{"meta":{"query_hash":"1c88955bf92a","filters":{"venue":"Journal of Mathematical Modelling and Algorithms in Operations Research"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/1c88955bf92a","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Mathematical+Modelling+and+Algorithms+in+Operations+Research"},"results":[{"id":"W1982031241","doi":"10.1007/s10852-013-9244-6","title":"Optimal SVM Classification for Compact Polarimetric Data Using Stokes Parameters","year":2013,"lang":"en","type":"article","venue":"Journal of Mathematical Modelling and Algorithms in Operations Research","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Polarimetry; Support vector machine; Stokes parameters; Remote sensing; Radar; Computer science; Polarization (electrochemistry); Pattern recognition (psychology); Artificial intelligence; Physics; Optics; Geology; Scattering; Telecommunications","score_opus":0.26386566178537596,"score_gpt":0.4056497321135825,"score_spread":0.14178407032820656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982031241","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09783633,0.00042353495,0.90089756,0.00030326124,0.000021261136,0.00039242735,0.000016717659,0.000019134397,0.00008977904],"genre_scores_gemma":[0.29824272,0.00018935575,0.70147234,0.0000036389793,0.00004566837,0.000010899558,0.000007161394,0.000016042817,0.000012157649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987644,0.00006453744,0.0005033907,0.0001400392,0.00030555227,0.00022208455],"domain_scores_gemma":[0.9985262,0.00074527814,0.000030351268,0.00029707624,0.00030358313,0.00009747456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016067398,0.00009915303,0.00023512848,0.00045622292,0.00014815271,0.00019544372,0.00031050644,0.00008628353,0.000017089591],"category_scores_gemma":[0.00023141736,0.0000783259,0.000035400888,0.0003468591,0.00008906328,0.0003643652,0.000035286386,0.000380491,0.0000035547014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021752308,0.0004280999,0.000059399026,0.0003944473,0.00017147095,0.000003849781,0.0010091279,0.773777,0.0038273802,0.015229456,0.0014279064,0.20365012],"study_design_scores_gemma":[0.00014894683,0.000047866048,0.000015783513,0.00009509528,0.000012243703,0.000035370216,0.0003327065,0.9935333,0.00069029327,0.0043104393,0.0006940012,0.00008392284],"about_ca_topic_score_codex":0.00009343592,"about_ca_topic_score_gemma":0.0000011513522,"teacher_disagreement_score":0.21975634,"about_ca_system_score_codex":0.00007979692,"about_ca_system_score_gemma":0.000048733607,"threshold_uncertainty_score":0.31940374},"labels":[],"label_agreement":null},{"id":"W1990344701","doi":"10.1007/s10852-012-9214-4","title":"A Higher-Order Hidden Markov Chain-Modulated Model for Asset Allocation","year":2012,"lang":"en","type":"article","venue":"Journal of Mathematical Modelling and Algorithms in Operations Research","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Markov chain; Hidden Markov model; Hidden semi-Markov model; Asset (computer security); Portfolio; Markov model; Asset allocation; Econometrics; Variable-order Markov model; Computer science; Benchmark (surveying); Discrete time and continuous time; Order (exchange); Portfolio optimization; Markov property; Multivariate statistics; Mathematical optimization; Economics; Mathematics; Finance; Statistics; Artificial intelligence; Machine learning","score_opus":0.1488583176428479,"score_gpt":0.3559526080630695,"score_spread":0.2070942904202216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990344701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018306144,0.0012239695,0.97801846,0.0016875572,0.00006414266,0.0003132144,0.000031348736,0.000005518043,0.00034964446],"genre_scores_gemma":[0.642739,0.00018015418,0.35639468,0.000029073048,0.00019256887,0.00009617465,0.0000054058446,0.000015836038,0.00034709126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860585,0.00001402759,0.0007709135,0.0001593172,0.000119650686,0.0003302687],"domain_scores_gemma":[0.99895096,0.0002691481,0.00009979668,0.00014402336,0.0004080477,0.00012802756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029037218,0.00009515626,0.00031584484,0.0003502306,0.0002010926,0.000092963746,0.0001658702,0.0001088805,0.000021842712],"category_scores_gemma":[0.00040661098,0.000089528934,0.000053673408,0.00037864863,0.00007052522,0.00030528748,0.000038144764,0.00030763494,0.00001741959],"study_design_candidate":"theoretical_or_conceptual","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":[0.000017767314,0.00036359832,0.000029465116,0.00007313914,0.000019953046,3.311947e-7,0.0010262906,0.08702314,0.000024570445,0.90900815,0.00005687357,0.0023567586],"study_design_scores_gemma":[0.00026645535,0.000037823396,0.000030024676,0.00003505882,0.000003345982,0.000005705017,0.00005632403,0.66552943,0.000008399279,0.33385336,0.00010647413,0.00006759079],"about_ca_topic_score_codex":0.000023135868,"about_ca_topic_score_gemma":0.0000023182295,"teacher_disagreement_score":0.62443286,"about_ca_system_score_codex":0.00008169252,"about_ca_system_score_gemma":0.000064642016,"threshold_uncertainty_score":0.36508837},"labels":[],"label_agreement":null},{"id":"W1999320738","doi":"10.1007/s10852-013-9238-4","title":"Optimum Allocation in Multivariate Stratified Random Sampling: A Modified Prékopa’s Approach","year":2013,"lang":"en","type":"article","venue":"Journal of Mathematical Modelling and Algorithms in Operations Research","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Consejo Nacional de Ciencia y Tecnología; Centre de Recherches Mathématiques","keywords":"Stratified sampling; Multivariate statistics; Sampling (signal processing); Statistics; Multivariate t-distribution; Multivariate analysis; Mathematics; Optimal allocation; Multivariate normal distribution; Computer science; Mathematical optimization","score_opus":0.3513715485652514,"score_gpt":0.447891761590875,"score_spread":0.09652021302562364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999320738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2027489,0.00007697639,0.7956191,0.0003640846,0.000025137875,0.0006482259,0.0000018252041,0.000022916549,0.00049285346],"genre_scores_gemma":[0.5027459,0.00007625561,0.49691248,0.00000554936,0.0000445201,0.00010072752,0.00000289627,0.000017030294,0.00009460547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966866,0.0006405175,0.0013311276,0.00025357882,0.0007069626,0.0003812286],"domain_scores_gemma":[0.9961805,0.0024959752,0.00011548905,0.0002566452,0.0008043809,0.0001470596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008892364,0.00017965058,0.0005431064,0.0007849045,0.00017195467,0.0003209664,0.00026471866,0.00018033719,0.000034563574],"category_scores_gemma":[0.0025538455,0.00014033732,0.00007033759,0.00049694383,0.00012721986,0.0004813712,0.000058030935,0.0009525118,0.0000069171333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111206005,0.0016457572,0.00003535028,0.000387739,0.0000469411,0.0000075323205,0.005772479,0.9164924,0.00080638536,0.067945756,0.00009911577,0.0066493405],"study_design_scores_gemma":[0.0009636699,0.00006961687,0.000028147517,0.00028194403,0.000005718335,0.00002599928,0.0006616297,0.73581475,0.00020482262,0.26183584,0.0000017091273,0.0001061668],"about_ca_topic_score_codex":0.00022636227,"about_ca_topic_score_gemma":0.0000098517585,"teacher_disagreement_score":0.29999703,"about_ca_system_score_codex":0.00010992018,"about_ca_system_score_gemma":0.00015291857,"threshold_uncertainty_score":0.572279},"labels":[],"label_agreement":null}]}