Introduction: Central Interior Ecoregional Assessment
Bibliographic record
Abstract
Ecoregional assessments provide a regional scale, biodiversity-based context for implementing conservation efforts by identifying a portfolio of sites for conservation action with a goal of protecting representative biodiversity and ecologically significant populations. The intent of these assessments is to create a shared vision for agencies and other organizations at the regional, state, and local levels to form partnerships and to ensure efficient allocation of conservation resources. The Nature Conservancy of Canada recently completed an ecoregional assessment of British Columbia’s Central Interior, the main components of which are presented as articles in this special issue of the BC Journal of Ecosystems and Management. These components include terrestrial ecosystems and animals, and freshwater ecosystems and species. The assessment also incorporates some new and innovative approaches to considering conservation priorities along with climate change, ecosystem services, and wildlife habitat modelling.The Central Interior Ecoregional Assessment provides a guide for prioritizing work on the conservation of habitats that support the extraordinary biological diversity of the ecoregion. Issues associated with land use and resource management planning are incredibly complex and this complexity is accelerating as a result of a changing climate and the cumulative effects of human impacts on species and ecosystems. The methods and results described in the following articles reflect the growing body of conservation planning experience.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".