{"id":"W2952149978","doi":"10.7554/elife.31200","title":"Fast and accurate edge orientation processing during object manipulation","year":2018,"lang":"en","type":"article","venue":"eLife","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Western University","funders":"Canadian Institutes of Health Research; Vetenskapsrådet","keywords":"Orientation (vector space); Enhanced Data Rates for GSM Evolution; Object (grammar); Computer science; Computer vision; Artificial intelligence; Mathematics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009897336,0.0000824286,0.00006275232,0.00006379344,0.0004284148,0.0001646716,0.00006042008,0.00003877047,0.00009618651],"category_scores_gemma":[0.0001314665,0.00007707877,0.00001130958,0.0002021312,0.00006318768,0.0004342118,0.00003097219,0.00006181349,0.0001534197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001912345,"about_ca_system_score_gemma":0.00002169959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002604313,"about_ca_topic_score_gemma":0.000004374264,"domain_scores_codex":[0.9992304,0.00004003224,0.0001278631,0.0002649185,0.0001927662,0.0001439994],"domain_scores_gemma":[0.9997271,0.00001208683,0.00007342346,0.00007560663,0.00005652805,0.00005524176],"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":[0.00003143126,0.00001570899,0.0003568399,0.0000391379,4.316339e-7,0.000001713636,0.002433829,0.000009489957,0.976176,0.0002723457,0.00003398954,0.02062914],"study_design_scores_gemma":[0.0004083882,0.00009129877,0.02568116,0.00005413256,0.000005410715,0.00003041348,0.0003065772,0.01312579,0.9594722,0.0002958169,0.0003589507,0.0001698823],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889819,0.00001098314,0.008631463,0.00007419771,0.0002384552,0.00008932553,0.000001354395,0.0001338026,0.001838544],"genre_scores_gemma":[0.9981302,0.00001141592,0.0004031166,0.0005300004,0.000286862,0.000005733399,0.000002091715,0.00001304117,0.0006175496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02532432,"threshold_uncertainty_score":0.3295063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07448992597911128,"score_gpt":0.3514179049530173,"score_spread":0.2769279789739061,"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."}}