{"id":"W2155928924","doi":"10.1071/en09074","title":"Enhancing reliability of elemental speciation results – quo vadis?","year":2009,"lang":"en","type":"article","venue":"Environmental Chemistry","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Traceability; Context (archaeology); Status quo; Reliability (semiconductor); Genetic algorithm; Computer science; Quality (philosophy); Field (mathematics); Set (abstract data type); Data science; Risk analysis (engineering); Management science; Political science; Engineering; Business; Epistemology; Geography; Archaeology; Biology; Mathematics; Software engineering; Law","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":[0.0005720977,0.0002899502,0.0002748751,0.00001389433,0.0001069421,0.0000113714,0.0003547888,0.0001485999,0.007610773],"category_scores_gemma":[0.00007920925,0.0003035709,0.0001422277,0.0001075385,0.000334048,0.000251953,0.0002211063,0.0002335544,0.0005653665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005508,"about_ca_system_score_gemma":0.000006461959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001892159,"about_ca_topic_score_gemma":0.000002272488,"domain_scores_codex":[0.9972838,0.00005678191,0.0007679824,0.0006837794,0.0007766603,0.0004310251],"domain_scores_gemma":[0.9987062,0.00005518705,0.0003041085,0.0007416733,0.000001014422,0.0001918312],"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":[0.00005809159,0.000519564,0.03019621,0.00001043231,0.000006897259,0.000005619745,0.0002331673,0.000635085,0.9651616,0.000001120624,0.0003906155,0.002781552],"study_design_scores_gemma":[0.0005585289,0.00008433094,0.2444639,0.00001246969,0.00001723266,0.000007530662,0.0001830023,0.00006442122,0.7503479,0.0001053575,0.003898798,0.0002565771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761702,0.00004020431,0.0001321367,0.0001594645,0.00006609181,0.0002412222,0.00007385322,0.00003940655,0.02307739],"genre_scores_gemma":[0.9956532,0.00006147709,0.002303907,0.0001320917,0.000095973,0.00001049128,0.0001116857,0.00002025393,0.001610942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2148138,"threshold_uncertainty_score":0.9999416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004213405257231438,"score_gpt":0.2045901394722639,"score_spread":0.2003767342150325,"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."}}