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Record W2031820326 · doi:10.1037//1076-898x.7.1.13

Target detection in scientific visualization.

2001· article· en· W2031820326 on OpenAlex
Ian Spence, Adele Efendov

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

VenueJournal of Experimental Psychology Applied · 2001
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHueColor spaceArtificial intelligenceColor visionContext (archaeology)ColoredComputer visionComputer scienceTrichromacyBrightnessSpace (punctuation)OpticsPhysicsImage (mathematics)Geography

Abstract

fetched live from OpenAlex

Three experiments were conducted to test participants' ability to detect targets in colored spatial displays using 7-level bipolar scales. Experiment 1 assessed the ability of participants to detect high or low targets using 12 scales whose poles either were directly opposed in color space or had a primary and an intermediate hue at each pole. Experiment 2 used 8 scales whose arms were orthogonal in color space. Experiment 3 examined the simultaneous detection of high and low targets. Although there are notable exceptions, scales that are close to or above the horizontal (red-green) axis in color space perform best. Of the scales with orthogonal arms, those that are oriented downward, toward the blues, in color space are least satisfactory. Scales that are asymmetrically effective are common, and applications requiring good detectability at both extremes must take this into account. The results are discussed in the context of the evolution of trichromatic color vision.

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 categoriesnone
Consensus categoriesnone
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 score0.022
Threshold uncertainty score0.821

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.403
Teacher spread0.336 · 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