USING A WEB-BASED GIS FOR ENVIRONMENTAL MANAGEMENT OF A CRUCIAL WINTER TRANSPORTATION ROUTE IN NORTHERN CANADA
Bibliographic record
Abstract
A seasonal snow and ice road – the Tibbitt to Contwoyto Winter Road (TCWR) – is constructed each winter to provide a reliable transportation route to and from the operating diamond mines and various other exploration projects in northern mainland Canada. The TCWR is an approximately 600 km long route northeast of the community of Yellowknife, Northwest Territories, that supplies critical goods and materials, including fuel, cement, ammonium nitrate (prill), building materials and mining equipment, to these northern destinations each winter.The TCWR Joint Venture currently consists of BHP Billiton Diamonds Inc. and Diavik Diamond Mines Inc., and has managed the TCWR for the past eight years; before that, from 1982 to 1999, it was managed by Echo Bay Mines. Each year, the TCWR is constructed from the end of the Ingraham Trail about 70 kilometres east of Yellowknife at Tibbitt Lake, across 495 km of frozen lakes and streams and 64 land portages, to Contwoyto Lake in Nunavut. Since 2001, the Joint Venture has carried out an integrated environmental management program that includes extensive baseline inventories and a GIS-based Winter Road Environmental Management System (WREMS). WREMS includes environmental baseline mapping based on high resolution aerial imagery, LIDAR, Landsat TM, and detailed ground-based data on ecosystems, wildlife habitat and wildlife movements, aquatic resources, and heritage resources. WREMS provides a means to organize, manage, disseminate and regularly update spatial information. One of the core components of the WREMS is live, interactive mapping that provides access to the various spatial data layers available for each of the 64 terrestrial portages. Users can select layers, zoom, pan, overlay and query data. Other components
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".