{"id":"W3031262063","doi":"10.1021/acs.est.0c01437","title":"Identification of Potential PBT/POP-Like Chemicals by a Deep Learning Approach Based on 2D Structural Features","year":2020,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Environment and Climate Change Canada","funders":"National Natural Science Foundation of China","keywords":"Organic chemicals; Chemical industry; Bioaccumulation; Identification (biology); Computer science; Environmental science; Environmental chemistry; Artificial intelligence; Biochemical engineering; Chemistry; Engineering; Environmental engineering; Biology","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":[0.000257833,0.001062122,0.0006515606,0.001210049,0.000211564,0.0006084603,0.0007661069,0.0007685141,0.0009267295],"category_scores_gemma":[0.0005645585,0.0004387782,0.001167728,0.0007575615,0.0003225613,0.0007856457,0.0006414613,0.000736123,0.0003368515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006133295,"about_ca_system_score_gemma":0.0009014266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007425023,"about_ca_topic_score_gemma":0.009218263,"domain_scores_codex":[0.9998546,0.00001520332,0.00000803013,0.00004856579,0.00004379799,0.00002996238],"domain_scores_gemma":[0.9997855,0.00006289591,0.00004232691,0.00001793785,0.00007440026,0.00001704195],"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":[0.0003421592,0.0003944232,0.0163602,0.0003486933,0.0002307486,0.000512904,0.00008311222,0.6715259,0.07849851,0.002640824,0.003729909,0.2253327],"study_design_scores_gemma":[0.000004209374,0.00003235981,0.0005690978,0.000004284904,0.00001567293,0.00002417809,0.000007108134,0.99471,0.00382393,0.0005043545,0.0002995099,0.00000531568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4188097,0.001129323,0.5709965,0.0004361133,0.00008106854,0.0001465427,0.001672646,0.002467821,0.004260396],"genre_scores_gemma":[0.8573325,0.0007002861,0.1345246,0.0002895528,0.00003171471,0.000148098,0.003237685,0.00007056482,0.003664921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007425023,"threshold_uncertainty_score":0.01476359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003057936043787285,"score_gpt":0.1861571283516016,"score_spread":0.1830991923078144,"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."}}