{"id":"W1985111585","doi":"10.1016/j.envpol.2014.01.011","title":"Predicting criteria continuous concentrations of 34 metals or metalloids by use of quantitative ion character-activity relationships–species sensitivity distributions (QICAR–SSD) model","year":2014,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Environmental Toxicology and Ecotoxicology","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"National Key Research and Development Program of China; State Administration of Foreign Experts Affairs; National Natural Science Foundation of China; U.S. Environmental Protection Agency","keywords":"Metalloid; Environmental chemistry; Heavy metals; Pollutant; Environmental science; Sensitivity (control systems); Chemistry; Ecotoxicology; Metal","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000716362,0.0002893862,0.0004945841,0.00005748825,0.0004788718,0.00002086937,0.0001273626,0.0002592755,0.001176761],"category_scores_gemma":[0.0003325863,0.0002804728,0.0001591279,0.0001858563,0.001520148,0.0009931137,0.0002160526,0.0003093093,0.00005831984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004557073,"about_ca_system_score_gemma":0.0000108791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001153181,"about_ca_topic_score_gemma":0.0001683587,"domain_scores_codex":[0.9973462,0.0008314172,0.0005988725,0.0004941185,0.0003414196,0.0003879928],"domain_scores_gemma":[0.9984841,0.0004646634,0.0005574007,0.0003492492,0.000004632065,0.0001399337],"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":[0.0001755744,0.0007375703,0.07198857,0.000007560886,0.00006478666,0.000001217163,0.0002302162,0.004863196,0.9205635,0.0008339118,0.0002906811,0.0002432888],"study_design_scores_gemma":[0.0006490772,0.0005329139,0.4501816,0.00001812729,0.0001744782,0.00001628724,0.00029762,0.07704195,0.4693327,0.0003199572,0.001099277,0.0003360078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9525037,0.00002287945,0.04416553,0.0001828856,0.0001311601,0.0005016103,0.001771615,0.00003675696,0.0006838951],"genre_scores_gemma":[0.9958075,0.00006438491,0.002619175,0.00006728502,0.00002176036,0.00004410161,0.0003271039,0.00002236573,0.001026292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4512307,"threshold_uncertainty_score":0.9999648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04486600593845075,"score_gpt":0.2605707198496528,"score_spread":0.2157047139112021,"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."}}