Designing natural heritage systems in southern Ontario using a systematic conservation planning approach
Bibliographic record
Abstract
Landscape planning in settled landscapes includes identifying larger areas of natural vegetation to be conserved protected and/or managed for various environmental and public services. These “green backbones” of the landscape, called Natural Heritage Systems (NHS) in the settled landscapes of southern Ontario, Canada, should have appropriate land use planning and natural areas management actions and related policies to protect and enhance biodiversity and ecological functions. As such, an NHS should be derived using a rigorous and defensible methodology while ensuring public involvement and input during this process. This paper describes the methodology for regional NHS design currently being implemented by OMNR in collaboration with numerous conservation partners and municipalities in southern Ontario. The methodology combines the principles and methods of landscape planning, conservation planning, and spatial analysis, while ensuring that the process is adaptable and repeatable over time and different scales. For each landscape, explicit and transparent conservation objectives, features and targets are identified based on stakeholder inputs. Numerous conservation and restoration objectives are translated into explicit quantitative targets for each analysis unit, and a mathematical optimization algorithm is used to represent all the targets at minimal cost (least land area). The methodology is illustrated using examples from a pilot study in Ecodistrict 7E–5 with some references to ongoing NHS implementation projects as well as potential applications of this method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".