{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001174826,0.0002197164,0.0003952993,0.00001547399,0.0000398535,0.000007949463,0.000139824,0.0005726124,0.01089117],"category_scores_gemma":[0.000111199,0.0001606074,0.00009521405,0.00002002466,0.0003172143,0.00001770355,0.00009372715,0.000386555,0.00001123406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001407434,"about_ca_system_score_gemma":0.00003735949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.844166e-7,"about_ca_topic_score_gemma":2.401675e-7,"domain_scores_codex":[0.9989951,0.00001224203,0.0002853755,0.0003019887,0.0002550883,0.0001502043],"domain_scores_gemma":[0.99933,0.0001423722,0.0001368644,0.0001789154,0.00005394677,0.0001578461],"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":[0.00003349863,0.00004487148,6.456003e-7,0.00004982767,0.00002569656,0.00001209621,0.0000032597,6.635574e-9,0.00310414,0.79278,0.00008009007,0.2038658],"study_design_scores_gemma":[0.00008455284,0.000179806,0.000002193211,0.0001257318,0.0000420221,0.00001323429,0.000005657437,0.000003772979,0.01022137,0.9608542,0.02827511,0.0001923818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0006030534,0.0008732962,0.02878956,0.0004219999,0.00002628318,0.0001745236,0.0007943074,0.0001622029,0.9681548],"genre_scores_gemma":[0.7832427,0.03826796,0.02764709,0.00199631,0.002125644,0.0002293681,0.007366455,0.0002033656,0.1389211],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8292337,"threshold_uncertainty_score":0.990013,"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."}}