{"id":"W2323704915","doi":"10.1021/es101417u","title":"Role of Detection Limits in Drinking Water Regulation","year":2010,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Environmental Justice and Health Disparities","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Concordia University; U.S. Environmental Protection Agency","keywords":"Context (archaeology); Safe Drinking Water Act; Environmental regulation; Regulatory agency; Agency (philosophy); Maximum Contaminant Level; Process (computing); Environmental planning; Risk analysis (engineering); Business; Environmental science; Water quality; Groundwater; Computer science; Engineering; Political science; Ecology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006372218,0.00006970497,0.00009316636,0.0003893159,0.000469803,0.00001413092,0.0003139591,0.0001694032,0.0004858298],"category_scores_gemma":[0.00004032734,0.00006472891,0.0000180328,0.0003781124,0.002904654,0.0003727084,0.0001019861,0.0002451815,0.00005978876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000211221,"about_ca_system_score_gemma":0.00002587564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002627087,"about_ca_topic_score_gemma":0.005159674,"domain_scores_codex":[0.998775,0.00002178254,0.0001924115,0.0002453947,0.0003467086,0.0004186947],"domain_scores_gemma":[0.9996902,0.00001612135,0.00005988928,0.0001718105,0.00000317374,0.00005874882],"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":[0.000003090416,0.00005142355,0.0700789,0.000002947628,4.516016e-7,4.202245e-7,0.00206371,0.00001281422,0.9011865,0.004392954,5.622755e-7,0.02220621],"study_design_scores_gemma":[0.0001254465,0.00005160605,0.1933947,0.00001495508,0.00000405102,0.000002385314,0.0130168,0.00005202231,0.780252,0.00478827,0.008187223,0.0001105582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930046,0.00006575906,0.0000151804,0.0004755787,0.0001972022,0.0001655479,8.182444e-7,0.00003936731,0.006035975],"genre_scores_gemma":[0.999351,0.0001754191,0.0002230417,0.00003877197,0.00004312575,0.00001783617,0.000001263508,0.000005071506,0.0001444379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1233158,"threshold_uncertainty_score":0.9998088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006165170396278569,"score_gpt":0.2465558992740994,"score_spread":0.2403907288778208,"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."}}