{"id":"W2409648650","doi":"10.1038/srep17142","title":"Shape recognition: convexities, concavities and things in between","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal General Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Position (finance); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Scale (ratio); Set (abstract data type); Convexity; Segmentation; Computer vision; Geometry; Mathematics; Geography; Cartography","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":[0.0005653929,0.0004259376,0.0004277444,0.0004781505,0.0002241401,0.001306331,0.0004799824,0.0005611117,0.002012983],"category_scores_gemma":[0.004739168,0.0003909626,0.0004801812,0.0003360722,0.001153758,0.002175129,0.001045462,0.0006059986,0.0005608548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004077603,"about_ca_system_score_gemma":0.0003390066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002325909,"about_ca_topic_score_gemma":0.001922068,"domain_scores_codex":[0.9995316,0.00007478002,0.00002161589,0.0002172966,0.0001183406,0.00003642663],"domain_scores_gemma":[0.9987561,0.0004448223,0.000191642,0.0004159215,0.00009883134,0.00009271524],"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":[0.00185659,0.0001166409,0.03037998,0.0002568886,0.0001483034,0.0002154485,0.001409473,0.007877241,0.692342,0.007480142,0.0008639419,0.2570532],"study_design_scores_gemma":[0.00006842845,0.00144893,0.3958788,0.0001040878,0.000196058,0.001989414,0.001266542,0.2346931,0.3247059,0.03465948,0.004764835,0.0002245901],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9298783,0.0002470585,0.06366543,0.00008841106,0.00002581031,0.00004992287,0.0001068264,0.0003642516,0.005573889],"genre_scores_gemma":[0.9804265,0.0001227193,0.01810427,0.00003957274,0.000007697638,0.00001539813,0.0002494017,0.0000665548,0.0009678929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002325909,"threshold_uncertainty_score":0.006734133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.177138351303153,"score_gpt":0.3308327038762534,"score_spread":0.1536943525731004,"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."}}