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Record W2982190758 · doi:10.4095/226198

High resolution digital elevation models and orthophotos for three landslide-prone areas in the Mackenzie Valley, Northwest Territories: Thunder River, East of Travaillant Lake, and Mount Morrow

2008· report· en· W2982190758 on OpenAlexaffabout
R Couture, S Riopel, Costas Armenakis, Florin Savopol

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsOrthophotoThunderDigital elevation modelLandslideMountGeologyArchaeologyElevation (ballistics)LaharRemote sensingGeographyGeomorphologyPhysical geographySeismologyVolcanoPyroclastic rockMeteorologyEngineering

Abstract

fetched live from OpenAlex

Northern communities and infrastructure in the Mackenzie Valley may be impacted by landslides and slope movements. In recent geological history of the Mackenzie Valley, hundreds of landslides, often affecting areas several hectares in size, have been identified and mapped. In order to improve the geoscience information in the Mackenzie Valley, Natural Resources Canada initiated a regional landslide mapping project to i) provide baseline knowledge on types, regional distribution, and controlling and aggravating factors of landslides in the Mackenzie Valley through a compilation of existing and new information; ii) assess the influence of environmental factors (e.g. forest fire, climate variability, global warming); iii) map and monitor zones of potentially unstable slopes using remote sensing technologies; and iv) map susceptibility to landslides in a permafrost environment.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.574

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.023
GPT teacher head0.225
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

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