{"id":"W2162373124","doi":"10.1002/etc.5620220626","title":"Predicting the bioavailability of copper and zinc in soils: Modeling the partitioning of potentially bioavailable copper and zinc from soil solid to soil solution","year":2003,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bioavailability; Soil water; Environmental chemistry; Zinc; Copper; Chemistry; Organic matter; Linear regression; Soil science; Environmental science; Mathematics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000465471,0.0006030265,0.0004616023,0.0003473241,0.0001856004,0.000580866,0.0005572594,0.0005972401,0.0003052427],"category_scores_gemma":[0.001138326,0.0003519615,0.0005230602,0.0004786877,0.0002592806,0.0004471769,0.0002299106,0.0003026201,0.000131314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363659,"about_ca_system_score_gemma":0.001604955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05983025,"about_ca_topic_score_gemma":0.04443513,"domain_scores_codex":[0.9998559,0.00004853498,0.000008224866,0.00003687874,0.00002987579,0.00002053709],"domain_scores_gemma":[0.999638,0.0002572788,0.00004327286,0.00001117553,0.00004005148,0.00001026195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004738162,0.00004077051,0.008910175,0.00005772205,0.0000343477,0.00004261514,0.0000442534,0.9751533,0.008869053,0.000433866,0.00006228374,0.006304167],"study_design_scores_gemma":[0.000007958701,0.00004578313,0.001352457,0.000001460252,0.00000943132,0.00001699849,0.00002111622,0.9947814,0.003291088,0.0003233831,0.0001438011,0.00000503092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473207,0.0001271099,0.05166135,0.00005488676,0.000002124753,0.00003624619,0.0002143138,0.00009839074,0.0004848051],"genre_scores_gemma":[0.9803184,0.000180804,0.01819587,0.000008117444,0.000001867265,0.00007318635,0.0002375903,0.00001586447,0.0009681882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05983025,"threshold_uncertainty_score":0.118964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0112754090568832,"score_gpt":0.2188224642428203,"score_spread":0.2075470551859371,"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."}}