{"id":"W2982216862","doi":"10.48550/arxiv.1910.10685","title":"Leffingwell Odor Dataset","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Olfactory and Sensory Function Studies","field":"Neuroscience","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Perception; Odor; Computer science; Graph; Machine learning; Artificial intelligence; Set (abstract data type); Artificial neural network; Representation (politics); Cognitive science; Sensory system; Psychology; Cognitive psychology; Neuroscience; Theoretical computer science","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":[0.0006236001,0.002381676,0.001190557,0.002055689,0.0009193273,0.001382689,0.003759926,0.002349249,0.04710713],"category_scores_gemma":[0.002470116,0.0004720836,0.001634302,0.002624776,0.0003874466,0.001468574,0.001401664,0.002248621,0.05753476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001392048,"about_ca_system_score_gemma":0.001573297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0234514,"about_ca_topic_score_gemma":0.04856965,"domain_scores_codex":[0.9993631,0.000084472,0.00004688272,0.0001777616,0.0002229983,0.0001046447],"domain_scores_gemma":[0.9993954,0.0001224134,0.00004681874,0.0001863743,0.0001726825,0.0000763724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001615803,0.00009065863,0.001187188,0.0008141496,0.00005943203,0.00009792593,0.00001215373,0.001341375,0.001073664,0.0007141184,0.9823593,0.01208836],"study_design_scores_gemma":[0.0003484114,0.0001551704,0.006958765,0.0002560996,0.00006905843,0.0002805693,0.00007981847,0.007243206,0.004200027,0.002137141,0.9781823,0.00008939373],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002986106,0.001010557,0.0009756161,0.0003716201,0.0001652213,0.0001019354,0.9838005,0.004928767,0.005659626],"genre_scores_gemma":[0.002530414,0.0002406264,0.001707051,0.0001770613,0.00001412211,0.0001385514,0.9931276,0.0001881803,0.001876328],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04710713,"threshold_uncertainty_score":0.1575891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3543371564403947,"score_gpt":0.2180125084697963,"score_spread":0.1363246479705985,"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."}}