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Record W2322013815 · doi:10.3722/cadaps.2012.795-810

Conceptual Design of Hemp Fibre Production Lines in Virtual Environments

2012· article· en· W2322013815 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputer-Aided Design and Applications · 2012
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of ManitobaMcGill University
FundersUniversity of Minnesota
KeywordsConceptual designProduction (economics)Computer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

The goal of virtual orthodontic treatment planning is to re-position the teeth in a digital dental model so that the desired alignment of the teeth on each dental arch and occlusion (i.e., matching) of the upper and lower arches is achieved.The input to the planning process is a collection of individual tooth objects obtained by segmenting a noisy 3D surface mesh that is generated by laserscanning a plaster model of the dental arch built from patient-specific dental impressions.A key step in the planning is the identification of features on the surface of the teeth such as cusps, grooves, incisal edges, marginal ridges, and occlusal surface boundary, that are important both for carrying out the alignment and evaluating its quality.This paper presents a collection of techniques to identify such features automatically, with minimal user intervention.Experimental results are presented that show the effectiveness of the approach.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.419

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 head0.023
GPT teacher head0.215
Teacher spread0.192 · 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