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Record W322919817 · doi:10.1096/fasebj.20.5.a847-c

Computerized modeling and animation system for use in Anatomy lectures

2006· article· en· W322919817 on OpenAlexaff
Sarah Rae Takekawa, Michael Farrell, Beth K. Lozanoff, Scott Lozanoff, Eric Neufeld

Bibliographic record

VenueThe FASEB Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceFile formatComputer graphics (images)SoftwareLaptopAnatomy3d modelScannerSurface anatomyEngineering drawingArtificial intelligenceDatabaseEngineeringBiology

Abstract

fetched live from OpenAlex

Traditional lectures in Anatomy rely on two‐dimensional illustrations or photographs derived from standard textbooks. However, with increased sophistication of laptop computing, three‐dimensional model reconstruction systems have increased the ease with which models and animations can be used for introducing complex concepts within a didactic lecture. The purpose of this study was to develop a technique to facilitate computerized anatomical models for use in anatomy lectures. Cadaveric material was obtained and scanned using a Polhemus hand‐held scanner and exported as an.obj file. The file was imported into Maya (Alias Software) and vertices were edited accordingly. The models were then exported as a.dxf file and imported into WinSURF (SURFdriver Software) and rendered with realistic texture and surface maps (SURFdriver Maps software). Once loaded, the reconstructions can be manipulated using rotation and enlargement tools, or colors can be changed to highlight specific areas. Examples are provided for the skull and cervical plexus, ocular testing, and vertebral morphology. Results of this study show that anatomical models can be generated easily and effectively and can be tailored for a specific lecture.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.136

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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
Published2006
Admission routes1
Has abstractyes

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