{"id":"W2535551971","doi":"10.1101/082495","title":"Reverse-engineering human olfactory perception from chemical features of odor molecules","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Olfactory and Sensory Function Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Russian Science Foundation; Javna Agencija za Raziskovalno Dejavnost RS; Canadian Institutes of Health Research; KU Leuven; Council of Scientific and Industrial Research, India; Howard Hughes Medical Institute; Government of Ontario; National Institutes of Health; Ontario Institute for Cancer Research","keywords":"Percept; Olfaction; Odor; Computer science; Perception; Artificial intelligence; Machine learning; Olfactory system; Psychology; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006065744,0.0005670513,0.0003320992,0.0002059715,0.000114351,0.000443898,0.0003766817,0.0003887318,0.001316151],"category_scores_gemma":[0.00141314,0.000165163,0.0006194016,0.0001322322,0.000360573,0.0004301091,0.0004524306,0.0005990763,0.0005169213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003197845,"about_ca_system_score_gemma":0.0004032224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002970264,"about_ca_topic_score_gemma":0.002057162,"domain_scores_codex":[0.9998426,0.00003686418,0.000005521525,0.00005375664,0.00004334806,0.00001784435],"domain_scores_gemma":[0.99963,0.0001737836,0.00003998098,0.00007057058,0.00006125054,0.00002430885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008195259,0.0004078378,0.0100204,0.0005677887,0.0002285384,0.0003839849,0.0001879176,0.3774931,0.4145274,0.006064082,0.006052529,0.183247],"study_design_scores_gemma":[0.0000294552,0.000201605,0.003533875,0.00001712052,0.00003737069,0.00007738684,0.00005565827,0.9032732,0.08375885,0.006465765,0.002516237,0.00003344543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7559972,0.0006787376,0.2346608,0.0009804863,0.0002016419,0.0001104448,0.00113839,0.001322078,0.00491026],"genre_scores_gemma":[0.9584196,0.0002113492,0.03818251,0.0001774666,0.00002297098,0.00003540228,0.0005590779,0.00007876063,0.002312977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002970264,"threshold_uncertainty_score":0.005905986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05283687941775261,"score_gpt":0.2304089222086572,"score_spread":0.1775720427909046,"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."}}