Bibliographic record
Abstract
The interaction between high relief, steep slopes, heavy precipitation, complex tectonic and geomorphic history, land uses, and the range of surficial deposits and bedrock can produce a variety of landslide types in many regions of the. In this paper, an effective approach is presented for the classification and mapping of terrain and landslide geohazards from stereo-pair air photographs, satellite imagery and benchmarking field studies in a region of mountainous terrain and discontinuous permafrost in Northwest Canada prone to earthquakes, mass wasting, wildfires and sensitive to the impacts of climate change. Digital terrain and landslide hazard maps and their accompanying geodatabases provide essential information for land management decisions regarding construction of pipelines, highways and settlements; evaluation of property rights decisions; extraction of fossil fuels, minerais, aggregates and groundwater; assessments of environmental risk and impact, ecological sensitivity and archaeological potential. GIS maps and geodatabases also provide calibration for future predictive landslide mapping and hazard analyses. Qualitative, semi-quantitative and quantitative analyses of landslide distribution, activity and density maps derived from terrain and landslide inventory geodatabases can improve the understanding of landslide processes in a region. Important outcomes that can be achieved through the use of geoscience databases, landslide hazard maps and related products include the attraction of new investment and reduction of risks for regional development. Outreach initiatives can also increase professional and public understanding and awareness of landslide hazards and related geoenvironmental issues.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".