{"id":"W1891503450","doi":"10.2136/2001.humicsubstances.c3","title":"Predicting Chemical Reactivity of Humic Substances for Minerals and Xenobiotics: Use of Computational Chemistry, Scanning Probe Microscopy, and Virtual Reality","year":2001,"lang":"en","type":"book-chapter","venue":"ASSA, CSSA and SSSA","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Xenobiotic; Chemistry; Reactivity (psychology); Microscopy; Environmental chemistry; Chemical engineering; Organic chemistry; Engineering; Optics; Pathology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002616202,0.0005635982,0.0005374411,0.0003674095,0.0003018844,0.0007483211,0.0007343157,0.0006690762,0.001316982],"category_scores_gemma":[0.0006379152,0.0003948484,0.0007243552,0.0003358838,0.0002025585,0.0006219643,0.0002986527,0.0003787494,0.0003220024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003541801,"about_ca_system_score_gemma":0.0003936189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005137763,"about_ca_topic_score_gemma":0.007776168,"domain_scores_codex":[0.9999374,0.00001086358,0.000002905567,0.00001471028,0.00003004951,0.000004069766],"domain_scores_gemma":[0.9997765,0.0001602087,0.00001352495,0.00001590601,0.00002687709,0.00000694608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000878061,0.00008383896,0.003425015,0.0002078199,0.0001157119,0.0001345208,0.0001214921,0.7672827,0.0403906,0.004186209,0.002192525,0.1817717],"study_design_scores_gemma":[0.000003955547,0.00001978439,0.000647463,0.000004612149,0.00001740374,0.00004735418,0.00002038336,0.9792386,0.01638426,0.002472424,0.001131438,0.00001232055],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2728639,0.001711312,0.7085132,0.0004282778,0.00005795491,0.00008015736,0.00102244,0.003593676,0.01172914],"genre_scores_gemma":[0.5801957,0.001826398,0.411199,0.00006750281,0.00001873969,0.00009607104,0.0007600333,0.0002316738,0.005604803],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005137763,"threshold_uncertainty_score":0.01021576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03227073292260814,"score_gpt":0.2797142129968195,"score_spread":0.2474434800742114,"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."}}