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Record W2784789369

Robotic wood crafting: An arctic shelter in the canadian tundra

2018· article· en· W2784789369 on OpenAlexaboutno aff
Daniel Fischer

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2018
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsTourismResource (disambiguation)ArcticBusinessAccommodationThe arcticProcess (computing)TundraEngineeringEnvironmental resource managementGeographyEconomicsComputer scienceEcologyGeologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

With a declining mining industry, Canada is looking for new models of economy. Arctic Eco-Tourism has already started growing over the last decade and is expected to grow even more rapid in future. This brings the chance of economic growth and employment. But it is also a danger¬ous situation for nature. Since the Canadian north is to more than 98% untouched, uncontrolled growth of traditional building complexes must not destroy nature as is already did in the south. The concept suggests combining two aspects of Canadian past with one of the future. The declared goal is not to destroy any more nature by infrastructure of insensible build-ings for tourism. The site of the Diavik Diamond Mine has been chosen for two things: First, to re-naturalize the area by money made from tourism. Second, to build an invisible starting point for Eco-Tourism in this area. The huge holes, which have been the result of a process called open-pit-mining, are a perfect spot to implement a building on a site from which nature was already banished. Another main point is to use the old airfield as infrastructure for tourism. No additional streets need to be built. The aim is to create a resource efficient and fully autocratic building complex for arctic Eco-Tourism. This complex will mainly offer temporary accommodation for tourists. It is the goal to use this project to examine the opportunities with robotically fabricated CLT structures as prefabricated building parts.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.268
Teacher spread0.230 · 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
Published2018
Admission routes1
Has abstractyes

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