{"id":"W2964331222","doi":"10.1109/ccgrid.2019.00059","title":"Performance evaluation of big data processing strategies for neuroimaging","year":2019,"lang":"","type":"article","venue":"Espace ÉTS (ETS)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Concordia University","funders":"Dell EMC","keywords":"Computer science; Big data; Lazy evaluation; Locality; Cache; Neuroimaging; SPARK (programming language); Data processing; Database; Parallel computing; Data mining; Theoretical computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006672167,0.002302454,0.001023991,0.001922074,0.001200905,0.002282419,0.003132698,0.001225116,0.001402628],"category_scores_gemma":[0.01587452,0.0006118011,0.000843909,0.002530249,0.001207492,0.003457087,0.001683904,0.001527606,0.0006936355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001727335,"about_ca_system_score_gemma":0.002715051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008301351,"about_ca_topic_score_gemma":0.005784797,"domain_scores_codex":[0.9942285,0.001606601,0.000642253,0.001032811,0.001748845,0.0007408745],"domain_scores_gemma":[0.985315,0.0068819,0.0007461161,0.002459699,0.003175654,0.001421717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01872821,0.003920523,0.0895144,0.003471359,0.001892739,0.0009736203,0.002372899,0.3839083,0.09211887,0.01235397,0.05045621,0.3402889],"study_design_scores_gemma":[0.0005210146,0.002244831,0.01883716,0.00007043283,0.0002070468,0.0002365834,0.0008148956,0.9124802,0.05472044,0.004831485,0.004896307,0.0001395283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9466474,0.002564519,0.02969896,0.0008210374,0.0003777818,0.0003961091,0.001579831,0.01232095,0.005593495],"genre_scores_gemma":[0.9452292,0.0004430117,0.04993231,0.0001825245,0.00005666149,0.0001940246,0.002652669,0.000558666,0.0007509073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008301351,"threshold_uncertainty_score":0.03528625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2584591674489424,"score_gpt":0.4128559192004845,"score_spread":0.1543967517515422,"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."}}