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Record W2767645280 · doi:10.1080/0142159x.2017.1395401

The relationship between spatial ability, cerebral blood flow and learning with dynamic images: A transcranial Doppler ultrasonography study

2017· article· en· W2767645280 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

VenueMedical Teacher · 2017
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsWestern UniversityUniversity of Calgary
Fundersnot available
KeywordsCerebral blood flowNeurophysiologyPerspective (graphical)Blood flowTranscranial DopplerDynamic imagingArtificial intelligenceComputer scienceDoppler effectUltrasonographyNeuroscienceComputer visionPsychologyMedicineImage (mathematics)RadiologyImage processingInternal medicine

Abstract

fetched live from OpenAlex

Determining the effect of dynamic images on learning is often limited to performance measures. This study explores the impact from the perspective of cerebral blood flow in the brain during learning. Performance and neurophysiological response in high and low spatial ability were compared during learning with dynamic images. Individuals with high spatial ability appear to be better suited to learn with complex images such as dynamic images that move in time and space. The results presented here suggest that spatial ability can help to determine the effectiveness of the media we use for teaching.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.355
Teacher spread0.320 · 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