Strategic Conservation Planning for Terrestrial Animal Species in the Central Interior of British Columbia
Bibliographic record
Abstract
The Nature Conservancy of Canada used an expert-driven approach to incorporate multiple animal species into an ecoregional assessment for the purpose of conservation planning in the Central Interior of British Columbia. This method has been applied in 14 ecoregions across Canada as part of the organization’s mission to “protect areas of biological diversity for their intrinsic value and for future generations” through land purchases and other land protection measures.A team of biologists identified 100 vertebrate species considered to be of conservation concern in the study area (3 amphibians, 5 reptiles, 28 mammals, and 64 birds) and set targets for spatial representation of their occurrences and habitat. The level of conservation concern associated with each species was assessed based on its formal conservation ranking, conservation priorities set by other organizations, and observed trends and vulnerabilities in a local and provincial context. To identify areas of high conservation priority, targets for the representation of animal species, and those identified separately for plants and ecosystem units, were collectively applied in a series of simulations using Marxan site-selection software. Marxan was directed to meet coarse-filter targets for terrestrial ecosystem units as well as optimally represent fine-filter targets for plants and animals and their habitats.The final portfolio of conservation areas is based on a “best solution” of planning units (500‑ha hexagons) that provide the most effective representation of targets at least cost over 500 Marxan simulations. These areas achieved all of the representation targets for terrestrial animals in terms of the number of element occurrences and percent area of habitat selected. Priority conservation areas are distributed across the study area, building on existing protected areas and providing increased connectivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".