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Record W2895794756 · doi:10.14393/ppv21n2a2017-04

Percepção da profundidade de máscaras côncavas branca, cinza-médio e preta

2017· article· pt· W2895794756 on OpenAlexaff
Esther Sampaio Santos, Lívia da Silva Bachetti, Maria Amélia Cesari Quaglia, Branda Garcia da Silva, Caio Rafael Silveira

Bibliographic record

VenuePerspectivas em Psicologia · 2017
Typearticle
Languagept
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsBrock University
FundersUniversidade Federal de São João del-Rei
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

ResumoO presente estudo objetivou investigar a percepção monocular da profundidade ou relevo de máscaras côncavas: branca, cinza-médio e preta, iluminadas por baixo e apresentadas nas posições vertical e invertida.Quarenta observadores estimaram a profundidade ou relevo dos reversos ocos das máscaras por meio de uma escala do tipo Likert e a profundidade métrica visualmente percebida, julgada por meio de uma trena retrátil.Não foram observadas diferenças nas atribuições métricas da profundidade ou relevo da máscara côncava frente às variações de e posicionamento e de luminosidade ou brilho.Houve tendências para perceber a máscara branca com maiores profundidades e a preta com menores durante a inversão monocular.Entretanto, maiores profundidades foram designadas às máscaras preta quando não foi percebida a ilusão.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.113
GPT teacher head0.368
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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