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Record W70819578

An Avalanche Terrain Assessment of Proposed Manitoba Mountain and Whistle Stop Hut-to-Hut Systems Using a Geographical Information System

2010· article· en· W70819578 on OpenAlexaboutno aff
E. N. Cleary, Eeva Latosuo, Jason Geck, Jonathan P. Wolfe

Bibliographic record

Venue2010 International Snow Science Workshop · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsTerrainGeographyRecreationWilderness areaRaster graphicsGeographic information systemPeninsulaPhysical geographyCartographyWildernessRemote sensingArchaeologyComputer scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

Alaska Mountain and Wilderness Hut Association has proposed a hut-to-hut system on the Kenai Peninsula of Southcentral Alaska. A terrain assessment is first of several steps necessary before construction of huts begins. Locating huts in this remote area may increase the use of the area, with potential for an increase in less skilled users. It is important to provide all recreational users with safe routes and safes resting locations. A model was created to determine the amount of terrain in potential release areas. An equation was applied to the ridgelines above proposed hut sites to estimate potential run-out distances of slides. Results were compared to actual historical run-out in the area observed by the Alaska Railroad. All of this was calculated and displayed using both a GIS and knowledge of avalanche terrain. Raster data available for this area, and most of Alaska, has a fairly large cell size so many micro-terrain features are missed in the assessment. As a result of the cell size limitations there is a need for further study of these areas including field observations. Outlined in the following article is a preliminary assessment of the avalanche terrain along the proposed hut-to-hut systems at Manitoba Mountain and along the Whistle Stop route.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.272
Teacher spread0.263 · 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 teacher head, 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
Published2010
Admission routes1
Has abstractyes

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