{"id":"W4393493115","doi":"10.5281/zenodo.1490064","title":"Hit Expansion using Substructure Search, Virtual Screening & Free Energy Perturbation","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"High-Velocity Impact and Material Behavior","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Substructure; Perturbation (astronomy); Physics; Statistical physics; Computer science; Engineering; Quantum mechanics; Structural engineering","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009084011,0.0004289722,0.0004069891,0.0004579268,0.003194386,0.00204683,0.002374747,0.000422232,0.05395462],"category_scores_gemma":[0.0007873278,0.0004263212,0.0001006942,0.0004786396,0.0003938084,0.0005334497,0.002886155,0.0004218534,0.005030552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003339083,"about_ca_system_score_gemma":0.00002454126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006507342,"about_ca_topic_score_gemma":0.00001231865,"domain_scores_codex":[0.9961241,0.0007513916,0.0005031478,0.0008186402,0.001039706,0.0007630003],"domain_scores_gemma":[0.9971284,0.00003275911,0.0002977612,0.00132795,0.0008499575,0.0003632297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001130223,0.00005058088,8.226056e-8,0.00005817289,0.00001496282,0.00001253653,0.0002967391,0.00003744781,0.1798342,0.00008482948,0.8177282,0.001769206],"study_design_scores_gemma":[0.0005020492,0.0002457126,0.00002503635,0.0001192532,0.00006495544,0.000134712,0.00008583267,0.0000484821,0.01090131,0.00004772233,0.9873543,0.0004705663],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06089967,0.00003064842,0.003292352,0.0001038094,0.0008727878,0.0003788427,0.9334955,0.0006399493,0.0002864526],"genre_scores_gemma":[0.005512939,0.00006683211,0.001020062,0.0001224253,0.001134223,8.006467e-8,0.9903134,0.001272824,0.0005572901],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1696262,"threshold_uncertainty_score":0.9998189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05603416882354272,"score_gpt":0.2766527027360785,"score_spread":0.2206185339125358,"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."}}