{"id":"W2013278693","doi":"10.1080/19440049.2012.688068","title":"Modelling aluminium leaching into food from different foodware materials with multi-level factorial design of experiments","year":2012,"lang":"en","type":"article","venue":"Food Additives & Contaminants Part A","topic":"Aluminum toxicity and tolerance in plants and animals","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Julius-Maximilians-Universität Würzburg","keywords":"Factorial experiment; Leaching (pedology); Aluminium; Fractional factorial design; Metallurgy; Ceramic; Materials science; Inductively coupled plasma atomic emission spectroscopy; Inductively coupled plasma; Analytical Chemistry (journal); Mathematics; Chemistry; Environmental science; Chromatography; Statistics; Soil science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001612215,0.0003522226,0.0005618156,0.0000169432,0.000298281,0.00006703971,0.0002594695,0.0001571122,0.0005849338],"category_scores_gemma":[0.00001911634,0.000144652,0.00008907362,0.00006991711,0.0001035666,0.0004257355,0.00008570578,0.0001327947,0.00001196427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002530853,"about_ca_system_score_gemma":0.00001123767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000254389,"about_ca_topic_score_gemma":0.0001844803,"domain_scores_codex":[0.998139,0.0001900089,0.0004292307,0.0003894628,0.0003115776,0.0005407606],"domain_scores_gemma":[0.9990535,0.0003370608,0.0002992463,0.00008649924,0.00005600947,0.0001676963],"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":[0.002052272,0.002264045,0.01149289,0.00005041685,0.0006870477,0.000008940175,0.01042495,0.00008180112,0.9488233,0.0003156564,0.0002315999,0.02356711],"study_design_scores_gemma":[0.001487147,0.004535105,0.05855056,0.0006484842,0.0000920025,0.000005006801,0.004114971,0.0003924208,0.9269208,0.0001037091,0.002295781,0.0008539686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898098,0.0004403768,0.004931284,0.00001370153,0.0006080731,0.0003960837,0.003734807,0.00004121448,0.00002462125],"genre_scores_gemma":[0.9973894,0.0000505492,0.0009463982,0.00002914294,0.00108113,0.00007103715,0.0003797277,0.000005477343,0.00004710295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04705767,"threshold_uncertainty_score":0.6404616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1138997163257598,"score_gpt":0.2623413346689623,"score_spread":0.1484416183432025,"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."}}