{"id":"W2072043291","doi":"10.1016/j.bpj.2014.08.007","title":"Determining Intrachain Diffusion Coefficients for Biopolymer Dynamics from Single-Molecule Force Spectroscopy Measurements","year":2014,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"","keywords":"Force spectroscopy; Chemical physics; Chemistry; Diffusion; Folding (DSP implementation); Optical tweezers; Biopolymer; Brownian dynamics; Molecular dynamics; Spectroscopy; Molecule; Energy landscape; Dynamics (music); Analytical Chemistry (journal); Brownian motion; Computational chemistry; Thermodynamics; Polymer; Physics; Optics","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":[0.0001131384,0.0002051656,0.0002275783,0.00004890972,0.0003731181,0.0001695167,0.0002787425,0.00005222267,0.00005029492],"category_scores_gemma":[0.000008218729,0.0001820459,0.0002020833,0.0001095451,0.00006993069,0.00008077909,0.00006230144,0.0002018353,0.00001626216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008419871,"about_ca_system_score_gemma":0.00002175899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003240732,"about_ca_topic_score_gemma":0.00000140339,"domain_scores_codex":[0.9988048,0.00003451762,0.0003052074,0.0002711864,0.0002157851,0.0003684998],"domain_scores_gemma":[0.9992684,0.00005346499,0.0002144592,0.0002173294,0.0000856584,0.0001606582],"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":[0.00001984774,0.0003184595,0.001086015,0.000002529877,0.00002640308,2.194899e-7,0.00002889366,0.00001097733,0.9815183,0.002533857,0.0001545345,0.0143],"study_design_scores_gemma":[0.000762368,0.0002785955,0.0001770925,0.00005672267,0.00005506989,0.000001783394,0.00007706003,0.06014379,0.9338369,0.003614153,0.0007232949,0.0002731083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4446406,0.000002379043,0.5544608,0.00007952855,0.000100535,0.0001400348,0.00005915542,0.00002582069,0.0004911589],"genre_scores_gemma":[0.9793916,5.808757e-7,0.01937488,0.00009602289,0.0008566904,0.00003241797,0.0001095523,0.00003742171,0.0001008676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5350859,"threshold_uncertainty_score":0.7423617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01498264997078972,"score_gpt":0.2739207568005919,"score_spread":0.2589381068298022,"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."}}