{"id":"W7066680275","doi":"","title":"Indexing and Retrieval of Multiple 3D Protein Structure Representations for the Protein Data Bank","year":2009,"lang":"en","type":"other","venue":"NPARC","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Search engine indexing; Protein Data Bank; Representation (politics); Protein structure; Pattern recognition (psychology); Data retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001768837,0.00123332,0.001291912,0.007529093,0.001425548,0.003452685,0.002233212,0.001484388,0.1509013],"category_scores_gemma":[0.01120573,0.0007717597,0.0008128912,0.005654315,0.0003952094,0.002856574,0.002176883,0.001013569,0.1546169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009831758,"about_ca_system_score_gemma":0.002295107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002350255,"about_ca_topic_score_gemma":0.005253695,"domain_scores_codex":[0.9986777,0.0001413552,0.0001311038,0.0001815286,0.0007687605,0.00009959268],"domain_scores_gemma":[0.9950621,0.0007350916,0.0003897573,0.001428131,0.002020587,0.0003642938],"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":[0.0002297386,0.000109426,0.0008420392,0.0004006319,0.00002552853,0.000188282,0.00006082732,0.0005668284,0.01033635,0.002931528,0.8147161,0.1695927],"study_design_scores_gemma":[0.0002486849,0.0001350755,0.00771374,0.0002316949,0.0000861869,0.0006439185,0.0002348395,0.04217993,0.03963819,0.01082044,0.8979492,0.000118154],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.04704125,0.003763064,0.1978907,0.009631531,0.004465205,0.002157929,0.3019305,0.1935522,0.2395677],"genre_scores_gemma":[0.07618548,0.003151715,0.209409,0.0006020677,0.0012668,0.0009123865,0.4566066,0.01276985,0.2390961],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1509013,"threshold_uncertainty_score":0.5048152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02567205305005702,"score_gpt":0.2979949687761367,"score_spread":0.2723229157260797,"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."}}