{"id":"W3012369157","doi":"10.6084/m9.figshare.11929629.v1","title":"Worldwide Trends in Computer Architectures for Data Science","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"IBM; Aerospace; Architecture; Computer science; Key (lock); Supercomputer; Focus (optics); State (computer science); Data science; Engineering management; Engineering; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002918456,0.0006472545,0.000421559,0.002905128,0.0009957094,0.00493251,0.001139182,0.00239074,0.0725371],"category_scores_gemma":[0.006429799,0.0003520157,0.0004696715,0.007309228,0.0009653691,0.006395152,0.002738227,0.00388053,0.03931593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002052112,"about_ca_system_score_gemma":0.002118755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001473996,"about_ca_topic_score_gemma":0.002697364,"domain_scores_codex":[0.9972543,0.0004473694,0.0001768566,0.0004090286,0.001463015,0.0002494535],"domain_scores_gemma":[0.9936353,0.001348039,0.0004395347,0.0006889621,0.003027435,0.0008606361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005253304,0.00005118015,0.001490087,0.0006373158,0.00001440665,0.00006673075,0.0002171089,0.0005250815,0.002592604,0.1173343,0.3954343,0.4815843],"study_design_scores_gemma":[0.000004093981,0.00001681527,0.0008893133,0.0001384504,0.000004500825,0.0001643106,0.00009941137,0.0003556787,0.0003175283,0.007318757,0.9906822,0.000008904259],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0100843,0.2347285,0.03949891,0.1539804,0.01399713,0.0002280048,0.002615009,0.00338836,0.5414793],"genre_scores_gemma":[0.07579809,0.3001716,0.1199537,0.05264442,0.01530131,0.0005110148,0.007448613,0.001780265,0.4263911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0725371,"threshold_uncertainty_score":0.2426608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1104270385893483,"score_gpt":0.3142649118481562,"score_spread":0.2038378732588079,"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."}}