{"id":"W2666956742","doi":"10.1021/acs.jpcb.7b04871","title":"Optimization and Automation of the Construction of Smooth Free Energy Profiles","year":2017,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Spectroscopy and Quantum Chemical Studies","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Histogram; Computation; Parametric statistics; Energy (signal processing); Algorithm; Mathematical optimization; Computer science; Mathematics; Applied mathematics; Statistics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002137904,0.0006727059,0.0007939942,0.0009734607,0.0005660305,0.0008977841,0.001379778,0.0005223483,0.001714326],"category_scores_gemma":[0.006564187,0.0006929665,0.0006863497,0.0007707055,0.0006990543,0.000878789,0.001410891,0.00149311,0.0006129304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006813698,"about_ca_system_score_gemma":0.00189007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001981432,"about_ca_topic_score_gemma":0.0019185,"domain_scores_codex":[0.9993344,0.0001795891,0.0000429442,0.0001240392,0.0002522019,0.00006689299],"domain_scores_gemma":[0.9981249,0.0009128251,0.0001459288,0.0004283036,0.0003252423,0.00006287821],"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":[0.0001299114,0.0001051232,0.001646929,0.0001797558,0.00005146608,0.0001277008,0.000213944,0.8121453,0.03884346,0.0413682,0.0009022101,0.104286],"study_design_scores_gemma":[0.00001236459,0.00002664974,0.0001664221,0.000004470547,0.00000406359,0.0000163544,0.00001206985,0.9865915,0.0054669,0.007017383,0.0006701368,0.00001168916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03102271,0.0000433602,0.967088,0.00004396954,0.00001227144,0.00005390786,0.00006323853,0.0007475428,0.0009250846],"genre_scores_gemma":[0.2355349,0.0001030131,0.7628599,0.00002784378,0.000008535638,0.0002597533,0.0002175395,0.0004257284,0.000562758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002137904,"threshold_uncertainty_score":0.01130641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005565563631437489,"score_gpt":0.2298802958936995,"score_spread":0.224314732262262,"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."}}