discourage investors from making long-term investments. To encourage reforestation of underused degraded land, policy makers should consider regulatory reform, permitting multiple-use urban forestry near the city, and that this be combined with land tenure reform, thus encouraging private investment in green infrastructure.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".