Landscape Connectivity Analysis for Conservation Planning in Southern Ontario
Bibliographic record
Abstract
The strategic planning of land conservation is a critical undertaking in urban/peri-urban areas. Natural areas in cities and their surroundings exist in an environment of competitive land use pressures, where the allocation of available land may be complex and politically charged. Organizations pursuing land conservation in these areas must balance biodiversity aims with fiscal and resource limitations, a competitive market, and the need for decision-making accountability. \n \nTo support the prioritization of conservation lands for protection, analysts may incorporate landscape connectivity analysis. By quantifying how the configuration of habitat facilitates species movement, connectivity analysis provides a rationale for conservation planning that supports the dispersal of species across the urban/peri-urban matrix. \n \nWhile connectivity analysis is useful for conservation planners, several factors have created a confusing environment for those interested in employing it. These include the rapid proliferation of connectivity research, the inconsistent use of methods and terminology, and an absence of updated selection guidelines for practitioners. Thus, my research evaluates how conservation organizations may best use landscape connectivity analysis to support conservation planning in urban/peri-urban areas. \n \nIn this thesis, a systematic review of urban/peri-urban connectivity literature is followed by application of review results to a conservation planning case study in Southern Ontario. Reflections on these two research phases support a proposed framework that outlines the pivotal decisions, organizational limitations, and best practices for using landscape connectivity analysis for conservation planning. This provides tangible benefit for organizations protecting and stewarding natural lands, particularly in areas like the urban/peri-urban matrix of Southern Ontario.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".