{"meta":{"query_hash":"53b7471dafcf","filters":{"venue":"International Journal of Image Graphics and Signal Processing"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/53b7471dafcf","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Image+Graphics+and+Signal+Processing"},"results":[{"id":"W2171219185","doi":"10.5815/ijigsp.2012.11.05","title":"Efficient Algorithm for Railway Tracks Detection Using Satellite Imagery","year":2012,"lang":"en","type":"article","venue":"International Journal of Image Graphics and Signal Processing","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":15,"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":"Track (disk drive); Schedule; Satellite; Satellite imagery; Computer science; Satellite image; Quality (philosophy); Remote sensing; Geography; Engineering","score_opus":0.012760121528392662,"score_gpt":0.2640609657932626,"score_spread":0.2513008442648699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171219185","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":[0.008862338,0.00015930133,0.98626685,0.00004833207,0.000057245914,0.000118058786,0.00025855436,0.0031911125,0.0010382141],"genre_scores_gemma":[0.044366635,0.0002022837,0.94969743,0.000037954407,0.000031156884,0.00016126847,0.0014367892,0.00014921739,0.0039171805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994,0.000045146688,0.000046284797,0.0001655688,0.00027399606,0.000068975525],"domain_scores_gemma":[0.9996209,0.000054201333,0.00003894374,0.000058251448,0.00021175477,0.000015837064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043110925,0.00088651455,0.00088663324,0.0021156531,0.00063382124,0.0009893181,0.001296007,0.00096967525,0.004038964],"category_scores_gemma":[0.0008752004,0.0005608187,0.00096847,0.0018302822,0.00029163295,0.0009175088,0.0006570629,0.00074897305,0.004379454],"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":[0.0001507049,0.00011190447,0.0017987756,0.0002215941,0.00009339809,0.00016462686,0.00007495921,0.032354906,0.09077116,0.0024112933,0.009944523,0.861902],"study_design_scores_gemma":[0.00010847176,0.00022976309,0.008716022,0.000049561404,0.00011724959,0.00073919666,0.00014975628,0.8385904,0.11744584,0.0034864023,0.030286595,0.000080816586],"about_ca_topic_score_codex":0.0053135054,"about_ca_topic_score_gemma":0.00788686,"teacher_disagreement_score":0.0053135054,"about_ca_system_score_codex":0.00041537694,"about_ca_system_score_gemma":0.0011613104,"threshold_uncertainty_score":0.013511658},"labels":[],"label_agreement":null},{"id":"W2752756920","doi":"10.5815/ijigsp.2017.09.01","title":"Appearance-based Salient Features Extraction in Medical Images Using Sparse Contourlet-based Representation","year":2017,"lang":"en","type":"article","venue":"International Journal of Image Graphics and Signal Processing","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":2,"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 Alberta","funders":"U.S. National Library of Medicine","keywords":"Salient; Computer science; Artificial intelligence; Contourlet; Pattern recognition (psychology); Feature extraction; Representation (politics); Computer vision; Feature (linguistics); Sparse approximation; Matching (statistics); Noise (video); Region of interest; Image (mathematics); Mathematics","score_opus":0.0356045966273654,"score_gpt":0.36958272567811074,"score_spread":0.3339781290507453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752756920","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":[0.09702615,0.0010349024,0.8996447,0.00034634362,0.00006874113,0.00011059422,0.00027557384,0.0005843336,0.0009086763],"genre_scores_gemma":[0.578344,0.0016886782,0.41749048,0.00022055696,0.00024826257,0.00010421424,0.00062675355,0.00011507019,0.0011619738],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983394,0.000025902407,0.00001438294,0.00003076832,0.0000738076,0.000021147402],"domain_scores_gemma":[0.99947983,0.00019196515,0.00008697504,0.00005090297,0.00015378463,0.000036507477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045720147,0.00038093768,0.0005606231,0.0023143222,0.00014341349,0.00050430617,0.00038657253,0.0005540731,0.0007200255],"category_scores_gemma":[0.0019202717,0.00025040153,0.00041861366,0.0011985941,0.0002469975,0.0006402337,0.00042942294,0.000505838,0.00037781472],"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":[0.0006050255,0.00018971782,0.004492503,0.00052528665,0.000072321076,0.00092108734,0.00029032349,0.028079458,0.38134325,0.0034307968,0.005015175,0.575035],"study_design_scores_gemma":[0.000052317417,0.0003998904,0.021307677,0.000071268354,0.00012414755,0.0027541765,0.00015182905,0.87233853,0.089838445,0.007656716,0.0052471543,0.000057732985],"about_ca_topic_score_codex":0.0006707208,"about_ca_topic_score_gemma":0.0007010688,"teacher_disagreement_score":0.0023143222,"about_ca_system_score_codex":0.00016859279,"about_ca_system_score_gemma":0.00027688342,"threshold_uncertainty_score":0.002417922},"labels":[],"label_agreement":null},{"id":"W2774513401","doi":"10.5815/ijigsp.2017.12.04","title":"Traffic Video Enhancement based Vehicle Correct Tracked Methodology","year":2017,"lang":"en","type":"article","venue":"International Journal of Image Graphics and Signal Processing","topic":"Image Enhancement Techniques","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":"University of Regina","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Kalman filter; Background subtraction; Noise (video); Filter (signal processing); Median filter; Frame (networking); Video tracking; Video processing; Real-time computing; Pixel; Image processing; Image (mathematics); Telecommunications","score_opus":0.04400486062145907,"score_gpt":0.3387757092417898,"score_spread":0.2947708486203307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774513401","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":[0.030269101,0.00018237992,0.9666471,0.000039459544,0.000039636052,0.00010114135,0.000054649052,0.0006025438,0.00206393],"genre_scores_gemma":[0.48218882,0.0005608016,0.5106277,0.00006508127,0.000057101468,0.000118067896,0.00030633667,0.00007235188,0.0060037645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973565,0.000033427084,0.000010916917,0.000086225984,0.00009843552,0.000035299876],"domain_scores_gemma":[0.9997466,0.00003236268,0.000031797415,0.000028465827,0.0001467311,0.0000139714675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041598463,0.00044603428,0.00039936078,0.000950917,0.00022198269,0.0005360835,0.0005579146,0.0004497242,0.0013661106],"category_scores_gemma":[0.00069074216,0.00020286835,0.00041336793,0.0005092128,0.00021745774,0.00054615334,0.00037533196,0.00029544206,0.00044483555],"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":[0.00037071714,0.0001414159,0.004267079,0.00023561328,0.000067937755,0.00023828309,0.0001179498,0.09986156,0.12644313,0.0067081936,0.0013796523,0.7601685],"study_design_scores_gemma":[0.000027795977,0.00028173198,0.0038141957,0.00002362932,0.0000861124,0.00036319665,0.000061400715,0.91372067,0.07410811,0.0018007278,0.005683048,0.000029385184],"about_ca_topic_score_codex":0.0019378786,"about_ca_topic_score_gemma":0.0016650987,"teacher_disagreement_score":0.0019378786,"about_ca_system_score_codex":0.0003218806,"about_ca_system_score_gemma":0.0005486068,"threshold_uncertainty_score":0.004570067},"labels":[],"label_agreement":null},{"id":"W2911019237","doi":"10.5815/ijigsp.2019.01.03","title":"Recognizing Bangla Handwritten Numeral Utilizing Deep Long Short Term Memory","year":2019,"lang":"en","type":"article","venue":"International Journal of Image Graphics and Signal Processing","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Numeral system; Bengali; Artificial intelligence; Speech recognition; Benchmark (surveying); Scripting language; Task (project management); Artificial neural network; Pattern recognition (psychology); Natural language processing","score_opus":0.014827059975069182,"score_gpt":0.27187528704786,"score_spread":0.25704822707279085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911019237","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":[0.40127632,0.0041050236,0.5375808,0.00084352324,0.0007563865,0.00021817912,0.004211356,0.019151524,0.03185681],"genre_scores_gemma":[0.81512153,0.0012742286,0.1517345,0.00035601307,0.00008511838,0.00009180848,0.006148102,0.00016521556,0.025023507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976367,0.000026286041,0.00001816154,0.000087829714,0.00006776173,0.000036300033],"domain_scores_gemma":[0.999777,0.000044823533,0.000033627315,0.000041127365,0.000083581974,0.000019914978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002198802,0.00072585954,0.0004386653,0.0005371013,0.00022155048,0.0007474426,0.00061789044,0.00042050987,0.0038085936],"category_scores_gemma":[0.00067650527,0.00015996241,0.00041243035,0.0004907994,0.0001847444,0.0011089224,0.00050229195,0.0005874293,0.0026657183],"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":[0.00033207278,0.00020006325,0.0031282476,0.0002724215,0.000086408785,0.00034807835,0.00009903614,0.02200497,0.086902425,0.0012733494,0.011249343,0.87410355],"study_design_scores_gemma":[0.000035619258,0.0004372447,0.0090922965,0.00007182508,0.00009978927,0.0006121231,0.00018026421,0.8559412,0.11483301,0.0035177968,0.015118657,0.000060225037],"about_ca_topic_score_codex":0.0038749492,"about_ca_topic_score_gemma":0.009278071,"teacher_disagreement_score":0.0038749492,"about_ca_system_score_codex":0.00036169763,"about_ca_system_score_gemma":0.00045862756,"threshold_uncertainty_score":0.01274097},"labels":[],"label_agreement":null},{"id":"W4385876368","doi":"10.5815/ijigsp.2023.04.03","title":"An Experimental and Statistical Analysis to Assess impact of Regional Accent on Distress Non-linguistic Scream of Young Women","year":2023,"lang":"en","type":"article","venue":"International Journal of Image Graphics and Signal Processing","topic":"Speech and Audio Processing","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":"University of Windsor","funders":"","keywords":"Stress (linguistics); Distress; Psychology; Linguistics; Correlation; Speech recognition; Audiology; Computer science; Mathematics; Clinical psychology; Medicine","score_opus":0.028175923429334757,"score_gpt":0.36506213071888693,"score_spread":0.33688620728955215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385876368","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":[0.9948955,0.00005873209,0.002533534,0.000024346524,0.00004960622,0.0006558786,0.0001262057,0.000017377819,0.001638801],"genre_scores_gemma":[0.98140967,0.00017493512,0.011279786,0.000071583214,0.00005978201,0.0031798426,0.00023183206,0.000018441615,0.003574138],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9981851,0.0006820926,0.00020714257,0.00030425636,0.0004447181,0.00017666617],"domain_scores_gemma":[0.99462366,0.0036020558,0.00048565347,0.00047637138,0.00053434086,0.0002778753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001897815,0.00041028473,0.00035645528,0.00037808457,0.00047772497,0.00038281363,0.00032718727,0.00037218802,0.0056389184],"category_scores_gemma":[0.0059149116,0.0002369066,0.00038237643,0.0002801191,0.000688633,0.00025656747,0.0005050448,0.00049049885,0.00035998673],"study_design_candidate":"observational","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":[0.031710695,0.060195968,0.0332093,0.0014440619,0.0002137653,0.0009934115,0.0121938875,0.001232637,0.7435136,0.0024597233,0.00079543964,0.11203756],"study_design_scores_gemma":[0.0019469743,0.48463035,0.32133162,0.0001502077,0.0005994112,0.0007883565,0.0061284746,0.003918791,0.17168193,0.0018839198,0.0067835734,0.00015651621],"about_ca_topic_score_codex":0.0003912853,"about_ca_topic_score_gemma":0.0005483912,"teacher_disagreement_score":0.0056389184,"about_ca_system_score_codex":0.00021608247,"about_ca_system_score_gemma":0.0004858604,"threshold_uncertainty_score":0.018864095},"labels":[],"label_agreement":null}]}