{"id":"W2175138761","doi":"10.1139/v11-041","title":"Modeling of <sup>13</sup>C NMR chemical shifts of benzene derivatives using the RC–PC–ANN method: A comparative study of original molecular descriptors and multivariate image analysis descriptors","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Chemistry; Chemical shift; Molecular descriptor; Artificial neural network; Rank (graph theory); Artificial intelligence; Carbon-13 NMR; Proton NMR; Multivariate statistics; Biological system; Pattern recognition (psychology); Quantitative structure–activity relationship; Computational chemistry; Stereochemistry; Mathematics; Machine learning; Physical chemistry; Computer science; Combinatorics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0005052771,0.0005681108,0.0004255536,0.0003804231,0.0001263271,0.0003897231,0.0004088634,0.0003350606,0.0009255507],"category_scores_gemma":[0.001053647,0.0001770106,0.0005134947,0.0004505622,0.0002173846,0.0004613756,0.0001429988,0.0004108164,0.0002030909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005612749,"about_ca_system_score_gemma":0.0005735579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006050762,"about_ca_topic_score_gemma":0.005123406,"domain_scores_codex":[0.9998685,0.00004258954,0.000006150673,0.0000251279,0.00004352358,0.00001402868],"domain_scores_gemma":[0.9994899,0.0003518714,0.00004981942,0.00002371733,0.00007353276,0.00001111355],"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":[0.0001241909,0.00005381503,0.001046335,0.00005940075,0.00002609985,0.00003642152,0.00001834119,0.9570258,0.006875828,0.0005287037,0.0001830141,0.03402208],"study_design_scores_gemma":[9.478558e-7,0.0000126157,0.0001662002,8.704569e-7,0.000002692473,0.000003957569,0.0000016565,0.998341,0.001343653,0.00007021753,0.00005422369,0.000001984306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4607056,0.0007186517,0.5328126,0.0002214535,0.0000400032,0.00007613816,0.0002836307,0.0007981109,0.004343955],"genre_scores_gemma":[0.9102727,0.0003831475,0.08687688,0.00002951521,0.00001385227,0.0000966291,0.0003145036,0.0000525647,0.001960215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006050762,"threshold_uncertainty_score":0.01203108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06804703128074051,"score_gpt":0.319570522123245,"score_spread":0.2515234908425045,"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."}}