{"id":"W2950168270","doi":"10.1101/577064","title":"Rapid invariant encoding of scene layout in human OPA","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Academy of Finland","keywords":"Computer vision; Functional magnetic resonance imaging; Artificial intelligence; Magnetoencephalography; Computer science; Visual cortex; Invariant (physics); Decoding methods; Representation (politics); Pattern recognition (psychology); Psychology; Neuroscience; Mathematics","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":[0.00006524601,0.0001049603,0.0001089584,0.0001565916,0.00008037529,0.000301098,0.00008957028,0.0001046967,0.002108618],"category_scores_gemma":[0.0005665635,0.0001031231,0.00007880662,0.00009199415,0.0002649803,0.0001955719,0.0002415136,0.0001562536,0.0001983581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001112673,"about_ca_system_score_gemma":0.0001001897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004606,"about_ca_topic_score_gemma":0.001092507,"domain_scores_codex":[0.999969,0.000004591481,0.000001041282,0.00001094296,0.000007109756,0.000007219081],"domain_scores_gemma":[0.999928,0.00002462639,0.00001219795,0.00001176949,0.00001136859,0.00001206386],"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":[0.0001904432,0.00001139508,0.002017552,0.00006299604,0.0000112978,0.0001425121,0.000197116,0.000819265,0.9641747,0.001208532,0.0004480562,0.03071629],"study_design_scores_gemma":[0.0001089955,0.000678056,0.6476178,0.0000490378,0.00006420473,0.002451653,0.0007416187,0.03118376,0.2850258,0.02114779,0.0108674,0.00006379963],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793137,0.000152224,0.01659464,0.0001504042,0.00002807185,0.00002243956,0.0002882483,0.0001862067,0.003264209],"genre_scores_gemma":[0.9951231,0.00005397372,0.003940694,0.00003129717,0.000005603994,0.00001003546,0.00008484644,0.00002225448,0.0007282183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002108618,"threshold_uncertainty_score":0.007053971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05868470649391429,"score_gpt":0.2843280752306662,"score_spread":0.2256433687367519,"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."}}