{"id":"W2793019704","doi":"10.1002/etc.4125","title":"Adverse outcome pathway networks I: Development and applications","year":2018,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":235,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Joint Research Centre; European Commission; Humane Society International; European Crop Protection Association; Society of Environmental Toxicology and Chemistry; European Chemical Industry Council; U.S. Environmental Protection Agency","keywords":"Adverse Outcome Pathway; Context (archaeology); Computer science; Aspect-oriented programming; Outcome (game theory); Stakeholder; Set (abstract data type); Software engineering; Data science; Programming language; Software; Computational biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000118789,0.00009576231,0.00008466631,0.00001023421,0.0001771483,0.00001483197,0.0001631458,0.00007914715,0.00006666125],"category_scores_gemma":[0.000004156042,0.0000987423,0.00001381092,0.00003814624,0.0002502731,0.00008976038,0.0003437976,0.00009172064,0.00001798742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004461547,"about_ca_system_score_gemma":0.00001749098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.891776e-7,"about_ca_topic_score_gemma":3.948757e-7,"domain_scores_codex":[0.9993297,0.00002171678,0.0001409963,0.0002973306,0.00006908944,0.0001412007],"domain_scores_gemma":[0.9996432,0.00007085119,0.00004470433,0.0001558182,0.000002262427,0.00008317886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004517498,0.0007707254,0.1114551,0.0001022632,0.0001515635,0.00004557767,0.001718921,0.001799285,0.08488697,0.01161954,0.0002533379,0.7871515],"study_design_scores_gemma":[0.001777819,0.0001577918,0.4351086,0.00001866953,0.00002408689,0.0004295871,0.0003560775,0.06305789,0.2580264,0.00619512,0.2336239,0.001224077],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7433488,0.0001916508,0.2544962,0.00009508571,0.00005204931,0.00009006569,0.000002628402,0.00003358133,0.001689945],"genre_scores_gemma":[0.973772,0.00001386436,0.02523746,0.0003189719,0.00009716598,0.00004316174,0.000007611955,0.000004470969,0.0005053011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7859274,"threshold_uncertainty_score":0.4026594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127986443004717,"score_gpt":0.2460256742919014,"score_spread":0.2347458098618543,"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."}}