{"meta":{"query_hash":"8cb548769300","filters":{"venue":"International Journal of Image and Graphics"},"cohort_total":35,"direct_labels_cover":0,"predictions_cover":35,"exported":35,"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/8cb548769300","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Image+and+Graphics"},"results":[{"id":"W1966039687","doi":"10.1142/s0219467806002288","title":"RAYSET: A TAXONOMY FOR IMAGE-BASED RENDERING","year":2006,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":8,"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 Alberta; Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Rendering (computer graphics); Image warping; Computer science; Image-based modeling and rendering; Artificial intelligence; Computer vision; Taxonomy (biology); Image-based lighting; Computer graphics (images)","score_opus":0.0173661186197663,"score_gpt":0.283817664984287,"score_spread":0.26645154636452073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966039687","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":[0.0009821239,0.009020451,0.96596545,0.00041616493,0.0004616249,0.00029380244,0.00039286623,0.0026779105,0.019789642],"genre_scores_gemma":[0.021007774,0.016255626,0.94259125,0.000612276,0.00066807057,0.00093257043,0.0014915315,0.0014891153,0.01495171],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960508,0.0007060191,0.0005656055,0.0005853251,0.0018665382,0.00022579997],"domain_scores_gemma":[0.9964204,0.0010642351,0.00030334882,0.0010556477,0.0009041083,0.00025233327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003256247,0.0022500493,0.0021561638,0.00792866,0.0019872596,0.009134692,0.005446637,0.0031288958,0.00962478],"category_scores_gemma":[0.006187569,0.0013813693,0.0029124538,0.00777516,0.0043442994,0.011389842,0.0039313273,0.006265783,0.006120135],"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":[0.00007865063,0.000060578124,0.0003837319,0.0012788014,0.000045815905,0.00034651288,0.001573329,0.005723917,0.004561459,0.68829274,0.01955333,0.27810115],"study_design_scores_gemma":[0.000021211845,0.00011242801,0.00037600045,0.0007663613,0.000046281784,0.0015987185,0.0004112304,0.03519014,0.0045752227,0.26502082,0.6917466,0.0001349652],"about_ca_topic_score_codex":0.0021409316,"about_ca_topic_score_gemma":0.001645434,"teacher_disagreement_score":0.00962478,"about_ca_system_score_codex":0.002008986,"about_ca_system_score_gemma":0.0016727025,"threshold_uncertainty_score":0.03219807},"labels":[],"label_agreement":null},{"id":"W2007581571","doi":"10.1142/s0219467806002343","title":"FREEFORM SURFACE RECONSTRUCTION FROM SCATTERED POINTS USING A DEFORMABLE SPHERICAL MAP","year":2006,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Rehabilitation; Western University","funders":"","keywords":"Surface (topology); Parametric surface; Process (computing); Surface reconstruction; Feature (linguistics); Computer science; Artificial intelligence; Parametric statistics; Coordinate system; Computer vision; Data point; Algorithm; Mathematics; Geometry","score_opus":0.006503877746537573,"score_gpt":0.2344470762656905,"score_spread":0.22794319851915293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007581571","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":[0.015050513,0.000025384325,0.98407906,0.000038313254,0.000007979729,0.000012889717,0.000027993388,0.0003288187,0.00042894777],"genre_scores_gemma":[0.32170346,0.00013460562,0.67622167,0.00003122232,0.0000150363685,0.000042003212,0.00022567734,0.0001178154,0.0015084471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997377,0.000034045508,0.000011003768,0.00004819886,0.0001517231,0.000017398697],"domain_scores_gemma":[0.9994764,0.00018758774,0.000045544457,0.00015043243,0.00011459947,0.000025523479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040186758,0.0004865906,0.000606754,0.0008516569,0.00024640546,0.00067163236,0.00055550033,0.00070634735,0.0013833178],"category_scores_gemma":[0.0017319262,0.00038674672,0.0007184983,0.0006859991,0.00052373676,0.0011006687,0.0011159409,0.000679692,0.0005209955],"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":[0.00016400637,0.00006031996,0.0015709948,0.00012013275,0.00005481734,0.00043367315,0.00037246192,0.56735665,0.07081941,0.016864695,0.0015071428,0.34067565],"study_design_scores_gemma":[0.000006350756,0.000029040948,0.00036299092,0.0000049625196,0.000004117759,0.00016060045,0.00004299237,0.98425275,0.009464625,0.004487036,0.00116805,0.00001649155],"about_ca_topic_score_codex":0.0014369662,"about_ca_topic_score_gemma":0.001433784,"teacher_disagreement_score":0.0014369662,"about_ca_system_score_codex":0.00028908948,"about_ca_system_score_gemma":0.00045096574,"threshold_uncertainty_score":0.0046275854},"labels":[],"label_agreement":null},{"id":"W2016411822","doi":"10.1142/s0219467801000141","title":"COMPUTING LINE INTERSECTIONS","year":2001,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","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":"University of Calgary","funders":"","keywords":"Intersection (aeronautics); Line (geometry); Algorithm; Point (geometry); Computation; Subroutine; Floating point; Computer science; Binary number; Mathematics; Arithmetic; Geometry","score_opus":0.015543154640741812,"score_gpt":0.2920000189865417,"score_spread":0.2764568643457999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016411822","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":[0.016087709,0.000173135,0.97833705,0.000048189948,0.00005059572,0.000080297476,0.00021943203,0.0018603508,0.0031433164],"genre_scores_gemma":[0.1686904,0.00029631378,0.82454634,0.0000393281,0.00004876765,0.00018509928,0.0020445087,0.0006636308,0.003485634],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974368,0.00024826705,0.00016042967,0.000567758,0.0013285357,0.00025831448],"domain_scores_gemma":[0.9976803,0.00084377103,0.0002517418,0.0003397219,0.00078876753,0.000095618576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007691163,0.0015185738,0.002202107,0.0035722298,0.0015308631,0.003219811,0.002834885,0.0014098905,0.016828377],"category_scores_gemma":[0.00760552,0.00083021435,0.0012517972,0.0031426656,0.00094567623,0.005095412,0.0038242927,0.0016799035,0.0061729397],"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":[0.00059864187,0.00018009568,0.004031241,0.00063246366,0.00015329807,0.0006252393,0.0006386174,0.16866153,0.019697772,0.08589624,0.010822366,0.70806247],"study_design_scores_gemma":[0.000103134174,0.00029381004,0.0011101831,0.000105658604,0.0000671329,0.00074027444,0.00057389267,0.81469786,0.047165144,0.101850115,0.033174902,0.000117891825],"about_ca_topic_score_codex":0.0016605502,"about_ca_topic_score_gemma":0.0013647551,"teacher_disagreement_score":0.016828377,"about_ca_system_score_codex":0.0009937576,"about_ca_system_score_gemma":0.0010518784,"threshold_uncertainty_score":0.056296587},"labels":[],"label_agreement":null},{"id":"W2021396313","doi":"10.1142/s0219467806002215","title":"A VR ENHANCED COLLABORATIVE SYSTEM FOR 3D CONFOCAL MICROSCOPIC IMAGE PROCESSING AND VISUALIZATION","year":2006,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","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 Toronto","funders":"Canadian Institutes of Health Research; Zhejiang University; Nanyang Technological University; Heart and Stroke Foundation of Canada","keywords":"Visualization; Confocal; Computer science; Image processing; Confocal microscopy; Feature (linguistics); Computer vision; Artificial intelligence; Computer graphics (images); Image (mathematics); Optics","score_opus":0.003488501445035841,"score_gpt":0.29113431378394605,"score_spread":0.28764581233891023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021396313","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":[0.005611151,0.0002875517,0.94142765,0.0001581508,0.00014023563,0.0005171505,0.0014957653,0.041967425,0.008394945],"genre_scores_gemma":[0.053251956,0.0003963256,0.92386943,0.0002846046,0.00009808928,0.0013697046,0.0034491175,0.0028144182,0.014466274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989976,0.0002041088,0.000062589155,0.0002095052,0.00042596358,0.00010015682],"domain_scores_gemma":[0.9990048,0.00025625541,0.000046166173,0.0002827072,0.0002343631,0.00017570169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012752449,0.00086955546,0.0009417614,0.0009438661,0.0004137409,0.0013367248,0.002593694,0.00126153,0.03937821],"category_scores_gemma":[0.0019432646,0.0006772049,0.0009523219,0.000668594,0.0003945034,0.00094170263,0.0027890918,0.0011286449,0.009571561],"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":[0.0015076979,0.00040372123,0.0013583167,0.00077970483,0.00021569419,0.0009828422,0.0006761032,0.017966902,0.32706687,0.01632617,0.13742836,0.49528766],"study_design_scores_gemma":[0.001024761,0.0009962539,0.0041831145,0.0001817698,0.00027193697,0.0031617251,0.00013462498,0.2767292,0.07595568,0.00883203,0.6279672,0.00056164665],"about_ca_topic_score_codex":0.0028565559,"about_ca_topic_score_gemma":0.0031121755,"teacher_disagreement_score":0.03937821,"about_ca_system_score_codex":0.0004828382,"about_ca_system_score_gemma":0.0011118323,"threshold_uncertainty_score":0.13173318},"labels":[],"label_agreement":null},{"id":"W2023528786","doi":"10.1142/s0219467804001415","title":"AUTOMATIC IMAGE REGISTRATION USING VIRTUAL CIRCLES","year":2004,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Medical Image Segmentation Techniques","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":"University of Waterloo","funders":"Universidade do Porto; University of Toronto; Purdue University; National Aeronautics and Space Administration","keywords":"Computer vision; Computer science; Artificial intelligence; Similarity (geometry); Enhanced Data Rates for GSM Evolution; Set (abstract data type); Pixel; Image registration; RADIUS; Image (mathematics); Virtual image; Computer graphics (images)","score_opus":0.016932326916434447,"score_gpt":0.31308276158002607,"score_spread":0.29615043466359164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023528786","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":[0.017461088,0.00029220743,0.979215,0.000054003372,0.000039016053,0.0000673479,0.00004314493,0.0018883547,0.0009397455],"genre_scores_gemma":[0.26759148,0.00034140612,0.7292092,0.000047891335,0.0000827676,0.00011989522,0.00025239165,0.0007026763,0.0016523631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99579537,0.0014290563,0.00023780053,0.0010153116,0.0012904545,0.0002320231],"domain_scores_gemma":[0.9954869,0.0015496773,0.00068917184,0.001575031,0.0005656746,0.00013358002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014926781,0.001003795,0.001594249,0.003174742,0.00072414393,0.0020986083,0.0018810037,0.0014210639,0.002861236],"category_scores_gemma":[0.0074769743,0.0008738809,0.0012608934,0.0025350896,0.0013231542,0.0035240916,0.0029164557,0.0011045313,0.0019275058],"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":[0.00084895187,0.00008944257,0.0012063998,0.0003210086,0.0001337428,0.00033538134,0.00050862966,0.051755276,0.14038816,0.016166624,0.002795982,0.7854504],"study_design_scores_gemma":[0.000084605424,0.00056433346,0.0030660457,0.000055170007,0.000091903596,0.0030607507,0.0002596736,0.66050464,0.2809372,0.01742435,0.03366419,0.0002871567],"about_ca_topic_score_codex":0.0006648739,"about_ca_topic_score_gemma":0.00053702854,"teacher_disagreement_score":0.003174742,"about_ca_system_score_codex":0.00039317357,"about_ca_system_score_gemma":0.00052361545,"threshold_uncertainty_score":0.009571791},"labels":[],"label_agreement":null},{"id":"W2026324054","doi":"10.1142/s0219467814500132","title":"Multibiometric System Using Level Set, Modified LBP and Random Forest","year":2014,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"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 Waterloo","funders":"","keywords":"Biometrics; Computer science; Local binary patterns; Artificial intelligence; Pattern recognition (psychology); Face (sociological concept); Iris recognition; Feature (linguistics); IRIS (biosensor); Set (abstract data type); Boundary (topology); Feature extraction; Random forest; Process (computing); Feature vector; Feature selection; Computer vision; Histogram; Image (mathematics); Mathematics","score_opus":0.0491726033402247,"score_gpt":0.2956917217472233,"score_spread":0.2465191184069986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026324054","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":[0.02929263,0.0004181301,0.9672375,0.00010411993,0.000077874465,0.000086463784,0.000081668884,0.0016060484,0.0010955831],"genre_scores_gemma":[0.33360052,0.00035785508,0.66270834,0.00014344712,0.00006383793,0.00016389145,0.0002883608,0.000113820235,0.0025599448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992084,0.00010563509,0.000042865053,0.00017975013,0.00039328678,0.0000700346],"domain_scores_gemma":[0.9994728,0.00008103498,0.00008557196,0.000102245256,0.00022321494,0.00003522547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009584448,0.00048872916,0.0008064489,0.0016172034,0.00041875598,0.00068233476,0.00095066335,0.0008246373,0.0018089148],"category_scores_gemma":[0.0016617198,0.00033679206,0.00076982024,0.0011672893,0.0003341607,0.001459348,0.00087074237,0.0006667271,0.00089338305],"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":[0.00035577605,0.00017110155,0.0017876821,0.00016940499,0.00008984342,0.00008147348,0.00009285665,0.023705266,0.12447751,0.0033299737,0.0020840946,0.84365493],"study_design_scores_gemma":[0.00004145167,0.00037605467,0.0053090886,0.00003272624,0.00007373045,0.0006089733,0.000046064157,0.891154,0.09322681,0.002910267,0.0061213593,0.00009951604],"about_ca_topic_score_codex":0.0014320858,"about_ca_topic_score_gemma":0.0014933201,"teacher_disagreement_score":0.0018089148,"about_ca_system_score_codex":0.00048986037,"about_ca_system_score_gemma":0.00048470075,"threshold_uncertainty_score":0.006051421},"labels":[],"label_agreement":null},{"id":"W2030907689","doi":"10.1142/s0219467807002829","title":"CAPTURING AND RE-USING ARTISTIC STYLES WITH REVERSE SUBDIVISION-BASED MULTIRESOLUTION METHODS","year":2007,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Computer Graphics and Visualization 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":"University of Calgary","funders":"","keywords":"Subdivision; Rendering (computer graphics); Computer science; Multiresolution analysis; Classification of discontinuities; Artificial intelligence; Non-photorealistic rendering; Computer vision; Computer graphics (images); Point (geometry); Mathematics; Geometry; Geography","score_opus":0.025073644438012525,"score_gpt":0.36772747964705627,"score_spread":0.34265383520904374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030907689","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":[0.012600134,0.0000916147,0.9856694,0.000034499746,0.000014366006,0.000029966752,0.000019022014,0.0006855231,0.00085547],"genre_scores_gemma":[0.13340159,0.00023808733,0.8646167,0.000033349836,0.000023516423,0.00005608467,0.000104773564,0.00037249713,0.0011532971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992804,0.00010912222,0.000047030735,0.0001078221,0.00039260872,0.000063024294],"domain_scores_gemma":[0.99877125,0.00034876796,0.00010646513,0.0005589104,0.00016458143,0.00005007673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007238235,0.0006984257,0.00070211856,0.0013991325,0.00032073035,0.0015832591,0.0009851481,0.0005933899,0.001986213],"category_scores_gemma":[0.0033965332,0.00062572764,0.0011166251,0.0010300856,0.0005359775,0.0015439902,0.001405812,0.0011608531,0.00093313336],"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":[0.00015144987,0.00007578509,0.00096863887,0.00024802543,0.000081200524,0.0002755277,0.0010158535,0.08536519,0.26304093,0.013682653,0.0018490498,0.63324565],"study_design_scores_gemma":[0.000047870326,0.00010129916,0.0015813413,0.000047580128,0.000052601896,0.00078119786,0.00014373696,0.86303866,0.10735657,0.010284328,0.016482953,0.00008189011],"about_ca_topic_score_codex":0.00096731953,"about_ca_topic_score_gemma":0.001316438,"teacher_disagreement_score":0.001986213,"about_ca_system_score_codex":0.00029440198,"about_ca_system_score_gemma":0.0003100537,"threshold_uncertainty_score":0.006644547},"labels":[],"label_agreement":null},{"id":"W2042318956","doi":"10.1142/s0219467805001902","title":"PALMPRINTS: A COOPERATIVE CO-EVOLUTIONARY ALGORITHM FOR CLUSTERING HAND IMAGES","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"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","funders":"Concordia University; Royal Society of Canada","keywords":"Cluster analysis; Fitness function; Genetic algorithm; Computer science; Curse of dimensionality; Artificial intelligence; Function (biology); Image (mathematics); Evolutionary algorithm; Correlation clustering; Space (punctuation); Pattern recognition (psychology); Data mining; Algorithm; Machine learning; Biology","score_opus":0.015548977745141402,"score_gpt":0.2969492022614951,"score_spread":0.2814002245163537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042318956","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":[0.015471599,0.0001376187,0.9822336,0.000066636654,0.00002698122,0.00005232107,0.000019448118,0.00043710537,0.0015547548],"genre_scores_gemma":[0.22175589,0.00017768823,0.7726627,0.000109580644,0.000024949262,0.00026866328,0.00008454018,0.00016517159,0.004750821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953926,0.00013130037,0.000017842644,0.000096930766,0.00016331405,0.000051356365],"domain_scores_gemma":[0.9995763,0.00019614304,0.000035339483,0.000057933397,0.000106619555,0.000027622258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010280737,0.0007932973,0.0009063131,0.0009586009,0.0005538392,0.0006612587,0.0013677934,0.0014349116,0.0016487326],"category_scores_gemma":[0.0019027204,0.00044528148,0.0006550365,0.0009312295,0.00071033824,0.0007191196,0.0010405321,0.00086085964,0.0005137784],"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":[0.00008671565,0.000084772095,0.0010795977,0.00008235244,0.00010712743,0.00011740607,0.0001784241,0.74257195,0.013088716,0.009393292,0.0015271861,0.2316824],"study_design_scores_gemma":[0.000013141591,0.000036195757,0.00017956027,0.000006572089,0.000010695731,0.00006025516,0.000015016114,0.9943223,0.0020255316,0.0017950216,0.0015268731,0.000008885238],"about_ca_topic_score_codex":0.004104439,"about_ca_topic_score_gemma":0.0038596292,"teacher_disagreement_score":0.004104439,"about_ca_system_score_codex":0.00060106546,"about_ca_system_score_gemma":0.0008797315,"threshold_uncertainty_score":0.008161068},"labels":[],"label_agreement":null},{"id":"W2043223183","doi":"10.1142/s0219467805001938","title":"STATE OF THE ART IN THE REALISTIC MODELING OF PLANT VENATION SYSTEMS","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":31,"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 Waterloo; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer graphics; Image (mathematics); Set (abstract data type); Artificial intelligence; Algorithm; Computer vision; Computer graphics (images); Theoretical computer science","score_opus":0.014582154764371526,"score_gpt":0.22540001247065383,"score_spread":0.2108178577062823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043223183","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010944226,0.007825265,0.96344614,0.0009257839,0.00011971379,0.00007697562,0.00021516286,0.0008013414,0.015645409],"genre_scores_gemma":[0.34508762,0.045554344,0.5978634,0.00041793907,0.000513854,0.00038824885,0.0012831083,0.00068692834,0.008204573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990376,0.00024661605,0.00008351857,0.00012408748,0.00044535525,0.00006276477],"domain_scores_gemma":[0.9974293,0.0016517671,0.00016658615,0.00040899348,0.0002770618,0.00006626029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015057621,0.0008198391,0.0013470151,0.0009173297,0.0005936824,0.003777778,0.0023740614,0.002312635,0.0045494665],"category_scores_gemma":[0.004981414,0.00088230614,0.0016585152,0.0011443447,0.0014565865,0.0027947023,0.0013342516,0.0016463952,0.0018899589],"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":[0.000082475824,0.000060070517,0.00082663354,0.0009608011,0.000045951096,0.00015780024,0.0002790177,0.7628962,0.01190898,0.12993553,0.002214299,0.09063234],"study_design_scores_gemma":[0.000013930135,0.000054723427,0.00024022645,0.00012752334,0.000027339347,0.00016355746,0.000044379864,0.93292916,0.005013952,0.029619059,0.03172267,0.000043556276],"about_ca_topic_score_codex":0.0023217858,"about_ca_topic_score_gemma":0.0012253299,"teacher_disagreement_score":0.0045494665,"about_ca_system_score_codex":0.001113992,"about_ca_system_score_gemma":0.00081746833,"threshold_uncertainty_score":0.01521951},"labels":[],"label_agreement":null},{"id":"W2045996208","doi":"10.1142/s0219467811004214","title":"IMPROVEMENT OF COLORIZATION REALISM VIA THE STRUCTURE TENSOR","year":2011,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Computer Graphics and Visualization 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":"Simon Fraser University","funders":"Engineering and Physical Sciences Research Council","keywords":"Grayscale; Computer science; Artificial intelligence; Contrast (vision); Computer vision; Image (mathematics); Structure tensor; Color image; Heuristic; Set (abstract data type); Projection (relational algebra); Algorithm; Image processing","score_opus":0.014657977655409512,"score_gpt":0.2713228532795176,"score_spread":0.2566648756241081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045996208","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":[0.03686618,0.0000821376,0.9558182,0.00021775663,0.000059230704,0.000041395386,0.000036078232,0.0010664634,0.005812512],"genre_scores_gemma":[0.4665692,0.00024230046,0.52855027,0.00016845769,0.000042543816,0.00005817108,0.000080582664,0.00062477536,0.003663675],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968743,0.000075993536,0.000011376979,0.00006916276,0.000119323726,0.000036869602],"domain_scores_gemma":[0.9992792,0.00020608756,0.00008255959,0.00022370229,0.00014955016,0.00005895673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061080477,0.0009534859,0.0004110219,0.0005392269,0.0004350338,0.0015594511,0.00067005027,0.00039666137,0.00343234],"category_scores_gemma":[0.0024767453,0.00047284947,0.0005546868,0.00033858314,0.001118239,0.0016755124,0.0016589289,0.0014963854,0.0005869069],"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":[0.00031823406,0.00010918868,0.001953509,0.00030584287,0.0000736739,0.00022529814,0.0006563009,0.19871257,0.37481147,0.1861033,0.0053153434,0.2314153],"study_design_scores_gemma":[0.000050077466,0.00015647172,0.0011206054,0.000034135606,0.00003847248,0.00028458127,0.000117761156,0.8704589,0.07930884,0.034785893,0.013572465,0.00007183672],"about_ca_topic_score_codex":0.0013800005,"about_ca_topic_score_gemma":0.0016820632,"teacher_disagreement_score":0.00343234,"about_ca_system_score_codex":0.0006837613,"about_ca_system_score_gemma":0.00062466937,"threshold_uncertainty_score":0.011482298},"labels":[],"label_agreement":null},{"id":"W2049086696","doi":"10.1142/s0219467803001044","title":"PERSONALIZED FACE ANIMATION IN SHOWFACE SYSTEM","year":2003,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Multimedia Communication and Technology","field":"Social Sciences","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":"University of British Columbia","funders":"","keywords":"Computer science; Animation; Parsing; Component (thermodynamics); Multimedia; Computer facial animation; Face (sociological concept); Presentation (obstetrics); Visual modeling; Human–computer interaction; Computer animation; Computer graphics (images); Artificial intelligence; Programming language; Unified Modeling Language","score_opus":0.023523551788536254,"score_gpt":0.33977879203314665,"score_spread":0.3162552402446104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049086696","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":[0.058825705,0.00021557845,0.9012594,0.00044674,0.00013664385,0.00020525607,0.00024707447,0.02378663,0.014876914],"genre_scores_gemma":[0.5167996,0.0005356658,0.44134772,0.00036881113,0.00022289732,0.00035120355,0.0012315356,0.0014763155,0.037666287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968207,0.00007467155,0.000013694237,0.00006124264,0.00012143621,0.000046870682],"domain_scores_gemma":[0.99966633,0.00011059577,0.000018105042,0.00008913968,0.000058386908,0.00005740775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044640983,0.00039211372,0.00058142043,0.00038225274,0.0005424897,0.00083780737,0.0009371655,0.0011981147,0.0129683595],"category_scores_gemma":[0.001299826,0.00028786558,0.00038114816,0.00018525543,0.00024583688,0.0017108758,0.0012738415,0.00075269124,0.0027225346],"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":[0.0015300821,0.00037085733,0.002509443,0.00026578142,0.00006383543,0.0021421812,0.0011145511,0.039992426,0.23080395,0.033873547,0.049656667,0.63767666],"study_design_scores_gemma":[0.00014138507,0.00038968225,0.0030359384,0.000043539345,0.00006778194,0.0021080915,0.00021339266,0.7269737,0.1493349,0.013830484,0.103739694,0.0001214718],"about_ca_topic_score_codex":0.0010713235,"about_ca_topic_score_gemma":0.0009913051,"teacher_disagreement_score":0.0129683595,"about_ca_system_score_codex":0.00031190907,"about_ca_system_score_gemma":0.0002360595,"threshold_uncertainty_score":0.04338348},"labels":[],"label_agreement":null},{"id":"W2050424943","doi":"10.1142/s0219467812500040","title":"CONTOUR INTERPOLATION USING LEVEL-SET ANALYSIS","year":2012,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"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":"Interpolation (computer graphics); Computer vision; Artificial intelligence; Curvature; Computer science; Visualization; Tracing; Contour line; Set (abstract data type); Boundary (topology); Trajectory; Motion (physics); Mathematics; Geometry; Geography; Cartography","score_opus":0.05400067830766171,"score_gpt":0.36270449708233565,"score_spread":0.30870381877467395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050424943","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":[0.001676626,0.000025699366,0.99767,0.000018334009,0.00000859465,0.00002183541,0.000012381926,0.00030362382,0.00026291338],"genre_scores_gemma":[0.09865544,0.00011443764,0.89970446,0.000038886763,0.000022064145,0.00011458486,0.00014715976,0.00028916093,0.0009137715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992803,0.000113507456,0.000042814045,0.00012628181,0.00038306502,0.000054132863],"domain_scores_gemma":[0.9990369,0.0004172864,0.00010145699,0.0002049634,0.00019662493,0.000042824973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013341162,0.00079788786,0.0009945281,0.0019788728,0.00048740953,0.0015459197,0.0021458932,0.001343923,0.0029680142],"category_scores_gemma":[0.0027982844,0.0009775064,0.0018234765,0.0011717587,0.0007919295,0.0015336352,0.0014614501,0.0019600312,0.0011889113],"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":[0.00014539255,0.00009251104,0.0010160909,0.0002165537,0.00009658455,0.0001401619,0.0002520336,0.5824287,0.03767632,0.043521933,0.0017322819,0.33268148],"study_design_scores_gemma":[0.0000059860536,0.000023062948,0.000080601996,0.000008429582,0.0000076278925,0.000028097158,0.0000049497803,0.9858251,0.005609471,0.0068886224,0.0015073185,0.000010783286],"about_ca_topic_score_codex":0.0016550102,"about_ca_topic_score_gemma":0.0014998927,"teacher_disagreement_score":0.0029680142,"about_ca_system_score_codex":0.0008913637,"about_ca_system_score_gemma":0.0010017095,"threshold_uncertainty_score":0.009929001},"labels":[],"label_agreement":null},{"id":"W2054255883","doi":"10.1142/s0219467805001859","title":"SCCI-HYBRID METHODS FOR 2D CURVE TRACING","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","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":"University of Calgary","funders":"","keywords":"Continuation; Iterated function; Function (biology); Mathematics; Interval (graph theory); Interval arithmetic; Curve fitting; Jacobian curve; Tripling-oriented Doche–Icart–Kohel curve; Subdivision; Computation; Applied mathematics; Computer science; Algorithm; Mathematical optimization; Mathematical analysis; Elliptic curve; Statistics; Combinatorics","score_opus":0.011940384537903704,"score_gpt":0.35234242354527207,"score_spread":0.3404020390073684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054255883","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":[0.0025073907,0.00021423545,0.9915501,0.000049290415,0.000058366975,0.00003172552,0.00007365221,0.0012072859,0.0043079196],"genre_scores_gemma":[0.1337515,0.00052198285,0.8509939,0.00009983188,0.00008796272,0.0002760256,0.00043413098,0.00084319996,0.0129915895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955255,0.000060639442,0.000015722188,0.000052675518,0.00028948393,0.000029003395],"domain_scores_gemma":[0.99947935,0.00018762516,0.000040593426,0.00012253947,0.00013589168,0.00003391663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005045001,0.00065412873,0.00051970175,0.0011191954,0.00039282924,0.0014209151,0.0013425804,0.0011345072,0.0071724867],"category_scores_gemma":[0.0011881153,0.000411842,0.0008313186,0.0009546694,0.0008053032,0.0008666385,0.0017867534,0.0012062553,0.0024672518],"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":[0.00012895274,0.0000768338,0.0014071603,0.00046214773,0.00007111904,0.00023614352,0.00061634893,0.42178217,0.034038614,0.17826834,0.009884498,0.35302764],"study_design_scores_gemma":[0.000013165792,0.000024156825,0.00015230631,0.000029375355,0.0000046750847,0.000120244076,0.000022469401,0.96124476,0.0040408974,0.014798592,0.019527337,0.000022011847],"about_ca_topic_score_codex":0.0022179098,"about_ca_topic_score_gemma":0.001868575,"teacher_disagreement_score":0.0071724867,"about_ca_system_score_codex":0.00074662996,"about_ca_system_score_gemma":0.0007280216,"threshold_uncertainty_score":0.023994386},"labels":[],"label_agreement":null},{"id":"W2059657882","doi":"10.1142/s0219467806002379","title":"REINFORCED CONTRAST ADAPTATION","year":2006,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":20,"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 Waterloo","funders":"","keywords":"Contrast (vision); Computer science; Artificial intelligence; Observer (physics); Image (mathematics); Reinforcement learning; Histogram; Computer vision; Ideal (ethics); Adaptation (eye); Transformation (genetics); Point (geometry); Algorithm; Mathematics","score_opus":0.00847445703570523,"score_gpt":0.26395502716419766,"score_spread":0.25548057012849246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059657882","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":[0.04816072,0.00019882758,0.94136435,0.000120968456,0.00008695342,0.00011125543,0.000026617045,0.0010659829,0.008864407],"genre_scores_gemma":[0.81132317,0.0001550826,0.18121555,0.0001609749,0.00003620939,0.00013178244,0.00004084026,0.00010403832,0.006832371],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996643,0.00007263402,0.000012772462,0.00008503261,0.00012964921,0.000035668367],"domain_scores_gemma":[0.99907434,0.00038533373,0.000095093055,0.000154602,0.00025077228,0.000039818267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053773314,0.0004624065,0.00036270916,0.0002310436,0.00017532028,0.00046765603,0.00087213115,0.0005661767,0.0030549602],"category_scores_gemma":[0.0028272686,0.0001996642,0.00030505474,0.00013683591,0.0003963267,0.00061700627,0.0007812764,0.0006721834,0.00069850555],"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":[0.0004039634,0.00046047676,0.0025585569,0.00019220203,0.00014730332,0.00029458036,0.00018043349,0.23552686,0.1752981,0.018194903,0.0032598788,0.5634828],"study_design_scores_gemma":[0.000055817065,0.00023810568,0.0016785397,0.000014882585,0.00003228522,0.00023945743,0.00001739866,0.9476893,0.037573755,0.006432208,0.0059991665,0.000029102444],"about_ca_topic_score_codex":0.0008601836,"about_ca_topic_score_gemma":0.0011113997,"teacher_disagreement_score":0.0030549602,"about_ca_system_score_codex":0.00037221218,"about_ca_system_score_gemma":0.00026444148,"threshold_uncertainty_score":0.010219812},"labels":[],"label_agreement":null},{"id":"W2062904527","doi":"10.1142/s0219467804001506","title":"INTER-FRAME PREDICTION OF MEDICAL AND VIDEOPHONE SEQUENCES: A DEFORMABLE TRIANGLE-BASED APPROACH","year":2004,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"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 Laval","funders":"","keywords":"Inter frame; Polygon mesh; Triangle mesh; Motion compensation; Computer vision; Quadtree; Artificial intelligence; Affine transformation; Computer science; Coding (social sciences); Motion estimation; Mathematics; Algorithm; Frame (networking); Computer graphics (images); Reference frame; Geometry","score_opus":0.01579229856697924,"score_gpt":0.28294464005189923,"score_spread":0.26715234148492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062904527","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":[0.021219091,0.00018555947,0.97727805,0.00007559456,0.000036729863,0.000035062658,0.000050132963,0.00047045512,0.00064926484],"genre_scores_gemma":[0.36962855,0.00064925983,0.625058,0.00008892577,0.000054924738,0.0000676449,0.00038239476,0.00020534829,0.003864895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978524,0.000029062956,0.000014186486,0.000051415023,0.00009669791,0.000023421771],"domain_scores_gemma":[0.9996635,0.00010214994,0.000040520838,0.00005592697,0.00011161487,0.000026274032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002857821,0.00054467435,0.00058221037,0.00082428736,0.00019972667,0.00038802173,0.00091590214,0.00073451194,0.0019543448],"category_scores_gemma":[0.0010691436,0.00026387282,0.000487334,0.00076431676,0.00021847023,0.0005984805,0.00030124554,0.0005834433,0.00064536656],"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":[0.00036156297,0.00009911371,0.00071020436,0.00008386656,0.000043894535,0.00023244825,0.00007978926,0.30362818,0.105112545,0.003140391,0.0013582574,0.5851498],"study_design_scores_gemma":[0.000003471816,0.000026025886,0.00015404358,0.0000020543207,0.000004554138,0.000034958994,0.00000639807,0.994054,0.004986937,0.00033673097,0.00038619468,0.0000047305443],"about_ca_topic_score_codex":0.0065653725,"about_ca_topic_score_gemma":0.0066450834,"teacher_disagreement_score":0.0065653725,"about_ca_system_score_codex":0.0003178859,"about_ca_system_score_gemma":0.00034977158,"threshold_uncertainty_score":0.013054311},"labels":[],"label_agreement":null},{"id":"W2063694967","doi":"10.1142/s0219467805001732","title":"SHOCK FILTER-BASED DIFFUSION FIELDS — APPLICATION TO GRAYSCALE CHARACTER IMAGE PROCESSING","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Grayscale; Character (mathematics); Computer science; Artificial intelligence; Image processing; Computer vision; Anisotropic diffusion; Image (mathematics); Diffusion; Noise (video); Spurious relationship; Partial differential equation; Algorithm; Pattern recognition (psychology); Mathematics; Geometry; Mathematical analysis","score_opus":0.004887411021763783,"score_gpt":0.25875230632738855,"score_spread":0.2538648953056248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063694967","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":[0.03397991,0.0003138679,0.9632313,0.00023961873,0.00005995961,0.00004071916,0.000026072192,0.00027774792,0.0018307971],"genre_scores_gemma":[0.53311056,0.0013072124,0.45496824,0.00013431274,0.00007693676,0.00006863675,0.0000857131,0.00008919137,0.010159187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993694,0.000013407295,0.000004551656,0.000010305466,0.00002825755,0.0000065816503],"domain_scores_gemma":[0.9998167,0.00007592325,0.000020008902,0.000013706744,0.00005995493,0.000013761794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028020403,0.0002962696,0.00028332448,0.00058966555,0.00020858138,0.00052884396,0.0002571628,0.0005647498,0.0012874882],"category_scores_gemma":[0.00075894914,0.00013620456,0.00026145243,0.00051514193,0.00035146993,0.0005646365,0.00032212463,0.00037394284,0.00020575398],"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":[0.0002629461,0.00009209437,0.0013126145,0.00026149393,0.000042535197,0.0005271872,0.00031690174,0.3000756,0.22125581,0.08250021,0.0019324992,0.3914202],"study_design_scores_gemma":[0.0000136531235,0.00004882657,0.00037752045,0.000010388205,0.000007101782,0.00016468756,0.000024922005,0.9663064,0.023702683,0.0062470753,0.003082202,0.000014603072],"about_ca_topic_score_codex":0.0012716079,"about_ca_topic_score_gemma":0.00078661507,"teacher_disagreement_score":0.0012874882,"about_ca_system_score_codex":0.00040836318,"about_ca_system_score_gemma":0.00026376237,"threshold_uncertainty_score":0.0043070912},"labels":[],"label_agreement":null},{"id":"W2068570931","doi":"10.1142/s0219467802000871","title":"FUZZY RELATION CALCULUS IN THE COMPRESSION AND DECOMPRESSION OF FUZZY RELATIONS","year":2002,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Algebra and Logic","field":"Computer Science","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":"University of Alberta","funders":"","keywords":"Fuzzy logic; Mathematics; Calculus (dental); Fuzzy associative matrix; Relation (database); Fuzzy subalgebra; Fuzzy number; Algebra over a field; Norm (philosophy); Computer science; Algorithm; Fuzzy set; Artificial intelligence; Pure mathematics; Data mining; Epistemology","score_opus":0.017047535592422235,"score_gpt":0.2717731045827834,"score_spread":0.2547255689903612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068570931","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":[0.009867059,0.012785611,0.9589928,0.0008355019,0.00047154876,0.00009348614,0.000085762425,0.00017933674,0.016688952],"genre_scores_gemma":[0.24001214,0.022404391,0.7169098,0.0008845341,0.002129225,0.00024321325,0.00019429393,0.00013384953,0.017088583],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99880123,0.00029726356,0.00010713384,0.00019565088,0.0005130179,0.00008557566],"domain_scores_gemma":[0.9994286,0.0003121494,0.000045458575,0.00008453177,0.00010234726,0.000026919215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00163198,0.00061935856,0.00084275997,0.0013532792,0.00085265114,0.002146851,0.00082863687,0.00112142,0.002896146],"category_scores_gemma":[0.002737835,0.0003115073,0.0010154848,0.0027196775,0.00344311,0.0040378436,0.0010902258,0.0023876533,0.0008974044],"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":[0.00004680568,0.000027142993,0.00011120351,0.00022625578,0.000015610085,0.00023589001,0.00037842288,0.014955715,0.005273049,0.92138845,0.0020789732,0.05526247],"study_design_scores_gemma":[0.000027890235,0.00011775984,0.00027296031,0.00010578235,0.00003364378,0.000526999,0.00012013696,0.088025935,0.0066464962,0.86177963,0.042278726,0.00006406559],"about_ca_topic_score_codex":0.0015158483,"about_ca_topic_score_gemma":0.0009421554,"teacher_disagreement_score":0.002896146,"about_ca_system_score_codex":0.0010951285,"about_ca_system_score_gemma":0.00069727516,"threshold_uncertainty_score":0.009688616},"labels":[],"label_agreement":null},{"id":"W2071347067","doi":"10.1142/s0219467801000359","title":"TWO ALGORITHMS FOR COMPUTING THE EUCLIDEAN DISTANCE TRANSFORM","year":2001,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Digital Image Processing Techniques","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":"University of Calgary","funders":"","keywords":"Algorithm; Euclidean distance; Pixel; Feature (linguistics); Metric (unit); Euclidean geometry; Computer science; Distance transform; Binary number; Image processing; Mathematics; Image (mathematics); Artificial intelligence; Arithmetic","score_opus":0.020549973665594027,"score_gpt":0.3258167522426751,"score_spread":0.30526677857708107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071347067","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":[0.0012817183,0.00023024944,0.99635065,0.000117739626,0.00013077309,0.000047684207,0.000060928392,0.0006120488,0.0011682627],"genre_scores_gemma":[0.018191997,0.00030485814,0.97852176,0.00006195813,0.00011866461,0.00021474418,0.00036790236,0.00014447026,0.0020735897],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965479,0.00043225623,0.00031615837,0.000735899,0.0016260439,0.00034189908],"domain_scores_gemma":[0.9972638,0.00068622676,0.00020686082,0.0006275388,0.0010800378,0.00013549221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014289676,0.0016184634,0.0017017922,0.0028654863,0.0008759029,0.0026352382,0.0028464196,0.0020109946,0.0073892144],"category_scores_gemma":[0.010242166,0.00087686273,0.0013252818,0.002786666,0.0013127145,0.0052482756,0.0027295726,0.0026702604,0.0049754474],"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":[0.00033187403,0.00018038585,0.000533864,0.00024816472,0.000057231104,0.00010045348,0.00011992784,0.038397074,0.009990929,0.097313106,0.012903712,0.83982325],"study_design_scores_gemma":[0.00041167127,0.00050850376,0.0013868595,0.0000937291,0.00009048118,0.0014240975,0.00021766912,0.71103674,0.047699425,0.16286369,0.07402254,0.00024464354],"about_ca_topic_score_codex":0.0024288255,"about_ca_topic_score_gemma":0.0034487564,"teacher_disagreement_score":0.0073892144,"about_ca_system_score_codex":0.0011766029,"about_ca_system_score_gemma":0.0023221206,"threshold_uncertainty_score":0.024719417},"labels":[],"label_agreement":null},{"id":"W2073105412","doi":"10.1142/s0219467805001823","title":"PROJECTIVE VOLUME RENDERING BY EXCLUDING OCCLUDED VOXELS","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":4,"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":"Institute of Software, Chinese Academy of Sciences; Chinese University of Hong Kong; University of British Columbia; National Natural Science Foundation of China; University of Alberta","keywords":"Voxel; Volume rendering; Rendering (computer graphics); Computer science; Pixel; Ray casting; Artificial intelligence; Computer vision; 3D rendering; Computer graphics (images); Ray tracing (physics); Optics; Physics","score_opus":0.016072731475303692,"score_gpt":0.30847397143667793,"score_spread":0.29240123996137424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073105412","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":[0.007131778,0.00010723892,0.9891495,0.000052614847,0.00003469923,0.000033414257,0.000028517452,0.001800675,0.0016615242],"genre_scores_gemma":[0.16214886,0.0005764186,0.8309525,0.000073974545,0.00014008292,0.00017407122,0.00030385828,0.0010997789,0.0045303195],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916255,0.00016512771,0.00003831187,0.00010138192,0.00044730073,0.000085295804],"domain_scores_gemma":[0.99877805,0.0006310962,0.00010129411,0.00025931737,0.00017538511,0.00005480231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080248376,0.0013957169,0.001220117,0.0006811899,0.00080906565,0.0020588895,0.0012269784,0.0005205374,0.0051907026],"category_scores_gemma":[0.0034546265,0.00070285326,0.0010076094,0.00087705324,0.001097902,0.0017863698,0.002099616,0.0014416449,0.0014034176],"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":[0.0004095915,0.0001465121,0.0022420788,0.00049256,0.000119363904,0.0007662217,0.0014569429,0.20885867,0.12620986,0.07603313,0.008985394,0.57427967],"study_design_scores_gemma":[0.000089267785,0.0002517106,0.0012855144,0.00003907501,0.00009562297,0.0009854417,0.0001701031,0.8187871,0.10788882,0.030131789,0.04018125,0.00009432416],"about_ca_topic_score_codex":0.002087547,"about_ca_topic_score_gemma":0.0020898192,"teacher_disagreement_score":0.0051907026,"about_ca_system_score_codex":0.00032439968,"about_ca_system_score_gemma":0.0009504755,"threshold_uncertainty_score":0.017364621},"labels":[],"label_agreement":null},{"id":"W2073187647","doi":"10.1142/s0219467801000372","title":"RECOGNIZING SYMBOLS BY DRAWING THEM","year":2001,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":0,"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 Calgary","funders":"","keywords":"Computer science; Symbol (formal); Orientation (vector space); Representation (politics); Artificial intelligence; Template matching; Matching (statistics); Variation (astronomy); Scale (ratio); Position (finance); Line (geometry); Pattern recognition (psychology); Computer vision; Arithmetic; Image (mathematics); Mathematics; Geometry; Statistics; Cartography","score_opus":0.020080088068414218,"score_gpt":0.28299166203880105,"score_spread":0.26291157397038684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073187647","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":[0.022103922,0.0006570407,0.9428402,0.00024466653,0.0004560257,0.00026986055,0.00062290643,0.009962279,0.022843031],"genre_scores_gemma":[0.094011955,0.0013465339,0.8869142,0.0002179189,0.00011513171,0.0001665546,0.0019596473,0.000986167,0.014281812],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992465,0.00013730049,0.000063908585,0.0002756183,0.00020991423,0.00006674945],"domain_scores_gemma":[0.99898475,0.00034741324,0.000064911124,0.0003472397,0.00020863737,0.0000470879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043213338,0.0014958889,0.0012238951,0.0014680456,0.0006203156,0.003576105,0.001852555,0.0015584974,0.02855141],"category_scores_gemma":[0.0046206736,0.0005241748,0.0009303848,0.0020760412,0.00094868045,0.0035970379,0.0014857018,0.0011734193,0.016479447],"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":[0.00018459538,0.000045306555,0.0008721702,0.0003962658,0.000033254702,0.00040604846,0.00047708355,0.008967671,0.051850427,0.020888751,0.011286887,0.9045914],"study_design_scores_gemma":[0.00009178704,0.00057535106,0.0045190263,0.0004305874,0.00026162836,0.0037860535,0.0016344243,0.3045762,0.21928138,0.07615268,0.3884352,0.0002557455],"about_ca_topic_score_codex":0.0016077085,"about_ca_topic_score_gemma":0.0017218507,"teacher_disagreement_score":0.02855141,"about_ca_system_score_codex":0.00039354578,"about_ca_system_score_gemma":0.00059804687,"threshold_uncertainty_score":0.09551394},"labels":[],"label_agreement":null},{"id":"W2076065916","doi":"10.1142/s0219467808003064","title":"HARDWARE-ACCELERATED PARALLEL-SPLIT SHADOW MAPS","year":2008,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":10,"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":"University of Alberta","keywords":"Rendering (computer graphics); Computer science; Anti-aliasing; Shadow mapping; Computer vision; Computer graphics (images); Shadow (psychology); Aliasing; Artificial intelligence; Frustum; Real-time rendering; Mathematics; Computer hardware; Geometry","score_opus":0.031377978824135296,"score_gpt":0.30172638606340074,"score_spread":0.27034840723926545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076065916","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":[0.102425486,0.00016697346,0.88917005,0.00006303692,0.000048752005,0.00008312727,0.000088349465,0.0032691068,0.0046850014],"genre_scores_gemma":[0.5775715,0.00016271067,0.4166999,0.000029065333,0.000027748672,0.00005870793,0.00017816175,0.0002970107,0.0049751536],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986315,0.00000914283,0.000004259051,0.000015994861,0.000084052735,0.000023333789],"domain_scores_gemma":[0.999762,0.00005706704,0.00001936002,0.00007707759,0.00006418747,0.00002029722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000119516604,0.00053950533,0.00043696142,0.0003591844,0.00028100287,0.0004383856,0.0005329489,0.00017215131,0.00488205],"category_scores_gemma":[0.000530662,0.00024395378,0.0003161646,0.00031900738,0.0002593043,0.0004546935,0.0007974523,0.0004038165,0.0006031909],"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":[0.00057248026,0.0001328766,0.0011335888,0.00016518397,0.00003874142,0.0002678688,0.00036491107,0.100702845,0.31416577,0.010199368,0.0038603195,0.568396],"study_design_scores_gemma":[0.00007199803,0.0001618853,0.0010970669,0.000007903467,0.000016996826,0.0003845126,0.00006743205,0.78082734,0.20318697,0.0042867814,0.009863439,0.000027685139],"about_ca_topic_score_codex":0.0015372407,"about_ca_topic_score_gemma":0.0019447457,"teacher_disagreement_score":0.00488205,"about_ca_system_score_codex":0.00029638744,"about_ca_system_score_gemma":0.0004659096,"threshold_uncertainty_score":0.01633203},"labels":[],"label_agreement":null},{"id":"W2076707216","doi":"10.1142/s0219467814500211","title":"Fingerprint Liveness Detection Using Multiple Static Features and Random Forests","year":2014,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","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 Natural Science Foundation of China; University of Calgary","keywords":"Liveness; Fingerprint (computing); Computer science; Artificial intelligence; Pattern recognition (psychology); Spoofing attack; Random forest; Classifier (UML); Fingerprint recognition; Biometrics; Fingerprint Verification Competition; Noise (video); Image (mathematics)","score_opus":0.013567145675621898,"score_gpt":0.2714141691401842,"score_spread":0.2578470234645623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076707216","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":[0.11542628,0.0008635426,0.8810254,0.00007706315,0.00004442575,0.000070906826,0.00012996516,0.0016565068,0.00070602534],"genre_scores_gemma":[0.7603247,0.00045162742,0.23729882,0.000049842143,0.00008714936,0.000059821752,0.00045900626,0.00008158383,0.0011873294],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878556,0.00019504681,0.00007478459,0.00030663464,0.0004551964,0.00018287949],"domain_scores_gemma":[0.9985544,0.00059378473,0.00024045713,0.00015887307,0.00038700947,0.000065481414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015872229,0.0008952611,0.0012513238,0.003310421,0.00044831497,0.0006677843,0.0009342386,0.0009826907,0.0006010566],"category_scores_gemma":[0.0024699618,0.00038312774,0.0011955485,0.001399703,0.00038757882,0.0012638512,0.00059049175,0.0005583639,0.00051639584],"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":[0.00058322604,0.00028704762,0.008347678,0.00014952182,0.00019456827,0.00033729113,0.00008476801,0.09205163,0.07157638,0.0011487885,0.0014651244,0.823774],"study_design_scores_gemma":[0.0000119791075,0.00013680225,0.0060235146,0.000015826135,0.000065838554,0.00041248152,0.000029705154,0.9710231,0.020331282,0.0011657949,0.000748816,0.000035027133],"about_ca_topic_score_codex":0.0029626302,"about_ca_topic_score_gemma":0.0028598893,"teacher_disagreement_score":0.003310421,"about_ca_system_score_codex":0.00030488,"about_ca_system_score_gemma":0.00040187276,"threshold_uncertainty_score":0.008394182},"labels":[],"label_agreement":null},{"id":"W2090978288","doi":"10.1142/s021946780600232x","title":"MTAR: A ROBUST 2D SHAPE REPRESENTATION","year":2006,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","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":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Maxima and minima; Affine transformation; Smoothing; Representation (politics); Wavelet transform; Curvature; Mathematics; Scale (ratio); Boundary (topology); Computer science; Artificial intelligence; Algorithm; Pattern recognition (psychology); Wavelet; Computer vision; Geometry; Mathematical analysis","score_opus":0.018925224918540633,"score_gpt":0.2817611683597778,"score_spread":0.2628359434412372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090978288","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":[0.004308699,0.0003197579,0.991527,0.000073277784,0.00006706562,0.000058488087,0.0004428869,0.0021075965,0.001095174],"genre_scores_gemma":[0.13186625,0.0011623754,0.85851604,0.000270122,0.0001888127,0.00024164905,0.0031462575,0.0008547991,0.0037537792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920636,0.000105957944,0.000044389726,0.00016229933,0.00042382488,0.000057208996],"domain_scores_gemma":[0.9994511,0.00011185475,0.00009164831,0.00016160398,0.00015987881,0.000023969258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046940745,0.0009136578,0.0009710385,0.0027352753,0.0002749785,0.0014813704,0.0015167356,0.0010406986,0.004879162],"category_scores_gemma":[0.0018005694,0.0005423855,0.0013091878,0.0024947715,0.00036941178,0.002078272,0.0011568641,0.0009020018,0.0038958201],"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":[0.0002450703,0.00005672856,0.0007609672,0.00022850449,0.00006799057,0.00021470802,0.00008491733,0.0440748,0.050141167,0.012138636,0.010848324,0.88113827],"study_design_scores_gemma":[0.000024545214,0.00017855414,0.0015333322,0.000047925016,0.000044286804,0.0011907169,0.000074372336,0.9360072,0.026810696,0.007874508,0.026118062,0.0000957778],"about_ca_topic_score_codex":0.0011984024,"about_ca_topic_score_gemma":0.001137241,"teacher_disagreement_score":0.004879162,"about_ca_system_score_codex":0.0003075313,"about_ca_system_score_gemma":0.0004418706,"threshold_uncertainty_score":0.016322494},"labels":[],"label_agreement":null},{"id":"W2092919318","doi":"10.1142/s0219467804001269","title":"SOLUTION OF CAMERA REGISTRATION PROBLEM VIA 3D-2D PARAMETERIZED MODEL MATCHING FOR ON-ROAD NAVIGATION","year":2004,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"GLS Industries (Canada)","funders":"","keywords":"Computer vision; Parameterized complexity; Artificial intelligence; Computer science; Position (finance); Matching (statistics); Algorithm; Mathematics","score_opus":0.013828776029222839,"score_gpt":0.25676744318211053,"score_spread":0.2429386671528877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092919318","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":[0.0045697284,0.00001869413,0.9949104,0.000020768948,0.000006656805,0.000009154592,0.000007907429,0.00016938557,0.00028726153],"genre_scores_gemma":[0.26486856,0.00014465115,0.73242193,0.000045483866,0.000028953578,0.000081724305,0.00019285703,0.00017025712,0.002045497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993506,0.00016665572,0.000028865514,0.00020025404,0.00019628005,0.000057292877],"domain_scores_gemma":[0.9995658,0.000097967335,0.000082575876,0.00014330137,0.000089331035,0.000020992135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004511046,0.00064574677,0.00089867454,0.0005966497,0.00053148257,0.0006915422,0.0011328673,0.0011449973,0.0018449042],"category_scores_gemma":[0.0016978557,0.0005297248,0.0009896165,0.0006890154,0.00049360335,0.0014923527,0.0012014855,0.0009500658,0.0007241654],"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":[0.000117752366,0.000117341406,0.0013347438,0.00011340951,0.00011714271,0.000213576,0.0002660909,0.51239604,0.034630213,0.026189413,0.002594273,0.42191005],"study_design_scores_gemma":[0.000010921333,0.00004187765,0.00033124772,0.000003882211,0.000013014963,0.00010757937,0.000042801057,0.9879792,0.005336191,0.0042603672,0.0018580871,0.000014962241],"about_ca_topic_score_codex":0.004628677,"about_ca_topic_score_gemma":0.0042033456,"teacher_disagreement_score":0.004628677,"about_ca_system_score_codex":0.00051740266,"about_ca_system_score_gemma":0.0011293972,"threshold_uncertainty_score":0.009203494},"labels":[],"label_agreement":null},{"id":"W2094283905","doi":"10.1142/s0219467809003514","title":"IMAGE WATERMARKING BASED ON THE HESSENBERG TRANSFORM","year":2009,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","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":"École de Technologie Supérieure","funders":"","keywords":"Digital watermarking; Watermark; Robustness (evolution); Embedding; Computer science; Artificial intelligence; Domain (mathematical analysis); Computer vision; Transformation (genetics); Frequency domain; Image (mathematics); Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.009864747731416344,"score_gpt":0.26122483495997856,"score_spread":0.25136008722856223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094283905","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":[0.037343744,0.0018212388,0.9466918,0.000458573,0.00023622157,0.000066086446,0.00010915116,0.0008241288,0.012448989],"genre_scores_gemma":[0.62854135,0.0045105265,0.34203494,0.00016678836,0.00033456867,0.000116129864,0.00027433084,0.00013588174,0.023885496],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980015,0.000042451677,0.000010265316,0.0000309444,0.00010125901,0.000014877957],"domain_scores_gemma":[0.99983525,0.000067110064,0.000026733851,0.000024586252,0.000038871935,0.0000074147433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018685435,0.0002605752,0.00034697313,0.00074282807,0.00016928029,0.00067206676,0.00028952913,0.0004949279,0.0020353806],"category_scores_gemma":[0.00061628834,0.00013248238,0.00030304908,0.0007222198,0.00063925295,0.0010418693,0.0003643168,0.000527407,0.00094909914],"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":[0.00036120834,0.000056068187,0.0005225644,0.00034103077,0.000056496297,0.00045796076,0.00014062772,0.084054075,0.305564,0.20411916,0.00414402,0.40018272],"study_design_scores_gemma":[0.00004723147,0.0003333867,0.0009811423,0.000033338114,0.000030602434,0.0011761374,0.00004964357,0.75963265,0.1506333,0.055708632,0.03127465,0.00009935331],"about_ca_topic_score_codex":0.00026664027,"about_ca_topic_score_gemma":0.00020884763,"teacher_disagreement_score":0.0020353806,"about_ca_system_score_codex":0.00023970239,"about_ca_system_score_gemma":0.000314758,"threshold_uncertainty_score":0.006808996},"labels":[],"label_agreement":null},{"id":"W2108373964","doi":"10.1142/s0219467809003551","title":"PECSI: A PRACTICAL PERCEPTUALLY-ENHANCED COMPRESSION FRAMEWORK FOR STILL IMAGES","year":2009,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"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 Waterloo","funders":"","keywords":"Upsampling; Image compression; Computer science; Artificial intelligence; Computer vision; Quantization (signal processing); Image quality; Compression (physics); Data compression; Perception; Data compression ratio; Texture compression; Encoding (memory); Image (mathematics); Image processing","score_opus":0.03312077197588812,"score_gpt":0.39446966265699707,"score_spread":0.3613488906811089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108373964","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":[0.0034264887,0.00031581018,0.99355716,0.00004782584,0.00004391392,0.00007952064,0.000056490633,0.0010033469,0.0014694172],"genre_scores_gemma":[0.09364891,0.00080225075,0.900062,0.00011529061,0.000109303604,0.0001373859,0.00039817655,0.00022193619,0.0045047067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996087,0.00003988081,0.0000139574895,0.000038362217,0.00027561307,0.000023523802],"domain_scores_gemma":[0.9996568,0.00009495088,0.00002961033,0.00006405817,0.00013121404,0.000023450117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005535956,0.00079066795,0.00042124387,0.0006367123,0.00024248911,0.00057331927,0.0010376569,0.0005015274,0.0031425355],"category_scores_gemma":[0.0011932577,0.00021383922,0.0004069077,0.00036175127,0.00040590702,0.00090801547,0.0008333332,0.001038264,0.0008639503],"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":[0.0003756416,0.00012283646,0.00042387407,0.00041705623,0.00007274697,0.0005167304,0.00014343845,0.07723706,0.23608634,0.03855121,0.008747496,0.63730556],"study_design_scores_gemma":[0.000050757528,0.00050358876,0.0008768313,0.00005558151,0.000041509542,0.0017147078,0.00004211556,0.8380683,0.11275716,0.008491216,0.037329905,0.00006840589],"about_ca_topic_score_codex":0.00088337576,"about_ca_topic_score_gemma":0.001310657,"teacher_disagreement_score":0.0031425355,"about_ca_system_score_codex":0.00024912157,"about_ca_system_score_gemma":0.0004506703,"threshold_uncertainty_score":0.010512829},"labels":[],"label_agreement":null},{"id":"W2125230560","doi":"10.1142/s021946780200069x","title":"SHAPE-BASED IMAGE RETRIEVAL APPLIED TO TRADEMARK IMAGES","year":2002,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":22,"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 Waterloo","funders":"","keywords":"Computer science; Image retrieval; Invariant (physics); Key (lock); Feature (linguistics); Set (abstract data type); Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Visual Word; Moment (physics); Information retrieval; Mathematics","score_opus":0.018038912021458426,"score_gpt":0.26387654970746394,"score_spread":0.2458376376860055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125230560","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":[0.067718826,0.0013607455,0.9227559,0.00022956997,0.00012998974,0.00021871235,0.00024004729,0.0041842144,0.0031619985],"genre_scores_gemma":[0.3613329,0.00081855495,0.63392687,0.00014307589,0.00013168833,0.00012761442,0.000513802,0.00018085503,0.0028246045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992029,0.00010125953,0.00005274072,0.00013599567,0.000444978,0.00006206593],"domain_scores_gemma":[0.99925095,0.00014905941,0.00010226205,0.00022791227,0.00023570018,0.000034067314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005066719,0.0005206356,0.001329118,0.0026591101,0.00035037045,0.001094518,0.0014198952,0.0008377191,0.0022369854],"category_scores_gemma":[0.002395828,0.00032433873,0.0008379386,0.0026816705,0.0005125656,0.0016034708,0.00065142184,0.00041552447,0.0016287931],"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":[0.00038771026,0.00012839603,0.0015877286,0.00019915328,0.000093538736,0.0002208786,0.00008169759,0.019590281,0.13000531,0.0042153737,0.0035781534,0.83991176],"study_design_scores_gemma":[0.00007751796,0.00041603856,0.0062052347,0.000021937649,0.00012148714,0.0021260937,0.00007885185,0.80338407,0.17174158,0.004853643,0.010870173,0.00010329248],"about_ca_topic_score_codex":0.0022143945,"about_ca_topic_score_gemma":0.0022027704,"teacher_disagreement_score":0.0026591101,"about_ca_system_score_codex":0.00056954974,"about_ca_system_score_gemma":0.000419118,"threshold_uncertainty_score":0.0074834824},"labels":[],"label_agreement":null},{"id":"W2128237130","doi":"10.1142/s0219467803000919","title":"VIEWS OR POINTS OF VIEW ON IMAGES","year":2003,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science; Object (grammar); GRASP; Representation (politics); Class (philosophy); Interpretation (philosophy); Image (mathematics); Exploit; Logical data model; Semantics (computer science); Point (geometry); Data model (GIS); Object model; Information retrieval; Artificial intelligence; Data modeling; Database; Programming language","score_opus":0.02259649952180506,"score_gpt":0.29857524039866734,"score_spread":0.2759787408768623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128237130","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015779028,0.009977717,0.83790284,0.0023270117,0.00154406,0.0003501134,0.0021091918,0.0031763946,0.12683372],"genre_scores_gemma":[0.2989752,0.021122672,0.6036691,0.0022491796,0.0024453294,0.00055502733,0.003760375,0.0015246007,0.065698415],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983658,0.00032706658,0.00011845148,0.000360803,0.00068999856,0.00013784503],"domain_scores_gemma":[0.9976948,0.00067651016,0.00019920722,0.00078276166,0.0005124969,0.00013415722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013847366,0.0010753936,0.00076862023,0.0035091785,0.00072349596,0.0065305135,0.0012285033,0.0020923116,0.013456218],"category_scores_gemma":[0.004921042,0.0005189807,0.0010071832,0.0028702235,0.002820851,0.0094935745,0.0031356288,0.001808046,0.0046848585],"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":[0.0002595518,0.000046404806,0.0012842057,0.0008417618,0.00009735518,0.0012200314,0.0022008389,0.0018730328,0.020349974,0.75420487,0.026920674,0.19070132],"study_design_scores_gemma":[0.00005321452,0.00013254222,0.0023900564,0.00065415754,0.000121162295,0.0023877246,0.001966032,0.010241922,0.017365776,0.40610695,0.5584584,0.00012202993],"about_ca_topic_score_codex":0.0010716971,"about_ca_topic_score_gemma":0.0009650573,"teacher_disagreement_score":0.013456218,"about_ca_system_score_codex":0.0005894798,"about_ca_system_score_gemma":0.00039560065,"threshold_uncertainty_score":0.045015574},"labels":[],"label_agreement":null},{"id":"W2151248231","doi":"10.1142/s0219467805001665","title":"BINARY IMAGE WATERMARKING THROUGH BLURRING AND BIASED BINARIZATION","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Steganography and Watermarking Techniques","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 Toronto","funders":"Shanghai Jiao Tong University","keywords":"Digital watermarking; Watermark; Discrete cosine transform; Artificial intelligence; Computer vision; Robustness (evolution); Computer science; Preprocessor; Binary number; Image (mathematics); Digital image; Binary image; Algorithm; Pattern recognition (psychology); Image processing; Mathematics; Arithmetic","score_opus":0.012145270461374374,"score_gpt":0.27532013049882803,"score_spread":0.26317486003745366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151248231","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":[0.12401265,0.0064648874,0.86580247,0.00026802803,0.00016154551,0.00009244834,0.000046021545,0.0007767988,0.002375085],"genre_scores_gemma":[0.60467595,0.0042880764,0.3865024,0.00020122403,0.00013724958,0.000055487326,0.000113166505,0.000101014324,0.0039254674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957615,0.000063691856,0.000027539767,0.00007776084,0.00021552062,0.000039347527],"domain_scores_gemma":[0.9991234,0.00024163304,0.00024410716,0.00014337435,0.00020293474,0.0000445251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004864522,0.00056932104,0.0005435637,0.00096075924,0.00031367716,0.0007137838,0.0004876544,0.00072059385,0.0010439642],"category_scores_gemma":[0.001862304,0.00034395207,0.0002991257,0.0010149422,0.0006525935,0.001618079,0.0007255044,0.00050030986,0.0004571196],"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":[0.0005181393,0.00006936515,0.0010613921,0.000408744,0.00004116783,0.0001711811,0.00017996892,0.012818583,0.64456344,0.018317139,0.00049436814,0.3213566],"study_design_scores_gemma":[0.00009841339,0.00079706823,0.0039493223,0.000070442824,0.000101751,0.0019625016,0.000059402537,0.21628733,0.74997747,0.01057848,0.015991949,0.0001259317],"about_ca_topic_score_codex":0.0004331508,"about_ca_topic_score_gemma":0.00043386495,"teacher_disagreement_score":0.0010439642,"about_ca_system_score_codex":0.000360489,"about_ca_system_score_gemma":0.00026659603,"threshold_uncertainty_score":0.0034924746},"labels":[],"label_agreement":null},{"id":"W2152260845","doi":"10.1142/s0219467805001756","title":"NATURAL SKELETONIZATION: NEW APPROACH FOR THE SKELETONIZATION OF HANDWRITTEN CHARACTERS","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Handwritten Text Recognition 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 Waterloo","funders":"","keywords":"Skeletonization; Artificial intelligence; Pixel; Computer science; Pattern recognition (psychology); Computer vision; Binary image; Aerenchyma; Medial axis; Image processing; Image (mathematics); Botany","score_opus":0.01214410654730863,"score_gpt":0.2683526044565506,"score_spread":0.256208497909242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152260845","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":[0.0023646052,0.00011606733,0.99610835,0.000025194446,0.000025642064,0.000033106862,0.000016946333,0.00079032435,0.00051967514],"genre_scores_gemma":[0.027147006,0.00025851457,0.9694577,0.00004669424,0.000039427996,0.00006109058,0.00018130481,0.0002368138,0.002571361],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934226,0.000084348925,0.000056415618,0.00020778638,0.00026222708,0.00004693588],"domain_scores_gemma":[0.99934417,0.00013876978,0.00008818204,0.00017046121,0.00021453458,0.00004387728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005410018,0.0008366095,0.0007831447,0.0016437719,0.00045587876,0.0008484606,0.0012949217,0.00080815365,0.0039551216],"category_scores_gemma":[0.0013818194,0.000498524,0.00084658386,0.0010705472,0.0009381633,0.001686627,0.0008989713,0.0009257093,0.0019110471],"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":[0.00012000308,0.000060760576,0.00053409656,0.00024229733,0.00005518755,0.00014986645,0.00015035462,0.025491942,0.082269005,0.01727975,0.004296867,0.86934984],"study_design_scores_gemma":[0.00007203199,0.0002344408,0.0019606578,0.00006173374,0.00006987022,0.0015203671,0.000108809676,0.7972623,0.11425502,0.02247219,0.06189982,0.0000827759],"about_ca_topic_score_codex":0.0011370443,"about_ca_topic_score_gemma":0.0017330066,"teacher_disagreement_score":0.0039551216,"about_ca_system_score_codex":0.00040495605,"about_ca_system_score_gemma":0.0008122071,"threshold_uncertainty_score":0.013231218},"labels":[],"label_agreement":null},{"id":"W2997716588","doi":"10.1142/s0219467819500220","title":"Face Identification Based on Discrete Wavelet Transform and Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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 Ottawa","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Facial recognition system; Context (archaeology); Wavelet; Pattern recognition (psychology); Face (sociological concept); Authentication (law); Identification (biology); Relevance (law); Machine learning; Computer vision; Computer security","score_opus":0.006847477560962715,"score_gpt":0.24794817667530256,"score_spread":0.24110069911433984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997716588","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":[0.01904478,0.00033291572,0.97861516,0.00007685758,0.000042121508,0.000020486275,0.000020764346,0.00019346425,0.0016533559],"genre_scores_gemma":[0.52435815,0.0010622536,0.4686276,0.000053399886,0.00006483565,0.00008939907,0.00011060684,0.000043716762,0.005589982],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998708,0.000027786487,0.00000570532,0.000022538157,0.000061032326,0.000012100506],"domain_scores_gemma":[0.9998759,0.000057469962,0.000013220147,0.000012849076,0.00003633518,0.0000041482763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026830882,0.00023996767,0.0003065679,0.00045271855,0.00015603993,0.00035857645,0.00030601426,0.00035032086,0.0010546639],"category_scores_gemma":[0.0007808305,0.00015634688,0.00029970377,0.0004662388,0.00020967388,0.00072809757,0.00027742222,0.00047722113,0.0003319577],"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":[0.00016588323,0.00006237534,0.00094275794,0.00012761026,0.00006848631,0.00015885988,0.00008756476,0.25755462,0.04048003,0.029011067,0.001319379,0.6700213],"study_design_scores_gemma":[0.0000020208624,0.000016169082,0.00030372117,0.0000056289728,0.0000060144125,0.00004325581,0.000006454363,0.99186796,0.004047499,0.0030154085,0.0006813338,0.0000044857675],"about_ca_topic_score_codex":0.0012204418,"about_ca_topic_score_gemma":0.0011410835,"teacher_disagreement_score":0.0012204418,"about_ca_system_score_codex":0.00022314048,"about_ca_system_score_gemma":0.00025619983,"threshold_uncertainty_score":0.0035282373},"labels":[],"label_agreement":null},{"id":"W3081030069","doi":"10.1142/s0219467821500108","title":"Classification of Mammogram Abnormalities Using Legendre Moments","year":2020,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fowler Kennedy Sport Medicine Clinic; Western University","funders":"","keywords":"Extractor; Legendre polynomials; Feature extraction; Classifier (UML); Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Computer science; Mathematics; Engineering","score_opus":0.04327668158494356,"score_gpt":0.3047382274338556,"score_spread":0.261461545848912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081030069","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":[0.525814,0.002597906,0.4637401,0.00055429497,0.00023067142,0.00013435274,0.00055422867,0.0027871542,0.003587323],"genre_scores_gemma":[0.8193562,0.0009961919,0.17668577,0.00010282596,0.00018444408,0.00003354709,0.00066670345,0.00011923413,0.0018551518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994942,0.000079584235,0.0000406034,0.00006621011,0.000261908,0.00005747323],"domain_scores_gemma":[0.9986161,0.0006325641,0.00022819535,0.00010822705,0.00036713717,0.000047775495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008583373,0.0004326045,0.00052749173,0.0030657072,0.0001527676,0.0006312786,0.0003539648,0.0004897987,0.0013884347],"category_scores_gemma":[0.0031899272,0.00012712144,0.0004969922,0.0010438438,0.00026456986,0.0010258671,0.00024382123,0.00032864034,0.0007030948],"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":[0.00088316214,0.00012058191,0.030138934,0.00021752199,0.00011570809,0.00048784196,0.00011142278,0.007522221,0.20187955,0.0010752814,0.002845127,0.7546027],"study_design_scores_gemma":[0.000070054055,0.001312664,0.2758773,0.00010289917,0.00036346703,0.008158906,0.00051881705,0.4292906,0.27167127,0.0028569982,0.00954779,0.00022922506],"about_ca_topic_score_codex":0.0007065756,"about_ca_topic_score_gemma":0.0010410447,"teacher_disagreement_score":0.0030657072,"about_ca_system_score_codex":0.00022233493,"about_ca_system_score_gemma":0.00015999994,"threshold_uncertainty_score":0.0046447515},"labels":[],"label_agreement":null},{"id":"W3161193347","doi":"10.1142/s0219467822500152","title":"Histogram of Marked Background (HMB) Feature Extraction Method for Arabic Handwriting Recognition","year":2021,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Handwriting; Histogram; Computer science; Artificial intelligence; Pattern recognition (psychology); Hidden Markov model; Handwriting recognition; Pixel; Feature extraction; Feature (linguistics); Arabic; Image (mathematics); Linguistics","score_opus":0.026623481588054023,"score_gpt":0.3361669086376732,"score_spread":0.3095434270496192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161193347","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":[0.06421263,0.0023557974,0.92209995,0.00022444292,0.00025214924,0.00022918153,0.0011959922,0.0056727305,0.0037570863],"genre_scores_gemma":[0.40666798,0.0016512882,0.5802356,0.00015381492,0.00013650257,0.00024886683,0.0025666854,0.00034529468,0.0079940455],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996947,0.000029318262,0.000023852177,0.00006929253,0.00014888303,0.000034002125],"domain_scores_gemma":[0.9996152,0.000080342616,0.00006348681,0.000049439343,0.00016006544,0.000031475374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022320962,0.0006215942,0.00061582273,0.001759751,0.0003045462,0.00047447512,0.00049295893,0.00038932948,0.0029878363],"category_scores_gemma":[0.0008005309,0.00022100024,0.00044196815,0.0014764477,0.0002310621,0.0007198207,0.00037134826,0.0005102319,0.0018018906],"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":[0.00019975478,0.00008834013,0.0015989895,0.0002455236,0.00005039468,0.0001770315,0.00005603685,0.0031343095,0.16518468,0.00073937717,0.0053466763,0.8231789],"study_design_scores_gemma":[0.000113034046,0.0007499116,0.051998764,0.00014851811,0.00019008116,0.003385622,0.00026811592,0.39532414,0.48476365,0.0030547646,0.059803724,0.0001997539],"about_ca_topic_score_codex":0.0021518592,"about_ca_topic_score_gemma":0.0030074741,"teacher_disagreement_score":0.0029878363,"about_ca_system_score_codex":0.00023589931,"about_ca_system_score_gemma":0.00045593895,"threshold_uncertainty_score":0.009995282},"labels":[],"label_agreement":null},{"id":"W3178965751","doi":"10.1142/s0219467822500358","title":"Spatial Distribution of Ink at Keypoints (SDIK): A Novel Feature for Word Spotting in Arabic Documents","year":2021,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Handwritten Text Recognition 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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Handwriting; Spotting; Artificial intelligence; Feature (linguistics); Word (group theory); Task (project management); Arabic; Handwriting recognition; Inkwell; Matching (statistics); Natural language processing; Pattern recognition (psychology); Pixel; Feature extraction; Speech recognition; Mathematics; Linguistics","score_opus":0.010769024176913864,"score_gpt":0.2792518819922136,"score_spread":0.26848285781529974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178965751","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":[0.35095474,0.0040646265,0.62933373,0.00034909786,0.0004080356,0.00029146753,0.0028116673,0.0058912914,0.005895287],"genre_scores_gemma":[0.84902316,0.0012907124,0.14340907,0.00009700817,0.00022440442,0.000098292374,0.0018392344,0.00028286898,0.003735235],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963224,0.00002499459,0.000035484452,0.00007951252,0.00017071371,0.000056967743],"domain_scores_gemma":[0.99914277,0.0001667061,0.00018192155,0.00010618891,0.00033231874,0.000070189264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021350101,0.0006373856,0.0006930197,0.0034864582,0.000262907,0.00080852496,0.0005444434,0.00042939183,0.0014861362],"category_scores_gemma":[0.0010422638,0.00015617136,0.00046587203,0.0024203635,0.00043781011,0.0011974085,0.0005683234,0.0005474769,0.001003361],"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":[0.0012760095,0.00011517906,0.013866303,0.0005975491,0.00010242537,0.00084219,0.0002472861,0.010183073,0.21054938,0.0016301905,0.006601562,0.7539889],"study_design_scores_gemma":[0.00012388584,0.00089758605,0.08974331,0.00012091379,0.0002987504,0.007833272,0.00077487965,0.48445278,0.37133232,0.0042192824,0.039958756,0.0002442589],"about_ca_topic_score_codex":0.0014972935,"about_ca_topic_score_gemma":0.001982067,"teacher_disagreement_score":0.0034864582,"about_ca_system_score_codex":0.00030892363,"about_ca_system_score_gemma":0.00036517394,"threshold_uncertainty_score":0.004971564},"labels":[],"label_agreement":null},{"id":"W4206100981","doi":"10.1142/s0219467823500195","title":"Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks","year":2021,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","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":"Biometrics; Computer science; Artificial intelligence; Feature (linguistics); Identification (biology); Convolutional neural network; Face (sociological concept); Feature extraction; Pattern recognition (psychology); Modality (human–computer interaction); Authentication (law); Facial recognition system; Convolution (computer science); Artificial neural network; Computer vision; Computer security","score_opus":0.028528613818144755,"score_gpt":0.27689701224312946,"score_spread":0.2483683984249847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206100981","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":[0.24605079,0.0030363407,0.73512584,0.00052453787,0.00038260128,0.00016737568,0.00081129593,0.0045170686,0.009384145],"genre_scores_gemma":[0.87141293,0.0010281294,0.11704232,0.00017633534,0.000065892964,0.000067797904,0.00078660704,0.000047642585,0.009372279],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963796,0.000042727297,0.000021155864,0.00010185162,0.0001441154,0.000052152453],"domain_scores_gemma":[0.9998374,0.000028747265,0.000027890319,0.000024256638,0.00007082933,0.000010933752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038312556,0.0005118262,0.0007147112,0.00053775695,0.0002604237,0.00041484946,0.0004475549,0.00054223195,0.0030336643],"category_scores_gemma":[0.00060518883,0.00015348686,0.000579097,0.00044816092,0.00022851714,0.00084179244,0.0005443453,0.00041683033,0.001016108],"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":[0.0011253143,0.00026227205,0.0071526603,0.0002457885,0.00019986773,0.0004973619,0.00009310148,0.04596754,0.15340672,0.002554779,0.00548239,0.7830122],"study_design_scores_gemma":[0.000021781181,0.00041839672,0.013438918,0.000046578538,0.0001352136,0.0014672924,0.000055975222,0.8788824,0.09936434,0.0017697421,0.004337799,0.000061590246],"about_ca_topic_score_codex":0.00271783,"about_ca_topic_score_gemma":0.0034092227,"teacher_disagreement_score":0.0030336643,"about_ca_system_score_codex":0.0004832934,"about_ca_system_score_gemma":0.00032728398,"threshold_uncertainty_score":0.010148644},"labels":[],"label_agreement":null}]}