{"id":"W2034422936","doi":"10.1088/1742-6596/341/1/012027","title":"High-throughput protein crystallization on the World Community Grid and the GPU","year":2012,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Enzyme Structure and Function","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Discovery Centre; University Health Network","funders":"University of Toronto; National Institutes of Health; Nvidia","keywords":"Xeon; Computer science; Grayscale; Grid; Parallel computing; Throughput; Classifier (UML); CUDA; Crystallization; Computational science; Pixel; Computer graphics (images); Artificial intelligence; Operating system; Mathematics","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":[0.002280616,0.0008920748,0.001886177,0.001025651,0.001342389,0.002288633,0.003664781,0.001075239,0.02085199],"category_scores_gemma":[0.005827972,0.0007059426,0.001324042,0.004563362,0.0008861645,0.002743624,0.00218679,0.002732579,0.007677753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001956933,"about_ca_system_score_gemma":0.004098722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02746655,"about_ca_topic_score_gemma":0.03294417,"domain_scores_codex":[0.9983568,0.0005040424,0.00009275405,0.0002678261,0.0005797269,0.000198889],"domain_scores_gemma":[0.9973863,0.0004139108,0.0001004781,0.001094477,0.0006590767,0.0003458453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002504812,0.0008246707,0.01062197,0.0005214533,0.0007412791,0.0005433562,0.0005467322,0.1389007,0.01808002,0.08718995,0.4140184,0.3255067],"study_design_scores_gemma":[0.0009170129,0.0001081928,0.00228789,0.00005153754,0.00006346128,0.0001208389,0.0001293464,0.8288975,0.00703678,0.06578217,0.09449734,0.0001079929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.105164,0.001831105,0.6765866,0.005850135,0.001195222,0.0005225526,0.01113692,0.09597021,0.1017433],"genre_scores_gemma":[0.3303534,0.001350977,0.6161699,0.000801339,0.0001657719,0.0009948023,0.02413869,0.007934619,0.01809049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02746655,"threshold_uncertainty_score":0.06975687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02821701107132304,"score_gpt":0.2384559276278014,"score_spread":0.2102389165564783,"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."}}