{"id":"W2797837540","doi":"10.1016/j.bpj.2018.02.026","title":"Probing Position-Dependent Diffusion in Folding Reactions Using Single-Molecule Force Spectroscopy","year":2018,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; National Institute for Nanotechnology","keywords":"Position (finance); Force spectroscopy; Reaction coordinate; Energy landscape; Work (physics); Folding (DSP implementation); Chemistry; Diffusion; Statistical physics; Protein folding; Spectroscopy; Chemical physics; Physics; Computational chemistry; Molecule; Thermodynamics; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008489381,0.0001428328,0.0001485385,0.00009727373,0.0004289203,0.0001545754,0.0001489956,0.00003868381,0.0001437881],"category_scores_gemma":[0.000002199105,0.0001339406,0.0001118037,0.0002431575,0.00007465306,0.0001916296,0.00006213268,0.0002984878,0.00002429019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001612505,"about_ca_system_score_gemma":0.00004068909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001233617,"about_ca_topic_score_gemma":0.000002465412,"domain_scores_codex":[0.9990143,0.00002886959,0.0002902827,0.0002072422,0.0001461678,0.0003131479],"domain_scores_gemma":[0.9994988,0.00001585749,0.0001599998,0.0001543419,0.00007196171,0.00009901825],"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":[0.00001113097,0.0002614655,0.0004281704,0.000002297862,0.000009409619,0.000002022227,0.000080816,0.00001736225,0.9909185,0.007864453,0.00006046116,0.0003439596],"study_design_scores_gemma":[0.0002995106,0.000149202,0.0001875289,0.0001276882,0.00001980681,0.00002735834,0.0001456846,0.004557452,0.988763,0.005264422,0.0002704699,0.0001878894],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.811713,0.000002724442,0.1854882,0.0001278341,0.00008665743,0.000146024,0.000007274368,0.00002973796,0.002398511],"genre_scores_gemma":[0.987601,0.000001408502,0.01114273,0.00003734989,0.001056343,0.00001144941,0.000007420528,0.00002312899,0.0001191555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.175888,"threshold_uncertainty_score":0.5461938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465397444774073,"score_gpt":0.292043961378999,"score_spread":0.2773899869312583,"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."}}