{"id":"W3015692433","doi":"10.1039/d0sc00445f","title":"Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex","year":2020,"lang":"en","type":"article","venue":"Chemical Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":189,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; Toronto Metropolitan University; University of Toronto; Toronto Public Health","funders":"Natural Resources Canada; Horizon 2020 Framework Programme; Norges Forskningsråd; European Commission; Compute Canada","keywords":"Chemical space; Space (punctuation); Computer science; Chemistry; Materials science; Biochemistry; Drug discovery; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002583301,0.000219849,0.0002592086,0.0000659178,0.0003764519,0.00048609,0.002132258,0.00007009695,0.0005733876],"category_scores_gemma":[0.003376649,0.00015796,0.0000501072,0.001637721,0.001073826,0.0006246619,0.0005196428,0.0004761098,0.0002416001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001578533,"about_ca_system_score_gemma":0.0001074188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001455049,"about_ca_topic_score_gemma":0.000001528745,"domain_scores_codex":[0.9966935,0.0002004366,0.0003665377,0.0008318038,0.00118281,0.0007249295],"domain_scores_gemma":[0.9988257,0.0003682608,0.0001807805,0.0003222411,0.0000666061,0.000236385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001958846,0.00002679844,0.001744965,0.00001657631,4.1659e-7,0.000003432315,0.001551663,0.0006219172,0.9949415,0.0008497133,0.00006181734,0.0001616459],"study_design_scores_gemma":[0.0002199914,0.00003253853,0.0008860869,0.00002190165,0.000002711877,0.000011836,0.00018381,0.05275233,0.944079,0.0003095722,0.001268029,0.00023222],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898614,0.0000134813,0.001586012,0.005954979,0.0001260384,0.0002094486,0.000002600414,0.0001524206,0.002093621],"genre_scores_gemma":[0.9945978,0.000001979715,0.004011407,0.001137021,0.0001999751,0.00001685251,0.000008346895,0.00001377179,0.00001285406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05213041,"threshold_uncertainty_score":0.644142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03534743907417005,"score_gpt":0.2820316854995478,"score_spread":0.2466842464253778,"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."}}