{"id":"W2168908108","doi":"10.1007/978-3-540-88787-4_10","title":"A Statistical Mechanics Theory of Molecular Recognition","year":2009,"lang":"en","type":"book-chapter","venue":"Biological and medical physics series","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"","keywords":"Molecular mechanics; Molecular recognition; Statistical mechanics; Molecular dynamics; Chemistry; Biomolecule; Distribution (mathematics); Function (biology); Ligand (biochemistry); Computational chemistry; Molecular orbital theory; Statistical physics; Biological system; Physics; Molecule; Mathematics; Biology; Mathematical analysis","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":[0.0009852678,0.0009462589,0.001243568,0.001314818,0.0008610901,0.002238713,0.001859481,0.0021186,0.005746428],"category_scores_gemma":[0.002226937,0.0007678538,0.0009535295,0.001438654,0.003932533,0.003622322,0.0009883308,0.003185816,0.001968878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001326576,"about_ca_system_score_gemma":0.001158586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001248489,"about_ca_topic_score_gemma":0.001044401,"domain_scores_codex":[0.9993542,0.0001585462,0.00002863246,0.0001119185,0.0002999699,0.00004674466],"domain_scores_gemma":[0.9991826,0.0005205207,0.00004342377,0.00009949568,0.0001214841,0.00003234813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004409361,0.00001579949,0.00005805304,0.00006459993,0.00001455495,0.00001956914,0.00005065364,0.007854389,0.0005987933,0.9666834,0.008206246,0.01642943],"study_design_scores_gemma":[0.000002749407,0.00000656506,0.00006808407,0.00001598155,0.00000359866,0.0000399021,0.0000076515,0.02256471,0.000227158,0.9627839,0.01426861,0.00001109354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006425981,0.03854071,0.848552,0.007642031,0.002816855,0.00006476302,0.000326832,0.000469005,0.0951618],"genre_scores_gemma":[0.4104834,0.04602346,0.3281013,0.004957822,0.008082123,0.0006572956,0.0007792815,0.0007410658,0.2001741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005746428,"threshold_uncertainty_score":0.01922369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02431704290302712,"score_gpt":0.2456898314425274,"score_spread":0.2213727885395003,"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."}}