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Record W7127181780 · doi:10.18357/wg2620243

Mapping Hazardous Terrain for Search and Rescue Pre-Planning

2024· article· W7127181780 on OpenAlexaff
Scott Shupe

Bibliographic record

VenueWestern Geography · 2024
Typearticle
Language
FieldEngineering
TopicAutomated Road and Building Extraction
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsTerrainElevation (ballistics)Hazardous wasteMainlandRidgeDigital elevation modelWatershedPopulation

Abstract

fetched live from OpenAlex

The rugged, mountainous terrain of British Columbia's Lower Mainland attracts a growing population seeking outdoor experiences, but it also poses significant challenges and dangers, often leading to injuries requiring search and rescue (SAR) assistance. This research aimed to enhance SAR operations by providing detailed terrain characterizations through Remotely Piloted Aircraft System (RPAS) imagery. The study focused on Evans Valley in Golden Ears Provincial Park, an area identified by Ridge Meadows SAR (RMSAR) as particularly hazardous for hikers, especially those venturing off-trail. The acquired imagery was used to create 2D and 3D data products, with a particular focus on the area around Evans Valley Trail. These procedures were based on a proof of concept conducted in the lower elevation Kanaka Creek Watershed south of the park. The resulting orthophotos, elevation models, and 3D models offer various perspectives of the terrain and map potential access routes between Evans Trail and Evans Creek to aid SAR teams in navigating the valley.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.015
GPT teacher head0.277
Teacher spread0.262 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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