Bibliographic record
Abstract
British Columbia’s Flathead Valley has been part of a century-old conversation about expanding Waterton Glacier National Park. The area is home to the highest density of non-coastal grizzlies of anywhere in North America. This valley boasts the highest plant diversity in all of Canada. There is no human population centers dwelling in this valley, but many stakeholders have interests there, including logging companies like CANFOR and Jemi Fibre, outfitters and hunting guides, ATV or off road vehicle groups, environmentalists, conservationists, First Nations people, British Columbia’s provincial government and the Canadian government. The First Nations group is the Ktunaxa and they are the areas oldest human inhabitants. They have used this area for 11,000 years. The conversation may be riper for a solution than it has ever been. The Ktunaxa are currently settling their land claims with British Columbia’s provincial government. Canada has biodiversity targets to protect 17 percent of their landscape by 2020. Currently, they are at 10 percent. Yellowstone to Yukon Initiative, a Canadian and U.S. nonprofit group, is spearheading the campaign, hoping to create a continent-sized wildlife corridor from the Yellowstone River to the Yukon River. This valley is a critical piece of the puzzle. This corridor would allow genetic diversity to flow and connect to other protected areas and foster species survivability. Bill Bennett, British Columbia’s Minister of Energy and Mines, is retiring in May 2017. Polls are showing more and more people of southeastern British Columbia are in favor of protecting this valley. Canada is celebrating its 150th birthday this year. My long-form piece of journalism focuses on this area, the people who use it and the obstacles that have blocked the expansion of Waterton Park but also explains why the timing may be right for a solution that protects both the area’s biodiversity and the needs of its human residents.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.704 | 0.483 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".