{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001152847,0.0003306417,0.0003579518,0.0001792059,0.0002531553,0.00006356733,0.0006936862,0.0002333114,0.0005155205],"category_scores_gemma":[0.0001863364,0.0003565229,0.0001719814,0.0002604237,0.0001898318,0.000213326,0.001280337,0.0006742796,0.003230236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009838405,"about_ca_system_score_gemma":0.00007741815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002499154,"about_ca_topic_score_gemma":0.00001142297,"domain_scores_codex":[0.9978923,0.0002144917,0.0001705571,0.001289299,0.00009727717,0.0003361409],"domain_scores_gemma":[0.9982736,0.0002805017,0.0002112431,0.001074903,0.0000465202,0.0001132097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001061847,0.0008009788,0.01294665,0.0008144063,0.0003163786,0.002523704,0.0008264592,0.6956394,0.0197688,0.07534262,0.1895222,0.0004365358],"study_design_scores_gemma":[0.003178007,0.0003420149,0.004155174,0.0002483829,0.0006043492,0.00004011868,0.0008658601,0.07027473,0.03588678,0.01289897,0.8679656,0.003540062],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677321,0.00003468474,0.004039936,0.0001157642,0.0033106,0.0005207564,0.002838204,0.0003080823,0.02109984],"genre_scores_gemma":[0.9826688,0.0001908516,0.000008982737,0.0005732979,0.0001112793,4.139781e-7,0.0001741591,0.00002418993,0.01624805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6784433,"threshold_uncertainty_score":0.9998887,"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."}}