{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002505704,0.0001954623,0.0003553095,0.00002749578,0.00006912061,0.00003001107,0.00005823873,0.0001994452,0.00007849037],"category_scores_gemma":[0.0000581504,0.000193375,0.0000444371,0.00002385058,0.0004676775,0.0001623482,0.00007262873,0.000120397,2.693299e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003231426,"about_ca_system_score_gemma":0.00001718775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001876915,"about_ca_topic_score_gemma":0.00006010996,"domain_scores_codex":[0.9989019,0.00001691508,0.0004050893,0.0003528374,0.0002001323,0.0001231048],"domain_scores_gemma":[0.9991729,0.0002261729,0.0003726342,0.0001111408,0.00004496435,0.00007217455],"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":[0.0001199642,0.00005871592,0.01127255,0.0002581329,0.00005019284,0.000001507624,0.0005510814,0.00008331607,0.9773302,0.002225229,0.003398175,0.004650898],"study_design_scores_gemma":[0.003610408,0.0009235501,0.01955763,0.001527106,0.0004691778,0.0001404468,0.0002961935,0.01978859,0.8403484,0.04687503,0.06416216,0.002301283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805349,0.0001814826,0.00188399,0.00009161574,0.00002054458,0.0004669388,0.0002736887,0.00003174641,0.01651507],"genre_scores_gemma":[0.9728846,0.0003216794,0.006000562,0.00005607563,0.00004575103,0.00001400185,0.0002117536,0.00002536244,0.02044023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1369818,"threshold_uncertainty_score":0.7885606,"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."}}