Mapping Bio-Physical and Cultural Values in the Mackenzie Valley: Preparing a Balanced Development Package
Bibliographic record
Abstract
For the past decade, World Wildlife Fund Canada has been at the leading edge of GIS mapping initiatives and gap analyses regarding the establishment of protected areas across Canada’s lands and waters. In the Northwest Territories (NWT), we have led an open, multi-stakeholder exercise this past year to compile and digitise all existing bio-physical and cultural information for the Mackenzie Valley and NWT to produce high-quality readily available GIS maps showing the regional distribution of these values. These data will be available to all interested stakeholders to highlight information gaps and to consider the placement of pipeline related developments. Furthermore, consistent with the NWT Protected Areas Strategy (PAS) [1], communities and other groups can use the information to assist in identifying areas of high natural and cultural value which should be reserved for protection as pipeline development plans and approvals are being made. Based on these data, a defensible network of protected areas representing the diversity of landform features in the Mackenzie Valley natural regions can be established to provide all parties with greater certainty and confidence as the development proceeds. This poster shows some key preliminary results from this mapping project, describes the various data layers and analytical techniques used, and highlights spatial examples where pipeline routing, associated infrastructure and conservation/protected areas in affected natural regions could all be achieved and widely supported.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".