{"meta":{"query_hash":"cc5029772568","filters":{"venue":"IEE Proceedings - Vision Image and Signal Processing"},"cohort_total":14,"direct_labels_cover":0,"predictions_cover":14,"exported":14,"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/cc5029772568","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEE+Proceedings+-+Vision+Image+and+Signal+Processing"},"results":[{"id":"W1966109143","doi":"10.1049/ip-vis:20010561","title":"Laplace spectrum for exponential decomposition and pole–zero estimation","year":2001,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Mathematics; Exponential function; Laplace transform; Mathematical analysis; Weighting; Complex plane; Fourier transform; Inverse Laplace transform; Exponential decay; Laplace distribution; Divergence (linguistics); Physics","score_opus":0.010842276703254865,"score_gpt":0.30397485090198423,"score_spread":0.29313257419872935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966109143","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":[0.808841,0.0006464618,0.18897447,0.00025578914,0.00009153244,0.00034034628,0.0000029255273,0.0005969254,0.00025057976],"genre_scores_gemma":[0.9475949,0.00012854105,0.051907554,0.000040754552,0.00022443708,0.000041036372,0.000005963641,0.000038921284,0.000017884626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900913,0.000003430066,0.00026115743,0.00027014923,0.00015661962,0.00029952984],"domain_scores_gemma":[0.99962264,0.000034584096,0.00007243413,0.000036346686,0.00009419199,0.00013981639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017861855,0.00019502692,0.00017911854,0.00014711644,0.0003180282,0.00034058886,0.000069190944,0.00010391428,0.0000064410647],"category_scores_gemma":[0.000022963512,0.00019245238,0.000024700423,0.0001441606,0.000051068484,0.0010619521,0.000030101204,0.0001477438,0.000001213942],"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":[0.00030081617,0.000021904234,0.00060262496,0.0019604962,0.000012635864,0.0000054035904,0.0009953054,0.00008689341,0.28290161,0.00015480345,0.0019022335,0.7110553],"study_design_scores_gemma":[0.0010575374,0.00042057747,0.0059223985,0.0009823792,0.000055862405,0.0002915716,0.00017396048,0.79175943,0.17694817,0.020885123,0.0008791111,0.0006238814],"about_ca_topic_score_codex":0.000007627141,"about_ca_topic_score_gemma":3.4662264e-7,"teacher_disagreement_score":0.7916725,"about_ca_system_score_codex":0.00005847241,"about_ca_system_score_gemma":0.000013398066,"threshold_uncertainty_score":0.784798},"labels":[],"label_agreement":null},{"id":"W1978545753","doi":"10.1049/ip-vis:20020190","title":"Neighbourhood-blocks motion vector estimation technique using pyramidal data structure","year":2002,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Advanced Vision and Imaging","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":"Concordia University","funders":"","keywords":"Motion vector; Motion estimation; Quarter-pixel motion; Motion (physics); Artificial intelligence; Computer science; Mathematics; Computer vision; Motion field; Algorithm","score_opus":0.02715559283864038,"score_gpt":0.29809083187404295,"score_spread":0.27093523903540256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978545753","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":[0.01153868,0.0007262937,0.98589224,0.00062359305,0.00007758178,0.0003136574,0.000007764903,0.00036730373,0.00045290307],"genre_scores_gemma":[0.64883065,0.000016967188,0.35080215,0.00018596994,0.00010561686,0.0000043910286,0.000006110554,0.000022736067,0.000025387244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758077,0.00001678992,0.0004449438,0.00098891,0.0005237445,0.00044485752],"domain_scores_gemma":[0.9987566,0.00003116112,0.00033021288,0.00031323437,0.0003411107,0.00022768622],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00034453534,0.00033050764,0.00027961994,0.0002769898,0.0007261626,0.0014910182,0.00089809636,0.00013425345,0.00009018937],"category_scores_gemma":[0.00015857261,0.00029783393,0.000037286438,0.0007108645,0.0001319525,0.008644124,0.00065695855,0.0004179867,0.0000060815446],"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":[0.000007853128,0.000051808787,0.000115085415,0.000180849,0.0000047925505,0.000010606991,0.0005320802,0.000067605724,0.3179168,0.00015091739,0.00039497166,0.6805666],"study_design_scores_gemma":[0.00033461142,0.00006314137,0.00017361632,0.00036088668,0.000016776245,0.00026725727,0.00007391368,0.9660853,0.028113639,0.0039642816,0.0001825418,0.0003640443],"about_ca_topic_score_codex":0.000008373843,"about_ca_topic_score_gemma":1.671894e-7,"teacher_disagreement_score":0.96601766,"about_ca_system_score_codex":0.00006167248,"about_ca_system_score_gemma":0.000041719777,"threshold_uncertainty_score":0.99994737},"labels":[],"label_agreement":null},{"id":"W2011695814","doi":"10.1049/ip-vis:20050289","title":"Unbiased homomorphic system and its application in reducing multiplicative noise","year":2006,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Multiplicative noise; Gaussian noise; Noise (video); Salt-and-pepper noise; Value noise; Homomorphic filtering; Algorithm; Filter (signal processing); Gradient noise; Computer science; Additive white Gaussian noise; Multiplicative function; Speckle noise; Homomorphic encryption; Mathematics; Median filter; Speckle pattern; Control theory (sociology); White noise; Noise reduction; Noise measurement; Noise floor; Statistics; Artificial intelligence; Computer vision; Telecommunications; Image (mathematics); Image processing; Image enhancement","score_opus":0.012565609479285918,"score_gpt":0.27107871102337255,"score_spread":0.25851310154408663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011695814","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":[0.34793976,0.0023787273,0.64644986,0.0005527291,0.000034375604,0.00058925786,0.0000018372531,0.0004207966,0.0016326512],"genre_scores_gemma":[0.95371443,0.00001832716,0.045883738,0.00010691076,0.00011475962,0.000065255976,0.0000017958882,0.000030933414,0.000063866115],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775743,0.0000484603,0.0005233425,0.0009003025,0.00035702524,0.00041340938],"domain_scores_gemma":[0.99889076,0.000107166255,0.00030447953,0.00012553067,0.00041761738,0.00015443657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010843035,0.00029020125,0.0003514395,0.0003535177,0.0004454871,0.000918691,0.0003622697,0.00012599667,0.0000010487571],"category_scores_gemma":[0.00007252018,0.00026973189,0.000038411607,0.00085915555,0.0001037021,0.0022884258,0.00019593658,0.0002916974,0.000009051051],"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":[0.000054158107,0.00008376176,0.00041560965,0.0006368161,0.000003725119,0.000024676638,0.0012386686,0.000027109154,0.8514784,0.0020060563,0.000099477234,0.14393157],"study_design_scores_gemma":[0.0015927478,0.00012168336,0.0038511567,0.0010255374,0.000022011582,0.00018679358,0.00037162995,0.8404187,0.14904991,0.0026956247,0.00013307175,0.0005311355],"about_ca_topic_score_codex":0.000093669274,"about_ca_topic_score_gemma":0.0000013276042,"teacher_disagreement_score":0.8403916,"about_ca_system_score_codex":0.00007721209,"about_ca_system_score_gemma":0.00007442876,"threshold_uncertainty_score":0.9999755},"labels":[],"label_agreement":null},{"id":"W2013963117","doi":"10.1049/ip-vis:20051183","title":"Novel embedded image coding algorithms based on wavelet difference reduction","year":2005,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":24,"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":"","keywords":"Algorithm; Computer science; Coding (social sciences); Wavelet; Algorithmic efficiency; Mathematics; Artificial intelligence; Statistics","score_opus":0.020992708264122795,"score_gpt":0.2962540663232436,"score_spread":0.2752613580591208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013963117","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":[0.012164571,0.00018801907,0.9811386,0.001963885,0.00013534099,0.00026867888,0.000003297418,0.0004037618,0.0037338405],"genre_scores_gemma":[0.48531944,0.000014255295,0.51298904,0.0008640591,0.00039466866,0.000018818613,0.0000029707496,0.000035583187,0.0003611672],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666214,0.000044334596,0.00059212797,0.0011586396,0.0008473148,0.0006954158],"domain_scores_gemma":[0.9983104,0.00012413561,0.00034737992,0.00023137045,0.00064363965,0.000343101],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012411473,0.00048572506,0.0004442818,0.00045446545,0.00088978396,0.0024320472,0.0007052884,0.00017436538,0.000033168366],"category_scores_gemma":[0.00019302119,0.00042806513,0.00011593488,0.0008022913,0.00021813964,0.0034050818,0.00021412072,0.0005678943,0.000030515275],"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":[0.00006470417,0.00013849407,0.0000037039872,0.00010909791,0.000004550458,0.000008930112,0.00075326575,0.000017296006,0.58538467,0.00022183445,0.00049412245,0.41279933],"study_design_scores_gemma":[0.0014292183,0.00030343456,0.00019474974,0.00053503003,0.000022841848,0.00015929129,0.00013808478,0.74747866,0.247783,0.00103387,0.0003757329,0.0005460865],"about_ca_topic_score_codex":0.000007465536,"about_ca_topic_score_gemma":1.4035862e-7,"teacher_disagreement_score":0.7474614,"about_ca_system_score_codex":0.000101877,"about_ca_system_score_gemma":0.00017504023,"threshold_uncertainty_score":0.99981713},"labels":[],"label_agreement":null},{"id":"W2028577761","doi":"10.1049/ip-vis:20040463","title":"Simple and practical cyclostationary beamforming algorithms","year":2004,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cyclostationary process; Algorithm; Computer science; Convergence (economics); SIGNAL (programming language); Simple (philosophy); Beamforming; Interference (communication); Telecommunications","score_opus":0.015446153105200151,"score_gpt":0.31767224202735356,"score_spread":0.3022260889221534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028577761","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":[0.051989377,0.00021596941,0.94125766,0.004182758,0.000015220253,0.00019719836,9.685464e-7,0.000372911,0.0017679624],"genre_scores_gemma":[0.60947406,0.000038202645,0.3893877,0.0010024341,0.00004920778,0.000013264798,0.0000020224602,0.000012957752,0.00002012603],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985677,0.000010267222,0.00029616195,0.0004925959,0.0003698494,0.00026345244],"domain_scores_gemma":[0.9992149,0.000057228066,0.00016977775,0.00006711443,0.00029916578,0.0001917968],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005258607,0.00018410171,0.00017075933,0.0001741467,0.00046123876,0.0012450813,0.00014926275,0.00009158496,0.000004406792],"category_scores_gemma":[0.00010336521,0.00016712048,0.000026165446,0.00033023645,0.00012448546,0.005292327,0.00022107325,0.00025851166,0.0000043288096],"study_design_candidate":"design_other","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":[0.00008827422,0.0004516905,0.0007861045,0.00064438616,0.000031522985,0.00012754131,0.021212302,0.000040598632,0.21243924,0.057567008,0.0025132329,0.7040981],"study_design_scores_gemma":[0.002718537,0.0013110553,0.0029132976,0.0007994417,0.00005437365,0.0020829947,0.0022915644,0.3492361,0.17102039,0.4576899,0.008251637,0.0016307065],"about_ca_topic_score_codex":0.00000863144,"about_ca_topic_score_gemma":3.1353176e-7,"teacher_disagreement_score":0.7024674,"about_ca_system_score_codex":0.00003157144,"about_ca_system_score_gemma":0.00015710368,"threshold_uncertainty_score":0.99979174},"labels":[],"label_agreement":null},{"id":"W2032556782","doi":"10.1049/ip-vis:20045109","title":"Techniques for automated reverse storyboarding","year":2005,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":29,"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 Guelph","funders":"","keywords":"Storyboard; Pluralistic walkthrough; Computer science; Shot (pellet); Variety (cybernetics); Representation (politics); Artificial intelligence; Motion (physics); Computer vision; Object (grammar); Computer graphics (images); Key (lock); Human–computer interaction; Multimedia; Usability","score_opus":0.01152962299780315,"score_gpt":0.3071226133002286,"score_spread":0.2955929903024255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032556782","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":[0.0032512676,0.00059043977,0.9888626,0.0023808924,0.000050213544,0.00036203812,0.0000015374036,0.002059188,0.002441878],"genre_scores_gemma":[0.36243573,0.000029270295,0.63578606,0.0013089796,0.00018790957,0.00003456648,0.0000010197522,0.00002566966,0.00019079451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822766,0.0000082628285,0.00036957124,0.000651603,0.00030663164,0.0004362725],"domain_scores_gemma":[0.9989345,0.000056837776,0.00022223369,0.000108176486,0.00046840237,0.00020982347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054046063,0.00026290902,0.00027085753,0.00025611164,0.000635253,0.0009422536,0.0004329242,0.000083757826,0.000008779959],"category_scores_gemma":[0.00012832732,0.00022799894,0.000074527146,0.00042895,0.000097711476,0.0052567674,0.000218101,0.00020044144,0.000009514884],"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":[0.000020452704,0.000037233876,0.00004725073,0.00014835439,0.000004071292,0.0000023580951,0.00076153816,0.0000031659204,0.19161531,0.00030035077,0.008659569,0.79840034],"study_design_scores_gemma":[0.00053033617,0.00014818959,0.000056775163,0.0004285793,0.000011073534,0.00005857856,0.0002198473,0.89787596,0.052124247,0.0018005092,0.046325084,0.0004208356],"about_ca_topic_score_codex":0.0000011307693,"about_ca_topic_score_gemma":1.0603071e-7,"teacher_disagreement_score":0.8978728,"about_ca_system_score_codex":0.00006346504,"about_ca_system_score_gemma":0.00006952166,"threshold_uncertainty_score":0.9297527},"labels":[],"label_agreement":null},{"id":"W2034793316","doi":"10.1049/ip-vis:20030236","title":"Generalisation of the Dirac-delta impulse extending Laplace and z transform domains","year":2003,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Mathematical functions and polynomials","field":"Mathematics","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":"Université de Montréal; Polytechnique Montréal","funders":"National Technical University of Athens","keywords":"Laplace transform; Mathematics; Mathematical analysis; Laplace transform applied to differential equations; Two-sided Laplace transform; Inverse Laplace transform; Convolution theorem; Convolution (computer science); Mellin transform; Fourier transform; Dirac delta function; Laplace–Stieltjes transform; Pure mathematics; Fractional Fourier transform; Fourier analysis","score_opus":0.0230526106585254,"score_gpt":0.29954400081798827,"score_spread":0.2764913901594629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034793316","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":[0.94651246,0.0006637521,0.038333252,0.0006377568,0.00006004868,0.00050284684,0.000009556177,0.000064250686,0.013216054],"genre_scores_gemma":[0.9805535,0.000056918994,0.018799169,0.00009118155,0.000059384674,0.00001545534,6.779179e-7,0.000026935595,0.00039676097],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987481,0.00002689396,0.00045689684,0.00026293367,0.00027635152,0.00022880672],"domain_scores_gemma":[0.9992156,0.00018946108,0.00023639435,0.00007857506,0.00016766264,0.00011231867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088806316,0.00019322826,0.0002878212,0.00008443039,0.00043041614,0.00022341679,0.00009690746,0.00010033965,0.00009301701],"category_scores_gemma":[0.00043479077,0.00012615568,0.00006645083,0.00024355795,0.00017079011,0.0005987261,0.000037979822,0.00017371256,7.449405e-7],"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":[0.00016760315,0.00042042733,0.0008281122,0.005748751,0.00007147345,0.0000034227714,0.012557874,0.0000025295226,0.70967084,0.14793406,0.005511201,0.11708366],"study_design_scores_gemma":[0.0024128235,0.0003711601,0.0003858933,0.0019867804,0.00040947393,0.00029523665,0.004281554,0.0140053965,0.3445185,0.6244711,0.0059687216,0.00089333794],"about_ca_topic_score_codex":0.0000041415183,"about_ca_topic_score_gemma":0.0000011806799,"teacher_disagreement_score":0.47653705,"about_ca_system_score_codex":0.00001880226,"about_ca_system_score_gemma":0.000042248877,"threshold_uncertainty_score":0.5144479},"labels":[],"label_agreement":null},{"id":"W2046415639","doi":"10.1049/ip-vis:20045192","title":"Similarity measures for efficient content-based image retrieval","year":2005,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Similarity (geometry); Euclidean distance; Pattern recognition (psychology); Similarity measure; Image retrieval; Mathematics; Content-based image retrieval; Distance measures; Artificial intelligence; Histogram; Latent Dirichlet allocation; Computer science; Image (mathematics); Topic model","score_opus":0.03393622792951042,"score_gpt":0.293427888195497,"score_spread":0.2594916602659866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046415639","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":[0.010098742,0.0006818866,0.98241806,0.0050265575,0.000041080242,0.0005704073,0.000007325634,0.00058850803,0.0005674288],"genre_scores_gemma":[0.7328376,0.000018786151,0.26575083,0.0010051945,0.00016760759,0.000036889418,0.0000035780115,0.000027496435,0.00015201575],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974991,0.000019491312,0.0005302417,0.0007939073,0.0006541036,0.0005031498],"domain_scores_gemma":[0.9976581,0.00009730978,0.00031707349,0.00016098047,0.0015000891,0.00026648882],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011846354,0.0003185497,0.00032938508,0.00022618637,0.00069133734,0.0015450212,0.0006136354,0.00014899252,0.000011801278],"category_scores_gemma":[0.00034342555,0.00027025925,0.00013284084,0.00054329436,0.00023437581,0.001762422,0.00014269081,0.00026768658,0.000009723758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015981593,0.00018362346,0.000033544475,0.00026478877,0.0000068640175,0.0000022143824,0.00031968972,0.0000011630381,0.7957264,0.00089271297,0.0006664857,0.20174268],"study_design_scores_gemma":[0.0007737575,0.00021736659,0.0001250586,0.00016677874,0.000020583384,0.000014260134,0.00010644638,0.27405503,0.7197736,0.0007963063,0.0036120734,0.00033877842],"about_ca_topic_score_codex":0.0000025077404,"about_ca_topic_score_gemma":1.8240787e-7,"teacher_disagreement_score":0.72273886,"about_ca_system_score_codex":0.00008569915,"about_ca_system_score_gemma":0.00019019897,"threshold_uncertainty_score":0.99997497},"labels":[],"label_agreement":null},{"id":"W2046616728","doi":"10.1049/ip-vis:20045260","title":"Adaptive filtering with decorrelation for coloured AR environments","year":2005,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Decorrelation; Computer science; Adaptive filter; Convergence (economics); Algorithm; Noise (video); Noise power; SIGNAL (programming language); Filter (signal processing); Power (physics); Control theory (sociology); Artificial intelligence","score_opus":0.00927471484322034,"score_gpt":0.23700055440545031,"score_spread":0.22772583956222997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046616728","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":[0.0901186,0.00027455913,0.90775234,0.000058883277,0.000017518068,0.00043183233,0.000009160073,0.00046582503,0.00087129395],"genre_scores_gemma":[0.73592114,0.000028938148,0.26366153,0.00004099286,0.00010737311,0.00009263681,0.0000050010585,0.000060091177,0.000082283026],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989128,0.0000027554509,0.00024701346,0.00033739928,0.00018358557,0.0003164532],"domain_scores_gemma":[0.99962735,0.000031131432,0.00009880798,0.000049872746,0.00007567505,0.0001171875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001322773,0.00026255377,0.00021136271,0.00011756149,0.00022384236,0.00016114174,0.00010515162,0.00007946769,0.000011283807],"category_scores_gemma":[0.000017609393,0.00024137566,0.000030931416,0.000116104144,0.0000805474,0.00158715,0.000042389576,0.00018150242,0.0000055164314],"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":[0.00025303065,0.000041497737,0.00011029797,0.00027705514,0.000028132954,0.0000032677049,0.0008754406,0.0032735115,0.6932709,0.00005871514,0.0006938143,0.30111435],"study_design_scores_gemma":[0.0010277722,0.00062200613,0.0007150137,0.0007764619,0.00005107667,0.00004764899,0.0002821787,0.72077274,0.26447275,0.0009924271,0.009550467,0.0006894378],"about_ca_topic_score_codex":5.0700913e-7,"about_ca_topic_score_gemma":4.750295e-7,"teacher_disagreement_score":0.71749926,"about_ca_system_score_codex":0.0001014614,"about_ca_system_score_gemma":0.0000119767055,"threshold_uncertainty_score":0.9843014},"labels":[],"label_agreement":null},{"id":"W2055556793","doi":"10.1049/ip-vis:20045062","title":"Robust image watermarking using a chirp detection-based technique","year":2005,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Watermark; Digital watermarking; Robustness (evolution); Chirp; Computer science; Parametric statistics; Detector; Artificial intelligence; Benchmark (surveying); Hough transform; Computer vision; Algorithm; Image (mathematics); Mathematics; Telecommunications; Optics; Statistics","score_opus":0.016198322754264877,"score_gpt":0.2625354919164231,"score_spread":0.24633716916215823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055556793","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":[0.026739892,0.00025108884,0.97026235,0.00037629085,0.00003682719,0.0003933567,0.0000014178968,0.001010913,0.00092784106],"genre_scores_gemma":[0.5733131,0.00001056809,0.4262226,0.00025167136,0.00012168398,0.000040965766,8.2852915e-7,0.000027347665,0.000011246511],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975428,0.000026750587,0.0005137884,0.0008631226,0.00043307192,0.0006204639],"domain_scores_gemma":[0.99881446,0.00004048253,0.00032711896,0.00018911301,0.00040444187,0.00022439891],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00070922397,0.00041751238,0.00033513358,0.0005579554,0.0010733801,0.0015246969,0.0006507388,0.00018095672,0.0000074058785],"category_scores_gemma":[0.00003706409,0.00037053172,0.00012143481,0.00078742113,0.00022013519,0.004946312,0.0002780947,0.00046038537,0.000002856536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036324287,0.00006287283,0.00012711658,0.00021726797,0.000006082854,0.000014312848,0.00040363337,0.00005277647,0.7969337,0.000058840655,0.000049048816,0.20203799],"study_design_scores_gemma":[0.00030059245,0.000111607354,0.000056065877,0.00052229885,0.00001570662,0.00013932599,0.000033133598,0.37728268,0.6179045,0.0023421056,0.00084793015,0.00044405507],"about_ca_topic_score_codex":0.000008563965,"about_ca_topic_score_gemma":7.4309014e-7,"teacher_disagreement_score":0.5465732,"about_ca_system_score_codex":0.00008226296,"about_ca_system_score_gemma":0.00008380852,"threshold_uncertainty_score":0.99987465},"labels":[],"label_agreement":null},{"id":"W2064405736","doi":"10.1049/ip-vis:20041238","title":"Frequency shift keyed narrowband interference rejection: optimal exponential weighting factor for the RLS algorithm","year":2005,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Narrowband; Adaptive filter; Frequency-shift keying; Control theory (sociology); Algorithm; Adjacent-channel interference; Mathematics; Intersymbol interference; Weighting; Interference (communication); Frequency offset; Filter (signal processing); Zero-forcing precoding; Computer science; Channel (broadcasting); Telecommunications; Acoustics; Physics; Precoding; Demodulation; Orthogonal frequency-division multiplexing","score_opus":0.013791336031774997,"score_gpt":0.2645771429898171,"score_spread":0.2507858069580421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064405736","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":[0.057486206,0.001363104,0.93913335,0.00025205934,0.00010983938,0.00043193987,0.000020352178,0.00079287385,0.00041028226],"genre_scores_gemma":[0.7740937,0.00007147533,0.22499663,0.00004562902,0.0005761811,0.000101136786,0.0000025674492,0.00006116758,0.000051525618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984723,0.000006088993,0.00041923946,0.0004358584,0.00023131803,0.0004352405],"domain_scores_gemma":[0.9993374,0.00008826489,0.00013409818,0.000084539955,0.00023230366,0.00012340902],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002413701,0.0003407428,0.0002616081,0.00013246817,0.000563804,0.0005405305,0.00027047493,0.00012246874,0.000059787326],"category_scores_gemma":[0.000060251856,0.00026971375,0.00007704326,0.00019730713,0.00013680906,0.0019158676,0.000086649066,0.0003757945,0.0000039317893],"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":[0.00004266428,0.000022931164,0.000030202142,0.00027000572,0.000024639106,0.0000022942675,0.0020410912,0.00012305808,0.4782,0.000046493165,0.000400433,0.5187962],"study_design_scores_gemma":[0.00071101234,0.0002985045,0.0002584116,0.0008451951,0.00005157514,0.000066844645,0.00050282717,0.5554819,0.43588766,0.0018656072,0.0032845575,0.0007458509],"about_ca_topic_score_codex":0.0000026127234,"about_ca_topic_score_gemma":7.793199e-7,"teacher_disagreement_score":0.71660745,"about_ca_system_score_codex":0.00008356501,"about_ca_system_score_gemma":0.000026275127,"threshold_uncertainty_score":0.9999755},"labels":[],"label_agreement":null},{"id":"W2093473251","doi":"10.1049/ip-vis:20020626","title":"Nonlinear filtering for phase image denoising","year":2002,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":16,"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 Toronto","funders":"","keywords":"Noise reduction; Noise (video); Filter (signal processing); Image (mathematics); Phase (matter); Nonlinear system; Artificial intelligence; Algorithm; Computer science; Median filter; Monte Carlo method; Computer vision; Pattern recognition (psychology); Mathematics; Image processing; Statistics; Physics","score_opus":0.030425265653614267,"score_gpt":0.3227734435597693,"score_spread":0.292348177906155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093473251","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":[0.019063886,0.0014179948,0.97462595,0.0010171701,0.0001165832,0.00039933226,0.0000059139716,0.0003874597,0.002965728],"genre_scores_gemma":[0.23854439,0.00006334823,0.75933564,0.0008830244,0.0004918744,0.000042308606,0.000003450491,0.0000684683,0.0005674794],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971322,0.000029242055,0.0006066201,0.0009836792,0.00048864627,0.00075963733],"domain_scores_gemma":[0.99833846,0.0001621166,0.0002993311,0.000189015,0.00066591596,0.0003451546],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010422088,0.000425726,0.00045582533,0.00033510875,0.0010273481,0.0031129278,0.00071528635,0.00014134722,0.00005005789],"category_scores_gemma":[0.00026593526,0.00039381487,0.00014563257,0.00061524275,0.00017662202,0.0050775222,0.0003057087,0.00033206935,0.000025529404],"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":[0.000046805962,0.00013379588,0.0000039920574,0.0002805978,0.000008272751,0.000024544874,0.0011622307,7.405993e-7,0.513862,0.00008928158,0.0014845849,0.48290312],"study_design_scores_gemma":[0.003741033,0.00073952647,0.000010456107,0.0005541173,0.00004986191,0.00026295928,0.00018359874,0.6929664,0.29011646,0.0036847726,0.006909459,0.0007813557],"about_ca_topic_score_codex":0.0000036322056,"about_ca_topic_score_gemma":1.11991305e-7,"teacher_disagreement_score":0.6929657,"about_ca_system_score_codex":0.00004547698,"about_ca_system_score_gemma":0.000048234982,"threshold_uncertainty_score":0.99985135},"labels":[],"label_agreement":null},{"id":"W2094171535","doi":"10.1049/ip-vis:20045231","title":"Optimum time–frequency distribution for detecting a discrete-time chirp signal in noise","year":2006,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence","funders":"","keywords":"Chirp; Algorithm; SIGNAL (programming language); Mathematics; Noise (video); Additive white Gaussian noise; Time–frequency analysis; Time domain; Gaussian noise; Gaussian; Discrete-time signal; Wigner distribution function; Detection theory; White noise; Discrete frequency domain; Computer science; Frequency domain; Statistics; Signal transfer function; Mathematical analysis; Artificial intelligence; Radar; Analog signal; Physics; Telecommunications; Detector; Optics; Computer vision","score_opus":0.004188422976163322,"score_gpt":0.25265339881344623,"score_spread":0.24846497583728291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094171535","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":[0.7803837,0.0014691547,0.20920336,0.00028175156,0.000042612166,0.0014755626,0.000101758334,0.0018197327,0.005222337],"genre_scores_gemma":[0.9775601,0.000017129973,0.021717811,0.00003177121,0.00024205176,0.0002098591,0.00007679247,0.00008800172,0.00005647795],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979824,0.000010104492,0.00061117153,0.0005194816,0.00028833764,0.00058851927],"domain_scores_gemma":[0.99933386,0.00009956628,0.00015193818,0.00007268075,0.00020747408,0.00013445715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005596544,0.0004040811,0.00041059518,0.00023826236,0.00030222756,0.00054703315,0.00020513173,0.00019215255,0.000046602872],"category_scores_gemma":[0.000113357026,0.00039546148,0.000092905946,0.00044288515,0.000086890584,0.0014684049,0.00006349063,0.00035298898,0.000011364846],"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":[0.000058305806,0.00008933318,0.0011695981,0.0008102263,0.000009382658,0.000008411108,0.00019652653,0.0002685225,0.9432632,0.00004024862,0.0029845964,0.051101685],"study_design_scores_gemma":[0.00093133847,0.00024076134,0.0012760318,0.0010012407,0.000046138666,0.000034058736,0.000050699877,0.76656175,0.22371909,0.005062143,0.00034079203,0.0007359354],"about_ca_topic_score_codex":0.00003139117,"about_ca_topic_score_gemma":0.0000017153837,"teacher_disagreement_score":0.7662932,"about_ca_system_score_codex":0.00014182959,"about_ca_system_score_gemma":0.000033873544,"threshold_uncertainty_score":0.99984974},"labels":[],"label_agreement":null},{"id":"W2534913894","doi":"10.1049/ip-vis:20030564","title":"Low-power data-dependent 8×8  DCT/IDCT for video compression","year":2003,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundo para o Desenvolvimento das Ciências e da Tecnologia","keywords":"Discrete cosine transform; Compression (physics); Data compression; Power (physics); Computer science; Computer vision; Materials science; Image (mathematics); Physics; Composite material","score_opus":0.03489356663537603,"score_gpt":0.31695968366902055,"score_spread":0.28206611703364454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2534913894","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":[0.015061213,0.0003563745,0.9774157,0.00032842538,0.00014945412,0.0005504615,0.000018923212,0.0002052359,0.0059142346],"genre_scores_gemma":[0.92722654,0.000011339125,0.07156169,0.0008370631,0.00006028403,0.000034891902,0.000018789127,0.000027869388,0.00022153757],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977366,0.00001706077,0.00044897845,0.0008761878,0.00048156877,0.00043963818],"domain_scores_gemma":[0.9988562,0.000074390795,0.0002472691,0.00022576987,0.00036933742,0.00022705224],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00071819837,0.000264522,0.0002457513,0.00015985174,0.00046639246,0.0024359997,0.00076314947,0.000060853898,0.00002965322],"category_scores_gemma":[0.0001311809,0.00022593884,0.000046059886,0.0002916505,0.00006712146,0.007375036,0.00034053216,0.00013938943,0.000014423458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012521264,0.00035468212,0.00021222125,0.0006970529,0.000029132834,0.000014987888,0.001828365,0.000003644452,0.5201848,0.0066607357,0.031092029,0.4387971],"study_design_scores_gemma":[0.006371134,0.0015755082,0.00046302573,0.0016616177,0.000094915835,0.00032819746,0.0015211621,0.262699,0.6055025,0.047322452,0.07030388,0.0021566493],"about_ca_topic_score_codex":0.0000019506044,"about_ca_topic_score_gemma":2.2714076e-7,"teacher_disagreement_score":0.91216534,"about_ca_system_score_codex":0.00002966017,"about_ca_system_score_gemma":0.000111706584,"threshold_uncertainty_score":0.9985996},"labels":[],"label_agreement":null}]}