{"id":"W3217447180","doi":"10.5281/zenodo.4085098","title":"Leffingwell Odor Dataset","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Odor; Computer science; Artificial intelligence; Biology; Neuroscience","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":[0.0007315124,0.003068638,0.001567097,0.002180559,0.001184888,0.002817504,0.004104379,0.002616857,0.1061273],"category_scores_gemma":[0.002998635,0.000583604,0.002209586,0.002913705,0.0004502029,0.002281919,0.002125353,0.002822495,0.1541817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001531065,"about_ca_system_score_gemma":0.001842771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02130832,"about_ca_topic_score_gemma":0.05239503,"domain_scores_codex":[0.9992251,0.0001091803,0.00005167779,0.000221971,0.0002782107,0.0001137019],"domain_scores_gemma":[0.9992344,0.0001515847,0.00004472757,0.0002509235,0.0002325643,0.00008586772],"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":[0.0001801756,0.00006879552,0.0008278489,0.0007983816,0.00006872759,0.00006556394,0.00001401844,0.0009129877,0.0008745503,0.0007716657,0.9821987,0.01321862],"study_design_scores_gemma":[0.0001688896,0.00007927638,0.003505724,0.0002097213,0.00005509443,0.0001250866,0.00006189308,0.003253121,0.002352353,0.002288112,0.9878215,0.00007915506],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001463581,0.001294138,0.001606308,0.0004168248,0.000401776,0.00009728981,0.9709481,0.01112218,0.01264981],"genre_scores_gemma":[0.002427666,0.0004141387,0.002341525,0.0003087245,0.00003336069,0.0001927594,0.9875818,0.000751734,0.005948188],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1061273,"threshold_uncertainty_score":0.3550313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02998196691530896,"score_gpt":0.2330045639142267,"score_spread":0.2030225969989177,"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."}}