{"meta":{"query_hash":"b2a4abd2f89a","filters":{"venue":"Journal of Next Generation Information Technology"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/b2a4abd2f89a","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Next+Generation+Information+Technology"},"results":[{"id":"W2982613029","doi":"","title":"Performance evaluation of fast smith-waterman algorithm for sequence database searches using CUDA GPU-based parallel computing","year":2014,"lang":"en","type":"article","venue":"Journal of Next Generation Information Technology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; CUDA; Parallel computing; Smith–Waterman algorithm; General-purpose computing on graphics processing units; Sequence (biology); Supercomputer; Computational science; Algorithm; Computer graphics (images); Sequence alignment; Graphics","score_opus":0.08745999493304632,"score_gpt":0.3131370536118424,"score_spread":0.2256770586787961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982613029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7709843,0.003452541,0.1735696,0.0007230114,0.00050363137,0.00028506276,0.0023604163,0.027945949,0.020175483],"genre_scores_gemma":[0.71158236,0.0006289517,0.2777323,0.0001504662,0.000030090543,0.0001702768,0.004102327,0.0008767435,0.004726471],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998552,0.00033283475,0.00013286025,0.00027322865,0.00045854424,0.00025050834],"domain_scores_gemma":[0.9974952,0.0007967831,0.000094961855,0.00024217904,0.0011735045,0.00019738707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013004235,0.0011776957,0.0010381246,0.0010715838,0.0011194326,0.0012687257,0.0025131605,0.0009194827,0.0058575],"category_scores_gemma":[0.0038910483,0.00039636937,0.00053602445,0.003098966,0.00041544132,0.0011085897,0.00059256534,0.00074251,0.0013123801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006694513,0.001504044,0.012768751,0.0015218741,0.0007221735,0.000867982,0.000689734,0.31113493,0.076766215,0.007841965,0.046813875,0.53267395],"study_design_scores_gemma":[0.00016147949,0.00023385302,0.0018694366,0.000016400953,0.000044825974,0.0000838955,0.00017196093,0.9726892,0.020586286,0.00081349956,0.003301508,0.000027705591],"about_ca_topic_score_codex":0.033663355,"about_ca_topic_score_gemma":0.021514094,"teacher_disagreement_score":0.033663355,"about_ca_system_score_codex":0.0011011038,"about_ca_system_score_gemma":0.003027098,"threshold_uncertainty_score":0.066934824},"labels":[],"label_agreement":null}]}