{"id":"W2126727423","doi":"10.4000/vertigo.11945","title":"Évaluation de la contamination métallique d’une ressource en eau de la ville de Curitiba, Brésil","year":2012,"lang":"fr","type":"article","venue":"VertigO","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Physics; Forestry; Art; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005448796,0.0002650827,0.0002182867,0.00005444686,0.0002461355,0.00006689382,0.0003121946,0.000513812,0.002858126],"category_scores_gemma":[0.0007676692,0.0003082143,0.0001079981,0.0001997358,0.000841841,0.0005216462,0.0002015941,0.0004666388,0.001194252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002601493,"about_ca_system_score_gemma":0.00007554195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002454649,"about_ca_topic_score_gemma":0.00009038278,"domain_scores_codex":[0.9944658,0.003333991,0.0003578344,0.0003469882,0.0005737884,0.0009216484],"domain_scores_gemma":[0.9980224,0.00105447,0.0001239295,0.0003890736,0.000006501954,0.0004036147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005143979,0.001373378,0.5332426,0.0001056485,0.00006506701,0.00006298759,0.04199676,0.01721383,0.2897616,0.007220997,0.004743047,0.1041626],"study_design_scores_gemma":[0.0005398754,0.00007349336,0.6447117,0.00009394533,0.00008560217,0.0001050917,0.0003446494,0.01116757,0.06153458,0.00201325,0.2790208,0.0003094416],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9312252,0.003196084,0.04355472,0.001414222,0.000309175,0.0003229309,0.0000147898,0.00005153353,0.01991135],"genre_scores_gemma":[0.9711283,0.001792239,0.02203554,0.0006691848,0.0003344574,0.00008002762,0.00001304068,0.00005516269,0.003892011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2742777,"threshold_uncertainty_score":0.999937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228185682132,"score_gpt":0.2737699169936177,"score_spread":0.2614880601722977,"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."}}