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Record W2715949390 · doi:10.4050/f-0070-2014-9601

Conducting Helicopter Operations in Northern Labrador and the Arctic

2014· article· en· W2715949390 on OpenAlexaffabout

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsGovernment of Newfoundland and Labrador
Fundersnot available
KeywordsArcticThe arcticComputer scienceAeronauticsGeographyOceanographyEngineeringGeology

Abstract

fetched live from OpenAlex

While helicopters are used for a myriad of purposes in rural and urban environments, their true potential can be measured by the support they can offer in extreme and remote areas. This paper describes a Northern Canadian operator, Universal Helicopters Newfoundland and Labrador LP, the equipment used, the tasks performed, the working conditions and the risks and challenges faced . The principal areas of operation include the Province of Newfoundland and Labrador, the Ungava Peninsula and Canada's high and eastern Arctic. The company operates 19 light and intermediate helicopters in one of the most challenging environments in the world. The aircraft are equipped with operational equipment and accessories for operation in temperature extremes which test not only the machinery but the crews that fly and maintain them. A Safety Management System is in place to properly identify and manage the unique risks of operating in the north as well as logistical support that recognizes associated added costs. The presence of multiple aircraft and their adjacency to remote communities often results in requests from authorities to assist in Search and Rescue operations. Despite challenges from wildlife, weather, topography and a long distance supply and communications chains, operators are able to conduct helicopter operations to support scientific research and natural resource development.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

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

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 head0.035
GPT teacher head0.263
Teacher spread0.229 · 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 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
Published2014
Admission routes2
Has abstractyes

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