{"id":"W3157603425","doi":"10.26434/chemrxiv.12996665.v1","title":"A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of Toronto","funders":"Division of Chemistry; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Deutsche Forschungsgemeinschaft; National Science Foundation; Compute Canada; École de technologie supérieure; University of Toronto; University of Utah; Government of Ontario; AstraZeneca; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Blueprint; Computer science; Intuition; Workflow; Biochemical engineering; Engineering; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001150584,0.0007468637,0.0009757692,0.001224053,0.0006232543,0.001322723,0.001099928,0.0007091252,0.00298493],"category_scores_gemma":[0.001405675,0.0004681129,0.0006723842,0.001440745,0.0003896815,0.001268248,0.001127434,0.001078214,0.001106374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009130851,"about_ca_system_score_gemma":0.001453131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001536771,"about_ca_topic_score_gemma":0.002692422,"domain_scores_codex":[0.9994476,0.00009768009,0.00002988565,0.0001161125,0.0002623948,0.00004630771],"domain_scores_gemma":[0.9995839,0.0001337457,0.00004672541,0.0001374555,0.00007065979,0.00002751032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001330912,0.0006939567,0.008489599,0.001270993,0.0004375194,0.0004306864,0.0002180962,0.5135521,0.1904958,0.0655437,0.03632418,0.1812125],"study_design_scores_gemma":[0.0002526411,0.0006188322,0.002214054,0.00005313674,0.0001030684,0.0001539539,0.00008228025,0.7602167,0.1513952,0.03104612,0.05377253,0.00009142142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6384811,0.00536822,0.2698,0.001501556,0.000135549,0.0003862165,0.03178499,0.02876646,0.02377588],"genre_scores_gemma":[0.7202193,0.002791836,0.2395553,0.0003207483,0.00004234571,0.0006838594,0.0314907,0.0008540971,0.004041955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00298493,"threshold_uncertainty_score":0.009985626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02673192777899917,"score_gpt":0.2850189676736468,"score_spread":0.2582870398946476,"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."}}