MétaCan
Menu
Back to cohort

The LINDSAY Virtual Human Project: Anatomy and Physiology Come to Life

2012· article· en· W3174878037 on OpenAlexaff
Heather A. Jamniczky, Christian Jacob, Scott Novakowski, Timothy Davison, Sebastian von Mammen, Carey Gingras, Scott Steil, Mike Paget, Benedikt Hallgrímsson, Bruce Wright

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPresentation (obstetrics)Computer scienceHuman–computer interactionSoftwareVisualizationHuman anatomyMultimediaInterface (matter)Artificial intelligenceAnatomyMedicineProgramming language

Abstract

fetched live from OpenAlex

The LINDSAY Virtual Human Project encompasses anatomy teaching and physiology simulation for medical education. We have built a novel visualization and simulation framework that allows instructors and students to interact with three dimensional anatomical models and physiological simulations in real time. These tools allow the creation of learning objects in a number of forms, intended for use by both instructors and students. These tools are inquiry‐learning ready and include features designed to make the creation and dissemination of learner‐centered content easy and accessible. The software uses a unique, visual programming language that harnesses the power and versatility of agent‐based modeling. Users can therefore create, save, and modify presentations and simulations, and the objects created can be stored and linked hierarchically to explore anatomy and physiology at multiple scales. We aim to take advantage of recent exciting advances in portable touch interface technology. Our presentation software is currently in beta‐release, with planned full release in Spring of 2012. Our simulation software is planned for beta‐release in Summer of 2012.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.314

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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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 designBench or experimental
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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicHuman Motion and AnimationFrench-language works237,207