Bibliographic record
Abstract
A variety of reserve design software programs are available to assist in the selection and spatial configuration of new protected areas. One such application, Marxan, produces spatially cohesive reserve configurations which meet representation targets efficiently. Conservation agencies worldwide are adopting Marxan as a conservation planning tool, however it is not currently used by Parks Canada when conducting feasibility studies for potential national park reserves. This thesis evaluates whether Marxan could be a useful decision-support tool for Parks Canada to use when selecting and designing potential park areas. The assessment is based on four usability criteria and three park selection criteria, developed in consultation with Parks Canada. Concurrent with this thesis, Parks Canada is conducting a feasibility study for a national park reserve in the South Okanagan-Lower Similkameen region of British Columbia, Canada. This region serves as a case study for the thesis. Marxan was used to create 36 unique reserve configuration options for the case study area and to help evaluate the performance of each reserve. Overall, Marxan fully satisfied three criteria, partially satisfied three, and failed to meet one. This study demonstrates that Marxan provides a useful means to design and explore a range of representative and scientifically defensible reserves. However, to use it effectively requires technical and ecological expertise, a comprehensive GIS infrastructure, good data and time. This analysis concludes that Marxan would be a very appropriate tool to assist Parks Canada in selecting and designing potential national park reserves. Marxan would be best used in conjunction with other decision-support tools, expert knowledge and public consultation.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".