Species richness and biodiversity conservation priorities in British Columbia, Canada
Bibliographic record
Abstract
Patterns in the geographic distribution of seven species groups were used to identify important areas for conservation in British Columbia, Canada. Potential priority sites for conservation were determined using an integer programming algorithm that maximized the number of species represented in the minimum number of sites. Sweep analyses were used to determine how well the set of priority sites identified for each species group represented the other species groups. Although areas of highest species richness were different for each species group, they all included sites in the southern interior of British Columbia, where there is limited protection. Furthermore, less than 13% of the distribution ranges for 23 of 25 bird species of special conservation concern were located within existing protected areas. Species at risk of extinction were poorly represented (26%–42%) in priority sets of sites selected for amphibians, reptiles, birds, and mammals, since these sites were generally scattered throughout the province. However, priority sites for species at risk represented 72%–91% of the species in other groups. Therefore, conservation activities in sites identified for such species have the potential to benefit many other species. These sites could be investigated in more detail to augment existing conservation and protection efforts in British Columbia.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".