{"id":"W2146999240","doi":"10.1002/etc.2837","title":"Metal Mixture Modeling Evaluation project: 3. Lessons learned and steps forward","year":2014,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Environmental Toxicology and Ecotoxicology","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Rio Tinto; International Zinc Association; Nickel Producers Environmental Research Association","keywords":"Biotic Ligand Model; Chemistry; Metal; Metal toxicity; Calibration; Toxicity; Quantitative structure–activity relationship; Ligand (biochemistry); Biological system; Environmental chemistry; Biochemical engineering; Stereochemistry; Mathematics; Organic chemistry; Statistics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03836975,0.001942081,0.001529749,0.001277384,0.0009858786,0.004801971,0.006463823,0.003017107,0.00529053],"category_scores_gemma":[0.02071958,0.0007564242,0.001701341,0.0009727779,0.0007979514,0.00534247,0.003412818,0.003011868,0.001973816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002866431,"about_ca_system_score_gemma":0.006375428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03225768,"about_ca_topic_score_gemma":0.02492064,"domain_scores_codex":[0.9916363,0.003791773,0.0004282216,0.0007588827,0.002962227,0.0004226121],"domain_scores_gemma":[0.9861004,0.002695406,0.0005506231,0.001647532,0.008039651,0.0009664673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001662078,0.003723866,0.03201249,0.00135021,0.0006623506,0.0006373926,0.001215866,0.1919325,0.02421027,0.03803971,0.07631933,0.6282339],"study_design_scores_gemma":[0.0009041765,0.004425721,0.01779253,0.001862395,0.0004621195,0.0005027543,0.00174094,0.6172068,0.06239949,0.04518639,0.2468942,0.0006224863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2648683,0.007494587,0.5867795,0.0766207,0.001823166,0.003613569,0.01351437,0.01325099,0.03203491],"genre_scores_gemma":[0.1923888,0.003454122,0.7747266,0.004023049,0.0003950672,0.001405893,0.01051835,0.001793336,0.01129481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03836975,"threshold_uncertainty_score":0.202921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02374322260553492,"score_gpt":0.271549481211901,"score_spread":0.2478062586063661,"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."}}