{"id":"W2002519761","doi":"10.1167/13.14.1","title":"The dichoptiscope: An instrument for investigating cues to motion in depth","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Stereoscopy; Vergence (optics); Computer vision; Parallax; Monocular; Depth perception; Binocular disparity; Artificial intelligence; Stereopsis; Accommodation; Object (grammar); Binocular vision; Computer science; Motion (physics); Computer graphics (images); Optics; Psychology; Perception; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006757205,0.0005492268,0.0005058359,0.001225181,0.0003868247,0.0005454276,0.0006691703,0.0006750211,0.002339826],"category_scores_gemma":[0.0006156402,0.0003995729,0.000384474,0.0005669384,0.0006526393,0.001094184,0.001451742,0.0009849619,0.0003922838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003788101,"about_ca_system_score_gemma":0.0005434614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003331222,"about_ca_topic_score_gemma":0.0005894312,"domain_scores_codex":[0.9996781,0.00006173703,0.00001610619,0.00006989227,0.0001372083,0.00003698272],"domain_scores_gemma":[0.9994401,0.0002141506,0.0000760962,0.0001018328,0.00006201049,0.0001058001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004152152,0.00009687109,0.003373293,0.0003626686,0.00003038827,0.0002126157,0.0001179289,0.0003190641,0.9049881,0.004302712,0.001565357,0.08421576],"study_design_scores_gemma":[0.0002616639,0.001875492,0.03092022,0.0001424457,0.0001711818,0.008281189,0.0002485805,0.01252137,0.8472615,0.003773157,0.09429857,0.0002445262],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.229871,0.01134446,0.738377,0.0006073447,0.000459889,0.0008264497,0.002118008,0.002867213,0.01352873],"genre_scores_gemma":[0.2176572,0.003815959,0.7715584,0.0003598924,0.0001159263,0.00069543,0.000612967,0.0002113371,0.004972878],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002339826,"threshold_uncertainty_score":0.00782752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941015397243234,"score_gpt":0.2978781827170822,"score_spread":0.2784680287446498,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}