{"id":"W6920744447","doi":"10.6084/m9.figshare.19703849","title":"Additional file 4 of KnotAli: informed energy minimization through the use of evolutionary information","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Minification; Set (abstract data type); Energy (signal processing); Test (biology); Energy minimization; Data set; Selection (genetic algorithm)","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":[0.00000170797,0.0000490543,0.00005386557,0.00004972668,0.00008016013,0.000009201708,0.00007300207,0.00002271299,0.9858431],"category_scores_gemma":[0.0005897348,0.00004637403,0.00003635859,0.0002126781,0.000004061557,0.0005698967,0.00004586487,0.00006625208,0.00009204711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003853232,"about_ca_system_score_gemma":0.00004346122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005238131,"about_ca_topic_score_gemma":0.000001718327,"domain_scores_codex":[0.9995223,0.00001749005,0.0001917866,0.00002906365,0.0001796839,0.00005963686],"domain_scores_gemma":[0.9992861,0.0004443394,0.0001049506,0.00008476373,0.00007016934,0.000009694975],"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":[0.000001333903,0.000001971269,3.634252e-7,0.00001943856,0.000004297184,4.123181e-8,0.0001078554,0.4617897,8.403905e-8,0.00003802116,0.5376224,0.0004145672],"study_design_scores_gemma":[0.00003789908,0.00000762818,0.0009402653,0.00007349716,9.608996e-7,0.000001834883,0.00006337689,0.2943587,0.000003823794,0.000005065147,0.7044724,0.00003456268],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004751339,0.00001283115,0.00002049074,0.000008100447,0.00002463207,0.00005352822,0.9967392,0.00004892238,0.003087517],"genre_scores_gemma":[0.00757215,7.438229e-7,0.0007960474,0.00008377529,0.00002023713,0.0003608035,0.9909079,0.000006496097,0.0002518267],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9857511,"threshold_uncertainty_score":0.1891078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04726610556279764,"score_gpt":0.2054260423379023,"score_spread":0.1581599367751047,"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."}}