{"meta":{"query_hash":"660cddb0f7d4","filters":{"topic":"Image Enhancement Techniques"},"cohort_total":473,"direct_labels_cover":0,"predictions_cover":473,"exported":473,"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/660cddb0f7d4","api":"https://metacan.xera.ac/api/v1/cohort?topic=Image+Enhancement+Techniques"},"results":[{"id":"W120056940","doi":"","title":"Real-time video matting using multichannel poisson equations","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"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; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Poisson distribution; Process (computing); Set (abstract data type); Image processing; Image (mathematics); Algorithm; Poisson's equation; Pattern recognition (psychology); Mathematics","score_opus":0.02088920695793188,"score_gpt":0.29158433192946603,"score_spread":0.27069512497153414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W120056940","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":[0.004369233,0.000110616034,0.9939977,0.000044590615,0.00003122536,0.000018243998,0.000019512885,0.00020146876,0.0012073378],"genre_scores_gemma":[0.1376561,0.00049569854,0.85292554,0.000052060375,0.00006786754,0.00006616339,0.00011532598,0.0001577035,0.008463581],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998074,0.000020857045,0.000009374597,0.000036148645,0.000112871334,0.000013318117],"domain_scores_gemma":[0.9997408,0.000104914994,0.00004205119,0.00003285984,0.00006527124,0.000014091713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002633912,0.000433214,0.00038673752,0.00039597397,0.00020440202,0.00073761697,0.00068481325,0.0004515034,0.0024803258],"category_scores_gemma":[0.0009669777,0.00026696766,0.00048302626,0.00033055586,0.00025954176,0.0007899228,0.0004458727,0.0007085017,0.00055186555],"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":[0.000103687686,0.00004913373,0.0011624487,0.0002921781,0.000068545334,0.0002827359,0.00032499345,0.36919415,0.13159147,0.05221873,0.003031534,0.4416804],"study_design_scores_gemma":[0.000003363767,0.000012272613,0.00012303733,0.000004210126,0.000003014853,0.00008494471,0.00001007034,0.9852164,0.010529227,0.0011712023,0.0028353126,0.0000069014886],"about_ca_topic_score_codex":0.0018811928,"about_ca_topic_score_gemma":0.0022007076,"teacher_disagreement_score":0.0024803258,"about_ca_system_score_codex":0.000507349,"about_ca_system_score_gemma":0.00049049844,"threshold_uncertainty_score":0.008297503},"labels":[],"label_agreement":null},{"id":"W1459202278","doi":"10.1016/j.neucom.2015.07.056","title":"Fast colorization for single-band thermal video sequences","year":2015,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Monochrome; Luminance; Frame (networking); Night vision; Pixel; Matching (statistics); Mathematics; Telecommunications","score_opus":0.0504467732059745,"score_gpt":0.273334713597184,"score_spread":0.22288794039120946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1459202278","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":[0.05195809,0.0005249188,0.94437605,0.000091671114,0.00006110423,0.000052458072,0.000087579196,0.00061905425,0.0022290214],"genre_scores_gemma":[0.3339654,0.0014402426,0.6580868,0.00006575013,0.00006838236,0.00007273539,0.00023624371,0.00017380093,0.0058907503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999899,0.000015111208,0.000004340095,0.0000181462,0.000046041227,0.000017426983],"domain_scores_gemma":[0.9996824,0.000093939045,0.000027811288,0.00004503532,0.00012729822,0.000023527016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022723632,0.00046566204,0.00024704178,0.00066733925,0.00017935874,0.00049083494,0.00033612936,0.00025645082,0.0032922158],"category_scores_gemma":[0.00095727755,0.00017335809,0.00026867207,0.00051316957,0.00018525402,0.00061682635,0.0003311596,0.00047209603,0.000654231],"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":[0.0004889124,0.0000814061,0.0005766375,0.00022535883,0.00003981486,0.0000944176,0.00009823421,0.023897257,0.36093,0.0046242,0.0017932833,0.60715055],"study_design_scores_gemma":[0.000020849468,0.00015642427,0.0029856602,0.000031416646,0.000045357952,0.00054151274,0.00007017075,0.7380686,0.24970797,0.0030195222,0.005327923,0.000024628045],"about_ca_topic_score_codex":0.001546865,"about_ca_topic_score_gemma":0.0031425015,"teacher_disagreement_score":0.0032922158,"about_ca_system_score_codex":0.00022195285,"about_ca_system_score_gemma":0.0003702985,"threshold_uncertainty_score":0.011013508},"labels":[],"label_agreement":null},{"id":"W1495386528","doi":"10.1007/11559573_20","title":"Type-2 Fuzzy Image Enhancement","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Fuzzy logic; Image (mathematics); Artificial intelligence; Image processing; Computer vision; Contrast (vision); Image enhancement","score_opus":0.013053205138489267,"score_gpt":0.25944042010565366,"score_spread":0.2463872149671644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495386528","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":[0.028064027,0.0028859056,0.88992554,0.00024015844,0.0005398795,0.00009284026,0.00009847842,0.0007461191,0.07740711],"genre_scores_gemma":[0.37312278,0.0039782417,0.47251356,0.00027194308,0.00030063782,0.000085811924,0.00019741617,0.00016424792,0.14936535],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988127,0.000008692118,0.00000493807,0.000023497902,0.000067488625,0.000014176027],"domain_scores_gemma":[0.99987876,0.000030635947,0.000009322964,0.000019181543,0.000056133587,0.0000059422105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018053115,0.00043532692,0.00037428417,0.00043248635,0.00026424995,0.0005220297,0.00048777252,0.00046642838,0.008246859],"category_scores_gemma":[0.00026923596,0.00018801528,0.0003817336,0.00035122843,0.0002409195,0.0005837924,0.00029331242,0.00059737975,0.0015231154],"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":[0.00030498777,0.000079589394,0.00031437428,0.0006319527,0.000036174253,0.00035577934,0.00012671182,0.011443566,0.3270424,0.047854163,0.010319001,0.6014913],"study_design_scores_gemma":[0.000048102575,0.0005736725,0.0035908362,0.00019020964,0.000120498145,0.004724073,0.00012768511,0.35596693,0.45344424,0.047329545,0.13377127,0.000112895454],"about_ca_topic_score_codex":0.00030635027,"about_ca_topic_score_gemma":0.0004497971,"teacher_disagreement_score":0.008246859,"about_ca_system_score_codex":0.00021074859,"about_ca_system_score_gemma":0.00014757115,"threshold_uncertainty_score":0.027588546},"labels":[],"label_agreement":null},{"id":"W1531045795","doi":"10.1007/978-3-642-15567-3_26","title":"A New Algorithmic Approach for Contrast Enhancement","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Contrast (vision); Computer science; Histogram equalization; Measure (data warehouse); Tone mapping; Equalization (audio); Histogram; Mathematical optimization; Algorithm; Distortion (music); Linear programming; Artificial intelligence; Mathematics; Computer vision; Bandwidth (computing); Image (mathematics); Data mining","score_opus":0.01373552411811472,"score_gpt":0.2504953992873359,"score_spread":0.23675987516922117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531045795","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":[0.0005052794,0.00013490698,0.99625045,0.000044679655,0.000052184176,0.000021907452,0.000011412543,0.00017453665,0.0028046824],"genre_scores_gemma":[0.01070341,0.00040538094,0.98033845,0.000078352525,0.00009604558,0.00006894555,0.00005774709,0.00013150994,0.0081201205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996691,0.000035593883,0.00001896471,0.000088908484,0.00016357344,0.000023904442],"domain_scores_gemma":[0.9997336,0.00009267934,0.000014234404,0.00006401034,0.00008237539,0.000013195832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035219343,0.0010304335,0.0005656845,0.00092561677,0.0005253845,0.0015097801,0.0015922694,0.0010173353,0.008332819],"category_scores_gemma":[0.0009239453,0.0005169415,0.0008644991,0.0006847088,0.0008672052,0.0017888781,0.0013743497,0.0016507022,0.0031535625],"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":[0.00006376836,0.00006865576,0.00015435014,0.0002892032,0.000046543813,0.0001459923,0.00012384122,0.017594736,0.08203323,0.3071827,0.008763257,0.5835337],"study_design_scores_gemma":[0.000038173053,0.00017033836,0.00043822528,0.00010126111,0.00008576587,0.0016481688,0.000071524264,0.55917656,0.05824672,0.2621731,0.11777306,0.00007714597],"about_ca_topic_score_codex":0.00052645674,"about_ca_topic_score_gemma":0.00087275874,"teacher_disagreement_score":0.008332819,"about_ca_system_score_codex":0.00033478535,"about_ca_system_score_gemma":0.00044637854,"threshold_uncertainty_score":0.02787608},"labels":[],"label_agreement":null},{"id":"W1543165708","doi":"10.1109/iscas.2015.7169272","title":"A general histogram modification framework for efficient contrast enhancement","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":15,"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":"Histogram; Histogram matching; Brightness; Contrast (vision); Computer science; Artificial intelligence; Adaptive histogram equalization; Contrast enhancement; Histogram equalization; Transformation (genetics); Image histogram; Matching (statistics); Computer vision; Pattern recognition (psychology); Balanced histogram thresholding; Image (mathematics); Mathematics; Image processing; Color image; Optics; Statistics","score_opus":0.04558731359528416,"score_gpt":0.31867967111373763,"score_spread":0.27309235751845345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543165708","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":[0.0014095231,0.00015541712,0.9974872,0.000024886545,0.000018530587,0.000023217306,0.000013256797,0.00023431166,0.0006337692],"genre_scores_gemma":[0.16840269,0.0012050247,0.82197547,0.00016203438,0.0001592136,0.00014864502,0.00017515232,0.00026771775,0.0075040245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997919,0.00003521931,0.000009645996,0.000053406908,0.00008918158,0.000020721036],"domain_scores_gemma":[0.9998388,0.000034429424,0.00001535916,0.00005387515,0.00004613229,0.000011384107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042321876,0.000620459,0.0004291788,0.0004269455,0.00018564817,0.0005501303,0.0010563844,0.0005525679,0.0022183773],"category_scores_gemma":[0.00055585115,0.00022774367,0.0007033545,0.00035741949,0.0004821212,0.0010779183,0.00062921864,0.00089352217,0.0010047441],"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":[0.00018196531,0.00012060206,0.0004601773,0.0004101835,0.00011225344,0.00029082215,0.0001212163,0.09683966,0.35776803,0.12115037,0.0044816365,0.41806313],"study_design_scores_gemma":[0.000028759638,0.00021532283,0.00069339166,0.00002842748,0.00006199192,0.00075590867,0.000023294107,0.853988,0.08765728,0.028774675,0.027716687,0.000056238277],"about_ca_topic_score_codex":0.00085931894,"about_ca_topic_score_gemma":0.00076361716,"teacher_disagreement_score":0.0022183773,"about_ca_system_score_codex":0.00031132202,"about_ca_system_score_gemma":0.00032603863,"threshold_uncertainty_score":0.0074211955},"labels":[],"label_agreement":null},{"id":"W1557726572","doi":"10.1007/978-3-540-36420-7_11","title":"Observer-Dependent Image Enhancement","year":2003,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Observer (physics); Artificial intelligence; Computer vision; Image quality; Image (mathematics); Computer science; Human visual system model; Smoothness; Image fusion; Contrast (vision); Perception; Image enhancement; Image processing; Mathematics; Psychology","score_opus":0.05088679874303899,"score_gpt":0.3102564428172652,"score_spread":0.25936964407422625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557726572","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":[0.021402374,0.002095949,0.9527074,0.0002610496,0.00012642833,0.00003836081,0.000024403926,0.0003513919,0.022992589],"genre_scores_gemma":[0.7070693,0.00441061,0.21889667,0.00020923192,0.00015201433,0.00005340461,0.00011414941,0.00022294487,0.06887172],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983,0.000035421886,0.0000055439855,0.000049867947,0.00006538934,0.000013655284],"domain_scores_gemma":[0.9994466,0.0002878083,0.000039780334,0.00011814253,0.00009288169,0.000014735706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004812803,0.00039251352,0.00046053476,0.00019291716,0.00015587159,0.00078190904,0.00051292876,0.00043586403,0.0031959857],"category_scores_gemma":[0.0014021891,0.00025833255,0.0003412731,0.00020018173,0.00070660055,0.0013187238,0.0006734055,0.0011659163,0.00059028296],"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":[0.00030535064,0.00011520346,0.00049890805,0.00040272667,0.000057433794,0.00025268388,0.0004575745,0.03702768,0.23716635,0.41750404,0.0042238995,0.3019882],"study_design_scores_gemma":[0.00002523898,0.00024745727,0.0019542403,0.00007510281,0.000052917378,0.0007319799,0.000076650205,0.54809755,0.3007525,0.118714444,0.02921157,0.000060352504],"about_ca_topic_score_codex":0.00024607702,"about_ca_topic_score_gemma":0.00019929901,"teacher_disagreement_score":0.0031959857,"about_ca_system_score_codex":0.0002897012,"about_ca_system_score_gemma":0.00013049538,"threshold_uncertainty_score":0.010691583},"labels":[],"label_agreement":null},{"id":"W1572725906","doi":"10.1109/nafips.2005.1548561","title":"Applications of fuzzy morphology to contrast enhancement","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of Guelph","funders":"","keywords":"Sharpening; Unsharp masking; Contrast (vision); Artificial intelligence; Masking (illustration); Subtext; Contrast enhancement; Computer vision; Fuzzy logic; Image (mathematics); Computer science; Image enhancement; Edge enhancement; Deblurring; Image restoration; Image processing; Mathematics","score_opus":0.00865544015866231,"score_gpt":0.2702902409410699,"score_spread":0.2616348007824076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1572725906","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":[0.0066390457,0.0051006055,0.97587717,0.00054558826,0.0002449593,0.00003588842,0.000015268326,0.00016560916,0.011375874],"genre_scores_gemma":[0.26337242,0.010589967,0.7174417,0.00036274406,0.00075578847,0.00008030605,0.00003911278,0.00006925819,0.0072887097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968016,0.000063729414,0.00002098829,0.000050141716,0.00016438955,0.000020614949],"domain_scores_gemma":[0.99944645,0.00031049296,0.00003509765,0.000058347352,0.00013000499,0.000019517429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050795457,0.00047896086,0.0004684549,0.001364115,0.00049693463,0.00089398,0.0005206128,0.00084076554,0.0017077073],"category_scores_gemma":[0.002062615,0.00030911923,0.0007646304,0.0006291902,0.0011859648,0.0008562849,0.000871369,0.0011572372,0.00039566966],"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":[0.00009212421,0.0000603898,0.0008831092,0.00052478287,0.000093083196,0.0013111052,0.0005420907,0.056581683,0.08090378,0.3596982,0.0032093744,0.49610022],"study_design_scores_gemma":[0.00005532593,0.00029861348,0.0021857868,0.00018342209,0.00008088722,0.0037160392,0.00015431055,0.3737371,0.06806101,0.45226657,0.099139445,0.000121412675],"about_ca_topic_score_codex":0.0006275283,"about_ca_topic_score_gemma":0.0006291101,"teacher_disagreement_score":0.0017077073,"about_ca_system_score_codex":0.00045535408,"about_ca_system_score_gemma":0.00026097425,"threshold_uncertainty_score":0.005712807},"labels":[],"label_agreement":null},{"id":"W1593941226","doi":"","title":"Accelerating a modified Gaussian pyramid with a customized processor","year":2013,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Speedup; Bottleneck; Overhead (engineering); Parallel computing; Computation; SIMD; Vectorization (mathematics); Pyramid (geometry); Computer hardware; Embedded system; Algorithm","score_opus":0.012295548775878507,"score_gpt":0.22743024004342727,"score_spread":0.21513469126754875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1593941226","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":[0.16518806,0.00022250279,0.81941617,0.00014684239,0.000090759924,0.00013380765,0.000100062956,0.0077113537,0.0069905496],"genre_scores_gemma":[0.42628554,0.00012700075,0.56598896,0.00017558625,0.000031348907,0.00009592098,0.00029056083,0.0002592711,0.0067458497],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998228,0.000015282061,0.000010810695,0.000042107735,0.000070091635,0.000038968254],"domain_scores_gemma":[0.9998404,0.00003437387,0.000014707052,0.000040967472,0.000057466714,0.0000121315425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014709924,0.00039962985,0.00023973029,0.00028256536,0.00017757574,0.000361799,0.0008442304,0.00025564275,0.002970209],"category_scores_gemma":[0.00041391878,0.00019110777,0.00029912998,0.0003771682,0.00015967972,0.0005705979,0.00035092552,0.00042026723,0.00071848737],"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":[0.00064708426,0.00019697538,0.0022053916,0.0001585426,0.00007881003,0.00046120473,0.0001506567,0.045946933,0.56773406,0.009110686,0.01086872,0.36244103],"study_design_scores_gemma":[0.00013207002,0.00066307123,0.0032226555,0.000015285459,0.00009945988,0.0006519032,0.000039488525,0.6527967,0.31745443,0.0016983108,0.023179365,0.00004722184],"about_ca_topic_score_codex":0.002221438,"about_ca_topic_score_gemma":0.0031350162,"teacher_disagreement_score":0.002970209,"about_ca_system_score_codex":0.00046337987,"about_ca_system_score_gemma":0.0007246928,"threshold_uncertainty_score":0.009936392},"labels":[],"label_agreement":null},{"id":"W1594139388","doi":"10.1155/2015/607407","title":"Color Enhancement in Endoscopic Images Using Adaptive Sigmoid Function and Space Variant Color Reproduction","year":2015,"lang":"en","type":"article","venue":"Computational and Mathematical Methods in Medicine","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":35,"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 Saskatchewan","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Grand Challenges Canada; Canada Foundation for Innovation","keywords":"Chrominance; Artificial intelligence; Computer vision; Color image; Computer science; Color space; Color balance; Sigmoid function; Color histogram; Grayscale; Pixel; Luminance; Image (mathematics); Image processing","score_opus":0.09612988115925784,"score_gpt":0.4053410985244286,"score_spread":0.30921121736517077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594139388","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":[0.10679669,0.0007139143,0.88936716,0.00008450621,0.00004389559,0.000051715284,0.000021430433,0.00055725407,0.0023633635],"genre_scores_gemma":[0.55415887,0.00096701167,0.4393797,0.000044444965,0.000029721332,0.000044519584,0.00004998316,0.00009366911,0.005232065],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998411,0.000032398537,0.000008756064,0.000028387618,0.00007597124,0.000013461767],"domain_scores_gemma":[0.9997762,0.00008422732,0.000030689298,0.000030929168,0.00006696616,0.000011013063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035965483,0.00040511746,0.00022548922,0.0004430035,0.00011704573,0.00040993173,0.00036456296,0.00033004605,0.0009973805],"category_scores_gemma":[0.00072819646,0.00015040555,0.0005166567,0.0004582146,0.00031813903,0.0005032178,0.00028770478,0.00030956633,0.00031873438],"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":[0.0004736914,0.000120033925,0.0020660842,0.00027131056,0.00007038988,0.000417167,0.00019253501,0.051769704,0.43985748,0.0057377126,0.0008609832,0.49816284],"study_design_scores_gemma":[0.00005017549,0.00043718106,0.0041723847,0.00002581673,0.00006445162,0.0019136613,0.000060834645,0.6953621,0.29019454,0.001630365,0.0060357573,0.000052690688],"about_ca_topic_score_codex":0.00066078396,"about_ca_topic_score_gemma":0.0006017915,"teacher_disagreement_score":0.0009973805,"about_ca_system_score_codex":0.0002054711,"about_ca_system_score_gemma":0.00019979199,"threshold_uncertainty_score":0.0033366084},"labels":[],"label_agreement":null},{"id":"W1652982787","doi":"10.1109/icvrv.2014.10","title":"Stereoscopic Image Recoloring via Consistent Intrinsic Decomposition","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"York University","funders":"National Natural Science Foundation of China","keywords":"Stereoscopy; Computer vision; Artificial intelligence; Pixel; Computer science; Image (mathematics); Enhanced Data Rates for GSM Evolution; Decomposition; Biology","score_opus":0.008770746370801812,"score_gpt":0.2590528268702108,"score_spread":0.250282080499409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1652982787","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":[0.04469131,0.00022869275,0.9507666,0.000066256376,0.00002423711,0.000039006056,0.00008448714,0.002221016,0.0018785346],"genre_scores_gemma":[0.22233604,0.0002689264,0.7730759,0.00011750565,0.000029465074,0.000046003803,0.0003225788,0.0003700423,0.0034334846],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997727,0.00003029615,0.000009006452,0.00006186671,0.00009891968,0.000027101822],"domain_scores_gemma":[0.9996086,0.00005440107,0.00005237261,0.00015387822,0.000104154184,0.000026553578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002624902,0.00045598304,0.0004546658,0.00075291254,0.00024487005,0.00048365173,0.0008012597,0.0003678751,0.002630863],"category_scores_gemma":[0.00066315994,0.00032478804,0.0004778172,0.0003712996,0.00027136877,0.00091089366,0.0010409734,0.00071235775,0.0008859944],"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":[0.00028021558,0.000059461563,0.0006682426,0.00016175449,0.000040161864,0.00015932205,0.00018756728,0.0072154403,0.63579655,0.003163896,0.0020848124,0.3501826],"study_design_scores_gemma":[0.000056380137,0.00024833795,0.0027700549,0.0000343239,0.00008092488,0.0012987431,0.000113146605,0.28364733,0.68810534,0.0054136473,0.018166937,0.000064891225],"about_ca_topic_score_codex":0.0006312131,"about_ca_topic_score_gemma":0.0011665835,"teacher_disagreement_score":0.002630863,"about_ca_system_score_codex":0.00023829007,"about_ca_system_score_gemma":0.00025887648,"threshold_uncertainty_score":0.008801103},"labels":[],"label_agreement":null},{"id":"W1709676693","doi":"","title":"Non-linear normalized entropy based exposure blending","year":2013,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Tone mapping; Normalization (sociology); Dynamic range; Classification of discontinuities; Entropy (arrow of time); High dynamic range; High-dynamic-range imaging; Computer science; Dynamic range compression; Linearity; Radiometry; Artificial intelligence; Computer vision; Irradiance; Algorithm; Mathematics; Optics; Physics; Engineering; Electronic engineering","score_opus":0.014132461759905322,"score_gpt":0.264718053958632,"score_spread":0.25058559219872667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1709676693","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":[0.0441996,0.000315057,0.95245343,0.0000704119,0.00003262905,0.000033395663,0.000029424386,0.00037070888,0.0024953373],"genre_scores_gemma":[0.5883902,0.0005174121,0.39970365,0.00010107565,0.00008570132,0.0000526196,0.0001337122,0.00034476098,0.010670942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965525,0.00005585119,0.000015332209,0.00007247049,0.00017470444,0.000026402773],"domain_scores_gemma":[0.99951935,0.00021001382,0.000052960422,0.00013722434,0.000053871492,0.000026608977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006036941,0.00050034944,0.00052464136,0.00044238838,0.00019028355,0.0010243102,0.0008279739,0.00042601326,0.0032976514],"category_scores_gemma":[0.001755546,0.00032058117,0.00046255355,0.00045487948,0.0006753004,0.0014680973,0.0014244452,0.000652029,0.00045781967],"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":[0.00070998154,0.00018274927,0.0018511167,0.0003558422,0.00011388899,0.00036403365,0.00041873276,0.20884098,0.19115758,0.05067695,0.00093616307,0.54439193],"study_design_scores_gemma":[0.00001831222,0.00022579462,0.0016056697,0.00002001877,0.00005288407,0.00045378951,0.00004960849,0.8899664,0.08735494,0.01606857,0.0041554314,0.00002860253],"about_ca_topic_score_codex":0.00019469633,"about_ca_topic_score_gemma":0.00031507865,"teacher_disagreement_score":0.0032976514,"about_ca_system_score_codex":0.00036150735,"about_ca_system_score_gemma":0.00018139525,"threshold_uncertainty_score":0.011031747},"labels":[],"label_agreement":null},{"id":"W1809620427","doi":"","title":"Invariant Image Improvement by sRGB colour space sharpening","year":2005,"lang":"en","type":"article","venue":"UEA Digital Repository (University of East Anglia)","topic":"Image Enhancement 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":"Simon Fraser University","funders":"","keywords":"Sharpening; Artificial intelligence; Computer vision; Invariant (physics); Mathematics; Color image; Color space; Computer science; Image processing; Image (mathematics)","score_opus":0.005327444168061117,"score_gpt":0.17503790537391092,"score_spread":0.16971046120584982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1809620427","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":[0.15201442,0.0004796705,0.8341517,0.00016109388,0.000059590922,0.00005484781,0.00009815761,0.0038229672,0.009157593],"genre_scores_gemma":[0.3960292,0.00049493415,0.59565884,0.00010850798,0.000025086623,0.000028754605,0.00016824936,0.00062712794,0.0068593943],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976116,0.000027805443,0.000011684059,0.000052936728,0.00010372329,0.000042764448],"domain_scores_gemma":[0.9996124,0.00007799962,0.000053651416,0.00013979916,0.000098737386,0.000017425176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000369248,0.0005579027,0.00043127616,0.0009349403,0.00016272398,0.00086325617,0.00050414033,0.00029806755,0.0031670656],"category_scores_gemma":[0.0009449031,0.0002667791,0.00050138356,0.0007425128,0.00057496154,0.00091586774,0.0007244843,0.00066972635,0.0013322955],"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":[0.00016744282,0.000044451535,0.00063970516,0.00016063814,0.00003011306,0.0001607022,0.00022265292,0.008836813,0.767594,0.00832998,0.0012753215,0.21253814],"study_design_scores_gemma":[0.000020834215,0.00012938869,0.0028143392,0.000011164788,0.000046367008,0.00066157384,0.000067407454,0.09241783,0.89188385,0.0025425907,0.009362858,0.000041800708],"about_ca_topic_score_codex":0.00085191464,"about_ca_topic_score_gemma":0.00087881496,"teacher_disagreement_score":0.0031670656,"about_ca_system_score_codex":0.000320853,"about_ca_system_score_gemma":0.00022985732,"threshold_uncertainty_score":0.010594904},"labels":[],"label_agreement":null},{"id":"W183335773","doi":"10.1016/s1076-5670(05)40004-x","title":"A Taxonomy of Color Image Filtering and Enhancement Solutions","year":2006,"lang":"en","type":"book-chapter","venue":"Advances in imaging and electron physics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sharpening; Smoothing; Pixel; Artificial intelligence; Computer science; Computer vision; Process (computing); Noise (video); Image (mathematics); Pattern recognition (psychology)","score_opus":0.009652451264130411,"score_gpt":0.2449034496298116,"score_spread":0.23525099836568117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W183335773","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":[0.003389953,0.032913428,0.90384793,0.00068573264,0.00041118442,0.00026646658,0.00033225043,0.0031538678,0.05499911],"genre_scores_gemma":[0.023128066,0.04119846,0.8956122,0.00056293857,0.00027055226,0.00027817002,0.000730441,0.00038999954,0.037829183],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993531,0.000044230248,0.000061253966,0.00011231955,0.00037009595,0.000058967275],"domain_scores_gemma":[0.9994842,0.00012619716,0.000046150024,0.0000945403,0.0002243505,0.00002463133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064648077,0.0012985423,0.0008363943,0.0032900292,0.0006336044,0.0027623065,0.002018793,0.0015099283,0.006877945],"category_scores_gemma":[0.0012306994,0.000575033,0.0009230641,0.0043821703,0.0006138693,0.0026481005,0.00092545146,0.0015340896,0.0049687503],"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":[0.000054508793,0.00012692905,0.00039282016,0.0008248447,0.000027247832,0.00014102308,0.000115765026,0.0030361926,0.017437492,0.112903945,0.018110063,0.84682924],"study_design_scores_gemma":[0.000039979608,0.0003853621,0.0013782079,0.0008660235,0.00011986056,0.0053014057,0.00020572667,0.07850995,0.062050447,0.16925198,0.68173015,0.00016083659],"about_ca_topic_score_codex":0.00085519796,"about_ca_topic_score_gemma":0.001106126,"teacher_disagreement_score":0.006877945,"about_ca_system_score_codex":0.0005517703,"about_ca_system_score_gemma":0.0006175225,"threshold_uncertainty_score":0.023009062},"labels":[],"label_agreement":null},{"id":"W1846524963","doi":"10.1007/s00530-015-0493-2","title":"Video chroma keying via global sampling and trimap propagation","year":2015,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Opacity; Segmentation; Keying; Frame (networking); Background subtraction; Pixel; Optics","score_opus":0.04546578659677351,"score_gpt":0.2920024648441564,"score_spread":0.24653667824738287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1846524963","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":[0.027491998,0.00048292158,0.9670913,0.00010222879,0.00008319087,0.000058638805,0.000055745553,0.00074862514,0.0038852792],"genre_scores_gemma":[0.21122418,0.0008920013,0.77662206,0.00011735244,0.00012830709,0.00007401065,0.00014505236,0.00024914145,0.0105478],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999765,0.00003738421,0.000009472242,0.000046834353,0.00010848163,0.000032991546],"domain_scores_gemma":[0.99954695,0.00015473636,0.00004414758,0.0001241112,0.000093352464,0.000036719546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038505046,0.0008732827,0.0005018335,0.0008616894,0.00041997887,0.0007461573,0.000507631,0.00050821464,0.0049684267],"category_scores_gemma":[0.0010960178,0.0003051196,0.00045114395,0.0009196194,0.0005052557,0.0014382628,0.0010452658,0.0008087496,0.0012115274],"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":[0.0007762217,0.00010899817,0.0005621288,0.0001759763,0.00006193907,0.00016536519,0.00013346216,0.01223838,0.3530478,0.015655361,0.0017911524,0.61528325],"study_design_scores_gemma":[0.00008221326,0.00033339186,0.0018882279,0.00005359804,0.000109714994,0.00078576064,0.000087784036,0.5092422,0.4647928,0.010194764,0.012360993,0.00006860853],"about_ca_topic_score_codex":0.0012541674,"about_ca_topic_score_gemma":0.0030059617,"teacher_disagreement_score":0.0049684267,"about_ca_system_score_codex":0.00040718535,"about_ca_system_score_gemma":0.0005124877,"threshold_uncertainty_score":0.016621053},"labels":[],"label_agreement":null},{"id":"W1849986882","doi":"10.1109/iscas.2004.1328896","title":"Color image filtering and enhancement based genetic algorithms","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":19,"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; Bell (Canada)","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Color image; Color filter array; Demosaicing; Genetic algorithm; Image restoration; Image (mathematics); Filter (signal processing); Image enhancement; Noise (video); Color correction; Algorithm; Color gel; Image processing","score_opus":0.009457314258301179,"score_gpt":0.243649193872918,"score_spread":0.23419187961461682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1849986882","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":[0.014728106,0.00019915108,0.98184896,0.00007345731,0.00003631874,0.000043754153,0.000012641181,0.00033442775,0.0027232345],"genre_scores_gemma":[0.30285057,0.00056577625,0.69044024,0.0001779644,0.000048411155,0.00023213487,0.000081378246,0.00008203262,0.005521509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996886,0.00008759326,0.00001329182,0.00006272127,0.00011510558,0.000032603355],"domain_scores_gemma":[0.9996325,0.00016781804,0.000042934193,0.000032920707,0.00011250245,0.000011433847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007069241,0.0006019217,0.0005274935,0.0007323913,0.00027864636,0.0007517486,0.0006549475,0.00095352763,0.0010275743],"category_scores_gemma":[0.0015013738,0.00024749985,0.00055904395,0.0006702218,0.0005955498,0.0004322617,0.00033280664,0.00056160666,0.00028772812],"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":[0.00007702897,0.00009731925,0.0007757225,0.00007430261,0.00007939785,0.0000736657,0.00008778313,0.7270632,0.021721225,0.027440637,0.0010644322,0.22144517],"study_design_scores_gemma":[0.00001705866,0.00004081839,0.00017630913,0.00000854253,0.000018069883,0.000032150165,0.00000698876,0.9914677,0.003982013,0.0028641564,0.0013792252,0.000007101146],"about_ca_topic_score_codex":0.0030006147,"about_ca_topic_score_gemma":0.0028838976,"teacher_disagreement_score":0.0030006147,"about_ca_system_score_codex":0.0005676432,"about_ca_system_score_gemma":0.000587732,"threshold_uncertainty_score":0.0059663057},"labels":[],"label_agreement":null},{"id":"W1854231432","doi":"10.1007/s11760-015-0833-x","title":"Propagating sparse labels through edge-aware filters","year":2015,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Toronto East General Hospital","funders":"","keywords":"Minification; Computer science; Enhanced Data Rates for GSM Evolution; Filter (signal processing); Contrast (vision); Kernel (algebra); Artificial intelligence; Algorithm; Invariant (physics); Energy minimization; Image (mathematics); Energy (signal processing); Computer vision; Pattern recognition (psychology); Mathematics; Physics","score_opus":0.04333037417773052,"score_gpt":0.29901727833417774,"score_spread":0.25568690415644724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1854231432","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":[0.009133592,0.000056638117,0.98934305,0.00008921217,0.00003670157,0.000015087568,0.00003558609,0.00030297312,0.0009871578],"genre_scores_gemma":[0.1288662,0.00029180662,0.86418694,0.00013948217,0.00006791973,0.0000380035,0.0001875841,0.00018164757,0.006040368],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970347,0.00006083426,0.000012351487,0.00006255458,0.00013061913,0.00003010957],"domain_scores_gemma":[0.9988933,0.00044768135,0.00011484061,0.00021585044,0.0002772722,0.000051105242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005303351,0.0007399822,0.0003859758,0.00086197344,0.00037965248,0.0011396175,0.00070503796,0.00094371685,0.0022918393],"category_scores_gemma":[0.0028483206,0.0004619457,0.0003105649,0.0008682109,0.00050928973,0.0017338927,0.001014284,0.0012314445,0.00082230725],"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":[0.00046224994,0.0002615975,0.0010706316,0.00017926445,0.000063202475,0.00013091687,0.0002353272,0.13014913,0.2248959,0.058212794,0.005991514,0.5783476],"study_design_scores_gemma":[0.000017102082,0.000048809943,0.0003062681,0.000015117802,0.000020991272,0.00007096436,0.00002673159,0.91519624,0.06430365,0.016946733,0.0030306177,0.000016902823],"about_ca_topic_score_codex":0.0020448822,"about_ca_topic_score_gemma":0.0054337243,"teacher_disagreement_score":0.0022918393,"about_ca_system_score_codex":0.00045046114,"about_ca_system_score_gemma":0.00059737294,"threshold_uncertainty_score":0.0076669455},"labels":[],"label_agreement":null},{"id":"W1859002903","doi":"10.1007/978-3-642-31254-0_1","title":"Bayesian Image Matting Using Infrared and Color Cues","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Infrared; Image (mathematics); Bayesian probability; Pattern recognition (psychology)","score_opus":0.016203095952034216,"score_gpt":0.2623876608477602,"score_spread":0.246184564895726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1859002903","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":[0.003070885,0.00012528151,0.9952317,0.000041026215,0.000015473117,0.000007787438,0.000027265207,0.00022585347,0.0012546609],"genre_scores_gemma":[0.12208205,0.00056581985,0.86654204,0.00007139736,0.00006150647,0.000035592664,0.00028926987,0.0003690142,0.009983419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964094,0.000044974913,0.000016719792,0.000071286886,0.00019720798,0.00002891198],"domain_scores_gemma":[0.9992316,0.00027773718,0.000090269066,0.0001362787,0.00022458148,0.000039635695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006674382,0.0005638344,0.0006326706,0.00062023813,0.00019855739,0.0008856736,0.0008870376,0.00066162396,0.0043232897],"category_scores_gemma":[0.0020631214,0.0007802099,0.0006604613,0.0006004434,0.0005012933,0.0015645652,0.00088187837,0.0011301124,0.001609574],"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":[0.00026003263,0.00009679114,0.00081503246,0.00027470785,0.00009579349,0.00008372326,0.00014331649,0.20127392,0.13246025,0.04674116,0.005287992,0.61246717],"study_design_scores_gemma":[0.0000127903195,0.000038879083,0.00072768703,0.000025144462,0.000027074782,0.00017668265,0.000015755062,0.9523602,0.028668657,0.014081353,0.0038361773,0.000029444162],"about_ca_topic_score_codex":0.0015020602,"about_ca_topic_score_gemma":0.0036649653,"teacher_disagreement_score":0.0043232897,"about_ca_system_score_codex":0.0003433707,"about_ca_system_score_gemma":0.00048412126,"threshold_uncertainty_score":0.014462888},"labels":[],"label_agreement":null},{"id":"W192534383","doi":"10.1007/978-0-387-21790-1_8","title":"A Faster and Smarter Deep Blue","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Deep blue; Victory; Competition (biology); Adversary; Artificial intelligence; Computer science; Political science; Law; Computer security; Chemistry","score_opus":0.011605960998319642,"score_gpt":0.21328158611997822,"score_spread":0.20167562512165857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W192534383","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":[0.008906997,0.00676119,0.8226592,0.0013575784,0.0010456333,0.00006373576,0.00021704214,0.004892967,0.15409563],"genre_scores_gemma":[0.035778284,0.005373998,0.45441407,0.001341375,0.00022830944,0.000055328634,0.00030271005,0.002685206,0.4998207],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998814,0.000008152652,0.0000035794792,0.000027152008,0.000066255976,0.000013449144],"domain_scores_gemma":[0.9998061,0.000045726672,0.000010022627,0.000039936687,0.00007960497,0.000018566829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019810673,0.0008195755,0.00037626037,0.00052498904,0.0003062726,0.0010038448,0.00091574376,0.00072350627,0.03469532],"category_scores_gemma":[0.00040679815,0.0004550243,0.0003179862,0.0004887976,0.00048119927,0.0024709934,0.00084104115,0.0019889523,0.0133976275],"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":[0.000097820644,0.000073648735,0.00014213343,0.0004093257,0.000020238305,0.000105920146,0.00014894034,0.0028013794,0.113946415,0.04720386,0.050608795,0.7844416],"study_design_scores_gemma":[0.000035404595,0.00015540406,0.0007108979,0.00023524131,0.000051313877,0.0015284974,0.000115466464,0.04316354,0.12030913,0.05221163,0.7814269,0.000056545425],"about_ca_topic_score_codex":0.0008900402,"about_ca_topic_score_gemma":0.0016561325,"teacher_disagreement_score":0.03469532,"about_ca_system_score_codex":0.00037531587,"about_ca_system_score_gemma":0.0003268858,"threshold_uncertainty_score":0.11606741},"labels":[],"label_agreement":null},{"id":"W194852316","doi":"","title":"Retinex in Matlab.","year":2000,"lang":"en","type":"article","venue":"Color Imaging Conference","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":71,"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":"","keywords":"Computer science; MATLAB; Color constancy; Pixel; Computer vision; Computation; Computer graphics (images); Artificial intelligence; Calibration; Code (set theory); Image (mathematics); Algorithm; Programming language; Mathematics","score_opus":0.00879149229839444,"score_gpt":0.2407244846352333,"score_spread":0.23193299233683887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W194852316","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014744082,0.0009923715,0.72781485,0.00044245875,0.0003646414,0.00019611308,0.010980225,0.22395366,0.03378121],"genre_scores_gemma":[0.03488451,0.0019161068,0.83041966,0.0011023999,0.00017852926,0.001859156,0.016311849,0.049273007,0.064054735],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992199,0.00015368804,0.00009945631,0.00014164888,0.00029950516,0.00008586713],"domain_scores_gemma":[0.99861896,0.00058492547,0.00013222257,0.00027229823,0.000330696,0.00006090559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013192156,0.0013367833,0.0012633367,0.0015809912,0.0005037618,0.0018922772,0.0023707817,0.0011129795,0.13725238],"category_scores_gemma":[0.0037923367,0.0009648583,0.001175813,0.0010022698,0.00058508525,0.0015725228,0.0016030129,0.0021159793,0.07787405],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006457355,0.00019220752,0.0012366067,0.0027654872,0.00035635656,0.00045785707,0.0004388554,0.023052732,0.013604169,0.085884206,0.4625975,0.40876833],"study_design_scores_gemma":[0.0003715466,0.0001431798,0.0012298167,0.00051526394,0.00011962549,0.0010461875,0.000111756795,0.10515781,0.04238007,0.07095232,0.7777992,0.00017318362],"about_ca_topic_score_codex":0.0020785974,"about_ca_topic_score_gemma":0.0032161723,"teacher_disagreement_score":0.13725238,"about_ca_system_score_codex":0.0006586337,"about_ca_system_score_gemma":0.0011791538,"threshold_uncertainty_score":0.45915496},"labels":[],"label_agreement":null},{"id":"W1953078193","doi":"10.1109/mce.2015.2463294","title":"Demystifying High-Dynamic-Range Technology: A new evolution in digital media","year":2015,"lang":"en","type":"article","venue":"IEEE Consumer Electronics Magazine","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada); University of British Columbia","funders":"","keywords":"Luminance; High dynamic range; Dynamic range; Human eye; High-dynamic-range imaging; Computer science; Computer graphics (images); Optics; Range (aeronautics); Adaptation (eye); Digital photography; Tone mapping; Computer vision; Artificial intelligence; Photography; Physics; Engineering; Art","score_opus":0.013936830433924711,"score_gpt":0.2505120857818698,"score_spread":0.23657525534794507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953078193","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029436396,0.5347575,0.25252193,0.036147222,0.0076827994,0.00019238757,0.0005881703,0.002311451,0.13636212],"genre_scores_gemma":[0.21239142,0.36229965,0.34243813,0.010677083,0.011376982,0.0003097865,0.0007925257,0.001180924,0.05853354],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981337,0.00026726496,0.00009999475,0.0002966292,0.0010644452,0.00013798157],"domain_scores_gemma":[0.9961159,0.0017434716,0.00021429926,0.0005549692,0.0011163688,0.00025499758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002699445,0.000759949,0.00068273064,0.0026767151,0.0009884669,0.004720378,0.0016536233,0.0025185451,0.0062698964],"category_scores_gemma":[0.005075263,0.00050602324,0.0005706814,0.0027430204,0.004078572,0.012680473,0.002691137,0.004488116,0.0027332145],"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":[0.000106616244,0.000103928905,0.0009989132,0.0011650786,0.000022208478,0.0004090325,0.0012155023,0.0010975735,0.023678863,0.3961019,0.022690048,0.5524105],"study_design_scores_gemma":[0.000019757532,0.0002057467,0.0010023274,0.00066574797,0.000024483395,0.0018244437,0.0006297753,0.0045496006,0.019810311,0.09071167,0.88041073,0.00014546335],"about_ca_topic_score_codex":0.0007138573,"about_ca_topic_score_gemma":0.0006352803,"teacher_disagreement_score":0.0062698964,"about_ca_system_score_codex":0.0016889386,"about_ca_system_score_gemma":0.00095306494,"threshold_uncertainty_score":0.020974934},"labels":[],"label_agreement":null},{"id":"W1963640979","doi":"10.1109/ist.2013.6729679","title":"Color range determination and alpha matting for color images","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ross Video (Canada); University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Cluster analysis; Segmentation; Computer vision; Metric (unit); Image segmentation; Alpha (finance); Color space; Image (mathematics); Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.010090247363267298,"score_gpt":0.2563839124623894,"score_spread":0.2462936650991221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963640979","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":[0.004592461,0.0002564578,0.99318343,0.000030912848,0.000045538116,0.000024626723,0.00002312368,0.0010342074,0.0008092739],"genre_scores_gemma":[0.10695141,0.0007273127,0.88837093,0.00006832284,0.000098079116,0.00006008117,0.0002167776,0.00039191707,0.00311511],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989335,0.00012923828,0.00006342476,0.00033821785,0.00047279082,0.00006286797],"domain_scores_gemma":[0.998696,0.0002499559,0.00020718161,0.0002695368,0.00051935203,0.000058087535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061824184,0.0010664352,0.00062592805,0.0016775603,0.0003844183,0.0011771658,0.0011326467,0.000599641,0.002375758],"category_scores_gemma":[0.0022551646,0.0005262666,0.00086288765,0.0012799077,0.0006543931,0.0017953645,0.00061546656,0.0011012355,0.0017463862],"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":[0.00022202285,0.000048966154,0.0010632835,0.00032158912,0.000064455584,0.00016871869,0.0002447164,0.017818816,0.16510087,0.009705693,0.0022950817,0.80294585],"study_design_scores_gemma":[0.00002362451,0.00023255308,0.0034861814,0.000049196027,0.000083195555,0.001656737,0.00013300662,0.57011396,0.39254636,0.0061010313,0.025468977,0.00010513755],"about_ca_topic_score_codex":0.0012114969,"about_ca_topic_score_gemma":0.0012593584,"teacher_disagreement_score":0.002375758,"about_ca_system_score_codex":0.00047058682,"about_ca_system_score_gemma":0.00046437746,"threshold_uncertainty_score":0.007947683},"labels":[],"label_agreement":null},{"id":"W1964061830","doi":"10.1109/iscas.2010.5537871","title":"Saturated-pixel enhancement for color images","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pixel; Smoothing; Artificial intelligence; Channel (broadcasting); Computer vision; Partition (number theory); Saturation (graph theory); Color image; Computer science; Mathematics; Image (mathematics); Image processing","score_opus":0.007776509763982172,"score_gpt":0.2682814503225999,"score_spread":0.2605049405586177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964061830","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":[0.023929374,0.00040325822,0.97256553,0.000053246717,0.000049577087,0.000051591116,0.000037425783,0.0012482586,0.0016617398],"genre_scores_gemma":[0.14281626,0.00060171064,0.8522654,0.00008663158,0.000045667046,0.000043711654,0.000119131044,0.00017453641,0.0038469231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997969,0.000027517624,0.000010590612,0.00004129451,0.00009997681,0.000023668888],"domain_scores_gemma":[0.9996542,0.0000900423,0.000040029216,0.00006679866,0.00013131797,0.000017629818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042349478,0.00065407704,0.00049521343,0.0007493665,0.00030001078,0.0004731358,0.00066866894,0.00040017194,0.0026126364],"category_scores_gemma":[0.0008397215,0.00030329998,0.00061691867,0.0005434078,0.00039463787,0.000701008,0.00052170095,0.0005515855,0.0010295135],"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":[0.00031414488,0.00011163435,0.00211504,0.00035589744,0.00010739672,0.00023925598,0.00017098007,0.02904785,0.3563564,0.0077156806,0.0022912796,0.6011745],"study_design_scores_gemma":[0.000045406905,0.00027367627,0.0046287486,0.000043116605,0.00016076113,0.0012359974,0.000065351334,0.50434685,0.4646554,0.006693946,0.017789543,0.00006128281],"about_ca_topic_score_codex":0.0010232837,"about_ca_topic_score_gemma":0.0018695883,"teacher_disagreement_score":0.0026126364,"about_ca_system_score_codex":0.00028605908,"about_ca_system_score_gemma":0.00045001757,"threshold_uncertainty_score":0.008740187},"labels":[],"label_agreement":null},{"id":"W1968571232","doi":"10.1145/1073204.1073242","title":"Evaluation of tone mapping operators using a High Dynamic Range display","year":2005,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":289,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Tone mapping; High dynamic range; Computer science; Contrast (vision); Visibility; High-dynamic-range imaging; Impression; Tone (literature); Brightness; Computer vision; Range (aeronautics); Artificial intelligence; Dynamic range; Computer graphics (images); Geography; Optics; Engineering","score_opus":0.04059747123573715,"score_gpt":0.3283221605109995,"score_spread":0.28772468927526235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968571232","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":[0.97023284,0.00013832067,0.027627308,0.000044442433,0.000034733155,0.00024597728,0.00003750088,0.0001605584,0.0014783337],"genre_scores_gemma":[0.93322265,0.00020294363,0.064854585,0.000060704635,0.000021607615,0.00014227134,0.00006370036,0.00010510298,0.0013264234],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981583,0.00082168245,0.00014782802,0.00021173125,0.00053742627,0.00012303365],"domain_scores_gemma":[0.9826612,0.013546517,0.0007590096,0.001041003,0.0015728852,0.00041947642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029055427,0.00084917207,0.0003287588,0.0005523126,0.00036079533,0.0010286665,0.00074772467,0.0007217898,0.002488192],"category_scores_gemma":[0.01949111,0.0002387103,0.0003418281,0.0002527524,0.0005562556,0.0012774956,0.00088197074,0.0004944736,0.00026559064],"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":[0.007130541,0.0020608008,0.008816942,0.001751941,0.0001766219,0.0008298835,0.0070489463,0.0049296897,0.7936762,0.0020473273,0.00074841396,0.17078261],"study_design_scores_gemma":[0.0012123568,0.049135655,0.09660356,0.00033432036,0.0008908618,0.004778763,0.0075617386,0.079089224,0.7456073,0.0027054392,0.011649769,0.0004309447],"about_ca_topic_score_codex":0.0003976926,"about_ca_topic_score_gemma":0.0003998446,"teacher_disagreement_score":0.0029055427,"about_ca_system_score_codex":0.00022285046,"about_ca_system_score_gemma":0.00024051259,"threshold_uncertainty_score":0.015366197},"labels":[],"label_agreement":null},{"id":"W1970076768","doi":"10.1109/tmm.2014.2299515","title":"Illumination Robust Video Foreground Prediction Based on Color Recovering","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Foreground detection; Frame (networking); Optical flow; Segmentation; Pixel; Background subtraction; Opacity; Image segmentation; Pattern recognition (psychology); Image (mathematics)","score_opus":0.01567198960955278,"score_gpt":0.23206444117904998,"score_spread":0.2163924515694972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970076768","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":[0.027467068,0.0003184937,0.9698238,0.00004679431,0.00003593844,0.000037879436,0.00004591313,0.001551888,0.00067238],"genre_scores_gemma":[0.49543342,0.0007006376,0.5013783,0.00008165124,0.00008288456,0.000053267493,0.0002975281,0.00019417133,0.0017781751],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997105,0.000024990994,0.000012040139,0.00009813834,0.00011596875,0.00003833376],"domain_scores_gemma":[0.99950624,0.000123111,0.000088641485,0.00009222856,0.00016001059,0.000029783374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035646206,0.0008347665,0.00072478736,0.0009260181,0.00040810637,0.0005957002,0.0009217146,0.00044154873,0.0006982936],"category_scores_gemma":[0.0014609973,0.00029327357,0.000493209,0.00070733187,0.00040632742,0.0010712729,0.000545723,0.00074548495,0.0004570675],"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":[0.0004898233,0.00008367359,0.0024387937,0.000102115264,0.000054160584,0.0002570237,0.0001526552,0.10135341,0.14238925,0.003080695,0.0022071549,0.74739134],"study_design_scores_gemma":[0.000010588875,0.000046789817,0.0009781684,0.000009133936,0.000024765048,0.00017723365,0.000017589004,0.92993605,0.06687761,0.0009607756,0.0009391491,0.000022117718],"about_ca_topic_score_codex":0.004939525,"about_ca_topic_score_gemma":0.0034536598,"teacher_disagreement_score":0.004939525,"about_ca_system_score_codex":0.00046300964,"about_ca_system_score_gemma":0.0006462048,"threshold_uncertainty_score":0.009821534},"labels":[],"label_agreement":null},{"id":"W1971696071","doi":"10.1117/12.603601","title":"In harbor underwater threat detection/identification using active imaging","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Underwater; Remote sensing; Laser; Computer vision; Artificial intelligence; Optics; Geology; Physics","score_opus":0.011805586179098574,"score_gpt":0.24639152672346576,"score_spread":0.23458594054436718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971696071","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":[0.94520986,0.00070848415,0.04372254,0.000077936784,0.000030335581,0.00018419819,0.0001839861,0.00068310124,0.009199555],"genre_scores_gemma":[0.9421968,0.00038950323,0.05100775,0.000062866195,0.000018347124,0.00007380828,0.00041413345,0.00004250127,0.005794238],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993325,0.00012271803,0.000018577688,0.00010700085,0.0003296767,0.00008951731],"domain_scores_gemma":[0.999584,0.0001058608,0.0000745169,0.00005684848,0.00013174226,0.00004697025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008499103,0.00046605576,0.00031166058,0.00080074905,0.00034765888,0.0008064819,0.00059408345,0.00048779344,0.0021817903],"category_scores_gemma":[0.0010867438,0.00024671177,0.00022697626,0.0003637917,0.00051656464,0.00089318,0.0011735471,0.00031443886,0.0005409662],"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":[0.00255886,0.00080342457,0.05579178,0.00043658217,0.00014698965,0.00077595504,0.0008124084,0.01566318,0.38599604,0.001272187,0.0018916018,0.53385097],"study_design_scores_gemma":[0.0002537665,0.0064399466,0.2089608,0.00013831201,0.00041942677,0.002897892,0.0010860121,0.19935744,0.5562889,0.0013579581,0.022601742,0.00019775234],"about_ca_topic_score_codex":0.004293081,"about_ca_topic_score_gemma":0.01126062,"teacher_disagreement_score":0.004293081,"about_ca_system_score_codex":0.0004825074,"about_ca_system_score_gemma":0.00033775042,"threshold_uncertainty_score":0.00853622},"labels":[],"label_agreement":null},{"id":"W1973646532","doi":"10.1016/j.rti.2003.11.001","title":"Fast color correction using principal regions mapping in different color spaces","year":2003,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gamut; Artificial intelligence; Computer vision; Computer science; Object (grammar); Color space; Pixel; Color correction; Principal component analysis; Principal (computer security); Color image; Pattern recognition (psychology); Color balance; Degree (music); Color histogram; Mathematics; Image (mathematics); Image processing","score_opus":0.017303948046962937,"score_gpt":0.26315507149457373,"score_spread":0.2458511234476108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973646532","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":[0.006124564,0.0002878269,0.991996,0.000057841306,0.00005426717,0.000023032226,0.000024697201,0.0009029473,0.0005288031],"genre_scores_gemma":[0.037496462,0.00056522025,0.9595033,0.000028231596,0.000024869381,0.000032120726,0.00006534163,0.00046824946,0.0018162463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993919,0.0001310195,0.000024868856,0.00009321949,0.0002976411,0.00006132894],"domain_scores_gemma":[0.9983569,0.0006049319,0.00013949285,0.0003535754,0.00047559576,0.00006956717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010354157,0.0016238767,0.0008055001,0.0015307647,0.0005664366,0.0013799479,0.0012064197,0.0008416072,0.004901659],"category_scores_gemma":[0.0028556867,0.0008077726,0.0009249261,0.0017027694,0.00063065416,0.002195298,0.0011739223,0.0017539714,0.0019720288],"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":[0.0008117004,0.0000852514,0.0005523706,0.00037324312,0.00011427661,0.00012028383,0.00020917822,0.012898497,0.27998877,0.011182953,0.0026875017,0.6909759],"study_design_scores_gemma":[0.00009653099,0.00020196816,0.0029203524,0.000062459265,0.0002320526,0.0014193218,0.0001177579,0.39593387,0.5694135,0.010854878,0.0186142,0.00013313477],"about_ca_topic_score_codex":0.001805206,"about_ca_topic_score_gemma":0.0026661747,"teacher_disagreement_score":0.004901659,"about_ca_system_score_codex":0.00042439095,"about_ca_system_score_gemma":0.00084614917,"threshold_uncertainty_score":0.016397655},"labels":[],"label_agreement":null},{"id":"W1974832347","doi":"10.1007/s00138-009-0186-y","title":"Is local colour normalization good enough for local appearance-based classification?","year":2009,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Dalhousie University","funders":"","keywords":"Normalization (sociology); Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Computer science; Computer vision","score_opus":0.01233703732910055,"score_gpt":0.30478333112799344,"score_spread":0.29244629379889286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974832347","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":[0.13224241,0.005622252,0.8354227,0.0047226185,0.0010654654,0.00014514221,0.00058446865,0.0044281436,0.015766857],"genre_scores_gemma":[0.829344,0.0028511523,0.1576861,0.0012827965,0.0006706988,0.00008676103,0.00081373146,0.0009966139,0.006268194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820924,0.00048877276,0.0000799847,0.00049041555,0.00045251104,0.00027910026],"domain_scores_gemma":[0.9956287,0.0012451335,0.00038576004,0.0012589315,0.0012946308,0.00018685349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038487893,0.00084504695,0.0021549484,0.0011498054,0.0006783692,0.0022977663,0.0010651777,0.0023015374,0.0055502746],"category_scores_gemma":[0.01323633,0.000559216,0.0010575162,0.0013871917,0.0016938556,0.004536198,0.0007460286,0.0014057284,0.0058698533],"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":[0.0013207495,0.00026415568,0.019401073,0.000744603,0.00050005136,0.0002358584,0.00009207598,0.017416306,0.17664991,0.008360866,0.015278984,0.75973535],"study_design_scores_gemma":[0.00024165827,0.00074220495,0.095698565,0.00037034787,0.00088147516,0.0027625957,0.0005053117,0.5357174,0.28638408,0.051622562,0.024787283,0.00028648027],"about_ca_topic_score_codex":0.0020616425,"about_ca_topic_score_gemma":0.0029694142,"teacher_disagreement_score":0.0055502746,"about_ca_system_score_codex":0.0005821466,"about_ca_system_score_gemma":0.0006228642,"threshold_uncertainty_score":0.020354569},"labels":[],"label_agreement":null},{"id":"W1975558697","doi":"10.1155/2010/891703","title":"Emerging Methods for Color Image and Video Quality Enhancement","year":2010,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biometrics; Artificial intelligence; Computer science; Computer vision; Image enhancement; Image quality; Color image; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.029363475256498385,"score_gpt":0.4061497491996857,"score_spread":0.3767862739431873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975558697","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":[0.0016333185,0.010927223,0.9730546,0.0004705137,0.00027386346,0.00011357512,0.00022612134,0.0009869371,0.012313911],"genre_scores_gemma":[0.06232404,0.025319992,0.8455152,0.00040928394,0.0005755665,0.00032898967,0.0011674735,0.000745462,0.06361404],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990305,0.00015076062,0.00003764385,0.00016986721,0.00056062033,0.000050759016],"domain_scores_gemma":[0.99856013,0.00033661697,0.000089583344,0.00025882793,0.00070276466,0.000052052714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020357845,0.000987155,0.00067735015,0.0018916287,0.00038306587,0.0017455884,0.0013783304,0.0012407949,0.018204533],"category_scores_gemma":[0.0019068859,0.0005365213,0.00068760244,0.0013586922,0.00087852153,0.002162068,0.0012347137,0.0016291001,0.007604383],"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":[0.00014604095,0.0001008481,0.00046144443,0.00095859054,0.0000617992,0.00008558785,0.00013176311,0.0040055467,0.12471998,0.04928427,0.023604726,0.7964394],"study_design_scores_gemma":[0.00011609934,0.00043243848,0.0036021024,0.0005722261,0.00017056467,0.001990663,0.00026232246,0.20118943,0.28976673,0.09031022,0.41140997,0.00017720027],"about_ca_topic_score_codex":0.0010023868,"about_ca_topic_score_gemma":0.0013014079,"teacher_disagreement_score":0.018204533,"about_ca_system_score_codex":0.00065213215,"about_ca_system_score_gemma":0.00046199962,"threshold_uncertainty_score":0.06090027},"labels":[],"label_agreement":null},{"id":"W1975957562","doi":"10.1109/icip.2010.5649486","title":"An improved Bayesian algorithm for color image desaturation","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 British Columbia","funders":"","keywords":"Pixel; Artificial intelligence; Algorithm; Color image; Bayesian probability; Computer vision; Computer science; Luminance; Spatial correlation; Digital image; Mathematics; Pattern recognition (psychology); Image (mathematics); Image processing; Statistics","score_opus":0.005393288642825423,"score_gpt":0.26987803235610264,"score_spread":0.26448474371327724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975957562","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":[0.0014198427,0.00010042588,0.99749917,0.000056821635,0.00001889714,0.000026614522,0.000027228316,0.00043762688,0.00041344177],"genre_scores_gemma":[0.033600263,0.00023014359,0.9628048,0.00012347502,0.000064581596,0.00013753424,0.0002311746,0.00021494807,0.0025931387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997851,0.00044188497,0.00015097288,0.0004454257,0.00096822303,0.00014247661],"domain_scores_gemma":[0.99723274,0.0010066439,0.00020139002,0.00028686805,0.0011878613,0.00008452425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024299428,0.0012166168,0.0015106632,0.0019561378,0.0009226846,0.0014238139,0.002759572,0.0016855019,0.0041995053],"category_scores_gemma":[0.0072920765,0.0010933711,0.0011738344,0.0019270529,0.0008285784,0.002576636,0.0018988278,0.0021429649,0.0018161909],"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":[0.00029035815,0.00010804788,0.0011906795,0.00013267816,0.000117877375,0.00010286882,0.0002081363,0.19327237,0.022254508,0.022005988,0.0051668454,0.7551496],"study_design_scores_gemma":[0.000029988752,0.000023529397,0.00029049427,0.000011447556,0.000024591456,0.0000878664,0.000011932843,0.98326355,0.005609223,0.00669611,0.0039223125,0.000028902045],"about_ca_topic_score_codex":0.013820819,"about_ca_topic_score_gemma":0.016352188,"teacher_disagreement_score":0.013820819,"about_ca_system_score_codex":0.0014851941,"about_ca_system_score_gemma":0.0030168765,"threshold_uncertainty_score":0.027480781},"labels":[],"label_agreement":null},{"id":"W1976478698","doi":"10.1109/tip.2012.2221725","title":"Objective Quality Assessment of Tone-Mapped Images","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":630,"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":"Tone mapping; Computer science; Naturalness; Artificial intelligence; Image quality; Measure (data warehouse); Visualization; Computer vision; Tone (literature); High dynamic range; Pattern recognition (psychology); Ranking (information retrieval); Image (mathematics); Dynamic range; Data mining","score_opus":0.03168889954894181,"score_gpt":0.3745780248808502,"score_spread":0.34288912533190835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976478698","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":[0.3234554,0.000677822,0.6718777,0.00011764006,0.00006702661,0.00020563648,0.00027742598,0.0006096725,0.002711672],"genre_scores_gemma":[0.78384244,0.00059001945,0.21264598,0.0000651762,0.000066995664,0.00010428624,0.0004014436,0.00016955829,0.0021141674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991258,0.00015371446,0.00006845202,0.00014063294,0.0004667895,0.000044600383],"domain_scores_gemma":[0.9968003,0.00094027247,0.0004715545,0.00030074216,0.0013592282,0.0001278855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015782085,0.0004949485,0.0004278724,0.0012828276,0.00019787511,0.0010853279,0.0004215118,0.0004885682,0.0022590389],"category_scores_gemma":[0.006056968,0.00017094499,0.0003250662,0.0004797749,0.00040870297,0.0011650976,0.0007676742,0.00035679777,0.0003324049],"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":[0.0014389158,0.00021289993,0.013778662,0.0009286684,0.00020786322,0.00030174977,0.00049528154,0.032532506,0.5370904,0.0039369725,0.0015503705,0.40752566],"study_design_scores_gemma":[0.00012991577,0.0019566782,0.0725746,0.00012048095,0.00031863913,0.0019017024,0.000521804,0.4553725,0.45582384,0.005314711,0.0057192445,0.00024585897],"about_ca_topic_score_codex":0.0004404282,"about_ca_topic_score_gemma":0.0004826753,"teacher_disagreement_score":0.0022590389,"about_ca_system_score_codex":0.00024710892,"about_ca_system_score_gemma":0.00019367319,"threshold_uncertainty_score":0.008346498},"labels":[],"label_agreement":null},{"id":"W1977397434","doi":"10.1109/crv.2010.17","title":"The Effect of Colour Space on Image Sharpening Algorithms","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sharpening; Image (mathematics); Computer science; Space (punctuation); Computer vision; Artificial intelligence; Algorithm","score_opus":0.0036234891947671717,"score_gpt":0.2569606791597683,"score_spread":0.2533371899650011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977397434","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":[0.35004735,0.013390857,0.6251784,0.00046138925,0.00026735227,0.0001532421,0.00009203795,0.0023078208,0.008101515],"genre_scores_gemma":[0.71268517,0.006438385,0.27603558,0.00021269116,0.00012051595,0.000054266788,0.00009446298,0.00056369934,0.0037951842],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779725,0.00095140253,0.00017007126,0.00024529579,0.0007110721,0.00012494333],"domain_scores_gemma":[0.97986126,0.016797157,0.00065072655,0.0011699017,0.0013831012,0.00013785297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034883705,0.001045884,0.00054056215,0.001115206,0.00045963418,0.0013867748,0.00056248525,0.0010321637,0.0030116017],"category_scores_gemma":[0.016927708,0.00047176465,0.0006308576,0.0011627492,0.0010075317,0.0022728657,0.0010106084,0.00093418563,0.0006786851],"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":[0.003227925,0.00015389071,0.002736888,0.0011566724,0.00018951044,0.00036048953,0.0005090868,0.0471054,0.5443357,0.009203614,0.00045135635,0.39056933],"study_design_scores_gemma":[0.00007696833,0.0019056088,0.006499575,0.00010530602,0.00032665915,0.0014001819,0.00013738975,0.12248006,0.85543346,0.0045161108,0.006980731,0.0001379359],"about_ca_topic_score_codex":0.0005192737,"about_ca_topic_score_gemma":0.00034097585,"teacher_disagreement_score":0.0034883705,"about_ca_system_score_codex":0.00026703512,"about_ca_system_score_gemma":0.00023282821,"threshold_uncertainty_score":0.018448532},"labels":[],"label_agreement":null},{"id":"W1977627410","doi":"10.4319/lo.2012.57.4.1025","title":"Radiance fluctuations induced by surface waves can enhance the appearance of underwater objects","year":2012,"lang":"en","type":"article","venue":"Limnology and Oceanography","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Innovation Trust","keywords":"Radiance; Contrast (vision); Underwater; Optics; Physics; Fish <Actinopterygii>; Spatial frequency; Biology; Geology; Fishery; Oceanography","score_opus":0.009533629727923388,"score_gpt":0.23722962940950632,"score_spread":0.22769599968158294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977627410","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":[0.9975804,0.000050076836,0.0019420647,0.0000058883197,0.000002986085,0.000006048446,0.000011229522,0.000016810278,0.00038447548],"genre_scores_gemma":[0.9976109,0.00007193837,0.001958417,0.000012599747,0.0000033794854,0.0000071669288,0.000025589115,0.000011225316,0.0002987467],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999912,0.000013065626,0.0000045368643,0.00001680933,0.00003222217,0.000021357271],"domain_scores_gemma":[0.999668,0.00014606326,0.00007643725,0.000029092656,0.000043098466,0.000037362766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001485977,0.0001846287,0.0001486059,0.00025804332,0.00007775943,0.00025397207,0.00012663001,0.00016912185,0.0007345585],"category_scores_gemma":[0.0008354317,0.00014586898,0.00013625872,0.00014464995,0.00022416125,0.00025228533,0.00025451698,0.00030854275,0.00008814585],"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":[0.00018725809,0.000017175551,0.0031573598,0.000030789382,0.000008090842,0.00010429004,0.00005413882,0.0002816003,0.99077445,0.00006245398,0.000023075647,0.0052993023],"study_design_scores_gemma":[0.000032247757,0.0012073123,0.49690706,0.000019101899,0.000064422384,0.0007275642,0.00023014245,0.0076734642,0.49221882,0.00029124602,0.00059822865,0.00003031558],"about_ca_topic_score_codex":0.0003127947,"about_ca_topic_score_gemma":0.00043895785,"teacher_disagreement_score":0.0007345585,"about_ca_system_score_codex":0.00012414377,"about_ca_system_score_gemma":0.00007234928,"threshold_uncertainty_score":0.0024573803},"labels":[],"label_agreement":null},{"id":"W1978606138","doi":"10.1109/icip.2010.5653283","title":"Visually-favorable tone-mapping with high compression performance","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of British Columbia","funders":"","keywords":"Tone mapping; Computer science; Tone (literature); Data compression; Coding (social sciences); Image compression; Artificial intelligence; Computer vision; Layer (electronics); Compression (physics); Image quality; Speech recognition; Image (mathematics); Mathematics; Image processing; High dynamic range","score_opus":0.007996775351713194,"score_gpt":0.24899719236049248,"score_spread":0.24100041700877928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978606138","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":[0.17302544,0.00022498902,0.82113624,0.00009885107,0.000036358684,0.00005541321,0.000039953233,0.000527634,0.0048550544],"genre_scores_gemma":[0.7648066,0.00026121916,0.23061787,0.000108088636,0.000045948753,0.00004746428,0.00008224934,0.00013429635,0.003896219],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998455,0.000022857534,0.0000060300854,0.00002348064,0.000088102846,0.000014021064],"domain_scores_gemma":[0.9996499,0.00012532952,0.000056236084,0.000060820268,0.000078643796,0.000029039076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028295277,0.00046845243,0.00029278465,0.00023758365,0.00012746567,0.0005400057,0.00026034747,0.00035028547,0.0015209247],"category_scores_gemma":[0.0012060627,0.00013777873,0.00019723993,0.0002226816,0.00029076068,0.0005618321,0.00046754704,0.00036164006,0.0004172239],"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":[0.00027058978,0.000104831786,0.0010434927,0.00018074189,0.000033002296,0.00032293404,0.000073133655,0.03914508,0.8107119,0.013205692,0.0008365798,0.13407204],"study_design_scores_gemma":[0.000056051573,0.0003866688,0.0023913148,0.000021208434,0.000041077015,0.0012651974,0.000049010054,0.6136684,0.37022915,0.008275911,0.0035699003,0.00004608285],"about_ca_topic_score_codex":0.00020602724,"about_ca_topic_score_gemma":0.00035005945,"teacher_disagreement_score":0.0015209247,"about_ca_system_score_codex":0.000110610126,"about_ca_system_score_gemma":0.00020577438,"threshold_uncertainty_score":0.0050880313},"labels":[],"label_agreement":null},{"id":"W1983974932","doi":"10.1109/tbc.2015.2419181","title":"Robust Image Chroma-Keying: A Quadmap Approach Based on Global Sampling and Local Affinity","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":15,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Color space; Hue; Computer science; HSL and HSV; Keying; Color image; Color depth; Color quantization; Color balance; Color histogram; Multispectral image; Mathematics; Image processing; Image (mathematics); Telecommunications","score_opus":0.0891255971570916,"score_gpt":0.28816858363758974,"score_spread":0.19904298648049812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983974932","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":[0.0091215605,0.00028010397,0.988816,0.00004887138,0.000031951662,0.00004507927,0.000036568432,0.00053987093,0.0010800293],"genre_scores_gemma":[0.2662046,0.0005838371,0.72779346,0.000171361,0.0000885327,0.00014661868,0.0002850146,0.00039566628,0.0043309284],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994616,0.00008073487,0.00001851104,0.00013944093,0.00022196998,0.000077818535],"domain_scores_gemma":[0.99951386,0.00009067296,0.000046095614,0.00009516557,0.0002086637,0.000045518373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046994534,0.0007785841,0.0009068262,0.0014678906,0.00054056855,0.0010444332,0.0013884276,0.00058035913,0.0032805717],"category_scores_gemma":[0.0014094999,0.00044393077,0.00079594227,0.0011655643,0.00058297976,0.0015243653,0.0012209585,0.0007247394,0.0011263738],"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":[0.00049967103,0.00013593282,0.0008668427,0.00019772432,0.000085021355,0.00013373962,0.00029533386,0.050531413,0.12663582,0.0154360095,0.0040616426,0.8011209],"study_design_scores_gemma":[0.000040501713,0.00023067335,0.0014206963,0.000020304318,0.000057623747,0.00030159563,0.00011062394,0.942403,0.038649596,0.008535298,0.008177722,0.000052416064],"about_ca_topic_score_codex":0.0035755325,"about_ca_topic_score_gemma":0.0025567245,"teacher_disagreement_score":0.0035755325,"about_ca_system_score_codex":0.0006222489,"about_ca_system_score_gemma":0.0006873279,"threshold_uncertainty_score":0.010974646},"labels":[],"label_agreement":null},{"id":"W1987819388","doi":"10.1109/iscas.2010.5537769","title":"On-the-fly tone mapping for backward-compatible high dynamic range image/video compression","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"University of British Columbia","funders":"","keywords":"Tone mapping; High dynamic range; Tone (literature); Computer science; Data compression; Distortion (music); Mean squared error; Algorithm; Range (aeronautics); Image compression; Dynamic range; Dynamic range compression; Computer vision; Compression (physics); Artificial intelligence; Image (mathematics); Mathematics; Image processing","score_opus":0.013306026797594512,"score_gpt":0.2823575781858291,"score_spread":0.26905155138823456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987819388","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":[0.054105207,0.00018591202,0.94421816,0.000054139975,0.000029831317,0.000030742653,0.000012785952,0.00017687818,0.0011863302],"genre_scores_gemma":[0.57442075,0.00038055173,0.42227387,0.000063169406,0.000044124547,0.000032973494,0.000042507476,0.000045977715,0.0026961183],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99989676,0.000023218723,0.0000048024704,0.000012431833,0.000056713317,0.0000060767643],"domain_scores_gemma":[0.99985206,0.000059352584,0.000022885559,0.000030450923,0.000027710761,0.000007547713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002275505,0.00023417198,0.00015530146,0.00014082345,0.00011121393,0.00022601117,0.00032039237,0.00021520123,0.0010088711],"category_scores_gemma":[0.0005220046,0.00008521622,0.00017915621,0.00010258402,0.00021125679,0.00031248294,0.00024044518,0.00027924316,0.00023431805],"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":[0.0002195722,0.00012453138,0.0012787769,0.00017889676,0.00003137888,0.00024632583,0.00010139992,0.06331496,0.58526164,0.015714299,0.0008538568,0.33267432],"study_design_scores_gemma":[0.000019508916,0.00018505573,0.00073559006,0.000011080094,0.000016825807,0.00061531545,0.000023628609,0.83924156,0.15349531,0.0018695886,0.0037642224,0.000022298165],"about_ca_topic_score_codex":0.00025993006,"about_ca_topic_score_gemma":0.0004692672,"teacher_disagreement_score":0.0010088711,"about_ca_system_score_codex":0.00010014333,"about_ca_system_score_gemma":0.00016852094,"threshold_uncertainty_score":0.0033749938},"labels":[],"label_agreement":null},{"id":"W1991944709","doi":"10.1109/ccece.2014.6901000","title":"Real-time automatic chroma-key matting using perceptual analysis and prediction","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Hue; Computer vision; Monochromatic color; Pattern recognition (psychology); Graphics; Color space; Lightness; Sample (material); Perception; Computer graphics (images); Image (mathematics)","score_opus":0.00919148522397013,"score_gpt":0.24581518259819193,"score_spread":0.23662369737422181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991944709","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":[0.03119793,0.0001828964,0.96396106,0.000029663735,0.00003914848,0.000041307612,0.000059710135,0.0036838218,0.00080433296],"genre_scores_gemma":[0.3544984,0.0002229291,0.6430776,0.00004984373,0.000039549544,0.00005143045,0.00020205144,0.00038674686,0.0014713882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974996,0.000023594757,0.00001084634,0.00007480536,0.00011182025,0.000029013248],"domain_scores_gemma":[0.99938095,0.00016759237,0.00009932948,0.00013451948,0.0001712798,0.000046252582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038072327,0.0006319772,0.0004699308,0.00079826696,0.00021637708,0.000628012,0.0008139345,0.00025666557,0.0022150162],"category_scores_gemma":[0.0011692363,0.00027165972,0.00037604608,0.00045030957,0.00030478468,0.0008038829,0.000537014,0.00046785822,0.00075996923],"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":[0.0005698049,0.00011010357,0.0018715874,0.00019422399,0.00005018316,0.00013662069,0.00012616478,0.014048201,0.4718459,0.0021475113,0.0021045823,0.5067951],"study_design_scores_gemma":[0.00003668034,0.0002468827,0.0042281994,0.00001956639,0.000045322042,0.00048490506,0.00005001249,0.6334025,0.3545149,0.0019258352,0.004997068,0.000048072696],"about_ca_topic_score_codex":0.00077400904,"about_ca_topic_score_gemma":0.00091912365,"teacher_disagreement_score":0.0022150162,"about_ca_system_score_codex":0.00022233855,"about_ca_system_score_gemma":0.00028042504,"threshold_uncertainty_score":0.00740999},"labels":[],"label_agreement":null},{"id":"W1993025405","doi":"10.3390/jlpea3040337","title":"Hardware Implementation of an Automatic Rendering Tone Mapping Algorithm for a Wide Dynamic Range Display","year":2013,"lang":"en","type":"article","venue":"Journal of Low Power Electronics and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":15,"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; Tone mapping; Algorithm; High dynamic range; Field-programmable gate array; Hardware architecture; Verilog; Rendering (computer graphics); Software; Computer hardware; Dynamic range; Artificial intelligence; Computer vision","score_opus":0.005719410040972606,"score_gpt":0.2926608664350821,"score_spread":0.2869414563941095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993025405","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":[0.0710197,0.00022544133,0.9157413,0.00013055796,0.00014165879,0.0002237456,0.000087870496,0.007455172,0.004974584],"genre_scores_gemma":[0.44659826,0.00015167388,0.5475069,0.000102780374,0.000043703032,0.00014721444,0.00017525716,0.00027732423,0.0049968343],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981934,0.0000228277,0.000016883601,0.000034363606,0.00008176598,0.000024785948],"domain_scores_gemma":[0.9996195,0.00011980454,0.00003981774,0.000068768575,0.00012956011,0.000022551088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026963875,0.00048202378,0.00024513376,0.000528753,0.00022269772,0.00056500983,0.0008966573,0.00031853173,0.006099172],"category_scores_gemma":[0.00079894654,0.00019499769,0.0001976279,0.00025463203,0.00015441196,0.00042488237,0.00022920688,0.00038759084,0.001056329],"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":[0.00044807495,0.00016485658,0.0014271658,0.0002407664,0.000051383144,0.00039268218,0.00023964758,0.010162628,0.5829708,0.0043684146,0.003959344,0.39557433],"study_design_scores_gemma":[0.00021054417,0.0010983536,0.004014219,0.000054171516,0.00007343749,0.0021935115,0.000066841494,0.31042778,0.64726716,0.0011111633,0.033388287,0.00009444266],"about_ca_topic_score_codex":0.0006749353,"about_ca_topic_score_gemma":0.00071952405,"teacher_disagreement_score":0.006099172,"about_ca_system_score_codex":0.0002843612,"about_ca_system_score_gemma":0.0004257129,"threshold_uncertainty_score":0.020403743},"labels":[],"label_agreement":null},{"id":"W1993582662","doi":"10.1049/el:20031198","title":"Digital camera zooming on colour filter array","year":2003,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Image Enhancement 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 Toronto","funders":"","keywords":"Computer vision; Zoom; Artificial intelligence; Computer science; Noise (video); Filter (signal processing); Enhanced Data Rates for GSM Evolution; Color filter array; Adaptive filter; Digital filter; Image sensor; Digital camera; Computer graphics (images); Image (mathematics); Engineering; Color gel; Algorithm","score_opus":0.006350863864631526,"score_gpt":0.21326190722781546,"score_spread":0.20691104336318394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993582662","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":[0.04636469,0.00043078297,0.9439717,0.00007953139,0.00010442516,0.00009312084,0.00008421694,0.0023989335,0.0064726276],"genre_scores_gemma":[0.17914046,0.00056599744,0.81025666,0.00010150156,0.000053071468,0.000079907426,0.00014166233,0.0001954064,0.009465309],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998628,0.000013941411,0.0000040690506,0.000027175414,0.00008020487,0.000011852782],"domain_scores_gemma":[0.99977416,0.00006374641,0.000018449004,0.000039234164,0.00008569782,0.000018718063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016858264,0.00030719783,0.00023256062,0.00047761455,0.00020287048,0.0002520204,0.000454674,0.00021469237,0.004573031],"category_scores_gemma":[0.00040183845,0.00018959896,0.00017950915,0.0004151919,0.00017653394,0.00047141875,0.00030451862,0.00027206665,0.0010140582],"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":[0.00020383934,0.000026859505,0.0002728364,0.00015579007,0.000017175153,0.00009952182,0.000094507675,0.004520351,0.65648127,0.007069818,0.002291978,0.32876614],"study_design_scores_gemma":[0.000064619286,0.0003617152,0.003610513,0.000053956537,0.000050336588,0.0012183455,0.000051662555,0.24964234,0.70372736,0.0047455244,0.036411088,0.000062588166],"about_ca_topic_score_codex":0.00070886366,"about_ca_topic_score_gemma":0.0014990888,"teacher_disagreement_score":0.004573031,"about_ca_system_score_codex":0.00029001097,"about_ca_system_score_gemma":0.00021187727,"threshold_uncertainty_score":0.015298247},"labels":[],"label_agreement":null},{"id":"W1994322580","doi":"10.1117/12.2087624","title":"Local adaptive tone mapping for video enhancement","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Enhancement 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":"Qualcomm (Canada)","funders":"","keywords":"Tone mapping; Computer science; Tone (literature); Computer vision","score_opus":0.02154071342223346,"score_gpt":0.2556331132541277,"score_spread":0.23409239983189423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994322580","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":[0.037830833,0.0039758217,0.944442,0.00014232032,0.00014706214,0.000076665645,0.00005541856,0.0011206142,0.012209293],"genre_scores_gemma":[0.63735133,0.0046907123,0.33288136,0.00030935445,0.00018312814,0.00011194976,0.00017764885,0.00017523908,0.02411927],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99991155,0.000014129514,0.000003090244,0.000022476333,0.000039194078,0.000009497585],"domain_scores_gemma":[0.9999268,0.000023937459,0.000009119899,0.0000127604,0.000022564283,0.00000480239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012162164,0.00032181368,0.00017034051,0.00027878906,0.0001263126,0.00036860933,0.000358598,0.00023518337,0.003042807],"category_scores_gemma":[0.00027358157,0.000083361505,0.00016693427,0.00030284922,0.00016800722,0.0003931436,0.00030486984,0.0003394333,0.0008566664],"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":[0.00014853444,0.000053832657,0.00034621524,0.00024984463,0.000022686809,0.00023900699,0.00008656652,0.0056410255,0.5398464,0.011632006,0.002769389,0.43896434],"study_design_scores_gemma":[0.000041690982,0.00042448365,0.0021320952,0.000070938164,0.00008322971,0.0019362848,0.0001040412,0.3009565,0.6035507,0.009339736,0.08130833,0.000051961375],"about_ca_topic_score_codex":0.0002503779,"about_ca_topic_score_gemma":0.00031419584,"teacher_disagreement_score":0.003042807,"about_ca_system_score_codex":0.00012996489,"about_ca_system_score_gemma":0.00008401623,"threshold_uncertainty_score":0.010179222},"labels":[],"label_agreement":null},{"id":"W1997706488","doi":"10.1109/ccece.2014.6901124","title":"Real-time HDR video imaging on FPGA with compressed comparametric lookup tables","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Toronto","funders":"","keywords":"Computer science; Lookup table; Field-programmable gate array; Table (database); Multiplexer; Computer vision; Computer graphics (images); Artificial intelligence; Computer hardware; Multiplexing; Data mining","score_opus":0.008767988513452823,"score_gpt":0.23579347606513312,"score_spread":0.2270254875516803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997706488","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":[0.2967288,0.00092705863,0.6600596,0.00028764436,0.00021643176,0.0003771269,0.0007112887,0.020469492,0.020222567],"genre_scores_gemma":[0.7763994,0.00021370262,0.21690395,0.00011506127,0.000037089227,0.00010995056,0.00049953983,0.00028322393,0.0054381327],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970204,0.000041638974,0.000020415779,0.000046523004,0.00015215312,0.000037171263],"domain_scores_gemma":[0.9995701,0.00015614556,0.000049670092,0.0000887969,0.00012033703,0.000014908688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030549656,0.00042620703,0.00026217633,0.0005317278,0.00017761417,0.000720984,0.00073705794,0.00024121878,0.0074377446],"category_scores_gemma":[0.0010698731,0.00015310111,0.00013189274,0.00048663933,0.0001726802,0.00080620765,0.00021910755,0.0002494417,0.00096649816],"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":[0.0022210518,0.00027713506,0.0034917917,0.0008246147,0.000096866504,0.0013678703,0.0003854048,0.049266335,0.39627308,0.020518793,0.015811613,0.5094654],"study_design_scores_gemma":[0.00024914407,0.0010575267,0.0026792013,0.00008919066,0.00008958399,0.0016721314,0.00013030527,0.45042098,0.5097746,0.0033797652,0.03039029,0.00006726727],"about_ca_topic_score_codex":0.0014709234,"about_ca_topic_score_gemma":0.0019736567,"teacher_disagreement_score":0.0074377446,"about_ca_system_score_codex":0.00055487483,"about_ca_system_score_gemma":0.00039181052,"threshold_uncertainty_score":0.02488172},"labels":[],"label_agreement":null},{"id":"W1999129255","doi":"10.1080/10867651.2002.10487554","title":"Parameter Estimation for Photographic Tone Reproduction","year":2002,"lang":"en","type":"article","venue":"Journal of Graphics Tools","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":130,"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 Utah; Ryerson University; U.S. Department of Energy","keywords":"Computer science; Tone (literature); Set (abstract data type); Photography; Range (aeronautics); Operator (biology); Process (computing); Tone mapping; Pixel; Sample (material); Code (set theory); Computer vision; Artificial intelligence; Algorithm; High dynamic range; Dynamic range; Engineering; Art","score_opus":0.050019627075917845,"score_gpt":0.30348795514842064,"score_spread":0.2534683280725028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999129255","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":[0.008215113,0.000086525695,0.9901215,0.0000339321,0.000010100633,0.00002331574,0.000031981337,0.0010285833,0.00044893567],"genre_scores_gemma":[0.32225204,0.00016377302,0.6748665,0.00005417049,0.000027229904,0.00011263407,0.00025394442,0.0003639525,0.0019057314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921834,0.00028671318,0.000042982883,0.0001560473,0.00025158725,0.000044363514],"domain_scores_gemma":[0.99764687,0.0014270466,0.0001908344,0.0003286028,0.00036419384,0.000042427688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094985944,0.00071983953,0.00051803305,0.0007723601,0.0003575803,0.0010209121,0.0007252528,0.0009956638,0.0035159595],"category_scores_gemma":[0.009736936,0.00036868663,0.000394046,0.00045893213,0.00036879544,0.0006667515,0.0007653711,0.0010416658,0.0014044725],"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":[0.00033654537,0.00008318619,0.0024179537,0.00021435051,0.000061537095,0.0001664507,0.0002757362,0.14769591,0.07501633,0.0074389973,0.003758618,0.76253444],"study_design_scores_gemma":[0.000028731922,0.000070290145,0.0019593171,0.00002332067,0.000022797854,0.00024990967,0.000041791907,0.946741,0.04359509,0.0034170428,0.0038109887,0.00003967797],"about_ca_topic_score_codex":0.0021372836,"about_ca_topic_score_gemma":0.0016223792,"teacher_disagreement_score":0.0035159595,"about_ca_system_score_codex":0.00033244482,"about_ca_system_score_gemma":0.00046296176,"threshold_uncertainty_score":0.011762083},"labels":[],"label_agreement":null},{"id":"W2001140770","doi":"10.1109/tip.2011.2159804","title":"An Energy-Based Model for the Image Edge-Histogram Specification Problem","year":2011,"lang":"en","type":"letter","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement 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":"Université de Montréal","funders":"","keywords":"Histogram matching; Histogram; Image histogram; Adaptive histogram equalization; Image gradient; Mathematics; Artificial intelligence; Algorithm; Image processing; Luminance; Image (mathematics); Computer science; Mathematical optimization; Edge detection; Histogram equalization; Image texture","score_opus":0.03407910834105616,"score_gpt":0.2740392342262335,"score_spread":0.23996012588517734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001140770","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":[0.0030191485,0.000049846407,0.995501,0.00015266491,0.000010472628,0.000017262853,0.000016195714,0.00005053514,0.0011829297],"genre_scores_gemma":[0.52479696,0.00048594756,0.4525751,0.00042894232,0.00010271878,0.00041586353,0.00028943326,0.00032143685,0.020583622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996954,0.00008403663,0.000011878033,0.000063165964,0.000118397445,0.000027100037],"domain_scores_gemma":[0.99956316,0.00022416485,0.00004057744,0.00005593186,0.00009443846,0.000021600454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071467,0.00045306628,0.00060308195,0.00031311632,0.0002685146,0.0007280161,0.0018517148,0.0015895533,0.0026575425],"category_scores_gemma":[0.001824963,0.00041774404,0.00048765834,0.00047387937,0.00072402356,0.0016964267,0.0008128293,0.001149225,0.00074738386],"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":[0.000054047643,0.0000462811,0.00027498434,0.000065990826,0.000016832933,0.00014309016,0.00006436104,0.81724405,0.011347813,0.14649701,0.0015847832,0.022660745],"study_design_scores_gemma":[0.0000025291251,0.000006528627,0.000025743866,0.0000014037154,0.0000010906643,0.00001977172,0.0000022474455,0.9927452,0.0004765008,0.0063153794,0.00039990648,0.0000036084875],"about_ca_topic_score_codex":0.0012194541,"about_ca_topic_score_gemma":0.0012886046,"teacher_disagreement_score":0.0026575425,"about_ca_system_score_codex":0.00081141654,"about_ca_system_score_gemma":0.00065878534,"threshold_uncertainty_score":0.008890331},"labels":[],"label_agreement":null},{"id":"W2001373948","doi":"10.1117/12.2050816","title":"A hybrid frequency-spatial domain infrared image enhancement approach evaluated by fuzzy entropy","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Enhancement 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":"Université Laval","funders":"Université de Toulouse","keywords":"Homomorphic filtering; Computer science; Frequency domain; Homomorphic encryption; Entropy (arrow of time); Computer vision; Artificial intelligence; Spatial frequency; Image processing; Image (mathematics); Algorithm; Image enhancement; Optics; Physics","score_opus":0.00742099912251115,"score_gpt":0.22408886790043456,"score_spread":0.2166678687779234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001373948","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":[0.053012684,0.00043874356,0.9436014,0.0000982723,0.000045855104,0.00004198581,0.000015341237,0.00023279349,0.0025129176],"genre_scores_gemma":[0.68504107,0.0004978017,0.31040624,0.00005978111,0.00005709415,0.00005131375,0.000038937127,0.000036620175,0.0038111312],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971837,0.000040441908,0.000013765586,0.000038849754,0.00016954268,0.000018865527],"domain_scores_gemma":[0.9998215,0.000051082727,0.00002457131,0.000020282056,0.00007055294,0.000012058531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048597736,0.00040000468,0.0003611472,0.0007608953,0.00020859968,0.0005012758,0.0004027324,0.00038365883,0.0007518113],"category_scores_gemma":[0.0006097708,0.0001807923,0.000510087,0.0003049268,0.00029160292,0.00082194846,0.00045233182,0.00032436286,0.00013802647],"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":[0.00038109912,0.00020250834,0.0022491647,0.0002548747,0.00014412387,0.00020934992,0.00019149896,0.13865007,0.36872005,0.02091606,0.0012032766,0.46687794],"study_design_scores_gemma":[0.0000179332,0.00020961606,0.0016691462,0.000010904974,0.000061745304,0.0003081271,0.000025729705,0.9234414,0.07056654,0.0020335752,0.0016178653,0.000037454494],"about_ca_topic_score_codex":0.00060850295,"about_ca_topic_score_gemma":0.00083670835,"teacher_disagreement_score":0.0007608953,"about_ca_system_score_codex":0.00032210682,"about_ca_system_score_gemma":0.00026839302,"threshold_uncertainty_score":0.0025700927},"labels":[],"label_agreement":null},{"id":"W2003099669","doi":"10.1080/2151237x.2007.10129236","title":"Adaptive Thresholding using the Integral Image","year":2007,"lang":"en","type":"article","venue":"Journal of Graphics Tools","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1397,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Carleton University","funders":"","keywords":"Thresholding; Artificial intelligence; Computer science; Computer vision; Balanced histogram thresholding; Pixel; Image (mathematics); Frame (networking); Graphics; Image processing; Computer graphics (images); Computer graphics","score_opus":0.05634350697886052,"score_gpt":0.32065325778834175,"score_spread":0.2643097508094812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003099669","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":[0.008497601,0.0004938942,0.98555297,0.00009371156,0.00008946225,0.000045561217,0.000043223423,0.0013394048,0.0038441438],"genre_scores_gemma":[0.11802206,0.00091006234,0.87495404,0.00012270555,0.00009390789,0.000080079684,0.00015931307,0.0006081935,0.005049758],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99936026,0.0000512857,0.000034113178,0.00017853738,0.0003201504,0.000055734163],"domain_scores_gemma":[0.9994066,0.00019575193,0.00006171973,0.00013512518,0.0001655579,0.000035290715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006458514,0.00059728127,0.0007425666,0.0012251018,0.00037486744,0.0015798474,0.0011123493,0.00084235135,0.005112337],"category_scores_gemma":[0.0023429214,0.00037615365,0.00061040843,0.0011699359,0.00081718224,0.0013883434,0.0011001151,0.0011701711,0.001762108],"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":[0.00021495117,0.000058879883,0.000775708,0.00036345262,0.00007179775,0.0002323753,0.00025028162,0.0127934925,0.3505408,0.024952818,0.0050818226,0.6046636],"study_design_scores_gemma":[0.000045011766,0.00021185189,0.004004787,0.00011257401,0.00015054345,0.0021960824,0.00013653112,0.43056926,0.47442538,0.034727328,0.053316824,0.00010385858],"about_ca_topic_score_codex":0.00057517417,"about_ca_topic_score_gemma":0.00060398417,"teacher_disagreement_score":0.005112337,"about_ca_system_score_codex":0.0004410204,"about_ca_system_score_gemma":0.0004387301,"threshold_uncertainty_score":0.01710254},"labels":[],"label_agreement":null},{"id":"W2004052938","doi":"10.1080/2151237x.2009.10129276","title":"Visualizing High Dynamic Range Images in a Web Browser","year":2009,"lang":"en","type":"article","venue":"Journal of Graphics GPU and Game Tools","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Callback; JavaScript; Computer science; Computer graphics (images); Basis (linear algebra); Embedding; High dynamic range; Computer vision; Artificial intelligence; World Wide Web; Dynamic range; Mathematics; Programming language","score_opus":0.01215452792875708,"score_gpt":0.2781677793258045,"score_spread":0.2660132513970474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004052938","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":[0.037313335,0.00059338246,0.91807,0.00033443977,0.00009594708,0.0001808774,0.00081844855,0.029784352,0.012809151],"genre_scores_gemma":[0.18140833,0.0010813246,0.79532546,0.000255967,0.00007237604,0.00022505621,0.0011351715,0.0057523246,0.014743942],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997625,0.000054946748,0.000017770744,0.00003736858,0.00009969393,0.000027862283],"domain_scores_gemma":[0.99911004,0.00037237626,0.000047295172,0.00015561846,0.00020938416,0.00010521973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004617467,0.0007834723,0.00033259887,0.0009848385,0.00025768412,0.0012635352,0.0005758911,0.000631724,0.015409482],"category_scores_gemma":[0.001360161,0.00041188815,0.00039060463,0.0005341055,0.00021684509,0.001262959,0.0010601459,0.00097212935,0.0037981288],"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":[0.0007320481,0.00045342653,0.0027872704,0.0007903203,0.0001340219,0.0020598723,0.0017812672,0.006353785,0.5128443,0.010747952,0.038645156,0.42267057],"study_design_scores_gemma":[0.00027302332,0.0007210308,0.01469498,0.00048522343,0.00018183459,0.007957619,0.0009554758,0.15267034,0.49461278,0.02302494,0.30408022,0.0003425453],"about_ca_topic_score_codex":0.0008890773,"about_ca_topic_score_gemma":0.0016859014,"teacher_disagreement_score":0.015409482,"about_ca_system_score_codex":0.00015647693,"about_ca_system_score_gemma":0.00030891594,"threshold_uncertainty_score":0.051549792},"labels":[],"label_agreement":null},{"id":"W2005537211","doi":"10.1145/1012551.1012566","title":"Subband encoding of high dynamic range imagery","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Tone mapping; Computer science; RGB color model; Computer vision; Artificial intelligence; Pixel; High dynamic range; Grayscale; Encoding (memory); Decoding methods; JPEG; Color depth; Data compression; Image compression; Tone (literature); Computer graphics (images); Dynamic range; Color image; Image processing; Image (mathematics); Algorithm","score_opus":0.008005038882747856,"score_gpt":0.24062009448228835,"score_spread":0.2326150555995405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005537211","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":[0.23605615,0.001268803,0.73033243,0.00025624866,0.0002446539,0.00010044359,0.00047590645,0.0030916254,0.028173706],"genre_scores_gemma":[0.47681513,0.0015797691,0.49432078,0.00018311132,0.000108523396,0.000065768625,0.0008959041,0.00046901652,0.025561977],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999269,0.0000106191155,0.000004033684,0.000013222611,0.000033651417,0.000011550986],"domain_scores_gemma":[0.9997614,0.000061646686,0.000019402429,0.00008460719,0.000058548485,0.000014354054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008725577,0.00027964092,0.00018067718,0.00043576263,0.00013463141,0.00039417692,0.00026048927,0.00016609073,0.0040794765],"category_scores_gemma":[0.00039348367,0.00009464791,0.0001484187,0.00038030033,0.0001416193,0.00049612956,0.00029735966,0.000263744,0.0013492632],"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":[0.00034472285,0.00005036161,0.0003107049,0.00016679679,0.000013568988,0.00021582132,0.00017010666,0.0057061496,0.62585324,0.01010946,0.002059093,0.355],"study_design_scores_gemma":[0.00003225442,0.0004309753,0.0023627672,0.000035730314,0.000052448737,0.0011067892,0.00008920418,0.07045848,0.8705224,0.0064794025,0.0483968,0.000032752756],"about_ca_topic_score_codex":0.00035227687,"about_ca_topic_score_gemma":0.00068733445,"teacher_disagreement_score":0.0040794765,"about_ca_system_score_codex":0.00014109146,"about_ca_system_score_gemma":0.0001163124,"threshold_uncertainty_score":0.013647258},"labels":[],"label_agreement":null},{"id":"W2005653733","doi":"10.1145/2554688.2554738","title":"Producing high-quality real-time HDR video system with FPGA (abstract only)","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Compositing; High dynamic range; Hash function; Interpolation (computer graphics); Computer graphics (images); Computer vision; Artificial intelligence; Real-time computing; Computer hardware; Dynamic range; Image (mathematics)","score_opus":0.012060234102409364,"score_gpt":0.2533005077961506,"score_spread":0.24124027369374124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005653733","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":[0.15789199,0.0008490524,0.732626,0.00033651362,0.00045950979,0.0006094451,0.0014292646,0.054972913,0.050825298],"genre_scores_gemma":[0.6677277,0.00039065216,0.29987085,0.0003004592,0.000115945,0.00022781464,0.0017685387,0.001330507,0.028267464],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997696,0.000024187544,0.000016743777,0.00005663353,0.000102818885,0.000029999146],"domain_scores_gemma":[0.99979025,0.000042603104,0.000024135945,0.000054813972,0.00007369445,0.000014454021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023018892,0.0004021324,0.00023153759,0.0003428493,0.00016727542,0.00057215954,0.0005636428,0.00022013421,0.02469407],"category_scores_gemma":[0.0004086428,0.00013146646,0.00017635156,0.00015637193,0.00014361425,0.00050598755,0.00038191018,0.00024726518,0.004652682],"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":[0.00072420406,0.0001160653,0.0016527488,0.00074214314,0.000064467255,0.0009808867,0.00023885838,0.0075979177,0.4906072,0.007818029,0.028792325,0.46066517],"study_design_scores_gemma":[0.00018512268,0.0011821357,0.0043046894,0.00010740759,0.000074981515,0.0019161275,0.000078559344,0.07144644,0.8138214,0.0012714508,0.105536014,0.00007568464],"about_ca_topic_score_codex":0.00047067946,"about_ca_topic_score_gemma":0.0005376573,"teacher_disagreement_score":0.02469407,"about_ca_system_score_codex":0.00030710179,"about_ca_system_score_gemma":0.00018117747,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2007651542","doi":"10.1109/istas.2013.6613107","title":"High dynamic range tone mapping based on Per-Pixel Exposure Mapping","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Tone mapping; High dynamic range; Computer science; Compositing; High-dynamic-range imaging; Pixel; Computer vision; Artificial intelligence; Multiple exposure; Dynamic range; Set (abstract data type); Process (computing); Range (aeronautics); Tone (literature); Pairwise comparison; Computer graphics (images); Image (mathematics); Engineering","score_opus":0.011516622926351168,"score_gpt":0.23511002376039927,"score_spread":0.2235934008340481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007651542","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":[0.05752879,0.00019756917,0.93616223,0.000045379544,0.000038619484,0.00007001066,0.000030928575,0.00078487745,0.005141593],"genre_scores_gemma":[0.33860838,0.00032624952,0.6546008,0.000058125406,0.000046631023,0.000078629426,0.000107537344,0.00020814974,0.0059654447],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997627,0.00003242712,0.000009338343,0.000042653683,0.00013286957,0.000019982173],"domain_scores_gemma":[0.9996258,0.0001220355,0.000035304245,0.00011370675,0.00007959601,0.000023511777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022668559,0.00044657712,0.0003366903,0.00042186014,0.00020101739,0.0005849211,0.0005668517,0.00025775243,0.0031905551],"category_scores_gemma":[0.0007973843,0.00022625762,0.0002855144,0.00034458996,0.0004138811,0.0008835719,0.0006419454,0.00055400626,0.0007047252],"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":[0.00024775552,0.000110720954,0.0007867777,0.0001880098,0.000028055096,0.00022430853,0.00021147037,0.031868484,0.5720343,0.017786207,0.0012068545,0.3753071],"study_design_scores_gemma":[0.000028055481,0.00036502042,0.0015762874,0.000024684752,0.000036672787,0.0013290151,0.00006495384,0.39772552,0.5822748,0.0050114263,0.011508634,0.000054958575],"about_ca_topic_score_codex":0.00030436364,"about_ca_topic_score_gemma":0.0004833019,"teacher_disagreement_score":0.0031905551,"about_ca_system_score_codex":0.00018987522,"about_ca_system_score_gemma":0.00021122253,"threshold_uncertainty_score":0.010673463},"labels":[],"label_agreement":null},{"id":"W2008866578","doi":"10.1109/icassp.2013.6637979","title":"High dynamic range image tone mapping by maximizing a structural fidelity measure","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"University of Waterloo","funders":"","keywords":"Tone mapping; High dynamic range; Computer science; Fidelity; Range (aeronautics); Measure (data warehouse); Image (mathematics); Visualization; High fidelity; Point (geometry); Computer vision; Image quality; Artificial intelligence; Dynamic range; Quality (philosophy); Mathematics; Data mining; Engineering","score_opus":0.008782753005313523,"score_gpt":0.24542727619550816,"score_spread":0.23664452319019463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008866578","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":[0.06873943,0.00014183167,0.9284481,0.00006694978,0.000016451153,0.00006458275,0.000014504916,0.00035263458,0.00215557],"genre_scores_gemma":[0.39539617,0.00021497479,0.6018338,0.00006722025,0.000023737572,0.00008451321,0.000044652203,0.00012536188,0.0022095172],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997371,0.00005638067,0.000015703255,0.00006118937,0.00010112697,0.00002846067],"domain_scores_gemma":[0.9992624,0.0003020859,0.000103122016,0.00013988995,0.00014207323,0.000050325474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067611405,0.0006594901,0.00046226018,0.0005190203,0.00023171931,0.00083381013,0.0006563972,0.00060969096,0.0014506122],"category_scores_gemma":[0.0022840123,0.00022569291,0.00032894188,0.00032645694,0.0005145273,0.0011503883,0.00077189464,0.0005637562,0.00039329223],"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":[0.00037527666,0.0002524856,0.0014460024,0.00032541988,0.000057444297,0.00014859375,0.0002917888,0.13220984,0.4329302,0.023620203,0.0015622181,0.40678057],"study_design_scores_gemma":[0.000047025005,0.00039403717,0.0010820967,0.0000225209,0.00004503302,0.00040925073,0.000070745344,0.83383685,0.15540569,0.00597535,0.0026790337,0.000032383523],"about_ca_topic_score_codex":0.00037356996,"about_ca_topic_score_gemma":0.00054221856,"teacher_disagreement_score":0.0014506122,"about_ca_system_score_codex":0.00027242306,"about_ca_system_score_gemma":0.00035441236,"threshold_uncertainty_score":0.004852712},"labels":[],"label_agreement":null},{"id":"W2012579661","doi":"10.1109/mmsp.2014.6958791","title":"A fusion-based enhancing approach for single sandstorm image","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":70,"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":"Artificial intelligence; Visibility; Brightness; Computer vision; Computer science; Luminance; Pixel; Image fusion; Naturalness; Image (mathematics); Color image; Distortion (music); Image quality; Fusion; Image processing; Optics; Physics","score_opus":0.013013607652742502,"score_gpt":0.23716374152821157,"score_spread":0.22415013387546906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012579661","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":[0.02814358,0.0006441849,0.9684899,0.0000842696,0.000051607185,0.00005274337,0.000025667905,0.0007955142,0.0017125643],"genre_scores_gemma":[0.2862655,0.0010740908,0.70866346,0.000119872,0.00006758451,0.00005270107,0.00010610139,0.00010161186,0.0035490764],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997445,0.000022505565,0.000013297367,0.0000611529,0.00012731487,0.00003121318],"domain_scores_gemma":[0.9997844,0.000038563132,0.000028648887,0.000040475836,0.00009407719,0.000013950572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003622715,0.0006549609,0.00066627766,0.0010641732,0.00029178787,0.0006052191,0.0005929295,0.00058306253,0.0012096299],"category_scores_gemma":[0.00048713267,0.00028870773,0.0008920859,0.0005349383,0.00037514843,0.001113454,0.00056997745,0.00056191935,0.0004923787],"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":[0.00026089558,0.00007147482,0.00089850265,0.000406332,0.00011811376,0.00030997052,0.0002414944,0.023081059,0.53912944,0.0043882015,0.0013445618,0.42974997],"study_design_scores_gemma":[0.00003293853,0.00035888885,0.0032686973,0.000045480796,0.00030232855,0.0015191812,0.00013297859,0.54122096,0.4365663,0.0038211753,0.012655518,0.00007564748],"about_ca_topic_score_codex":0.000846402,"about_ca_topic_score_gemma":0.0011271551,"teacher_disagreement_score":0.0012096299,"about_ca_system_score_codex":0.00030770726,"about_ca_system_score_gemma":0.00036099556,"threshold_uncertainty_score":0.0040465593},"labels":[],"label_agreement":null},{"id":"W2013670523","doi":"10.1109/tmm.2013.2266633","title":"Visually Favorable Tone-Mapping With High Compression Performance in Bit-Depth Scalable Video Coding","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":27,"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; Scalability; Coding (social sciences); Encoder; Algorithm; Artificial intelligence; Speech recognition; Mathematics; Database; Statistics","score_opus":0.0158407353988739,"score_gpt":0.2545894113385192,"score_spread":0.2387486759396453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013670523","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":[0.21992579,0.00048589046,0.77208215,0.00020892348,0.0000532937,0.000087558714,0.00007160831,0.0005602104,0.0065245423],"genre_scores_gemma":[0.8768023,0.00032176528,0.12058351,0.0000895618,0.000016211878,0.000040162457,0.000062393585,0.00006946363,0.002014616],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997861,0.000040900562,0.000006271096,0.000020549627,0.00012876373,0.000017433622],"domain_scores_gemma":[0.9995716,0.00021484388,0.000052579257,0.000053503303,0.00008250811,0.000025053712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003775537,0.0003702212,0.00023228052,0.00018696502,0.00015142192,0.00047171925,0.00037762494,0.000396199,0.0009084681],"category_scores_gemma":[0.002283333,0.00016242806,0.00012862978,0.0002661237,0.00044364601,0.00065271044,0.00047844296,0.00046935107,0.00023925852],"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":[0.00024646916,0.00012640237,0.0009874058,0.00025126708,0.000021497699,0.00044477647,0.00024682048,0.28742254,0.5743808,0.045127504,0.0013396752,0.089404814],"study_design_scores_gemma":[0.000014985912,0.00008891175,0.00036275855,0.000016644814,0.000005197602,0.00013804491,0.000019238621,0.9295357,0.06411193,0.004725936,0.0009587167,0.000022015789],"about_ca_topic_score_codex":0.0013541855,"about_ca_topic_score_gemma":0.0014249954,"teacher_disagreement_score":0.0013541855,"about_ca_system_score_codex":0.00037193124,"about_ca_system_score_gemma":0.0003332721,"threshold_uncertainty_score":0.0030391216},"labels":[],"label_agreement":null},{"id":"W2017626840","doi":"10.1109/icip.2013.6738487","title":"Near-infrared guided color image dehazing","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Qualcomm (Canada)","funders":"","keywords":"RGB color model; Artificial intelligence; Computer science; Computer vision; Color image; Haze; Image (mathematics); Near-infrared spectroscopy; Image processing; Optics; Physics","score_opus":0.01326278127835131,"score_gpt":0.25722867930405785,"score_spread":0.24396589802570653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017626840","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":[0.100496985,0.0008887649,0.8933245,0.00012504289,0.00008623102,0.0000500735,0.00004032269,0.00064011675,0.0043479456],"genre_scores_gemma":[0.49566513,0.0010799863,0.4935897,0.00013551083,0.000078936326,0.000034902074,0.00010631773,0.00010326842,0.009206177],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983394,0.000013739332,0.0000054885804,0.000030984098,0.00010041309,0.000015505522],"domain_scores_gemma":[0.99980015,0.00003521033,0.00003789894,0.00005643017,0.00005971388,0.000010642566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013956912,0.00042086258,0.000325909,0.00043045142,0.00018624193,0.00033787955,0.00048899313,0.00031388627,0.0011189103],"category_scores_gemma":[0.00037636943,0.00016433583,0.00043106737,0.00024746917,0.00032389193,0.0005914822,0.00051048776,0.0004969377,0.00036639514],"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":[0.00016052664,0.000109185436,0.00092974777,0.00021105818,0.00005130612,0.000103748804,0.00012717002,0.025476176,0.6246597,0.005288326,0.0013670305,0.341516],"study_design_scores_gemma":[0.000022405793,0.00015492825,0.0032706163,0.000017385997,0.000054388038,0.0009641991,0.00005659308,0.28455827,0.6965857,0.0020447096,0.012215785,0.000054924705],"about_ca_topic_score_codex":0.00080840784,"about_ca_topic_score_gemma":0.0013184443,"teacher_disagreement_score":0.0011189103,"about_ca_system_score_codex":0.0002139716,"about_ca_system_score_gemma":0.00024877052,"threshold_uncertainty_score":0.003743112},"labels":[],"label_agreement":null},{"id":"W2021696730","doi":"10.1145/2343456.2343467","title":"HDRchitecture","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Toronto","funders":"","keywords":"Computer graphics (images); Computer science; High-dynamic-range imaging; High dynamic range; Photography; Digital photography; Computer vision; Dynamic range; Artificial intelligence; Art; Visual arts","score_opus":0.008135398321538334,"score_gpt":0.2390173290825643,"score_spread":0.23088193076102595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021696730","genre_codex":"other","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":[0.003531105,0.0046177413,0.043998983,0.0025520276,0.012796105,0.00039702037,0.028007444,0.023730153,0.8803694],"genre_scores_gemma":[0.017275458,0.0017518082,0.014182139,0.0014704918,0.0030357298,0.00013629488,0.02352588,0.004989322,0.93363297],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995888,0.000027987871,0.000016644866,0.00009049204,0.00023860173,0.00003755266],"domain_scores_gemma":[0.9985475,0.0001545896,0.000031502535,0.0005268952,0.00049518794,0.00024426376],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055147323,0.0006117049,0.00051794073,0.0023693047,0.001250257,0.0024366584,0.0015231203,0.00095385074,0.6320015],"category_scores_gemma":[0.0015600749,0.00042112745,0.00043802784,0.0014879254,0.00035164942,0.001660404,0.0020355782,0.0012931513,0.36065117],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016404492,0.0000512395,0.00043206726,0.00022082953,0.0000103318325,0.0002684497,0.00007831815,0.00017673726,0.006116597,0.004417244,0.79560614,0.19245796],"study_design_scores_gemma":[0.000007731454,0.000019931022,0.00043633507,0.000028356615,0.0000036425185,0.0003160477,0.000014559392,0.00015171003,0.001480987,0.00069015095,0.99684227,0.000008277942],"about_ca_topic_score_codex":0.0016609242,"about_ca_topic_score_gemma":0.0025996505,"teacher_disagreement_score":0.6320015,"about_ca_system_score_codex":0.00053273427,"about_ca_system_score_gemma":0.0006409523,"threshold_uncertainty_score":0.52490515},"labels":[],"label_agreement":null},{"id":"W2023295001","doi":"10.1117/12.666061","title":"Marine environment background synthesis using MODTRAN 4","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"MODTRAN; Bidirectional reflectance distribution function; Computer science; GRASP; Code (set theory); Radiative transfer; Remote sensing; Computation; Implementation; Reflectivity; Computational science; Algorithm; Optics; Geology","score_opus":0.01333660292717117,"score_gpt":0.22224391688226394,"score_spread":0.20890731395509277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023295001","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":[0.025248552,0.00009263706,0.9454855,0.00017968597,0.00015103251,0.0000587297,0.0011892328,0.011427827,0.016166858],"genre_scores_gemma":[0.15471356,0.0002583896,0.8304778,0.00013064897,0.00004772658,0.00015349426,0.003162381,0.0027527225,0.008303284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989927,0.0000110281035,0.0000044147873,0.000018096089,0.00005654847,0.000010743952],"domain_scores_gemma":[0.99988663,0.000017082628,0.000007032071,0.000024795487,0.000055712917,0.0000088129445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021246455,0.00061719917,0.00031384078,0.00052185735,0.0003174292,0.0009321674,0.00054458476,0.0003837331,0.0083728805],"category_scores_gemma":[0.00065847806,0.00026673562,0.00046859798,0.00054132106,0.000121906574,0.00055158837,0.00063903484,0.0006258785,0.0031769415],"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":[0.00026284653,0.000071028655,0.003670538,0.00025033244,0.00009867137,0.000601267,0.00027825343,0.30059105,0.14498605,0.04232607,0.034158837,0.47270507],"study_design_scores_gemma":[0.000047480124,0.000028550778,0.0010058462,0.00002024206,0.000018380912,0.0001533281,0.0000492054,0.8808178,0.059401166,0.005912419,0.052514482,0.00003108861],"about_ca_topic_score_codex":0.001844193,"about_ca_topic_score_gemma":0.0023607016,"teacher_disagreement_score":0.0083728805,"about_ca_system_score_codex":0.00026947042,"about_ca_system_score_gemma":0.0006527282,"threshold_uncertainty_score":0.02801007},"labels":[],"label_agreement":null},{"id":"W2023963773","doi":"10.1109/cvprw.2012.6239193","title":"Gradient domain color restoration of clipped highlights","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of British Columbia","funders":"","keywords":"Hue; Clipping (morphology); Artificial intelligence; Colored; Computer vision; Color space; Color correction; Computer science; Lightness; Boundary (topology); Color balance; Mathematics; Color image; Image (mathematics); Image processing; Mathematical analysis; Materials science","score_opus":0.015383712123320487,"score_gpt":0.25867264085761116,"score_spread":0.24328892873429067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023963773","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":[0.19941655,0.0003694261,0.79513425,0.00016507109,0.00012627144,0.000043171854,0.00013376397,0.0014541233,0.0031574634],"genre_scores_gemma":[0.6486726,0.0006107814,0.34539327,0.00009458023,0.00007420488,0.00002543365,0.00020581706,0.00025758153,0.0046656337],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987686,0.00001326355,0.0000036406684,0.000021027583,0.00006155998,0.000023628987],"domain_scores_gemma":[0.9996766,0.000046320747,0.000042217573,0.00006866561,0.00013783514,0.000028421138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023436296,0.0005667842,0.00040561322,0.0006230138,0.00021103595,0.00064385094,0.000414944,0.00033962444,0.0012523415],"category_scores_gemma":[0.00085200387,0.00018550342,0.00030758264,0.00043694917,0.00040422377,0.0004801672,0.00046023197,0.0006422318,0.00035558382],"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":[0.000724154,0.00011126861,0.0014767267,0.00021341519,0.000075017764,0.0005600799,0.00018298162,0.09861423,0.5919619,0.008560356,0.003319598,0.29420027],"study_design_scores_gemma":[0.00003709308,0.00012907859,0.003328257,0.000019018351,0.000047993126,0.0007594333,0.00007981697,0.59099936,0.39440387,0.0038130735,0.0063337698,0.00004916406],"about_ca_topic_score_codex":0.0013313891,"about_ca_topic_score_gemma":0.0015706253,"teacher_disagreement_score":0.0013313891,"about_ca_system_score_codex":0.00026035344,"about_ca_system_score_gemma":0.0003354969,"threshold_uncertainty_score":0.004189551},"labels":[],"label_agreement":null},{"id":"W2025134675","doi":"10.1109/icme.2014.6890304","title":"High dynamic range image tone mapping by optimizing tone mapped image quality index","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":31,"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":"Tone mapping; Tone (literature); High dynamic range; Image (mathematics); Dynamic range; High-dynamic-range imaging; Computer science; Index (typography); Range (aeronautics); Image quality; Quality (philosophy); Computer vision; Artificial intelligence; Physics; Engineering","score_opus":0.011271779404769469,"score_gpt":0.29893617623041635,"score_spread":0.2876643968256469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025134675","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":[0.08866932,0.00022595389,0.9078609,0.000048227706,0.000024032477,0.00009650984,0.000026658367,0.0005262652,0.0025221794],"genre_scores_gemma":[0.44246605,0.0003047066,0.5548967,0.000067044595,0.000022309432,0.00011127601,0.000074620024,0.00022199716,0.0018353619],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997608,0.000044324523,0.000014723269,0.00006393185,0.000093508774,0.000022585702],"domain_scores_gemma":[0.9993468,0.00019727692,0.00012706072,0.00009506195,0.00019381357,0.00003990338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044533706,0.00071266707,0.0002916085,0.00047341935,0.0001621925,0.0007027675,0.0004667382,0.00030397504,0.0013390732],"category_scores_gemma":[0.0014610862,0.00016679283,0.00026629018,0.0003447444,0.00035216,0.00084932573,0.00048234005,0.00043174613,0.00037774863],"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":[0.0002582055,0.00018670545,0.0015703003,0.00025020575,0.000038471164,0.00011085778,0.00016034378,0.04194268,0.72355235,0.00644014,0.0008131428,0.22467662],"study_design_scores_gemma":[0.000063071355,0.0007258358,0.0034100136,0.000031546704,0.00008948207,0.0006119803,0.00011063005,0.54640543,0.43850413,0.0043536345,0.0056294235,0.000064796455],"about_ca_topic_score_codex":0.00033984208,"about_ca_topic_score_gemma":0.00042917285,"teacher_disagreement_score":0.0013390732,"about_ca_system_score_codex":0.00022089512,"about_ca_system_score_gemma":0.00026385847,"threshold_uncertainty_score":0.0044796467},"labels":[],"label_agreement":null},{"id":"W2025293472","doi":"10.1109/icip.2012.6467480","title":"Image dependent energy-constrained local backlight dimming","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","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":"McMaster University","funders":"","keywords":"Backlight; Computer science; Weighting; Luminance; Energy consumption; Image (mathematics); Energy (signal processing); Power (physics); Image quality; Extension (predicate logic); Function (biology); Power consumption; Computer vision; Artificial intelligence; Mathematics; Liquid-crystal display; Engineering; Electrical engineering","score_opus":0.007646526870443313,"score_gpt":0.23237291860649714,"score_spread":0.22472639173605383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025293472","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":[0.027464813,0.0001500902,0.9707879,0.00003833238,0.000017200338,0.000019549901,0.000011743234,0.0002793279,0.0012310992],"genre_scores_gemma":[0.6010996,0.00020422271,0.39256993,0.0001209413,0.000036526984,0.000062552164,0.000083258834,0.00016378924,0.005659258],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985266,0.000030514257,0.000006219493,0.00003216978,0.000064992186,0.000013526151],"domain_scores_gemma":[0.9998357,0.00004622767,0.000022111682,0.000034142886,0.000049697224,0.000012018657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021272138,0.00039767686,0.00042321798,0.00024636014,0.0001543076,0.00037368428,0.00077533553,0.0003262208,0.001470091],"category_scores_gemma":[0.000502799,0.0001405459,0.00030915724,0.00021862112,0.00018813348,0.0005563155,0.0005124623,0.00036949306,0.00029802747],"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":[0.00042165973,0.00018286463,0.0008465491,0.00030031768,0.0000870337,0.00010292985,0.00015704526,0.2560353,0.21504024,0.009104924,0.0016781411,0.51604295],"study_design_scores_gemma":[0.00002497067,0.00007702078,0.00056313374,0.0000073980955,0.000018729083,0.00006582108,0.000012148917,0.9452024,0.050534386,0.0018343969,0.0016443905,0.000015165075],"about_ca_topic_score_codex":0.00047507795,"about_ca_topic_score_gemma":0.0009017289,"teacher_disagreement_score":0.001470091,"about_ca_system_score_codex":0.00023543688,"about_ca_system_score_gemma":0.00024650956,"threshold_uncertainty_score":0.0049179196},"labels":[],"label_agreement":null},{"id":"W2026765491","doi":"10.1109/tip.2012.2211371","title":"Optimal local dimming for LC image formation with controllable backlighting","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Backlight; Computer science; Power consumption; Quality (philosophy); Image quality; Computer vision; Artificial intelligence; Algorithm; Liquid-crystal display; Image (mathematics); Computer graphics (images); Power (physics); Physics","score_opus":0.01137369302690209,"score_gpt":0.2574405113955734,"score_spread":0.2460668183686713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026765491","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":[0.15104964,0.00053242233,0.84341705,0.00008011871,0.000021043055,0.000037241254,0.000022440221,0.00054207654,0.004298049],"genre_scores_gemma":[0.8031305,0.00020898136,0.19477744,0.000051277344,0.0000129181435,0.000036763602,0.000026261188,0.000046587505,0.0017092109],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999925,0.000013493257,0.0000032095847,0.000013386439,0.00003642429,0.000008385325],"domain_scores_gemma":[0.9998777,0.000049617003,0.000025278981,0.000019383417,0.000016755235,0.000011187715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013965352,0.00018042307,0.00018664724,0.00015557908,0.00014905179,0.0002272931,0.00032979192,0.00018288309,0.0010202621],"category_scores_gemma":[0.00043891228,0.000105014784,0.0001234486,0.00018399148,0.0002665391,0.0003516806,0.0005339139,0.00026270328,0.0001589927],"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":[0.00031428866,0.00010744161,0.00097100815,0.00024584835,0.00002483018,0.00016683515,0.00028151943,0.13812324,0.57435805,0.019801341,0.0016025142,0.26400316],"study_design_scores_gemma":[0.00003753661,0.00015348822,0.00037853586,0.000010435127,0.00000826289,0.00013479078,0.000025695774,0.8549305,0.13950977,0.0028529698,0.0019403662,0.000017689421],"about_ca_topic_score_codex":0.00038765534,"about_ca_topic_score_gemma":0.00068170164,"teacher_disagreement_score":0.0010202621,"about_ca_system_score_codex":0.00032831088,"about_ca_system_score_gemma":0.00018221453,"threshold_uncertainty_score":0.0034130812},"labels":[],"label_agreement":null},{"id":"W2028943866","doi":"10.1109/icip.2011.6116447","title":"Image contrast enhancement in compressed wavelet domain","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Concordia University","funders":"","keywords":"Wavelet; Artificial intelligence; Computer science; Computer vision; Wavelet transform; JPEG; Discrete wavelet transform; Image quality; Stationary wavelet transform; Pattern recognition (psychology); Image (mathematics)","score_opus":0.018869154389314104,"score_gpt":0.23950536355750815,"score_spread":0.22063620916819404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028943866","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":[0.037399285,0.0011438921,0.95763415,0.00012655123,0.000079283054,0.000046166213,0.00003266885,0.00032818972,0.0032098156],"genre_scores_gemma":[0.2965024,0.0021391076,0.69562966,0.00016559033,0.00015622108,0.00006508141,0.00016425956,0.0001378913,0.00503972],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998004,0.000025548461,0.0000060132634,0.000023975028,0.00012881409,0.000015319178],"domain_scores_gemma":[0.9998134,0.00006286678,0.000022849235,0.000024080106,0.0000662679,0.000010564297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023840119,0.00041471384,0.00041092944,0.0005187614,0.00013302658,0.00036858543,0.0003694993,0.00033788392,0.00095303945],"category_scores_gemma":[0.000683978,0.00012659053,0.00033894097,0.00034262825,0.00027708255,0.0006466903,0.0004495153,0.0005466812,0.00028981664],"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":[0.00025673158,0.00006736189,0.00040142328,0.00030044635,0.000055816698,0.0003855902,0.00010345805,0.020925876,0.6589686,0.019764038,0.001880806,0.29688975],"study_design_scores_gemma":[0.00007068308,0.00039822896,0.002168724,0.000054448345,0.000097605465,0.0021714452,0.000052584346,0.44971508,0.5122682,0.0073051406,0.025647469,0.000050545765],"about_ca_topic_score_codex":0.00043402563,"about_ca_topic_score_gemma":0.000507713,"teacher_disagreement_score":0.00095303945,"about_ca_system_score_codex":0.00017839663,"about_ca_system_score_gemma":0.00016571613,"threshold_uncertainty_score":0.0031882524},"labels":[],"label_agreement":null},{"id":"W2029484270","doi":"10.1109/icip.2010.5651778","title":"Objective assessment of tone mapping algorithms","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":27,"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":"Tone mapping; Tone (literature); Computer science; Algorithm; High dynamic range; Similarity (geometry); Range (aeronautics); Scale (ratio); Dynamic range; Data mining; Artificial intelligence; Image (mathematics); Computer vision; Engineering","score_opus":0.012675990898948396,"score_gpt":0.3232595060140638,"score_spread":0.3105835151151154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029484270","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":[0.11720979,0.00036457088,0.87492514,0.00011109073,0.000065969776,0.00030591106,0.00010955133,0.0008652139,0.0060427655],"genre_scores_gemma":[0.5389966,0.00034603564,0.4564201,0.000074028874,0.00005645263,0.0002509603,0.00030245457,0.0002967237,0.003256682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967325,0.00089887966,0.00029099203,0.00031844433,0.0016417915,0.00011746191],"domain_scores_gemma":[0.9853572,0.0062255166,0.0012691981,0.0011971935,0.005552326,0.00039858543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045419508,0.00094636757,0.00064458925,0.0018279812,0.00045807706,0.0023698446,0.00075705524,0.00083892216,0.0036399602],"category_scores_gemma":[0.024309218,0.00022670375,0.00037302778,0.00068613223,0.0005077038,0.0021258176,0.0012806851,0.000696519,0.0006360217],"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":[0.0012748272,0.00043131143,0.010615048,0.0008502601,0.00018790633,0.00013726101,0.00069155724,0.06123521,0.14741376,0.009542753,0.0026558659,0.7649643],"study_design_scores_gemma":[0.00016251138,0.0019488315,0.017081838,0.00014650823,0.00015700092,0.00061696686,0.00062068604,0.77598166,0.18444942,0.010386291,0.008243467,0.00020486156],"about_ca_topic_score_codex":0.000480819,"about_ca_topic_score_gemma":0.000542761,"teacher_disagreement_score":0.0045419508,"about_ca_system_score_codex":0.00042038,"about_ca_system_score_gemma":0.00043578172,"threshold_uncertainty_score":0.024020433},"labels":[],"label_agreement":null},{"id":"W2035346787","doi":"10.1145/2393347.2396525","title":"High dynamic range (HDR) video image processing for digital glass","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; High dynamic range; Compositing; Computer vision; Pixel; Computer graphics (images); Image processing; Frame rate; Frame (networking); Artificial intelligence; Arc welding; Welding; Dynamic range; Engineering; Image (mathematics); Mechanical engineering","score_opus":0.009671035708695638,"score_gpt":0.26454305190274,"score_spread":0.25487201619404437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035346787","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":[0.018451309,0.00020076869,0.97446615,0.00008526332,0.000041123752,0.0000783095,0.00013534036,0.0040250043,0.0025167773],"genre_scores_gemma":[0.110337906,0.00034299566,0.8849756,0.0000774583,0.00002629138,0.000056796693,0.0003028595,0.00034775704,0.0035323023],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985564,0.00001318939,0.000008993834,0.000031968273,0.00007517586,0.000015059582],"domain_scores_gemma":[0.99986327,0.00003479906,0.000016669503,0.0000306533,0.000039728136,0.000014923859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002161779,0.00046922197,0.0002246613,0.0005040334,0.00025394277,0.0005744592,0.0006274135,0.00034290474,0.0076698875],"category_scores_gemma":[0.00051744556,0.00017848765,0.00030024917,0.0004137896,0.0002471405,0.00049088546,0.00039011304,0.0003949444,0.0013669073],"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":[0.00024683875,0.00007847227,0.0008860208,0.00023543197,0.00004211449,0.00023978142,0.00015621874,0.02219594,0.44750082,0.006721586,0.007873168,0.5138237],"study_design_scores_gemma":[0.00006305098,0.00029486752,0.0024774075,0.00003694623,0.000039533323,0.0007961195,0.00008069652,0.5638885,0.39482594,0.0038992101,0.033534497,0.00006326149],"about_ca_topic_score_codex":0.0037173722,"about_ca_topic_score_gemma":0.0060179657,"teacher_disagreement_score":0.0076698875,"about_ca_system_score_codex":0.00036771258,"about_ca_system_score_gemma":0.00038348587,"threshold_uncertainty_score":0.025658369},"labels":[],"label_agreement":null},{"id":"W2040406368","doi":"10.1109/ist.2010.5548529","title":"Using temporal correlation for fast and highdetailed video tone mapping","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of British Columbia","funders":"","keywords":"Tone mapping; Computer science; Frame (networking); Tone (literature); Reference frame; Computer vision; Filter (signal processing); Artificial intelligence; Range (aeronautics); Block-matching algorithm; Motion estimation; High-dynamic-range imaging; High dynamic range; Video processing; Dynamic range; Video tracking","score_opus":0.029291931163253272,"score_gpt":0.30064751424345726,"score_spread":0.27135558308020397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040406368","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":[0.018485263,0.00015564985,0.9800514,0.000030811694,0.000028325354,0.000028791286,0.00001810345,0.00026692153,0.00093468145],"genre_scores_gemma":[0.17902511,0.00035367557,0.8186768,0.00005668423,0.000051168587,0.00006873545,0.00009487147,0.00009586811,0.0015771333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997472,0.000056826048,0.000011509557,0.000038783866,0.00012307083,0.00002250116],"domain_scores_gemma":[0.9993142,0.00031262994,0.00007852036,0.00012698716,0.00013410994,0.00003359102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050536863,0.00045965982,0.00026093147,0.00047910993,0.00022748714,0.0004912845,0.000372678,0.0003288629,0.0021161898],"category_scores_gemma":[0.0019002878,0.00018720691,0.00024401215,0.0006239114,0.0003242936,0.00080756546,0.0006071233,0.00043489865,0.00044635113],"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":[0.00037526787,0.00009033125,0.0010238171,0.00017440006,0.000043019056,0.00022908719,0.000166664,0.02096261,0.38304216,0.021036033,0.0020916036,0.570765],"study_design_scores_gemma":[0.00007777652,0.0003837865,0.0029281583,0.000038820104,0.000063494976,0.0014640332,0.00007308484,0.6558999,0.3118878,0.0075609353,0.01954584,0.000076326694],"about_ca_topic_score_codex":0.00069337315,"about_ca_topic_score_gemma":0.0013646497,"teacher_disagreement_score":0.0021161898,"about_ca_system_score_codex":0.0001980735,"about_ca_system_score_gemma":0.0003991033,"threshold_uncertainty_score":0.0070794225},"labels":[],"label_agreement":null},{"id":"W2044477653","doi":"10.1109/vcip.2011.6115909","title":"Image matting based on mutual information","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Markov random field; Image (mathematics); Artificial intelligence; Similarity (geometry); Pixel; Computer science; Computer vision; Markov chain; Energy (signal processing); Function (biology); Image quality; Pattern recognition (psychology); Mutual information; Image editing; Mathematics; Image segmentation; Statistics; Machine learning","score_opus":0.015929486916904663,"score_gpt":0.2251189639099014,"score_spread":0.20918947699299675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044477653","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":[0.003399164,0.00014636898,0.99556893,0.000042568674,0.000012770579,0.000016616754,0.0000114020495,0.00022986133,0.0005722956],"genre_scores_gemma":[0.28133518,0.0006790208,0.7126799,0.00012369509,0.00014120739,0.00009325908,0.00019667101,0.00032568158,0.0044253827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901533,0.00019972824,0.000047481528,0.00019507922,0.00046197692,0.00008034266],"domain_scores_gemma":[0.9984133,0.0006041633,0.00022250463,0.00029262077,0.0003862793,0.00008120107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011650515,0.00085440016,0.0011966771,0.0013771607,0.00045768815,0.0010781649,0.0013337962,0.00088321377,0.0023250112],"category_scores_gemma":[0.0031516023,0.0004993503,0.0011463527,0.0010033934,0.0009714387,0.002371312,0.0011096499,0.0011113208,0.000729947],"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":[0.00023236958,0.000092868926,0.00083463074,0.00036772364,0.0001799278,0.00025195495,0.00022890758,0.3385518,0.060925834,0.052053582,0.0029673951,0.54331297],"study_design_scores_gemma":[0.000011148207,0.00010421655,0.0003603086,0.000012094122,0.00003485691,0.00022753481,0.00002264564,0.964185,0.020008348,0.012600863,0.0024025145,0.000030602372],"about_ca_topic_score_codex":0.0012895537,"about_ca_topic_score_gemma":0.0017222696,"teacher_disagreement_score":0.0023250112,"about_ca_system_score_codex":0.00063074124,"about_ca_system_score_gemma":0.0005589654,"threshold_uncertainty_score":0.0077778697},"labels":[],"label_agreement":null},{"id":"W2045222564","doi":"10.1109/ccece.2012.6334945","title":"High dynamic range simultaneous signal compositing, applied to audio","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Compositing; High dynamic range; Computer science; Dynamic range; Computer vision; SIGNAL (programming language); Artificial intelligence; Range (aeronautics); Sampling (signal processing); Audio signal; Audio signal processing; Digital signal processing; Computer graphics (images); Image (mathematics); Computer hardware; Engineering; Filter (signal processing)","score_opus":0.006291283350280593,"score_gpt":0.2394465629270768,"score_spread":0.23315527957679621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045222564","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":[0.11029577,0.0011945713,0.87809056,0.00010917313,0.00006535079,0.00007840764,0.000027128059,0.0007399705,0.00939911],"genre_scores_gemma":[0.65085787,0.0010694577,0.34249648,0.000095770214,0.00008162908,0.00006628712,0.0000574116,0.00015012725,0.005124946],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999749,0.00003948847,0.000010288664,0.00006616828,0.00011273995,0.000022357674],"domain_scores_gemma":[0.99965465,0.0001576768,0.0000505005,0.00006776352,0.000047214882,0.000022195938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022955136,0.0005247276,0.00025398028,0.0003175097,0.00024750174,0.0006477439,0.00036523782,0.00029459514,0.0015913546],"category_scores_gemma":[0.0007652343,0.00023176329,0.00021207372,0.00042178092,0.00046264305,0.00049097696,0.0005204612,0.0003555413,0.0005451049],"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":[0.00012173104,0.000030746156,0.00042051813,0.00010529208,0.000012691865,0.0001539309,0.00007904595,0.005756005,0.8583928,0.0026936566,0.00027498836,0.1319586],"study_design_scores_gemma":[0.000017213666,0.0002557407,0.0016984834,0.000015859305,0.000030526433,0.0008995372,0.000037128546,0.08752234,0.8985612,0.0014524766,0.009487931,0.000021467482],"about_ca_topic_score_codex":0.0003045828,"about_ca_topic_score_gemma":0.0005423457,"teacher_disagreement_score":0.0015913546,"about_ca_system_score_codex":0.00017496674,"about_ca_system_score_gemma":0.00014522861,"threshold_uncertainty_score":0.005323589},"labels":[],"label_agreement":null},{"id":"W2046562944","doi":"10.1109/icip.2013.6738183","title":"Image enhancement revisited: From first order to second order statistics","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"McMaster University","funders":"","keywords":"Histogram; Histogram equalization; Tone mapping; Pixel; Computer science; Artificial intelligence; Adaptive histogram equalization; Contrast (vision); Image (mathematics); Image quality; Image histogram; Pattern recognition (psychology); Computer vision; Mathematics; Image processing; Binary image","score_opus":0.007745095960639995,"score_gpt":0.24503160918124448,"score_spread":0.23728651322060448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046562944","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":[0.004778499,0.00054287794,0.9926857,0.00016937985,0.000036457197,0.000009678915,0.000006219936,0.00012293349,0.001648153],"genre_scores_gemma":[0.3579256,0.003135345,0.6301808,0.0004039155,0.00027930542,0.00006023258,0.000047710735,0.00030162625,0.007665496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999493,0.00009442562,0.000024510682,0.000103319675,0.00024726242,0.00003748931],"domain_scores_gemma":[0.9988048,0.0007298444,0.00011455524,0.00013297463,0.00018580235,0.00003214422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009821412,0.00050324417,0.00059120974,0.00051876216,0.00022786878,0.0011955719,0.00071335776,0.00052195136,0.0011097647],"category_scores_gemma":[0.002834413,0.00032994503,0.0005674054,0.00046277253,0.001057642,0.0017690993,0.00069754734,0.001562894,0.00039999574],"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":[0.00022573325,0.00012598622,0.0017600144,0.00037175845,0.000091059854,0.00030113486,0.00033905142,0.19255923,0.06748636,0.2961613,0.002097108,0.4384812],"study_design_scores_gemma":[0.000014396209,0.0001445631,0.00093917485,0.000035629633,0.00003406324,0.00044947973,0.000032674332,0.91453063,0.026681358,0.0495429,0.0075588706,0.000036373414],"about_ca_topic_score_codex":0.00095205667,"about_ca_topic_score_gemma":0.0008387743,"teacher_disagreement_score":0.0011955719,"about_ca_system_score_codex":0.000512549,"about_ca_system_score_gemma":0.0006030558,"threshold_uncertainty_score":0.005194068},"labels":[],"label_agreement":null},{"id":"W2049127974","doi":"10.1007/s11042-010-0583-2","title":"A low distortion image enhancement scheme based on multi-resolutions analysis in next generation network","year":2010,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Nanjing University of Information Science and Technology; Nanjing University of Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Next-generation network; Distortion (music); Contrast (vision); Image processing; Focus (optics); Noise (video); Image (mathematics); Computer vision; Artificial intelligence; Telecommunications; The Internet; Bandwidth (computing); World Wide Web","score_opus":0.02932996924552414,"score_gpt":0.2806801718192954,"score_spread":0.2513502025737713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049127974","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":[0.09354874,0.0018697614,0.8974915,0.00018674116,0.00022925217,0.000075548814,0.000057021804,0.0007178509,0.0058236085],"genre_scores_gemma":[0.5094283,0.0016739285,0.47682345,0.00011833259,0.00014074687,0.00005376664,0.00013396172,0.00006615056,0.01156132],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985623,0.00003214242,0.00000813311,0.000025103642,0.000062427294,0.00001598964],"domain_scores_gemma":[0.99984884,0.00003249614,0.000016181813,0.000025766865,0.00006673067,0.00000987589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025955934,0.0003777904,0.000326149,0.000417004,0.00029010858,0.00038142008,0.00041736654,0.00028152094,0.0012981113],"category_scores_gemma":[0.00027978598,0.00016839821,0.00027103236,0.0003288522,0.00018175504,0.0006389889,0.0002747699,0.00037704114,0.0004146907],"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":[0.0005060513,0.00012439753,0.0010421933,0.00016320759,0.0000631204,0.00027487584,0.000088884844,0.015460636,0.550753,0.00917448,0.0027349235,0.41961426],"study_design_scores_gemma":[0.000038652823,0.0003316765,0.0029813293,0.000027576178,0.00015324565,0.001296491,0.000055295794,0.519458,0.4580047,0.0018450943,0.015748566,0.000059352813],"about_ca_topic_score_codex":0.0007933416,"about_ca_topic_score_gemma":0.0013548158,"teacher_disagreement_score":0.0012981113,"about_ca_system_score_codex":0.00022177811,"about_ca_system_score_gemma":0.00018542135,"threshold_uncertainty_score":0.004342556},"labels":[],"label_agreement":null},{"id":"W2053677670","doi":"10.1109/icdsp.2014.6900712","title":"Pixel classification algorithms for noise removal and signal preservation in low-pass filtering for contrast enhancement","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Concordia University","funders":"","keywords":"Pixel; Thresholding; Smoothing; Homogeneity (statistics); Computer science; Artificial intelligence; Histogram; Histogram equalization; Homogeneous; Computation; Algorithm; Computer vision; Bilateral filter; Pattern recognition (psychology); Mathematics; Image (mathematics)","score_opus":0.03298011299208212,"score_gpt":0.2854785162967694,"score_spread":0.2524984033046873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053677670","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":[0.00822916,0.00022689131,0.9902597,0.00003593528,0.000033404067,0.00006155173,0.00001703201,0.000566549,0.0005697572],"genre_scores_gemma":[0.0637224,0.00023703928,0.93418455,0.000051019764,0.000037497615,0.00013726972,0.000104784056,0.000067195586,0.0014582349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994646,0.00007955804,0.00005458363,0.00011786568,0.0002282625,0.00005515478],"domain_scores_gemma":[0.9992741,0.00022391352,0.00008077593,0.00010473888,0.00028871547,0.000027756483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008400781,0.0006630648,0.00080094935,0.0012083126,0.00060016615,0.0009072341,0.0010801007,0.00096118863,0.0022717111],"category_scores_gemma":[0.0018079894,0.00030919383,0.00067554886,0.00096270733,0.00049934036,0.0011799689,0.00044033123,0.00082303747,0.0010801341],"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":[0.00033532767,0.00015828108,0.0013621624,0.00019128247,0.000050539664,0.00009669265,0.00014004456,0.019921817,0.09035405,0.0098404065,0.0019095675,0.87563986],"study_design_scores_gemma":[0.00006675871,0.00024814703,0.004012586,0.00003143627,0.00009065685,0.0004676071,0.0000611259,0.8193354,0.15814312,0.008247206,0.009239424,0.00005647165],"about_ca_topic_score_codex":0.0013530768,"about_ca_topic_score_gemma":0.0013763419,"teacher_disagreement_score":0.0022717111,"about_ca_system_score_codex":0.00057518354,"about_ca_system_score_gemma":0.00061331317,"threshold_uncertainty_score":0.007599652},"labels":[],"label_agreement":null},{"id":"W2053936387","doi":"10.1145/1569901.1569973","title":"Tone mapping by interactive evolution","year":2009,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Tone mapping; Tweaking; Tone (literature); Computer science; Context (archaeology); Task (project management); Operator (biology); High dynamic range; Computational complexity theory; Range (aeronautics); Algorithm; Dynamic range; Computer vision; Engineering","score_opus":0.007317682381824835,"score_gpt":0.2703470360397264,"score_spread":0.26302935365790153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053936387","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":[0.02518883,0.00016845547,0.96511024,0.00007414724,0.00005271438,0.00006893832,0.000021506932,0.001149006,0.008166085],"genre_scores_gemma":[0.39793032,0.00029524206,0.5920617,0.0001255555,0.00003653302,0.00020006583,0.00008616614,0.0004844727,0.008779878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969494,0.000080323895,0.000015053515,0.000066058274,0.00011255134,0.00003117311],"domain_scores_gemma":[0.9995715,0.00021417633,0.000027593744,0.0001032545,0.000058874735,0.00002451097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004981015,0.00052917376,0.00040005712,0.00045279623,0.00036509908,0.00080408633,0.00087607524,0.0005412906,0.004805477],"category_scores_gemma":[0.0019383951,0.00023851784,0.0004595792,0.00034743722,0.0006980484,0.00081468065,0.001389131,0.0005607666,0.0008230049],"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":[0.00031035667,0.00014215981,0.0012231894,0.00027151254,0.000093806026,0.00038307745,0.0008148893,0.12446886,0.25261375,0.091647804,0.0036093947,0.5244212],"study_design_scores_gemma":[0.00006988623,0.0002656515,0.000772092,0.000047150497,0.000053735563,0.00063463877,0.00012325982,0.837675,0.084440954,0.041429747,0.03442038,0.00006744722],"about_ca_topic_score_codex":0.0005954402,"about_ca_topic_score_gemma":0.0005005283,"teacher_disagreement_score":0.004805477,"about_ca_system_score_codex":0.0002964889,"about_ca_system_score_gemma":0.00019748551,"threshold_uncertainty_score":0.01607591},"labels":[],"label_agreement":null},{"id":"W2054834801","doi":"10.1117/12.2041322","title":"Improved global-sampling matting using sequential pair-selection strategy","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pixel; Benchmark (surveying); Artificial intelligence; Sampling (signal processing); Computer science; Selection (genetic algorithm); Image (mathematics); Alpha (finance); Pattern recognition (psychology); Computer vision; Mathematics; Statistics","score_opus":0.01932983790887014,"score_gpt":0.26363295404865217,"score_spread":0.24430311613978203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054834801","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":[0.026009016,0.0002445331,0.9713841,0.000037052658,0.00003280668,0.00003519918,0.00003805736,0.0010896309,0.0011295736],"genre_scores_gemma":[0.25301316,0.00016739585,0.74229854,0.00007431328,0.00006639239,0.000061565115,0.00032461074,0.00025586714,0.003738225],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993812,0.000104968414,0.000025503748,0.00015583061,0.0002834607,0.00004901871],"domain_scores_gemma":[0.9992835,0.00019533695,0.0000616489,0.00020904618,0.00019827629,0.000052237127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062105415,0.0009734097,0.0010220293,0.0009524844,0.000335291,0.0005769721,0.0010703657,0.0005795145,0.0025767463],"category_scores_gemma":[0.0011565671,0.00031575342,0.0007214569,0.0010327519,0.00039710975,0.0009152116,0.0007385714,0.00071121,0.0010567594],"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":[0.00041114012,0.00017257106,0.0016558088,0.00016676052,0.00011163467,0.0002622172,0.00018397109,0.0949927,0.1389071,0.0056824,0.004193287,0.7532605],"study_design_scores_gemma":[0.000031646825,0.00022214411,0.0008578687,0.000005474804,0.000036752503,0.0004338171,0.000026187792,0.9468817,0.046377238,0.002147822,0.0029593294,0.000020034655],"about_ca_topic_score_codex":0.001003198,"about_ca_topic_score_gemma":0.0017444802,"teacher_disagreement_score":0.0025767463,"about_ca_system_score_codex":0.0002504672,"about_ca_system_score_gemma":0.00039236684,"threshold_uncertainty_score":0.008620024},"labels":[],"label_agreement":null},{"id":"W2058468730","doi":"10.1109/icecs.2011.6122293","title":"Customized embedded processor design for global photographic tone mapping","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Tone mapping; Overhead (engineering); Logarithm; Graphics; Luminance; Reduced instruction set computing; High dynamic range; Computer hardware; Range (aeronautics); Flexibility (engineering); Embedded system; Instruction set; Dynamic range; Parallel computing; Computer graphics (images); Artificial intelligence; Computer vision; Operating system; Engineering","score_opus":0.06849034144122347,"score_gpt":0.29970985891730406,"score_spread":0.2312195174760806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058468730","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":[0.114302374,0.00047936666,0.86653435,0.0001373757,0.00016637606,0.00040329437,0.00021132351,0.008343768,0.009421672],"genre_scores_gemma":[0.53278154,0.0003198192,0.45276138,0.00034213337,0.00007893149,0.0003510464,0.0007929563,0.0006445178,0.011927597],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974984,0.000025572033,0.000023538028,0.00007200638,0.00008291457,0.00004608865],"domain_scores_gemma":[0.9997242,0.00004028217,0.000033103406,0.00007316495,0.0001125483,0.000016726417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016394095,0.0005353084,0.0002699976,0.0002894756,0.00018247601,0.00045725578,0.0011049344,0.00022966585,0.004467065],"category_scores_gemma":[0.0004989469,0.00020713164,0.0003022285,0.0002671981,0.00012940408,0.00047488709,0.00032818477,0.00060956116,0.0012535236],"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":[0.0005450951,0.00015582825,0.0018402333,0.00038691796,0.000093513685,0.00060952187,0.00015991235,0.021289153,0.6049329,0.008055101,0.009705017,0.35222676],"study_design_scores_gemma":[0.00025552456,0.0017440824,0.004548622,0.000075143325,0.00027069057,0.0025679686,0.0000681822,0.2535925,0.65398705,0.001980916,0.08081806,0.00009127251],"about_ca_topic_score_codex":0.0004865178,"about_ca_topic_score_gemma":0.0008379068,"teacher_disagreement_score":0.004467065,"about_ca_system_score_codex":0.00035753177,"about_ca_system_score_gemma":0.00057676015,"threshold_uncertainty_score":0.014943838},"labels":[],"label_agreement":null},{"id":"W2061261185","doi":"10.1109/icce.2015.7066441","title":"A new hybrid tone mapping scheme for high dynamic range (HDR) videos","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"Telus (Canada); University of British Columbia","funders":"","keywords":"Tone mapping; High dynamic range; Computer science; Frame (networking); Scheme (mathematics); Computer vision; Flicker; Tone (literature); Artificial intelligence; Dynamic range; Range (aeronautics); Computer graphics (images); Mathematics; Engineering; Telecommunications","score_opus":0.0277749760674757,"score_gpt":0.2907689895672202,"score_spread":0.2629940134997445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061261185","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":[0.16858083,0.0017270744,0.8224896,0.00018040811,0.00021145174,0.0001872237,0.00011752911,0.0010430794,0.005462777],"genre_scores_gemma":[0.5505151,0.000916289,0.4413624,0.00018803038,0.00016742694,0.000109425564,0.00015905224,0.00008133772,0.006501008],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982387,0.00003698808,0.000012065174,0.00004482529,0.00006244135,0.000019884199],"domain_scores_gemma":[0.9997398,0.000059313672,0.00003360988,0.00007262489,0.00006360455,0.000030991258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022365765,0.00041516655,0.00028353848,0.0004855574,0.0002557776,0.0005044398,0.00070008566,0.00032127448,0.0023397508],"category_scores_gemma":[0.00056189526,0.0001576622,0.00023859333,0.0003070599,0.00023511429,0.0009543433,0.0005320997,0.00038567532,0.00055019796],"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":[0.00037490446,0.00007790855,0.00037293817,0.000212481,0.00003728196,0.00018401102,0.00012831995,0.003226557,0.7041904,0.004435579,0.0009418726,0.2858178],"study_design_scores_gemma":[0.00013501752,0.0021706223,0.0034881337,0.00008272486,0.00021005125,0.004322411,0.00012005546,0.26883775,0.67433584,0.0037584447,0.042397093,0.00014175593],"about_ca_topic_score_codex":0.00026041947,"about_ca_topic_score_gemma":0.00034977772,"teacher_disagreement_score":0.0023397508,"about_ca_system_score_codex":0.00014148877,"about_ca_system_score_gemma":0.00009396276,"threshold_uncertainty_score":0.007827222},"labels":[],"label_agreement":null},{"id":"W2061745545","doi":"10.1111/j.1467-8659.2012.03027.x","title":"Metering for Exposure Stacks","year":2012,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nokia (Canada)","funders":"","keywords":"High dynamic range; Computer science; Computer vision; Histogram; Set (abstract data type); Artificial intelligence; Irradiance; Range (aeronautics); Metering mode; Sequence (biology); Image quality; Image (mathematics); Computer graphics (images); Dynamic range; Optics","score_opus":0.021042614154264845,"score_gpt":0.2653379218052852,"score_spread":0.24429530765102037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061745545","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":[0.07764974,0.0005130474,0.90714985,0.00018287532,0.00012147457,0.00017090127,0.0003116301,0.0051573208,0.008743092],"genre_scores_gemma":[0.4197015,0.0003126201,0.57407266,0.00011386676,0.000052708852,0.00009948841,0.00046897065,0.00064300926,0.0045352327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995783,0.000086041975,0.00002615565,0.000108969,0.00015541769,0.00004506517],"domain_scores_gemma":[0.9993629,0.00016232261,0.00006706436,0.00021737162,0.00015680629,0.000033565244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064339704,0.0006320107,0.00047960092,0.0007231468,0.00039501107,0.00095096184,0.00082025444,0.00041762955,0.0102899475],"category_scores_gemma":[0.001893708,0.00046639703,0.0004328431,0.00060171343,0.00024754705,0.0014882719,0.0009808382,0.0008664998,0.0015311613],"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":[0.00051365833,0.00017964044,0.0048398753,0.00041361753,0.00009000719,0.00024478254,0.0003716696,0.031625137,0.32960054,0.011147775,0.00653438,0.61443895],"study_design_scores_gemma":[0.00007703462,0.0005604036,0.01538457,0.00012223472,0.00015973323,0.0010241071,0.0003631774,0.30509257,0.5866655,0.017535727,0.07286758,0.00014737491],"about_ca_topic_score_codex":0.00050897116,"about_ca_topic_score_gemma":0.00071974285,"teacher_disagreement_score":0.0102899475,"about_ca_system_score_codex":0.0003080269,"about_ca_system_score_gemma":0.00026560304,"threshold_uncertainty_score":0.03442329},"labels":[],"label_agreement":null},{"id":"W2062550983","doi":"10.1117/12.2083481","title":"A novel framework for automatic trimap generation using the Gestalt laws of grouping","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Gestalt psychology; Computer science; Artificial intelligence; Process (computing); Unsupervised learning; Image (mathematics); Task (project management); Pattern recognition (psychology); Computer vision; Engineering","score_opus":0.0373020677535344,"score_gpt":0.27662486216383453,"score_spread":0.23932279441030013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062550983","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":[0.0005579569,0.00003484003,0.9980453,0.000018698529,0.000010753628,0.000019407253,0.000012470229,0.00079187704,0.0005086689],"genre_scores_gemma":[0.02042496,0.00007079078,0.9774674,0.00004063312,0.000030323405,0.000055620196,0.00009476106,0.00034923106,0.0014661828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991111,0.000111743684,0.00005455621,0.00027908056,0.0003693914,0.00007411194],"domain_scores_gemma":[0.9988851,0.00022332997,0.00012494305,0.0004489154,0.00024226523,0.00007541777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093141716,0.000899467,0.00092193903,0.0017791366,0.0010361252,0.0017224656,0.002678628,0.0014761251,0.004892207],"category_scores_gemma":[0.0020823148,0.00081677735,0.0014304299,0.0011094819,0.0017989906,0.0024024176,0.002163385,0.0020894185,0.0023970832],"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":[0.0001528519,0.00019560594,0.00079947495,0.0003993764,0.000096020056,0.0004565853,0.0007543024,0.13369982,0.100009955,0.13888882,0.012091039,0.61245614],"study_design_scores_gemma":[0.000024674107,0.000109563334,0.00038150695,0.0000401118,0.000028122175,0.00069465884,0.00009673902,0.8759687,0.036298815,0.053318657,0.032980613,0.000057791072],"about_ca_topic_score_codex":0.0017477957,"about_ca_topic_score_gemma":0.002793262,"teacher_disagreement_score":0.004892207,"about_ca_system_score_codex":0.0006917462,"about_ca_system_score_gemma":0.0009820014,"threshold_uncertainty_score":0.016366065},"labels":[],"label_agreement":null},{"id":"W2063080140","doi":"10.1137/130919696","title":"Single Image Dehazing and Denoising: A Fast Variational Approach","year":2014,"lang":"en","type":"article","venue":"SIAM Journal on Imaging Sciences","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":69,"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":"Noise reduction; Image (mathematics); Noise (video); Uniqueness; Transmission (telecommunications); Convergence (economics); Computer science; Enhanced Data Rates for GSM Evolution; Computer vision; Algorithm; Image restoration; Channel (broadcasting); Image denoising; Artificial intelligence; Haze; Artifact (error); Mathematics; Image processing; Mathematical analysis; Telecommunications","score_opus":0.014326218493146513,"score_gpt":0.25541526985822294,"score_spread":0.2410890513650764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063080140","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":[0.0021634158,0.00017232347,0.997059,0.00005087915,0.00001819072,0.000010440744,0.000008922566,0.00004831726,0.0004686219],"genre_scores_gemma":[0.12345841,0.0012209163,0.86738795,0.00008946895,0.000101725025,0.00007085863,0.00009397078,0.00017271416,0.007403869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997534,0.00004574232,0.000008799981,0.00005162421,0.00011591635,0.000024453459],"domain_scores_gemma":[0.99971455,0.00013328399,0.00002821142,0.000041096257,0.00006249082,0.000020325735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077953876,0.0007138457,0.0007474163,0.00067168067,0.00033685824,0.0006674668,0.001224326,0.0010182313,0.0013954624],"category_scores_gemma":[0.0010228817,0.00061528856,0.00092858076,0.00038900948,0.000748941,0.0013061683,0.0010296641,0.0012531028,0.00039323184],"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":[0.00008823561,0.000050201445,0.00053089304,0.00026902938,0.00012076964,0.00013719517,0.00020405304,0.5932442,0.07987098,0.111342594,0.0015630824,0.21257874],"study_design_scores_gemma":[0.0000035184669,0.000017495679,0.00006776213,0.0000048088846,0.0000064805727,0.00007218768,0.000007914957,0.9884189,0.0042053047,0.0056224647,0.0015614555,0.000011857421],"about_ca_topic_score_codex":0.0028645396,"about_ca_topic_score_gemma":0.002917342,"teacher_disagreement_score":0.0028645396,"about_ca_system_score_codex":0.00055534946,"about_ca_system_score_gemma":0.000768376,"threshold_uncertainty_score":0.0056957603},"labels":[],"label_agreement":null},{"id":"W2063744373","doi":"10.1109/mwscas.2011.6026420","title":"Contrast enhancement by adaptive mapping function with local information","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Concordia University","funders":"","keywords":"Adaptive histogram equalization; Pixel; Artificial intelligence; Computer science; Computer vision; Histogram; Clipping (morphology); Contrast (vision); Histogram equalization; Image quality; Image restoration; Histogram matching; Pattern recognition (psychology); Image (mathematics); Image processing","score_opus":0.013391256501888832,"score_gpt":0.1828670764325989,"score_spread":0.16947581993071006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063744373","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":[0.08172656,0.0005459533,0.9153341,0.00007403514,0.000039862673,0.000040460305,0.00001079749,0.00057328196,0.0016548283],"genre_scores_gemma":[0.57754797,0.00045291093,0.4185286,0.000068731584,0.000047810903,0.00006133652,0.000044446202,0.000074731404,0.0031734598],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998739,0.000024035697,0.000006023486,0.000030496574,0.000049750524,0.000015733604],"domain_scores_gemma":[0.99981993,0.000068847075,0.000027523929,0.000026124973,0.000047240523,0.000010395038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002675698,0.00041974493,0.00031862623,0.00049191853,0.00016853507,0.00033450333,0.0004663579,0.00035656488,0.0010327749],"category_scores_gemma":[0.0007555068,0.00015782434,0.00033150578,0.00036135592,0.00030809152,0.0008253177,0.00040092896,0.0004086726,0.00031444753],"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":[0.00033616152,0.00015900392,0.0010969308,0.00016631698,0.00006311332,0.00022635423,0.00013085545,0.032328155,0.46828863,0.0055444203,0.00089963595,0.49076042],"study_design_scores_gemma":[0.0000622272,0.00049283286,0.0030504595,0.000019500878,0.00007717596,0.0014017359,0.000043238724,0.67347854,0.31128782,0.0030733934,0.006959953,0.00005310426],"about_ca_topic_score_codex":0.00037904092,"about_ca_topic_score_gemma":0.0004210251,"teacher_disagreement_score":0.0010327749,"about_ca_system_score_codex":0.0001752713,"about_ca_system_score_gemma":0.00015821338,"threshold_uncertainty_score":0.0034549832},"labels":[],"label_agreement":null},{"id":"W2064055503","doi":"10.1155/2015/493142","title":"Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Image Enhancement 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":"University of Guelph","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Curvelet; Artificial intelligence; Inverse; Computer science; Channel (broadcasting); Image (mathematics); Computer vision; Sonar; Algorithm; Contrast (vision); Pattern recognition (psychology); Nonlinear system; Noise (video); Wavelet transform; Mathematics; Wavelet; Physics; Telecommunications","score_opus":0.015265868627888948,"score_gpt":0.24191238597975973,"score_spread":0.22664651735187077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064055503","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":[0.017959619,0.00023324606,0.97996473,0.00006554633,0.000042257867,0.000029580753,0.000019028521,0.00032827753,0.0013577287],"genre_scores_gemma":[0.15302882,0.0006681555,0.841489,0.00008663644,0.00008654049,0.000057003723,0.00013433494,0.00009707727,0.0043524443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997203,0.000038967453,0.000014946493,0.000050570427,0.00015508069,0.00002017231],"domain_scores_gemma":[0.9995491,0.00012727203,0.000049352315,0.00007818667,0.00017730636,0.000018761348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038499132,0.00057059276,0.00053854665,0.00051311654,0.00014886672,0.00045389798,0.00060323544,0.0006033142,0.0012662128],"category_scores_gemma":[0.0007305677,0.00025337926,0.00056756637,0.00046293047,0.00033639156,0.0010072634,0.00045471278,0.0007182607,0.000866007],"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":[0.0002899141,0.00010038469,0.0007342249,0.00017326452,0.000057844718,0.00018919024,0.00009921953,0.03537181,0.44469416,0.007345629,0.0015776224,0.50936675],"study_design_scores_gemma":[0.00003416997,0.00028806782,0.0009994122,0.000017002309,0.000044931556,0.0009890205,0.00002108281,0.7252711,0.26247984,0.0021639194,0.007659383,0.000032106145],"about_ca_topic_score_codex":0.00025391564,"about_ca_topic_score_gemma":0.0003056571,"teacher_disagreement_score":0.0012662128,"about_ca_system_score_codex":0.00014157285,"about_ca_system_score_gemma":0.00023742375,"threshold_uncertainty_score":0.0042359233},"labels":[],"label_agreement":null},{"id":"W2067169367","doi":"10.1117/12.476748","title":"Joint temporal and spatial color demosaic","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Enhancement 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":"McMaster University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Frame (networking); RGB color model; Reference frame","score_opus":0.012153655044272087,"score_gpt":0.2264219801500652,"score_spread":0.2142683251057931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067169367","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":[0.0141662415,0.00059200934,0.9741416,0.00016475936,0.00018757844,0.000034559354,0.00028505025,0.0009449751,0.00948311],"genre_scores_gemma":[0.3206614,0.0014651646,0.65720075,0.00022447052,0.00033285082,0.00008497381,0.0015197085,0.00047975447,0.018030956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910384,0.00010698861,0.000040369978,0.00015608106,0.00046035004,0.0001324096],"domain_scores_gemma":[0.9989794,0.00009007767,0.0000826607,0.00036467478,0.00042166308,0.00006143519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010403671,0.00085093244,0.0005976885,0.0011249117,0.00046317917,0.001433242,0.00070423615,0.00047328143,0.0039259177],"category_scores_gemma":[0.0023101575,0.00040411323,0.00091006607,0.0014447675,0.00072765397,0.0015928744,0.0018278603,0.0006354271,0.0016112084],"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":[0.00057849,0.00010009753,0.0041002096,0.00032843277,0.00025174237,0.00019844182,0.0002189087,0.07248694,0.14602251,0.0493305,0.010175741,0.716208],"study_design_scores_gemma":[0.000054866807,0.000236307,0.011501675,0.00007217494,0.00025503951,0.0014946125,0.00042981317,0.7219312,0.14128661,0.053686574,0.06891877,0.00013241357],"about_ca_topic_score_codex":0.0031920872,"about_ca_topic_score_gemma":0.0072332257,"teacher_disagreement_score":0.0039259177,"about_ca_system_score_codex":0.00039577697,"about_ca_system_score_gemma":0.0012169356,"threshold_uncertainty_score":0.013133526},"labels":[],"label_agreement":null},{"id":"W2067470403","doi":"10.1109/icassp.2014.6854045","title":"A statistical derivation of an automatic tone mapping algorithm for wide dynamic range display","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Tone mapping; Brightness; Computer science; Tone (literature); High dynamic range; Computation; Algorithm; Dynamic range; Computer vision; Range (aeronautics); Artificial intelligence; Image (mathematics); Image quality; Contrast (vision); Key (lock)","score_opus":0.00958141293687698,"score_gpt":0.29000854490823097,"score_spread":0.280427131971354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067470403","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":[0.0011635697,0.00002987606,0.99826413,0.00001125675,0.0000138887435,0.000020977117,0.000006760814,0.00026589012,0.00022373116],"genre_scores_gemma":[0.04602715,0.00011122197,0.95231605,0.00004581295,0.000052084157,0.00013332108,0.000071065115,0.00026897152,0.0009742944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924695,0.00013711814,0.000046325575,0.00014440375,0.00038763633,0.00003760037],"domain_scores_gemma":[0.99707353,0.0014238387,0.00022508384,0.0004167198,0.00078783993,0.00007300938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009654159,0.000666232,0.0005437215,0.00076148217,0.00035328514,0.0010283936,0.0011179753,0.00067086087,0.004294875],"category_scores_gemma":[0.007367039,0.00039221335,0.0005950216,0.00056532497,0.00047638663,0.0010048685,0.00073265063,0.0013054146,0.0021460953],"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":[0.00019651794,0.00012781667,0.0011664502,0.0002685846,0.00009499036,0.00017389831,0.00015886304,0.07763661,0.15978347,0.032954186,0.0027858526,0.7246528],"study_design_scores_gemma":[0.0000279338,0.000119690805,0.0012095898,0.000021393154,0.000026784272,0.00044178774,0.000019016526,0.9489568,0.03670435,0.0067055994,0.0057209632,0.000046141573],"about_ca_topic_score_codex":0.0007748171,"about_ca_topic_score_gemma":0.0007891114,"teacher_disagreement_score":0.004294875,"about_ca_system_score_codex":0.00040381038,"about_ca_system_score_gemma":0.0006276787,"threshold_uncertainty_score":0.014367759},"labels":[],"label_agreement":null},{"id":"W2069025511","doi":"10.5220/0005107102610266","title":"2D Hair Strands Generation Based on Template Matching","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Histogram; Artificial intelligence; Computer vision; Orientation (vector space); Computer science; Matching (statistics); Pattern recognition (psychology); Gabor filter; Feature (linguistics); Template matching; Image (mathematics); Mathematics","score_opus":0.017507753840658054,"score_gpt":0.25270156229279683,"score_spread":0.23519380845213878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069025511","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":[0.009541329,0.0001300723,0.9876873,0.000024761293,0.00005568949,0.00007578481,0.00006277271,0.0015160005,0.00090640015],"genre_scores_gemma":[0.12279865,0.00032420663,0.8723842,0.000054603745,0.000046248704,0.000102062964,0.00040182722,0.0004971844,0.0033910384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948263,0.000047808924,0.000028640457,0.0001531341,0.0002375452,0.00005016988],"domain_scores_gemma":[0.9994789,0.00011678627,0.000044561715,0.00015985157,0.00016434188,0.00003563452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043954808,0.0008138912,0.00083705684,0.0016939774,0.00032274253,0.0007399191,0.0010612549,0.0007405405,0.0041845483],"category_scores_gemma":[0.0011461229,0.0005120038,0.0012165508,0.00110704,0.00032023995,0.0010023066,0.000972392,0.00048814368,0.002375445],"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":[0.00022064445,0.000073673,0.00090778456,0.00019405123,0.00006809786,0.00041769654,0.0002038558,0.02795256,0.20914198,0.0038692618,0.0044633676,0.752487],"study_design_scores_gemma":[0.00004917871,0.00023663799,0.0018369288,0.000033731958,0.000058705333,0.0018511992,0.000096853175,0.81179726,0.16184491,0.0065073497,0.015612717,0.00007460517],"about_ca_topic_score_codex":0.000869592,"about_ca_topic_score_gemma":0.0010196612,"teacher_disagreement_score":0.0041845483,"about_ca_system_score_codex":0.00023664947,"about_ca_system_score_gemma":0.00039225694,"threshold_uncertainty_score":0.013998687},"labels":[],"label_agreement":null},{"id":"W2070255845","doi":"10.1118/1.3244101","title":"Sci—Wed PM: Delivery—09: Automatic Contrast Enhancement on Electronic Portal Images Based on Human Visual System Properties","year":2009,"lang":"en","type":"article","venue":"Medical Physics","topic":"Image Enhancement 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":"BC Cancer Agency; University of Victoria","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Image-guided radiation therapy; Contrast (vision); Human visual system model; Noise (video); Image quality; Medical imaging; Imaging phantom; Fiducial marker; Image noise; Metric (unit); Image (mathematics); Medicine; Nuclear medicine","score_opus":0.01247581731438002,"score_gpt":0.27091989038165554,"score_spread":0.2584440730672755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070255845","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":[0.18573695,0.001969041,0.6294641,0.001836981,0.0014922348,0.0009396448,0.0013485391,0.019476343,0.1577362],"genre_scores_gemma":[0.61967695,0.0013413605,0.24783383,0.0003805283,0.0005423914,0.00019752952,0.0015935036,0.0033364801,0.12509748],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985254,0.00003831497,0.000008684345,0.000024611054,0.00005866953,0.000017125369],"domain_scores_gemma":[0.999676,0.00008228051,0.000024702967,0.000072507944,0.00010615129,0.0000383584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040750398,0.00029013763,0.00023058828,0.00068822555,0.00018896314,0.0010237601,0.00029428664,0.00037500024,0.030841257],"category_scores_gemma":[0.0010022761,0.00018343444,0.00019593028,0.0003073994,0.0003147325,0.0005000832,0.0005144429,0.00039004072,0.009412955],"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":[0.0011183964,0.00023300793,0.0014854631,0.0003205757,0.000031319003,0.00028480907,0.00008255433,0.005866431,0.31642166,0.005965421,0.048371322,0.61981905],"study_design_scores_gemma":[0.00021825313,0.0011537395,0.03053316,0.00015618837,0.00005931443,0.002094441,0.00009646007,0.23007013,0.53565794,0.004105825,0.19577736,0.00007721737],"about_ca_topic_score_codex":0.0003352162,"about_ca_topic_score_gemma":0.00064635294,"teacher_disagreement_score":0.030841257,"about_ca_system_score_codex":0.00024643107,"about_ca_system_score_gemma":0.00022195834,"threshold_uncertainty_score":0.10317427},"labels":[],"label_agreement":null},{"id":"W2071406240","doi":"10.1109/mecbme.2014.6783224","title":"A color reproduction method with image enhancement for endoscopic images","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":11,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chrominance; Artificial intelligence; Computer vision; RGB color model; Color image; Color histogram; Color balance; False color; Computer science; Color quantization; Color space; Image gradient; Image texture; Luminance; Mathematics; Image segmentation; Image processing; Image (mathematics)","score_opus":0.010261448803171896,"score_gpt":0.29194084141913174,"score_spread":0.28167939261595987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071406240","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":[0.014283871,0.0007451286,0.9811149,0.000062112194,0.00008677336,0.0000824563,0.000014347793,0.0008590506,0.0027512948],"genre_scores_gemma":[0.19534668,0.0010357866,0.79564655,0.00008758273,0.00007898884,0.000076533055,0.000058945956,0.00013065229,0.0075383903],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997646,0.000027561016,0.000011532568,0.000041801293,0.00014214178,0.000012284834],"domain_scores_gemma":[0.9998023,0.00006251174,0.000025251271,0.00004130399,0.000057227066,0.000011399987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025821265,0.00044296507,0.00028289016,0.0005657139,0.00019947391,0.00029943185,0.0005679647,0.00046920413,0.0020739054],"category_scores_gemma":[0.0005346849,0.00021737441,0.0004540689,0.00043294462,0.00025766547,0.00064382027,0.00032989096,0.00040097177,0.0006646237],"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":[0.00016595687,0.00008589497,0.00039113718,0.00030056998,0.000034790297,0.0002661237,0.00010481283,0.01064129,0.49235576,0.0049981093,0.0016140866,0.48904136],"study_design_scores_gemma":[0.00006386615,0.00053466164,0.001976535,0.0000449658,0.00008457107,0.0049713375,0.000030555126,0.36924875,0.5855705,0.0013852878,0.03598375,0.000105161394],"about_ca_topic_score_codex":0.00033640076,"about_ca_topic_score_gemma":0.0003261101,"teacher_disagreement_score":0.0020739054,"about_ca_system_score_codex":0.00016881783,"about_ca_system_score_gemma":0.00014627932,"threshold_uncertainty_score":0.006937921},"labels":[],"label_agreement":null},{"id":"W2071780790","doi":"10.1145/1401132.1401170","title":"High dynamic range imaging &amp; image-based lighting","year":2008,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Dolby (Canada)","funders":"","keywords":"High dynamic range; High-dynamic-range imaging; Computer science; Tone mapping; Computer graphics (images); Computer vision; Dynamic range; Artificial intelligence; Range (aeronautics); Image file formats; Image-based lighting; Image (mathematics); Image processing; Engineering","score_opus":0.010697163996807549,"score_gpt":0.24545750869789962,"score_spread":0.23476034470109208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071780790","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":[0.018776169,0.037747145,0.75002515,0.0012487156,0.0016196193,0.00025723697,0.00023041821,0.0045793424,0.18551628],"genre_scores_gemma":[0.1963537,0.06384172,0.5004044,0.0019434487,0.0022144953,0.00025087377,0.00068085163,0.0012953402,0.2330151],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995888,0.00004002591,0.000012776905,0.00007440208,0.00023902283,0.000044949884],"domain_scores_gemma":[0.99974674,0.000115848605,0.0000166558,0.000039318336,0.000061251616,0.000020140185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030730915,0.00063179986,0.00048086664,0.0009703781,0.00041813726,0.0019589858,0.0009854161,0.0010351075,0.021971293],"category_scores_gemma":[0.00056549313,0.00030201825,0.0003337099,0.0006928916,0.0006353565,0.0015227745,0.001147934,0.0012943791,0.009738381],"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":[0.00020734123,0.000090022804,0.00032394135,0.00081243896,0.000018688315,0.000510781,0.0002253662,0.0019674737,0.18564722,0.031142488,0.029890798,0.7491635],"study_design_scores_gemma":[0.000031827916,0.00044284976,0.0012121622,0.0003190789,0.000034000856,0.00618684,0.00015941847,0.022616992,0.28960663,0.016985439,0.66228133,0.00012348675],"about_ca_topic_score_codex":0.0001867715,"about_ca_topic_score_gemma":0.00031185287,"teacher_disagreement_score":0.021971293,"about_ca_system_score_codex":0.0003437952,"about_ca_system_score_gemma":0.00013584916,"threshold_uncertainty_score":0.07350129},"labels":[],"label_agreement":null},{"id":"W2072642623","doi":"10.1016/j.imavis.2006.10.012","title":"Novel depth cues from light scattering","year":2006,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Artificial intelligence; Computer vision; Scattering; Computer science; Depth perception; Light scattering; Optics; Physics; Psychology; Perception; Neuroscience","score_opus":0.01008488705805457,"score_gpt":0.28516808059309456,"score_spread":0.27508319353504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072642623","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":[0.061650325,0.0014853431,0.92544293,0.000363015,0.00020909723,0.000043581116,0.0002282464,0.0006639331,0.009913494],"genre_scores_gemma":[0.38198313,0.0026654992,0.6026339,0.00030865954,0.00026269074,0.000054714084,0.00040695746,0.00028767067,0.011396757],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997987,0.000022136983,0.000006400391,0.000024858015,0.000120759156,0.000027185855],"domain_scores_gemma":[0.99968266,0.000096051735,0.00004314207,0.000038988586,0.000105009894,0.000034184115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016126785,0.00075807306,0.0004067561,0.00050443254,0.00019792511,0.0006625039,0.0005212911,0.0005064713,0.003294207],"category_scores_gemma":[0.0008474979,0.00031144524,0.00029794767,0.0005843981,0.00029470582,0.001110998,0.0012042457,0.0008744777,0.00064953417],"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":[0.00053628447,0.00008550327,0.0005242375,0.00030014987,0.00004064456,0.00023108661,0.00010335009,0.018361142,0.6465345,0.030713525,0.004063871,0.29850578],"study_design_scores_gemma":[0.00008498455,0.00023500853,0.001624791,0.0000695003,0.00007381249,0.0013936954,0.00009418691,0.58086354,0.37722257,0.017066747,0.021189114,0.00008201313],"about_ca_topic_score_codex":0.00046734116,"about_ca_topic_score_gemma":0.0012469288,"teacher_disagreement_score":0.003294207,"about_ca_system_score_codex":0.00024383167,"about_ca_system_score_gemma":0.0003156213,"threshold_uncertainty_score":0.011020184},"labels":[],"label_agreement":null},{"id":"W2073925482","doi":"10.1109/mwscas.2013.6674864","title":"Low-pass filtering aiming at noise generated in a contrast enhancement","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Smoothing; Pixel; Computer science; Histogram equalization; Artificial intelligence; Noise (video); Computer vision; Histogram; Bilateral filter; Adaptive histogram equalization; Binary image; Edge enhancement; Enhanced Data Rates for GSM Evolution; Computation; Process (computing); Contrast (vision); Image (mathematics); Algorithm; Image enhancement; Image processing","score_opus":0.0106251826224689,"score_gpt":0.22828673596481236,"score_spread":0.21766155334234347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073925482","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":[0.03565022,0.00055798487,0.9613017,0.000057314082,0.00006270491,0.00005142008,0.000015267096,0.0005109547,0.0017923921],"genre_scores_gemma":[0.35019788,0.00089549145,0.64307016,0.00013553142,0.00009947874,0.00006341994,0.00008127531,0.00011559584,0.005341075],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998136,0.00002254476,0.0000098771625,0.000043462212,0.000091933805,0.000018643477],"domain_scores_gemma":[0.99974495,0.00010630684,0.00003581649,0.000039883696,0.000060600814,0.000012406116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027399661,0.00067654176,0.0004612588,0.00045418172,0.0003072453,0.00059203635,0.0005711287,0.0006472144,0.0013807511],"category_scores_gemma":[0.0006380882,0.00022173434,0.0005026502,0.00031413592,0.00040864007,0.000640523,0.00026930706,0.000507728,0.0007074344],"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":[0.00023445317,0.00009273922,0.00089188345,0.00037527087,0.000059112957,0.00034912437,0.000111417896,0.013286243,0.833551,0.0069785467,0.00055044034,0.14351986],"study_design_scores_gemma":[0.000029561754,0.00037972437,0.0019451595,0.000026803753,0.0001289136,0.0010053441,0.000026942014,0.17447497,0.80853397,0.0030776234,0.010339057,0.00003199056],"about_ca_topic_score_codex":0.00055588194,"about_ca_topic_score_gemma":0.00062695314,"teacher_disagreement_score":0.0013807511,"about_ca_system_score_codex":0.0002282184,"about_ca_system_score_gemma":0.0003006327,"threshold_uncertainty_score":0.004619062},"labels":[],"label_agreement":null},{"id":"W2075187447","doi":"10.1109/ccece.2012.6335012","title":"Realtime HDR (High Dynamic Range) video for eyetap wearable computers, FPGA-based seeing aids, and glasseyes (EyeTaps)","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Wearable computer; Field-programmable gate array; High dynamic range; Augmented reality; Compositing; Context (archaeology); Computer graphics (images); Computer vision; Image processing; Task (project management); Set (abstract data type); Computer hardware; Artificial intelligence; Embedded system; Dynamic range; Image (mathematics); Engineering","score_opus":0.009742533162552334,"score_gpt":0.25711274663361605,"score_spread":0.24737021347106372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075187447","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":[0.06123239,0.0023803662,0.8933622,0.00035876292,0.00037983296,0.0004560939,0.0006818962,0.011759351,0.029389123],"genre_scores_gemma":[0.3295908,0.0030412714,0.61545277,0.0005316936,0.0002658659,0.00039852338,0.0010108161,0.001178737,0.04852963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997868,0.000034264056,0.000012783704,0.000056804834,0.000088174,0.000021230486],"domain_scores_gemma":[0.99970704,0.000093236704,0.000040315455,0.000073161325,0.000053088766,0.000033276767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035752443,0.0005276491,0.00020246331,0.00044746042,0.00017705803,0.0007284908,0.00070237217,0.0005470401,0.024996733],"category_scores_gemma":[0.0009380544,0.00025058351,0.00026679182,0.0003162074,0.00025049108,0.0007483153,0.0005782053,0.00040689023,0.0031830734],"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":[0.00058781565,0.000088875626,0.00076857157,0.00061588275,0.000035970592,0.00054099824,0.00027112558,0.0012900308,0.48741144,0.007136061,0.01816791,0.48308536],"study_design_scores_gemma":[0.00023385571,0.0028479185,0.011283055,0.00036992863,0.0001957147,0.008752236,0.00028886003,0.039635587,0.5775692,0.0035788813,0.35505402,0.00019065097],"about_ca_topic_score_codex":0.0006310449,"about_ca_topic_score_gemma":0.0013858342,"teacher_disagreement_score":0.024996733,"about_ca_system_score_codex":0.00022676427,"about_ca_system_score_gemma":0.00020622695,"threshold_uncertainty_score":0.083622396},"labels":[],"label_agreement":null},{"id":"W2076491823","doi":"10.1145/2601097.2601206","title":"Intrinsic images in the wild","year":2014,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":403,"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":"Division of Information and Intelligent Systems; Natural Sciences and Engineering Research Council of Canada; Intel Corporation","keywords":"Ground truth; Computer science; Crowdsourcing; Benchmark (surveying); Image (mathematics); Range (aeronautics); Scalability; Artificial intelligence; Decomposition; Computer vision; Database; Geography; Cartography","score_opus":0.014021267338712522,"score_gpt":0.25135166114244883,"score_spread":0.2373303938037363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076491823","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":[0.05975595,0.0027238855,0.7427607,0.0010279409,0.0008715975,0.0007231103,0.08520077,0.080761306,0.026174812],"genre_scores_gemma":[0.17465648,0.0008522814,0.63505024,0.00073088554,0.00016244836,0.00050602446,0.17523971,0.0048026307,0.0079992],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974113,0.00028868538,0.00011575866,0.0011292199,0.0008382371,0.00021674282],"domain_scores_gemma":[0.9963069,0.0004828484,0.00017177769,0.0021003783,0.0007691438,0.00016905036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015432399,0.0016074206,0.0011869932,0.0023196815,0.00080682756,0.0024203127,0.0031218522,0.0016912073,0.009680135],"category_scores_gemma":[0.008228479,0.0007595949,0.0014934408,0.001906866,0.0011133623,0.0053151087,0.0028543577,0.0020563724,0.009843915],"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":[0.001016188,0.00047118595,0.006649091,0.0010769689,0.00026770678,0.00039021356,0.0003132627,0.032527845,0.0375676,0.019465555,0.26036423,0.6398902],"study_design_scores_gemma":[0.00021440598,0.00042823676,0.016236963,0.00034863246,0.00010922787,0.0020974511,0.00084105524,0.5110569,0.08269567,0.07280278,0.31290135,0.0002673036],"about_ca_topic_score_codex":0.009137458,"about_ca_topic_score_gemma":0.016816957,"teacher_disagreement_score":0.009680135,"about_ca_system_score_codex":0.0011294505,"about_ca_system_score_gemma":0.0010872144,"threshold_uncertainty_score":0.032383263},"labels":[],"label_agreement":null},{"id":"W2083458772","doi":"10.1016/j.visres.2005.01.028","title":"Generic and customised digital image enhancement filters for the visually impaired","year":2005,"lang":"en","type":"article","venue":"Vision Research","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Baker Foundation","keywords":"Unsharp masking; Computer vision; Artificial intelligence; Visibility; Histogram equalization; Adaptive histogram equalization; Thresholding; Filter (signal processing); Masking (illustration); Computer science; Psychophysics; Adaptive filter; Edge enhancement; Contrast (vision); Digital filter; Image enhancement; Image processing; Image (mathematics); Psychology; Optics; Perception; Algorithm","score_opus":0.050967488156663666,"score_gpt":0.39985542069132096,"score_spread":0.3488879325346573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083458772","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":[0.17935118,0.0016687235,0.8112713,0.00021748479,0.00020052228,0.00015758655,0.000262564,0.0018330157,0.0050374796],"genre_scores_gemma":[0.51570934,0.0018805743,0.46848425,0.00018549878,0.00008250666,0.0001241513,0.0002942969,0.0003114488,0.012927905],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998273,0.000027086015,0.0000126800405,0.000040388346,0.00006873226,0.000023879948],"domain_scores_gemma":[0.9995185,0.00014087377,0.000042310567,0.000113277216,0.00015687363,0.000028128146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049043016,0.0004381901,0.00034184096,0.00038904397,0.00015223437,0.0005228825,0.00054874306,0.00086385274,0.003634776],"category_scores_gemma":[0.0013241078,0.00024390235,0.0004408806,0.00025380045,0.00029618462,0.0007507366,0.0005335229,0.00057720894,0.00078752375],"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":[0.0017297477,0.00024985344,0.0019310105,0.00056674966,0.000083043415,0.000410526,0.0001565781,0.012054931,0.5350694,0.0046687895,0.003114664,0.4399647],"study_design_scores_gemma":[0.00020164368,0.0013507075,0.018813964,0.00021760316,0.0004653104,0.007253775,0.00018387532,0.16737835,0.7715568,0.004633054,0.027802946,0.0001420151],"about_ca_topic_score_codex":0.000512927,"about_ca_topic_score_gemma":0.0011721841,"teacher_disagreement_score":0.003634776,"about_ca_system_score_codex":0.00023588019,"about_ca_system_score_gemma":0.0002452876,"threshold_uncertainty_score":0.012159526},"labels":[],"label_agreement":null},{"id":"W2085318923","doi":"10.1117/1.jei.21.1.013016","title":"Non-local pairwise energy-based model for the high-dynamic-range image compression problem","year":2012,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","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":"Université de Montréal","funders":"","keywords":"Tone mapping; Computer science; Pairwise comparison; Range (aeronautics); Gradient descent; Pixel; High dynamic range; Representation (politics); Image compression; Image (mathematics); Artificial intelligence; Algorithm; Energy (signal processing); Computer vision; Dynamic range; Mathematics; Image processing; Artificial neural network","score_opus":0.006310101333078258,"score_gpt":0.24947706604647352,"score_spread":0.24316696471339527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085318923","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":[0.013379074,0.00021509377,0.9835352,0.00025967564,0.000022763421,0.00002294235,0.000032884218,0.000046495425,0.002485846],"genre_scores_gemma":[0.79052925,0.0011856761,0.18278931,0.00028385143,0.00012705255,0.00036540625,0.00022125922,0.00017242027,0.024325779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977237,0.00006877903,0.0000070260458,0.000044669443,0.00008622184,0.000020963958],"domain_scores_gemma":[0.9997074,0.00016024195,0.0000431201,0.000028191798,0.000037132206,0.000023848386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048028122,0.00072772754,0.00068817765,0.00043514848,0.00024723524,0.0006752882,0.0017039606,0.001257761,0.0026533725],"category_scores_gemma":[0.0011175921,0.00027365072,0.0006161401,0.0004457006,0.00073097204,0.001383763,0.0008800413,0.0012700895,0.00042188226],"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":[0.000030669227,0.000047459594,0.00017945327,0.00007369166,0.000025944648,0.00014858865,0.00004622426,0.8997657,0.005312476,0.07733616,0.00094755716,0.016086103],"study_design_scores_gemma":[0.0000025347547,0.000010667777,0.0000336502,0.0000015352556,0.0000023906837,0.000026185095,0.000002780877,0.9931102,0.00029107786,0.0063275453,0.00018802023,0.0000034131467],"about_ca_topic_score_codex":0.0012297321,"about_ca_topic_score_gemma":0.0011873031,"teacher_disagreement_score":0.0026533725,"about_ca_system_score_codex":0.000540252,"about_ca_system_score_gemma":0.00048231753,"threshold_uncertainty_score":0.008876383},"labels":[],"label_agreement":null},{"id":"W2092305139","doi":"10.1109/lsp.2014.2381458","title":"FSITM: A Feature Similarity Index For Tone-Mapped Images","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":108,"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":"Pattern recognition (psychology); Feature (linguistics); Similarity (geometry); Feature extraction; Range (aeronautics); Image (mathematics)","score_opus":0.01226481585074939,"score_gpt":0.2747091463863644,"score_spread":0.262444330535615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092305139","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":[0.14434882,0.00213616,0.8475963,0.00011970531,0.00019593617,0.00024286317,0.00061569584,0.0009846019,0.003759937],"genre_scores_gemma":[0.6681269,0.0010958469,0.32604587,0.00012223954,0.00021870856,0.00027282236,0.0015136957,0.00020225595,0.0024016807],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904245,0.0001231903,0.00008798843,0.00013066016,0.0005664701,0.000049305527],"domain_scores_gemma":[0.9984806,0.00043902732,0.0002652039,0.00017420219,0.00056842266,0.00007244195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081752177,0.00058165187,0.0006569514,0.0031876597,0.00026565092,0.00107639,0.00054745446,0.00052785163,0.0013883606],"category_scores_gemma":[0.003979107,0.00012445668,0.00051303854,0.0016619453,0.00034355425,0.001989259,0.0007423253,0.00046554735,0.00044843953],"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":[0.000987825,0.00020308683,0.010269992,0.00059365813,0.0002783752,0.00018554046,0.0001924925,0.015868917,0.14516254,0.0053337137,0.003907442,0.8170165],"study_design_scores_gemma":[0.000120935074,0.0021155141,0.06259669,0.00014306606,0.00048492415,0.0031420432,0.00048803858,0.69984925,0.20099716,0.01020565,0.019585893,0.00027082267],"about_ca_topic_score_codex":0.0007234235,"about_ca_topic_score_gemma":0.0007761806,"teacher_disagreement_score":0.0031876597,"about_ca_system_score_codex":0.00040764964,"about_ca_system_score_gemma":0.00035106976,"threshold_uncertainty_score":0.0046445727},"labels":[],"label_agreement":null},{"id":"W2092723316","doi":"10.1109/istas.2013.6613108","title":"Comparametric HDR (High Dynamic Range) imaging for digital eye glass, wearable cameras, and sousveillance","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Toronto","funders":"","keywords":"High dynamic range; High-dynamic-range imaging; Wearable computer; Computer science; Dynamic range; Computer graphics (images); Computer vision; Digital imaging; Range (aeronautics); Artificial intelligence; Digital image; Materials science; Image processing; Embedded system; Image (mathematics)","score_opus":0.004854445377539829,"score_gpt":0.2323027588049177,"score_spread":0.22744831342737787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092723316","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":[0.05865809,0.0037067176,0.89734584,0.0007630632,0.00043219156,0.00035489185,0.0006360843,0.0048914375,0.033211693],"genre_scores_gemma":[0.39290914,0.0028503104,0.5746121,0.00076904666,0.00021712271,0.00017511217,0.0007779413,0.00044194655,0.027247312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969447,0.000050089955,0.000012153521,0.00007054418,0.00014615267,0.000026665519],"domain_scores_gemma":[0.99963844,0.000102442,0.000039700983,0.00008466692,0.00010332091,0.00003138376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004796432,0.0004898882,0.0002818298,0.00051390234,0.00026224556,0.0010429064,0.0006724012,0.0006129325,0.014572236],"category_scores_gemma":[0.00092800107,0.00024681012,0.00035352656,0.00033813785,0.0002748624,0.0009028323,0.0006301924,0.00049301254,0.0027349205],"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":[0.0004636944,0.000120692894,0.0022450956,0.0005666609,0.0000687007,0.00048933056,0.00030287867,0.003364974,0.37649694,0.0108874915,0.017143233,0.58785033],"study_design_scores_gemma":[0.00011454352,0.0018591089,0.025171626,0.000408909,0.00022424267,0.006643451,0.0005169851,0.127071,0.44165865,0.010920839,0.3851935,0.00021714208],"about_ca_topic_score_codex":0.0010489539,"about_ca_topic_score_gemma":0.0021979786,"teacher_disagreement_score":0.014572236,"about_ca_system_score_codex":0.00038879892,"about_ca_system_score_gemma":0.00024940653,"threshold_uncertainty_score":0.04874897},"labels":[],"label_agreement":null},{"id":"W2093536002","doi":"10.1109/cse.2014.173","title":"Chroma-Keying Based on Global Weighted Sampling and Laplacian Propagation","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Transparency (behavior); Computer science; Keying; Cluster analysis; Color space; Mathematics; Pattern recognition (psychology); Image (mathematics); Telecommunications","score_opus":0.012069496033379321,"score_gpt":0.25688020776125753,"score_spread":0.24481071172787822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093536002","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":[0.010732521,0.00016826588,0.987645,0.00003828406,0.000035323155,0.000040865725,0.000023382654,0.0004742436,0.0008421803],"genre_scores_gemma":[0.25349164,0.00052221125,0.7408842,0.00010097386,0.00009719975,0.00009425066,0.00021330267,0.000223385,0.0043728612],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995565,0.000056505476,0.000016201508,0.00009637735,0.00023134568,0.000042976153],"domain_scores_gemma":[0.9995272,0.00012673014,0.00004118781,0.000071195755,0.00019048956,0.00004311418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049092405,0.0007920095,0.0006511919,0.0012767025,0.00043603854,0.000647947,0.00084620365,0.00045668363,0.0018363056],"category_scores_gemma":[0.0015855771,0.00029172335,0.0006503834,0.0011534913,0.00055062765,0.0012852809,0.0007395187,0.0006559801,0.0005560006],"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":[0.0004901623,0.00013505288,0.0011240874,0.00016802538,0.00007348995,0.00015592581,0.00020612103,0.056970645,0.20390841,0.013302433,0.002820794,0.7206449],"study_design_scores_gemma":[0.000033092154,0.00009806771,0.0008450088,0.000007693191,0.000034290108,0.00019467247,0.000034583114,0.9397673,0.052814174,0.0032324838,0.0029047995,0.00003390587],"about_ca_topic_score_codex":0.005556358,"about_ca_topic_score_gemma":0.006755428,"teacher_disagreement_score":0.005556358,"about_ca_system_score_codex":0.0006421966,"about_ca_system_score_gemma":0.00068669155,"threshold_uncertainty_score":0.011048019},"labels":[],"label_agreement":null},{"id":"W2095533569","doi":"10.1145/2668904.2668939","title":"Saliency-based parameter tuning for tone mapping","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tone mapping; Distortion (music); Divergence (linguistics); Tone (literature); Computer science; Artificial intelligence; Minification; Computer vision; Process (computing); Saliency map; Pattern recognition (psychology); Algorithm; Image (mathematics); High dynamic range","score_opus":0.023815149893432384,"score_gpt":0.2851582781084426,"score_spread":0.2613431282150102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095533569","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":[0.025971062,0.00013312447,0.9718979,0.000038917955,0.000019759804,0.00005053567,0.000008991272,0.0004796941,0.0014000987],"genre_scores_gemma":[0.6129603,0.0001051574,0.38524204,0.00009616309,0.000028086064,0.00012786157,0.00003770465,0.00017237762,0.0012303805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975353,0.00005577971,0.000015379073,0.00006594199,0.00008840201,0.000020950554],"domain_scores_gemma":[0.99942005,0.00027402272,0.00007020151,0.00008620929,0.00012425095,0.000025279944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006417459,0.00067812554,0.00044692864,0.00052008056,0.00030072356,0.0005379294,0.0007672543,0.00063061743,0.0015803492],"category_scores_gemma":[0.0030789888,0.00024924608,0.000307093,0.00027134494,0.00043141062,0.0006795216,0.00055532006,0.00052223774,0.00033163128],"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":[0.00016657515,0.00018543127,0.0017368069,0.00017188542,0.00009511372,0.00015971597,0.00028973832,0.37549275,0.17469741,0.010037624,0.0016510185,0.43531585],"study_design_scores_gemma":[0.00002197446,0.00009741446,0.00045686855,0.0000066176785,0.000018647417,0.00010863938,0.000019376605,0.9803929,0.014796961,0.002853598,0.0012123265,0.000014647809],"about_ca_topic_score_codex":0.00089382794,"about_ca_topic_score_gemma":0.00091565214,"teacher_disagreement_score":0.0015803492,"about_ca_system_score_codex":0.00038088646,"about_ca_system_score_gemma":0.00032898108,"threshold_uncertainty_score":0.005286813},"labels":[],"label_agreement":null},{"id":"W2096589459","doi":"10.1109/nafips.2005.1548575","title":"Quality evaluation of fuzzy contrast enhancement algorithms","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Fuzzy logic; Contrast (vision); Implementation; Task (project management); Algorithm; Contrast enhancement; MATLAB; Quality (philosophy); Artificial intelligence; Machine learning; Data mining; Engineering","score_opus":0.05363996604403682,"score_gpt":0.36453503822393596,"score_spread":0.31089507217989915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096589459","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":[0.110238306,0.0032999134,0.8822685,0.00016637365,0.00008682372,0.00011856739,0.00006889117,0.00072656275,0.0030261152],"genre_scores_gemma":[0.48711562,0.0017014884,0.50908947,0.00007982876,0.00009114435,0.000059660306,0.00025411876,0.00018466548,0.0014240328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973018,0.0005932263,0.00017501217,0.00024167278,0.0015737433,0.00011452256],"domain_scores_gemma":[0.98679346,0.005876082,0.0009311678,0.0009022873,0.0052770223,0.00021998321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005165099,0.00069709506,0.000660846,0.00238851,0.00041048625,0.0017174573,0.0008869096,0.0011456158,0.0014626866],"category_scores_gemma":[0.021947242,0.00025039152,0.0005535764,0.0011275278,0.00068165787,0.0014410685,0.0006986068,0.000534212,0.00032211206],"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":[0.0014852813,0.00016919768,0.009774133,0.0006172313,0.00033178992,0.00017554271,0.00024129577,0.11491499,0.07617666,0.0074493606,0.0017906056,0.78687394],"study_design_scores_gemma":[0.00011065593,0.00096416427,0.012161769,0.00010568606,0.00022036151,0.00109071,0.00011565728,0.83665186,0.14023452,0.004394828,0.0038452244,0.00010454944],"about_ca_topic_score_codex":0.001214299,"about_ca_topic_score_gemma":0.001004325,"teacher_disagreement_score":0.005165099,"about_ca_system_score_codex":0.00070335955,"about_ca_system_score_gemma":0.00039915068,"threshold_uncertainty_score":0.027315974},"labels":[],"label_agreement":null},{"id":"W2097032383","doi":"10.1109/ccece.2003.1225991","title":"Real-time image processing for remote sensing","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Polytechnique Montréal","funders":"","keywords":"Sewerage; Computer science; Real-time computing; Image processing; Java; Enhanced Data Rates for GSM Evolution; Architecture; Image (mathematics); Remote sensing; Embedded system; Computer vision; Engineering; Operating system","score_opus":0.012508471031372776,"score_gpt":0.27803830005581714,"score_spread":0.2655298290244444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097032383","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":[0.0019751773,0.0031157066,0.98462844,0.00040414804,0.00030263673,0.000059892052,0.00006618619,0.0017364662,0.0077113626],"genre_scores_gemma":[0.090513,0.004562282,0.8798074,0.0006500114,0.0005784371,0.00021023377,0.0004864314,0.00049341185,0.022698786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999311,0.00012031421,0.00003697383,0.00015962712,0.00033075162,0.00004140588],"domain_scores_gemma":[0.9994873,0.00017117376,0.000042783115,0.00012235361,0.00015213813,0.0000242555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057824655,0.0007252268,0.00042738733,0.0005918736,0.00027422083,0.0010597743,0.0010915914,0.0009921055,0.009564611],"category_scores_gemma":[0.001031481,0.00020283432,0.00040819836,0.00064665027,0.0005652194,0.0012107942,0.0006152288,0.0009770613,0.005424594],"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":[0.00017887366,0.000077699064,0.00029306937,0.0006078373,0.000054264103,0.00021709532,0.00013658515,0.0074509834,0.15201548,0.0476512,0.013917762,0.7773992],"study_design_scores_gemma":[0.00009030638,0.00037136296,0.0017395103,0.00022780467,0.0001255286,0.0017207207,0.0001292558,0.21034606,0.16033487,0.06662087,0.5581605,0.000133149],"about_ca_topic_score_codex":0.00064535055,"about_ca_topic_score_gemma":0.0005642927,"teacher_disagreement_score":0.009564611,"about_ca_system_score_codex":0.00036282226,"about_ca_system_score_gemma":0.000294807,"threshold_uncertainty_score":0.031996787},"labels":[],"label_agreement":null},{"id":"W2097771239","doi":"10.1109/robot.2006.1642244","title":"On the performance of color tracking algorithms for underwater robots under varying lighting and visibility","year":2006,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":32,"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","funders":"","keywords":"Visibility; Computer vision; Underwater; Tracking (education); Artificial intelligence; Computer science; Histogram; Robot; Tracking system; Context (archaeology); Eye tracking; Color histogram; Image processing; Kalman filter; Color image; Image (mathematics); Optics","score_opus":0.02773919852638555,"score_gpt":0.2691706863740841,"score_spread":0.24143148784769855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097771239","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":[0.7636463,0.0013243366,0.23082095,0.00013022109,0.000060535567,0.00006764588,0.000106516534,0.0013846023,0.0024588886],"genre_scores_gemma":[0.93110394,0.0004916673,0.06716948,0.000039259987,0.000020860947,0.000031013078,0.00019673887,0.00010579423,0.00084122585],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904543,0.00021638253,0.00007418343,0.00017718357,0.0003644362,0.00012240768],"domain_scores_gemma":[0.98970467,0.007288797,0.00078903296,0.00048746142,0.0015906203,0.00013940722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021165623,0.00055014394,0.0006210477,0.0008513408,0.00042125114,0.0007768402,0.0005491012,0.0009088182,0.0006981352],"category_scores_gemma":[0.01822272,0.0002332608,0.0003003611,0.0008864103,0.0005264944,0.0010179427,0.0004593705,0.000393093,0.00030488524],"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":[0.0026883793,0.0003165942,0.018591728,0.00045733835,0.00024325425,0.00020315392,0.00037610176,0.4072908,0.13837028,0.0018424611,0.0008070281,0.42881286],"study_design_scores_gemma":[0.000044340144,0.0007434317,0.0132643385,0.000020906806,0.00009462072,0.00023696129,0.00009561218,0.8851761,0.09918986,0.00060439593,0.0004815063,0.000047936268],"about_ca_topic_score_codex":0.0036255212,"about_ca_topic_score_gemma":0.0022781705,"teacher_disagreement_score":0.0036255212,"about_ca_system_score_codex":0.00055101427,"about_ca_system_score_gemma":0.00056762155,"threshold_uncertainty_score":0.0111935735},"labels":[],"label_agreement":null},{"id":"W2101661007","doi":"10.1109/ical.2009.5262814","title":"Contrast enhancement using morphological scale space","year":2009,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of Alberta","funders":"","keywords":"Contrast (vision); Computer science; Segmentation; Scale (ratio); Artificial intelligence; Image segmentation; Measure (data warehouse); Image (mathematics); Image enhancement; Computer vision; Contrast enhancement; Object (grammar); Scale space; Pattern recognition (psychology); Image processing; Algorithm; Data mining","score_opus":0.019928605015022027,"score_gpt":0.2790694731349363,"score_spread":0.25914086811991427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101661007","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":[0.018416714,0.00039918334,0.97780085,0.00011245511,0.000053473592,0.00004680572,0.00002485297,0.0008513114,0.0022943937],"genre_scores_gemma":[0.1647307,0.00096897874,0.83034045,0.00010026242,0.000083235806,0.000049909984,0.00007636945,0.00026127382,0.0033887702],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967146,0.00004182507,0.000020542802,0.000071327406,0.00016371366,0.00003116759],"domain_scores_gemma":[0.999343,0.000246219,0.00009987625,0.00013089868,0.00015106674,0.00002896009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006718443,0.00063131144,0.00047141494,0.0011477246,0.0003173932,0.0012080612,0.00051408436,0.0006840951,0.0020658919],"category_scores_gemma":[0.0015958445,0.00038656738,0.00075419253,0.0008661415,0.0007343804,0.0012680666,0.00074553327,0.000888902,0.0010976394],"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":[0.00017344404,0.00005951721,0.0008746616,0.00031525377,0.000091210524,0.0003460043,0.00016411427,0.016803691,0.6078099,0.015307338,0.0015660081,0.3564888],"study_design_scores_gemma":[0.00004502205,0.00030575314,0.0047224057,0.00004491077,0.000120785066,0.002592411,0.00009424315,0.24925952,0.69985765,0.013230788,0.029634837,0.0000916302],"about_ca_topic_score_codex":0.00030274357,"about_ca_topic_score_gemma":0.0004890607,"teacher_disagreement_score":0.0020658919,"about_ca_system_score_codex":0.0002986168,"about_ca_system_score_gemma":0.000296598,"threshold_uncertainty_score":0.006911099},"labels":[],"label_agreement":null},{"id":"W2104666850","doi":"10.1109/icip.2004.1419467","title":"Contrast enhancement of radiograph images based on local heterogeneity measures","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"University of Windsor","funders":"","keywords":"Pixel; Artificial intelligence; Computer science; Contrast (vision); Computer vision; Contrast enhancement; Image enhancement; Grayscale; Pattern recognition (psychology); Image (mathematics)","score_opus":0.012482327497048432,"score_gpt":0.25280578906407175,"score_spread":0.24032346156702333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104666850","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":[0.128786,0.0004793301,0.8691456,0.000065229775,0.00001973581,0.000050337116,0.000032207026,0.00031738155,0.0011042176],"genre_scores_gemma":[0.5771359,0.0006786072,0.42031538,0.000037266207,0.00004943194,0.000053280317,0.00008345017,0.00008558309,0.0015610944],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998229,0.00003655709,0.000010136686,0.000033476164,0.00007882133,0.000018197881],"domain_scores_gemma":[0.9993407,0.0003572413,0.00009445554,0.00006911424,0.00011309043,0.000025390817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004878657,0.00036322422,0.0002885107,0.00077276095,0.000104702514,0.0005321821,0.00030195058,0.00028002964,0.0009239541],"category_scores_gemma":[0.0018548656,0.00017662586,0.00031421022,0.00032276192,0.0002944727,0.0006112383,0.00040432438,0.00036184886,0.0002547175],"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":[0.0004720124,0.000058334103,0.0030639595,0.00026884684,0.00007887727,0.00033232235,0.000112887166,0.024831751,0.66278964,0.004901835,0.00045016804,0.30263934],"study_design_scores_gemma":[0.000060684044,0.0005116963,0.018446892,0.0000400605,0.00018782339,0.0022603523,0.000080454105,0.4119183,0.55804574,0.003481898,0.0049039247,0.000062221014],"about_ca_topic_score_codex":0.00021609112,"about_ca_topic_score_gemma":0.00033512866,"teacher_disagreement_score":0.0009239541,"about_ca_system_score_codex":0.00015238425,"about_ca_system_score_gemma":0.00016227775,"threshold_uncertainty_score":0.0030909777},"labels":[],"label_agreement":null},{"id":"W2104759024","doi":"10.1145/2492494.2501888","title":"Modelling perceptually efficient aquatic environments","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Waterloo","funders":"","keywords":"Computer science; Perception; Artificial intelligence; Deep water; Waves and shallow water; Geology; Marine engineering; Engineering; Oceanography","score_opus":0.014619659409170113,"score_gpt":0.2122516215166142,"score_spread":0.19763196210744408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104759024","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":[0.36294636,0.0007525685,0.6128109,0.00051696075,0.00008533348,0.00012728445,0.0003541409,0.0004406014,0.021965781],"genre_scores_gemma":[0.9205934,0.0005538439,0.072446115,0.00006700684,0.000033555974,0.00009018166,0.00014110492,0.00014372998,0.005931096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984145,0.00004039269,0.000006584462,0.00003822509,0.000039825514,0.000033482695],"domain_scores_gemma":[0.99947244,0.0002688762,0.00006679671,0.00005532685,0.00007511975,0.00006146423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024864223,0.00077150785,0.00060641434,0.0004527334,0.0004155837,0.001794171,0.0011474584,0.0011214414,0.0033460634],"category_scores_gemma":[0.0017942,0.0007641849,0.0006858069,0.0003664318,0.00090098433,0.0018602756,0.0013839931,0.0007556885,0.00041524193],"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":[0.00007405785,0.000034502194,0.0005152101,0.000072153234,0.000015469795,0.00011938028,0.00016404633,0.97249705,0.0075376015,0.013301068,0.00031732806,0.0053520747],"study_design_scores_gemma":[0.000008737534,0.00002140444,0.00019766075,0.0000050404924,0.0000035872415,0.000023008506,0.00002611184,0.99357104,0.000393017,0.005449033,0.00029422055,0.000007090171],"about_ca_topic_score_codex":0.0052484553,"about_ca_topic_score_gemma":0.005637221,"teacher_disagreement_score":0.0052484553,"about_ca_system_score_codex":0.0008107117,"about_ca_system_score_gemma":0.00054889347,"threshold_uncertainty_score":0.011193693},"labels":[],"label_agreement":null},{"id":"W2104771767","doi":"10.1109/icassp.2012.6288176","title":"Computationally efficient tone-mapping of high-bit-depth video in the YCbCr domain","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 British Columbia","funders":"","keywords":"Tone mapping; YCbCr; Computer vision; Artificial intelligence; Computer science; Luminance; High dynamic range; Color space; Color depth; Gamma correction; Chrominance; Pipeline (software); Transformation (genetics); Dynamic range; Color image; Image (mathematics); Image processing","score_opus":0.01698122103098512,"score_gpt":0.2786052219429144,"score_spread":0.26162400091192933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104771767","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":[0.08870442,0.00065405923,0.90423673,0.00010879053,0.00006580864,0.00013825449,0.000060379498,0.00081230944,0.0052192546],"genre_scores_gemma":[0.3068253,0.0008210261,0.6867142,0.00007059288,0.00005180298,0.00006856984,0.00016995247,0.000083521256,0.005195029],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998541,0.000023383722,0.0000073125584,0.000020472213,0.00007999136,0.000014792695],"domain_scores_gemma":[0.9998313,0.00004492294,0.000026448733,0.000048153153,0.00004035594,0.00000869933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015339424,0.00043753663,0.0002713429,0.00040485838,0.00018887866,0.00046510133,0.00037764816,0.0002823144,0.0031624325],"category_scores_gemma":[0.00053661334,0.00013060107,0.00025767603,0.00032917247,0.0001733394,0.000673824,0.0003495725,0.000362405,0.0008285875],"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":[0.00017700931,0.00008928174,0.00038357763,0.00027323884,0.000029281235,0.00014891839,0.00008991893,0.011799997,0.43304613,0.0048135025,0.0011562882,0.5479928],"study_design_scores_gemma":[0.00008948529,0.0005030318,0.0031583305,0.000046657467,0.00005559544,0.0017547032,0.00011593694,0.4046807,0.5686666,0.0032046924,0.01767315,0.00005112585],"about_ca_topic_score_codex":0.00094456284,"about_ca_topic_score_gemma":0.0024643089,"teacher_disagreement_score":0.0031624325,"about_ca_system_score_codex":0.00020144545,"about_ca_system_score_gemma":0.00040363774,"threshold_uncertainty_score":0.010579407},"labels":[],"label_agreement":null},{"id":"W2106466407","doi":"10.1109/crv.2005.35","title":"Detection of Occlusion Edges from the Derivatives of Weather Degraded Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visibility; Computer vision; Artificial intelligence; Computer science; Occlusion; Pattern recognition (psychology); Geography","score_opus":0.013340176401912034,"score_gpt":0.24533699986221477,"score_spread":0.23199682346030273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106466407","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":[0.7820957,0.0005953942,0.21406403,0.00008256889,0.000029641546,0.000045648034,0.0002010062,0.0008651639,0.0020207996],"genre_scores_gemma":[0.9007053,0.0005211743,0.097381465,0.000018718307,0.000025041656,0.00001589093,0.00028307756,0.000104849954,0.00094439636],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986815,0.000015244573,0.000005560522,0.00002185828,0.00006476043,0.000024454237],"domain_scores_gemma":[0.9994887,0.00017168539,0.00011631137,0.00006330932,0.00011496091,0.000045041586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021770399,0.0003882378,0.00033329814,0.000859975,0.00014542927,0.00041074125,0.00020912143,0.0002722992,0.00075106265],"category_scores_gemma":[0.0010061048,0.00022318514,0.00017954681,0.00037710782,0.00032698366,0.0004935236,0.00020813319,0.0003832505,0.00016534774],"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":[0.0007406222,0.00008309139,0.004889783,0.00020565395,0.0000444956,0.00051176944,0.00020294888,0.004437703,0.8244447,0.0007324923,0.00043698106,0.16326965],"study_design_scores_gemma":[0.00004214049,0.00037819427,0.13205443,0.000035913017,0.00011056957,0.0027824892,0.00013719696,0.14331253,0.7162013,0.0008589821,0.004008424,0.000077808916],"about_ca_topic_score_codex":0.00072852866,"about_ca_topic_score_gemma":0.0010803188,"teacher_disagreement_score":0.000859975,"about_ca_system_score_codex":0.00013670341,"about_ca_system_score_gemma":0.0001622222,"threshold_uncertainty_score":0.0025125146},"labels":[],"label_agreement":null},{"id":"W2107051770","doi":"10.1109/mmsp.2006.285326","title":"Fast Image/Video Contrast Enhancement Based on WTHE","year":2006,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 British Columbia","funders":"","keywords":"Histogram equalization; Adaptive histogram equalization; Thresholding; Computer science; Artificial intelligence; Histogram; Balanced histogram thresholding; Weighting; Computer vision; Image (mathematics); Histogram matching; Pattern recognition (psychology); Contrast (vision); Image histogram; Contrast enhancement; Image enhancement; Process (computing); Image processing; Color image","score_opus":0.005608157530604221,"score_gpt":0.22857891203085867,"score_spread":0.22297075450025444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107051770","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":[0.016717592,0.0004931517,0.9807351,0.000060793896,0.0000622762,0.000047983634,0.000014914876,0.0004528027,0.0014152528],"genre_scores_gemma":[0.24397278,0.0008476321,0.7499976,0.00014297043,0.00013034488,0.00008065928,0.00006291554,0.00011276589,0.004652295],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996767,0.00004498707,0.000016664342,0.00006024381,0.00016860993,0.000032812546],"domain_scores_gemma":[0.99966824,0.00012515207,0.00005024528,0.00006376184,0.000073608906,0.000018906529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038815418,0.00044737724,0.00043531446,0.0006194415,0.00017978063,0.0004628049,0.0005284461,0.0003720841,0.0014642759],"category_scores_gemma":[0.0007122749,0.0002717045,0.0003331933,0.00031803956,0.00045377272,0.000991265,0.00061085453,0.00072567386,0.00054817717],"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":[0.00017149484,0.00008809758,0.00042644845,0.00017342025,0.00004258435,0.00024728593,0.000048331945,0.012411886,0.6753151,0.0112075275,0.0008273575,0.2990405],"study_design_scores_gemma":[0.000035442226,0.0002701825,0.0012446127,0.000017829256,0.000037269063,0.0012345562,0.000012974735,0.30640084,0.6785167,0.0026759882,0.009504572,0.000049038015],"about_ca_topic_score_codex":0.00026975526,"about_ca_topic_score_gemma":0.00046144528,"teacher_disagreement_score":0.0014642759,"about_ca_system_score_codex":0.00014850618,"about_ca_system_score_gemma":0.00018321628,"threshold_uncertainty_score":0.0048984885},"labels":[],"label_agreement":null},{"id":"W2107138380","doi":"10.1109/tip.2008.2001414","title":"Minimal-Bracketing Sets for High-Dynamic-Range Image Capture","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":70,"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 Victoria","funders":"","keywords":"Bracketing (phenomenology); Computer vision; Artificial intelligence; Image quality; Computer science; Set (abstract data type); High dynamic range; Noise (video); High-dynamic-range imaging; Dynamic range; Image processing; Mathematics; Range (aeronautics); Image (mathematics); Algorithm","score_opus":0.01714098219781213,"score_gpt":0.27969393822028,"score_spread":0.26255295602246786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107138380","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":[0.021851594,0.000076074844,0.9769249,0.000038270642,0.000007022644,0.000038070353,0.000020873133,0.00040309466,0.00064011],"genre_scores_gemma":[0.15452349,0.00010945912,0.84406835,0.00004828713,0.000029190474,0.00008904982,0.00013768008,0.00015345018,0.00084100163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989724,0.00018439596,0.00005682188,0.0001837207,0.0005341708,0.00006844346],"domain_scores_gemma":[0.99768,0.0013429731,0.00022778388,0.00040031492,0.00027572238,0.00007328896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009192707,0.0006756004,0.0008081459,0.00059723033,0.00043236988,0.0007611822,0.0011513267,0.00059288566,0.0029668575],"category_scores_gemma":[0.004589727,0.00042435675,0.0005669035,0.0004834125,0.0006601882,0.0016355265,0.0012771118,0.0008759216,0.00058597064],"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":[0.00046915386,0.0002313291,0.0018619635,0.0003754101,0.000080182734,0.00026935845,0.0006318052,0.21013595,0.15472713,0.026589729,0.00242656,0.6022014],"study_design_scores_gemma":[0.000041939453,0.00031880866,0.0020828429,0.00002879555,0.000031914067,0.0004719219,0.00013736461,0.8356414,0.13865283,0.017317705,0.0052186307,0.000055843408],"about_ca_topic_score_codex":0.0006247703,"about_ca_topic_score_gemma":0.0009806593,"teacher_disagreement_score":0.0029668575,"about_ca_system_score_codex":0.00063396164,"about_ca_system_score_gemma":0.00059738435,"threshold_uncertainty_score":0.009925127},"labels":[],"label_agreement":null},{"id":"W2109079788","doi":"10.2200/s00601ed1v01y201410ivm017","title":"Combating Bad Weather Part II: Fog Removal from Image and Video","year":2015,"lang":"en","type":"article","venue":"Synthesis lectures on image, video, and multimedia processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsemi (Canada)","funders":"","keywords":"Meteorology; Weather modification; Computer science; Environmental science; Aeronautics; Computer vision; Artificial intelligence; Geography; Engineering","score_opus":0.017933361138546607,"score_gpt":0.2671048665950275,"score_spread":0.24917150545648092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109079788","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":[0.11056247,0.013469248,0.8465705,0.00048393823,0.0014901315,0.00030105602,0.000580931,0.0018892772,0.02465248],"genre_scores_gemma":[0.57608724,0.021655167,0.27652895,0.00047630188,0.0012258008,0.00024404337,0.00203099,0.0007678657,0.120983645],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99991894,0.0000045557213,0.000003497486,0.000016922055,0.000039937917,0.000016234018],"domain_scores_gemma":[0.9999193,0.000018438945,0.000008928669,0.000010856571,0.000033340726,0.000009079702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001079788,0.0007614593,0.00045366914,0.00046833543,0.00030544764,0.00052673084,0.00031364543,0.00052480464,0.0035330092],"category_scores_gemma":[0.00021065697,0.00023427517,0.0003120328,0.0003994569,0.00030567514,0.00048261366,0.00031233937,0.00046052225,0.0012135954],"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":[0.00033815854,0.00010377549,0.0011923733,0.00054145616,0.000058356625,0.0005414911,0.00014286657,0.012578368,0.48187602,0.0028119984,0.01612205,0.48369297],"study_design_scores_gemma":[0.000030125493,0.00051120744,0.022395663,0.00012963395,0.00013588929,0.00193194,0.00029215968,0.19467239,0.6819137,0.0053970553,0.092515595,0.0000746348],"about_ca_topic_score_codex":0.0023946683,"about_ca_topic_score_gemma":0.0028081536,"teacher_disagreement_score":0.0035330092,"about_ca_system_score_codex":0.00018918506,"about_ca_system_score_gemma":0.00024805762,"threshold_uncertainty_score":0.011819065},"labels":[],"label_agreement":null},{"id":"W2109484310","doi":"10.1109/ccece.2007.13","title":"An Efficient Compression Scheme for Colour Filter Array Images Using Estimated Colour Differences","year":2007,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"YCbCr; RGB color model; Artificial intelligence; Bayer filter; Demosaicing; Computer vision; Color filter array; Chrominance; Computer science; Pixel; Mathematics; Sample (material); Color gel; Color image; Image (mathematics); Image processing; Materials science; Luminance","score_opus":0.053479747782831336,"score_gpt":0.35197412398357586,"score_spread":0.29849437620074454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109484310","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":[0.031658527,0.00068092573,0.9641845,0.00014367113,0.0001792549,0.000098575016,0.00013215916,0.00069996517,0.0022223399],"genre_scores_gemma":[0.18416148,0.00086674205,0.8085612,0.00015071547,0.00015273424,0.00011934837,0.0004976699,0.0000815313,0.005408636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965036,0.000027579425,0.000021821137,0.000053308777,0.00021831514,0.000028612172],"domain_scores_gemma":[0.9995141,0.0000981011,0.000054857395,0.00010085134,0.000210515,0.000021546562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034055318,0.0006706342,0.00040064013,0.00087350793,0.00028387253,0.00051695603,0.0005644536,0.00043000196,0.0019091753],"category_scores_gemma":[0.00119861,0.00022011995,0.00047940828,0.0010165917,0.0003344035,0.00084754074,0.0005243839,0.0006399073,0.00083846785],"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":[0.0004248037,0.00008587831,0.00089933246,0.00016840718,0.00005953322,0.0002783556,0.00017468342,0.034231536,0.24455407,0.010650414,0.0038151855,0.70465773],"study_design_scores_gemma":[0.00007901237,0.000389936,0.0028965885,0.000072017225,0.000078538775,0.001406553,0.00008131022,0.7099761,0.25687578,0.0047327694,0.023310395,0.000101063364],"about_ca_topic_score_codex":0.0020568753,"about_ca_topic_score_gemma":0.0025796844,"teacher_disagreement_score":0.0020568753,"about_ca_system_score_codex":0.00039707814,"about_ca_system_score_gemma":0.0005295254,"threshold_uncertainty_score":0.0063868165},"labels":[],"label_agreement":null},{"id":"W2116827294","doi":"10.1109/crv.2006.77","title":"Toward a Realistic Interpretation of Blue-spill for Blue-screen Matting","year":2006,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Université de Sherbrooke","funders":"","keywords":"Interpretation (philosophy); Phenomenon; Composition (language); Computer science; Object (grammar); Artificial intelligence; Computer vision; Computer graphics (images); Physics; Art; Programming language","score_opus":0.019754845814650892,"score_gpt":0.27393165907202194,"score_spread":0.25417681325737107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116827294","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":[0.0108197,0.00019801667,0.98376113,0.00027238776,0.0000845187,0.000021035601,0.000014544695,0.00019577002,0.004632902],"genre_scores_gemma":[0.5252888,0.00059082423,0.46601394,0.00035075584,0.00015737403,0.00007142045,0.00006555817,0.00028211938,0.0071791783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994561,0.00015350623,0.000020870804,0.000073626485,0.00026008527,0.00003583935],"domain_scores_gemma":[0.99939907,0.00021958184,0.000081547165,0.00011924007,0.00014466309,0.00003594197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000719206,0.00055307616,0.00041684997,0.00047740698,0.00046697023,0.0014777734,0.0011516705,0.0012476192,0.0024671273],"category_scores_gemma":[0.0028576183,0.00038601118,0.00040956805,0.0002658412,0.0014931309,0.0021792287,0.0013613773,0.0016115537,0.0004402024],"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":[0.00011652277,0.00008450229,0.0007110573,0.00025376363,0.000021337628,0.0005775975,0.00061122776,0.17346947,0.09538368,0.672126,0.0027368246,0.053908005],"study_design_scores_gemma":[0.000015754815,0.000040600225,0.00029177545,0.000025501817,0.000008347998,0.00029993613,0.000074244,0.9050083,0.012911848,0.07139667,0.009897917,0.000029094033],"about_ca_topic_score_codex":0.00056748907,"about_ca_topic_score_gemma":0.00054239976,"teacher_disagreement_score":0.0024671273,"about_ca_system_score_codex":0.0005691886,"about_ca_system_score_gemma":0.00037881173,"threshold_uncertainty_score":0.008253336},"labels":[],"label_agreement":null},{"id":"W2118810955","doi":"10.1109/icip.2008.4712138","title":"The tradeoff between SNR and exposure-set size in HDR imaging","year":2008,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of Victoria","funders":"","keywords":"Radiance; Pixel; Computer vision; Computer science; Artificial intelligence; High dynamic range; Dynamic range; Noise (video); Range (aeronautics); Image resolution; Digital imaging; Set (abstract data type); Signal-to-noise ratio (imaging); Digital image; Image (mathematics); Remote sensing; Image processing; Geography; Engineering","score_opus":0.019165220952389046,"score_gpt":0.249694403098997,"score_spread":0.23052918214660795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118810955","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":[0.33108705,0.0020512596,0.65668404,0.0009294185,0.000083260784,0.0001100939,0.0001850381,0.0014898541,0.0073799486],"genre_scores_gemma":[0.739058,0.0012175067,0.25616762,0.00041078922,0.00017307585,0.000175636,0.00034877527,0.00050225697,0.0019463934],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768174,0.0007713468,0.00014372436,0.0004377556,0.0008282958,0.00013715937],"domain_scores_gemma":[0.9749381,0.02079284,0.0008738108,0.0014882021,0.0015647683,0.00034231585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003681068,0.0008145808,0.00086605566,0.00073298655,0.0005147397,0.0009813411,0.0013582691,0.0016896712,0.001817848],"category_scores_gemma":[0.024786655,0.00069803576,0.0004363833,0.0008596764,0.0009030513,0.0031718656,0.0016475274,0.00096539245,0.00052916363],"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":[0.0030378557,0.00068528636,0.016900202,0.0009963391,0.0003172241,0.0016423918,0.0013146041,0.16347365,0.5254928,0.017971404,0.0028150864,0.2653532],"study_design_scores_gemma":[0.00028693472,0.0022524246,0.038291022,0.00019116234,0.00037169558,0.0067942385,0.0004277668,0.37795123,0.535859,0.030684331,0.006649696,0.00024047615],"about_ca_topic_score_codex":0.0005074658,"about_ca_topic_score_gemma":0.0005840423,"teacher_disagreement_score":0.003681068,"about_ca_system_score_codex":0.00060581556,"about_ca_system_score_gemma":0.0002719391,"threshold_uncertainty_score":0.019467533},"labels":[],"label_agreement":null},{"id":"W2119356981","doi":"10.1111/j.1467-8659.2009.01594.x","title":"TouchTone: Interactive Local Image Adjustment Using Point‐and‐Swipe","year":2010,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement 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":"Nokia (Canada)","funders":"","keywords":"Computer science; SwIPe; Computer vision; Computer graphics (images); Mobile device; Process (computing); Image (mathematics); Enhanced Data Rates for GSM Evolution; Computational photography; Gesture; Artificial intelligence; Image sharing; Image editing; Image processing; World Wide Web","score_opus":0.008446527489725294,"score_gpt":0.25920438022784853,"score_spread":0.25075785273812323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119356981","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":[0.03419497,0.0002600326,0.9551745,0.0000789197,0.0000684594,0.000099111785,0.0000756189,0.0068835937,0.0031648364],"genre_scores_gemma":[0.31952235,0.0002832915,0.67054665,0.00017391193,0.00005409122,0.00016170138,0.00017099979,0.0009176448,0.008169495],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997447,0.00003791122,0.000010561687,0.00007439112,0.00010933734,0.000023057913],"domain_scores_gemma":[0.9996476,0.0001661754,0.000028639231,0.00007919703,0.000043365155,0.000034999754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030353866,0.0006482716,0.0005181701,0.00055506313,0.00024631902,0.0005923293,0.00106619,0.00058313337,0.010825982],"category_scores_gemma":[0.0010730844,0.00034961602,0.00038600768,0.00036830266,0.0003422564,0.00082168955,0.0013583364,0.00047307284,0.0011804276],"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":[0.0006199678,0.00013141797,0.0009964621,0.00021893604,0.00007972993,0.0005004051,0.00040082162,0.017282845,0.29235393,0.0029253736,0.009627635,0.6748625],"study_design_scores_gemma":[0.00021748383,0.00049895304,0.0046781963,0.000058596586,0.00006876089,0.0016786922,0.0002012045,0.6569038,0.27738342,0.0043126014,0.053817563,0.0001807201],"about_ca_topic_score_codex":0.0007880545,"about_ca_topic_score_gemma":0.0011866541,"teacher_disagreement_score":0.010825982,"about_ca_system_score_codex":0.00018905505,"about_ca_system_score_gemma":0.00012010922,"threshold_uncertainty_score":0.036216557},"labels":[],"label_agreement":null},{"id":"W2119922429","doi":"10.1109/titb.2011.2164259","title":"Nonlinear Unsharp Masking for Mammogram Enhancement","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":199,"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 Calgary","keywords":"Unsharp masking; Computer science; Masking (illustration); Artificial intelligence; Nonlinear system; Flexibility (engineering); Measure (data warehouse); A priori and a posteriori; Image (mathematics); Image enhancement; Computer vision; Visualization; Pattern recognition (psychology); Mathematics; Data mining; Statistics","score_opus":0.017926443399578534,"score_gpt":0.2594391597498786,"score_spread":0.24151271635030006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119922429","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":[0.029907841,0.0016815068,0.96445745,0.00013414549,0.00010922345,0.00007311024,0.000033812783,0.00045995845,0.0031429308],"genre_scores_gemma":[0.26597354,0.0018324006,0.7277941,0.00014925787,0.00011493896,0.00008740243,0.000080469916,0.000095239404,0.003872609],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975175,0.00006563907,0.00001417253,0.000036757767,0.00011402299,0.000017653883],"domain_scores_gemma":[0.99954754,0.0002295008,0.000055593835,0.000077406716,0.000069977104,0.000019956717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056245015,0.0005542392,0.0003393639,0.00042796868,0.0002465754,0.00035496554,0.00047119526,0.00043410197,0.0018788942],"category_scores_gemma":[0.0012028557,0.0001887564,0.00033084906,0.00031077518,0.00038875738,0.00057659356,0.00059477123,0.00047533592,0.0006012411],"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":[0.0005061884,0.00004359951,0.00070087105,0.0004709967,0.00004597505,0.00024886633,0.00013104695,0.013191905,0.62772894,0.0138971545,0.001149919,0.34188464],"study_design_scores_gemma":[0.000034735305,0.00041672966,0.0023398462,0.00008044944,0.000113897244,0.0017810835,0.0000483239,0.3227853,0.64005595,0.005888025,0.026383335,0.00007225926],"about_ca_topic_score_codex":0.00021676962,"about_ca_topic_score_gemma":0.00052973843,"teacher_disagreement_score":0.0018788942,"about_ca_system_score_codex":0.0002263176,"about_ca_system_score_gemma":0.00021618881,"threshold_uncertainty_score":0.0062854886},"labels":[],"label_agreement":null},{"id":"W2121009375","doi":"10.1109/qomex.2015.7148154","title":"Compression efficiency of HDR/LDR content","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"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":"Gamut; High dynamic range; Computer science; Luminance; Metadata; Computer vision; High-dynamic-range imaging; Transform coding; Dynamic range; Computer graphics (images); Tone mapping; Data compression; Artificial intelligence; Dynamic range compression; Discrete cosine transform; Image (mathematics)","score_opus":0.09239125790355197,"score_gpt":0.2912168387772989,"score_spread":0.19882558087374697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121009375","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9618688,0.0017488896,0.024949748,0.00011233924,0.00006906686,0.0000868792,0.00080320815,0.0007531955,0.0096079055],"genre_scores_gemma":[0.97423786,0.000917122,0.019305693,0.00007296181,0.000029761091,0.000032503292,0.0015771178,0.00013177864,0.0036951734],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995216,0.00004606855,0.000030601004,0.000058476417,0.0002815686,0.00006168041],"domain_scores_gemma":[0.99881786,0.00037897454,0.000091907255,0.0001356229,0.000533517,0.000042049523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003757023,0.00038980745,0.00031563424,0.0011038776,0.00020119168,0.00062119507,0.00038513835,0.0003465591,0.0030933334],"category_scores_gemma":[0.0019354268,0.000074050105,0.00017301872,0.0007841457,0.00024381797,0.0006688483,0.0003119421,0.00021183123,0.0006047624],"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":[0.003388274,0.00038409152,0.005948838,0.0004770328,0.00009399605,0.0008005342,0.00022875071,0.038264737,0.6022855,0.002611455,0.0027859968,0.3427308],"study_design_scores_gemma":[0.000105166044,0.0011696991,0.025194954,0.00008555355,0.0001282174,0.0014459388,0.00018418496,0.26161173,0.7040143,0.0007777465,0.005213823,0.00006869984],"about_ca_topic_score_codex":0.0018156334,"about_ca_topic_score_gemma":0.0010990943,"teacher_disagreement_score":0.0030933334,"about_ca_system_score_codex":0.0003695556,"about_ca_system_score_gemma":0.0002161746,"threshold_uncertainty_score":0.010348201},"labels":[],"label_agreement":null},{"id":"W2123851435","doi":"10.1109/iccv.2011.6126366","title":"Cluster-based color space optimizations","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":53,"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":"Gamut; Color space; Computer science; Computer vision; Artificial intelligence; Grayscale; Computer graphics (images); Multispectral image; RGB color space; Color quantization; Color histogram; ICC profile; Color image; Color model; Image processing; Image (mathematics)","score_opus":0.027954308714992954,"score_gpt":0.23647759804528465,"score_spread":0.20852328933029168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123851435","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":[0.030227311,0.00015018087,0.9597673,0.0001532446,0.00005389343,0.00006249715,0.00009537324,0.0012079756,0.008282276],"genre_scores_gemma":[0.45852324,0.00017227468,0.5300762,0.00012559514,0.000041339496,0.00017950944,0.00029690683,0.0011025732,0.009482335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995802,0.00007771674,0.000015291735,0.00008525548,0.00017239565,0.000069111426],"domain_scores_gemma":[0.9995338,0.00009615355,0.000029435683,0.00011660572,0.00019598175,0.00002802554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049837504,0.0010410934,0.00080273085,0.0008235635,0.00066190056,0.0009546707,0.0012920974,0.0005555096,0.004619248],"category_scores_gemma":[0.0015271867,0.00030049944,0.00076995377,0.0011788938,0.00058224174,0.00093941874,0.0013216636,0.0008998823,0.00072958396],"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":[0.00022744843,0.00012346478,0.0008437987,0.00010479939,0.00007081526,0.000061196486,0.00016337655,0.75409067,0.034884904,0.050898932,0.00765716,0.15087335],"study_design_scores_gemma":[0.000019593277,0.000029453107,0.00020105521,0.0000032891126,0.000013023154,0.000037231468,0.000039802424,0.97648627,0.007553872,0.012439842,0.0031638106,0.0000126916875],"about_ca_topic_score_codex":0.004054656,"about_ca_topic_score_gemma":0.0054286886,"teacher_disagreement_score":0.004619248,"about_ca_system_score_codex":0.0011306684,"about_ca_system_score_gemma":0.0008799587,"threshold_uncertainty_score":0.015452921},"labels":[],"label_agreement":null},{"id":"W2125543507","doi":"10.1109/iembs.2010.5626149","title":"Local image enhancement for fiducial marker detection in electronic portal images of prostate radiotherapy","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of Victoria","funders":"","keywords":"Fiducial marker; Contrast (vision); Artificial intelligence; Computer science; Metric (unit); Computer vision; Workflow; Pattern recognition (psychology); Image (mathematics); Engineering","score_opus":0.003388104934084862,"score_gpt":0.24911253738473416,"score_spread":0.2457244324506493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125543507","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":[0.019249167,0.00057023973,0.9792346,0.00003116335,0.000013002171,0.000029886785,0.000007408293,0.00031956678,0.0005449751],"genre_scores_gemma":[0.2846701,0.00061542937,0.7133284,0.00005875893,0.00003864977,0.000059239497,0.00004085859,0.00015166482,0.0010369037],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953115,0.0001868843,0.000021757922,0.000057455243,0.00017970892,0.00002308489],"domain_scores_gemma":[0.99921906,0.00048499927,0.00009464493,0.00008521225,0.00009321359,0.0000227753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007235714,0.00049465464,0.00039714682,0.00059439434,0.00017674346,0.00056577346,0.0006672645,0.00053081784,0.00097017887],"category_scores_gemma":[0.0028736987,0.00028575893,0.0003534406,0.0003750701,0.00039431296,0.00063282595,0.0007458335,0.00052047486,0.00045008407],"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":[0.0005422482,0.00009579962,0.0011756733,0.0004263362,0.00005484128,0.0002678511,0.0001582946,0.033094056,0.42601433,0.0049426984,0.0006933411,0.5325345],"study_design_scores_gemma":[0.00006320776,0.00052951026,0.0037883539,0.000057757396,0.000099073244,0.0020665943,0.000047067617,0.5265878,0.45712182,0.002498446,0.0070812223,0.000059178292],"about_ca_topic_score_codex":0.00013286926,"about_ca_topic_score_gemma":0.00023931806,"teacher_disagreement_score":0.00097017887,"about_ca_system_score_codex":0.00016957754,"about_ca_system_score_gemma":0.00018373744,"threshold_uncertainty_score":0.0038266778},"labels":[],"label_agreement":null},{"id":"W2129294876","doi":"10.1364/josaa.29.001580","title":"Nature-inspired color-filter array for enhancing the quality of images","year":2012,"lang":"en","type":"article","venue":"Journal of the Optical Society of America A","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Color filter array; Image quality; Rendering (computer graphics); Cichlid; Biology; Color gel; Image (mathematics); Fish <Actinopterygii>; Physics","score_opus":0.019133038374189133,"score_gpt":0.31891657008454133,"score_spread":0.2997835317103522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129294876","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":[0.25793198,0.0007430215,0.7371821,0.00009201155,0.000042688585,0.00002847548,0.000032206506,0.00041926242,0.003528349],"genre_scores_gemma":[0.6489718,0.0004275302,0.34880015,0.000044016437,0.000020866843,0.000015693528,0.00004711802,0.0000593366,0.0016134845],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999007,0.0000173212,0.0000022609886,0.00001651351,0.000052538166,0.000010662635],"domain_scores_gemma":[0.9998447,0.00006734298,0.000023319255,0.000018619538,0.000037100595,0.000008879372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021244388,0.0002434788,0.00019815566,0.00023478724,0.000086992244,0.00027091533,0.00017790232,0.00022573207,0.000587464],"category_scores_gemma":[0.0003959419,0.00011135643,0.0002469683,0.00023227034,0.00020262787,0.00030908326,0.00015753057,0.00021699752,0.00013689033],"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":[0.00016526353,0.00006471671,0.0013585279,0.000113336835,0.000055093966,0.00011982061,0.00011835376,0.08445891,0.7924058,0.004653624,0.00047333853,0.11601317],"study_design_scores_gemma":[0.00001973834,0.00018829058,0.003841271,0.000009803922,0.00004177455,0.00048883737,0.00003458769,0.7595421,0.23146853,0.0012946448,0.0030454043,0.000024964285],"about_ca_topic_score_codex":0.0005992702,"about_ca_topic_score_gemma":0.0010280135,"teacher_disagreement_score":0.0005992702,"about_ca_system_score_codex":0.00024899145,"about_ca_system_score_gemma":0.00012950147,"threshold_uncertainty_score":0.0019652843},"labels":[],"label_agreement":null},{"id":"W2130525912","doi":"10.1109/tvcg.2010.63","title":"Correction of Clipped Pixels in Color Images","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":28,"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":"Pixel; Clipping (morphology); Artificial intelligence; Computer vision; Brightness; Computer science; Distortion (music); Smoothing; Color image; Mathematics; Image processing; Image (mathematics); Optics; Physics","score_opus":0.010501500650109652,"score_gpt":0.2775310637100161,"score_spread":0.2670295630599065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130525912","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":[0.11003292,0.0019736008,0.8778164,0.00025852767,0.0008031519,0.00017119198,0.00028788252,0.004960059,0.003696333],"genre_scores_gemma":[0.3090795,0.0026351423,0.6776807,0.00043493017,0.0003759472,0.0000896754,0.00068211823,0.0011976748,0.007824321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994042,0.000045456334,0.000028315419,0.00011912236,0.00032884057,0.00007404111],"domain_scores_gemma":[0.9980732,0.00049124623,0.00022886286,0.00042697313,0.0006886569,0.000090930545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061395153,0.001000248,0.00072799384,0.001500612,0.0006321074,0.0012318306,0.0009494631,0.000701138,0.0031871626],"category_scores_gemma":[0.0040892423,0.0003869326,0.0006548486,0.001507714,0.0005844599,0.0010862274,0.000979577,0.0013623248,0.0008737876],"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":[0.0009815632,0.00010064026,0.004062676,0.0007696678,0.00018661754,0.0020147555,0.0006147198,0.016066544,0.3576637,0.0057079783,0.008717874,0.60311335],"study_design_scores_gemma":[0.00009136886,0.00041744817,0.01781395,0.00016513992,0.00044671498,0.0059500034,0.00039298253,0.2303579,0.6883933,0.0050183316,0.050782584,0.00017023618],"about_ca_topic_score_codex":0.0027681566,"about_ca_topic_score_gemma":0.0032438214,"teacher_disagreement_score":0.0031871626,"about_ca_system_score_codex":0.00044891454,"about_ca_system_score_gemma":0.0006800126,"threshold_uncertainty_score":0.010662079},"labels":[],"label_agreement":null},{"id":"W2130987107","doi":"10.1111/j.1467-8659.2012.03056.x","title":"Unsharp Masking, Countershading and Halos: Enhancements or Artifacts?","year":2012,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":39,"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":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Computer vision; Tone mapping; Artificial intelligence; Unsharp masking; Sharpening; Contrast (vision); Artifact (error); Image (mathematics); Context (archaeology); Masking (illustration); Human visual system model; High dynamic range; Image processing; Computer graphics (images); Dynamic range","score_opus":0.02248035548607913,"score_gpt":0.26737519972726764,"score_spread":0.24489484424118851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130987107","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":[0.9419159,0.00087420264,0.0538245,0.00014284215,0.000034515986,0.000041304986,0.000035674526,0.00027015628,0.002860926],"genre_scores_gemma":[0.9926835,0.00019923455,0.006500997,0.000056955447,0.000012580512,0.000009495526,0.000022436983,0.000056044097,0.00045875608],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994282,0.00013809756,0.000034143068,0.00009176481,0.00024272382,0.000065036],"domain_scores_gemma":[0.99416167,0.0030183117,0.0011232155,0.00094357244,0.0005590914,0.00019399411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007968263,0.00042703934,0.00025695376,0.00031265843,0.00032537288,0.0008647083,0.00031571096,0.00040635458,0.0015406111],"category_scores_gemma":[0.006943154,0.00028119556,0.00017635425,0.00025374163,0.00094486744,0.0009635896,0.00059459975,0.00062879664,0.00019621331],"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":[0.0022997684,0.00013481067,0.013954967,0.0003957291,0.000066892484,0.000978363,0.0012849519,0.0059554167,0.9016474,0.0060174624,0.00066253636,0.066601746],"study_design_scores_gemma":[0.00006747992,0.0013154352,0.061210092,0.00015729236,0.00013126199,0.004729165,0.001070484,0.042402778,0.8764599,0.0069155567,0.0054489123,0.000091693335],"about_ca_topic_score_codex":0.00040502744,"about_ca_topic_score_gemma":0.00031926204,"teacher_disagreement_score":0.0015406111,"about_ca_system_score_codex":0.00020707714,"about_ca_system_score_gemma":0.00013840142,"threshold_uncertainty_score":0.005153835},"labels":[],"label_agreement":null},{"id":"W2132819211","doi":"10.5539/cis.v5n3p49","title":"An Improved Guidance Image Based Method to Remove Rain and Snow in a Single Image","year":2012,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Rain and snow mixed; Visibility; Image (mathematics); Computer vision; Artificial intelligence; Snow removal; Snow; Image processing; Feature detection (computer vision); Remote sensing; Geology; Geography; Meteorology","score_opus":0.01194089419617635,"score_gpt":0.29845805723573554,"score_spread":0.2865171630395592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132819211","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":[0.020395864,0.00085774274,0.9748189,0.00015690603,0.0001245879,0.00010642928,0.00006689966,0.001454747,0.0020178708],"genre_scores_gemma":[0.09732068,0.00082132575,0.89546263,0.00014401447,0.000079466925,0.00009728629,0.00023055471,0.00011868788,0.00572533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963963,0.000029671726,0.00001579064,0.00006831409,0.00021524468,0.000031355972],"domain_scores_gemma":[0.9996824,0.000040827974,0.000029205523,0.000050951177,0.0001785728,0.000018141267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023421599,0.0007110169,0.0005382015,0.0011600245,0.00028835644,0.00045585632,0.00072527945,0.00083477126,0.0016628276],"category_scores_gemma":[0.00055627176,0.00033913524,0.0007851712,0.00056302163,0.00032680665,0.0008423425,0.00047969213,0.0007911228,0.0008023703],"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":[0.00015798009,0.00007717,0.0007966008,0.00035493288,0.000090606976,0.00028040225,0.00024235006,0.010013786,0.4439738,0.0022782548,0.004487436,0.53724664],"study_design_scores_gemma":[0.00014718661,0.00069321546,0.006703454,0.00007368357,0.0002461035,0.0045261243,0.00016415218,0.4921097,0.42949817,0.0021789116,0.06343664,0.00022268727],"about_ca_topic_score_codex":0.003247309,"about_ca_topic_score_gemma":0.0045694574,"teacher_disagreement_score":0.003247309,"about_ca_system_score_codex":0.0002804967,"about_ca_system_score_gemma":0.0006130292,"threshold_uncertainty_score":0.006456852},"labels":[],"label_agreement":null},{"id":"W2133478173","doi":"10.1109/icsmc.1995.538328","title":"Enhancing cable television pictures","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 British Columbia","funders":"","keywords":"Computer science; Cable television; Computer vision; Computer graphics (images); Noise (video); Video capture; Multimedia; Professional video camera; Video processing; Artificial intelligence; High-definition television; Image (mathematics); Telecommunications","score_opus":0.014303615616717197,"score_gpt":0.2387713971146116,"score_spread":0.2244677814978944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133478173","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":[0.18497466,0.008495385,0.6975739,0.0005664691,0.000573158,0.0002660107,0.0001646293,0.0017503585,0.10563541],"genre_scores_gemma":[0.61577004,0.014004204,0.26383278,0.00033381893,0.00047248727,0.000119937584,0.00027934927,0.00032640435,0.10486096],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998865,0.00001774064,0.0000040098876,0.000016158305,0.000059248538,0.00001624006],"domain_scores_gemma":[0.99981576,0.000051201,0.000019897316,0.00002695521,0.00007316411,0.000013002358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012264686,0.00032433504,0.00018699408,0.00044025888,0.00020860841,0.0004633472,0.00032907864,0.0004569207,0.0067245564],"category_scores_gemma":[0.0004988479,0.00013435044,0.00018089851,0.00048948673,0.00018154997,0.0007022955,0.00036869932,0.00035623668,0.0019519196],"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":[0.00024817052,0.00004331773,0.00037260173,0.0004105516,0.0000122749725,0.00063124317,0.00018484303,0.0016339736,0.6112534,0.006034689,0.003121177,0.3760538],"study_design_scores_gemma":[0.000055769804,0.0010275391,0.006285239,0.00011249994,0.00011452215,0.0096701365,0.00028114073,0.02340872,0.82337904,0.0033577231,0.13224643,0.00006116469],"about_ca_topic_score_codex":0.00032619716,"about_ca_topic_score_gemma":0.00030878725,"teacher_disagreement_score":0.0067245564,"about_ca_system_score_codex":0.000119080236,"about_ca_system_score_gemma":0.00009367423,"threshold_uncertainty_score":0.022495925},"labels":[],"label_agreement":null},{"id":"W2136001449","doi":"10.1109/tip.2011.2150235","title":"Generalized Random Walks for Fusion of Multi-Exposure Images","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":236,"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","funders":"Natural Sciences and Engineering Research Council of Canada; Beihang University","keywords":"Image fusion; Artificial intelligence; Computer science; Probabilistic logic; Fusion; Computer vision; Image (mathematics); Image quality; Consistency (knowledge bases); Random walk; Random walker algorithm; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.033846584122962375,"score_gpt":0.28310746632066447,"score_spread":0.2492608821977021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136001449","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":[0.004252836,0.00007888901,0.99521184,0.00003376592,0.0000075769453,0.000015509137,0.00000974829,0.00019380386,0.00019596907],"genre_scores_gemma":[0.3307118,0.00035484892,0.6660602,0.00009609448,0.00003834291,0.00014926416,0.00019133373,0.00021532549,0.0021826932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890625,0.00036094084,0.0000499977,0.00022115756,0.00038761002,0.00007399738],"domain_scores_gemma":[0.99906737,0.00048434816,0.00012824594,0.00014051453,0.00012969172,0.000049826107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017625289,0.0010916798,0.0012584692,0.0012841332,0.0003908026,0.0009515084,0.0012139712,0.0015156268,0.0014523054],"category_scores_gemma":[0.0034942697,0.00077945186,0.001753398,0.0010513654,0.00085599197,0.0018778791,0.0015117272,0.0011665729,0.00048519354],"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":[0.00013317839,0.00005505924,0.00045174357,0.000116900956,0.00013485097,0.00016347077,0.00010104689,0.8547367,0.016588826,0.024889559,0.0007000058,0.10192874],"study_design_scores_gemma":[0.0000067971096,0.000016293448,0.00011466952,0.0000036434456,0.000009504981,0.000032798736,0.0000045872353,0.99016434,0.0016851904,0.0076314504,0.00031603075,0.000014596372],"about_ca_topic_score_codex":0.0027948564,"about_ca_topic_score_gemma":0.003130181,"teacher_disagreement_score":0.0027948564,"about_ca_system_score_codex":0.00090640516,"about_ca_system_score_gemma":0.000655403,"threshold_uncertainty_score":0.009321213},"labels":[],"label_agreement":null},{"id":"W2139092464","doi":"10.1109/crv.2009.24","title":"Adaptive Monte Carlo Retinex Method for Illumination and Reflectance Separation and Color Image Enhancement","year":2009,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"University of Waterloo","funders":"","keywords":"Color constancy; Artificial intelligence; Monte Carlo method; Computer vision; Monochromatic color; Computer science; Contrast (vision); Chromatic adaptation; Optics; Image (mathematics); Mathematics; Physics; Statistics","score_opus":0.015022098877401599,"score_gpt":0.34186938634586217,"score_spread":0.3268472874684606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139092464","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":[0.0016361343,0.000101534635,0.9975376,0.000018598554,0.0000095763635,0.00001448059,0.0000060901375,0.00017803442,0.0004979987],"genre_scores_gemma":[0.09053846,0.0003283925,0.9058692,0.000054154312,0.000032908552,0.00012516236,0.000046860572,0.000096334574,0.0029084352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936527,0.00021807893,0.000020772428,0.0000767325,0.00028040612,0.000038772858],"domain_scores_gemma":[0.9993728,0.0003362615,0.00006918941,0.00007739377,0.00012186507,0.000022463066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008389349,0.00067314174,0.00087699934,0.0007191645,0.00039568663,0.00064805633,0.0008031578,0.0006495385,0.0015903066],"category_scores_gemma":[0.0015208944,0.00044273346,0.00073638506,0.00045436682,0.00053178176,0.00058451894,0.0005993296,0.00077568955,0.00061599567],"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":[0.00036129588,0.00017830943,0.0010634268,0.00024449112,0.00018831686,0.0002634765,0.00022172986,0.48424238,0.09630644,0.043259397,0.002815473,0.37085533],"study_design_scores_gemma":[0.000014455937,0.00003111748,0.0002164171,0.000010398676,0.000015203402,0.0002125243,0.0000066911275,0.97939473,0.0150272185,0.0021157372,0.002927696,0.00002778245],"about_ca_topic_score_codex":0.002422921,"about_ca_topic_score_gemma":0.0027743434,"teacher_disagreement_score":0.002422921,"about_ca_system_score_codex":0.0006568196,"about_ca_system_score_gemma":0.00088908954,"threshold_uncertainty_score":0.005320132},"labels":[],"label_agreement":null},{"id":"W2139689981","doi":"10.1109/icsmc.1989.71428","title":"On the informativeness of edges","year":2003,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Waterloo","funders":"","keywords":"Coding (social sciences); Filter (signal processing); Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Contrast (vision); Segmentation; Image segmentation; Computer vision; Image (mathematics); Mathematics; Statistics","score_opus":0.016749026165025993,"score_gpt":0.24015242922131352,"score_spread":0.22340340305628753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139689981","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":[0.05149661,0.005261109,0.9247865,0.00068692275,0.00023630318,0.000076732285,0.00022486488,0.00047408315,0.01675686],"genre_scores_gemma":[0.7139341,0.009833722,0.2621544,0.0010152376,0.0016387216,0.00018178165,0.0006294917,0.00041675402,0.010195821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834216,0.00032875277,0.00010511519,0.00043407228,0.00064487284,0.00014494128],"domain_scores_gemma":[0.9897996,0.007201806,0.0008033992,0.0007048811,0.0012211608,0.00026914416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025125758,0.0008638663,0.0008229076,0.0037770402,0.0007079071,0.002208067,0.0009356925,0.0017595214,0.003040284],"category_scores_gemma":[0.015149,0.000630735,0.0007003863,0.0019772358,0.0029859317,0.0045436826,0.0014513411,0.001813167,0.0008692436],"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":[0.0010672897,0.00013307531,0.0031908725,0.00096900016,0.00015968588,0.0011632195,0.00087258435,0.0668682,0.12164982,0.43314624,0.006542998,0.36423704],"study_design_scores_gemma":[0.00011261499,0.0007180034,0.0099564,0.00049428403,0.00028249467,0.0031244631,0.00030559715,0.3292808,0.097248726,0.5210686,0.037172966,0.00023517484],"about_ca_topic_score_codex":0.0006409452,"about_ca_topic_score_gemma":0.0004536685,"teacher_disagreement_score":0.0037770402,"about_ca_system_score_codex":0.00068020093,"about_ca_system_score_gemma":0.0003246447,"threshold_uncertainty_score":0.013287902},"labels":[],"label_agreement":null},{"id":"W2140557988","doi":"10.1109/tip.2010.2095866","title":"Optimizing a Tone Curve for Backward-Compatible High Dynamic Range Image and Video Compression","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":153,"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":"Tone mapping; High dynamic range; Data compression; Tone (literature); Computer science; Algorithm; Image compression; Distortion (music); Computer vision; Range (aeronautics); Mean squared error; Dynamic range; Image quality; High-dynamic-range imaging; Artificial intelligence; Iterative reconstruction; Dynamic range compression; Mathematics; Image (mathematics); Image processing","score_opus":0.01182586325777499,"score_gpt":0.2934184721824124,"score_spread":0.2815926089246374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140557988","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":[0.046613354,0.00011280966,0.95214415,0.0000556433,0.000010118285,0.000021452382,0.000009972784,0.000103370905,0.0009290499],"genre_scores_gemma":[0.6250311,0.00028436357,0.3724392,0.00004966344,0.000019247564,0.000042004318,0.000036743222,0.00006428379,0.0020333435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983275,0.0000392727,0.000007744531,0.000024990499,0.00008347328,0.000011823885],"domain_scores_gemma":[0.9997327,0.00013068573,0.000042489803,0.000037629954,0.000044304517,0.000012088846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042721687,0.00030479804,0.0002508388,0.00019345908,0.00014311295,0.00039623585,0.000304921,0.00042256326,0.0008089565],"category_scores_gemma":[0.001460806,0.00014898459,0.00020929257,0.00020370277,0.0003870056,0.00048505707,0.0003830821,0.00031923465,0.00020948084],"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":[0.0002016028,0.00014599085,0.0015092852,0.00022154451,0.000030603856,0.00021017985,0.0001359962,0.470876,0.3085107,0.024197096,0.0009094233,0.19305158],"study_design_scores_gemma":[0.0000073136234,0.00005192646,0.00023256693,0.0000040811547,0.0000044858853,0.00013206125,0.00001001655,0.97379225,0.023673255,0.0013543012,0.00072838954,0.00000941483],"about_ca_topic_score_codex":0.0005331135,"about_ca_topic_score_gemma":0.00060171925,"teacher_disagreement_score":0.0008089565,"about_ca_system_score_codex":0.0002588308,"about_ca_system_score_gemma":0.0003560109,"threshold_uncertainty_score":0.0027062297},"labels":[],"label_agreement":null},{"id":"W2141204096","doi":"10.1016/j.jvcir.2007.06.006","title":"Photometric image processing for high dynamic range displays","year":2007,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dolby (Canada); University of British Columbia","funders":"","keywords":"High dynamic range; Computer science; Brightness; Dynamic range; Range (aeronautics); Tone mapping; High-dynamic-range imaging; Computer vision; Computer graphics (images); Artificial intelligence; Image processing; Image (mathematics); Optics; Physics; Materials science","score_opus":0.020462536995938713,"score_gpt":0.3905687104745346,"score_spread":0.3701061734785959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141204096","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":[0.04755863,0.001431569,0.9310981,0.00034025763,0.000105918945,0.00006669313,0.00011096675,0.0018448131,0.017443046],"genre_scores_gemma":[0.3847538,0.0016936727,0.5795928,0.00022410764,0.00013590592,0.000085228494,0.0002976231,0.00064382516,0.03257298],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99982005,0.00002889428,0.000005906821,0.00002370938,0.00010062412,0.000020872134],"domain_scores_gemma":[0.9996891,0.000117353055,0.000021648551,0.00007056466,0.00008598302,0.00001526001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002252797,0.00036615226,0.00030446117,0.00039860315,0.00034402768,0.0010413537,0.00067687227,0.0003987428,0.011115176],"category_scores_gemma":[0.00085366814,0.00032593854,0.00026934917,0.0005227378,0.00026858062,0.0010305056,0.0006598058,0.00077909353,0.0024087743],"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":[0.00044887865,0.00009001577,0.00052992464,0.00028216673,0.000032303113,0.000114908486,0.00013476676,0.0057262247,0.63215506,0.021265272,0.0046946104,0.33452576],"study_design_scores_gemma":[0.00004402457,0.0002202274,0.0038014762,0.000057376932,0.00006075825,0.0012089739,0.00008975386,0.19241627,0.756793,0.012665264,0.03258948,0.00005346073],"about_ca_topic_score_codex":0.0003231418,"about_ca_topic_score_gemma":0.00066504057,"teacher_disagreement_score":0.011115176,"about_ca_system_score_codex":0.00027813224,"about_ca_system_score_gemma":0.00020158956,"threshold_uncertainty_score":0.03718394},"labels":[],"label_agreement":null},{"id":"W2141924422","doi":"10.1007/978-3-642-21593-3_12","title":"Structural Fidelity vs. Naturalness - Objective Assessment of Tone Mapped Images","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Naturalness; Tone mapping; Computer science; Fidelity; Measure (data warehouse); Tone (literature); Brightness; Artificial intelligence; High dynamic range; Range (aeronautics); Similarity (geometry); Similarity measure; Computer vision; Pattern recognition (psychology); Dynamic range; Image (mathematics); Data mining","score_opus":0.01601109477786206,"score_gpt":0.29577368326102355,"score_spread":0.2797625884831615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141924422","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":[0.7022085,0.0026187727,0.27151984,0.00023230638,0.00020076717,0.00034622,0.00052508805,0.000356152,0.021992356],"genre_scores_gemma":[0.9654336,0.00095123606,0.029139176,0.000051184474,0.00007300446,0.000058650723,0.00020788948,0.00008844777,0.0039969133],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994862,0.000091915615,0.000029304043,0.000056715126,0.0002994787,0.00003628574],"domain_scores_gemma":[0.997544,0.0014058194,0.000268371,0.00015186057,0.000516174,0.000113698836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092283107,0.00034974352,0.00023904555,0.0008390153,0.00013186294,0.0010236888,0.00025515672,0.0005294969,0.004439979],"category_scores_gemma":[0.0058777407,0.00015108817,0.00023471315,0.00030117057,0.000445454,0.0011004522,0.00058286084,0.00039202123,0.00047591125],"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":[0.0024538941,0.00024047098,0.014820436,0.0014938433,0.00016880936,0.00029207222,0.0005732743,0.011533893,0.53231776,0.0049472195,0.0011054451,0.43005297],"study_design_scores_gemma":[0.00016826096,0.00614365,0.24465409,0.00060845807,0.00062153366,0.0054737735,0.0014981191,0.18311356,0.5340217,0.013594078,0.009813972,0.00028888843],"about_ca_topic_score_codex":0.00042101645,"about_ca_topic_score_gemma":0.0006362673,"teacher_disagreement_score":0.004439979,"about_ca_system_score_codex":0.00017611934,"about_ca_system_score_gemma":0.00013421189,"threshold_uncertainty_score":0.014853239},"labels":[],"label_agreement":null},{"id":"W2146297113","doi":"10.1109/icsmc.1991.169692","title":"Direct mapping between histograms: an improved interactive image enhancement method","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Histogram; Histogram matching; Balanced histogram thresholding; Adaptive histogram equalization; Contouring; Image histogram; Computer science; Artificial intelligence; Histogram equalization; Pattern recognition (psychology); Image (mathematics); Algorithm; Computer vision; Mathematics; Image processing; Computer graphics (images); Color image","score_opus":0.031031252790352678,"score_gpt":0.30547225578237347,"score_spread":0.2744410029920208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146297113","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":[0.0065592,0.00011219418,0.98986965,0.000041538067,0.00002147155,0.00003803943,0.000018406337,0.0015680273,0.0017715051],"genre_scores_gemma":[0.08437996,0.00021808411,0.9092643,0.000080777434,0.000035021305,0.000077835815,0.00008099783,0.00032171808,0.005541332],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999616,0.00005565658,0.000016410262,0.00007251411,0.00020638363,0.000032964836],"domain_scores_gemma":[0.9996112,0.00013843301,0.0000272333,0.00008503604,0.000114243674,0.00002382786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039662587,0.00046022824,0.0004500141,0.00061476073,0.00017464622,0.0005745096,0.0011419316,0.00047288463,0.004724029],"category_scores_gemma":[0.0010450318,0.00031210182,0.0004336199,0.0004598989,0.00031067862,0.0010076386,0.000977018,0.0007071485,0.0013533062],"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":[0.00025731346,0.000121207806,0.00039506142,0.00018096427,0.000035783545,0.00017930975,0.00025010738,0.015287195,0.2629654,0.009420415,0.0035398307,0.7073673],"study_design_scores_gemma":[0.000096371616,0.00023311605,0.0016293238,0.00002561291,0.000060128466,0.0015437852,0.000062785846,0.7388647,0.22821058,0.004592416,0.024612853,0.0000683381],"about_ca_topic_score_codex":0.00065282214,"about_ca_topic_score_gemma":0.00080777024,"teacher_disagreement_score":0.004724029,"about_ca_system_score_codex":0.00019163694,"about_ca_system_score_gemma":0.00028796523,"threshold_uncertainty_score":0.015803456},"labels":[],"label_agreement":null},{"id":"W2146782705","doi":"10.1109/icip.2007.4379997","title":"Camera Response Function Recovery from Auto-Exposure Cameras","year":2007,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Superposition principle; Computer science; Computer vision; Function (biology); Artificial intelligence; Property (philosophy); Ambiguity; Homogeneity (statistics); Algorithm; Mathematics; Machine learning; Mathematical analysis","score_opus":0.009315842196969676,"score_gpt":0.2402606286902578,"score_spread":0.23094478649328812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146782705","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":[0.005629538,0.00011575863,0.9933842,0.000031624444,0.000009881603,0.000012823134,0.00001816032,0.00030148542,0.0004965485],"genre_scores_gemma":[0.13897601,0.0005314239,0.85571563,0.00008657165,0.000040629526,0.00010208204,0.00019496909,0.00029138572,0.0040613045],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992293,0.0001473185,0.000031871437,0.00014355729,0.00040168632,0.000046243626],"domain_scores_gemma":[0.9991497,0.00027260697,0.000128564,0.00020730004,0.00021209361,0.000029734441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078104,0.00078886445,0.0010301125,0.00055301737,0.00027792176,0.00065318466,0.0008547971,0.00085000956,0.002196958],"category_scores_gemma":[0.002122029,0.0004899685,0.0006529214,0.0005486944,0.00043069688,0.0013231522,0.0012104858,0.0014157856,0.0008343581],"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":[0.00025886076,0.00011671719,0.0010321846,0.00038446486,0.00014201182,0.00024628665,0.000210909,0.08009986,0.39299178,0.017405815,0.0025512094,0.5045599],"study_design_scores_gemma":[0.000038553495,0.00017805581,0.0026439575,0.00004428374,0.00005679598,0.00093157386,0.00004666014,0.66648287,0.31155077,0.008175915,0.0097481245,0.00010253202],"about_ca_topic_score_codex":0.000688241,"about_ca_topic_score_gemma":0.000901296,"teacher_disagreement_score":0.002196958,"about_ca_system_score_codex":0.000415202,"about_ca_system_score_gemma":0.00055160205,"threshold_uncertainty_score":0.0073496103},"labels":[],"label_agreement":null},{"id":"W2146870294","doi":"10.1109/tip.2010.2092438","title":"A Linear Programming Approach for Optimal Contrast-Tone Mapping","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Tone mapping; Histogram equalization; Contrast (vision); Computer science; Equalization (audio); Distortion (music); Histogram; Tone (literature); Linear programming; Limit (mathematics); Artificial intelligence; Image (mathematics); Algorithm; Mathematical optimization; Computer vision; Mathematics; Bandwidth (computing); Dynamic range","score_opus":0.01890979099548158,"score_gpt":0.29247349658558897,"score_spread":0.2735637055901074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146870294","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":[0.00046109897,0.00007139339,0.99778795,0.00007732729,0.000011173727,0.000013011092,0.000008313816,0.000029265047,0.001540462],"genre_scores_gemma":[0.09208885,0.0006542618,0.8975333,0.00023406037,0.00013726659,0.00038217488,0.00009007891,0.00018208052,0.008697987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995198,0.00017605769,0.000016406852,0.00010070266,0.00014712219,0.000039780713],"domain_scores_gemma":[0.9994154,0.0004389667,0.000030096588,0.000020895108,0.00007675574,0.000017876555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010512943,0.0012490435,0.0008742898,0.0005461411,0.0004002774,0.001121521,0.0010231034,0.0010477259,0.005031492],"category_scores_gemma":[0.0022600505,0.0007158554,0.0007465158,0.00054182543,0.00079553586,0.0010306633,0.0011498224,0.002005466,0.00086728536],"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":[0.000034610897,0.00008179568,0.00013406952,0.00017078391,0.000038555976,0.00009027677,0.0000773369,0.7931783,0.0038863549,0.13277163,0.0030616305,0.06647464],"study_design_scores_gemma":[0.00000962043,0.000031755248,0.000028506269,0.000011832316,0.000007240071,0.00003473521,0.000007808103,0.970318,0.0006662727,0.026542932,0.0023325237,0.000008776426],"about_ca_topic_score_codex":0.0015586814,"about_ca_topic_score_gemma":0.001688581,"teacher_disagreement_score":0.005031492,"about_ca_system_score_codex":0.0007970257,"about_ca_system_score_gemma":0.0010435132,"threshold_uncertainty_score":0.016832054},"labels":[],"label_agreement":null},{"id":"W2153227084","doi":"10.1109/icdsp.2011.6004915","title":"Subjective evaluation of tone-mapping methods on 3D images","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","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 British Columbia","funders":"","keywords":"Tone mapping; Stereoscopy; Computer science; High dynamic range; Computer vision; Artificial intelligence; Tone (literature); Context (archaeology); Contrast (vision); Brightness; Texture mapping; High-dynamic-range imaging; Process (computing); Dynamic range; Geography","score_opus":0.1296380026587658,"score_gpt":0.41844602127194336,"score_spread":0.28880801861317756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153227084","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":[0.9519449,0.0005920725,0.042102285,0.000073258285,0.00009223678,0.00028215788,0.00024443824,0.00016978667,0.004498871],"genre_scores_gemma":[0.95733666,0.00047342284,0.038166147,0.00012683336,0.000056458885,0.00015712711,0.00040268275,0.00010580027,0.003174827],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9983432,0.0006316032,0.00015428908,0.00018178376,0.00062918337,0.00005996937],"domain_scores_gemma":[0.9880249,0.0077081635,0.0008607261,0.00064000837,0.002285044,0.00048105337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022124136,0.00060495205,0.0003127935,0.0007895465,0.00027627405,0.0006555698,0.00031447693,0.000561357,0.004382973],"category_scores_gemma":[0.0131620625,0.00016612715,0.00039564967,0.000315593,0.000414485,0.00061118853,0.00081296713,0.00030854097,0.00039068057],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0088403085,0.001114757,0.024848355,0.0017664226,0.00034977504,0.0007264284,0.0033669455,0.008627115,0.76323116,0.00093644566,0.001995696,0.18419665],"study_design_scores_gemma":[0.0007002926,0.024575083,0.48917297,0.00048825963,0.0009779809,0.0055911536,0.0064080367,0.08748057,0.36680466,0.002603849,0.014413134,0.000784034],"about_ca_topic_score_codex":0.00048555463,"about_ca_topic_score_gemma":0.0006396497,"teacher_disagreement_score":0.004382973,"about_ca_system_score_codex":0.00018497107,"about_ca_system_score_gemma":0.0001117366,"threshold_uncertainty_score":0.014662504},"labels":[],"label_agreement":null},{"id":"W2154257987","doi":"10.1109/icpr.1992.202045","title":"Color image enhancement through 3-D histogram equalization","year":2003,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Histogram equalization; Adaptive histogram equalization; Histogram matching; Histogram; Balanced histogram thresholding; Color normalization; Color histogram; Artificial intelligence; Image histogram; RGB color model; Computer vision; Computer science; Pattern recognition (psychology); Mathematics; Color image; Image (mathematics); Image processing","score_opus":0.017505439592255402,"score_gpt":0.28315292113044777,"score_spread":0.26564748153819234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154257987","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":[0.008555781,0.00034780777,0.9857393,0.00007333813,0.00006715993,0.000043397362,0.00003998662,0.001064449,0.00406875],"genre_scores_gemma":[0.19370034,0.0015606283,0.79256725,0.000193834,0.00006806316,0.00008637203,0.00016386561,0.0003091668,0.011350407],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982685,0.000021431228,0.00001076699,0.000031167205,0.000089303336,0.00002061355],"domain_scores_gemma":[0.99974054,0.00008464538,0.000022690881,0.000049159622,0.000092523405,0.0000104937035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024652577,0.00034091147,0.00029599748,0.00055008836,0.0001868408,0.0005883984,0.0004043589,0.0003290529,0.0031796906],"category_scores_gemma":[0.0006350375,0.00020974287,0.00042135592,0.0005136318,0.00028658946,0.0006123404,0.0006132237,0.000485578,0.0011622073],"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":[0.0001451925,0.000049332783,0.00048858795,0.00026473703,0.00003899256,0.0002252563,0.00015093772,0.012306256,0.55614555,0.012710453,0.002734601,0.41474003],"study_design_scores_gemma":[0.000023173314,0.00011995078,0.0029508765,0.000031986525,0.000049229537,0.0014136529,0.00004643487,0.18500857,0.7632278,0.005462413,0.041592445,0.000073429765],"about_ca_topic_score_codex":0.0006163383,"about_ca_topic_score_gemma":0.0007642006,"teacher_disagreement_score":0.0031796906,"about_ca_system_score_codex":0.00019631999,"about_ca_system_score_gemma":0.00022077742,"threshold_uncertainty_score":0.010637164},"labels":[],"label_agreement":null},{"id":"W2156292614","doi":"10.1109/jstsp.2012.2193555","title":"Rendering 3-D High Dynamic Range Images: Subjective Evaluation of Tone-Mapping Methods and Preferred 3-D Image Attributes","year":2012,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Image Enhancement 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":"University of British Columbia","funders":"","keywords":"Tone mapping; High dynamic range; Stereoscopy; Rendering (computer graphics); Computer science; Artificial intelligence; Computer vision; Brightness; Tone (literature); High-dynamic-range imaging; Image quality; Dynamic range; Computer graphics (images); Image (mathematics); Physics; Optics","score_opus":0.04946989989573047,"score_gpt":0.37611264856638377,"score_spread":0.3266427486706533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156292614","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":[0.97891176,0.0005429655,0.017706217,0.00005228091,0.000050280505,0.00019434033,0.00021821492,0.00012786653,0.0021961362],"genre_scores_gemma":[0.9709816,0.0004975112,0.02575905,0.00011555833,0.000049161772,0.00015121461,0.0004286476,0.00009620313,0.0019209461],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99897885,0.0003942241,0.00010875564,0.00013316407,0.00033357803,0.00005150203],"domain_scores_gemma":[0.9928388,0.004559244,0.0005228941,0.00033607197,0.0012951174,0.0004478979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018104323,0.00073264225,0.00036399098,0.0007282725,0.00021680995,0.0006789238,0.00026758638,0.0005038607,0.0030036573],"category_scores_gemma":[0.00832367,0.0001695735,0.00044144268,0.00030391538,0.00037435762,0.00055683596,0.00069421454,0.0003456952,0.00032070527],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008797581,0.0017200735,0.042206697,0.0029280067,0.0005034627,0.00096323824,0.00599793,0.008253485,0.77965826,0.000741021,0.0024564662,0.14577383],"study_design_scores_gemma":[0.0010395622,0.021082345,0.5794222,0.00049139565,0.0012258617,0.0062208762,0.009325132,0.082471,0.28419283,0.0017356985,0.011969284,0.0008238111],"about_ca_topic_score_codex":0.0006613926,"about_ca_topic_score_gemma":0.0008051382,"teacher_disagreement_score":0.0030036573,"about_ca_system_score_codex":0.00013296127,"about_ca_system_score_gemma":0.00012930627,"threshold_uncertainty_score":0.010048211},"labels":[],"label_agreement":null},{"id":"W2156406314","doi":"10.1109/tip.2015.2401516","title":"Integrated Foreground Segmentation and Boundary Matting for Live Videos","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Segmentation; Image segmentation; Discriminative model; Process (computing); Pixel; Video Graphics Array; Boundary (topology); Background subtraction; Image processing; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.030390682244751673,"score_gpt":0.29812254841588565,"score_spread":0.267731866171134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156406314","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":[0.016588217,0.00019454015,0.9804052,0.00005378533,0.000029938443,0.000048238337,0.000049114187,0.0019206934,0.00071029447],"genre_scores_gemma":[0.17606023,0.00021004963,0.82081807,0.00007437966,0.00006539838,0.000058057292,0.00036172845,0.0002948272,0.0020572795],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995808,0.000038320308,0.000019241133,0.0001381326,0.00016021248,0.00006331582],"domain_scores_gemma":[0.99949646,0.00013650111,0.00007731364,0.00008789772,0.00015626918,0.00004542651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049433485,0.00087624975,0.00080832234,0.0011522943,0.0004245866,0.00091422594,0.0013667796,0.0009877901,0.0024657103],"category_scores_gemma":[0.0016090439,0.00040202643,0.00061488175,0.00059661816,0.00044687837,0.0013974154,0.00080718094,0.00092569133,0.0010241171],"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":[0.0003182113,0.000104569124,0.0009440184,0.00013254867,0.00004403992,0.00026800972,0.0001453799,0.040300388,0.21053885,0.0030985514,0.001910122,0.74219537],"study_design_scores_gemma":[0.000014828697,0.00011647566,0.0014577764,0.000016454745,0.000015790125,0.00038604287,0.00006141414,0.8789825,0.11251073,0.0033730508,0.0030458868,0.000019089732],"about_ca_topic_score_codex":0.0024015538,"about_ca_topic_score_gemma":0.0035440186,"teacher_disagreement_score":0.0024657103,"about_ca_system_score_codex":0.0005412677,"about_ca_system_score_gemma":0.0005008748,"threshold_uncertainty_score":0.008248687},"labels":[],"label_agreement":null},{"id":"W2160600073","doi":"10.1109/iscas.2011.5937699","title":"Comparative analysis of contrast enhancement algorithms in surveillance imaging","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","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":"Polytechnique Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Contrast (vision); Computer science; Brightness; Histogram; Contrast enhancement; Artificial intelligence; Computer vision; Image enhancement; Key (lock); Image quality; Image (mathematics); Image processing; Optics","score_opus":0.03046446875800011,"score_gpt":0.2900713880307718,"score_spread":0.2596069192727717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160600073","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5441283,0.020289712,0.4261863,0.00022717741,0.00020524925,0.00023130025,0.00022825673,0.0015011078,0.0070027346],"genre_scores_gemma":[0.71743715,0.005745529,0.27329704,0.00007262803,0.000095909425,0.00009139609,0.0006987557,0.00022448802,0.0023371652],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908173,0.00024243473,0.00009068546,0.00010249524,0.00040500058,0.00007768339],"domain_scores_gemma":[0.995159,0.0030564293,0.00024433705,0.0002213173,0.00123896,0.000079903795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017781553,0.00056487095,0.00065982336,0.0021293114,0.00024578322,0.00073472166,0.0004604841,0.0006942353,0.0009515451],"category_scores_gemma":[0.0073701325,0.00018476466,0.000486592,0.0011514188,0.00017487818,0.00081072253,0.00030018532,0.00027344623,0.00026786842],"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":[0.002665735,0.00034850513,0.0075198794,0.00088118,0.00039947187,0.00025822545,0.0001320883,0.05896234,0.12368773,0.0018382077,0.0014251128,0.80188155],"study_design_scores_gemma":[0.00014650445,0.003163722,0.03900166,0.000148876,0.0006693536,0.0021784022,0.0001739594,0.6591545,0.28633875,0.0017850943,0.0071224933,0.00011674439],"about_ca_topic_score_codex":0.0007599504,"about_ca_topic_score_gemma":0.0007278399,"teacher_disagreement_score":0.0021293114,"about_ca_system_score_codex":0.0003444533,"about_ca_system_score_gemma":0.00022931902,"threshold_uncertainty_score":0.009403884},"labels":[],"label_agreement":null},{"id":"W2161396854","doi":"10.1111/j.1467-8659.2009.01358.x","title":"Color correction for tone mapping","year":2009,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":164,"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":"Luminance; Tone mapping; Artificial intelligence; Contrast (vision); Computer science; Computer vision; Tone (literature); Color balance; Color image; High dynamic range; Image (mathematics); Dynamic range; Image processing","score_opus":0.015544283052243977,"score_gpt":0.2726449650855383,"score_spread":0.25710068203329434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161396854","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":[0.12379037,0.0002667185,0.86816657,0.000119575125,0.00018719892,0.00008069435,0.00003253535,0.0018885263,0.0054678516],"genre_scores_gemma":[0.7853522,0.00017181924,0.2104496,0.000067130844,0.00003726331,0.000031600088,0.00003850558,0.00026375998,0.0035880846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967825,0.000057901743,0.000017232305,0.000058406094,0.00016219786,0.000026031546],"domain_scores_gemma":[0.99879336,0.00037307316,0.00011179212,0.0003163982,0.00037046426,0.000034978184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043558216,0.00043163745,0.00023102993,0.00049940817,0.00025766646,0.0007268123,0.00054780813,0.00026372843,0.003024733],"category_scores_gemma":[0.0032779938,0.0001218394,0.00023372787,0.00035166868,0.0003557393,0.0006137128,0.00041900532,0.0005066883,0.00040173522],"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":[0.00046356584,0.00011809226,0.0031619892,0.00024699,0.00005039468,0.0003547719,0.00017560877,0.024421204,0.36607468,0.026759787,0.002924613,0.5752483],"study_design_scores_gemma":[0.000043471606,0.0003226085,0.0029202276,0.000026210273,0.00006921522,0.00089887297,0.00006292148,0.49915707,0.47689348,0.0076105893,0.0119597055,0.000035582725],"about_ca_topic_score_codex":0.00057487027,"about_ca_topic_score_gemma":0.00047233974,"teacher_disagreement_score":0.003024733,"about_ca_system_score_codex":0.00025530928,"about_ca_system_score_gemma":0.0002109822,"threshold_uncertainty_score":0.010118723},"labels":[],"label_agreement":null},{"id":"W2161533094","doi":"10.1109/iscas.2011.5937756","title":"Edge detection using histogram equalization and multi-filtering process","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Concordia University","funders":"","keywords":"Adaptive histogram equalization; Histogram equalization; Artificial intelligence; Histogram; Computer science; Computer vision; Histogram matching; Noise (video); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Process (computing); Image histogram; Image (mathematics); Image processing; Image texture","score_opus":0.10004214308941557,"score_gpt":0.30909309229264914,"score_spread":0.20905094920323358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161533094","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":[0.015525177,0.00035426966,0.9822481,0.000047202,0.000044426746,0.000048681868,0.000022342581,0.00059112697,0.0011186311],"genre_scores_gemma":[0.23972486,0.0006572931,0.75443596,0.00007712639,0.0000753251,0.00007821614,0.00011164373,0.000083666506,0.0047559305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954885,0.00004896348,0.000029003262,0.000092347436,0.00023714287,0.000043692708],"domain_scores_gemma":[0.99965835,0.00012176709,0.00003769845,0.000046354497,0.0001220921,0.00001374372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044663288,0.00038056253,0.0005697265,0.0009666801,0.0002997392,0.00064658804,0.0006478065,0.00069113105,0.002155739],"category_scores_gemma":[0.0008127855,0.0002317275,0.00055742374,0.0008352626,0.00032158307,0.0010492787,0.00046702632,0.0004903278,0.0009443824],"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":[0.00031514766,0.00014050471,0.0013146752,0.00028174373,0.00008338262,0.00021676686,0.00011924104,0.014551439,0.32864827,0.0047884877,0.0009633091,0.64857703],"study_design_scores_gemma":[0.00005343517,0.00045753474,0.007814225,0.000040053896,0.00009684371,0.001230729,0.00005629263,0.57029307,0.4033071,0.004959559,0.011593447,0.0000977387],"about_ca_topic_score_codex":0.0007038611,"about_ca_topic_score_gemma":0.00076841377,"teacher_disagreement_score":0.002155739,"about_ca_system_score_codex":0.00023313497,"about_ca_system_score_gemma":0.00028022807,"threshold_uncertainty_score":0.007211685},"labels":[],"label_agreement":null},{"id":"W2161579921","doi":"10.1109/isspit.2006.270857","title":"Minimal Capture Sets for Multi-Exposure Enhanced-Dynamic-Range Imaging","year":2006,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Victoria","funders":"","keywords":"Dynamic range; High dynamic range; Computer science; Set (abstract data type); Range (aeronautics); Computer vision; Multiple exposure; High-dynamic-range imaging; Artificial intelligence; Greedy algorithm; Software; Process (computing); Digital imaging; Image (mathematics); Digital image; Image processing; Algorithm; Engineering","score_opus":0.011965177647361998,"score_gpt":0.27732746081675846,"score_spread":0.2653622831693965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161579921","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":[0.0059171678,0.00007751275,0.99301296,0.000044486704,0.00000384828,0.000028793562,0.000014217828,0.00011003679,0.0007910792],"genre_scores_gemma":[0.13132074,0.00018824832,0.86704266,0.000047155117,0.000017625942,0.00016867697,0.00010991491,0.000112697795,0.0009922724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990508,0.0002862867,0.000045195175,0.00014011958,0.00039794954,0.000079723075],"domain_scores_gemma":[0.997988,0.0013607776,0.00019241782,0.0002268856,0.00017907572,0.000052893156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010517141,0.0007383719,0.00071548636,0.00082253217,0.0005551404,0.00086653937,0.0011840205,0.00075549306,0.0023944061],"category_scores_gemma":[0.0051143505,0.000727834,0.00065607857,0.000501778,0.0007183922,0.0012470066,0.0015185435,0.00079756515,0.0005620535],"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":[0.000287707,0.00021167369,0.0011768887,0.0003585473,0.00008653623,0.00025453256,0.0003190376,0.59504145,0.05191751,0.09189965,0.0036791353,0.25476736],"study_design_scores_gemma":[0.000026353648,0.00013781933,0.0006170884,0.00002848229,0.000021355678,0.00033863596,0.00006309233,0.9339119,0.020365547,0.03992917,0.004522011,0.000038485745],"about_ca_topic_score_codex":0.0007040563,"about_ca_topic_score_gemma":0.0013438725,"teacher_disagreement_score":0.0023944061,"about_ca_system_score_codex":0.0009800863,"about_ca_system_score_gemma":0.0009215746,"threshold_uncertainty_score":0.008010089},"labels":[],"label_agreement":null},{"id":"W2161833896","doi":"10.1080/14622200701485026","title":"Digital image analysis of cigarette filter staining to estimate smoke exposure","year":2007,"lang":"en","type":"article","venue":"Nicotine & Tobacco Research","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"American Cancer Society; U.S. Department of Health and Human Services","keywords":"Library science; Population; Art; Medicine; Humanities; Environmental health; Computer science","score_opus":0.05076171069003247,"score_gpt":0.40629354334930234,"score_spread":0.35553183265926985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161833896","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":[0.65011376,0.0019014839,0.33140016,0.00020377593,0.00013060514,0.00074669474,0.0019936827,0.0024246334,0.0110852355],"genre_scores_gemma":[0.62629515,0.0014585165,0.3640544,0.0001637834,0.000044616518,0.00041174478,0.00093333836,0.00021817625,0.0064202617],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972504,0.000046825608,0.00002278366,0.000047576974,0.00012676851,0.00003091939],"domain_scores_gemma":[0.9992663,0.0002414064,0.00008033082,0.000079684854,0.00030049356,0.00003180521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008168116,0.00031948523,0.000230647,0.0024754733,0.00014236302,0.0004101326,0.00026237537,0.0002914334,0.0055827755],"category_scores_gemma":[0.0013378483,0.00019752393,0.00023565922,0.0011655649,0.00018765118,0.00031121608,0.00025118366,0.00038670452,0.00086381927],"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":[0.0006408173,0.00021684625,0.022823082,0.00038546085,0.00010197777,0.00020112113,0.00022499336,0.0012839277,0.7325968,0.0008284211,0.0016980241,0.2389984],"study_design_scores_gemma":[0.000070058355,0.00081415224,0.38073277,0.00008172737,0.0002944029,0.0034972036,0.00038299878,0.071199566,0.52747643,0.0011725816,0.014184187,0.000093928225],"about_ca_topic_score_codex":0.0011308123,"about_ca_topic_score_gemma":0.0019289303,"teacher_disagreement_score":0.0055827755,"about_ca_system_score_codex":0.00021899998,"about_ca_system_score_gemma":0.00022776666,"threshold_uncertainty_score":0.018676281},"labels":[],"label_agreement":null},{"id":"W2165093818","doi":"10.1109/icsmc.1994.399852","title":"Homomorphic vs. multiplicative lateral inhibition models for image enhancement","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiplicative function; Homomorphic filtering; Lateral inhibition; Homomorphic encryption; Contrast (vision); Nonlinear system; Image (mathematics); Multiplicative noise; Mathematics; Computer science; Image enhancement; Algorithm; Artificial intelligence; Psychology; Physics","score_opus":0.029716314629214344,"score_gpt":0.25476871398512163,"score_spread":0.2250523993559073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165093818","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":[0.11267921,0.0030812933,0.8269075,0.0024378977,0.00015181258,0.00008012078,0.00014838214,0.0006408354,0.053872943],"genre_scores_gemma":[0.9403335,0.001253437,0.031790655,0.00029741062,0.00010238028,0.00008690775,0.00005200026,0.00006801849,0.026015716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997758,0.000047972913,0.0000078025205,0.000033674518,0.00009901636,0.000035771205],"domain_scores_gemma":[0.999519,0.0002728643,0.00006617589,0.000053794873,0.00006064018,0.00002763759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064104056,0.00046911646,0.0003347742,0.00042572748,0.00022228618,0.0009338172,0.0012177931,0.0009993074,0.004250472],"category_scores_gemma":[0.0012926895,0.00024108954,0.0006765411,0.00022970505,0.00095602474,0.0015934798,0.000531302,0.0007452413,0.00086503837],"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":[0.00018936642,0.00012309341,0.0011272996,0.00024918563,0.00006993413,0.00031319057,0.00032061554,0.23459186,0.029833283,0.68656623,0.0028930788,0.043722857],"study_design_scores_gemma":[0.000026403604,0.000060848437,0.000794492,0.000018559034,0.00003025133,0.00025673275,0.000035560577,0.8310091,0.0031478528,0.16245705,0.00213885,0.000024335786],"about_ca_topic_score_codex":0.0012075003,"about_ca_topic_score_gemma":0.001507798,"teacher_disagreement_score":0.004250472,"about_ca_system_score_codex":0.00092580065,"about_ca_system_score_gemma":0.00037881968,"threshold_uncertainty_score":0.014219224},"labels":[],"label_agreement":null},{"id":"W2170379784","doi":"10.1109/ccece.2003.1226027","title":"Filter fusion for image enhancement using reinforcement learning","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"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":"Reinforcement learning; Computer science; Sharpening; Artificial intelligence; Filter (signal processing); Image fusion; Image (mathematics); Computer vision; Fusion; Composite image filter; Reinforcement; Machine learning; Engineering","score_opus":0.02213869561766121,"score_gpt":0.2870051712883829,"score_spread":0.2648664756707217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170379784","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":[0.0059346915,0.00015383538,0.99264246,0.000042403815,0.00002138621,0.000032383745,0.0000032918113,0.0003544433,0.0008151516],"genre_scores_gemma":[0.4784386,0.00032918932,0.5171283,0.00009806333,0.000046180514,0.0001771207,0.000023236506,0.00007021221,0.0036890758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995524,0.000111342975,0.000023040444,0.000075229655,0.0001937749,0.000044193486],"domain_scores_gemma":[0.999423,0.00028702582,0.00007026361,0.000058610105,0.00013089394,0.000030126943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012084098,0.0006402529,0.00071486476,0.00034371985,0.0002965347,0.0004943532,0.00084727997,0.0008123164,0.0015171487],"category_scores_gemma":[0.00143435,0.00026373495,0.00050913007,0.00024457093,0.0005860885,0.000702793,0.0007264512,0.0010979201,0.00036645363],"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":[0.00026031732,0.0002706046,0.0006760204,0.00017404958,0.00013056824,0.0001913994,0.00016672276,0.5620204,0.07433837,0.017679991,0.0012085872,0.34288296],"study_design_scores_gemma":[0.00002105361,0.00012702947,0.00014927445,0.0000092140845,0.000019295187,0.00006255672,0.000005352865,0.98330843,0.011391823,0.0032073548,0.0016848118,0.000013917604],"about_ca_topic_score_codex":0.0015712829,"about_ca_topic_score_gemma":0.0014216643,"teacher_disagreement_score":0.0015712829,"about_ca_system_score_codex":0.00059939385,"about_ca_system_score_gemma":0.00038786247,"threshold_uncertainty_score":0.0063907504},"labels":[],"label_agreement":null},{"id":"W2171120124","doi":"10.1007/s11263-011-0471-x","title":"Automatic Real-Time Video Matting Using Time-of-Flight Camera and Multichannel Poisson Equations","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Memorial University of Newfoundland","funders":"University of Kentucky; National Science Foundation","keywords":"Artificial intelligence; Computer vision; Computer science; Segmentation","score_opus":0.02337258113584984,"score_gpt":0.30079753286147926,"score_spread":0.2774249517256294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171120124","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":[0.02040076,0.00010656906,0.97849077,0.00004631876,0.000023868788,0.000020958292,0.00003366058,0.000392465,0.0004846472],"genre_scores_gemma":[0.29768485,0.0002879829,0.69934857,0.000033202847,0.000037705224,0.000041569594,0.00010945958,0.00019646247,0.0022601623],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998037,0.00002891178,0.000008975126,0.00005158685,0.00008924108,0.000017590786],"domain_scores_gemma":[0.9994326,0.00021537009,0.0000954189,0.00005001622,0.000175819,0.000030705563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033192892,0.00050867215,0.00062450237,0.0005414547,0.0002651931,0.00076129206,0.0007197679,0.0006070255,0.0018379608],"category_scores_gemma":[0.0012030693,0.00043121676,0.0005343125,0.00049587357,0.0002640341,0.00090707117,0.0003962419,0.0008031127,0.00041156294],"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":[0.00038859976,0.00008685282,0.002579919,0.0003470692,0.00009432651,0.00021652856,0.00024505175,0.16110827,0.33800223,0.010717371,0.0022398634,0.48397395],"study_design_scores_gemma":[0.000008754449,0.000020451154,0.0007006756,0.0000056757663,0.00001111806,0.00014782166,0.000012748512,0.979261,0.018265525,0.0008135746,0.000738113,0.0000145382655],"about_ca_topic_score_codex":0.0031413692,"about_ca_topic_score_gemma":0.0046077915,"teacher_disagreement_score":0.0031413692,"about_ca_system_score_codex":0.00038888823,"about_ca_system_score_gemma":0.00073423097,"threshold_uncertainty_score":0.0062461495},"labels":[],"label_agreement":null},{"id":"W2171529458","doi":"10.1109/icpr.2014.163","title":"A New Filter for Reducing HALO Artifacts in Tone Mapped Images","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Calgary","funders":"","keywords":"Tone mapping; Halo; Computer science; Tone (literature); Artificial intelligence; Filter (signal processing); Computer vision; Gaussian; Pattern recognition (psychology); Focus (optics); High dynamic range; Dynamic range; Physics","score_opus":0.015863640351642724,"score_gpt":0.28626692397483194,"score_spread":0.2704032836231892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171529458","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":[0.03209148,0.00041209641,0.9661479,0.00006753345,0.00006227499,0.000042633008,0.000023836099,0.0006071146,0.0005451815],"genre_scores_gemma":[0.13798672,0.0005581728,0.8577993,0.00021352217,0.000082860606,0.00007835215,0.00010018326,0.00016794234,0.0030129536],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997217,0.00003152196,0.0000179364,0.000054967302,0.0001492628,0.00002456661],"domain_scores_gemma":[0.99939764,0.00017284548,0.000063900705,0.000078469646,0.00023416388,0.000053135536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039153456,0.0006065091,0.0004571272,0.0006629656,0.00023555834,0.00052717555,0.00068440905,0.00064206315,0.0017281397],"category_scores_gemma":[0.0010851967,0.00021227889,0.0005186724,0.00045480704,0.00033237098,0.0007803727,0.0004535059,0.0005757737,0.0006375253],"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":[0.00032760575,0.000093317336,0.000699529,0.00029202565,0.00007515303,0.0001867471,0.00011764531,0.0053959973,0.63860935,0.0023053328,0.0011494337,0.35074785],"study_design_scores_gemma":[0.00012562492,0.0010256597,0.0063930536,0.000054597167,0.00025400263,0.0022014363,0.00009138893,0.3437808,0.6257395,0.0014843873,0.018726604,0.0001229273],"about_ca_topic_score_codex":0.0006933176,"about_ca_topic_score_gemma":0.0011061302,"teacher_disagreement_score":0.0017281397,"about_ca_system_score_codex":0.00025425965,"about_ca_system_score_gemma":0.00028665178,"threshold_uncertainty_score":0.0057812333},"labels":[],"label_agreement":null},{"id":"W2171648450","doi":"10.1109/tce.2004.1277871","title":"A novel cost effective demosaicing approach","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Demosaicing; Interpolation (computer graphics); Computer vision; Artificial intelligence; Color filter array; Computer science; Color difference; Color correction; Process (computing); Enhanced Data Rates for GSM Evolution; Color image; Image processing; Image (mathematics); Color gel; Physics","score_opus":0.012073676609027178,"score_gpt":0.24956228078840473,"score_spread":0.23748860417937756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171648450","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":[0.0019011471,0.00018348331,0.9956275,0.000047022568,0.00006604038,0.000020992262,0.000035528774,0.00044674997,0.0016716019],"genre_scores_gemma":[0.03118129,0.00039361845,0.9614875,0.00008328568,0.000067037035,0.000038452315,0.00021262371,0.000079803045,0.006456355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997008,0.000020341377,0.000010769483,0.000063352294,0.000180023,0.00002475393],"domain_scores_gemma":[0.99986804,0.000015209821,0.000011122012,0.000036462076,0.0000589611,0.000010197824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015912313,0.00074557454,0.0005193927,0.0007809685,0.00031414282,0.0006394601,0.00092231814,0.0005692743,0.0028941666],"category_scores_gemma":[0.0003676402,0.0003214813,0.00049665855,0.00066142005,0.00023927998,0.00073971495,0.0008185257,0.0008501512,0.0021773654],"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":[0.00006896566,0.00004558582,0.00027910908,0.00017554774,0.00005263906,0.0001113808,0.00006279544,0.020083752,0.21533605,0.01795718,0.005672893,0.7401541],"study_design_scores_gemma":[0.000035952256,0.0001160697,0.0011662588,0.00003452167,0.000072391274,0.0013649089,0.00006539595,0.7488195,0.15312645,0.0131069925,0.0820157,0.00007587843],"about_ca_topic_score_codex":0.0016060293,"about_ca_topic_score_gemma":0.002877066,"teacher_disagreement_score":0.0028941666,"about_ca_system_score_codex":0.00034587717,"about_ca_system_score_gemma":0.0007570715,"threshold_uncertainty_score":0.00968194},"labels":[],"label_agreement":null},{"id":"W2178359507","doi":"10.1117/12.2194837","title":"The shower curtain effect paradoxes","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Enhancement 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":"Defence Research and Development Canada","funders":"","keywords":"Shower; Rendering (computer graphics); Cutoff frequency; Cutoff; Physics; Detector; Optics; Computer science; Acoustics; Computer vision","score_opus":0.01182170197740446,"score_gpt":0.24445380368647146,"score_spread":0.232632101709067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178359507","genre_codex":"empirical","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":[0.47263545,0.006397574,0.39143738,0.016300188,0.0007337468,0.00021828795,0.00041447207,0.0014880545,0.11037486],"genre_scores_gemma":[0.9749127,0.00073056686,0.017759355,0.0015112713,0.0002941517,0.000062944004,0.00007265845,0.0001508086,0.00450558],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967709,0.0006976805,0.00009099577,0.0005201699,0.0016684118,0.00025172444],"domain_scores_gemma":[0.97757155,0.016296126,0.0012717862,0.0025609464,0.0018459276,0.0004537267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004484709,0.00061550207,0.000983202,0.0010658395,0.0010623098,0.0018507509,0.0017632715,0.0026716804,0.0054527284],"category_scores_gemma":[0.021674223,0.0005499982,0.0007782577,0.00049899967,0.0050105625,0.0055041686,0.0035630795,0.0040991874,0.0005395971],"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":[0.00081229804,0.00028319706,0.008332898,0.0007876983,0.00023139146,0.0043560076,0.0060964087,0.0120788235,0.051934835,0.8418334,0.014295402,0.05895773],"study_design_scores_gemma":[0.00023801213,0.00045163924,0.0138151795,0.00014581604,0.00013256924,0.005688928,0.0016458429,0.06730739,0.032647666,0.8484272,0.029226115,0.0002737813],"about_ca_topic_score_codex":0.0008789636,"about_ca_topic_score_gemma":0.00048526403,"teacher_disagreement_score":0.0054527284,"about_ca_system_score_codex":0.0008635354,"about_ca_system_score_gemma":0.00042050626,"threshold_uncertainty_score":0.023717701},"labels":[],"label_agreement":null},{"id":"W2203198397","doi":"10.1109/have.2015.7359452","title":"Stereoscopic chroma key matting using statistical analysis in CIECAM02 color space","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Artificial intelligence; Computer vision; Color space; Computer science; Hue; HSL and HSV; RGB color model; Color histogram; Color image; Pixel; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.04324711285960314,"score_gpt":0.32190366609247073,"score_spread":0.2786565532328676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2203198397","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":[0.009923719,0.00013994718,0.98783827,0.000041796513,0.000028341468,0.000029462342,0.000076482276,0.000825141,0.0010968862],"genre_scores_gemma":[0.21271855,0.0005791344,0.7826809,0.000080043574,0.00006713778,0.00009227511,0.0004591375,0.00033906728,0.002983711],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964595,0.000040666073,0.000013678524,0.0000679513,0.00019584679,0.000035962064],"domain_scores_gemma":[0.99945813,0.00009231019,0.000060530183,0.000078481804,0.00027361274,0.000036984988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040254334,0.000535658,0.00033459297,0.0013833183,0.00025087924,0.0008853998,0.00049291196,0.0002570334,0.003093585],"category_scores_gemma":[0.0012161267,0.00022291421,0.0005950981,0.00131911,0.0004159947,0.00067267905,0.0005291962,0.00065249304,0.00091611774],"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":[0.0002853612,0.00009291866,0.0023510656,0.00029130792,0.00006625113,0.00019064218,0.00017927187,0.043017324,0.21258295,0.018327128,0.004968278,0.71764755],"study_design_scores_gemma":[0.000027760654,0.00012642462,0.004991372,0.000021339345,0.00003868872,0.00046539612,0.00007000973,0.84084475,0.13425379,0.006387165,0.012707355,0.00006591763],"about_ca_topic_score_codex":0.0031311142,"about_ca_topic_score_gemma":0.0032452461,"teacher_disagreement_score":0.0031311142,"about_ca_system_score_codex":0.0004922788,"about_ca_system_score_gemma":0.00079772057,"threshold_uncertainty_score":0.010349035},"labels":[],"label_agreement":null},{"id":"W2217818766","doi":"10.1109/iccv.2015.70","title":"Video Restoration Against Yin-Yang Phasing","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"McMaster University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Object (grammar); Chromaticity; Video tracking; Consistency (knowledge bases); Video quality; Motion compensation; Point (geometry); Computer graphics (images); Mathematics; Engineering","score_opus":0.044619476774717545,"score_gpt":0.2907178656961216,"score_spread":0.24609838892140407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2217818766","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":[0.08272807,0.0019664383,0.90815926,0.00038233591,0.0001229894,0.00003774544,0.00002428542,0.00058597396,0.0059929015],"genre_scores_gemma":[0.7285921,0.0027357296,0.2620964,0.00022669905,0.00011921266,0.000051051447,0.00006447713,0.00011861885,0.005995672],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996964,0.000069482565,0.000012684136,0.00005504549,0.00013588712,0.00003057765],"domain_scores_gemma":[0.9994954,0.00020936246,0.00009018894,0.00009335645,0.00008601786,0.00002562875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005104776,0.00058709935,0.00051492616,0.00042153415,0.00033505677,0.00048406908,0.0003422055,0.00060258235,0.0011603113],"category_scores_gemma":[0.0018553884,0.00019353806,0.0002663313,0.0003821068,0.00078299874,0.00089294306,0.0008359581,0.0008160691,0.0003758182],"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":[0.0006558699,0.000079201855,0.0012459756,0.000567872,0.000083219136,0.00076976535,0.00054161006,0.055566587,0.5050704,0.0676047,0.002768827,0.3650461],"study_design_scores_gemma":[0.000050807175,0.00043312393,0.0017428132,0.000087977765,0.0000582646,0.0022992752,0.00019154049,0.61688125,0.33571556,0.024814975,0.017675504,0.000048910577],"about_ca_topic_score_codex":0.00036703786,"about_ca_topic_score_gemma":0.00034105338,"teacher_disagreement_score":0.0011603113,"about_ca_system_score_codex":0.00021893358,"about_ca_system_score_gemma":0.00033260617,"threshold_uncertainty_score":0.003881693},"labels":[],"label_agreement":null},{"id":"W2226241173","doi":"10.1186/s40064-015-1612-4","title":"Color reproduction and processing algorithm based on real-time mapping for endoscopic images","year":2016,"lang":"en","type":"article","venue":"SpringerPlus","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"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 Saskatchewan","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Grand Challenges Canada; McMaster University","keywords":"Computer science; Artificial intelligence; Computer vision; Grayscale; Color balance; Color space; Color image; Hue; Similarity (geometry); Color quantization; Preprocessor; Color difference; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.011932207248427647,"score_gpt":0.25195549809275813,"score_spread":0.2400232908443305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2226241173","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":[0.008265194,0.00020598352,0.9894762,0.00004446773,0.000051442825,0.00003841497,0.000014741876,0.00086071884,0.0010428951],"genre_scores_gemma":[0.10903371,0.0004920346,0.8861283,0.000058402387,0.000050897994,0.00007246755,0.000119649485,0.00016595646,0.0038784908],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996667,0.000041537987,0.000020363837,0.00007972894,0.00016305316,0.000028622846],"domain_scores_gemma":[0.9996136,0.00008950996,0.00004320962,0.00008183793,0.00015447068,0.000017373724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031160412,0.00051598845,0.00037730325,0.00069798087,0.00023143362,0.00063667767,0.0007528827,0.0004512728,0.002999334],"category_scores_gemma":[0.00087906834,0.00021930966,0.00047193872,0.00063677115,0.00031755056,0.00094235735,0.00036861564,0.00060135545,0.0014512554],"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":[0.00027349498,0.00008042893,0.0004948337,0.00018746124,0.000033242624,0.00014541995,0.000105697734,0.013465132,0.22921641,0.004705383,0.0021154836,0.7491771],"study_design_scores_gemma":[0.00005930439,0.0003849274,0.00237885,0.000025380952,0.00006544982,0.0017114396,0.00008081943,0.61508006,0.3530719,0.0031173977,0.023946643,0.00007777445],"about_ca_topic_score_codex":0.0007428748,"about_ca_topic_score_gemma":0.00063921395,"teacher_disagreement_score":0.002999334,"about_ca_system_score_codex":0.00025741794,"about_ca_system_score_gemma":0.00031686286,"threshold_uncertainty_score":0.010033786},"labels":[],"label_agreement":null},{"id":"W2238590479","doi":"10.1142/s0219691315500381","title":"A color image enhancement algorithm based on quaternion representation of vector rotation","year":2015,"lang":"en","type":"article","venue":"International Journal of Wavelets Multiresolution and Information Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"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":"Engineering and Physical Sciences Research Council; McMaster University","keywords":"Quaternion; Algorithm; Chrominance; Computer science; Color image; Artificial intelligence; Luminance; Rotation (mathematics); Unsharp masking; Computer vision; Mathematics; Image (mathematics); Image processing","score_opus":0.020508700426288797,"score_gpt":0.3056195897972589,"score_spread":0.2851108893709701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2238590479","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":[0.0044511463,0.00041354753,0.993115,0.00007489563,0.00008105427,0.00007110025,0.00003400316,0.0004855425,0.0012737165],"genre_scores_gemma":[0.083984,0.001296076,0.90947646,0.0001191414,0.00010191559,0.00010508818,0.00020671541,0.000084371655,0.0046261516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978703,0.000025810697,0.000016681448,0.000050206796,0.000101303456,0.000018947296],"domain_scores_gemma":[0.9998259,0.000031943004,0.000026011765,0.000023999568,0.00008230587,0.000009823849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042459063,0.00076890376,0.0005339874,0.0006695693,0.0002582518,0.0005622381,0.00047916,0.0004535963,0.002647229],"category_scores_gemma":[0.00054725516,0.00026188328,0.0006167923,0.0007076596,0.0002896114,0.0008383452,0.00035421457,0.0007227775,0.0010354384],"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":[0.00018448352,0.00005612298,0.0008678109,0.00030949674,0.00005936723,0.00018725813,0.00015094272,0.025639605,0.22195064,0.022217572,0.005144869,0.7232318],"study_design_scores_gemma":[0.00009720905,0.00054323795,0.003141925,0.000071720395,0.00012782178,0.0016239779,0.00010626481,0.6825555,0.24323961,0.007500781,0.06087649,0.000115417795],"about_ca_topic_score_codex":0.0008761069,"about_ca_topic_score_gemma":0.00075763295,"teacher_disagreement_score":0.002647229,"about_ca_system_score_codex":0.0003104311,"about_ca_system_score_gemma":0.00039569737,"threshold_uncertainty_score":0.00885582},"labels":[],"label_agreement":null},{"id":"W2283479202","doi":"10.1007/978-3-319-19387-8_114","title":"A novel system for real-time planning and guidance of breast HDR brachytherapy","year":2015,"lang":"en","type":"book-chapter","venue":"World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Brachytherapy; Imaging phantom; Radiation treatment planning; Computer science; Tracking (education); Medicine; Medical physics; Computer vision; Radiology; Radiation therapy; Psychology","score_opus":0.01630738162815255,"score_gpt":0.25768661096339035,"score_spread":0.2413792293352378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283479202","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":[0.014903652,0.002407038,0.95799124,0.0004004137,0.0006813882,0.0003720966,0.00047128007,0.012151275,0.010621525],"genre_scores_gemma":[0.08338498,0.0013424319,0.883297,0.00081037096,0.000189976,0.00035561304,0.00070135936,0.0011208375,0.028797405],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973816,0.00003079079,0.000016170368,0.00007385735,0.000118039025,0.000023047825],"domain_scores_gemma":[0.99979836,0.000065802895,0.000016266078,0.00003418716,0.000060909406,0.000024615094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048059216,0.0005440945,0.000506891,0.0004768126,0.00041964886,0.0010982492,0.001479351,0.0012803723,0.012028326],"category_scores_gemma":[0.00045998438,0.00065053464,0.00037311454,0.00042967658,0.00023483402,0.0008403383,0.0009850477,0.00060983247,0.0028685702],"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":[0.00054387783,0.00012989684,0.000681474,0.00048893533,0.00007945495,0.00059388106,0.00029677985,0.0036127479,0.57537806,0.0045827525,0.027590908,0.38602135],"study_design_scores_gemma":[0.00028196257,0.0010788627,0.005958174,0.00020294597,0.00039003493,0.010049846,0.00011108405,0.18529302,0.4319594,0.0023951486,0.36195084,0.00032875477],"about_ca_topic_score_codex":0.0010900851,"about_ca_topic_score_gemma":0.0018810584,"teacher_disagreement_score":0.012028326,"about_ca_system_score_codex":0.0004562385,"about_ca_system_score_gemma":0.0010225816,"threshold_uncertainty_score":0.040238798},"labels":[],"label_agreement":null},{"id":"W2288685579","doi":"","title":"Color Transfer and Colorization based on Textural Properties","year":2015,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Image Enhancement 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":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Grayscale; Coherence (philosophical gambling strategy); Pattern recognition (psychology); Image (mathematics); Color image; Color quantization; Enhanced Data Rates for GSM Evolution; Texture (cosmology); Image processing; Mathematics","score_opus":0.019724046586337196,"score_gpt":0.23177512077079254,"score_spread":0.21205107418445535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288685579","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":[0.027677767,0.00049141754,0.9678526,0.00007098538,0.000045125103,0.00005962708,0.000037290374,0.0011740295,0.0025910942],"genre_scores_gemma":[0.38127726,0.0010894758,0.6085131,0.00014857635,0.000102654114,0.0000956473,0.00019272903,0.0008323382,0.0077480962],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968266,0.000047582034,0.000011661057,0.00011212988,0.00011084604,0.000035103836],"domain_scores_gemma":[0.99931705,0.00021480494,0.00008807837,0.00022505481,0.000122029815,0.000033036922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041152412,0.00071001577,0.00056504435,0.0010437021,0.0002597591,0.0010841887,0.0009121899,0.00050973945,0.0032210997],"category_scores_gemma":[0.0015185587,0.00028573113,0.00062131643,0.00083241257,0.0007774756,0.001389497,0.00070605834,0.0009868987,0.0009939143],"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":[0.00030052094,0.000112502385,0.00062329805,0.00026021118,0.00006584284,0.00011101897,0.00013289487,0.029170074,0.51133335,0.0129117295,0.0013876068,0.44359088],"study_design_scores_gemma":[0.00003891316,0.00019240286,0.0021734207,0.00002002501,0.00008736703,0.00057286944,0.000055331657,0.39686736,0.58100456,0.0084570935,0.01047559,0.00005497415],"about_ca_topic_score_codex":0.00059336703,"about_ca_topic_score_gemma":0.000737894,"teacher_disagreement_score":0.0032210997,"about_ca_system_score_codex":0.00041121177,"about_ca_system_score_gemma":0.00029202973,"threshold_uncertainty_score":0.010775626},"labels":[],"label_agreement":null},{"id":"W2293876531","doi":"10.1109/tip.2015.2436340","title":"High Dynamic Range Image Compression by Optimizing Tone Mapped Image Quality Index","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":133,"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":"Tone mapping; High dynamic range; Computer science; Image quality; Naturalness; Artificial intelligence; Computer vision; Image (mathematics); Range (aeronautics); Metric (unit); Tone (literature); Image compression; Dynamic range; Image processing","score_opus":0.02238372510821498,"score_gpt":0.3246670638641913,"score_spread":0.30228333875597635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293876531","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":[0.113128416,0.00025953347,0.8831176,0.00007491762,0.000028042363,0.00010506038,0.000030217394,0.0005014455,0.0027547986],"genre_scores_gemma":[0.51105237,0.00025998213,0.486773,0.000068375,0.000022285652,0.000090424204,0.00008845801,0.00016083007,0.0014842721],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997003,0.000049500166,0.000018562527,0.000051044546,0.00015426456,0.000026405023],"domain_scores_gemma":[0.9992532,0.0002743305,0.00012251093,0.000092775466,0.00021667639,0.000040577936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005890083,0.000688469,0.00026368795,0.0005526614,0.00015848351,0.00068090984,0.00046965314,0.00040089033,0.0008973453],"category_scores_gemma":[0.002042829,0.00013893271,0.00025690257,0.00039664155,0.00042554227,0.0008430411,0.00053280767,0.0005270191,0.00022729982],"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":[0.00035878835,0.00022873444,0.0021673788,0.00029816598,0.000063739106,0.0002142845,0.00021738515,0.13077971,0.58686143,0.013905289,0.0012856666,0.26361948],"study_design_scores_gemma":[0.00003839876,0.000404588,0.0017418754,0.00001939433,0.000040015424,0.0003937091,0.000059331654,0.7626609,0.22935964,0.0029881154,0.002257431,0.000036571513],"about_ca_topic_score_codex":0.00046863192,"about_ca_topic_score_gemma":0.00057266204,"teacher_disagreement_score":0.0008973453,"about_ca_system_score_codex":0.00028114143,"about_ca_system_score_gemma":0.00034242155,"threshold_uncertainty_score":0.003115058},"labels":[],"label_agreement":null},{"id":"W2294269662","doi":"10.1145/1377980.1377990","title":"Artistic thresholding","year":2008,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":46,"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; Thresholding; Artificial intelligence; Computer vision; Image (mathematics)","score_opus":0.025834817699415875,"score_gpt":0.2361036377572803,"score_spread":0.21026882005786443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294269662","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":[0.019986192,0.00095938734,0.94555676,0.00047069893,0.00015612415,0.00008562456,0.00011461723,0.0005627523,0.032107897],"genre_scores_gemma":[0.42715383,0.0018141501,0.54117155,0.00037601087,0.00026266015,0.00013443288,0.0004269747,0.0007244551,0.027936038],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987921,0.00019730756,0.000055211,0.0003451275,0.00046676002,0.00014348094],"domain_scores_gemma":[0.99851817,0.00069845107,0.0001366912,0.00033462385,0.00020356326,0.000108577005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012781682,0.0010960564,0.0012035777,0.001073513,0.0011108256,0.003720466,0.0023067235,0.0018908333,0.013537867],"category_scores_gemma":[0.004886778,0.0007435381,0.0010135536,0.0012084168,0.001750676,0.0029457186,0.0024274334,0.0014415255,0.0024071776],"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":[0.00029806438,0.0001640553,0.0010925458,0.0006403624,0.00008575376,0.0005915765,0.00046176385,0.32795978,0.049541757,0.20122327,0.013588717,0.40435234],"study_design_scores_gemma":[0.000055013457,0.00016911096,0.00063308596,0.000113820846,0.00006624552,0.0013367926,0.00019485464,0.77994406,0.023604583,0.15608928,0.037728332,0.00006475305],"about_ca_topic_score_codex":0.00093427696,"about_ca_topic_score_gemma":0.0009225945,"teacher_disagreement_score":0.013537867,"about_ca_system_score_codex":0.0008949195,"about_ca_system_score_gemma":0.0006089784,"threshold_uncertainty_score":0.045288622},"labels":[],"label_agreement":null},{"id":"W2294368874","doi":"10.1109/icip.2015.7351748","title":"Saliency weighted quality assessment of tone-mapped images","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":18,"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 Manitoba","funders":"","keywords":"Tone mapping; Artificial intelligence; Ranking (information retrieval); Metric (unit); Image quality; Computer science; Tone (literature); Computer vision; High dynamic range; Quality (philosophy); Image (mathematics); Range (aeronautics); Judgement; Quality Score; Pattern recognition (psychology); Dynamic range","score_opus":0.0564457346565031,"score_gpt":0.3992333884117835,"score_spread":0.3427876537552804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294368874","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":[0.59498835,0.0004885559,0.40005955,0.000092015,0.00006615998,0.00019129642,0.00013957503,0.00056308217,0.003411348],"genre_scores_gemma":[0.9185324,0.00018985622,0.08026574,0.000024671344,0.00002944896,0.000028301673,0.00013590945,0.00007076644,0.0007229213],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956685,0.0000840938,0.000027136583,0.000060941664,0.00022803151,0.00003290875],"domain_scores_gemma":[0.9975713,0.0007543003,0.0003673548,0.00020884965,0.0009526505,0.00014549844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009573408,0.00040947844,0.00032979628,0.001402857,0.00014677712,0.0008120694,0.0003442872,0.00030797874,0.0016313322],"category_scores_gemma":[0.005346597,0.00014044154,0.00030048913,0.0004463324,0.0003196211,0.00078074384,0.00046409422,0.0002559652,0.00022112482],"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":[0.0016846183,0.00016772553,0.017145956,0.00060179905,0.00028588818,0.00031450792,0.00046836608,0.03303662,0.5607991,0.0035287465,0.0011290977,0.38083753],"study_design_scores_gemma":[0.00011972141,0.0023340771,0.11185485,0.00006534623,0.00037081615,0.0019037178,0.00046124266,0.5586738,0.31547654,0.005631983,0.0029283927,0.00017952801],"about_ca_topic_score_codex":0.0006962828,"about_ca_topic_score_gemma":0.00072255713,"teacher_disagreement_score":0.0016313322,"about_ca_system_score_codex":0.00023995164,"about_ca_system_score_gemma":0.00016087099,"threshold_uncertainty_score":0.005457282},"labels":[],"label_agreement":null},{"id":"W2294776398","doi":"10.1109/icip.2015.7351342","title":"Analysis on spectral effects of dark-channel prior for haze removal","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Haze; Channel (broadcasting); Sky; RGB color model; Computer science; Pixel; Image (mathematics); Zero (linguistics); Artificial intelligence; Star (game theory); Computer vision; Mathematics; Astrophysics; Physics; Telecommunications","score_opus":0.01781674258247954,"score_gpt":0.2768310665038917,"score_spread":0.25901432392141216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294776398","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":[0.3367256,0.0015293345,0.6549822,0.00023551185,0.00007284908,0.00003959746,0.00010609055,0.00033891204,0.0059698513],"genre_scores_gemma":[0.9157664,0.001480384,0.07961036,0.000065774984,0.00003740604,0.000012678889,0.00013745653,0.00009405968,0.0027954304],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996604,0.000053280237,0.000009989992,0.000046983918,0.00019304785,0.00003634883],"domain_scores_gemma":[0.9976006,0.0013260378,0.00023118053,0.00023374773,0.00056223525,0.000046136556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068277546,0.00035485966,0.00022745159,0.00050803035,0.00025615952,0.0003611899,0.00026107454,0.00031207624,0.0010228514],"category_scores_gemma":[0.003921948,0.00016382296,0.00025623475,0.00032419222,0.0004874079,0.0007220439,0.0003441863,0.00048689125,0.00020053706],"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":[0.00076421566,0.0001710529,0.0132853165,0.00059670885,0.0001606544,0.0009312402,0.0005033315,0.29132,0.41902578,0.026455095,0.0021155588,0.24467105],"study_design_scores_gemma":[0.000010237284,0.00012697477,0.012315299,0.000040230094,0.0000703606,0.0006144249,0.00016049614,0.82112855,0.15745299,0.004712078,0.0033274388,0.000040988638],"about_ca_topic_score_codex":0.0020100772,"about_ca_topic_score_gemma":0.0024848531,"teacher_disagreement_score":0.0020100772,"about_ca_system_score_codex":0.00028378106,"about_ca_system_score_gemma":0.00033138445,"threshold_uncertainty_score":0.0039966702},"labels":[],"label_agreement":null},{"id":"W2295858151","doi":"10.1109/icip.2015.7351183","title":"High dynamic range map estimation via fully connected random fields with stochastic cliques","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Waterloo","funders":"","keywords":"Tone mapping; High dynamic range; Computer vision; Computer science; Artificial intelligence; Noise (video); Context (archaeology); Dynamic range; High-dynamic-range imaging; Random field; Range (aeronautics); Conditional random field; Image noise; Image (mathematics); Mathematics; Geography; Statistics; Engineering","score_opus":0.009189222057934638,"score_gpt":0.24400759986960202,"score_spread":0.2348183778116674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295858151","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":[0.0249643,0.00020797654,0.9736424,0.00020720622,0.000013062132,0.000031480264,0.000093365146,0.0003046757,0.000535588],"genre_scores_gemma":[0.75239056,0.0004149442,0.24248129,0.00020541892,0.00014043185,0.00019188222,0.000963849,0.00017966972,0.0030319167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999275,0.00028591955,0.000026518757,0.00020584783,0.00013151816,0.00007516755],"domain_scores_gemma":[0.9970611,0.0021317208,0.000329451,0.00014917053,0.00023685196,0.00009162765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016923711,0.0009698586,0.0017588648,0.0015916434,0.00052748946,0.0010838623,0.0019907032,0.0015937482,0.0012939514],"category_scores_gemma":[0.004841222,0.0011081944,0.0013248149,0.0010633336,0.0011721139,0.0016481283,0.0013611879,0.0012060037,0.0002793526],"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":[0.00008799056,0.00003642802,0.0005364777,0.000049211114,0.00005282211,0.000077254444,0.00003770478,0.96517533,0.0013840118,0.006664299,0.00053219975,0.025366252],"study_design_scores_gemma":[0.0000024066942,0.0000041562776,0.0000660273,0.0000020947882,0.0000024902788,0.0000068768372,0.0000012967599,0.9980406,0.000110057845,0.0017110533,0.000049912895,0.0000030635683],"about_ca_topic_score_codex":0.008947337,"about_ca_topic_score_gemma":0.008168768,"teacher_disagreement_score":0.008947337,"about_ca_system_score_codex":0.001141564,"about_ca_system_score_gemma":0.00088520034,"threshold_uncertainty_score":0.017790556},"labels":[],"label_agreement":null},{"id":"W2296344202","doi":"10.1109/icip.2015.7351729","title":"Hybrid key: An automatic tool for real-time high quality chroma keying","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Keying; Compositing; Key (lock); Asynchronous communication; Real-time computing; Embedded system; Computer hardware; Artificial intelligence; Telecommunications; Operating system","score_opus":0.04072218575178477,"score_gpt":0.32384287319757415,"score_spread":0.2831206874457894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296344202","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":[0.0077888053,0.00017454183,0.97351956,0.000049040595,0.00006932024,0.00007795645,0.00009356103,0.01682401,0.001403253],"genre_scores_gemma":[0.17156975,0.0001734823,0.8213307,0.00012221065,0.0000539131,0.00012988354,0.00027919954,0.0022746816,0.0040660654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995413,0.000058585967,0.000029831464,0.000095157826,0.00021244663,0.000062808984],"domain_scores_gemma":[0.99943393,0.00019315511,0.000059446644,0.00012552454,0.00012398088,0.00006384034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063024106,0.0009109744,0.00043565527,0.0009952068,0.00027018122,0.001135674,0.0018801572,0.00075255707,0.012242434],"category_scores_gemma":[0.0016161605,0.00045202553,0.0004231671,0.0004074412,0.0005527436,0.0017196253,0.0011254686,0.0006525929,0.0024823982],"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":[0.0012950739,0.00013810743,0.0016266312,0.0005034431,0.000117983516,0.0005097245,0.00026322203,0.011263752,0.32065657,0.013281373,0.017091721,0.63325244],"study_design_scores_gemma":[0.0002958469,0.00059999083,0.0017716542,0.00007017003,0.000091988244,0.0013243174,0.000112177586,0.4063089,0.5095475,0.006180925,0.073529504,0.00016702956],"about_ca_topic_score_codex":0.00063986186,"about_ca_topic_score_gemma":0.0006857001,"teacher_disagreement_score":0.012242434,"about_ca_system_score_codex":0.00043154918,"about_ca_system_score_gemma":0.00041445,"threshold_uncertainty_score":0.040955007},"labels":[],"label_agreement":null},{"id":"W2296434085","doi":"10.1109/icip.2015.7351694","title":"Adaptive exposure fusion for high dynamic range imaging","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"High dynamic range; Fusion; Computer science; Image fusion; Artificial intelligence; High-dynamic-range imaging; Dynamic range; Image (mathematics); Computer vision; Human visual system model; Range (aeronautics); Perception; Engineering","score_opus":0.019291539869272116,"score_gpt":0.2628638592240528,"score_spread":0.2435723193547807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296434085","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":[0.03359263,0.0005283913,0.9637067,0.000050958024,0.000031482057,0.000031001302,0.0000214026,0.00044958486,0.0015878677],"genre_scores_gemma":[0.39804384,0.0006356988,0.59749204,0.00009323835,0.00006751373,0.000044419867,0.00009996626,0.000117420386,0.003405894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970585,0.000041659467,0.000011518503,0.000059866412,0.0001538125,0.000027184808],"domain_scores_gemma":[0.99978465,0.00007506895,0.00003171573,0.00004234452,0.00005148467,0.000014720658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031225153,0.00040372837,0.00035383573,0.00043716645,0.00023217204,0.00039467908,0.0005619488,0.0004675582,0.0023655521],"category_scores_gemma":[0.0006550442,0.00020036908,0.00042326434,0.00030249255,0.00027749498,0.00090819504,0.0007575015,0.0006058031,0.00053403305],"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":[0.00031722384,0.00009680813,0.00073186896,0.00017695621,0.00008200781,0.00015610851,0.00018949664,0.030545928,0.47617123,0.006539019,0.0009943517,0.48399895],"study_design_scores_gemma":[0.000045383975,0.0006320814,0.0048858607,0.000050174243,0.00011009921,0.0017361729,0.00008713934,0.5192904,0.44801322,0.0082535865,0.016814372,0.00008155516],"about_ca_topic_score_codex":0.00034476147,"about_ca_topic_score_gemma":0.0004155423,"teacher_disagreement_score":0.0023655521,"about_ca_system_score_codex":0.000242835,"about_ca_system_score_gemma":0.00019252354,"threshold_uncertainty_score":0.0079135895},"labels":[],"label_agreement":null},{"id":"W2296478018","doi":"10.1109/icip.2015.7351094","title":"Multi-exposure image fusion: A patch-wise approach","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":115,"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":"Artificial intelligence; RGB color model; Computer science; Computer vision; Image fusion; Color image; Pixel; Image (mathematics); Key (lock); SIGNAL (programming language); Pattern recognition (psychology); Image processing","score_opus":0.03744123302101541,"score_gpt":0.2716292558924889,"score_spread":0.23418802287147347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296478018","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":[0.004503576,0.0002646198,0.99406636,0.000064127526,0.000026828666,0.000033177606,0.00001587831,0.00024654318,0.0007788527],"genre_scores_gemma":[0.11460836,0.00068985537,0.88185865,0.00014075256,0.000091492795,0.00007383897,0.00009703339,0.00013773901,0.002302207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993349,0.00012406547,0.000029921115,0.00017011841,0.00027374813,0.00006719807],"domain_scores_gemma":[0.9994493,0.00013543532,0.000059645863,0.00016378658,0.00015975538,0.000031981865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008788209,0.00079004664,0.0010313469,0.0011408824,0.00031008324,0.0010179586,0.0011401805,0.0011822021,0.0027690951],"category_scores_gemma":[0.001357773,0.00049280224,0.0013921293,0.0008090783,0.0006246881,0.001977219,0.0018522897,0.0012303384,0.0009994074],"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":[0.00027137162,0.00015309147,0.0010515504,0.0004073046,0.00023093019,0.00024271879,0.00029858295,0.042979896,0.34522846,0.013225041,0.0019606035,0.59395045],"study_design_scores_gemma":[0.000045416906,0.0005774316,0.003117109,0.00007150269,0.0002769374,0.0018774664,0.00014551134,0.66112864,0.30059293,0.014148791,0.017899174,0.00011909282],"about_ca_topic_score_codex":0.00033884018,"about_ca_topic_score_gemma":0.00038939592,"teacher_disagreement_score":0.0027690951,"about_ca_system_score_codex":0.00025156068,"about_ca_system_score_gemma":0.00028568486,"threshold_uncertainty_score":0.0092635155},"labels":[],"label_agreement":null},{"id":"W2296690173","doi":"10.1109/icip.2015.7351475","title":"Perceptual evaluation of single image dehazing algorithms","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":139,"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":"Visibility; Computer science; Haze; Artificial intelligence; Computer vision; Image quality; Perception; Image (mathematics)","score_opus":0.11751984033806594,"score_gpt":0.3400034959865563,"score_spread":0.22248365564849037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296690173","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":[0.9406569,0.0013741537,0.054681562,0.00006340701,0.0000836221,0.0001573871,0.00019975213,0.00031422667,0.0024689138],"genre_scores_gemma":[0.94890547,0.0005910462,0.04853027,0.00002944914,0.000026864911,0.000038495975,0.00035925972,0.00008980628,0.0014293465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989454,0.00020248278,0.00012858701,0.00017698665,0.00046698796,0.000079485406],"domain_scores_gemma":[0.99179626,0.0040689083,0.0006754215,0.0005435447,0.0025116168,0.00040420418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018069888,0.0006018681,0.0004914564,0.0016774469,0.00023715655,0.0007005882,0.0005308406,0.00046036998,0.0018862062],"category_scores_gemma":[0.007180212,0.0001564923,0.00034709537,0.0005397638,0.00035961304,0.00087423564,0.00072587305,0.00030023683,0.00030393968],"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":[0.0071290825,0.0014022052,0.018833272,0.0022797089,0.00051645853,0.00034776673,0.00096742663,0.05478342,0.42739767,0.0010325719,0.0020484054,0.48326197],"study_design_scores_gemma":[0.00039027695,0.016596401,0.14827739,0.00023557743,0.00064846757,0.0025349867,0.0015359907,0.40777296,0.41452304,0.0014958684,0.005717109,0.0002719191],"about_ca_topic_score_codex":0.0009720923,"about_ca_topic_score_gemma":0.00094085425,"teacher_disagreement_score":0.0018862062,"about_ca_system_score_codex":0.00021473986,"about_ca_system_score_gemma":0.00015583965,"threshold_uncertainty_score":0.009556413},"labels":[],"label_agreement":null},{"id":"W2310078822","doi":"10.1109/ism.2015.43","title":"Quantitative Evaluation of Hair Texture","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Texture (cosmology); Artificial intelligence; Computer science; Computer vision; Face (sociological concept); Relation (database); Pattern recognition (psychology); Image (mathematics); Data mining","score_opus":0.1502532455763075,"score_gpt":0.3857534373602812,"score_spread":0.23550019178397372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2310078822","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":[0.82996935,0.002297287,0.15760566,0.0001284572,0.00013337235,0.00015002378,0.0013278184,0.0017338855,0.006654219],"genre_scores_gemma":[0.97782975,0.00029211954,0.019570667,0.000046422392,0.000045891706,0.000022742448,0.00082140445,0.00017724604,0.0011936072],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989806,0.00016044949,0.0000432106,0.00018838135,0.0005224304,0.00010492694],"domain_scores_gemma":[0.99675876,0.0012758655,0.0004407562,0.00033220914,0.0009846148,0.00020787254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012460011,0.00045821266,0.0005194519,0.0024758854,0.00030867013,0.00088175404,0.00034492963,0.0004866878,0.0026730695],"category_scores_gemma":[0.0061381646,0.00018336483,0.00027439164,0.00082868454,0.00049867894,0.0007497536,0.00057524245,0.0002962925,0.0006144093],"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":[0.002099319,0.00026288314,0.08650586,0.0013416659,0.00040519977,0.0004508138,0.0003780607,0.046918478,0.49564117,0.0016116312,0.0045082825,0.3598767],"study_design_scores_gemma":[0.000101795216,0.001298305,0.42269978,0.00016905504,0.00022699714,0.0035222992,0.0006787283,0.3606716,0.20042886,0.0025892498,0.007456751,0.00015655029],"about_ca_topic_score_codex":0.0010389403,"about_ca_topic_score_gemma":0.0014150032,"teacher_disagreement_score":0.0026730695,"about_ca_system_score_codex":0.00026720497,"about_ca_system_score_gemma":0.0001333723,"threshold_uncertainty_score":0.008942246},"labels":[],"label_agreement":null},{"id":"W2317939074","doi":"10.1109/embc.2014.6944477","title":"Image enhancement and space-variant color reproduction method for endoscopic images using adaptive sigmoid function","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"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 Saskatchewan","funders":"","keywords":"Chrominance; Artificial intelligence; Color space; Computer vision; RGB color model; Computer science; Luminance; Sigmoid function; Color image; Pixel; Mathematics; Image (mathematics); Image processing; Artificial neural network","score_opus":0.02145717594194335,"score_gpt":0.29515888595874545,"score_spread":0.2737017100168021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317939074","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":[0.058957502,0.00064324215,0.93803555,0.00008654106,0.000045870744,0.00003683288,0.000018941273,0.0005266387,0.0016489817],"genre_scores_gemma":[0.6809855,0.0010943168,0.31178045,0.000071229035,0.00004383801,0.000045935707,0.000052726256,0.00009731194,0.005828628],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982065,0.000029949364,0.000011925322,0.00003128649,0.00009049024,0.000015793588],"domain_scores_gemma":[0.99974865,0.00008812775,0.000028362698,0.000032459186,0.0000884909,0.000013836274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035158524,0.00037480865,0.0002748304,0.00037838027,0.00012912773,0.0003428547,0.00042451074,0.00035906612,0.0009940403],"category_scores_gemma":[0.0006682153,0.00015849363,0.00047212525,0.00035055738,0.0002622808,0.0005457901,0.00026530214,0.00038000642,0.00028698758],"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":[0.00036374177,0.00009858323,0.0013497205,0.00026959306,0.000059698883,0.00036390344,0.00016803095,0.02909326,0.5855929,0.0044382457,0.00096822483,0.377234],"study_design_scores_gemma":[0.00004026417,0.0004160482,0.0028989804,0.000024178958,0.000065614564,0.0021073623,0.000047730737,0.6123024,0.3739392,0.0010736565,0.007012396,0.00007217127],"about_ca_topic_score_codex":0.0004883682,"about_ca_topic_score_gemma":0.00040246948,"teacher_disagreement_score":0.0009940403,"about_ca_system_score_codex":0.00020764815,"about_ca_system_score_gemma":0.0001645635,"threshold_uncertainty_score":0.0033253431},"labels":[],"label_agreement":null},{"id":"W2337186434","doi":"10.1007/s40846-016-0120-5","title":"Efficient Color Reproduction Algorithm for Endoscopic Images Based on Dynamic Color Map","year":2016,"lang":"en","type":"article","venue":"Journal of Medical and Biological Engineering","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Grayscale; Artificial intelligence; Computer vision; Color image; Computer science; Color balance; Color space; Hue; Color histogram; Similarity (geometry); Color quantization; Histogram equalization; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.009320791514561947,"score_gpt":0.24730322438462943,"score_spread":0.23798243287006748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337186434","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":[0.02343055,0.00033366025,0.9730296,0.00008431475,0.000055394456,0.00004744189,0.00004497095,0.0012328377,0.001741204],"genre_scores_gemma":[0.17759074,0.0006188253,0.8166793,0.00007225832,0.00004714529,0.00006906705,0.0001819001,0.0002870322,0.0044537615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998072,0.000026251562,0.000009149104,0.000038782273,0.00009650785,0.000022056563],"domain_scores_gemma":[0.99969506,0.00007817714,0.000027556154,0.000045762208,0.00013305787,0.000020389134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026922312,0.000594041,0.00042805905,0.00089329673,0.00024436627,0.0007645477,0.00065088726,0.00043819635,0.0032491493],"category_scores_gemma":[0.00073654787,0.00029583636,0.00046887773,0.00067598326,0.00020563239,0.000764849,0.00051912497,0.00055500603,0.0008670065],"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":[0.00046048817,0.00008102157,0.0007476891,0.00018186096,0.00005114447,0.00021538326,0.00012124398,0.017674843,0.34124148,0.004865678,0.003294495,0.6310648],"study_design_scores_gemma":[0.000074102216,0.00017175634,0.0017890086,0.000022111622,0.00008642615,0.0012500826,0.000050351868,0.72814924,0.25855833,0.001821135,0.0079647945,0.000062733445],"about_ca_topic_score_codex":0.00091804744,"about_ca_topic_score_gemma":0.00094250555,"teacher_disagreement_score":0.0032491493,"about_ca_system_score_codex":0.00026208634,"about_ca_system_score_gemma":0.0003817682,"threshold_uncertainty_score":0.010869443},"labels":[],"label_agreement":null},{"id":"W2367944403","doi":"","title":"The rain drops are measured and get rid of the method to study in the video picture","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Frame (networking); Cluster analysis; Remote sensing; Real-time computing; Computer vision; Meteorology; Artificial intelligence; Telecommunications; Geology","score_opus":0.012177355045995332,"score_gpt":0.29820095901478205,"score_spread":0.28602360396878673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2367944403","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":[0.14041792,0.0026686776,0.8223149,0.0010060412,0.0011435915,0.00043821812,0.0011827726,0.0034979302,0.02732993],"genre_scores_gemma":[0.5737189,0.0038537465,0.39065686,0.0006855539,0.00040290528,0.00039906896,0.0011055835,0.000692917,0.028484492],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948716,0.00003766408,0.000025111873,0.00009913155,0.00031662526,0.000034284894],"domain_scores_gemma":[0.999203,0.00020384241,0.00007634634,0.000102697224,0.00036527088,0.000048824855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004244031,0.00050198776,0.0005269643,0.0015795797,0.0003959381,0.00059728697,0.0006336929,0.0005280476,0.0051844916],"category_scores_gemma":[0.00190111,0.0002822673,0.0003572183,0.00073949614,0.0003727132,0.0016313781,0.00042894998,0.00083153974,0.0021527912],"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":[0.00026393207,0.000105887484,0.011643029,0.00081444473,0.00009389121,0.0003272759,0.0005764988,0.0018057938,0.46418452,0.0039257477,0.008081634,0.50817734],"study_design_scores_gemma":[0.0001132425,0.0009033101,0.090433285,0.00029757625,0.00029455932,0.0025061695,0.0020859407,0.057761967,0.7225465,0.0066539715,0.116180114,0.00022333494],"about_ca_topic_score_codex":0.0016046169,"about_ca_topic_score_gemma":0.0021222744,"teacher_disagreement_score":0.0051844916,"about_ca_system_score_codex":0.0002726616,"about_ca_system_score_gemma":0.0003823083,"threshold_uncertainty_score":0.017343879},"labels":[],"label_agreement":null},{"id":"W2398015170","doi":"10.2352/cic.2013.21.1.art00027","title":"Maximum Entropy Spectral Modeling Approach to Mesopic Tone Mapping","year":2013,"lang":"en","type":"article","venue":"Color and Imaging Conference","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"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":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Mesopic vision; Color rendering index; Tone mapping; Scotopic vision; Computer science; Artificial intelligence; Photopic vision; Smoothing; Computer vision; Mathematics; Optics; Physics; High dynamic range; Dynamic range","score_opus":0.02270678378251497,"score_gpt":0.24959648449722255,"score_spread":0.22688970071470757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398015170","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":[0.005553536,0.0001303426,0.99174577,0.00009166253,0.000013860965,0.000014123985,0.000032219195,0.00012340449,0.0022950342],"genre_scores_gemma":[0.7042465,0.00086873583,0.28298467,0.00016742838,0.00012888049,0.00015225945,0.00021116485,0.00023271992,0.011007525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997842,0.00006703122,0.0000075543226,0.000035927853,0.000090609734,0.000014756703],"domain_scores_gemma":[0.99964476,0.00018570111,0.00003729561,0.00004607014,0.00006800192,0.000018165598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042039284,0.0005511443,0.00040546322,0.0004553825,0.0002684785,0.00067966053,0.0008357052,0.0005973063,0.0018734427],"category_scores_gemma":[0.0011794784,0.00029412817,0.0005860743,0.00033407946,0.00046650151,0.00086102786,0.0006539455,0.0008130411,0.00044535956],"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":[0.00004210764,0.00004094549,0.0004839782,0.000070139824,0.000028797323,0.0001021132,0.00009414378,0.8797918,0.011678539,0.053840797,0.0010711139,0.05275558],"study_design_scores_gemma":[9.132201e-7,0.0000073019264,0.000064119005,0.0000024640206,0.0000023998089,0.000015443427,0.000002768429,0.9928647,0.00057292,0.0060664415,0.0003960282,0.0000044453027],"about_ca_topic_score_codex":0.002310958,"about_ca_topic_score_gemma":0.0017388545,"teacher_disagreement_score":0.002310958,"about_ca_system_score_codex":0.0005414395,"about_ca_system_score_gemma":0.00039576172,"threshold_uncertainty_score":0.0062672496},"labels":[],"label_agreement":null},{"id":"W2461502243","doi":"10.1016/b978-012077790-7/50004-7","title":"Fundamental Enhancement Techniques","year":2000,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science","score_opus":0.012240406866388446,"score_gpt":0.2439509916156011,"score_spread":0.23171058474921266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461502243","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":[0.0057460456,0.014497737,0.6328792,0.0005035578,0.001032897,0.000117232696,0.00028009334,0.003553753,0.34138945],"genre_scores_gemma":[0.041233115,0.01670948,0.25514543,0.0004392142,0.0004603737,0.00013365161,0.00072470336,0.0008172218,0.6843368],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998659,0.0000067760543,0.0000038706344,0.000033323468,0.00007897935,0.000011097588],"domain_scores_gemma":[0.99985766,0.000039450333,0.000007783404,0.000034079483,0.000050678864,0.000010388918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016767993,0.0009745855,0.00042697747,0.0009025404,0.00035703668,0.0009336873,0.0008492141,0.00077787763,0.06368754],"category_scores_gemma":[0.00033562217,0.00038953146,0.00032272746,0.000742626,0.0004141416,0.0014404717,0.00072658755,0.001336525,0.029402439],"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":[0.00004687136,0.00004707851,0.00007136595,0.00041225244,0.0000117713,0.00011133959,0.0001181349,0.0009097392,0.11534059,0.038459107,0.046663858,0.7978078],"study_design_scores_gemma":[0.000016493379,0.000113707756,0.0009218973,0.00024768154,0.000036438763,0.0021405274,0.00007821708,0.012293869,0.119376145,0.026322512,0.8384111,0.000041466847],"about_ca_topic_score_codex":0.00023937329,"about_ca_topic_score_gemma":0.00050733954,"teacher_disagreement_score":0.06368754,"about_ca_system_score_codex":0.00022127878,"about_ca_system_score_gemma":0.00025687244,"threshold_uncertainty_score":0.21305603},"labels":[],"label_agreement":null},{"id":"W2478396075","doi":"10.48550/arxiv.1607.06235","title":"Haze Visibility Enhancement: A Survey and Quantitative Benchmarking","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Enhancement 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":"York University","funders":"","keywords":"Visibility; Benchmark (surveying); Benchmarking; Haze; Ground truth; Computer science; Image (mathematics); Image enhancement; Computer vision; Artificial intelligence; Remote sensing; Optics; Geography; Physics; Cartography","score_opus":0.09210372285924155,"score_gpt":0.2331432360318526,"score_spread":0.14103951317261104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478396075","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":[0.06370571,0.21060507,0.69616336,0.0010664042,0.00062692404,0.0006448895,0.0030391912,0.0062332177,0.01791526],"genre_scores_gemma":[0.31649435,0.12434333,0.539956,0.0006033276,0.0005975188,0.0005136456,0.009910636,0.0025871194,0.004994097],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99480754,0.00093700434,0.00039751668,0.00103887,0.0026312703,0.00018778004],"domain_scores_gemma":[0.98990154,0.0052635926,0.0008808434,0.0013254969,0.002414091,0.000214508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044686133,0.0022696187,0.0016774342,0.007344377,0.0006747562,0.0024889,0.0020524177,0.001766506,0.0017240444],"category_scores_gemma":[0.017175123,0.0007952698,0.0011060955,0.0050287307,0.0011303519,0.0032963161,0.001526106,0.0014840362,0.0011020977],"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":[0.00027657748,0.0003045119,0.0063532917,0.008958377,0.00041563375,0.00010890382,0.00033179155,0.039881665,0.024706418,0.0046052686,0.016464403,0.8975931],"study_design_scores_gemma":[0.00010768877,0.0021326067,0.036781136,0.006401254,0.00088387815,0.0048261583,0.0016664766,0.5252198,0.19536765,0.024236122,0.20178485,0.0005923521],"about_ca_topic_score_codex":0.002499915,"about_ca_topic_score_gemma":0.002444762,"teacher_disagreement_score":0.007344377,"about_ca_system_score_codex":0.00078240054,"about_ca_system_score_gemma":0.0007935098,"threshold_uncertainty_score":0.023632586},"labels":[],"label_agreement":null},{"id":"W2484555229","doi":"10.1109/i2mtc.2016.7520354","title":"A novel perception oriented image color representation","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Color image; Color balance; Color space; Artificial intelligence; Computer vision; Color histogram; Color quantization; Pixel; Computer science; Mathematics; False color; Representation (politics); Image (mathematics); Image processing","score_opus":0.017525831823083055,"score_gpt":0.29065463790109536,"score_spread":0.2731288060780123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2484555229","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":[0.004800892,0.00038525325,0.9892265,0.00023126451,0.00013525905,0.000044593024,0.00014952548,0.00085306127,0.0041736774],"genre_scores_gemma":[0.24383497,0.0015300509,0.7437381,0.00050540984,0.00027668287,0.00022021649,0.00061757176,0.0003457181,0.008931262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996785,0.00004799268,0.000013851371,0.000096667216,0.00012220113,0.00004091313],"domain_scores_gemma":[0.9997557,0.000027620814,0.000023593542,0.00005042509,0.00011971841,0.000022876808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028019468,0.00062358263,0.00050564716,0.0011569186,0.0002756828,0.0014646322,0.0014191145,0.0007733385,0.0031375713],"category_scores_gemma":[0.0008348389,0.00024086943,0.00083058624,0.0013558393,0.0005877628,0.0018423804,0.000899179,0.0011540799,0.0012189292],"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":[0.00026990223,0.000121006735,0.0007657977,0.00043601973,0.000075662574,0.00041760562,0.0002908462,0.07116183,0.1359384,0.2285204,0.015632754,0.5463698],"study_design_scores_gemma":[0.00003279547,0.00013860062,0.0008406765,0.000050324838,0.000059042264,0.0009035181,0.00008769344,0.88025266,0.028352473,0.050677057,0.038532075,0.00007307891],"about_ca_topic_score_codex":0.0016780259,"about_ca_topic_score_gemma":0.00095959555,"teacher_disagreement_score":0.0031375713,"about_ca_system_score_codex":0.0005874075,"about_ca_system_score_gemma":0.0005697134,"threshold_uncertainty_score":0.010496199},"labels":[],"label_agreement":null},{"id":"W2488024148","doi":"10.4018/978-1-61350-153-5.ch004","title":"Wavelet Filters Evaluation in Power Constrained Visual Sensor Networks","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Image Enhancement 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":"Université de Sherbrooke","funders":"","keywords":"Wavelet; Computer science; Wavelet transform; Context (archaeology); Set (abstract data type); Lifting scheme; Artificial intelligence; Filter (signal processing); Computer vision; Wavelet packet decomposition; Real-time computing","score_opus":0.023481482541942874,"score_gpt":0.27360198462462293,"score_spread":0.25012050208268005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2488024148","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":[0.1890072,0.0021456254,0.80015045,0.0001552289,0.0000789691,0.00013706155,0.000096831114,0.00056783104,0.0076607945],"genre_scores_gemma":[0.82943356,0.0019708138,0.16189322,0.00006640879,0.000030032437,0.000093824485,0.00018817879,0.00013314029,0.00619079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929166,0.00016423322,0.000035469795,0.000078134864,0.00039327604,0.00003723752],"domain_scores_gemma":[0.9987148,0.0007345118,0.00010166948,0.000075773205,0.00033902953,0.000034218374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009993574,0.0005844613,0.00047222627,0.0006080525,0.00018871432,0.000990214,0.00060183794,0.00049734244,0.0015287362],"category_scores_gemma":[0.0031950655,0.00017140787,0.00022491634,0.00071162323,0.00027225088,0.0009431922,0.000371602,0.0002705697,0.00029257147],"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":[0.00068617676,0.00016659567,0.002382708,0.0004891996,0.00008568448,0.00027551522,0.00017544457,0.47101608,0.1383686,0.006350226,0.0018871147,0.37811658],"study_design_scores_gemma":[0.000010084924,0.00019551207,0.000945597,0.000030673546,0.000019186687,0.000103607075,0.000048505557,0.95927244,0.03648802,0.0017605798,0.0011137833,0.000011935768],"about_ca_topic_score_codex":0.0015625294,"about_ca_topic_score_gemma":0.001624356,"teacher_disagreement_score":0.0015625294,"about_ca_system_score_codex":0.00076627155,"about_ca_system_score_gemma":0.00031533558,"threshold_uncertainty_score":0.0055597425},"labels":[],"label_agreement":null},{"id":"W2488672052","doi":"10.1117/3.887920.ch4","title":"Enhancement of Color Images","year":2011,"lang":"en","type":"book-chapter","venue":"SPIE eBooks","topic":"Image Enhancement Techniques","field":"Computer Science","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":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer vision; Brightness; Visibility; Computer science; Image quality; Image (mathematics); Contrast (vision); Hue; Image processing; Geography; Optics","score_opus":0.024570282015957626,"score_gpt":0.23964909524424383,"score_spread":0.2150788132282862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2488672052","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":[0.09094875,0.004921857,0.8481837,0.0005787781,0.00070790265,0.0003393938,0.00032312653,0.00331635,0.05068017],"genre_scores_gemma":[0.38873643,0.008018406,0.5656663,0.0011502354,0.0004171663,0.0002190927,0.00054302195,0.0008375506,0.034411807],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996766,0.000045978028,0.00001331117,0.00006355148,0.00014653023,0.000054023363],"domain_scores_gemma":[0.9991391,0.00019745875,0.00007002841,0.00015315037,0.0004073968,0.000032921842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005434976,0.0008828355,0.00042265354,0.0010845135,0.00023874648,0.0012247079,0.0005699299,0.00059281645,0.00743683],"category_scores_gemma":[0.0015799617,0.00027111868,0.0005065005,0.0008846515,0.000456342,0.0010455074,0.000778977,0.00074402615,0.0031791239],"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":[0.0004419033,0.000069487105,0.00044514242,0.0009983672,0.000050779756,0.0004970251,0.00022696894,0.0031287458,0.65282714,0.007456756,0.0060825134,0.32777515],"study_design_scores_gemma":[0.000042651525,0.0005003096,0.0044159153,0.00017600074,0.00015457167,0.0037365202,0.00018739006,0.03398199,0.8650278,0.005831883,0.08586343,0.00008155491],"about_ca_topic_score_codex":0.00020361405,"about_ca_topic_score_gemma":0.00028776104,"teacher_disagreement_score":0.00743683,"about_ca_system_score_codex":0.00023387877,"about_ca_system_score_gemma":0.00017148764,"threshold_uncertainty_score":0.02487874},"labels":[],"label_agreement":null},{"id":"W2502557613","doi":"","title":"Image Enhancement Based on Edge Profile Acutance","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Region of interest; Pixel; Artificial intelligence; Computer vision; Image gradient; Image (mathematics); Measure (data warehouse); Enhanced Data Rates for GSM Evolution; Boundary (topology); Mathematics; Feature (linguistics); Morphological gradient; Edge detection; Image quality; Computer science; Pattern recognition (psychology); Image processing","score_opus":0.008427641815396915,"score_gpt":0.24305554115793973,"score_spread":0.23462789934254283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502557613","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":[0.15461494,0.0016562183,0.83022743,0.0001952731,0.00016886977,0.00013915298,0.00007512791,0.0011733762,0.011749631],"genre_scores_gemma":[0.51132095,0.0018137124,0.474545,0.00021698032,0.00011066091,0.00008746705,0.00014055955,0.00024019068,0.011524401],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998419,0.000025254583,0.00000859717,0.000030401692,0.00007073029,0.000023087707],"domain_scores_gemma":[0.9996382,0.00010424343,0.000036008834,0.000057515623,0.000138328,0.000025726404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023927746,0.00062114885,0.00030444635,0.00067716517,0.00027273412,0.0005563754,0.00037480955,0.0004733902,0.003355722],"category_scores_gemma":[0.00066113676,0.00023092248,0.00034832867,0.0005512615,0.00027441778,0.0008196689,0.0004967549,0.0005785374,0.00066504],"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":[0.0006163544,0.00009495514,0.0008517087,0.00024382603,0.000027991486,0.0003636087,0.00008241007,0.0025279615,0.734594,0.0029679653,0.0008645327,0.2567647],"study_design_scores_gemma":[0.000045176886,0.0004115021,0.004859232,0.00005137763,0.00011570345,0.0026312144,0.000058470312,0.10345105,0.87637097,0.0010508314,0.010912364,0.000042123225],"about_ca_topic_score_codex":0.00027788786,"about_ca_topic_score_gemma":0.00041778685,"teacher_disagreement_score":0.003355722,"about_ca_system_score_codex":0.00012843746,"about_ca_system_score_gemma":0.00020174762,"threshold_uncertainty_score":0.011225998},"labels":[],"label_agreement":null},{"id":"W2508520635","doi":"10.1109/icip.2016.7532487","title":"Objective quality assessment of tone-mapped videos","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"University of Waterloo","funders":"","keywords":"Tone mapping; Naturalness; Computer science; High dynamic range; Tone (literature); Visualization; High fidelity; Fidelity; Perception; Artificial intelligence; Range (aeronautics); Quality (philosophy); Computer vision; Speech recognition; Dynamic range; Engineering","score_opus":0.03137359623902838,"score_gpt":0.3851761088677536,"score_spread":0.35380251262872525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508520635","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":[0.44083688,0.0013880474,0.55293053,0.00011258694,0.00008181927,0.00033045321,0.00066535256,0.00062655675,0.003027836],"genre_scores_gemma":[0.904941,0.0011054473,0.09023762,0.00006995816,0.00008520775,0.000118018426,0.0006161816,0.00013941423,0.0026871504],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921906,0.0001782104,0.000050797124,0.0001374858,0.00037737936,0.000037068716],"domain_scores_gemma":[0.9970198,0.0009514413,0.000563101,0.00018848808,0.0011568718,0.00012020343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013048111,0.0006218585,0.0003860152,0.0010319508,0.00012985476,0.0009011852,0.0003998175,0.000456468,0.0018543411],"category_scores_gemma":[0.006342569,0.00015818412,0.0002997289,0.00039944323,0.0003008859,0.0007916367,0.0005733955,0.00028948137,0.00036258783],"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":[0.0018605002,0.0003764464,0.030704688,0.001748651,0.0003145906,0.0004968564,0.0008168733,0.058211666,0.423141,0.0024349715,0.0023527753,0.4775409],"study_design_scores_gemma":[0.00012605259,0.002271798,0.1246191,0.00022897929,0.00030883576,0.00168944,0.0005561467,0.6756801,0.18664497,0.0029314396,0.0047253543,0.0002177983],"about_ca_topic_score_codex":0.0010504184,"about_ca_topic_score_gemma":0.00097367313,"teacher_disagreement_score":0.0018543411,"about_ca_system_score_codex":0.00023151639,"about_ca_system_score_gemma":0.00017107313,"threshold_uncertainty_score":0.006900549},"labels":[],"label_agreement":null},{"id":"W2511484517","doi":"10.1109/iscas.2016.7539021","title":"Hardware implementation of a real-time tone mapping algorithm based on a mantissa-exponent representation","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Tone mapping; Exponent; Algorithm; Computer science; Pixel; Field-programmable gate array; Data compression; Representation (politics); Tone (literature); Range (aeronautics); Process (computing); Reset (finance); High dynamic range; Dynamic range; Artificial intelligence; Computer vision; Computer hardware; Engineering","score_opus":0.020033547944678983,"score_gpt":0.31797052972711953,"score_spread":0.29793698178244055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511484517","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":[0.039573763,0.0004258917,0.95085096,0.00009393895,0.00014032442,0.000167149,0.000064985885,0.004539209,0.004143692],"genre_scores_gemma":[0.3114933,0.0002872545,0.6821222,0.00010370612,0.000073369265,0.000105073574,0.0001734273,0.00017965301,0.0054620947],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983525,0.000022366256,0.000018376271,0.000040706524,0.00006372666,0.000019563136],"domain_scores_gemma":[0.9997037,0.000095067786,0.000034443063,0.00006204991,0.00008825416,0.000016395621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022913437,0.00051621714,0.00027594576,0.000530037,0.00020139571,0.0006206275,0.0009630544,0.00032080282,0.0058098095],"category_scores_gemma":[0.0006806533,0.0002068859,0.00020932211,0.00028193567,0.0001757942,0.0005392141,0.00021454626,0.00037912442,0.0016472286],"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":[0.0004888068,0.00012789443,0.0010112282,0.0003098189,0.00006332393,0.00044507513,0.00014584872,0.0067609265,0.5003454,0.0065488573,0.0025311958,0.48122162],"study_design_scores_gemma":[0.00020990607,0.0015171475,0.003049111,0.00007405056,0.00013423,0.0038926508,0.000077448385,0.25631997,0.68900657,0.0016398162,0.04397528,0.00010384149],"about_ca_topic_score_codex":0.00035689576,"about_ca_topic_score_gemma":0.000490139,"teacher_disagreement_score":0.0058098095,"about_ca_system_score_codex":0.00020699462,"about_ca_system_score_gemma":0.0002698967,"threshold_uncertainty_score":0.019435763},"labels":[],"label_agreement":null},{"id":"W2522349080","doi":"10.1007/s11554-016-0635-6","title":"An FPGA implementation of a tone mapping algorithm with a halo-reducing filter","year":2016,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates - Technology Futures","keywords":"Tone mapping; Computer science; Field-programmable gate array; Algorithm; Verilog; Brightness; High dynamic range; Rendering (computer graphics); Filter (signal processing); Dynamic range; Computer vision; Computer hardware","score_opus":0.010970598467660875,"score_gpt":0.30900170278814826,"score_spread":0.2980311043204874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522349080","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":[0.15786868,0.0009286634,0.8026745,0.00030413552,0.00054282014,0.00029788632,0.00021589424,0.010000806,0.027166657],"genre_scores_gemma":[0.5170924,0.00031231294,0.46816498,0.0002467133,0.00008110726,0.00009370907,0.00023447062,0.00022525649,0.01354907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998964,0.000013888268,0.0000058076075,0.000018637162,0.000042568397,0.000022705697],"domain_scores_gemma":[0.9998839,0.00003360411,0.000008912451,0.00002073955,0.000039017494,0.000013892492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011395177,0.0004948204,0.0002607534,0.0004407365,0.00030469723,0.0005725823,0.0005892237,0.00035524045,0.009594547],"category_scores_gemma":[0.00025198245,0.00019047342,0.00021109892,0.00032531982,0.00010797015,0.00025890462,0.00017154962,0.0002694866,0.0017713787],"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":[0.0009936178,0.00023107934,0.0013703043,0.00032980472,0.00013341197,0.0007586044,0.00014127226,0.011667551,0.47968316,0.0062655006,0.0075410157,0.49088472],"study_design_scores_gemma":[0.00049514894,0.0019030898,0.006757687,0.0001015457,0.00022027637,0.0031469034,0.00012316313,0.31265488,0.61633193,0.0018620843,0.056304116,0.00009919746],"about_ca_topic_score_codex":0.0017198365,"about_ca_topic_score_gemma":0.0027233555,"teacher_disagreement_score":0.009594547,"about_ca_system_score_codex":0.00027171645,"about_ca_system_score_gemma":0.00038809294,"threshold_uncertainty_score":0.032096982},"labels":[],"label_agreement":null},{"id":"W2525169039","doi":"10.1109/dmiaf.2016.7574892","title":"Perception-based Histogram Equalization for tone mapping applications","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tone mapping; High dynamic range; Computer science; Histogram equalization; Luminance; High-dynamic-range imaging; Computer vision; Histogram; Dynamic range; Brightness; Artificial intelligence; Backward compatibility; Noise (video); Gamma correction; Image (mathematics)","score_opus":0.02593642413928434,"score_gpt":0.31035082324902774,"score_spread":0.2844143991097434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525169039","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":[0.024817007,0.00071135955,0.97035193,0.00007333483,0.00007280876,0.000058587295,0.000053716285,0.0012180662,0.0026433114],"genre_scores_gemma":[0.57984245,0.0010595212,0.41280225,0.00014909878,0.00010551101,0.00007373868,0.00022879611,0.00019565705,0.005542987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998012,0.00003071489,0.000010232241,0.000041041534,0.000091999056,0.000024815725],"domain_scores_gemma":[0.9997156,0.00011563814,0.00002111566,0.000047766058,0.00008533903,0.000014549068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022490218,0.0003054217,0.000270676,0.00035783337,0.00014211534,0.00046167406,0.00044083144,0.00027493184,0.003901142],"category_scores_gemma":[0.000790062,0.00012408613,0.00021874621,0.00039607423,0.0002525686,0.000580974,0.00042468414,0.00045269867,0.0008110915],"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":[0.0003960028,0.000115038434,0.0006998049,0.0002330316,0.000033709497,0.00009428113,0.00007717844,0.014852088,0.31231037,0.0047348705,0.0022482988,0.6642054],"study_design_scores_gemma":[0.000048673526,0.00039484046,0.004401923,0.00003893243,0.0000624742,0.000595418,0.000077675846,0.6072384,0.36163303,0.0063079144,0.0191433,0.00005744262],"about_ca_topic_score_codex":0.0006681513,"about_ca_topic_score_gemma":0.0008117564,"teacher_disagreement_score":0.003901142,"about_ca_system_score_codex":0.00019435208,"about_ca_system_score_gemma":0.00023744434,"threshold_uncertainty_score":0.013050675},"labels":[],"label_agreement":null},{"id":"W2526765766","doi":"10.1109/icmew.2016.7574752","title":"Rain removal via shrinkage of sparse codes and learned rain dictionary","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":31,"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":"Computer science; Image (mathematics); Enhanced Data Rates for GSM Evolution; Feature (linguistics); Neural coding; Shrinkage; Artificial intelligence; Pattern recognition (psychology); Algorithm; Machine learning","score_opus":0.01714515695603149,"score_gpt":0.2553179014091655,"score_spread":0.238172744453134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526765766","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":[0.028037023,0.00016038286,0.9706336,0.000072287534,0.000033687462,0.000020578873,0.00004611746,0.00029502442,0.0007012247],"genre_scores_gemma":[0.3730147,0.0006441251,0.62179506,0.00018003628,0.000100594894,0.00007783237,0.00048189217,0.0001417497,0.0035640954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963653,0.000051820603,0.000020454156,0.00007267011,0.00018382286,0.00003457861],"domain_scores_gemma":[0.9992507,0.0002640683,0.00010276405,0.00013318035,0.00021379684,0.000035369707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041330783,0.0005392799,0.0006596329,0.0007163631,0.00019790887,0.0003936827,0.0004971452,0.0005102073,0.00070735713],"category_scores_gemma":[0.0017723223,0.00030062496,0.000580444,0.000694561,0.00046656758,0.0008947234,0.00092820136,0.00082529295,0.00032384132],"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":[0.00043583682,0.00011004705,0.0021616782,0.00029541805,0.00012568031,0.00039742078,0.0002789511,0.24383445,0.2560644,0.0118770115,0.0030223313,0.48139685],"study_design_scores_gemma":[0.000020100708,0.000081582046,0.00096385233,0.000010457031,0.000028685772,0.0002544643,0.000029919309,0.9452464,0.04779883,0.0029177703,0.0026241972,0.00002380462],"about_ca_topic_score_codex":0.0014743346,"about_ca_topic_score_gemma":0.0016410266,"teacher_disagreement_score":0.0014743346,"about_ca_system_score_codex":0.00021998315,"about_ca_system_score_gemma":0.00044260098,"threshold_uncertainty_score":0.0029314756},"labels":[],"label_agreement":null},{"id":"W2548991416","doi":"10.1109/ccece.2016.7726650","title":"A platform for subjective image quality evaluation on mobile devices","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Mobile device; Quality (philosophy); Point (geometry); Multimedia; Interface (matter); Image quality; Human–computer interaction; Electronics; Mobile apps; Image (mathematics); Computer vision; Engineering; World Wide Web","score_opus":0.0541719438200548,"score_gpt":0.3815285703153809,"score_spread":0.3273566264953261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548991416","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":[0.17630483,0.00087067083,0.7282597,0.00022626134,0.00033851244,0.007735157,0.0043208646,0.06492417,0.017019833],"genre_scores_gemma":[0.3638274,0.00062759104,0.59224445,0.00035865384,0.00018825129,0.0075925677,0.005268281,0.003892577,0.026000341],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988539,0.00023661983,0.00008591807,0.00012998046,0.0006260937,0.000067409186],"domain_scores_gemma":[0.9970932,0.0010144407,0.0002371998,0.00042364065,0.0009363415,0.00029505667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001905731,0.0013009897,0.00102037,0.002250502,0.0003891803,0.0008267424,0.0010330899,0.00071495917,0.019394072],"category_scores_gemma":[0.0029754045,0.00047066194,0.00062517804,0.00057109003,0.0003354347,0.00086927915,0.001665418,0.000544761,0.0049661854],"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":[0.0040448983,0.0017374194,0.006624611,0.0017910373,0.00021772322,0.0015004046,0.00094972516,0.0029509633,0.47892544,0.0032111881,0.025715802,0.47233072],"study_design_scores_gemma":[0.0019656327,0.013528751,0.14017603,0.001388806,0.0006297995,0.008069051,0.0010292197,0.21881147,0.46355793,0.008654556,0.14105392,0.0011348843],"about_ca_topic_score_codex":0.00046860497,"about_ca_topic_score_gemma":0.0007146915,"teacher_disagreement_score":0.019394072,"about_ca_system_score_codex":0.00019999631,"about_ca_system_score_gemma":0.0003739134,"threshold_uncertainty_score":0.064879656},"labels":[],"label_agreement":null},{"id":"W2558326025","doi":"10.1109/cec.2016.7744278","title":"Automatic blended tone mapping through evolutionary optimization","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Dalhousie University","funders":"","keywords":"Tone mapping; Computer science; Tone (literature); High dynamic range; Brightness; Operator (biology); Process (computing); High-dynamic-range imaging; Artificial intelligence; Range (aeronautics); Computer vision; Optimization problem; Evolutionary algorithm; Quality (philosophy); Dynamic range; Algorithm; Engineering","score_opus":0.017408555042144388,"score_gpt":0.2669348199992565,"score_spread":0.2495262649571121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558326025","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":[0.05260621,0.00011756402,0.943127,0.00007294734,0.000021044523,0.00006403078,0.000015133711,0.0004939101,0.003482176],"genre_scores_gemma":[0.48572257,0.00013596096,0.5105953,0.00009068622,0.000016728582,0.00016398804,0.00006681695,0.0002041964,0.0030036876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974936,0.00005408741,0.000010936698,0.000053304495,0.000097927965,0.000034300956],"domain_scores_gemma":[0.9995461,0.000245962,0.00005163522,0.00004039863,0.00008882399,0.000027156084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006497434,0.00079186575,0.00065375044,0.0007480137,0.00039746007,0.0008216313,0.0007782988,0.00080114487,0.0016491801],"category_scores_gemma":[0.0019542156,0.00041864702,0.0005503621,0.00047603535,0.0005221467,0.0006728676,0.0009393248,0.00063301617,0.00029525705],"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":[0.00008678223,0.00011534193,0.0010937672,0.000076679615,0.000048886846,0.0001245848,0.0001600729,0.78256834,0.034178223,0.0064545167,0.00084461604,0.1742482],"study_design_scores_gemma":[0.000010105122,0.000027252468,0.00013363062,0.0000039826346,0.000007575868,0.000024365669,0.000013490694,0.995902,0.0020248338,0.0014184678,0.0004293897,0.0000049541813],"about_ca_topic_score_codex":0.001442184,"about_ca_topic_score_gemma":0.0016147055,"teacher_disagreement_score":0.0016491801,"about_ca_system_score_codex":0.0004782193,"about_ca_system_score_gemma":0.00046373782,"threshold_uncertainty_score":0.005517006},"labels":[],"label_agreement":null},{"id":"W2564997479","doi":"10.1109/icip.2016.7533126","title":"A fusion-based method for single backlit image enhancement","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":66,"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":"Backlight; Visibility; Computer science; Computer vision; Artificial intelligence; Histogram; Fusion; Image enhancement; Contrast (vision); Focus (optics); Image (mathematics); Optics; Liquid-crystal display","score_opus":0.018896579563350432,"score_gpt":0.3004797838235976,"score_spread":0.28158320426024713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564997479","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":[0.014642564,0.0011444014,0.9812593,0.000079600395,0.000113946764,0.00007001663,0.000028186081,0.00068356586,0.001978451],"genre_scores_gemma":[0.24352469,0.0016109609,0.74806386,0.0001765959,0.00011747657,0.00009300636,0.00013988737,0.00013711264,0.0061363042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957436,0.000036161044,0.000023480003,0.00009579097,0.00023339277,0.000036760426],"domain_scores_gemma":[0.99974185,0.000044409873,0.000029925535,0.000044382265,0.00012153118,0.000017890361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038072947,0.0006871354,0.0007185558,0.0012155934,0.00033649136,0.0006713889,0.00086503755,0.0007061589,0.0019526259],"category_scores_gemma":[0.00049911597,0.00037462593,0.00080366957,0.00061545806,0.00040773535,0.0015156694,0.0009008088,0.0007655257,0.00077006716],"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":[0.00028388668,0.00010374834,0.0004346054,0.0004045063,0.000111678724,0.00021520622,0.00016798811,0.008605294,0.44005752,0.0046353247,0.002018358,0.5429619],"study_design_scores_gemma":[0.0000642991,0.00045295435,0.0022626389,0.00006284389,0.00023585458,0.002184796,0.00008067621,0.5225906,0.4474352,0.0035832645,0.02093196,0.00011488732],"about_ca_topic_score_codex":0.0006658102,"about_ca_topic_score_gemma":0.0007688484,"teacher_disagreement_score":0.0019526259,"about_ca_system_score_codex":0.00030174683,"about_ca_system_score_gemma":0.00032550783,"threshold_uncertainty_score":0.0065321326},"labels":[],"label_agreement":null},{"id":"W2569534636","doi":"10.1145/3072959.2990495","title":"Antialiasing Complex Global Illumination Effects in Path-Space","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":23,"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; Université de Montréal","funders":"National Science Foundation","keywords":"Path (computing); Global illumination; Computer science; Radiance; Context (archaeology); Filter (signal processing); Computer vision; Fourier transform; Algorithm; Space (punctuation); Artificial intelligence; Topology (electrical circuits); Mathematics; Optics; Physics; Mathematical analysis; Geology","score_opus":0.026837618802682154,"score_gpt":0.30686319713513616,"score_spread":0.280025578332454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2569534636","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":[0.013755048,0.000040411116,0.9843828,0.000026249412,0.000016902102,0.000012216672,0.000023332888,0.00079899636,0.00094402273],"genre_scores_gemma":[0.30877295,0.00025287416,0.68461895,0.00007998754,0.00003649796,0.000055925047,0.00019069258,0.0011667041,0.0048254193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984086,0.000020757969,0.0000037679326,0.000026511085,0.000086925174,0.000021095217],"domain_scores_gemma":[0.99955505,0.00016110066,0.000058168338,0.000110248606,0.000092263945,0.00002315993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002861728,0.00078887906,0.0003706918,0.0004226632,0.0002349189,0.00094233174,0.0006632275,0.0004507255,0.0026873425],"category_scores_gemma":[0.0013213985,0.00032451164,0.00057618297,0.00029205202,0.0005002565,0.0010623306,0.00075553986,0.0010630307,0.0008340839],"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":[0.00016595708,0.00009566874,0.0021911091,0.00016025596,0.00006516381,0.00021215588,0.00039301658,0.48090142,0.14392334,0.05282724,0.004025426,0.31503916],"study_design_scores_gemma":[0.0000068749055,0.000028036522,0.00042814057,0.000008096687,0.000010828522,0.00012137952,0.0000248225,0.96289814,0.024303712,0.007829559,0.004324867,0.00001558748],"about_ca_topic_score_codex":0.0021759558,"about_ca_topic_score_gemma":0.0034972846,"teacher_disagreement_score":0.0026873425,"about_ca_system_score_codex":0.0004481816,"about_ca_system_score_gemma":0.00064643024,"threshold_uncertainty_score":0.008990049},"labels":[],"label_agreement":null},{"id":"W2571871059","doi":"10.1109/ism.2016.0031","title":"Feedback Control System for Exposure Optimization in High-Dynamic-Range Multimedia Sensing","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"","keywords":"Compositing; Computer science; High dynamic range; Dynamic range; Range (aeronautics); Wide dynamic range; Metric (unit); Process (computing); Computer vision; Real-time computing; Image (mathematics); Engineering","score_opus":0.0065666867658139925,"score_gpt":0.2191254666972003,"score_spread":0.2125587799313863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571871059","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":[0.015095689,0.0001292884,0.98227155,0.000063187894,0.000039142877,0.00003090925,0.000009741194,0.00038705138,0.001973419],"genre_scores_gemma":[0.92036897,0.00011034255,0.07636138,0.000105688785,0.000041306543,0.00012373878,0.00002228289,0.000040858413,0.0028255386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995838,0.00006892442,0.000018056802,0.00014476906,0.00015059029,0.000033756878],"domain_scores_gemma":[0.99961495,0.00015383784,0.00006634744,0.000034245688,0.00010800025,0.000022580125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052649464,0.00055992807,0.00037896994,0.00019105256,0.0003636641,0.0005525906,0.0007692065,0.00039044267,0.0019355881],"category_scores_gemma":[0.0011941907,0.00021770607,0.00025501178,0.00014280964,0.00049403025,0.0005016654,0.00084312743,0.0006140147,0.0002868374],"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":[0.00046240853,0.00025919074,0.0012080285,0.0003538723,0.00008743388,0.0002948241,0.00054650655,0.4274966,0.25426903,0.02334174,0.0023018005,0.28937852],"study_design_scores_gemma":[0.00003671143,0.00029048877,0.00067266624,0.000015384025,0.000022760563,0.00008374362,0.000020764088,0.96921855,0.024043376,0.0030891097,0.0024788505,0.00002760871],"about_ca_topic_score_codex":0.0015046921,"about_ca_topic_score_gemma":0.0015325529,"teacher_disagreement_score":0.0019355881,"about_ca_system_score_codex":0.0005209471,"about_ca_system_score_gemma":0.00031986437,"threshold_uncertainty_score":0.00647521},"labels":[],"label_agreement":null},{"id":"W2573180828","doi":"10.2312/vmv.20161349","title":"Matting with Sequential Pair Selection Using Graph Transduction","year":2016,"lang":"en","type":"article","venue":"Eurographics","topic":"Image Enhancement Techniques","field":"Computer Science","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":"University of Ottawa","funders":"","keywords":"Pixel; Alpha (finance); Artificial intelligence; Benchmark (surveying); Computer science; Opacity; Sampling (signal processing); Graph; Pattern recognition (psychology); Computer vision; Image (mathematics); Object (grammar); Selection (genetic algorithm); Mathematics; Statistics; Theoretical computer science","score_opus":0.0239615852183684,"score_gpt":0.2542826501765405,"score_spread":0.23032106495817212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573180828","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":[0.02184823,0.00008578234,0.9739214,0.000084410785,0.000034921177,0.000078556615,0.0000541089,0.0020799597,0.0018126278],"genre_scores_gemma":[0.40902478,0.0001269445,0.583216,0.00019463366,0.00006465087,0.00016953809,0.00038194374,0.0005596334,0.0062618223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929154,0.00020710142,0.00002323199,0.00019249247,0.0002204106,0.00006518183],"domain_scores_gemma":[0.99928087,0.0002994226,0.00005653808,0.00019976952,0.000107812295,0.00005554318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006800616,0.0010513599,0.00086941343,0.0010060268,0.000487885,0.0007205004,0.0011489957,0.00086894963,0.0056209],"category_scores_gemma":[0.0015535081,0.00036701703,0.0009424844,0.000995312,0.00074578094,0.001329508,0.0014109719,0.0009527353,0.0014947548],"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":[0.0003112605,0.00038425886,0.00090380403,0.000228678,0.00010064548,0.00039988963,0.00024731742,0.24437115,0.0842952,0.030282265,0.007350962,0.6311246],"study_design_scores_gemma":[0.000028616065,0.0002121905,0.0002793211,0.0000071106747,0.000021181493,0.00019220046,0.000043731226,0.9495479,0.02229508,0.024136664,0.0032199603,0.00001603964],"about_ca_topic_score_codex":0.0010880786,"about_ca_topic_score_gemma":0.0014971374,"teacher_disagreement_score":0.0056209,"about_ca_system_score_codex":0.00045509767,"about_ca_system_score_gemma":0.0005303483,"threshold_uncertainty_score":0.018803775},"labels":[],"label_agreement":null},{"id":"W2580777197","doi":"10.2352/issn.2470-1173.2016.12.imse-263","title":"Novel Real-Time Tone-Mapping Operator for Noisy Logarithmic CMOS Image Sensors","year":2016,"lang":"en","type":"article","venue":"Electronic Imaging","topic":"Image Enhancement 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":"CMC Microsystems","keywords":"Tone mapping; Fixed-pattern noise; Computer science; Computer vision; Noise (video); Artificial intelligence; Logarithm; Image sensor; Histogram; CMOS; Distortion (music); Dynamic range; Operator (biology); High dynamic range; Image (mathematics); Electronic engineering; Mathematics; Engineering","score_opus":0.006939548437493282,"score_gpt":0.25950609834150573,"score_spread":0.25256654990401245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580777197","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":[0.03152716,0.00016047337,0.96629035,0.00006475454,0.000055181397,0.000046281646,0.000021486254,0.0005251669,0.0013090767],"genre_scores_gemma":[0.49218875,0.00022450031,0.5027553,0.00013238599,0.00006124429,0.000069646514,0.000046669134,0.00009390374,0.0044275215],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976045,0.000036152465,0.000012791609,0.000058108315,0.00011746803,0.000015050367],"domain_scores_gemma":[0.999556,0.00019602089,0.000060167524,0.000058709993,0.000102307706,0.00002665852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002663458,0.0003056973,0.00016476134,0.00015055736,0.00016540845,0.00041166515,0.0006124863,0.00027418582,0.0017834487],"category_scores_gemma":[0.001234799,0.00011644181,0.00017676233,0.00014999509,0.0003492919,0.0005562092,0.00041192136,0.00032776737,0.00025932927],"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":[0.00035001768,0.000049181795,0.00059750676,0.00014386227,0.000015398977,0.00024454674,0.00023118798,0.0059783706,0.7781273,0.0071730493,0.001237492,0.20585209],"study_design_scores_gemma":[0.000049318678,0.0007674701,0.0018992008,0.000027900845,0.0000405174,0.002168333,0.00009354596,0.29246527,0.6781637,0.0037411714,0.020516716,0.00006678814],"about_ca_topic_score_codex":0.00024365274,"about_ca_topic_score_gemma":0.00032362217,"teacher_disagreement_score":0.0017834487,"about_ca_system_score_codex":0.00020862534,"about_ca_system_score_gemma":0.00017897114,"threshold_uncertainty_score":0.005966246},"labels":[],"label_agreement":null},{"id":"W2584629362","doi":"10.1109/icecs.2016.7841297","title":"Evaluation of chroma subsampling for high dynamic range video compression","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"Telus (Canada); University of British Columbia","funders":"","keywords":"Gamut; Computer science; Upsampling; Codec; High dynamic range; Data compression; Computer vision; Artificial intelligence; Coding (social sciences); Range (aeronautics); Pipeline transport; Dynamic range; Computer graphics (images); Computer hardware; Mathematics; Statistics","score_opus":0.038795911545603655,"score_gpt":0.32859451771035997,"score_spread":0.28979860616475633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584629362","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":[0.94009006,0.0014604308,0.054181926,0.00008308774,0.000051730272,0.00008946317,0.00014250341,0.00054104836,0.0033596554],"genre_scores_gemma":[0.9615664,0.0005349115,0.0363837,0.000028645407,0.00001850448,0.000020269405,0.00020041564,0.00006452548,0.0011826751],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951684,0.00008357059,0.000025821595,0.000050710456,0.0002727914,0.000050234223],"domain_scores_gemma":[0.9982364,0.0009905061,0.00013125133,0.00015076641,0.0004220905,0.00006896826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007946278,0.0004482246,0.00027156778,0.0007355718,0.00018803573,0.0004874908,0.0003701949,0.00035793145,0.0015279069],"category_scores_gemma":[0.0032659231,0.000103639926,0.00022640394,0.0005505135,0.00025794443,0.00048430634,0.0002072075,0.00018606124,0.00018975469],"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":[0.0059090075,0.00079272926,0.0074947276,0.00048401227,0.00017639354,0.00040284076,0.00014167809,0.06719892,0.61876404,0.0013847288,0.0007964806,0.29645446],"study_design_scores_gemma":[0.0001477508,0.004606945,0.036900878,0.000046227375,0.00022395869,0.0007705371,0.00012337956,0.3288686,0.625348,0.00039927277,0.0025007545,0.00006373017],"about_ca_topic_score_codex":0.0022395113,"about_ca_topic_score_gemma":0.0018459231,"teacher_disagreement_score":0.0022395113,"about_ca_system_score_codex":0.00032924625,"about_ca_system_score_gemma":0.00023394234,"threshold_uncertainty_score":0.0051113367},"labels":[],"label_agreement":null},{"id":"W2588581370","doi":"10.1109/tcsii.2017.2669866","title":"Iterative Graph-Based Filtering for Image Abstraction and Stylization","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":25,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Graph; Artificial intelligence; Iterative method; Computer vision; Filter (signal processing); Pattern recognition (psychology); Algorithm; Theoretical computer science","score_opus":0.025571565527947284,"score_gpt":0.2780490045367101,"score_spread":0.25247743900876285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588581370","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":[0.004234027,0.0001434865,0.99452126,0.000035539255,0.00002133719,0.00002037827,0.000013207979,0.00034158913,0.00066925364],"genre_scores_gemma":[0.16504614,0.0006936828,0.8298003,0.00011498287,0.00006336255,0.000059350103,0.00012749236,0.00017868538,0.0039160443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996542,0.000052495012,0.0000176545,0.00007420697,0.00016015946,0.000041306495],"domain_scores_gemma":[0.99950457,0.000169547,0.000056565837,0.00013829261,0.000107272695,0.000023702529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000411259,0.00080545095,0.0006666566,0.000954097,0.0003604428,0.0009217159,0.00086364325,0.00071505597,0.002435436],"category_scores_gemma":[0.0012602882,0.00033865595,0.0009936307,0.00083779416,0.00072332256,0.0013030266,0.0007683142,0.0010080502,0.00084016414],"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":[0.00020101135,0.000068887464,0.0005918155,0.00033639366,0.00010125309,0.00028641534,0.00033934126,0.15180874,0.24734184,0.04533608,0.0037530647,0.5498352],"study_design_scores_gemma":[0.000013526195,0.00008812107,0.00043033966,0.000014520335,0.0000352917,0.00020898985,0.000038102047,0.9187539,0.060631577,0.012444071,0.007309086,0.000032529762],"about_ca_topic_score_codex":0.001917165,"about_ca_topic_score_gemma":0.0023714234,"teacher_disagreement_score":0.002435436,"about_ca_system_score_codex":0.0005461822,"about_ca_system_score_gemma":0.00049992686,"threshold_uncertainty_score":0.008147359},"labels":[],"label_agreement":null},{"id":"W2596107950","doi":"","title":"HDR Video Compression Using High Efficiency Video Coding (HEVC)","year":2012,"lang":"en","type":"article","venue":"Ubiquitous Computing Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multiview Video Coding; Computer science; Video compression picture types; Data compression; Context-adaptive binary arithmetic coding; Coding (social sciences); Coding tree unit; Computer vision; Video processing; Video tracking; Decoding methods; Algorithm; Mathematics","score_opus":0.026211923078675207,"score_gpt":0.2798143065770063,"score_spread":0.2536023834983311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596107950","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":[0.1413577,0.0024529204,0.8172158,0.00041609444,0.00047629687,0.00041869542,0.00059116964,0.003613823,0.033457518],"genre_scores_gemma":[0.58529186,0.0024220322,0.3720434,0.00037652015,0.00019253526,0.00011462571,0.0009842468,0.00025784588,0.038316946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974614,0.000027397557,0.000012412381,0.000030482852,0.00015184264,0.000031771782],"domain_scores_gemma":[0.9996784,0.000060059254,0.000019389518,0.00007230269,0.00015780058,0.000012087197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002837037,0.00033959027,0.00020569828,0.0009768598,0.0002065162,0.00052689627,0.00027897305,0.00044098042,0.003297974],"category_scores_gemma":[0.00078457774,0.00008547063,0.00017935585,0.00075361744,0.00020647935,0.00041544726,0.00027166517,0.00046568294,0.0010591778],"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":[0.0002633796,0.00016238618,0.0011100023,0.00019491246,0.000035143094,0.00027962675,0.00007754546,0.0059055057,0.38699618,0.0073382556,0.0051147635,0.5925223],"study_design_scores_gemma":[0.000054820448,0.000287745,0.007854771,0.000098025965,0.000065882734,0.0012142241,0.00008096368,0.13616942,0.8217017,0.0014647709,0.03096115,0.000046523513],"about_ca_topic_score_codex":0.0037939728,"about_ca_topic_score_gemma":0.004945596,"teacher_disagreement_score":0.0037939728,"about_ca_system_score_codex":0.00026264592,"about_ca_system_score_gemma":0.00033524114,"threshold_uncertainty_score":0.011032879},"labels":[],"label_agreement":null},{"id":"W2602101963","doi":"10.1016/j.isprsjprs.2017.02.016","title":"Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency","year":2017,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Technology Futures","keywords":"Color constancy; Visibility; Contrast (vision); Artificial intelligence; Computer vision; Computer science; Histogram; Consistency (knowledge bases); Generality; Representation (politics); Scale (ratio); Truncation (statistics); High dynamic range; Mathematics; Image (mathematics); Dynamic range; Machine learning","score_opus":0.026021668995040728,"score_gpt":0.3312813776359922,"score_spread":0.3052597086409515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602101963","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":[0.16061525,0.00033510104,0.83321196,0.00013709802,0.000046655223,0.000056388577,0.00009696687,0.00054048974,0.0049601486],"genre_scores_gemma":[0.5250303,0.00049407093,0.46958905,0.00005776908,0.000034237328,0.000041575826,0.00019543325,0.00018541828,0.004372057],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998584,0.000027891489,0.0000075316543,0.000017678496,0.00006709453,0.000021461028],"domain_scores_gemma":[0.999665,0.00011936943,0.000037361762,0.00007037862,0.00009234408,0.000015504469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002985277,0.00042751734,0.0003308421,0.00048528437,0.0002004694,0.00070812635,0.00036824087,0.00027066696,0.001956935],"category_scores_gemma":[0.00080880045,0.0001899125,0.000376189,0.0004281803,0.00028805446,0.0006696449,0.0005870865,0.00046404317,0.00035966208],"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":[0.00054123125,0.00014790372,0.0010535095,0.00026559032,0.000051835876,0.00014498977,0.00019248419,0.07976042,0.5680086,0.011349822,0.0011854249,0.33729818],"study_design_scores_gemma":[0.000050242717,0.00014669028,0.00316445,0.000037206835,0.00007434192,0.00034298454,0.000065672306,0.7220877,0.2664684,0.0032647168,0.004258038,0.000039682236],"about_ca_topic_score_codex":0.0011235409,"about_ca_topic_score_gemma":0.001977855,"teacher_disagreement_score":0.001956935,"about_ca_system_score_codex":0.0002839632,"about_ca_system_score_gemma":0.00038652594,"threshold_uncertainty_score":0.0065466166},"labels":[],"label_agreement":null},{"id":"W2605060370","doi":"10.1145/3130800.3130891","title":"Learning to predict indoor illumination from a single image","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":332,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Artificial intelligence; Computer vision; High dynamic range; Classifier (UML); High-dynamic-range imaging; Artificial neural network; Light field; Deep learning; Range (aeronautics); Image-based lighting; Global illumination; Deep neural networks; Field (mathematics); Computer graphics (images); Image (mathematics); Image processing; Dynamic range; Rendering (computer graphics); Mathematics","score_opus":0.0204952666595602,"score_gpt":0.27084924516339537,"score_spread":0.2503539785038352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605060370","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":[0.0349725,0.00012436132,0.9580426,0.000072297946,0.000042840995,0.000037211033,0.00030984115,0.004462145,0.0019362017],"genre_scores_gemma":[0.5041875,0.00036527935,0.48816243,0.0002071103,0.00008264294,0.00009914864,0.0016281153,0.0005036158,0.0047641136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997626,0.000020435366,0.0000052169576,0.00011483361,0.0000657016,0.000031201544],"domain_scores_gemma":[0.99975723,0.000044297405,0.000033216384,0.000070970906,0.00007609639,0.000018198527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023105572,0.00087940844,0.0005804512,0.00068021036,0.00017698226,0.00061189936,0.0009437137,0.00043594048,0.0021788348],"category_scores_gemma":[0.0008762705,0.0005248652,0.0006420173,0.00041656636,0.00030099033,0.0009517563,0.00071304984,0.0010968309,0.0015523317],"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":[0.00027847572,0.00027557753,0.005163557,0.00014685796,0.00014419475,0.000114029266,0.00010640684,0.254634,0.10706175,0.0019974366,0.0070018205,0.62307596],"study_design_scores_gemma":[0.0000082886245,0.00003491439,0.0021161677,0.000011046056,0.000016843673,0.00006456896,0.000014317471,0.973435,0.021545608,0.00122199,0.0015178185,0.000013468759],"about_ca_topic_score_codex":0.0033557012,"about_ca_topic_score_gemma":0.009652897,"teacher_disagreement_score":0.0033557012,"about_ca_system_score_codex":0.00048198726,"about_ca_system_score_gemma":0.00052614295,"threshold_uncertainty_score":0.0072889924},"labels":[],"label_agreement":null},{"id":"W2606720601","doi":"10.1093/mnras/stx1492","title":"Finding strong lenses in CFHTLS using convolutional neural networks","year":2017,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Physics; Convolutional neural network; Astrophysics; Astronomy; Remote sensing; Artificial intelligence","score_opus":0.026071055781200655,"score_gpt":0.2623057557832162,"score_spread":0.23623470000201552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606720601","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":[0.96288687,0.00041498776,0.031890742,0.00013643639,0.000011790757,0.000037189773,0.00090580265,0.0008004646,0.0029156578],"genre_scores_gemma":[0.9780254,0.00011435843,0.018923726,0.000038983075,0.000012550973,0.000008458417,0.0018690332,0.000022546876,0.0009849519],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996871,0.00003498051,0.000012493208,0.00008088464,0.000101571924,0.00008299172],"domain_scores_gemma":[0.9993117,0.00021183991,0.00017895023,0.00009353632,0.00013769302,0.000066295754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050082663,0.0005949949,0.00019854128,0.001829325,0.00037069956,0.0007767514,0.0004143013,0.00039168485,0.00085221743],"category_scores_gemma":[0.0017104081,0.00021679526,0.00043557264,0.0010454943,0.0003429011,0.0006000237,0.0006283928,0.00026981026,0.00029276067],"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":[0.0005716329,0.00018161943,0.46230155,0.00021019313,0.00028052853,0.0013452971,0.000413781,0.19517234,0.0526331,0.0029887934,0.004515445,0.27938572],"study_design_scores_gemma":[0.00002200486,0.0001265405,0.15700395,0.000036336492,0.000059509417,0.0003097416,0.00024157093,0.81584847,0.021168713,0.002354093,0.0027928578,0.00003613248],"about_ca_topic_score_codex":0.048845157,"about_ca_topic_score_gemma":0.075491704,"teacher_disagreement_score":0.048845157,"about_ca_system_score_codex":0.001070544,"about_ca_system_score_gemma":0.000545444,"threshold_uncertainty_score":0.097121716},"labels":[],"label_agreement":null},{"id":"W2612766801","doi":"10.1109/mmul.2017.40","title":"Extreme-Dynamic-Range Sensing: Real-Time Adaptation to Extreme Signals","year":2017,"lang":"en","type":"article","venue":"IEEE Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Compositing; Computer science; Dynamic range; High dynamic range; Real-time computing; Salience (neuroscience); Wide dynamic range; Range (aeronautics); Computer vision; Tone mapping; Artificial intelligence; Engineering","score_opus":0.06164693396937925,"score_gpt":0.2969614810597339,"score_spread":0.23531454709035465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612766801","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":[0.020011768,0.00025197575,0.97713876,0.000066654575,0.00005096746,0.000018231383,0.000009487905,0.0004153003,0.0020369433],"genre_scores_gemma":[0.70369476,0.0003081398,0.29217654,0.00015840288,0.00008668197,0.000056491925,0.00004196506,0.0001325203,0.0033444706],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964416,0.0000654216,0.000015284257,0.0001118818,0.00013768431,0.000025582598],"domain_scores_gemma":[0.999624,0.00018660477,0.000045946905,0.00006812364,0.000052903928,0.000022308877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036296705,0.00042292112,0.00032895882,0.00021935168,0.00017185546,0.0005314481,0.000780707,0.00043347382,0.0012745636],"category_scores_gemma":[0.0012807114,0.00020843564,0.00024586124,0.00022463342,0.000563537,0.0008224338,0.0008857648,0.00055512134,0.00030634704],"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":[0.00040008538,0.000113792725,0.00079176616,0.0001660589,0.000056048113,0.00024891715,0.0003614562,0.07800515,0.5320856,0.023904936,0.0015324998,0.3623336],"study_design_scores_gemma":[0.000021902993,0.00021625664,0.0009921122,0.00001847813,0.000018269022,0.0004401132,0.00004270872,0.8815787,0.09541544,0.013107683,0.008093478,0.000054824548],"about_ca_topic_score_codex":0.0002916199,"about_ca_topic_score_gemma":0.000365987,"teacher_disagreement_score":0.0012745636,"about_ca_system_score_codex":0.00023092951,"about_ca_system_score_gemma":0.00009053325,"threshold_uncertainty_score":0.0042638183},"labels":[],"label_agreement":null},{"id":"W2615700559","doi":"10.1007/s10916-017-0738-z","title":"Tri-Scan: A Three Stage Color Enhancement Tool for Endoscopic Images","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stage (stratigraphy); Computer science; Computer vision; Artificial intelligence; Health informatics; Computer graphics (images); Medicine; Geology; Pathology","score_opus":0.030703700967576415,"score_gpt":0.33319161550350646,"score_spread":0.30248791453593005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615700559","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":[0.024452638,0.0017612508,0.9504277,0.00026414945,0.00013885954,0.0003320284,0.00076095096,0.017814485,0.004047955],"genre_scores_gemma":[0.08015292,0.0015193269,0.90255743,0.00047442853,0.00011927261,0.00040438081,0.0010924282,0.0043346616,0.00934517],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999699,0.000058002373,0.000026257283,0.000033421522,0.00015446324,0.000028897339],"domain_scores_gemma":[0.9988815,0.00053583644,0.0000991505,0.0001496357,0.00023027122,0.000103492675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009822348,0.0009219109,0.00047901817,0.0020270362,0.00023429058,0.001027667,0.00110086,0.0009144647,0.017898602],"category_scores_gemma":[0.002144608,0.00072541833,0.00062974,0.0007900711,0.00023773793,0.001236833,0.0010313134,0.0010384574,0.0036625722],"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":[0.0016785424,0.00017455152,0.0027141708,0.000918115,0.00018584846,0.0010617322,0.00019940855,0.0018567414,0.320653,0.00212479,0.023585739,0.64484745],"study_design_scores_gemma":[0.0004544239,0.0013606925,0.01548748,0.00036702302,0.00049048226,0.024551203,0.00023044547,0.14497358,0.6900205,0.0031170282,0.118470415,0.00047678742],"about_ca_topic_score_codex":0.0004297288,"about_ca_topic_score_gemma":0.0009408116,"teacher_disagreement_score":0.017898602,"about_ca_system_score_codex":0.00014191696,"about_ca_system_score_gemma":0.00043663566,"threshold_uncertainty_score":0.05987674},"labels":[],"label_agreement":null},{"id":"W2616330482","doi":"10.1109/tmi.2017.2701861","title":"Vision-Based Surgical Field Defogging","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Joseph's Hospital; University Hospital; Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Visibility; Computer vision; Computer science; Visualization; Artificial intelligence; Contrast (vision); Luminance; Field (mathematics); Domain (mathematical analysis); Mathematics; Optics","score_opus":0.01118872924543164,"score_gpt":0.31345210479920543,"score_spread":0.3022633755537738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616330482","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":[0.056909923,0.001169123,0.9390931,0.00012311201,0.000077332,0.00007043469,0.00005906179,0.00066140486,0.0018364765],"genre_scores_gemma":[0.5760278,0.001741806,0.41816977,0.00021814217,0.00010589588,0.00006281859,0.000268546,0.00014888677,0.003256387],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971884,0.00003257121,0.00001276786,0.000051052837,0.00014853703,0.000036272246],"domain_scores_gemma":[0.999706,0.00007836183,0.000056391073,0.00004747273,0.00008979663,0.000022065757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032690697,0.0005131158,0.00041265017,0.0007804318,0.00021379151,0.0005263647,0.00053111056,0.0004933376,0.0008921679],"category_scores_gemma":[0.0010563859,0.00023189813,0.00051507395,0.0003615092,0.0003221879,0.00070159783,0.00093095406,0.0005888807,0.00022351652],"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":[0.00031085568,0.000088650995,0.0014938556,0.0002160683,0.00005895228,0.0003290083,0.00024684082,0.06325682,0.2702913,0.0034189187,0.0018998759,0.6583889],"study_design_scores_gemma":[0.000036797108,0.00030560076,0.0044858307,0.000045245677,0.00006043176,0.0013898703,0.00009516358,0.86114126,0.12187949,0.0026501724,0.007844283,0.000065865606],"about_ca_topic_score_codex":0.001589545,"about_ca_topic_score_gemma":0.0018069984,"teacher_disagreement_score":0.001589545,"about_ca_system_score_codex":0.0002811532,"about_ca_system_score_gemma":0.000495974,"threshold_uncertainty_score":0.003160596},"labels":[],"label_agreement":null},{"id":"W2617393214","doi":"10.1109/tip.2017.2708841","title":"Single Image Rain Streak Decomposition Using Layer Priors","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China; National Research Foundation Singapore","keywords":"Streak; Computer science; Prior probability; Visibility; Artificial intelligence; Computer vision; Image (mathematics); Pattern recognition (psychology); Algorithm; Optics; Bayesian probability","score_opus":0.032796066534784646,"score_gpt":0.3288948794674522,"score_spread":0.2960988129326676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617393214","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":[0.012729222,0.00021735228,0.98503125,0.00008881971,0.00002334425,0.000034149714,0.00008670473,0.0007735237,0.0010155716],"genre_scores_gemma":[0.17768535,0.00095270446,0.8157409,0.00017373299,0.0000690252,0.00007399273,0.0007825791,0.0004815731,0.0040400987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999684,0.0000433827,0.000016386886,0.000063203624,0.00014682926,0.000046243847],"domain_scores_gemma":[0.9994843,0.00012433538,0.000060351234,0.00014209287,0.00014482092,0.000044103996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055055413,0.00095316704,0.0006961723,0.0008622125,0.00023087669,0.0009771385,0.00070254423,0.0007425163,0.0018367026],"category_scores_gemma":[0.0013290631,0.00049551704,0.00089942926,0.0005876827,0.00043512194,0.001447535,0.0010795343,0.001453494,0.0011816494],"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":[0.00053272536,0.00015942896,0.0015558015,0.00042831738,0.00015431884,0.00031895627,0.00018889409,0.21579164,0.25494707,0.011518648,0.0063243485,0.5080798],"study_design_scores_gemma":[0.000023636896,0.00005989744,0.0011013894,0.000025436826,0.000046850764,0.00022723808,0.000031089676,0.9422509,0.046378765,0.0048821406,0.0049473746,0.000025205825],"about_ca_topic_score_codex":0.0029609504,"about_ca_topic_score_gemma":0.004619632,"teacher_disagreement_score":0.0029609504,"about_ca_system_score_codex":0.0003561107,"about_ca_system_score_gemma":0.00091645913,"threshold_uncertainty_score":0.0061444044},"labels":[],"label_agreement":null},{"id":"W2751187725","doi":"10.1016/b978-0-08-100412-8.00001-2","title":"The Fundamental Basis of HDR","year":2016,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Basis (linear algebra); Computer science; Mathematics; Geometry","score_opus":0.01336847205931918,"score_gpt":0.2397358443336088,"score_spread":0.22636737227428963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751187725","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005049306,0.07436766,0.32306856,0.003545788,0.004158523,0.00004556487,0.00025732335,0.00068612816,0.5888211],"genre_scores_gemma":[0.16684414,0.061774917,0.17466119,0.0018733767,0.0059138592,0.00012327234,0.0003475553,0.0005580937,0.58790356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99986553,0.00002276905,0.000006986102,0.00003862206,0.00005348868,0.000012548723],"domain_scores_gemma":[0.9998406,0.000072738614,0.000009538647,0.000033360207,0.000034942135,0.000008751518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027683197,0.0004676255,0.00034187402,0.0006338702,0.00040202276,0.0015676738,0.00068667775,0.0009229867,0.017688237],"category_scores_gemma":[0.00045861508,0.00034330325,0.00023624998,0.000532651,0.0018463068,0.0019629102,0.0006804438,0.001828721,0.0052851303],"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":[0.000013674347,0.000010552336,0.000035726905,0.00018366036,0.000005263089,0.000060749757,0.00013719028,0.0010294742,0.005170014,0.8762127,0.016395813,0.10074516],"study_design_scores_gemma":[0.0000067204232,0.00002316748,0.00028292017,0.00013380035,0.0000071364093,0.0004914719,0.00006466252,0.005022174,0.0055120136,0.45198902,0.53644043,0.0000265034],"about_ca_topic_score_codex":0.00043565713,"about_ca_topic_score_gemma":0.00037996113,"teacher_disagreement_score":0.017688237,"about_ca_system_score_codex":0.0005692697,"about_ca_system_score_gemma":0.00025507135,"threshold_uncertainty_score":0.059173048},"labels":[],"label_agreement":null},{"id":"W2751461496","doi":"10.1007/s11042-017-5143-6","title":"Parametric ratio-based method for efficient contrast-preserving decolorization","year":2017,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Robustness (evolution); Contrast (vision); Parametric statistics; Rendering (computer graphics); Optimal distinctiveness theory; Algorithm; Convergence (economics); Mathematical optimization; Artificial intelligence; Computer vision; Mathematics","score_opus":0.035272215253519035,"score_gpt":0.3379535830233419,"score_spread":0.30268136776982285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751461496","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":[0.011643644,0.0005627256,0.9843285,0.00010460986,0.000048938702,0.00003723273,0.000046546113,0.000549666,0.002678089],"genre_scores_gemma":[0.14070389,0.0013601703,0.8495863,0.00013388216,0.00009172297,0.00010552227,0.00020577885,0.0002169041,0.0075958106],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995758,0.000085789,0.000021323414,0.000069849004,0.000208963,0.000038207432],"domain_scores_gemma":[0.9995499,0.00014869518,0.000043590037,0.00010438911,0.00013263052,0.000020749574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004329921,0.0008717136,0.0005669225,0.0009369468,0.00034467585,0.0007233908,0.0010199109,0.0006345196,0.0046612076],"category_scores_gemma":[0.0011582341,0.00028026334,0.00059865456,0.0008549449,0.00044951012,0.0014345188,0.0010076723,0.0011726577,0.0018486344],"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":[0.00033790077,0.00012488608,0.00024274972,0.00026984877,0.00006781131,0.00011838814,0.00010788331,0.007457278,0.52342695,0.014484677,0.0025971837,0.4507644],"study_design_scores_gemma":[0.000035104964,0.00020752296,0.0007828101,0.000035468674,0.00010462192,0.001202944,0.000045167282,0.38530844,0.5928885,0.0042285654,0.015086509,0.00007437394],"about_ca_topic_score_codex":0.0004517503,"about_ca_topic_score_gemma":0.00083307276,"teacher_disagreement_score":0.0046612076,"about_ca_system_score_codex":0.000269729,"about_ca_system_score_gemma":0.00048907864,"threshold_uncertainty_score":0.01559329},"labels":[],"label_agreement":null},{"id":"W2758824372","doi":"10.1177/1541931213601600","title":"Nighttime Photography &amp; Videography: Techniques &amp; Tips","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Image Enhancement 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":"Advantage Forensics (Canada)","funders":"","keywords":"Videography; Visibility; Session (web analytics); Collision; Photography; Computer science; Perspective (graphical); Object (grammar); Aeronautics; Simulation; Transport engineering; Computer security; Visual arts; Engineering; Artificial intelligence; Meteorology; Geography; World Wide Web; Art","score_opus":0.019999397057122788,"score_gpt":0.25831128420698307,"score_spread":0.2383118871498603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758824372","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":[0.022030972,0.026339669,0.7122658,0.049293533,0.008278879,0.0028930653,0.0006249348,0.006351792,0.17192136],"genre_scores_gemma":[0.117469035,0.05093577,0.6476877,0.0099973045,0.008355754,0.0017800881,0.00080960605,0.0021793274,0.16078544],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856216,0.0005839352,0.000101106714,0.000113167,0.00054688455,0.00009289194],"domain_scores_gemma":[0.99629575,0.001329569,0.00013403322,0.0005861733,0.0013487202,0.00030574104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004440769,0.0011145014,0.00040082197,0.0015305316,0.0009288954,0.002246949,0.0015364387,0.0026456874,0.027549753],"category_scores_gemma":[0.0061026793,0.0004372371,0.00057109573,0.0006169586,0.0009795005,0.0026109559,0.0017724448,0.0026635574,0.011334189],"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":[0.00011759714,0.00026548887,0.0008570845,0.00131519,0.000025578205,0.00083549065,0.002385883,0.0007509551,0.02497267,0.003871291,0.13886665,0.82573617],"study_design_scores_gemma":[0.000051363062,0.00049884897,0.00749085,0.0017977275,0.000031117666,0.0118760625,0.0048334263,0.0027053775,0.021615103,0.011086019,0.9378685,0.00014557857],"about_ca_topic_score_codex":0.00058185676,"about_ca_topic_score_gemma":0.001513857,"teacher_disagreement_score":0.027549753,"about_ca_system_score_codex":0.0003045976,"about_ca_system_score_gemma":0.0004248603,"threshold_uncertainty_score":0.092163086},"labels":[],"label_agreement":null},{"id":"W2759158333","doi":"10.1109/iscas.2017.8050236","title":"A color frame reproduction technique for IoT-based video surveillance application","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"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 Saskatchewan","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Frame (networking); Grayscale; Real-time computing; Telecommunications; Pixel","score_opus":0.015977463632560748,"score_gpt":0.29905110478438773,"score_spread":0.283073641151827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759158333","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":[0.015400261,0.0005144808,0.9787627,0.00009786224,0.0000993608,0.00007357908,0.00003919505,0.00075673487,0.0042558582],"genre_scores_gemma":[0.337598,0.0010997545,0.654309,0.00021402423,0.00013350841,0.000103416816,0.00019627383,0.00015801427,0.0061881165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998847,0.000016165057,0.0000062215795,0.00002303547,0.00005920076,0.0000105752515],"domain_scores_gemma":[0.9998405,0.000027607357,0.000022262313,0.00002978771,0.00006914182,0.000010646854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016762142,0.0003767377,0.00017725685,0.0005025587,0.00024714426,0.00025334398,0.00043525864,0.00029655476,0.0018905228],"category_scores_gemma":[0.00037175402,0.00012446725,0.00029800704,0.0003984502,0.0001601709,0.00051537424,0.00022804918,0.0003509722,0.00063205103],"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":[0.0002780715,0.000109364984,0.0011637334,0.00016971458,0.000038931597,0.00035225746,0.000086723485,0.009103143,0.41592783,0.007673185,0.005216509,0.55988044],"study_design_scores_gemma":[0.000072454,0.00051111996,0.004527549,0.00006207043,0.000118753465,0.0038587153,0.000066971246,0.46033597,0.47595966,0.0031081813,0.0512971,0.00008154042],"about_ca_topic_score_codex":0.00072978874,"about_ca_topic_score_gemma":0.0010137999,"teacher_disagreement_score":0.0018905228,"about_ca_system_score_codex":0.00021906137,"about_ca_system_score_gemma":0.00021974188,"threshold_uncertainty_score":0.0063244104},"labels":[],"label_agreement":null},{"id":"W2760767862","doi":"10.1109/mwscas.2017.8053239","title":"A late adaptive graph-based edge-aware filtering with iterative weight updating process","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Computer science; Graph; Artificial intelligence; Filter (signal processing); Enhanced Data Rates for GSM Evolution; Computer vision; Image quality; Pattern recognition (psychology); Image (mathematics); Theoretical computer science","score_opus":0.018004771481686548,"score_gpt":0.2717972320739808,"score_spread":0.25379246059229427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760767862","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":[0.0039629787,0.00008221697,0.9952251,0.00002648359,0.000018814382,0.000014658387,0.000008521719,0.0002598479,0.0004013741],"genre_scores_gemma":[0.13576777,0.00028920034,0.85947627,0.00009449289,0.000052692514,0.00005744498,0.000102187056,0.00011118045,0.0040487614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997284,0.00003185386,0.000011724796,0.00007204261,0.00012845716,0.000027514741],"domain_scores_gemma":[0.9997291,0.00005933612,0.000028901297,0.000056899473,0.00010392205,0.000021731206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035355703,0.00070266245,0.0006655973,0.0007038752,0.00035212812,0.00065985793,0.0012671647,0.00079426175,0.0017767925],"category_scores_gemma":[0.0006245607,0.00034803725,0.0007945668,0.0006514231,0.00039261498,0.0011272818,0.00060684275,0.00087858347,0.0006192042],"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":[0.0002700468,0.00019013888,0.0009888618,0.00020054512,0.00015900658,0.0002137699,0.00018937717,0.19624317,0.2562195,0.018702937,0.0035351326,0.5230875],"study_design_scores_gemma":[0.000016219368,0.000072510906,0.00030651517,0.000005274816,0.00003345028,0.000103815386,0.000011772876,0.972231,0.0214121,0.002829967,0.0029568938,0.000020587375],"about_ca_topic_score_codex":0.0036945727,"about_ca_topic_score_gemma":0.0052847844,"teacher_disagreement_score":0.0036945727,"about_ca_system_score_codex":0.000408651,"about_ca_system_score_gemma":0.0006630066,"threshold_uncertainty_score":0.0073460937},"labels":[],"label_agreement":null},{"id":"W2761288201","doi":"10.1111/cgf.13274","title":"Group‐Theme Recoloring for Multi‐Image Color Consistency","year":2017,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Canada First Research Excellence Fund","keywords":"Theme (computing); Consistency (knowledge bases); Computer science; Artificial intelligence; Color image; Computer vision; Image (mathematics); Image processing; World Wide Web","score_opus":0.05004088575321006,"score_gpt":0.3087094459312401,"score_spread":0.2586685601780301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761288201","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":[0.040759027,0.00015145759,0.9527347,0.000092378665,0.000046515717,0.00007901038,0.00002327278,0.0025843065,0.0035293514],"genre_scores_gemma":[0.36536545,0.00013999817,0.62955827,0.000106380736,0.000041978663,0.00010230673,0.00008280801,0.00081793836,0.0037848293],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926513,0.0001565601,0.000031722775,0.00017601383,0.00026790317,0.0001026659],"domain_scores_gemma":[0.99807966,0.00040262344,0.00016134344,0.00086548296,0.00040361355,0.000087318775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011091976,0.00068287377,0.0005696071,0.00086789334,0.00067243516,0.0007663515,0.0015287421,0.000534528,0.004555588],"category_scores_gemma":[0.002255717,0.00035266194,0.00075290713,0.0006213223,0.0007901844,0.0012624995,0.002033849,0.001210476,0.00079958054],"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":[0.00047693684,0.00028200969,0.0017119269,0.00038520063,0.00008933624,0.00044913183,0.0009236932,0.044023015,0.45544112,0.036595095,0.006399811,0.4532227],"study_design_scores_gemma":[0.00007895353,0.00030596607,0.001764773,0.000041311625,0.00007212713,0.00095616136,0.0002607797,0.4343053,0.50229406,0.020480635,0.03936642,0.00007357089],"about_ca_topic_score_codex":0.0008870727,"about_ca_topic_score_gemma":0.0010802298,"teacher_disagreement_score":0.004555588,"about_ca_system_score_codex":0.00050208444,"about_ca_system_score_gemma":0.00042969972,"threshold_uncertainty_score":0.015239954},"labels":[],"label_agreement":null},{"id":"W2763247712","doi":"10.1111/cgf.13275","title":"ℒ0 Gradient‐Preserving Color Transfer","year":2017,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"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":"","keywords":"Artificial intelligence; Computer vision; Computer science; Palette (painting); Pixel; Color balance; Color histogram; Color image; Color quantization; High color; Similarity (geometry); Color normalization; Color depth; Image (mathematics); Pattern recognition (psychology); Image processing","score_opus":0.020836679226788977,"score_gpt":0.262990408689435,"score_spread":0.24215372946264604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763247712","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":[0.027395852,0.00032793323,0.96325046,0.00011881447,0.00014290707,0.00007373841,0.00005157038,0.0016802836,0.006958477],"genre_scores_gemma":[0.4491567,0.00050763343,0.52817637,0.00022259392,0.00013583232,0.00010044114,0.00021297635,0.00032907087,0.021158371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999798,0.000024426045,0.0000071862382,0.000045820634,0.00010316961,0.000021364043],"domain_scores_gemma":[0.999833,0.00002313074,0.000014211946,0.00005145187,0.00006512641,0.000013106075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019722187,0.00050635857,0.0003074431,0.00055239676,0.0002612578,0.0005835991,0.0008886708,0.0004030214,0.0053757005],"category_scores_gemma":[0.00043798026,0.00020869417,0.0004141254,0.0005085507,0.00037588106,0.0008656716,0.0006045405,0.00070165197,0.0016981637],"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":[0.00029150303,0.00015122328,0.00050175627,0.00017397561,0.000048294976,0.00014534267,0.00008965709,0.024030663,0.25509632,0.0127142975,0.005607375,0.70114964],"study_design_scores_gemma":[0.00007132368,0.00022400111,0.0014353081,0.000014821045,0.000040322215,0.0008151043,0.00003590069,0.6076218,0.3579714,0.0070126574,0.02470356,0.0000537777],"about_ca_topic_score_codex":0.0009856184,"about_ca_topic_score_gemma":0.0007042449,"teacher_disagreement_score":0.0053757005,"about_ca_system_score_codex":0.0003158624,"about_ca_system_score_gemma":0.00035605367,"threshold_uncertainty_score":0.017983556},"labels":[],"label_agreement":null},{"id":"W2765376230","doi":"10.1109/avss.2017.8078533","title":"Multi-Scale histogram tone mapping algorithm enables better object detection in wide dynamic range images","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Tone mapping; Histogram; Computer science; Artificial intelligence; Brightness; Pixel; Computer vision; High dynamic range; Scale (ratio); Contrast (vision); Pattern recognition (psychology); Face (sociological concept); Object detection; Consistency (knowledge bases); Algorithm; Dynamic range; Image (mathematics)","score_opus":0.013288257227169696,"score_gpt":0.27791333049698547,"score_spread":0.26462507326981577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765376230","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":[0.072516516,0.0004505178,0.923212,0.00010417853,0.00010805119,0.00007312699,0.000057422007,0.001198653,0.0022794874],"genre_scores_gemma":[0.34960902,0.0004909904,0.646406,0.00011768396,0.00008159795,0.0000645104,0.00014577014,0.00018275583,0.0029016105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997913,0.000029027593,0.000010880502,0.000049933402,0.000093099814,0.00002585074],"domain_scores_gemma":[0.99956626,0.00013221567,0.000039108494,0.00009394093,0.00014246235,0.00002594417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038585885,0.00040038791,0.00033301802,0.0007929146,0.00021255694,0.00066824333,0.0004561989,0.00040345732,0.0026162758],"category_scores_gemma":[0.0010381221,0.00016240234,0.00031045655,0.0004889286,0.000253167,0.0010474499,0.00043763072,0.00041591979,0.00073759054],"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":[0.00029644222,0.00009372089,0.0017388957,0.00017445213,0.00004460983,0.00014445235,0.00010500347,0.0073682996,0.27099413,0.0029487016,0.0015618756,0.71452945],"study_design_scores_gemma":[0.000048214177,0.00046994866,0.013156196,0.000030048472,0.0001009253,0.0023091075,0.00016549767,0.41049883,0.5517045,0.0039555766,0.017465968,0.000095112395],"about_ca_topic_score_codex":0.0003691296,"about_ca_topic_score_gemma":0.0005708253,"teacher_disagreement_score":0.0026162758,"about_ca_system_score_codex":0.00016884938,"about_ca_system_score_gemma":0.00017087588,"threshold_uncertainty_score":0.008752346},"labels":[],"label_agreement":null},{"id":"W2767269772","doi":"10.1109/tip.2017.2771142","title":"Learning-Based Restoration of Backlit Images","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Backlight; Computer vision; Artificial intelligence; Computer science; Image quality; Image (mathematics); Liquid-crystal display","score_opus":0.017870332825650464,"score_gpt":0.2931462082675908,"score_spread":0.2752758754419403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767269772","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":[0.18836099,0.0006107045,0.80820084,0.00011598478,0.00006928879,0.000040302097,0.000036012363,0.0009835407,0.0015823543],"genre_scores_gemma":[0.77283067,0.00054754433,0.22299035,0.00012501974,0.000046726316,0.00002161247,0.000102442296,0.000087607084,0.0032480378],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975234,0.000025785837,0.000009890866,0.000055705965,0.00012976765,0.000026504895],"domain_scores_gemma":[0.99956924,0.00009673641,0.000079327416,0.0000942474,0.00013907146,0.000021380127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002935004,0.00041494338,0.0005701103,0.0005328613,0.00017765889,0.00048519537,0.0005217121,0.0004591435,0.0007299059],"category_scores_gemma":[0.000937772,0.00018125435,0.0003714267,0.00029099418,0.0005347385,0.0007680078,0.00057285634,0.00058529497,0.00033272797],"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":[0.00032573086,0.00014454951,0.0016782924,0.0002507998,0.000074847114,0.0002675868,0.00016998983,0.049774405,0.47174543,0.0018570945,0.0010797522,0.47263154],"study_design_scores_gemma":[0.000016577047,0.00021875725,0.003016502,0.000017808525,0.00005019818,0.000622917,0.000060244594,0.73679894,0.2552914,0.0017657353,0.0021136955,0.000027163278],"about_ca_topic_score_codex":0.0005862958,"about_ca_topic_score_gemma":0.0009037778,"teacher_disagreement_score":0.0007299059,"about_ca_system_score_codex":0.00020362667,"about_ca_system_score_gemma":0.00026865158,"threshold_uncertainty_score":0.0024418235},"labels":[],"label_agreement":null},{"id":"W2771220351","doi":"10.1109/tpami.2017.2760833","title":"Color Homography: Theory and Applications","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":48,"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":"Engineering and Physical Sciences Research Council; Simon Fraser University","keywords":"Artificial intelligence; Computer vision; Homography; Computer science; Color balance; Color normalization; Color image; Chromaticity; Color histogram; Camera resectioning; Mathematics; Image (mathematics); Image processing","score_opus":0.014418066596099344,"score_gpt":0.28472610939714077,"score_spread":0.2703080428010414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771220351","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":[0.0044627762,0.012371333,0.9722645,0.0005209809,0.00021245563,0.000024854398,0.00010962969,0.0003214201,0.009712033],"genre_scores_gemma":[0.5219122,0.039228503,0.4162668,0.00089580676,0.002304243,0.00028914804,0.00073191896,0.00045928772,0.017912023],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990803,0.00023029707,0.000040276504,0.00022718657,0.00036308123,0.000058862028],"domain_scores_gemma":[0.99882025,0.0005062819,0.000113415605,0.00023136429,0.00028144778,0.000047121113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084737915,0.0008356933,0.0009947546,0.0027964516,0.00072288356,0.0022365064,0.0011321973,0.001449725,0.003891411],"category_scores_gemma":[0.0025328058,0.0005534197,0.0010329861,0.004901769,0.002837114,0.002561536,0.0019348301,0.002537401,0.0011619747],"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":[0.000055501347,0.000040634783,0.0008659909,0.00034724854,0.00011443759,0.00017321765,0.0002610592,0.061755437,0.0034804903,0.6645431,0.0068740887,0.2614887],"study_design_scores_gemma":[0.00001577433,0.000051722578,0.0011949829,0.00009644122,0.00003822716,0.0005045858,0.00011345869,0.36626896,0.0019101145,0.5996642,0.030079732,0.00006171684],"about_ca_topic_score_codex":0.0035393287,"about_ca_topic_score_gemma":0.0012944463,"teacher_disagreement_score":0.003891411,"about_ca_system_score_codex":0.0012731544,"about_ca_system_score_gemma":0.0006835353,"threshold_uncertainty_score":0.013018072},"labels":[],"label_agreement":null},{"id":"W2774513401","doi":"10.5815/ijigsp.2017.12.04","title":"Traffic Video Enhancement based Vehicle Correct Tracked Methodology","year":2017,"lang":"en","type":"article","venue":"International Journal of Image Graphics and Signal Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Kalman filter; Background subtraction; Noise (video); Filter (signal processing); Median filter; Frame (networking); Video tracking; Video processing; Real-time computing; Pixel; Image processing; Image (mathematics); Telecommunications","score_opus":0.04400486062145907,"score_gpt":0.3387757092417898,"score_spread":0.2947708486203307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774513401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030269101,0.00018237992,0.9666471,0.000039459544,0.000039636052,0.00010114135,0.000054649052,0.0006025438,0.00206393],"genre_scores_gemma":[0.48218882,0.0005608016,0.5106277,0.00006508127,0.000057101468,0.000118067896,0.00030633667,0.00007235188,0.0060037645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973565,0.000033427084,0.000010916917,0.000086225984,0.00009843552,0.000035299876],"domain_scores_gemma":[0.9997466,0.00003236268,0.000031797415,0.000028465827,0.0001467311,0.0000139714675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041598463,0.00044603428,0.00039936078,0.000950917,0.00022198269,0.0005360835,0.0005579146,0.0004497242,0.0013661106],"category_scores_gemma":[0.00069074216,0.00020286835,0.00041336793,0.0005092128,0.00021745774,0.00054615334,0.00037533196,0.00029544206,0.00044483555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037071714,0.0001414159,0.004267079,0.00023561328,0.000067937755,0.00023828309,0.0001179498,0.09986156,0.12644313,0.0067081936,0.0013796523,0.7601685],"study_design_scores_gemma":[0.000027795977,0.00028173198,0.0038141957,0.00002362932,0.0000861124,0.00036319665,0.000061400715,0.91372067,0.07410811,0.0018007278,0.005683048,0.000029385184],"about_ca_topic_score_codex":0.0019378786,"about_ca_topic_score_gemma":0.0016650987,"teacher_disagreement_score":0.0019378786,"about_ca_system_score_codex":0.0003218806,"about_ca_system_score_gemma":0.0005486068,"threshold_uncertainty_score":0.004570067},"labels":[],"label_agreement":null},{"id":"W2776574544","doi":"10.1109/tci.2017.2786138","title":"Multi-Exposure Image Fusion by Optimizing A Structural Similarity Index","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":200,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Similarity (geometry); Algorithm; Image (mathematics); Image fusion; Transformation (genetics); Convergence (economics)","score_opus":0.015683323755942613,"score_gpt":0.2937431099667134,"score_spread":0.27805978621077077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776574544","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":[0.008122403,0.00008841431,0.99068844,0.00009165917,0.00001649353,0.000030388854,0.000013178109,0.00020741027,0.00074156356],"genre_scores_gemma":[0.12508439,0.00016332879,0.8727901,0.00011119557,0.000033177384,0.00007670333,0.00009070933,0.00010242136,0.0015479729],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921715,0.00010817762,0.000046465775,0.00017142543,0.00038693927,0.000069885566],"domain_scores_gemma":[0.99938273,0.0001584212,0.000099177836,0.00011427269,0.00020449207,0.00004103649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013193148,0.0007762474,0.00091535127,0.0010383679,0.0003545991,0.0012901428,0.0013386365,0.0014178116,0.0016977005],"category_scores_gemma":[0.0025210008,0.00036654764,0.0010976666,0.00084283866,0.00062544027,0.0022425395,0.0016084326,0.0012255441,0.00056641584],"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":[0.00031391784,0.00023889309,0.0013640437,0.00018072868,0.00013927417,0.00009864693,0.00021159629,0.30757433,0.121869646,0.026007447,0.0020553072,0.53994614],"study_design_scores_gemma":[0.000017868566,0.000100494246,0.0005359954,0.000009075059,0.000026526865,0.00010414699,0.00002001654,0.95668423,0.037244678,0.003593095,0.0016384778,0.000025523339],"about_ca_topic_score_codex":0.0012936747,"about_ca_topic_score_gemma":0.0016334272,"teacher_disagreement_score":0.0016977005,"about_ca_system_score_codex":0.00088057935,"about_ca_system_score_gemma":0.0010139843,"threshold_uncertainty_score":0.0069773197},"labels":[],"label_agreement":null},{"id":"W2779235975","doi":"10.1142/s0218001418540186","title":"Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement","year":2017,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":148,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"RGB color model; Color histogram; Color space; Artificial intelligence; Computer vision; Color balance; Color image; Adaptive histogram equalization; Color normalization; RGB color space; Mathematics; Histogram equalization; Color depth; HSL and HSV; Computer science; Histogram; Image processing; Image (mathematics)","score_opus":0.09437467462078489,"score_gpt":0.34245048782312254,"score_spread":0.24807581320233765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779235975","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":[0.041412406,0.0007186237,0.9552687,0.000094445626,0.000060768867,0.000044522847,0.000024516881,0.0005549454,0.0018210922],"genre_scores_gemma":[0.5295525,0.00090757187,0.4649569,0.00013473241,0.000057969242,0.00006658514,0.00012744438,0.0001011015,0.0040952167],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967027,0.0000408383,0.000020783305,0.000064873086,0.00017034079,0.000032983135],"domain_scores_gemma":[0.9997482,0.00006309819,0.000029685818,0.00003278352,0.00011505536,0.000011292315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004418881,0.00039536352,0.00040792156,0.0007260058,0.0002267219,0.0003881771,0.00046468628,0.0003308374,0.0008572237],"category_scores_gemma":[0.0007765523,0.00020318117,0.00056049915,0.000592136,0.00032621864,0.0011447301,0.0006202491,0.00055308326,0.00026133005],"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":[0.00031725733,0.000114647686,0.00185233,0.00023414574,0.00011852888,0.00013699004,0.00012784898,0.027530493,0.42851424,0.0046648956,0.0010548074,0.5353339],"study_design_scores_gemma":[0.0000390431,0.00025865357,0.004815321,0.000031291816,0.00011768697,0.00067792414,0.00008301819,0.5561834,0.4275268,0.0025108024,0.0076732547,0.00008287059],"about_ca_topic_score_codex":0.001121887,"about_ca_topic_score_gemma":0.0015409697,"teacher_disagreement_score":0.001121887,"about_ca_system_score_codex":0.000258817,"about_ca_system_score_gemma":0.0003338302,"threshold_uncertainty_score":0.0028677583},"labels":[],"label_agreement":null},{"id":"W2787164094","doi":"10.1109/pesgm.2017.8274521","title":"Digital image expert system for corrosion analysis of steel transmission structures","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of Waterloo; McMaster University; Hydro One (Canada)","funders":"","keywords":"RGB color model; Pixel; Shadow (psychology); Artificial intelligence; Computer science; Corrosion; Computer vision; Digital image; Transmission (telecommunications); Artificial neural network; Image (mathematics); Image processing; Materials science; Telecommunications","score_opus":0.015361416862902988,"score_gpt":0.2910723167181858,"score_spread":0.2757108998552828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787164094","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":[0.037641503,0.00037889776,0.92734236,0.0001604276,0.000116550065,0.00070018286,0.0014331253,0.021777155,0.010449828],"genre_scores_gemma":[0.17622261,0.00032084208,0.79733646,0.00027766122,0.00004536803,0.00066456705,0.0019495343,0.00036675233,0.022816157],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996729,0.00003574519,0.000026075904,0.00008408476,0.00015508874,0.000026123224],"domain_scores_gemma":[0.9993905,0.00011018631,0.00002712442,0.000052740157,0.0003864058,0.000033000422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006402294,0.0003774934,0.0005615802,0.0011707626,0.0002131457,0.00042186215,0.0006340605,0.0006756319,0.011905421],"category_scores_gemma":[0.0011458521,0.00022085849,0.00028904318,0.00045031708,0.00012301699,0.00046366244,0.00033485208,0.00044556658,0.0031183537],"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":[0.00059843087,0.00019905895,0.0015314856,0.00047555665,0.00006257912,0.00029623247,0.00013451609,0.010038078,0.24767357,0.0017170826,0.026135942,0.7111374],"study_design_scores_gemma":[0.0003619487,0.0005851615,0.021877797,0.000096667776,0.00018661175,0.0015684918,0.00011052764,0.6382159,0.23880509,0.0015663288,0.096486874,0.00013855223],"about_ca_topic_score_codex":0.0026707456,"about_ca_topic_score_gemma":0.0043998873,"teacher_disagreement_score":0.011905421,"about_ca_system_score_codex":0.00043275522,"about_ca_system_score_gemma":0.00053729507,"threshold_uncertainty_score":0.039827645},"labels":[],"label_agreement":null},{"id":"W2790580379","doi":"10.1016/j.cam.2018.01.027","title":"Rhythm oscillation in fractional-order Relaxation oscillator and its application in image enhancement","year":2018,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"National High-tech Research and Development Program; Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China","keywords":"Mathematics; Relaxation (psychology); Oscillation (cell signaling); Relaxation oscillator; Limit (mathematics); Limit cycle; Rhythm; Image (mathematics); Order (exchange); Stability (learning theory); Mathematical analysis; Control theory (sociology); Physics; Artificial intelligence; Voltage-controlled oscillator; Quantum mechanics; Computer science; Neuroscience; Voltage","score_opus":0.010318003852846562,"score_gpt":0.2655614980590466,"score_spread":0.25524349420620007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790580379","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":[0.12422961,0.0029853513,0.8648937,0.0003058462,0.00017291197,0.00003920003,0.000053534222,0.00019358483,0.007126324],"genre_scores_gemma":[0.86215097,0.002448295,0.1290935,0.00008311926,0.0001501736,0.000043018397,0.000060735823,0.000058372545,0.0059117656],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994373,0.000014567204,0.000002765937,0.000020089172,0.000012922823,0.000006004463],"domain_scores_gemma":[0.9998171,0.00010900219,0.000022641063,0.000013926516,0.000024816829,0.000012577204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002499543,0.00028964676,0.0002736241,0.00029649003,0.00024391818,0.00031350914,0.00037827098,0.00051493006,0.0012496404],"category_scores_gemma":[0.00089246215,0.00012250496,0.00034453018,0.00038112383,0.0003829711,0.00043992783,0.0002820682,0.00036518593,0.00011645324],"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":[0.0005181532,0.00022456552,0.0038234454,0.0007096479,0.00012632197,0.0009918014,0.0008086977,0.14339103,0.37107974,0.21836506,0.0023441117,0.25761735],"study_design_scores_gemma":[0.00001577427,0.00009937001,0.0009914157,0.000018359215,0.00003559203,0.00034020844,0.000042976426,0.9736602,0.009571315,0.012574756,0.002624198,0.000025812415],"about_ca_topic_score_codex":0.0004860209,"about_ca_topic_score_gemma":0.00039729147,"teacher_disagreement_score":0.0012496404,"about_ca_system_score_codex":0.00011194455,"about_ca_system_score_gemma":0.00012464469,"threshold_uncertainty_score":0.004180491},"labels":[],"label_agreement":null},{"id":"W2791122516","doi":"10.1049/el.2017.3227","title":"Local tone mapping algorithm and hardware implementation","year":2018,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Tone mapping; Pixel; Computer science; Algorithm; Block (permutation group theory); High dynamic range; Computer hardware; Brightness; Logarithm; Tone (literature); Computer vision; Dynamic range; Artificial intelligence; Mathematics","score_opus":0.00762877152240727,"score_gpt":0.27477082485862825,"score_spread":0.267142053336221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791122516","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":[0.011180577,0.0002865495,0.9809729,0.00010177298,0.000082218554,0.0001272882,0.000036455393,0.0019233192,0.0052889516],"genre_scores_gemma":[0.21681607,0.00034374703,0.77103347,0.00016137626,0.00009519501,0.00026279598,0.00018281791,0.00012452446,0.010980034],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997875,0.000023987115,0.000015718537,0.00004196878,0.00010638611,0.00002444429],"domain_scores_gemma":[0.9997948,0.00003717857,0.000019578396,0.0000395565,0.000094152485,0.0000147875435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017420381,0.0004189816,0.00026242642,0.00046359128,0.0002976101,0.00072216475,0.000913626,0.0004039133,0.006502245],"category_scores_gemma":[0.0005561697,0.00019380859,0.00019453082,0.00042970566,0.00019729504,0.0007245357,0.00035776768,0.0004420261,0.001971053],"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":[0.00027830616,0.00010870733,0.0008134632,0.000322337,0.000043011212,0.0003655479,0.00018670554,0.013559144,0.266333,0.021133428,0.0065440354,0.6903122],"study_design_scores_gemma":[0.00021201532,0.0008734389,0.002165812,0.00005868931,0.00010378102,0.003999871,0.00012484794,0.49481118,0.39530584,0.006512623,0.095722064,0.000109927605],"about_ca_topic_score_codex":0.0005765002,"about_ca_topic_score_gemma":0.0006419748,"teacher_disagreement_score":0.006502245,"about_ca_system_score_codex":0.00028085877,"about_ca_system_score_gemma":0.00047342904,"threshold_uncertainty_score":0.021752179},"labels":[],"label_agreement":null},{"id":"W2791501217","doi":"10.1109/camsap.2017.8313070","title":"Multi-Scale histogram tone mapping algorithm for display of wide dynamic range images","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Calgary","funders":"","keywords":"Tone mapping; Brightness; Computer science; Histogram; High dynamic range; Computer vision; Computation; Algorithm; Artificial intelligence; Scale (ratio); Image (mathematics); Contrast (vision); Filter (signal processing); Adaptive histogram equalization; Dynamic range; Histogram matching; Histogram equalization","score_opus":0.01889362949920371,"score_gpt":0.31510292736793033,"score_spread":0.29620929786872663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791501217","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":[0.023568101,0.0004717483,0.97250664,0.000087060675,0.000052212174,0.000056551657,0.000031407548,0.0010453544,0.0021809468],"genre_scores_gemma":[0.22171904,0.00062136,0.7727147,0.0000908047,0.000052378204,0.00007752972,0.0001222841,0.00016769243,0.0044342596],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998673,0.000015713948,0.000007659498,0.00002272345,0.000074704905,0.000011839291],"domain_scores_gemma":[0.9997845,0.00005356382,0.000023320299,0.00004519228,0.00007922676,0.00001417025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020461243,0.00028980806,0.00022810375,0.0005159783,0.00021428622,0.00041507138,0.00041718848,0.00025948585,0.0033113034],"category_scores_gemma":[0.0006275815,0.00014165872,0.00024686943,0.00048807575,0.00017612772,0.0007148471,0.00030809917,0.00041554627,0.0006646956],"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":[0.0001754122,0.000056614488,0.00059433666,0.00013666759,0.000029787027,0.00013685708,0.00011363946,0.008910852,0.23490277,0.0070977407,0.0027853982,0.74505985],"study_design_scores_gemma":[0.000059165348,0.00031895953,0.003939702,0.000027486616,0.0000721477,0.0015628309,0.00010316789,0.56854075,0.3887637,0.005459542,0.031085674,0.00006682328],"about_ca_topic_score_codex":0.00062749325,"about_ca_topic_score_gemma":0.0009106541,"teacher_disagreement_score":0.0033113034,"about_ca_system_score_codex":0.00022034107,"about_ca_system_score_gemma":0.00028683932,"threshold_uncertainty_score":0.011077404},"labels":[],"label_agreement":null},{"id":"W2792071900","doi":"10.1007/978-3-319-78133-4_13","title":"Evolutionary Optimization of Tone Mapped Image Quality Index","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Tone mapping; Computer science; Tone (literature); Image quality; Image (mathematics); Measure (data warehouse); Artificial intelligence; Quality (philosophy); Operator (biology); Range (aeronautics); Evolutionary algorithm; Computer vision; High dynamic range; Pattern recognition (psychology); Data mining; Dynamic range","score_opus":0.016870665998142624,"score_gpt":0.29241045049500564,"score_spread":0.275539784496863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792071900","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":[0.15528998,0.00092946313,0.81369334,0.00019246392,0.0001283838,0.00007315787,0.00007063465,0.0005038315,0.029118702],"genre_scores_gemma":[0.8237145,0.00039220948,0.16305973,0.00008682643,0.00004169996,0.00006610593,0.000091467926,0.00020294305,0.012344538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987817,0.00001749157,0.000003895151,0.000023760102,0.000056211768,0.000020336936],"domain_scores_gemma":[0.9997415,0.000096414034,0.00002441812,0.00002316624,0.00009621462,0.000018284334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003195315,0.0003758429,0.0004131341,0.00044911483,0.00017271,0.00070843275,0.00064466306,0.0005156,0.0048259827],"category_scores_gemma":[0.0011272797,0.00017997342,0.0002996492,0.00036138378,0.00024055823,0.00049695856,0.00046073852,0.0002979167,0.00036575962],"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":[0.00015379034,0.00016236311,0.0010229264,0.00018684851,0.0000674614,0.00012147616,0.00009828503,0.6738072,0.069523916,0.015799383,0.002558043,0.23649828],"study_design_scores_gemma":[0.000010465714,0.00008401266,0.00048343735,0.000011253501,0.000017868231,0.000056169767,0.0000133981775,0.99182105,0.004837279,0.0017661098,0.0008916548,0.0000072653147],"about_ca_topic_score_codex":0.0008740337,"about_ca_topic_score_gemma":0.0007810184,"teacher_disagreement_score":0.0048259827,"about_ca_system_score_codex":0.0004858671,"about_ca_system_score_gemma":0.00027153775,"threshold_uncertainty_score":0.016144514},"labels":[],"label_agreement":null},{"id":"W2801094975","doi":"10.2316/j.2010.216.680-0139","title":"Medical Image Enhancement Algorithm based on Local Contrast Enhancement and Human Visual Characteristics","year":2010,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Contrast (vision); Contrast enhancement; Image enhancement; Artificial intelligence; Image (mathematics); Computer science; Computer vision; Human visual system model; Image contrast; Algorithm; Medicine; Radiology","score_opus":0.0034148423510362725,"score_gpt":0.24064409362412437,"score_spread":0.23722925127308808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801094975","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":[0.032079235,0.002563176,0.9605236,0.00021586812,0.00011913925,0.00018557494,0.00008093087,0.0006178992,0.0036146282],"genre_scores_gemma":[0.24987377,0.0034894429,0.7387585,0.0002410915,0.00016850975,0.00021209153,0.00020762505,0.00015818798,0.006890728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997975,0.00002855581,0.000013260347,0.000043063043,0.0001005339,0.000017024448],"domain_scores_gemma":[0.99970704,0.00010943316,0.000035049296,0.000020529866,0.0001104506,0.000017497268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000696154,0.0004498432,0.0004634095,0.0009846138,0.00013752148,0.0005737364,0.00035392735,0.00057367235,0.0014375672],"category_scores_gemma":[0.0010778963,0.00018246088,0.00046688123,0.00046213376,0.0003166014,0.0007022591,0.00031687706,0.00055825344,0.00065512507],"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":[0.00043151816,0.00013408405,0.002009059,0.0006596802,0.00014309768,0.00030441448,0.00009794823,0.0093721915,0.47350067,0.004356457,0.0021033566,0.5068875],"study_design_scores_gemma":[0.00013815472,0.001017739,0.021929922,0.00018179572,0.00064169505,0.0067590773,0.00011736451,0.3585158,0.57815444,0.00550407,0.026922962,0.00011693103],"about_ca_topic_score_codex":0.00035761253,"about_ca_topic_score_gemma":0.00059624924,"teacher_disagreement_score":0.0014375672,"about_ca_system_score_codex":0.00020439968,"about_ca_system_score_gemma":0.0003437937,"threshold_uncertainty_score":0.004809141},"labels":[],"label_agreement":null},{"id":"W2802408754","doi":"10.3390/rs10050682","title":"Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":69,"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":"Artificial intelligence; Histogram; Grayscale; Computer science; Histogram matching; Computer vision; Brightness; Smoothing; Image histogram; Adaptive histogram equalization; Maxima and minima; Pattern recognition (psychology); Histogram equalization; Image processing; Pixel; Mathematics; Image (mathematics); Color image; Optics; Physics","score_opus":0.020235939648078953,"score_gpt":0.2699998635961172,"score_spread":0.24976392394803826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802408754","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":[0.024056528,0.0002898515,0.973751,0.00004271324,0.000034584496,0.000049001617,0.000016376727,0.0007059171,0.001054005],"genre_scores_gemma":[0.3166025,0.0006239026,0.67886627,0.00008502011,0.000056819816,0.00008196353,0.00011815362,0.00018779235,0.0033776509],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997168,0.000032714157,0.00001366996,0.000076061544,0.00012998424,0.000030713825],"domain_scores_gemma":[0.9996941,0.00008112593,0.000052067568,0.0000574679,0.00010158823,0.000013663563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034388667,0.00053501426,0.00046096128,0.0008853935,0.00024409384,0.00048607073,0.00070768956,0.00036261772,0.0012302229],"category_scores_gemma":[0.00080612476,0.0002702812,0.000550498,0.00085096003,0.0004082244,0.00088305847,0.0006530474,0.0005039645,0.0004518556],"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":[0.00027939185,0.00014709149,0.0011951198,0.00018850331,0.000056376277,0.00014251798,0.00019659163,0.03717055,0.49903727,0.0037118837,0.0012765395,0.45659816],"study_design_scores_gemma":[0.000046995374,0.00024734833,0.004555493,0.000022029146,0.00009976702,0.00081767887,0.00008183602,0.55235696,0.43169436,0.002284083,0.007722272,0.000071158436],"about_ca_topic_score_codex":0.0010051352,"about_ca_topic_score_gemma":0.0010425941,"teacher_disagreement_score":0.0012302229,"about_ca_system_score_codex":0.00025443305,"about_ca_system_score_gemma":0.0003103311,"threshold_uncertainty_score":0.0041154623},"labels":[],"label_agreement":null},{"id":"W2806707183","doi":"10.1002/sdtp.12106","title":"85‐1: Visually Lossless Compression of High Dynamic Range Images: A Large‐Scale Evaluation","year":2018,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Image Enhancement 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":"Qualcomm (Canada); York University","funders":"","keywords":"Lossless compression; High dynamic range; Computer science; Flicker; Dynamic range compression; Codec; Compression (physics); Dynamic range; Computer vision; Scale (ratio); Image compression; Data compression; Artificial intelligence; Computer graphics (images); Computer hardware; Image (mathematics); Image processing; Materials science; Telecommunications; Cartography; Geography","score_opus":0.007825180964762707,"score_gpt":0.2883745563859382,"score_spread":0.2805493754211755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806707183","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":[0.96927845,0.00043108888,0.02431744,0.000061289866,0.000043786105,0.00032639722,0.00057212374,0.00050200085,0.004467378],"genre_scores_gemma":[0.97432363,0.00035402953,0.019381182,0.00006699666,0.00002738029,0.00012215393,0.0009005813,0.00017075171,0.004653276],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994549,0.00008653553,0.000031308646,0.00005296726,0.0003281824,0.000046105473],"domain_scores_gemma":[0.99858224,0.0005560515,0.00008489287,0.0001299243,0.00050939806,0.00013755944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080928183,0.00045338552,0.0002831414,0.00092606054,0.0002667926,0.000341833,0.00039126165,0.00048196132,0.0033258754],"category_scores_gemma":[0.0017291583,0.00010457409,0.0002082799,0.00054780295,0.00050257356,0.0005294411,0.00042264306,0.00033376526,0.00041144073],"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":[0.0010444614,0.0008366731,0.0026545203,0.00044386243,0.00006208995,0.00038297576,0.00033115258,0.006101872,0.90600765,0.0004183806,0.0017731519,0.079943195],"study_design_scores_gemma":[0.00016296636,0.008996193,0.046206996,0.000033729513,0.00010825546,0.0011769396,0.00024105501,0.034692943,0.90383583,0.00017841552,0.004289008,0.000077574],"about_ca_topic_score_codex":0.0013448751,"about_ca_topic_score_gemma":0.0017710752,"teacher_disagreement_score":0.0033258754,"about_ca_system_score_codex":0.00026612167,"about_ca_system_score_gemma":0.00018643239,"threshold_uncertainty_score":0.011126161},"labels":[],"label_agreement":null},{"id":"W2807141917","doi":"10.1111/cgf.13341","title":"From Faces to Outdoor Light Probes","year":2018,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":51,"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":"Computer science; Artificial intelligence; Computer vision; High dynamic range; Rendering (computer graphics); Computer graphics (images); High-dynamic-range imaging; Image-based lighting; Dynamic range; Image-based modeling and rendering","score_opus":0.010833960205919735,"score_gpt":0.2539730568964078,"score_spread":0.24313909669048808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807141917","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":[0.29302207,0.00061586295,0.6937125,0.00023158194,0.000095206866,0.000087674394,0.0013828884,0.0041826093,0.006669652],"genre_scores_gemma":[0.81512606,0.00047163785,0.17722455,0.00013040937,0.000083911174,0.00005156101,0.002492332,0.00036502696,0.0040544355],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997421,0.000050022452,0.000005826992,0.00009336571,0.00007526797,0.000033311877],"domain_scores_gemma":[0.99968374,0.00007414225,0.000042401785,0.00009493307,0.000085508684,0.000019169025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025763255,0.000647863,0.00044884434,0.0007188651,0.00015360661,0.00071387494,0.0004256307,0.00047588133,0.002615277],"category_scores_gemma":[0.0013786631,0.00034000882,0.000466546,0.00034576954,0.0003369742,0.00064210664,0.00073240633,0.0007873309,0.0010597158],"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":[0.00064009806,0.00013337709,0.008057676,0.00024116166,0.0001489772,0.00039463755,0.00039255247,0.23220095,0.11250731,0.0046372926,0.010350787,0.63029516],"study_design_scores_gemma":[0.000014850688,0.00007034667,0.009996815,0.000044958408,0.000033161607,0.00036448907,0.00015965477,0.9256586,0.052133974,0.0048973635,0.006585439,0.000040393767],"about_ca_topic_score_codex":0.0030299663,"about_ca_topic_score_gemma":0.0035863959,"teacher_disagreement_score":0.0030299663,"about_ca_system_score_codex":0.00033222398,"about_ca_system_score_gemma":0.00021510113,"threshold_uncertainty_score":0.008748949},"labels":[],"label_agreement":null},{"id":"W2807192771","doi":"10.5194/isprs-archives-xlii-2-15-2018","title":"UNDERWATER PHOTOGRAMMETRY IN VERY SHALLOW WATERS: MAIN CHALLENGES AND CAUSTICS EFFECT REMOVAL","year":2018,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":34,"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":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Underwater; Photogrammetry; Computer science; Caustic (mathematics); Robustness (evolution); Convolutional neural network; Artificial intelligence; Computer vision; Buoyancy; Object (grammar); Ground truth; Geology; Set (abstract data type); Neutral buoyancy; Marine engineering; Engineering; Mathematics; Geometry","score_opus":0.018402855825562142,"score_gpt":0.25911631656498024,"score_spread":0.2407134607394181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807192771","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":[0.074485525,0.0011140181,0.91996926,0.0007100088,0.00008612221,0.000069412185,0.00010230221,0.00064427895,0.0028190613],"genre_scores_gemma":[0.5020533,0.001766448,0.491508,0.00022610878,0.00006692744,0.00005263706,0.00035377275,0.00023106718,0.003741732],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993529,0.0001094308,0.0000314147,0.00011985921,0.000338585,0.00004775779],"domain_scores_gemma":[0.9992322,0.0002423961,0.00009531601,0.00020673215,0.00019059285,0.000032838427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059793575,0.00061466347,0.0004983242,0.00048305435,0.00032139214,0.0009839142,0.00052369485,0.0010065722,0.0011632019],"category_scores_gemma":[0.0016333408,0.000393506,0.00038393782,0.0005583304,0.00066811184,0.0011274156,0.0010585095,0.0010659904,0.00077802816],"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":[0.00013051253,0.000051223775,0.0052505066,0.0006025189,0.00011096766,0.00057415664,0.00057854,0.09008817,0.3268978,0.0052125296,0.0025306079,0.5679725],"study_design_scores_gemma":[0.000023938199,0.00021558991,0.015786888,0.0001790538,0.00008594496,0.0022729384,0.0007402135,0.66369694,0.27172372,0.018268391,0.02689214,0.000114235976],"about_ca_topic_score_codex":0.0016963934,"about_ca_topic_score_gemma":0.0024242671,"teacher_disagreement_score":0.0016963934,"about_ca_system_score_codex":0.00035681328,"about_ca_system_score_gemma":0.00056957686,"threshold_uncertainty_score":0.0038913488},"labels":[],"label_agreement":null},{"id":"W2809049503","doi":"10.1109/iccnc.2018.8390342","title":"A Flickering Reduction Scheme for Tone Mapped HDR Video","year":2018,"lang":"en","type":"article","venue":"2018 International Conference on Computing, Networking and Communications (ICNC)","topic":"Image Enhancement 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":"Telus (Canada); University of British Columbia","funders":"","keywords":"Flicker; Tone mapping; Computer vision; Computer science; Artificial intelligence; Ghosting; Brightness; Artifact (error); Noise (video); High dynamic range; Filter (signal processing); High-dynamic-range imaging; Tone (literature); Noise reduction; Computer graphics (images); Dynamic range; Image (mathematics)","score_opus":0.09174540967469165,"score_gpt":0.3658306294377614,"score_spread":0.27408521976306977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809049503","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":[0.048083026,0.00020234357,0.94967777,0.000055113553,0.000042794753,0.000092283044,0.000029151242,0.00073120283,0.0010863795],"genre_scores_gemma":[0.39332235,0.00040084522,0.6019538,0.0000982484,0.00006105485,0.00009967316,0.00011862744,0.000110349814,0.0038350585],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975806,0.000030804385,0.000016164735,0.000056982415,0.00011561924,0.000022384651],"domain_scores_gemma":[0.99960476,0.00007410133,0.00005771975,0.000089632515,0.00013799293,0.000035851044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026331714,0.0004417883,0.0003647251,0.00070688274,0.00029104634,0.00043937925,0.0004965961,0.00029340948,0.0013820509],"category_scores_gemma":[0.000885729,0.00015661813,0.0003409292,0.00040029897,0.00030259724,0.00043060223,0.00037565324,0.00048651037,0.00047463502],"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":[0.00029399188,0.00010551438,0.0005632725,0.00014093393,0.00002397087,0.00017011295,0.00017588456,0.0081058815,0.5192083,0.002996079,0.00087302114,0.46734294],"study_design_scores_gemma":[0.000056144625,0.0010372952,0.0055036433,0.00003146612,0.00009617592,0.0015969067,0.00012297358,0.55933964,0.41986546,0.0022042661,0.010058601,0.000087337816],"about_ca_topic_score_codex":0.0009988063,"about_ca_topic_score_gemma":0.0013479565,"teacher_disagreement_score":0.0013820509,"about_ca_system_score_codex":0.00021946945,"about_ca_system_score_gemma":0.00029707036,"threshold_uncertainty_score":0.004623413},"labels":[],"label_agreement":null},{"id":"W2888025659","doi":"10.1049/htl.2018.5067","title":"Endoscopic image enhancement with noise suppression","year":2018,"lang":"en","type":"article","venue":"Healthcare Technology Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Noise (video); Image quality; Image restoration; Naturalness; Stereoscopy; Image processing; Image enhancement; Ghosting; Image (mathematics); Physics","score_opus":0.008719117927630034,"score_gpt":0.2718686376306004,"score_spread":0.26314951970297035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888025659","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":[0.11253878,0.0016400308,0.88039917,0.00011186725,0.00006693993,0.00008612521,0.000035611556,0.001040218,0.0040813726],"genre_scores_gemma":[0.3950281,0.0014223797,0.5975915,0.00015516626,0.0000698997,0.00006912924,0.00010282942,0.000117967844,0.005443073],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971,0.00004221854,0.00001842463,0.00006563939,0.0001413085,0.000022322884],"domain_scores_gemma":[0.99968445,0.000105110266,0.000048885664,0.000045597924,0.00010364158,0.000012363084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041785275,0.0006000518,0.00040424053,0.00050491915,0.0001517221,0.0003960176,0.0004097483,0.0005228544,0.0010124765],"category_scores_gemma":[0.000823795,0.00021481173,0.0005105603,0.0003702667,0.0003015711,0.00054473896,0.00046931594,0.0002905218,0.0005559482],"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":[0.00034804663,0.00007378965,0.0007281736,0.0002734577,0.000036504774,0.00021020675,0.00010028341,0.008285163,0.70481807,0.0010791218,0.00073505973,0.28331214],"study_design_scores_gemma":[0.000044061893,0.0004828371,0.0043530334,0.00004567873,0.00012387517,0.002302415,0.000044885026,0.21078445,0.7708953,0.0007756455,0.010103789,0.000044154353],"about_ca_topic_score_codex":0.00023733368,"about_ca_topic_score_gemma":0.00031425426,"teacher_disagreement_score":0.0010124765,"about_ca_system_score_codex":0.00015032754,"about_ca_system_score_gemma":0.00013670915,"threshold_uncertainty_score":0.0033870935},"labels":[],"label_agreement":null},{"id":"W2903967792","doi":"10.1155/2018/2365414","title":"An Efficient Color Space for Deep-Learning Based Traffic Light Recognition","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Ministry of Education; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Artificial intelligence; Computer science; RGB color model; YCbCr; HSL and HSV; Deep learning; Color space; Computer vision; Traffic signal; Task (project management); Pattern recognition (psychology); Image processing; Real-time computing; Color image; Engineering","score_opus":0.009293734751485604,"score_gpt":0.2674057458900855,"score_spread":0.2581120111385999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903967792","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":[0.056580435,0.00041758703,0.93663615,0.00013694652,0.000064085965,0.000049654118,0.00017053269,0.003516184,0.0024285282],"genre_scores_gemma":[0.6298735,0.0003576354,0.3651415,0.0001615448,0.000034058638,0.000085382606,0.0005732063,0.00016411989,0.0036090356],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998679,0.000022267532,0.0000051114603,0.000028426308,0.00004795936,0.000028296803],"domain_scores_gemma":[0.9997899,0.000028853565,0.000016210082,0.000030253477,0.00011631468,0.000018487232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024175202,0.0005474699,0.0003122929,0.00047319036,0.0002129048,0.00047591783,0.00081608933,0.00031610613,0.0024442726],"category_scores_gemma":[0.0005959311,0.00018026488,0.00030224407,0.0005100928,0.00022443819,0.0009808559,0.0004973737,0.00053372746,0.0007093624],"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":[0.00038856972,0.00026110758,0.0022071584,0.00014037715,0.00006019746,0.000107144944,0.00005875624,0.21320379,0.11898605,0.010843849,0.0071362937,0.6466067],"study_design_scores_gemma":[0.000005352413,0.00002974509,0.00027063474,0.0000033629265,0.0000068755653,0.000025501402,0.0000066093608,0.9795491,0.017762698,0.0010385364,0.0012946889,0.000006777244],"about_ca_topic_score_codex":0.0068520214,"about_ca_topic_score_gemma":0.00821158,"teacher_disagreement_score":0.0068520214,"about_ca_system_score_codex":0.000684322,"about_ca_system_score_gemma":0.0007031487,"threshold_uncertainty_score":0.0136243105},"labels":[],"label_agreement":null},{"id":"W2918212426","doi":"10.1109/tpami.2019.2903035","title":"Computational Imaging on the Electric Grid","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"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":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Israel Science Foundation","keywords":"Rendering (computer graphics); Computer science; Grid; Computer graphics (images); Dynamic range; High dynamic range; Electric light; Grid cell; Image-based lighting; Computer vision; Image-based modeling and rendering; Optics; Physics","score_opus":0.011331846541207086,"score_gpt":0.25629321327268695,"score_spread":0.24496136673147986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918212426","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":[0.28244835,0.00036497178,0.693034,0.00070741173,0.00010708173,0.00007012707,0.0017909445,0.0009657744,0.020511385],"genre_scores_gemma":[0.8135011,0.00019410485,0.17965588,0.00013105065,0.000046575657,0.00008922826,0.0012064764,0.00017177462,0.005003849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998797,0.000029448733,0.0000051062293,0.000039818187,0.000029920244,0.000015959078],"domain_scores_gemma":[0.999706,0.00015617097,0.00002861101,0.00004328233,0.000042605527,0.000023225652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001601518,0.00027637125,0.00027211258,0.0002610646,0.00016383344,0.0006931862,0.0004518332,0.00036931704,0.0028206296],"category_scores_gemma":[0.001107147,0.00015796602,0.0002881764,0.00045595397,0.0004303702,0.00055828673,0.0007652434,0.00043240472,0.0002951816],"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":[0.00020627902,0.00008094506,0.0030203562,0.0001458116,0.00004777125,0.0001842799,0.00016336418,0.9016909,0.016602965,0.021546109,0.0042525786,0.05205873],"study_design_scores_gemma":[0.000016085458,0.000015048691,0.00080917176,0.000004268202,0.0000022879854,0.000036114052,0.000031851003,0.9881555,0.0015024084,0.0076271095,0.0017938655,0.0000063707894],"about_ca_topic_score_codex":0.0027478964,"about_ca_topic_score_gemma":0.0028359834,"teacher_disagreement_score":0.0028206296,"about_ca_system_score_codex":0.00024407009,"about_ca_system_score_gemma":0.00033324095,"threshold_uncertainty_score":0.009436011},"labels":[],"label_agreement":null},{"id":"W2918587444","doi":"10.23919/eusipco.2019.8903046","title":"Learning of Image Dehazing Models for Segmentation Tasks","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement 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":"Université Laval","funders":"","keywords":"Computer science; Artificial intelligence; Discriminator; Segmentation; Computer vision; Image segmentation; Image (mathematics); Ground truth; Generator (circuit theory); Perception; Image restoration; Pixel; Pattern recognition (psychology); Image processing","score_opus":0.028191200136593198,"score_gpt":0.30514239501245893,"score_spread":0.2769511948758657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918587444","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":[0.044123318,0.00064832106,0.9523358,0.00022912065,0.000051825013,0.00011948838,0.00010188715,0.0010378623,0.0013524481],"genre_scores_gemma":[0.6838323,0.00067124836,0.3091541,0.00037302604,0.000091062,0.0002231316,0.000631381,0.00029109008,0.0047327937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963045,0.000084289364,0.000015827642,0.00013941423,0.0000810065,0.00004891445],"domain_scores_gemma":[0.99877363,0.00065769564,0.00016336552,0.00016193505,0.00017913007,0.00006426219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013638559,0.0013202928,0.0008129075,0.0006917346,0.00021784953,0.0008783763,0.0014967403,0.0017488103,0.0015189202],"category_scores_gemma":[0.0037508977,0.000534461,0.00076301524,0.00032457197,0.00080010423,0.0012175652,0.001102421,0.0021192965,0.0005780292],"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":[0.00028400682,0.0001682521,0.0009881546,0.00015764724,0.00010918303,0.00006354344,0.00006877758,0.8383546,0.01963587,0.004279928,0.0016202149,0.13426985],"study_design_scores_gemma":[0.0000045144357,0.0000436085,0.000115938856,0.000006795531,0.000007759186,0.000021113448,0.000004277011,0.99449253,0.0036334353,0.0015019695,0.00016445454,0.0000036822491],"about_ca_topic_score_codex":0.0013809301,"about_ca_topic_score_gemma":0.0019425757,"teacher_disagreement_score":0.0017488103,"about_ca_system_score_codex":0.000853001,"about_ca_system_score_gemma":0.0005054732,"threshold_uncertainty_score":0.0072128773},"labels":[],"label_agreement":null},{"id":"W2922871176","doi":"10.3934/ipi.2019023","title":"A variational gamma correction model for image contrast enhancement","year":2019,"lang":"en","type":"article","venue":"Inverse Problems and Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":42,"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":"Pixel; Gamma correction; Regularization (linguistics); Contrast (vision); Computer science; Benchmark (surveying); Uniqueness; Energy functional; Artificial intelligence; Image (mathematics); Algorithm; Image quality; Function (biology); Mathematics; Computer vision","score_opus":0.012078138051701763,"score_gpt":0.23217166339494158,"score_spread":0.2200935253432398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922871176","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":[0.0039767064,0.00031723504,0.99390054,0.00013709717,0.000023383895,0.000018066054,0.000020366871,0.00005287306,0.0015537456],"genre_scores_gemma":[0.527526,0.001792651,0.4432336,0.0003778484,0.00011368293,0.00025050767,0.00025117627,0.00034108962,0.02611344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998123,0.000060141756,0.000006751761,0.000041204472,0.00005907477,0.000020413823],"domain_scores_gemma":[0.9998098,0.00009627912,0.000020478119,0.000015326215,0.00004423438,0.000013912729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000652025,0.00076500536,0.0006543947,0.00045737802,0.00027614078,0.0006921636,0.0013728596,0.0011573094,0.0015041435],"category_scores_gemma":[0.0011153006,0.0004169243,0.0009923578,0.0003892957,0.0009534868,0.000965547,0.0008159085,0.0010389428,0.0003412934],"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":[0.000047976824,0.00003035705,0.0003858156,0.000116170435,0.000053779066,0.0001476083,0.00010478392,0.8317623,0.021327736,0.116356716,0.0013605754,0.02830626],"study_design_scores_gemma":[0.0000033867693,0.00001225182,0.000053381427,0.000004068505,0.0000052742384,0.00003800035,0.0000041310973,0.9903494,0.00095803983,0.007708921,0.00085567025,0.0000074941318],"about_ca_topic_score_codex":0.0041786944,"about_ca_topic_score_gemma":0.0031264604,"teacher_disagreement_score":0.0041786944,"about_ca_system_score_codex":0.00086442573,"about_ca_system_score_gemma":0.0007546872,"threshold_uncertainty_score":0.008308768},"labels":[],"label_agreement":null},{"id":"W2941547246","doi":"10.1109/lsp.2019.2910403","title":"Single Image Dehazing with a Generic Model-Agnostic Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Image (mathematics); Artificial intelligence; Atmosphere (unit); Simple (philosophy); Artificial neural network; Plug and play; Computer vision; Pattern recognition (psychology)","score_opus":0.014948161483332039,"score_gpt":0.2192740983239218,"score_spread":0.20432593684058978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941547246","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":[0.07951582,0.0003791012,0.91458076,0.00018730982,0.000113368886,0.000055985405,0.00011340361,0.0018966731,0.0031575502],"genre_scores_gemma":[0.6236267,0.0004509087,0.36748788,0.0002041095,0.000041466174,0.00003920356,0.00031470903,0.00013650776,0.007698463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999853,0.000013769817,0.000005798606,0.000045246175,0.000057132846,0.000025030862],"domain_scores_gemma":[0.99972326,0.000039638107,0.00003497337,0.00012583673,0.000056105593,0.00002014392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031830408,0.0005836063,0.00039065228,0.00023726444,0.00016312682,0.00046140177,0.0009556572,0.00073593884,0.00095863745],"category_scores_gemma":[0.0006273178,0.0002467052,0.00047169137,0.00018368244,0.00036019404,0.00093683647,0.00076320104,0.0010235772,0.00037725075],"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":[0.0003916335,0.00020442205,0.0017221706,0.00023074921,0.0002057538,0.0002466946,0.000086075685,0.38159853,0.27382615,0.008747013,0.0043037743,0.3284369],"study_design_scores_gemma":[0.0000054208963,0.000082219354,0.0005064986,0.000007759641,0.000022297687,0.00013257281,0.000006470347,0.9485194,0.047756616,0.0010269135,0.0019203998,0.000013372865],"about_ca_topic_score_codex":0.00217074,"about_ca_topic_score_gemma":0.004783185,"teacher_disagreement_score":0.00217074,"about_ca_system_score_codex":0.00041245198,"about_ca_system_score_gemma":0.00043448864,"threshold_uncertainty_score":0.0043162107},"labels":[],"label_agreement":null},{"id":"W2943585339","doi":"10.1109/iscas.2019.8702404","title":"A High Contrast Video Inverse Tone Mapping Operator for High Dynamic Range Applications","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 British Columbia","funders":"","keywords":"Tone mapping; High dynamic range; Contrast (vision); High-dynamic-range imaging; Computer science; Luminance; Computer vision; Artificial intelligence; Range (aeronautics); Operator (biology); Dynamic range; Inverse; Human visual system model; Quality (philosophy); Image (mathematics); Mathematics; Engineering","score_opus":0.008513261113782613,"score_gpt":0.25451792778591453,"score_spread":0.24600466667213192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943585339","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":[0.04262107,0.000308591,0.95267045,0.0000963029,0.00009980643,0.000096279946,0.00003744019,0.00078178244,0.0032882567],"genre_scores_gemma":[0.26434368,0.00055941293,0.7266758,0.00018697856,0.00011882939,0.00008600878,0.00014454663,0.00015624463,0.0077284253],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987245,0.000021971107,0.00000625866,0.000023135799,0.000064755215,0.0000114415225],"domain_scores_gemma":[0.99975485,0.00007357934,0.000030874933,0.000045079836,0.00006753874,0.000028078293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024384099,0.00033411547,0.00017810633,0.00034517012,0.0001674417,0.00039864512,0.0004258945,0.00032507582,0.003098296],"category_scores_gemma":[0.0005578532,0.00010678111,0.0002783455,0.000189676,0.00023540568,0.000509685,0.00040382845,0.0005868971,0.00065741304],"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":[0.00024811554,0.00007631136,0.00055695453,0.00016642391,0.000018062603,0.00027354207,0.00009921755,0.0029079635,0.6432929,0.005679958,0.002079458,0.34460112],"study_design_scores_gemma":[0.000064697044,0.00091980107,0.0034029966,0.000051869258,0.000083015235,0.0039792326,0.00016009697,0.29482785,0.6443457,0.0026814814,0.04941169,0.000071519404],"about_ca_topic_score_codex":0.00035666162,"about_ca_topic_score_gemma":0.0006502006,"teacher_disagreement_score":0.003098296,"about_ca_system_score_codex":0.00012108334,"about_ca_system_score_gemma":0.00020594063,"threshold_uncertainty_score":0.01036489},"labels":[],"label_agreement":null},{"id":"W2950315129","doi":"10.48550/arxiv.1906.04909","title":"All-Weather Deep Outdoor Lighting Estimation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Enhancement 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":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Panorama; Computer science; Artificial intelligence; Overcast; Sky; Computer vision; Image (mathematics); Artificial neural network; Computer graphics (images); Geography; Meteorology","score_opus":0.05451961097098654,"score_gpt":0.20378445133172324,"score_spread":0.1492648403607367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950315129","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":[0.18637303,0.0012850788,0.77883387,0.00035493856,0.00032158388,0.00006801646,0.002083676,0.015447971,0.01523179],"genre_scores_gemma":[0.831611,0.00045281107,0.14675963,0.00021544704,0.00012398824,0.00003988427,0.0033018615,0.00048704204,0.017008275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998791,0.000009719588,0.0000028482948,0.000058043428,0.000023363533,0.000026904832],"domain_scores_gemma":[0.9999033,0.00001169009,0.000012733028,0.000031973454,0.000029168856,0.000011052914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016463554,0.00086469436,0.0004714711,0.0003930008,0.00019806047,0.0005386596,0.00091868185,0.0005148864,0.004123052],"category_scores_gemma":[0.00052001665,0.00039632103,0.0006121525,0.00029606998,0.00019845802,0.0010021758,0.0005584736,0.00085699133,0.0014679709],"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":[0.00034733486,0.00020094686,0.004487309,0.00014342957,0.00020118558,0.00016387561,0.00006746653,0.41019264,0.045787245,0.0027687158,0.0153795425,0.52026033],"study_design_scores_gemma":[0.0000072923585,0.000026097114,0.0013221004,0.000009389631,0.000017269615,0.000045933746,0.00001001182,0.9829732,0.011879895,0.001293337,0.0024058234,0.00000961131],"about_ca_topic_score_codex":0.007092791,"about_ca_topic_score_gemma":0.016844172,"teacher_disagreement_score":0.007092791,"about_ca_system_score_codex":0.00048055573,"about_ca_system_score_gemma":0.00032935702,"threshold_uncertainty_score":0.014103055},"labels":[],"label_agreement":null},{"id":"W2952722698","doi":"10.1016/j.brachy.2019.04.136","title":"Commissioning and Clinical Use of the Uronav Therapy System with the Electromagnetic Tracking Technology for Intra-Op US Guided Prostate HDR","year":2019,"lang":"en","type":"article","venue":"Brachytherapy","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Medicine; Medical physics; Prostate; Project commissioning; Tracking (education); Publishing; Internal medicine","score_opus":0.025048910047464783,"score_gpt":0.2881905479950642,"score_spread":0.2631416379475994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952722698","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":[0.94857967,0.0017686805,0.043524604,0.00018578944,0.00006476033,0.00024612423,0.00021370719,0.0005695826,0.0048469757],"genre_scores_gemma":[0.9910533,0.00026066857,0.007763068,0.000040261264,0.00001927436,0.000033050997,0.00006880489,0.00016099609,0.0006005483],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985341,0.00082824845,0.00009080559,0.00018262348,0.0002687224,0.0000954756],"domain_scores_gemma":[0.9982135,0.00073252036,0.00022263455,0.00051260594,0.00020375283,0.00011499778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014480575,0.0004480255,0.000540114,0.00067707227,0.0005650338,0.0007697964,0.0006764841,0.0004952248,0.0020462798],"category_scores_gemma":[0.00541428,0.00048843015,0.00034932003,0.00065987837,0.001194106,0.00050946116,0.00071265345,0.0006266542,0.00051474216],"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":[0.014744577,0.003053433,0.1337452,0.0007284755,0.00017935294,0.0045864224,0.0061354,0.020040734,0.3995639,0.0021530318,0.0020369075,0.41303262],"study_design_scores_gemma":[0.0008245845,0.037506543,0.29194993,0.00013301932,0.0006424729,0.018964617,0.0013613867,0.026108753,0.595555,0.00076963,0.02581619,0.00036785987],"about_ca_topic_score_codex":0.0020542832,"about_ca_topic_score_gemma":0.0015512103,"teacher_disagreement_score":0.0020542832,"about_ca_system_score_codex":0.00076673465,"about_ca_system_score_gemma":0.0008586956,"threshold_uncertainty_score":0.007658124},"labels":[],"label_agreement":null},{"id":"W2954819764","doi":"10.3390/rs11131591","title":"Underwater Image Restoration Based on a Parallel Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Underwater; Computer science; Convolutional neural network; Artificial intelligence; Visibility; Computer vision; Image restoration; Image (mathematics); Transmission (telecommunications); Pattern recognition (psychology); Image processing; Telecommunications; Geology","score_opus":0.014496012717467754,"score_gpt":0.24589786999226487,"score_spread":0.23140185727479712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954819764","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":[0.03821371,0.00040852642,0.95758134,0.00015887272,0.00006185557,0.000034940404,0.000041386327,0.0010562197,0.0024431143],"genre_scores_gemma":[0.66774976,0.00066736166,0.3238075,0.00017215076,0.000058012436,0.00006959507,0.00020633954,0.00009834998,0.0071708667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987054,0.000013729461,0.0000050919784,0.00003742055,0.000055249795,0.000017841137],"domain_scores_gemma":[0.9998652,0.00003260768,0.000020694511,0.000022189945,0.000050463615,0.000008799352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027706483,0.0005382869,0.0003836149,0.00035979206,0.00021697953,0.00030280533,0.0006919562,0.0004956099,0.0010398469],"category_scores_gemma":[0.0005061441,0.0002880237,0.00039070757,0.0003034779,0.00034194347,0.0006391643,0.0005253524,0.00064708426,0.00024820407],"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":[0.00017441298,0.00009572469,0.0011411976,0.00010243653,0.000094669835,0.00018291229,0.000054346587,0.6369608,0.059136406,0.0030464153,0.0019197584,0.29709092],"study_design_scores_gemma":[0.0000023501177,0.000016692196,0.00012941776,0.0000023163075,0.000008822592,0.000025093348,0.0000025279846,0.9947213,0.0043713627,0.00031612412,0.0004000551,0.000003929226],"about_ca_topic_score_codex":0.0067742188,"about_ca_topic_score_gemma":0.007946256,"teacher_disagreement_score":0.0067742188,"about_ca_system_score_codex":0.0004395068,"about_ca_system_score_gemma":0.000504776,"threshold_uncertainty_score":0.013469577},"labels":[],"label_agreement":null},{"id":"W2957953566","doi":"10.1109/meco.2019.8760100","title":"Image Enhancement by Jetson TX2 Embedded AI Computing Device","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Process (computing); Image processing; General-purpose computing on graphics processing units; Embedded system; Image (mathematics); Artificial intelligence; Computer graphics (images); Operating system; Graphics","score_opus":0.006837671724916876,"score_gpt":0.2719617519101993,"score_spread":0.2651240801852824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957953566","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":[0.07346398,0.0009114713,0.80979896,0.0007603167,0.0006289958,0.00027571796,0.0010587388,0.047914524,0.06518731],"genre_scores_gemma":[0.3732902,0.0004916269,0.54969347,0.00090323534,0.00012733063,0.00037100675,0.0019401879,0.0046116784,0.06857131],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998056,0.000016354266,0.000013972146,0.000037774727,0.00008863557,0.00003763266],"domain_scores_gemma":[0.9997687,0.00004414642,0.000016445043,0.000037724374,0.00010604346,0.000026891497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026069573,0.0006142544,0.00031251032,0.00039097658,0.0002886689,0.0009627228,0.0010112113,0.00034299708,0.021776509],"category_scores_gemma":[0.0006713492,0.0002453449,0.00027419967,0.00039558666,0.00018683741,0.000973632,0.00058225146,0.00058879086,0.0052083647],"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":[0.0012616847,0.00017592628,0.0018291879,0.00050949014,0.000048833117,0.0010580863,0.00033359483,0.009550054,0.5908206,0.02060265,0.075450115,0.29835972],"study_design_scores_gemma":[0.00010626657,0.00026596268,0.0025222877,0.000063898944,0.00004092665,0.00086008396,0.000057316953,0.20198148,0.6566636,0.0024981652,0.13485622,0.0000837374],"about_ca_topic_score_codex":0.0012096207,"about_ca_topic_score_gemma":0.0012899922,"teacher_disagreement_score":0.021776509,"about_ca_system_score_codex":0.00045697144,"about_ca_system_score_gemma":0.00040411553,"threshold_uncertainty_score":0.07284963},"labels":[],"label_agreement":null},{"id":"W2963780738","doi":"10.1016/j.cviu.2017.09.003","title":"Haze visibility enhancement: A Survey and quantitative benchmarking","year":2017,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":172,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation Singapore; Nvidia","keywords":"Benchmark (surveying); Visibility; Benchmarking; Ground truth; Computer science; Haze; Artificial intelligence; Image (mathematics); Filter (signal processing); Computer vision; Remote sensing; Pattern recognition (psychology); Optics; Geography; Cartography; Physics","score_opus":0.08099546624211718,"score_gpt":0.350093309666092,"score_spread":0.2690978434239748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963780738","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":[0.07916511,0.19368398,0.69959784,0.00051699975,0.00024803888,0.00052573916,0.00177098,0.0034007274,0.021090653],"genre_scores_gemma":[0.51718867,0.14452344,0.32730117,0.00027743183,0.00040244358,0.00027457526,0.003295845,0.000978844,0.0057576676],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961665,0.00059891917,0.00027229896,0.00084942824,0.0019532077,0.0001595453],"domain_scores_gemma":[0.99018735,0.0047319536,0.0012795338,0.0010429084,0.002575548,0.00018265485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041854344,0.0016414274,0.0019248695,0.0071278107,0.00051223545,0.0023395047,0.0019444302,0.0012929924,0.0023750814],"category_scores_gemma":[0.0110184075,0.0006729109,0.0008541828,0.0064709624,0.0008296102,0.0033173126,0.0011435716,0.00074417284,0.00084375835],"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":[0.0003248532,0.00036148762,0.009792144,0.0053571677,0.00032925376,0.000051195675,0.00016572853,0.01971981,0.02495816,0.003909853,0.004323388,0.93070686],"study_design_scores_gemma":[0.00012941519,0.0041088015,0.06768648,0.003779387,0.0016236288,0.0040816604,0.0015165062,0.47029677,0.30724528,0.018590646,0.120422974,0.0005186028],"about_ca_topic_score_codex":0.0017202778,"about_ca_topic_score_gemma":0.0018422806,"teacher_disagreement_score":0.0071278107,"about_ca_system_score_codex":0.0007094919,"about_ca_system_score_gemma":0.0007976172,"threshold_uncertainty_score":0.02213496},"labels":[],"label_agreement":null},{"id":"W2963882477","doi":"10.1109/iccv.2017.484","title":"Learning High Dynamic Range from Outdoor Panoramas","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"High dynamic range; Computer science; Autoencoder; Artificial intelligence; Ranging; Computer vision; Dynamic range; Ground truth; Range (aeronautics); Set (abstract data type); Process (computing); Deep learning; High-dynamic-range imaging; Computer graphics (images); Engineering","score_opus":0.015902143543326343,"score_gpt":0.2780582301056642,"score_spread":0.26215608656233785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963882477","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":[0.15234852,0.0007694501,0.8386597,0.00019377089,0.00008303003,0.00006140623,0.00053304,0.0031556147,0.00419552],"genre_scores_gemma":[0.7301325,0.0010001045,0.25985014,0.00024844738,0.00015029755,0.000059765367,0.002240695,0.0003664419,0.005951511],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973994,0.000025081146,0.000007678209,0.00012907707,0.000056645404,0.000041633004],"domain_scores_gemma":[0.99971956,0.00007835028,0.00003889113,0.00008054757,0.00006126667,0.000021359738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028558925,0.00072828634,0.0006457655,0.00084809336,0.00016630799,0.0007168316,0.00060164323,0.00052222854,0.0014865432],"category_scores_gemma":[0.0010794381,0.00043317128,0.0005965832,0.0006802587,0.00032562733,0.000986245,0.00061193836,0.0010048748,0.001045363],"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":[0.00022822483,0.00021808375,0.0058570527,0.00029261268,0.00018832256,0.00022722148,0.00016854312,0.1604829,0.124406494,0.0021373117,0.006306351,0.6994869],"study_design_scores_gemma":[0.000024163335,0.00009600056,0.010722238,0.000040568873,0.000057537392,0.0004877482,0.00012224073,0.93740785,0.03931869,0.0050684283,0.006623931,0.000030634736],"about_ca_topic_score_codex":0.0023187383,"about_ca_topic_score_gemma":0.005458265,"teacher_disagreement_score":0.0023187383,"about_ca_system_score_codex":0.00027185172,"about_ca_system_score_gemma":0.00031119442,"threshold_uncertainty_score":0.0049729943},"labels":[],"label_agreement":null},{"id":"W2966398006","doi":"10.1109/crv.2019.00026","title":"Instance Segmentation Based Semantic Matting for Compositing Applications","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"McGill University","funders":"","keywords":"Compositing; Computer science; Artificial intelligence; Computer vision; Segmentation; Image (mathematics); Task (project management); Image segmentation; Keying; Computer graphics (images)","score_opus":0.009102799490976402,"score_gpt":0.2681075259153567,"score_spread":0.25900472642438027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966398006","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":[0.010833941,0.00019139261,0.9764357,0.000040303403,0.00003105315,0.000055452183,0.00014524627,0.010680954,0.00158591],"genre_scores_gemma":[0.13776158,0.00028329395,0.85712785,0.00006987537,0.00004586623,0.00005585452,0.0008858682,0.001573102,0.0021967078],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996061,0.000055283555,0.000026831769,0.00012124959,0.00014771441,0.00004288555],"domain_scores_gemma":[0.99944407,0.00016693695,0.0000462724,0.00019275461,0.00010758851,0.000042421183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036599243,0.0012611864,0.0007302862,0.0011400366,0.0003430811,0.0014124237,0.0014564977,0.0009347858,0.0044990266],"category_scores_gemma":[0.0012301684,0.00043590847,0.0010657345,0.0012277003,0.0004402396,0.00167022,0.0007973478,0.00116865,0.0018398409],"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":[0.00054534414,0.00014098547,0.0009862643,0.00041901707,0.00012543166,0.000507231,0.00035334763,0.033583954,0.28577107,0.011481238,0.008574177,0.6575119],"study_design_scores_gemma":[0.000023018507,0.00011768542,0.0011800894,0.000023655653,0.000058896076,0.00078604603,0.00012908081,0.71641237,0.24860801,0.011911191,0.020705748,0.00004422038],"about_ca_topic_score_codex":0.0009065519,"about_ca_topic_score_gemma":0.001594587,"teacher_disagreement_score":0.0044990266,"about_ca_system_score_codex":0.00042853307,"about_ca_system_score_gemma":0.00027052843,"threshold_uncertainty_score":0.015050709},"labels":[],"label_agreement":null},{"id":"W2967854230","doi":"10.2312/exp.20191083","title":"Stipple Removal in Extreme-tone Regions","year":2019,"lang":"en","type":"article","venue":"Eurographics","topic":"Image Enhancement 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":"Carleton University","funders":"","keywords":"Tone (literature); Computer science; Cover (algebra); Computer vision; Artificial intelligence; Tone mapping; Image (mathematics); Engineering","score_opus":0.041692071887794314,"score_gpt":0.2729771001400755,"score_spread":0.23128502825228117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967854230","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":[0.17127815,0.00042886066,0.81982785,0.00016339196,0.00016179623,0.00014083064,0.00019111019,0.0022133416,0.0055947504],"genre_scores_gemma":[0.53952456,0.00039581637,0.45012516,0.00024774377,0.000059913924,0.00007008664,0.0004104249,0.000925081,0.008241254],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996371,0.000053126074,0.000024150735,0.000084761254,0.00014037601,0.000060452294],"domain_scores_gemma":[0.99902546,0.00029282251,0.00009660195,0.00034771842,0.00015227825,0.00008510024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040820573,0.0006605535,0.00057546125,0.0005243564,0.00041716066,0.00086797646,0.0007327858,0.00062212406,0.0044929],"category_scores_gemma":[0.0021372521,0.0003493225,0.00047806185,0.00046366264,0.0006045742,0.000890791,0.0011655039,0.0011559657,0.0013808373],"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":[0.0006288479,0.00009534797,0.0010163862,0.0003110017,0.000042391992,0.0010342485,0.00030120817,0.028484182,0.66238075,0.006541554,0.003598572,0.2955655],"study_design_scores_gemma":[0.000056650744,0.00031104262,0.0056142523,0.00006384387,0.000054645752,0.0023527571,0.00018228476,0.28943458,0.66538113,0.00861237,0.027857801,0.00007854757],"about_ca_topic_score_codex":0.0005137674,"about_ca_topic_score_gemma":0.00089327374,"teacher_disagreement_score":0.0044929,"about_ca_system_score_codex":0.00020956734,"about_ca_system_score_gemma":0.00035017828,"threshold_uncertainty_score":0.015030265},"labels":[],"label_agreement":null},{"id":"W2970435330","doi":"10.1109/icip.2019.8803161","title":"Cloudmaskgan: A Content-Aware Unpaired Image-To-Image Translation Algorithm for Remote Sensing Imagery","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","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":"Simon Fraser University","funders":"","keywords":"Computer science; Translation (biology); Artificial intelligence; Ground truth; Computer vision; Image translation; Image (mathematics); Obstacle; Task (project management); Land cover; Cloud computing; Image segmentation; Pixel; Segmentation; Automatic image annotation; Cover (algebra); Algorithm; Image retrieval; Land use","score_opus":0.031448456038306205,"score_gpt":0.27136755586306693,"score_spread":0.23991909982476073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970435330","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":[0.025768599,0.0004728884,0.96604806,0.00015154501,0.00017313834,0.00017351605,0.00029541983,0.0045733075,0.0023435773],"genre_scores_gemma":[0.119082816,0.0002909959,0.87374246,0.00021518885,0.00007790146,0.00017081546,0.001251148,0.0007806534,0.0043879524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997714,0.000025378127,0.000009937118,0.000062077735,0.00010102836,0.000030190835],"domain_scores_gemma":[0.99980384,0.000043332868,0.00002624479,0.00005026058,0.000056695557,0.000019615072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003597452,0.0008990695,0.00062061864,0.00095675007,0.00040182544,0.000740586,0.001170326,0.00077915704,0.0036037827],"category_scores_gemma":[0.0009600798,0.00028747338,0.00063945283,0.00092021143,0.00046403808,0.00084175717,0.00088419556,0.0010153058,0.0019010386],"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":[0.0005057099,0.00017794309,0.00075095025,0.00021849814,0.000133364,0.0002511653,0.0000896476,0.05541994,0.105269946,0.005463579,0.013278182,0.8184412],"study_design_scores_gemma":[0.000056192268,0.00013764424,0.0009445774,0.000018385286,0.000026831809,0.00028348595,0.000030013714,0.9315004,0.053530715,0.003709553,0.009737241,0.000024904415],"about_ca_topic_score_codex":0.0027057775,"about_ca_topic_score_gemma":0.005307236,"teacher_disagreement_score":0.0036037827,"about_ca_system_score_codex":0.00043641886,"about_ca_system_score_gemma":0.0007148709,"threshold_uncertainty_score":0.012055874},"labels":[],"label_agreement":null},{"id":"W2970781471","doi":"10.1109/iccv.2019.01030","title":"Physics-Based Rendering for Improving Robustness to Rain","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Université Laval","funders":"","keywords":"Robustness (evolution); Computer science; Rendering (computer graphics); Segmentation; Object detection; Artificial intelligence; Computer vision; Image segmentation","score_opus":0.01622788749615066,"score_gpt":0.2605844668561997,"score_spread":0.244356579360049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970781471","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":[0.057432335,0.00031470484,0.91394234,0.0002832645,0.00019186678,0.000172199,0.0007252151,0.01907065,0.007867453],"genre_scores_gemma":[0.4803757,0.0004925238,0.5048658,0.00034888132,0.0000920425,0.00012916047,0.0019799676,0.006384231,0.0053317584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996044,0.000049412367,0.000018955763,0.000089847126,0.00017960713,0.000057790614],"domain_scores_gemma":[0.99943763,0.00018570831,0.00003391848,0.00017028848,0.000127137,0.000045329067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005925478,0.0011728029,0.00078117586,0.0008167035,0.0004077766,0.0018826913,0.0013655969,0.000798618,0.008726031],"category_scores_gemma":[0.0026386546,0.0007114307,0.0012527251,0.00045057645,0.0005238321,0.0013713255,0.0016530156,0.0017117744,0.002234103],"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":[0.0007018063,0.0002594114,0.0034227355,0.00053423253,0.0002696751,0.0004999494,0.0005381349,0.4880828,0.2453382,0.010749298,0.016365271,0.2332384],"study_design_scores_gemma":[0.000043250344,0.000055796947,0.00094164367,0.00001637,0.000033408083,0.00015416967,0.000035395908,0.94899255,0.03796099,0.0036623501,0.008074467,0.000029686065],"about_ca_topic_score_codex":0.005311807,"about_ca_topic_score_gemma":0.0075564296,"teacher_disagreement_score":0.008726031,"about_ca_system_score_codex":0.0007198166,"about_ca_system_score_gemma":0.00067287206,"threshold_uncertainty_score":0.029191494},"labels":[],"label_agreement":null},{"id":"W2977375802","doi":"10.3390/jimaging5100079","title":"A Contrast-Guided Approach for the Enhancement of Low-Lighting Underwater Images","year":2019,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ocean Networks Canada Society; University of Victoria","funders":"University of Victoria","keywords":"Underwater; Computer science; Visibility; Computer vision; Contrast (vision); Artificial intelligence; Radiance; Image enhancement; Image (mathematics); Remote sensing; Optics; Geology","score_opus":0.013058984662390591,"score_gpt":0.2651244751821631,"score_spread":0.2520654905197725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977375802","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":[0.03753698,0.00083702,0.95799834,0.00010860656,0.00004985067,0.00013395117,0.00015111249,0.0010645941,0.002119648],"genre_scores_gemma":[0.13986684,0.0009160488,0.8557881,0.00012130993,0.000048939102,0.00008450436,0.00041176286,0.00022678316,0.0025357234],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999762,0.000027441087,0.000010801567,0.000054859487,0.000111203095,0.000033704637],"domain_scores_gemma":[0.99962544,0.00008254899,0.00005340708,0.000066642824,0.00014401744,0.000027889515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043872424,0.0007440804,0.00050208776,0.0013742934,0.00027752222,0.000700423,0.00075260794,0.0006012878,0.00095459254],"category_scores_gemma":[0.0009829098,0.0002709657,0.0006179867,0.0006291488,0.00045966438,0.0006938309,0.00094460783,0.0010079978,0.00047426394],"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":[0.00026810815,0.00013517708,0.0013967297,0.00052510056,0.000111738314,0.00024232068,0.00018800108,0.023311306,0.54900783,0.004808501,0.0030374292,0.41696772],"study_design_scores_gemma":[0.000040956365,0.00036826526,0.006721676,0.00007527399,0.00019437843,0.0019825192,0.000133806,0.44515362,0.52085465,0.0036549005,0.02074155,0.000078314755],"about_ca_topic_score_codex":0.0017062529,"about_ca_topic_score_gemma":0.004241714,"teacher_disagreement_score":0.0017062529,"about_ca_system_score_codex":0.00029780337,"about_ca_system_score_gemma":0.00054074556,"threshold_uncertainty_score":0.0033926368},"labels":[],"label_agreement":null},{"id":"W2982143932","doi":"10.1109/iccv.2019.00033","title":"What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Color constancy; Artificial intelligence; Robustness (evolution); Deep neural networks; Deep learning; Artificial neural network; Color balance; Segmentation; Computer vision; Image segmentation; Pattern recognition (psychology); Image (mathematics); Image processing; Color image","score_opus":0.01532608444085527,"score_gpt":0.25246836347472773,"score_spread":0.23714227903387247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982143932","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":[0.29940686,0.020455552,0.59577024,0.04906049,0.0018341363,0.00015335286,0.0005861178,0.012913206,0.019820048],"genre_scores_gemma":[0.8765632,0.003560014,0.10763372,0.0043655285,0.00050436106,0.00007281426,0.00023723718,0.00078451994,0.006278647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990513,0.0002977375,0.00004726484,0.00021313413,0.0002613863,0.00012916443],"domain_scores_gemma":[0.99581945,0.0020731206,0.00038496434,0.0010187798,0.0005327492,0.00017084497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032917792,0.0016136288,0.0005653926,0.0004648139,0.0005653506,0.0015010893,0.0015423953,0.0019653563,0.0036803302],"category_scores_gemma":[0.021616522,0.00037179174,0.0003695262,0.0005458728,0.0017337945,0.0053831046,0.0016619699,0.0034459336,0.0011142036],"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":[0.00085306435,0.00027255458,0.01202811,0.0006271988,0.00036981338,0.0005507416,0.0005820251,0.14140472,0.07051119,0.030670818,0.03064888,0.71148086],"study_design_scores_gemma":[0.00012417285,0.0008280992,0.005783391,0.0005528187,0.00020345232,0.00074741605,0.00036615308,0.74856365,0.11251157,0.10170023,0.02850165,0.00011731204],"about_ca_topic_score_codex":0.0035665857,"about_ca_topic_score_gemma":0.005688186,"teacher_disagreement_score":0.0036803302,"about_ca_system_score_codex":0.00086142373,"about_ca_system_score_gemma":0.0008487623,"threshold_uncertainty_score":0.017408848},"labels":[],"label_agreement":null},{"id":"W2983723229","doi":"10.1007/s00779-019-01334-w","title":"Estimating ambient visibility in the presence of fog: a deep convolutional neural network approach","year":2019,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Zayed University","keywords":"Visibility; Computer science; Convolutional neural network; Artificial intelligence; Computer vision; Remote sensing; Meteorology; Geology; Geography","score_opus":0.014201172880600665,"score_gpt":0.25100037801562153,"score_spread":0.23679920513502087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983723229","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":[0.39422125,0.0011716032,0.60038173,0.00023121203,0.00015228085,0.000024722724,0.00031793522,0.00065746123,0.0028418703],"genre_scores_gemma":[0.9728309,0.00032035136,0.02531141,0.00003690502,0.00006280893,0.0000055279634,0.00021553943,0.000028455912,0.0011879918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989414,0.000011035096,0.0000034068491,0.000031227562,0.00002021669,0.000039862003],"domain_scores_gemma":[0.99984765,0.000052395928,0.000023255476,0.000014791528,0.00004212119,0.000019782834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018824631,0.0006186686,0.00052122626,0.00046429466,0.00018563365,0.000558092,0.00061435683,0.00061515253,0.0004237688],"category_scores_gemma":[0.0006929735,0.0003861197,0.00042291143,0.00038001838,0.0002539033,0.00083513965,0.0007069524,0.00095733313,0.00013829324],"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":[0.0007288472,0.0003613784,0.020705355,0.00017696079,0.00029383108,0.0006061075,0.00019449322,0.6545385,0.053682923,0.0036493454,0.0030805408,0.26198167],"study_design_scores_gemma":[0.0000034610368,0.0000150981405,0.0018750756,0.0000044592157,0.000013227865,0.000027318896,0.000011531146,0.9955884,0.0014445812,0.0008436983,0.00016784066,0.000005324781],"about_ca_topic_score_codex":0.014706274,"about_ca_topic_score_gemma":0.018985117,"teacher_disagreement_score":0.014706274,"about_ca_system_score_codex":0.00031416348,"about_ca_system_score_gemma":0.00045723308,"threshold_uncertainty_score":0.029241383},"labels":[],"label_agreement":null},{"id":"W2986869486","doi":"10.48550/arxiv.1911.07262","title":"Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Decomposition; Image (mathematics); Computer vision; Object (grammar); Representation (politics); Pattern recognition (psychology); Exploit; Construct (python library); Range (aeronautics)","score_opus":0.061217597128764095,"score_gpt":0.25430754892190105,"score_spread":0.19308995179313695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986869486","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":[0.029101038,0.0002121187,0.96739465,0.000075547396,0.000028886778,0.00004910443,0.00019770034,0.0017766537,0.0011643454],"genre_scores_gemma":[0.25043416,0.00037113574,0.74340326,0.00018258761,0.00006644391,0.0001055632,0.0017155979,0.00049288786,0.0032283044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995797,0.00006739336,0.0000151533495,0.00016611532,0.000111861795,0.000059770442],"domain_scores_gemma":[0.9991968,0.00016316267,0.00009324495,0.0003096316,0.00017360164,0.00006356546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055672583,0.0011481106,0.0008070348,0.0009217014,0.00029380614,0.00074138364,0.0011702167,0.00074093195,0.0011609857],"category_scores_gemma":[0.0013404301,0.0005331933,0.0011001241,0.0007817176,0.00053070224,0.0010852747,0.0012770133,0.0016111917,0.0012647405],"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":[0.0003137313,0.0003235664,0.0030595106,0.00022150215,0.00019616012,0.00014475588,0.00019432961,0.07996453,0.28246275,0.0042974814,0.006458024,0.6223636],"study_design_scores_gemma":[0.0000200503,0.00013528629,0.0035181404,0.000023253207,0.000056788733,0.0002678937,0.00005984133,0.89341915,0.0925712,0.004209609,0.0056878626,0.000031008345],"about_ca_topic_score_codex":0.0018705727,"about_ca_topic_score_gemma":0.0058741895,"teacher_disagreement_score":0.0018705727,"about_ca_system_score_codex":0.00030391885,"about_ca_system_score_gemma":0.0005226117,"threshold_uncertainty_score":0.0038838387},"labels":[],"label_agreement":null},{"id":"W2986969740","doi":"10.3390/e21111123","title":"A Novel Residual Dense Pyramid Network for Image Dehazing","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"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","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Pyramid (geometry); Residual; Encoder; Artificial intelligence; Block (permutation group theory); Memory footprint; Pooling; Context (archaeology); Convolutional neural network; Computer vision; Pattern recognition (psychology); Pixel; Algorithm","score_opus":0.01078914216391536,"score_gpt":0.25295398774799893,"score_spread":0.24216484558408358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986969740","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":[0.037503317,0.0007655094,0.9545759,0.00023648767,0.000081973514,0.0000753033,0.00020125897,0.0016143969,0.0049458053],"genre_scores_gemma":[0.66962767,0.000827978,0.31454894,0.00038882397,0.00006359599,0.00011891383,0.0010043316,0.00015972577,0.01325997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983025,0.000012815948,0.0000063825137,0.00004879465,0.00006911421,0.00003256391],"domain_scores_gemma":[0.9998479,0.000032173866,0.000018283192,0.000027213911,0.00006063191,0.000013803155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003022066,0.0007324416,0.0005345683,0.00047414823,0.00021171714,0.00040641442,0.0013999382,0.00057056494,0.0020388046],"category_scores_gemma":[0.00069377106,0.00032288808,0.0004934199,0.00039742485,0.00038958268,0.0012596893,0.0009169512,0.00081637397,0.0005087783],"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":[0.00024773407,0.00019570922,0.0013405993,0.00022259576,0.0001534795,0.00024178975,0.00010481125,0.34637004,0.06402864,0.011880515,0.009240251,0.5659739],"study_design_scores_gemma":[0.000007755661,0.000048736303,0.0001988736,0.0000058426895,0.000020169173,0.000057160458,0.0000068572954,0.98716694,0.00906322,0.0019265999,0.0014908034,0.0000070934134],"about_ca_topic_score_codex":0.0052949865,"about_ca_topic_score_gemma":0.0063008047,"teacher_disagreement_score":0.0052949865,"about_ca_system_score_codex":0.0006293626,"about_ca_system_score_gemma":0.0006580809,"threshold_uncertainty_score":0.010528326},"labels":[],"label_agreement":null},{"id":"W2988303725","doi":"10.1109/globalsip45357.2019.8969197","title":"Image Alpha Matting via Residual Convolutional Grid Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Alpha (finance); Residual; Computer vision; Grid; Image (mathematics); Digital image; Pattern recognition (psychology); Image processing; Mathematics; Algorithm","score_opus":0.005623416330174262,"score_gpt":0.22717442689619677,"score_spread":0.2215510105660225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988303725","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":[0.043176997,0.00027033236,0.95128024,0.00012046243,0.0000599732,0.000034277087,0.00005084256,0.002451661,0.0025552288],"genre_scores_gemma":[0.6499186,0.0004343536,0.34045392,0.00016873878,0.000051220744,0.000048735823,0.00025680786,0.00019998691,0.008467527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998828,0.000012769724,0.0000047635763,0.000030547322,0.000044633754,0.000024518884],"domain_scores_gemma":[0.99982685,0.000038214766,0.000024030463,0.00003946643,0.00005577806,0.000015661502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026070492,0.0005090961,0.00041699287,0.00035260143,0.00014793506,0.00036239947,0.0007736886,0.00040790683,0.001372438],"category_scores_gemma":[0.00058874994,0.00022981633,0.00035270213,0.00037553292,0.00031719415,0.0006630313,0.0004642856,0.0006053365,0.00052288605],"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":[0.0003542553,0.00010251406,0.0015191614,0.00010632311,0.000096205826,0.00023799072,0.000091309535,0.26898462,0.08924751,0.0055786017,0.004941651,0.6287398],"study_design_scores_gemma":[0.0000080465825,0.00004730674,0.00032366606,0.0000037701745,0.0000136248345,0.00007519209,0.000006230259,0.98001397,0.017109046,0.0011945788,0.0011989393,0.0000055376086],"about_ca_topic_score_codex":0.0037001497,"about_ca_topic_score_gemma":0.0043391283,"teacher_disagreement_score":0.0037001497,"about_ca_system_score_codex":0.00034845725,"about_ca_system_score_gemma":0.00037878682,"threshold_uncertainty_score":0.00735718},"labels":[],"label_agreement":null},{"id":"W2990007814","doi":"10.1109/iccv.2019.00741","title":"GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1006,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Bottleneck; Convolutional neural network; Artificial intelligence; Atmosphere (unit); Grid; Scale (ratio); Dimension (graph theory); Image (mathematics); Image processing; Computer vision; Pattern recognition (psychology); Embedded system","score_opus":0.012104924922010861,"score_gpt":0.2618624223646093,"score_spread":0.24975749744259845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990007814","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":[0.040628467,0.00043988405,0.9533179,0.00014103074,0.00008054657,0.00006527739,0.00014383373,0.0029921972,0.002190823],"genre_scores_gemma":[0.59269273,0.00044191786,0.39679685,0.0003195933,0.000053780164,0.00009705481,0.00076740404,0.0003019691,0.008528724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998667,0.000009807616,0.000004254844,0.00004254968,0.000047186317,0.000029350165],"domain_scores_gemma":[0.99982494,0.00004004518,0.000021775902,0.00004996495,0.000048245285,0.000014978125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002741301,0.0006766185,0.0004654575,0.00041693047,0.00022090987,0.00041127193,0.0016059224,0.0006782493,0.0020638658],"category_scores_gemma":[0.00065999286,0.0003028966,0.0004767769,0.0003573019,0.00035269858,0.0010219427,0.0010318987,0.00094050827,0.00046843968],"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":[0.00020478282,0.0001644781,0.0015795268,0.0001557289,0.00016145919,0.00017937078,0.00008298906,0.36050203,0.07371667,0.003738114,0.0061208555,0.553394],"study_design_scores_gemma":[0.000007635802,0.00004057017,0.0004086673,0.000004224772,0.000015507088,0.000048390873,0.000006675224,0.9819047,0.0148958685,0.0013222209,0.0013384334,0.000007128416],"about_ca_topic_score_codex":0.0061668637,"about_ca_topic_score_gemma":0.0104637565,"teacher_disagreement_score":0.0061668637,"about_ca_system_score_codex":0.0005104839,"about_ca_system_score_gemma":0.00046253123,"threshold_uncertainty_score":0.012261987},"labels":[],"label_agreement":null},{"id":"W2990123081","doi":"10.1109/avss.2019.8909891","title":"Deep Single Image Enhancer","year":2019,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Convolutional neural network; High dynamic range; Image (mathematics); Pyramid (geometry); Terrain; Feature (linguistics); Range (aeronautics); Luminance; Dynamic range; Geography; Mathematics; Engineering","score_opus":0.0066734867818406774,"score_gpt":0.23137502561178236,"score_spread":0.22470153882994168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990123081","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":[0.023719274,0.0017590517,0.9414193,0.00042088013,0.0003588395,0.00015832006,0.00090731686,0.0062611015,0.024995979],"genre_scores_gemma":[0.47518688,0.0018420082,0.44050846,0.0009233104,0.00017076373,0.00022896151,0.003799144,0.0008389518,0.07650147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997545,0.000014729425,0.000009009715,0.000064262145,0.000094952105,0.00006244863],"domain_scores_gemma":[0.9997967,0.00003374727,0.000020203252,0.00004929435,0.00008157813,0.00001852569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033574662,0.0009544138,0.0006356939,0.000547437,0.00024848513,0.00084256125,0.0016091036,0.001004379,0.0186229],"category_scores_gemma":[0.00080581056,0.00028326776,0.0005879275,0.0005578897,0.000400344,0.0016307133,0.001292703,0.0010808633,0.004721859],"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":[0.00038865133,0.00020699858,0.0006167178,0.00034877638,0.00010156042,0.00030896795,0.00006784165,0.057453543,0.06931835,0.024268312,0.027691009,0.8192293],"study_design_scores_gemma":[0.000032328942,0.00018642543,0.00053970644,0.000045600376,0.000041336058,0.00034890027,0.000038158283,0.8922572,0.06096507,0.013704838,0.031817168,0.000023215283],"about_ca_topic_score_codex":0.0022689956,"about_ca_topic_score_gemma":0.0046553616,"teacher_disagreement_score":0.0186229,"about_ca_system_score_codex":0.0005491886,"about_ca_system_score_gemma":0.0007299529,"threshold_uncertainty_score":0.062299848},"labels":[],"label_agreement":null},{"id":"W2994853525","doi":"10.48550/arxiv.1912.06960","title":"What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Enhancement 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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung; Canada First Research Excellence Fund; York University","keywords":"Computer science; Artificial intelligence; Color constancy; Robustness (evolution); Deep neural networks; Segmentation; Color balance; Artificial neural network; Deep learning; Computer vision; Image (mathematics); Pattern recognition (psychology); Image processing; Color image","score_opus":0.05493843091998473,"score_gpt":0.20621500046045813,"score_spread":0.1512765695404734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994853525","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":[0.2839314,0.021098435,0.6124517,0.046827175,0.0017383886,0.00013483428,0.00057925784,0.012925155,0.020313647],"genre_scores_gemma":[0.86989444,0.003673291,0.11422325,0.0040245014,0.0005045918,0.00007143964,0.0002551977,0.00083791174,0.0065152943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989913,0.00032634614,0.000048343172,0.0002271358,0.00027755127,0.00012925896],"domain_scores_gemma":[0.9958255,0.0020812887,0.00037092954,0.0010326938,0.0005262211,0.00016331246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033905408,0.0016346097,0.0005794841,0.00046536696,0.0005484761,0.0015720624,0.001600642,0.0020839474,0.0036264998],"category_scores_gemma":[0.021367632,0.00039162437,0.0003747917,0.00054610724,0.0018144941,0.0055568004,0.0017059962,0.003651805,0.0012003919],"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":[0.0008442873,0.0002706313,0.011165407,0.0006503448,0.00037402654,0.00050170283,0.00055869215,0.14650987,0.06638572,0.03167576,0.032019455,0.7090441],"study_design_scores_gemma":[0.0001134821,0.0007140383,0.004945233,0.0004902363,0.0001827646,0.00064333016,0.00031534125,0.7570772,0.10510528,0.104678735,0.025632376,0.000101920654],"about_ca_topic_score_codex":0.0032838162,"about_ca_topic_score_gemma":0.005181986,"teacher_disagreement_score":0.0036264998,"about_ca_system_score_codex":0.0009107613,"about_ca_system_score_gemma":0.00082886347,"threshold_uncertainty_score":0.017931104},"labels":[],"label_agreement":null},{"id":"W2996883610","doi":"10.1609/aaai.v34i07.6961","title":"Leveraging Multi-View Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Decomposition; Image (mathematics); Computer vision; Representation (politics); Object (grammar); Pattern recognition (psychology); Exploit; Range (aeronautics); Construct (python library)","score_opus":0.09186159896244678,"score_gpt":0.35005124372389584,"score_spread":0.25818964476144907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996883610","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":[0.035686333,0.00021243237,0.9600873,0.0000802929,0.00003239996,0.000060441216,0.00026215863,0.0023032478,0.0012753133],"genre_scores_gemma":[0.29508153,0.00031851925,0.698143,0.00020352325,0.000068650486,0.00012173482,0.0024012674,0.000508934,0.0031526745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995505,0.00006787021,0.00001540742,0.00018151826,0.000114493705,0.00007017212],"domain_scores_gemma":[0.9991541,0.00016532911,0.00009665789,0.0003108035,0.0002003952,0.00007266005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005392781,0.0011952001,0.0008960787,0.0010069242,0.0003387402,0.00078549905,0.001302003,0.0007642413,0.001093758],"category_scores_gemma":[0.0013942623,0.0005630447,0.0012315137,0.00081808056,0.00050908345,0.0011466832,0.0012702117,0.0016299076,0.0012602931],"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":[0.00030626234,0.0004002248,0.004266836,0.00019889891,0.00021769259,0.00014798317,0.00021059565,0.085423075,0.2455451,0.0036713756,0.007018978,0.6525929],"study_design_scores_gemma":[0.00001909052,0.00012908383,0.0035778978,0.000019002278,0.000051699477,0.00020764433,0.00005997765,0.9189241,0.06909506,0.0032945299,0.004590459,0.000031364078],"about_ca_topic_score_codex":0.0027427517,"about_ca_topic_score_gemma":0.008471714,"teacher_disagreement_score":0.0027427517,"about_ca_system_score_codex":0.00033727384,"about_ca_system_score_gemma":0.0006101043,"threshold_uncertainty_score":0.0054535866},"labels":[],"label_agreement":null},{"id":"W2997224767","doi":"10.3844/ajassp.2019.336.345","title":"Multi-Level Image Thresholding via Nonlinear Fitting of the Histogram","year":2019,"lang":"en","type":"article","venue":"American Journal of Applied Sciences","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Humber College","funders":"","keywords":"Hyperbolic function; Thresholding; Histogram; Mathematics; Polynomial; Balanced histogram thresholding; Maxima and minima; Adaptive histogram equalization; Applied mathematics; Gaussian function; Function (biology); Curve fitting; Nonlinear system; Algorithm; Gaussian; Histogram equalization; Image (mathematics); Artificial intelligence; Computer science; Mathematical analysis; Statistics","score_opus":0.018812255234551302,"score_gpt":0.2730952223982115,"score_spread":0.25428296716366017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997224767","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":[0.009264323,0.000106841224,0.98940057,0.000031109772,0.000019145093,0.000019896188,0.000012915866,0.00048357656,0.0006616358],"genre_scores_gemma":[0.26109034,0.00027255144,0.73562974,0.00005318964,0.000034404296,0.00004047929,0.000075064025,0.00018892034,0.0026153387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961627,0.000038306538,0.000020745134,0.00009690866,0.00019016545,0.000037581496],"domain_scores_gemma":[0.99963903,0.00009414563,0.000052861047,0.000103762264,0.000085953056,0.00002418277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003538047,0.00040332577,0.0006172436,0.0006333001,0.00031849777,0.0006586378,0.001016381,0.00056574325,0.0018169509],"category_scores_gemma":[0.0010585499,0.00033989912,0.00051887764,0.0007085932,0.0004370579,0.0012394872,0.0009814281,0.0008064363,0.0008842899],"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":[0.00017443547,0.00008700153,0.001147811,0.0002235647,0.000043921853,0.0001552059,0.00017942971,0.07135296,0.28818148,0.0141818775,0.0011206366,0.6231516],"study_design_scores_gemma":[0.000011185312,0.00008442944,0.00096761127,0.000014362608,0.000024793086,0.00044618789,0.000025692518,0.8844976,0.104748905,0.0043253317,0.004811647,0.000042269003],"about_ca_topic_score_codex":0.0009549205,"about_ca_topic_score_gemma":0.0012600577,"teacher_disagreement_score":0.0018169509,"about_ca_system_score_codex":0.00048629806,"about_ca_system_score_gemma":0.000479938,"threshold_uncertainty_score":0.006078303},"labels":[],"label_agreement":null},{"id":"W3008774220","doi":"10.1109/tmm.2020.2976573","title":"Automated Colorization of a Grayscale Image With Seed Points Propagation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":155,"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":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Grayscale; Artificial intelligence; Computer science; Pixel; Computer vision; RGB color model; Similarity (geometry); Pattern recognition (psychology); Image (mathematics); Color image; Artificial neural network; Image processing","score_opus":0.011251000751002212,"score_gpt":0.2371668884619035,"score_spread":0.2259158877109013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008774220","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":[0.011393533,0.00009644768,0.9865019,0.000042958698,0.000018782413,0.000038209968,0.000018363546,0.0013380523,0.0005518945],"genre_scores_gemma":[0.13579093,0.00017382618,0.8614168,0.000087581015,0.00002379363,0.000062081264,0.000119395736,0.00028217517,0.0020433161],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995939,0.000050640712,0.00001779702,0.00012356079,0.00016735241,0.000046895966],"domain_scores_gemma":[0.99948883,0.000112516434,0.00006096033,0.00013079448,0.00018438119,0.000022513932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058007124,0.0010107659,0.0007641901,0.001147766,0.00034685142,0.0007994179,0.0012376803,0.00062517356,0.0020397953],"category_scores_gemma":[0.001180689,0.00048413742,0.0006780386,0.0007516877,0.00059868104,0.0011686634,0.0007891024,0.00087070046,0.00075624004],"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":[0.0002614449,0.0001072452,0.001042073,0.00013112617,0.00006246162,0.00011536936,0.00011320698,0.1096734,0.16942793,0.0056752646,0.0021604788,0.71122986],"study_design_scores_gemma":[0.000016930051,0.000045010132,0.00058123446,0.000008244747,0.000018357794,0.00009254241,0.000013696245,0.9286566,0.06675407,0.0020690619,0.0017270816,0.000017183036],"about_ca_topic_score_codex":0.004884548,"about_ca_topic_score_gemma":0.0064344094,"teacher_disagreement_score":0.004884548,"about_ca_system_score_codex":0.0007501363,"about_ca_system_score_gemma":0.00091145624,"threshold_uncertainty_score":0.009712219},"labels":[],"label_agreement":null},{"id":"W3014111670","doi":"10.1109/iros45743.2020.9340821","title":"Semantic Segmentation of Underwater Imagery: Dataset and Benchmark","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":17,"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":"McGill University; University of Minnesota; National Science Foundation","keywords":"Computer science; Benchmark (surveying); Underwater; Artificial intelligence; Segmentation; Pipeline (software); Robot; Inference; Computer vision; Geography","score_opus":0.02547458921488926,"score_gpt":0.28846957285038727,"score_spread":0.262994983635498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014111670","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31749168,0.0077722906,0.0755735,0.0016472363,0.0011506693,0.002398552,0.50546336,0.06054927,0.027953515],"genre_scores_gemma":[0.11900814,0.00095239776,0.08876966,0.0003418217,0.00008983199,0.00059611636,0.78563803,0.0013485934,0.0032554765],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989311,0.00009754613,0.00009355815,0.00036892373,0.00034066953,0.00016813971],"domain_scores_gemma":[0.99924195,0.00012335552,0.00006922925,0.0002522883,0.00021814085,0.000095094765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007854649,0.0024070847,0.0009742396,0.0035564543,0.0008839787,0.0010131921,0.0026048003,0.002017435,0.0035688828],"category_scores_gemma":[0.0022033008,0.00045205338,0.0013598703,0.0035581563,0.0008887152,0.0014198553,0.0020443995,0.0011529305,0.0038363177],"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":[0.001517408,0.0013654797,0.013375182,0.005259769,0.00063372235,0.0012862994,0.00046080566,0.0645007,0.050263334,0.0030059495,0.4882774,0.3700538],"study_design_scores_gemma":[0.00075403997,0.0011828518,0.08736205,0.0008627469,0.00048744734,0.0048892256,0.0023984376,0.4136753,0.11791139,0.011311528,0.35871357,0.0004514097],"about_ca_topic_score_codex":0.020575263,"about_ca_topic_score_gemma":0.043442953,"teacher_disagreement_score":0.020575263,"about_ca_system_score_codex":0.0012273011,"about_ca_system_score_gemma":0.0013903262,"threshold_uncertainty_score":0.04091096},"labels":[],"label_agreement":null},{"id":"W3029167364","doi":"10.48550/arxiv.2005.13736","title":"L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Luminance; Underwater; Contrast (vision); Computer vision; Artificial intelligence; Computer science; Image enhancement; Image fusion; Contrast enhancement; Fusion; Process (computing); Scale (ratio); Image (mathematics); Geography; Cartography","score_opus":0.05949389584570021,"score_gpt":0.22526197949579918,"score_spread":0.16576808365009899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029167364","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":[0.0044163326,0.00018435832,0.992708,0.000051659645,0.000021810618,0.00004084537,0.000053607244,0.0015509308,0.0009724382],"genre_scores_gemma":[0.108377784,0.00043923286,0.88569355,0.00014128661,0.00004125363,0.00011285318,0.00027155955,0.0007267947,0.004195696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978715,0.00003377177,0.000008546327,0.00004105094,0.00010335442,0.000026099262],"domain_scores_gemma":[0.9997378,0.00008177491,0.00003330125,0.00006151186,0.000061525476,0.000024146235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069181214,0.00078217924,0.0006061739,0.0007657942,0.00022548018,0.00086842483,0.0014120034,0.0007912242,0.0025913233],"category_scores_gemma":[0.0011320922,0.00041347512,0.0009470549,0.00039991547,0.00051531114,0.0011443071,0.0016593444,0.0012623606,0.0008792526],"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":[0.00026414797,0.00017695419,0.00094694033,0.00053713145,0.00019686407,0.0003282237,0.0002495449,0.14713217,0.30784154,0.023441715,0.007988542,0.51089627],"study_design_scores_gemma":[0.000026392863,0.000107730084,0.0006481237,0.000030393936,0.000038037415,0.0004181162,0.000034936063,0.8902388,0.08740296,0.0064512915,0.014560513,0.00004268724],"about_ca_topic_score_codex":0.0014459464,"about_ca_topic_score_gemma":0.0025577547,"teacher_disagreement_score":0.0025913233,"about_ca_system_score_codex":0.0004072498,"about_ca_system_score_gemma":0.0003857018,"threshold_uncertainty_score":0.00866878},"labels":[],"label_agreement":null},{"id":"W3034660707","doi":"10.1109/icmew46912.2020.9106053","title":"Color Balanced Histogram Equalization for Image Enhancement","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of Alberta","funders":"","keywords":"Histogram equalization; Computer science; Artificial intelligence; Computer vision; Histogram; Object detection; Visibility; Detector; Color histogram; Color normalization; Merge (version control); Image (mathematics); Pattern recognition (psychology); Color image; Image processing","score_opus":0.026471622126041573,"score_gpt":0.2851985325404298,"score_spread":0.2587269104143882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034660707","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":[0.03224039,0.00236471,0.9575265,0.00017390295,0.0001297791,0.00010838012,0.00019877547,0.0017669165,0.0054907114],"genre_scores_gemma":[0.40587437,0.002348447,0.5788395,0.00026632374,0.000111061345,0.00009022638,0.00079534727,0.00024052947,0.011434295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980503,0.000018299153,0.0000072959556,0.000051936113,0.00009258322,0.000024932911],"domain_scores_gemma":[0.9998023,0.00005247296,0.000021480666,0.000042689517,0.000070099486,0.000011032335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026297267,0.00045557224,0.00028352387,0.0007168353,0.00017866542,0.00043313656,0.0004883775,0.00036967103,0.002923593],"category_scores_gemma":[0.0005877432,0.0001561328,0.00027503434,0.00061763934,0.00026800585,0.0006110391,0.0004521296,0.0005101431,0.0012186104],"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":[0.00023580532,0.00011194466,0.0006626044,0.00021358076,0.000055623495,0.00007413994,0.000042056625,0.019629385,0.35158816,0.0038879334,0.0055385404,0.6179603],"study_design_scores_gemma":[0.000032932356,0.00020414985,0.004074745,0.000031445143,0.000057762107,0.000571508,0.00004436231,0.42895707,0.52982557,0.005946272,0.030206632,0.000047528043],"about_ca_topic_score_codex":0.0013172801,"about_ca_topic_score_gemma":0.0020720274,"teacher_disagreement_score":0.002923593,"about_ca_system_score_codex":0.00033482016,"about_ca_system_score_gemma":0.00028906824,"threshold_uncertainty_score":0.009780347},"labels":[],"label_agreement":null},{"id":"W3035082476","doi":"10.1109/cvprw50498.2020.00277","title":"L<sup>2</sup>UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":103,"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 Victoria","funders":"","keywords":"Contrast (vision); Underwater; Artificial intelligence; Luminance; Computer vision; Scale (ratio); Computer science; Fusion; Image fusion; Image (mathematics); Physics; Geography","score_opus":0.02114234276352955,"score_gpt":0.2724236443399395,"score_spread":0.25128130157640993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035082476","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":[0.0065592187,0.00014000633,0.9912981,0.000038214366,0.000014498618,0.0000359336,0.000029849807,0.0010888394,0.00079531386],"genre_scores_gemma":[0.12767316,0.00030125098,0.86852026,0.00007492483,0.000029220795,0.0000749572,0.0001389255,0.00043180204,0.002755429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980396,0.000033835036,0.000009959015,0.000037970643,0.00009045335,0.000023774295],"domain_scores_gemma":[0.9996916,0.00009474768,0.000043435197,0.00006940091,0.00007428608,0.000026669612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007173667,0.00071157847,0.00048162538,0.0006594238,0.00021920532,0.00075223163,0.0011691343,0.00065419514,0.0018433356],"category_scores_gemma":[0.0010188931,0.00032090317,0.0007870724,0.00033204255,0.00057294016,0.001097029,0.0010840864,0.0008627607,0.0006949102],"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":[0.00027267577,0.00016550532,0.0007699323,0.00037647452,0.00011828674,0.0003052633,0.00022667291,0.13942009,0.40183625,0.025791194,0.0044789882,0.42623866],"study_design_scores_gemma":[0.000017455375,0.00013895782,0.0007233358,0.000019541672,0.000038204005,0.00033313213,0.000031714848,0.8510062,0.13385484,0.004474715,0.009320753,0.000041206473],"about_ca_topic_score_codex":0.0015875788,"about_ca_topic_score_gemma":0.0023666092,"teacher_disagreement_score":0.0018433356,"about_ca_system_score_codex":0.0003539135,"about_ca_system_score_gemma":0.00033170433,"threshold_uncertainty_score":0.0061665773},"labels":[],"label_agreement":null},{"id":"W3036992002","doi":"10.1109/tpami.2021.3070580","title":"CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Enhancement 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":"York University","funders":"","keywords":"Deblurring; Artificial intelligence; Computer vision; Computer science; RGB color model; Pipeline (software); Color balance; Image (mathematics); Image processing; Color image; Image restoration","score_opus":0.02800213758311258,"score_gpt":0.30916749438315094,"score_spread":0.28116535680003835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036992002","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":[0.018684147,0.0006715499,0.91267365,0.00037944707,0.00023720675,0.0002583959,0.00134769,0.04711126,0.018636677],"genre_scores_gemma":[0.2439356,0.0008239138,0.7167657,0.0007942801,0.0001094058,0.00047143066,0.0055262884,0.0019162918,0.029657086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997069,0.000016314785,0.000011610495,0.00008187617,0.00012928605,0.000054004347],"domain_scores_gemma":[0.99972636,0.00005354967,0.000028279634,0.000078336685,0.00007955096,0.000033870187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039863677,0.0013596034,0.0004599813,0.0006509561,0.0003676157,0.0012083091,0.0027198985,0.00090393197,0.017428752],"category_scores_gemma":[0.0010284793,0.0004615572,0.0006387682,0.0004976798,0.0005933597,0.0018290883,0.0015740119,0.0017762564,0.005307891],"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":[0.0006189339,0.0003843988,0.0017539905,0.00047872568,0.00010189256,0.0002610754,0.00010354363,0.063708976,0.041325644,0.028170623,0.078175224,0.784917],"study_design_scores_gemma":[0.00004602514,0.00019329983,0.0010662639,0.00005200878,0.000032446656,0.00017917679,0.00003074303,0.86548537,0.07532288,0.017450567,0.04009574,0.00004534167],"about_ca_topic_score_codex":0.0053499253,"about_ca_topic_score_gemma":0.010447886,"teacher_disagreement_score":0.017428752,"about_ca_system_score_codex":0.0011396734,"about_ca_system_score_gemma":0.0012534079,"threshold_uncertainty_score":0.058305025},"labels":[],"label_agreement":null},{"id":"W3042695477","doi":"10.23977/acss.2020.040106","title":"Nature Inspired Algorithms multi-objective histogram equalization for Grey image enhancement","year":2020,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Histogram equalization; Artificial intelligence; Particle swarm optimization; Computer science; Image quality; Computer vision; Histogram; Image processing; Adaptive histogram equalization; Pattern recognition (psychology); Image segmentation; Image (mathematics); Algorithm","score_opus":0.023353416720413477,"score_gpt":0.30769202052037986,"score_spread":0.2843386037999664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042695477","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":[0.009042207,0.0014188495,0.9793761,0.00015327548,0.00008212168,0.00007540718,0.000026668888,0.0004759027,0.0093495045],"genre_scores_gemma":[0.47135016,0.0023568335,0.5021721,0.0002682899,0.00009985882,0.0003156962,0.00015924998,0.00015572498,0.023122065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998135,0.000026359803,0.000009127044,0.00002586283,0.00011043645,0.000014663607],"domain_scores_gemma":[0.99988055,0.00004426372,0.00002047371,0.000009128806,0.000041171028,0.0000044336402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026884326,0.00046127467,0.00045214,0.0004464547,0.00020941054,0.0005078396,0.0005186084,0.00051413855,0.0028760128],"category_scores_gemma":[0.00044629685,0.00017748044,0.000535524,0.00038823372,0.00024614553,0.00047299193,0.00035260967,0.00056190166,0.00054975424],"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":[0.00010578052,0.00014692335,0.0011122583,0.00050675333,0.00014769207,0.00017958823,0.00016905477,0.40213487,0.066621095,0.024545718,0.0054069157,0.49892336],"study_design_scores_gemma":[0.000014138137,0.000091349866,0.00068515626,0.000030090194,0.00002574207,0.00013248375,0.000024162568,0.97556794,0.009507629,0.0046217106,0.009279798,0.00001996636],"about_ca_topic_score_codex":0.001287463,"about_ca_topic_score_gemma":0.0011835712,"teacher_disagreement_score":0.0028760128,"about_ca_system_score_codex":0.00035954893,"about_ca_system_score_gemma":0.00033702992,"threshold_uncertainty_score":0.009621203},"labels":[],"label_agreement":null},{"id":"W3043271413","doi":"10.5430/air.v9n1p12","title":"An algorithm for glare detection via photometric, colorimetric, and global positioning features","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; GLARE; Ground truth; Global Positioning System","score_opus":0.11478013586893686,"score_gpt":0.4263289298579716,"score_spread":0.31154879398903473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043271413","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":[0.009152851,0.00013038956,0.98827285,0.00007035304,0.00003902762,0.00013536676,0.000088981404,0.0015236918,0.00058652274],"genre_scores_gemma":[0.051594056,0.00012891911,0.9464084,0.00005969707,0.00002615282,0.0001450713,0.00028557584,0.000062108775,0.0012901119],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995813,0.0000308465,0.0000315099,0.00012695571,0.0001877782,0.00004164649],"domain_scores_gemma":[0.99954873,0.0000642955,0.0000681113,0.000054513417,0.0002426147,0.000021715932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055969495,0.0007986272,0.0007554596,0.001882082,0.00054984563,0.00093812356,0.001221485,0.0008874366,0.0014135184],"category_scores_gemma":[0.0011775178,0.00039864588,0.0007750833,0.0013215138,0.00037563374,0.0008967168,0.0006667662,0.00087836426,0.0011525709],"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":[0.00017447492,0.00013977881,0.0025262635,0.000107995395,0.00010326399,0.00007586507,0.00008322237,0.015316756,0.093233734,0.0022660154,0.004067237,0.88190544],"study_design_scores_gemma":[0.00016073739,0.00040856277,0.017606284,0.000040872666,0.00018362407,0.00095513853,0.0001686986,0.8336785,0.12320612,0.004912444,0.018549081,0.00012990928],"about_ca_topic_score_codex":0.0045713554,"about_ca_topic_score_gemma":0.0064859954,"teacher_disagreement_score":0.0045713554,"about_ca_system_score_codex":0.0005708775,"about_ca_system_score_gemma":0.0011038876,"threshold_uncertainty_score":0.00908947},"labels":[],"label_agreement":null},{"id":"W3048468193","doi":"10.1007/s11042-020-09383-7","title":"Aerial image dehazing using a deep convolutional autoencoder","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Autoencoder; Computer vision; Image (mathematics); Encoder; Convolutional neural network; Deep learning","score_opus":0.03979328032176318,"score_gpt":0.2812061359720002,"score_spread":0.24141285565023704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048468193","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":[0.075230405,0.00035212538,0.91930544,0.00012842812,0.00007801224,0.000042457174,0.00009554914,0.0011266792,0.003640857],"genre_scores_gemma":[0.56419545,0.0005978772,0.4224168,0.00012607011,0.000045495875,0.00003004213,0.00032710796,0.00011814155,0.012143039],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999311,0.000004127794,0.0000027257774,0.000014983073,0.00003600036,0.000011076394],"domain_scores_gemma":[0.9999019,0.000020200794,0.000010339798,0.000021213622,0.00003868329,0.000007592127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013817527,0.0003587464,0.00023494265,0.00028411028,0.00011661281,0.00026232962,0.00037162664,0.00035540116,0.0015844081],"category_scores_gemma":[0.00023150202,0.00019067804,0.00034849162,0.0001848859,0.00017835513,0.0003869326,0.00035520183,0.0005444452,0.00043241252],"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":[0.00018936441,0.00012653547,0.0013252966,0.00012896165,0.00010682399,0.00014565143,0.000058382906,0.16699876,0.30888245,0.004219565,0.0024448768,0.5153734],"study_design_scores_gemma":[0.000005868002,0.00004012389,0.0010431239,0.0000080633035,0.000024644147,0.00009495608,0.000009056966,0.9262597,0.06969777,0.00061309076,0.002195038,0.000008487315],"about_ca_topic_score_codex":0.0043108077,"about_ca_topic_score_gemma":0.0067843744,"teacher_disagreement_score":0.0043108077,"about_ca_system_score_codex":0.0002740392,"about_ca_system_score_gemma":0.00037032992,"threshold_uncertainty_score":0.008571446},"labels":[],"label_agreement":null},{"id":"W3082546531","doi":"10.48550/arxiv.2009.00702","title":"Unsupervised Single-Image Reflection Separation Using Perceptual Deep Image Priors","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Image (mathematics); Pattern recognition (psychology); Deep learning; Reflection (computer programming); Prior probability; Convolutional neural network; Unsupervised learning; Process (computing); Perception; Computer vision; Machine learning; Bayesian probability","score_opus":0.13097666085328344,"score_gpt":0.25074664819514225,"score_spread":0.11976998734185881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082546531","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":[0.021131633,0.00016396327,0.9757764,0.00007870113,0.000023466917,0.000036240464,0.00008124538,0.0013223981,0.0013860412],"genre_scores_gemma":[0.41931677,0.0004750767,0.5710768,0.00027176092,0.00006950265,0.00013457707,0.0009757256,0.0006452477,0.0070346305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956745,0.00007530831,0.000017153237,0.00014678667,0.00012903819,0.00006428678],"domain_scores_gemma":[0.9994425,0.00012400004,0.00008674018,0.0001749083,0.00013437362,0.000037471298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006581509,0.0012598424,0.0008043779,0.0007536878,0.00025180136,0.0008402298,0.0015033224,0.00074868795,0.0016787826],"category_scores_gemma":[0.0015649637,0.0005304663,0.0010105271,0.0005911886,0.00077532046,0.0017282038,0.0016829572,0.0019337531,0.0010107562],"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":[0.00058076595,0.00029732578,0.0013123427,0.00027949156,0.0001848167,0.00015292894,0.00019057051,0.19800639,0.141445,0.009768262,0.0053729825,0.6424091],"study_design_scores_gemma":[0.000022320011,0.00007179534,0.00069674844,0.000017112514,0.000034546574,0.00009646057,0.000027750455,0.9368988,0.05318186,0.0064986013,0.0024365387,0.00001749939],"about_ca_topic_score_codex":0.0017815686,"about_ca_topic_score_gemma":0.0033097018,"teacher_disagreement_score":0.0017815686,"about_ca_system_score_codex":0.00052079913,"about_ca_system_score_gemma":0.0010023997,"threshold_uncertainty_score":0.0056161284},"labels":[],"label_agreement":null},{"id":"W3083374363","doi":"10.1109/mwscas48704.2020.9184525","title":"Image Segmentation and Adaptive Contrast Enhancement for Haze Removal","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Haze; Artificial intelligence; Adaptive histogram equalization; Computer science; Computer vision; Noise (video); Image restoration; Bilateral filter; Image segmentation; Pixel; Contrast (vision); Image (mathematics); Filter (signal processing); Noise reduction; Pattern recognition (psychology); Histogram; Image processing; Physics; Histogram equalization","score_opus":0.022841008062326994,"score_gpt":0.2679188595837792,"score_spread":0.2450778515214522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083374363","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":[0.059740882,0.0007310054,0.93738127,0.00008978242,0.00004639187,0.0000712222,0.000028615947,0.00043623234,0.0014746294],"genre_scores_gemma":[0.28765443,0.00059671927,0.7075997,0.00009639601,0.000046148918,0.00006398826,0.000104200924,0.000087325396,0.003751099],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997447,0.00002156144,0.000009671074,0.00007618097,0.000117700096,0.00003014296],"domain_scores_gemma":[0.99978846,0.00004923677,0.000036423487,0.000033129378,0.00007848326,0.000014193692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025116262,0.00048305828,0.00040718826,0.00069176103,0.00025948635,0.00048492235,0.00066638214,0.00077696017,0.0008198008],"category_scores_gemma":[0.00055654626,0.00032507302,0.0005089685,0.00042033632,0.000499081,0.00067350495,0.00045614978,0.00052755384,0.00034561133],"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":[0.00017154358,0.000063970765,0.0011364629,0.00016567014,0.000048842754,0.000187583,0.00010381213,0.017511852,0.83549166,0.0026166043,0.000374053,0.142128],"study_design_scores_gemma":[0.000034458073,0.0003117429,0.006088927,0.000020826556,0.00006322409,0.0010824179,0.00005028076,0.310478,0.67117757,0.0016664726,0.008970031,0.000056058278],"about_ca_topic_score_codex":0.0012919116,"about_ca_topic_score_gemma":0.0020134374,"teacher_disagreement_score":0.0012919116,"about_ca_system_score_codex":0.0003168662,"about_ca_system_score_gemma":0.00044347392,"threshold_uncertainty_score":0.002742529},"labels":[],"label_agreement":null},{"id":"W3083841659","doi":"10.1109/access.2020.3022393","title":"BITPNet: Unsupervised Bio-Inspired Two-Path Network for Nighttime Traffic Image Enhancement","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":18,"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":"National Key Research and Development Program of China","keywords":"Computer science; Artificial intelligence; Luminance; Convolutional neural network; Computer vision; Kernel (algebra); Path (computing); Pattern recognition (psychology); Mathematics","score_opus":0.03382974433866514,"score_gpt":0.3027715946810007,"score_spread":0.26894185034233553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083841659","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":[0.14990632,0.0021584174,0.8321787,0.000477261,0.000311985,0.00019697036,0.0008273574,0.0072831097,0.0066598607],"genre_scores_gemma":[0.7470756,0.0010874959,0.2340041,0.0005618214,0.00006458436,0.00024302183,0.0027568701,0.0002704442,0.013936017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998803,0.000015059753,0.000005029168,0.00003829843,0.00003722444,0.000024115294],"domain_scores_gemma":[0.99985504,0.000041143598,0.000021960965,0.000017677863,0.000051792547,0.000012374017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002889812,0.00094431854,0.0005129637,0.00040580888,0.00021928122,0.00041160866,0.0014382505,0.00072639616,0.0011818841],"category_scores_gemma":[0.0006827468,0.00028935476,0.00048842945,0.00040598228,0.0003083845,0.00095975056,0.00062442117,0.000937629,0.00030647265],"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":[0.00042916022,0.00031681103,0.0019067777,0.000245588,0.00017498586,0.00029451353,0.000062904495,0.5389197,0.04103079,0.0028978954,0.009615365,0.40410554],"study_design_scores_gemma":[0.000008182434,0.000055912576,0.0003441051,0.0000068403706,0.000013224387,0.000042669144,0.0000048544875,0.991106,0.006746802,0.00077798386,0.0008865345,0.000007031736],"about_ca_topic_score_codex":0.0057552513,"about_ca_topic_score_gemma":0.0075951293,"teacher_disagreement_score":0.0057552513,"about_ca_system_score_codex":0.00074345944,"about_ca_system_score_gemma":0.00056956935,"threshold_uncertainty_score":0.011443496},"labels":[],"label_agreement":null},{"id":"W3091712247","doi":"10.1109/icbaie49996.2020.00072","title":"An Approach to Embedding a Style Transfer Model into a Mobile APP","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 British Columbia","funders":"","keywords":"Computer science; Convolutional neural network; Transfer (computing); Bitmap; Mobile device; Transfer of learning; Mobile phone; Process (computing); Artificial intelligence; Feature (linguistics); Artificial neural network; Feed forward; Computer vision; Real-time computing; Computer graphics (images); Parallel computing; Operating system","score_opus":0.02221616839484756,"score_gpt":0.284699298984483,"score_spread":0.2624831305896354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091712247","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":[0.012969472,0.00023641216,0.97839934,0.0002135199,0.00013837276,0.00010570182,0.00015122817,0.0030524964,0.0047335243],"genre_scores_gemma":[0.49800378,0.00068298896,0.47511727,0.00039632493,0.00013820699,0.00030509246,0.00057315745,0.00038505337,0.024398053],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99984336,0.000020190868,0.000009267179,0.00005221473,0.00005490017,0.000019999075],"domain_scores_gemma":[0.99987566,0.000020078342,0.000010596983,0.00003334872,0.000046904534,0.000013336747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024846973,0.00079066114,0.0003123206,0.00027623374,0.00023071098,0.00062201795,0.0008334149,0.0006143999,0.0056761135],"category_scores_gemma":[0.0006331623,0.00034924858,0.0007880567,0.00022824493,0.00029485702,0.0010566481,0.00074382446,0.0012155194,0.0021111548],"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":[0.00028886748,0.0003101393,0.0016612534,0.00022511433,0.00016206989,0.00052616186,0.00026410318,0.28063506,0.06795256,0.02656899,0.011179783,0.61022586],"study_design_scores_gemma":[0.000008540697,0.00009327647,0.0002908016,0.000012705836,0.000024448598,0.00010388191,0.000018715024,0.9775869,0.00974285,0.0048898202,0.0072132107,0.0000148511845],"about_ca_topic_score_codex":0.0039268164,"about_ca_topic_score_gemma":0.0048068548,"teacher_disagreement_score":0.0056761135,"about_ca_system_score_codex":0.00039325978,"about_ca_system_score_gemma":0.00055612915,"threshold_uncertainty_score":0.01898849},"labels":[],"label_agreement":null},{"id":"W3092270034","doi":"10.1049/iet-ipr.2019.0873","title":"Image dehazing with uneven illumination prior by dense residual channel attention network","year":2020,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Image Enhancement 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":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Residual; Computer science; Channel (broadcasting); Computer vision; Artificial intelligence; Image (mathematics); Computer network; Algorithm","score_opus":0.01138116621971622,"score_gpt":0.24596569919083994,"score_spread":0.23458453297112372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092270034","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":[0.053503998,0.0004534698,0.94265366,0.0001277404,0.000053622694,0.000051267554,0.000048408736,0.0009064593,0.0022013008],"genre_scores_gemma":[0.71405673,0.0006490139,0.27593464,0.0002633948,0.000102342325,0.00008049897,0.00030804472,0.00018842494,0.008416905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979264,0.000021948452,0.000007799393,0.00005481588,0.00007616015,0.000046706875],"domain_scores_gemma":[0.9996234,0.00011915803,0.00005132749,0.00006228653,0.000119173026,0.000024532614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004039476,0.0008059968,0.00068807276,0.0007966635,0.00027874627,0.00045546328,0.00097580685,0.00065482553,0.0011854639],"category_scores_gemma":[0.0009009225,0.0004586588,0.00075256574,0.00042513944,0.00046709622,0.0010006445,0.00088155235,0.0009914057,0.0002710546],"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":[0.0004719654,0.00019203279,0.0013176042,0.00016449482,0.00015594655,0.00016438865,0.00015610384,0.35135195,0.09524771,0.004854824,0.003201774,0.5427212],"study_design_scores_gemma":[0.0000095153055,0.000050816798,0.00055610103,0.0000059617423,0.000036156467,0.000049255817,0.000011549403,0.98237944,0.014880794,0.0012862819,0.00072365225,0.000010422003],"about_ca_topic_score_codex":0.008484465,"about_ca_topic_score_gemma":0.009880336,"teacher_disagreement_score":0.008484465,"about_ca_system_score_codex":0.00056970696,"about_ca_system_score_gemma":0.0007510397,"threshold_uncertainty_score":0.016870141},"labels":[],"label_agreement":null},{"id":"W3092484881","doi":"","title":"A Statistical Learning-Based Method for Color Correction of Underwater Images","year":2005,"lang":"en","type":"article","venue":"Research in computing science","topic":"Image Enhancement Techniques","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":"McGill University","funders":"","keywords":"Underwater; Artificial intelligence; Computer science; Computer vision; Markov random field; Color correction; Monochrome; Color balance; Pixel; Color histogram; Color image; Pattern recognition (psychology); Image (mathematics); Image processing; Image segmentation; Geography","score_opus":0.07207089326841064,"score_gpt":0.46695625285872616,"score_spread":0.3948853595903155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092484881","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":[0.0012773074,0.000050532504,0.99824214,0.000033216536,0.000013611228,0.0000096778995,0.000011008987,0.00024537425,0.000117083546],"genre_scores_gemma":[0.10331585,0.00031682278,0.893974,0.00013343057,0.00009770799,0.00008307599,0.00016626365,0.00020198974,0.0017107883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999283,0.000164294,0.000027853728,0.000189759,0.0002925778,0.000042495732],"domain_scores_gemma":[0.99828976,0.0006878653,0.0002342731,0.0002945752,0.00044390242,0.000049604907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011717253,0.0005824599,0.0007881428,0.0010560382,0.0003621558,0.00045753657,0.0015745859,0.0008188604,0.0011380786],"category_scores_gemma":[0.0042080884,0.00047493848,0.0008799038,0.0009561395,0.00078566256,0.0010738682,0.00071488315,0.00154781,0.00061961653],"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":[0.00013482543,0.00009646931,0.0010388544,0.00013996502,0.00011414127,0.00009983826,0.00007623226,0.27302974,0.03769075,0.013475786,0.00244441,0.67165893],"study_design_scores_gemma":[0.0000071254935,0.000033541543,0.00037757598,0.000008824004,0.000018852998,0.0001115005,0.0000053221816,0.9841801,0.009569532,0.003845524,0.001819484,0.000022666272],"about_ca_topic_score_codex":0.002693043,"about_ca_topic_score_gemma":0.003095774,"teacher_disagreement_score":0.002693043,"about_ca_system_score_codex":0.0005827528,"about_ca_system_score_gemma":0.00088247267,"threshold_uncertainty_score":0.0061967373},"labels":[],"label_agreement":null},{"id":"W3109970185","doi":"10.1109/iai50351.2020.9262172","title":"Pre-processing for UAV Based Wildfire Detection: A Loss U-net Enhanced GAN for Image Restoration","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminator; Computer science; Feature (linguistics); Net (polyhedron); Simulated annealing; Artificial intelligence; Generator (circuit theory); Real-time computing; Algorithm; Detector; Telecommunications; Mathematics","score_opus":0.019213812546200165,"score_gpt":0.2811697038350284,"score_spread":0.26195589128882824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109970185","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":[0.034210794,0.00026463304,0.96112406,0.00013728393,0.000077889126,0.000041989275,0.000056515782,0.0011403549,0.002946609],"genre_scores_gemma":[0.77726185,0.00028973588,0.21327902,0.0003710121,0.000046724625,0.00006732607,0.0002881201,0.00023396916,0.008162212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998603,0.000020677995,0.0000045921724,0.000042040938,0.000045983976,0.000026299165],"domain_scores_gemma":[0.99987984,0.00003696899,0.000015746982,0.000022340946,0.00003490417,0.000010206311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035964028,0.0006584913,0.00039342477,0.0002541886,0.00016735135,0.00031100135,0.00061405625,0.0004600866,0.0013323564],"category_scores_gemma":[0.0005361581,0.00022656436,0.0005021368,0.0001652204,0.00036215154,0.0006064432,0.0004164044,0.0009779587,0.0003625237],"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":[0.00024500434,0.000121756806,0.0020067843,0.00010549963,0.00008391494,0.00030463276,0.000097019685,0.61553663,0.06734097,0.0075183474,0.0049285768,0.3017108],"study_design_scores_gemma":[0.0000034783734,0.00004480849,0.0003120707,0.0000052853147,0.000008831608,0.00008135387,0.0000075449598,0.9853768,0.012023977,0.0011788596,0.0009514366,0.000005564477],"about_ca_topic_score_codex":0.0016665363,"about_ca_topic_score_gemma":0.002725606,"teacher_disagreement_score":0.0016665363,"about_ca_system_score_codex":0.00032724158,"about_ca_system_score_gemma":0.0003184774,"threshold_uncertainty_score":0.004457116},"labels":[],"label_agreement":null},{"id":"W3111127010","doi":"10.18280/ts.370505","title":"Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Mosul","keywords":"Color constancy; Artificial intelligence; Brightness; Computer science; Normalization (sociology); Computer vision; Sigmoid function; Gamma correction; Image quality; Pattern recognition (psychology); Image (mathematics); Algorithm; Artificial neural network","score_opus":0.015078126314241241,"score_gpt":0.25190358643192373,"score_spread":0.2368254601176825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111127010","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":[0.011383459,0.00040292562,0.9857987,0.00006883236,0.000028848597,0.00006858474,0.00001893244,0.00041894618,0.0018107763],"genre_scores_gemma":[0.10141662,0.0007150876,0.8925015,0.0000664624,0.00002743193,0.00013925266,0.00008807104,0.00007469334,0.0049708425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995609,0.00009619963,0.000026600925,0.00007766782,0.00020663733,0.000032069427],"domain_scores_gemma":[0.9996798,0.00009192649,0.000041965934,0.00004811267,0.00012668822,0.000011499741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006752927,0.00061121356,0.0005525263,0.0008396187,0.00031401828,0.00073464884,0.0006417885,0.000611038,0.0020448694],"category_scores_gemma":[0.00082825834,0.00021224284,0.000660327,0.0006808346,0.00039023315,0.00078556186,0.000422116,0.0006111102,0.00094684516],"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":[0.00037625566,0.00017701015,0.0008465934,0.0002391859,0.00009984769,0.00014022742,0.0002859979,0.07928978,0.20271482,0.01637693,0.0024014234,0.697052],"study_design_scores_gemma":[0.000043666554,0.00021524489,0.0012349298,0.00003460993,0.000053004194,0.0004978118,0.0000497643,0.8547183,0.12617429,0.0030488882,0.01387899,0.00005059003],"about_ca_topic_score_codex":0.001648972,"about_ca_topic_score_gemma":0.0017491292,"teacher_disagreement_score":0.0020448694,"about_ca_system_score_codex":0.0005298756,"about_ca_system_score_gemma":0.00070311996,"threshold_uncertainty_score":0.006840706},"labels":[],"label_agreement":null},{"id":"W3114965225","doi":"10.1109/tcsvt.2020.3048114","title":"A Fully Automatic Content Adaptive Inverse Tone Mapping Operator With Improved Color Accuracy","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Image Enhancement Techniques","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":"Telus (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tone mapping; High dynamic range; Computer science; Computer vision; Artificial intelligence; Brightness; High-dynamic-range imaging; Hue; Visualization; Human visual system model; Segmentation; Dynamic range; Image (mathematics)","score_opus":0.051254126525365755,"score_gpt":0.25753705035361724,"score_spread":0.20628292382825147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114965225","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":[0.043431554,0.00021821084,0.95240957,0.00006393248,0.00009508692,0.00006951727,0.000050239545,0.0017295787,0.0019323552],"genre_scores_gemma":[0.29313758,0.00029824543,0.70056695,0.00017955541,0.00010746527,0.000095781885,0.0002745198,0.00043202323,0.00490792],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996581,0.000049517483,0.000018406407,0.00008175092,0.00016271127,0.000029617542],"domain_scores_gemma":[0.99944025,0.00016241337,0.000052547985,0.00012525545,0.00018623857,0.000033346183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037707045,0.0005602943,0.00039154894,0.0005494609,0.00022300285,0.000667589,0.00075740844,0.0003798811,0.002305509],"category_scores_gemma":[0.0013759499,0.0001749564,0.00048937806,0.00032789676,0.00033989412,0.0008504831,0.00068581355,0.00070649985,0.00076941797],"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":[0.00044884472,0.0001270386,0.0009333034,0.0001560689,0.000041459658,0.00014736678,0.00015607667,0.013725597,0.39115128,0.0044256398,0.0026646177,0.5860226],"study_design_scores_gemma":[0.00006168621,0.00029366548,0.002072007,0.000020393076,0.000064612694,0.0006094798,0.00008282986,0.7465215,0.23583075,0.0020895826,0.012286489,0.00006711151],"about_ca_topic_score_codex":0.0011421297,"about_ca_topic_score_gemma":0.0013583248,"teacher_disagreement_score":0.002305509,"about_ca_system_score_codex":0.00020860463,"about_ca_system_score_gemma":0.00037684082,"threshold_uncertainty_score":0.007712662},"labels":[],"label_agreement":null},{"id":"W3115729019","doi":"10.1145/3426239","title":"Deep Learning Thermal Image Translation for Night Vision Perception","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Image translation; Computer science; Artificial intelligence; Convolutional neural network; Translation (biology); Deep learning; Computer vision; Perception; Grayscale; Image (mathematics); Pattern recognition (psychology)","score_opus":0.021768421573466524,"score_gpt":0.27530560188629055,"score_spread":0.253537180312824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115729019","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":[0.05747888,0.0010383769,0.9341554,0.00028510223,0.00019567992,0.000046742407,0.00012561264,0.0018799413,0.004794328],"genre_scores_gemma":[0.7892332,0.001038904,0.2003613,0.00042709662,0.00015125921,0.00005868769,0.00049075705,0.0003187648,0.0079199495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998424,0.000025049962,0.000004194253,0.00005207633,0.000046225687,0.000030045394],"domain_scores_gemma":[0.99987614,0.000027848344,0.000018084245,0.000031830903,0.000033952394,0.000012087226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025335702,0.0006895488,0.00040783777,0.00024979943,0.00020514279,0.00052915205,0.00076194055,0.0004940293,0.0018549486],"category_scores_gemma":[0.0007222184,0.00019450115,0.0005967399,0.000300382,0.00036472338,0.0009657383,0.00076809525,0.0013014227,0.00046636857],"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":[0.0003227711,0.00016697902,0.001324314,0.0002033322,0.00011402835,0.0002072522,0.00014130367,0.25863457,0.13366584,0.011590611,0.0060507436,0.58757824],"study_design_scores_gemma":[0.000008537116,0.00006705695,0.00075696385,0.000010949461,0.000028233308,0.000075086966,0.000022064964,0.9698379,0.021390336,0.0047841934,0.0030058632,0.000012822427],"about_ca_topic_score_codex":0.002420663,"about_ca_topic_score_gemma":0.0031538757,"teacher_disagreement_score":0.002420663,"about_ca_system_score_codex":0.00047471828,"about_ca_system_score_gemma":0.00048431096,"threshold_uncertainty_score":0.0062054396},"labels":[],"label_agreement":null},{"id":"W311602103","doi":"10.5120/20413-2825","title":"Smooth Context based Color Transfer","year":2015,"lang":"en","type":"article","venue":"International Journal of Computer Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"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":"McGill University","keywords":"Computer science; Context (archaeology); Transfer (computing); Computer graphics (images); Information retrieval; Artificial intelligence; Geology; Operating system","score_opus":0.024431254536479086,"score_gpt":0.29367488205128367,"score_spread":0.2692436275148046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W311602103","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":[0.035653297,0.00042415853,0.9605105,0.00005549727,0.000064701766,0.000047705136,0.000025482173,0.0007470335,0.0024715902],"genre_scores_gemma":[0.65800667,0.0005110307,0.33551297,0.000120112956,0.00008893053,0.00007140142,0.00007394202,0.00017364358,0.0054412764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968636,0.000059055386,0.000009088363,0.00009881205,0.00010971638,0.000036994952],"domain_scores_gemma":[0.99978584,0.000050782335,0.000018577823,0.000053723812,0.00007360703,0.000017391805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030805366,0.0005999091,0.000371461,0.0006504519,0.00031869853,0.0006128977,0.00073748373,0.00045469566,0.0029682228],"category_scores_gemma":[0.00087345834,0.00019296684,0.00048934587,0.0004811977,0.0004329514,0.0009679714,0.0010477798,0.00049956073,0.00061447953],"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":[0.00040804571,0.00012545515,0.00091460557,0.00019802628,0.00007148055,0.000164606,0.00024511234,0.030094825,0.34418777,0.015038784,0.0016732729,0.606878],"study_design_scores_gemma":[0.000058253107,0.00045558094,0.0029274495,0.000023269142,0.000109670575,0.0006033579,0.00013168216,0.76376235,0.19618589,0.020525804,0.015144449,0.000072243725],"about_ca_topic_score_codex":0.0009794935,"about_ca_topic_score_gemma":0.0010422057,"teacher_disagreement_score":0.0029682228,"about_ca_system_score_codex":0.00031191442,"about_ca_system_score_gemma":0.000318153,"threshold_uncertainty_score":0.009929717},"labels":[],"label_agreement":null},{"id":"W3118575477","doi":"10.1109/ssci47803.2020.9308535","title":"Adversarial and Adaptive Tone Mapping Operator for High Dynamic Range Images","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":17,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Biomet","keywords":"Tone mapping; High dynamic range; Computer science; Tone (literature); Artificial intelligence; Computer vision; Metric (unit); Range (aeronautics); Operator (biology); Image (mathematics); Dynamic range; High-dynamic-range imaging; Engineering","score_opus":0.017539444711632143,"score_gpt":0.25853607631544656,"score_spread":0.2409966316038144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118575477","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":[0.051618073,0.0003130348,0.9436635,0.0002702554,0.0000899281,0.00006781658,0.000045706573,0.0009913628,0.002940268],"genre_scores_gemma":[0.7578458,0.0003360879,0.2329529,0.00044562054,0.00008276905,0.00007487922,0.00017301032,0.00022070693,0.007868179],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994766,0.00013257375,0.00001841778,0.0001210555,0.00018724962,0.00006399174],"domain_scores_gemma":[0.99893385,0.00057252083,0.00011223946,0.00019160776,0.000133629,0.000056078934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012616689,0.0007192385,0.00042313806,0.0003549329,0.00021256365,0.0005279175,0.0008053911,0.0006530088,0.0024576588],"category_scores_gemma":[0.0031313791,0.00020466687,0.0004770883,0.00019449818,0.0008451146,0.000982929,0.0010582957,0.001374896,0.00044155988],"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":[0.00043709305,0.00015008533,0.001023726,0.0001421847,0.00009442267,0.00031444957,0.00014055714,0.6045035,0.09897703,0.016569085,0.003399531,0.27424827],"study_design_scores_gemma":[0.000006970865,0.0000861515,0.00024667958,0.0000063635184,0.000010020275,0.000117162126,0.000010659429,0.97980624,0.015799936,0.0028753602,0.00102543,0.000009090613],"about_ca_topic_score_codex":0.0011123614,"about_ca_topic_score_gemma":0.0010769033,"teacher_disagreement_score":0.0024576588,"about_ca_system_score_codex":0.0004466088,"about_ca_system_score_gemma":0.0003212702,"threshold_uncertainty_score":0.008221626},"labels":[],"label_agreement":null},{"id":"W3120043704","doi":"10.18280/ts.370616","title":"Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Remote sensing; Computer vision; Computer science; Segmentation; Filter (signal processing); Mathematical morphology; Image segmentation; High resolution; Image (mathematics); Image processing; Geography","score_opus":0.017455131909667253,"score_gpt":0.2527137869069884,"score_spread":0.23525865499732118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120043704","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":[0.11582744,0.0003511093,0.88074774,0.00008052988,0.00002903533,0.00008712897,0.000048890957,0.0010000905,0.0018279604],"genre_scores_gemma":[0.40298036,0.00055790367,0.59349674,0.00007329991,0.000033285793,0.000057030957,0.00014994049,0.00020553303,0.0024458752],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997873,0.000023261213,0.000013225632,0.00006197645,0.000090697424,0.000023580815],"domain_scores_gemma":[0.9997197,0.00009556016,0.00005560558,0.000035941674,0.000076246084,0.000016853173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002794485,0.0005056582,0.00034621646,0.0010146603,0.00020365849,0.00049620454,0.00041155415,0.00039737936,0.00079826463],"category_scores_gemma":[0.00060690835,0.00029867087,0.00040483678,0.00057656836,0.00043198158,0.00076860085,0.00042701716,0.00031240546,0.00041016232],"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":[0.00011503231,0.000038472543,0.0010662589,0.00012971241,0.000020253141,0.00018944616,0.00017411255,0.008465692,0.83996046,0.000884666,0.00033484824,0.14862104],"study_design_scores_gemma":[0.000028401673,0.00020856985,0.01384611,0.000029568182,0.00007448016,0.0009924155,0.00014594235,0.33427197,0.64199185,0.0015509288,0.00681519,0.00004449271],"about_ca_topic_score_codex":0.0007211039,"about_ca_topic_score_gemma":0.0012457086,"teacher_disagreement_score":0.0010146603,"about_ca_system_score_codex":0.00019874092,"about_ca_system_score_gemma":0.00024649146,"threshold_uncertainty_score":0.0026704073},"labels":[],"label_agreement":null},{"id":"W3123806511","doi":"10.15353/jcvis.v6i1.3556","title":"Locally Adaptive Thresholding for Single-Shot Structured Light Patterns","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thresholding; Artificial intelligence; Computer science; Balanced histogram thresholding; Projector; Image (mathematics); Computer vision; Sensitivity (control systems); Pattern recognition (psychology); Image processing; Engineering","score_opus":0.021682651122192236,"score_gpt":0.286774309870284,"score_spread":0.26509165874809176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123806511","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":[0.041681856,0.00028365955,0.95616364,0.00006761437,0.000028688843,0.00003801049,0.000027370203,0.00046823424,0.0012410162],"genre_scores_gemma":[0.44448134,0.00042526968,0.5518644,0.00010655891,0.00003243605,0.00009481397,0.00013232628,0.00030136041,0.0025615743],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946934,0.000080544916,0.000032933167,0.000118286036,0.00026270875,0.00003620286],"domain_scores_gemma":[0.9990646,0.0003606388,0.0001477826,0.00016127949,0.00022126478,0.000044400826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004646234,0.0003215524,0.00044849407,0.00059485994,0.00019621388,0.00071355904,0.0006630392,0.00045914896,0.001216841],"category_scores_gemma":[0.002043595,0.00025477482,0.00027415287,0.0005112628,0.0004995041,0.0006414509,0.0005912024,0.00059066794,0.00042631678],"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":[0.00022178293,0.00006534687,0.00075077626,0.0003389781,0.000041107767,0.00016200119,0.00019748842,0.025357442,0.6829537,0.005469875,0.0011052791,0.28333622],"study_design_scores_gemma":[0.000026445947,0.00019930043,0.0030298447,0.00005164402,0.000042739208,0.00070312497,0.00010397694,0.60549015,0.3776063,0.008243846,0.0044606393,0.00004197872],"about_ca_topic_score_codex":0.00032233354,"about_ca_topic_score_gemma":0.0006906896,"teacher_disagreement_score":0.001216841,"about_ca_system_score_codex":0.00030839478,"about_ca_system_score_gemma":0.00029303096,"threshold_uncertainty_score":0.004070699},"labels":[],"label_agreement":null},{"id":"W3130368773","doi":"10.1109/bmsb53066.2021.9547023","title":"Photograph enhancement via imitation-to-innovation training scheme","year":2021,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Computer science; Scheme (mathematics); Amateur; Imitation; Tacking; Artificial intelligence; Piecewise; Deep learning; Function (biology); Adversarial system; Smoothness; Training (meteorology); Computer vision; Engineering","score_opus":0.033722943711214315,"score_gpt":0.2852529594143138,"score_spread":0.25153001570309946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130368773","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":[0.024950057,0.00023737126,0.9702036,0.00012134346,0.000061707295,0.00006814746,0.000037913138,0.0012752237,0.0030446043],"genre_scores_gemma":[0.62380296,0.0003969065,0.3643825,0.00022041466,0.000052738076,0.00009265288,0.00015690914,0.00018446268,0.010710412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974924,0.000034876684,0.000010446784,0.000073683834,0.00010323604,0.000028457867],"domain_scores_gemma":[0.99971646,0.00008259974,0.000034021163,0.000087654786,0.000060184702,0.000019108418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047949256,0.0004648593,0.0005149475,0.00030359972,0.00017271066,0.00028946428,0.0008972316,0.00056043616,0.0022758436],"category_scores_gemma":[0.0010290139,0.0002248027,0.00051413436,0.00023927494,0.0004580301,0.0006798653,0.00081255863,0.0008556642,0.00059845735],"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":[0.0003009505,0.00012311,0.0012512188,0.00019798613,0.000082603445,0.0003585074,0.00017638897,0.2385725,0.149417,0.0101945205,0.0050704414,0.5942548],"study_design_scores_gemma":[0.000013034776,0.00009561257,0.0005180522,0.000009147159,0.000019507148,0.00023349529,0.000009224038,0.9574618,0.036572255,0.0019008131,0.0031526764,0.000014357521],"about_ca_topic_score_codex":0.0011803422,"about_ca_topic_score_gemma":0.0017549277,"teacher_disagreement_score":0.0022758436,"about_ca_system_score_codex":0.00028924333,"about_ca_system_score_gemma":0.00031441698,"threshold_uncertainty_score":0.00761348},"labels":[],"label_agreement":null},{"id":"W3133216609","doi":"10.1109/tim.2021.3060598","title":"Kalman Filter-Based Convolutional Neural Network for Robust Tracking of Froth-Middling Interface in a Primary Separation Vessel in Presence of Occlusions","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Image Enhancement Techniques","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":"Convolutional neural network; Computer science; Interface (matter); Kalman filter; Artificial intelligence; Gravity separation; Computer vision; Process (computing); Artificial neural network; Filter (signal processing); Pattern recognition (psychology); Engineering","score_opus":0.058676369718725044,"score_gpt":0.29445276146334953,"score_spread":0.2357763917446245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133216609","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":[0.13326591,0.0006993489,0.8613802,0.00014349195,0.00010260425,0.000039704104,0.0001347319,0.0017591072,0.0024748517],"genre_scores_gemma":[0.9332446,0.00024696806,0.06272753,0.00006434196,0.000023111537,0.000040114155,0.00022940135,0.00003939647,0.003384529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981064,0.000012871698,0.000010501787,0.00007609544,0.000050774637,0.000039183356],"domain_scores_gemma":[0.9997105,0.000076682256,0.000051166033,0.000023294855,0.00012515903,0.00001328613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004186324,0.0005777362,0.00048104845,0.00036771872,0.00023842076,0.0003640635,0.0006878636,0.0005172239,0.00071165815],"category_scores_gemma":[0.00094115053,0.0003182962,0.00031847073,0.00027839091,0.00022706718,0.00041700917,0.00032683281,0.00057886884,0.0002391819],"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":[0.00044473997,0.000115538925,0.0071705896,0.000116875,0.00012068672,0.00015479847,0.0001205142,0.48979503,0.043099903,0.0015447531,0.0023365507,0.45498],"study_design_scores_gemma":[0.000002973288,0.000018164768,0.0010407142,0.0000028842758,0.00001018644,0.000011258169,0.0000044242624,0.9947519,0.0038381354,0.00013313038,0.00018195984,0.000004333446],"about_ca_topic_score_codex":0.032739427,"about_ca_topic_score_gemma":0.03377888,"teacher_disagreement_score":0.032739427,"about_ca_system_score_codex":0.00080531277,"about_ca_system_score_gemma":0.0009467112,"threshold_uncertainty_score":0.06509775},"labels":[],"label_agreement":null},{"id":"W3135349823","doi":"10.1007/978-3-030-67070-2_30","title":"AIM 2020: Scene Relighting and Illumination Estimation Challenge","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Standard illuminant; Computer science; Track (disk drive); Artificial intelligence; Computer vision; Orientation (vector space); Position (finance); Computer graphics (images); Mathematics","score_opus":0.013392730387762496,"score_gpt":0.24703800825525146,"score_spread":0.23364527786748895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135349823","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":[0.08413439,0.0069276504,0.767782,0.007987879,0.0024588297,0.0007777132,0.02549057,0.026193224,0.07824772],"genre_scores_gemma":[0.22358127,0.0022942533,0.63974184,0.003138667,0.0011766488,0.00040725758,0.07947059,0.00401136,0.046178136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851686,0.00031810207,0.00004172898,0.00031214376,0.00064301665,0.0001680607],"domain_scores_gemma":[0.99838114,0.00058260345,0.00004669679,0.00044384782,0.00036473628,0.00018100477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019319417,0.0015148686,0.0017371741,0.00072434306,0.0008814369,0.0018713741,0.0021685709,0.0026160297,0.010263511],"category_scores_gemma":[0.004322806,0.00059732224,0.0014886163,0.00086641806,0.0009007663,0.0015571082,0.002831113,0.0030968795,0.009254971],"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":[0.0016493115,0.00072258286,0.0013332969,0.0019471522,0.00024291483,0.00043713537,0.00022943704,0.047284734,0.041904666,0.0137616,0.37470376,0.5157834],"study_design_scores_gemma":[0.0005073516,0.001484608,0.008122224,0.0003251566,0.00018710115,0.0022247988,0.00054070476,0.61959815,0.0786071,0.06708149,0.22118086,0.0001404554],"about_ca_topic_score_codex":0.0037630103,"about_ca_topic_score_gemma":0.0049703275,"teacher_disagreement_score":0.010263511,"about_ca_system_score_codex":0.0004798396,"about_ca_system_score_gemma":0.0013495178,"threshold_uncertainty_score":0.03433484},"labels":[],"label_agreement":null},{"id":"W3135932884","doi":"10.1109/access.2021.3065968","title":"Image Dehazing in Disproportionate Haze Distributions","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image Enhancement 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":"McGill University","funders":"King Mongkut's Institute of Technology Ladkrabang; National Taipei University of Technology","keywords":"Haze; Visibility; Computer science; Image restoration; Robustness (evolution); Computer vision; Artificial intelligence; Channel (broadcasting); Image (mathematics); Image processing; Geography; Optics; Physics; Telecommunications","score_opus":0.022656333755515838,"score_gpt":0.3295803921278598,"score_spread":0.30692405837234393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135932884","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":[0.46530157,0.0016018283,0.5274377,0.00020166235,0.00008692974,0.000088411005,0.000066814464,0.0009818734,0.004233247],"genre_scores_gemma":[0.87129617,0.0011133067,0.123753674,0.00008637557,0.000032900767,0.000024448862,0.00008802056,0.00008098679,0.0035240846],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974126,0.000025822048,0.000012446387,0.00005794108,0.0001268476,0.000035731202],"domain_scores_gemma":[0.9994622,0.00015741838,0.00012606567,0.00009415492,0.00012882726,0.00003141907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004179179,0.00058364077,0.00045905903,0.0008545838,0.00026214108,0.00068124477,0.0005084717,0.0004708152,0.0008125827],"category_scores_gemma":[0.0009450084,0.0002749675,0.00036161402,0.00038219022,0.0005840868,0.0008758469,0.00070624304,0.0005305437,0.00022022743],"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":[0.0004665067,0.00013168406,0.0024251088,0.00057020586,0.000104745064,0.0006494265,0.00043185774,0.034585513,0.77930707,0.0026147538,0.00087767944,0.17783552],"study_design_scores_gemma":[0.00003288691,0.00033311764,0.00940495,0.000042478332,0.00012114126,0.0017434937,0.00023946154,0.26075596,0.72065264,0.0016867196,0.004939639,0.000047444442],"about_ca_topic_score_codex":0.0010169647,"about_ca_topic_score_gemma":0.0016119803,"teacher_disagreement_score":0.0010169647,"about_ca_system_score_codex":0.00017907082,"about_ca_system_score_gemma":0.00029910318,"threshold_uncertainty_score":0.002718389},"labels":[],"label_agreement":null},{"id":"W3137661749","doi":"10.48550/arxiv.2103.13998","title":"GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Bottleneck; Artificial intelligence; Task (project management); Grid; Synthetic data; Block (permutation group theory); Process (computing); Transfer of learning; Scale (ratio); Pattern recognition (psychology); Machine learning","score_opus":0.05724932680637841,"score_gpt":0.20898588992740585,"score_spread":0.15173656312102746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137661749","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":[0.046292566,0.00059819967,0.94636226,0.0002485495,0.00011960765,0.000093037954,0.00018275926,0.0030412187,0.003061798],"genre_scores_gemma":[0.5554457,0.00048055712,0.4313303,0.0004556319,0.000086661246,0.00017089031,0.0008537654,0.00035876266,0.010817678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999772,0.00002226032,0.000007650665,0.00007712814,0.000077797435,0.00004316399],"domain_scores_gemma":[0.999665,0.000083847466,0.000038780126,0.000105793886,0.00007826214,0.00002834518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045898248,0.00090936874,0.00069954986,0.0005339834,0.00031507446,0.000596842,0.0021876623,0.0011011321,0.0022686578],"category_scores_gemma":[0.0012578525,0.00040442654,0.0006204161,0.00043950864,0.0004669186,0.0017773743,0.001603683,0.0011625793,0.00064335903],"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":[0.00024957172,0.00020216458,0.001180074,0.00015681573,0.00016247951,0.00020074594,0.000111035915,0.43824002,0.041844156,0.0036631373,0.0060853134,0.5079045],"study_design_scores_gemma":[0.000011143535,0.00005153288,0.0002870783,0.0000054718917,0.000016874605,0.00005837185,0.0000126800005,0.9881589,0.007817177,0.0018522942,0.0017179404,0.000010626553],"about_ca_topic_score_codex":0.004989286,"about_ca_topic_score_gemma":0.0075122737,"teacher_disagreement_score":0.004989286,"about_ca_system_score_codex":0.0005474493,"about_ca_system_score_gemma":0.0005309233,"threshold_uncertainty_score":0.0099205375},"labels":[],"label_agreement":null},{"id":"W3138218344","doi":"10.1145/2010324.1964935","title":"HDR-VDP-2","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":644,"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":"Engineering and Physical Sciences Research Council","keywords":"Luminance; Metric (unit); Computer science; Contrast (vision); Rendering (computer graphics); Visibility; Artificial intelligence; Image quality; Computer vision; Image (mathematics); Optics","score_opus":0.04519748489046131,"score_gpt":0.2568153888724903,"score_spread":0.21161790398202895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138218344","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":[0.0714709,0.0010782368,0.74419355,0.00044983876,0.00054023345,0.0010628746,0.022821778,0.092232555,0.06615001],"genre_scores_gemma":[0.5085602,0.0006753244,0.40064144,0.00039382273,0.00015392771,0.0008710321,0.052478075,0.010489321,0.025736803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887866,0.00014141334,0.0000697029,0.00015437874,0.0006794634,0.000076485136],"domain_scores_gemma":[0.9984523,0.0002568666,0.00010988778,0.00040128632,0.0006945956,0.000085076645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092460663,0.0010351307,0.0006535047,0.0013270116,0.00030226453,0.0016193305,0.0012113035,0.0008733398,0.01888948],"category_scores_gemma":[0.004104088,0.00028409754,0.00045920565,0.00081865996,0.00035496082,0.0014980707,0.0011961386,0.0006170266,0.007936635],"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":[0.0012024586,0.00037842777,0.007987571,0.00090373465,0.00017011556,0.000285942,0.00018206824,0.04936493,0.10160305,0.01851575,0.13175088,0.68765503],"study_design_scores_gemma":[0.00018938624,0.0006691035,0.018229648,0.0001121166,0.00006031849,0.0012019911,0.00010416258,0.69099337,0.14885883,0.012266793,0.12711436,0.0001999688],"about_ca_topic_score_codex":0.0023749706,"about_ca_topic_score_gemma":0.002212083,"teacher_disagreement_score":0.01888948,"about_ca_system_score_codex":0.0006863405,"about_ca_system_score_gemma":0.0004613085,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3142697761","doi":"10.18280/ts.380104","title":"Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Johnson Space Center; University of Mosul; National Aeronautics and Space Administration","keywords":"Grayscale; Contrast (vision); Artificial intelligence; Brightness; Computer science; Computer vision; Set (abstract data type); Contrast enhancement; Feature (linguistics); Pattern recognition (psychology); Algorithm; Digital image; Fuzzy logic; Image (mathematics); Mathematics; Image processing","score_opus":0.02263554681864367,"score_gpt":0.2692621098322484,"score_spread":0.24662656301360475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142697761","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":[0.022501424,0.00021754955,0.9752328,0.000066082255,0.000047444577,0.00008533755,0.000017452321,0.0002802429,0.0015517422],"genre_scores_gemma":[0.26093486,0.00026468825,0.7366525,0.00008013691,0.000033155393,0.00013830356,0.00006117467,0.00003337139,0.0018017658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939895,0.000082848404,0.000057267138,0.000116680305,0.00030423238,0.000039899413],"domain_scores_gemma":[0.9992107,0.00025798488,0.000080839854,0.0000483241,0.000381513,0.000020572026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011373856,0.00055320683,0.0007328825,0.00097411993,0.00042864768,0.000935495,0.0009904546,0.0008070601,0.0011512163],"category_scores_gemma":[0.002272922,0.0002529865,0.0009328479,0.00058110466,0.0004054004,0.0008134325,0.0004082754,0.0008563367,0.00029369158],"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":[0.0005572733,0.00021424615,0.002161852,0.00041062335,0.00015775808,0.0002471128,0.00036994877,0.22560582,0.12937644,0.008185179,0.0017182726,0.6309955],"study_design_scores_gemma":[0.000032586217,0.00018208337,0.00090721436,0.000027703816,0.000054712335,0.00021103898,0.000029462502,0.96607715,0.029598797,0.0010960599,0.0017504592,0.000032734348],"about_ca_topic_score_codex":0.003093563,"about_ca_topic_score_gemma":0.0029582754,"teacher_disagreement_score":0.003093563,"about_ca_system_score_codex":0.0006793306,"about_ca_system_score_gemma":0.00064817397,"threshold_uncertainty_score":0.0061511397},"labels":[],"label_agreement":null},{"id":"W3145991252","doi":"10.18280/isi.260110","title":"Image Pixel Contrast Enhancement Using Enhanced Multi Histogram Equalization Method","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Histogram equalization; Artificial intelligence; Computer science; Computer vision; Contrast (vision); Histogram; Pixel; Adaptive histogram equalization; Brightness; Equalization (audio); Image (mathematics); Image enhancement; Pattern recognition (psychology); Channel (broadcasting)","score_opus":0.02623312701486845,"score_gpt":0.30240469002737536,"score_spread":0.2761715630125069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145991252","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":[0.028384518,0.001010418,0.9619715,0.00015459291,0.00014052696,0.00013802803,0.000114267204,0.0014421832,0.0066439547],"genre_scores_gemma":[0.31935042,0.0017935804,0.65951157,0.00022695509,0.000103985134,0.00015445969,0.0003792793,0.00015582991,0.018323978],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958175,0.000042097417,0.000029340961,0.0000987241,0.00019935287,0.00004873988],"domain_scores_gemma":[0.9996753,0.00007101594,0.000035552206,0.000042543354,0.000163706,0.000011951879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036395906,0.00039574696,0.00044146265,0.00084149337,0.00023231484,0.00065999065,0.0005765386,0.00049061974,0.0042613717],"category_scores_gemma":[0.0007755122,0.00022073787,0.00049590616,0.0006334501,0.00027004318,0.0010083243,0.00057167164,0.00064404245,0.0013658506],"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":[0.0003195984,0.00015769569,0.0012437948,0.00051613094,0.00009516631,0.0003072147,0.00013787611,0.010640552,0.43075067,0.005688665,0.004554544,0.5455882],"study_design_scores_gemma":[0.000047433652,0.00039131608,0.0061278916,0.000067178305,0.00010486816,0.001781806,0.000093060655,0.22548911,0.7249605,0.0023869954,0.038456626,0.00009318455],"about_ca_topic_score_codex":0.00046613257,"about_ca_topic_score_gemma":0.00073687395,"teacher_disagreement_score":0.0042613717,"about_ca_system_score_codex":0.00024837736,"about_ca_system_score_gemma":0.00027736853,"threshold_uncertainty_score":0.0142557025},"labels":[],"label_agreement":null},{"id":"W3158623384","doi":"10.1049/ipr2.12247","title":"Underwater image enhancement based on colour correction and fusion","year":2021,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Underwater; Image fusion; Fusion; Image enhancement; Computer vision; Artificial intelligence; Image (mathematics); Computer science; Remote sensing; Optics; Geology; Physics; Oceanography","score_opus":0.01022510103826426,"score_gpt":0.2636183492321107,"score_spread":0.25339324819384645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158623384","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":[0.28358635,0.0010580705,0.70865566,0.00018942209,0.0001292778,0.000082531784,0.000057654892,0.0012701884,0.0049708267],"genre_scores_gemma":[0.72353935,0.00082495576,0.27155966,0.00008972548,0.000049857805,0.000031313608,0.00007997614,0.00008130192,0.0037438658],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980253,0.000020148147,0.000009029631,0.000038591425,0.0001042213,0.0000255046],"domain_scores_gemma":[0.9998178,0.00003431489,0.000033621796,0.000027842469,0.000075680284,0.0000108383065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002527883,0.0004647678,0.00038254893,0.0005823472,0.0001601654,0.0003594233,0.00025979083,0.00036306863,0.0010152309],"category_scores_gemma":[0.00041943692,0.00019836795,0.00038220556,0.00041834638,0.00031385964,0.00064317696,0.0004996739,0.00029343634,0.00030298176],"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":[0.00020381805,0.000048393027,0.0009612678,0.0001430942,0.000029764959,0.00016186501,0.00006322328,0.010606001,0.8248467,0.0013384655,0.0005823471,0.16101508],"study_design_scores_gemma":[0.000025030817,0.00024024438,0.0047211605,0.000023484423,0.00009268962,0.000620294,0.000039559036,0.24278094,0.7464219,0.0007568343,0.004232362,0.00004548343],"about_ca_topic_score_codex":0.00053280185,"about_ca_topic_score_gemma":0.00045753983,"teacher_disagreement_score":0.0010152309,"about_ca_system_score_codex":0.00015148835,"about_ca_system_score_gemma":0.00018843668,"threshold_uncertainty_score":0.003396213},"labels":[],"label_agreement":null},{"id":"W3159558183","doi":"10.1155/2021/6658763","title":"Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Transmission (telecommunications); Image (mathematics); Computer vision; Minification; Optimization problem; Representation (politics); Image fusion; Algorithm","score_opus":0.008980893311572936,"score_gpt":0.27866136930791463,"score_spread":0.2696804759963417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159558183","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":[0.006038248,0.00041619805,0.9922002,0.00015459012,0.000024505518,0.000015653526,0.000023415998,0.000084474836,0.0010427954],"genre_scores_gemma":[0.56963915,0.00137247,0.41907346,0.00026225546,0.00011053076,0.00018601539,0.00025390784,0.00022426635,0.008878027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998555,0.00004422586,0.0000073314504,0.000028751296,0.00004348804,0.000020547977],"domain_scores_gemma":[0.99979836,0.00010839942,0.000021394675,0.000010928394,0.00004751879,0.000013349407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068655604,0.0006575305,0.00076524814,0.00037028705,0.00024076129,0.00053363584,0.0008244923,0.00089387095,0.0012183482],"category_scores_gemma":[0.0010754849,0.0004429492,0.0007492119,0.00034601224,0.00057989685,0.00065763487,0.00093887467,0.0014040653,0.00018196103],"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":[0.000031247513,0.000021768621,0.0003084148,0.00007696655,0.00004082839,0.00003659903,0.000043867516,0.95145124,0.004177698,0.01182824,0.0009347035,0.031048315],"study_design_scores_gemma":[0.0000011266379,0.0000034415825,0.00001716844,0.0000016799347,0.0000015934163,0.0000031354318,0.0000014983073,0.99888223,0.00018722074,0.0007484984,0.00015103913,0.0000014093964],"about_ca_topic_score_codex":0.007675922,"about_ca_topic_score_gemma":0.0059695994,"teacher_disagreement_score":0.007675922,"about_ca_system_score_codex":0.00074018247,"about_ca_system_score_gemma":0.0011825074,"threshold_uncertainty_score":0.015262485},"labels":[],"label_agreement":null},{"id":"W3162612338","doi":"10.1155/2021/5598390","title":"CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Visibility; Haze; Robustness (evolution); Artificial intelligence; Feature extraction; Computer vision; Feature (linguistics); Finite element method; Fuse (electrical); Pattern recognition (psychology); Engineering","score_opus":0.010964599463472582,"score_gpt":0.2688476561394518,"score_spread":0.2578830566759792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162612338","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":[0.07023353,0.000650668,0.9243146,0.00016130833,0.00008304447,0.000049890325,0.00018057237,0.0015908162,0.0027355782],"genre_scores_gemma":[0.78022414,0.00069471303,0.21284299,0.00017083836,0.00006788782,0.00005059285,0.0006006859,0.000129194,0.0052189888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998907,0.0000074387194,0.000003745849,0.00003381249,0.000035001114,0.00002932714],"domain_scores_gemma":[0.9999099,0.000012432303,0.000014585336,0.000014145318,0.00003856422,0.000010334872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021106288,0.0007015778,0.0003801055,0.00037952798,0.00014885991,0.00034534372,0.00087311515,0.00043852776,0.0008819494],"category_scores_gemma":[0.00051333394,0.00024830797,0.00046914796,0.00019369165,0.00020647128,0.0007259609,0.00062268256,0.0006761218,0.00020065758],"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":[0.00028192785,0.00014247744,0.0039092656,0.00019534513,0.00018829996,0.00033955008,0.00011301623,0.37602708,0.15009792,0.0059411624,0.0044468744,0.4583171],"study_design_scores_gemma":[0.0000043229134,0.00004417676,0.000703061,0.000006159229,0.000028016046,0.000070266215,0.0000076657,0.9831141,0.014334691,0.0008110941,0.00086982234,0.000006620522],"about_ca_topic_score_codex":0.010083992,"about_ca_topic_score_gemma":0.01301565,"teacher_disagreement_score":0.010083992,"about_ca_system_score_codex":0.0004558327,"about_ca_system_score_gemma":0.0006326293,"threshold_uncertainty_score":0.020050645},"labels":[],"label_agreement":null},{"id":"W3163092774","doi":"10.18280/ts.380231","title":"An Image Sharpness Enhancement Algorithm Based on Green Function","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Image (mathematics); Algorithm; Boundary (topology); Artificial intelligence; Image enhancement; Function (biology); Computer vision; Brightness; Consistency (knowledge bases); Computer science; Mathematics; Domain (mathematical analysis)","score_opus":0.011077455936806318,"score_gpt":0.24692405247012042,"score_spread":0.2358465965333141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163092774","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":[0.009653487,0.0002698526,0.9884322,0.000070273374,0.00004123192,0.00002983305,0.0000091988495,0.000432131,0.0010617892],"genre_scores_gemma":[0.12291829,0.0008661015,0.8718451,0.00012662304,0.00006784563,0.000058218466,0.000058892598,0.000117514406,0.0039412845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975044,0.000027245456,0.000014566137,0.000055727378,0.00013026423,0.000021742448],"domain_scores_gemma":[0.9997625,0.00006231057,0.000023414472,0.000028474667,0.000110116474,0.000013228584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004000783,0.0004696437,0.0005477113,0.00087546854,0.00027890416,0.0005156956,0.0005624265,0.0006457459,0.0010327145],"category_scores_gemma":[0.00068972755,0.00025945887,0.000662977,0.00058308255,0.00036813592,0.0010623909,0.00037578322,0.0007767092,0.0005030739],"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":[0.00013599811,0.0000817943,0.0005397832,0.00018245216,0.000055307173,0.00021412548,0.00015653658,0.035870064,0.4411477,0.014966239,0.0018886401,0.5047614],"study_design_scores_gemma":[0.000059037688,0.00029439648,0.001423878,0.000031118983,0.00008682548,0.0014850419,0.000043366206,0.7131711,0.26300824,0.0051044235,0.015200718,0.00009189893],"about_ca_topic_score_codex":0.00086925685,"about_ca_topic_score_gemma":0.0007460528,"teacher_disagreement_score":0.0010327145,"about_ca_system_score_codex":0.0002763428,"about_ca_system_score_gemma":0.0004528137,"threshold_uncertainty_score":0.0034547448},"labels":[],"label_agreement":null},{"id":"W3164005373","doi":"10.1186/s40494-021-00504-5","title":"Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter","year":2021,"lang":"en","type":"article","venue":"Heritage Science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":30,"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":"Key Technologies Research and Development Program; Beijing Postdoctoral Science Foundation","keywords":"Artificial intelligence; Computer vision; Bilateral filter; Color constancy; Computer science; Hyperspectral imaging; Brightness; Hue; Channel (broadcasting); Image restoration; Mathematics; Image processing; Pixel; Optics; Image (mathematics); Physics","score_opus":0.013488257889989884,"score_gpt":0.2845251537257964,"score_spread":0.27103689583580653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164005373","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":[0.16430123,0.0005477015,0.82754636,0.00022072192,0.00014357966,0.00008910303,0.000093361195,0.0010276957,0.006030284],"genre_scores_gemma":[0.6198485,0.0008844691,0.37021783,0.0000973032,0.000060778755,0.00006510014,0.00016925384,0.00011768411,0.008539041],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995796,0.000044437485,0.000021380674,0.00007631287,0.0002346494,0.000043630633],"domain_scores_gemma":[0.99971586,0.00004139391,0.00003884121,0.000049102415,0.00013704428,0.000017769426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003659647,0.0007137504,0.0005348873,0.0012322805,0.0003062542,0.00070232834,0.00043661418,0.00058576354,0.0023986355],"category_scores_gemma":[0.00072349387,0.0002434484,0.00089005585,0.00054572773,0.000499219,0.0007461705,0.00046548032,0.00045615368,0.00056841306],"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":[0.0006440605,0.0001636005,0.00263677,0.00049616635,0.00015323299,0.00035597585,0.00035192491,0.062262766,0.537268,0.006152483,0.002056934,0.3874582],"study_design_scores_gemma":[0.00008111706,0.00032177466,0.011094655,0.00006893144,0.00019289393,0.0010900112,0.00034116302,0.6329272,0.33908948,0.002804427,0.011833637,0.00015468188],"about_ca_topic_score_codex":0.0025162369,"about_ca_topic_score_gemma":0.0023657153,"teacher_disagreement_score":0.0025162369,"about_ca_system_score_codex":0.00033802856,"about_ca_system_score_gemma":0.00050377403,"threshold_uncertainty_score":0.008024275},"labels":[],"label_agreement":null},{"id":"W3171489778","doi":"10.1109/lgrs.2021.3084932","title":"Pragmatic Augmentation Algorithms for Deep Learning-Based Cloud and Cloud Shadow Detection in Remote Sensing Imagery","year":2021,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Government of Canada","keywords":"Cloud computing; Computer science; Ground truth; Artificial intelligence; Shadow (psychology); Preprocessor; Boosting (machine learning); Segmentation; Deep learning; Computer vision; Identification (biology); Image segmentation; Remote sensing; Algorithm; Machine learning; Geology","score_opus":0.011659963894478322,"score_gpt":0.2542562490691613,"score_spread":0.24259628517468296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171489778","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":[0.094898,0.0010925531,0.8964179,0.00053512875,0.00010261145,0.00012915459,0.00026155563,0.0033168646,0.0032462538],"genre_scores_gemma":[0.6172773,0.00051509915,0.37757188,0.00036353164,0.0000871764,0.00015227965,0.0009887867,0.00023182438,0.0028121301],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997342,0.000056629255,0.000015356469,0.000060116072,0.000083799634,0.00004985799],"domain_scores_gemma":[0.9994978,0.00020889529,0.00006702091,0.000081847495,0.0001140374,0.0000305146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084428274,0.0007589785,0.00052838796,0.0006333608,0.0002737055,0.0005900456,0.0012441961,0.0006759807,0.00134771],"category_scores_gemma":[0.0019029566,0.00032386382,0.00057737174,0.00068064325,0.0005678859,0.0010853455,0.0010544398,0.0014655174,0.00050313625],"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":[0.00038280894,0.0002765463,0.0022643926,0.00016895267,0.00008602529,0.000111087116,0.00011353953,0.36461246,0.029618263,0.0046820664,0.005041579,0.5926422],"study_design_scores_gemma":[0.000004980869,0.000022292073,0.0002914693,0.00000675816,0.000005135711,0.000016557091,0.000008301698,0.99231213,0.0052247103,0.0015065185,0.00059726357,0.000003863385],"about_ca_topic_score_codex":0.0047591776,"about_ca_topic_score_gemma":0.007190094,"teacher_disagreement_score":0.0047591776,"about_ca_system_score_codex":0.0007016196,"about_ca_system_score_gemma":0.00069653656,"threshold_uncertainty_score":0.009462953},"labels":[],"label_agreement":null},{"id":"W3175067501","doi":"10.1109/cvpr46437.2021.00987","title":"Seeing in Extra Darkness Using a Deep-Red Flash","year":2021,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Flash (photography); Artificial intelligence; Computer vision; RGB color model; Frame (networking); Computer graphics (images); Optics; Physics; Telecommunications","score_opus":0.024240768634344698,"score_gpt":0.27811742937987105,"score_spread":0.25387666074552634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175067501","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":[0.16552554,0.00039085324,0.8279799,0.00023520077,0.00005231518,0.000040366634,0.0001325993,0.001780848,0.0038624662],"genre_scores_gemma":[0.70412284,0.00040240135,0.29088157,0.00029992426,0.000031043106,0.000031504733,0.00021097135,0.00015151562,0.0038682476],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999292,0.000009837371,0.0000026452394,0.000020217341,0.000021613807,0.000016608157],"domain_scores_gemma":[0.9998857,0.0000279276,0.000016760212,0.000028830278,0.000020656958,0.000020089154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020849548,0.00045235208,0.00028139912,0.00023166301,0.00011362646,0.0003525055,0.00061791996,0.00035269186,0.0016549454],"category_scores_gemma":[0.0003341868,0.00021189344,0.0003132937,0.00012440886,0.0003253412,0.0007251436,0.0007327121,0.0005880172,0.00028410647],"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":[0.0004053576,0.00011109182,0.0013099137,0.00021049319,0.000100717,0.00042309865,0.00026934792,0.06392059,0.6047192,0.006960052,0.0019994269,0.31957084],"study_design_scores_gemma":[0.000040801457,0.00028560238,0.0018454116,0.000037484107,0.00006865212,0.0005696742,0.000065206885,0.77804637,0.20803383,0.0054402384,0.0055245426,0.000042142306],"about_ca_topic_score_codex":0.00109589,"about_ca_topic_score_gemma":0.0016775389,"teacher_disagreement_score":0.0016549454,"about_ca_system_score_codex":0.00024709196,"about_ca_system_score_gemma":0.00024023573,"threshold_uncertainty_score":0.0055363774},"labels":[],"label_agreement":null},{"id":"W3177140187","doi":"10.1609/aaai.v35i2.16266","title":"Deep Low-Contrast Image Enhancement using Structure Tensor Representation","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Image Enhancement 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":"Nexen (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Contrast (vision); Structure tensor; Image (mathematics); Computer science; Artificial intelligence; Representation (politics); Ground truth; Tensor (intrinsic definition); Pattern recognition (psychology); Function (biology); Deep learning; Image quality; Mathematics; Geometry","score_opus":0.05306238292628465,"score_gpt":0.3160405930976323,"score_spread":0.2629782101713477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177140187","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":[0.013465753,0.00043523873,0.98322994,0.00014929513,0.00004074439,0.0000395092,0.000058242047,0.0011084335,0.0014728158],"genre_scores_gemma":[0.32727867,0.0009413912,0.66280305,0.00036148026,0.00008248653,0.00009287911,0.00042266262,0.00035485488,0.007662553],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997745,0.000034253964,0.000009135814,0.00004626003,0.00009686753,0.00003884823],"domain_scores_gemma":[0.9996617,0.00009350967,0.00005739686,0.00006945961,0.00008816968,0.000029716162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006739355,0.0010658776,0.00071872567,0.0005861279,0.00022240535,0.00095881877,0.0010695684,0.0007901652,0.0020536028],"category_scores_gemma":[0.0012305212,0.00036353237,0.0007789328,0.00039221093,0.0005938081,0.0016372879,0.001174882,0.0017113334,0.00068212446],"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":[0.00033810528,0.00020821302,0.0010820411,0.00036085927,0.0001429494,0.00024787415,0.00012715504,0.2968374,0.2044745,0.017015455,0.0049396297,0.47422576],"study_design_scores_gemma":[0.000010951836,0.000087158296,0.000271765,0.000017418262,0.000026410406,0.00012779517,0.000008666517,0.94449615,0.048471548,0.0040911795,0.0023771487,0.000013811542],"about_ca_topic_score_codex":0.0016863903,"about_ca_topic_score_gemma":0.0024295824,"teacher_disagreement_score":0.0020536028,"about_ca_system_score_codex":0.00060100644,"about_ca_system_score_gemma":0.00053343194,"threshold_uncertainty_score":0.006869912},"labels":[],"label_agreement":null},{"id":"W3192513540","doi":"10.24963/ijcai.2021/90","title":"Direction-aware Feature-level Frequency Decomposition for Single Image Deraining","year":2021,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Decomposition; Artificial intelligence; Image (mathematics); Filter (signal processing); Pattern recognition (psychology); Computer vision","score_opus":0.03242755780204999,"score_gpt":0.30402833167248716,"score_spread":0.2716007738704372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192513540","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":[0.017944457,0.00025786893,0.9785848,0.00008840354,0.000046953897,0.000041364812,0.000082914645,0.00132346,0.0016296863],"genre_scores_gemma":[0.3854747,0.00045891685,0.60203034,0.00033132936,0.00007488402,0.00012658877,0.0007145632,0.00027964037,0.010508871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997583,0.000029313976,0.000011741486,0.00007293337,0.00008282438,0.000044837827],"domain_scores_gemma":[0.9996269,0.00009009703,0.00003809179,0.00011050685,0.000108860644,0.000025448204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046410706,0.00087651523,0.0006593167,0.0005487627,0.00027658924,0.00050522055,0.0011995055,0.000714888,0.0029808441],"category_scores_gemma":[0.0012207591,0.00030520224,0.0005511994,0.0005625284,0.00040799295,0.0012417631,0.0009929012,0.000899644,0.0011368841],"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":[0.00033732102,0.00015095572,0.001007009,0.000116975054,0.00006900225,0.00013236517,0.00009513579,0.08439313,0.09858339,0.004737876,0.005495975,0.8048808],"study_design_scores_gemma":[0.000015774433,0.00010954818,0.0007709941,0.000013692379,0.0000348195,0.00017557076,0.000032081876,0.94643456,0.042562395,0.004414979,0.005416882,0.000018738085],"about_ca_topic_score_codex":0.0027548478,"about_ca_topic_score_gemma":0.00504137,"teacher_disagreement_score":0.0029808441,"about_ca_system_score_codex":0.00037116706,"about_ca_system_score_gemma":0.00052792934,"threshold_uncertainty_score":0.009971976},"labels":[],"label_agreement":null},{"id":"W3193021599","doi":"10.1007/s11042-021-11209-z","title":"A multi-scale attentive recurrent network for image dehazing","year":2021,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Haze; Computer science; Encoder; Artificial intelligence; Image (mathematics); Computer vision; Scale (ratio); Residual; Network architecture; Algorithm; Computer network","score_opus":0.031535775594435767,"score_gpt":0.30081212315879347,"score_spread":0.2692763475643577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193021599","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":[0.023790345,0.0005520993,0.9730958,0.00011277529,0.000106665924,0.000031098236,0.000058672384,0.00082584406,0.0014267311],"genre_scores_gemma":[0.6640213,0.0005843149,0.3247643,0.00022077288,0.00014485088,0.000092687595,0.00032071467,0.00015571875,0.009695307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984455,0.000025669433,0.000008821386,0.00005283391,0.000042918862,0.000025194535],"domain_scores_gemma":[0.9997638,0.00008752119,0.000022881291,0.000028494427,0.00007963812,0.000017737768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044211437,0.00058347225,0.00067796366,0.00032907174,0.0002912795,0.00041979976,0.0013463118,0.00087110506,0.0021531754],"category_scores_gemma":[0.00074526726,0.00034086488,0.00055128563,0.00029927288,0.0002741824,0.00071128865,0.0006321035,0.00084619346,0.0005754168],"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":[0.0003597549,0.000259661,0.00094326737,0.0001563374,0.00018163964,0.0002301674,0.00009768698,0.47534212,0.07424784,0.006714902,0.004682047,0.43678454],"study_design_scores_gemma":[0.0000027810645,0.00002379652,0.00007279326,0.0000020337836,0.000013412366,0.000014065559,0.000002143258,0.99704725,0.0021705066,0.0004070672,0.00024071046,0.0000035395374],"about_ca_topic_score_codex":0.0049365633,"about_ca_topic_score_gemma":0.007996418,"teacher_disagreement_score":0.0049365633,"about_ca_system_score_codex":0.0004047966,"about_ca_system_score_gemma":0.00032725531,"threshold_uncertainty_score":0.009815693},"labels":[],"label_agreement":null},{"id":"W3194636806","doi":"10.1155/2021/9470895","title":"Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Visibility; Computer science; Artificial intelligence; Haze; Feature (linguistics); Object detection; Computer vision; Adverse weather; Generalization; Artificial neural network; Remote sensing; Pattern recognition (psychology)","score_opus":0.006829666765030715,"score_gpt":0.25641791442964246,"score_spread":0.24958824766461174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194636806","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":[0.2048454,0.0007881099,0.79038274,0.00022592736,0.000075357864,0.000059568538,0.00007108862,0.00085633487,0.0026955423],"genre_scores_gemma":[0.90774965,0.00029134014,0.09072597,0.00006676469,0.00002448628,0.000024424662,0.00008505117,0.000020689286,0.0010117146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999105,0.000016362674,0.0000038893713,0.000026129233,0.000026690923,0.000016382379],"domain_scores_gemma":[0.9998951,0.000027586493,0.000016431193,0.000010581333,0.00004210841,0.000008217615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021750378,0.00033692663,0.00026265797,0.00031116654,0.00013699115,0.0002952765,0.0004068532,0.00041895502,0.0004368261],"category_scores_gemma":[0.00046988757,0.00014116643,0.00025190655,0.0002137846,0.00022593081,0.0005772793,0.00031537106,0.00044557443,0.000117445874],"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":[0.00038508922,0.00025318743,0.0034222282,0.00014408915,0.00006286876,0.00020369516,0.00013571198,0.3610643,0.24628429,0.0031544894,0.00193167,0.38295835],"study_design_scores_gemma":[0.000002431181,0.00003350235,0.0006144263,0.0000029632013,0.0000070318233,0.000021983053,0.000009417133,0.9843539,0.014355109,0.0003121183,0.00028268245,0.00000448024],"about_ca_topic_score_codex":0.0039428254,"about_ca_topic_score_gemma":0.0040595457,"teacher_disagreement_score":0.0039428254,"about_ca_system_score_codex":0.00036636597,"about_ca_system_score_gemma":0.00032608336,"threshold_uncertainty_score":0.007839739},"labels":[],"label_agreement":null},{"id":"W3195389496","doi":"10.1117/1.jei.30.4.043020","title":"Adversarial and adaptive tone mapping operator: multi-scheme generation and multi-metric evaluation","year":2021,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Image Enhancement Techniques","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Tone mapping; Artificial intelligence; Discriminator; Computer science; Computer vision; High dynamic range; Image resolution; Generator (circuit theory); Metric (unit); Pattern recognition (psychology); Dynamic range","score_opus":0.03515593848719149,"score_gpt":0.3155375243738519,"score_spread":0.2803815858866604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195389496","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":[0.23361477,0.0015237024,0.7542239,0.00080728356,0.0002957897,0.00053477346,0.0003736067,0.0026025882,0.006023651],"genre_scores_gemma":[0.8765816,0.00018303026,0.119660564,0.00026858944,0.00004540559,0.00016515185,0.00045884927,0.00014310569,0.002493569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855727,0.000573419,0.00006252167,0.0002548942,0.0004177393,0.000134276],"domain_scores_gemma":[0.9970734,0.0015252035,0.00021579771,0.0005599129,0.0004610033,0.00016458912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043308563,0.0013778408,0.00086546794,0.00090374984,0.0003174861,0.00076839037,0.0014622541,0.0014271098,0.0017718197],"category_scores_gemma":[0.009157128,0.0002585743,0.0005468923,0.00032235414,0.0009963774,0.001467488,0.0017373685,0.001360783,0.00033606304],"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":[0.0007001567,0.00025586123,0.0027115166,0.00016116818,0.00014945703,0.00018645373,0.000085943975,0.7982454,0.013940144,0.009305886,0.0046402784,0.16961777],"study_design_scores_gemma":[0.0000117343825,0.00010038075,0.00019061875,0.0000057715674,0.000007048715,0.00004103279,0.000006398189,0.9949792,0.003016185,0.0013991707,0.00023541441,0.000007030434],"about_ca_topic_score_codex":0.0019691507,"about_ca_topic_score_gemma":0.0017031279,"teacher_disagreement_score":0.0043308563,"about_ca_system_score_codex":0.001335458,"about_ca_system_score_gemma":0.0006540973,"threshold_uncertainty_score":0.022904038},"labels":[],"label_agreement":null},{"id":"W3198430278","doi":"10.1109/access.2021.3110428","title":"Indirect Domain Shift for Single Image Dehazing","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image Enhancement Techniques","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Computer science; Rendering (computer graphics); Artificial intelligence; Convolution (computer science); Convolutional neural network; Image (mathematics); Domain (mathematical analysis); Deep learning; Computer vision; Pattern recognition (psychology); Artificial neural network; Mathematics","score_opus":0.03757794971571524,"score_gpt":0.32248684956955,"score_spread":0.2849088998538347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198430278","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":[0.05708437,0.00043654427,0.93807214,0.00013322014,0.000051289328,0.000035512076,0.000046095356,0.000607733,0.0035331366],"genre_scores_gemma":[0.61665857,0.00078106375,0.37552345,0.00011077782,0.00003544354,0.00003886635,0.00014332993,0.00010317261,0.0066053774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998803,0.000013816576,0.0000059229324,0.000029334893,0.000055322093,0.000015239917],"domain_scores_gemma":[0.9997495,0.00006466129,0.000034814653,0.00008786055,0.00005004095,0.000013081617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028614773,0.0004928511,0.00028736104,0.00029240275,0.00014166914,0.000322609,0.00051120843,0.00034050678,0.0016927335],"category_scores_gemma":[0.000738877,0.0001938378,0.00029473842,0.00015514516,0.0004998859,0.0009109618,0.0008968705,0.00085772533,0.00040033297],"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":[0.00025189776,0.0000960918,0.0016470432,0.0002938326,0.000060824364,0.00012475328,0.00014540971,0.120159134,0.38117263,0.016806914,0.0020083876,0.477233],"study_design_scores_gemma":[0.000014234399,0.00010622857,0.00090942084,0.000020226762,0.000026890648,0.0003364431,0.0000372348,0.7075959,0.27829182,0.0058767805,0.006763907,0.000020849593],"about_ca_topic_score_codex":0.0008045369,"about_ca_topic_score_gemma":0.0016383103,"teacher_disagreement_score":0.0016927335,"about_ca_system_score_codex":0.00026451386,"about_ca_system_score_gemma":0.00035873355,"threshold_uncertainty_score":0.005662799},"labels":[],"label_agreement":null},{"id":"W3199549300","doi":"10.1109/tpami.2021.3115139","title":"Learning Frequency Domain Priors for Image Demoireing","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Key Research and Development Program of China; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Discrete cosine transform; Frequency domain; Block (permutation group theory); Convolution (computer science); Convolutional neural network; Computer vision; Margin (machine learning); Pattern recognition (psychology); Image restoration; Image (mathematics); Prior probability; Image processing; Mathematics; Artificial neural network","score_opus":0.013457621250449423,"score_gpt":0.27425942374643425,"score_spread":0.26080180249598484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199549300","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":[0.04332144,0.0009163399,0.95156384,0.00037753358,0.00006416595,0.00005932185,0.00020446909,0.0019337528,0.0015591049],"genre_scores_gemma":[0.57866734,0.0012287224,0.40645024,0.00080298015,0.00015930834,0.00011402063,0.0017705468,0.0004916683,0.01031525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995969,0.00006700537,0.000018213088,0.00014681311,0.00011875124,0.000052241452],"domain_scores_gemma":[0.99923027,0.00024368806,0.00009333686,0.00022978138,0.00015836864,0.000044457935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011812842,0.000969268,0.0010267558,0.00086406874,0.00026760728,0.00062695617,0.001276626,0.0014602073,0.0012598531],"category_scores_gemma":[0.002486072,0.00041019125,0.00076017133,0.00052252307,0.0007716105,0.0015328892,0.0010490083,0.0018586888,0.0005920234],"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":[0.00035831827,0.00016474348,0.0014406312,0.00020083161,0.00010314793,0.00016573769,0.000085037704,0.42074192,0.04402281,0.006648838,0.007335695,0.5187322],"study_design_scores_gemma":[0.000009556537,0.00003978607,0.00050611125,0.00001198616,0.000014382281,0.00008935081,0.0000130663,0.9841601,0.009582397,0.0039166487,0.0016466088,0.000009957375],"about_ca_topic_score_codex":0.003039109,"about_ca_topic_score_gemma":0.0043141614,"teacher_disagreement_score":0.003039109,"about_ca_system_score_codex":0.00078005705,"about_ca_system_score_gemma":0.00072592654,"threshold_uncertainty_score":0.0062473416},"labels":[],"label_agreement":null},{"id":"W3199989184","doi":"10.1007/978-3-030-86365-4_34","title":"FMSNet: Underwater Image Restoration by Learning from a Synthesized Dataset","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Underwater; Computer science; Convolutional neural network; Artificial intelligence; Image restoration; Process (computing); Feature (linguistics); Image (mathematics); Artificial neural network; Pattern recognition (psychology); Decomposition; Image processing; Computer vision","score_opus":0.016406195931919245,"score_gpt":0.2518439014843075,"score_spread":0.23543770555238827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199989184","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":[0.03328883,0.0012144194,0.86855197,0.00034064965,0.00069379987,0.00043224712,0.013030918,0.07579781,0.0066492264],"genre_scores_gemma":[0.07682337,0.0004965305,0.862501,0.00023588874,0.00014540697,0.0003087288,0.044169415,0.002539264,0.012780326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995833,0.0000467285,0.000015727803,0.00017150951,0.0001287316,0.00005404672],"domain_scores_gemma":[0.99969745,0.00006999181,0.000018065763,0.000121969904,0.00007403508,0.000018519047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000775033,0.0017772382,0.0011500573,0.0014094132,0.00041219767,0.00076766836,0.0021527603,0.0013401335,0.0075836764],"category_scores_gemma":[0.0015515226,0.0005567839,0.0013170337,0.0011659857,0.00032750683,0.0010360596,0.0012382759,0.0012601274,0.0050888145],"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":[0.0006525747,0.0002944539,0.0009802848,0.00034114442,0.0002458296,0.00019038044,0.00003715892,0.060810443,0.026641425,0.0018048312,0.104260124,0.8037412],"study_design_scores_gemma":[0.000089686095,0.00017909365,0.0012048677,0.00004310797,0.000066379456,0.00023344498,0.000040836974,0.93776435,0.030795233,0.0036501447,0.025893085,0.000039691684],"about_ca_topic_score_codex":0.007591587,"about_ca_topic_score_gemma":0.012112014,"teacher_disagreement_score":0.007591587,"about_ca_system_score_codex":0.0004662926,"about_ca_system_score_gemma":0.0009154441,"threshold_uncertainty_score":0.025369942},"labels":[],"label_agreement":null},{"id":"W3200044457","doi":"10.32393/csme.2021.178","title":"Validated Cfd Simulation For Flashing Flow In Inflow Control Devices In Sagd","year":2021,"lang":"en","type":"article","venue":"Progress in Canadian Mechanical Engineering. Volume 4","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Flashing; Inflow; Computational fluid dynamics; Marine engineering; Petroleum engineering; Flow (mathematics); Flow control (data); Computer science; Geology; Engineering; Aerospace engineering; Mechanics; Materials science; Telecommunications; Oceanography; Physics","score_opus":0.009238362437964928,"score_gpt":0.25325486226701194,"score_spread":0.244016499829047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200044457","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":[0.83135843,0.00079508114,0.115424484,0.00076912163,0.00028608428,0.00020748498,0.002484463,0.0024083706,0.04626642],"genre_scores_gemma":[0.98889786,0.00010942571,0.0067134476,0.00003879077,0.000007997617,0.000031951713,0.00035192858,0.00006437322,0.0037842006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998741,0.000022335536,0.000007960731,0.000015730937,0.000052723175,0.000027090744],"domain_scores_gemma":[0.99965703,0.00017560995,0.000019790365,0.000027441372,0.000095631665,0.000024615581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022683111,0.0003218352,0.00051987567,0.00040709839,0.00046028948,0.0008801512,0.00053878874,0.0009207144,0.0042933254],"category_scores_gemma":[0.00082056446,0.00019231916,0.00038324978,0.00031006787,0.0004573909,0.00040199826,0.00041969272,0.00036865167,0.0004275654],"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":[0.00022309572,0.00013910152,0.0036765854,0.00010127228,0.000020078845,0.00028693522,0.00013551288,0.9639208,0.01300538,0.0019876154,0.002394679,0.014108942],"study_design_scores_gemma":[0.000019464076,0.000048580092,0.0008824409,0.000009315199,0.0000054391376,0.000020918143,0.000033880846,0.9948702,0.0029066058,0.00017883032,0.001014857,0.000009472798],"about_ca_topic_score_codex":0.017510936,"about_ca_topic_score_gemma":0.011934185,"teacher_disagreement_score":0.017510936,"about_ca_system_score_codex":0.0007277584,"about_ca_system_score_gemma":0.0010973958,"threshold_uncertainty_score":0.034818053},"labels":[],"label_agreement":null},{"id":"W3201726107","doi":"10.18280/ts.380409","title":"Pipeline of Optimization Techniques for Multi-Level Thresholding in Medical Image Compression Using 2D Histogram","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Thresholding; Histogram; Computer science; Peak signal-to-noise ratio; Artificial intelligence; Particle swarm optimization; Image compression; Pattern recognition (psychology); Computer vision; Image (mathematics); Algorithm; Image processing","score_opus":0.07446878541876178,"score_gpt":0.34359075309433934,"score_spread":0.26912196767557756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201726107","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":[0.013082383,0.00026162583,0.9847819,0.00008808949,0.000021146136,0.000045034165,0.00002084965,0.00042847684,0.0012704972],"genre_scores_gemma":[0.25053585,0.00057017483,0.7460105,0.00007033548,0.00003035657,0.00011615964,0.00012060641,0.00012340448,0.002422722],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978966,0.000029425702,0.000017037242,0.00004481666,0.00009870499,0.000020398495],"domain_scores_gemma":[0.9997886,0.000085013904,0.000027896913,0.00002502722,0.00006503973,0.000008441827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039849107,0.00041381287,0.00045006088,0.0006398137,0.0002625543,0.000544706,0.00055154134,0.0004982264,0.0022434304],"category_scores_gemma":[0.00075782096,0.00023866432,0.000564015,0.00064391724,0.00026772646,0.00054180395,0.0004282464,0.0004630198,0.0004497699],"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":[0.00014083809,0.000115525996,0.0015616319,0.00029357267,0.00006977046,0.0001535787,0.00020373968,0.20798956,0.10186859,0.008337453,0.001380559,0.6778852],"study_design_scores_gemma":[0.0000109077055,0.00011251662,0.000870861,0.000012702513,0.000018726105,0.00013657348,0.000028465345,0.9707333,0.024043972,0.0019807639,0.0020379908,0.000013331228],"about_ca_topic_score_codex":0.0018833874,"about_ca_topic_score_gemma":0.0017674521,"teacher_disagreement_score":0.0022434304,"about_ca_system_score_codex":0.00029259242,"about_ca_system_score_gemma":0.0005736847,"threshold_uncertainty_score":0.0075049996},"labels":[],"label_agreement":null},{"id":"W3201787295","doi":"10.18280/isi.260402","title":"Transfer Learning Approach - An Efficient Method to Predict Rainfall Based on Ground-Based Cloud Images","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Precipitation; Computer science; Transfer of learning; Meteorology; Task (project management); Environmental science; Identification (biology); Machine learning; Artificial intelligence; Remote sensing; Geography","score_opus":0.014448866295728674,"score_gpt":0.25552201916615663,"score_spread":0.24107315287042796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201787295","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":[0.07727241,0.0010368916,0.9064002,0.0003713441,0.0003454531,0.00025896865,0.0007341736,0.00888234,0.004698182],"genre_scores_gemma":[0.7575981,0.0008487203,0.22687078,0.00026952758,0.00019537767,0.00025741837,0.002021758,0.00030507913,0.011633304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976295,0.00001794217,0.00001323341,0.000081368154,0.00007883863,0.000045663954],"domain_scores_gemma":[0.9998098,0.000036755482,0.000024546245,0.000026664906,0.00008859385,0.000013715933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036714235,0.00087536254,0.0006033237,0.0011514708,0.00031647732,0.0005245929,0.0013552681,0.00080845173,0.0030302007],"category_scores_gemma":[0.00079300447,0.00032449956,0.0009674573,0.0010145482,0.00026575604,0.0012066195,0.000610608,0.0009142748,0.0015459078],"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":[0.00022155808,0.00024306306,0.004943905,0.00010760808,0.00013109142,0.00021666028,0.0000690792,0.19915475,0.01738037,0.001382172,0.0066882167,0.7694616],"study_design_scores_gemma":[0.000009909782,0.00004485456,0.0009927434,0.0000074230184,0.000013368686,0.000058864476,0.000017195616,0.9909128,0.0056968974,0.0009873874,0.0012472192,0.000011370943],"about_ca_topic_score_codex":0.0101821255,"about_ca_topic_score_gemma":0.007911591,"teacher_disagreement_score":0.0101821255,"about_ca_system_score_codex":0.0006758486,"about_ca_system_score_gemma":0.0008199645,"threshold_uncertainty_score":0.020245671},"labels":[],"label_agreement":null},{"id":"W3205781905","doi":"10.1109/icip46576.2022.9897680","title":"Deep Image Debanding","year":2022,"lang":"en","type":"preprint","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Image Enhancement 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 Waterloo","funders":"","keywords":"Artifact (error); Deep learning; Artificial intelligence; Computer science; Construct (python library); Image (mathematics); Computer vision","score_opus":0.06057705323253225,"score_gpt":0.35423229935712647,"score_spread":0.2936552461245942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205781905","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":[0.105424546,0.0016181961,0.88013715,0.0003840601,0.00016471861,0.00018594778,0.0013567933,0.0053781997,0.005350314],"genre_scores_gemma":[0.4911471,0.0012683596,0.48644966,0.00039049805,0.00007035718,0.00013115177,0.005095732,0.00056275114,0.014884379],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996401,0.000035136974,0.000022489421,0.00010773982,0.00013920023,0.000055249493],"domain_scores_gemma":[0.9993709,0.000119348755,0.00007127075,0.00023077501,0.00018469521,0.000023101624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053256325,0.0009514078,0.00081199274,0.0009891886,0.0003436186,0.0008349315,0.0009433316,0.00079514354,0.0035725944],"category_scores_gemma":[0.0015765617,0.00031034174,0.0008156085,0.0007168292,0.00046057653,0.0010749605,0.001073629,0.0009017891,0.0013203984],"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":[0.00041697768,0.00011346401,0.0022213904,0.00032449444,0.0001259432,0.00019583407,0.000107577915,0.08918585,0.11989114,0.0033557496,0.010887624,0.77317405],"study_design_scores_gemma":[0.000022270407,0.0001364457,0.0029645648,0.000035435645,0.000064310305,0.0004172567,0.000056781584,0.81180733,0.16680749,0.005538426,0.012119372,0.000030414853],"about_ca_topic_score_codex":0.0027841667,"about_ca_topic_score_gemma":0.004554909,"teacher_disagreement_score":0.0035725944,"about_ca_system_score_codex":0.0006537224,"about_ca_system_score_gemma":0.00043934237,"threshold_uncertainty_score":0.011951506},"labels":[],"label_agreement":null},{"id":"W3215328494","doi":"10.18280/ts.380509","title":"Contrast Enhancement of Images Using Meta-Heuristic Algorithm","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Contrast (vision); Chaotic; Artificial intelligence; Computer science; Metaheuristic; Enhanced Data Rates for GSM Evolution; Image quality; Entropy (arrow of time); Fitness function; Heuristic; Pattern recognition (psychology); Algorithm; Image (mathematics); Mathematics; Machine learning; Genetic algorithm","score_opus":0.03374137909179412,"score_gpt":0.2725278883002244,"score_spread":0.23878650920843028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215328494","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":[0.03495011,0.000994552,0.9579601,0.0001680831,0.00007108451,0.00012301176,0.000031791144,0.0003210048,0.0053802165],"genre_scores_gemma":[0.50299984,0.00071826595,0.4920767,0.00016901431,0.000056199686,0.00034281082,0.000105234925,0.00007695459,0.0034549374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977666,0.00005858937,0.000015057116,0.000047811314,0.00006754911,0.00003436591],"domain_scores_gemma":[0.9996921,0.00017185876,0.000036940128,0.000018105351,0.00006605411,0.000014980177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064073544,0.0008789377,0.0009826261,0.0012553033,0.00042659827,0.00102069,0.0010637054,0.0011639594,0.0014518414],"category_scores_gemma":[0.0011255189,0.00044187572,0.0012522134,0.0007362917,0.00047284336,0.00062611484,0.00057179073,0.00059598475,0.00021766018],"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":[0.00006787632,0.00007687633,0.00079134817,0.00013373526,0.00014540032,0.00011775112,0.00009367655,0.9037555,0.0060010315,0.006582071,0.00069847156,0.081536226],"study_design_scores_gemma":[0.000017501214,0.000042428113,0.000102490216,0.00001123412,0.000024985076,0.000025813295,0.000013094247,0.99698156,0.0008190733,0.0014295188,0.0005262495,0.0000059677395],"about_ca_topic_score_codex":0.0034674515,"about_ca_topic_score_gemma":0.003219635,"teacher_disagreement_score":0.0034674515,"about_ca_system_score_codex":0.0007664381,"about_ca_system_score_gemma":0.00095598324,"threshold_uncertainty_score":0.006894529},"labels":[],"label_agreement":null},{"id":"W3216889993","doi":"10.1109/tcds.2021.3131045","title":"Recurrent Network Knowledge Distillation for Image Rain Removal","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Xiamen University; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Computer science; Block (permutation group theory); Streak; Residual; Artificial intelligence; Convolutional neural network; Image (mathematics); Artificial neural network; Channel (broadcasting); Computer vision; Deep learning; Pattern recognition (psychology); Machine learning; Algorithm; Mathematics; Telecommunications","score_opus":0.025689469394975406,"score_gpt":0.2803272227409553,"score_spread":0.2546377533459799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216889993","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":[0.05696161,0.0008678591,0.93717766,0.00024699038,0.00006485661,0.000048871807,0.00012904896,0.0026679542,0.001835167],"genre_scores_gemma":[0.7711621,0.0005522013,0.22084783,0.0003272313,0.00006578012,0.00009129303,0.00070977723,0.00016008895,0.0060836207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979514,0.000030586474,0.0000111611525,0.00006779721,0.000056187324,0.00003916235],"domain_scores_gemma":[0.9996222,0.0001488129,0.00004688978,0.00006390379,0.00009647841,0.000021730353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005194457,0.0007941384,0.00067884004,0.00045297298,0.00025426256,0.00040057534,0.0012700671,0.0007340568,0.001605009],"category_scores_gemma":[0.0014168209,0.00032815352,0.00058384944,0.0003953863,0.00035957515,0.0009760365,0.0007534419,0.0011456241,0.00040167515],"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":[0.0002302869,0.00017320896,0.00094255677,0.00012274813,0.00012705843,0.00019723533,0.00011013979,0.38971445,0.032338932,0.004242934,0.0036158334,0.56818473],"study_design_scores_gemma":[0.000005234982,0.00003676064,0.00017968295,0.00000386996,0.000021542271,0.000023853217,0.0000059227414,0.99068016,0.007331309,0.0011484085,0.0005576911,0.0000055632213],"about_ca_topic_score_codex":0.009159703,"about_ca_topic_score_gemma":0.014020234,"teacher_disagreement_score":0.009159703,"about_ca_system_score_codex":0.00057705573,"about_ca_system_score_gemma":0.0008042484,"threshold_uncertainty_score":0.018212795},"labels":[],"label_agreement":null},{"id":"W331567734","doi":"10.21236/ada417137","title":"Using a Laser Underwater Camera Image Enhancer for Mine Warfare Applications: What is Gained?","year":2002,"lang":"en","type":"report","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Underwater; Laser; Enhancer; Image (mathematics); Computer science; Computer vision; Computer graphics (images); Artificial intelligence; Engineering; Geology; Optics; Physics; Oceanography; Chemistry","score_opus":0.06997831454547994,"score_gpt":0.35048847065689753,"score_spread":0.2805101561114176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W331567734","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":[0.70935464,0.051660992,0.17264618,0.0103809675,0.00044843528,0.0009484526,0.00046721144,0.002175695,0.0519175],"genre_scores_gemma":[0.7646136,0.043322466,0.16641192,0.0020231616,0.00041626478,0.0001879987,0.0010758522,0.00015423715,0.021794518],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945194,0.00015266381,0.000028213852,0.00006379762,0.0002229737,0.00008039311],"domain_scores_gemma":[0.998917,0.0003043523,0.000102090984,0.00008835525,0.00051096984,0.000077248034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017981174,0.00075541384,0.00058693823,0.00055015757,0.0002988193,0.0012680806,0.0008154216,0.0013602886,0.0034595055],"category_scores_gemma":[0.0015418804,0.0001643344,0.00031848834,0.0004390916,0.0005745846,0.0024899975,0.00047513048,0.00047721475,0.0011618796],"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":[0.0009700574,0.0014982925,0.023065034,0.002530067,0.0001924989,0.0009090868,0.000471209,0.0020104905,0.1639878,0.0018428285,0.0045305444,0.7979921],"study_design_scores_gemma":[0.0005308193,0.027317181,0.07003585,0.0011549399,0.001647658,0.014532817,0.003464481,0.03382077,0.7369216,0.0020148128,0.10810993,0.0004491738],"about_ca_topic_score_codex":0.0026401733,"about_ca_topic_score_gemma":0.0065586516,"teacher_disagreement_score":0.0034595055,"about_ca_system_score_codex":0.00033255926,"about_ca_system_score_gemma":0.0005114302,"threshold_uncertainty_score":0.011573195},"labels":[],"label_agreement":null},{"id":"W4200019380","doi":"10.3390/atmos12121657","title":"Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications","year":2021,"lang":"en","type":"article","venue":"Atmosphere","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Environment and Climate Change Canada","funders":"Financiadora de Estudos e Projetos","keywords":"Categorical variable; Visibility; Ceiling (cloud); Nowcasting; Computer science; Artificial intelligence; Machine learning; Statistics; Meteorology; Environmental science; Pattern recognition (psychology); Mathematics; Geography","score_opus":0.04116492801534197,"score_gpt":0.32337329468432147,"score_spread":0.2822083666689795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200019380","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":[0.3036845,0.0005554808,0.68980587,0.00018175642,0.00008030876,0.00005997859,0.00012124214,0.00094539626,0.0045654834],"genre_scores_gemma":[0.9116212,0.00019062974,0.08735049,0.000018243272,0.0000137999705,0.000029743504,0.00009234812,0.00002031151,0.00066310965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999094,0.000025956195,0.0000066372518,0.000019195162,0.000027584047,0.000011277282],"domain_scores_gemma":[0.9998331,0.00008409816,0.000022367552,0.000016829626,0.00003635019,0.0000071662807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022437592,0.00036682692,0.00020463936,0.0002990071,0.00014635982,0.0004749867,0.00032237257,0.00036497816,0.00039468493],"category_scores_gemma":[0.0007120374,0.00012887543,0.0004359814,0.00024064608,0.00014291222,0.00034024636,0.00021327926,0.00041058112,0.000096567244],"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":[0.000046466714,0.000057362206,0.0044695595,0.00004999433,0.000040477647,0.00005803582,0.000046750076,0.8932878,0.013784224,0.0011694075,0.0002805055,0.0867095],"study_design_scores_gemma":[0.0000014923013,0.000022945782,0.0011128758,0.0000027749827,0.0000061170404,0.000007544609,0.000006443462,0.99632806,0.0019145756,0.0003254392,0.0002682057,0.0000034702505],"about_ca_topic_score_codex":0.005794412,"about_ca_topic_score_gemma":0.006010975,"teacher_disagreement_score":0.005794412,"about_ca_system_score_codex":0.00026091156,"about_ca_system_score_gemma":0.00031262237,"threshold_uncertainty_score":0.011521339},"labels":[],"label_agreement":null},{"id":"W4200096726","doi":"10.1109/iai53119.2021.9619442","title":"Development of New Efficient Transposed Convolution Techniques for Flame Segmentation from UAV-captured Images","year":2021,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Upsampling; Computer science; Convolution (computer science); Artificial intelligence; Feature (linguistics); Bilinear interpolation; Segmentation; Bicubic interpolation; Computer vision; Kernel (algebra); Deep learning; Interpolation (computer graphics); Convolutional neural network; Encoder; Pattern recognition (psychology); Image (mathematics); Linear interpolation; Artificial neural network; Mathematics","score_opus":0.01899137984741532,"score_gpt":0.2677675899054451,"score_spread":0.24877621005802975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200096726","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":[0.058473486,0.00031766354,0.93845415,0.000102845326,0.000043422708,0.00005322337,0.00006237635,0.0010832482,0.0014095564],"genre_scores_gemma":[0.24795379,0.00033277902,0.7494434,0.00006550096,0.00001789633,0.000038632224,0.00019746141,0.00007297041,0.001877537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985147,0.000015063255,0.000008868414,0.000033161054,0.0000724834,0.000018989529],"domain_scores_gemma":[0.999788,0.000053021715,0.000032221094,0.000041936706,0.0000722831,0.000012669098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043382766,0.0004724314,0.00026584827,0.00058480795,0.00016517928,0.00038043378,0.00053024484,0.00040103655,0.00084117224],"category_scores_gemma":[0.00078924635,0.0002149117,0.00038911446,0.00038924007,0.0002903803,0.0006945993,0.00040068783,0.00057615136,0.0002482282],"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":[0.00023310843,0.000088735374,0.0017962231,0.0001463969,0.00008751985,0.00020202558,0.00011989374,0.11981547,0.26820397,0.0062022083,0.0016516926,0.6014527],"study_design_scores_gemma":[0.0000069265457,0.00007152496,0.00086876366,0.0000095465075,0.000018418159,0.00017535663,0.000015386351,0.88030356,0.11537428,0.0009281972,0.002215977,0.000012038949],"about_ca_topic_score_codex":0.0028446447,"about_ca_topic_score_gemma":0.005243436,"teacher_disagreement_score":0.0028446447,"about_ca_system_score_codex":0.00043190914,"about_ca_system_score_gemma":0.00072113005,"threshold_uncertainty_score":0.005656123},"labels":[],"label_agreement":null},{"id":"W4200301643","doi":"10.1049/ell2.12391","title":"Context‐wise attention‐guided network for single image deraining","year":2021,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Image Enhancement 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":"Alberta Bone and Joint Health Institute","funders":"National Natural Science Foundation of China","keywords":"Context (archaeology); Computer science; Exploit; Streak; Artificial intelligence; Image (mathematics); Layer (electronics); Residual; Scale (ratio); Pattern recognition (psychology); Network architecture; Artificial neural network; Machine learning; Algorithm","score_opus":0.01701222594110548,"score_gpt":0.25684798549417764,"score_spread":0.23983575955307215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200301643","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":[0.06093943,0.0013631377,0.9290688,0.0004490427,0.00022992914,0.000093139286,0.00018717261,0.0028072465,0.0048621073],"genre_scores_gemma":[0.836897,0.00047421665,0.14896834,0.00080217,0.00018120623,0.000101183046,0.000487355,0.00022346512,0.01186503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969685,0.000045539604,0.000012630548,0.000115940515,0.000059231137,0.00006984853],"domain_scores_gemma":[0.9996724,0.00010293338,0.000030234607,0.00006138536,0.00010529471,0.000027667496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005359054,0.0012530355,0.0007617841,0.00046836046,0.00043656636,0.00051308714,0.0018827146,0.0011737747,0.003381388],"category_scores_gemma":[0.0012939502,0.00038500087,0.0005873305,0.0004679878,0.00056075805,0.0013231195,0.0011873371,0.0013377112,0.0006424222],"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":[0.00042975217,0.00021914738,0.0011384574,0.00013759494,0.00011905216,0.00023849736,0.0001287224,0.29268074,0.053678688,0.005731486,0.008648642,0.63684916],"study_design_scores_gemma":[0.00000965982,0.000059381546,0.00031336208,0.000006992323,0.00002391063,0.00005246223,0.000013567711,0.9855592,0.009832309,0.002711725,0.0014075631,0.000009749798],"about_ca_topic_score_codex":0.0076636183,"about_ca_topic_score_gemma":0.009999678,"teacher_disagreement_score":0.0076636183,"about_ca_system_score_codex":0.00078360265,"about_ca_system_score_gemma":0.00074868836,"threshold_uncertainty_score":0.015237987},"labels":[],"label_agreement":null},{"id":"W4205672093","doi":"10.18280/ts.380618","title":"Color Enhancement of Low Illumination Garden Landscape Images","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Robustness (evolution); Image enhancement; Image (mathematics); Biology","score_opus":0.007971594735694646,"score_gpt":0.23076158895889415,"score_spread":0.2227899942231995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205672093","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":[0.48563385,0.0007121394,0.5007076,0.00019270857,0.00010138569,0.0000621467,0.00011736162,0.0020243905,0.010448411],"genre_scores_gemma":[0.8487203,0.0006856735,0.14462294,0.00009050101,0.000030915588,0.000019794392,0.00010701422,0.00016856167,0.0055543347],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99994266,0.0000074646614,0.000001785113,0.000013477547,0.000023095912,0.000011510852],"domain_scores_gemma":[0.9998858,0.000024623001,0.000016704524,0.000016547694,0.000045155837,0.000011161057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014167913,0.00032169223,0.00017293048,0.0003801718,0.00008166786,0.00029404153,0.00019102888,0.00017379054,0.0012138995],"category_scores_gemma":[0.00030369032,0.00010218783,0.0002236007,0.00020332148,0.00017621306,0.0003182349,0.00021432892,0.00034615063,0.00029254972],"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":[0.00029205266,0.000044476325,0.0010732122,0.00022993756,0.000024695073,0.0003806913,0.00012596368,0.009242514,0.805357,0.0015854046,0.0012996239,0.18034448],"study_design_scores_gemma":[0.00003210022,0.00034524617,0.012419456,0.000038059294,0.00008315586,0.0013213382,0.000095381314,0.1604724,0.81163365,0.00092438416,0.012589145,0.00004570047],"about_ca_topic_score_codex":0.00048472037,"about_ca_topic_score_gemma":0.0008605845,"teacher_disagreement_score":0.0012138995,"about_ca_system_score_codex":0.00014993988,"about_ca_system_score_gemma":0.00011301529,"threshold_uncertainty_score":0.004060924},"labels":[],"label_agreement":null},{"id":"W4206692274","doi":"10.18280/ts.380610","title":"A Framework for Cross-Modality Guided Contrast Enhancement of CT Liver Using MRI","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Histogram; Artificial intelligence; Computer science; Modality (human–computer interaction); Contrast (vision); Visualization; Medical imaging; Contrast enhancement; Transformation (genetics); Computer vision; Computed tomography; Radiology; Magnetic resonance imaging; Image (mathematics); Pattern recognition (psychology); Medicine","score_opus":0.05322746630489459,"score_gpt":0.3429635269415525,"score_spread":0.2897360606366579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206692274","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":[0.00075421314,0.00020371903,0.99799746,0.000045231645,0.000013040161,0.000024718565,0.000008547544,0.00012271,0.0008302253],"genre_scores_gemma":[0.075231604,0.00086490926,0.9197615,0.00007911662,0.00006722729,0.00015531523,0.00008116261,0.000099576886,0.0036595976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996307,0.00010887269,0.000021510947,0.00008161174,0.00011798706,0.000039271992],"domain_scores_gemma":[0.99975914,0.000070975104,0.00003722092,0.00003951339,0.000059566108,0.000033655622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009713808,0.00063209864,0.00049434614,0.0008262868,0.00044984577,0.0009856988,0.0015010021,0.00082528347,0.0022172737],"category_scores_gemma":[0.0008973069,0.00032504142,0.0008492185,0.00060015474,0.0008714441,0.0010384149,0.0014941692,0.0008590561,0.0008553117],"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":[0.00015490834,0.00015184523,0.00082567526,0.00043780898,0.0000960405,0.0013845027,0.00061353506,0.15695967,0.084383816,0.46386302,0.004964589,0.2861646],"study_design_scores_gemma":[0.000021397751,0.00023245775,0.0004279297,0.00008080727,0.000045534223,0.0011594192,0.0000723508,0.9037222,0.01192961,0.053540304,0.028708339,0.00005971403],"about_ca_topic_score_codex":0.0020700346,"about_ca_topic_score_gemma":0.002028221,"teacher_disagreement_score":0.0022172737,"about_ca_system_score_codex":0.00047415117,"about_ca_system_score_gemma":0.000695962,"threshold_uncertainty_score":0.0074175},"labels":[],"label_agreement":null},{"id":"W4210257365","doi":"10.1049/ipr2.12433","title":"Underwater image enhancement with latent consistency learning‐based color transfer","year":2022,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Image Enhancement 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 Windsor","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Underwater; Consistency (knowledge bases); Computer science; Artificial intelligence; Transfer of learning; Computer vision; Image (mathematics); Pattern recognition (psychology); Geology","score_opus":0.012847348644730578,"score_gpt":0.24399101250131877,"score_spread":0.2311436638565882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210257365","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":[0.065106414,0.00060238416,0.93003494,0.00015342428,0.00006864962,0.00006030166,0.00008526221,0.0019876848,0.0019010035],"genre_scores_gemma":[0.6358825,0.0006198574,0.35758948,0.00023794375,0.00005439898,0.00005660841,0.0004056517,0.00024463402,0.004908838],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997631,0.000042371237,0.000011184982,0.00007546894,0.00007447106,0.00003340359],"domain_scores_gemma":[0.99962175,0.00009168941,0.00005393433,0.00009296816,0.0001155445,0.000024036213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006267169,0.0007667372,0.0006090332,0.0006351304,0.00016012402,0.00052492425,0.0007379284,0.00041475275,0.0010384906],"category_scores_gemma":[0.0011018473,0.00023371229,0.00070521835,0.00043681797,0.00044373993,0.00089989306,0.0008403374,0.00095140917,0.00039159486],"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":[0.0005192764,0.00023424749,0.0022295369,0.00020964663,0.00017967427,0.0001764703,0.00011483668,0.2305347,0.15087683,0.003648425,0.0038316494,0.6074447],"study_design_scores_gemma":[0.000014694701,0.000056178083,0.00052580785,0.000007668457,0.000032321983,0.000059944745,0.000012578853,0.95848566,0.03877642,0.000966161,0.0010504787,0.000012151251],"about_ca_topic_score_codex":0.0023158258,"about_ca_topic_score_gemma":0.002278887,"teacher_disagreement_score":0.0023158258,"about_ca_system_score_codex":0.00032185265,"about_ca_system_score_gemma":0.0004097004,"threshold_uncertainty_score":0.004604697},"labels":[],"label_agreement":null},{"id":"W4211132728","doi":"10.32920/ryerson.14655897","title":"Adaptive Exposure Fusion for HDR Imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement 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":"Toronto Metropolitan University","funders":"","keywords":"Sequence (biology); Computer science; Fusion; Artificial intelligence; Metric (unit); Image fusion; Computer vision; Multiple exposure; Image (mathematics); Pattern recognition (psychology); Engineering","score_opus":0.023041533534400558,"score_gpt":0.2757639509186194,"score_spread":0.2527224173842188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211132728","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":[0.028835688,0.0020300555,0.9644871,0.00012172529,0.00006626249,0.00009716353,0.00012580983,0.0013492478,0.0028870204],"genre_scores_gemma":[0.27607125,0.002169062,0.71664643,0.0001696723,0.000103153376,0.000080643425,0.0004643434,0.00023175581,0.0040637716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994541,0.0000790808,0.000025667508,0.00013492069,0.00026844465,0.00003792381],"domain_scores_gemma":[0.99956673,0.0001228345,0.00005647556,0.00011161823,0.00012268455,0.000019559917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064845855,0.0005565961,0.0004415544,0.0008925331,0.00028065345,0.00070477027,0.0006488041,0.00061578664,0.002613151],"category_scores_gemma":[0.0012080974,0.0002600917,0.00066598534,0.0006816747,0.00037411432,0.0010858787,0.0007812462,0.0008353832,0.0009056747],"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":[0.00033105683,0.00009851892,0.0011962802,0.00042955668,0.00011111186,0.0001517969,0.00022568139,0.024409795,0.30339462,0.0057478873,0.003091074,0.6608127],"study_design_scores_gemma":[0.000049269252,0.0007682099,0.008911101,0.000116450676,0.00022822646,0.0023555898,0.00018025863,0.44554082,0.48649308,0.009911232,0.045338396,0.000107358705],"about_ca_topic_score_codex":0.00077086483,"about_ca_topic_score_gemma":0.00076233165,"teacher_disagreement_score":0.002613151,"about_ca_system_score_codex":0.00039266076,"about_ca_system_score_gemma":0.00028040126,"threshold_uncertainty_score":0.008741856},"labels":[],"label_agreement":null},{"id":"W4220870497","doi":"10.1109/icce53296.2022.9730460","title":"Deep Learning-Based HDR Image Upscaling Approach for 8K UHD Applications","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Consumer Electronics (ICCE)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"High dynamic range; Computer science; Deep learning; Artificial intelligence; Residual; Computer vision; Multimedia; Computer graphics (images); Dynamic range; Algorithm","score_opus":0.03149384909518813,"score_gpt":0.3054671157361049,"score_spread":0.27397326664091676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220870497","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":[0.034179084,0.00040288694,0.9605842,0.00019648534,0.000053940126,0.000044967856,0.00008302814,0.0014328741,0.0030225166],"genre_scores_gemma":[0.7036163,0.00048213778,0.28471032,0.00034191392,0.000056191355,0.00007575651,0.000365829,0.00027569974,0.010075756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998468,0.000020449213,0.000006329774,0.000036186702,0.000063609,0.000026518575],"domain_scores_gemma":[0.9998474,0.000047881,0.000019203511,0.000024534016,0.000048230086,0.000012687989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039580322,0.00051987096,0.00036969324,0.0003310472,0.00015286858,0.0003524919,0.00080436835,0.00044696804,0.0025986875],"category_scores_gemma":[0.00058700534,0.00023369266,0.0004445628,0.00023770961,0.00031180322,0.00054025307,0.0006854626,0.00095420337,0.0006340723],"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":[0.00018134527,0.00014189459,0.0008581701,0.000110316185,0.000062342195,0.00017420274,0.00008121647,0.6181419,0.06453744,0.006851687,0.0036399413,0.30521947],"study_design_scores_gemma":[0.0000027366837,0.000021454118,0.00010259874,0.0000033512777,0.0000056381914,0.000021050939,0.0000036104868,0.99249226,0.0058824304,0.00070251524,0.0007589314,0.0000033738468],"about_ca_topic_score_codex":0.0022344645,"about_ca_topic_score_gemma":0.0031197576,"teacher_disagreement_score":0.0025986875,"about_ca_system_score_codex":0.0004734669,"about_ca_system_score_gemma":0.00034090874,"threshold_uncertainty_score":0.008693457},"labels":[],"label_agreement":null},{"id":"W4220917622","doi":"10.1109/tnnls.2022.3153955","title":"Image Matting With Deep Gaussian Process","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Shandong Province; Taishan Scholar Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Deep learning; Kernel (algebra); Image (mathematics); Computer science; Pixel; Scalability; Pattern recognition (psychology); Process (computing); Set (abstract data type); Gaussian process; Gaussian; Computer vision; Machine learning; Mathematics; Physics","score_opus":0.007203179155005628,"score_gpt":0.22514300176396784,"score_spread":0.21793982260896222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220917622","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":[0.0052427743,0.00008344884,0.9925053,0.0001570282,0.000024703331,0.0000128979855,0.000036734564,0.0007746614,0.001162502],"genre_scores_gemma":[0.3944485,0.0003920386,0.5935054,0.0003188355,0.00012387242,0.000078671226,0.00029549169,0.00064692786,0.010190314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997298,0.000046219287,0.000009685854,0.000072739575,0.00010688447,0.00003468406],"domain_scores_gemma":[0.9994178,0.00021863641,0.000060964256,0.00014717046,0.00011039927,0.000044987773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000641812,0.00068862154,0.00063755905,0.0006355464,0.0002382317,0.0011005936,0.0011813905,0.0011787342,0.0036019273],"category_scores_gemma":[0.002238968,0.0004327765,0.0010723012,0.00077180273,0.00097224384,0.0019392829,0.0013292764,0.00201249,0.0010831559],"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":[0.00010254413,0.000044667675,0.00053032825,0.00009760188,0.000060184477,0.0001746292,0.00011813107,0.7795095,0.015816847,0.072409585,0.003471547,0.12766448],"study_design_scores_gemma":[0.0000030046078,0.0000071635554,0.00003048862,0.0000020500318,0.000002327685,0.00001588227,0.0000024674935,0.98931354,0.0015884965,0.008547072,0.0004842103,0.0000032431496],"about_ca_topic_score_codex":0.00416987,"about_ca_topic_score_gemma":0.004190591,"teacher_disagreement_score":0.00416987,"about_ca_system_score_codex":0.0010915784,"about_ca_system_score_gemma":0.0008277995,"threshold_uncertainty_score":0.012049675},"labels":[],"label_agreement":null},{"id":"W4221108711","doi":"10.21923/jesd.931771","title":"DEĞİŞTİRİLMİŞ AYRIK HAAR DALGACIK DÖNÜŞÜMÜ İLE YENİ BİR HİSTOGRAM EŞİTLEME YÖNTEMİ","year":2022,"lang":"tr","type":"article","venue":"Mühendislik Bilimleri ve Tasarım Dergisi","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"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":"Istanbul Üniversitesi; Canadian Institute for Theoretical Astrophysics","keywords":"Mathematics; Haar wavelet; Physics; Computer science; Artificial intelligence; Discrete wavelet transform; Wavelet transform; Wavelet","score_opus":0.01710442871929526,"score_gpt":0.24978653451233662,"score_spread":0.23268210579304135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221108711","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":[0.29823306,0.00790828,0.6175246,0.001980836,0.0019238638,0.00044225005,0.0042944155,0.010237373,0.057455324],"genre_scores_gemma":[0.7857357,0.003931187,0.16325496,0.00049238565,0.00030918527,0.0003152165,0.005472867,0.0015292381,0.03895931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985967,0.0001563611,0.00008081422,0.00033185503,0.0006336703,0.00020058293],"domain_scores_gemma":[0.99738854,0.00082036474,0.00018974401,0.00027055468,0.0012278954,0.00010292637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001164985,0.0014900983,0.0012178667,0.0020660728,0.0010073119,0.0031749501,0.0009863428,0.0012666477,0.019054739],"category_scores_gemma":[0.0060825334,0.0005179523,0.0009830409,0.0018574825,0.0008375882,0.0027661314,0.0010757544,0.0016675388,0.005840185],"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":[0.0015582449,0.0002692371,0.024307298,0.0015768136,0.00028195753,0.0010279489,0.0014851941,0.03572481,0.08535991,0.017578475,0.023435138,0.8073949],"study_design_scores_gemma":[0.00014585527,0.0016142004,0.1284676,0.00078935357,0.00076563563,0.0055043497,0.006603456,0.3686829,0.23369929,0.040939134,0.21193969,0.00084858935],"about_ca_topic_score_codex":0.0075223986,"about_ca_topic_score_gemma":0.0049519315,"teacher_disagreement_score":0.019054739,"about_ca_system_score_codex":0.0010310368,"about_ca_system_score_gemma":0.0012107488,"threshold_uncertainty_score":0.063744485},"labels":[],"label_agreement":null},{"id":"W4225269231","doi":"10.53469/jissr.2022.09(04).11","title":"Improving Machine Learning Based Color Optimization Efficiency by Using a New Image Restoration Technique","year":2022,"lang":"en","type":"article","venue":"Journal of Innovation and Social Science Research","topic":"Image Enhancement 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 Waterloo","funders":"","keywords":"Computer science; Autoencoder; Image (mathematics); Artificial intelligence; Task (project management); Artificial neural network; Constant (computer programming); Acceleration; Quadratic growth; Computational complexity theory; Deep learning; Computer vision; Machine learning; Computer engineering; Algorithm; Engineering","score_opus":0.048432368428189036,"score_gpt":0.3823108108174045,"score_spread":0.33387844238921544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225269231","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":[0.011681412,0.00033053817,0.98586756,0.00012074505,0.000080631,0.000018784518,0.000009630403,0.0006979898,0.001192655],"genre_scores_gemma":[0.15860489,0.00046727245,0.8377919,0.00016070688,0.00010210515,0.000037212823,0.000044482695,0.00017052024,0.0026208938],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966216,0.00004752963,0.000021063086,0.00007688306,0.00016143291,0.000030825115],"domain_scores_gemma":[0.9996092,0.00009148501,0.000049103448,0.000072512776,0.00015422341,0.000023523722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005367422,0.0005466559,0.0007595786,0.00094255316,0.00030376966,0.00048327012,0.0007220185,0.0006608592,0.0012529519],"category_scores_gemma":[0.0009434359,0.00029829584,0.0008664726,0.00069852424,0.00059226435,0.0010678407,0.00058443204,0.00097385637,0.00038962316],"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":[0.00021410339,0.00028473677,0.00137051,0.00030135538,0.00016420799,0.00022172034,0.00016749144,0.16675632,0.21722402,0.021689262,0.0036951224,0.5879111],"study_design_scores_gemma":[0.000015925123,0.000044381093,0.00028101445,0.0000047554677,0.000023593291,0.0001514884,0.000006468385,0.9752299,0.020551385,0.0015342328,0.0021430044,0.000013826792],"about_ca_topic_score_codex":0.0017536726,"about_ca_topic_score_gemma":0.0021357515,"teacher_disagreement_score":0.0017536726,"about_ca_system_score_codex":0.00034451194,"about_ca_system_score_gemma":0.00056951365,"threshold_uncertainty_score":0.004191518},"labels":[],"label_agreement":null},{"id":"W4225307975","doi":"10.1109/icassp43922.2022.9747174","title":"Low-Light Image Enhancement via Feature Restoration","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Distortion (music); Computer science; Artificial intelligence; Feature (linguistics); Visibility; Image restoration; Subnet; Computer vision; Noise (video); Noise reduction; Code (set theory); Decoding methods; Pattern recognition (psychology); Image (mathematics); Image processing; Algorithm","score_opus":0.023724034428417994,"score_gpt":0.2909566991952492,"score_spread":0.2672326647668312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225307975","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":[0.03700056,0.00033038267,0.95491433,0.00012730579,0.00006437347,0.00007920216,0.00017677323,0.0034481653,0.0038589581],"genre_scores_gemma":[0.3937923,0.0006159008,0.59440184,0.00021891668,0.000061343846,0.00012042416,0.00060620275,0.0004302472,0.009752781],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998393,0.000016852826,0.000006167486,0.000052327574,0.00005900885,0.000026323754],"domain_scores_gemma":[0.99975663,0.000046508245,0.00003124459,0.00006945779,0.000076044256,0.000020104078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029923237,0.00091021694,0.0005955651,0.0005572116,0.00024234349,0.00061760313,0.0009429749,0.00051994674,0.002718409],"category_scores_gemma":[0.00086045376,0.00027005965,0.000516445,0.00034209673,0.00051828596,0.0011157586,0.0010737517,0.0007570119,0.0013820891],"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":[0.0003795993,0.0002072609,0.0014577808,0.0003463075,0.000096869524,0.0003302262,0.00017656578,0.08613003,0.31973115,0.009568067,0.0071491315,0.574427],"study_design_scores_gemma":[0.000024524954,0.0002000776,0.0011032899,0.000032522865,0.00006671375,0.0005983611,0.000046164263,0.75228876,0.22570415,0.008133532,0.01176489,0.000037074402],"about_ca_topic_score_codex":0.001231319,"about_ca_topic_score_gemma":0.0020828946,"teacher_disagreement_score":0.002718409,"about_ca_system_score_codex":0.0003291941,"about_ca_system_score_gemma":0.000398462,"threshold_uncertainty_score":0.009094},"labels":[],"label_agreement":null},{"id":"W4226084239","doi":"10.1007/978-3-031-19787-1_31","title":"Layered Controllable Video Generation","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Government of British Columbia; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; University of British Columbia","funders":"","keywords":"Computer science; Frame (networking); Benchmark (surveying); Artificial intelligence; Generator (circuit theory); Parameterized complexity; Key (lock); Set (abstract data type); Process (computing); Computer vision; Algorithm; Power (physics)","score_opus":0.018552475249241404,"score_gpt":0.2464461734108119,"score_spread":0.2278936981615705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226084239","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":[0.015924,0.00060013373,0.929834,0.0001172469,0.0002303084,0.000069527654,0.00020071524,0.0023994455,0.050624654],"genre_scores_gemma":[0.5341002,0.00087950495,0.41173843,0.00015684756,0.00012139048,0.00015681838,0.0008661965,0.00072518317,0.051255397],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998565,0.00001643211,0.0000044945177,0.000030468516,0.00007285912,0.00001929722],"domain_scores_gemma":[0.9998493,0.000043483666,0.000012839926,0.00004492105,0.00003090935,0.000018535182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015950137,0.0005562349,0.00024927862,0.00038159153,0.00021467681,0.00067789387,0.0007389896,0.000392707,0.0115393],"category_scores_gemma":[0.00037032113,0.0002762764,0.00032275668,0.00027425168,0.0002818889,0.0007051542,0.0010778491,0.0007830615,0.0021978344],"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":[0.00029098013,0.00009220221,0.00029077043,0.00021993824,0.000038654645,0.00027644035,0.00012384854,0.03296901,0.37533966,0.15097167,0.013162926,0.4262239],"study_design_scores_gemma":[0.000046231744,0.00018378103,0.0005900515,0.00007539741,0.000030604973,0.0007369246,0.000039611743,0.59896046,0.3024531,0.04098417,0.055843852,0.000055867644],"about_ca_topic_score_codex":0.00028291956,"about_ca_topic_score_gemma":0.0003592911,"teacher_disagreement_score":0.0115393,"about_ca_system_score_codex":0.00029908406,"about_ca_system_score_gemma":0.00016064795,"threshold_uncertainty_score":0.03860283},"labels":[],"label_agreement":null},{"id":"W4226194913","doi":"10.1007/978-3-031-04112-9_3","title":"Color Vision Deficiency and Live Recoloring","year":2022,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"RGB color model; Computer science; Artificial intelligence; Computer vision; Trichromacy; Blindness; Color space; Scheme (mathematics); Color vision; Compensation (psychology); Classification scheme; Optometry; Medicine; Mathematics; Image (mathematics); Psychology; Information retrieval","score_opus":0.030875507558446914,"score_gpt":0.2985303995455743,"score_spread":0.2676548919871274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226194913","genre_codex":"other","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":[0.26957226,0.171964,0.09620366,0.007826887,0.0032694847,0.00027218217,0.000452021,0.0016605865,0.44877893],"genre_scores_gemma":[0.63382703,0.047072247,0.014883521,0.0015542168,0.0006294015,0.000064950334,0.0002679162,0.00032440474,0.3013764],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999026,0.0000102459835,0.000005347584,0.000022555583,0.000034272074,0.00002489018],"domain_scores_gemma":[0.9998122,0.00007222407,0.000027107468,0.00003081152,0.00003421483,0.000023490025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017047375,0.0004492159,0.00032817698,0.0009498902,0.00032485026,0.00094624044,0.0008513827,0.00090779265,0.009291668],"category_scores_gemma":[0.00054324773,0.00015559004,0.0002898114,0.00045459947,0.0011654439,0.0012597032,0.00056426635,0.0011266646,0.0012319842],"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":[0.0009496194,0.0004684778,0.004010062,0.0013140521,0.00005160621,0.03135677,0.0012442494,0.0021740468,0.10295291,0.15508704,0.042487536,0.6579037],"study_design_scores_gemma":[0.0000941614,0.0010432218,0.012741514,0.00069372443,0.00013481475,0.35580412,0.0014069956,0.0067179077,0.10212843,0.13837019,0.3807648,0.00010023055],"about_ca_topic_score_codex":0.0010526347,"about_ca_topic_score_gemma":0.000762088,"teacher_disagreement_score":0.009291668,"about_ca_system_score_codex":0.00039606553,"about_ca_system_score_gemma":0.00024466723,"threshold_uncertainty_score":0.031083763},"labels":[],"label_agreement":null},{"id":"W4230327142","doi":"10.1145/2990495","title":"Antialiasing Complex Global Illumination Effects in Path-Space","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"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; Université de Montréal","funders":"National Science Foundation","keywords":"Path (computing); Computer science; Global illumination; Radiance; Context (archaeology); Filter (signal processing); Computer vision; Fourier transform; Algorithm; Space (punctuation); Artificial intelligence; Topology (electrical circuits); Mathematics; Optics; Physics; Geology; Mathematical analysis","score_opus":0.026837618802682154,"score_gpt":0.30686319713513616,"score_spread":0.280025578332454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230327142","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":[0.013755048,0.000040411116,0.9843828,0.000026249412,0.000016902102,0.000012216672,0.000023332888,0.00079899636,0.00094402273],"genre_scores_gemma":[0.30877295,0.00025287416,0.68461895,0.00007998754,0.00003649796,0.000055925047,0.00019069258,0.0011667041,0.0048254193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99984086,0.000020757969,0.0000037679326,0.000026511085,0.000086925174,0.000021095217],"domain_scores_gemma":[0.99955505,0.00016110066,0.000058168338,0.000110248606,0.000092263945,0.00002315993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002861728,0.00078887906,0.0003706918,0.0004226632,0.0002349189,0.00094233174,0.0006632275,0.0004507255,0.0026873425],"category_scores_gemma":[0.0013213985,0.00032451164,0.00057618297,0.00029205202,0.0005002565,0.0010623306,0.00075553986,0.0010630307,0.0008340839],"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":[0.00016595708,0.00009566874,0.0021911091,0.00016025596,0.00006516381,0.00021215588,0.00039301658,0.48090142,0.14392334,0.05282724,0.004025426,0.31503916],"study_design_scores_gemma":[0.0000068749055,0.000028036522,0.00042814057,0.000008096687,0.000010828522,0.00012137952,0.0000248225,0.96289814,0.024303712,0.007829559,0.004324867,0.00001558748],"about_ca_topic_score_codex":0.0021759558,"about_ca_topic_score_gemma":0.0034972846,"teacher_disagreement_score":0.0026873425,"about_ca_system_score_codex":0.0004481816,"about_ca_system_score_gemma":0.00064643024,"threshold_uncertainty_score":0.008990049},"labels":[],"label_agreement":null},{"id":"W4233454584","doi":"10.1145/1882262.1866165","title":"Optical computing for fast light transport analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Toronto","funders":"","keywords":"Krylov subspace; Computer science; Subspace topology; Subroutine; Eigenvalues and eigenvectors; Matrix (chemical analysis); Computer graphics (images); Computer vision; Computation; Generalized minimal residual method; Algorithm; Computational science; Artificial intelligence; Physics; Iterative method","score_opus":0.007176076722101655,"score_gpt":0.2617171068293055,"score_spread":0.25454103010720386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233454584","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":[0.0015508453,0.0001967444,0.9935109,0.00018085264,0.000048043315,0.000024131536,0.000077232406,0.0006349025,0.0037763016],"genre_scores_gemma":[0.11615691,0.0008973669,0.8740324,0.0001681441,0.00014331508,0.00024213282,0.0003181046,0.0006163494,0.007425219],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996885,0.0000648549,0.000013138492,0.00004787695,0.00015160673,0.000034096538],"domain_scores_gemma":[0.9993926,0.00021054244,0.000050058505,0.00016961775,0.00015223895,0.000024910745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046890456,0.0009702085,0.00053899805,0.0009190837,0.0008143352,0.0015356014,0.0009148691,0.0006686823,0.010764543],"category_scores_gemma":[0.0018844914,0.00040257405,0.00068792095,0.001045613,0.00090054405,0.0019294323,0.0014374212,0.0014649824,0.0022015877],"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":[0.00009267285,0.000049748982,0.00046337798,0.0002422814,0.000045099074,0.000090226495,0.00013021281,0.18624699,0.0164428,0.53919274,0.011006527,0.24599737],"study_design_scores_gemma":[0.000012626895,0.000013946581,0.00013065185,0.000026509131,0.0000075102744,0.000043755,0.00002760878,0.8190353,0.0044398927,0.15746418,0.018780654,0.000017457965],"about_ca_topic_score_codex":0.0035797516,"about_ca_topic_score_gemma":0.003577439,"teacher_disagreement_score":0.010764543,"about_ca_system_score_codex":0.0010181395,"about_ca_system_score_gemma":0.0010577061,"threshold_uncertainty_score":0.03601098},"labels":[],"label_agreement":null},{"id":"W4234285608","doi":"10.1109/icpr.2004.1334013","title":"Sparse scene structure recovery from atmospheric degradation","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visibility; Computer vision; Computer science; Artificial intelligence; Degradation (telecommunications); Computer graphics (images); Geography; Telecommunications","score_opus":0.02969399127566385,"score_gpt":0.25223223855419236,"score_spread":0.2225382472785285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234285608","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":[0.23772644,0.0005594794,0.7579401,0.00021903189,0.000061346254,0.00002910488,0.00015765883,0.0010417367,0.0022650587],"genre_scores_gemma":[0.7722172,0.00066329003,0.22372681,0.00006751745,0.000062880266,0.000021332366,0.00065418,0.00017748724,0.0024092728],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997899,0.00002844204,0.0000060146685,0.000032287753,0.00011497076,0.000028440872],"domain_scores_gemma":[0.99956995,0.00011589076,0.0000652515,0.00012057526,0.00010527248,0.000023032171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030826076,0.0005379943,0.0005039347,0.00067950867,0.00021076588,0.00045009286,0.00038587188,0.0005400036,0.0008727816],"category_scores_gemma":[0.0013950326,0.00033040022,0.00039823266,0.0005573712,0.0004265552,0.00093011407,0.00059245183,0.0007733816,0.00034770177],"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":[0.00038706476,0.00006946873,0.0026633847,0.0002344561,0.00009537833,0.00054331747,0.00021984956,0.089056954,0.52095824,0.0031009098,0.0016371045,0.38103387],"study_design_scores_gemma":[0.000043676086,0.00016769265,0.014541346,0.000026846696,0.000075497686,0.0016127483,0.00008892621,0.76637125,0.20758645,0.0055884523,0.00383308,0.000064022744],"about_ca_topic_score_codex":0.0012387879,"about_ca_topic_score_gemma":0.0024174983,"teacher_disagreement_score":0.0012387879,"about_ca_system_score_codex":0.00017938031,"about_ca_system_score_gemma":0.0003400753,"threshold_uncertainty_score":0.0029197931},"labels":[],"label_agreement":null},{"id":"W4234833824","doi":"10.32920/ryerson.14653488","title":"Human visual system inspired saliency guided edge preserving tone-mapping for high dynamic range imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement 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":"Toronto Metropolitan University","funders":"","keywords":"Tone mapping; Artificial intelligence; Human visual system model; Computer vision; Computer science; High dynamic range; High-dynamic-range imaging; Enhanced Data Rates for GSM Evolution; Filter (signal processing); Pixel; Tone (literature); Bilateral filter; Dynamic range; Naturalness; Image (mathematics); Physics","score_opus":0.026323784104012104,"score_gpt":0.3324057861757245,"score_spread":0.30608200207171243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234833824","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":[0.11495761,0.0009287235,0.879685,0.00010353997,0.00006114984,0.000055831497,0.000034269193,0.0005243346,0.003649572],"genre_scores_gemma":[0.6921193,0.00078638317,0.30252227,0.00008253412,0.00005849365,0.00003593692,0.00006329861,0.00008198342,0.0042497953],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994266,0.000012055874,0.0000019391969,0.000012308202,0.000023593275,0.0000075152475],"domain_scores_gemma":[0.99989307,0.000036073365,0.000013574551,0.000019781053,0.000029043425,0.000008405882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013922763,0.00030181618,0.00018971108,0.0002590718,0.000111352194,0.0003020983,0.00027388535,0.00024643977,0.0015695916],"category_scores_gemma":[0.0003628591,0.00010147634,0.00029316035,0.00015725728,0.0002151204,0.00038506603,0.00026500228,0.00028032268,0.00027247317],"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":[0.00020670185,0.00005266955,0.00044643768,0.00023865774,0.000053365013,0.00019582406,0.0001384546,0.023467006,0.7169026,0.004981275,0.00096549565,0.25235155],"study_design_scores_gemma":[0.000035768804,0.00045795727,0.0034313255,0.000022486889,0.000076373835,0.0011293615,0.000069491296,0.6448015,0.3347478,0.006331806,0.008862791,0.000033411492],"about_ca_topic_score_codex":0.0003368197,"about_ca_topic_score_gemma":0.00053649774,"teacher_disagreement_score":0.0015695916,"about_ca_system_score_codex":0.0001280597,"about_ca_system_score_gemma":0.00014393106,"threshold_uncertainty_score":0.0052508116},"labels":[],"label_agreement":null},{"id":"W4235126022","doi":"10.32920/ryerson.14647569","title":"K-means clustering based tone-mapping operator for high dynamic range video","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement 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":"Toronto Metropolitan University","funders":"","keywords":"Tone mapping; High dynamic range; Cluster analysis; Computer science; Luminance; Centroid; Artificial intelligence; Dynamic range; Computer vision; Range (aeronautics); Flicker; Frame (networking); Algorithm; Computer graphics (images); Engineering","score_opus":0.02036936154178004,"score_gpt":0.29091480783651075,"score_spread":0.2705454462947307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235126022","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":[0.01610011,0.00008624122,0.98187387,0.00004929775,0.00003392197,0.000059618087,0.000040739684,0.0009248989,0.00083132816],"genre_scores_gemma":[0.1199438,0.00011821502,0.87676704,0.000077778794,0.000037359692,0.000115084076,0.0001810962,0.0002440879,0.0025154985],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993156,0.00009547436,0.00004456872,0.00018798413,0.00028483028,0.00007158122],"domain_scores_gemma":[0.999185,0.00021846547,0.00006273202,0.00014828383,0.00033983606,0.000045652923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066187844,0.0006801394,0.00066130375,0.0009650872,0.00070655136,0.0008843956,0.0012795902,0.00076962623,0.003293391],"category_scores_gemma":[0.0026364995,0.0002811894,0.00077093096,0.00094087346,0.00058457354,0.0010966407,0.00089102186,0.0011495004,0.0012146675],"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":[0.00054772646,0.00015670044,0.00096268154,0.00020481432,0.000084487605,0.00013429682,0.00039312406,0.082306996,0.12731509,0.0125619145,0.005779605,0.7695525],"study_design_scores_gemma":[0.00003263283,0.00011382528,0.0014404701,0.000013622381,0.000024421242,0.0002603256,0.000112421956,0.91437304,0.071029596,0.007761744,0.0047900556,0.000047858863],"about_ca_topic_score_codex":0.003557235,"about_ca_topic_score_gemma":0.0041443342,"teacher_disagreement_score":0.003557235,"about_ca_system_score_codex":0.00067417126,"about_ca_system_score_gemma":0.0007292934,"threshold_uncertainty_score":0.011017501},"labels":[],"label_agreement":null},{"id":"W4237848840","doi":"10.5594/m001838","title":"Quantitative Evaluation and Attribute of Overall Brightness in a High Dynamic Range World","year":2018,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of British Columbia","funders":"","keywords":"Brightness; Metric (unit); Pixel; Computer science; High dynamic range; Luminance; Dynamic range; Computer vision; Artificial intelligence; High-dynamic-range imaging; Range (aeronautics); Code (set theory); Mathematics; Optics; Physics","score_opus":0.027347491269461247,"score_gpt":0.3330831547322355,"score_spread":0.30573566346277425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237848840","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":[0.58502257,0.0013146396,0.40508276,0.00012106015,0.000079072124,0.00018211434,0.00072852266,0.00118741,0.0062818057],"genre_scores_gemma":[0.91945994,0.0004467049,0.07777179,0.000041853153,0.000031070456,0.00007978628,0.0006152884,0.0001647772,0.0013888944],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999086,0.00022476773,0.0000629198,0.00014800932,0.0004165429,0.00006183819],"domain_scores_gemma":[0.9960008,0.0019197509,0.0004810292,0.00033654348,0.0011127972,0.00014915167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001721781,0.00043517112,0.00038593297,0.0021994861,0.00021203552,0.0011249981,0.00036857874,0.00050247484,0.0020779634],"category_scores_gemma":[0.006079602,0.00015621574,0.00032598388,0.0010974397,0.0005544849,0.0013036006,0.00056578644,0.0003664173,0.0004504916],"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":[0.0016038023,0.00039544632,0.050230708,0.0013902464,0.0003155369,0.00033448508,0.0008152387,0.044582386,0.48631856,0.0057926606,0.0024683739,0.40575254],"study_design_scores_gemma":[0.00007207364,0.002039106,0.25073358,0.00017964545,0.0003976864,0.0020350711,0.0011104549,0.37704718,0.35145062,0.0057291677,0.008949927,0.00025555058],"about_ca_topic_score_codex":0.00070774683,"about_ca_topic_score_gemma":0.0006950678,"teacher_disagreement_score":0.0021994861,"about_ca_system_score_codex":0.00031694717,"about_ca_system_score_gemma":0.00017212737,"threshold_uncertainty_score":0.009105802},"labels":[],"label_agreement":null},{"id":"W4239618954","doi":"10.1109/bmsb.2017.7986154","title":"Chroma keying based on stereo images","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Alpha (finance); Computer science; Pixel; Laplace operator; Keying; Object (grammar); Process (computing); Image (mathematics); Matrix (chemical analysis); Matching (statistics); Pattern recognition (psychology); Mathematics","score_opus":0.02039192191817729,"score_gpt":0.2911229525810238,"score_spread":0.2707310306628465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239618954","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":[0.038807057,0.0004771986,0.956059,0.00008880507,0.00010423835,0.00006871577,0.000075704294,0.0007683236,0.0035509616],"genre_scores_gemma":[0.4043607,0.0010659121,0.5883443,0.00015179507,0.00014594941,0.00006823603,0.00020724967,0.0001854013,0.0054704784],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996643,0.000030889692,0.000012296284,0.00006498316,0.00019149459,0.000036076046],"domain_scores_gemma":[0.9994611,0.00011491619,0.00008104285,0.00010233284,0.00019702816,0.000043607284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002842132,0.0006145876,0.00044785664,0.001003425,0.00025407353,0.000644871,0.0004552957,0.00039309842,0.0028813083],"category_scores_gemma":[0.0012628284,0.00024079547,0.00037804822,0.00082428777,0.00044356508,0.0012657775,0.0006354907,0.0006509012,0.0009177672],"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":[0.0004692076,0.00005705849,0.0007322153,0.000251479,0.000034971385,0.0002649558,0.00016382169,0.013333242,0.4778197,0.008298288,0.0021041997,0.49647093],"study_design_scores_gemma":[0.00005906682,0.00036948995,0.0033176076,0.000040866737,0.00007886004,0.001525462,0.00014804488,0.38588792,0.5869801,0.0055389833,0.015988208,0.000065366046],"about_ca_topic_score_codex":0.0009155405,"about_ca_topic_score_gemma":0.0011863919,"teacher_disagreement_score":0.0028813083,"about_ca_system_score_codex":0.0003098108,"about_ca_system_score_gemma":0.0003798523,"threshold_uncertainty_score":0.009638965},"labels":[],"label_agreement":null},{"id":"W4246250782","doi":"10.32920/ryerson.14655897.v1","title":"Adaptive Exposure Fusion for HDR Imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement 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":"Toronto Metropolitan University","funders":"","keywords":"Sequence (biology); Fusion; Computer science; Artificial intelligence; Metric (unit); Multiple exposure; Computer vision; Image fusion; Image (mathematics); Pattern recognition (psychology); Engineering","score_opus":0.023041533534400558,"score_gpt":0.2757639509186194,"score_spread":0.2527224173842188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246250782","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":[0.028835688,0.0020300555,0.9644871,0.00012172529,0.00006626249,0.00009716353,0.00012580983,0.0013492478,0.0028870204],"genre_scores_gemma":[0.27607125,0.002169062,0.71664643,0.0001696723,0.000103153376,0.000080643425,0.0004643434,0.00023175581,0.0040637716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994541,0.0000790808,0.000025667508,0.00013492069,0.00026844465,0.00003792381],"domain_scores_gemma":[0.99956673,0.0001228345,0.00005647556,0.00011161823,0.00012268455,0.000019559917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064845855,0.0005565961,0.0004415544,0.0008925331,0.00028065345,0.00070477027,0.0006488041,0.00061578664,0.002613151],"category_scores_gemma":[0.0012080974,0.0002600917,0.00066598534,0.0006816747,0.00037411432,0.0010858787,0.0007812462,0.0008353832,0.0009056747],"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":[0.00033105683,0.00009851892,0.0011962802,0.00042955668,0.00011111186,0.0001517969,0.00022568139,0.024409795,0.30339462,0.0057478873,0.003091074,0.6608127],"study_design_scores_gemma":[0.000049269252,0.0007682099,0.008911101,0.000116450676,0.00022822646,0.0023555898,0.00018025863,0.44554082,0.48649308,0.009911232,0.045338396,0.000107358705],"about_ca_topic_score_codex":0.00077086483,"about_ca_topic_score_gemma":0.00076233165,"teacher_disagreement_score":0.002613151,"about_ca_system_score_codex":0.00039266076,"about_ca_system_score_gemma":0.00028040126,"threshold_uncertainty_score":0.008741856},"labels":[],"label_agreement":null},{"id":"W4250438722","doi":"10.5594/m001708","title":"Luminance-Preserving Colour Conversion","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"BC Research (Canada)","funders":"","keywords":"Luminance; Computer science; Computer vision; Artificial intelligence; Pixel; Colorimetry; Computer graphics (images); Process (computing); Representation (politics)","score_opus":0.009408413362329367,"score_gpt":0.22655771001182917,"score_spread":0.2171492966494998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250438722","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":[0.06554854,0.00056684157,0.89434135,0.00012638746,0.00022971001,0.0000809017,0.00021815604,0.0023845856,0.036503527],"genre_scores_gemma":[0.5112288,0.001074791,0.44189417,0.00017799565,0.000098541626,0.00006837474,0.0003174214,0.0004954571,0.044644434],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998485,0.000010702951,0.0000058871196,0.000042411728,0.000066400484,0.000026137182],"domain_scores_gemma":[0.9998609,0.000014972308,0.000012788041,0.000062621024,0.000040071027,0.00000875334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114952774,0.00032295397,0.00020244825,0.0003650067,0.00025891102,0.0006498949,0.00043032484,0.000251114,0.0050549693],"category_scores_gemma":[0.00035027493,0.00016031024,0.00029146895,0.00032915207,0.00040578787,0.0004223617,0.00050967775,0.0004950063,0.0022130578],"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":[0.00012075629,0.000047256923,0.00040552617,0.00015158587,0.000013519543,0.00018598442,0.00011003282,0.002978633,0.71876204,0.020956729,0.002412183,0.2538558],"study_design_scores_gemma":[0.000013226147,0.00012731737,0.0016759917,0.000018482047,0.000030098538,0.0012319087,0.000054068867,0.02680153,0.90958315,0.0049715806,0.05546404,0.000028588263],"about_ca_topic_score_codex":0.00059022533,"about_ca_topic_score_gemma":0.00075671653,"teacher_disagreement_score":0.0050549693,"about_ca_system_score_codex":0.0001868294,"about_ca_system_score_gemma":0.0002118999,"threshold_uncertainty_score":0.016910613},"labels":[],"label_agreement":null},{"id":"W4251444399","doi":"10.1145/1276377.1276426","title":"Ldr2Hdr","year":2007,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":177,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dolby (Canada); University of British Columbia","funders":"","keywords":"Computer science; High dynamic range; Computer graphics (images); Robustness (evolution); Video processing; Fidelity; Graphics; Dynamic range; Emphasis (telecommunications); Artificial intelligence; Computer vision; Telecommunications","score_opus":0.01901070370005295,"score_gpt":0.2775036654296376,"score_spread":0.2584929617295847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251444399","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":[0.047484275,0.0022522165,0.46346238,0.0011656332,0.0014721212,0.00032959142,0.005069737,0.05276073,0.4260033],"genre_scores_gemma":[0.3432783,0.00096255663,0.22036217,0.0020177544,0.00047831808,0.00029787674,0.009235795,0.0037953015,0.41957197],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995146,0.0000530629,0.000020253277,0.00012357491,0.00022219501,0.00006632388],"domain_scores_gemma":[0.99963284,0.00004074516,0.0000201134,0.000151835,0.00012067062,0.00003374521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039134707,0.0004744484,0.0004147425,0.0008255454,0.00057357096,0.001216684,0.00084962,0.0008020267,0.08613322],"category_scores_gemma":[0.0006951493,0.00023180972,0.00027838108,0.00063398527,0.00023742986,0.00095335284,0.0011416886,0.00062188576,0.044508234],"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":[0.00067690224,0.00014410637,0.0015185231,0.00042410652,0.000041794847,0.0005235476,0.00018245178,0.0025490161,0.19067247,0.030100498,0.1745766,0.59859],"study_design_scores_gemma":[0.00010067307,0.00019750117,0.0024935496,0.000042534095,0.00003863496,0.0015171786,0.000095636206,0.031197848,0.16265003,0.00402474,0.79755557,0.000086069675],"about_ca_topic_score_codex":0.00079883117,"about_ca_topic_score_gemma":0.0012067253,"teacher_disagreement_score":0.08613322,"about_ca_system_score_codex":0.00042113345,"about_ca_system_score_gemma":0.0003284186,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4253014881","doi":"10.32920/ryerson.14653488.v1","title":"Human visual system inspired saliency guided edge preserving tone-mapping for high dynamic range imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement 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":"Toronto Metropolitan University","funders":"","keywords":"Tone mapping; High dynamic range; Artificial intelligence; Computer vision; Human visual system model; High-dynamic-range imaging; Computer science; Filter (signal processing); Enhanced Data Rates for GSM Evolution; Pixel; Dynamic range; Tone (literature); Naturalness; Bilateral filter; Image (mathematics); Physics","score_opus":0.026323784104012104,"score_gpt":0.3324057861757245,"score_spread":0.30608200207171243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253014881","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":[0.11495761,0.0009287235,0.879685,0.00010353997,0.00006114984,0.000055831497,0.000034269193,0.0005243346,0.003649572],"genre_scores_gemma":[0.6921193,0.00078638317,0.30252227,0.00008253412,0.00005849365,0.00003593692,0.00006329861,0.00008198342,0.0042497953],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99994266,0.000012055874,0.0000019391969,0.000012308202,0.000023593275,0.0000075152475],"domain_scores_gemma":[0.99989307,0.000036073365,0.000013574551,0.000019781053,0.000029043425,0.000008405882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013922763,0.00030181618,0.00018971108,0.0002590718,0.000111352194,0.0003020983,0.00027388535,0.00024643977,0.0015695916],"category_scores_gemma":[0.0003628591,0.00010147634,0.00029316035,0.00015725728,0.0002151204,0.00038506603,0.00026500228,0.00028032268,0.00027247317],"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":[0.00020670185,0.00005266955,0.00044643768,0.00023865774,0.000053365013,0.00019582406,0.0001384546,0.023467006,0.7169026,0.004981275,0.00096549565,0.25235155],"study_design_scores_gemma":[0.000035768804,0.00045795727,0.0034313255,0.000022486889,0.000076373835,0.0011293615,0.000069491296,0.6448015,0.3347478,0.006331806,0.008862791,0.000033411492],"about_ca_topic_score_codex":0.0003368197,"about_ca_topic_score_gemma":0.00053649774,"teacher_disagreement_score":0.0015695916,"about_ca_system_score_codex":0.0001280597,"about_ca_system_score_gemma":0.00014393106,"threshold_uncertainty_score":0.0052508116},"labels":[],"label_agreement":null},{"id":"W4254886313","doi":"10.32920/ryerson.14647569.v1","title":"K-means clustering based tone-mapping operator for high dynamic range video","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement 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":"Toronto Metropolitan University","funders":"","keywords":"Tone mapping; High dynamic range; Cluster analysis; Computer science; Luminance; Centroid; Artificial intelligence; Computer vision; Dynamic range; Range (aeronautics); Flicker; Frame (networking); Algorithm; Computer graphics (images); Engineering","score_opus":0.02036936154178004,"score_gpt":0.29091480783651075,"score_spread":0.2705454462947307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254886313","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":[0.01610011,0.00008624122,0.98187387,0.00004929775,0.00003392197,0.000059618087,0.000040739684,0.0009248989,0.00083132816],"genre_scores_gemma":[0.1199438,0.00011821502,0.87676704,0.000077778794,0.000037359692,0.000115084076,0.0001810962,0.0002440879,0.0025154985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993156,0.00009547436,0.00004456872,0.00018798413,0.00028483028,0.00007158122],"domain_scores_gemma":[0.999185,0.00021846547,0.00006273202,0.00014828383,0.00033983606,0.000045652923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066187844,0.0006801394,0.00066130375,0.0009650872,0.00070655136,0.0008843956,0.0012795902,0.00076962623,0.003293391],"category_scores_gemma":[0.0026364995,0.0002811894,0.00077093096,0.00094087346,0.00058457354,0.0010966407,0.00089102186,0.0011495004,0.0012146675],"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":[0.00054772646,0.00015670044,0.00096268154,0.00020481432,0.000084487605,0.00013429682,0.00039312406,0.082306996,0.12731509,0.0125619145,0.005779605,0.7695525],"study_design_scores_gemma":[0.00003263283,0.00011382528,0.0014404701,0.000013622381,0.000024421242,0.0002603256,0.000112421956,0.91437304,0.071029596,0.007761744,0.0047900556,0.000047858863],"about_ca_topic_score_codex":0.003557235,"about_ca_topic_score_gemma":0.0041443342,"teacher_disagreement_score":0.003557235,"about_ca_system_score_codex":0.00067417126,"about_ca_system_score_gemma":0.0007292934,"threshold_uncertainty_score":0.011017501},"labels":[],"label_agreement":null},{"id":"W4281698687","doi":"10.1117/12.2621608","title":"Deep visible to thermal infrared style transfer in dynamic video sequences","year":2022,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Université de Moncton","funders":"","keywords":"Computer science; Artificial intelligence; Thermal infrared; Infrared; Computer vision; Deep learning; Transfer of learning; Thermal; Pattern recognition (psychology); Optics","score_opus":0.008176835126935234,"score_gpt":0.2474092782686171,"score_spread":0.23923244314168188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281698687","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":[0.30061266,0.0004581052,0.69225913,0.00027499776,0.00014548733,0.00008277681,0.00015821744,0.0012507112,0.004757974],"genre_scores_gemma":[0.8936664,0.00031149507,0.09970251,0.00011461754,0.000042236716,0.000031950214,0.00024286412,0.000080611986,0.005807397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990416,0.000018576868,0.0000033942483,0.000029064,0.000024452807,0.00002037734],"domain_scores_gemma":[0.9998697,0.000039153063,0.000016793303,0.000025587786,0.000032146472,0.00001652497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034123403,0.0004555186,0.00025791983,0.0003307576,0.00011005294,0.00034513176,0.00039030888,0.00031635788,0.0013034063],"category_scores_gemma":[0.00086612627,0.00016872626,0.00031453272,0.0002619155,0.00026558226,0.000570344,0.00041844154,0.0006318032,0.00033371308],"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":[0.00036928017,0.0002224019,0.0016201345,0.0000968452,0.00004795474,0.00012017837,0.00010370778,0.39766324,0.08942033,0.005662218,0.0018695649,0.5028041],"study_design_scores_gemma":[0.0000056900094,0.0000489435,0.0006198563,0.000006433081,0.000004963397,0.000026708587,0.000011108194,0.9818812,0.015192472,0.0017008012,0.00049628015,0.000005504327],"about_ca_topic_score_codex":0.0027222305,"about_ca_topic_score_gemma":0.003351903,"teacher_disagreement_score":0.0027222305,"about_ca_system_score_codex":0.00034675637,"about_ca_system_score_gemma":0.00028189886,"threshold_uncertainty_score":0.005412698},"labels":[],"label_agreement":null},{"id":"W4283369524","doi":"10.1145/3478457","title":"SADnet: Semi-supervised Single Image Dehazing Method Based on an Attention Mechanism","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Computer science; Generalizability theory; Artificial intelligence; Image (mathematics); Computer vision; Haze; Process (computing)","score_opus":0.029037222435524045,"score_gpt":0.3118934650462394,"score_spread":0.28285624261071535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283369524","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":[0.051029354,0.00054895063,0.9445584,0.00017142342,0.00011306262,0.00012567922,0.000107744025,0.0015284093,0.0018169543],"genre_scores_gemma":[0.502069,0.0004892883,0.48618624,0.00039344205,0.00011504293,0.00016964617,0.0006795639,0.00018303125,0.009714802],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997732,0.000020886666,0.000010806016,0.00007803933,0.000084415726,0.000032597654],"domain_scores_gemma":[0.99967074,0.00007337814,0.000039140345,0.000047731995,0.00014377118,0.000025355639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000480819,0.0007657807,0.0008078349,0.00070936413,0.0003500885,0.00043806038,0.0016192995,0.00070953683,0.0014058555],"category_scores_gemma":[0.00077982777,0.00031876782,0.00066253985,0.00035586942,0.00045349856,0.0009506688,0.00081324996,0.00087413867,0.00038757978],"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":[0.00043786378,0.00028589004,0.001665055,0.00020177194,0.0001475736,0.00014786833,0.00014053025,0.13330233,0.08720765,0.0038666683,0.0057906765,0.7668062],"study_design_scores_gemma":[0.000018004694,0.000096072625,0.0007575779,0.000007732933,0.000027702128,0.00012841477,0.000019742198,0.9726715,0.023125114,0.001247084,0.0018891913,0.000011875933],"about_ca_topic_score_codex":0.005590474,"about_ca_topic_score_gemma":0.0097424975,"teacher_disagreement_score":0.005590474,"about_ca_system_score_codex":0.0005685396,"about_ca_system_score_gemma":0.00088894635,"threshold_uncertainty_score":0.011115849},"labels":[],"label_agreement":null},{"id":"W4283815594","doi":"10.1609/aaai.v36i3.20276","title":"Efficient Model-Driven Network for Shadow Removal","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":58,"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":"University of Science and Technology of China; National Natural Science Foundation of China","keywords":"Shadow (psychology); Interpretability; Computer science; Artificial intelligence; Convolutional neural network; Task (project management); Computer vision; Deep learning; Point (geometry); Shadow mapping; FLOPS; Image (mathematics); Algorithm; Mathematics","score_opus":0.07048946113948087,"score_gpt":0.29685503739243746,"score_spread":0.22636557625295659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283815594","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":[0.017391175,0.00038176466,0.97568464,0.00017508556,0.000074058655,0.000044242057,0.00014353592,0.0031854296,0.0029200776],"genre_scores_gemma":[0.6413545,0.0004234352,0.34260875,0.0004414994,0.0000842485,0.00015116243,0.0010721615,0.000630275,0.013233889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982834,0.000016492231,0.0000053772715,0.00005022305,0.00006525517,0.0000343867],"domain_scores_gemma":[0.9998472,0.00004292233,0.000016438338,0.000029920413,0.00004818731,0.00001530488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024719705,0.00087148434,0.00073554204,0.00034843315,0.0003029165,0.00047271317,0.0014115127,0.0006985791,0.0033320691],"category_scores_gemma":[0.0006316816,0.00048467002,0.0007278066,0.00033957636,0.00032851912,0.00081428903,0.001006389,0.0012229285,0.00079015654],"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":[0.0001186693,0.00006697268,0.00046129627,0.00008555405,0.00006543106,0.00010712047,0.00004310412,0.77232563,0.020571683,0.0043051415,0.0054461625,0.19640322],"study_design_scores_gemma":[0.000002129763,0.0000044627677,0.00002576801,9.560108e-7,0.0000027262538,0.000008037979,0.0000014905878,0.9977804,0.001099097,0.0007460663,0.00032727778,0.0000016023854],"about_ca_topic_score_codex":0.011658877,"about_ca_topic_score_gemma":0.021422146,"teacher_disagreement_score":0.011658877,"about_ca_system_score_codex":0.0010443586,"about_ca_system_score_gemma":0.0014212378,"threshold_uncertainty_score":0.023182034},"labels":[],"label_agreement":null},{"id":"W4285129685","doi":"10.1109/tmm.2022.3185929","title":"Unsupervised Single-Image Reflection Removal","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Convolutional neural network; Reflection (computer programming); Image (mathematics); Process (computing); Deep learning; Feature (linguistics); Pattern recognition (psychology); Artificial neural network; Feature extraction; Supervised learning; Image quality; Computer vision","score_opus":0.027653636013142982,"score_gpt":0.2694604788557707,"score_spread":0.2418068428426277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285129685","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":[0.04404083,0.00046117717,0.9496142,0.000094616174,0.0000701826,0.00010617964,0.00027533446,0.0029754124,0.0023620357],"genre_scores_gemma":[0.40492877,0.0008045441,0.5801901,0.00029689484,0.0000899041,0.0002215844,0.0021443134,0.0009560613,0.010367887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921334,0.00008819236,0.000033540895,0.00026250674,0.00030344314,0.00009893158],"domain_scores_gemma":[0.9991798,0.00014551695,0.000099251636,0.00029789482,0.00024789813,0.00002974532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069635874,0.001434967,0.0013048018,0.0008264324,0.00032434092,0.0007686679,0.0017508335,0.00080338476,0.0016737976],"category_scores_gemma":[0.0019374341,0.00053933135,0.001259834,0.0007633014,0.00067137886,0.0011681241,0.0014601459,0.0012388616,0.0014597852],"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":[0.00047296972,0.00022557743,0.0020673852,0.00052407105,0.00024820596,0.00023937506,0.00019676339,0.0856896,0.21579105,0.0036863612,0.007691644,0.683167],"study_design_scores_gemma":[0.000034247987,0.00016313656,0.0029834644,0.00003836811,0.00011350654,0.0005249445,0.00007691686,0.7636155,0.21981655,0.0036905713,0.008899296,0.000043541204],"about_ca_topic_score_codex":0.0019428263,"about_ca_topic_score_gemma":0.0045492547,"teacher_disagreement_score":0.0019428263,"about_ca_system_score_codex":0.00039578,"about_ca_system_score_gemma":0.0011610138,"threshold_uncertainty_score":0.0055993795},"labels":[],"label_agreement":null},{"id":"W4285218761","doi":"10.1109/access.2022.3178745","title":"Learning Tone Curves for Local Image Enhancement","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Social Innovation","funders":"","keywords":"Computer science; Tone mapping; Tone (literature); Artificial intelligence; Software; Computer vision; Pixel; Image (mathematics); Interpretability","score_opus":0.023426746537441565,"score_gpt":0.3463302204229295,"score_spread":0.3229034738854879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285218761","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":[0.042087,0.00093381753,0.94311744,0.00021649877,0.000115601855,0.00021266816,0.0006490162,0.006564245,0.0061036707],"genre_scores_gemma":[0.29448345,0.0011665156,0.6838242,0.00034316775,0.00013011397,0.00027306983,0.0033459913,0.0011727945,0.015260742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953556,0.000060604358,0.000022844499,0.00021698557,0.00011756117,0.00004636512],"domain_scores_gemma":[0.9991671,0.00022795769,0.00007668744,0.00029416647,0.00018720592,0.000046820096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083307276,0.0014596956,0.00083304656,0.0013590137,0.0003901727,0.0012394753,0.001566043,0.0010339785,0.004281716],"category_scores_gemma":[0.002735481,0.00040312958,0.0010254361,0.0010551785,0.0006121992,0.0021552714,0.0013759566,0.0016325605,0.002531634],"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":[0.00033114318,0.00022937289,0.0020602515,0.000296527,0.0000942755,0.000121250625,0.000116481,0.07641529,0.060906094,0.0053692157,0.013174719,0.8408854],"study_design_scores_gemma":[0.000039622413,0.0001955295,0.001877298,0.000037939844,0.000055512726,0.00035112604,0.00008407655,0.91640407,0.05533393,0.011513892,0.0140722655,0.00003470709],"about_ca_topic_score_codex":0.0018754641,"about_ca_topic_score_gemma":0.004299047,"teacher_disagreement_score":0.004281716,"about_ca_system_score_codex":0.0007247726,"about_ca_system_score_gemma":0.00043675344,"threshold_uncertainty_score":0.014323831},"labels":[],"label_agreement":null},{"id":"W4285261750","doi":"10.1109/tits.2022.3170328","title":"Cycle-SNSPGAN: Towards Real-World Image Dehazing via Cycle Spectral Normalized Soft Likelihood Estimation Patch GAN","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":109,"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; Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Leverage (statistics); Computer vision; Image (mathematics); Image editing","score_opus":0.013825704135623585,"score_gpt":0.2671288536452248,"score_spread":0.25330314950960126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285261750","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":[0.029493112,0.00025680667,0.96493375,0.00017846967,0.000046305522,0.000048086615,0.00012212824,0.001276048,0.003645294],"genre_scores_gemma":[0.7120682,0.0003439616,0.27472785,0.00055336335,0.000045589306,0.00011696824,0.0008143753,0.0005012653,0.010828483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998159,0.000044980894,0.000004776901,0.000051741794,0.00006141186,0.000021324202],"domain_scores_gemma":[0.9996828,0.00012930021,0.000031460277,0.00007484092,0.000054112952,0.000027471879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050538237,0.0007166212,0.0005061192,0.0002729344,0.0001416274,0.00039942685,0.0011623534,0.0006036438,0.0020493525],"category_scores_gemma":[0.0010201747,0.00028370073,0.0005244381,0.00018451149,0.0006942628,0.00075856625,0.0011512032,0.0013904287,0.0005523607],"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":[0.00012261933,0.00006977091,0.00085666653,0.000090067704,0.00006772362,0.00011670531,0.00007337489,0.86905885,0.018322673,0.010156959,0.0052030836,0.09586152],"study_design_scores_gemma":[0.0000028329341,0.000016325,0.00007717958,0.000003079623,0.0000032068517,0.000025769215,0.0000036044703,0.99542433,0.0019265137,0.0020434198,0.0004705173,0.0000032007197],"about_ca_topic_score_codex":0.0019399077,"about_ca_topic_score_gemma":0.0030775825,"teacher_disagreement_score":0.0020493525,"about_ca_system_score_codex":0.00047791353,"about_ca_system_score_gemma":0.00035428503,"threshold_uncertainty_score":0.006855786},"labels":[],"label_agreement":null},{"id":"W4288462704","doi":"10.18280/ts.390313","title":"Non-Local Retinex Based Dehazing and Low Light Enhancement of Images","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Color constancy; Computer science; Artificial intelligence; Computer vision; Image (mathematics)","score_opus":0.006520076381859834,"score_gpt":0.21812996627752712,"score_spread":0.21160988989566729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288462704","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":[0.05490669,0.00088800176,0.9376875,0.00021415623,0.000055066943,0.00009284827,0.00017792822,0.0014364598,0.0045413426],"genre_scores_gemma":[0.3361268,0.0012324369,0.65178823,0.00022846358,0.000062331455,0.00008657316,0.00069746806,0.00049959245,0.009278034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958867,0.000064009546,0.00001638423,0.000101769794,0.00017643154,0.000052774372],"domain_scores_gemma":[0.9995229,0.0001010468,0.000053119347,0.00018026188,0.00011779582,0.000024760506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071420876,0.0007437599,0.000746158,0.0009365086,0.00030483847,0.0009698624,0.00092734693,0.00067453884,0.001539336],"category_scores_gemma":[0.0011175957,0.00030518768,0.0012368219,0.0004382392,0.00083326525,0.0009361454,0.00092165713,0.0009705385,0.00083671237],"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":[0.00033398197,0.0001955659,0.0020077538,0.00060419133,0.00023276909,0.00022921979,0.00033407533,0.2629618,0.2379599,0.017561937,0.005445154,0.47213358],"study_design_scores_gemma":[0.000022735785,0.00010053967,0.0019495953,0.000037310998,0.000059744943,0.00049279386,0.00007584763,0.8446275,0.13845861,0.005682123,0.008452418,0.000040823026],"about_ca_topic_score_codex":0.0036873359,"about_ca_topic_score_gemma":0.0064872457,"teacher_disagreement_score":0.0036873359,"about_ca_system_score_codex":0.00058035634,"about_ca_system_score_gemma":0.0007785985,"threshold_uncertainty_score":0.007331729},"labels":[],"label_agreement":null},{"id":"W4296700619","doi":"10.18280/isi.270416","title":"Swarm Based Optimization for Image Dehazing from Noise Filtering Perspective","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Soundness; Flexibility (engineering); Haze; Swarm behaviour; Image restoration; Artificial intelligence; Noise (video); Computer vision; Perspective (graphical); Noise reduction; Image (mathematics); Algorithm; Image processing; Mathematics; Geography","score_opus":0.01227286508632741,"score_gpt":0.24057148828580094,"score_spread":0.22829862319947353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296700619","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":[0.015611709,0.0004065187,0.98079,0.00017708192,0.000049862865,0.000029682733,0.000021115118,0.00010799467,0.002805945],"genre_scores_gemma":[0.69395214,0.00097176986,0.29675454,0.00013915001,0.00009947506,0.0001979821,0.00013479411,0.000057538215,0.0076926164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998605,0.000041097785,0.000008263453,0.00003091338,0.000041384268,0.000017826978],"domain_scores_gemma":[0.99972266,0.00016321025,0.000031687887,0.000015634681,0.000053557233,0.00001325335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004993777,0.00074492395,0.000744886,0.0003909777,0.00026442055,0.0005261239,0.00048507212,0.00078985316,0.0012028328],"category_scores_gemma":[0.0010034264,0.0002588809,0.0005179928,0.00032920417,0.0004998351,0.00051815255,0.0005858676,0.0007374943,0.0001688027],"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":[0.00003684706,0.000020391892,0.00034198764,0.000082244565,0.00004023653,0.00003977015,0.00007022363,0.9596036,0.003187703,0.0069846497,0.0007099343,0.028882489],"study_design_scores_gemma":[0.0000036003341,0.000021322274,0.00006068014,0.0000035917776,0.0000038917865,0.0000066911975,0.0000073048363,0.9983021,0.0002940618,0.0009548415,0.00033967488,0.0000022396187],"about_ca_topic_score_codex":0.003624102,"about_ca_topic_score_gemma":0.002292846,"teacher_disagreement_score":0.003624102,"about_ca_system_score_codex":0.0003843223,"about_ca_system_score_gemma":0.0005871713,"threshold_uncertainty_score":0.0072060227},"labels":[],"label_agreement":null},{"id":"W4298009622","doi":"10.18280/ts.390433","title":"Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cuckoo search; Masking (illustration); Computer science; Cuckoo; Wavelet; Image enhancement; Artificial intelligence; Algorithm; Contrast (vision); Contrast enhancement; Pattern recognition (psychology); Image (mathematics); Magnetic resonance imaging","score_opus":0.018561998286934785,"score_gpt":0.26330973420597703,"score_spread":0.24474773591904225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298009622","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":[0.10279534,0.0011942455,0.8910644,0.00015987705,0.000048473867,0.00007197947,0.000020405523,0.00034314345,0.0043021264],"genre_scores_gemma":[0.78896135,0.0005309734,0.20721836,0.00008051914,0.000016836091,0.00007159335,0.000031657997,0.00007216973,0.003016561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988484,0.000030267807,0.0000047331146,0.000016581198,0.000052602805,0.000010918879],"domain_scores_gemma":[0.9997732,0.00011653981,0.0000383505,0.000017265576,0.00004156552,0.00001298453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030130128,0.00046609284,0.0003837744,0.0003911423,0.0002837056,0.00029961093,0.00040959206,0.000478611,0.00088760955],"category_scores_gemma":[0.0008656296,0.00018573545,0.00029780396,0.00037202894,0.00040471964,0.00037758428,0.00030730595,0.0003022239,0.00014281005],"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":[0.00021301724,0.00005304664,0.00078547304,0.00024978496,0.000053767122,0.00018522833,0.00014601319,0.73038757,0.15415281,0.017710006,0.0007940414,0.09526918],"study_design_scores_gemma":[0.000009060371,0.000059151735,0.00025570556,0.000008876404,0.000010010531,0.00006208331,0.00000755538,0.98545873,0.011569481,0.0016529276,0.00089593034,0.000010506546],"about_ca_topic_score_codex":0.0019295573,"about_ca_topic_score_gemma":0.0020180533,"teacher_disagreement_score":0.0019295573,"about_ca_system_score_codex":0.00048363116,"about_ca_system_score_gemma":0.0004929129,"threshold_uncertainty_score":0.0038366914},"labels":[],"label_agreement":null},{"id":"W4298009677","doi":"10.18280/ts.390413","title":"Low-Light Image Enhancement and Target Detection Based on Deep Learning","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Object detection; Transformation (genetics); Deep learning; Image (mathematics); Pattern recognition (psychology)","score_opus":0.005138381293065445,"score_gpt":0.20820614679251398,"score_spread":0.20306776549944852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298009677","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":[0.018462308,0.00042247245,0.9787966,0.000107443586,0.00002354557,0.000022902299,0.000023481884,0.00050341926,0.001637827],"genre_scores_gemma":[0.6555922,0.001260689,0.33515808,0.0002563784,0.000065309156,0.00007801202,0.00018816708,0.00009945882,0.0073016295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998062,0.000028229291,0.000008609217,0.000052650463,0.00007157213,0.000032664087],"domain_scores_gemma":[0.99983525,0.00005708211,0.00002240652,0.00002188572,0.00005202111,0.000011383654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038265908,0.00057989516,0.00041713938,0.000425912,0.00015617003,0.0004227097,0.00080463855,0.0005040519,0.00084388943],"category_scores_gemma":[0.00059871416,0.00027119726,0.0005372864,0.00035819173,0.00041975672,0.00093056244,0.00064440933,0.0010086539,0.00026255104],"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":[0.00020623826,0.00018811437,0.0015167147,0.00021979505,0.000105709645,0.00018401012,0.00013723409,0.3323412,0.1039309,0.014872926,0.002278054,0.5440191],"study_design_scores_gemma":[0.0000032696007,0.000027757751,0.00020146654,0.0000047468534,0.00000978277,0.00003417454,0.0000039015654,0.9863906,0.011160779,0.0015544613,0.0006039312,0.000005097018],"about_ca_topic_score_codex":0.0024430032,"about_ca_topic_score_gemma":0.0029093218,"teacher_disagreement_score":0.0024430032,"about_ca_system_score_codex":0.00049183954,"about_ca_system_score_gemma":0.00037393978,"threshold_uncertainty_score":0.0048575997},"labels":[],"label_agreement":null},{"id":"W4298009692","doi":"10.18280/ts.390405","title":"An Approach for Enhancement of MR Images of Brain Tumor","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Metric (unit); Pattern recognition (psychology); Similarity (geometry); Peak signal-to-noise ratio; Computer science; Feature (linguistics); Histogram; Mathematics; Sigmoid function; Mean squared error; Statistics; Image (mathematics); Artificial neural network","score_opus":0.015954862544574632,"score_gpt":0.261322416980864,"score_spread":0.24536755443628938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298009692","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":[0.033938866,0.00057517324,0.96348995,0.000114776354,0.00003477383,0.000049613514,0.00002445203,0.0003772506,0.0013950603],"genre_scores_gemma":[0.28197822,0.0008518344,0.71319425,0.00012561167,0.00004737035,0.00005146678,0.00008813913,0.00006582463,0.0035972719],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982446,0.000026548601,0.000012505726,0.000049597234,0.00006810151,0.000018856359],"domain_scores_gemma":[0.99985063,0.000031637646,0.00002522041,0.000025988875,0.00005698285,0.000009422003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041496116,0.00041357387,0.00031481532,0.00073381164,0.00017651357,0.00034642697,0.0004370206,0.00045833472,0.00081140694],"category_scores_gemma":[0.00062465685,0.0001496876,0.00047011892,0.00044311013,0.00025808983,0.00059443095,0.00046304738,0.00042270325,0.00031398682],"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":[0.00015394586,0.0001069039,0.0015256719,0.00025058998,0.000087255336,0.00025126673,0.00015466606,0.040585667,0.37255955,0.005399862,0.0013465156,0.577578],"study_design_scores_gemma":[0.000020745178,0.000477813,0.0044459817,0.000031290216,0.000110495275,0.0016620281,0.00007480506,0.7787345,0.20195785,0.0039928504,0.008452187,0.000039411265],"about_ca_topic_score_codex":0.0005781703,"about_ca_topic_score_gemma":0.00096077385,"teacher_disagreement_score":0.00081140694,"about_ca_system_score_codex":0.00016921041,"about_ca_system_score_gemma":0.00028001843,"threshold_uncertainty_score":0.0027143955},"labels":[],"label_agreement":null},{"id":"W4298009708","doi":"10.18280/ts.390425","title":"Enhancement of Images with Very Low Light by Using Modified Brightness Low Lightness Areas Algorithm Based on Sigmoid Function","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sigmoid function; Brightness; Histogram equalization; Artificial intelligence; Computer vision; Mathematics; Computer science; Lightness; Gamma correction; Algorithm; Histogram; Image (mathematics); Optics","score_opus":0.007976007268453957,"score_gpt":0.2107538548232034,"score_spread":0.20277784755474945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298009708","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":[0.09970769,0.00028729008,0.89761394,0.000089403926,0.000037537844,0.00004816816,0.000019904737,0.00043205544,0.0017640407],"genre_scores_gemma":[0.54715353,0.00053569285,0.44755012,0.000051677813,0.000022889286,0.00006675009,0.000056096807,0.00007592389,0.0044874153],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998184,0.000027739918,0.000015096048,0.00003832919,0.00008349382,0.000016878208],"domain_scores_gemma":[0.99966526,0.0001026513,0.000043786233,0.000024221697,0.00015104831,0.0000130132885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047381522,0.00037986008,0.00031309784,0.0005769652,0.00015570344,0.00050893985,0.00038041075,0.00035350025,0.0010262448],"category_scores_gemma":[0.0008975763,0.00014969245,0.00045423408,0.00039272537,0.0003139551,0.000738126,0.00024321406,0.00033554935,0.00028204347],"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":[0.0005069287,0.00015748914,0.0029601608,0.0003342729,0.00008125314,0.00018558596,0.00028831998,0.06566219,0.44813108,0.0057685752,0.0008387386,0.4750854],"study_design_scores_gemma":[0.000039583312,0.00039948913,0.005422852,0.00003165007,0.00006232747,0.00057221396,0.000087636356,0.766669,0.22067302,0.0015474773,0.004449665,0.000045100835],"about_ca_topic_score_codex":0.0007670829,"about_ca_topic_score_gemma":0.0007906842,"teacher_disagreement_score":0.0010262448,"about_ca_system_score_codex":0.0002726127,"about_ca_system_score_gemma":0.0003147985,"threshold_uncertainty_score":0.0034331083},"labels":[],"label_agreement":null},{"id":"W4298108646","doi":"10.1155/2022/2160044","title":"Contrastive Learning-Based Haze Visibility Enhancement in Intelligent Maritime Transportation System","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Haze; Visibility; Intelligent transportation system; Computer science; Transport engineering; Artificial intelligence; Engineering; Meteorology; Geography","score_opus":0.006977003426466522,"score_gpt":0.2464950726563257,"score_spread":0.2395180692298592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298108646","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":[0.26916584,0.0008232396,0.7253781,0.00028347364,0.00007887341,0.00007389494,0.00007689744,0.0008413504,0.003278413],"genre_scores_gemma":[0.90170884,0.00025826762,0.095872454,0.000094579846,0.000035103916,0.000030303398,0.00010627005,0.00003287252,0.0018613474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998468,0.000021030843,0.0000073097303,0.00004415274,0.000052653857,0.000027993201],"domain_scores_gemma":[0.9998159,0.00004625065,0.000026545842,0.000019170024,0.00007736742,0.000014788744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038862237,0.00038762944,0.00047871692,0.00043645318,0.00018205337,0.00040894578,0.0006685669,0.00043678674,0.00056565943],"category_scores_gemma":[0.0006471395,0.00017930917,0.00044011872,0.00024130617,0.00031196975,0.0006699698,0.00043643924,0.00051089155,0.00014998221],"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":[0.0005411882,0.0002769697,0.003226764,0.0001576904,0.000085260406,0.00022679642,0.00015475933,0.52445036,0.14903465,0.002396901,0.0017780451,0.31767055],"study_design_scores_gemma":[0.0000074511604,0.00006319687,0.000621013,0.0000024567523,0.00001149706,0.000029206127,0.00000815119,0.9874449,0.011182441,0.00033280873,0.00029064575,0.000006267952],"about_ca_topic_score_codex":0.004279629,"about_ca_topic_score_gemma":0.003209203,"teacher_disagreement_score":0.004279629,"about_ca_system_score_codex":0.000489972,"about_ca_system_score_gemma":0.0004656794,"threshold_uncertainty_score":0.008509457},"labels":[],"label_agreement":null},{"id":"W4306411318","doi":"10.1016/j.knosys.2022.109997","title":"Degradation-aware and color-corrected network for underwater image enhancement","year":2022,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Underwater; Computer science; Artificial intelligence; Degradation (telecommunications); Computer vision; Image restoration; Sharpening; Channel (broadcasting); Convolutional neural network; Image (mathematics); Image processing; Geology","score_opus":0.016825293554553807,"score_gpt":0.25855726817390573,"score_spread":0.24173197461935192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306411318","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":[0.16248775,0.00086707075,0.8300674,0.00022451735,0.00008863314,0.000034563927,0.000091216185,0.000677318,0.005461462],"genre_scores_gemma":[0.81875294,0.0008263379,0.17197348,0.00009390243,0.00004138626,0.000020333036,0.00013472638,0.000043465803,0.008113452],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992645,0.000011491001,0.0000028383936,0.00001927025,0.000028962742,0.000010954567],"domain_scores_gemma":[0.99984956,0.000037143152,0.000021130558,0.00002453009,0.000059706694,0.000007987319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001677957,0.00029282487,0.00017920781,0.00022776495,0.00017430185,0.00028933762,0.00029473254,0.00026988052,0.00090929936],"category_scores_gemma":[0.00041154787,0.00009192164,0.00010918357,0.00023051244,0.00018715087,0.0007120055,0.00034159893,0.00030638385,0.00024597193],"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":[0.00049892603,0.00013921015,0.0017379792,0.00013444017,0.000041119452,0.0002443079,0.00013423801,0.06047213,0.5005773,0.0049631447,0.0022784167,0.42877883],"study_design_scores_gemma":[0.000013230187,0.00015692196,0.0023512573,0.000019354937,0.000053484033,0.00034258206,0.0000738169,0.75816256,0.2320517,0.0022808474,0.0044709067,0.00002328665],"about_ca_topic_score_codex":0.0017512451,"about_ca_topic_score_gemma":0.0034108008,"teacher_disagreement_score":0.0017512451,"about_ca_system_score_codex":0.00026041453,"about_ca_system_score_gemma":0.00026382602,"threshold_uncertainty_score":0.0034821033},"labels":[],"label_agreement":null},{"id":"W4306651443","doi":"10.3390/app122010453","title":"X-ray Image Enhancement Based on Adaptive Gradient Domain Guided Image Filtering","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Image Enhancement 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":"McGill University","funders":"","keywords":"Image (mathematics); Computer science; Contrast enhancement; Image enhancement; Artificial intelligence; Image contrast; Edge enhancement; Contrast (vision); Domain (mathematical analysis); Image gradient; Algorithm; Enhanced Data Rates for GSM Evolution; Computer vision; Edge detection; Pattern recognition (psychology); Mathematics; Image processing","score_opus":0.021987860540974128,"score_gpt":0.26320707237209373,"score_spread":0.2412192118311196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306651443","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":[0.021451857,0.0004081142,0.976607,0.000058972324,0.000032856897,0.000036941823,0.000011754848,0.00040445232,0.0009880268],"genre_scores_gemma":[0.20440277,0.0007908199,0.79098105,0.0001006441,0.00004386136,0.000055285702,0.00006367006,0.000064876076,0.0034970278],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981207,0.000026507743,0.000012218303,0.000040709856,0.00009091964,0.000017522887],"domain_scores_gemma":[0.99978215,0.000069770336,0.000030029807,0.000025338812,0.00008186889,0.000010859834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029783265,0.0004660444,0.00046869472,0.0005189318,0.00019011436,0.00043439565,0.0005088089,0.0005499987,0.00092288334],"category_scores_gemma":[0.00051085744,0.00018967057,0.00047842224,0.0003641805,0.0003069803,0.0006310672,0.00030941702,0.00043360173,0.0004327649],"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":[0.00029259108,0.000093330884,0.0010338076,0.00020921724,0.00006146398,0.00024592204,0.00011595295,0.024325902,0.61221683,0.004071518,0.0010666538,0.35626686],"study_design_scores_gemma":[0.000046194302,0.0003192667,0.002957249,0.00003086977,0.00009018316,0.0018181304,0.000040464012,0.61771977,0.36630407,0.0015874252,0.009028359,0.000058035534],"about_ca_topic_score_codex":0.00060182187,"about_ca_topic_score_gemma":0.000715809,"teacher_disagreement_score":0.00092288334,"about_ca_system_score_codex":0.00018098477,"about_ca_system_score_gemma":0.0002726665,"threshold_uncertainty_score":0.0030873418},"labels":[],"label_agreement":null},{"id":"W4310431166","doi":"","title":"Effet du débit sur le bruit propre d’un étage soufflante/redresseur en présence d’une bulle de recirculation","year":2022,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Image Enhancement 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":"Safran Electronics (Canada)","funders":"","keywords":"Physics; Computer science","score_opus":0.015109916659468033,"score_gpt":0.22358246662944894,"score_spread":0.2084725499699809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310431166","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":[0.99369603,0.00059655646,0.0028660882,0.0001623155,0.00016930148,0.000011237514,0.000072214425,0.00024326067,0.0021828986],"genre_scores_gemma":[0.9898241,0.00022833626,0.0012878977,0.00006653778,0.000027862185,0.000010394552,0.00008689004,0.00010728331,0.008360713],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999696,0.000048102345,0.000011269522,0.00006421569,0.00008502055,0.00009540565],"domain_scores_gemma":[0.99758744,0.0015639297,0.00014380799,0.00012524426,0.0003271571,0.0002524318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039052792,0.00045361771,0.00041184906,0.00029501953,0.0004522613,0.0007578192,0.00039923214,0.0013877269,0.012151404],"category_scores_gemma":[0.0017406248,0.0003029249,0.00032496397,0.0001633005,0.000409353,0.00054110005,0.00038461149,0.0007346289,0.0012462082],"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":[0.011736038,0.00034279676,0.0017548557,0.00019668236,0.00005254938,0.0005594867,0.00029823484,0.004547369,0.95216936,0.00024349902,0.0008295951,0.027269688],"study_design_scores_gemma":[0.00020652568,0.0061535966,0.03511027,0.00005853827,0.00017030725,0.0005376627,0.0006481493,0.025292091,0.92577076,0.00023957236,0.0057197367,0.000092895796],"about_ca_topic_score_codex":0.0029724767,"about_ca_topic_score_gemma":0.0017441697,"teacher_disagreement_score":0.012151404,"about_ca_system_score_codex":0.00021885677,"about_ca_system_score_gemma":0.00014321465,"threshold_uncertainty_score":0.040650427},"labels":[],"label_agreement":null},{"id":"W4312939004","doi":"10.1007/978-3-031-22061-6_30","title":"Lighting Enhancement Using Self-attention Guided HDR Reconstruction","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Computer vision; Computer graphics (images); Psychology","score_opus":0.021444870988904983,"score_gpt":0.2659043081285082,"score_spread":0.2444594371396032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312939004","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":[0.03579424,0.0005654801,0.95264566,0.00009377044,0.000098788885,0.000043724234,0.00011450993,0.0016448069,0.008999013],"genre_scores_gemma":[0.2608024,0.0012170356,0.71880037,0.00019045264,0.00009747354,0.000040085855,0.00039838007,0.0006161648,0.017837698],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989927,0.000013109531,0.00000418199,0.000020555777,0.000047156635,0.000015654989],"domain_scores_gemma":[0.9998419,0.000046779256,0.0000151251525,0.00004325714,0.00004284579,0.000010076302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015232904,0.00050770625,0.0003658597,0.00043681465,0.0001481923,0.0005565894,0.00039187758,0.00035285146,0.0060652206],"category_scores_gemma":[0.00032390398,0.00024810067,0.00042664932,0.00038532898,0.00020785179,0.00052373327,0.00050558185,0.00053456164,0.0015696163],"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":[0.0002952734,0.00008663538,0.00044033217,0.0002151087,0.00004071729,0.00015733323,0.000085485786,0.010762053,0.6007924,0.004012204,0.0034250808,0.37968728],"study_design_scores_gemma":[0.000034672266,0.00014705746,0.0024553444,0.000043441472,0.00009751613,0.001234417,0.000055195644,0.29902232,0.67240244,0.002031313,0.022433486,0.000042666372],"about_ca_topic_score_codex":0.00047181168,"about_ca_topic_score_gemma":0.0007638726,"teacher_disagreement_score":0.0060652206,"about_ca_system_score_codex":0.00016094402,"about_ca_system_score_gemma":0.000182881,"threshold_uncertainty_score":0.020290196},"labels":[],"label_agreement":null},{"id":"W4313064273","doi":"10.1109/tits.2022.3210455","title":"GridDehazeNet+: An Enhanced Multi-Scale Network With Intra-Task Knowledge Transfer for Single Image Dehazing","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Bottleneck; Artificial intelligence; Task (project management); Process (computing); Block (permutation group theory); Grid; Synthetic data; Transfer of learning; Domain (mathematical analysis); Machine learning","score_opus":0.029485905267752154,"score_gpt":0.2707081033393839,"score_spread":0.24122219807163173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313064273","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":[0.072455466,0.0004512008,0.91881627,0.00032291445,0.00013382727,0.000112820424,0.00025390022,0.0036054694,0.0038481285],"genre_scores_gemma":[0.6838289,0.00030856975,0.30443114,0.00041940177,0.000067167115,0.0002045332,0.0009912627,0.00027177582,0.009477305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998522,0.000015951517,0.000005380262,0.00005654117,0.000039547078,0.000030310754],"domain_scores_gemma":[0.99971455,0.0000838629,0.00002944008,0.000079671496,0.0000653376,0.000027088334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039759706,0.0007272956,0.00047915333,0.00036425516,0.00029155597,0.00047101584,0.0018075394,0.00094161247,0.0021325904],"category_scores_gemma":[0.0012582672,0.000351008,0.0004956349,0.00033472345,0.00040229995,0.001429271,0.001502114,0.0010768853,0.0004895257],"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":[0.00026616055,0.0002587552,0.0016955336,0.00011994691,0.0001325131,0.00019774301,0.00011674672,0.5825778,0.032555237,0.0036317026,0.007099856,0.371348],"study_design_scores_gemma":[0.000010810729,0.000041657317,0.0002659586,0.0000034845725,0.000010339858,0.000027836082,0.000009209381,0.99222296,0.0045868326,0.0015873718,0.0012257131,0.0000078051135],"about_ca_topic_score_codex":0.0058926805,"about_ca_topic_score_gemma":0.009604267,"teacher_disagreement_score":0.0058926805,"about_ca_system_score_codex":0.0005205324,"about_ca_system_score_gemma":0.000563674,"threshold_uncertainty_score":0.011716783},"labels":[],"label_agreement":null},{"id":"W4315630387","doi":"10.1109/globecom48099.2022.10000829","title":"Object-Based Resolution Selection for Efficient Edge-Assisted Multi-Task Video Analytics","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Image Enhancement 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":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Analytics; Video tracking; Bandwidth (computing); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Real-time computing; Computer vision; Latency (audio); Task (project management); Video processing; Computation; Data mining; Algorithm; Computer network; Telecommunications","score_opus":0.05635500627788053,"score_gpt":0.32118535474640997,"score_spread":0.26483034846852943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315630387","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":[0.02835276,0.0002652538,0.9693996,0.000059445207,0.000022081951,0.000033728644,0.000025661895,0.0005044864,0.0013370153],"genre_scores_gemma":[0.47651204,0.00025136585,0.5207119,0.000107870634,0.000046404228,0.000057467067,0.00013707342,0.0001142882,0.002061604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997094,0.000049979622,0.000016598826,0.00006606546,0.00011282949,0.000045026747],"domain_scores_gemma":[0.9996166,0.00012920574,0.000052197414,0.000072634015,0.000098838704,0.000030580984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004173896,0.0005293614,0.00041707058,0.0005140849,0.0002630908,0.0006739981,0.0008758178,0.00047019825,0.0010699965],"category_scores_gemma":[0.0013107137,0.00020380771,0.00026605977,0.00045940836,0.0002456888,0.0011430864,0.0007460635,0.00061774853,0.00048251063],"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":[0.00053920725,0.00022732018,0.002855702,0.000125514,0.000063797735,0.0002933098,0.00021352281,0.14266025,0.1729414,0.0084188925,0.0036942353,0.6679669],"study_design_scores_gemma":[0.000017684239,0.00007766802,0.0007752719,0.00000805892,0.000014569482,0.00017317083,0.0000378336,0.9524194,0.04207106,0.002449638,0.0019379193,0.000017767681],"about_ca_topic_score_codex":0.000766399,"about_ca_topic_score_gemma":0.0011187887,"teacher_disagreement_score":0.0010699965,"about_ca_system_score_codex":0.00025269482,"about_ca_system_score_gemma":0.00033237,"threshold_uncertainty_score":0.0035794973},"labels":[],"label_agreement":null},{"id":"W4315777911","doi":"10.1109/icsc56524.2022.10009340","title":"Performance Analysis of Conditional GANs based Image-to-Image Translation Models for Low-Light Image Enhancement","year":2022,"lang":"en","type":"article","venue":"2022 8th International Conference on Signal Processing and Communication (ICSC)","topic":"Image Enhancement 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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Image quality; Image processing; Digital image; Preprocessor; Image (mathematics); Computer vision; Pattern recognition (psychology)","score_opus":0.04581721029550958,"score_gpt":0.31114888270770824,"score_spread":0.2653316724121987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315777911","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":[0.21416341,0.00429621,0.75812346,0.0011410747,0.00025114202,0.00014938807,0.00046717914,0.0066140774,0.01479405],"genre_scores_gemma":[0.91097987,0.0008475484,0.0809053,0.00038846838,0.00004741929,0.00006931545,0.000796805,0.00028546425,0.005679716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996674,0.00009227889,0.000014611131,0.00007818824,0.000088641405,0.000058865342],"domain_scores_gemma":[0.999278,0.00039440044,0.00005752133,0.00008116636,0.00014635702,0.00004245331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012893535,0.0011561365,0.0006570397,0.00038980096,0.00021035972,0.00080747553,0.0011473049,0.0007884481,0.0025905038],"category_scores_gemma":[0.0022931297,0.0003288867,0.00075737847,0.00027910926,0.0004972061,0.0009226345,0.0008083355,0.001537068,0.00054058863],"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":[0.00040434118,0.00011202822,0.0014427415,0.0001575928,0.00012276781,0.00009640494,0.000038496408,0.89172095,0.0071731084,0.0042927135,0.0027981116,0.09164077],"study_design_scores_gemma":[0.0000031775808,0.000030330102,0.00010072243,0.0000040950017,0.0000074550235,0.000012596818,0.0000028265924,0.9976635,0.0016638135,0.00038365112,0.00012513358,0.0000027060084],"about_ca_topic_score_codex":0.0058637895,"about_ca_topic_score_gemma":0.005220252,"teacher_disagreement_score":0.0058637895,"about_ca_system_score_codex":0.00096196966,"about_ca_system_score_gemma":0.000783023,"threshold_uncertainty_score":0.011659324},"labels":[],"label_agreement":null},{"id":"W4315781033","doi":"10.3390/app13021034","title":"Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Kernel (algebra); Residual; Computer vision; Algorithm; Mathematics","score_opus":0.02785724476311708,"score_gpt":0.2849439010123938,"score_spread":0.25708665624927673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315781033","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":[0.20809156,0.0010928495,0.77593374,0.00026364197,0.00017707564,0.00014072066,0.00042155027,0.007018746,0.00686012],"genre_scores_gemma":[0.645601,0.00057662325,0.34491503,0.00031539073,0.00005337417,0.0000766002,0.0008759885,0.0003977107,0.0071883267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998037,0.000015761678,0.000007931753,0.00007271035,0.000061591934,0.000038223578],"domain_scores_gemma":[0.9997911,0.00003083491,0.000029209894,0.000051361858,0.000075314165,0.000022240585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043902255,0.00082605885,0.00069692836,0.00071785395,0.00019752902,0.0005193548,0.001004871,0.00040186103,0.001251532],"category_scores_gemma":[0.000548554,0.00023981121,0.00076911337,0.0003710351,0.00037900606,0.00093986775,0.0007888993,0.0006560254,0.0005354249],"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":[0.0007575475,0.0003202451,0.0033500658,0.0003186174,0.00018660484,0.00028553273,0.00014154374,0.13139275,0.32973826,0.002948235,0.0053195213,0.5252411],"study_design_scores_gemma":[0.000029000497,0.00017408899,0.0022815631,0.000018624754,0.00007878847,0.0001930302,0.000033148935,0.85414785,0.13734326,0.0016699694,0.0039987005,0.000032104548],"about_ca_topic_score_codex":0.0028459462,"about_ca_topic_score_gemma":0.0051254258,"teacher_disagreement_score":0.0028459462,"about_ca_system_score_codex":0.0004566096,"about_ca_system_score_gemma":0.0005007994,"threshold_uncertainty_score":0.0056587458},"labels":[],"label_agreement":null},{"id":"W4317555115","doi":"10.1109/pcs56426.2022.10018035","title":"Rate-Distortion in Image Coding for Machines","year":2022,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"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":"","keywords":"Computer science; Codec; Scalability; Artificial intelligence; Coding (social sciences); Server; Machine learning; Computer vision; Computer engineering; Computer hardware; Computer network","score_opus":0.013444650824426543,"score_gpt":0.273831382027988,"score_spread":0.2603867312035615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317555115","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":[0.013897343,0.0026541837,0.9711329,0.0014156556,0.00020976724,0.00007723311,0.000113418624,0.00044346406,0.0100560505],"genre_scores_gemma":[0.50883883,0.0037370897,0.47072297,0.0009596085,0.00042363923,0.00048350397,0.0002922231,0.0004648296,0.014077194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976405,0.0008067901,0.00013996314,0.00036271306,0.0008570766,0.00019294553],"domain_scores_gemma":[0.9909293,0.005985978,0.00042168316,0.0016775375,0.00088991615,0.000095589356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004135164,0.0012019319,0.0008692485,0.0007667763,0.00047297703,0.0017507293,0.0016325356,0.001706624,0.004537891],"category_scores_gemma":[0.021102006,0.00041687684,0.0006754266,0.00093366066,0.002747008,0.0036855077,0.0021178902,0.0037555797,0.0014858348],"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":[0.00032710892,0.00006330341,0.0006498281,0.00031298285,0.000059255184,0.00019741202,0.00024452794,0.2737149,0.01161573,0.58804166,0.005852261,0.11892108],"study_design_scores_gemma":[0.000029644054,0.000114427,0.00027305435,0.00008118084,0.000013848065,0.00022512909,0.000036845362,0.77912587,0.010569742,0.20378542,0.005713556,0.000031330852],"about_ca_topic_score_codex":0.0023250831,"about_ca_topic_score_gemma":0.0015213477,"teacher_disagreement_score":0.004537891,"about_ca_system_score_codex":0.0020949265,"about_ca_system_score_gemma":0.0011335971,"threshold_uncertainty_score":0.021869063},"labels":[],"label_agreement":null},{"id":"W4319336357","doi":"10.1109/wacvw58289.2023.00026","title":"An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"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 Victoria","funders":"","keywords":"Deblurring; Underwater; Computer science; Benchmark (surveying); Artificial intelligence; Color correction; Computer vision; Image restoration; Encoder; Image quality; Encoding (memory); Image (mathematics); Image processing","score_opus":0.012789851811987706,"score_gpt":0.26966348382604344,"score_spread":0.2568736320140557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319336357","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":[0.03097842,0.00082876463,0.96035534,0.00026703463,0.00011145724,0.00009367997,0.00016763512,0.0041260957,0.0030716376],"genre_scores_gemma":[0.21191174,0.0007298735,0.7765283,0.00026578555,0.00005089906,0.00006748305,0.00061550754,0.0003709153,0.009459449],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997197,0.000025762925,0.000012113004,0.00008028201,0.00012124845,0.00004091946],"domain_scores_gemma":[0.99964714,0.00006143107,0.000029149927,0.00009558467,0.00014498079,0.000021611335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004178052,0.0010192188,0.00065273314,0.0005435479,0.00028239863,0.00060391537,0.0011123678,0.00072655984,0.0024323703],"category_scores_gemma":[0.0010637657,0.0003210014,0.00057379977,0.00036616676,0.00040637082,0.0011245164,0.0011154058,0.0014010665,0.0012879727],"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":[0.00024185936,0.00013839998,0.001084412,0.00023029395,0.0000995017,0.000116712865,0.0000754592,0.036008086,0.22262318,0.003246619,0.0060224948,0.73011297],"study_design_scores_gemma":[0.00002978634,0.00021436818,0.0015360069,0.000039099596,0.00008596257,0.00036899975,0.0000432343,0.7366358,0.24733664,0.0028664838,0.010807968,0.000035657195],"about_ca_topic_score_codex":0.0037393812,"about_ca_topic_score_gemma":0.009535911,"teacher_disagreement_score":0.0037393812,"about_ca_system_score_codex":0.000502296,"about_ca_system_score_gemma":0.00073273794,"threshold_uncertainty_score":0.008137107},"labels":[],"label_agreement":null},{"id":"W4319599285","doi":"10.2352/cic.2022.30.1.23","title":"A 360&amp;#xB0; Omnidirectional Photometer using a Ricoh Theta Z1","year":2022,"lang":"en","type":"article","venue":"Color and Imaging Conference","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Luminance; Photometer; Omnidirectional antenna; Computer vision; Computer science; Calibration; Optics; Artificial intelligence; Computer graphics (images); Metre; Remote sensing; Physics; Geography; Telecommunications","score_opus":0.0383423169572753,"score_gpt":0.2800574390001231,"score_spread":0.24171512204284779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319599285","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":[0.14835955,0.00079675857,0.80980194,0.00039385917,0.00023096622,0.0009862761,0.0015893655,0.012515518,0.025325784],"genre_scores_gemma":[0.26625192,0.0006391975,0.7179937,0.00038409932,0.000042108266,0.00036055915,0.0007984419,0.00052882964,0.01300106],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990368,0.0001226457,0.00004259508,0.0002912359,0.00043679465,0.00006991314],"domain_scores_gemma":[0.99905473,0.00012555905,0.00013123335,0.00022788077,0.00038974333,0.00007086412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006539047,0.00062012556,0.00044799648,0.0009743608,0.00021363178,0.0010022268,0.0011879478,0.0005536215,0.0085896095],"category_scores_gemma":[0.0015249664,0.0004797644,0.00040744414,0.00086620764,0.0003760513,0.0013450416,0.00085108826,0.00067540497,0.0033356054],"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":[0.00033044736,0.00017222187,0.0058922744,0.0005510075,0.000062520296,0.00016249629,0.00021211428,0.0016069928,0.6931477,0.0033146509,0.008473502,0.28607407],"study_design_scores_gemma":[0.000088194945,0.00085338316,0.02635239,0.000108052096,0.000102454665,0.0019185793,0.00018836411,0.027771983,0.87563217,0.00066236634,0.06612981,0.00019207926],"about_ca_topic_score_codex":0.0015864847,"about_ca_topic_score_gemma":0.0023454195,"teacher_disagreement_score":0.0085896095,"about_ca_system_score_codex":0.0005817932,"about_ca_system_score_gemma":0.00063276774,"threshold_uncertainty_score":0.028735042},"labels":[],"label_agreement":null},{"id":"W4321192579","doi":"10.1109/icce56470.2023.10043487","title":"Investigating Suitability of Inverse Tone Mapping for Medical Images","year":2023,"lang":"en","type":"article","venue":"2023 IEEE International Conference on Consumer Electronics (ICCE)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Contrast (vision); Tone mapping; Computer vision; Artificial intelligence; Medical imaging; Brightness; Process (computing); Contrast enhancement; Image quality; High dynamic range; Dynamic range; Image (mathematics); Radiology; Medicine","score_opus":0.07035064885530755,"score_gpt":0.3616351436788329,"score_spread":0.29128449482352536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321192579","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":[0.87447613,0.0016643049,0.11741518,0.00024906965,0.000123894,0.00013778484,0.00012375691,0.0004311357,0.0053788144],"genre_scores_gemma":[0.9541491,0.0010322011,0.043003112,0.00006947495,0.00004377132,0.000034558238,0.00012675064,0.00007836244,0.0014627674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944097,0.00014979477,0.000032118594,0.00011082653,0.00020845793,0.000057765297],"domain_scores_gemma":[0.9975495,0.0015053174,0.0001453581,0.00017472108,0.00055781007,0.00006735044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000886929,0.00039810623,0.00021963022,0.000720981,0.00019557899,0.0008855691,0.00028290527,0.0005800168,0.0015916267],"category_scores_gemma":[0.007080896,0.00013852038,0.00027646954,0.00046101576,0.00035266113,0.00081567257,0.00033215177,0.000274035,0.0004206057],"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":[0.0019395635,0.0002100702,0.008694797,0.000805293,0.00009194308,0.0006789834,0.00046888491,0.025429958,0.73914164,0.0017838539,0.0006716864,0.22008339],"study_design_scores_gemma":[0.000057086225,0.004164675,0.030652484,0.00009742418,0.00022631978,0.0035440815,0.0007062434,0.30153534,0.65253437,0.0015635997,0.0048084445,0.00010996011],"about_ca_topic_score_codex":0.0006331843,"about_ca_topic_score_gemma":0.0004028117,"teacher_disagreement_score":0.0015916267,"about_ca_system_score_codex":0.00015527733,"about_ca_system_score_gemma":0.00013220122,"threshold_uncertainty_score":0.005324483},"labels":[],"label_agreement":null},{"id":"W4321789582","doi":"10.3390/s23052516","title":"Tone Mapping Operator for High Dynamic Range Images Based on Modified iCAM06","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Shenzhen Science and Technology Innovation Program; National Natural Science Foundation of China","keywords":"Tone mapping; Hue; High dynamic range; Computer science; Computer vision; Gamma correction; Artificial intelligence; Operator (biology); Tone (literature); Compensation (psychology); Dynamic range; High-dynamic-range imaging; Reduction (mathematics); Range (aeronautics); Image (mathematics); Scale (ratio); Mathematics; Engineering; Geography","score_opus":0.01933495047820745,"score_gpt":0.2868652313833108,"score_spread":0.2675302809051034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321789582","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":[0.0716332,0.00011401008,0.92621523,0.000059894544,0.00005695502,0.00006095598,0.00001645726,0.00039580872,0.0014474913],"genre_scores_gemma":[0.43341184,0.00020769422,0.56351745,0.000100450925,0.00004914715,0.00006573261,0.00007642982,0.00006784584,0.002503416],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997811,0.0000354409,0.00001294192,0.000043376698,0.00010850417,0.000018694074],"domain_scores_gemma":[0.99966776,0.000094956464,0.000040418738,0.000063571344,0.00010527947,0.000028074242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003726054,0.00032053379,0.00023183622,0.00030306526,0.0001398806,0.00042324376,0.00045786728,0.00032830873,0.0013386747],"category_scores_gemma":[0.0009440212,0.000095192176,0.0003263191,0.00025738333,0.0002631254,0.0006567705,0.00041880624,0.00049156207,0.00024998837],"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":[0.00049350323,0.00012280824,0.0020817663,0.00021503534,0.000040610787,0.0002371883,0.00022222052,0.020629007,0.50319916,0.009411505,0.0014491505,0.461898],"study_design_scores_gemma":[0.000055430068,0.00077996054,0.005019442,0.000022191078,0.00005953134,0.0014284627,0.00012968906,0.76396185,0.21440226,0.003101654,0.010979495,0.00006007445],"about_ca_topic_score_codex":0.00033184444,"about_ca_topic_score_gemma":0.00039572624,"teacher_disagreement_score":0.0013386747,"about_ca_system_score_codex":0.00012652947,"about_ca_system_score_gemma":0.00018176352,"threshold_uncertainty_score":0.004478276},"labels":[],"label_agreement":null},{"id":"W4328108583","doi":"10.1016/j.neunet.2023.03.021","title":"Adams-based hierarchical features fusion network for image dehazing","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Ode; Computer science; Block (permutation group theory); Ordinary differential equation; Image (mathematics); Feature (linguistics); Euler method; Artificial neural network; Artificial intelligence; Algorithm; Euler's formula; Backward Euler method; Differential equation; Euler equations; Mathematics; Applied mathematics","score_opus":0.014794505588443606,"score_gpt":0.2765292793257823,"score_spread":0.2617347737373387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328108583","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":[0.045660533,0.00037640118,0.95132935,0.0000895381,0.00006386337,0.00005009429,0.000069056056,0.0005777628,0.0017833995],"genre_scores_gemma":[0.7410125,0.00039118176,0.25097916,0.00009222039,0.000045423145,0.00007027201,0.00024796047,0.00005804957,0.007103214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998129,0.000017711713,0.0000112985945,0.000042667336,0.00008840549,0.000026959913],"domain_scores_gemma":[0.9997981,0.000036093268,0.000018319703,0.000021816933,0.000115227245,0.000010365488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037349144,0.0003961737,0.00052499276,0.00044798345,0.00033933026,0.0003465556,0.00082279823,0.00052901614,0.0018896231],"category_scores_gemma":[0.0005479285,0.0002371206,0.00043142957,0.0004517363,0.00021947951,0.00083175645,0.0006647973,0.0006517246,0.00037590184],"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":[0.00042315497,0.00021446504,0.0014313665,0.000092642964,0.00011292873,0.000090786656,0.00007188942,0.17025775,0.07265201,0.0067799767,0.0034584836,0.74441445],"study_design_scores_gemma":[0.000005308233,0.000052135543,0.00046302725,0.000002733557,0.000020777936,0.000026260386,0.000006514299,0.98829144,0.009892315,0.0007057791,0.0005271957,0.000006466033],"about_ca_topic_score_codex":0.006142218,"about_ca_topic_score_gemma":0.008351524,"teacher_disagreement_score":0.006142218,"about_ca_system_score_codex":0.00045617673,"about_ca_system_score_gemma":0.000595652,"threshold_uncertainty_score":0.012212932},"labels":[],"label_agreement":null},{"id":"W4352977397","doi":"10.1109/tcsvt.2023.3260025","title":"Sampling Propagation Attention With Trimap Generation Network for Natural Image Matting","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Image Enhancement 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":"McGill University","funders":"Shenzhen Science and Technology Innovation Program; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Sampling (signal processing); Image segmentation; Image (mathematics); Pattern recognition (psychology)","score_opus":0.03357188084469661,"score_gpt":0.2777539786086095,"score_spread":0.2441820977639129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4352977397","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":[0.040158402,0.0005608289,0.94894314,0.00029417788,0.000121463134,0.000086485335,0.0002079241,0.0062256083,0.0034019726],"genre_scores_gemma":[0.68701005,0.00046539024,0.29816785,0.00073127117,0.00015917407,0.00020656765,0.0011429982,0.00049396785,0.011622628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974626,0.000026572967,0.000009680279,0.00010932614,0.000058397258,0.00004977094],"domain_scores_gemma":[0.9995776,0.000121760495,0.000052346917,0.00007595403,0.00013086226,0.000041548155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053287623,0.0013313403,0.0006758026,0.00076958514,0.00052071776,0.0006705847,0.0021535412,0.0012558169,0.0041216463],"category_scores_gemma":[0.0016295437,0.00051502214,0.0008448714,0.0005934176,0.0007879902,0.0013951375,0.0012270737,0.001588247,0.0009134337],"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":[0.00028221236,0.00016823906,0.002280994,0.00017435382,0.00012414153,0.0004056056,0.00022814446,0.41120502,0.035760503,0.011084327,0.010004152,0.5282823],"study_design_scores_gemma":[0.0000050112035,0.00003841679,0.00024169315,0.0000058036144,0.000017803522,0.00004556415,0.000008335514,0.9899714,0.0056908014,0.003108653,0.0008587348,0.000007725517],"about_ca_topic_score_codex":0.0077697695,"about_ca_topic_score_gemma":0.011686999,"teacher_disagreement_score":0.0077697695,"about_ca_system_score_codex":0.0010429234,"about_ca_system_score_gemma":0.0008079215,"threshold_uncertainty_score":0.015449047},"labels":[],"label_agreement":null},{"id":"W4353047582","doi":"10.1016/j.asoc.2023.110204","title":"High-order Adams Network (HIAN) for image dehazing","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Ode; Euler's formula; Residual; Convolutional neural network; Convergence (economics); Image (mathematics); Ordinary differential equation; Stability (learning theory); Artificial intelligence; Backward Euler method; Artificial neural network; Algorithm; Machine learning; Applied mathematics; Euler equations; Differential equation; Mathematics","score_opus":0.01183653076091093,"score_gpt":0.2559113424535329,"score_spread":0.244074811692622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353047582","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":[0.014337923,0.0001737891,0.981948,0.0000785472,0.00008393904,0.00003239603,0.00006736566,0.0004192032,0.002858813],"genre_scores_gemma":[0.4545038,0.00047901832,0.5253886,0.00010669514,0.000081396814,0.00013059114,0.0003366919,0.00012806787,0.018845154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998574,0.000036101053,0.000007381852,0.000024312245,0.000058478046,0.000016190412],"domain_scores_gemma":[0.9996966,0.000103862396,0.000025251515,0.000048865564,0.000104992934,0.000020425307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037174602,0.00035780232,0.00033085316,0.00033672914,0.0003207866,0.0003915508,0.000624675,0.00046646822,0.0038688541],"category_scores_gemma":[0.00092586555,0.00016201382,0.00021850025,0.00034519593,0.00023840391,0.0005239163,0.00055542326,0.0008535494,0.0008735233],"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":[0.000369715,0.00014638412,0.0016457397,0.0001555166,0.000050082555,0.0001249699,0.00009790794,0.42732534,0.0200279,0.046689015,0.0065121897,0.49685526],"study_design_scores_gemma":[0.0000024942717,0.000018247638,0.000108837055,0.0000027839415,0.0000029000275,0.000015172439,0.0000046345845,0.99424815,0.0023128367,0.002018389,0.0012624591,0.0000031680981],"about_ca_topic_score_codex":0.0037147172,"about_ca_topic_score_gemma":0.006008301,"teacher_disagreement_score":0.0038688541,"about_ca_system_score_codex":0.0003038105,"about_ca_system_score_gemma":0.00048134473,"threshold_uncertainty_score":0.012942612},"labels":[],"label_agreement":null},{"id":"W4353100361","doi":"10.18280/ts.400127","title":"Rapid Classification of Massive Images Based on Cloud Computing Platform","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Computer science; Data science; Operating system","score_opus":0.03663024007049511,"score_gpt":0.27225832522455606,"score_spread":0.23562808515406095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353100361","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":[0.11612607,0.0003085589,0.88003314,0.00031543395,0.000092930146,0.00011300052,0.00005866869,0.000732746,0.002219423],"genre_scores_gemma":[0.86644053,0.00027367737,0.13090922,0.00008894066,0.00006105828,0.00005257417,0.00012704101,0.000046575868,0.0020003559],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994765,0.000060732345,0.00002058908,0.00013002566,0.00022316127,0.00008897822],"domain_scores_gemma":[0.9995419,0.00010708554,0.00006350783,0.00006610285,0.00018116682,0.000040360104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005163752,0.0005808372,0.0006167923,0.001008504,0.0005782339,0.00091143535,0.0007894111,0.00055551383,0.00072630483],"category_scores_gemma":[0.0012642641,0.00019659517,0.00054214185,0.0007036441,0.0004541161,0.0019715887,0.00072945957,0.0007904574,0.00024338247],"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":[0.00065654295,0.00026436284,0.010153895,0.00015405183,0.000090167705,0.00075793796,0.00035306526,0.27622697,0.14812511,0.018288465,0.0040858625,0.5408436],"study_design_scores_gemma":[0.000004407996,0.000034436976,0.0012968369,0.0000042154793,0.000008581324,0.000070039445,0.00004106896,0.98268986,0.012523642,0.0026727922,0.0006447126,0.000009394356],"about_ca_topic_score_codex":0.0040554395,"about_ca_topic_score_gemma":0.003023202,"teacher_disagreement_score":0.0040554395,"about_ca_system_score_codex":0.00075836206,"about_ca_system_score_gemma":0.0005815407,"threshold_uncertainty_score":0.008063674},"labels":[],"label_agreement":null},{"id":"W4360605076","doi":"10.1109/icnc57223.2023.10074176","title":"A Generative Adversarial Network Based Tone Mapping Operator for 4K HDR Images","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tone mapping; High dynamic range; Computer science; Deep learning; Artificial intelligence; Process (computing); Representation (politics); Code (set theory); Range (aeronautics); Computer vision; Dynamic range","score_opus":0.02743570306176923,"score_gpt":0.29948705548719795,"score_spread":0.2720513524254287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360605076","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":[0.074524365,0.00071814016,0.9141787,0.0004969414,0.0002130883,0.0001696817,0.0005954182,0.0026457212,0.006457963],"genre_scores_gemma":[0.7069542,0.0004233351,0.27603745,0.0005843136,0.000082840925,0.00016662777,0.001676809,0.00046454417,0.013609916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997409,0.00006346768,0.0000071566046,0.00007771975,0.000073039424,0.00003767716],"domain_scores_gemma":[0.99961984,0.000167565,0.000033845135,0.000086275875,0.000058740905,0.00003366114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007755783,0.0008013823,0.00047149853,0.00037216712,0.00019566946,0.00049099565,0.0008912035,0.00059814466,0.0025366829],"category_scores_gemma":[0.0013887634,0.00024147925,0.0005891308,0.00024528388,0.0006086169,0.0006686609,0.00090385746,0.0015046699,0.0005766101],"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":[0.00045278558,0.00016537822,0.0016374646,0.00014952237,0.00010067315,0.0002640615,0.00009566091,0.7007493,0.0365354,0.011110622,0.010820302,0.2379188],"study_design_scores_gemma":[0.000007697102,0.000039691353,0.0002161115,0.0000072716207,0.0000067459687,0.00007049951,0.0000061784763,0.9911628,0.005066372,0.0022738166,0.0011350677,0.000007773313],"about_ca_topic_score_codex":0.0019178075,"about_ca_topic_score_gemma":0.0032146825,"teacher_disagreement_score":0.0025366829,"about_ca_system_score_codex":0.000537314,"about_ca_system_score_gemma":0.0003128955,"threshold_uncertainty_score":0.0084860325},"labels":[],"label_agreement":null},{"id":"W4362468669","doi":"10.1038/s41598-023-30548-5","title":"Dental image enhancement network for early diagnosis of oral dental disease","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":18,"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":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Adaptability; Computer vision; Contrast (vision); Visibility; Image quality; Brightness; Image (mathematics); Optics","score_opus":0.01611661679717694,"score_gpt":0.2821527644833666,"score_spread":0.26603614768618966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362468669","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":[0.18963626,0.0017472658,0.80176574,0.00043444856,0.00012013702,0.0002040334,0.00037755666,0.0014073941,0.0043071886],"genre_scores_gemma":[0.8245463,0.0010182053,0.1670801,0.00022023742,0.00009103943,0.00014229187,0.0007057006,0.00004107905,0.0061551803],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998115,0.000024084582,0.000012935822,0.00006813505,0.000053399956,0.00002993033],"domain_scores_gemma":[0.99982136,0.00005163018,0.00002796698,0.000015953276,0.00007061466,0.000012560406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039515944,0.0005458145,0.00045381015,0.0007559486,0.00023249412,0.0003985893,0.00059225224,0.0005858031,0.0013901308],"category_scores_gemma":[0.0007207292,0.0002079965,0.00045165216,0.0003291072,0.00017368999,0.0005581652,0.00049950863,0.00040316573,0.0003223855],"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":[0.000769097,0.0002596804,0.009600933,0.00020305662,0.00011497442,0.00041660937,0.00014633015,0.11169008,0.084472716,0.0020770265,0.0040338347,0.78621554],"study_design_scores_gemma":[0.000017704891,0.0001608025,0.0053253067,0.00001581,0.00008719458,0.00027312373,0.000044304907,0.9624383,0.028168486,0.0010700307,0.002383083,0.000015970389],"about_ca_topic_score_codex":0.0016354978,"about_ca_topic_score_gemma":0.0025587974,"teacher_disagreement_score":0.0016354978,"about_ca_system_score_codex":0.00034178165,"about_ca_system_score_gemma":0.0003402559,"threshold_uncertainty_score":0.0046504736},"labels":[],"label_agreement":null},{"id":"W4362496938","doi":"10.1109/iconat57137.2023.10080156","title":"Image De-hazing techniques for Vision based applications - A survey","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Horizon College and Seminary","funders":"","keywords":"Visibility; Computer science; Computer vision; Artificial intelligence; Haze; Image processing; CLARITY; Image quality; Image (mathematics)","score_opus":0.02468044012274178,"score_gpt":0.3417589543570828,"score_spread":0.317078514234341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362496938","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":[0.011645993,0.3919266,0.5788313,0.0011052445,0.0008573417,0.00017489343,0.00020851272,0.0012579886,0.013992156],"genre_scores_gemma":[0.11976093,0.5231806,0.33462682,0.000704556,0.0011354026,0.0001646079,0.0008320357,0.00033105086,0.019264007],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999537,0.000038004913,0.00003043674,0.0000818877,0.00028472496,0.000028031192],"domain_scores_gemma":[0.9995473,0.00014075749,0.000043736243,0.000058695823,0.00019103261,0.000018524037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004839141,0.00076254277,0.0007109586,0.0015950591,0.00037554526,0.0013004749,0.0010793745,0.0011227978,0.0024565952],"category_scores_gemma":[0.00068571937,0.0004022058,0.00064281106,0.0020949238,0.0005240563,0.001769987,0.0005562031,0.0012859715,0.0019951598],"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":[0.000052831016,0.00009833509,0.00044888462,0.0019559038,0.00006252246,0.00014646689,0.00014437364,0.0030590245,0.037322044,0.0061286683,0.009322983,0.94125813],"study_design_scores_gemma":[0.00004053476,0.00075607194,0.008583042,0.001542553,0.0002921159,0.0056938794,0.0007826953,0.11665891,0.152292,0.022117183,0.6910258,0.00021521187],"about_ca_topic_score_codex":0.0014306892,"about_ca_topic_score_gemma":0.0013201815,"teacher_disagreement_score":0.0024565952,"about_ca_system_score_codex":0.0003392253,"about_ca_system_score_gemma":0.00042162716,"threshold_uncertainty_score":0.008218169},"labels":[],"label_agreement":null},{"id":"W4362514524","doi":"10.1109/access.2023.3263948","title":"A Novel Unbiased Deep Learning Approach (DL-Net) in Feature Space for Converting Gray to Color Image","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":16,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Color space; Deep learning; Feature (linguistics); Backpropagation; Ambiguity; Color image; Image (mathematics); Artificial neural network; Image processing","score_opus":0.032746210294011445,"score_gpt":0.323519817488017,"score_spread":0.2907736071940056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362514524","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":[0.009102309,0.00015122123,0.98851997,0.00010012352,0.000040492232,0.000025712794,0.00005977606,0.001048217,0.0009522472],"genre_scores_gemma":[0.47076797,0.00041521108,0.51547885,0.00053346297,0.000071978706,0.0001728866,0.00066932983,0.00022354176,0.011666751],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998254,0.000022366505,0.00000816804,0.000055551725,0.000059262322,0.000029208057],"domain_scores_gemma":[0.9997818,0.000052176474,0.00002511803,0.000030148794,0.00009212905,0.000018608192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037792404,0.0007293528,0.00048324664,0.000537134,0.00021470955,0.00067132775,0.0012713997,0.00069913757,0.0023599155],"category_scores_gemma":[0.0008502164,0.00024239549,0.00044519652,0.0004908061,0.00037541872,0.0010302543,0.0008642441,0.00096992846,0.00062601635],"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":[0.00019592019,0.00016001219,0.0014017664,0.00016383849,0.00009318761,0.00014081839,0.000056019133,0.25291708,0.041012168,0.012800816,0.005828255,0.6852301],"study_design_scores_gemma":[0.0000063547877,0.00005218701,0.00020141242,0.0000072525654,0.0000122085885,0.000049744645,0.0000062277873,0.98599875,0.009911782,0.0024444705,0.0013026518,0.000006953193],"about_ca_topic_score_codex":0.0033534165,"about_ca_topic_score_gemma":0.0054831556,"teacher_disagreement_score":0.0033534165,"about_ca_system_score_codex":0.00075826887,"about_ca_system_score_gemma":0.00071507297,"threshold_uncertainty_score":0.007894754},"labels":[],"label_agreement":null},{"id":"W4377098794","doi":"10.21203/rs.3.rs-2946470/v1","title":"DMPH-Net: A Deep Multiscale Pyramid Hybrid Network for Low-Light Image Enhancement with Attention Mechanism and Noise Reduction","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Image Enhancement 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":"MD Precision (Canada)","funders":"","keywords":"Artificial intelligence; Fuse (electrical); Computer science; Pyramid (geometry); Computer vision; Brightness; Feature (linguistics); Noise (video); Noise reduction; Reduction (mathematics); Pattern recognition (psychology); Image (mathematics); Mathematics; Optics; Physics","score_opus":0.03141243557161738,"score_gpt":0.3414934217926988,"score_spread":0.31008098622108143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377098794","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":[0.072495654,0.0013631836,0.91342217,0.00045367636,0.0002031184,0.00013448385,0.00045378998,0.0049757706,0.0064981743],"genre_scores_gemma":[0.63197863,0.0007949752,0.3488743,0.0009416816,0.00013238307,0.00017997727,0.001850888,0.00028351918,0.014963689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983895,0.000016511261,0.0000063091247,0.000050867868,0.000055394346,0.000031852887],"domain_scores_gemma":[0.9998516,0.00003504584,0.000014354665,0.000019018273,0.00006476281,0.0000152853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003547607,0.00085754594,0.0006294799,0.00054771913,0.00024338967,0.0005756651,0.0014198634,0.0005907166,0.0020160358],"category_scores_gemma":[0.00065035705,0.0002620199,0.00054662337,0.00037077395,0.00030013925,0.0010503973,0.0007966161,0.00071511866,0.00048142453],"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":[0.000456892,0.00028368828,0.0018713903,0.00021891772,0.0002215555,0.00021274602,0.00006403341,0.21317045,0.051378645,0.005073437,0.0141065465,0.71294177],"study_design_scores_gemma":[0.00001876687,0.000073429735,0.00042930254,0.00000898497,0.00003354527,0.000046152036,0.000011652615,0.98597324,0.009770131,0.0018972551,0.0017271754,0.000010362132],"about_ca_topic_score_codex":0.0059800013,"about_ca_topic_score_gemma":0.009013564,"teacher_disagreement_score":0.0059800013,"about_ca_system_score_codex":0.0008003119,"about_ca_system_score_gemma":0.00053913856,"threshold_uncertainty_score":0.011890411},"labels":[],"label_agreement":null},{"id":"W4378554333","doi":"10.22214/ijraset.2023.52728","title":"Satellite Image Dehazing","year":2023,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Image Enhancement 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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Adaptability; Image restoration; Computer vision; Net (polyhedron); Transformation (genetics); Set (abstract data type); Satellite; Haze; Image quality; Degradation (telecommunications); Deep learning; Satellite image; Image processing; Mathematics; Telecommunications; Geography; Engineering","score_opus":0.05793989301492493,"score_gpt":0.41088352501918624,"score_spread":0.3529436320042613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378554333","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":[0.41149384,0.00391415,0.5441391,0.0012853461,0.0006681528,0.00021248123,0.0013957025,0.0043036253,0.032587465],"genre_scores_gemma":[0.89202976,0.0019553485,0.08867041,0.00031601827,0.00012732252,0.0000260601,0.0013733966,0.0001827481,0.015318971],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99985063,0.000012787177,0.0000053602184,0.000038450773,0.0000742173,0.000018496608],"domain_scores_gemma":[0.9997987,0.000034089953,0.000029444045,0.000067673755,0.00005851104,0.00001163262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024174026,0.0004759709,0.00027997492,0.0005485435,0.00019436635,0.00034404852,0.00033582587,0.0003498164,0.0023650052],"category_scores_gemma":[0.000502614,0.00011562679,0.00039620278,0.00036343848,0.00037407814,0.00051660603,0.0004478406,0.0006243661,0.00074875966],"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":[0.0003429478,0.00009284114,0.005819155,0.0004986022,0.00017681088,0.00043987643,0.00015178241,0.12791245,0.16509372,0.0058486317,0.012536558,0.68108654],"study_design_scores_gemma":[0.000029255334,0.00028973253,0.013601249,0.00008131723,0.00012466627,0.0018111203,0.00014184763,0.6267969,0.3073816,0.005955637,0.043735348,0.000051378167],"about_ca_topic_score_codex":0.0019792647,"about_ca_topic_score_gemma":0.0024527316,"teacher_disagreement_score":0.0023650052,"about_ca_system_score_codex":0.00028794107,"about_ca_system_score_gemma":0.00026035047,"threshold_uncertainty_score":0.007911682},"labels":[],"label_agreement":null},{"id":"W4381299650","doi":"10.2139/ssrn.4457390","title":"Multi-Wavelength Blending Speckle Reduction for Color Improvement","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reduction (mathematics); Speckle pattern; Wavelength; Computer science; Optics; Artificial intelligence; Computer vision; Materials science; Optoelectronics; Physics; Mathematics","score_opus":0.031988234161713966,"score_gpt":0.3013571386105475,"score_spread":0.26936890444883355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381299650","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":[0.09402758,0.00096766214,0.89670855,0.00016752494,0.0001137649,0.000043738557,0.00007301751,0.0009523657,0.006945805],"genre_scores_gemma":[0.30306178,0.0014134129,0.68189883,0.0001005568,0.00009956904,0.000052295505,0.00016764656,0.00036705445,0.012838949],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997789,0.000032811495,0.0000111668705,0.00004566995,0.00010938958,0.000021957178],"domain_scores_gemma":[0.9996371,0.00012095757,0.000038138416,0.00009199773,0.000091277274,0.000020439233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002810854,0.0006266085,0.0004810342,0.00066674774,0.0002932772,0.00058755866,0.00039275756,0.00048630175,0.0048037875],"category_scores_gemma":[0.00054627465,0.0002798628,0.00052439165,0.0009731172,0.00039593037,0.0009338132,0.0007889182,0.00079682027,0.0010902715],"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":[0.0003848947,0.00020124493,0.0007853095,0.00016656477,0.000055998982,0.0000767675,0.0000921966,0.009724018,0.7137772,0.0076165376,0.00085251115,0.26626676],"study_design_scores_gemma":[0.00003540696,0.00018962403,0.00229109,0.000025760264,0.00009447079,0.00047066328,0.00003567816,0.337738,0.6470995,0.004802134,0.0071882894,0.000029373728],"about_ca_topic_score_codex":0.0003199165,"about_ca_topic_score_gemma":0.0009260971,"teacher_disagreement_score":0.0048037875,"about_ca_system_score_codex":0.0002475374,"about_ca_system_score_gemma":0.00024355008,"threshold_uncertainty_score":0.016070247},"labels":[],"label_agreement":null},{"id":"W4385151536","doi":"10.1109/tim.2023.3295026","title":"Computational Framework for Turbid Water Single-Pixel Imaging by Polynomial Regression and Feature Enhancement","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Image Enhancement Techniques","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":"Western University","funders":"Natural Science Foundation of Anhui Province; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Underwater; Computer science; Pixel; Artificial intelligence; Feature (linguistics); Image quality; Computer vision; Remote sensing; Turbidity; Image (mathematics); Geology","score_opus":0.02869992473278911,"score_gpt":0.27520796860717733,"score_spread":0.24650804387438824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385151536","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":[0.0014836158,0.000043902906,0.9981128,0.000029707891,0.0000038825083,0.000010712551,0.000011564167,0.00007303197,0.00023088687],"genre_scores_gemma":[0.12226397,0.00045993997,0.8739936,0.000065675245,0.00005524206,0.00020134915,0.0001865453,0.00016596809,0.0026077908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996062,0.00009645533,0.000017851737,0.0000823454,0.00016122186,0.00003588393],"domain_scores_gemma":[0.99943084,0.00026306437,0.00008294584,0.000058769576,0.00013160642,0.000032691765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000857783,0.000738734,0.0007389192,0.0006724877,0.00027210946,0.00071519805,0.0016163266,0.00077126967,0.002051088],"category_scores_gemma":[0.0017979607,0.00036025452,0.00096363184,0.0006306376,0.00062716566,0.00088465394,0.0012032384,0.001156219,0.0006312179],"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":[0.00007424798,0.00007203358,0.0005572552,0.00018483769,0.000043640273,0.00018069596,0.00010166843,0.8312075,0.024695178,0.04291078,0.0013568854,0.09861527],"study_design_scores_gemma":[0.0000016765891,0.000009547466,0.0000276283,0.0000013830892,0.0000019836132,0.000015027637,0.0000026613457,0.99763954,0.00070986553,0.0012556756,0.00033216004,0.0000029483285],"about_ca_topic_score_codex":0.0037160092,"about_ca_topic_score_gemma":0.0031917093,"teacher_disagreement_score":0.0037160092,"about_ca_system_score_codex":0.0005830428,"about_ca_system_score_gemma":0.0009849998,"threshold_uncertainty_score":0.0073887706},"labels":[],"label_agreement":null},{"id":"W4385151940","doi":"10.1109/jsen.2023.3296167","title":"An Improved CycleGAN-Based Model for Low-Light Image Enhancement","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Normalization (sociology); Light field; Computer vision; Image quality; Deep learning; Generator (circuit theory); Image (mathematics); Pattern recognition (psychology)","score_opus":0.017309102031716414,"score_gpt":0.2957204517177125,"score_spread":0.27841134968599607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385151940","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":[0.026160069,0.0006697884,0.9660176,0.00028658338,0.000091438276,0.000049922986,0.00011752277,0.00067066314,0.0059363907],"genre_scores_gemma":[0.7977343,0.00094669376,0.1798046,0.0005309453,0.00007502535,0.0001684592,0.00042424392,0.00021308707,0.02010265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999889,0.000018479015,0.0000037230886,0.0000357264,0.000033068376,0.000020050253],"domain_scores_gemma":[0.9998779,0.000044313274,0.000014971749,0.000016748754,0.000037922167,0.000008095337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028315868,0.00060150505,0.00041476465,0.000310203,0.00015917502,0.00043745362,0.00091666036,0.0005755907,0.002058388],"category_scores_gemma":[0.0005221019,0.00028339686,0.00056646735,0.00020117653,0.00040232143,0.0007517318,0.00057956367,0.0009376433,0.00043382854],"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":[0.00016728559,0.000068944195,0.00087149267,0.0001186281,0.000064377404,0.00017043197,0.00008001619,0.8112821,0.03457885,0.017657893,0.0033809573,0.13155901],"study_design_scores_gemma":[0.0000026611258,0.000017358247,0.00007821002,0.000004617889,0.0000062203103,0.000032509673,0.000001984206,0.99557257,0.002348331,0.0013122369,0.0006195064,0.000003826251],"about_ca_topic_score_codex":0.0024713674,"about_ca_topic_score_gemma":0.0034707482,"teacher_disagreement_score":0.0024713674,"about_ca_system_score_codex":0.00052968215,"about_ca_system_score_gemma":0.00035403212,"threshold_uncertainty_score":0.0068860054},"labels":[],"label_agreement":null},{"id":"W4385407477","doi":"10.2139/ssrn.4521008","title":"Perceptual Quality Assessment of Underwater Image Enhancement","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Underwater; Perception; Image quality; Quality (philosophy); Artificial intelligence; Computer science; Computer vision; Image (mathematics); Psychology; Geology; Physics; Oceanography","score_opus":0.04170734470321166,"score_gpt":0.35889006503418774,"score_spread":0.31718272033097605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385407477","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":[0.5248816,0.0020838908,0.46097553,0.00020823859,0.00013719965,0.00016160829,0.00029917186,0.0006150755,0.0106377555],"genre_scores_gemma":[0.92342377,0.0011203735,0.07057043,0.000054586424,0.00005292146,0.000022681452,0.00024365264,0.00014332111,0.004368103],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996451,0.00008035736,0.000020872203,0.00004934247,0.00016842011,0.000035887035],"domain_scores_gemma":[0.9985678,0.0005300248,0.0001232119,0.00010047222,0.0006138394,0.000064515545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000841841,0.00044503657,0.00034687706,0.0008217852,0.00016678171,0.0007390404,0.00024299875,0.00041223285,0.0036711486],"category_scores_gemma":[0.0033737717,0.00013975716,0.000213711,0.00050518155,0.00030273086,0.0005991034,0.0005169484,0.00032876036,0.00036070848],"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":[0.0028770603,0.00018259171,0.0072239777,0.00079785456,0.00013976767,0.00044241932,0.00026787078,0.028163197,0.53052974,0.002529225,0.0014319889,0.42541435],"study_design_scores_gemma":[0.00012111465,0.0020359391,0.06411304,0.00016051055,0.00047550944,0.002146389,0.00043318135,0.4539774,0.4674997,0.0033601697,0.0055774096,0.000099732395],"about_ca_topic_score_codex":0.00079656974,"about_ca_topic_score_gemma":0.00059054577,"teacher_disagreement_score":0.0036711486,"about_ca_system_score_codex":0.00015996088,"about_ca_system_score_gemma":0.00014099544,"threshold_uncertainty_score":0.012281179},"labels":[],"label_agreement":null},{"id":"W4385484066","doi":"10.1145/3596711.3596717","title":"High Dynamic Range Display Systems","year":2023,"lang":"en","type":"book-chapter","venue":"ACM eBooks","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of British Columbia; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Projector; Computer science; Workspace; Software; Liquid-crystal display; Computer graphics (images); Range (aeronautics); Display device; High dynamic range; Computer hardware; Dynamic range; Engineering; Computer vision; Artificial intelligence; Operating system; Aerospace engineering","score_opus":0.020875104241912967,"score_gpt":0.24733605823162558,"score_spread":0.2264609539897126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385484066","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":[0.013151111,0.021893702,0.689229,0.00089710875,0.00092699594,0.00025759306,0.0005252423,0.010189873,0.26292935],"genre_scores_gemma":[0.1137547,0.022502126,0.40196013,0.0013795635,0.00063017523,0.0003104813,0.0012186209,0.0014301997,0.4568141],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99946374,0.000059020615,0.000021070797,0.00010636454,0.00030652405,0.000043205288],"domain_scores_gemma":[0.9995034,0.00020886138,0.00002005032,0.00008848101,0.00014032096,0.000038882754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042562402,0.0006505108,0.0004986561,0.0009872443,0.00052902015,0.0025093462,0.0015699929,0.0013509529,0.05948498],"category_scores_gemma":[0.00088420516,0.00046411017,0.00045605825,0.0013084967,0.00034447748,0.0021371525,0.0013778637,0.0012226822,0.025801677],"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":[0.00015480266,0.000093224364,0.00049590884,0.0012411958,0.000041508945,0.00039736924,0.00039000207,0.0025567668,0.15196432,0.050305616,0.07953673,0.7128225],"study_design_scores_gemma":[0.000045098976,0.00019725623,0.0012018963,0.00024563947,0.000048832677,0.002954117,0.00012546706,0.011227061,0.06060167,0.00882227,0.9144436,0.00008702079],"about_ca_topic_score_codex":0.00053226424,"about_ca_topic_score_gemma":0.0006046789,"teacher_disagreement_score":0.05948498,"about_ca_system_score_codex":0.00040900367,"about_ca_system_score_gemma":0.00022390428,"threshold_uncertainty_score":0.19899714},"labels":[],"label_agreement":null},{"id":"W4385624643","doi":"10.15701/kcgs.2023.29.3.13","title":"Developing an HDR Imaging Method for an Ultra-thin Light-Field Camera","year":2023,"lang":"en","type":"article","venue":"Journal of the Korea Computer Graphics Society","topic":"Image Enhancement 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":"Kootenay Association for Science & Technology; Nexen (Canada)","funders":"Defense Acquisition Program Administration","keywords":"Computer vision; Artificial intelligence; Computer graphics (images); Field (mathematics); Computer science; Light field; Mathematics","score_opus":0.03282943509311777,"score_gpt":0.32817247384434134,"score_spread":0.2953430387512236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385624643","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":[0.00988734,0.0005825739,0.9831049,0.00030281467,0.00010170481,0.00014342554,0.000028014447,0.00060533575,0.005243885],"genre_scores_gemma":[0.07176042,0.0008356103,0.9197937,0.00015692314,0.00006143658,0.0001091631,0.00004999457,0.000080747464,0.007151929],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946123,0.00007600947,0.000031868407,0.00013365391,0.00026550057,0.00003184541],"domain_scores_gemma":[0.9994272,0.00012382207,0.000059181344,0.000053754495,0.0002945932,0.000041389303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083584135,0.0005834928,0.00030350158,0.0010392308,0.0004762423,0.0012413664,0.00092699716,0.00085070304,0.0037924987],"category_scores_gemma":[0.0010080448,0.0004930675,0.00060847047,0.00054368185,0.00060151133,0.0021458757,0.00073510053,0.00092787103,0.0012817236],"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":[0.0000939948,0.00011104166,0.0027069945,0.0008335713,0.000057455676,0.00056080736,0.00079383596,0.009905043,0.40742335,0.02865774,0.0060617966,0.54279435],"study_design_scores_gemma":[0.000101967475,0.00036629225,0.005877493,0.00024344008,0.00015958164,0.004804789,0.00079906185,0.39433038,0.49202195,0.011323028,0.08970245,0.00026967376],"about_ca_topic_score_codex":0.0019271419,"about_ca_topic_score_gemma":0.002088999,"teacher_disagreement_score":0.0037924987,"about_ca_system_score_codex":0.0006168308,"about_ca_system_score_gemma":0.0011049459,"threshold_uncertainty_score":0.012687147},"labels":[],"label_agreement":null},{"id":"W4385689129","doi":"10.21203/rs.3.rs-3236568/v1","title":"Research on Low-light Image Enhancement Based on MER-Retinex Algorithm","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MD Precision (Canada)","funders":"Government of Jiangsu Province; Jiangsu University; National Natural Science Foundation of China","keywords":"Fuse (electrical); Color constancy; Artificial intelligence; Computer vision; Computer science; Brightness; Image enhancement; Pyramid (geometry); Image (mathematics); Convolution (computer science); Feature (linguistics); Algorithm; Mathematics; Artificial neural network; Optics; Engineering","score_opus":0.1074420871726595,"score_gpt":0.4573890434734251,"score_spread":0.3499469563007656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385689129","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":[0.03952536,0.0018373224,0.95365196,0.00018950587,0.00007128843,0.000054315264,0.000028742434,0.00064850174,0.0039928863],"genre_scores_gemma":[0.30929443,0.0022988059,0.67843443,0.00018463207,0.00009179434,0.000062342486,0.00014735335,0.00013181036,0.009354472],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947244,0.0000956202,0.000028604625,0.00013553484,0.00021565532,0.000052212283],"domain_scores_gemma":[0.9994753,0.00013818429,0.000043730513,0.00009551239,0.00022098115,0.00002625942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080422044,0.00067316263,0.00089258025,0.0010780359,0.00034236195,0.0009926802,0.0008881955,0.0007231933,0.0019495441],"category_scores_gemma":[0.0009739733,0.00029547838,0.00087462517,0.0006499664,0.0005361798,0.0014840623,0.0005252162,0.0007031645,0.0005759705],"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":[0.00052665675,0.00020574158,0.0021264392,0.00031746854,0.00020838968,0.00020405078,0.00020965147,0.10976794,0.18106912,0.021197213,0.0028517973,0.68131554],"study_design_scores_gemma":[0.000049322625,0.00023369983,0.0016067785,0.000030897005,0.0000808098,0.0005405453,0.000061691106,0.8935385,0.09157123,0.004194001,0.008049872,0.0000427407],"about_ca_topic_score_codex":0.002311864,"about_ca_topic_score_gemma":0.0016303803,"teacher_disagreement_score":0.002311864,"about_ca_system_score_codex":0.000599407,"about_ca_system_score_gemma":0.00058506074,"threshold_uncertainty_score":0.0065218806},"labels":[],"label_agreement":null},{"id":"W4385801669","doi":"10.1109/cvprw59228.2023.00145","title":"A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Homogeneous; Preprocessor; Haze; Artificial intelligence; RGB color model; Transformer; Image (mathematics); Computer vision; Data mining; Mathematics","score_opus":0.03368884128365148,"score_gpt":0.3145019100628809,"score_spread":0.28081306877922946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385801669","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":[0.008048068,0.00011074084,0.9896632,0.000113005706,0.000024584575,0.00003970041,0.000075564654,0.00087272306,0.0010523505],"genre_scores_gemma":[0.28442115,0.00036505514,0.70966864,0.00023710115,0.000050853527,0.00009957226,0.0007252753,0.00029623884,0.0041361493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950993,0.000048952188,0.000022939645,0.00014757743,0.00021343745,0.00005713313],"domain_scores_gemma":[0.99939656,0.000108020635,0.000056464225,0.00024976867,0.0001505664,0.000038611972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006805521,0.00085864164,0.00066562364,0.00077730266,0.00035287632,0.0009225273,0.0021552448,0.0008256929,0.0017233619],"category_scores_gemma":[0.0015777758,0.00036467228,0.0009106282,0.0008259108,0.0009404868,0.0022668573,0.002556715,0.0021946377,0.00078711705],"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":[0.00025311857,0.00017474202,0.0019075469,0.00035315257,0.00013606042,0.0002687935,0.00024472663,0.21137069,0.14677939,0.03301727,0.007793601,0.597701],"study_design_scores_gemma":[0.000017165345,0.00007794769,0.00033225835,0.000013007336,0.000020798572,0.00023827417,0.000043636042,0.9361596,0.04331219,0.015206328,0.0045607346,0.000018110655],"about_ca_topic_score_codex":0.0026028634,"about_ca_topic_score_gemma":0.0036906851,"teacher_disagreement_score":0.0026028634,"about_ca_system_score_codex":0.0005058274,"about_ca_system_score_gemma":0.0010232315,"threshold_uncertainty_score":0.0057651997},"labels":[],"label_agreement":null},{"id":"W4385834335","doi":"10.1109/access.2023.3305576","title":"UnShadowNet: Illumination Critic Guided Contrastive Learning for Shadow Removal","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Artificial intelligence; Shadow (psychology)","score_opus":0.05600335142623282,"score_gpt":0.3614986325934254,"score_spread":0.3054952811671926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385834335","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":[0.043173097,0.0005027106,0.9473223,0.0003164469,0.00013340592,0.00007918041,0.00020035215,0.0035656902,0.0047068913],"genre_scores_gemma":[0.715881,0.00023337698,0.2667025,0.0006302194,0.00012557584,0.00015276815,0.0010426098,0.00051898806,0.014713104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968755,0.000055754383,0.000008976368,0.00010928042,0.0000847543,0.000053819393],"domain_scores_gemma":[0.9994273,0.00023233055,0.000054761058,0.0001246461,0.00011338307,0.000047523085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073013693,0.0010228748,0.00064723945,0.00030018046,0.0002916886,0.00061953656,0.0021023583,0.0010696842,0.0038697917],"category_scores_gemma":[0.002177558,0.00040816804,0.0005107462,0.00025341913,0.0009340352,0.0011486893,0.0015286366,0.0022216358,0.0009462549],"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":[0.0005430589,0.00026922056,0.0016843353,0.00013887552,0.00011891835,0.00019370807,0.000114118404,0.624651,0.033246033,0.009984035,0.010063363,0.31899345],"study_design_scores_gemma":[0.000009886751,0.000047337933,0.00015724721,0.000004527966,0.000005018654,0.000016831875,0.0000035365542,0.9926818,0.004654512,0.0017480113,0.0006668173,0.000004510941],"about_ca_topic_score_codex":0.0036047401,"about_ca_topic_score_gemma":0.0064150137,"teacher_disagreement_score":0.0038697917,"about_ca_system_score_codex":0.00079378014,"about_ca_system_score_gemma":0.00083742145,"threshold_uncertainty_score":0.012945771},"labels":[],"label_agreement":null},{"id":"W4386071615","doi":"10.1109/cvpr52729.2023.01598","title":"Computational Flash Photography through Intrinsics","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Simon Fraser University","funders":"","keywords":"Flash (photography); Intrinsics; Computer science; Computational photography; Photography; Computer graphics (images); Computer vision; Artificial intelligence; Image processing; Optics; Image (mathematics); Visual arts","score_opus":0.018516586042425433,"score_gpt":0.2766200847689285,"score_spread":0.25810349872650307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386071615","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":[0.04437635,0.000300275,0.9457481,0.00029833915,0.00006025498,0.00004615444,0.000098042285,0.0001824622,0.008889981],"genre_scores_gemma":[0.80518836,0.00055356376,0.18079878,0.0001732089,0.00008772452,0.00017367418,0.0002334413,0.00031315436,0.0124780005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998586,0.000040268304,0.000006158113,0.00003303519,0.000043064563,0.00001881949],"domain_scores_gemma":[0.9995035,0.0002744887,0.000058418555,0.000073470816,0.000052764637,0.00003743115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004680188,0.0005206361,0.00044769552,0.00029486095,0.00028427283,0.0008843986,0.00079991773,0.0007691174,0.0047002253],"category_scores_gemma":[0.0016411899,0.0003510567,0.00053796766,0.00017273685,0.0010891483,0.0014295242,0.0015168425,0.0011835266,0.00031403953],"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":[0.00004483306,0.00003474505,0.00043681276,0.00011801319,0.000022289041,0.00008070705,0.00007805896,0.84542537,0.0059718466,0.13129693,0.0012496124,0.0152407475],"study_design_scores_gemma":[0.0000041596513,0.000011228136,0.00006022447,0.000006274965,0.0000022799532,0.0000093233775,0.000007919593,0.9869221,0.0004530857,0.011955156,0.0005650311,0.0000031699724],"about_ca_topic_score_codex":0.0012804265,"about_ca_topic_score_gemma":0.0014142044,"teacher_disagreement_score":0.0047002253,"about_ca_system_score_codex":0.0006137023,"about_ca_system_score_gemma":0.0005466489,"threshold_uncertainty_score":0.015723765},"labels":[],"label_agreement":null},{"id":"W4386076237","doi":"10.1109/cvpr52729.2023.01752","title":"GamutMLP: A Lightweight MLP for Color Loss Recovery","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gamut; Computer science; Artificial intelligence; RGB color model; Color space; Computer vision; RGB color space; Lightness; Color depth; Color balance; Color constancy; Clipping (morphology); Color management; Computer graphics (images); Color image; Image processing; Image (mathematics)","score_opus":0.0182752534558556,"score_gpt":0.27574208937657674,"score_spread":0.2574668359207211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386076237","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":[0.12166627,0.0016088984,0.7705094,0.0005675961,0.0004555972,0.00039566818,0.008200957,0.087089084,0.009506452],"genre_scores_gemma":[0.37717187,0.0007910965,0.58042336,0.00058314693,0.000078764904,0.0006208307,0.021487858,0.0031884836,0.01565453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996736,0.00004115301,0.000013460975,0.00010819153,0.00011026883,0.000053398042],"domain_scores_gemma":[0.99957603,0.00010801479,0.000029990677,0.00015719567,0.000106245796,0.000022385166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006356085,0.0021323417,0.00077792915,0.00085475564,0.00036551856,0.000931715,0.0031192033,0.0011421171,0.005502438],"category_scores_gemma":[0.002539415,0.00062511757,0.0009598717,0.0008146829,0.00046705393,0.0017691794,0.0013277176,0.00221248,0.0028889987],"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":[0.00064248865,0.00035020427,0.0026694974,0.00044373694,0.00022639953,0.00028292194,0.00007243612,0.25336587,0.029570602,0.0026724266,0.04867886,0.6610247],"study_design_scores_gemma":[0.000030796306,0.00006489102,0.0007111429,0.00002177166,0.000020767691,0.000086605854,0.000014143762,0.9750277,0.016888145,0.0019106977,0.005202284,0.000020977455],"about_ca_topic_score_codex":0.008842263,"about_ca_topic_score_gemma":0.016068732,"teacher_disagreement_score":0.008842263,"about_ca_system_score_codex":0.0010278309,"about_ca_system_score_gemma":0.00083275215,"threshold_uncertainty_score":0.018407464},"labels":[],"label_agreement":null},{"id":"W4386291091","doi":"10.1002/sdtp.16931","title":"P‐17: All Weather‐Robust Image Quality Enhancement based on Image Feature Fusion and Multiscale Degradation Profile","year":2023,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Faurecia (Canada)","funders":"","keywords":"Visibility; Degradation (telecommunications); Computer science; Feature (linguistics); Image (mathematics); Artificial intelligence; Image fusion; Computer vision; Fusion; Scale (ratio); Image quality; Pattern recognition (psychology); Geography; Cartography","score_opus":0.016153658823900154,"score_gpt":0.28170906529026873,"score_spread":0.2655554064663686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386291091","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":[0.074533165,0.0010692339,0.91604704,0.0001349654,0.00007611458,0.00012123318,0.00012071586,0.0017774645,0.0061202087],"genre_scores_gemma":[0.67338145,0.0010660457,0.3163309,0.00013751016,0.00008488059,0.00008763472,0.00043736532,0.00022853308,0.008245674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997763,0.000024084013,0.000011389346,0.000049326074,0.000110283734,0.000028669372],"domain_scores_gemma":[0.99981195,0.0000222562,0.00003270429,0.000038005288,0.00007975817,0.000015420184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038641374,0.000566027,0.00037391396,0.00047892414,0.0001956038,0.00057842187,0.00041843313,0.00046576592,0.0018797087],"category_scores_gemma":[0.0004524683,0.00017025736,0.00047154704,0.00035343875,0.0003665427,0.00094036304,0.00054852286,0.0004740161,0.000696613],"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":[0.0003404885,0.00010633165,0.002076424,0.00027830654,0.00009093788,0.00039853214,0.00010856088,0.015899496,0.6233225,0.0042165727,0.0030847457,0.3500772],"study_design_scores_gemma":[0.000031281397,0.00047397325,0.016695665,0.000038159185,0.00011431549,0.001838751,0.000053173608,0.31020695,0.6495451,0.0026765242,0.01826345,0.00006267462],"about_ca_topic_score_codex":0.0010457041,"about_ca_topic_score_gemma":0.001106894,"teacher_disagreement_score":0.0018797087,"about_ca_system_score_codex":0.00022917324,"about_ca_system_score_gemma":0.0004047459,"threshold_uncertainty_score":0.00628829},"labels":[],"label_agreement":null},{"id":"W4386473479","doi":"10.2139/ssrn.4564021","title":"Cnn-Transformer Blend Pyramid Network for Underwater Image","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Underwater; Transformer; Pyramid (geometry); Computer science; Artificial intelligence; Computer vision; Geology; Engineering; Electrical engineering; Physics; Optics; Voltage; Oceanography","score_opus":0.018536735357580363,"score_gpt":0.2801252455824888,"score_spread":0.2615885102249084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386473479","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":[0.03887079,0.000723336,0.94507176,0.00031169984,0.00020485157,0.00012855926,0.0008981095,0.0045445752,0.009246298],"genre_scores_gemma":[0.4846157,0.0011826976,0.47298017,0.0002802717,0.0001284201,0.00014144692,0.0030323956,0.00053903926,0.03709986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998927,0.0000069303574,0.0000036738186,0.000034154276,0.00003590952,0.000026608763],"domain_scores_gemma":[0.99990225,0.00001118942,0.0000073092083,0.000026510537,0.000043228032,0.000009500821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001970035,0.000676971,0.00046062717,0.00046556487,0.00020540979,0.000511517,0.00090589304,0.0006322351,0.0076806266],"category_scores_gemma":[0.00047181934,0.00031893773,0.00048981764,0.0006107032,0.0001698554,0.0008052775,0.0008816393,0.00082809967,0.0021964356],"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":[0.00025312518,0.00012844385,0.0011185734,0.00016509858,0.00010535931,0.00015926844,0.000046794077,0.07539121,0.079871766,0.005973934,0.014698722,0.82208776],"study_design_scores_gemma":[0.000010220168,0.000052722,0.00070215005,0.0000114259665,0.000030464773,0.00008030724,0.00001579039,0.9646332,0.027332293,0.0026696203,0.004452666,0.000009185891],"about_ca_topic_score_codex":0.010474175,"about_ca_topic_score_gemma":0.0149899945,"teacher_disagreement_score":0.010474175,"about_ca_system_score_codex":0.00046275815,"about_ca_system_score_gemma":0.00063379435,"threshold_uncertainty_score":0.025694251},"labels":[],"label_agreement":null},{"id":"W4386598344","doi":"10.1109/icip49359.2023.10222331","title":"Retinex-based Image Denoising / Contrast Enhancement Using Gradient Graph Laplacian Regularizer","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Color constancy; Piecewise; Laplacian matrix; Computer science; Pixel; Artificial intelligence; Graph; Regularization (linguistics); Laplace operator; Mathematics; Noise reduction; Computation; Computer vision; Algorithm; Image (mathematics); Theoretical computer science","score_opus":0.019883796264862825,"score_gpt":0.2713705764216916,"score_spread":0.2514867801568288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386598344","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":[0.009076406,0.00008366679,0.98913854,0.00008786469,0.000016591946,0.000023994746,0.000021706779,0.00039471276,0.0011565121],"genre_scores_gemma":[0.141219,0.00022933776,0.8538565,0.00010418237,0.000028712051,0.00006513792,0.00011657707,0.00016196641,0.004218567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975616,0.000039645045,0.000009497613,0.000053842443,0.00011400173,0.000026734795],"domain_scores_gemma":[0.99978334,0.00006432299,0.00003638719,0.000043795495,0.00005587407,0.000016258684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003907871,0.0007111765,0.0007526704,0.00049639755,0.00026200316,0.00059648993,0.00066845317,0.00071734545,0.0015088682],"category_scores_gemma":[0.00065375294,0.00033591842,0.0007136028,0.0003870569,0.0005997599,0.00067381765,0.00082727865,0.000905274,0.0006329317],"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":[0.00022533559,0.000126824,0.0007165703,0.00019148875,0.00009844018,0.00023929236,0.00022142158,0.5268612,0.20238842,0.020877564,0.0041888095,0.24386463],"study_design_scores_gemma":[0.000012230129,0.00003277044,0.00011133277,0.0000055882338,0.000009481442,0.00008826932,0.0000121430585,0.9759462,0.019919999,0.002241914,0.0016078419,0.000012275492],"about_ca_topic_score_codex":0.0023553048,"about_ca_topic_score_gemma":0.0036224958,"teacher_disagreement_score":0.0023553048,"about_ca_system_score_codex":0.0004135104,"about_ca_system_score_gemma":0.0007800459,"threshold_uncertainty_score":0.0050476193},"labels":[],"label_agreement":null},{"id":"W4386759882","doi":"10.1016/j.patcog.2023.109956","title":"Restoring vision in hazy weather with hierarchical contrastive learning","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Exploit; Hierarchy; Image (mathematics); Haze; Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.019807655704650134,"score_gpt":0.26893450516104417,"score_spread":0.24912684945639404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386759882","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":[0.17426363,0.00035535352,0.82154644,0.00018203072,0.00006394402,0.000032597425,0.000071750634,0.00088544417,0.0025986978],"genre_scores_gemma":[0.7528685,0.00020542066,0.24363084,0.00009217916,0.00004578172,0.0000211082,0.0001026714,0.00007709352,0.0029564793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992776,0.000009237984,0.0000023978369,0.000015727595,0.000025686142,0.000019253006],"domain_scores_gemma":[0.9997907,0.00007352124,0.000028171382,0.000038779122,0.00004921167,0.000019583118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020782622,0.00029039555,0.00028485042,0.00030505503,0.00018417349,0.00041887275,0.00057480147,0.00036377902,0.0011971758],"category_scores_gemma":[0.0007080204,0.00019020193,0.0003121557,0.00023584669,0.00036349392,0.0006181231,0.00053584715,0.0007117321,0.00022821405],"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":[0.00038018537,0.00020818191,0.002372288,0.00014780847,0.00008661611,0.00015940807,0.0001589334,0.38269338,0.17369767,0.0063090725,0.0025602551,0.43122625],"study_design_scores_gemma":[0.000011059663,0.000048361522,0.0010000402,0.000004236423,0.0000106476045,0.000038585273,0.00001481415,0.9793449,0.016886594,0.0020292946,0.00060408306,0.000007359992],"about_ca_topic_score_codex":0.0047282143,"about_ca_topic_score_gemma":0.007339703,"teacher_disagreement_score":0.0047282143,"about_ca_system_score_codex":0.00027791806,"about_ca_system_score_gemma":0.00041159318,"threshold_uncertainty_score":0.009401441},"labels":[],"label_agreement":null},{"id":"W4386919844","doi":"10.1109/sas58821.2023.10254059","title":"Fog-Aware Adaptive YOLO for Object Detection in Adverse Weather","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Adverse weather; Computer science; Object detection; Object (grammar); Meteorology; Computer security; Artificial intelligence; Geography; Pattern recognition (psychology)","score_opus":0.022586218570994604,"score_gpt":0.27404635031919966,"score_spread":0.25146013174820503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386919844","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":[0.2047469,0.0022218903,0.7827779,0.00025531996,0.00020556488,0.00027629244,0.00028897816,0.004797156,0.0044299955],"genre_scores_gemma":[0.5983719,0.0011439769,0.3939733,0.00045277536,0.00012735206,0.00014234232,0.00088651263,0.00032955804,0.004572215],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997502,0.000018629002,0.000011144017,0.00008981697,0.000064461725,0.00006562638],"domain_scores_gemma":[0.9997081,0.00006429515,0.000048386533,0.00004424507,0.00010597294,0.00002901541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039126683,0.0007094375,0.00074243214,0.0011845721,0.00036478808,0.00090215437,0.0010084934,0.00048718302,0.0007967664],"category_scores_gemma":[0.0008920421,0.00030549947,0.00046370842,0.0004801116,0.000416059,0.0010199503,0.0008141238,0.00052410306,0.00046000083],"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":[0.00090020057,0.00034165368,0.010227894,0.00035722592,0.00016852366,0.00028237075,0.00031661536,0.028880626,0.22488609,0.0017192913,0.0075013535,0.7244182],"study_design_scores_gemma":[0.00007893332,0.0004720789,0.025001796,0.00006771798,0.00018616683,0.0005377266,0.00031068505,0.8182945,0.14217001,0.0023178519,0.010492483,0.00007000444],"about_ca_topic_score_codex":0.0048696008,"about_ca_topic_score_gemma":0.01020337,"teacher_disagreement_score":0.0048696008,"about_ca_system_score_codex":0.000469409,"about_ca_system_score_gemma":0.00065042806,"threshold_uncertainty_score":0.009682536},"labels":[],"label_agreement":null},{"id":"W4388043359","doi":"10.1111/cgf.14964","title":"Integrating High‐Level Features for Consistent Palette‐based Multi‐image Recoloring","year":2023,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement Techniques","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":"York University","funders":"Natural Science Basic Research Program of Shaanxi Province; European Regional Development Fund; Canada Research Chairs; Agencia Estatal de Investigación; Canada First Research Excellence Fund; National Natural Science Foundation of China","keywords":"Palette (painting); Computer science; Consistency (knowledge bases); Workflow; Artificial intelligence; Computer vision; Image (mathematics); Image editing; Computer graphics (images); Information retrieval; Database","score_opus":0.05160179138991332,"score_gpt":0.29442523560496264,"score_spread":0.2428234442150493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388043359","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":[0.011391546,0.00012403559,0.98239166,0.00006123634,0.000028397431,0.000057212394,0.0000310927,0.0044800313,0.0014349114],"genre_scores_gemma":[0.19328867,0.00013117446,0.80338216,0.00007654776,0.000031758544,0.000071448005,0.00014128584,0.0010264067,0.001850625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999363,0.00012383761,0.000033740478,0.000118192045,0.00029392017,0.00006738729],"domain_scores_gemma":[0.9980823,0.00047258261,0.00017278038,0.0006406336,0.0004834764,0.00014829713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014641986,0.0007110416,0.000586298,0.0017748027,0.00046740982,0.0016339468,0.0018107471,0.0006818501,0.0043205055],"category_scores_gemma":[0.0034775252,0.0004716671,0.000749369,0.00076094246,0.00071402395,0.0019833085,0.002305488,0.001364269,0.0012026408],"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":[0.00041519897,0.0004144758,0.0025793866,0.00037918577,0.00010079574,0.0002800432,0.00063134584,0.03976935,0.1959205,0.021395463,0.008511441,0.72960275],"study_design_scores_gemma":[0.000052293602,0.00018558832,0.0027274033,0.00006069312,0.000060379345,0.00041810732,0.00016681229,0.79239887,0.16412486,0.017284758,0.022423865,0.0000963911],"about_ca_topic_score_codex":0.0012922536,"about_ca_topic_score_gemma":0.0019220575,"teacher_disagreement_score":0.0043205055,"about_ca_system_score_codex":0.0004500804,"about_ca_system_score_gemma":0.0004260748,"threshold_uncertainty_score":0.01445353},"labels":[],"label_agreement":null},{"id":"W4388099189","doi":"10.18280/ts.400536","title":"A New Deep Learning-Based Restoration Method for Colour Images","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Deep learning; Computer science; Computer vision; Image restoration; Image (mathematics); Image processing","score_opus":0.02191710735487966,"score_gpt":0.3062397973469447,"score_spread":0.284322689992065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388099189","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":[0.0042296993,0.0003568336,0.9936469,0.0001195968,0.00007881455,0.000022541288,0.000031699215,0.0005113102,0.0010025188],"genre_scores_gemma":[0.21155581,0.0012034343,0.769425,0.00054829876,0.0001643537,0.00012207884,0.00032779094,0.00028111422,0.016372101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977154,0.000026004842,0.000011573236,0.00005961203,0.000097491706,0.000033811648],"domain_scores_gemma":[0.99977607,0.000038706872,0.000019640549,0.000028361852,0.00011338067,0.00002386791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048369987,0.000665985,0.00076772936,0.000751678,0.00032524447,0.0005944541,0.0011921058,0.00087290886,0.0023513741],"category_scores_gemma":[0.00076054776,0.00034019802,0.0010801723,0.00063783984,0.00047498738,0.001096046,0.00092549424,0.0014770215,0.00068953366],"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":[0.00016698953,0.00013991645,0.0007776366,0.00025295353,0.00015137884,0.00011149529,0.00008937615,0.18040982,0.05182912,0.015765235,0.0069012986,0.74340487],"study_design_scores_gemma":[0.0000075210587,0.000026526544,0.00012956646,0.000007581355,0.000016076385,0.000051714334,0.0000047443996,0.99021614,0.0054201637,0.0019134007,0.0021982314,0.000008296947],"about_ca_topic_score_codex":0.0054172003,"about_ca_topic_score_gemma":0.007021394,"teacher_disagreement_score":0.0054172003,"about_ca_system_score_codex":0.00065965735,"about_ca_system_score_gemma":0.00095079973,"threshold_uncertainty_score":0.010771334},"labels":[],"label_agreement":null},{"id":"W4389195994","doi":"10.1016/j.eswa.2023.122710","title":"A multi-level wavelet-based underwater image enhancement network with color compensation prior","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Wavelet; Wavelet transform; Pattern recognition (psychology); Normalization (sociology); Frequency domain; Color image; Image processing; Image (mathematics)","score_opus":0.03348413158142738,"score_gpt":0.2829039192260364,"score_spread":0.24941978764460904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389195994","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":[0.027008634,0.00025043136,0.96935844,0.000090547954,0.000045953726,0.000034011613,0.000045274774,0.00042182562,0.002744872],"genre_scores_gemma":[0.34923154,0.0006856205,0.63754684,0.0001290645,0.000054576223,0.00007779737,0.00020006397,0.00006693767,0.012007539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998406,0.000019279698,0.0000065952786,0.000039300325,0.000075818476,0.000018367364],"domain_scores_gemma":[0.9998555,0.000029957151,0.000014939165,0.000020668058,0.00006732419,0.000011641049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023407713,0.00037624995,0.00039203544,0.00038417467,0.00022630012,0.00032384225,0.00060959737,0.00040234847,0.0017577215],"category_scores_gemma":[0.0003746865,0.00023775313,0.00028462242,0.00038655155,0.00020722648,0.00070098095,0.0006368066,0.00046710603,0.0007158056],"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":[0.0003680075,0.00016893176,0.0009539954,0.0001236733,0.000050665993,0.00015293244,0.0000698413,0.07675997,0.3070326,0.0051481714,0.0027067102,0.60646445],"study_design_scores_gemma":[0.000013871684,0.00012305398,0.00076329964,0.000015259433,0.000040096424,0.00016715036,0.00001746159,0.9147267,0.079710625,0.000773243,0.0036282067,0.000021073345],"about_ca_topic_score_codex":0.002294213,"about_ca_topic_score_gemma":0.0043569426,"teacher_disagreement_score":0.002294213,"about_ca_system_score_codex":0.00029340334,"about_ca_system_score_gemma":0.00048045907,"threshold_uncertainty_score":0.0058801174},"labels":[],"label_agreement":null},{"id":"W4389272191","doi":"10.1007/978-3-031-47966-3_18","title":"Unsupervised Deep-Learning Approach for Underwater Image Enhancement","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Underwater; Computer science; Artificial intelligence; Deep learning; Image (mathematics); Similarity (geometry); Ground truth; Inference; Computer vision; Image quality; Pattern recognition (psychology); Geology","score_opus":0.022051903179661915,"score_gpt":0.258757274960943,"score_spread":0.23670537178128107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389272191","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":[0.0074394094,0.0004716388,0.9894521,0.00006477812,0.000034512464,0.00001971044,0.00007331919,0.0006581306,0.0017862347],"genre_scores_gemma":[0.21885622,0.0010979312,0.756172,0.00018212562,0.00008113345,0.00007856579,0.000572262,0.00027193088,0.022687813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989915,0.000014753349,0.000004833432,0.00002432184,0.000036893525,0.000019894422],"domain_scores_gemma":[0.99984944,0.00004484431,0.000013388592,0.000024749277,0.000058622754,0.000008937713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030616863,0.0006314682,0.00052305177,0.000390786,0.00017460743,0.0004215628,0.0008487097,0.0006276219,0.0027573628],"category_scores_gemma":[0.00039553313,0.00029963147,0.00059587625,0.00047284202,0.00025707728,0.00055529707,0.0008215166,0.0009985314,0.0009462941],"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":[0.00012232616,0.00012517917,0.0004975844,0.00015737441,0.00007939998,0.00007572137,0.00005030732,0.17613894,0.08125704,0.008006548,0.00486443,0.72862506],"study_design_scores_gemma":[0.0000034259351,0.000029012092,0.00025125008,0.000009671761,0.000015169424,0.000047189773,0.000007459759,0.9796011,0.015371067,0.0023543136,0.002304475,0.000005988074],"about_ca_topic_score_codex":0.002690299,"about_ca_topic_score_gemma":0.0053801667,"teacher_disagreement_score":0.0027573628,"about_ca_system_score_codex":0.0003366343,"about_ca_system_score_gemma":0.0005086503,"threshold_uncertainty_score":0.009224296},"labels":[],"label_agreement":null},{"id":"W4390150961","doi":"10.1016/j.engappai.2023.107692","title":"Visual Attention and ODE-inspired Fusion Network for image dehazing","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Ode; Artificial intelligence; Runge–Kutta methods; Ordinary differential equation; Leverage (statistics); Fuse (electrical); Computer vision; Machine learning; Differential equation; Applied mathematics","score_opus":0.01931160014540108,"score_gpt":0.29806699251730734,"score_spread":0.2787553923719063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390150961","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":[0.10881353,0.0010408886,0.88446224,0.0003016477,0.00020395493,0.00004553654,0.00007370012,0.0005179013,0.0045405757],"genre_scores_gemma":[0.9045631,0.00041986746,0.08788593,0.00017294496,0.00008145031,0.000032588217,0.00010455146,0.00003552973,0.0067041544],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998535,0.000016054031,0.0000073605092,0.00004454765,0.000045094777,0.000033415534],"domain_scores_gemma":[0.9997929,0.00005027369,0.00002211145,0.000017108643,0.00010090718,0.00001668829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004794768,0.0004480742,0.0005350046,0.00046659936,0.00029672362,0.00047407183,0.0008313349,0.000787332,0.0011345402],"category_scores_gemma":[0.00084901345,0.00020249808,0.000508812,0.0003994514,0.00029509945,0.0008004942,0.0008192249,0.00065643474,0.0001642722],"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":[0.00038928038,0.00031891107,0.002242565,0.00013566199,0.00018679694,0.00016528598,0.000112903945,0.41394675,0.09060618,0.012583508,0.003093226,0.47621894],"study_design_scores_gemma":[0.0000033618392,0.000029598761,0.00034300654,0.0000024163437,0.000013621216,0.000023546843,0.000004246534,0.9931166,0.0051329266,0.0010633885,0.0002627423,0.0000045178604],"about_ca_topic_score_codex":0.005468942,"about_ca_topic_score_gemma":0.0050208666,"teacher_disagreement_score":0.005468942,"about_ca_system_score_codex":0.0006238564,"about_ca_system_score_gemma":0.00043743508,"threshold_uncertainty_score":0.010874212},"labels":[],"label_agreement":null},{"id":"W4390190318","doi":"10.1109/iccvw60793.2023.00089","title":"IDTransformer: Transformer for Intrinsic Image Decomposition","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Invariant (physics); Computer science; Artificial intelligence; Shading; Prior probability; Computer vision; Photometric stereo; Transformer; Algorithm; Image (mathematics); Mathematics; Physics; Computer graphics (images)","score_opus":0.013501955572723243,"score_gpt":0.30783303625481356,"score_spread":0.2943310806820903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390190318","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":[0.0041521015,0.00021497751,0.983238,0.00009358029,0.00007307234,0.00006263509,0.0005191456,0.00931489,0.0023315747],"genre_scores_gemma":[0.15097137,0.0005818353,0.82828087,0.00039896945,0.00006966141,0.00022776278,0.004242868,0.0028674577,0.012359225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967074,0.00003478387,0.00001439684,0.000116586336,0.00011495673,0.00004849074],"domain_scores_gemma":[0.99972874,0.000058028498,0.00002347886,0.00011732615,0.000046288453,0.000026182797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006356606,0.0011979738,0.00065002555,0.000898164,0.0002459548,0.0010368071,0.0016825725,0.0007281756,0.009820271],"category_scores_gemma":[0.0016364183,0.00041437458,0.0010396512,0.00067160226,0.00059170864,0.0017470656,0.002034423,0.0019555665,0.0057767457],"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":[0.0003008482,0.00015459982,0.0008857966,0.00028881835,0.00010655658,0.00017323343,0.0001098748,0.04424633,0.06278472,0.03395859,0.040560395,0.8164301],"study_design_scores_gemma":[0.000049479982,0.0001275998,0.0007525215,0.000036121975,0.000036886657,0.0004501686,0.000045471337,0.8555575,0.06731053,0.04085555,0.034740124,0.00003805818],"about_ca_topic_score_codex":0.0021051883,"about_ca_topic_score_gemma":0.0034979957,"teacher_disagreement_score":0.009820271,"about_ca_system_score_codex":0.00062026444,"about_ca_system_score_gemma":0.0006472428,"threshold_uncertainty_score":0.032852113},"labels":[],"label_agreement":null},{"id":"W4390190377","doi":"10.1109/iccvw60793.2023.00030","title":"Data Efficient Single Image Dehazing via Adversarial Auto-Augmentation and extended Atmospheric Scattering Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Faurecia (Canada)","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Prior probability; Image restoration; Encoder; Regularization (linguistics); Pattern recognition (psychology); Image (mathematics); Computer vision; Image processing","score_opus":0.03535594029357073,"score_gpt":0.2890037861150139,"score_spread":0.2536478458214432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390190377","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":[0.026976703,0.0001190858,0.97092,0.00012212803,0.000022551054,0.00002717604,0.00003947128,0.00049320265,0.0012797425],"genre_scores_gemma":[0.6860007,0.00021444033,0.30908135,0.0001877729,0.000033014952,0.000096859745,0.00021569432,0.00016162648,0.0040085483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973243,0.000061578794,0.000010022501,0.000059440415,0.00010480192,0.000031781103],"domain_scores_gemma":[0.99925584,0.00033439108,0.00009450852,0.00016816871,0.00011492229,0.00003218125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081141386,0.00074738136,0.00055917504,0.00035076734,0.0001993796,0.0004518593,0.0010600868,0.0006157098,0.0010983167],"category_scores_gemma":[0.0017288785,0.00031462839,0.00053438486,0.00027493105,0.0008012983,0.00087829557,0.0012444811,0.0012679314,0.00030568274],"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":[0.000104242856,0.000058163096,0.000482752,0.00005533382,0.000035416415,0.000065519314,0.00006041116,0.9068065,0.019175142,0.0050160005,0.0010224617,0.06711801],"study_design_scores_gemma":[0.0000020655357,0.000013756909,0.000047209873,0.0000023252608,0.0000023460877,0.00001955074,0.0000029057808,0.9951381,0.0035778428,0.0009880445,0.00020304607,0.0000027550868],"about_ca_topic_score_codex":0.0017547781,"about_ca_topic_score_gemma":0.0020031303,"teacher_disagreement_score":0.0017547781,"about_ca_system_score_codex":0.00037363477,"about_ca_system_score_gemma":0.00048492162,"threshold_uncertainty_score":0.0042912364},"labels":[],"label_agreement":null},{"id":"W4390872162","doi":"10.1109/iccv51070.2023.00741","title":"Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Luminance; High dynamic range; Artificial intelligence; Pixel; Illuminance; Computer vision; Calibration; High-dynamic-range imaging; Range (aeronautics); Computer graphics (images); Dynamic range; Mathematics; Optics","score_opus":0.01923343781638274,"score_gpt":0.27821547088272996,"score_spread":0.2589820330663472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390872162","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033352394,0.0022582556,0.025063287,0.0005010063,0.0005030543,0.0004310955,0.8907282,0.036400467,0.010762341],"genre_scores_gemma":[0.026811423,0.00034281574,0.026129926,0.00019884808,0.00005480847,0.0002464833,0.94330376,0.0009909037,0.0019210461],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998743,0.00013448067,0.0000808785,0.0004486277,0.00042367805,0.00016940423],"domain_scores_gemma":[0.9987924,0.00011886973,0.00008760133,0.0005260876,0.0003726376,0.000102249534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000779333,0.0025142853,0.001434883,0.0022011988,0.00081126584,0.0018383502,0.003296495,0.0017910985,0.009333515],"category_scores_gemma":[0.0025329515,0.00064414437,0.0017291699,0.0028345007,0.0005624365,0.0017585818,0.002048236,0.0023934643,0.017424507],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008836589,0.00067058677,0.011832323,0.0019193743,0.00039628515,0.00027590097,0.00017063707,0.010967824,0.014084276,0.0019734525,0.83288085,0.12394481],"study_design_scores_gemma":[0.00055124867,0.00035979008,0.07813437,0.0006891489,0.00022325113,0.0015837346,0.0004955134,0.09608351,0.041472856,0.0067847995,0.77321345,0.00040836137],"about_ca_topic_score_codex":0.019277938,"about_ca_topic_score_gemma":0.04775252,"teacher_disagreement_score":0.019277938,"about_ca_system_score_codex":0.0011998952,"about_ca_system_score_gemma":0.0011084519,"threshold_uncertainty_score":0.03833145},"labels":[],"label_agreement":null},{"id":"W4390873415","doi":"10.1109/iccv51070.2023.01202","title":"Examining Autoexposure for Challenging Scenes","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Canada First Research Excellence Fund","keywords":"Computer science; Shutter; Artificial intelligence; Point (geometry); Range (aeronautics); Computer vision; Software; Mathematics; Engineering","score_opus":0.0798597046753562,"score_gpt":0.2984972285027806,"score_spread":0.2186375238274244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390873415","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":[0.6225294,0.012366187,0.20144485,0.0014769384,0.001807051,0.001395662,0.09167525,0.02928149,0.0380232],"genre_scores_gemma":[0.6231645,0.003749624,0.22566551,0.0009233169,0.00039062754,0.0003545136,0.13407214,0.0030306869,0.008649157],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905497,0.00009685849,0.000050025847,0.00031995983,0.00030324433,0.00017500679],"domain_scores_gemma":[0.9988764,0.00027591272,0.00009566768,0.000287331,0.00039021563,0.000074568976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075631245,0.0012857553,0.0007734756,0.0021805344,0.0006483658,0.0013457977,0.00089969457,0.00079454336,0.0033692007],"category_scores_gemma":[0.0028446934,0.0003503255,0.0009581731,0.0011877838,0.00048884214,0.0015271534,0.0013524545,0.0009335535,0.0019956573],"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":[0.0013052903,0.00071209157,0.038847048,0.0040520104,0.0006486881,0.0011667347,0.000893692,0.025145413,0.10074816,0.003294522,0.16711563,0.65607065],"study_design_scores_gemma":[0.0002157283,0.0013415201,0.26265734,0.0011434805,0.00048026151,0.009115088,0.003118716,0.22237833,0.17505056,0.0112858545,0.31285617,0.0003569651],"about_ca_topic_score_codex":0.005729869,"about_ca_topic_score_gemma":0.024306403,"teacher_disagreement_score":0.005729869,"about_ca_system_score_codex":0.00055151875,"about_ca_system_score_gemma":0.00047710084,"threshold_uncertainty_score":0.011393011},"labels":[],"label_agreement":null},{"id":"W4391095562","doi":"10.1109/icicsp59554.2023.10390579","title":"FPGA Implementation of Multi-Spectral HDR Imaging","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Toronto","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; High dynamic range; Field-programmable gate array; Image sensor; Computer graphics (images); Dynamic range; Computer hardware","score_opus":0.025975638277956542,"score_gpt":0.3436136635606767,"score_spread":0.3176380252827201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391095562","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":[0.06654617,0.0009895975,0.8919707,0.0002850133,0.00028469707,0.00024084628,0.00043113568,0.011861576,0.027390229],"genre_scores_gemma":[0.61309826,0.00042784927,0.3716443,0.00028555427,0.00007952715,0.00015084595,0.00057325495,0.00024108974,0.013499347],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997634,0.000029059773,0.00001295451,0.00004709729,0.00010234407,0.000045104818],"domain_scores_gemma":[0.999835,0.00003825497,0.000016949138,0.000039281855,0.000060260416,0.000010170721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018486775,0.00049338414,0.00021029054,0.00044603762,0.0002135945,0.00057810824,0.00088741543,0.00030914517,0.009825727],"category_scores_gemma":[0.00037121872,0.00017947149,0.0001878105,0.00022921104,0.00014817675,0.0005121715,0.00025543664,0.00034327482,0.0022662869],"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":[0.0010836226,0.00025037298,0.002804423,0.000639958,0.00014905552,0.000916118,0.00022041227,0.044903554,0.2532462,0.013653371,0.02062041,0.6615126],"study_design_scores_gemma":[0.0002751345,0.0014055987,0.004864842,0.00011339918,0.000118625845,0.0020118186,0.00012055289,0.46578148,0.43284646,0.003499838,0.088870876,0.000091391106],"about_ca_topic_score_codex":0.001466647,"about_ca_topic_score_gemma":0.002126879,"teacher_disagreement_score":0.009825727,"about_ca_system_score_codex":0.00042914835,"about_ca_system_score_gemma":0.00033402216,"threshold_uncertainty_score":0.032870352},"labels":[],"label_agreement":null},{"id":"W4391102977","doi":"10.3390/jimaging10010028","title":"Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion Approach","year":2024,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":15,"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 Saskatchewan","funders":"Kermanshah University of Medical Sciences","keywords":"Artificial intelligence; Image fusion; Computer science; Computer vision; Filter (signal processing); Wavelet transform; Endoscope; Fusion; Wavelet; Image quality; Process (computing); Set (abstract data type); Image (mathematics); Pattern recognition (psychology); Radiology; Medicine","score_opus":0.010169694028764545,"score_gpt":0.28268526408759687,"score_spread":0.2725155700588323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391102977","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":[0.012649217,0.00034222135,0.98618996,0.0000547445,0.000018852517,0.000023070736,0.000013651242,0.00014476986,0.0005634756],"genre_scores_gemma":[0.28880724,0.0012292446,0.7072652,0.00008324961,0.000054118467,0.00007480245,0.00013218744,0.00006249782,0.0022914615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970335,0.000051477633,0.000018290839,0.000056403722,0.00013834123,0.000032095148],"domain_scores_gemma":[0.99980503,0.00005112687,0.000030542382,0.000024663217,0.00007614816,0.000012549986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007435346,0.00054104,0.0005951546,0.00073244714,0.00017389696,0.00049483,0.00049060385,0.0007469574,0.0006542765],"category_scores_gemma":[0.00081144174,0.00024042361,0.0009120178,0.00068333145,0.00031172892,0.0008526982,0.00057843677,0.00068271527,0.00031806534],"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":[0.00038954124,0.00018035772,0.0012390503,0.00022818687,0.00015856908,0.00020246507,0.00017466937,0.13279738,0.19839726,0.009806529,0.0015075394,0.65491843],"study_design_scores_gemma":[0.00001309121,0.00014995548,0.0009265803,0.000016183627,0.00006383314,0.0002473204,0.000024545834,0.9611945,0.03329768,0.0021228374,0.001923643,0.000019752233],"about_ca_topic_score_codex":0.00093713915,"about_ca_topic_score_gemma":0.0007107825,"teacher_disagreement_score":0.00093713915,"about_ca_system_score_codex":0.00026764607,"about_ca_system_score_gemma":0.00035650827,"threshold_uncertainty_score":0.0039322376},"labels":[],"label_agreement":null},{"id":"W4391661581","doi":"10.1109/tetci.2024.3358200","title":"Joint Self-Supervised Enhancement and Denoising of Low-Light Images","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Carleton University","funders":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Noise reduction; Computer vision; Color constancy; Noise (video); Pattern recognition (psychology); Feature (linguistics); Supervised learning; Global illumination; Image (mathematics); Artificial neural network","score_opus":0.021639192825561107,"score_gpt":0.2938804522355884,"score_spread":0.2722412594100273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391661581","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":[0.08729854,0.0003797442,0.9097012,0.00010843863,0.00004075409,0.00005775369,0.00007817442,0.0007601834,0.0015751485],"genre_scores_gemma":[0.57258123,0.0004995001,0.42146817,0.00015209403,0.00007200622,0.00008836448,0.0004189083,0.00021818568,0.004501488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996117,0.00007107815,0.000017016511,0.00013114006,0.00011872808,0.00005040047],"domain_scores_gemma":[0.9993154,0.00016074638,0.000109994726,0.00014605072,0.00023355962,0.00003423549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001034653,0.00080687786,0.00089422794,0.000723669,0.0002542742,0.00061175675,0.0007759202,0.0005739918,0.0006900145],"category_scores_gemma":[0.0015418482,0.00029843408,0.0007546426,0.0004263383,0.000625268,0.0009702177,0.0008251156,0.00079560967,0.00031160115],"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":[0.00060943904,0.0003313017,0.0033802956,0.00039928022,0.00020586984,0.00021526123,0.00030070436,0.16616382,0.26449856,0.0042825057,0.0026873767,0.5569256],"study_design_scores_gemma":[0.00001389178,0.00008419561,0.0022867203,0.000016118573,0.000044191416,0.00014996057,0.000038707625,0.9129619,0.08050064,0.002406917,0.0014775713,0.00001911811],"about_ca_topic_score_codex":0.0008975368,"about_ca_topic_score_gemma":0.0019763967,"teacher_disagreement_score":0.001034653,"about_ca_system_score_codex":0.00031089256,"about_ca_system_score_gemma":0.000440026,"threshold_uncertainty_score":0.0054718256},"labels":[],"label_agreement":null},{"id":"W4391661592","doi":"10.1109/tetci.2024.3359051","title":"Self-Supervised Adaptive Illumination Estimation for Low-Light Image Enhancement","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Smoothing; Artificial intelligence; Computer science; Gaussian blur; Computer vision; Pattern recognition (psychology); Feature (linguistics); Kernel (algebra); Image (mathematics); Image restoration; Mathematics; Image processing","score_opus":0.022616150682669624,"score_gpt":0.31275846648426914,"score_spread":0.2901423158015995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391661592","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":[0.056369934,0.0006703529,0.93918616,0.00012244307,0.00004937857,0.000054552995,0.00010872774,0.0018735335,0.0015650137],"genre_scores_gemma":[0.6018869,0.00073492207,0.39008835,0.0002573465,0.00008506603,0.00008348923,0.00067198655,0.0003389634,0.0058529214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997938,0.000033525834,0.000008884884,0.00007336485,0.000058067213,0.00003227572],"domain_scores_gemma":[0.99971336,0.000076898286,0.000044459925,0.000056412893,0.000093984956,0.000014772963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005649011,0.00063014793,0.0006545029,0.0005552717,0.00018710304,0.00052667153,0.0008481531,0.0004218961,0.0010415427],"category_scores_gemma":[0.00097447686,0.0002641026,0.0005989319,0.00043566525,0.0003584457,0.0009573761,0.0005979414,0.0008403907,0.00045797683],"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":[0.0003870333,0.00022000828,0.0029006342,0.00024013472,0.00014919022,0.00010962508,0.0001183088,0.111832425,0.13239212,0.0029750078,0.0047592428,0.7439163],"study_design_scores_gemma":[0.000012722971,0.00005791348,0.0017639386,0.000013998206,0.00004180332,0.00010958201,0.000019022313,0.9490012,0.044900555,0.0023406337,0.0017250088,0.0000136538565],"about_ca_topic_score_codex":0.0011481412,"about_ca_topic_score_gemma":0.0022573692,"teacher_disagreement_score":0.0011481412,"about_ca_system_score_codex":0.00039559288,"about_ca_system_score_gemma":0.00030917965,"threshold_uncertainty_score":0.003484249},"labels":[],"label_agreement":null},{"id":"W4391697048","doi":"10.1109/tip.2024.3362153","title":"UCL-Dehaze: Toward Real-World Image Dehazing via Unsupervised Contrastive Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":172,"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":"Basic and Applied Basic Research Foundation of Guangdong Province; Shenzhen Science and Technology Innovation Program; National Defense Basic Scientific Research Program of China; Lingnan University; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Image editing; Embedding; Image (mathematics); Generalization; Leverage (statistics); Deep learning; Computer vision; Pattern recognition (psychology); Mathematics","score_opus":0.0164759686231717,"score_gpt":0.28861610939888155,"score_spread":0.27214014077570986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391697048","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":[0.062081996,0.0005560778,0.93217176,0.0002990166,0.000059422422,0.00007457465,0.00008480253,0.0014094238,0.003263048],"genre_scores_gemma":[0.6045274,0.00049820216,0.3862464,0.00056500884,0.000061742365,0.00012519014,0.00043915267,0.0002705451,0.0072662607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973994,0.00004829891,0.00000806594,0.00008584728,0.00008717166,0.000030668933],"domain_scores_gemma":[0.99920195,0.00030651042,0.00011013695,0.00018270084,0.00015900338,0.000039654784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009374526,0.00088974164,0.0005040363,0.000522976,0.00029165938,0.00057203765,0.0015760732,0.0010229109,0.00122965],"category_scores_gemma":[0.0023187404,0.00035854857,0.0005698422,0.00024610845,0.0010878708,0.0017389661,0.0015678729,0.0019719847,0.0004558909],"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":[0.00021059145,0.00024447436,0.0021125758,0.00021033754,0.00009964385,0.00015258038,0.00016589602,0.63110787,0.057341095,0.010708995,0.003791219,0.2938548],"study_design_scores_gemma":[0.0000063309435,0.00006653378,0.00023461808,0.000009971966,0.000007688466,0.000050265462,0.000012684531,0.980232,0.015353856,0.0029342326,0.0010832318,0.00000859875],"about_ca_topic_score_codex":0.0015653089,"about_ca_topic_score_gemma":0.0028045413,"teacher_disagreement_score":0.0015760732,"about_ca_system_score_codex":0.0005782827,"about_ca_system_score_gemma":0.00043276284,"threshold_uncertainty_score":0.0049577355},"labels":[],"label_agreement":null},{"id":"W4392152399","doi":"10.1109/globecom54140.2023.10436757","title":"End-Edge Coordinated Joint Encoding and Neural Enhancement for Low-Light Video Analytics","year":2023,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 Windsor","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hubei Province; Henan University of Science and Technology; National Natural Science Foundation of China","keywords":"Encoding (memory); Computer science; Joint (building); Analytics; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Computer vision; Data mining; Engineering","score_opus":0.031548776550971186,"score_gpt":0.2801304745264532,"score_spread":0.248581697975482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392152399","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":[0.048600215,0.0003928764,0.94637185,0.00012692338,0.00006747481,0.00006957819,0.000047358557,0.001870216,0.0024534552],"genre_scores_gemma":[0.81442875,0.00019551515,0.1808687,0.00021928403,0.00006981414,0.000059235572,0.00011580907,0.0000758808,0.0039670398],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996729,0.00004364249,0.000014887829,0.000115241055,0.00009567222,0.000057635178],"domain_scores_gemma":[0.99950945,0.00013864932,0.000058436126,0.00008941771,0.0001561087,0.000048055732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034884494,0.00065922184,0.00050701614,0.00031580613,0.00039275215,0.0007320749,0.0012569819,0.000609483,0.0018405057],"category_scores_gemma":[0.00091787166,0.00025257486,0.0002285921,0.00023032744,0.0003415351,0.0014758854,0.0009652651,0.00079601124,0.0005227552],"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":[0.00192925,0.000714511,0.006099281,0.00027759463,0.0001246596,0.00066001544,0.00040251776,0.12542616,0.26667914,0.0089554535,0.0053283894,0.58340317],"study_design_scores_gemma":[0.000028299197,0.00020966206,0.0011355311,0.0000135031405,0.00003604548,0.00014431344,0.00004742881,0.93222976,0.061001983,0.0026952212,0.0024370598,0.000021114922],"about_ca_topic_score_codex":0.0018645623,"about_ca_topic_score_gemma":0.003072189,"teacher_disagreement_score":0.0018645623,"about_ca_system_score_codex":0.0003656531,"about_ca_system_score_gemma":0.00045687714,"threshold_uncertainty_score":0.0061571},"labels":[],"label_agreement":null},{"id":"W4392756246","doi":"10.1049/cit2.12310","title":"Multi‐granularity feature enhancement network for maritime ship detection","year":2024,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Image Enhancement 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 Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Granularity; Feature (linguistics); Computer science; Artificial intelligence; Benchmark (surveying); Convolutional neural network; Pattern recognition (psychology); Data mining; Remote sensing; Computer vision; Geography; Cartography","score_opus":0.02069290628437447,"score_gpt":0.2924868966955325,"score_spread":0.27179399041115804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392756246","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":[0.3256118,0.004755885,0.6508424,0.0006881909,0.00040330694,0.00025899275,0.0018070696,0.00883415,0.0067981896],"genre_scores_gemma":[0.82681346,0.00076508103,0.16182327,0.00035726491,0.00011280631,0.0000867812,0.003107572,0.000099344375,0.0068345466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969184,0.00003565714,0.000016727472,0.000109793815,0.00008981764,0.000056143544],"domain_scores_gemma":[0.9996655,0.00008945205,0.00004348832,0.000060788047,0.00012171803,0.000019152012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066139956,0.00096222403,0.00076499296,0.0012533404,0.00026697325,0.0005124247,0.0010916106,0.0006823738,0.0012089267],"category_scores_gemma":[0.0010063804,0.00023266952,0.0006920248,0.0006802606,0.00023294009,0.0012351191,0.00064567477,0.0007950012,0.00050050067],"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":[0.0008826401,0.00048647745,0.0061803246,0.00015800384,0.00023491413,0.0002829514,0.00005820419,0.17053366,0.037759867,0.0018427976,0.013799582,0.76778054],"study_design_scores_gemma":[0.000011804985,0.00008664162,0.0017666575,0.000009778028,0.000036226855,0.00007690523,0.000012263903,0.98600835,0.009382812,0.0006729573,0.0019236048,0.0000119583965],"about_ca_topic_score_codex":0.0061749844,"about_ca_topic_score_gemma":0.0074786833,"teacher_disagreement_score":0.0061749844,"about_ca_system_score_codex":0.00064889627,"about_ca_system_score_gemma":0.00043736008,"threshold_uncertainty_score":0.01227808},"labels":[],"label_agreement":null},{"id":"W4393034911","doi":"10.1109/tim.2024.3372230","title":"Low-FaceNet: Face Recognition-Driven Low-Light Image Enhancement","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Facial recognition system; Computer vision; Artificial intelligence; Face (sociological concept); Computer science; Pattern recognition (psychology)","score_opus":0.026304807646043267,"score_gpt":0.2596976998678941,"score_spread":0.23339289222185083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393034911","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":[0.11320173,0.0012551952,0.8315469,0.00044920013,0.00043449164,0.0005732723,0.0028032511,0.036127456,0.0136084575],"genre_scores_gemma":[0.42099535,0.0006123163,0.5456549,0.0009798552,0.00009100784,0.0006264241,0.008299701,0.0009698277,0.021770658],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998104,0.000019509273,0.0000051883885,0.00007086809,0.000059437625,0.000034522407],"domain_scores_gemma":[0.99981624,0.00004678541,0.000018921124,0.000049357688,0.000053489868,0.000015205885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004612557,0.0010187606,0.00050619955,0.000529768,0.0002525332,0.00050814514,0.0021258206,0.00064716884,0.0053816983],"category_scores_gemma":[0.0009850955,0.0003766987,0.0004887997,0.00025191662,0.00035793622,0.0011204612,0.0010548935,0.0009373556,0.0019039524],"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":[0.0006373824,0.00051509397,0.0034243471,0.00039743425,0.00019658868,0.00025450316,0.00010680381,0.068743646,0.106010094,0.006383608,0.043499406,0.7698311],"study_design_scores_gemma":[0.000051718445,0.0002843221,0.0020808345,0.00003728794,0.000051460298,0.00029126427,0.000031232765,0.88686025,0.09099884,0.004811882,0.014458286,0.000042671567],"about_ca_topic_score_codex":0.0032081308,"about_ca_topic_score_gemma":0.007235945,"teacher_disagreement_score":0.0053816983,"about_ca_system_score_codex":0.00078040647,"about_ca_system_score_gemma":0.00046309948,"threshold_uncertainty_score":0.018003583},"labels":[],"label_agreement":null},{"id":"W4395076625","doi":"10.18280/ria.380229","title":"Enhancement of Very Low Light Images Using the YIQ Space Based on the CLAHE and Sigmoid Mapping with High Colour Restoration","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sigmoid function; Computer vision; Mathematics; Artificial intelligence; Computer science","score_opus":0.027749811406278295,"score_gpt":0.25767174095808615,"score_spread":0.22992192955180785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395076625","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":[0.17817952,0.00087389007,0.8168264,0.0001531314,0.000076978824,0.00006429901,0.00004512862,0.00050747144,0.0032732603],"genre_scores_gemma":[0.68828964,0.0008253644,0.30524218,0.00011719247,0.00004436851,0.000049308852,0.00008238706,0.00010654684,0.0052429363],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997514,0.00004759566,0.000012015706,0.00004675419,0.000115829025,0.000026365859],"domain_scores_gemma":[0.99959296,0.00013699137,0.000048203645,0.000054210257,0.00014791927,0.000019667687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005428893,0.0003933539,0.00031861162,0.0005596744,0.00014599727,0.00071466045,0.00035056387,0.0003320106,0.0014635378],"category_scores_gemma":[0.0010347466,0.00013200396,0.0005014025,0.0004476889,0.0004378913,0.00091808336,0.0004899727,0.00046752143,0.00037624422],"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":[0.0006219706,0.00016106071,0.0022845883,0.00040184948,0.000082223734,0.00018437303,0.00022822048,0.029472997,0.53070986,0.005364607,0.00083833013,0.42964998],"study_design_scores_gemma":[0.00004332124,0.0005780888,0.0101063745,0.000047208316,0.000102549246,0.0011550464,0.00014401479,0.44205213,0.5355596,0.0026502993,0.0074734064,0.00008794771],"about_ca_topic_score_codex":0.00051193085,"about_ca_topic_score_gemma":0.00059716596,"teacher_disagreement_score":0.0014635378,"about_ca_system_score_codex":0.00021189505,"about_ca_system_score_gemma":0.00023812926,"threshold_uncertainty_score":0.004895985},"labels":[],"label_agreement":null},{"id":"W4395449669","doi":"10.1016/j.cag.2024.103924","title":"Virtual cleaning of sooty murals in ancient temples using twice colour attenuation prior","year":2024,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Mural; Computer vision; Computer science; Artificial intelligence; Attenuation; Oil painting; Image restoration; Optics; Art; Visual arts; Image (mathematics); Image processing; Painting; Physics","score_opus":0.036098456077417386,"score_gpt":0.3001318443401022,"score_spread":0.2640333882626848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395449669","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":[0.9287185,0.0006062144,0.030207783,0.00028041415,0.0002931563,0.00006748411,0.00012086817,0.00078055676,0.03892509],"genre_scores_gemma":[0.9618129,0.00010909061,0.019348368,0.00005502585,0.000025851214,0.000010988767,0.00008004732,0.00017974648,0.018378047],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984145,0.000019332409,0.0000036822516,0.00003325871,0.0000598622,0.00004246092],"domain_scores_gemma":[0.99983716,0.000036380196,0.000011198427,0.00006221393,0.000026250422,0.0000268118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021709148,0.00033701526,0.00031378542,0.0005877393,0.0013060693,0.0008854699,0.00049672864,0.0006416753,0.010105548],"category_scores_gemma":[0.00057604787,0.00028883407,0.00031474448,0.00031024765,0.00095279346,0.00051914057,0.0011187674,0.00072981836,0.0010004058],"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":[0.004305898,0.0004939863,0.008068833,0.00083705614,0.0001343446,0.0052305437,0.012104721,0.030163392,0.5707812,0.021858027,0.011944493,0.33407748],"study_design_scores_gemma":[0.00033070517,0.0028769085,0.13329253,0.00041363982,0.00037697997,0.008547402,0.018602965,0.114415355,0.46034735,0.013356815,0.24698639,0.00045301623],"about_ca_topic_score_codex":0.0014650571,"about_ca_topic_score_gemma":0.004057458,"teacher_disagreement_score":0.010105548,"about_ca_system_score_codex":0.00022777056,"about_ca_system_score_gemma":0.0002898871,"threshold_uncertainty_score":0.033806443},"labels":[],"label_agreement":null},{"id":"W4396505468","doi":"10.1109/tip.2024.3393390","title":"Learning to Recover Spectral Reflectance From RGB Images","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","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 Manitoba; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; University of Manitoba","keywords":"RGB color model; Artificial intelligence; Computer science; Computer vision; Ground truth; Reflectivity; Pattern recognition (psychology); Optics","score_opus":0.01255581173888426,"score_gpt":0.2886021015375174,"score_spread":0.27604628979863316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396505468","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":[0.024820557,0.00032643447,0.9700965,0.00015296569,0.000055346794,0.00004336603,0.00010398556,0.0021998307,0.002201027],"genre_scores_gemma":[0.4864965,0.00074229995,0.50339776,0.00042566576,0.00012424836,0.00012960262,0.0008137322,0.0005562744,0.007314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996226,0.00006826708,0.0000105246345,0.0001738584,0.00007967572,0.000045020042],"domain_scores_gemma":[0.9995646,0.00012061855,0.000062356856,0.00014013857,0.00009225005,0.000020056901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007662355,0.0013415081,0.0006999176,0.0005663289,0.00030432735,0.0006611254,0.0015648237,0.0009434282,0.001491469],"category_scores_gemma":[0.0021490555,0.0005253632,0.00081478973,0.0005174458,0.0008102449,0.0015626528,0.0010535853,0.0014872808,0.0012214164],"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":[0.00021721878,0.00019618562,0.0016203824,0.00028591795,0.00012690606,0.00015855921,0.00014675662,0.34308505,0.07799047,0.005771999,0.0040914887,0.5663091],"study_design_scores_gemma":[0.0000039457327,0.000042107935,0.00039556914,0.000012068658,0.000014587725,0.000077671946,0.000015013228,0.9816156,0.013953631,0.0026978082,0.0011604126,0.00001169032],"about_ca_topic_score_codex":0.002297245,"about_ca_topic_score_gemma":0.0036022714,"teacher_disagreement_score":0.002297245,"about_ca_system_score_codex":0.000475718,"about_ca_system_score_gemma":0.00052902143,"threshold_uncertainty_score":0.004989445},"labels":[],"label_agreement":null},{"id":"W4396696562","doi":"10.1016/j.jvcir.2024.104163","title":"Convolution-transformer blend pyramid network for underwater image enhancement","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Encoder; Underwater; Transformer; Convolutional neural network; Artificial intelligence; Computer vision; Pattern recognition (psychology); Engineering","score_opus":0.02330316752039279,"score_gpt":0.36545225592502567,"score_spread":0.3421490884046329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396696562","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":[0.02842392,0.0002971009,0.96769017,0.000084283885,0.000051917876,0.00003356862,0.00007036758,0.0005150474,0.0028335901],"genre_scores_gemma":[0.4472265,0.0008546854,0.53856295,0.000116937445,0.000051682116,0.000058676047,0.00033075054,0.00011067234,0.012687208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990666,0.000013673586,0.0000043649175,0.000019259545,0.000040858635,0.0000150888445],"domain_scores_gemma":[0.9998971,0.000024909206,0.000008862207,0.000017691493,0.000043816064,0.000007562442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002226107,0.0003520516,0.00031789741,0.00028719395,0.00017269321,0.0002970378,0.00042425992,0.0003058754,0.002394028],"category_scores_gemma":[0.0003595916,0.00016529336,0.0003064411,0.00046078113,0.0001679497,0.0005869016,0.0004991381,0.00047706923,0.00053756963],"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":[0.00037824424,0.00016175769,0.0011640358,0.0001483588,0.00007298003,0.00017684099,0.000084553016,0.06927833,0.2348507,0.010973245,0.004152042,0.678559],"study_design_scores_gemma":[0.00000992631,0.000084786065,0.00057819457,0.000008024609,0.000032635795,0.00015605999,0.000018223758,0.93379676,0.06033676,0.0018124974,0.003155872,0.00001019995],"about_ca_topic_score_codex":0.0028220867,"about_ca_topic_score_gemma":0.0040525417,"teacher_disagreement_score":0.0028220867,"about_ca_system_score_codex":0.00026755754,"about_ca_system_score_gemma":0.00042202344,"threshold_uncertainty_score":0.008008778},"labels":[],"label_agreement":null},{"id":"W4398132044","doi":"10.1088/1361-6501/ad4dca","title":"Underwater image enhancement via color correction and multi-feature image fusion","year":2024,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Image Enhancement Techniques","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":"PricewaterhouseCoopers (Canada)","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Underwater; Computer vision; Feature (linguistics); Image (mathematics); Image fusion; Color correction; Computer science; Color image; Feature detection (computer vision); Image enhancement; Pattern recognition (psychology); Image processing; Geology","score_opus":0.015072708997525741,"score_gpt":0.2558000399511282,"score_spread":0.24072733095360246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398132044","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":[0.054525513,0.00038367833,0.94238544,0.00008410646,0.000057171204,0.000042167627,0.00003491814,0.000994116,0.0014929184],"genre_scores_gemma":[0.52223927,0.00044777797,0.47448456,0.00010496643,0.000056619043,0.00005181365,0.00012334227,0.00014015725,0.002351482],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959415,0.000043369622,0.000021472302,0.00009411438,0.00020294615,0.000043971],"domain_scores_gemma":[0.99966025,0.000055667115,0.000053592412,0.000058440088,0.00015526051,0.0000167584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041553596,0.000620171,0.00059863256,0.0010117943,0.00023941825,0.0004442751,0.0005478327,0.00044884539,0.0011531382],"category_scores_gemma":[0.0006807869,0.0002678,0.0007352974,0.00076044956,0.00032478588,0.0010914494,0.0007940741,0.0005532728,0.00034503915],"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":[0.00025802097,0.0000986233,0.0015300363,0.00020454837,0.000090436864,0.00017405266,0.00011064431,0.030405404,0.52912444,0.002760471,0.0013411265,0.4339021],"study_design_scores_gemma":[0.000025766081,0.00017392528,0.0032020006,0.000017959548,0.00008963586,0.0004731018,0.00004797157,0.59152883,0.39838162,0.0014656752,0.0045310245,0.00006249326],"about_ca_topic_score_codex":0.0010769079,"about_ca_topic_score_gemma":0.0009016293,"teacher_disagreement_score":0.0011531382,"about_ca_system_score_codex":0.00028615698,"about_ca_system_score_gemma":0.0003112253,"threshold_uncertainty_score":0.0038576126},"labels":[],"label_agreement":null},{"id":"W4398781982","doi":"10.1145/3665498","title":"Light-Aware Contrastive Learning for Low-Light Image Enhancement","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"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":"Computer science; Artificial intelligence; Feature (linguistics); Exploit; Computer vision; Noise (video); Image (mathematics); Global illumination; Pattern recognition (psychology); Regularization (linguistics); Representation (politics)","score_opus":0.014691638935582016,"score_gpt":0.30149225820239683,"score_spread":0.2868006192668148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398781982","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":[0.026573721,0.0007176557,0.96967655,0.00016883304,0.000046494442,0.00003897053,0.000060738577,0.00082832715,0.0018887327],"genre_scores_gemma":[0.47807243,0.0012175071,0.5107189,0.0006161905,0.00011868838,0.00011409449,0.00041324276,0.00037018297,0.008358683],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978787,0.000034494024,0.000009667894,0.000059516206,0.000077733705,0.000030790914],"domain_scores_gemma":[0.999603,0.00015929733,0.000053447253,0.000060781927,0.00010050025,0.00002304594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052797864,0.0007813903,0.00061094103,0.0005240094,0.00016895101,0.00068580045,0.000904028,0.00060921756,0.0017378721],"category_scores_gemma":[0.0013963924,0.00030585064,0.0007668465,0.0003636088,0.00054860004,0.0009384821,0.0009005432,0.001304375,0.0005327607],"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":[0.00044678358,0.00023116298,0.001428914,0.00039065568,0.00015953273,0.00019216436,0.00013086686,0.23209211,0.18822396,0.010460867,0.0045251166,0.56171787],"study_design_scores_gemma":[0.000016023596,0.000074205134,0.00048435573,0.00001947671,0.000035140856,0.00010398328,0.000012620234,0.9578294,0.035726245,0.0034507643,0.0022337602,0.000013970284],"about_ca_topic_score_codex":0.0013268635,"about_ca_topic_score_gemma":0.00230621,"teacher_disagreement_score":0.0017378721,"about_ca_system_score_codex":0.00047451333,"about_ca_system_score_gemma":0.00044209076,"threshold_uncertainty_score":0.005813718},"labels":[],"label_agreement":null},{"id":"W4399038492","doi":"10.1007/s00371-024-03458-4","title":"TransDehaze: transformer-enhanced texture attention for end-to-end single image dehaze","year":2024,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"End-to-end principle; Transformer; Artificial intelligence; Computer science; Computer vision; Electrical engineering; Engineering","score_opus":0.015376343580582992,"score_gpt":0.30072375436496235,"score_spread":0.28534741078437936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399038492","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":[0.01836781,0.00046157106,0.97460496,0.000082974904,0.00013208187,0.000070126414,0.00018264049,0.0028679299,0.0032299233],"genre_scores_gemma":[0.2979033,0.001009835,0.6705749,0.00037670278,0.00012237401,0.00008747345,0.0009979499,0.0006819192,0.028245423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982494,0.000011854108,0.000005296179,0.000028559753,0.00009981185,0.000029699313],"domain_scores_gemma":[0.99984324,0.000038048875,0.00001112891,0.000037873328,0.000052084022,0.000017583649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022872617,0.0006551051,0.0006100792,0.0005163729,0.00021083641,0.0005072624,0.00085611356,0.00045089118,0.0076799365],"category_scores_gemma":[0.00045897043,0.00023587546,0.000364105,0.0004229183,0.00026272883,0.0007476381,0.0010848121,0.00084680814,0.0020056628],"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":[0.000589813,0.00018518289,0.00036608282,0.00012735702,0.000062926156,0.00016478708,0.00004846316,0.008994994,0.23144327,0.0032396636,0.009636999,0.74514043],"study_design_scores_gemma":[0.000059635713,0.0003107624,0.001477265,0.000028367096,0.00006360048,0.00091608235,0.000060403396,0.61785614,0.35330337,0.004877639,0.02100237,0.000044513665],"about_ca_topic_score_codex":0.0022299816,"about_ca_topic_score_gemma":0.0070760264,"teacher_disagreement_score":0.0076799365,"about_ca_system_score_codex":0.00026483333,"about_ca_system_score_gemma":0.00040547017,"threshold_uncertainty_score":0.025691986},"labels":[],"label_agreement":null},{"id":"W4399951420","doi":"10.1109/icaeee62219.2024.10561664","title":"Reinforcement Learning Based Dark Image Enhancement Through Color Feature Balancing","year":2024,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Université du Québec à Montréal","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Feature (linguistics); Computer vision; Pattern recognition (psychology)","score_opus":0.008563155070031305,"score_gpt":0.2655146841948817,"score_spread":0.2569515291248504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399951420","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":[0.12270093,0.0002572346,0.8712844,0.00016932815,0.0000514324,0.000095475814,0.000015710068,0.0011092423,0.004316188],"genre_scores_gemma":[0.9021404,0.00008585162,0.09527912,0.0000881411,0.000014314526,0.00005016485,0.00001938899,0.000042993634,0.00227974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998097,0.00003638697,0.000009479787,0.000048267557,0.00005979548,0.000036322206],"domain_scores_gemma":[0.9994129,0.00023457222,0.00011833972,0.0000553615,0.00012732422,0.000051519197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005274651,0.0006078969,0.0004663906,0.0002450646,0.00022682463,0.00039126127,0.0007185825,0.00040496126,0.0012137452],"category_scores_gemma":[0.0011985507,0.00017691965,0.00026246143,0.0001271446,0.00049047644,0.0005407773,0.00065076567,0.0006119051,0.00021414916],"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":[0.0003957705,0.00047010952,0.0021652163,0.0001599156,0.000066832756,0.00032003465,0.00021995924,0.6132622,0.13224486,0.0070619904,0.001617933,0.24201514],"study_design_scores_gemma":[0.000014123829,0.000109178865,0.00025896353,0.000005344461,0.000008518567,0.00004713433,0.000007859925,0.98598784,0.012051932,0.00096765906,0.0005331665,0.00000830153],"about_ca_topic_score_codex":0.0012008271,"about_ca_topic_score_gemma":0.0012199797,"teacher_disagreement_score":0.0012137452,"about_ca_system_score_codex":0.00034833004,"about_ca_system_score_gemma":0.00039961803,"threshold_uncertainty_score":0.004060447},"labels":[],"label_agreement":null},{"id":"W4400041310","doi":"10.18280/ts.410346","title":"Adaptive Fine-Tuned AdaBoost and Improved Firefly Algorithm for Skin Cancer Detection","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Firefly algorithm; AdaBoost; Firefly protocol; Computer science; Artificial intelligence; Skin cancer; Pattern recognition (psychology); Computer vision; Algorithm; Cancer; Medicine; Biology; Internal medicine; Support vector machine","score_opus":0.013041042355165702,"score_gpt":0.25952954513151677,"score_spread":0.24648850277635106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400041310","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":[0.07635061,0.0010444579,0.9189571,0.00018960575,0.00017302799,0.00011488551,0.000063707,0.0014209397,0.0016855934],"genre_scores_gemma":[0.55414623,0.00039712945,0.44011554,0.00020093314,0.00007438873,0.00015790715,0.0002066206,0.0001040961,0.0045971395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955875,0.000080760576,0.000024750861,0.0001275194,0.0001262019,0.00008194331],"domain_scores_gemma":[0.99967015,0.000099597964,0.00004086514,0.000025888756,0.00014029111,0.000023190092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011252799,0.0009231525,0.0010315626,0.0010136169,0.00044876646,0.0005233744,0.0013408387,0.0010343948,0.0008715309],"category_scores_gemma":[0.0010033485,0.00034396976,0.0010330544,0.0006770106,0.00037355802,0.00062798394,0.00035361148,0.00083758065,0.00031933753],"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":[0.00048967917,0.0004726318,0.0038834813,0.00016407782,0.00021958229,0.00011427637,0.000105315696,0.5221238,0.034919184,0.0023115447,0.0035449092,0.4316515],"study_design_scores_gemma":[0.000012913292,0.00006164149,0.0004839932,0.000004727986,0.000013559861,0.000025968637,0.0000066123434,0.9956573,0.003057521,0.000268308,0.00039969638,0.000007703771],"about_ca_topic_score_codex":0.010714957,"about_ca_topic_score_gemma":0.008085634,"teacher_disagreement_score":0.010714957,"about_ca_system_score_codex":0.0008990677,"about_ca_system_score_gemma":0.0010848494,"threshold_uncertainty_score":0.021305144},"labels":[],"label_agreement":null},{"id":"W4400390978","doi":"10.1016/j.neucom.2024.128132","title":"Generalizing event-based HDR imaging to various exposures","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Computer science; Event (particle physics); Artificial intelligence; Computer vision; Pattern recognition (psychology); Physics","score_opus":0.011106978602433584,"score_gpt":0.27722994384461336,"score_spread":0.26612296524217977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400390978","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":[0.027479693,0.00026711714,0.96906316,0.00014203836,0.00004822383,0.000063167434,0.00017844852,0.0010621198,0.0016959424],"genre_scores_gemma":[0.29279253,0.001627305,0.696199,0.00027465753,0.000172057,0.000104190185,0.0010529492,0.0007374749,0.0070399046],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981886,0.000031220563,0.000011359132,0.000051310115,0.000057059075,0.000030185818],"domain_scores_gemma":[0.99965334,0.00008336881,0.000033260225,0.0001291043,0.00008029352,0.0000205917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056888134,0.00088517676,0.0004483173,0.000748663,0.00026641996,0.0008349638,0.0007368255,0.0006506993,0.002997954],"category_scores_gemma":[0.0014303058,0.00037554366,0.0009160143,0.00077182776,0.00032764586,0.0009691696,0.000911504,0.0008587222,0.0012262978],"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":[0.00039823138,0.00017451713,0.0026862388,0.0003366518,0.00018568555,0.0004207997,0.00018299115,0.108612575,0.3484026,0.005996659,0.0030285607,0.52957445],"study_design_scores_gemma":[0.000020730846,0.0001467588,0.0119970525,0.000035504218,0.0001425485,0.001115946,0.00009658974,0.7778382,0.18777578,0.009522767,0.011266686,0.000041556996],"about_ca_topic_score_codex":0.0028684158,"about_ca_topic_score_gemma":0.005096331,"teacher_disagreement_score":0.002997954,"about_ca_system_score_codex":0.00030419714,"about_ca_system_score_gemma":0.00048716262,"threshold_uncertainty_score":0.010029197},"labels":[],"label_agreement":null},{"id":"W4400970593","doi":"10.1145/3641234.3671053","title":"Interactive RGB+NIR Photo Editing","year":2024,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Simon Fraser University","funders":"","keywords":"Computer science; RGB color model; Computer graphics (images); Image editing; Computer vision; Artificial intelligence; Image (mathematics)","score_opus":0.00961866820912033,"score_gpt":0.28347512320090973,"score_spread":0.2738564549917894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400970593","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012082485,0.002553621,0.3458602,0.0016187491,0.0038216398,0.00085971603,0.015921826,0.060759146,0.5565226],"genre_scores_gemma":[0.08541984,0.0020428724,0.19142833,0.00068986544,0.00059122226,0.0006292118,0.00773164,0.01021164,0.70125544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998622,0.000018838486,0.0000064754204,0.000033210083,0.000058108464,0.000021089954],"domain_scores_gemma":[0.9996648,0.000113217,0.000009975879,0.00010363716,0.000067963665,0.000040405896],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031270197,0.0011136063,0.0005520249,0.0006915563,0.00034231824,0.0007662055,0.0010278955,0.00058631506,0.49618846],"category_scores_gemma":[0.00083784765,0.00039025734,0.0005127943,0.0004608382,0.00022264551,0.0006502759,0.0012878396,0.00064906164,0.124621436],"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":[0.0006521512,0.0001191001,0.0003091162,0.0010228844,0.00003452159,0.0005761241,0.00009869866,0.0012999746,0.17076975,0.0028921573,0.38896915,0.4332564],"study_design_scores_gemma":[0.00008459215,0.000104132814,0.003640273,0.00020877342,0.000041491112,0.0018588405,0.000083349325,0.009886063,0.060089618,0.004197257,0.9197415,0.000064138156],"about_ca_topic_score_codex":0.0009924967,"about_ca_topic_score_gemma":0.002570918,"teacher_disagreement_score":0.49618846,"about_ca_system_score_codex":0.00015360018,"about_ca_system_score_gemma":0.00023966105,"threshold_uncertainty_score":0.718626},"labels":[],"label_agreement":null},{"id":"W4400971925","doi":"10.2316/j.2024.206-1111","title":"AN IMPROVED ILLUMINATION ADAPTIVE ORB-SLAM3 ALGORITHM","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Orb (optics); Computer science; Computer vision; Artificial intelligence; Algorithm; Image (mathematics)","score_opus":0.00883932470383306,"score_gpt":0.28026935213368065,"score_spread":0.27143002742984756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400971925","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":[0.011008823,0.0004374933,0.98129004,0.00008895712,0.00024950565,0.00006426801,0.00021455917,0.0038958243,0.0027505434],"genre_scores_gemma":[0.09066652,0.00027783276,0.89942616,0.00015607539,0.00011357238,0.00012613733,0.0011609672,0.00057782687,0.0074947947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993624,0.00006675064,0.000022788998,0.00013646172,0.0003325973,0.00007905734],"domain_scores_gemma":[0.999716,0.000026655463,0.000015822267,0.000088460845,0.00013388293,0.000019166282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047261265,0.0007891611,0.0011208176,0.00091940997,0.00059282157,0.00089640403,0.0012845405,0.0009654555,0.005846526],"category_scores_gemma":[0.00074351014,0.000512475,0.0011488867,0.0016731635,0.00027750529,0.00080763135,0.0016134568,0.0012535701,0.0042571086],"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":[0.00023586347,0.00007929656,0.0006697839,0.00009882592,0.00008969535,0.00005492848,0.00005051859,0.025173998,0.040308367,0.0020769804,0.009160725,0.92200106],"study_design_scores_gemma":[0.00015611078,0.00017744939,0.0038134716,0.000034016288,0.00008837892,0.00040693904,0.000047955105,0.92805827,0.028604884,0.004762103,0.03376611,0.00008432257],"about_ca_topic_score_codex":0.004686153,"about_ca_topic_score_gemma":0.0056612208,"teacher_disagreement_score":0.005846526,"about_ca_system_score_codex":0.00032175359,"about_ca_system_score_gemma":0.0011531991,"threshold_uncertainty_score":0.019558609},"labels":[],"label_agreement":null},{"id":"W4401642994","doi":"10.1049/ipr2.13197","title":"Insulator detection based on FA‐YOLO network with improved feature extraction ability","year":2024,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Feature extraction; Computer science; Extraction (chemistry); Insulator (electricity); Pattern recognition (psychology); Artificial intelligence; Optoelectronics; Materials science; Chemistry; Chromatography","score_opus":0.00614672184423073,"score_gpt":0.2595468013850189,"score_spread":0.2534000795407882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401642994","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":[0.2652621,0.00059096474,0.7235721,0.00035205393,0.00012266854,0.00009785475,0.00024546077,0.0031274648,0.0066292337],"genre_scores_gemma":[0.9173867,0.00021527264,0.07642882,0.00014101548,0.000049082293,0.000050812603,0.00035081917,0.000047698897,0.0053297314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984694,0.000011933849,0.000004907559,0.000052755193,0.000035414985,0.000048113965],"domain_scores_gemma":[0.99986744,0.000028897275,0.000019610186,0.000014664621,0.000057978632,0.000011379505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020034233,0.0006554311,0.0005003738,0.0006814325,0.00023191294,0.000498415,0.0007088897,0.00040921426,0.0009807354],"category_scores_gemma":[0.00047104727,0.00019270978,0.00042704708,0.00032224905,0.00022101036,0.0006894381,0.0005034944,0.00040813143,0.0004043157],"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":[0.00057009707,0.00028772675,0.009754811,0.00011082232,0.00011041174,0.00035422767,0.0001268809,0.279605,0.07668423,0.0026104678,0.005854709,0.62393063],"study_design_scores_gemma":[0.0000048308116,0.000040998122,0.001484133,0.000003281315,0.000015901343,0.00004114665,0.000010289596,0.9908674,0.006644924,0.00028990943,0.0005921339,0.0000050154517],"about_ca_topic_score_codex":0.0099758245,"about_ca_topic_score_gemma":0.0124237435,"teacher_disagreement_score":0.0099758245,"about_ca_system_score_codex":0.00057546754,"about_ca_system_score_gemma":0.0004885978,"threshold_uncertainty_score":0.019835532},"labels":[],"label_agreement":null},{"id":"W4402350183","doi":"10.1145/3678582","title":"ChromaFlash: Snapshot Hyperspectral Imaging Using Rolling Shutter Cameras","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Image Enhancement 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":"Universitas Brawijaya","keywords":"Hyperspectral imaging; Artificial intelligence; Computer vision; Computer science; Shutter; Snapshot (computer storage); Pixel; RGB color model; Spectral imaging; Full spectral imaging; Frame rate; Rolling shutter; Remote sensing; Optics; Geography; Physics","score_opus":0.01184275207493695,"score_gpt":0.27079514118570996,"score_spread":0.258952389110773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402350183","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":[0.066962026,0.00059275894,0.92110354,0.000273217,0.00008361505,0.00013002145,0.0004712962,0.0055774054,0.004806087],"genre_scores_gemma":[0.31308118,0.000549681,0.68184507,0.00029932676,0.00004724903,0.000111868794,0.00069504615,0.00030217713,0.0030684809],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984586,0.000024654302,0.0000040142236,0.000044047465,0.000065302636,0.000015993213],"domain_scores_gemma":[0.9997565,0.00007689831,0.000032153806,0.000059658334,0.00005080366,0.000023963004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003248156,0.0004692799,0.00025384157,0.00031438912,0.00019583521,0.00067001424,0.0007040194,0.0003798103,0.0020484086],"category_scores_gemma":[0.00070379867,0.00028313007,0.00029388652,0.00025533463,0.00036166364,0.0010100127,0.00068721327,0.00072060863,0.00040303986],"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":[0.0006226216,0.00024269606,0.0048268153,0.00037179462,0.00017773463,0.00033625207,0.00027194762,0.052862007,0.42597345,0.008535197,0.016632011,0.4891474],"study_design_scores_gemma":[0.000082389524,0.00036122566,0.0054109055,0.000040972394,0.000047869806,0.0007733335,0.00010038904,0.71449035,0.25626278,0.0037321986,0.0185774,0.000120198085],"about_ca_topic_score_codex":0.002280235,"about_ca_topic_score_gemma":0.0058840103,"teacher_disagreement_score":0.002280235,"about_ca_system_score_codex":0.00031846465,"about_ca_system_score_gemma":0.00047582577,"threshold_uncertainty_score":0.006852567},"labels":[],"label_agreement":null},{"id":"W4402753812","doi":"10.1109/cvpr52733.2024.01268","title":"Posterior Distillation Sampling","year":2024,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Samsung; Neurosciences Research Foundation","keywords":"Sampling (signal processing); Computer science; Distillation; Chromatography; Computer vision; Chemistry","score_opus":0.023285961961492293,"score_gpt":0.30424597018315236,"score_spread":0.28096000822166006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402753812","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":[0.0027514487,0.00016309162,0.9938694,0.00013022493,0.00004589077,0.000034794724,0.00008461817,0.00053778914,0.0023826025],"genre_scores_gemma":[0.20258087,0.00055365445,0.78176636,0.0005150803,0.00017808644,0.00034004636,0.00077911164,0.0015483658,0.011738478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990238,0.0002787934,0.000037538488,0.00023619739,0.00033559138,0.00008804639],"domain_scores_gemma":[0.9984617,0.000860937,0.00009943508,0.00029004712,0.00019836526,0.00008947846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014266017,0.001220293,0.0010044053,0.00079402264,0.0005388216,0.0016796547,0.001713722,0.0012191852,0.008533125],"category_scores_gemma":[0.005757025,0.0006798138,0.0010072283,0.0007540711,0.0012076193,0.0021212373,0.0025779488,0.002305303,0.0019337669],"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":[0.0002875646,0.00012555614,0.0013507378,0.00034060443,0.000100921454,0.00029133618,0.00024263065,0.5015022,0.019099155,0.17896548,0.011971064,0.28572273],"study_design_scores_gemma":[0.000020225052,0.000035253077,0.00011042879,0.000020035,0.000010667382,0.000093079,0.0000146557095,0.9483639,0.0043798396,0.039392587,0.007537832,0.000021411843],"about_ca_topic_score_codex":0.002024732,"about_ca_topic_score_gemma":0.0028749874,"teacher_disagreement_score":0.008533125,"about_ca_system_score_codex":0.0008711834,"about_ca_system_score_gemma":0.0011958235,"threshold_uncertainty_score":0.028546154},"labels":[],"label_agreement":null},{"id":"W4402927981","doi":"10.23977/acss.2024.080606","title":"A hybrid enhancement algorithm for polarised images based on a dark primary color prior","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Primary color; Artificial intelligence; Primary (astronomy); Computer science; Algorithm; Computer vision; Pattern recognition (psychology); Physics; Astrophysics","score_opus":0.009607874648336151,"score_gpt":0.2709524547090753,"score_spread":0.26134458006073913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402927981","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":[0.022439787,0.00035796867,0.9756026,0.000073081996,0.000050668255,0.000037264246,0.000027190792,0.0003560557,0.0010554136],"genre_scores_gemma":[0.14351419,0.00071162404,0.8507865,0.00008529375,0.000048050864,0.000057191693,0.00014794867,0.00009157144,0.004557587],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997261,0.000033139066,0.000017413136,0.000067940295,0.00012629623,0.000029058632],"domain_scores_gemma":[0.9996468,0.000089703164,0.000034201472,0.00004672345,0.00016457262,0.00001804952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048632958,0.0005930354,0.00046230952,0.0008383264,0.000223335,0.00074868446,0.0005201108,0.0005183635,0.001512175],"category_scores_gemma":[0.0009806985,0.0002904293,0.0005686881,0.00060273544,0.00038732486,0.0010114411,0.0005846716,0.0006568187,0.00067108724],"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":[0.00046839143,0.00010862768,0.0010388726,0.00019146892,0.00007793782,0.00014197709,0.00012507466,0.03252342,0.35118937,0.005690522,0.0012861672,0.6071582],"study_design_scores_gemma":[0.000047441405,0.00034041,0.0029137298,0.000035633064,0.00010560164,0.0008501518,0.000053970616,0.73615897,0.24642372,0.0022274733,0.010781053,0.00006187384],"about_ca_topic_score_codex":0.0010073598,"about_ca_topic_score_gemma":0.001191352,"teacher_disagreement_score":0.001512175,"about_ca_system_score_codex":0.00028905174,"about_ca_system_score_gemma":0.00041894984,"threshold_uncertainty_score":0.005058706},"labels":[],"label_agreement":null},{"id":"W4402946686","doi":"10.1167/jov.24.10.1136","title":"Lightness constancy can be very weak in an immersive VR environment","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Image Enhancement 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":"York University","funders":"","keywords":"Lightness; Psychology; Computer science; Computer vision","score_opus":0.011070088876107992,"score_gpt":0.2830359336722399,"score_spread":0.2719658447961319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402946686","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":[0.9909609,0.00020434608,0.0067061833,0.000035444424,0.00001507667,0.000024472154,0.000039479615,0.000100051366,0.0019139122],"genre_scores_gemma":[0.9973878,0.00005076146,0.0021275575,0.000035635538,0.0000047953836,0.000013795388,0.00005886754,0.00004211336,0.00027875413],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9984835,0.00032142078,0.00013737305,0.00032972975,0.0005330048,0.00019504009],"domain_scores_gemma":[0.99311066,0.0039639096,0.001004803,0.0010498659,0.00058461993,0.00028618565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012000627,0.00038618542,0.00039272368,0.00041539533,0.00025301112,0.0006097706,0.00055591244,0.00030863896,0.0022421083],"category_scores_gemma":[0.011845237,0.00041744843,0.0002847592,0.00015935968,0.00067067734,0.00075430254,0.0017502418,0.00067069434,0.00017224705],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084312004,0.000100040845,0.0142363515,0.00031618087,0.000062266765,0.00016753051,0.0009987789,0.001001366,0.95414066,0.00060215755,0.00023595618,0.027295593],"study_design_scores_gemma":[0.000117501906,0.003553798,0.5867753,0.00013534464,0.0002697465,0.0019091514,0.0012034674,0.0183742,0.38067192,0.002473035,0.0043413336,0.0001751859],"about_ca_topic_score_codex":0.0010638108,"about_ca_topic_score_gemma":0.0009783645,"teacher_disagreement_score":0.0022421083,"about_ca_system_score_codex":0.00023554062,"about_ca_system_score_gemma":0.0001794417,"threshold_uncertainty_score":0.0075006485},"labels":[],"label_agreement":null},{"id":"W4402949346","doi":"10.1038/s41598-024-73243-9","title":"UICE-MIRNet guided image enhancement for underwater object detection","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":20,"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":"Canadian Institute for Advanced Research; University of Minnesota","keywords":"Underwater; Computer science; Artificial intelligence; Computer vision; Image enhancement; Object (grammar); Object detection; Image (mathematics); Pattern recognition (psychology); Geology; Oceanography","score_opus":0.018915409697716306,"score_gpt":0.29197249403575204,"score_spread":0.2730570843380357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402949346","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":[0.07062525,0.0009878366,0.9187455,0.0002254427,0.00017494416,0.00013475053,0.00017513146,0.0031488014,0.005782324],"genre_scores_gemma":[0.37660885,0.00089585275,0.6077713,0.0003339466,0.00005975741,0.000103638165,0.00080027693,0.00034823478,0.013078146],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997881,0.000027976534,0.000010476443,0.0000404041,0.000103839215,0.000029288693],"domain_scores_gemma":[0.99976474,0.000046655754,0.000029742192,0.000038869268,0.00010657195,0.00001344192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040654285,0.0007022012,0.00044069614,0.0007790691,0.00018338668,0.0004545897,0.00073777506,0.0005217949,0.002257975],"category_scores_gemma":[0.0008520416,0.00022816705,0.0005369674,0.00033399666,0.00031027725,0.0008700992,0.00068224146,0.0005895759,0.0007789035],"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":[0.0006497734,0.00020779685,0.0017216788,0.0003614904,0.00010680909,0.00039079503,0.000106749925,0.059455037,0.33943775,0.004891511,0.0056585944,0.58701193],"study_design_scores_gemma":[0.000020379668,0.0002607329,0.0028183712,0.00004204626,0.0000626956,0.00044537344,0.000046145105,0.75448495,0.22887409,0.0014066914,0.01150463,0.000033995686],"about_ca_topic_score_codex":0.0018252904,"about_ca_topic_score_gemma":0.0034216747,"teacher_disagreement_score":0.002257975,"about_ca_system_score_codex":0.0004266792,"about_ca_system_score_gemma":0.0005030737,"threshold_uncertainty_score":0.0075536966},"labels":[],"label_agreement":null},{"id":"W4403048615","doi":"10.1007/978-3-031-72378-0_1","title":"A New Benchmark In Vivo Paired Dataset for Laparoscopic Image De-smoking","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Benchmark (surveying); Computer science; Image (mathematics); Artificial intelligence; Computer vision; Cartography","score_opus":0.01583438203735291,"score_gpt":0.27684375427300506,"score_spread":0.2610093722356521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403048615","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3190454,0.017626032,0.33914995,0.0016012518,0.0020909868,0.0029612172,0.25389618,0.031948127,0.031680834],"genre_scores_gemma":[0.2298324,0.003707674,0.20016907,0.001208378,0.00033213667,0.001355499,0.5326698,0.002835989,0.027889049],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998855,0.00019559145,0.000086749904,0.00032335127,0.00040614235,0.00013314806],"domain_scores_gemma":[0.9987846,0.00020609931,0.000082997096,0.00041376875,0.00040048498,0.000111975736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014077077,0.0017597483,0.0012811475,0.0021401423,0.00073717494,0.001492767,0.0021292788,0.0023212668,0.0064562946],"category_scores_gemma":[0.0026814598,0.0006051274,0.0012017362,0.0018171195,0.00046923943,0.0007649132,0.0015476629,0.0010429611,0.0061847284],"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":[0.0031573786,0.002149856,0.012319668,0.0029879294,0.0012693165,0.0011499792,0.00016701258,0.025534643,0.087992206,0.0015363836,0.24956131,0.61217433],"study_design_scores_gemma":[0.0009486352,0.0033550588,0.10028218,0.0010527237,0.0015511571,0.020535495,0.0009948588,0.32417813,0.21621291,0.007836956,0.32247385,0.0005779708],"about_ca_topic_score_codex":0.0068561207,"about_ca_topic_score_gemma":0.019595131,"teacher_disagreement_score":0.0068561207,"about_ca_system_score_codex":0.00054788153,"about_ca_system_score_gemma":0.00128541,"threshold_uncertainty_score":0.021598399},"labels":[],"label_agreement":null},{"id":"W4403071580","doi":"10.1007/978-3-031-72384-1_1","title":"A Clinical-Oriented Lightweight Network for High-Resolution Medical Image Enhancement","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Resolution (logic); Image (mathematics); High resolution; Computer vision; Artificial intelligence; Computer graphics (images); Remote sensing; Geology","score_opus":0.01499314472784809,"score_gpt":0.298016404245675,"score_spread":0.2830232595178269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403071580","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":[0.017990729,0.0009845457,0.9381456,0.00061518827,0.0004078652,0.0004317288,0.0005496624,0.020065608,0.020809056],"genre_scores_gemma":[0.32970107,0.001794863,0.57891923,0.001454697,0.00051972206,0.00080240204,0.0030555169,0.0022579785,0.08149445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997459,0.00005300528,0.000019875253,0.000044012497,0.00009858017,0.000038611764],"domain_scores_gemma":[0.9993635,0.00017935829,0.000055863125,0.00016695344,0.00014189545,0.00009247916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008442767,0.000498041,0.0003979867,0.0008520518,0.0002991414,0.0009709015,0.0014627806,0.00067638414,0.016843602],"category_scores_gemma":[0.0013408253,0.0002805997,0.00026150676,0.0005530582,0.00017939486,0.0011366339,0.0015851846,0.00060228154,0.0060286573],"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":[0.0010345799,0.00039006805,0.0022907215,0.00028825522,0.000073811665,0.0006384794,0.00013956644,0.009723903,0.055039153,0.011551424,0.071136266,0.8476937],"study_design_scores_gemma":[0.00033360734,0.0012494383,0.007219762,0.00028150185,0.00032892547,0.0053509944,0.00025323348,0.49920732,0.089370616,0.034243744,0.3619739,0.00018687976],"about_ca_topic_score_codex":0.00058049546,"about_ca_topic_score_gemma":0.0011971656,"teacher_disagreement_score":0.016843602,"about_ca_system_score_codex":0.00037944296,"about_ca_system_score_gemma":0.00050885254,"threshold_uncertainty_score":0.05634743},"labels":[],"label_agreement":null},{"id":"W4403659185","doi":"10.1038/s41598-024-75801-7","title":"Virtual cleaning of sooty mural hyperspectral images using the LIME model and improved dark channel prior","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Mural; Hyperspectral imaging; Channel (broadcasting); Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Art; Telecommunications; Art history","score_opus":0.018474216837101213,"score_gpt":0.2666517155663366,"score_spread":0.2481774987292354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403659185","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":[0.22624026,0.0005325951,0.7679921,0.00022911173,0.000084453815,0.00006371286,0.00009892917,0.0012282705,0.0035304944],"genre_scores_gemma":[0.6782185,0.00049756246,0.3178751,0.0001378245,0.000029640243,0.000056882327,0.0002749169,0.00020350811,0.0027059522],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997851,0.000030855557,0.000007288953,0.00004271851,0.00010357148,0.000030528143],"domain_scores_gemma":[0.999772,0.00006109192,0.000038112874,0.00004862022,0.000061874045,0.000018231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037687385,0.000684653,0.00040968988,0.00065591134,0.00022822036,0.0007512645,0.00047985106,0.000567802,0.00074661575],"category_scores_gemma":[0.0007196657,0.00023119213,0.00076900894,0.0003623214,0.0005547511,0.00087990804,0.0006854698,0.00059625093,0.00027023559],"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":[0.0006041793,0.00022289144,0.0039684214,0.00048519354,0.00014409615,0.00055236515,0.00059728994,0.24990194,0.44408438,0.0071962555,0.002576288,0.2896668],"study_design_scores_gemma":[0.000023103445,0.00009166401,0.0034442064,0.000020889729,0.000056713332,0.00024551884,0.00012182041,0.87868327,0.11183453,0.0012502446,0.0041653602,0.00006272291],"about_ca_topic_score_codex":0.0018709414,"about_ca_topic_score_gemma":0.0026768288,"teacher_disagreement_score":0.0018709414,"about_ca_system_score_codex":0.000310245,"about_ca_system_score_gemma":0.00047863065,"threshold_uncertainty_score":0.0037201047},"labels":[],"label_agreement":null},{"id":"W4403769396","doi":"10.1007/s10489-024-05827-x","title":"A typhoon optimization algorithm and difference of CNN integrated bi-level network for unsupervised underwater image enhancement","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Underwater; Typhoon; Image (mathematics); Artificial intelligence; Algorithm; Pattern recognition (psychology); Geology; Oceanography","score_opus":0.025933125677780187,"score_gpt":0.2664714264684644,"score_spread":0.2405383007906842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403769396","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":[0.02084538,0.00016477614,0.9767032,0.000082356055,0.000054981847,0.000039274983,0.00003242311,0.00043341616,0.0016442427],"genre_scores_gemma":[0.35789466,0.0002915602,0.63182306,0.00015003368,0.00004831869,0.00012483184,0.0003200955,0.00017519383,0.009172233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988353,0.0000137521,0.000007206475,0.000037528534,0.00003848737,0.000019476476],"domain_scores_gemma":[0.9999068,0.000021537786,0.0000099275885,0.000012448732,0.000041670504,0.000007645569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031988294,0.000495238,0.00043945084,0.00033342972,0.00025300338,0.00033252817,0.0007932134,0.0005443832,0.0016759112],"category_scores_gemma":[0.00046024116,0.00030513448,0.00050791877,0.00033630215,0.0002186161,0.0006471306,0.00062920095,0.00064720825,0.00028452446],"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":[0.0002353484,0.00014472703,0.001971484,0.00009974758,0.00013685111,0.000116584044,0.00008618252,0.36345014,0.07470382,0.00940147,0.0037227122,0.545931],"study_design_scores_gemma":[0.0000048690144,0.000025186375,0.0002604993,0.000001960695,0.000010869334,0.000020059008,0.0000053035683,0.9935155,0.0050827996,0.00047906733,0.0005894079,0.0000043848113],"about_ca_topic_score_codex":0.006215154,"about_ca_topic_score_gemma":0.009215128,"teacher_disagreement_score":0.006215154,"about_ca_system_score_codex":0.00040965065,"about_ca_system_score_gemma":0.0007296288,"threshold_uncertainty_score":0.01235795},"labels":[],"label_agreement":null},{"id":"W4403777039","doi":"10.1111/cgf.15209","title":"A TransISP Based Image Enhancement Method for Visual Disbalance in Low‐light Images","year":2024,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer vision; Computer science; Artificial intelligence; Image (mathematics); Image enhancement; Computer graphics (images); Visualization","score_opus":0.007436790959442617,"score_gpt":0.2993326905107109,"score_spread":0.29189589955126827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403777039","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":[0.06338796,0.00042390343,0.9313772,0.0001442201,0.00008188765,0.000060284197,0.000041180487,0.0012969298,0.0031864517],"genre_scores_gemma":[0.628516,0.000585006,0.36234477,0.0002896686,0.00006467304,0.00006155199,0.00015804761,0.00022473701,0.0077555715],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998505,0.000017068496,0.0000072609555,0.000033521912,0.000071524395,0.000020080059],"domain_scores_gemma":[0.9997167,0.000060094615,0.000041182026,0.000051363815,0.0001062023,0.00002445114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003047902,0.00050404976,0.00032780797,0.0005084627,0.00014442847,0.00040368774,0.00060992147,0.0003224159,0.0019963183],"category_scores_gemma":[0.0005980865,0.00020529875,0.00045621526,0.00023018167,0.0003599692,0.0006492105,0.00063604297,0.00070465705,0.00041021002],"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":[0.00038437094,0.00012518986,0.0011027436,0.00022052178,0.000078304394,0.00032858105,0.00011774808,0.051208448,0.54386413,0.0059339106,0.0028991979,0.39373684],"study_design_scores_gemma":[0.000027440145,0.00020767273,0.0012085658,0.00002371015,0.000069647016,0.00065059285,0.0000372618,0.7560575,0.23456281,0.0019934461,0.005137482,0.000023770626],"about_ca_topic_score_codex":0.00066574826,"about_ca_topic_score_gemma":0.0011789128,"teacher_disagreement_score":0.0019963183,"about_ca_system_score_codex":0.00024149929,"about_ca_system_score_gemma":0.0003002732,"threshold_uncertainty_score":0.006678343},"labels":[],"label_agreement":null},{"id":"W4403871489","doi":"10.3390/e26110918","title":"A Comprehensive Method for Example-Based Color Transfer with Holistic–Local Balancing and Unit-Wise Riemannian Information Gradient Acceleration","year":2024,"lang":"en","type":"article","venue":"Entropy","topic":"Image Enhancement 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":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Transformation (genetics); Metric (unit); Artificial intelligence; Gaussian; Algorithm; Computer vision","score_opus":0.03179598981152161,"score_gpt":0.2954948037724573,"score_spread":0.2636988139609357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403871489","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":[0.0017487324,0.00015603563,0.9954856,0.00005153961,0.00003808688,0.000035421643,0.000024741243,0.001061365,0.001398503],"genre_scores_gemma":[0.06915524,0.0004008157,0.92275673,0.00012854015,0.000075111064,0.00009468614,0.0002442746,0.00051063154,0.0066339513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996569,0.000039481478,0.00001757087,0.00007980332,0.00017998077,0.000026387514],"domain_scores_gemma":[0.9997588,0.00003214165,0.000022127191,0.00007653421,0.000088306755,0.000022029913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040652117,0.0007830089,0.0005413536,0.0010087803,0.00029750456,0.00072023756,0.0011141362,0.00056210015,0.004841594],"category_scores_gemma":[0.0009179311,0.0002954212,0.0008111479,0.0006869998,0.0004490003,0.00092777784,0.0011059806,0.0008942684,0.0022512635],"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":[0.000072863186,0.00007237915,0.000405402,0.00023037108,0.00007528797,0.00011328976,0.0001222892,0.033812337,0.10384399,0.028585754,0.008337991,0.82432806],"study_design_scores_gemma":[0.000020067731,0.00008880345,0.0006663689,0.000018702347,0.000041380936,0.0005050657,0.000026527377,0.8946753,0.06299632,0.009513392,0.031397816,0.000050305465],"about_ca_topic_score_codex":0.001451017,"about_ca_topic_score_gemma":0.0016928898,"teacher_disagreement_score":0.004841594,"about_ca_system_score_codex":0.00043417915,"about_ca_system_score_gemma":0.00063510257,"threshold_uncertainty_score":0.016196728},"labels":[],"label_agreement":null},{"id":"W4403921649","doi":"10.2139/ssrn.5004880","title":"Enhanced Underwater Image Dehazing Using an Improved Dark Channel Prior Method","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Underwater; Channel (broadcasting); Image (mathematics); Computer science; Computer vision; Artificial intelligence; Geology; Telecommunications; Oceanography","score_opus":0.019280096762856285,"score_gpt":0.3193430172909612,"score_spread":0.3000629205281049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403921649","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":[0.027551748,0.00043478413,0.9679602,0.00014493598,0.000086960055,0.000041145162,0.000088162844,0.00037268834,0.0033194788],"genre_scores_gemma":[0.2026566,0.0015509697,0.7764496,0.00013603052,0.00009142147,0.00006623209,0.0003361263,0.00026710454,0.018445987],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980897,0.00001829413,0.000008885171,0.000031232197,0.00011482832,0.000017798318],"domain_scores_gemma":[0.9996315,0.000086168024,0.000032403732,0.00007162121,0.0001577276,0.00002051338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027752115,0.00062676467,0.00042819703,0.0006764402,0.00020409607,0.0005740468,0.00039885327,0.00056515203,0.0029741346],"category_scores_gemma":[0.00088719313,0.00031103072,0.00045177725,0.00052758906,0.00037824718,0.00097468763,0.00084511697,0.0010311577,0.00087806454],"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":[0.00031480446,0.00016033085,0.00089489913,0.00036867338,0.000066600864,0.00017351082,0.00014762794,0.030865764,0.5355876,0.008725538,0.0023302631,0.42036444],"study_design_scores_gemma":[0.000038786322,0.00017370295,0.0039468077,0.000054881715,0.00012382987,0.0009170221,0.00007542265,0.5467329,0.4280425,0.0031470146,0.016666044,0.00008110706],"about_ca_topic_score_codex":0.0012955779,"about_ca_topic_score_gemma":0.0025743197,"teacher_disagreement_score":0.0029741346,"about_ca_system_score_codex":0.00019776626,"about_ca_system_score_gemma":0.00060075766,"threshold_uncertainty_score":0.009949446},"labels":[],"label_agreement":null},{"id":"W4403947296","doi":"10.1007/978-3-031-73247-8_10","title":"Intrinsic Single-Image HDR Reconstruction","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Computer vision; Computer graphics (images); Artificial intelligence; Image (mathematics)","score_opus":0.014960113985878121,"score_gpt":0.24218485180645954,"score_spread":0.22722473782058142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403947296","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":[0.004934355,0.00089936773,0.96121037,0.00008396626,0.00014121454,0.00003477334,0.00024283797,0.0013592116,0.031093916],"genre_scores_gemma":[0.09483926,0.0035366656,0.77132833,0.00018452291,0.0001734269,0.000046203906,0.0011299475,0.0011345134,0.12762713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998697,0.00000972967,0.0000051523452,0.00002603668,0.000077642704,0.000011836824],"domain_scores_gemma":[0.9997719,0.000045854234,0.000014780142,0.0000892368,0.00006670314,0.000011495099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022115857,0.0005536785,0.0004035673,0.00047589486,0.00015394548,0.00094031426,0.0006229877,0.00050577224,0.02396485],"category_scores_gemma":[0.00040773925,0.0003403515,0.0003481222,0.00047999102,0.00027102203,0.0011547541,0.0007042211,0.000733819,0.013407154],"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":[0.00013928826,0.00006939937,0.00036704005,0.0005640792,0.00003993687,0.0002134235,0.00011082946,0.010964917,0.26357988,0.036909588,0.018717771,0.66832393],"study_design_scores_gemma":[0.00001803289,0.00015893033,0.0024238804,0.00016688733,0.000074432835,0.0053662043,0.00009276825,0.20061901,0.56635195,0.01875007,0.20591922,0.000058661284],"about_ca_topic_score_codex":0.0001311912,"about_ca_topic_score_gemma":0.0003438529,"teacher_disagreement_score":0.02396485,"about_ca_system_score_codex":0.00017310701,"about_ca_system_score_gemma":0.000219558,"threshold_uncertainty_score":0.08017039},"labels":[],"label_agreement":null},{"id":"W4404130826","doi":"10.1007/s00371-024-03700-z","title":"DMDC: a cross-attention network for dynamic mask-based dual-camera snapshot hyperspectral Photography","year":2024,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Snapshot (computer storage); Hyperspectral imaging; Photography; Computer graphics (images); Computer science; Computer graphics; Artificial intelligence; Computational photography; Computer vision; Image processing; Art; Visual arts; Image (mathematics)","score_opus":0.012717569483331333,"score_gpt":0.3190374146363427,"score_spread":0.30631984515301136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404130826","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":[0.05709665,0.0011008848,0.92696023,0.00030234008,0.00032916063,0.0002705116,0.000667401,0.006343654,0.006929176],"genre_scores_gemma":[0.37201676,0.0007259727,0.6076531,0.00068722473,0.00019388527,0.00028496355,0.0013069302,0.0002561835,0.016875038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997321,0.000027303899,0.0000072833973,0.000070713206,0.00012357962,0.000039026636],"domain_scores_gemma":[0.99972934,0.000059518512,0.000018720473,0.00004244252,0.000115057584,0.00003499747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003356823,0.00070451706,0.00055996544,0.00097126974,0.0005128085,0.00049981364,0.0015707816,0.00060344447,0.0068630595],"category_scores_gemma":[0.00053911697,0.00031279944,0.0002645643,0.0006588723,0.00033151652,0.0007836818,0.0012679363,0.00063347217,0.0014319917],"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":[0.00068382244,0.00030769338,0.0012004921,0.0001720887,0.000087064385,0.00018583499,0.000070649985,0.0120089045,0.25841138,0.0028490184,0.016985254,0.70703787],"study_design_scores_gemma":[0.00005085887,0.0002564032,0.0026320443,0.000025512958,0.000060076425,0.00036996714,0.000041911233,0.82304484,0.1536657,0.0017234161,0.018071957,0.000057397316],"about_ca_topic_score_codex":0.008285484,"about_ca_topic_score_gemma":0.019125607,"teacher_disagreement_score":0.008285484,"about_ca_system_score_codex":0.00084983173,"about_ca_system_score_gemma":0.0008886196,"threshold_uncertainty_score":0.022959232},"labels":[],"label_agreement":null},{"id":"W4404722489","doi":"10.1007/978-3-031-73195-2_3","title":"GLARE: Low Light Image Enhancement via Generative Latent Feature Based Codebook Retrieval","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Codebook; Computer science; Artificial intelligence; Feature (linguistics); GLARE; Computer vision; Pattern recognition (psychology); Materials science","score_opus":0.008879571686920685,"score_gpt":0.23993863958591338,"score_spread":0.2310590678989927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404722489","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":[0.007226661,0.0006362225,0.9830088,0.00010609029,0.00010286742,0.000086479675,0.00026013536,0.0048007537,0.0037719568],"genre_scores_gemma":[0.14173324,0.00088441937,0.81523067,0.00036461503,0.000114701346,0.00013829199,0.0021678966,0.0011589221,0.03820721],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974483,0.000026943608,0.000007938914,0.000043553817,0.00014867652,0.000027949374],"domain_scores_gemma":[0.99979967,0.00004615138,0.000012962925,0.000072884446,0.000052070085,0.000016213582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002652614,0.00054948573,0.0006904313,0.0005813354,0.00024006057,0.00074595964,0.0011145959,0.00055212755,0.011113588],"category_scores_gemma":[0.0005543676,0.00027805535,0.0005609537,0.0007421202,0.00039684784,0.0008687724,0.0012360087,0.0008163728,0.0052349763],"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":[0.00037021053,0.00015869709,0.00022612997,0.0001758759,0.00005014939,0.00014086519,0.00006229977,0.018958874,0.14050375,0.009154597,0.02459887,0.8055997],"study_design_scores_gemma":[0.00015560021,0.0003086345,0.0011611318,0.000038939805,0.00006176871,0.0008565361,0.000056475765,0.79925066,0.14335573,0.014270511,0.04040533,0.000078621415],"about_ca_topic_score_codex":0.002093696,"about_ca_topic_score_gemma":0.0039839954,"teacher_disagreement_score":0.011113588,"about_ca_system_score_codex":0.00035881292,"about_ca_system_score_gemma":0.0003812868,"threshold_uncertainty_score":0.037178636},"labels":[],"label_agreement":null},{"id":"W4404838573","doi":"10.1016/j.eswa.2024.125820","title":"Hierarchical candidate recursive network for highlight restoration in endoscopic videos","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence","score_opus":0.011494996805427045,"score_gpt":0.2811939768989583,"score_spread":0.26969898009353127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404838573","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":[0.016676564,0.00020635113,0.9821034,0.00005743491,0.000014032289,0.000022675958,0.000036826903,0.00035492147,0.00052774575],"genre_scores_gemma":[0.4142023,0.00047264568,0.57843506,0.00009314671,0.00005712338,0.000109639506,0.0002906363,0.00017116328,0.0061681694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997658,0.000059024493,0.000009029782,0.00006535747,0.000056782792,0.000044045373],"domain_scores_gemma":[0.99954706,0.0002262378,0.000047585654,0.000048928803,0.000102785016,0.000027424356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006461359,0.0005705299,0.00071464485,0.0006756516,0.00035180195,0.0005405219,0.0009911243,0.00097038224,0.0021028726],"category_scores_gemma":[0.0015160519,0.00039981064,0.00058183813,0.00045475957,0.00033961504,0.0008091088,0.0007483367,0.0008133643,0.00056481577],"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":[0.00054575305,0.00014940646,0.0010790176,0.00015436953,0.00008722336,0.0001977418,0.00014977454,0.35682848,0.04531542,0.0105716,0.0036954505,0.5812258],"study_design_scores_gemma":[0.0000036667373,0.000026342239,0.00014061919,0.000004135019,0.000009119822,0.000023983986,0.000005937869,0.9956801,0.0029605203,0.0007955733,0.00034615686,0.0000038030198],"about_ca_topic_score_codex":0.00518444,"about_ca_topic_score_gemma":0.008661443,"teacher_disagreement_score":0.00518444,"about_ca_system_score_codex":0.00048342723,"about_ca_system_score_gemma":0.0007336847,"threshold_uncertainty_score":0.010308504},"labels":[],"label_agreement":null},{"id":"W4404959384","doi":"10.3390/electronics13234776","title":"Vision-Based Prediction of Flashover Using Transformers and Convolutional Long Short-Term Memory Model","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Transformer; Arc flash; Computer science; Term (time); Long short term memory; Artificial intelligence; Engineering; Voltage; Electrical engineering; Physics","score_opus":0.014400604902166853,"score_gpt":0.2705836610358198,"score_spread":0.256183056133653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404959384","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":[0.36648563,0.0007317755,0.6271783,0.0002942359,0.00011120514,0.000042899286,0.00037379426,0.0017285767,0.0030535501],"genre_scores_gemma":[0.9829737,0.00015803253,0.0153409345,0.000036637382,0.000010787294,0.000012579803,0.00021841814,0.000017286397,0.0012316705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999298,0.0000068851778,0.0000044416715,0.00002276044,0.000017127411,0.000018979988],"domain_scores_gemma":[0.9998393,0.00005507053,0.000029656097,0.000011846209,0.000050661696,0.000013419137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024466423,0.0005649116,0.0003219742,0.00055122154,0.000113711016,0.00038843014,0.0005380297,0.000349365,0.000899974],"category_scores_gemma":[0.0007916447,0.00018396761,0.00039546395,0.0003443016,0.00020639905,0.0007111396,0.00030048884,0.00063262414,0.00020047283],"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":[0.00026408397,0.00015419503,0.007822304,0.00007463307,0.00006418072,0.00017101709,0.000038337734,0.8191293,0.020265179,0.0023091326,0.0021405248,0.14756712],"study_design_scores_gemma":[8.7755353e-7,0.000008536815,0.00027100556,0.0000010600692,0.0000029660537,0.0000059156587,0.0000020157252,0.9982059,0.0011935234,0.00027288398,0.000033959197,0.0000012983842],"about_ca_topic_score_codex":0.008017741,"about_ca_topic_score_gemma":0.009265032,"teacher_disagreement_score":0.008017741,"about_ca_system_score_codex":0.00060978805,"about_ca_system_score_gemma":0.00047763984,"threshold_uncertainty_score":0.015942156},"labels":[],"label_agreement":null},{"id":"W4405603297","doi":"10.2316/j.2025.206-1111","title":"AN IMPROVED ILLUMINATION ADAPTIVE ORB-SLAM3 ALGORITHM, 115-123.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Orb (optics); Computer science; Algorithm; Artificial intelligence; Image (mathematics)","score_opus":0.008906515755286358,"score_gpt":0.2801432405546302,"score_spread":0.27123672479934385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405603297","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":[0.019990154,0.001822009,0.9613344,0.00024314465,0.00056915707,0.0001219265,0.0009294793,0.0064695915,0.008520134],"genre_scores_gemma":[0.12048493,0.000680076,0.8572113,0.00014844199,0.000096106975,0.00010799601,0.003093639,0.0008561107,0.017321516],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996189,0.000041182713,0.000017144015,0.00005968199,0.0002185916,0.00004457289],"domain_scores_gemma":[0.99976975,0.000019889912,0.000011441036,0.00005061582,0.00013391658,0.000014400079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045637638,0.0008258086,0.0007784694,0.0009590892,0.00055409875,0.00083225186,0.0008923622,0.00072029897,0.006579578],"category_scores_gemma":[0.0009733792,0.00044523645,0.00083157694,0.0017173578,0.00027471516,0.0008211292,0.0008860171,0.0010616806,0.004867378],"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":[0.000296706,0.00010071182,0.0010533221,0.00016442203,0.00007322505,0.00008560867,0.000034685752,0.017452061,0.038582813,0.0028642819,0.022012584,0.9172795],"study_design_scores_gemma":[0.00024266477,0.00024183471,0.010644138,0.000080765436,0.00013334636,0.0008334402,0.00011640531,0.802114,0.07697261,0.009342772,0.0991645,0.00011353162],"about_ca_topic_score_codex":0.007915854,"about_ca_topic_score_gemma":0.015797552,"teacher_disagreement_score":0.007915854,"about_ca_system_score_codex":0.00039261064,"about_ca_system_score_gemma":0.0012011736,"threshold_uncertainty_score":0.022010863},"labels":[],"label_agreement":null},{"id":"W4406391458","doi":"10.1145/3711929","title":"Wakeup-Darkness: When Multimodal Meets Unsupervised Low-Light Image Enhancement","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":16,"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 Ottawa","funders":"Canadian Mental Health Association; National Natural Science Foundation of China","keywords":"Computer science; Darkness; Artificial intelligence; Image (mathematics); Computer vision; Optics","score_opus":0.011798200161796137,"score_gpt":0.28557658021766186,"score_spread":0.27377838005586574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406391458","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":[0.10272113,0.0003806288,0.8862179,0.00022383695,0.00008567934,0.00015256523,0.000108803906,0.002332003,0.0077774553],"genre_scores_gemma":[0.70319414,0.00029168464,0.28940263,0.0003005736,0.00005482108,0.0001532328,0.00018939942,0.0004912797,0.0059222556],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977773,0.000047271114,0.00000946503,0.000058737467,0.000060445225,0.000046445046],"domain_scores_gemma":[0.9995981,0.00013357498,0.000036328405,0.000114809794,0.00006846367,0.00004867843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043407115,0.00051876885,0.00031699793,0.0002690761,0.00031914032,0.0006328407,0.0006422008,0.00047566337,0.0039242213],"category_scores_gemma":[0.001575623,0.00021731015,0.00029302062,0.00015930712,0.00069676567,0.00124376,0.0019384865,0.0006476364,0.00073050894],"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":[0.00092804723,0.0001865507,0.0019738774,0.00028160875,0.000050392737,0.0004276413,0.000762525,0.033628657,0.52995497,0.021663794,0.0054031634,0.4047388],"study_design_scores_gemma":[0.000086776265,0.0006239894,0.005201573,0.000103348844,0.000077582605,0.00082937995,0.00038184176,0.5907507,0.3376883,0.036910985,0.027238855,0.00010664455],"about_ca_topic_score_codex":0.0005729366,"about_ca_topic_score_gemma":0.0011834393,"teacher_disagreement_score":0.0039242213,"about_ca_system_score_codex":0.00019670861,"about_ca_system_score_gemma":0.000272281,"threshold_uncertainty_score":0.013127804},"labels":[],"label_agreement":null},{"id":"W4407264220","doi":"10.1016/j.neucom.2025.129572","title":"MDANet: A multi-stage domain adaptation framework for generalizable low-light image enhancement","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs","funders":"Shenzhen Fundamental Research and Discipline Layout project","keywords":"Computer science; Adaptation (eye); Image (mathematics); Domain adaptation; Artificial intelligence; Domain (mathematical analysis); Stage (stratigraphy); Image enhancement; Computer vision; Pattern recognition (psychology); Mathematics; Optics; Physics; Geology","score_opus":0.028648783688789713,"score_gpt":0.31908963357847553,"score_spread":0.2904408498896858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407264220","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":[0.002589756,0.00012738245,0.99455833,0.000032862357,0.000028634615,0.000020340509,0.000067618115,0.0021084594,0.0004666404],"genre_scores_gemma":[0.10003093,0.0003538994,0.8903487,0.00019640276,0.000050160925,0.00011266293,0.00058228325,0.0006172018,0.007707805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987924,0.000022057924,0.000005308279,0.000033752498,0.000041893407,0.000017783654],"domain_scores_gemma":[0.9998374,0.00004616351,0.00001297008,0.000031105297,0.00006034205,0.000012020076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046096937,0.0007571782,0.0005543932,0.00042178793,0.00021852713,0.0004944208,0.0012396548,0.0007457272,0.003896832],"category_scores_gemma":[0.0007220329,0.0003397522,0.0006288646,0.00038954034,0.00022347046,0.0006061488,0.0007729764,0.0013378132,0.0015267876],"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":[0.00027099482,0.00016762322,0.0005375988,0.00015006517,0.00014677914,0.00010867538,0.00005305697,0.16040891,0.08941196,0.006407132,0.011578833,0.73075837],"study_design_scores_gemma":[0.000008364162,0.000024936766,0.00023111938,0.000006472983,0.00001414029,0.000042837964,0.0000060185644,0.9790852,0.014723747,0.0018750045,0.003972626,0.00000964512],"about_ca_topic_score_codex":0.00485662,"about_ca_topic_score_gemma":0.010715557,"teacher_disagreement_score":0.00485662,"about_ca_system_score_codex":0.0003207582,"about_ca_system_score_gemma":0.0005041218,"threshold_uncertainty_score":0.013036191},"labels":[],"label_agreement":null},{"id":"W4407475854","doi":"10.1109/dicta63115.2024.00081","title":"Light Field Resolution Enhancement Framework","year":2024,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Brandon University","funders":"","keywords":"Field (mathematics); Computer science; Resolution (logic); Light field; Artificial intelligence; Mathematics","score_opus":0.009838698932998461,"score_gpt":0.2784354592717786,"score_spread":0.26859676033878016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407475854","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":[0.0038052965,0.00038555608,0.9899503,0.000083885956,0.000033383887,0.00007867995,0.000085436914,0.00082233374,0.004755172],"genre_scores_gemma":[0.15473998,0.0011315175,0.8286176,0.00024615994,0.00012858673,0.00014511184,0.00038578466,0.00030156653,0.014303673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999539,0.00005486692,0.000013711905,0.00008125945,0.00026028775,0.000050936873],"domain_scores_gemma":[0.9996031,0.00008003422,0.0000367249,0.00009052078,0.00015762808,0.000032055126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049576606,0.00091724395,0.00062685757,0.00088833924,0.0002863887,0.0010340494,0.0015323951,0.00074947387,0.006378295],"category_scores_gemma":[0.0009901164,0.00034609702,0.000656375,0.0005052268,0.00041784224,0.0011194001,0.0012997895,0.0011530536,0.0023243022],"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":[0.00034268058,0.00024458393,0.0007401816,0.0004400137,0.00010079422,0.00039847937,0.00012089264,0.1230743,0.23746489,0.056846,0.01327525,0.566952],"study_design_scores_gemma":[0.000035696085,0.000116183954,0.00035049068,0.00003242234,0.000052747244,0.0007370126,0.000028655106,0.85972506,0.09993744,0.012266652,0.02668106,0.000036674144],"about_ca_topic_score_codex":0.0012098731,"about_ca_topic_score_gemma":0.0014602427,"teacher_disagreement_score":0.006378295,"about_ca_system_score_codex":0.00047255235,"about_ca_system_score_gemma":0.0006337693,"threshold_uncertainty_score":0.02133751},"labels":[],"label_agreement":null},{"id":"W4408812414","doi":"10.2139/ssrn.5192888","title":"Adaptive Dark Channel Prior with Feature Fusion Attention for Image Dehazing","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Privy Council Office","funders":"","keywords":"Feature (linguistics); Channel (broadcasting); Computer science; Artificial intelligence; Fusion; Image (mathematics); Computer vision; Image fusion; Pattern recognition (psychology); Computer network","score_opus":0.00861030105794788,"score_gpt":0.2572990334544956,"score_spread":0.24868873239654773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408812414","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":[0.028336061,0.00057591125,0.9687561,0.00013239656,0.00006941528,0.000031355692,0.00007072297,0.00041600142,0.0016121651],"genre_scores_gemma":[0.50168914,0.0011734058,0.48514235,0.00026115886,0.00015123338,0.000058561505,0.00037586942,0.00025445287,0.010893774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997886,0.000029522278,0.000008736521,0.00004883488,0.000087029206,0.000037386522],"domain_scores_gemma":[0.9996418,0.000116949996,0.000029342722,0.000075089076,0.000113836926,0.000023028291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037619725,0.00057634333,0.000703958,0.0006639801,0.0002747081,0.00051296223,0.0006200723,0.000665813,0.0031000902],"category_scores_gemma":[0.0010000198,0.00025421576,0.00051359826,0.0006569721,0.00046073282,0.00081955636,0.0011135797,0.000985189,0.00068441423],"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":[0.00072940905,0.00026025847,0.0006436052,0.00024787942,0.00009970167,0.0001030309,0.00009254534,0.04896822,0.27605984,0.010003279,0.004075688,0.65871656],"study_design_scores_gemma":[0.000027701712,0.00017704655,0.0019299622,0.000028107188,0.00010171913,0.0002681251,0.000034695135,0.8552633,0.13042313,0.006579304,0.0051351357,0.000031710984],"about_ca_topic_score_codex":0.002257654,"about_ca_topic_score_gemma":0.0036910973,"teacher_disagreement_score":0.0031000902,"about_ca_system_score_codex":0.0002893222,"about_ca_system_score_gemma":0.0006603766,"threshold_uncertainty_score":0.010370851},"labels":[],"label_agreement":null},{"id":"W4409149816","doi":"10.1016/j.rineng.2025.104773","title":"SI-CL-SDEO algorithm for improving HDFS performance and data reliability","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Reliability (semiconductor); Algorithm; Reliability engineering; Engineering; Physics","score_opus":0.010277865154845101,"score_gpt":0.2583167193673282,"score_spread":0.2480388542124831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409149816","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":[0.050818384,0.0004711619,0.9325548,0.0004637477,0.00016088746,0.00020799822,0.00023919865,0.0021979124,0.012885845],"genre_scores_gemma":[0.5168519,0.00029446298,0.46854118,0.00027283764,0.0000542071,0.00044363426,0.00091059745,0.00023308181,0.012398045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983096,0.000023937242,0.00001261984,0.000036764042,0.00006619232,0.000029515086],"domain_scores_gemma":[0.9997588,0.00007047502,0.000026442427,0.000026583642,0.00009529779,0.000022380676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003493307,0.0005789536,0.00054026645,0.0004445293,0.00040615568,0.0006026869,0.0008643035,0.00059047795,0.0032145556],"category_scores_gemma":[0.0009650439,0.00021383556,0.00050516805,0.00037368946,0.00026634708,0.00055264164,0.0007497625,0.00057347695,0.00059589813],"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":[0.00017755687,0.0001562426,0.0033353646,0.00020770698,0.00007358275,0.00011907333,0.00012417923,0.71639794,0.011507002,0.008995218,0.008281526,0.25062466],"study_design_scores_gemma":[0.000031181306,0.00003250058,0.00020913755,0.000006505387,0.000007009999,0.00002328489,0.000015123284,0.99413,0.0018976558,0.0011112574,0.0025307206,0.000005584756],"about_ca_topic_score_codex":0.0046239053,"about_ca_topic_score_gemma":0.00625607,"teacher_disagreement_score":0.0046239053,"about_ca_system_score_codex":0.0005471851,"about_ca_system_score_gemma":0.0013725039,"threshold_uncertainty_score":0.01075381},"labels":[],"label_agreement":null},{"id":"W4409235847","doi":"10.7717/peerj-cs.2799","title":"Two-stage object detection in low-light environments using deep learning image enhancement","year":2025,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Image Enhancement 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":"Lakehead University","funders":"","keywords":"Stage (stratigraphy); Artificial intelligence; Computer vision; Computer science; Image (mathematics); Object detection; Object (grammar); Pattern recognition (psychology); Geology","score_opus":0.007814860085145968,"score_gpt":0.2672527838775381,"score_spread":0.2594379237923921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409235847","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":[0.14133751,0.00070106634,0.8480842,0.00019194795,0.00010009669,0.00023724549,0.00025554464,0.0052626934,0.0038297058],"genre_scores_gemma":[0.5329605,0.0004483859,0.45739996,0.00033300082,0.00004916383,0.00015642318,0.00084961456,0.00021866048,0.007584382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945086,0.000048068447,0.000020770462,0.00017247036,0.00021608484,0.00009170163],"domain_scores_gemma":[0.99943286,0.00013381388,0.00006558904,0.00009184114,0.00023376134,0.000042115644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009877369,0.000971677,0.00079378206,0.000816584,0.00025633592,0.0009361504,0.0015000341,0.0008197052,0.0015677139],"category_scores_gemma":[0.0013272485,0.00039890857,0.00067818403,0.0003692063,0.00033551865,0.0012364674,0.0014554822,0.0009372097,0.0008451894],"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":[0.00084016996,0.00054265006,0.005840963,0.00032792782,0.00016593606,0.00028482047,0.00016145514,0.03796158,0.23727883,0.0018438764,0.003383314,0.7113685],"study_design_scores_gemma":[0.00004105861,0.0004584429,0.006630342,0.00003913747,0.00009247893,0.00032253054,0.00004881181,0.81318325,0.17371958,0.0018217687,0.0035965515,0.00004611886],"about_ca_topic_score_codex":0.0019575926,"about_ca_topic_score_gemma":0.003920296,"teacher_disagreement_score":0.0019575926,"about_ca_system_score_codex":0.0005854518,"about_ca_system_score_gemma":0.0005550155,"threshold_uncertainty_score":0.005244553},"labels":[],"label_agreement":null},{"id":"W4409365439","doi":"10.1609/aaai.v39i10.33168","title":"Low-Light Image Enhancement via Generative Perceptual Priors","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Image Enhancement Techniques","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":"McMaster University","funders":"","keywords":"Prior probability; Artificial intelligence; Perception; Generative grammar; Image (mathematics); Computer science; Computer vision; Generative model; Pattern recognition (psychology); Psychology; Bayesian probability","score_opus":0.029731458059625418,"score_gpt":0.29693778354428346,"score_spread":0.26720632548465806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409365439","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":[0.013296,0.00037990857,0.9823637,0.00028906687,0.00003750103,0.00005162439,0.00008216099,0.0011124347,0.0023875488],"genre_scores_gemma":[0.5463765,0.0011220885,0.4392744,0.0007429128,0.0001387112,0.00015706921,0.0004185332,0.00061459764,0.011155075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978036,0.000046678517,0.000007956173,0.00006621081,0.00006520638,0.000033609573],"domain_scores_gemma":[0.99945456,0.00024183717,0.00006273888,0.00008629239,0.000111986534,0.00004251292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007951356,0.0009799841,0.00065512507,0.00062663143,0.00023815311,0.0010568394,0.0015191959,0.00083949394,0.0024193355],"category_scores_gemma":[0.0022187394,0.0005060218,0.0009400563,0.00037694036,0.00084779476,0.0016632591,0.0015775026,0.0019222175,0.0009810345],"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":[0.0003210579,0.00016838007,0.0011610078,0.0003406794,0.000114732255,0.00023065749,0.00027455908,0.46294,0.10930443,0.03665862,0.0056584817,0.3828274],"study_design_scores_gemma":[0.00001272631,0.00003901209,0.00021809098,0.000016771068,0.000023099772,0.00008397258,0.00001378685,0.9731419,0.013209345,0.01136873,0.0018583401,0.000014194002],"about_ca_topic_score_codex":0.0026838253,"about_ca_topic_score_gemma":0.004088066,"teacher_disagreement_score":0.0026838253,"about_ca_system_score_codex":0.0007315888,"about_ca_system_score_gemma":0.00062017667,"threshold_uncertainty_score":0.008093417},"labels":[],"label_agreement":null},{"id":"W4409433275","doi":"10.1016/j.neunet.2025.107495","title":"Driving scene image Dehazing model based on multi-branch and multi-scale feature fusion","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision; Scale (ratio); Feature (linguistics); Image (mathematics); Image fusion; Fusion; Pattern recognition (psychology); Cartography; Geography","score_opus":0.011112674688946571,"score_gpt":0.2652916251813153,"score_spread":0.25417895049236877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409433275","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":[0.0389955,0.00022443697,0.95871174,0.00008812612,0.00004680267,0.000032893917,0.000047069763,0.00037209355,0.0014813306],"genre_scores_gemma":[0.84198844,0.0007387723,0.1512262,0.00007086074,0.00005092667,0.000056790268,0.0002186252,0.00009000796,0.0055593625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999869,0.000009274038,0.0000053977033,0.000042688374,0.000056880686,0.000016788119],"domain_scores_gemma":[0.99991226,0.000013684568,0.000012237112,0.000009146943,0.000046389894,0.000006239172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022376087,0.00052199786,0.00045314056,0.00047070414,0.00022084417,0.00044818697,0.0007171302,0.00045388975,0.00082854717],"category_scores_gemma":[0.00033356424,0.00029033187,0.00057618757,0.0003818068,0.00022299924,0.00092630234,0.00035128015,0.00062777323,0.00024901802],"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":[0.00020250115,0.00012061375,0.0020769802,0.00013225824,0.00009828467,0.00013057771,0.000113173955,0.623356,0.058051012,0.0051486925,0.0016507871,0.30891916],"study_design_scores_gemma":[0.0000020908728,0.000012484547,0.0004514223,0.0000021659043,0.000011494549,0.000027104408,0.000004640481,0.9951202,0.0037431398,0.00037116947,0.0002487405,0.000005175414],"about_ca_topic_score_codex":0.00654418,"about_ca_topic_score_gemma":0.0059575015,"teacher_disagreement_score":0.00654418,"about_ca_system_score_codex":0.00044065964,"about_ca_system_score_gemma":0.000500452,"threshold_uncertainty_score":0.013012171},"labels":[],"label_agreement":null},{"id":"W4410277212","doi":"10.1007/978-3-031-92747-8_12","title":"Virtual Exposure Bracketing for HDR Imaging Using Fuzzy Membership","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Bracketing (phenomenology); Computer science; Fuzzy logic; Computer vision; Artificial intelligence; Computer graphics (images)","score_opus":0.02260006118908298,"score_gpt":0.27853214262346543,"score_spread":0.25593208143438245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410277212","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":[0.013540777,0.000090031834,0.9843693,0.00002402224,0.000023804789,0.000021796808,0.000015852722,0.00030894257,0.0016055031],"genre_scores_gemma":[0.3272554,0.00016917846,0.6674578,0.00003542874,0.000028982464,0.000044506472,0.000069196765,0.00010827028,0.0048311274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997514,0.000036444373,0.000013691556,0.00006427107,0.000109349334,0.000024753697],"domain_scores_gemma":[0.9997563,0.00009374398,0.000019709996,0.00005322024,0.00006170555,0.000015357497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035383337,0.00032262685,0.00048786734,0.0003640387,0.0004074995,0.0006784639,0.0007739677,0.00045627807,0.006060933],"category_scores_gemma":[0.00079673145,0.00025976283,0.0004996812,0.00037287202,0.00033461754,0.0008002472,0.0007183867,0.0006676768,0.0007909792],"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":[0.00036506026,0.00014324399,0.00043700598,0.00020538957,0.000028445862,0.00014493983,0.00032047654,0.06848683,0.21049422,0.02627683,0.0018448255,0.69125277],"study_design_scores_gemma":[0.000014447605,0.00021712254,0.00073700707,0.000027239124,0.000027747614,0.000280276,0.00008397061,0.905903,0.07673456,0.008676247,0.0072682183,0.000030200632],"about_ca_topic_score_codex":0.0011621509,"about_ca_topic_score_gemma":0.0012348313,"teacher_disagreement_score":0.006060933,"about_ca_system_score_codex":0.00033293167,"about_ca_system_score_gemma":0.0003321828,"threshold_uncertainty_score":0.020275831},"labels":[],"label_agreement":null},{"id":"W4410341644","doi":"10.1109/jiot.2025.3569663","title":"Zero-DCE With Global Information for Low-Light Image Enhancement in Coal Mine IoVT","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Image Enhancement 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":"Carleton University; University of British Columbia","funders":"Foundation Research Project of Jiangsu Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Coal mining; Zero (linguistics); Computer vision; Artificial intelligence; Coal","score_opus":0.004787896775071343,"score_gpt":0.255340501173855,"score_spread":0.25055260439878363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410341644","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":[0.036183666,0.0011526417,0.95907634,0.00017459954,0.000057179932,0.000061024482,0.00009664462,0.00094228605,0.0022556859],"genre_scores_gemma":[0.36912462,0.0018235185,0.6194488,0.0003799957,0.00007444359,0.00008338113,0.00060280215,0.0004395262,0.008022981],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998018,0.000026720596,0.0000110547435,0.000044737266,0.00008478816,0.000030979616],"domain_scores_gemma":[0.9997017,0.000090472226,0.00003124234,0.000062235646,0.00009596804,0.000018462326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046782836,0.0005838038,0.000641642,0.00085946184,0.00019329849,0.0008522897,0.00057567767,0.0005202638,0.0016072906],"category_scores_gemma":[0.001023978,0.00024817474,0.00070012506,0.00050785823,0.00041492877,0.0008877804,0.000825004,0.00084655115,0.00072151946],"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":[0.00046395554,0.00017050872,0.0016853791,0.00038608906,0.00008805139,0.00025660914,0.00015838478,0.06519689,0.20032413,0.006216019,0.004022797,0.7210313],"study_design_scores_gemma":[0.000032116735,0.00013409504,0.0022992704,0.0000512267,0.000083444575,0.0006599445,0.00007159671,0.8680258,0.11445677,0.0041393656,0.010015674,0.000030739233],"about_ca_topic_score_codex":0.0015366144,"about_ca_topic_score_gemma":0.002964752,"teacher_disagreement_score":0.0016072906,"about_ca_system_score_codex":0.00035364606,"about_ca_system_score_gemma":0.0005209759,"threshold_uncertainty_score":0.005376935},"labels":[],"label_agreement":null},{"id":"W4410884675","doi":"10.1016/j.engappai.2025.111115","title":"A codebook-driven approach for low-light image enhancement","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Image Enhancement Techniques","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 Alberta","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Shenzhen Science and Technology Innovation Program; National Natural Science Foundation of China","keywords":"Computer science; Codebook; Image enhancement; Image (mathematics); Artificial intelligence; Computer vision","score_opus":0.012649928381298265,"score_gpt":0.27717084797827324,"score_spread":0.264520919596975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410884675","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":[0.0069191684,0.00058934285,0.98893905,0.00010680463,0.000055740868,0.00007312849,0.00016128202,0.0013683137,0.001787207],"genre_scores_gemma":[0.15887457,0.0010145231,0.8297511,0.0003668323,0.000079659505,0.0001567583,0.0013406362,0.0006070107,0.0078089144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943906,0.00007623733,0.000023394223,0.00013409845,0.00027254474,0.00005469166],"domain_scores_gemma":[0.9991773,0.00018625331,0.00006294733,0.00025036943,0.00026364907,0.000059591595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000689039,0.00089463784,0.0008296821,0.0009141393,0.00026375358,0.00084500347,0.001358055,0.00062212406,0.003402928],"category_scores_gemma":[0.0020146775,0.00032995385,0.00075276074,0.00076575,0.0006473458,0.0011701059,0.0015184279,0.0015247414,0.0019518983],"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":[0.00040643668,0.00019608461,0.0009120025,0.0004727951,0.00008176361,0.00015601325,0.00013491124,0.07420909,0.19755559,0.014497319,0.011263946,0.70011413],"study_design_scores_gemma":[0.0000770654,0.00028356063,0.0012173138,0.000058551825,0.000066954686,0.000571714,0.000063454514,0.82661307,0.1323357,0.013152672,0.025497958,0.000062087944],"about_ca_topic_score_codex":0.0018916299,"about_ca_topic_score_gemma":0.0035283924,"teacher_disagreement_score":0.003402928,"about_ca_system_score_codex":0.00047864523,"about_ca_system_score_gemma":0.0007145566,"threshold_uncertainty_score":0.011383891},"labels":[],"label_agreement":null},{"id":"W4410982446","doi":"10.1007/s42452-025-07163-2","title":"Brightness adjustment and contrast matching in low-light underwater images using feedforward neural networks","year":2025,"lang":"en","type":"article","venue":"Discover Applied Sciences","topic":"Image Enhancement 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":"Becton Dickinson (Canada)","funders":"","keywords":"Brightness; Contrast (vision); Underwater; Feedforward neural network; Matching (statistics); Artificial intelligence; Feed forward; Computer vision; Computer science; Artificial neural network; Pattern recognition (psychology); Optics; Mathematics; Geology; Physics; Engineering; Statistics","score_opus":0.009539752483074043,"score_gpt":0.261366968090301,"score_spread":0.251827215607227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410982446","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":[0.10091044,0.00054801145,0.89481246,0.00019141265,0.00007851544,0.00006609286,0.000061704464,0.0010278262,0.002303625],"genre_scores_gemma":[0.7860238,0.00041338985,0.20801383,0.00019994404,0.000066658846,0.00008275559,0.00023596964,0.00010091144,0.0048626917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998441,0.00002242506,0.000009099077,0.000054455057,0.00003897748,0.000030971354],"domain_scores_gemma":[0.9996649,0.00013801176,0.000047124002,0.000023804427,0.00010864149,0.000017574997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050348963,0.0006622722,0.00057545735,0.00052691036,0.00023596566,0.00058832276,0.0008800329,0.0007823999,0.00107458],"category_scores_gemma":[0.0013864078,0.0003613304,0.0005541988,0.00035210798,0.0003268294,0.00076885364,0.0004691936,0.0008271758,0.0002488047],"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":[0.00032471961,0.00021546535,0.002309692,0.0001309642,0.00009628123,0.00015274205,0.00009467271,0.49306858,0.04409833,0.0024120887,0.0016035239,0.45549303],"study_design_scores_gemma":[0.00000419456,0.000017454198,0.00024069469,0.000004159434,0.00000906668,0.0000121264975,0.0000036589372,0.9960163,0.0031743064,0.00039470798,0.00011986324,0.00000338167],"about_ca_topic_score_codex":0.005855304,"about_ca_topic_score_gemma":0.006819552,"teacher_disagreement_score":0.005855304,"about_ca_system_score_codex":0.00064417167,"about_ca_system_score_gemma":0.00046259738,"threshold_uncertainty_score":0.011642396},"labels":[],"label_agreement":null},{"id":"W4411282518","doi":"10.3390/electronics14122416","title":"Model-Based Design of Contrast-Limited Histogram Equalization for Low-Complexity, High-Speed, and Low-Power Tone-Mapping Operation","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Image Enhancement 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 Alberta","funders":"Mitacs","keywords":"Tone (literature); Contrast (vision); Equalization (audio); Histogram equalization; Computer science; Tone mapping; Power (physics); Histogram; Adaptive histogram equalization; Electronic engineering; Speech recognition; Engineering; Artificial intelligence; Computer vision; Algorithm; Physics; Art; Image (mathematics); Dynamic range","score_opus":0.028813487371821378,"score_gpt":0.2941536552694838,"score_spread":0.2653401678976624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411282518","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":[0.020261599,0.00018052482,0.9669114,0.00011261833,0.00004817125,0.00011629333,0.00007870894,0.0012381705,0.011052502],"genre_scores_gemma":[0.7924605,0.00031119183,0.19672659,0.00011067701,0.000022124172,0.0002770528,0.00014176988,0.00016999056,0.009780131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998753,0.000015774162,0.000005451773,0.000028445826,0.000058625108,0.000016335547],"domain_scores_gemma":[0.99988747,0.00002650509,0.000017371658,0.00001588363,0.00004701158,0.000005720987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015419642,0.00039247313,0.00027035412,0.00017839442,0.00019853485,0.00063406775,0.00081898103,0.0003085242,0.0031364337],"category_scores_gemma":[0.00033653385,0.00018214156,0.00029083385,0.00009414105,0.00019850077,0.00040375302,0.00027343293,0.00043268467,0.0006804574],"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":[0.00027348046,0.00014345054,0.0011614448,0.00047960595,0.00009281183,0.00028722532,0.0003323637,0.58430064,0.2355116,0.028768096,0.004096292,0.14455295],"study_design_scores_gemma":[0.000024358495,0.00018279171,0.00031427544,0.000024224548,0.000029993478,0.000108255175,0.00002275562,0.93243796,0.05505252,0.0013064694,0.01048332,0.000013119636],"about_ca_topic_score_codex":0.0017937953,"about_ca_topic_score_gemma":0.002763469,"teacher_disagreement_score":0.0031364337,"about_ca_system_score_codex":0.0005156563,"about_ca_system_score_gemma":0.00069093675,"threshold_uncertainty_score":0.010492384},"labels":[],"label_agreement":null},{"id":"W4412201592","doi":"10.3390/app15147762","title":"Dual-Branch Luminance–Chrominance Attention Network for Hydraulic Concrete Image Enhancement","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Chrominance; Luminance; Dual (grammatical number); Computer science; Artificial intelligence; Computer vision; Art","score_opus":0.010356303545461167,"score_gpt":0.27086418367948284,"score_spread":0.26050788013402165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412201592","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":[0.40576595,0.003963381,0.5705,0.0006190035,0.0002987687,0.0003614891,0.002368115,0.004891719,0.011231487],"genre_scores_gemma":[0.79267997,0.0013067155,0.18755488,0.00049460045,0.0001515226,0.00020865636,0.005772993,0.00019379426,0.011636765],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980897,0.000026511907,0.0000058912624,0.00006165626,0.000050766175,0.00004630135],"domain_scores_gemma":[0.9998288,0.000041751857,0.000016772769,0.000026573032,0.00006957479,0.000016419111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003367115,0.00066426274,0.0004984306,0.0007280435,0.00017440824,0.00035567067,0.00076919876,0.00043253435,0.0014616832],"category_scores_gemma":[0.0006721187,0.00014792109,0.0004074348,0.00045496557,0.0002241166,0.00052639755,0.00064006867,0.00060358824,0.0005226583],"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":[0.0008709481,0.00041618897,0.0052642487,0.00034521284,0.00014821813,0.00032475984,0.00011523221,0.090975806,0.13524981,0.0018904894,0.014095812,0.75030327],"study_design_scores_gemma":[0.000045830824,0.00025269197,0.008927782,0.000030134166,0.00010805415,0.00026405856,0.00006730754,0.92375517,0.056151167,0.0016165293,0.0087507,0.000030593827],"about_ca_topic_score_codex":0.007386781,"about_ca_topic_score_gemma":0.014119221,"teacher_disagreement_score":0.007386781,"about_ca_system_score_codex":0.00049880875,"about_ca_system_score_gemma":0.0004413956,"threshold_uncertainty_score":0.014687538},"labels":[],"label_agreement":null},{"id":"W4412446484","doi":"10.1109/dsp65409.2025.11074896","title":"A Perceptually-Based Deep Learning Approach for Energy-Preserving Image Enhancement","year":2025,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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 British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Image (mathematics); Deep learning; Computer vision; Image enhancement; Energy (signal processing); Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.010575608335785028,"score_gpt":0.2616783209231831,"score_spread":0.2511027125873981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412446484","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":[0.020519398,0.00059004436,0.9758336,0.0001509047,0.00005705159,0.000030804513,0.0000643658,0.0006527982,0.0021010488],"genre_scores_gemma":[0.5279642,0.0009903193,0.4553241,0.00045199445,0.000081138634,0.000085670545,0.00037894616,0.0001901754,0.01453355],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99991405,0.000011512676,0.0000039765496,0.0000208403,0.000034817956,0.000014848905],"domain_scores_gemma":[0.999895,0.00002932615,0.000011795102,0.000016439282,0.000038695238,0.000008708898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002739389,0.0005089874,0.00029812157,0.00027646666,0.00010572476,0.0003460508,0.00071635423,0.0004218925,0.0017907625],"category_scores_gemma":[0.0005231298,0.00018905774,0.0003787074,0.0002335245,0.00025383988,0.0005809518,0.0005989707,0.0009713507,0.00040768075],"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":[0.00020289933,0.0002069815,0.00076316495,0.00021012432,0.00010464662,0.000122387,0.000067892805,0.28958082,0.122544914,0.008861417,0.004079176,0.57325554],"study_design_scores_gemma":[0.000005916532,0.00005395072,0.00023317702,0.000011114614,0.000015925323,0.000047146623,0.0000047879553,0.982268,0.014075141,0.0018108707,0.0014678891,0.0000060492303],"about_ca_topic_score_codex":0.0016300738,"about_ca_topic_score_gemma":0.0037595835,"teacher_disagreement_score":0.0017907625,"about_ca_system_score_codex":0.00033952168,"about_ca_system_score_gemma":0.00029265825,"threshold_uncertainty_score":0.005990684},"labels":[],"label_agreement":null},{"id":"W4412713258","doi":"10.1109/icicv64824.2025.11085854","title":"Multimodal Low-Light Image Enhancement using Retinex-based Decomposition and Transformer Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Color constancy; Computer science; Computer vision; Artificial intelligence; Image enhancement; Transformer; Decomposition; Image (mathematics); Engineering","score_opus":0.005237054553555992,"score_gpt":0.27623106892562993,"score_spread":0.27099401437207393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412713258","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":[0.02218538,0.00025661683,0.9744203,0.00006694799,0.000023379213,0.000035339453,0.00003630192,0.00040895987,0.0025668144],"genre_scores_gemma":[0.4660676,0.00077894673,0.5251935,0.00012546733,0.000047530415,0.00008335232,0.00018132664,0.0001565383,0.00736582],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998184,0.00004434471,0.000006691564,0.00003749508,0.000067170215,0.000025927957],"domain_scores_gemma":[0.99983,0.000047048437,0.000025332074,0.00002791952,0.000054869448,0.000014818601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037223595,0.00072818226,0.0004417464,0.00061812927,0.00017633039,0.00056443067,0.0005231994,0.00031225898,0.0016639188],"category_scores_gemma":[0.0005441777,0.00019473606,0.0005555363,0.00039742558,0.0003853297,0.00078280334,0.0007005976,0.00052230025,0.0005040464],"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":[0.00044278958,0.00018164751,0.0008640209,0.0001809861,0.00012670971,0.00024408566,0.00014032322,0.23058194,0.31796226,0.017670175,0.0028729043,0.4287322],"study_design_scores_gemma":[0.000013507081,0.00008014341,0.00036532772,0.000013032542,0.000038164966,0.00018980754,0.00002045748,0.93620336,0.05679719,0.0037730664,0.0024881656,0.000017780243],"about_ca_topic_score_codex":0.0012739768,"about_ca_topic_score_gemma":0.0020550857,"teacher_disagreement_score":0.0016639188,"about_ca_system_score_codex":0.0004497971,"about_ca_system_score_gemma":0.00029107963,"threshold_uncertainty_score":0.0055663586},"labels":[],"label_agreement":null},{"id":"W4412873526","doi":"10.2139/ssrn.5358561","title":"Dynamic Mutual Adversarial Learning for Semi-Supervised Semantic Segmentation of Underwater Images with Limited and Noisy Annotations","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Segmentation; Adversarial system; Computer science; Underwater; Artificial intelligence; Computer vision; Pattern recognition (psychology); Machine learning; Geography","score_opus":0.007017174008908051,"score_gpt":0.2579206903466361,"score_spread":0.25090351633772806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412873526","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":[0.020852877,0.00020545928,0.97705245,0.00016770633,0.000026987685,0.000039010312,0.00013384032,0.0007866863,0.0007349308],"genre_scores_gemma":[0.6912494,0.0003914864,0.2989514,0.00031368248,0.00014299118,0.00025283507,0.0013230238,0.00059487997,0.006780419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989962,0.00032020535,0.000042186664,0.00032652894,0.00018698015,0.00012792375],"domain_scores_gemma":[0.99774987,0.0014341742,0.0002661432,0.00028127487,0.00018368619,0.00008481369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019803944,0.0011888348,0.0017492633,0.0010469276,0.00048418148,0.0011504781,0.00234237,0.0022858647,0.0016846813],"category_scores_gemma":[0.0049785976,0.0012012704,0.0013474951,0.001054004,0.0016004079,0.0019342942,0.0029557818,0.0021651345,0.00066925894],"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":[0.0004646164,0.00009958913,0.0005375418,0.00016691645,0.0001054968,0.00015646605,0.00019063457,0.85449255,0.011687956,0.008487799,0.0020579877,0.12155246],"study_design_scores_gemma":[0.0000032333915,0.000014999794,0.00008639494,0.00000584309,0.000005030458,0.000018141807,0.0000066648013,0.99543506,0.0012626366,0.0030034157,0.00015425803,0.0000043976975],"about_ca_topic_score_codex":0.0037305327,"about_ca_topic_score_gemma":0.0041674217,"teacher_disagreement_score":0.0037305327,"about_ca_system_score_codex":0.0009766401,"about_ca_system_score_gemma":0.0011249835,"threshold_uncertainty_score":0.01047343},"labels":[],"label_agreement":null},{"id":"W4412989788","doi":"10.3390/wevj16080441","title":"Enhanced Real-Time Method Traffic Light Signal Color Recognition Using Advanced Convolutional Neural Network Techniques","year":2025,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Image Enhancement 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":"Carleton University","funders":"","keywords":"Convolutional neural network; Traffic signal; Computer science; Pattern recognition (psychology); Artificial intelligence; Artificial neural network; SIGNAL (programming language); Real-time computing","score_opus":0.0108552735704038,"score_gpt":0.28051787891490626,"score_spread":0.2696626053445025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412989788","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":[0.17289077,0.0011757662,0.807773,0.00026247924,0.00030240594,0.000120288714,0.0007611455,0.009824883,0.006889265],"genre_scores_gemma":[0.7702836,0.0006053793,0.20769691,0.00027059755,0.00007491426,0.00010090877,0.002470654,0.00031012803,0.018186813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997876,0.000018355151,0.0000071609197,0.00007172926,0.00006976273,0.00004546714],"domain_scores_gemma":[0.9997688,0.000027081149,0.000020463378,0.000037740785,0.0001312698,0.000014671398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035535594,0.0008415964,0.00047152012,0.00067720644,0.00017674835,0.0005435657,0.0010279563,0.00043297498,0.00199063],"category_scores_gemma":[0.0006727227,0.00023661,0.00048089714,0.00037253025,0.00018457272,0.0006915399,0.0006088217,0.000719864,0.001293182],"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":[0.0004930672,0.00031417434,0.0044760834,0.00015338359,0.00015922364,0.00014466829,0.00006297918,0.13718274,0.10595024,0.0020089522,0.008931147,0.74012345],"study_design_scores_gemma":[0.00001052862,0.000075055126,0.0015986828,0.000010222513,0.00003337282,0.00007254273,0.00000998607,0.9699167,0.025209518,0.00042392037,0.0026259485,0.000013531385],"about_ca_topic_score_codex":0.009853005,"about_ca_topic_score_gemma":0.015795706,"teacher_disagreement_score":0.009853005,"about_ca_system_score_codex":0.00061647635,"about_ca_system_score_gemma":0.00078332424,"threshold_uncertainty_score":0.019591331},"labels":[],"label_agreement":null},{"id":"W4413256811","doi":"10.1109/mlise66443.2025.11100248","title":"A Real-Time Deraining Network with Rain Feature Perception for Autonomous Driving","year":2025,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"Ministry of Education and Child Care","funders":"","keywords":"Feature (linguistics); Perception; Computer science; Artificial intelligence; Real-time computing; Psychology","score_opus":0.006212137310687626,"score_gpt":0.25262569329115897,"score_spread":0.24641355598047135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413256811","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":[0.2606259,0.0005980669,0.72900516,0.0003115645,0.00019530687,0.00015579583,0.00028842333,0.00434132,0.0044784257],"genre_scores_gemma":[0.9226994,0.00012737743,0.07384317,0.00012026866,0.00002769513,0.000052673382,0.00034222542,0.000041139763,0.002746171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999894,0.000010986854,0.0000035768971,0.00003645058,0.000028665367,0.000026313299],"domain_scores_gemma":[0.9998622,0.0000272498,0.000013000048,0.000022722725,0.000055519766,0.000019302228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028272974,0.0004730228,0.00031997627,0.00029535475,0.00026794735,0.00033960582,0.0011194801,0.0003218324,0.0009611451],"category_scores_gemma":[0.0005342486,0.0001656842,0.00019292322,0.000187178,0.00023667293,0.0007137079,0.00061842747,0.00034746472,0.00022903149],"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":[0.0010383135,0.00033687317,0.007260067,0.00014806502,0.00010105731,0.00039374994,0.00022398598,0.2413693,0.12551619,0.0034376432,0.009018277,0.61115646],"study_design_scores_gemma":[0.000016789973,0.00011587876,0.0012948661,0.0000036853314,0.000018181145,0.000067409695,0.000032180335,0.9826705,0.012638825,0.0008037802,0.0023254382,0.000012429267],"about_ca_topic_score_codex":0.00703668,"about_ca_topic_score_gemma":0.011065912,"teacher_disagreement_score":0.00703668,"about_ca_system_score_codex":0.0005534265,"about_ca_system_score_gemma":0.00056970643,"threshold_uncertainty_score":0.013991475},"labels":[],"label_agreement":null},{"id":"W4413407903","doi":"10.1016/j.oceaneng.2025.122391","title":"Dual-frequency multi-scale based submarine pipeline side-scan sonar image enhancement algorithm","year":2025,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"China University of Petroleum, Beijing; National Natural Science Foundation of China","keywords":"Side-scan sonar; Dual (grammatical number); Pipeline (software); Submarine; Scale (ratio); Sonar; Computer science; Algorithm; Artificial intelligence; Computer vision; Acoustics; Remote sensing; Geology; Marine engineering; Engineering; Physics; Geography; Cartography","score_opus":0.00617798740627436,"score_gpt":0.2298329559744344,"score_spread":0.22365496856816003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413407903","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":[0.050412115,0.00047981564,0.9435323,0.00012661643,0.00008819631,0.000052347114,0.00008538389,0.000638878,0.0045843525],"genre_scores_gemma":[0.24596052,0.0009359202,0.7354307,0.00014288817,0.00007783437,0.000077887686,0.00042765678,0.00011167393,0.016834894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998778,0.000009290276,0.000006006599,0.000028035349,0.000063128464,0.000015775313],"domain_scores_gemma":[0.9998504,0.000023416844,0.000015897847,0.000019090483,0.00008157839,0.000009735802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019346646,0.00045189005,0.00037234704,0.00047280712,0.00015235665,0.000325781,0.0003887526,0.0004105684,0.0021393022],"category_scores_gemma":[0.0003270167,0.00021634017,0.00042333576,0.0003473521,0.00013494001,0.00044715023,0.0004236852,0.00041607683,0.0010046541],"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":[0.00034582673,0.00009496206,0.0019915167,0.00014257398,0.000059475467,0.0001565912,0.000087328364,0.022522783,0.34738293,0.0023851297,0.002797994,0.6220328],"study_design_scores_gemma":[0.000041733623,0.00029968264,0.0075061307,0.000027338743,0.0001285496,0.0009010608,0.000059068643,0.7649667,0.21060254,0.0009430325,0.014485313,0.000038831295],"about_ca_topic_score_codex":0.0008859214,"about_ca_topic_score_gemma":0.0015374508,"teacher_disagreement_score":0.0021393022,"about_ca_system_score_codex":0.000119854885,"about_ca_system_score_gemma":0.00037163583,"threshold_uncertainty_score":0.00715667},"labels":[],"label_agreement":null},{"id":"W4414070233","doi":"10.1051/itmconf/20257804008","title":"A Review of Gan-Based Texture Reconstruction of Underwater Images","year":2025,"lang":"en","type":"article","venue":"ITM Web of Conferences","topic":"Image Enhancement 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 Toronto","funders":"","keywords":"Underwater; Image restoration; Adversarial system; Generalization; Image (mathematics); Image processing; Generative grammar; Image texture","score_opus":0.015213192331123952,"score_gpt":0.2818736460277652,"score_spread":0.26666045369664126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414070233","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":[0.006976809,0.365962,0.6050867,0.0011308527,0.0010260522,0.00008895053,0.00033451302,0.00076791056,0.01862618],"genre_scores_gemma":[0.1338168,0.59032536,0.24935769,0.0009272204,0.0019854049,0.00017177487,0.0013192605,0.00043541036,0.02166109],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970907,0.000053005904,0.000030858686,0.00007014845,0.00012131622,0.000015574331],"domain_scores_gemma":[0.99953127,0.00025437385,0.000036380236,0.000041053467,0.0001209653,0.000016094718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006154827,0.0008464959,0.00073783414,0.00097240537,0.00015135262,0.00076619384,0.0008736602,0.0007837131,0.0026099577],"category_scores_gemma":[0.0012291905,0.00046109926,0.0009680539,0.0014014923,0.00037208945,0.00094491564,0.00045641835,0.00090138236,0.0011977558],"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":[0.00008466969,0.000052155872,0.0007397022,0.0037491987,0.00015266612,0.00021962296,0.00008938835,0.05973687,0.010864781,0.013112342,0.013942994,0.89725554],"study_design_scores_gemma":[0.000022991353,0.0003643161,0.0039283396,0.0017330539,0.0003121287,0.0029658095,0.00013395099,0.54833066,0.02416271,0.016698388,0.40115184,0.00019579066],"about_ca_topic_score_codex":0.0015821266,"about_ca_topic_score_gemma":0.001342649,"teacher_disagreement_score":0.0026099577,"about_ca_system_score_codex":0.00034601172,"about_ca_system_score_gemma":0.00034007462,"threshold_uncertainty_score":0.008731186},"labels":[],"label_agreement":null},{"id":"W4414197797","doi":"10.1109/cvprw67362.2025.00122","title":"NTIRE 2025 Challenge on Single Image Reflection Removal in the Wild: Datasets, Methods and Results","year":2025,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Alexander von Humboldt-Stiftung","keywords":"Reflection (computer programming); Process (computing); Cover (algebra); Task (project management); Image (mathematics); Range (aeronautics)","score_opus":0.04440769089067901,"score_gpt":0.3965857864121933,"score_spread":0.35217809552151425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414197797","genre_codex":"dataset","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":[0.07135538,0.03605822,0.2322927,0.0073256665,0.008612155,0.0063637206,0.47313607,0.11772847,0.047127616],"genre_scores_gemma":[0.02979866,0.0028190475,0.14688358,0.0017856042,0.00041258367,0.0016194201,0.80272514,0.0027061538,0.011249825],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9915581,0.0014993532,0.00075414364,0.0020056905,0.003434164,0.0007485889],"domain_scores_gemma":[0.99407417,0.0010975461,0.0002977597,0.002345602,0.0017445261,0.0004403928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075185364,0.0068711475,0.0036134424,0.0042602876,0.002256287,0.00394471,0.0063861897,0.00534768,0.009215254],"category_scores_gemma":[0.014991606,0.000964344,0.003757714,0.003134052,0.0016896147,0.0045178835,0.0055782823,0.0043829107,0.022875521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009597688,0.0012190512,0.002656958,0.0035639561,0.00042675433,0.000427722,0.00012646116,0.012800293,0.009091217,0.0020615591,0.74175847,0.22490779],"study_design_scores_gemma":[0.000939677,0.0014549339,0.0149055,0.0020690183,0.0005379032,0.004427732,0.0011451076,0.14694017,0.058850218,0.014363689,0.75376004,0.00060603087],"about_ca_topic_score_codex":0.015879044,"about_ca_topic_score_gemma":0.02569832,"teacher_disagreement_score":0.015879044,"about_ca_system_score_codex":0.0018452809,"about_ca_system_score_gemma":0.002772372,"threshold_uncertainty_score":0.03976226},"labels":[],"label_agreement":null},{"id":"W4415158464","doi":"10.1371/journal.pone.0333928","title":"Improving object detection in challenging weather for autonomous driving via adversarial image translation","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Object detection; Robustness (evolution); Adversarial system; Adverse weather; Reliability (semiconductor); Object (grammar); Image translation; SAFER; Perception","score_opus":0.017642766903219798,"score_gpt":0.23450872456843302,"score_spread":0.21686595766521322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415158464","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":[0.08605249,0.0002255108,0.90987927,0.00021513978,0.00007513482,0.000039470513,0.00007799608,0.0010963149,0.0023386711],"genre_scores_gemma":[0.8975276,0.00016892329,0.099420734,0.00017306251,0.000047940215,0.000033192842,0.00017690587,0.00010470766,0.0023469066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978215,0.000043469216,0.0000060286566,0.0000705045,0.000057453508,0.000040322793],"domain_scores_gemma":[0.9996512,0.00015700478,0.000045395216,0.00006203151,0.000058372665,0.000025992937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005009455,0.00080695515,0.00043356247,0.00028793365,0.00019136595,0.00047098467,0.0008306127,0.0006402642,0.0010138357],"category_scores_gemma":[0.001509274,0.00028955727,0.00046922982,0.00016566587,0.00064534444,0.00061774947,0.0010082038,0.0010257399,0.00039166637],"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":[0.00021302488,0.00007289175,0.0020105715,0.00006795516,0.00006421416,0.00021633082,0.00010734645,0.8362113,0.050190378,0.005409881,0.0020945652,0.10334138],"study_design_scores_gemma":[0.000003175501,0.000020249292,0.00027898292,0.0000025537558,0.0000037514626,0.000033969478,0.000005118427,0.9939849,0.0039968747,0.0013760015,0.0002899055,0.0000046117207],"about_ca_topic_score_codex":0.0020763443,"about_ca_topic_score_gemma":0.0025089218,"teacher_disagreement_score":0.0020763443,"about_ca_system_score_codex":0.0003367415,"about_ca_system_score_gemma":0.0003707692,"threshold_uncertainty_score":0.004128456},"labels":[],"label_agreement":null},{"id":"W4415377194","doi":"10.1016/j.cviu.2025.104543","title":"XLITE-Unet: Extremely Light and Efficient Deep learning architecture with selective atrous and axial depthwise convolution for image segmentation","year":2025,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Centre National de la Recherche Scientifique; Canadian Nautical Research Society; University of Central Arkansas","keywords":"Deep learning; Benchmark (surveying); Segmentation; Convolution (computer science); Convolutional neural network; Range (aeronautics); Channel (broadcasting); Network architecture","score_opus":0.009156559389302646,"score_gpt":0.2545318479845313,"score_spread":0.24537528859522867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415377194","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":[0.02294168,0.00076594134,0.94688183,0.00037209876,0.00023884463,0.000107778775,0.001339737,0.021080626,0.006271495],"genre_scores_gemma":[0.19915995,0.00043633793,0.77133197,0.000791967,0.0000744531,0.00024424572,0.0045894342,0.0014200616,0.021951573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998343,0.000017111948,0.0000073185324,0.000044355056,0.000065254695,0.00003170055],"domain_scores_gemma":[0.9998184,0.00004260081,0.000013727253,0.00004053059,0.000058163856,0.000026690146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034935967,0.0009760537,0.0004691851,0.00047450207,0.00030036454,0.0007397371,0.0020013608,0.0008990878,0.008235537],"category_scores_gemma":[0.00080710126,0.00043297268,0.0004904306,0.00041504292,0.000309856,0.0009774598,0.0013499947,0.0018250534,0.0025050165],"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":[0.000597589,0.0002955632,0.0013732027,0.00034570132,0.00022285932,0.00020725778,0.00009872342,0.0901022,0.053539187,0.01917723,0.07360828,0.7604321],"study_design_scores_gemma":[0.00004513121,0.00013709294,0.00029107058,0.000026444703,0.00002676301,0.00009023398,0.00001592369,0.95463836,0.02677286,0.0061771977,0.011757754,0.000021076197],"about_ca_topic_score_codex":0.0053214678,"about_ca_topic_score_gemma":0.016429542,"teacher_disagreement_score":0.008235537,"about_ca_system_score_codex":0.00077852805,"about_ca_system_score_gemma":0.0012537738,"threshold_uncertainty_score":0.027550638},"labels":[],"label_agreement":null},{"id":"W4415540052","doi":"10.1145/3746027.3755125","title":"AtlantisGS: Underwater Sparse-View Scene Reconstruction via Gaussian Splatting","year":2025,"lang":"","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Underwater; Gaussian; Noise (video); Context (archaeology); Perspective (graphical)","score_opus":0.016017389565701933,"score_gpt":0.2670936622515454,"score_spread":0.2510762726858435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415540052","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":[0.006547845,0.000065379645,0.9873285,0.0000702588,0.00004736268,0.000030532843,0.00017585879,0.0037317001,0.0020025668],"genre_scores_gemma":[0.0660693,0.00013060828,0.9250994,0.00008937904,0.000033173317,0.000047453195,0.0009048499,0.000653447,0.006972441],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973804,0.00003179177,0.000008234225,0.00003569039,0.00016037405,0.00002587188],"domain_scores_gemma":[0.9998167,0.00003206476,0.0000135240525,0.00006409409,0.000053536893,0.000020154584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033425406,0.0007190333,0.00055461866,0.0005336794,0.00028702067,0.0006134643,0.00097350293,0.0006421057,0.0062913983],"category_scores_gemma":[0.00059338857,0.00035986304,0.00057547056,0.0006475846,0.00035153847,0.0008665274,0.0019411931,0.0009528175,0.0024043438],"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":[0.0004354965,0.00016719349,0.0009080419,0.00016418782,0.00010359809,0.00029887952,0.00017082001,0.053443003,0.117885716,0.014689611,0.018762013,0.79297155],"study_design_scores_gemma":[0.00004293517,0.00007486206,0.00058634375,0.0000113709175,0.000017197153,0.00031421107,0.000048261434,0.92556477,0.0535481,0.0043697674,0.015395824,0.000026483523],"about_ca_topic_score_codex":0.0030256277,"about_ca_topic_score_gemma":0.0060477112,"teacher_disagreement_score":0.0062913983,"about_ca_system_score_codex":0.00023779189,"about_ca_system_score_gemma":0.0007074957,"threshold_uncertainty_score":0.021046817},"labels":[],"label_agreement":null},{"id":"W4415697246","doi":"10.18280/ts.420530","title":"Hybrid Deep Learning Approach for Low-Light Image Enhancement Based on Attention-Guided Residual Networks","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deep learning; Residual; Image enhancement; Image (mathematics); Pattern recognition (psychology)","score_opus":0.012200456272244569,"score_gpt":0.2571792063438673,"score_spread":0.24497875007162276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415697246","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":[0.018045781,0.00059704116,0.97805196,0.00013173444,0.00005475188,0.00002691087,0.000041143892,0.00086991175,0.0021807225],"genre_scores_gemma":[0.5330376,0.0008924528,0.44659692,0.00041439667,0.0001331512,0.00008638202,0.0003294872,0.00025380505,0.018255878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998646,0.000017510192,0.0000056822555,0.000036429006,0.000044289995,0.000031455107],"domain_scores_gemma":[0.99984,0.00004744605,0.00001760272,0.000017219272,0.00006326826,0.000014368962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003341862,0.00076149736,0.0006332527,0.0004412283,0.00019764203,0.0004487395,0.0012405077,0.0007371939,0.0025011445],"category_scores_gemma":[0.00040755156,0.0002730953,0.00064312544,0.0003054357,0.0002713509,0.0007159458,0.00063974963,0.0009924675,0.000705453],"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":[0.00031466305,0.0002768938,0.00072550436,0.00019230186,0.00016091713,0.00016952059,0.00008209521,0.28969547,0.10962017,0.009071768,0.004220116,0.5854706],"study_design_scores_gemma":[0.0000049011433,0.000038736405,0.00012641241,0.000006001692,0.00001918621,0.000028724777,0.0000039780134,0.98989373,0.008179117,0.0010021636,0.00069182593,0.0000051741813],"about_ca_topic_score_codex":0.004112264,"about_ca_topic_score_gemma":0.007005772,"teacher_disagreement_score":0.004112264,"about_ca_system_score_codex":0.0003836018,"about_ca_system_score_gemma":0.0004676121,"threshold_uncertainty_score":0.008367121},"labels":[],"label_agreement":null},{"id":"W4416020257","doi":"10.1016/j.procs.2025.10.130","title":"LiteDHAZE: An Adversarial Dehazing Network for Robust Robotic Perception in Challenging Visual Conditions","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Image Enhancement 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":"Dalhousie University","funders":"","keywords":"Adversarial system; Low latency (capital markets); Perception; Encoding (memory); Perspective (graphical); Feature (linguistics); Latency (audio); Generative adversarial network; Object (grammar); Robot","score_opus":0.01676636026257652,"score_gpt":0.3066416635815305,"score_spread":0.289875303318954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416020257","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":[0.060312815,0.0009535196,0.93060577,0.00029099084,0.00015371329,0.0000938691,0.00021675974,0.0026330068,0.0047394307],"genre_scores_gemma":[0.76582557,0.0005567592,0.22353792,0.0004197992,0.00004336138,0.000093273455,0.0006118216,0.00028172243,0.008629927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988174,0.000017711782,0.000002958759,0.00003297624,0.00004433344,0.000020186155],"domain_scores_gemma":[0.9998085,0.00007694243,0.000021912494,0.000039993567,0.000037299797,0.000015313499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003701822,0.00073247263,0.00039456828,0.00027815424,0.00015366274,0.00035676855,0.0010433397,0.0005519415,0.001430955],"category_scores_gemma":[0.00082643476,0.00022820839,0.00031188322,0.00014473563,0.00041528017,0.00066286005,0.0010072802,0.00093838247,0.00038158754],"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":[0.00023844326,0.00013517319,0.0012558289,0.0001592122,0.00014746236,0.00021396246,0.00007502091,0.68714184,0.055756316,0.0050328583,0.006848716,0.24299528],"study_design_scores_gemma":[0.000007432535,0.00006955799,0.00027061353,0.00000911882,0.000010607463,0.00008996684,0.0000089700525,0.985843,0.010477521,0.001581057,0.0016224345,0.000009724798],"about_ca_topic_score_codex":0.0016987603,"about_ca_topic_score_gemma":0.0034898145,"teacher_disagreement_score":0.0016987603,"about_ca_system_score_codex":0.0003518942,"about_ca_system_score_gemma":0.00030780703,"threshold_uncertainty_score":0.004787028},"labels":[],"label_agreement":null},{"id":"W4416252369","doi":"10.1109/ijcnn64981.2025.11227405","title":"IllumiCurveNet: Low-Light Image Enhancement of Lunar Permanently Shadowed Regions Using a Self-Guided Loss Framework","year":2025,"lang":"","type":"article","venue":"","topic":"Image Enhancement 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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Image enhancement; Noise (video); Image quality; Illuminance; Contrast (vision); Feature (linguistics); Attenuation; Camouflage; Contrast enhancement","score_opus":0.01532279927838484,"score_gpt":0.3012156858857405,"score_spread":0.2858928866073557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416252369","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":[0.04061617,0.00070161105,0.95268404,0.0001235561,0.00007300121,0.000048721573,0.00010260593,0.0022413582,0.0034090641],"genre_scores_gemma":[0.41127285,0.0008771611,0.5705922,0.00032440168,0.00006583516,0.00009504904,0.000696937,0.00068612536,0.015389428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998864,0.000017857148,0.0000036523736,0.000023524683,0.00005352464,0.0000150570295],"domain_scores_gemma":[0.9998485,0.000044675402,0.000016816055,0.000030573432,0.000043060267,0.00001641533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003034128,0.00057862396,0.000378123,0.00032291794,0.00012883895,0.0005377508,0.0008374849,0.00036889582,0.0013832801],"category_scores_gemma":[0.0006238189,0.00014227873,0.00030393506,0.00019633534,0.0003092398,0.0006910774,0.00077732967,0.0006403475,0.00049292017],"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":[0.000467659,0.00021464226,0.001029422,0.0002678054,0.000108410146,0.00027727967,0.00013632503,0.114103876,0.32872233,0.009163666,0.008766216,0.53674245],"study_design_scores_gemma":[0.000024750421,0.00020073794,0.0006672301,0.000021570497,0.0000315792,0.00037620997,0.000023734201,0.8540139,0.13380271,0.0024207197,0.008392143,0.000024628574],"about_ca_topic_score_codex":0.0011538129,"about_ca_topic_score_gemma":0.0023197322,"teacher_disagreement_score":0.0013832801,"about_ca_system_score_codex":0.00030384527,"about_ca_system_score_gemma":0.00038298496,"threshold_uncertainty_score":0.004627526},"labels":[],"label_agreement":null},{"id":"W4416750031","doi":"10.1109/iros60139.2025.11246089","title":"The Common Objects Underwater (COU) Dataset for Robust Underwater Object Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Image Enhancement 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":"Environment and Natural Resources; National Science Foundation","keywords":"Underwater; Object (grammar); Object detection; Focus (optics); Class (philosophy); Field (mathematics)","score_opus":0.027316597876277316,"score_gpt":0.29176172330808214,"score_spread":0.2644451254318048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416750031","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12688933,0.005706717,0.060501922,0.0010809154,0.0012600736,0.0024130207,0.7300817,0.053994495,0.018071713],"genre_scores_gemma":[0.043117512,0.00057586253,0.052494183,0.00025135194,0.000069550275,0.0006320365,0.89913994,0.0010236814,0.002695848],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99846506,0.00013565362,0.00014467587,0.00047396106,0.0005354363,0.00024540725],"domain_scores_gemma":[0.99904066,0.00016049956,0.00011722684,0.00031411435,0.00025999974,0.00010747424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011468704,0.0037354059,0.0018191638,0.0041312855,0.0011018126,0.0016565946,0.003625828,0.0025649532,0.005009034],"category_scores_gemma":[0.0028872767,0.00078089716,0.0020859335,0.0030504875,0.000843393,0.0017875215,0.0029930482,0.0018082466,0.008391917],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009948732,0.0012221994,0.014844521,0.0045706467,0.0008184822,0.0011777391,0.0003629979,0.023082402,0.035604287,0.0019248223,0.6708755,0.24452163],"study_design_scores_gemma":[0.0006738544,0.0012130097,0.08793242,0.0010528323,0.00057064305,0.0053212284,0.0016318222,0.21936315,0.08376648,0.004981532,0.5929476,0.00054548745],"about_ca_topic_score_codex":0.024696717,"about_ca_topic_score_gemma":0.054304555,"teacher_disagreement_score":0.024696717,"about_ca_system_score_codex":0.0012881954,"about_ca_system_score_gemma":0.0015634485,"threshold_uncertainty_score":0.049105942},"labels":[],"label_agreement":null},{"id":"W4416944645","doi":"10.1145/3779223","title":"Toward Efficient Underwater Visual Perception through Image Enhancement, Compression, and Understanding","year":2025,"lang":"en","type":"article","venue":"ACM Computing Surveys","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Underwater; Image processing; Perception; Noise (video); Image quality; Image compression; Image (mathematics); Visualization; Visual perception","score_opus":0.04937269875666304,"score_gpt":0.33125037007848307,"score_spread":0.28187767132182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416944645","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":[0.01312949,0.04039132,0.93601304,0.0011347806,0.00017614194,0.000055308457,0.00010680349,0.00084212807,0.008151039],"genre_scores_gemma":[0.17925315,0.13625285,0.6711885,0.0008983527,0.0009441715,0.00014647035,0.00046697623,0.00023781082,0.010611774],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997136,0.000041047566,0.000018502487,0.000059200618,0.00014137573,0.000026346012],"domain_scores_gemma":[0.9996463,0.00015501882,0.000041494895,0.000049636845,0.00009557392,0.000011983672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048652102,0.0005818512,0.000634503,0.0013982522,0.00020826481,0.001217557,0.0007496529,0.00076245045,0.0014884284],"category_scores_gemma":[0.0009491041,0.00031960523,0.00045446944,0.001335764,0.0007026169,0.0019938585,0.00077167107,0.0010874466,0.0008448971],"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":[0.00005722732,0.00007054067,0.00064282893,0.0012357171,0.000056072193,0.0001154761,0.00023236581,0.017669609,0.065997265,0.02621636,0.007820441,0.8798862],"study_design_scores_gemma":[0.000033335444,0.00037207582,0.004910945,0.000735968,0.00019912337,0.0016191435,0.0005347672,0.59237087,0.12704149,0.072954215,0.19909677,0.00013120503],"about_ca_topic_score_codex":0.0009892441,"about_ca_topic_score_gemma":0.0010796887,"teacher_disagreement_score":0.0014884284,"about_ca_system_score_codex":0.00033225314,"about_ca_system_score_gemma":0.00041443342,"threshold_uncertainty_score":0.004979253},"labels":[],"label_agreement":null},{"id":"W4417277843","doi":"10.5194/ica-abs-10-2-2025","title":"Enhancing Aerial Data Semantic Segmentation with a Colour Range Mask Layer: A Deep Learning Approach","year":2025,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Image Enhancement 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":"MAB-Mackay Rehabilitation Centre","funders":"","keywords":"Deep learning; Segmentation; Range (aeronautics); Pattern recognition (psychology); Image segmentation; Feature (linguistics)","score_opus":0.016912575575405417,"score_gpt":0.2654545706953072,"score_spread":0.24854199511990177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417277843","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":[0.03853102,0.00056980114,0.9555256,0.00036852085,0.00006922394,0.00005896834,0.00034156977,0.0024090973,0.0021261314],"genre_scores_gemma":[0.41415653,0.00072246516,0.57650566,0.0004813275,0.00007353239,0.000083723775,0.0018372689,0.00032488303,0.0058146315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983287,0.000017667391,0.000009602188,0.000049797753,0.000049386104,0.000040640567],"domain_scores_gemma":[0.9998313,0.000039366914,0.000022138593,0.000035783192,0.000055800756,0.000015532594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043976412,0.0008369022,0.0005584927,0.0012142261,0.00023183998,0.00091036526,0.0010126285,0.0008917345,0.0016215523],"category_scores_gemma":[0.00068516907,0.00041838907,0.00086594344,0.0009453047,0.00047156864,0.0013922342,0.0010950487,0.0010627087,0.0007767777],"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":[0.0002605723,0.00021088097,0.0014403748,0.00015536521,0.00012081049,0.0001267933,0.00015420128,0.22505315,0.06529065,0.0076558525,0.0063476237,0.69318366],"study_design_scores_gemma":[0.0000058365804,0.000030151854,0.00046070694,0.00001225182,0.000022419523,0.00004027006,0.00001898993,0.9828655,0.011541501,0.0032100216,0.0017823082,0.0000100410225],"about_ca_topic_score_codex":0.005974513,"about_ca_topic_score_gemma":0.0073638908,"teacher_disagreement_score":0.005974513,"about_ca_system_score_codex":0.00061444734,"about_ca_system_score_gemma":0.0007551199,"threshold_uncertainty_score":0.011879444},"labels":[],"label_agreement":null},{"id":"W4417302281","doi":"10.1109/iccv51701.2025.01205","title":"Towards a Universal Image Degradation Model via Content-Degradation Disentanglement","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement 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 Waterloo","funders":"","keywords":"Degradation (telecommunications); Image (mathematics); Generalizability theory; Homogeneous; Set (abstract data type); Variety (cybernetics); Adaptability; Image restoration","score_opus":0.039655942382254714,"score_gpt":0.28632090632511237,"score_spread":0.24666496394285764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417302281","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":[0.013945301,0.00034818827,0.98302114,0.000092000635,0.000025848185,0.000044518227,0.00012467026,0.00091092143,0.0014873461],"genre_scores_gemma":[0.62757266,0.0013721767,0.3617256,0.00021267142,0.00006873513,0.00024155766,0.00067252497,0.0006127975,0.0075212917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967766,0.00005498266,0.00001806456,0.000090621164,0.00012059688,0.00003809805],"domain_scores_gemma":[0.9994647,0.00016750048,0.00008442542,0.00012883545,0.00012090096,0.000033525856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056822837,0.00097486685,0.00065228785,0.0007628447,0.0002063973,0.0008218102,0.00088125974,0.00096808793,0.0012540652],"category_scores_gemma":[0.0014945178,0.0004165215,0.00086193526,0.00042274897,0.00080601027,0.0012154671,0.00088288524,0.0011122719,0.00048056673],"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":[0.00016293436,0.00008719027,0.0010269014,0.00022938849,0.000047423608,0.00017137988,0.00015782338,0.8237285,0.079364866,0.014206475,0.0023307628,0.07848639],"study_design_scores_gemma":[0.000005228426,0.000019125664,0.00018606082,0.0000065979507,0.000009456214,0.000056214147,0.000006660298,0.98775566,0.008103303,0.0025453689,0.0012992539,0.000006991458],"about_ca_topic_score_codex":0.0030279888,"about_ca_topic_score_gemma":0.0024459672,"teacher_disagreement_score":0.0030279888,"about_ca_system_score_codex":0.00070500374,"about_ca_system_score_gemma":0.00048437284,"threshold_uncertainty_score":0.006020725},"labels":[],"label_agreement":null},{"id":"W4417306250","doi":"10.56536/jicet.v5i2.227","title":"Brightness-Preserving and Structure-Enhanced Image Enhancement using Dual-Domain Histogram Equalization","year":2025,"lang":"","type":"article","venue":"Journal of Innovative Computing and Emerging Technologies","topic":"Image Enhancement 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":"Queen's University","funders":"","keywords":"Histogram equalization; Adaptive histogram equalization; Histogram; Brightness; Clipping (morphology); Equalization (audio); Histogram matching; Balanced histogram thresholding","score_opus":0.01569385427447535,"score_gpt":0.31976813282713246,"score_spread":0.3040742785526571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417306250","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":[0.12708867,0.00082413154,0.8642227,0.00024234068,0.000098011085,0.0000724578,0.00007547968,0.00094983104,0.0064263023],"genre_scores_gemma":[0.54857314,0.00082831905,0.44403106,0.0001368063,0.000041292762,0.0000451733,0.00010961781,0.00007740274,0.0061571705],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985385,0.000014732968,0.000007577795,0.0000315811,0.000067855595,0.000024415904],"domain_scores_gemma":[0.9997583,0.000075313495,0.000029083745,0.000041378615,0.000079692305,0.000016250897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021912801,0.00028316487,0.00028938425,0.00058534014,0.0001481403,0.00040516918,0.00041951513,0.00033521332,0.0018378097],"category_scores_gemma":[0.00058486057,0.00018851379,0.00030418928,0.0004969858,0.00036367206,0.0008118911,0.00050873787,0.00046388526,0.00051249476],"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":[0.00026449616,0.00011445172,0.00083778077,0.00017317908,0.000027446442,0.00015236055,0.000056403358,0.007631618,0.75970864,0.0037077141,0.00082263426,0.22650322],"study_design_scores_gemma":[0.000034303113,0.00022267363,0.0034164512,0.000015624051,0.000037604386,0.00095073885,0.000045368593,0.14915581,0.8370411,0.0015147049,0.007524376,0.000041270574],"about_ca_topic_score_codex":0.00032848623,"about_ca_topic_score_gemma":0.000461152,"teacher_disagreement_score":0.0018378097,"about_ca_system_score_codex":0.0001714999,"about_ca_system_score_gemma":0.00018751597,"threshold_uncertainty_score":0.0061481},"labels":[],"label_agreement":null},{"id":"W65680539","doi":"","title":"Image Thresholding Using Differential Evolution.","year":2006,"lang":"en","type":"article","venue":"","topic":"Image Enhancement 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":"University of Waterloo","funders":"","keywords":"Thresholding; Artificial intelligence; Balanced histogram thresholding; Image (mathematics); Image processing; Computer science; Computer vision; Pattern recognition (psychology); Range (aeronautics); Mathematics; Engineering","score_opus":0.011853001463081549,"score_gpt":0.25037385121368705,"score_spread":0.2385208497506055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W65680539","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":[0.010494457,0.00065786054,0.9838578,0.00009744284,0.000043327047,0.000026235139,0.00002660255,0.00036855924,0.004427621],"genre_scores_gemma":[0.39296386,0.00082179805,0.5970986,0.00008983826,0.000026526652,0.00008421916,0.000103496335,0.00016512664,0.008646565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999833,0.00002538291,0.00000846878,0.00003172779,0.00009320888,0.000008181971],"domain_scores_gemma":[0.9998436,0.00006861588,0.000021131398,0.00002612011,0.000032452237,0.000008067854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028768915,0.00025174877,0.00042055378,0.00031542635,0.00012228543,0.00047129046,0.00037997979,0.0004156071,0.0013717122],"category_scores_gemma":[0.0008392072,0.00015089453,0.00023967476,0.0004218167,0.0003491973,0.0004795881,0.00052661006,0.00038402417,0.00046825552],"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":[0.0001109136,0.000054025393,0.0011295732,0.00044256353,0.00008701002,0.00028026893,0.00019893424,0.175959,0.27350333,0.05509668,0.0040700813,0.48906755],"study_design_scores_gemma":[0.000009435129,0.000045092915,0.00063624355,0.000020866297,0.000016298181,0.00035639384,0.000015305894,0.93052423,0.048414644,0.009244192,0.010703416,0.0000138637815],"about_ca_topic_score_codex":0.00034244065,"about_ca_topic_score_gemma":0.00039386892,"teacher_disagreement_score":0.0013717122,"about_ca_system_score_codex":0.00039365422,"about_ca_system_score_gemma":0.00014075874,"threshold_uncertainty_score":0.0045888424},"labels":[],"label_agreement":null},{"id":"W6889042501","doi":"10.25316/ir-12664","title":"Nanaimo Free Press [Tuesday, April 18, 1893]","year":2019,"lang":"en","type":"other","venue":"VIURRSpace (Vancouver Island University)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.010997122616514619,"score_gpt":0.20854779234592102,"score_spread":0.1975506697294064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889042501","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00080749474,0.01871246,0.000281101,0.0030615404,0.005826579,0.00003521053,0.0029639702,0.00022720463,0.9680845],"genre_scores_gemma":[0.0011016133,0.0020356597,0.00007716297,0.00012413958,0.00017810568,0.000007277526,0.00023305959,0.000072732764,0.9961702],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961096,0.000023806411,0.000016693737,0.000075414435,0.00020492674,0.00006818047],"domain_scores_gemma":[0.99972445,0.000036104444,0.000016546664,0.000020630438,0.00014659343,0.000055636563],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00034584667,0.001096083,0.0006649624,0.0022179182,0.003324331,0.0049554547,0.000822386,0.0017421005,0.34897417],"category_scores_gemma":[0.0013726341,0.00045960987,0.00035623717,0.0033150117,0.00063257775,0.001913395,0.0013292585,0.0022603138,0.1263654],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003941729,0.000010794056,0.00017284708,0.00009409201,0.000004561612,0.000086963555,0.00008279797,0.00009359004,0.00011706322,0.008605584,0.9393826,0.0513097],"study_design_scores_gemma":[0.0000019933752,0.0000030748063,0.00036629045,0.000059375452,0.0000010090207,0.000022345737,0.000029952274,0.00002120961,0.00004300235,0.00034518284,0.9991037,0.0000029217704],"about_ca_topic_score_codex":0.10826277,"about_ca_topic_score_gemma":0.46070373,"teacher_disagreement_score":0.65102583,"about_ca_system_score_codex":0.0031807094,"about_ca_system_score_gemma":0.0021188466,"threshold_uncertainty_score":0.9286093},"labels":[],"label_agreement":null},{"id":"W6890057620","doi":"10.34675/l34648","title":"Letter related to exploration and development of a mineral claim for U3O8, pitchblende, other uranium bearing minerals, and other minerals in the Northern Mining District, Saskatchewan, with royalty due upon transfer of ownership of claim.","year":2019,"lang":"en","type":"article","venue":"RoyaltyStat Library","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mineral; Bearing (navigation); Uranium; Uranium ore; Mineral exploration; Mineral resource classification","score_opus":0.0180362482651047,"score_gpt":0.2240150359397783,"score_spread":0.20597878767467362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890057620","genre_codex":"commentary","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":[0.014872831,0.0014931405,0.0010849048,0.77606684,0.04059852,0.00020357152,0.00061119435,0.0003680896,0.16470094],"genre_scores_gemma":[0.07405931,0.0012211341,0.0006863339,0.2915173,0.009180433,0.00007500997,0.00034373085,0.00013618467,0.6227805],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993943,0.000089982925,0.000040736486,0.00009793296,0.00022907139,0.00014794024],"domain_scores_gemma":[0.99794775,0.00097504305,0.000098052136,0.000077150566,0.0006705806,0.00023142071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054149755,0.00033051803,0.0003338862,0.00037917934,0.0033117488,0.0015868465,0.0006365142,0.011033086,0.040951543],"category_scores_gemma":[0.008026013,0.00024867995,0.00041600384,0.0002299119,0.0007900143,0.0008175249,0.0006775257,0.0072869766,0.0132869845],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011821524,0.000054856777,0.0010922087,0.000030239862,0.000011448143,0.006379272,0.00029276332,0.000066338354,0.0009843509,0.002827957,0.9780157,0.010126666],"study_design_scores_gemma":[0.000032202806,0.00012544378,0.0029304666,0.0001335113,0.000015760179,0.004588198,0.0011611102,0.0005023152,0.0011115362,0.0021559708,0.9872186,0.000024799348],"about_ca_topic_score_codex":0.013269417,"about_ca_topic_score_gemma":0.023041729,"teacher_disagreement_score":0.9867306,"about_ca_system_score_codex":0.0025098969,"about_ca_system_score_gemma":0.0023364238,"threshold_uncertainty_score":0.13699657},"labels":[],"label_agreement":null},{"id":"W6892648960","doi":"10.5281/zenodo.10798889","title":"Rapport du sondage sur les logiciels de recherche de l'Alliance","year":2025,"lang":"fr","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Image Enhancement 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":true,"ca_institutions":"Simon Fraser University; Toronto Dementia Research Alliance","funders":"","keywords":"Subject (documents); Information center; Context (archaeology); Oath","score_opus":0.32303906542666655,"score_gpt":0.35022508047280887,"score_spread":0.02718601504614232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892648960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02672821,0.024067955,0.65973485,0.07617371,0.002782545,0.0004249742,0.0010036837,0.0017801586,0.20730391],"genre_scores_gemma":[0.29704908,0.024617676,0.45451418,0.00728448,0.001989325,0.0005833019,0.0011116137,0.0012021668,0.2116482],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9706142,0.009148391,0.0015958302,0.003098496,0.013862262,0.0016807829],"domain_scores_gemma":[0.9507114,0.022368899,0.0016837285,0.0049239723,0.01857509,0.0017369482],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03406549,0.001228184,0.0012522468,0.0051978827,0.0058256243,0.018455496,0.0028688153,0.0040621897,0.013380456],"category_scores_gemma":[0.03842312,0.0009700694,0.0019487316,0.004901611,0.011001902,0.013758325,0.0057962555,0.006545314,0.0037903013],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108760396,0.000030580853,0.0006336473,0.00018346378,0.000026539125,0.00018302795,0.0013778062,0.0018584667,0.0020868417,0.93471384,0.011395031,0.047402043],"study_design_scores_gemma":[0.000076788594,0.0001237958,0.0013433185,0.00052338693,0.000110203015,0.00078240456,0.0016798698,0.022764187,0.0137978215,0.40189075,0.5567307,0.00017662143],"about_ca_topic_score_codex":0.12842268,"about_ca_topic_score_gemma":0.07955647,"teacher_disagreement_score":0.97799045,"about_ca_system_score_codex":0.022009527,"about_ca_system_score_gemma":0.022268062,"threshold_uncertainty_score":0.25535035},"labels":[],"label_agreement":null},{"id":"W6893806097","doi":"10.5281/zenodo.5378085","title":"Fig. 14 in Geographic Range Updates for the Tiger Beetles (Coleoptera: Carabidae: Cicindelinae) of Northern Ontario, Canada","year":2017,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tiger; Range (aeronautics); Distribution (mathematics); Geographic information system; Zoogeography; Data collection","score_opus":0.01941893719837021,"score_gpt":0.22916928713018236,"score_spread":0.20975034993181216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893806097","genre_codex":"dataset","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":[0.0062482376,0.0015523558,0.0016409584,0.00070527085,0.0007578129,0.0003725807,0.86209077,0.0013013002,0.1253307],"genre_scores_gemma":[0.022908282,0.0032304435,0.0087273605,0.00036172025,0.00011517595,0.0003813869,0.7600451,0.0007039628,0.20352647],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948174,0.000015180792,0.00002943606,0.00006548862,0.00029451068,0.00011360875],"domain_scores_gemma":[0.99569184,0.00011030345,0.00014655678,0.00014666107,0.0035766703,0.0003279429],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005603141,0.0008613413,0.0004522378,0.0068854634,0.0021929052,0.001374364,0.0011400818,0.00037294833,0.15682097],"category_scores_gemma":[0.0020365259,0.00045477954,0.00046952313,0.011625332,0.00041018074,0.000716173,0.000727541,0.0007329213,0.04128163],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035398392,0.0000125664355,0.0082406495,0.00032159968,0.000013468247,0.000057039815,0.00026216853,0.00016608946,0.00036693545,0.00048657958,0.9592358,0.030801732],"study_design_scores_gemma":[0.000017035796,0.000007987258,0.04825558,0.00017374688,0.0000230203,0.00007475953,0.00041825403,0.00012005222,0.0002340344,0.00011056364,0.95054674,0.00001822689],"about_ca_topic_score_codex":0.97349066,"about_ca_topic_score_gemma":0.9911985,"teacher_disagreement_score":0.84317905,"about_ca_system_score_codex":0.009552782,"about_ca_system_score_gemma":0.01974858,"threshold_uncertainty_score":0.5246184},"labels":[],"label_agreement":null},{"id":"W6925561358","doi":"10.18452/20712","title":"Biobehavioral Pathways Underlying Spousal Health Dynamics: Its Nature, Correlates, and Consequences","year":2014,"lang":"en","type":"other","venue":"edoc Publication server (Humboldt University of Berlin)","topic":"Image Enhancement Techniques","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":"Canada Research Chairs; Michael Smith Health Research BC","keywords":"Mental health; Focus (optics); Physical health; Psychological Theory; Health behavior; Public health","score_opus":0.023500312513647075,"score_gpt":0.2575454933708008,"score_spread":0.23404518085715373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925561358","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":[0.9896387,0.0016700095,0.002592493,0.00096614717,0.000010290607,0.000039381845,0.00028844582,0.000033539418,0.0047610337],"genre_scores_gemma":[0.997505,0.0008122916,0.0010243958,0.000044015396,0.000010123858,0.000022855758,0.00010858256,0.0000057744883,0.0004669897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997409,0.00009665868,0.000015803538,0.000056273453,0.000041795673,0.000048454523],"domain_scores_gemma":[0.99865985,0.0004841337,0.00040991182,0.00015722077,0.00009432091,0.00019465122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007169101,0.00024093536,0.00029604713,0.0011788617,0.00063217996,0.0011377486,0.00030464728,0.00039662942,0.004003047],"category_scores_gemma":[0.0033523222,0.00019841772,0.00022250567,0.0009679976,0.00087488315,0.00063182216,0.0010626597,0.0005880601,0.00023334511],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025620195,0.00034790864,0.9261128,0.00009497326,0.000114455004,0.00051138236,0.0034954408,0.00036327788,0.0024316018,0.0045197257,0.0003485689,0.06140385],"study_design_scores_gemma":[0.0000038068672,0.00006724981,0.9928034,0.000035298282,0.000026517644,0.00032290688,0.0018461093,0.00077947427,0.0002444606,0.0033929122,0.0004694541,0.000008243711],"about_ca_topic_score_codex":0.0023850775,"about_ca_topic_score_gemma":0.00378341,"teacher_disagreement_score":0.004003047,"about_ca_system_score_codex":0.00038650457,"about_ca_system_score_gemma":0.0005349763,"threshold_uncertainty_score":0.013391554},"labels":[],"label_agreement":null},{"id":"W6926121726","doi":"10.25316/ir-10194","title":"Nanaimo Free Press [Thursday, October 7, 1897]","year":2019,"lang":"en","type":"other","venue":"VIURRSpace (Vancouver Island University)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.008846267801077773,"score_gpt":0.20286131904909357,"score_spread":0.1940150512480158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926121726","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00087553327,0.018540932,0.00028198468,0.0035712114,0.007888665,0.000043337182,0.0032077683,0.00023748461,0.965353],"genre_scores_gemma":[0.0010434269,0.0018601742,0.000074413605,0.00012907642,0.00020095908,0.000007181298,0.0002380629,0.000067846435,0.99637884],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995827,0.000025820931,0.000016253793,0.00007373475,0.00022665707,0.00007483529],"domain_scores_gemma":[0.9997086,0.000035950463,0.000016903867,0.000020514199,0.00015199298,0.00006609887],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003714419,0.0010587928,0.0006745675,0.0022458194,0.0033679842,0.0053419056,0.00083203666,0.001843476,0.35631892],"category_scores_gemma":[0.0013217373,0.00045306928,0.00038936606,0.0028974905,0.0006399601,0.0019276362,0.0013934611,0.002264486,0.13642402],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043768654,0.000011007758,0.00016905756,0.00009331153,0.000004517859,0.0000893326,0.00007725513,0.00007860932,0.00012010404,0.007006262,0.9439005,0.04840626],"study_design_scores_gemma":[0.0000019317388,0.0000033071346,0.00036094504,0.000058248825,9.195149e-7,0.000019927405,0.00003227087,0.000019699995,0.000045761994,0.0002908611,0.99916327,0.000002935236],"about_ca_topic_score_codex":0.08233932,"about_ca_topic_score_gemma":0.40457177,"teacher_disagreement_score":0.35631892,"about_ca_system_score_codex":0.0028861512,"about_ca_system_score_gemma":0.0020560776,"threshold_uncertainty_score":0.91813296},"labels":[],"label_agreement":null},{"id":"W6931074633","doi":"10.5281/zenodo.3905423","title":"PlateCurie: Software for mapping Curie depth from a wavelet analysis of magnetic anomaly data","year":2020,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Image Enhancement 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 Ottawa","funders":"","keywords":"Magnetic anomaly; Curie temperature; Wavelet; Wavelet transform; Magnetization; Curie; Spectral density","score_opus":0.05558294080561262,"score_gpt":0.26448698273835886,"score_spread":0.20890404193274623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931074633","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071120006,0.00025532162,0.40899795,0.00019957942,0.00017322472,0.00023044144,0.064981826,0.5120636,0.00598589],"genre_scores_gemma":[0.05224822,0.00059442216,0.6035861,0.0005101351,0.00009159229,0.0016586138,0.13629934,0.19191346,0.013098199],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961793,0.000033644228,0.00003890428,0.00012549941,0.00014629654,0.000037660346],"domain_scores_gemma":[0.9993193,0.00028896463,0.000077382254,0.00010016376,0.00017368403,0.000040500086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012173309,0.0016460967,0.00077221985,0.0017113481,0.00041645835,0.0014831772,0.0020254268,0.00061080535,0.053834915],"category_scores_gemma":[0.0041094436,0.0012044247,0.0014279833,0.0011301312,0.000330815,0.0018462902,0.0020235223,0.0016880367,0.028524853],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000493474,0.000110100635,0.008792584,0.0018691547,0.0004757389,0.00039001618,0.0009975192,0.014795622,0.02535075,0.008474233,0.6704508,0.2677999],"study_design_scores_gemma":[0.00032607067,0.0001387221,0.030923398,0.00052002736,0.00027163007,0.0008076682,0.0004759354,0.283777,0.06058632,0.033364475,0.58831316,0.0004956288],"about_ca_topic_score_codex":0.0043555675,"about_ca_topic_score_gemma":0.0071286038,"teacher_disagreement_score":0.053834915,"about_ca_system_score_codex":0.00043771602,"about_ca_system_score_gemma":0.001185722,"threshold_uncertainty_score":0.18009573},"labels":[],"label_agreement":null},{"id":"W6931679238","doi":"10.5281/zenodo.7609191","title":"FIGURE 2. Macromorphological comparison between species A in Revealing a new species of Agarista (Ericaceae) endemic to the Chapada Diamantina, Brazil, segregated from A. revoluta by multiple evidence","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Image Enhancement 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":"McGill University","funders":"","keywords":"Pedicel; Calyx; Ovary; Taxonomy (biology); Locule","score_opus":0.07016795676687952,"score_gpt":0.28745073959052314,"score_spread":0.2172827828236436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931679238","genre_codex":"empirical","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":[0.89410836,0.0019152925,0.007271156,0.0002622377,0.00010688324,0.0003251358,0.008033467,0.0010551051,0.08692237],"genre_scores_gemma":[0.9814098,0.0002221466,0.010093403,0.00008662255,0.000023040482,0.000051405787,0.0019249034,0.00012339464,0.006065241],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999362,0.000007881992,0.00000459878,0.00002993849,0.000011835252,0.000009539104],"domain_scores_gemma":[0.9997991,0.000052594933,0.00005036355,0.000022998245,0.000034499506,0.000040482493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006536433,0.00028340498,0.00015685309,0.0020983156,0.00069970713,0.00035366172,0.00023078072,0.00029961034,0.011535009],"category_scores_gemma":[0.00024546005,0.00015086777,0.0002166025,0.0006103974,0.00036754468,0.00021213319,0.00047259824,0.00029984638,0.0022910621],"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":[0.0010578233,0.00010328847,0.0834653,0.0011053239,0.00009883668,0.0033384485,0.003236936,0.00047515827,0.744789,0.0019827785,0.0049718837,0.1553752],"study_design_scores_gemma":[0.00003586085,0.0001855906,0.8525319,0.000114714996,0.000106347885,0.0121804625,0.0024553312,0.0009728648,0.02054585,0.0006892805,0.11014966,0.0000320542],"about_ca_topic_score_codex":0.0033690212,"about_ca_topic_score_gemma":0.0066917827,"teacher_disagreement_score":0.011535009,"about_ca_system_score_codex":0.00018929224,"about_ca_system_score_gemma":0.00013408487,"threshold_uncertainty_score":0.038588464},"labels":[],"label_agreement":null},{"id":"W6976951064","doi":"10.6084/m9.figshare.28058607","title":"Additional file 2 of Glacial lakes inventory and susceptibility assessment in the Alsek River Basin, Yukon, Canada","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Glacial period; Meltwater; Hydrology (agriculture); Glacial lake; Permafrost","score_opus":0.0176066894339379,"score_gpt":0.25079070160517497,"score_spread":0.23318401217123708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976951064","genre_codex":"dataset","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":[0.00020951568,0.000007856019,0.00006154283,0.000029555931,0.000006492453,0.00003547178,0.9984561,0.00009640704,0.0010970376],"genre_scores_gemma":[0.011621862,0.00009044103,0.0015140862,0.00012924401,0.000019904943,0.0004803024,0.97510624,0.00038634072,0.0106516415],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9996736,0.000026579908,0.000052215924,0.0000757876,0.000080828264,0.00009107765],"domain_scores_gemma":[0.99414575,0.00242494,0.00025104138,0.0004173084,0.0024704316,0.00029049077],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00058302196,0.000612768,0.0007552926,0.0027505637,0.0013617884,0.001518308,0.0018009569,0.0005917007,0.7539235],"category_scores_gemma":[0.008393849,0.00042493522,0.00067092985,0.0072414996,0.0003477706,0.0010619224,0.0008242127,0.0005493388,0.067446545],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010172705,0.000035142722,0.005241671,0.0010325391,0.000029910008,0.000071256676,0.00009905509,0.00051630946,0.00006482051,0.00063702976,0.98480356,0.0073669436],"study_design_scores_gemma":[0.0014925236,0.0000469907,0.07417938,0.001338493,0.00014644199,0.00026427623,0.0011434079,0.0016645842,0.0005494736,0.003939727,0.915113,0.0001217546],"about_ca_topic_score_codex":0.579874,"about_ca_topic_score_gemma":0.68765664,"teacher_disagreement_score":0.7539235,"about_ca_system_score_codex":0.0034250796,"about_ca_system_score_gemma":0.00780254,"threshold_uncertainty_score":0.8452004},"labels":[],"label_agreement":null},{"id":"W6984026363","doi":"","title":"2003 promo for Sk8","year":2003,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.0062378162745881085,"score_gpt":0.19686966218671845,"score_spread":0.19063184591213034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6984026363","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005630291,0.0003181175,0.0015317114,0.0009822773,0.00080513174,0.00015646775,0.0036547317,0.0026575746,0.9893309],"genre_scores_gemma":[0.00074123364,0.00011160674,0.00038336054,0.00009761081,0.000045908797,0.00002496675,0.0013400097,0.00044151617,0.99681383],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997347,0.000019512558,0.0000084299,0.000044590724,0.00012914745,0.000063538144],"domain_scores_gemma":[0.9992046,0.000038713388,0.000025327265,0.0000917322,0.0003373595,0.00030232378],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005170558,0.0009330462,0.00041593582,0.0013918412,0.0016078906,0.0030190228,0.0009389203,0.0011992491,0.80787086],"category_scores_gemma":[0.0009767537,0.000490809,0.00043421803,0.001141196,0.00038580954,0.0019605267,0.0017408245,0.0012790937,0.7080781],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058859932,0.00002314367,0.000049883507,0.000052234907,0.0000015771469,0.000035413443,0.000029192906,0.00002240974,0.0006949743,0.0014623945,0.96105236,0.03651752],"study_design_scores_gemma":[0.000007591176,0.00001082498,0.00027690403,0.000016269028,0.0000012479471,0.00003943567,0.000023702652,0.000060024096,0.00016975917,0.0002709835,0.99911994,0.0000032975872],"about_ca_topic_score_codex":0.012447068,"about_ca_topic_score_gemma":0.047499772,"teacher_disagreement_score":0.19212914,"about_ca_system_score_codex":0.0010981681,"about_ca_system_score_gemma":0.001799823,"threshold_uncertainty_score":0.2740488},"labels":[],"label_agreement":null},{"id":"W7024214156","doi":"","title":"Rahanpesun sääntelyn haasteet: Painopisteenä itsepesun kriminalisointi","year":2012,"lang":"fi","type":"other","venue":"Osuva (University of Vaasa)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Population; Work (physics)","score_opus":0.02302802890000315,"score_gpt":0.21708098995537078,"score_spread":0.19405296105536762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024214156","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10905151,0.024643855,0.006111462,0.012708661,0.0049698255,0.00027594806,0.0013982961,0.0007712428,0.84006906],"genre_scores_gemma":[0.124999106,0.0077641006,0.004554461,0.0011426057,0.0003588721,0.00007520657,0.00080777606,0.0003576953,0.8599402],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999551,0.000056795176,0.000019426949,0.00009021443,0.00017626428,0.00010634361],"domain_scores_gemma":[0.99965596,0.000057677036,0.000030099842,0.000024192546,0.0001262209,0.00010580675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059964845,0.00046214517,0.00026696027,0.0005688271,0.0020670798,0.0037768483,0.0004638362,0.0009234344,0.06885708],"category_scores_gemma":[0.00063686457,0.00023528653,0.00035311902,0.0005511669,0.0005722678,0.002044527,0.002195532,0.0019548035,0.018274974],"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":[0.0011631893,0.00069741916,0.0068978625,0.0015222558,0.00004565624,0.0021024225,0.017569553,0.00044485647,0.054229125,0.0473724,0.14718658,0.72076863],"study_design_scores_gemma":[0.000011478195,0.00014713642,0.005671835,0.000190944,0.00001756607,0.00037620662,0.0032834504,0.0001058597,0.007642639,0.0015277815,0.9810022,0.000022882785],"about_ca_topic_score_codex":0.0043370724,"about_ca_topic_score_gemma":0.0129825715,"teacher_disagreement_score":0.06885708,"about_ca_system_score_codex":0.0015041657,"about_ca_system_score_gemma":0.0024609354,"threshold_uncertainty_score":0.23034984},"labels":[],"label_agreement":null},{"id":"W7066711385","doi":"","title":"Isoluminant color picking and its applications","year":2005,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Luminance; Perception; Color vision; Human visual system model; Field (mathematics); Color image","score_opus":0.013323144627219717,"score_gpt":0.25619638286276236,"score_spread":0.24287323823554263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066711385","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":[0.09962035,0.018678732,0.6668895,0.0011164546,0.000980556,0.0003347094,0.0004355831,0.011429907,0.20051418],"genre_scores_gemma":[0.33227876,0.010041147,0.3540245,0.00061478553,0.0002705749,0.0001360553,0.000548318,0.0010077656,0.30107805],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976104,0.000025631503,0.0000084032245,0.000056197892,0.00011531796,0.000033331162],"domain_scores_gemma":[0.99967563,0.000082770006,0.000022561648,0.000053088766,0.00012397091,0.000042063326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034164547,0.0006364577,0.0003381717,0.0018059122,0.0005701214,0.0011443016,0.00082160666,0.0006908749,0.03575527],"category_scores_gemma":[0.00047264306,0.00030232736,0.00041900156,0.0016724062,0.00044592816,0.0009875172,0.0007370562,0.0005304124,0.01033894],"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":[0.00036909484,0.000108358574,0.00073096505,0.00028618876,0.000014908152,0.00030401826,0.00014019618,0.001618578,0.2610124,0.007631602,0.010168761,0.717615],"study_design_scores_gemma":[0.00007822178,0.00090154895,0.0076326057,0.00019194736,0.00012115665,0.003426394,0.00034411295,0.04266563,0.5640911,0.007014367,0.37340844,0.00012442216],"about_ca_topic_score_codex":0.0014959879,"about_ca_topic_score_gemma":0.0020238322,"teacher_disagreement_score":0.03575527,"about_ca_system_score_codex":0.000521931,"about_ca_system_score_gemma":0.00038295356,"threshold_uncertainty_score":0.11961329},"labels":[],"label_agreement":null},{"id":"W7092290489","doi":"10.12194/j.ntu.20250620001","title":"4D radar-camera fusion algorithm for intelligent navigation of inland unmanned vessels","year":2025,"lang":"zh","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Image Enhancement 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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Radar; Feature (linguistics); Weighting; Sensor fusion; Representation (politics); Key (lock); Field (mathematics); Construct (python library); Artificial neural network","score_opus":0.14597600566708707,"score_gpt":0.5285023540551103,"score_spread":0.3825263483880232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7092290489","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":[0.030115005,0.0005126429,0.9663155,0.00016435649,0.00007963549,0.000038967304,0.000065578126,0.0010565011,0.0016518066],"genre_scores_gemma":[0.55459076,0.00049087126,0.43899256,0.00031126558,0.000067610315,0.000087112836,0.00047319924,0.000105276435,0.004881352],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965096,0.000040731524,0.000018282799,0.00010753066,0.00011559516,0.00006696543],"domain_scores_gemma":[0.9998287,0.000025133324,0.00002563011,0.000023186207,0.000082961524,0.000014364744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052231346,0.0007654494,0.0007586534,0.0008152631,0.0003218588,0.00059320446,0.0009994374,0.0008309433,0.0016426452],"category_scores_gemma":[0.00085477467,0.00036260806,0.00078054,0.00064338814,0.00022238115,0.00096406846,0.0009227599,0.0010558501,0.0005984765],"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":[0.00025792574,0.00014069502,0.0023620536,0.00007738902,0.00013979837,0.00016130056,0.00014756233,0.21812706,0.044834707,0.0033966324,0.0050308937,0.72532403],"study_design_scores_gemma":[0.0000066518805,0.000049047587,0.0006928095,0.0000061622363,0.000023136226,0.000047011425,0.000020095216,0.9905223,0.0063325525,0.0011626766,0.0011279149,0.000009550313],"about_ca_topic_score_codex":0.0060893316,"about_ca_topic_score_gemma":0.006610854,"teacher_disagreement_score":0.0060893316,"about_ca_system_score_codex":0.00059648254,"about_ca_system_score_gemma":0.00086575036,"threshold_uncertainty_score":0.0121077895},"labels":[],"label_agreement":null},{"id":"W7116658289","doi":"10.18280/mmep.121120","title":"Dark Lighter Pro-Net: A Deep Learning Model for Enhancement of Low Light Underwater Images","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deep learning; Underwater; Artificial neural network; Convolutional neural network; Camouflage","score_opus":0.015622286776074159,"score_gpt":0.23203486671186188,"score_spread":0.21641257993578772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116658289","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":[0.011856,0.0002489213,0.98526436,0.00013308672,0.000053828495,0.000025447327,0.00014884623,0.00068985746,0.0015796673],"genre_scores_gemma":[0.43471563,0.00086324004,0.5361348,0.00032819968,0.00009217804,0.0001296404,0.00071158074,0.00040731722,0.02661742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993885,0.000010308594,0.0000023140415,0.00001451052,0.00002371663,0.000010342468],"domain_scores_gemma":[0.9998462,0.000047860085,0.000015333397,0.000019202993,0.00005561681,0.000015619606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036118354,0.0005696293,0.00036715736,0.0002761147,0.00014626398,0.0004968115,0.0010964517,0.00070418784,0.0024285677],"category_scores_gemma":[0.00065747224,0.00032016457,0.0003626957,0.00025437385,0.00030180582,0.00081537955,0.00063880987,0.0011389783,0.00068206235],"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":[0.00028607223,0.00012924621,0.0007994658,0.00016769512,0.0000709111,0.000107089916,0.000042400006,0.67266047,0.035880707,0.014099897,0.006275884,0.26948017],"study_design_scores_gemma":[0.0000026194211,0.000014008877,0.000055485023,0.0000039974366,0.0000040207974,0.0000120464765,0.0000014297545,0.9945846,0.0033593278,0.0012761782,0.00068333454,0.0000029783757],"about_ca_topic_score_codex":0.0032546562,"about_ca_topic_score_gemma":0.005613019,"teacher_disagreement_score":0.0032546562,"about_ca_system_score_codex":0.0004229161,"about_ca_system_score_gemma":0.00044232808,"threshold_uncertainty_score":0.0081243515},"labels":[],"label_agreement":null},{"id":"W7117141926","doi":"10.1016/j.patcog.2025.112964","title":"AquaSlot-SAM: Coupling slot-based state space models with SAM for robust underwater video multi-object segmentation","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"China Scholarship Council; State Key Laboratory of Robotics; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Segmentation; Underwater; Consistency (knowledge bases); Task (project management); Image segmentation; Object (grammar); Coherence (philosophical gambling strategy); Fusion mechanism","score_opus":0.04770108571795957,"score_gpt":0.27882359617122826,"score_spread":0.2311225104532687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117141926","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":[0.005120189,0.000056871893,0.9918447,0.000026557831,0.000024443718,0.000017384642,0.000059294347,0.0022104876,0.0006401695],"genre_scores_gemma":[0.619569,0.00014288329,0.37456864,0.000104832056,0.00004404943,0.00020656234,0.00059111865,0.0005064583,0.004266521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997893,0.00005753452,0.000013462071,0.00005058525,0.000065334694,0.000023802077],"domain_scores_gemma":[0.9996959,0.00013574598,0.00003233932,0.000053745214,0.00006814615,0.000014146622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005299147,0.00073734426,0.0007080123,0.00027820148,0.00026254077,0.0005762052,0.00091408216,0.0006391677,0.0031732756],"category_scores_gemma":[0.0011005008,0.0003749602,0.00065586664,0.00029673945,0.00028124463,0.00076771184,0.0008100368,0.0008708457,0.0008803479],"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":[0.0004042242,0.00009860857,0.00091268285,0.00014844321,0.00012378316,0.00009435993,0.00014604234,0.74113226,0.01491506,0.008171704,0.0038943493,0.22995843],"study_design_scores_gemma":[0.0000036608621,0.000018354165,0.000055715776,0.0000021074914,0.0000035381597,0.0000063782186,0.0000033684748,0.997241,0.0013307848,0.00066937855,0.00066210644,0.0000035720022],"about_ca_topic_score_codex":0.004023831,"about_ca_topic_score_gemma":0.0058507007,"teacher_disagreement_score":0.004023831,"about_ca_system_score_codex":0.00030012266,"about_ca_system_score_gemma":0.00049764,"threshold_uncertainty_score":0.010615647},"labels":[],"label_agreement":null},{"id":"W7125250017","doi":"10.18280/mmep.121222","title":"Underwater Image Enhancement Through Smooth Gridded Adaptive Color Compensation with Green-Tint Removal and Integrated CLAHE","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Underwater; Adaptive histogram equalization; Compensation (psychology); Image enhancement; Noise (video); Color image","score_opus":0.02268686684587563,"score_gpt":0.2285823715020586,"score_spread":0.20589550465618298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125250017","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":[0.06535189,0.0005914835,0.92964333,0.00009249702,0.00006477483,0.00004236567,0.00004396033,0.0012456471,0.002923983],"genre_scores_gemma":[0.5061783,0.0006978541,0.48585853,0.0001535145,0.000037257625,0.00004559474,0.00018410622,0.00022297537,0.006621829],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998654,0.000014135897,0.000006279494,0.000032206535,0.000065610664,0.000016358908],"domain_scores_gemma":[0.999863,0.000025782594,0.000022636057,0.00002732644,0.000052232044,0.000008976377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017518543,0.00044508494,0.00025787237,0.0004737179,0.00013149265,0.00032886662,0.000460965,0.00023784497,0.00092558947],"category_scores_gemma":[0.0004025733,0.0001344122,0.00037691233,0.0004058472,0.00029230549,0.0004940185,0.00054828427,0.000306171,0.00032256503],"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":[0.00018678202,0.00007416707,0.0015169184,0.0001912999,0.00006514128,0.0002096197,0.00012137957,0.05090189,0.507052,0.0045724483,0.0023544442,0.43275395],"study_design_scores_gemma":[0.000029962708,0.00023024036,0.0035521009,0.000022253955,0.000069787406,0.00064931216,0.00006888141,0.6264169,0.3507247,0.0023464076,0.015831087,0.000058317066],"about_ca_topic_score_codex":0.0013872108,"about_ca_topic_score_gemma":0.0026438623,"teacher_disagreement_score":0.0013872108,"about_ca_system_score_codex":0.00018435287,"about_ca_system_score_gemma":0.0002712495,"threshold_uncertainty_score":0.0030964613},"labels":[],"label_agreement":null},{"id":"W7132943755","doi":"","title":"Intrinsic image decomposition via deformable reflectance models","year":2007,"lang":"","type":"dissertation","venue":"TSpace","topic":"Image Enhancement 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":"Canadian Heritage; University of Toronto; Library and Archives Canada","funders":"","keywords":"Reflectivity; Generative model; Image (mathematics); Artificial neural network; Probabilistic logic; Displacement (psychology); Decomposition; Image-based lighting","score_opus":0.02049375897173278,"score_gpt":0.3872187850126961,"score_spread":0.3667250260409633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132943755","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":[0.0076090805,0.00009779364,0.9906313,0.00008144762,0.000013842946,0.000015182405,0.00004457066,0.00040746672,0.0010993521],"genre_scores_gemma":[0.4434527,0.0008509657,0.54117846,0.00015178764,0.000059944814,0.00010992163,0.000502201,0.0007289963,0.012965079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978787,0.000042212687,0.000007897872,0.00006182347,0.000083006336,0.000017275679],"domain_scores_gemma":[0.99957794,0.00015398141,0.00006117374,0.00011972338,0.000060365524,0.000026765894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005318439,0.0007869753,0.0004980882,0.00065592414,0.00018373675,0.0008238798,0.0010179708,0.000913683,0.0023185827],"category_scores_gemma":[0.0014189902,0.0007357425,0.0013471858,0.00042527725,0.0005797717,0.0015878795,0.0008183378,0.0014589494,0.0009690953],"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":[0.000052309268,0.000033432716,0.00031098226,0.00006873131,0.00004305281,0.00007461054,0.000078146746,0.8799946,0.024650961,0.025095114,0.0010559313,0.068542205],"study_design_scores_gemma":[0.0000015837251,0.000004791461,0.000051629522,0.000002408351,0.0000024455169,0.00002007577,0.0000029522628,0.9948778,0.0010404703,0.0036376764,0.00035449697,0.0000035809735],"about_ca_topic_score_codex":0.0028007673,"about_ca_topic_score_gemma":0.0031456726,"teacher_disagreement_score":0.0028007673,"about_ca_system_score_codex":0.00064865756,"about_ca_system_score_gemma":0.00031413592,"threshold_uncertainty_score":0.0077564716},"labels":[],"label_agreement":null},{"id":"W7160111553","doi":"10.1109/iccv51701.2025.01730","title":"Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP","year":2025,"lang":"","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Encoding (memory); Field (mathematics); Pattern recognition (psychology); Feature (linguistics); Data compression; Artificial neural network","score_opus":0.010091439495930055,"score_gpt":0.25778981945306134,"score_spread":0.2476983799571313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160111553","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":[0.015880669,0.0003683327,0.97412217,0.00017855181,0.00013200096,0.00006219125,0.00020988607,0.0044022547,0.00464396],"genre_scores_gemma":[0.17564237,0.00064363255,0.8006056,0.00037096132,0.00015905427,0.00008704558,0.0006785879,0.000668189,0.021144502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997477,0.00003224131,0.000011576715,0.000039165945,0.00014162832,0.00002780642],"domain_scores_gemma":[0.99965835,0.000085895466,0.000022748056,0.000108218366,0.00009555102,0.000029214933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032072424,0.0008526784,0.00039513048,0.0005304582,0.00023336936,0.0006328419,0.00071985874,0.0005669916,0.008012419],"category_scores_gemma":[0.00094359583,0.00023712513,0.00032617227,0.0004780108,0.0003106958,0.0012167362,0.0012228696,0.0011831997,0.0027794628],"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":[0.00045442572,0.00017278561,0.00040638173,0.00017105328,0.00004605844,0.00016661189,0.000062399304,0.017922414,0.2707191,0.007024744,0.008341386,0.69451267],"study_design_scores_gemma":[0.000045250912,0.0002600035,0.0010432638,0.000048080947,0.000072781906,0.0005835669,0.000032076197,0.6402336,0.3243355,0.0048206113,0.028479865,0.000045479606],"about_ca_topic_score_codex":0.001488729,"about_ca_topic_score_gemma":0.0028400994,"teacher_disagreement_score":0.008012419,"about_ca_system_score_codex":0.0002941646,"about_ca_system_score_gemma":0.00040899354,"threshold_uncertainty_score":0.026804209},"labels":[],"label_agreement":null},{"id":"W7160842963","doi":"10.63575/cia.2025.30206","title":"A Comparative Evaluation of Deep Learning Paradigms for Low-Light Image Enhancement: From CNNs to Diffusion Models","year":2025,"lang":"","type":"article","venue":"Journal of Computing Innovations and Applications","topic":"Image Enhancement 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 Toronto","funders":"","keywords":"Deep learning; Benchmark (surveying); Inference; Convolutional neural network; Generalization; Fidelity; Image (mathematics); Artificial neural network","score_opus":0.03065897187692652,"score_gpt":0.34849832379297324,"score_spread":0.31783935191604673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160842963","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":[0.32296392,0.029462388,0.61663586,0.0014886851,0.00067388563,0.0006335159,0.0008621783,0.004410099,0.022869462],"genre_scores_gemma":[0.6347044,0.011311817,0.344206,0.00066259236,0.00010574355,0.00022341334,0.0016014111,0.0006509644,0.00653357],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917775,0.00018896739,0.000053687294,0.00017409954,0.00033353406,0.0000719376],"domain_scores_gemma":[0.99864763,0.0006454403,0.00010486876,0.0002231811,0.00030732004,0.00007165847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024639973,0.0013164212,0.00060389325,0.0011896618,0.00028052344,0.0013008224,0.0011646105,0.0009253465,0.0012944778],"category_scores_gemma":[0.0056944564,0.00034393583,0.0006782047,0.00058517896,0.0005674617,0.001905507,0.0013367618,0.0014501094,0.00046106978],"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":[0.0013843904,0.0004374147,0.003991642,0.002340888,0.0006051144,0.00022968603,0.00022136708,0.19436297,0.050458338,0.009645777,0.0065322015,0.72979015],"study_design_scores_gemma":[0.00012686492,0.0017635865,0.0028380377,0.0005011006,0.00028128186,0.0007559898,0.00020601795,0.86857134,0.10454299,0.0070955073,0.01322166,0.00009556853],"about_ca_topic_score_codex":0.0025408645,"about_ca_topic_score_gemma":0.004516522,"teacher_disagreement_score":0.0025408645,"about_ca_system_score_codex":0.00071368105,"about_ca_system_score_gemma":0.00063279667,"threshold_uncertainty_score":0.013031006},"labels":[],"label_agreement":null},{"id":"W91740730","doi":"10.1007/978-3-642-31298-4_38","title":"Adaptive Windowing for Optimal Visualization of Medical Images Based on a Structural Fidelity Measure","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tone mapping; Computer science; High dynamic range; Visualization; Fidelity; Computer vision; Measure (data warehouse); Artificial intelligence; High fidelity; High-dynamic-range imaging; Range (aeronautics); Dynamic range; Data mining; Engineering","score_opus":0.025413650508689822,"score_gpt":0.3017766647534292,"score_spread":0.27636301424473936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W91740730","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":[0.0126795145,0.00038767926,0.98634005,0.000049485774,0.000018345272,0.000019708124,0.000030256853,0.00016373869,0.00031125854],"genre_scores_gemma":[0.13261375,0.0011551469,0.8645928,0.000035402667,0.00005302172,0.00008077872,0.00010586969,0.00025009896,0.001113174],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998154,0.000056972753,0.000016823198,0.000030684005,0.00006352449,0.00001660736],"domain_scores_gemma":[0.99909544,0.00058293244,0.00007584193,0.000093194234,0.00011066952,0.000041881533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055011903,0.00058706745,0.00059050665,0.0005904281,0.00015614442,0.000892562,0.00068513054,0.0005354043,0.0021297894],"category_scores_gemma":[0.00257022,0.0004017119,0.0005715646,0.0006008401,0.0003222736,0.0011525155,0.0008499719,0.0011165518,0.00028047452],"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":[0.00055700063,0.00012015068,0.00061203283,0.00054093986,0.0000878618,0.00019622379,0.00027338386,0.09174868,0.43661612,0.046791527,0.0025299443,0.41992602],"study_design_scores_gemma":[0.000022678012,0.00013818935,0.0007095044,0.00003933051,0.00004426111,0.00034345797,0.000023371616,0.9307789,0.056992702,0.007992898,0.0028849365,0.000029695873],"about_ca_topic_score_codex":0.0005862014,"about_ca_topic_score_gemma":0.0005588194,"teacher_disagreement_score":0.0021297894,"about_ca_system_score_codex":0.00025497895,"about_ca_system_score_gemma":0.0003488157,"threshold_uncertainty_score":0.007124841},"labels":[],"label_agreement":null}]}