MétaCan
Menu
Back to cohort
Record W3099025090 · doi:10.1177/2041669520973698

Perception of a Black Room Seen Through a Veiling Luminance

2020· article· en· W3099025090 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuei-Perception · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsLuminanceLightnessContrast (vision)White (mutation)ReflectivityPerceptionOpticsComputer visionPsychologyArtComputer sciencePhysicsChemistry

Abstract

fetched live from OpenAlex

When a black room (a room painted black and filled with objects painted black) is viewed through a veiling luminance, how does it appear? Prior work on black rooms and white rooms suggests the room will appear white because mutual illumination in the high-reflectance white room lowers image contrast, and the veil also lowers image contrast. Other work reporting high lightness constancy for three-dimensional scenes viewed through a veil suggests the veil will not make the room appear lighter. Because mutual illumination also modifies the pattern of luminance gradients across the room while the veil does not, we were able to tease apart local luminance gradients from overall luminance contrast by presenting observers with a black room viewed through a veiling luminance. The room appeared white, and no veil was perceived. This suggests that lightness judgments in a room of one reflectance depend on overall luminance contrast only.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.086
GPT teacher head0.334
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