{"meta":{"query_hash":"ae5596e10ef6","filters":{"venue":"International Journal of Intelligent Computing Research"},"cohort_total":4,"direct_labels_cover":0,"predictions_cover":4,"exported":4,"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/ae5596e10ef6","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Intelligent+Computing+Research"},"results":[{"id":"W2511073537","doi":"10.20533/ijicr.2042.4655.2013.0040","title":"Dirt Jumper: A New and Fast Evolving Botnet-for-DDoS","year":2013,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing Research","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Botnet; Jumper; Denial-of-service attack; Dirt; Computer science; Computer security; Engineering; World Wide Web; Operating system; The Internet","score_opus":0.0552141237590453,"score_gpt":0.36510602055388425,"score_spread":0.309891896794839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511073537","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":[0.75578636,0.0045611723,0.13237286,0.004158214,0.0009446981,0.0006901837,0.0017926708,0.033803795,0.06589013],"genre_scores_gemma":[0.89068174,0.0020490494,0.0800314,0.001352295,0.00023262965,0.000119351054,0.0033814698,0.0017288425,0.020423241],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989293,0.000107806125,0.000037962902,0.00018965856,0.0005549889,0.00018019434],"domain_scores_gemma":[0.9984339,0.0003455561,0.00019465189,0.00042143764,0.0003372369,0.00026735786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010884524,0.00050802185,0.00043030005,0.0022718452,0.0013653343,0.0016843547,0.0007746166,0.0008474479,0.0010191576],"category_scores_gemma":[0.0027693505,0.0003482583,0.00030928757,0.0011854531,0.001467057,0.002985056,0.0015729421,0.0016807208,0.0004982176],"study_design_candidate":"not_applicable","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":[0.0007229642,0.0006038283,0.07788302,0.00052639993,0.0001447582,0.0041402746,0.0047998102,0.014782398,0.105211936,0.0553021,0.06545671,0.67042565],"study_design_scores_gemma":[0.000111954534,0.0016149975,0.106216766,0.00039727153,0.0001836894,0.018065825,0.0019434977,0.18573432,0.10564712,0.028810471,0.55088174,0.0003922887],"about_ca_topic_score_codex":0.0024920248,"about_ca_topic_score_gemma":0.0032855507,"teacher_disagreement_score":0.0024920248,"about_ca_system_score_codex":0.001276553,"about_ca_system_score_gemma":0.00083828054,"threshold_uncertainty_score":0.009262085},"labels":[],"label_agreement":null},{"id":"W2513411477","doi":"10.20533/ijicr.2042.4655.2012.0028","title":"Improving Finding and Re-finding Web search Results Using Clustering and Visualisation","year":2012,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing Research","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Cluster analysis; Computer science; Information retrieval; Visualization; World Wide Web; Data mining; Artificial intelligence","score_opus":0.21090346821690836,"score_gpt":0.45267100886614453,"score_spread":0.24176754064923617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513411477","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":[0.107218295,0.0017450929,0.7816364,0.0013508549,0.00016036298,0.00086099753,0.0018193582,0.095567115,0.00964151],"genre_scores_gemma":[0.16128585,0.0010772191,0.82980406,0.00016196401,0.00009477182,0.00043593335,0.0015644437,0.0026519736,0.0029238458],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738246,0.0011149796,0.00022463262,0.00033634345,0.00081462564,0.00012686782],"domain_scores_gemma":[0.9807482,0.013804212,0.0007361449,0.0018281722,0.0024336318,0.00044966175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072634756,0.0020285724,0.0022639213,0.006085716,0.00082542974,0.0041421996,0.002456572,0.001832315,0.009470913],"category_scores_gemma":[0.023067102,0.00075351365,0.0013427153,0.0034301614,0.00047035696,0.005523751,0.0029643096,0.0015225811,0.0037927958],"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":[0.0019390385,0.00086997665,0.0075286077,0.002860108,0.00031936384,0.00053859386,0.0066960054,0.017978676,0.06372503,0.0051431446,0.034674477,0.857727],"study_design_scores_gemma":[0.0016586323,0.00358786,0.027853684,0.0016784349,0.0010586968,0.003652638,0.0067297686,0.62358576,0.13831592,0.040778387,0.14945827,0.0016418862],"about_ca_topic_score_codex":0.0022410695,"about_ca_topic_score_gemma":0.0025362717,"teacher_disagreement_score":0.009470913,"about_ca_system_score_codex":0.00044011994,"about_ca_system_score_gemma":0.00071408995,"threshold_uncertainty_score":0.038413405},"labels":[],"label_agreement":null},{"id":"W2516824109","doi":"10.20533/ijicr.2042.4655.2012.0031","title":"Fraud Reduction on EMV Payment Cards by the Implementation of Stringent Security Features","year":2012,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing Research","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"Shandong Academy of Sciences; Concordia University of Edmonton","keywords":"Payment; Reduction (mathematics); Computer security; Business; Computer science; Finance; Mathematics","score_opus":0.05608683404324238,"score_gpt":0.4253049894310142,"score_spread":0.3692181553877718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516824109","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":[0.9251356,0.0005405577,0.028169908,0.0014960691,0.00013206848,0.0003846705,0.000096021504,0.0003405755,0.04370442],"genre_scores_gemma":[0.9837272,0.00014841036,0.012214919,0.00019166971,0.000029736466,0.000027205579,0.00006312542,0.00001531644,0.003582329],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99051815,0.0033242586,0.0005219359,0.0004401028,0.004311624,0.0008838828],"domain_scores_gemma":[0.98295677,0.0035722135,0.004043178,0.0061121588,0.0029110643,0.00040458856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004385519,0.00046928306,0.00045404467,0.0019338026,0.001205053,0.003857807,0.0015817712,0.0015337762,0.0038285258],"category_scores_gemma":[0.02277698,0.00025915183,0.000607845,0.0014149211,0.0013934486,0.003153915,0.0042854743,0.0013911964,0.0016860362],"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":[0.0017155291,0.0016520787,0.15431793,0.00031077064,0.00015523403,0.0016903261,0.001633968,0.007580073,0.036434054,0.0730323,0.0053139497,0.7161639],"study_design_scores_gemma":[0.00036773,0.006886687,0.48939112,0.00096429884,0.00048168495,0.016026257,0.006377654,0.09775112,0.15583567,0.046411864,0.17906526,0.00044060778],"about_ca_topic_score_codex":0.00064748345,"about_ca_topic_score_gemma":0.00051632395,"teacher_disagreement_score":0.004385519,"about_ca_system_score_codex":0.0010464849,"about_ca_system_score_gemma":0.0013367935,"threshold_uncertainty_score":0.023193121},"labels":[],"label_agreement":null},{"id":"W7110003881","doi":"10.20533/ijicr.2042.4655.2025.0168","title":"A Novel Learning Maturity Model: Using Generative AI Technology","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing Research","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University","funders":"Algoma University","keywords":"Transformative learning; Rubric; Generative grammar; Leverage (statistics); Mindset; Generative model; Metacognition; Craft; Maturity (psychological); Educational technology","score_opus":0.08751537420679868,"score_gpt":0.42517821874841705,"score_spread":0.3376628445416184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110003881","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056225553,0.0019643353,0.78161174,0.016021362,0.00016019896,0.00053693587,0.0001868338,0.000689365,0.1426037],"genre_scores_gemma":[0.71368724,0.0015024027,0.27164978,0.00096176774,0.000090103626,0.00071735453,0.0002443664,0.0001242721,0.011022764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99606425,0.0020147094,0.00024700924,0.0005048404,0.00085818983,0.00031108467],"domain_scores_gemma":[0.9941729,0.0031206242,0.00052822527,0.0005503704,0.0010770025,0.000550809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055570323,0.0007224938,0.0002700424,0.0021413553,0.0011086717,0.0068230308,0.0013611361,0.0023490374,0.0030204009],"category_scores_gemma":[0.01048173,0.00035854874,0.0008864115,0.001150825,0.00404319,0.011974608,0.004463873,0.002981064,0.0011671731],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022079095,0.000112822185,0.003958768,0.00026557792,0.000019572657,0.00020662924,0.008581803,0.0040665017,0.0014255772,0.9031511,0.0020080733,0.07618155],"study_design_scores_gemma":[0.000031119158,0.00027338817,0.0027582515,0.0005816562,0.000043809196,0.0008249249,0.005444567,0.04867987,0.0026746066,0.84132403,0.0972788,0.00008491743],"about_ca_topic_score_codex":0.0016686781,"about_ca_topic_score_gemma":0.0015580931,"teacher_disagreement_score":0.0068230308,"about_ca_system_score_codex":0.0037780812,"about_ca_system_score_gemma":0.004216845,"threshold_uncertainty_score":0.029388726},"labels":[],"label_agreement":null}]}