{"meta":{"query_hash":"c1cf37de6593","filters":{"venue":"Integra Journal of Integrated Mathematics and Computer Science"},"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/c1cf37de6593","api":"https://metacan.xera.ac/api/v1/cohort?venue=Integra+Journal+of+Integrated+Mathematics+and+Computer+Science"},"results":[{"id":"W4412510697","doi":"10.26554/integrajimcs.20241221","title":"Comparison of Support Vector Regression and Random Forest Regression Performance in Vehicle Fuel Consumption Prediction","year":2024,"lang":"en","type":"article","venue":"Integra Journal of Integrated Mathematics and Computer Science","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Random forest; Regression; Regression analysis; Statistics; Support vector machine; Consumption (sociology); Linear regression; Environmental science; Econometrics; Mathematics; Computer science; Machine learning","score_opus":0.01917749078834931,"score_gpt":0.27622489647925996,"score_spread":0.25704740569091067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412510697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9625294,0.0017982268,0.03495653,0.000028724826,0.0004931733,0.000079503676,0.00000441445,0.000028758854,0.00008125925],"genre_scores_gemma":[0.98998684,0.0017135501,0.0082352925,0.000004279919,0.00003891316,0.0000017739532,0.0000013822404,0.00000954333,0.000008405237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987976,0.000012930329,0.0006188145,0.00012833766,0.0002854226,0.00015692419],"domain_scores_gemma":[0.9994069,0.00009489393,0.00013360337,0.00010689683,0.00016145107,0.00009626285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007720337,0.00014527782,0.0003181837,0.00038511548,0.000058026937,0.00011142594,0.00017369467,0.000067576955,0.000011748087],"category_scores_gemma":[0.000027443019,0.000087138505,0.00003790444,0.0003917858,0.00019308731,0.0005161023,0.00004282059,0.00037935495,0.0000014202813],"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":[0.0003434362,0.00048100573,0.16995402,0.0064508417,0.00011249391,0.00008059571,0.023865901,0.015325518,0.15122332,0.0032421674,0.0015984293,0.62732226],"study_design_scores_gemma":[0.00041097097,0.00028585846,0.009273371,0.0041324976,0.000012706203,0.00016872659,0.0002048683,0.9707302,0.014292301,0.00020255095,0.00019830922,0.00008767194],"about_ca_topic_score_codex":0.000002623433,"about_ca_topic_score_gemma":0.000002494507,"teacher_disagreement_score":0.95540464,"about_ca_system_score_codex":0.00005866187,"about_ca_system_score_gemma":0.00007949554,"threshold_uncertainty_score":0.3553405},"labels":[],"label_agreement":null}]}