Field recognition, inventory, and analysis of landslides in the Camsell Bend map area, southern Mackenzie River watershed, Northwest Territories
Bibliographic record
Abstract
A terrain inventory in the Camsell Bend map area (NTS 95 J), southern Mackenzie River watershed, Northwest Territories, distinguishes among complex landslides involving bedrock and unconsolidated surfi cial deposits, landslides involving bedrock, landslides in surfi cial deposits, and landslides in earth materials containing permafrost. Geoscience data collected include the spatial distribution of surfi - cial deposits and landforms, and the classifi cation, dimensions, physiographic setting, and age of landslide events. Dominant factors in triggering and velocity of landslides are slope, drainage characteristics, and water content of surfi cial material or bedrock. Other controls include distribution and intensity of wildfi res, local relief, orientation of bedrock structures, debris thickness, and distribution of ground ice, precipitation and vegetation. The outcome of applying knowledge gained through this work is an increased awareness and response to landslide geohazards, risks, and consequences, and improved decisions on regional development of infrastructure by geoscientists, engineers, ecologists, planners, emergency response offi cials, lawyers, and local communities.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".