{"id":"W2973739168","doi":"10.1167/19.10.16","title":"The size of objects in visual space compared to pictorial space","year":2019,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Object (grammar); Space (punctuation); Contrast (vision); Computer vision; Perception; Artificial intelligence; Computer science; Computer graphics (images); Mathematics; Psychology","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":[0.000917748,0.0003076727,0.0002011537,0.0006554183,0.0001787148,0.001954678,0.0003565324,0.0004104855,0.005310487],"category_scores_gemma":[0.01297459,0.0002530975,0.0002783829,0.0004869732,0.0008860442,0.00261191,0.001290591,0.0005324411,0.0002509579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003382663,"about_ca_system_score_gemma":0.0001320405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008611303,"about_ca_topic_score_gemma":0.0007558963,"domain_scores_codex":[0.9990463,0.0002444967,0.00006646924,0.0002654815,0.000317462,0.00005974105],"domain_scores_gemma":[0.9945213,0.002980581,0.001285858,0.0005462813,0.0004078384,0.0002581068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003595778,0.0003812227,0.14,0.001338239,0.0003496271,0.0004087068,0.01539632,0.004153492,0.6873088,0.01015867,0.001063746,0.1358455],"study_design_scores_gemma":[0.0001074013,0.001316061,0.9508336,0.0000744674,0.0001131791,0.000511174,0.004015564,0.004978518,0.02988082,0.005338827,0.002733479,0.00009681493],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890142,0.0002447215,0.003867742,0.00005819683,0.00002518036,0.00002772028,0.00007542025,0.0000272114,0.006659576],"genre_scores_gemma":[0.9967963,0.00009501413,0.002323205,0.00002640239,0.000009937839,0.00004113259,0.00007864916,0.00002016919,0.0006090762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005310487,"threshold_uncertainty_score":0.01776528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425122720502111,"score_gpt":0.3230272944174372,"score_spread":0.3087760672124161,"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."}}