{"id":"W2019148660","doi":"10.1021/es0259638","title":"3-D Structural Modeling of Humic Acids through Experimental Characterization, Computer Assisted Structure Elucidation and Atomistic Simulations. 1. Chelsea Soil Humic Acid","year":2003,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Chemistry; Characterization (materials science); Humic acid; Spectroscopy; Molecular dynamics; Mass spectrometry; Electrospray ionization; Computational chemistry; Nuclear magnetic resonance spectroscopy; Physical chemistry; Analytical Chemistry (journal); Organic chemistry; Materials science; Chromatography; Nanotechnology; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001804934,0.0002680454,0.0003056862,0.0002397309,0.0005560908,0.00003816676,0.0005093428,0.0002180718,0.001678608],"category_scores_gemma":[0.00004464749,0.0002629632,0.00004576154,0.0008819285,0.002629797,0.0007466372,0.0004670268,0.0002154895,0.00003552693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005657187,"about_ca_system_score_gemma":0.0000251552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001777006,"about_ca_topic_score_gemma":0.0000197967,"domain_scores_codex":[0.9977602,0.00006791188,0.0004717311,0.0007772614,0.0004425774,0.0004803362],"domain_scores_gemma":[0.9991722,0.00001808746,0.0002524831,0.0004709937,0.000008812524,0.00007747379],"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":[0.000006122202,0.00005359903,0.07459535,0.000002336113,0.0000105455,0.0000017925,0.0003218194,0.02163052,0.9017898,0.000354768,0.000001457035,0.001231894],"study_design_scores_gemma":[0.0006794257,0.0001853924,0.07627346,0.000007679408,0.00004145159,0.00009724016,0.0004573006,0.2408556,0.6791144,0.001698846,0.0001569455,0.0004322881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910045,0.00004505348,0.008138099,0.00007191271,0.0001388634,0.000313691,0.00002603581,0.00005922027,0.0002025742],"genre_scores_gemma":[0.9938957,0.00001227676,0.005842987,0.0001093853,0.00001734055,0.00001426438,0.00005809504,0.00001960923,0.0000303246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2226754,"threshold_uncertainty_score":0.9999822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007716714641562794,"score_gpt":0.2270035926742257,"score_spread":0.2192868780326629,"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."}}