{"meta":{"query_hash":"69c870bb13a7","filters":{"venue":"Indian International Conference on Artificial Intelligence"},"cohort_total":6,"direct_labels_cover":0,"predictions_cover":6,"exported":6,"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/69c870bb13a7","api":"https://metacan.xera.ac/api/v1/cohort?venue=Indian+International+Conference+on+Artificial+Intelligence"},"results":[{"id":"W191311471","doi":"","title":"An Algorithm for the Estimation of a Time Period of 2-Sequences.","year":2009,"lang":"en","type":"article","venue":"Indian International Conference on Artificial Intelligence","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; University of New Brunswick","funders":"","keywords":"Period (music); Algorithm; Estimation; Computer science; Engineering","score_opus":0.03554230519960595,"score_gpt":0.332159717735415,"score_spread":0.2966174125358091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W191311471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35564172,0.000082272425,0.6384387,0.002746492,0.00028804629,0.00045694082,0.0002594374,0.000015277737,0.0020711224],"genre_scores_gemma":[0.99593997,0.000048840007,0.0035225113,0.00014990868,0.00010123488,0.0000141474275,0.00011622679,0.0000044735875,0.00010270244],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914706,0.000023587443,0.00031606553,0.00020659137,0.0001974307,0.00010924646],"domain_scores_gemma":[0.99920636,0.000027086295,0.00018366966,0.00021746673,0.00032707446,0.000038340382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018683709,0.000097198834,0.000115793715,0.00007685933,0.000056240697,0.000035507353,0.00043976645,0.00007115797,0.00017326203],"category_scores_gemma":[0.00011442974,0.000075814976,0.00009892415,0.00009636306,0.00017079906,0.000016111073,0.000013556958,0.000048330054,0.000013392],"study_design_candidate":"bench_or_experimental","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":[0.000095882046,0.00014197544,0.000014901554,0.0000028679851,0.000044997654,0.0000012646517,0.00023629282,0.004084613,0.30145437,0.01815302,0.000026103105,0.6757437],"study_design_scores_gemma":[0.000030367011,0.0008228586,0.00011373007,0.000026575452,0.000015712683,0.0000042651554,0.0003943174,0.31718946,0.65984064,0.021361576,0.000089591274,0.00011087661],"about_ca_topic_score_codex":0.00006038061,"about_ca_topic_score_gemma":0.00003225876,"teacher_disagreement_score":0.67563283,"about_ca_system_score_codex":0.000011610116,"about_ca_system_score_gemma":0.00009405127,"threshold_uncertainty_score":0.3091645},"labels":[],"label_agreement":null},{"id":"W22775118","doi":"10.1111/j.1460-9568.2012.08207.x","title":"ANN Application in End Depth Computation for Inverted Semicircular Channels.","year":2003,"lang":"en","type":"article","venue":"Indian International Conference on Artificial Intelligence","topic":"Advanced Surface Polishing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Canadian Institutes of Health Research","keywords":"Computation; Computer science; Algorithm","score_opus":0.07339428852188634,"score_gpt":0.3310227825704494,"score_spread":0.2576284940485631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W22775118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059338257,0.00002066003,0.93336827,0.00021463967,0.0006648916,0.00056195,0.00004017158,0.00031083627,0.005480323],"genre_scores_gemma":[0.9914182,0.00003321517,0.00802632,0.00011962265,0.00005838719,0.00020565777,0.00009061366,0.000031429267,0.000016592734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998818,0.00002659288,0.00039437893,0.00029036877,0.00021560393,0.0002550463],"domain_scores_gemma":[0.9994302,0.00010237568,0.00007158024,0.0001537738,0.0001762828,0.00006578938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024118676,0.00017912808,0.00015135443,0.00030457453,0.00004834195,0.00009237968,0.00028978026,0.00012025479,0.000056154986],"category_scores_gemma":[0.00019424676,0.00021720467,0.000046327565,0.00023348766,0.00005940969,0.00023068547,0.000012947961,0.00022118233,0.00007181726],"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":[0.000039717615,0.000109871464,0.00040008544,0.00003567546,0.00003308869,0.00000852286,0.0016696472,0.2164946,0.020035218,0.64153284,0.00018211089,0.119458616],"study_design_scores_gemma":[0.000043847973,0.000035212517,0.00006139785,0.000062598345,0.0000023696432,0.0000037537643,0.00027966505,0.54501534,0.14357148,0.31003544,0.00065140304,0.00023748181],"about_ca_topic_score_codex":0.0000687545,"about_ca_topic_score_gemma":0.00033383054,"teacher_disagreement_score":0.9320799,"about_ca_system_score_codex":0.0002486902,"about_ca_system_score_gemma":0.000042147534,"threshold_uncertainty_score":0.8857349},"labels":[],"label_agreement":null},{"id":"W2395748680","doi":"","title":"Shape from an Endoscope Image using Extended Fast Marching Method.","year":2011,"lang":"en","type":"article","venue":"Indian International Conference on Artificial Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fast marching method; Computer vision; Computer science; Artificial intelligence; Endoscope; Image (mathematics); Computer graphics (images); Medicine; Radiology","score_opus":0.19397083361867756,"score_gpt":0.39732343692643524,"score_spread":0.20335260330775767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395748680","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":[0.010398607,0.000006622019,0.9769291,0.00039980494,0.00069767167,0.00020120431,0.000045713103,0.00029470085,0.01102652],"genre_scores_gemma":[0.674049,0.000014199715,0.32542896,0.00026335227,0.00013127968,0.000018509336,0.000017346008,0.000014973811,0.0000623499],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974737,0.00022501229,0.0005807979,0.0007937768,0.0005698942,0.00035678505],"domain_scores_gemma":[0.9983068,0.000113063805,0.00027439508,0.0006581028,0.00043921729,0.00020841428],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004761037,0.00026922967,0.00021643908,0.00035953723,0.00021146653,0.00062454515,0.0023993074,0.00011590346,0.002178324],"category_scores_gemma":[0.00009890034,0.00026747925,0.00010060359,0.00033857068,0.00016620377,0.0013907192,0.00026511415,0.000386436,0.00042426557],"study_design_candidate":"bench_or_experimental","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":[0.000043720273,0.00017466652,0.000020565796,0.000001759296,0.000019377669,0.000045996687,0.0023138928,0.0000019280326,0.047951862,0.38568804,0.00000422933,0.56373394],"study_design_scores_gemma":[0.000023236422,0.0001440957,0.0002928484,0.00005474065,0.000004718721,0.00002156905,0.0005889915,0.29593495,0.5258845,0.17672597,0.000049083144,0.00027527643],"about_ca_topic_score_codex":0.0007965756,"about_ca_topic_score_gemma":0.00005502432,"teacher_disagreement_score":0.66365045,"about_ca_system_score_codex":0.00012287968,"about_ca_system_score_gemma":0.00018746729,"threshold_uncertainty_score":0.99997777},"labels":[],"label_agreement":null},{"id":"W2398755758","doi":"","title":"Mining Icebergs in Time-Stamped Databases.","year":2011,"lang":"en","type":"article","venue":"Indian International Conference on Artificial Intelligence","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Iceberg; Computer science; Database; Geology","score_opus":0.18212539124276206,"score_gpt":0.3412848615653043,"score_spread":0.15915947032254224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398755758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095981285,0.0000156343,0.7910767,0.0029798525,0.0015216165,0.00051339134,0.00020705389,0.0003720313,0.107332416],"genre_scores_gemma":[0.9282389,0.000022465998,0.07059541,0.00043459926,0.000098903605,0.00007079401,0.000066082415,0.000012703019,0.0004600872],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813324,0.000047679674,0.00047708311,0.00060058036,0.00042606535,0.00031535167],"domain_scores_gemma":[0.9988716,0.000102513615,0.00014557721,0.0006025055,0.0001499395,0.00012782583],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00038156964,0.00018256066,0.00015417111,0.00037608386,0.00009478515,0.00026904655,0.0022585297,0.00005660959,0.0013190565],"category_scores_gemma":[0.00012975393,0.00019160964,0.000046870766,0.00046475744,0.00010686,0.0006845264,0.00032099718,0.0002280032,0.0019684315],"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":[0.000018674255,0.00026289863,0.00034034412,0.0000020878688,0.000011854006,0.00006338299,0.002731279,0.000013887018,0.0004934725,0.74440706,0.00020631797,0.25144878],"study_design_scores_gemma":[0.00021240478,0.00036450167,0.0048960387,0.0005040477,0.000010738203,0.0000672591,0.0023138756,0.7395582,0.089723185,0.15381306,0.006993743,0.0015429407],"about_ca_topic_score_codex":0.0004539915,"about_ca_topic_score_gemma":0.00029006304,"teacher_disagreement_score":0.8322577,"about_ca_system_score_codex":0.000073857256,"about_ca_system_score_gemma":0.0001290688,"threshold_uncertainty_score":0.99959385},"labels":[],"label_agreement":null},{"id":"W2399886942","doi":"","title":"Clustering of Products to Identify Optimal Inventory Prediction Models.","year":2011,"lang":"en","type":"article","venue":"Indian International Conference on Artificial Intelligence","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Artificial intelligence","score_opus":0.2398035938578342,"score_gpt":0.3504457422213326,"score_spread":0.11064214836349842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399886942","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":[0.02342448,0.0000048892384,0.96755034,0.0006108811,0.0011944093,0.0001787414,0.000027740847,0.000075879994,0.006932639],"genre_scores_gemma":[0.90699935,0.000005647943,0.092598885,0.00019439802,0.00010425535,0.00002879545,0.000007845114,0.000008048532,0.00005275791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981598,0.00004388811,0.0005360361,0.00048506603,0.00055555144,0.00021966064],"domain_scores_gemma":[0.998743,0.000048152957,0.00013678538,0.0002795119,0.00065579626,0.00013673533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024094137,0.00015086311,0.00014232824,0.0003291464,0.0000747258,0.00011820558,0.0010891951,0.000055250486,0.0001626236],"category_scores_gemma":[0.00014411802,0.00015706675,0.000049656675,0.00031811377,0.00007379906,0.0005105843,0.00024324513,0.0001576434,0.0001999076],"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":[0.00006621832,0.00014068931,0.000030338642,0.000010251058,0.000021683452,0.000014685741,0.0039582537,0.04035263,0.0010090902,0.89040434,0.00003303399,0.06395879],"study_design_scores_gemma":[0.000017461387,0.00014521656,0.00037499366,0.0000771085,0.0000023770313,0.0000065518616,0.0001733834,0.75312114,0.010803147,0.23514438,0.000010232712,0.00012403615],"about_ca_topic_score_codex":0.00011807448,"about_ca_topic_score_gemma":0.000020762547,"teacher_disagreement_score":0.8835749,"about_ca_system_score_codex":0.00006643185,"about_ca_system_score_gemma":0.0001299714,"threshold_uncertainty_score":0.6404996},"labels":[],"label_agreement":null},{"id":"W73408484","doi":"","title":"Fuzzy Model: Time Dependent Dispersion in Rivers.","year":2009,"lang":"en","type":"article","venue":"Indian International Conference on Artificial Intelligence","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Dispersion (optics); Fuzzy logic; Computer science; Artificial intelligence; Physics; Optics","score_opus":0.06283129012735406,"score_gpt":0.3021028151528228,"score_spread":0.23927152502546872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W73408484","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":[0.8486896,0.000002007833,0.0034554263,0.004311263,0.0002474377,0.00019797363,0.000017208244,0.000084742416,0.14299434],"genre_scores_gemma":[0.997278,0.000010700706,0.0009948513,0.00096728303,0.0000473791,0.0000058165137,0.000013517231,0.000008237695,0.0006742551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981664,0.000053884556,0.0003680954,0.0004995466,0.0005681028,0.00034401208],"domain_scores_gemma":[0.9994914,0.00004417521,0.00009890212,0.0002074645,0.000026306503,0.00013174096],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00029650563,0.00018659604,0.00014929392,0.00014186864,0.000097311895,0.00010280894,0.000660527,0.00011978021,0.0047243135],"category_scores_gemma":[0.00015870437,0.00017805587,0.000059544458,0.00022197001,0.00017987858,0.00023101084,0.00010946943,0.00031866122,0.006393673],"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":[0.00027051725,0.00064274506,0.0013667091,0.0000014994856,0.000008698922,0.00019523605,0.0015786234,0.5800162,0.024621073,0.08159348,0.00032445838,0.30938077],"study_design_scores_gemma":[0.00005001783,0.00023267462,0.0011045232,0.000055346216,0.000002863685,0.000009782641,0.000048152204,0.67313427,0.007947029,0.3170256,0.00009025333,0.00029947108],"about_ca_topic_score_codex":0.00016033325,"about_ca_topic_score_gemma":0.00018971223,"teacher_disagreement_score":0.3090813,"about_ca_system_score_codex":0.00036111003,"about_ca_system_score_gemma":0.000024563136,"threshold_uncertainty_score":0.9961855},"labels":[],"label_agreement":null}]}