Delineating conservation areas on the Oak Ridges Moraine using a systematic conservation planning approach
Bibliographic record
Abstract
Protected lands form an essential component of landscape planning, and often extend beyond protection of existing natural areas to consider enhancement through restoration to improve existing conditions. We tested an automated conservation science-based methodology and systematic approach to delineate conservation and restoration priority areas on the Oak Ridges Moraine (ORM). The methodology comprised: a) preparing and assembling existing spatial (GIS) information and tessellating the study area to 5-ha hexagon planning units; b) conducting a gap analysis to provide a basis for setting conservation targets that protect, or that through future restoration activities might enhance, under-represented biodiversity elements; and c) applying a simulated annealing procedure (i.e., mathematical algorithm) to find solutions that optimize the set biodiversity targets. The final output of our work is a map of conservation priority area that enables the more than 50 conservation partners in this landscape to coordinate various conservation, stewardship and restoration activities by focusing on those areas that have the highest conservation value. Key words: restoration, settled landscapes, conservation planning, mathematical algorithm
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".