{"id":"W3170934469","doi":"10.1371/journal.pone.0258376","title":"Where to draw the line?","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Line drawings; Categorization; Set (abstract data type); Line (geometry); Computer science; Artificial intelligence; Computer vision; Psychology; Visual arts; Art; Mathematics; Geometry; Engineering drawing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00245416,0.0006653462,0.000376716,0.001055515,0.0009863775,0.004182127,0.001201107,0.002186807,0.03175586],"category_scores_gemma":[0.01938023,0.0003470535,0.0003985148,0.0007859246,0.003023876,0.008787349,0.001077526,0.002109905,0.01824145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007049449,"about_ca_system_score_gemma":0.0006883682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001639831,"about_ca_topic_score_gemma":0.002079306,"domain_scores_codex":[0.9985176,0.0007148244,0.00008761657,0.0003322708,0.0002621994,0.00008551309],"domain_scores_gemma":[0.9961903,0.001210558,0.0005413287,0.0006849474,0.001014379,0.000358433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000269,0.0001098482,0.007289852,0.0007964732,0.00006020896,0.001105606,0.01005806,0.0006699737,0.01112537,0.1002869,0.167643,0.7005858],"study_design_scores_gemma":[0.00005649345,0.0002258868,0.00500281,0.001334667,0.00005633485,0.003225744,0.01683052,0.002886562,0.008210118,0.1510312,0.8109737,0.0001660814],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08208407,0.0306528,0.5506899,0.1464479,0.007497056,0.0002678822,0.001387061,0.006062915,0.1749104],"genre_scores_gemma":[0.5373994,0.02424796,0.3473413,0.01670042,0.001548611,0.0002532769,0.001163124,0.002729502,0.06861646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03175586,"threshold_uncertainty_score":0.106234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1615238021136673,"score_gpt":0.3202103741213394,"score_spread":0.1586865720076721,"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."}}