{"meta":{"query_hash":"c046e460dfbf","filters":{"venue":"2007 International Conference on Convergence Information Technology (ICCIT 2007)"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"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/c046e460dfbf","api":"https://metacan.xera.ac/api/v1/cohort?venue=2007+International+Conference+on+Convergence+Information+Technology+%28ICCIT+2007%29"},"results":[{"id":"W2017733151","doi":"10.1109/iccit.2007.148","title":"Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers","year":2007,"lang":"en","type":"article","venue":"2007 International Conference on Convergence Information Technology (ICCIT 2007)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":151,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Naive Bayes classifier; Artificial intelligence; k-nearest neighbors algorithm; Computer science; Machine learning; Bayes error rate; Classifier (UML); Bayes classifier; Random subspace method; Pattern recognition (psychology); Bayesian probability; Bayes' theorem; Data mining; Support vector machine","score_opus":0.024900131717299163,"score_gpt":0.26046504088334227,"score_spread":0.2355649091660431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017733151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40799195,0.029043477,0.52071804,0.003364038,0.0020249733,0.0010355046,0.0017204485,0.0024473479,0.031654246],"genre_scores_gemma":[0.77948624,0.0031602485,0.21121487,0.00041924647,0.00046506262,0.00024048796,0.0014320785,0.00020890828,0.0033728566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.972265,0.009156005,0.002022311,0.0034004005,0.012219149,0.00093716406],"domain_scores_gemma":[0.9158292,0.061080836,0.0028181085,0.0036463363,0.015876478,0.00074896455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024978561,0.0017160461,0.0024701804,0.0043599885,0.0018942847,0.003885977,0.0019543234,0.0028710726,0.001898506],"category_scores_gemma":[0.10162636,0.0006303834,0.001129974,0.0032327026,0.0012517548,0.007553869,0.0010593983,0.0016107376,0.0011827209],"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":[0.0027377983,0.00071206247,0.03973011,0.0012196886,0.0009415638,0.00022954737,0.00084799,0.24045718,0.002506417,0.02304417,0.012110934,0.6754625],"study_design_scores_gemma":[0.000077883546,0.0006781075,0.0085443,0.00016241384,0.00017442151,0.00023166009,0.00048473087,0.96362764,0.0030334292,0.018525274,0.0043460513,0.00011403094],"about_ca_topic_score_codex":0.01523003,"about_ca_topic_score_gemma":0.011999207,"teacher_disagreement_score":0.024978561,"about_ca_system_score_codex":0.002820689,"about_ca_system_score_gemma":0.0022467182,"threshold_uncertainty_score":0.13210082},"labels":[],"label_agreement":null},{"id":"W3146200671","doi":"10.1109/iccit.2007.4420473","title":"Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers","year":2007,"lang":"en","type":"article","venue":"2007 International Conference on Convergence Information Technology (ICCIT 2007)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Naive Bayes classifier; Artificial intelligence; k-nearest neighbors algorithm; Computer science; Bayes error rate; Machine learning; Classifier (UML); Bayes classifier; Pattern recognition (psychology); Random subspace method; Bayesian probability; Bayes' theorem; Data mining; Support vector machine","score_opus":0.023613763142237787,"score_gpt":0.2723399187404118,"score_spread":0.24872615559817401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146200671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.404068,0.029894542,0.5219258,0.003411538,0.0020951775,0.0010809556,0.0017851866,0.0024950602,0.03324367],"genre_scores_gemma":[0.77965367,0.0032376803,0.21079576,0.00042614064,0.0004733659,0.0002529971,0.0014868663,0.00021634149,0.0034572459],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97177786,0.00922172,0.002028905,0.0034797993,0.0125622405,0.000929488],"domain_scores_gemma":[0.91667074,0.060208127,0.002845148,0.0036793118,0.01587223,0.0007245174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025298733,0.0017217312,0.0024907542,0.004416841,0.0019101395,0.0039246446,0.0019698068,0.0028687937,0.0019429751],"category_scores_gemma":[0.1014981,0.0006384532,0.0011716913,0.0032885785,0.0012718533,0.0075208996,0.0010422018,0.001648775,0.001260272],"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":[0.002682743,0.0006956996,0.04053821,0.0012565802,0.00096661673,0.00022520286,0.0008841181,0.22550462,0.002508153,0.024204642,0.012750305,0.6877831],"study_design_scores_gemma":[0.00008448183,0.00071767485,0.009430393,0.00018436558,0.00019230571,0.00024996765,0.00052975817,0.9598077,0.0032003578,0.020556133,0.004921972,0.00012492025],"about_ca_topic_score_codex":0.0146618355,"about_ca_topic_score_gemma":0.011731297,"teacher_disagreement_score":0.025298733,"about_ca_system_score_codex":0.002840949,"about_ca_system_score_gemma":0.002247396,"threshold_uncertainty_score":0.13379407},"labels":[],"label_agreement":null}]}