Conservation of Lakeshore Zones in the Northern Boreal Mountains: State of Knowledge, and Principles and Guidelines for Planning and Management
Bibliographic record
Abstract
Wildlife Conservation Society Canada (WCS Canada) was incorporated as a conservation organization in Canada in July 2004.Its mission is to save wildlife and wildlands by improving our understanding of -and seeking solutions to -critical problems that threaten vulnerable species and large wild ecosystems throughout Canada.WCS Canada implements and supports comprehensive field studies to gather information on the ecology and behavior of wildlife.Then, it applies that information to resolve key conservation problems by working with a broad array of stakeholders, including local community members, conservation groups, regulatory agencies, and commercial interests.It also provides technical assistance and biological expertise to local groups and agencies that lack the resources to tackle conservation dilemmas.Already, WCS Canada has worked on design of protected areas (Nahanni National Park), monitoring and recovery of species (grizzly bear, lynx, wolverine, and woodland caribou), restoration of ecosystems, integrated management of large landscapes, and community-based conservation.Although WCS Canada is independently registered and managed, it retains a strong collaborative working relationship with sister WCS programs in more than 55 countries around the world.The Wildlife Conservation Society is a recognized global leader in conservation, dedicated to saving wildlife and wildlands for species in peril, such as elephants, tigers, sharks, macaws and bears.For more than a century, WCS has worked in North America promoting conservation actions such as recovery of bison, establishment of parks, and legislation to protect endangered wildlife.Today, WCS Canada draws upon this legacy of experience and expertise to inform its strategic programs from Yukon to Labrador.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".