{"id":"W3181327668","doi":"10.1145/3465084.3467939","title":"An Efficient Adaptive Partial Snapshot Implementation","year":2021,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Snapshot (computer storage); Computer science; Parallel computing; Algorithm; Theoretical computer science; Computer engineering; Distributed computing; Operating system","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":[0.0004568223,0.0005101575,0.0005724936,0.0004217059,0.0005204074,0.001471278,0.002111973,0.000500903,0.01111467],"category_scores_gemma":[0.002029701,0.0004228425,0.0003762223,0.0008833379,0.0004165578,0.002041871,0.00191038,0.0008408104,0.002735424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005190812,"about_ca_system_score_gemma":0.001256605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002240811,"about_ca_topic_score_gemma":0.004812332,"domain_scores_codex":[0.9993794,0.00009950103,0.00004402692,0.0001225727,0.0002296198,0.0001248215],"domain_scores_gemma":[0.9990527,0.0001232218,0.00002970044,0.0005110041,0.0002199437,0.00006339285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001320558,0.0002303509,0.002066528,0.0002751591,0.00009834335,0.000283568,0.000272854,0.05899479,0.05891564,0.09750677,0.04447017,0.7355652],"study_design_scores_gemma":[0.0001747459,0.0002010486,0.0006193985,0.00003028056,0.00006144871,0.0002372207,0.00009516346,0.8879387,0.04245214,0.03966772,0.02846661,0.00005548682],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02270319,0.0003159262,0.9513531,0.000234355,0.0001794618,0.0001125672,0.0003904469,0.01561226,0.009098709],"genre_scores_gemma":[0.4051915,0.0002437103,0.5811705,0.0002463076,0.00008580119,0.0002381824,0.0009960249,0.0005803847,0.01124765],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01111467,"threshold_uncertainty_score":0.03718221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02514256332474277,"score_gpt":0.3196502248659279,"score_spread":0.2945076615411851,"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."}}