Percepção da profundidade de máscaras côncavas branca, cinza-médio e preta
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".