{"id":"W2258466128","doi":"10.1016/j.envpol.2016.02.007","title":"Predictability of physicochemical properties of polychlorinated dibenzo-p-dioxins (PCDDs) based on single-molecular descriptor models","year":2016,"lang":"en","type":"review","venue":"Environmental Pollution","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science, ICT and Future Planning; U.S. Environmental Protection Agency","keywords":"Quantitative structure–activity relationship; Chemistry; Partition coefficient; Polychlorinated dibenzofurans; Molecular descriptor; Bioaccumulation; Environmental chemistry; Vapor pressure; Polychlorinated Dibenzo-p-dioxins; Computational chemistry; Thermodynamics; Organic chemistry; Stereochemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003263083,0.0004386521,0.001012404,0.0001974461,0.00004298899,0.00002031866,0.0008007081,0.0002037566,0.00001215285],"category_scores_gemma":[0.0000545232,0.0003388228,0.000555268,0.0002548861,0.0003418702,0.0003910701,0.0003453076,0.0002038209,0.00001344216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000694604,"about_ca_system_score_gemma":0.0001706288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008392803,"about_ca_topic_score_gemma":8.89001e-8,"domain_scores_codex":[0.9968722,0.000536693,0.0007981518,0.0007103821,0.0007959075,0.0002866442],"domain_scores_gemma":[0.9983032,0.0001224888,0.0005820436,0.0008785236,0.00001541292,0.00009826911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009934703,0.002550812,0.000002695934,0.004893138,0.0001952207,0.000006517783,0.0001370511,0.01271717,0.1050426,0.004166815,0.00002603056,0.8701627],"study_design_scores_gemma":[0.003271818,0.002562877,0.0002187811,0.05046472,0.0009679946,0.00005009268,0.00003541332,0.2720439,0.6269234,0.01032934,0.02943666,0.003694946],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.008194213,0.6044504,0.3850341,0.0000568644,0.0003856412,0.001171387,0.0004430372,0.00008655115,0.0001777742],"genre_scores_gemma":[0.87484,0.1164746,0.008291315,0.00004195143,0.00008325234,0.00009961199,0.00007842766,0.00006310254,0.00002769138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8666458,"threshold_uncertainty_score":0.9999064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05245573959666096,"score_gpt":0.2688481546929626,"score_spread":0.2163924150963017,"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."}}