{"id":"W2122689181","doi":"10.1897/ieam_2009-026.1","title":"Efficiency of sediment quality guidelines for predicting toxicity: The case of the St. Lawrence river","year":2009,"lang":"en","type":"article","venue":"Integrated Environmental Assessment and Management","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada); Environment and Climate Change Canada; Ministère des Ressources naturelles et des Forêts","funders":"","keywords":"Sediment; Dispose pattern; Pollutant; Environmental science; Quotient; Drainage basin; Environmental engineering; Environmental chemistry; Engineering; Waste management; Geology; Mathematics; Chemistry; Geography","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":[0.001387385,0.0002871327,0.0002958617,0.0000327339,0.00028631,0.00001951173,0.0004810033,0.00006750522,0.0002819009],"category_scores_gemma":[0.00002857323,0.0001675103,0.0001555775,0.0001798617,0.0007801222,0.000136615,0.0005443668,0.0001608775,0.000004369324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003378775,"about_ca_system_score_gemma":0.000007923088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002573711,"about_ca_topic_score_gemma":0.00004843065,"domain_scores_codex":[0.9975865,0.0001894532,0.0008222908,0.0004791337,0.000585252,0.0003373488],"domain_scores_gemma":[0.9987602,0.0001200827,0.0004086605,0.0006258953,0.000007922962,0.00007724404],"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":[0.0002865251,0.005407854,0.1740708,0.000361861,0.0007604062,0.0001021738,0.003530991,0.02530988,0.1926515,0.01318406,0.0115721,0.5727618],"study_design_scores_gemma":[0.003580189,0.001637391,0.8455555,0.0003118282,0.0007843187,0.0001196586,0.01244018,0.03124803,0.06926948,0.005349938,0.02855203,0.001151415],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859836,0.0001038614,0.008053671,0.001539489,0.0001761805,0.00171984,0.000111665,0.00001996002,0.002291718],"genre_scores_gemma":[0.9891994,0.0002738626,0.009347722,0.0006442147,0.00001953923,0.00009312707,0.00001234205,0.00001375532,0.0003960193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6714847,"threshold_uncertainty_score":0.6830873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03367602065567733,"score_gpt":0.3310904011114135,"score_spread":0.2974143804557361,"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."}}