{"meta":{"query_hash":"181cd309eac5","filters":{"topic":"Advanced Image Fusion Techniques"},"cohort_total":323,"direct_labels_cover":0,"predictions_cover":323,"exported":323,"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/181cd309eac5","api":"https://metacan.xera.ac/api/v1/cohort?topic=Advanced+Image+Fusion+Techniques"},"results":[{"id":"W1448017457","doi":"","title":"Summer flood mapping in a northern wetland using a combination of Radarsat and SPOT imagery","year":2001,"lang":"en","type":"article","venue":"IAHS-AISH publication","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Flood myth; Mahalanobis distance; Remote sensing; Wetland; Environmental science; Cartography; Geography; Artificial intelligence; Computer science; Ecology; Archaeology","score_opus":0.01819921295242186,"score_gpt":0.24493353218473846,"score_spread":0.2267343192323166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1448017457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89434713,0.00023416695,0.103106454,0.00018717542,0.000046744808,0.00027704478,0.0000044791036,0.00025075604,0.0015460496],"genre_scores_gemma":[0.98443097,0.00013623535,0.015265127,0.000030967298,0.00002485721,0.000014300886,0.000038765917,0.000028819879,0.000029959065],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992078,0.000024720901,0.0003093481,0.0001637373,0.00013271147,0.00016167434],"domain_scores_gemma":[0.99947596,0.00003890566,0.00008685682,0.00020132885,0.0001595503,0.00003741795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021723879,0.0001115099,0.00015420452,0.0003601701,0.000030417119,0.000041756022,0.000088135675,0.000069324284,0.000018363282],"category_scores_gemma":[0.00012014406,0.00012837333,0.00002206546,0.0005163246,0.000034243985,0.0006807076,0.000033812437,0.00011080384,0.0000027032136],"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":[0.000018066201,0.00017377835,0.42304355,0.0002068009,0.000027308488,0.000008616234,0.0008669646,0.0007554446,0.4297552,0.000317349,0.0007825188,0.1440444],"study_design_scores_gemma":[0.0034037144,0.00008028181,0.6071737,0.0005262509,0.000041348456,0.00015259007,0.00055579044,0.20895055,0.1500226,0.0063521136,0.021572722,0.0011683655],"about_ca_topic_score_codex":0.00009809195,"about_ca_topic_score_gemma":0.00012107456,"teacher_disagreement_score":0.2797326,"about_ca_system_score_codex":0.00009472848,"about_ca_system_score_gemma":0.000015418971,"threshold_uncertainty_score":0.52349126},"labels":[],"label_agreement":null},{"id":"W1489918042","doi":"10.5772/8940","title":"Curvelet Based Feature Extraction","year":2010,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Windsor; University of British Columbia","funders":"","keywords":"Curvelet; Pattern recognition (psychology); Artificial intelligence; Feature extraction; Extraction (chemistry); Feature (linguistics); Computer science; Chromatography; Chemistry; Philosophy; Wavelet transform; Linguistics; Wavelet","score_opus":0.010307436921643721,"score_gpt":0.24412279136242626,"score_spread":0.23381535444078255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1489918042","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005177573,0.00012603484,0.25764254,0.000031518106,0.00065899343,0.00031986283,0.000050716983,0.002692857,0.7384723],"genre_scores_gemma":[0.008484108,0.00004951035,0.09983331,0.00022126653,0.00056081114,0.00010205396,0.00008868192,0.0004612534,0.890199],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990064,0.0000044257727,0.00024252034,0.00029650007,0.00022529204,0.00022484921],"domain_scores_gemma":[0.9990567,0.00005554305,0.00009344967,0.00060555426,0.00010663928,0.000082142236],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0000784486,0.00044243087,0.0003169606,0.0002649361,0.00005148432,0.000038015285,0.00026934323,0.0008209908,0.0007545989],"category_scores_gemma":[0.000025741705,0.00046388642,0.00016625132,0.000010148201,0.000089457826,0.000058279405,0.00004594824,0.0023201657,0.00018921684],"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":[0.000011436553,0.000004048498,2.500928e-7,0.00011239378,0.000031773016,0.0000769002,0.000016396547,0.0000031992104,0.8527019,0.0013484971,0.00920661,0.1364866],"study_design_scores_gemma":[0.00005106598,0.000014339867,4.2187358e-7,0.00015060151,0.000011989511,0.000017839344,6.302455e-7,0.00006472897,0.45405936,0.0077552046,0.5376017,0.00027208048],"about_ca_topic_score_codex":0.000002728285,"about_ca_topic_score_gemma":0.000028342603,"teacher_disagreement_score":0.5283951,"about_ca_system_score_codex":0.00012036884,"about_ca_system_score_gemma":0.00003176052,"threshold_uncertainty_score":0.9999815},"labels":[],"label_agreement":null},{"id":"W1501319685","doi":"10.1007/11550822_83","title":"Image Segmentation Using Watershed Transform and Feed-Back Pulse Coupled Neural Network","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Guelph","funders":"","keywords":"Computer science; Artificial intelligence; Watershed; Image segmentation; Artificial neural network; Segmentation; Computer vision; Image (mathematics); Pattern recognition (psychology); Scale-space segmentation; Image fusion","score_opus":0.01085777516486672,"score_gpt":0.2391399182168302,"score_spread":0.22828214305196348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501319685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015764878,0.00043463675,0.9960492,0.0000815147,0.00042128062,0.00046820953,0.0000041769426,0.00028710582,0.0006773342],"genre_scores_gemma":[0.06321759,0.00019905058,0.9355745,0.000317401,0.0005209864,0.0000062567224,0.000017088378,0.000082016064,0.0000651133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835324,0.00000760045,0.00034693215,0.0005180741,0.00031161233,0.00046254165],"domain_scores_gemma":[0.9994038,0.00007358474,0.00006639901,0.00029870524,0.00006503696,0.00009247705],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019772808,0.00040136054,0.00034175257,0.00025244863,0.00012231461,0.00021582945,0.0003495366,0.00018269863,0.00008343074],"category_scores_gemma":[0.000006792927,0.00038303237,0.00005343428,0.00019160006,0.00034303975,0.0006831999,0.00011557146,0.0004773595,0.0000069387193],"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":[0.00000757231,0.0000035636608,0.000008437085,0.00006843015,0.00000621205,0.000020505136,0.0002557615,0.48205888,0.02172375,0.000014480914,0.000012186883,0.49582022],"study_design_scores_gemma":[0.00020279374,0.00004183734,0.000022795475,0.00017420267,0.000010742009,0.000050050436,2.2143246e-7,0.98304796,0.01071862,0.0051530446,0.00015226164,0.00042547905],"about_ca_topic_score_codex":0.00000832919,"about_ca_topic_score_gemma":0.00003188232,"teacher_disagreement_score":0.5009891,"about_ca_system_score_codex":0.00025856466,"about_ca_system_score_gemma":0.00003077646,"threshold_uncertainty_score":0.99986213},"labels":[],"label_agreement":null},{"id":"W1506977119","doi":"10.1007/978-3-642-02611-9_6","title":"Multifocus Image Fusion Using Local Phase Coherence Measurement","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Image fusion; Phase congruency; Coherence (philosophical gambling strategy); Computer vision; Contrast (vision); Fusion; Image (mathematics); Wavelet; Wavelet transform; Pattern recognition (psychology); Energy (signal processing); Mathematics","score_opus":0.02474515005688925,"score_gpt":0.27239849627769697,"score_spread":0.2476533462208077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506977119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007556839,0.0009527877,0.99535257,0.00002656229,0.00060313515,0.00048785194,0.000007675129,0.0006094192,0.0018844182],"genre_scores_gemma":[0.12754808,0.00008939132,0.8717299,0.00022129675,0.00027852933,0.00000718867,0.000004521746,0.00008383393,0.0000372154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99709034,0.000014399928,0.00047586253,0.00080037984,0.0010652166,0.0005538127],"domain_scores_gemma":[0.9986243,0.000067301546,0.0001203899,0.0007337648,0.00030497005,0.00014922046],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004823947,0.00055777177,0.00046939103,0.00053043675,0.0001472611,0.00013976022,0.00087570964,0.0002723554,0.000067959576],"category_scores_gemma":[0.000060825594,0.00054383354,0.00009301093,0.00029950717,0.00055364484,0.00036215744,0.00028581775,0.0008184047,0.000021613492],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054482252,0.000019224322,3.997526e-7,0.000031999443,0.0000031010509,0.00007272241,0.00006803627,0.06601031,0.066782,0.000021428072,0.000021766964,0.86696357],"study_design_scores_gemma":[0.00040320348,0.00014303376,0.0000038694425,0.00087494863,0.000011401228,0.000046712437,1.5218333e-7,0.7856148,0.19247702,0.01857981,0.0011330412,0.0007120249],"about_ca_topic_score_codex":0.000012622661,"about_ca_topic_score_gemma":0.000027681346,"teacher_disagreement_score":0.8662515,"about_ca_system_score_codex":0.00096378813,"about_ca_system_score_gemma":0.00015609384,"threshold_uncertainty_score":0.9997013},"labels":[],"label_agreement":null},{"id":"W1529028926","doi":"10.1007/978-3-540-87734-9_44","title":"Image Denoising Using Neighbouring Contourlet Coefficients","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Concordia University","funders":"","keywords":"Contourlet; Computer science; Image denoising; Noise reduction; Artificial intelligence; Computer vision; Image (mathematics); Pattern recognition (psychology); Wavelet; Wavelet transform","score_opus":0.013734670255425166,"score_gpt":0.24516549636796156,"score_spread":0.2314308261125364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529028926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000781075,0.00085723464,0.99283373,0.000012500642,0.00092060055,0.00030876166,0.0000086764,0.00067084015,0.0036065737],"genre_scores_gemma":[0.117462076,0.00022723297,0.8812998,0.00023904948,0.00050977047,0.0000038201897,0.00000530447,0.00016057584,0.00009239103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99753934,0.000010251001,0.00045201115,0.0007348529,0.0006196622,0.00064387935],"domain_scores_gemma":[0.9987716,0.00015394317,0.00012311558,0.00066802837,0.00015225535,0.00013104243],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001825401,0.0005273423,0.00049113686,0.0007264077,0.00023891877,0.00018145717,0.0008888065,0.000260032,0.00004390247],"category_scores_gemma":[0.00006472341,0.00055629027,0.00009900892,0.00031958942,0.00061399495,0.0004217122,0.0004266264,0.0008231033,0.000020434623],"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":[0.0000050757712,0.0000152318,0.000017266757,0.00010719893,0.000013115925,0.00043076754,0.0005243491,0.5243052,0.050831914,0.0000634658,0.00007321162,0.4236132],"study_design_scores_gemma":[0.00021759486,0.00003864168,0.000022196438,0.00087867334,0.000009756764,0.0002346124,1.7002843e-7,0.94498813,0.04723271,0.0037607322,0.0017167628,0.0009000343],"about_ca_topic_score_codex":0.000010689323,"about_ca_topic_score_gemma":0.000008441425,"teacher_disagreement_score":0.42271316,"about_ca_system_score_codex":0.0006059285,"about_ca_system_score_gemma":0.000111264475,"threshold_uncertainty_score":0.99968886},"labels":[],"label_agreement":null},{"id":"W1531930321","doi":"10.1111/j.1556-4029.2011.01850.x","title":"Latent Evidence Detection using a Combination of Near Infrared and High Dynamic Range Photography: An Example Using Bloodstains","year":2011,"lang":"en","type":"article","venue":"Journal of Forensic Sciences","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Windsor","funders":"","keywords":"Photography; High dynamic range; Near-infrared spectroscopy; Dynamic range; Visibility; Artificial intelligence; Computer vision; Computer science; Remote sensing; Materials science; Optics; Geography; Art; Physics; Visual arts","score_opus":0.07529086113862365,"score_gpt":0.28644137741310716,"score_spread":0.2111505162744835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531930321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8895336,0.00037290546,0.10980149,0.0000017847219,0.00016242314,0.00008325918,7.508108e-7,0.000031399268,0.0000124267335],"genre_scores_gemma":[0.8287333,0.000094708084,0.17115024,0.000004499343,0.000009352226,4.9878776e-7,7.01016e-8,0.0000068315057,5.0146076e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915683,0.000033132306,0.00031961777,0.000097681506,0.00026080367,0.00013190477],"domain_scores_gemma":[0.9993697,0.0000404044,0.00026235357,0.000088259345,0.00018446158,0.000054821525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004759861,0.00008838973,0.00017050217,0.00031726327,0.00011908055,0.000035965008,0.00013151171,0.000038038943,0.000008346601],"category_scores_gemma":[0.00005300559,0.00007538761,0.000042571563,0.00046119833,0.0003178476,0.0011952835,0.000023000975,0.0001003957,4.7557393e-8],"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":[0.00006930668,0.00007966791,0.009645397,0.000101041,0.000038851147,0.000018428073,0.004061825,0.010887206,0.9176674,0.000074042655,0.0000037090053,0.057353117],"study_design_scores_gemma":[0.0004013249,0.00096826645,0.029585809,0.00048291386,0.00006729932,0.00021631295,0.0007410098,0.6074254,0.34992582,0.009988848,0.000003454821,0.00019354616],"about_ca_topic_score_codex":0.00044158386,"about_ca_topic_score_gemma":0.00004902857,"teacher_disagreement_score":0.5965382,"about_ca_system_score_codex":0.00004772675,"about_ca_system_score_gemma":0.00003402633,"threshold_uncertainty_score":0.30742174},"labels":[],"label_agreement":null},{"id":"W153277671","doi":"10.1007/978-3-642-22407-2_4","title":"Fusion with Infrared Images for an Improved Performance and Perception","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"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":"Infrared; Artificial intelligence; Computer science; Computer vision; Fusion; Feature (linguistics); Pixel; Perception; Image fusion; Sensor fusion; Modalities; Pattern recognition (psychology); Optics; Physics; Psychology; Image (mathematics)","score_opus":0.011699534200944257,"score_gpt":0.20879884206479038,"score_spread":0.19709930786384613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W153277671","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069352705,0.00013277013,0.29144898,0.000004222903,0.00007318092,0.0010357092,0.00004343635,0.0016460249,0.70492214],"genre_scores_gemma":[0.0108306585,0.004230613,0.35186207,0.000064696986,0.00023468853,0.00018473713,0.00019216994,0.00034954704,0.6320508],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933296,0.0000016480747,0.00017324348,0.0002579624,0.00007466912,0.00015951281],"domain_scores_gemma":[0.99949455,0.000010677259,0.000049338953,0.00028684968,0.00009012393,0.00006844253],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000047322148,0.00030371966,0.00023017761,0.00011928155,0.000060138198,0.000024630437,0.00009475021,0.00021113445,0.0003926338],"category_scores_gemma":[0.0000026542295,0.0002491436,0.000030894273,0.000010343664,0.000060217273,0.0004068991,0.00003934191,0.00018307098,0.000008043818],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016226512,0.000009346642,0.000010557233,0.0006685745,0.000038823466,0.0000017748318,0.00028911207,0.000016930466,0.07758701,0.0010251736,0.006687765,0.9135027],"study_design_scores_gemma":[0.0035415061,0.008801428,0.0016341901,0.0022576312,0.00040737144,0.0001727155,0.00020446134,0.084585615,0.106099136,0.039945744,0.74577737,0.00657281],"about_ca_topic_score_codex":0.0000036515526,"about_ca_topic_score_gemma":0.0000063206676,"teacher_disagreement_score":0.90692985,"about_ca_system_score_codex":0.00003947496,"about_ca_system_score_gemma":0.000008659958,"threshold_uncertainty_score":0.99999607},"labels":[],"label_agreement":null},{"id":"W1542704817","doi":"10.1007/11559573_22","title":"All-in-Focus Imaging Using a Series of Images on Different Focal Planes","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":18,"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 Manitoba","funders":"","keywords":"Focus (optics); Series (stratigraphy); Computer vision; Computer science; Artificial intelligence; Geology; Optics; Physics; Paleontology","score_opus":0.015114107368166015,"score_gpt":0.251803693929482,"score_spread":0.23668958656131597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542704817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012801163,0.00070584385,0.99552137,0.00010528925,0.00039023027,0.00024264673,0.000014041554,0.00024809217,0.0014923841],"genre_scores_gemma":[0.6432874,0.00023620056,0.35573977,0.0002119825,0.00034904393,0.00000804647,0.0000054012658,0.00010244118,0.000059674618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818933,0.000010549851,0.00042482436,0.0005320298,0.00042764193,0.00041559854],"domain_scores_gemma":[0.99914354,0.00017187529,0.00010441182,0.00046670812,0.000052987823,0.00006047208],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013993582,0.00044088764,0.0004937896,0.0007409532,0.000042908217,0.00006560418,0.0005967865,0.0001375053,0.00002604535],"category_scores_gemma":[0.000030231806,0.00039984673,0.00006925191,0.00015605394,0.0004203724,0.00028661772,0.00025288863,0.00059052504,0.0000025502784],"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":[0.000017027634,0.00002494188,0.0001634254,0.00012369305,0.000009369282,0.0001413867,0.00034133004,0.29125252,0.021876333,0.00048028302,0.00001868706,0.685551],"study_design_scores_gemma":[0.00034422,0.00013954879,0.00048159895,0.002289956,0.00001574462,0.00015431178,7.881116e-7,0.5956874,0.34900126,0.049944006,0.00077835674,0.0011628432],"about_ca_topic_score_codex":0.000015664247,"about_ca_topic_score_gemma":0.000053536252,"teacher_disagreement_score":0.68438816,"about_ca_system_score_codex":0.00030369635,"about_ca_system_score_gemma":0.000039718285,"threshold_uncertainty_score":0.9998453},"labels":[],"label_agreement":null},{"id":"W1548437120","doi":"10.1109/ccece.2015.7129347","title":"A comparative study of directional filters with application to angiogram image enhancement","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Contourlet; Computer vision; Decimation; Artificial intelligence; Computer science; Filter bank; Filter (signal processing); Context (archaeology); Noise (video); Upsampling; Image (mathematics); Pattern recognition (psychology); Geology; Wavelet transform","score_opus":0.016020756564659535,"score_gpt":0.29224773199496074,"score_spread":0.2762269754303012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548437120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21798748,0.0000076057336,0.774646,0.00000923059,0.000015779005,0.0005851099,0.000001928652,0.00027835908,0.006468548],"genre_scores_gemma":[0.8569852,6.613427e-7,0.14270146,0.000010172493,0.000008296277,0.00017763632,0.0000038790886,0.000008155576,0.00010453535],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995598,0.000007650343,0.00011223394,0.00010188006,0.00014470243,0.00007372816],"domain_scores_gemma":[0.9996744,0.000010479429,0.000018839266,0.00013694026,0.00010440757,0.000054936474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000043596483,0.00007479955,0.00011258551,0.00007196555,0.000011173867,0.0000059111912,0.000059462527,0.000009386606,0.000023336375],"category_scores_gemma":[0.000002936992,0.00006152061,0.00001063858,0.0002211292,0.000012798581,0.000085156804,0.00002038054,0.00003114912,0.000014354186],"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":[0.0004039449,0.0026674287,0.0028352172,0.00006253764,0.00037105795,0.000005999003,0.020513944,0.07767788,0.7601602,0.00015311767,0.08952809,0.045620568],"study_design_scores_gemma":[0.0006462657,0.0014015337,0.0012765595,0.000014862678,0.000015430072,0.0000019344359,0.004405733,0.007819672,0.9807557,0.00009554538,0.0033374522,0.00022931822],"about_ca_topic_score_codex":0.000040492403,"about_ca_topic_score_gemma":0.000040369705,"teacher_disagreement_score":0.63899773,"about_ca_system_score_codex":0.000048376634,"about_ca_system_score_gemma":0.000004996013,"threshold_uncertainty_score":0.25087377},"labels":[],"label_agreement":null},{"id":"W1548785611","doi":"10.1109/tip.2015.2460015","title":"Objective Quality Assessment for Color-to-Gray Image Conversion","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":83,"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; Grayscale; Computer science; Luminance; Image quality; Computer vision; Pattern recognition (psychology); Color image; Gray (unit); Image (mathematics); Image processing","score_opus":0.028916218521561485,"score_gpt":0.33821847910847325,"score_spread":0.30930226058691174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548785611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054544094,0.000037456823,0.9894833,0.00012479226,0.00040982984,0.00082916504,0.000083959174,0.0014146884,0.0021623927],"genre_scores_gemma":[0.5793836,0.000009199991,0.41985086,0.00012363457,0.000038501774,0.00034593578,0.000004232061,0.000065976674,0.00017805559],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985345,0.00004639572,0.0003629427,0.00037390654,0.0003074967,0.0003747812],"domain_scores_gemma":[0.9988415,0.00011512618,0.0000676321,0.00026889215,0.00047149512,0.0002353245],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039329217,0.00028282913,0.0003010504,0.00024052999,0.0002480892,0.00013323825,0.00018775876,0.000102253965,0.000034258403],"category_scores_gemma":[0.000036895526,0.00029548124,0.000107071435,0.00038932162,0.00008326516,0.0008774302,0.000002842751,0.00032209454,0.00004416305],"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":[0.0002606673,0.00027275286,0.000008754035,0.00056657376,0.00004969509,0.000009421106,0.001399506,0.01784115,0.83884305,0.000013078666,0.002942508,0.13779283],"study_design_scores_gemma":[0.001007012,0.00024125181,0.00004770204,0.00012473362,0.00004071912,0.000007260769,0.0009145992,0.063405216,0.9318916,0.0005901372,0.0012521193,0.00047761656],"about_ca_topic_score_codex":0.0000145428585,"about_ca_topic_score_gemma":0.0000073995884,"teacher_disagreement_score":0.5739292,"about_ca_system_score_codex":0.00053437764,"about_ca_system_score_gemma":0.00012910391,"threshold_uncertainty_score":0.99994975},"labels":[],"label_agreement":null},{"id":"W1563797521","doi":"10.3233/ica-2005-12201","title":"A multiscale approach to pixel-level image fusion","year":2005,"lang":"en","type":"article","venue":"Integrated Computer-Aided Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":137,"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; Computer vision; Artificial intelligence; Image fusion; Image (mathematics); Computer science; Fusion; Linguistics; Philosophy","score_opus":0.010061018520926307,"score_gpt":0.20985114963417992,"score_spread":0.1997901311132536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563797521","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033179346,0.00012034784,0.9588569,0.000050032824,0.000425935,0.00043998272,0.000028433917,0.004943753,0.0019552677],"genre_scores_gemma":[0.23059812,0.00002488523,0.7685265,0.00009673442,0.00033358374,0.000107834494,0.00003585837,0.00014178084,0.00013470704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813014,0.000016707918,0.0005030333,0.00045938243,0.00025470043,0.0006360301],"domain_scores_gemma":[0.9989634,0.00005985319,0.000033150252,0.00052891765,0.00012688243,0.00028776022],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013361192,0.00051328016,0.00041177587,0.0004590955,0.00006308691,0.00011476728,0.00049681123,0.00017072678,0.000043920743],"category_scores_gemma":[0.00004343005,0.0005227672,0.00012019857,0.00071159756,0.00002152252,0.0004523903,0.0001687105,0.0005691598,0.0002123204],"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":[0.00000637566,0.00006017801,0.000003142815,0.000057511505,0.000029300085,0.000008933476,0.00025700926,0.5406112,0.33331472,0.00011983148,0.005073808,0.120458],"study_design_scores_gemma":[0.00028789855,0.00004615825,0.00014471375,0.0001390745,0.000007965378,0.00004901062,0.00001946715,0.8553023,0.11213882,0.000016863289,0.031292588,0.0005551637],"about_ca_topic_score_codex":0.000017403281,"about_ca_topic_score_gemma":0.000003911469,"teacher_disagreement_score":0.3146911,"about_ca_system_score_codex":0.00031917903,"about_ca_system_score_gemma":0.000014607206,"threshold_uncertainty_score":0.99972236},"labels":[],"label_agreement":null},{"id":"W1565540967","doi":"10.3233/idt-140217","title":"Gini index-based digital image complementing in the study of medical images","year":2014,"lang":"en","type":"article","venue":"Intelligent Decision Technologies","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Manitoba","funders":"","keywords":"Image (mathematics); Pixel; Artificial intelligence; Computer vision; Digital image; Computer science; Digital image analysis; Focus (optics); Index (typography); Transformation (genetics); Feature detection (computer vision); Line (geometry); Binary image; Function (biology); Pattern recognition (psychology); Image processing; Mathematics; Physics","score_opus":0.017549988421166403,"score_gpt":0.3086998902392936,"score_spread":0.2911499018181272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565540967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31963137,0.00012558907,0.6773345,0.00020067542,0.00007979609,0.00045732997,0.0000061737223,0.0014968405,0.0006677397],"genre_scores_gemma":[0.98958594,0.000082197424,0.010180552,0.000025455858,0.000009551007,0.00008223283,0.0000032193934,0.00002738633,0.0000034853026],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99770373,0.000041218525,0.00072486507,0.00026454814,0.0009746306,0.00029099072],"domain_scores_gemma":[0.9979693,0.0010608251,0.00009559749,0.00077110366,0.00007954346,0.000023582352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089847,0.00022220149,0.0003205295,0.00052199885,0.000049892064,0.0000976023,0.0015618881,0.00015431494,0.00006790362],"category_scores_gemma":[0.002486956,0.00015486674,0.00006738835,0.00063548726,0.00020360912,0.00020060019,0.00036556413,0.00049170555,0.000025796471],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001498217,0.00033917127,0.008428221,0.000026323807,0.000011326065,0.000030438689,0.00020477574,0.00093656505,0.0008731364,0.00021546507,0.0019231681,0.9869964],"study_design_scores_gemma":[0.0025296027,0.0016927338,0.009178567,0.0009404552,0.000030122032,0.0000350358,0.04998913,0.13317436,0.69610214,0.075015806,0.029984163,0.001327895],"about_ca_topic_score_codex":0.0000116210995,"about_ca_topic_score_gemma":0.000024295974,"teacher_disagreement_score":0.98566854,"about_ca_system_score_codex":0.00004106936,"about_ca_system_score_gemma":0.000010444674,"threshold_uncertainty_score":0.63152826},"labels":[],"label_agreement":null},{"id":"W1577158432","doi":"10.1109/icnn.1994.374703","title":"An analog-Hopfield neural network for recognition of partially visible 2-D objects","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial intelligence; Computer science; Pattern recognition (psychology); Matching (statistics); Computer vision; Artificial neural network; Similarity (geometry); Boundary (topology); Object (grammar); Similarity measure; Feature (linguistics); Cognitive neuroscience of visual object recognition; Mathematics; Image (mathematics)","score_opus":0.024126690625003493,"score_gpt":0.2536671432987916,"score_spread":0.2295404526737881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1577158432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041056003,0.00021787744,0.9408123,0.000049328555,0.0002552234,0.00045544677,0.000016273698,0.0013861263,0.015751459],"genre_scores_gemma":[0.8376903,0.00009285538,0.16174555,0.00012185438,0.00014088032,0.000044547163,0.000017740722,0.000026151929,0.0001201265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944377,0.000009845366,0.00018622376,0.00010893121,0.000061127976,0.0001901211],"domain_scores_gemma":[0.9996532,0.000052366966,0.000026695967,0.00016980905,0.000052010477,0.000045885514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058995105,0.00008988782,0.00012820448,0.00004494978,0.000028108854,0.00001085609,0.00008117574,0.000059116403,0.00055664626],"category_scores_gemma":[0.000024472469,0.00008945346,0.00004485072,0.00013370349,0.000012988967,0.0002152952,0.000008750292,0.00006361741,0.000009026593],"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":[0.000035493507,0.00010128683,0.00033590858,0.00013620347,0.000032648408,0.0000050358703,0.00017283253,0.10802793,0.10274321,0.00022634638,0.06945837,0.7187247],"study_design_scores_gemma":[0.00024407513,0.0003206691,0.0001459636,0.000026377673,0.000012951883,0.0000030944864,0.00003338928,0.7691024,0.22535084,0.0033265343,0.0012275191,0.0002061959],"about_ca_topic_score_codex":0.000005720212,"about_ca_topic_score_gemma":0.000046779758,"teacher_disagreement_score":0.7966343,"about_ca_system_score_codex":0.000009005579,"about_ca_system_score_gemma":0.0000015503906,"threshold_uncertainty_score":0.60948867},"labels":[],"label_agreement":null},{"id":"W1580436348","doi":"10.1109/tip.2015.2442920","title":"Perceptual Quality Assessment for Multi-Exposure Image Fusion","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1131,"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":"Image quality; Computer science; Image fusion; Artificial intelligence; Quality (philosophy); Consistency (knowledge bases); Image (mathematics); Perception; Fusion; Similarity (geometry); Pattern recognition (psychology); Computer vision; Data mining; Machine learning","score_opus":0.06301080638396696,"score_gpt":0.36345180591979614,"score_spread":0.3004409995358292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580436348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007748726,0.000118532196,0.9884374,0.00007110876,0.000380561,0.0005634171,0.0000762309,0.0017878228,0.000816196],"genre_scores_gemma":[0.45790136,0.00002513769,0.54138917,0.000057210946,0.00005217685,0.00022719454,0.000008826124,0.000081095604,0.00025781768],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99837786,0.00005127187,0.00046134097,0.0003781051,0.00033405152,0.00039736318],"domain_scores_gemma":[0.9989682,0.00006173951,0.00007728288,0.0003208545,0.0003778204,0.00019412834],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040141377,0.00032076324,0.00030425392,0.00018682794,0.00028436893,0.00015632658,0.00020606253,0.00013742894,0.000067824534],"category_scores_gemma":[0.000028480836,0.00032644527,0.00013517492,0.00025594528,0.00011030702,0.0010255523,0.000002809912,0.00042720084,0.000028385928],"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":[0.00006976977,0.00032556217,0.0000070680258,0.00038836157,0.000021590531,0.0000061274577,0.0011839033,0.0051668016,0.6763085,0.000004067534,0.00078466185,0.31573355],"study_design_scores_gemma":[0.002656321,0.00031992682,0.00016675591,0.00021859325,0.00007259339,0.000022093502,0.0021718573,0.3646564,0.6271741,0.0003738672,0.0012265358,0.00094099966],"about_ca_topic_score_codex":0.0000085034035,"about_ca_topic_score_gemma":0.000012347483,"teacher_disagreement_score":0.45015264,"about_ca_system_score_codex":0.00029936965,"about_ca_system_score_gemma":0.00010303615,"threshold_uncertainty_score":0.99991876},"labels":[],"label_agreement":null},{"id":"W1686453017","doi":"","title":"A united optimum images fusion based on analysis of color distortion","year":2004,"lang":"en","type":"article","venue":"Chinese Optics Letters","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Calgary","funders":"","keywords":"Image fusion; Wavelet transform; Wavelet; Artificial intelligence; Wavelet packet decomposition; Pixel; Color space; Hue; Distortion (music); Computer science; Mathematics; Pattern recognition (psychology); Computer vision; Image (mathematics); Telecommunications","score_opus":0.004527609379459866,"score_gpt":0.22864829742682818,"score_spread":0.22412068804736832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1686453017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58892274,0.000009514283,0.40999812,0.00029429363,0.0000675231,0.00010574447,0.000025148323,0.0002946462,0.00028229368],"genre_scores_gemma":[0.9415833,0.000018618954,0.057664044,0.0004887174,0.00002459382,0.00001748464,0.00015794007,0.000036220474,0.000009076392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991846,0.000013692565,0.00026016665,0.00016803379,0.00020805401,0.00016545359],"domain_scores_gemma":[0.9993971,0.000060199054,0.00006817153,0.00036186617,0.000056306704,0.000056352583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075959644,0.00019274025,0.00027196712,0.0008614093,0.000041704534,0.000015035761,0.00014579234,0.000056948225,0.00002871687],"category_scores_gemma":[0.00007548549,0.00017068861,0.00014183277,0.0016101119,0.00006083585,0.00010095165,0.000024241963,0.00014330629,0.000004907244],"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":[0.000015269083,0.00003670489,0.00034372997,0.000022488595,0.000055186654,0.000009551989,0.000044810553,0.731989,0.26713273,0.000018713572,0.000111253394,0.00022054512],"study_design_scores_gemma":[0.0008289541,0.00011754644,0.013952242,0.00008335133,0.00033405042,0.0000011352308,0.00001443826,0.8545815,0.12940663,0.0001209392,0.00012663167,0.0004325964],"about_ca_topic_score_codex":0.000011737502,"about_ca_topic_score_gemma":0.000003774221,"teacher_disagreement_score":0.35266057,"about_ca_system_score_codex":0.00015164158,"about_ca_system_score_gemma":0.00000688816,"threshold_uncertainty_score":0.69604796},"labels":[],"label_agreement":null},{"id":"W1865808852","doi":"","title":"Image fusion based on expectation maximization algorithm and steerable pyramid","year":2004,"lang":"en","type":"article","venue":"Chinese Optics Letters","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Calgary","funders":"","keywords":"Image fusion; Fuse (electrical); Pyramid (geometry); Computer science; Image (mathematics); Artificial intelligence; Fusion; Maximization; Inverse; Computer vision; Contourlet; Algorithm; Pattern recognition (psychology); Optics; Mathematics; Mathematical optimization; Physics; Wavelet transform","score_opus":0.0029782896108964487,"score_gpt":0.2056726513616436,"score_spread":0.20269436175074715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1865808852","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18154934,0.000024162138,0.8164683,0.00051636464,0.00013568951,0.00018408036,0.0000060344373,0.00056290405,0.0005531376],"genre_scores_gemma":[0.2994448,0.00006469818,0.6994475,0.00077241566,0.000089399946,0.00003171497,0.00006665808,0.000069606875,0.000013187126],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932945,0.000009250076,0.00015710018,0.0001793386,0.00015995733,0.00016490112],"domain_scores_gemma":[0.9996473,0.000030222614,0.00003055297,0.00020359285,0.000033970653,0.000054356668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005175734,0.00018273853,0.0001260054,0.00015286158,0.00007436375,0.000053753705,0.00007242042,0.00005169677,0.000016200594],"category_scores_gemma":[0.000040474937,0.00017170151,0.000028102078,0.00019421215,0.000040448555,0.00025385278,0.0000192844,0.0001400181,0.000011126321],"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":[0.000012040726,0.00004193777,0.00007766011,0.000069284986,0.0000071274826,0.000033898104,0.00028381372,0.47651154,0.50930125,0.00003946956,0.00028671007,0.013335276],"study_design_scores_gemma":[0.00090502016,0.000060072307,0.0012035085,0.00007030351,0.000008157946,0.0000064312767,0.000024320621,0.93970555,0.056894176,0.00073697156,0.000064724714,0.0003207853],"about_ca_topic_score_codex":0.0000033019157,"about_ca_topic_score_gemma":5.961915e-7,"teacher_disagreement_score":0.463194,"about_ca_system_score_codex":0.00009766981,"about_ca_system_score_gemma":0.0000056588897,"threshold_uncertainty_score":0.70017844},"labels":[],"label_agreement":null},{"id":"W1870190791","doi":"10.1109/ific.2000.859841","title":"A real time pixel-level based image fusion via adaptive weight averaging","year":2000,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Pixel; Computer vision; Artificial intelligence; Computer science; Image fusion; Fusion; Image (mathematics)","score_opus":0.006676807792915971,"score_gpt":0.2081836287325468,"score_spread":0.20150682093963085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1870190791","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013433693,0.000022445087,0.7848386,0.000055086755,0.000050692022,0.0002498057,0.000017173794,0.0031984765,0.19813399],"genre_scores_gemma":[0.14099336,0.00016627193,0.84490865,0.00020263753,0.0001288172,0.00005461436,0.000036536316,0.00013805134,0.013371075],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998987,0.000023049539,0.00022904249,0.00024626905,0.00019134564,0.0003232949],"domain_scores_gemma":[0.9994598,0.00005160104,0.00001987253,0.00032383666,0.0000455424,0.00009936178],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008548419,0.00023137484,0.00019592351,0.00011490388,0.00007823679,0.000024965479,0.0001665489,0.00008086515,0.017049586],"category_scores_gemma":[0.000006797611,0.00021989022,0.000070288734,0.00020028063,0.00003687157,0.0003306774,0.00003266806,0.00018085155,0.0013305122],"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":[0.000033778997,0.000031745363,0.0000065188497,0.000013871681,0.000010433152,0.000030347608,0.000072234616,0.00085009634,0.869246,0.00003305748,0.0068076937,0.12286424],"study_design_scores_gemma":[0.00043088215,0.000058673395,0.00029173543,0.00005337514,0.000010532511,0.000011106183,0.000007798806,0.52733576,0.46046025,0.0006884132,0.010199891,0.00045158953],"about_ca_topic_score_codex":0.000039591465,"about_ca_topic_score_gemma":0.000003331337,"teacher_disagreement_score":0.5264856,"about_ca_system_score_codex":0.000112684895,"about_ca_system_score_gemma":0.000013882439,"threshold_uncertainty_score":0.99944705},"labels":[],"label_agreement":null},{"id":"W1964189664","doi":"10.1016/j.eswa.2014.05.043","title":"A novel approach for multimodal medical image fusion","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":105,"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 Guelph","funders":"","keywords":"Image fusion; Fuse (electrical); Artificial intelligence; Computer science; Compressed sensing; Robustness (evolution); Fusion; Computer vision; Pattern recognition (psychology); Gaussian; Image (mathematics)","score_opus":0.008291136246485259,"score_gpt":0.2544536248195662,"score_spread":0.24616248857308093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964189664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000059876438,0.00021244946,0.9896143,0.000060545743,0.00005092265,0.0017359924,0.000020749412,0.0012367149,0.007008467],"genre_scores_gemma":[0.18514022,0.00002832551,0.79583675,0.000081162434,0.0004998247,0.017993914,0.00011157625,0.00010186892,0.00020638017],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896085,0.000011582394,0.00025922887,0.00027466207,0.00026064503,0.0002330087],"domain_scores_gemma":[0.99913573,0.00009236677,0.000046393543,0.0004719255,0.00009971041,0.0001538925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017594699,0.00017516226,0.00021143141,0.00007134343,0.00012998373,0.00003333055,0.00026312316,0.0001167702,0.000014325353],"category_scores_gemma":[0.000029243778,0.0001423604,0.00004020318,0.00016104139,0.000059932125,0.00011071816,0.000031641775,0.000117292926,0.000016951846],"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":[0.000060282422,0.00072202756,0.000048564903,0.0010606532,0.00011223687,0.000001246743,0.0011037006,0.010924424,0.8275123,0.033667266,0.04339345,0.08139382],"study_design_scores_gemma":[0.00070938753,0.000042317268,0.000015781172,0.00005627181,0.0000062439185,0.000046676436,0.00016571055,0.7956375,0.010489658,0.00003670149,0.19248043,0.00031335102],"about_ca_topic_score_codex":0.000032599746,"about_ca_topic_score_gemma":0.000002394998,"teacher_disagreement_score":0.8170227,"about_ca_system_score_codex":0.000053380278,"about_ca_system_score_gemma":0.000017614759,"threshold_uncertainty_score":0.58052886},"labels":[],"label_agreement":null},{"id":"W196696935","doi":"","title":"THE EFFECTS OF DIFFERENT TYPES OF WAVELETS ON IMAGE FUSION","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 New Brunswick","funders":"","keywords":"Wavelet; Image fusion; Artificial intelligence; Wavelet transform; Multispectral image; Computer vision; Biorthogonal wavelet; Biorthogonal system; Transformation (genetics); Stationary wavelet transform; Pattern recognition (psychology); Wavelet packet decomposition; Multiresolution analysis; Top-hat transform; Panchromatic film; Computer science; Discrete wavelet transform; Orthogonal wavelet; Second-generation wavelet transform; Mathematics; Image processing; Binary image; Image (mathematics)","score_opus":0.0021812893624196723,"score_gpt":0.2046310878814362,"score_spread":0.2024497985190165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W196696935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88131195,0.0002626679,0.10251169,0.00006170611,0.00022167331,0.00036224123,0.000001966407,0.00042690686,0.014839206],"genre_scores_gemma":[0.9943507,0.00032507486,0.0051661828,0.00001026772,0.000009498534,0.000008094787,6.523338e-7,0.000014389724,0.000115143936],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963176,0.000006465696,0.00012099543,0.00005442476,0.00009940403,0.000086955515],"domain_scores_gemma":[0.9996285,0.000121034376,0.00002286082,0.00018711895,0.000023753897,0.000016764723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000025292731,0.00007519238,0.0001029726,0.000030839026,0.000019075518,0.00000290582,0.00009028097,0.00002561433,0.000017646085],"category_scores_gemma":[0.000059860216,0.00004342268,0.00003417298,0.000051401374,0.00003371131,0.000029030212,0.000029345705,0.000060199138,0.0000058806877],"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":[0.000006630083,0.000027798193,0.0000064410187,0.00010211129,0.0000067647547,0.0000015002645,0.000038443708,0.00013368933,0.97129107,0.002087843,0.00024936488,0.026048323],"study_design_scores_gemma":[0.00015595784,0.0001171961,0.00097608444,0.000084814965,0.0000032680898,4.3737583e-7,0.000006051949,0.00021102584,0.9951032,0.003179505,0.00011502545,0.00004745746],"about_ca_topic_score_codex":0.00000431751,"about_ca_topic_score_gemma":0.0000030746342,"teacher_disagreement_score":0.113038756,"about_ca_system_score_codex":0.000022161541,"about_ca_system_score_gemma":0.0000024377423,"threshold_uncertainty_score":0.17707254},"labels":[],"label_agreement":null},{"id":"W1968396256","doi":"10.1117/12.391917","title":"&lt;title&gt;Improving spatial resolution of infrared images by means of sensor fusion&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Wavelet; Artificial intelligence; Image fusion; Computer vision; Computer science; Infrared; Transformation (genetics); Wavelet transform; Pattern recognition (psychology); Image resolution; Merge (version control); Image (mathematics); Optics; Physics","score_opus":0.005738803899624391,"score_gpt":0.20907641760937373,"score_spread":0.20333761370974934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968396256","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9312162,0.00037011996,0.0018578236,0.0002250392,0.00021880915,0.00043354448,0.00018042728,0.00027484557,0.06522323],"genre_scores_gemma":[0.47429246,0.0013028095,0.51764125,0.000058335132,0.00074607384,0.0001277211,0.00004561674,0.00023865388,0.005547082],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866736,2.2779489e-8,0.00050604745,0.00018899262,0.00041777777,0.00021978037],"domain_scores_gemma":[0.99906915,0.000053004504,0.0001736673,0.00006112782,0.00058609614,0.000056935925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020437036,0.00020053671,0.00029215624,0.00008511356,0.000029499713,0.000020609934,0.00040041766,0.00015004701,0.0002984618],"category_scores_gemma":[0.00021699368,0.00018530479,0.00027281477,0.00019483473,0.00014430736,0.00024229886,0.00006819062,0.00018665672,0.00000701512],"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":[0.000027216274,0.000036062233,0.0000125362885,0.00039150566,0.00009179551,6.130054e-8,0.00006129515,0.0002005753,0.94804394,0.01767092,0.026668647,0.0067954464],"study_design_scores_gemma":[0.00055281905,0.00017966652,0.00014445987,0.00035387007,0.00009031478,0.000007853244,0.00012321219,0.07687569,0.86999834,0.0010040853,0.050306346,0.0003633408],"about_ca_topic_score_codex":0.000005047906,"about_ca_topic_score_gemma":6.815702e-8,"teacher_disagreement_score":0.5157834,"about_ca_system_score_codex":0.000087578,"about_ca_system_score_gemma":0.000014876155,"threshold_uncertainty_score":0.755651},"labels":[],"label_agreement":null},{"id":"W1972697625","doi":"10.1109/iscas.2014.6865378","title":"Contourlet domain image modeling by using the alpha-stable family of distributions","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Contourlet; Histogram; Gaussian; Measure (data warehouse); Mathematics; Laplace operator; Generalized normal distribution; Domain (mathematical analysis); Gaussian network model; Distribution (mathematics); Heavy-tailed distribution; Image (mathematics); Statistical physics; Statistics; Normal distribution; Mathematical analysis; Artificial intelligence; Computer science; Physics; Wavelet transform; Wavelet; Data mining","score_opus":0.009755543025233758,"score_gpt":0.23768609341788588,"score_spread":0.22793055039265211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972697625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049500443,0.00017123173,0.94251317,0.000037571448,0.00004291455,0.00012519262,0.00006461299,0.00037933319,0.0071655284],"genre_scores_gemma":[0.82875174,0.000036178593,0.17101282,0.000046084708,0.000024171713,0.000011234471,0.000018013961,0.000025531346,0.0000742489],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993423,0.000022913766,0.00022181503,0.00010132779,0.000110828936,0.000200801],"domain_scores_gemma":[0.9995358,0.00005336513,0.000027857992,0.00028192645,0.00006499332,0.00003603265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001851366,0.00011167003,0.0001458856,0.000026343872,0.00008465896,0.000021087286,0.00016219843,0.000039961644,0.00005165751],"category_scores_gemma":[0.000038018712,0.00008427404,0.000044900327,0.00011578796,0.000051882933,0.00018173116,0.000047731606,0.000110442845,0.000003844518],"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":[0.0000016119359,0.000009560423,0.00000472846,0.000014119052,0.000008105656,2.3740215e-7,0.000038776154,0.018380547,0.9726922,0.0014949385,0.006219548,0.0011356615],"study_design_scores_gemma":[0.00011769394,0.0000080431455,0.0000021015946,0.00001678397,0.0000075902385,0.0000015921066,0.00014254639,0.8178861,0.17225288,0.0035627347,0.005888612,0.00011331163],"about_ca_topic_score_codex":0.00010116347,"about_ca_topic_score_gemma":0.0000033815556,"teacher_disagreement_score":0.8004393,"about_ca_system_score_codex":0.00004551611,"about_ca_system_score_gemma":0.0000056502,"threshold_uncertainty_score":0.34365958},"labels":[],"label_agreement":null},{"id":"W1974760906","doi":"10.1016/j.jag.2013.10.003","title":"Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area","year":2013,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; York University","funders":"Canadian Space Agency; York University","keywords":"Remote sensing; Synthetic aperture radar; Grassland; Image resolution; Ground truth; Satellite; Environmental science; Geography; Computer science; Artificial intelligence; Ecology","score_opus":0.0073360754579526654,"score_gpt":0.2106146390528238,"score_spread":0.20327856359487112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974760906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.877883,0.000056038152,0.12044696,0.0008321274,0.000068072404,0.00021578926,0.000007376472,0.000022998827,0.0004676538],"genre_scores_gemma":[0.9663447,0.00026679217,0.033175413,0.00014400647,0.000020847741,0.000006819037,0.000020837839,0.000007087596,0.000013452823],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99916905,0.0000062732074,0.00047066231,0.00006174869,0.00021509545,0.000077184646],"domain_scores_gemma":[0.99944973,0.00007954268,0.00015133909,0.000048222726,0.00021232967,0.000058853413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013709285,0.000102500104,0.00015359471,0.0002585578,0.000017903883,0.00007764161,0.00006620761,0.000054269123,0.000018774284],"category_scores_gemma":[0.000089488385,0.00008682349,0.000016655478,0.00007929372,0.00003192034,0.000833235,0.00002953546,0.00012809466,0.0000019194438],"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":[0.000217344,0.000059446113,0.004934016,0.00015319273,0.00008121703,0.0000031165835,0.0024028844,0.0025171544,0.31991673,0.004241158,0.0009857159,0.664488],"study_design_scores_gemma":[0.0018323218,0.00019027726,0.7986563,0.0004353288,0.000022955726,0.00008514964,0.00083051703,0.1042076,0.082422845,0.0072026397,0.0037660324,0.00034803993],"about_ca_topic_score_codex":0.00000778124,"about_ca_topic_score_gemma":0.00001245734,"teacher_disagreement_score":0.7937223,"about_ca_system_score_codex":0.000018411767,"about_ca_system_score_gemma":0.000007479477,"threshold_uncertainty_score":0.35405594},"labels":[],"label_agreement":null},{"id":"W1977092387","doi":"10.1007/s00500-005-0039-1","title":"A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing","year":2006,"lang":"en","type":"article","venue":"Soft Computing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Waterloo","funders":"","keywords":"Artificial intelligence; Evidential reasoning approach; Fuzzy logic; Probabilistic logic; Computer science; Neuro-fuzzy; Machine learning; Entropy (arrow of time); Fuzzy rule; Fuzzy classification; Fuzzy set operations; Fuzzy set; Data mining; Fuzzy control system; Decision support system","score_opus":0.014496995508119078,"score_gpt":0.27845380861146357,"score_spread":0.2639568131033445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977092387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009002216,0.000373645,0.98859775,0.000022327618,0.000028079787,0.0005789771,0.000017612445,0.00086793833,0.0005114692],"genre_scores_gemma":[0.44022277,0.0000030360015,0.55938447,0.000008810604,0.00014858726,0.0000412304,0.00013754281,0.00004479168,0.000008793577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989593,0.0000063844504,0.00028086253,0.0003322336,0.00012419873,0.00029703585],"domain_scores_gemma":[0.9993645,0.00010395932,0.00008050769,0.000376344,0.000045177145,0.000029521078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020025577,0.0001606341,0.00016923952,0.00010845242,0.00016550577,0.000091482194,0.00032582888,0.000047180572,0.000001523079],"category_scores_gemma":[0.000040010385,0.0001601136,0.00001976962,0.0003316823,0.000033853124,0.00042446158,0.00015764545,0.00015632392,0.0000014605416],"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":[0.0000662593,0.00031662354,0.005348393,0.0015074519,0.000029155251,0.00001645156,0.00068923354,0.13460188,0.31124538,0.003137027,0.0023230973,0.5407191],"study_design_scores_gemma":[0.0005242795,0.000017311711,0.0011905304,0.00057358237,0.000015686695,0.000018662138,0.00006556333,0.9876873,0.0056855655,0.0013464522,0.0025415588,0.00033351095],"about_ca_topic_score_codex":0.0000532539,"about_ca_topic_score_gemma":0.000053353764,"teacher_disagreement_score":0.8530854,"about_ca_system_score_codex":0.00006376937,"about_ca_system_score_gemma":0.000026534984,"threshold_uncertainty_score":0.6529243},"labels":[],"label_agreement":null},{"id":"W1978938759","doi":"10.1109/dsp.2009.4785984","title":"Gray-Scale Fingerprint Image Compression Based on the Hybrid-NRCT","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Memorial University of Newfoundland","funders":"","keywords":"Contourlet; Wavelet transform; Artificial intelligence; Computer vision; Redundancy (engineering); Pattern recognition (psychology); Mathematics; Wavelet; Computer science","score_opus":0.005465356866507609,"score_gpt":0.2215862759108487,"score_spread":0.21612091904434108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978938759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016114691,0.000030112553,0.8645116,0.0011062161,0.00008846244,0.00028544312,0.00000492371,0.0021741306,0.11568439],"genre_scores_gemma":[0.93872225,0.000015554699,0.05971398,0.001225816,0.000033920656,0.000017295919,0.000003971128,0.000022683153,0.0002445264],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993397,0.000019956167,0.00014314597,0.0001450722,0.00016062912,0.00019148595],"domain_scores_gemma":[0.9993881,0.000091866816,0.00001810818,0.00043074894,0.000022954002,0.000048208654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009565931,0.00014785652,0.00011234026,0.000054452506,0.0000735003,0.00003278914,0.00020738001,0.000027958846,0.0007392202],"category_scores_gemma":[0.00002640228,0.000094593335,0.00005829474,0.00008032537,0.000025777272,0.000087191795,0.000023424724,0.00020035193,0.000095205796],"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":[0.00003576847,0.00012813012,0.000031166044,0.000022394572,0.000005291003,0.00002870334,0.000049881786,0.017076917,0.7151702,0.0007182044,0.1110872,0.15564615],"study_design_scores_gemma":[0.000105733656,0.000049443886,0.00064047653,0.00004948566,0.0000021813153,0.0000019336917,0.0000084063695,0.18148425,0.80661863,0.0019397782,0.008947383,0.00015231413],"about_ca_topic_score_codex":0.000002537474,"about_ca_topic_score_gemma":7.3007897e-7,"teacher_disagreement_score":0.92260754,"about_ca_system_score_codex":0.000038081474,"about_ca_system_score_gemma":0.0000040860878,"threshold_uncertainty_score":0.80939424},"labels":[],"label_agreement":null},{"id":"W1979349676","doi":"10.1016/s0098-3004(03)00035-9","title":"Constant time 0(1) pixel averaging with applicability to kernel filtering","year":2003,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Lakehead University","keywords":"Kernel (algebra); Variable kernel density estimation; Computer science; Kernel embedding of distributions; Kernel method; Image processing; Algorithm; Radial basis function kernel; Kernel principal component analysis; Mathematics; Artificial intelligence; Image (mathematics); Discrete mathematics; Support vector machine","score_opus":0.005256384599260238,"score_gpt":0.20250520586434972,"score_spread":0.1972488212650895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979349676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12524927,0.00004477935,0.869517,0.000036556507,0.00016007604,0.00024412514,0.0000035508099,0.00074336655,0.0040012617],"genre_scores_gemma":[0.6602365,0.0000064704486,0.3394543,0.00016533253,0.000018422907,0.000030141027,8.080434e-7,0.000016754751,0.00007132128],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886894,0.000020090474,0.00017534073,0.0003568993,0.00021585997,0.00036287343],"domain_scores_gemma":[0.99940735,0.00006954714,0.000025807583,0.00031307113,0.00003718248,0.00014702354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021251079,0.00017591495,0.00018086651,0.00009840502,0.00012881939,0.000090226895,0.00033223975,0.000025746118,0.00004628535],"category_scores_gemma":[0.000025113684,0.00015049551,0.00002757815,0.0004348728,0.00016275271,0.00022262668,0.00007916911,0.00010332462,0.00004973035],"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":[0.000030220719,0.00015495192,0.0051836823,0.00018711788,0.00004662421,0.00007667746,0.0026411233,0.13049899,0.6050513,0.0031972283,0.00440892,0.24852319],"study_design_scores_gemma":[0.0009421594,0.0006476026,0.0048536696,0.00053981214,0.000025516782,0.00028005408,0.00060102815,0.5655472,0.2603125,0.005332505,0.15815254,0.0027654073],"about_ca_topic_score_codex":0.0000067822602,"about_ca_topic_score_gemma":0.0000022004212,"teacher_disagreement_score":0.5349872,"about_ca_system_score_codex":0.00006716746,"about_ca_system_score_gemma":0.000024082115,"threshold_uncertainty_score":0.6137029},"labels":[],"label_agreement":null},{"id":"W1979960625","doi":"10.1117/12.606410","title":"A wavelet integrated image fusion approach for target detection in very high resolution satellite imagery","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 New Brunswick","funders":"","keywords":"Panchromatic film; Multispectral image; Image fusion; Remote sensing; Artificial intelligence; Multispectral pattern recognition; Image resolution; Computer science; Computer vision; Wavelet; Wavelet transform; Satellite imagery; Geology; Image (mathematics)","score_opus":0.007405125410072436,"score_gpt":0.21524363268537627,"score_spread":0.20783850727530384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979960625","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97281796,0.0002107441,0.023485182,0.000390745,0.00019103943,0.00114154,0.00008337161,0.00044299307,0.0012364356],"genre_scores_gemma":[0.2606718,0.00031146914,0.73788285,0.000042573854,0.00036353682,0.0004889845,0.000047157344,0.00010998209,0.00008166135],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976648,3.1862697e-8,0.0008859801,0.0004421894,0.00048581624,0.0005211901],"domain_scores_gemma":[0.99827594,0.00012258155,0.0002493057,0.00007865608,0.0011734575,0.00010008551],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007150395,0.00041574682,0.00048673415,0.00027387828,0.000076497985,0.000096881755,0.00061884953,0.0002899101,0.000009408492],"category_scores_gemma":[0.0005237522,0.00038187564,0.00048243356,0.0005325188,0.00016397335,0.001121617,0.0001185422,0.00048370333,0.0000013081361],"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":[0.0001799311,0.00012480211,0.000025610652,0.0006511313,0.0001172515,8.620768e-8,0.00013483582,0.0019524498,0.97096944,0.018307826,0.0016419496,0.005894672],"study_design_scores_gemma":[0.0008498453,0.00014765892,0.0002552478,0.00018822016,0.00004267684,0.000009459585,0.0003935437,0.3747234,0.61846066,0.0009784892,0.0036040617,0.00034676175],"about_ca_topic_score_codex":0.000010606773,"about_ca_topic_score_gemma":4.3878853e-7,"teacher_disagreement_score":0.71439767,"about_ca_system_score_codex":0.0005313157,"about_ca_system_score_gemma":0.00002080911,"threshold_uncertainty_score":0.9998633},"labels":[],"label_agreement":null},{"id":"W1986688508","doi":"10.1117/12.931470","title":"A new reduced-reference metric for measuring spatial resolution enhanced images","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Space Agency","funders":"","keywords":"Artificial intelligence; Image resolution; Image quality; Computer vision; Sharpening; Metric (unit); Computer science; Image processing; Feature detection (computer vision); Image restoration; Binary image; Digital image processing; Image (mathematics); Sub-pixel resolution; Pattern recognition (psychology)","score_opus":0.01733919621039302,"score_gpt":0.24566457096054528,"score_spread":0.22832537475015227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986688508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9389152,0.00046742376,0.055331152,0.00031496995,0.00046417397,0.00096266717,0.00006124281,0.0004972676,0.0029859184],"genre_scores_gemma":[0.53367186,0.00016629318,0.46488172,0.0000210784,0.0006856883,0.0002823743,0.0000094722245,0.000094143405,0.00018736957],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976422,1.5637877e-8,0.00073269475,0.00033714814,0.00063988386,0.0006480102],"domain_scores_gemma":[0.9979726,0.00017357436,0.00028169094,0.0000808427,0.0012791822,0.00021212964],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061495067,0.00039685413,0.00046131294,0.0002142317,0.00009160133,0.000083707666,0.00081764895,0.00023091254,0.000017500663],"category_scores_gemma":[0.00091876206,0.0003648504,0.00050904,0.0004601707,0.00010442532,0.0010783173,0.00014797234,0.00035669626,0.000002159833],"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":[0.000071989394,0.000053255135,0.00003923774,0.0005038232,0.00020563122,1.544937e-8,0.00014635352,0.00037539206,0.93209904,0.055566277,0.005892465,0.005046488],"study_design_scores_gemma":[0.00072775694,0.0001635622,0.00045444455,0.00026011752,0.00011770793,0.000007395464,0.00027746556,0.016005196,0.9763525,0.00088417076,0.0043354095,0.00041427327],"about_ca_topic_score_codex":0.000016269209,"about_ca_topic_score_gemma":1.8342145e-7,"teacher_disagreement_score":0.40955058,"about_ca_system_score_codex":0.00031421788,"about_ca_system_score_gemma":0.000029901212,"threshold_uncertainty_score":0.9998804},"labels":[],"label_agreement":null},{"id":"W1987208657","doi":"10.1109/ccece.2014.6901097","title":"Camouflaged target detection using real-time video fusion algorithm based on multi-scale transforms","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer science; Thresholding; Artificial intelligence; Image fusion; Wavelet transform; Fusion; Computer vision; Algorithm; Pattern recognition (psychology); Image (mathematics); Object detection; Wavelet","score_opus":0.006719907205483841,"score_gpt":0.22568045655143334,"score_spread":0.2189605493459495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987208657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021206422,0.0000048854163,0.9714257,0.000014361252,0.00015543864,0.00033938044,0.000008732044,0.0025323145,0.0043127397],"genre_scores_gemma":[0.22178565,0.000013882413,0.7775398,0.0000749602,0.0001059684,0.000029989727,0.00001741213,0.00009571733,0.00033660344],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886686,0.00004401513,0.0002788236,0.00027395677,0.00021900101,0.00031733254],"domain_scores_gemma":[0.9994187,0.00006378996,0.00003237367,0.00032730366,0.000049962087,0.00010791404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024226675,0.00025413503,0.00023436327,0.00020352368,0.0001337443,0.000027093303,0.00011755485,0.00015289975,0.00036851692],"category_scores_gemma":[0.00002780181,0.00022781771,0.00009169086,0.00022463824,0.000027822338,0.00024085231,0.000014289243,0.00021498381,0.00007336128],"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":[0.000010548071,0.000029430952,0.000006031268,0.000020555459,0.000002839512,0.0000015569223,0.0000338481,0.026959086,0.84936166,7.5432877e-7,0.000078041296,0.12349565],"study_design_scores_gemma":[0.00029286675,0.00005512557,0.00008124278,0.000022710867,0.0000046821924,0.0000018883829,0.0000067775577,0.5364981,0.46214813,0.000040326227,0.0006814648,0.00016667187],"about_ca_topic_score_codex":0.00009555976,"about_ca_topic_score_gemma":0.000021641952,"teacher_disagreement_score":0.509539,"about_ca_system_score_codex":0.0001569355,"about_ca_system_score_gemma":0.000007783515,"threshold_uncertainty_score":0.92901367},"labels":[],"label_agreement":null},{"id":"W1988085614","doi":"10.1016/s0895-7177(00)00195-3","title":"Progressive transmission of images: PC-based computations, using orthogonal matrix polynomials","year":2000,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":20,"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":"Piecewise; Computation; Matrix (chemical analysis); Polynomial; Computer science; Image (mathematics); Algorithm; Set (abstract data type); Mathematics; Digital image; Base (topology); Computer vision; Image processing; Mathematical analysis","score_opus":0.015914947037065374,"score_gpt":0.2558667497094911,"score_spread":0.23995180267242572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988085614","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13618425,0.00040078134,0.8628726,0.000014785139,0.000011595678,0.00017407621,0.000005194082,0.00023085426,0.00010588033],"genre_scores_gemma":[0.31786865,0.000021158488,0.68202496,0.000012587592,0.000030205461,0.0000061678725,0.0000037830455,0.000021770375,0.000010734508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991437,0.000020701482,0.000371161,0.00015436838,0.00014337005,0.00016668475],"domain_scores_gemma":[0.99964714,0.00009261236,0.000037112244,0.00011325873,0.00003621132,0.00007368346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081562204,0.00015619682,0.00027160105,0.00006934226,0.000057891688,0.00003015629,0.0000834248,0.00005828361,0.00017486012],"category_scores_gemma":[0.0000010790687,0.00013662002,0.00006184034,0.00009281813,0.00005985406,0.00010881908,0.00001408076,0.00010094531,0.000004552422],"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":[0.000010094758,0.00004844964,0.0000017152034,0.00032851906,0.0000100114175,0.0000069515763,0.0001230611,0.9493368,0.0015315425,0.00049349,0.000018968194,0.04809039],"study_design_scores_gemma":[0.00019441103,0.00002875666,0.0000014317764,0.00036180948,0.000016837093,0.000016407925,0.0000042920883,0.9754372,0.014054885,0.009688741,0.000048693677,0.00014648546],"about_ca_topic_score_codex":8.560963e-7,"about_ca_topic_score_gemma":7.926136e-9,"teacher_disagreement_score":0.18168442,"about_ca_system_score_codex":0.000012462708,"about_ca_system_score_gemma":0.000010873346,"threshold_uncertainty_score":0.55712026},"labels":[],"label_agreement":null},{"id":"W1990299000","doi":"10.1109/tbme.2013.2243448","title":"Spine Image Fusion Via Graph Cuts","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Robarts Clinical Trials; St. Joseph’s Healthcare Hamilton; Lawson Health Research Institute; Western University","funders":"","keywords":"Image fusion; Pixel; Artificial intelligence; Wavelet; Graph; Mean squared error; Fusion; Computer science; Cut; Image (mathematics); Transparency (behavior); Pattern recognition (psychology); Algorithm; Computer vision; Mathematics; Image segmentation; Theoretical computer science; Statistics","score_opus":0.003660300798723203,"score_gpt":0.2009637957329538,"score_spread":0.1973034949342306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990299000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006223029,0.00006634642,0.9891059,0.00013496923,0.00095142156,0.00030412857,0.000011558803,0.0028974689,0.0003051957],"genre_scores_gemma":[0.891491,0.0003683712,0.10726981,0.00009265375,0.00013737583,0.00028267014,0.000010762526,0.000142112,0.00020525258],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998641,0.00000817753,0.0003412471,0.0002521401,0.0003199174,0.00043748773],"domain_scores_gemma":[0.9992689,0.000041176176,0.00001906268,0.00032389016,0.000046251087,0.00030071908],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006748159,0.00029945924,0.00023651333,0.00045394464,0.00007362051,0.000035785062,0.00020489946,0.00017759182,0.0008862311],"category_scores_gemma":[0.000008037167,0.00029314676,0.00012514499,0.0005897013,0.000072573726,0.00035285697,0.0000024974481,0.0005100559,0.00036066648],"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":[0.0000019634458,0.000068537,1.6316208e-7,0.000066075336,0.000025912263,0.000012610464,0.000032154912,0.022203645,0.860413,0.0000060210673,0.0013280405,0.11584184],"study_design_scores_gemma":[0.0004494389,0.00013470094,0.00007741004,0.00013453902,0.000019923471,0.000044645727,0.000012302504,0.47864825,0.51094764,0.00016392935,0.008804452,0.00056278304],"about_ca_topic_score_codex":0.00001696233,"about_ca_topic_score_gemma":9.529268e-7,"teacher_disagreement_score":0.885268,"about_ca_system_score_codex":0.00009360168,"about_ca_system_score_gemma":0.000007485967,"threshold_uncertainty_score":0.9999521},"labels":[],"label_agreement":null},{"id":"W1991539441","doi":"10.1109/igarss.2015.7326758","title":"Road damage detection from VHR remote sensing images based on multiscale texture analysis and dempster shafer theory","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Dempster–Shafer theory; Computer science; Computer vision; Artificial intelligence; Texture (cosmology); Remote sensing; Image texture; Pattern recognition (psychology); Image segmentation; Image (mathematics); Geology","score_opus":0.0075606609748243405,"score_gpt":0.2306090578529239,"score_spread":0.22304839687809958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991539441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08190172,0.000089306865,0.9131587,0.00004447085,0.00005950754,0.00011770507,0.000014447451,0.00086938345,0.0037447237],"genre_scores_gemma":[0.8370224,0.000008716411,0.16222471,0.00017816713,0.000038955197,7.6199746e-7,0.000015797425,0.000037039405,0.00047339915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991567,0.000075843505,0.00017073819,0.00026797067,0.00015661134,0.00017212993],"domain_scores_gemma":[0.9993369,0.00010982126,0.000029616995,0.00037447736,0.000058165864,0.00009102347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019543187,0.00019500301,0.00022006189,0.00025297463,0.00004807554,0.000053785792,0.00006705174,0.00011496465,0.000100138495],"category_scores_gemma":[0.00010106056,0.00016768152,0.000068187896,0.00030404096,0.000038936392,0.00019370609,0.00003874959,0.000203235,0.000018373334],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005735329,0.000008975733,0.00015576037,0.000009535731,0.000097914424,0.000021644215,0.00014373251,0.018505175,0.11640214,0.0000012417779,0.0004692002,0.86412734],"study_design_scores_gemma":[0.0002469087,0.000020139416,0.0036544667,0.000017202829,0.00009889083,0.0000011770844,0.00006928525,0.7753291,0.21937801,0.0006606263,0.0003343915,0.00018977617],"about_ca_topic_score_codex":0.00021982689,"about_ca_topic_score_gemma":0.00017789223,"teacher_disagreement_score":0.86393756,"about_ca_system_score_codex":0.00006129663,"about_ca_system_score_gemma":0.0000040803334,"threshold_uncertainty_score":0.6837854},"labels":[],"label_agreement":null},{"id":"W1999316653","doi":"10.1109/tmm.2013.2244870","title":"Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":472,"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":"Contourlet; Computer science; Image fusion; Artificial intelligence; Modalities; Contrast (vision); Medical imaging; Phase congruency; Fusion rules; Computer vision; Image (mathematics); Pattern recognition (psychology); Fuse (electrical); Modality (human–computer interaction); Wavelet transform; Engineering","score_opus":0.004383614316694057,"score_gpt":0.22823449048934416,"score_spread":0.2238508761726501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999316653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060588177,0.000030416068,0.93529487,0.00019203233,0.0005633104,0.00080547284,0.000045224697,0.0011641766,0.0013163117],"genre_scores_gemma":[0.90132457,0.00006235711,0.09773217,0.00013963589,0.00005130294,0.0005456618,0.000008510099,0.000078314464,0.000057507907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998294,0.00008424276,0.00040268034,0.00033472912,0.00044472187,0.0004396206],"domain_scores_gemma":[0.99887586,0.00043148908,0.00003319562,0.00031181175,0.00007915233,0.0002684699],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018139045,0.00030955358,0.00031633192,0.00036984798,0.00008068348,0.000029531599,0.0002219806,0.00025740615,0.003499431],"category_scores_gemma":[0.00003560486,0.00030287766,0.00011687793,0.00033920808,0.0001474398,0.00037423012,0.0000017548412,0.00074224797,0.00047836572],"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":[0.00008645288,0.0006168602,0.000055946584,0.000057184,0.000038161463,0.000093508876,0.0006914373,0.01758374,0.46110332,0.0000023851646,0.001135591,0.51853544],"study_design_scores_gemma":[0.0019389918,0.00006424535,0.0014496094,0.00012412031,0.00000836297,0.0000061801707,0.000099370416,0.6025287,0.39310056,0.00008241939,0.0002199431,0.0003775001],"about_ca_topic_score_codex":0.00024654032,"about_ca_topic_score_gemma":0.00023047277,"teacher_disagreement_score":0.8407364,"about_ca_system_score_codex":0.000220354,"about_ca_system_score_gemma":0.000042470874,"threshold_uncertainty_score":0.99994236},"labels":[],"label_agreement":null},{"id":"W2006322106","doi":"10.1155/2013/104684","title":"Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images","year":2013,"lang":"en","type":"article","venue":"Journal of Medical Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Université du Québec à Montréal","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Gabor filter; Gabor transform; Discrete wavelet transform; Feature extraction; Gabor wavelet; Computer science; Filter bank; Support vector machine; Filter (signal processing); Computer vision; Feature vector; Entropy (arrow of time); Image processing; Wavelet transform; Wavelet; Image (mathematics); Time–frequency analysis; Physics","score_opus":0.00404006233598976,"score_gpt":0.24338211269208673,"score_spread":0.23934205035609699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006322106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032302175,0.0022667395,0.9632301,0.001408698,0.0003718069,0.00018091252,0.000020605934,0.0001815503,0.000037458616],"genre_scores_gemma":[0.77940345,0.001146472,0.21808025,0.00010137807,0.0010905571,0.000045368277,0.000015186313,0.00008535168,0.00003200823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985764,0.0000069352172,0.0004755469,0.00011890123,0.000557146,0.0002650764],"domain_scores_gemma":[0.9992418,0.0001909477,0.00007389067,0.000080188816,0.00008342152,0.0003296954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027151965,0.0001887253,0.00030847758,0.00016566587,0.000035931622,0.00007594126,0.00018891801,0.00015013717,0.00020617688],"category_scores_gemma":[0.00034145132,0.0001478575,0.00008770844,0.000071300434,0.00004901641,0.0006373464,0.000020466783,0.0006294388,0.0000013790541],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021821132,0.000028731878,0.000005196006,0.00036583762,0.000069688576,0.000073836316,0.00016651154,0.00059680524,0.1345977,0.000013783358,0.013949412,0.85011065],"study_design_scores_gemma":[0.0023324415,0.0002923257,0.0025690342,0.0019010942,0.000106059306,0.0011341951,0.00013335972,0.6651452,0.2755901,0.0013307147,0.04875344,0.0007120493],"about_ca_topic_score_codex":0.0000065588115,"about_ca_topic_score_gemma":3.1067898e-7,"teacher_disagreement_score":0.8493986,"about_ca_system_score_codex":0.00005119502,"about_ca_system_score_gemma":0.000028436552,"threshold_uncertainty_score":0.6029454},"labels":[],"label_agreement":null},{"id":"W2008980862","doi":"10.1080/19479832.2010.525535","title":"Data fusion using aerial photographs and satellite images for detailed landslide assessment","year":2011,"lang":"en","type":"article","venue":"International Journal of Image and Data Fusion","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"","keywords":"Landslide; Orthophoto; Stereoscopy; Aerial photography; Photogrammetry; Remote sensing; Digital elevation model; Geology; Aerial photos; Aerial image; Computer vision; Computer science; Cartography; Artificial intelligence; Geography; Image (mathematics); Geomorphology","score_opus":0.06129989547750762,"score_gpt":0.3497254874513661,"score_spread":0.2884255919738585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008980862","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10717859,0.0014293266,0.88741434,0.00006255932,0.0008933444,0.0002588481,0.002203224,0.0000760566,0.00048371986],"genre_scores_gemma":[0.15137884,0.011262396,0.8359808,0.00007069412,0.0004509259,0.0000028402978,0.00080531434,0.00003881768,0.000009414287],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99888915,0.000024587787,0.0004432455,0.00023195716,0.00027215076,0.00013888763],"domain_scores_gemma":[0.9989502,0.00007991404,0.00020182868,0.00042442797,0.0002572335,0.0000864293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005135253,0.00015227309,0.00020700741,0.00022292492,0.000064158245,0.00012329237,0.0007945381,0.00005291976,0.0000819709],"category_scores_gemma":[0.0001204032,0.00012849792,0.00003251022,0.00005620189,0.00006003354,0.0017471053,0.00087484746,0.00015674571,5.872893e-7],"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":[0.00035657105,0.000084992425,0.001850155,0.000056363184,0.00013750281,0.00012798196,0.00015589912,0.000004400997,0.7603243,0.00003075873,0.0053122877,0.23155881],"study_design_scores_gemma":[0.013642187,0.0011021362,0.027119024,0.0022024557,0.0008438025,0.00380797,0.0014460543,0.20220126,0.46426436,0.01103638,0.270096,0.0022383633],"about_ca_topic_score_codex":0.00002046541,"about_ca_topic_score_gemma":0.000012696959,"teacher_disagreement_score":0.2960599,"about_ca_system_score_codex":0.00002573895,"about_ca_system_score_gemma":0.000028331147,"threshold_uncertainty_score":0.52399933},"labels":[],"label_agreement":null},{"id":"W2009605648","doi":"10.1117/12.527262","title":"Global semantic classification of scenes using ridgelet transform","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Curvelet; Artificial intelligence; Sparse approximation; Pattern recognition (psychology); Focus (optics); Representation (politics); Dimension (graph theory); Fourier transform; Computer vision; Filter (signal processing); Shearlet; Image (mathematics); Wavelet transform; Mathematics; Wavelet","score_opus":0.01390903779769868,"score_gpt":0.24943150320309124,"score_spread":0.23552246540539257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009605648","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911926,0.00014879594,0.0054123406,0.00050627615,0.00019162903,0.00056770956,0.00007122242,0.00027484397,0.0016346065],"genre_scores_gemma":[0.7596657,0.0001525948,0.23987089,0.000019489908,0.0001506621,0.000060795715,0.000006916235,0.0000601425,0.000012792627],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979021,8.475965e-9,0.0008208128,0.00029601456,0.00060919527,0.00037184637],"domain_scores_gemma":[0.9983412,0.000046294143,0.00027503222,0.00007191846,0.0011669614,0.00009860343],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003277809,0.0003345715,0.00045344562,0.000111712296,0.000058760124,0.000049578724,0.0007471478,0.0002067424,0.0000066770504],"category_scores_gemma":[0.0002043093,0.00030671296,0.0005230404,0.00050775683,0.00023847987,0.00066299347,0.00007403362,0.00022225195,7.1049595e-7],"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":[0.00003615987,0.00007982007,0.00022572545,0.0007408412,0.00020230141,7.4065326e-8,0.00010429028,0.0036943331,0.7809572,0.21289909,0.00030818247,0.0007519466],"study_design_scores_gemma":[0.0012958834,0.00023332307,0.0018415201,0.0007988192,0.00019766502,0.00003450272,0.00084659975,0.10781468,0.8763253,0.009292687,0.00079612277,0.00052290433],"about_ca_topic_score_codex":0.000015223121,"about_ca_topic_score_gemma":4.2223988e-7,"teacher_disagreement_score":0.23445855,"about_ca_system_score_codex":0.00038930506,"about_ca_system_score_gemma":0.00003594106,"threshold_uncertainty_score":0.9999385},"labels":[],"label_agreement":null},{"id":"W2009823620","doi":"10.1109/icip.2010.5653824","title":"Joint color decrosstalk and demosaicking for CFA cameras","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Demosaicing; Artificial intelligence; Deconvolution; Computer science; Computer vision; Crosstalk; Color filter array; Interpolation (computer graphics); Joint (building); A priori and a posteriori; Color image; Color gel; Algorithm; Image processing; Image (mathematics); Engineering; Electronic engineering; Layer (electronics)","score_opus":0.006755676805767536,"score_gpt":0.22606342015180247,"score_spread":0.21930774334603495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009823620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42452055,0.00007147998,0.5706262,0.00008088955,0.00020800818,0.00033492837,0.000008413408,0.0011107089,0.0030388546],"genre_scores_gemma":[0.6190862,0.000027950855,0.3803193,0.00010307964,0.00004335744,0.000063080544,0.0000027380065,0.00003062604,0.00032369184],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957794,0.0000015968151,0.000112668036,0.00010649243,0.00004167573,0.00015960103],"domain_scores_gemma":[0.9997317,0.000046816924,0.0000124092385,0.00012237526,0.000031385563,0.000055269094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006568231,0.00008947504,0.00010062139,0.000027719672,0.000057253506,0.000031542386,0.000047226764,0.00005681905,0.0000817618],"category_scores_gemma":[0.00007180507,0.00008209546,0.000022858123,0.000039524322,0.000037396967,0.00011500377,0.000030322526,0.000114941664,0.000004514631],"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":[0.0000024753115,0.000004670108,0.00011479296,0.000027669152,0.000005175085,0.0000013756146,0.00004779275,0.00003403201,0.9551772,0.0013476735,0.0038728118,0.039364353],"study_design_scores_gemma":[0.0002170394,0.00003148815,0.00046245253,0.000008370455,0.000004783117,0.000017308,0.000035872665,0.02430963,0.8982477,0.0024341645,0.074047156,0.00018404196],"about_ca_topic_score_codex":0.0000070600595,"about_ca_topic_score_gemma":0.00006171691,"teacher_disagreement_score":0.19456564,"about_ca_system_score_codex":0.000010990682,"about_ca_system_score_gemma":0.000005031861,"threshold_uncertainty_score":0.33477557},"labels":[],"label_agreement":null},{"id":"W2018576645","doi":"10.1002/j.2168-0159.2014.tb00093.x","title":"26.3: Edge Adaptive Method of Image Resampling and Enhancement","year":2014,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Qualcomm (Canada)","funders":"","keywords":"Resampling; Image scaling; Enhanced Data Rates for GSM Evolution; Interpolation (computer graphics); Edge enhancement; Artificial intelligence; Pixel; Computer vision; Image (mathematics); Computer science; Image gradient; Edge detection; Image enhancement; Image processing","score_opus":0.0074140908720201695,"score_gpt":0.2564535905021864,"score_spread":0.24903949963016625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018576645","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010655421,0.0005145038,0.33560443,0.00031484402,0.00021454584,0.00091545365,0.000030088771,0.0019186131,0.64983207],"genre_scores_gemma":[0.8467185,0.00030975053,0.15276547,0.00002756456,0.000028806955,0.000040531333,0.000002408424,0.000055932825,0.000051046332],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868226,0.00004300182,0.00050480565,0.0002807502,0.00023082299,0.00025835758],"domain_scores_gemma":[0.99897736,0.00030881297,0.00012183265,0.0004214373,0.000076946024,0.000093602124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036908322,0.00022061623,0.00044682727,0.00011237208,0.00003630376,0.000008485443,0.00023148058,0.00013242575,0.000029930572],"category_scores_gemma":[0.00015317096,0.00021277547,0.00009533892,0.00017471913,0.0002101073,0.00012661467,0.00015074846,0.00022717706,0.000003069733],"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":[0.000023011076,0.000032537126,0.000028245508,0.000109961635,0.000015665904,7.7094603e-7,0.00004299854,0.00032053905,0.99546427,0.0014332645,0.0001029937,0.002425765],"study_design_scores_gemma":[0.00023879393,0.00028962002,0.0005211763,0.00016299433,0.000027826067,0.000006132448,0.000034841163,0.00029872413,0.9947341,0.0004928953,0.0029551757,0.00023770671],"about_ca_topic_score_codex":0.000012581202,"about_ca_topic_score_gemma":0.000004960489,"teacher_disagreement_score":0.8360631,"about_ca_system_score_codex":0.00006377695,"about_ca_system_score_gemma":0.0000114672075,"threshold_uncertainty_score":0.8676732},"labels":[],"label_agreement":null},{"id":"W2019051317","doi":"10.1117/12.474886","title":"Electro-optical and SAR image fusion for improvements on target feature estimation","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Defence Research and Development Canada; AUG Signals (Canada)","funders":"","keywords":"Subpixel rendering; Computer science; Artificial intelligence; Image fusion; Computer vision; Pixel; Pyramid (geometry); Pattern recognition (psychology); Discrete wavelet transform; Image resolution; Wavelet; Principal component analysis; Fusion; Wavelet transform; Image (mathematics); Mathematics","score_opus":0.005592532098977658,"score_gpt":0.2275607111131913,"score_spread":0.22196817901421362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019051317","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98837066,0.000104237464,0.0068092267,0.0007495794,0.0001855633,0.0010852272,0.00005681543,0.0002840468,0.002354656],"genre_scores_gemma":[0.13009866,0.00014534305,0.8689406,0.00010864179,0.00016668478,0.0002516587,0.000018856677,0.0001138798,0.00015566073],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982129,1.4889417e-8,0.0004942861,0.0003744152,0.00047836363,0.00044007497],"domain_scores_gemma":[0.99864966,0.00014429465,0.00017608172,0.00006358991,0.0008434374,0.00012291574],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044049066,0.00037063006,0.00037968994,0.000117907664,0.00009543012,0.00009958689,0.00040910064,0.00022999338,0.000009073661],"category_scores_gemma":[0.00081888674,0.00032424097,0.00033672014,0.0002183209,0.00013476155,0.0005642643,0.000069014655,0.00035979485,9.34513e-7],"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":[0.000064107844,0.00006336838,0.000022663666,0.0004942672,0.00013084532,5.506237e-8,0.000052154748,0.00020730299,0.798325,0.1932736,0.0060061174,0.0013605409],"study_design_scores_gemma":[0.0010153541,0.0005039998,0.00012644853,0.0002088781,0.000077109224,0.000010468768,0.00021877157,0.123172335,0.8615594,0.0072699264,0.0054622367,0.0003750547],"about_ca_topic_score_codex":7.852459e-7,"about_ca_topic_score_gemma":2.0290262e-8,"teacher_disagreement_score":0.86213136,"about_ca_system_score_codex":0.00019618397,"about_ca_system_score_gemma":0.000015896267,"threshold_uncertainty_score":0.99992096},"labels":[],"label_agreement":null},{"id":"W2021188143","doi":"10.1016/j.ins.2014.03.015","title":"Performance analysis of statistical optimal data fusion algorithms","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":46,"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":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Covariance intersection; Fusion; Sensor fusion; Algorithm; Robustness (evolution); Computer science; Intersection (aeronautics); Pattern recognition (psychology); Artificial intelligence; Covariance matrix; Estimation of covariance matrices","score_opus":0.02251820317720614,"score_gpt":0.3001401519316516,"score_spread":0.27762194875444546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021188143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062391806,0.000006207257,0.93082213,0.0000103248185,0.00006565875,0.000046750793,0.00008465741,0.00015110683,0.0064213686],"genre_scores_gemma":[0.7977741,0.000040674044,0.2020144,0.000027240749,0.000008840059,0.0000027351552,0.00012596001,0.000001775988,0.0000043053265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991922,0.000009256323,0.0002947771,0.0000743441,0.0003158031,0.0001136203],"domain_scores_gemma":[0.9995005,0.000074785494,0.00006597938,0.00026762128,0.00005945384,0.000031623436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056280725,0.00006042509,0.00012346682,0.00033735312,0.00007083542,0.000040172883,0.00042844328,0.000023628647,0.0001125905],"category_scores_gemma":[0.00012141366,0.000050252213,0.000014694999,0.00090772245,0.00013691059,0.0023390963,0.00010669434,0.000046515757,0.000022897919],"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":[0.000002910832,0.00000675539,0.0012239581,0.000042155105,0.000029724324,6.8148054e-8,0.0003827387,0.2875883,0.00071391294,0.00079257623,0.0016009797,0.7076159],"study_design_scores_gemma":[0.000035267494,0.000030157045,0.005914444,0.0000057965676,0.000022372371,5.6054535e-7,0.00006336269,0.98562574,0.0026391554,0.000024494118,0.0055735093,0.00006512321],"about_ca_topic_score_codex":0.000008222051,"about_ca_topic_score_gemma":0.0000016338925,"teacher_disagreement_score":0.73538226,"about_ca_system_score_codex":0.000010529503,"about_ca_system_score_gemma":0.00001155747,"threshold_uncertainty_score":0.20492259},"labels":[],"label_agreement":null},{"id":"W2024270358","doi":"10.1109/qomex.2014.6982278","title":"Perceptual evaluation of multi-exposure image fusion algorithms","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":41,"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":"Image fusion; Computer science; Image quality; Artificial intelligence; Fusion; Image (mathematics); Perception; Computer vision; Quality (philosophy); Pattern recognition (psychology); Algorithm","score_opus":0.02086048952732526,"score_gpt":0.2873014324490734,"score_spread":0.26644094292174814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024270358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10202733,0.00007762337,0.88537717,0.000011013147,0.00011701081,0.0002345668,0.0000035648095,0.0006783489,0.011473378],"genre_scores_gemma":[0.7000913,0.000024944171,0.29963383,0.000014326742,0.000037619728,0.000019284307,0.000009278141,0.000023548868,0.000145835],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920875,0.000046171783,0.00019735795,0.00011890791,0.0003090326,0.000119809774],"domain_scores_gemma":[0.9994637,0.000027343296,0.000026264092,0.00023354379,0.0002136417,0.00003550775],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044534905,0.00010384347,0.00012387859,0.00007410997,0.000023012048,0.00000723644,0.000096663796,0.00006173959,0.0009828123],"category_scores_gemma":[0.00012271579,0.00009408122,0.000038881673,0.00009262793,0.00003288177,0.00016781491,0.00003416557,0.00008275528,0.00004633259],"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":[0.0000015307858,0.000026948,0.00006258855,0.000021062222,0.0000045899183,1.64329e-7,0.00019454154,0.0021442163,0.59903765,0.000041839598,0.0010150854,0.39744976],"study_design_scores_gemma":[0.00041027454,0.000051206865,0.0020184603,0.000014490201,0.000014161911,0.0000015270772,0.00012272544,0.7366154,0.25978512,0.00022806111,0.00061614445,0.0001224072],"about_ca_topic_score_codex":0.000008517602,"about_ca_topic_score_gemma":0.000006041328,"teacher_disagreement_score":0.7344712,"about_ca_system_score_codex":0.000040298306,"about_ca_system_score_gemma":0.0000066543184,"threshold_uncertainty_score":0.99993044},"labels":[],"label_agreement":null},{"id":"W2029148643","doi":"10.1007/bf02826650","title":"Production of high-resolution remote sensing images for navigation information infrastructures","year":2004,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Université de Sherbrooke","funders":"","keywords":"Panchromatic film; Image fusion; Image resolution; Computer vision; Pixel; Computer science; Artificial intelligence; Remote sensing; Sub-pixel resolution; Resolution (logic); Wavelet; Image (mathematics); Image processing; Digital image processing; Geography","score_opus":0.005456112715366274,"score_gpt":0.23806063554388215,"score_spread":0.23260452282851588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029148643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07569068,0.000008251016,0.92208886,0.00009379277,0.0007522995,0.0005872112,0.000032575237,0.00040726733,0.00033905878],"genre_scores_gemma":[0.789384,0.000016267391,0.21037321,0.000040986273,0.00004205467,0.0000049369983,0.00013097381,0.0000057556636,0.0000018311354],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856347,0.0000072580683,0.0006023403,0.00010265179,0.0004770572,0.00024723384],"domain_scores_gemma":[0.9985792,0.000018658187,0.00028866375,0.00024362082,0.00082518626,0.00004464763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047151983,0.0001410117,0.00013799546,0.00048967486,0.00024298482,0.00011490989,0.00017494652,0.00007302804,0.000004658872],"category_scores_gemma":[0.0005373418,0.0001432124,0.000035640205,0.0007304043,0.00021808897,0.010651134,0.0000456629,0.0001132422,0.000014282865],"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":[0.000020794376,0.0000020119821,0.000001842485,0.00013041495,0.0000026074222,4.0838955e-8,0.0011201471,0.36362723,0.06685885,0.0005541254,0.00020584217,0.5674761],"study_design_scores_gemma":[0.00037803873,0.0000654254,0.0021537347,0.0001094653,0.000007119639,0.000014842785,0.00017396545,0.18347016,0.80005884,0.011519181,0.0018431569,0.00020607821],"about_ca_topic_score_codex":0.0004782859,"about_ca_topic_score_gemma":0.000007840733,"teacher_disagreement_score":0.73319995,"about_ca_system_score_codex":0.00025900608,"about_ca_system_score_gemma":0.000098529614,"threshold_uncertainty_score":0.7721815},"labels":[],"label_agreement":null},{"id":"W2032230990","doi":"10.1109/icmlc.2013.6890419","title":"Proposal of a multi-frame images fusion model on dual tree complex wavelet transform domain","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1,"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":"Japan Society for the Promotion of Science; Fondation Brain Canada","keywords":"Complex wavelet transform; Artificial intelligence; Image fusion; Computer science; Frame (networking); Wavelet transform; Pattern recognition (psychology); Fusion; Computer vision; Wavelet; Image (mathematics); Discrete wavelet transform","score_opus":0.015599272860560325,"score_gpt":0.24828627742215692,"score_spread":0.2326870045615966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032230990","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040039573,0.0000176696,0.93237233,0.00029767226,0.000031677624,0.00086029083,0.00003646694,0.0010962844,0.02524805],"genre_scores_gemma":[0.40150723,0.000033944798,0.59747356,0.00005970991,0.000014546576,0.00007420748,0.000013529925,0.000044550477,0.000778722],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899024,0.000012559041,0.00032568464,0.00018569114,0.0002128168,0.00027301858],"domain_scores_gemma":[0.9994975,0.000029610936,0.00003091423,0.00029259553,0.000071473776,0.00007795831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006698525,0.00021905804,0.0002567069,0.00012394195,0.0000369442,0.000018160112,0.00015601344,0.00009501739,0.0008151574],"category_scores_gemma":[0.000012516055,0.00017722348,0.000080046084,0.00010832455,0.00007355008,0.00026133418,0.000035337867,0.00018328193,0.00006228162],"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":[0.000014018482,0.000107060165,0.0000024888127,0.00007899407,0.000010032273,0.0000026821583,0.0002405993,0.0034504854,0.9129463,0.0008957563,0.0055960193,0.07665557],"study_design_scores_gemma":[0.00056682655,0.00011944848,0.00023921607,0.00003719153,0.0000042901033,0.0000045882243,0.000091156166,0.5791563,0.41300946,0.006248927,0.00028602747,0.00023659757],"about_ca_topic_score_codex":0.000039847848,"about_ca_topic_score_gemma":0.000022045888,"teacher_disagreement_score":0.57570577,"about_ca_system_score_codex":0.00004831838,"about_ca_system_score_gemma":0.000012865751,"threshold_uncertainty_score":0.89254016},"labels":[],"label_agreement":null},{"id":"W2035115895","doi":"10.1109/tip.2006.888340","title":"Novel Cooperative Neural Fusion Algorithms for Image Restoration and Image Fusion","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of Waterloo","funders":"Chinese University of Hong Kong; University of Hong Kong","keywords":"Image restoration; Image fusion; Algorithm; Computer science; Artificial intelligence; Regularization (linguistics); Fusion; Artificial neural network; Gaussian noise; Noise (video); Image (mathematics); Image processing; Pattern recognition (psychology); Computer vision","score_opus":0.017522397463494425,"score_gpt":0.29157238798670265,"score_spread":0.2740499905232082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035115895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011167689,0.0002004171,0.9859278,0.00009431546,0.00032209503,0.00073602394,0.000068193054,0.001090908,0.0003925332],"genre_scores_gemma":[0.4312886,0.00012966554,0.567923,0.00009598868,0.000120825775,0.000125477,0.000018592405,0.00012267113,0.00017520337],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99839586,0.000017912564,0.00045680674,0.00043764402,0.00024898467,0.00044279182],"domain_scores_gemma":[0.99904615,0.0001230177,0.000089998946,0.00022241069,0.00037943423,0.0001389954],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033498404,0.0003597977,0.00026752445,0.00027781914,0.0006117398,0.00023120001,0.00013043015,0.00014869626,0.000030099889],"category_scores_gemma":[0.000030255718,0.0003665219,0.00007795687,0.00041691834,0.00017140477,0.0018175112,0.000004052475,0.0004200256,0.0000079539295],"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":[0.00007453203,0.0000666904,2.6420545e-7,0.00013323606,0.000005970428,0.000007290025,0.00032654975,0.00086304307,0.77392244,0.0000015925176,0.000116569376,0.22448184],"study_design_scores_gemma":[0.0007314511,0.0001346995,0.00003062324,0.000121024656,0.000030353565,0.0000494334,0.00022756384,0.27860507,0.7193905,0.00006691749,0.00025782693,0.00035453567],"about_ca_topic_score_codex":0.000008622492,"about_ca_topic_score_gemma":0.000021876624,"teacher_disagreement_score":0.4201209,"about_ca_system_score_codex":0.0001539905,"about_ca_system_score_gemma":0.00003240839,"threshold_uncertainty_score":0.99987864},"labels":[],"label_agreement":null},{"id":"W2035518358","doi":"10.1080/0143116031000102421","title":"Evaluation of the runoff potential in high relief semi-arid regions using remote sensing data: application to Bolivia","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Natural Resources Canada","funders":"","keywords":"Arid; Remote sensing; Surface runoff; Watershed; Environmental science; Hydrology (agriculture); Land cover; Vegetation (pathology); Scale (ratio); Land use; Radar; Cartography; Geography; Computer science; Geology; Civil engineering","score_opus":0.02381917302609505,"score_gpt":0.31004301352961816,"score_spread":0.2862238405035231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035518358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2923385,0.00008646372,0.70578957,0.00063713244,0.0008482531,0.00018238672,0.0000057440056,0.00003458286,0.000077392186],"genre_scores_gemma":[0.67794293,0.000059006892,0.32158324,0.000069714995,0.0003086807,9.365153e-9,0.0000056009585,0.000028299417,0.000002537625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974794,0.000102243066,0.0007641474,0.00019685666,0.0012787434,0.00017865737],"domain_scores_gemma":[0.99764377,0.0000463489,0.00043818864,0.00047578503,0.0013366701,0.00005922585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012548963,0.00016109264,0.00023465951,0.0004276239,0.000057024998,0.000046186517,0.00049087603,0.000087642155,0.000002499977],"category_scores_gemma":[0.00053326867,0.00014930335,0.000087014414,0.0003318772,0.00004514487,0.0004189505,0.0001973572,0.00037593747,0.000002421169],"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":[0.000018493889,0.0000065131453,0.0000018647255,0.000005020954,0.0000380398,0.000020386517,0.00013078016,0.40376806,0.29563972,0.000012654808,0.000055168024,0.3003033],"study_design_scores_gemma":[0.0006958902,0.000015580325,0.00041377067,0.0008029039,0.000090525726,0.00080464844,0.00007725432,0.8895401,0.0919961,0.015050497,0.00035656898,0.00015616757],"about_ca_topic_score_codex":0.0006306496,"about_ca_topic_score_gemma":0.00020116243,"teacher_disagreement_score":0.48577204,"about_ca_system_score_codex":0.00094381784,"about_ca_system_score_gemma":0.00018050503,"threshold_uncertainty_score":0.60884136},"labels":[],"label_agreement":null},{"id":"W2036825992","doi":"10.1109/tgrs.2014.2311815","title":"High-Fidelity Component Substitution Pansharpening by the Fitting of Substitution Data","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":80,"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 New Brunswick","funders":"","keywords":"Panchromatic film; Substitution (logic); Multispectral image; Component (thermodynamics); Convolution (computer science); Computer science; Image fusion; Distortion (music); Algorithm; Independent component analysis; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Physics; Artificial neural network","score_opus":0.019797024200286017,"score_gpt":0.24752906155791307,"score_spread":0.22773203735762707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036825992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19594193,0.00005845703,0.80327517,0.000053194228,0.00032192745,0.000110159956,0.000021354314,0.00018132961,0.00003648841],"genre_scores_gemma":[0.91160154,0.00015694488,0.08814775,0.00003861721,0.00002277879,1.2626991e-7,0.0000081232865,0.0000098169485,0.000014268094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990008,0.00004972725,0.00026358623,0.00027086365,0.00021472936,0.00020029908],"domain_scores_gemma":[0.9992457,0.00012621036,0.000063220774,0.00046761418,0.00004942155,0.000047846006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005180692,0.00013116708,0.00014832059,0.00007657611,0.0004324217,0.00003956484,0.00019484633,0.00005615685,0.0000028627255],"category_scores_gemma":[0.000027396847,0.000103949504,0.000026750806,0.0002569244,0.000290539,0.0003616378,0.0000054557545,0.0002352805,0.000002292342],"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":[0.000006817803,0.000009681184,7.1110617e-7,0.000030839412,0.000005851513,8.3757374e-7,0.00008441332,0.01377068,0.29342723,0.000036228153,0.000030156893,0.69259655],"study_design_scores_gemma":[0.00009863359,0.00001777459,0.00008444495,0.00011737205,0.000015369473,0.000021355994,0.000033046683,0.72419333,0.27468956,0.00015014064,0.0004696415,0.00010933841],"about_ca_topic_score_codex":0.00034475198,"about_ca_topic_score_gemma":0.00002868911,"teacher_disagreement_score":0.7156597,"about_ca_system_score_codex":0.000037353802,"about_ca_system_score_gemma":0.00001167024,"threshold_uncertainty_score":0.42389378},"labels":[],"label_agreement":null},{"id":"W2037346635","doi":"10.1016/j.optcom.2004.04.039","title":"Recognition invariant under unknown affine transformations of intensity","year":2004,"lang":"en","type":"article","venue":"Optics Communications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":8,"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 Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Invariant (physics); Affine transformation; Multiplicative function; Orthonormal basis; Intensity (physics); Computer science; Nonlinear system; Filter (signal processing); Artificial intelligence; Pattern recognition (psychology); Computer vision; Mathematics; Optics; Algorithm; Physics; Mathematical analysis; Geometry","score_opus":0.034997478062195594,"score_gpt":0.26102436760353154,"score_spread":0.22602688954133593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037346635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066831373,0.0001904366,0.9158545,0.0012868933,0.000050417544,0.0002469212,0.000050323863,0.00052202115,0.07511536],"genre_scores_gemma":[0.65417725,0.0015741586,0.34401846,0.000043876295,0.0000057595103,0.00002744015,0.00011766344,0.000015315529,0.000020102492],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950486,0.000013249837,0.00026590028,0.000052800384,0.00006865769,0.000094541305],"domain_scores_gemma":[0.9988898,0.000054777436,0.00003850269,0.00080306106,0.00017920206,0.000034648667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008158227,0.00008102299,0.00011468286,0.00009615872,0.00009459028,0.000011156164,0.00032630717,0.000052718486,0.000028636017],"category_scores_gemma":[0.000038852664,0.000090325164,0.000039776165,0.00024751696,0.00010040848,0.00021565972,0.00007490518,0.00019255134,0.000025082923],"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":[0.000019357592,0.00087371015,0.000013533717,0.00019652495,0.00018571602,0.0000017187807,0.0037313846,0.13069646,0.2170656,0.58683604,0.0006023317,0.05977762],"study_design_scores_gemma":[0.0019180508,0.00018539121,0.0011942729,0.0006182029,0.00019665883,0.00005597543,0.0013447141,0.15070967,0.45584217,0.37363255,0.0132307075,0.0010716398],"about_ca_topic_score_codex":0.000015169799,"about_ca_topic_score_gemma":0.00008237245,"teacher_disagreement_score":0.6474941,"about_ca_system_score_codex":0.00007011382,"about_ca_system_score_gemma":0.000021346737,"threshold_uncertainty_score":0.36833534},"labels":[],"label_agreement":null},{"id":"W2038093066","doi":"10.1080/01431161003621593","title":"An image fusion method taking into account phenological analogies and haze","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Pixel; Panchromatic film; Image fusion; Image resolution; Artificial intelligence; Computer vision; Haze; Computer science; Fusion; Remote sensing; Image (mathematics); Geography; Meteorology","score_opus":0.026151534314803493,"score_gpt":0.32241622287901034,"score_spread":0.29626468856420685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038093066","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15645726,0.00017970163,0.840897,0.00008509542,0.0004306194,0.00003370334,8.947732e-7,0.00013521544,0.0017805067],"genre_scores_gemma":[0.40723908,0.000178297,0.5923766,0.000053397627,0.00013414831,1.1279071e-8,7.0761854e-7,0.000014399483,0.000003359831],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904823,0.000046879795,0.00036871288,0.000119097145,0.0002848356,0.0001322704],"domain_scores_gemma":[0.99913615,0.000051346826,0.00023427788,0.00011156146,0.0003990827,0.000067604546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034600124,0.00013497239,0.00019091532,0.00026976655,0.00004233535,0.0000728444,0.00023723906,0.000072730494,0.000049796603],"category_scores_gemma":[0.00021949288,0.00011586495,0.000059423484,0.00007104293,0.00005996374,0.0006170114,0.00008073653,0.00028980518,0.0000019879615],"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":[0.000031775926,0.000006636301,0.000014959433,0.0000045849024,0.00003280083,0.0002405549,0.00066631206,0.00013301875,0.29379442,0.000037584698,0.000052802112,0.70498455],"study_design_scores_gemma":[0.0007579797,0.00031147094,0.0043863333,0.0005000293,0.000055536824,0.0043087536,0.0015037167,0.37737983,0.56000483,0.047375336,0.0028680703,0.0005480886],"about_ca_topic_score_codex":0.000050192586,"about_ca_topic_score_gemma":0.000009929323,"teacher_disagreement_score":0.7044365,"about_ca_system_score_codex":0.00010323061,"about_ca_system_score_gemma":0.000012303154,"threshold_uncertainty_score":0.47248352},"labels":[],"label_agreement":null},{"id":"W2040661103","doi":"10.1117/12.2053554","title":"Influence of image compression on the quality of UNB pan-sharpened imagery: a case study with security video image frames","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 New Brunswick","funders":"","keywords":"Computer vision; Panchromatic film; Artificial intelligence; Computer science; Color image; Monochrome; Image resolution; Sub-pixel resolution; False color; Multispectral image; Image quality; Image processing; Digital image processing; Image (mathematics)","score_opus":0.01030980728692399,"score_gpt":0.2631996483019799,"score_spread":0.2528898410150559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040661103","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99670506,0.00003258154,0.0006381502,0.0003320329,0.000047934453,0.0011011728,0.00008045276,0.00020670208,0.000855915],"genre_scores_gemma":[0.92313594,0.000033581622,0.07644463,0.000040553205,0.00007341317,0.00018437546,0.0000029415096,0.00007326416,0.000011273706],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99724495,1.79653e-7,0.0010711609,0.0003958817,0.00092948345,0.00035831096],"domain_scores_gemma":[0.9962624,0.00062867964,0.0006375263,0.00018135452,0.0021933184,0.00009676899],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012570049,0.0004287836,0.0007062555,0.0001240362,0.000089792105,0.0000703027,0.0010311721,0.00015042262,0.000011229576],"category_scores_gemma":[0.0013739762,0.00029410832,0.00039717028,0.00042223957,0.0004986883,0.00075684703,0.00025438337,0.0005328622,7.266414e-7],"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":[0.00020095425,0.00032948045,0.00059290003,0.0010763848,0.00032013434,9.586871e-7,0.0007903577,0.0008833744,0.9537205,0.04113283,0.00071036466,0.00024173394],"study_design_scores_gemma":[0.0014153476,0.0010592444,0.002829307,0.0008673051,0.00014923846,0.00004595771,0.004551199,0.034893356,0.9512298,0.0022905774,0.00016079027,0.00050786237],"about_ca_topic_score_codex":0.00006148908,"about_ca_topic_score_gemma":7.997815e-7,"teacher_disagreement_score":0.07580648,"about_ca_system_score_codex":0.00010284321,"about_ca_system_score_gemma":0.000020049045,"threshold_uncertainty_score":0.9999511},"labels":[],"label_agreement":null},{"id":"W2041414778","doi":"10.1016/j.ins.2010.05.010","title":"Covariance intersection based image fusion technique with application to pansharpening in remote sensing","year":2010,"lang":"en","type":"article","venue":"Information Sciences","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":77,"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":"Panchromatic film; Covariance intersection; Image fusion; Covariance; Artificial intelligence; Computer science; Computer vision; Image resolution; Covariance matrix; Pattern recognition (psychology); Multispectral image; Image (mathematics); Mathematics; Algorithm; Estimation of covariance matrices; Statistics","score_opus":0.005212414928674454,"score_gpt":0.2487673175382839,"score_spread":0.24355490260960946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041414778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032958284,0.0000015571702,0.96349263,0.00007375683,0.00010412271,0.00047573217,0.0000019888457,0.00050648116,0.0023854324],"genre_scores_gemma":[0.53708506,0.0000010034692,0.46281186,0.0000683003,0.000011207615,0.000012081009,0.0000048145753,0.0000044707826,0.0000012028322],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926364,0.00000942366,0.00024852637,0.00011221735,0.0002104957,0.00015570025],"domain_scores_gemma":[0.9996054,0.00003192285,0.000067286936,0.00015967211,0.000094770185,0.000040912713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005029412,0.00009668358,0.00007965496,0.0004317614,0.00010437214,0.0001088042,0.0001533075,0.000046125235,0.000011172357],"category_scores_gemma":[0.00007280711,0.00008637903,0.000011514024,0.0009497407,0.00006678073,0.0018753432,0.000024892946,0.00021709561,0.00003228734],"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":[0.0000105564295,0.0000019711229,0.000042780008,0.000029384699,3.5222763e-7,5.2527184e-7,0.00034012232,0.01349912,0.61983854,0.00006857694,0.00004882101,0.36611927],"study_design_scores_gemma":[0.00009340672,0.000031610638,0.0004158992,0.00010811253,7.8420675e-7,0.000014909517,0.000111802176,0.60793155,0.3869152,0.0003280374,0.0039033182,0.00014534287],"about_ca_topic_score_codex":0.000072807125,"about_ca_topic_score_gemma":0.000101558544,"teacher_disagreement_score":0.5944325,"about_ca_system_score_codex":0.000058003083,"about_ca_system_score_gemma":0.000030322983,"threshold_uncertainty_score":0.35224342},"labels":[],"label_agreement":null},{"id":"W2048608292","doi":"10.1142/s0219691312500257","title":"SUPER-RESOLUTION OF HYPERSPECTRAL IMAGERY USING COMPLEX RIDGELET TRANSFORM","year":2012,"lang":"en","type":"article","venue":"International Journal of Wavelets Multiresolution and Information Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Defence Research and Development Canada; Concordia University; Canadian Space Agency","funders":"","keywords":"Complex wavelet transform; Radon transform; Wavelet transform; Artificial intelligence; Harmonic wavelet transform; Stationary wavelet transform; Discrete wavelet transform; Pattern recognition (psychology); S transform; Computer vision; Image fusion; Mathematics; Wavelet; Computer science; Image (mathematics)","score_opus":0.019510095718314045,"score_gpt":0.2777335178042219,"score_spread":0.25822342208590787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048608292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34218025,0.00082140387,0.6546063,0.00018447873,0.0005506786,0.0001288464,0.000033120174,0.000096779775,0.0013981762],"genre_scores_gemma":[0.9313018,0.00027243656,0.06814768,0.000080863465,0.00016392428,0.0000013116628,0.000017711387,0.00001107584,0.0000032285172],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855447,0.000016441909,0.000774443,0.000041961346,0.00041731767,0.00019537834],"domain_scores_gemma":[0.9988616,0.000026564483,0.0003411359,0.00005058382,0.00062970474,0.000090376394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031519312,0.00013179875,0.00018662214,0.00044826244,0.00006708237,0.000057231715,0.00014514776,0.00006843706,0.00003437825],"category_scores_gemma":[0.00009202015,0.00012509659,0.00007439049,0.00012991384,0.00008270926,0.0066823424,0.000023101124,0.00018077926,0.0000019391027],"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":[0.00023513855,0.00015725622,0.0010034242,0.0003533412,0.00013447144,0.0000042080537,0.006708021,0.011267508,0.32975605,0.0011082257,0.000993418,0.64827895],"study_design_scores_gemma":[0.002063402,0.00009439463,0.012719431,0.000498598,0.00005457923,0.0010551228,0.0017145099,0.8331374,0.12845547,0.00021654801,0.019539488,0.00045106656],"about_ca_topic_score_codex":0.0000073049646,"about_ca_topic_score_gemma":3.1218147e-7,"teacher_disagreement_score":0.82186985,"about_ca_system_score_codex":0.0001941374,"about_ca_system_score_gemma":0.00003972691,"threshold_uncertainty_score":0.5101291},"labels":[],"label_agreement":null},{"id":"W2052381462","doi":"10.1109/tip.2012.2236346","title":"QoE-Based Multi-Exposure Fusion in Hierarchical Multivariate Gaussian CRF","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":62,"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":"Conditional random field; Artificial intelligence; Computer science; Multivariate statistics; Gaussian; Image fusion; Fusion; Image quality; Pattern recognition (psychology); Contrast (vision); Computer vision; Image (mathematics); Machine learning","score_opus":0.015657830253787332,"score_gpt":0.27616987360701817,"score_spread":0.26051204335323086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052381462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011544939,0.00030500573,0.98573107,0.000081473685,0.00030680312,0.00031275282,0.000017261464,0.0012524574,0.00044822553],"genre_scores_gemma":[0.74251163,0.000032502245,0.25707614,0.00007593726,0.000047610578,0.000106125466,0.000003417788,0.00008142737,0.00006518182],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984067,0.00006119798,0.00040515597,0.0002859972,0.00024162281,0.0005993071],"domain_scores_gemma":[0.9993625,0.000069538684,0.00005115457,0.00027602044,0.000054758133,0.00018599752],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017177178,0.00032244422,0.00026107114,0.00044081116,0.00020589711,0.00007139798,0.00017473618,0.0001775867,0.00013548904],"category_scores_gemma":[0.000016537091,0.000323032,0.00009105583,0.00052734517,0.00009083058,0.00095469574,0.0000021114245,0.0007930199,0.000051123003],"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":[0.000078518606,0.0006946954,0.00012516676,0.00030371177,0.000010448226,0.000020101994,0.0009684512,0.02267817,0.5454081,0.0000027499532,0.00002835681,0.4296815],"study_design_scores_gemma":[0.0014674036,0.000062324296,0.0014711597,0.0004227549,0.000023543855,0.0000139299955,0.00009642506,0.3024945,0.6928577,0.00004870735,0.0004601354,0.0005814717],"about_ca_topic_score_codex":0.000016824662,"about_ca_topic_score_gemma":0.000018743513,"teacher_disagreement_score":0.7309667,"about_ca_system_score_codex":0.00018364032,"about_ca_system_score_gemma":0.00004029801,"threshold_uncertainty_score":0.99992216},"labels":[],"label_agreement":null},{"id":"W2056076478","doi":"10.5589/m07-060","title":"Multi-scale feature tracking in sequential satellite images with wavelet analysis to measure sea surface currents in the Gulf of St. Lawrence","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"National Oceanic and Atmospheric Administration","keywords":"Advanced very-high-resolution radiometer; Wavelet; Remote sensing; Daubechies wavelet; Feature (linguistics); Wavelet transform; Satellite; Scale (ratio); Computer science; Tracking (education); Ocean current; Geology; Geodesy; Geography; Artificial intelligence; Computer vision; Discrete wavelet transform; Cartography; Climatology; Engineering","score_opus":0.0175788146456064,"score_gpt":0.2635016358723863,"score_spread":0.2459228212267799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056076478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49351963,0.0007129375,0.5052689,0.00013646935,0.00008544881,0.00013395365,0.000017017574,0.00001355908,0.000112112815],"genre_scores_gemma":[0.6674596,0.000035722733,0.33241558,0.00003496057,0.00002666305,9.115163e-9,0.0000023408118,0.000018630144,0.000006450776],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986482,0.000078169876,0.00042594376,0.00014140726,0.0002946148,0.00041162342],"domain_scores_gemma":[0.99907684,0.00006443109,0.00014161892,0.00021765674,0.00025780586,0.000241629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009947596,0.0001766778,0.0003623412,0.000795111,0.000045232264,0.000047902686,0.00022279879,0.000085661646,0.000003791838],"category_scores_gemma":[0.000098518874,0.00014389186,0.00009153251,0.0015700976,0.00005942928,0.00020223782,0.000008244816,0.00060942065,4.0035818e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006106647,0.000012651113,0.013170113,0.000065267195,0.00015535545,0.0021591415,0.006283682,0.08398488,0.074322134,5.4392433e-7,0.00019667375,0.8195885],"study_design_scores_gemma":[0.003099283,0.00033070377,0.30329332,0.0055403262,0.0007112057,0.0019301622,0.00865115,0.18025449,0.48457977,0.00013627212,0.009480169,0.0019931577],"about_ca_topic_score_codex":0.0033981714,"about_ca_topic_score_gemma":0.27009603,"teacher_disagreement_score":0.8175953,"about_ca_system_score_codex":0.00029940304,"about_ca_system_score_gemma":0.00013711488,"threshold_uncertainty_score":0.7432228},"labels":[],"label_agreement":null},{"id":"W2056189760","doi":"10.1080/01431160210155028","title":"Confusion in data fusion","year":2003,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Nova Scotia Hospital; Bedford Institute of Oceanography; Canadian Hydrographic Service","funders":"","keywords":"Computer science; Sensor fusion; Metadata; Overlay; Context (archaeology); Fusion; Term (time); Data mining; Confusion; Object (grammar); Information retrieval; Artificial intelligence; World Wide Web","score_opus":0.021915292324024387,"score_gpt":0.2952424418548685,"score_spread":0.2733271495308441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056189760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122543536,0.00046796346,0.86954147,0.00023345364,0.0018811309,0.000057954425,0.000004858896,0.00007390651,0.00519576],"genre_scores_gemma":[0.622472,0.00046452796,0.37678754,0.000082518854,0.00013438359,3.5946104e-9,0.0000055792166,0.000020021722,0.00003345999],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991089,0.000030479132,0.00036911166,0.00008330073,0.0003074013,0.00010079117],"domain_scores_gemma":[0.99942166,0.000050596555,0.000108230204,0.00017816883,0.00020343025,0.00003794676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035733584,0.00008209253,0.00012534538,0.00026889268,0.000012709439,0.000028484392,0.00029622807,0.00004262415,0.000032906653],"category_scores_gemma":[0.00032576945,0.00007923714,0.000030487712,0.00009473886,0.000016175041,0.00032821763,0.00006580613,0.00023804724,0.000004986958],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020603937,0.00000962082,0.000034580924,0.000005445034,0.000022126778,0.00049142155,0.000121146535,0.0019895406,0.149345,0.00005015753,0.0015451862,0.84636515],"study_design_scores_gemma":[0.001615592,0.000049683644,0.00042314298,0.001129842,0.00001632016,0.0039031552,0.00027742013,0.4689009,0.26362562,0.009269151,0.25037608,0.00041307893],"about_ca_topic_score_codex":0.000007780536,"about_ca_topic_score_gemma":0.000016907656,"teacher_disagreement_score":0.8459521,"about_ca_system_score_codex":0.00011136585,"about_ca_system_score_gemma":0.00002541554,"threshold_uncertainty_score":0.32311967},"labels":[],"label_agreement":null},{"id":"W2056728639","doi":"10.5555/1385289.1385295","title":"Unequal error protection based on objective video evaluation model","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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; Video quality; Fuzzy logic; Subjective video quality; Quality (philosophy); Artificial intelligence; Focus (optics); Data mining; Real-time computing; Computer vision; Image quality; Metric (unit); Image (mathematics); Engineering","score_opus":0.0329482793305361,"score_gpt":0.30464493193109926,"score_spread":0.27169665260056314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056728639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011123878,0.0000063061752,0.9516326,0.000024373863,0.000081801576,0.0007089297,0.000001824051,0.0013083183,0.035111938],"genre_scores_gemma":[0.923465,7.7587435e-7,0.07611409,0.00008503031,0.00003267467,0.00012446776,0.000005248826,0.0000291037,0.00014360211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999208,0.000017781927,0.00015793633,0.00015506381,0.0002961048,0.00016512019],"domain_scores_gemma":[0.9996079,0.000033668104,0.000022715805,0.00018135304,0.00011584089,0.000038526938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053920003,0.000115006296,0.00007868979,0.00016951852,0.00004364058,0.0000094365705,0.000051070918,0.00007416031,0.00012693128],"category_scores_gemma":[0.000111374655,0.00011021334,0.00003189084,0.00018457568,0.00001156297,0.00015928877,0.000007827048,0.00014987239,0.000033077493],"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":[0.000032031283,0.000020267544,0.000003606577,0.0000091098345,0.0000027207454,6.308913e-7,0.000050764225,0.8710259,0.052176893,0.00012507966,0.00024226162,0.076310694],"study_design_scores_gemma":[0.00017664277,0.00005592842,0.00011519263,0.000012454334,0.0000042465635,2.8314744e-7,0.000023989338,0.73315954,0.26488176,0.0014153384,0.000055226945,0.00009940618],"about_ca_topic_score_codex":0.000010175165,"about_ca_topic_score_gemma":0.000021625176,"teacher_disagreement_score":0.9123411,"about_ca_system_score_codex":0.0003072023,"about_ca_system_score_gemma":0.000020548301,"threshold_uncertainty_score":0.44943696},"labels":[],"label_agreement":null},{"id":"W2059440857","doi":"10.1117/12.2053551","title":"Image quality assessment of 2-chip color camera in comparison with 1-chip color and 3-chip color cameras in various lighting conditions: initial results","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Panchromatic film; Computer vision; Artificial intelligence; Color image; Computer science; Monochrome; Camera auto-calibration; Image sensor; Camera module; Smart camera; Multispectral image; Chip; Image resolution; Three-CCD camera; Color histogram; False color; RGB color model; Demosaicing; Image processing; Camera resectioning; Image (mathematics); Telecommunications","score_opus":0.014819600077010216,"score_gpt":0.2916400442656094,"score_spread":0.2768204441885992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059440857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945958,0.00005428444,0.0007938338,0.00057825004,0.00010377179,0.0009841037,0.00013368194,0.00018040492,0.0025758543],"genre_scores_gemma":[0.76804847,0.000051267514,0.23142211,0.00004603097,0.000090243506,0.0002491815,0.000019608757,0.00006132237,0.000011751902],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99691767,2.387632e-7,0.001466198,0.00047401435,0.0006574242,0.00048445928],"domain_scores_gemma":[0.9976178,0.00061576377,0.00058279186,0.00009232632,0.0009622918,0.00012907406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013502738,0.00043126388,0.00087394234,0.0002593045,0.0000733026,0.00009175521,0.0005822325,0.00023934973,0.0000064954324],"category_scores_gemma":[0.0011173855,0.00038858186,0.0002182302,0.00049042754,0.00040467968,0.0006447645,0.00017086613,0.000666022,4.3009388e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004042046,0.0003329113,0.004260186,0.0013766445,0.00025097324,7.2859086e-7,0.00088029006,0.0026921057,0.8320683,0.15678822,0.00054020283,0.00040519307],"study_design_scores_gemma":[0.0069740787,0.0017542854,0.044674452,0.0019741338,0.00018408644,0.000034022745,0.003949126,0.61032826,0.32504794,0.00321695,0.00066783227,0.0011948579],"about_ca_topic_score_codex":0.000081509046,"about_ca_topic_score_gemma":0.000010486275,"teacher_disagreement_score":0.60763615,"about_ca_system_score_codex":0.00032986805,"about_ca_system_score_gemma":0.000050104914,"threshold_uncertainty_score":0.9998566},"labels":[],"label_agreement":null},{"id":"W2060119334","doi":"10.1117/12.861564","title":"Enhancement of spatial resolution of hyperspectral imagery using iterative back projection","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Space Agency","funders":"","keywords":"Panchromatic film; Hyperspectral imaging; Image resolution; Multispectral image; Artificial intelligence; Computer science; Computer vision; Projection (relational algebra); Metric (unit); Sub-pixel resolution; Image quality; Cube (algebra); Remote sensing; Iterative method; Iterative reconstruction; Image (mathematics); Pattern recognition (psychology); Image processing; Mathematics; Geography; Digital image processing; Algorithm","score_opus":0.01118938494814528,"score_gpt":0.24465104456335326,"score_spread":0.23346165961520798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060119334","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99305624,0.00005284968,0.0040145195,0.00012498422,0.00034159457,0.0006276252,0.000045633526,0.000101863225,0.0016346864],"genre_scores_gemma":[0.5522519,0.000066519206,0.44726977,0.000008151268,0.00023960536,0.00006021662,0.000005845483,0.00005317893,0.000044862412],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980549,2.0778378e-8,0.0008191023,0.00027724146,0.0005434695,0.00030523702],"domain_scores_gemma":[0.9977269,0.000074429954,0.00041712416,0.00006976692,0.0016458117,0.00006595557],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000405501,0.0002935722,0.00044182022,0.00016372041,0.000044938723,0.000033633973,0.00048232483,0.00018871913,0.00003248785],"category_scores_gemma":[0.00034219437,0.00026333774,0.00043685554,0.0003030082,0.00027252315,0.0006299167,0.000108589215,0.00039854136,5.9080895e-7],"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":[0.00007737281,0.0001002855,0.00011101545,0.00062981056,0.0001864253,3.82353e-8,0.00026559306,0.0003603098,0.9628273,0.03458687,0.00039778085,0.00045716183],"study_design_scores_gemma":[0.00041869644,0.00021044475,0.00018866039,0.00025115468,0.00006461099,0.000008460704,0.00036906055,0.11450881,0.8829881,0.0005535438,0.000233295,0.00020515597],"about_ca_topic_score_codex":0.000024744431,"about_ca_topic_score_gemma":4.068013e-7,"teacher_disagreement_score":0.44325525,"about_ca_system_score_codex":0.00016264222,"about_ca_system_score_gemma":0.000030083856,"threshold_uncertainty_score":0.9999819},"labels":[],"label_agreement":null},{"id":"W2065193748","doi":"10.1109/tgrs.2012.2195666","title":"Enhancing Spatial Resolution of Hyperspectral Imagery Using Sensor's Intrinsic Keystone Distortion","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Canadian Space Agency","funders":"Defence Research and Development Canada","keywords":"Subpixel rendering; Hyperspectral imaging; Image resolution; Computer vision; Cube (algebra); Computer science; Data cube; Artificial intelligence; Remote sensing; Image fusion; Distortion (music); Sensor fusion; Full spectral imaging; Pixel; Projection (relational algebra); Spatial analysis; Image (mathematics); Geology; Algorithm; Data mining; Mathematics","score_opus":0.01202358645321253,"score_gpt":0.2402625696846139,"score_spread":0.22823898323140138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065193748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3673674,0.000073997326,0.631779,0.000008054201,0.0004833733,0.00007670617,0.0000026877115,0.00015113724,0.00005764616],"genre_scores_gemma":[0.74931324,0.000111589135,0.25045422,0.0000101538235,0.00006851081,7.152254e-8,4.014797e-7,0.000016940667,0.000024887504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99899775,0.000031412965,0.00025684288,0.00018080157,0.0001972049,0.0003359629],"domain_scores_gemma":[0.9995832,0.000041361523,0.00005939686,0.00017199935,0.00005178185,0.00009230346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002015776,0.00015025721,0.00017347507,0.00024431504,0.00022678147,0.000017398253,0.0000409369,0.000075917174,0.0000046046907],"category_scores_gemma":[0.000011911559,0.00015339164,0.00005621854,0.00031183506,0.000182532,0.00044830836,0.000001810837,0.00021329956,0.0000023869547],"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":[0.000009023638,0.000011064675,9.734039e-7,0.00002272548,0.000003647042,0.0000018805807,0.00031771793,0.0022509887,0.6919594,8.5215976e-7,9.325085e-7,0.30542082],"study_design_scores_gemma":[0.00008981546,0.000029398216,0.00020639008,0.00011984127,0.000022128706,0.00009836207,0.00018872585,0.20747168,0.7915628,0.000029339973,0.000021625869,0.00015987067],"about_ca_topic_score_codex":0.00037370974,"about_ca_topic_score_gemma":0.000038170092,"teacher_disagreement_score":0.38194582,"about_ca_system_score_codex":0.00015233154,"about_ca_system_score_gemma":0.000016435075,"threshold_uncertainty_score":0.6255129},"labels":[],"label_agreement":null},{"id":"W2067530150","doi":"10.1109/mwscas.2013.6674890","title":"A fast, low complexity image fusion algorithm based on multiscale transforms","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Concordia University","funders":"","keywords":"Contourlet; Image fusion; Fusion; Wavelet transform; Algorithm; Computer science; Image (mathematics); Fusion rules; Decomposition; Scale (ratio); Artificial intelligence; Pattern recognition (psychology); Wavelet","score_opus":0.007443247279319247,"score_gpt":0.22121293089224633,"score_spread":0.21376968361292709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067530150","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041644117,0.0000074269897,0.96403867,0.00016325946,0.00008311296,0.0005523012,0.000024746429,0.002143971,0.028822076],"genre_scores_gemma":[0.13910536,0.00001508575,0.8595145,0.00033532176,0.00006781521,0.00015406478,0.000037304937,0.00007303097,0.0006975672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908876,0.000011955245,0.00021010463,0.0001997831,0.00019309178,0.00029632077],"domain_scores_gemma":[0.99947464,0.000036411053,0.000015359168,0.0003050557,0.000053991665,0.000114562295],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005066176,0.0002113513,0.00017965098,0.000101681864,0.00006282677,0.000040338375,0.00015622126,0.00007573907,0.0036670037],"category_scores_gemma":[0.000008546923,0.00017097521,0.000076525575,0.00013817128,0.00006770147,0.0003305945,0.000019770392,0.00019693098,0.0005309749],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055353535,0.0001178884,0.000010282535,0.000072071176,0.0000042636957,0.000008034431,0.000057959118,0.0016075901,0.21675284,0.000037305286,0.01361905,0.76770717],"study_design_scores_gemma":[0.0003466827,0.00005414971,0.00056961173,0.0000306051,0.00000193275,0.0000015762898,0.000022406934,0.7208023,0.27639452,0.0006626043,0.00090168614,0.00021190007],"about_ca_topic_score_codex":0.00008816965,"about_ca_topic_score_gemma":0.000009172654,"teacher_disagreement_score":0.7674953,"about_ca_system_score_codex":0.000060680726,"about_ca_system_score_gemma":0.000005196954,"threshold_uncertainty_score":0.99724376},"labels":[],"label_agreement":null},{"id":"W2067977604","doi":"10.1109/tbme.2012.2211017","title":"Cross-Scale Coefficient Selection for Volumetric Medical Image Fusion","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":116,"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":"Faculty of Medicine and Dentistry, University of Alberta; Natural Sciences and Engineering Research Council of Canada","keywords":"Image fusion; Computer science; Artificial intelligence; Fusion; Fusion rules; Set (abstract data type); Scale (ratio); Sensor fusion; Medical imaging; Data mining; Image (mathematics); Selection (genetic algorithm); Pattern recognition (psychology); Decomposition; Information fusion; Computer vision","score_opus":0.006680005682623798,"score_gpt":0.25851943251749254,"score_spread":0.25183942683486876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067977604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019843295,0.00012332376,0.97530895,0.00004391533,0.0021365308,0.00035312088,0.000043658794,0.0020571176,0.00009007135],"genre_scores_gemma":[0.93592376,0.00015586251,0.06289118,0.000052653937,0.0003971021,0.00028462164,0.000015990148,0.00012015124,0.00015865409],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980833,0.0000114418,0.00040570705,0.00023970647,0.00059791957,0.00066195393],"domain_scores_gemma":[0.9989738,0.00019763646,0.000025953188,0.00019684338,0.00006990072,0.0005358839],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037327487,0.0002735985,0.00024769836,0.0005826713,0.00014364845,0.000034529025,0.00019633908,0.000316766,0.00056021655],"category_scores_gemma":[0.00007397996,0.0002763111,0.00016039236,0.0009894468,0.00008598437,0.0003095368,0.0000029725431,0.00050717796,0.00006553913],"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":[0.000072931165,0.0009907656,0.000052866195,0.00052279467,0.000112463014,0.0000056738813,0.00024659585,0.16927171,0.456735,0.000039393202,0.0032910062,0.36865884],"study_design_scores_gemma":[0.00053660333,0.000120518926,0.00012511043,0.00006633864,0.00002083393,0.00004021022,0.000008502647,0.74157727,0.23433077,0.0000038039068,0.02284078,0.00032928074],"about_ca_topic_score_codex":0.000005566256,"about_ca_topic_score_gemma":0.0000013782604,"teacher_disagreement_score":0.9160805,"about_ca_system_score_codex":0.00024594986,"about_ca_system_score_gemma":0.000023201736,"threshold_uncertainty_score":0.9999689},"labels":[],"label_agreement":null},{"id":"W2075129211","doi":"10.1109/sitis.2012.65","title":"A Versatile Demosaicing Algorithm for Performing Image Zooming","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Demosaicing; RGB color model; Color filter array; Bilinear interpolation; Computer science; Artificial intelligence; Computer vision; Bayer filter; Image (mathematics); Algorithm; Color gel; Color image; Image processing","score_opus":0.007945965121405515,"score_gpt":0.24367352996750027,"score_spread":0.23572756484609475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075129211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00326558,0.00016245394,0.98552954,0.0000093474055,0.00021562685,0.00024032022,0.0000057403554,0.0011740318,0.009397346],"genre_scores_gemma":[0.05894664,0.000017052163,0.9403209,0.000052117717,0.00020267486,0.000072356874,0.000006304688,0.000052227035,0.0003297072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992899,0.000004113232,0.00015122604,0.0000897078,0.00007496048,0.0003901002],"domain_scores_gemma":[0.99965936,0.00007515758,0.000017145008,0.0001402512,0.00002900084,0.000079062884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016070827,0.00012504902,0.00012205715,0.000067476816,0.00008191267,0.000018231658,0.00008133909,0.00004624225,0.00019149663],"category_scores_gemma":[0.00003582807,0.00012280507,0.000051153896,0.00007863031,0.0000142659665,0.0006622011,0.00003435046,0.000089637295,0.000034023487],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001796853,0.00001331658,0.000103208986,0.00006046196,0.000015182561,7.9850855e-7,0.00045323506,0.00003238317,0.23030429,0.00017096086,0.00511884,0.7637255],"study_design_scores_gemma":[0.00020880495,0.00001730769,0.000044936467,0.000025007317,0.000011598131,0.000009613466,0.00032106618,0.6041873,0.37396294,0.0001586785,0.020812605,0.00024013498],"about_ca_topic_score_codex":0.00000520919,"about_ca_topic_score_gemma":6.269211e-7,"teacher_disagreement_score":0.7634854,"about_ca_system_score_codex":0.00007892326,"about_ca_system_score_gemma":0.000003948536,"threshold_uncertainty_score":0.5007846},"labels":[],"label_agreement":null},{"id":"W2078942744","doi":"10.1109/mmsp.2012.6343424","title":"Wavelet-based gradient transform and its applications","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Wavelet transform; Stationary wavelet transform; Second-generation wavelet transform; Harmonic wavelet transform; Lifting scheme; Wavelet; Wavelet packet decomposition; Discrete wavelet transform; Artificial intelligence; Pattern recognition (psychology); Top-hat transform; Computer science; Mathematics; Diagonal; Computer vision; Image (mathematics); Image processing; Image texture; Geometry","score_opus":0.008015649332726063,"score_gpt":0.22636003271690183,"score_spread":0.21834438338417578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078942744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031711378,0.00058708433,0.9715318,0.000078938676,0.000023992898,0.00028150223,0.000004775485,0.0008988823,0.023421874],"genre_scores_gemma":[0.93379045,0.00009213499,0.06563081,0.0000696387,0.000028148184,0.00017841882,0.000004277752,0.000016612117,0.00018951792],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997039,0.0000016400933,0.00006901773,0.00004890353,0.00004009333,0.00013648084],"domain_scores_gemma":[0.9998213,0.000016281192,0.0000041413314,0.00007994346,0.000009983537,0.00006835106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031786258,0.00006057785,0.00004939557,0.000034496,0.000025360057,0.0000043107825,0.00003381479,0.000023603803,0.00009724391],"category_scores_gemma":[0.000002473972,0.00005468741,0.0000129474465,0.000062215055,0.000008313549,0.00011205121,0.00000422034,0.00004532405,0.000026444122],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002872639,0.00008049862,0.00007652551,0.00016973272,0.000012691417,4.9238156e-7,0.00020572111,0.000093109695,0.10708034,0.021796936,0.0029795575,0.8675015],"study_design_scores_gemma":[0.00016335835,0.000014285802,0.00038447205,0.0000075641333,0.000007382092,0.0000045232005,0.000023763487,0.02517979,0.75856304,0.0006715653,0.21476972,0.00021053069],"about_ca_topic_score_codex":6.786616e-7,"about_ca_topic_score_gemma":0.0000010329084,"teacher_disagreement_score":0.9306193,"about_ca_system_score_codex":0.000018074032,"about_ca_system_score_gemma":0.0000015548283,"threshold_uncertainty_score":0.2230088},"labels":[],"label_agreement":null},{"id":"W2079800045","doi":"10.3390/rs6053583","title":"An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Image resolution; Artificial intelligence; Computer vision; Segmentation; Pixel; Image segmentation; Geospatial analysis; Remote sensing; Homogeneity (statistics); Boundary (topology); Geology; Mathematics","score_opus":0.018739699416101277,"score_gpt":0.27441405044977063,"score_spread":0.25567435103366937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079800045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014730016,0.0001208597,0.9834628,0.000014002518,0.00028479207,0.0005077761,0.000021365508,0.0006307146,0.00022768069],"genre_scores_gemma":[0.06065322,0.000051421357,0.9389262,0.00006171937,0.00015660895,1.4976433e-7,0.000039291765,0.00007894556,0.000032397802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988615,0.00005999516,0.00035239424,0.00026730172,0.00017831128,0.0002804961],"domain_scores_gemma":[0.999229,0.000094466945,0.00010471932,0.0003140133,0.00017181627,0.00008594739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019352845,0.00021572236,0.0002956607,0.00013243264,0.000072897026,0.000023276445,0.000071967894,0.00009595192,0.0000030848394],"category_scores_gemma":[0.000038136473,0.00023803065,0.0000937803,0.00012414127,0.00006574328,0.00024212449,0.000023631048,0.00012971803,0.0000035313406],"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":[0.00000993615,0.000007946063,1.8611216e-7,0.000035307337,0.00000913013,0.000001675708,0.00027845264,0.0011679898,0.40145737,6.5986364e-7,0.00005805421,0.5969733],"study_design_scores_gemma":[0.00033400132,0.000087387554,0.00004873268,0.00006840225,0.000017195212,0.000013315091,0.00018851554,0.61374056,0.38484675,0.00026604495,0.00023406942,0.00015503986],"about_ca_topic_score_codex":0.000035772275,"about_ca_topic_score_gemma":0.000008116591,"teacher_disagreement_score":0.61257255,"about_ca_system_score_codex":0.00016508372,"about_ca_system_score_gemma":0.000018426346,"threshold_uncertainty_score":0.97066087},"labels":[],"label_agreement":null},{"id":"W2084382156","doi":"10.1109/igarss.2012.6351607","title":"A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion","year":2012,"lang":"en","type":"review","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":92,"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 New Brunswick","funders":"","keywords":"Panchromatic film; Sharpening; Multispectral image; Image fusion; Remote sensing; Computer science; Image resolution; Satellite; Artificial intelligence; Computer vision; Image (mathematics); Geography; Engineering","score_opus":0.06225181668600862,"score_gpt":0.352765513946137,"score_spread":0.2905136972601284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084382156","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.7609774e-8,0.904119,0.08872524,0.000009676782,0.00007036095,0.0030682236,0.00009595965,0.0010198697,0.0028916104],"genre_scores_gemma":[0.0000011584492,0.75458205,0.24415998,0.00003555315,0.00006272637,0.0005165951,0.00025710682,0.0001216388,0.00026318256],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976567,0.00009214046,0.0013282809,0.00035443238,0.00015594537,0.00041249738],"domain_scores_gemma":[0.9984702,0.00024179179,0.0004654019,0.0005679369,0.0001651434,0.00008953822],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000640292,0.00056836655,0.0027436572,0.00024371613,0.00008163968,0.00002835063,0.00030564328,0.00036739738,0.00023384976],"category_scores_gemma":[0.000085135456,0.00048790415,0.0002608957,0.0003030605,0.000077663695,0.00033714782,0.00021113516,0.0004150257,0.00003249949],"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":[0.0000018487947,0.000020182451,4.829565e-7,0.124979526,0.000028587445,2.1252826e-7,0.00000768571,3.035156e-8,0.00020151546,0.00010145657,0.018902171,0.8557563],"study_design_scores_gemma":[0.000053779575,0.00006293598,3.9886058e-7,0.047275405,0.0010302132,0.000009095393,0.000002433149,0.0000333815,0.0019553977,0.00006152327,0.9490494,0.00046601656],"about_ca_topic_score_codex":0.000022420812,"about_ca_topic_score_gemma":0.000007410276,"teacher_disagreement_score":0.93014723,"about_ca_system_score_codex":0.00012831848,"about_ca_system_score_gemma":0.000032912572,"threshold_uncertainty_score":0.9997573},"labels":[],"label_agreement":null},{"id":"W2089484870","doi":"10.1016/j.visres.2005.09.036","title":"The transparency is in the fused image not the monocular image","year":2005,"lang":"en","type":"article","venue":"Vision Research","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"York University","funders":"","keywords":"Transparency (behavior); Image (mathematics); Monocular; Computer vision; Artificial intelligence; Optics; Optometry; Computer science; Physics; Medicine; Computer security","score_opus":0.03882214109405198,"score_gpt":0.39623607671191446,"score_spread":0.3574139356178625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089484870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3044476,0.012798071,0.2539587,0.26782277,0.000602105,0.011544872,0.00017733275,0.003097192,0.14555138],"genre_scores_gemma":[0.98138654,0.0041712453,0.01230822,0.0005641133,0.00017926584,0.00037862404,0.000004552088,0.00007875445,0.0009286832],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975921,0.0003490861,0.00030573452,0.00023608618,0.0009344256,0.00058256317],"domain_scores_gemma":[0.9978826,0.0008835113,0.000017909368,0.0010237575,0.00014040893,0.00005183039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029193358,0.00015062645,0.0001182198,0.00014779161,0.0004971667,0.0002542181,0.001275502,0.00007103389,0.00028809093],"category_scores_gemma":[0.00024156022,0.000072101524,0.00007189501,0.00088819215,0.00029323756,0.00028350065,0.00011645485,0.0010527085,0.00029085026],"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":[0.00004749479,0.000086712455,0.000023496454,0.00003731453,0.000012824884,0.000045101162,0.003995,0.00021846346,0.38629037,0.00047815786,0.17683136,0.4319337],"study_design_scores_gemma":[0.0005684589,0.00013073705,0.0027691138,0.000073687326,0.00000506714,0.000009449883,0.0016259043,0.08543074,0.3650184,0.0032685066,0.5408078,0.00029216855],"about_ca_topic_score_codex":0.00004981867,"about_ca_topic_score_gemma":0.00006869709,"teacher_disagreement_score":0.67693895,"about_ca_system_score_codex":0.000077326185,"about_ca_system_score_gemma":0.000025656724,"threshold_uncertainty_score":0.45735532},"labels":[],"label_agreement":null},{"id":"W2090466915","doi":"10.1109/ccece.2009.5090278","title":"Contourlet based image compression using controlled modification of coefficients","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Contourlet; Algorithm; Compression (physics); Computer science; Wavelet transform; Image compression; Entropy (arrow of time); Data compression; Wavelet; Artificial intelligence; Mathematics; Pattern recognition (psychology); Image (mathematics); Image processing; Materials science","score_opus":0.015759073707609338,"score_gpt":0.28619649656890783,"score_spread":0.2704374228612985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090466915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051184967,0.00005888225,0.94289553,0.000021157684,0.000042232874,0.00035737315,0.0000054965394,0.0005230415,0.004911327],"genre_scores_gemma":[0.8459594,0.0000051147003,0.15391448,0.00005250329,0.000009745812,0.0000049611085,0.0000075339294,0.00001164886,0.000034590437],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993298,0.000020248744,0.00027789274,0.00010266174,0.00014252363,0.00012684888],"domain_scores_gemma":[0.9995421,0.000043345015,0.00006158018,0.00021455502,0.00010089153,0.00003753827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008894589,0.00010754275,0.0002438984,0.00010002431,0.000028583405,0.000011104503,0.00010061507,0.000046935715,0.00010123475],"category_scores_gemma":[0.00004024496,0.00009363624,0.000053024753,0.00011315422,0.000024619305,0.00011908852,0.000007408513,0.0000623378,0.0000037247705],"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":[0.000046907124,0.00004567918,0.0000067147766,0.000010024429,0.0000028701495,7.160125e-7,0.000011037353,0.02983174,0.9657919,0.00009580789,0.00029153872,0.0038651135],"study_design_scores_gemma":[0.001182225,0.000019509354,0.00010704028,0.000026500686,0.000005327735,3.4402748e-7,0.000006226372,0.5421738,0.45624995,0.00008721279,0.00007851861,0.00006335572],"about_ca_topic_score_codex":0.000004393701,"about_ca_topic_score_gemma":2.0825331e-7,"teacher_disagreement_score":0.7947745,"about_ca_system_score_codex":0.000037702746,"about_ca_system_score_gemma":0.0000073536135,"threshold_uncertainty_score":0.38183752},"labels":[],"label_agreement":null},{"id":"W2095325404","doi":"10.1049/iet-ipr.2009.0163","title":"Contrast-based fusion of noisy images using discrete wavelet transform","year":2010,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Concordia University","funders":"","keywords":"Artificial intelligence; Image fusion; Grayscale; Noise reduction; Wavelet; Computer science; Pattern recognition (psychology); Noise (video); Discrete wavelet transform; Computer vision; Wavelet transform; Fusion; Peak signal-to-noise ratio; Entropy (arrow of time); Algorithm; Image (mathematics)","score_opus":0.006127298203150533,"score_gpt":0.25600318445859704,"score_spread":0.24987588625544652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095325404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11032787,0.0002543213,0.88538677,0.0000717572,0.00015681016,0.0002924062,0.000048849823,0.00083435315,0.0026268594],"genre_scores_gemma":[0.64627856,0.000014029044,0.35350555,0.000028736207,0.000060054866,0.000013391274,0.000010383603,0.00007099328,0.000018299545],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860793,0.000013564615,0.0004491365,0.00025855823,0.00026948506,0.00040130442],"domain_scores_gemma":[0.99925363,0.00004736892,0.00011248576,0.0002885918,0.00019787917,0.000100064055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020336051,0.0002922678,0.00034308308,0.00018048332,0.00012988529,0.00008607874,0.00024715002,0.00013334832,0.000117224576],"category_scores_gemma":[0.00007428514,0.0002793003,0.000101441154,0.0002987669,0.00021501383,0.00083186233,0.000030234463,0.00047094384,0.0000041902417],"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":[0.000016533086,0.000021007152,0.000033402142,0.00037570499,0.000004674744,0.000009002406,0.00010614017,0.00018667942,0.8952429,0.0000048686516,0.00006835054,0.10393072],"study_design_scores_gemma":[0.00040865323,0.000024038356,0.00010806134,0.00020368471,0.000024230743,0.000015893345,0.000043837816,0.14219159,0.85583717,0.00048957084,0.0003586881,0.00029460524],"about_ca_topic_score_codex":0.0000140845395,"about_ca_topic_score_gemma":0.000007768335,"teacher_disagreement_score":0.53595066,"about_ca_system_score_codex":0.00003710057,"about_ca_system_score_gemma":0.00006680397,"threshold_uncertainty_score":0.9999659},"labels":[],"label_agreement":null},{"id":"W2095763972","doi":"10.1109/ccece.2008.4564754","title":"The nonredundant contourlet transform (NRCT): A multiresolution and multidirection image representation","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Contourlet; Wavelet transform; Artificial intelligence; Redundancy (engineering); Mathematics; Computer vision; Filter bank; Pattern recognition (psychology); Wavelet; Computer science; Filter (signal processing)","score_opus":0.012433409466116466,"score_gpt":0.20625829043081145,"score_spread":0.193824880964695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095763972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3458114,0.00079729065,0.6443488,0.0013975451,0.00044395492,0.0013516212,0.000019292745,0.0017069821,0.0041230964],"genre_scores_gemma":[0.99247116,0.0018957066,0.0052984273,0.000048421643,0.00009789821,0.00009020967,0.0000051152233,0.000032040214,0.00006104783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986804,0.0000073773144,0.00026122152,0.0003492468,0.00017028506,0.00053146237],"domain_scores_gemma":[0.99923116,0.0000714564,0.000035966164,0.00009762361,0.00020077628,0.00036302814],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009952308,0.00028325806,0.00023257398,0.0002220299,0.00037679254,0.00022069376,0.00015556614,0.00011806801,0.0000075861017],"category_scores_gemma":[0.000051519586,0.0002512726,0.000036111873,0.00027008902,0.00011089837,0.00035100704,0.000021501213,0.00040678747,0.0000037659704],"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":[0.00009941174,0.000052236803,0.0010086402,0.00019652788,0.0001226955,0.0000827551,0.0038563025,0.0007789073,0.0764487,0.02759359,0.0034370315,0.8863232],"study_design_scores_gemma":[0.000306211,0.00014181297,0.004754247,0.00007544265,0.000008811483,0.00013277786,0.000045716675,0.98420095,0.0069532953,0.0003470737,0.002688793,0.0003448485],"about_ca_topic_score_codex":0.0012687234,"about_ca_topic_score_gemma":0.000676684,"teacher_disagreement_score":0.98342204,"about_ca_system_score_codex":0.00016836851,"about_ca_system_score_gemma":0.000081225735,"threshold_uncertainty_score":0.999994},"labels":[],"label_agreement":null},{"id":"W2098621703","doi":"","title":"METHODS FOR IMAGE FUSION QUALITY ASSESSMENT - A REVIEW, COMPARISON AND ANALYSIS","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":93,"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 New Brunswick","funders":"","keywords":"Quality assessment; Image fusion; Image quality; Ranking (information retrieval); Standard deviation; Computer science; Artificial intelligence; Fusion; Sensor fusion; Quality (philosophy); Correlation coefficient; Statistics; Pattern recognition (psychology); Mathematics; Image (mathematics); Evaluation methods; Reliability engineering; Engineering","score_opus":0.06808736002439475,"score_gpt":0.4824693185722307,"score_spread":0.41438195854783594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098621703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012789676,0.0039670668,0.99000007,0.00007585858,0.000023872528,0.0003822829,0.0000074193144,0.00054319843,0.0037212835],"genre_scores_gemma":[0.00814974,0.007479872,0.98390037,0.00014164686,0.000009732952,0.00009981958,0.00002666029,0.000015457044,0.00017671888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991681,0.00008088209,0.000355685,0.00017299718,0.000087514716,0.00013484075],"domain_scores_gemma":[0.9993673,0.0001962636,0.000049028957,0.00026181104,0.00006928391,0.000056324836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006130024,0.00012066191,0.00044900295,0.000096740645,0.00007134665,0.000009078208,0.00007272848,0.00003611142,0.00017085084],"category_scores_gemma":[0.0000948956,0.00010408012,0.00011360087,0.00032474747,0.000032232434,0.00012671648,0.00004566214,0.0000868058,0.0000013460647],"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":[0.000014300535,0.00013799939,0.005443375,0.002454803,0.0005135908,0.0000045602087,0.00020906354,0.0003090908,0.29629114,0.00086471176,0.052620202,0.6411372],"study_design_scores_gemma":[0.0008167399,0.00015758444,0.043805476,0.0002493744,0.0009991877,0.00001822473,0.00010940187,0.64847845,0.19678941,0.0021529975,0.10526161,0.0011615318],"about_ca_topic_score_codex":0.000012827679,"about_ca_topic_score_gemma":0.000004189104,"teacher_disagreement_score":0.6481694,"about_ca_system_score_codex":0.000040774903,"about_ca_system_score_gemma":0.000005737957,"threshold_uncertainty_score":0.4244264},"labels":[],"label_agreement":null},{"id":"W2099292544","doi":"10.1109/igarss.2002.1027170","title":"Data fusion of multiple polarimetric SAR images using discrete wavelet transform (DWT)","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of Manitoba","funders":"","keywords":"Image fusion; Artificial intelligence; Synthetic aperture radar; Image resolution; Computer science; Remote sensing; Computer vision; Sensor fusion; Wavelet transform; Wavelet; Pattern recognition (psychology); Polarimetry; Pixel; Multiresolution analysis; Discrete wavelet transform; Geography; Image (mathematics)","score_opus":0.029156523232706347,"score_gpt":0.2798564195624212,"score_spread":0.25069989632971484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099292544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015498908,0.000587768,0.97312814,0.0000104642895,0.000094205854,0.0002729396,0.00030111388,0.0005239801,0.009582474],"genre_scores_gemma":[0.5273137,0.0001991347,0.4722479,0.000011648273,0.000012936371,9.901687e-7,0.000056407953,0.000042311636,0.00011497309],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897885,0.000020854073,0.00032434147,0.00022977349,0.00018898865,0.00025720822],"domain_scores_gemma":[0.99906474,0.00007496992,0.000037933674,0.00071520905,0.00004288985,0.000064261505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019324567,0.00018029302,0.00023098734,0.00024285718,0.000050338724,0.000015839882,0.00033200078,0.0000770967,0.00026024884],"category_scores_gemma":[0.00018510029,0.000161566,0.0000485263,0.00051774015,0.00004163139,0.0005638294,0.00008071314,0.00015364749,0.0000063350776],"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":[0.000008142891,0.00003727271,0.00043212276,0.0001238382,0.000025989306,0.0000061629985,0.000042314357,0.0005070128,0.96681136,0.00019250522,0.0015394766,0.03027381],"study_design_scores_gemma":[0.00029205834,0.000024113517,0.00023136094,0.000032679804,0.000018806051,0.000012004663,0.000048916234,0.046496235,0.9386263,0.00034198523,0.013633486,0.00024207866],"about_ca_topic_score_codex":0.000115075745,"about_ca_topic_score_gemma":0.000014487781,"teacher_disagreement_score":0.5118148,"about_ca_system_score_codex":0.000047452802,"about_ca_system_score_gemma":0.000015698017,"threshold_uncertainty_score":0.65884703},"labels":[],"label_agreement":null},{"id":"W2100933137","doi":"10.1109/icpr.2008.4761396","title":"Phase-adaptive image signal fusion using complex-valued wavelets","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"SIGNAL (programming language); Artificial intelligence; Image fusion; Computer science; Computer vision; Wavelet; Image (mathematics); Visualization; Fusion; Pattern recognition (psychology)","score_opus":0.1384835453245831,"score_gpt":0.32453687400908693,"score_spread":0.18605332868450383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100933137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68530184,0.000016146649,0.116771616,0.0012205074,0.0023882997,0.002050764,0.0032351676,0.002508088,0.1865076],"genre_scores_gemma":[0.9792855,0.00081064785,0.013643088,0.001418902,0.0014041872,0.0005924836,0.002321579,0.00030690662,0.000216749],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99235654,0.00003439026,0.0018359036,0.0019605786,0.002594014,0.0012185666],"domain_scores_gemma":[0.9918785,0.000102671234,0.0010860721,0.00023767112,0.0061461125,0.0005489403],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0005169411,0.0015413488,0.0010129778,0.0018135225,0.0006669769,0.0009553378,0.0016094493,0.0005812579,0.011732531],"category_scores_gemma":[0.000202956,0.0017249323,0.00047204044,0.0005853445,0.00046716188,0.0031150584,0.00035707172,0.0017008006,0.001634059],"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":[0.0017785617,0.0031936804,0.0017841505,0.00039088706,0.0011117565,0.0002207764,0.0019276653,0.000023548491,0.49370182,0.0039672023,0.011160683,0.48073927],"study_design_scores_gemma":[0.012039195,0.0029678724,0.0021928702,0.0048870714,0.000280468,0.0012803146,0.0035039505,0.61143297,0.3309403,0.024186192,0.0010935512,0.005195209],"about_ca_topic_score_codex":0.000087455664,"about_ca_topic_score_gemma":0.000014737624,"teacher_disagreement_score":0.6114094,"about_ca_system_score_codex":0.0011607262,"about_ca_system_score_gemma":0.00014133572,"threshold_uncertainty_score":0.9997335},"labels":[],"label_agreement":null},{"id":"W2102555533","doi":"10.1109/72.991418","title":"Neural data fusion algorithms based on a linearly constrained least square method","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of Calgary","funders":"","keywords":"Algorithm; Artificial neural network; Covariance intersection; Covariance matrix; Sensor fusion; Computer science; Covariance; Invertible matrix; Estimation of covariance matrices; Artificial intelligence; Mathematics; Statistics","score_opus":0.03510574400188336,"score_gpt":0.2758330014542319,"score_spread":0.24072725745234852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102555533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004702925,0.0001155291,0.9947364,0.00033967532,0.0011387776,0.0004851776,0.0002648768,0.0020085631,0.00044075452],"genre_scores_gemma":[0.9302318,0.00012927587,0.06818282,0.0006919542,0.00027230507,0.00007443607,0.0000701322,0.00013757266,0.00020971918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979795,0.0001259878,0.00042474887,0.0005961035,0.00033995695,0.0005336784],"domain_scores_gemma":[0.99821466,0.00033478,0.000060076312,0.0011288938,0.00005982745,0.00020176769],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014777733,0.00044456206,0.0003488128,0.00023449954,0.00024753934,0.000083937695,0.0005319644,0.00022827716,0.00075708225],"category_scores_gemma":[0.000010209493,0.00043676197,0.00015245011,0.00055718527,0.00008507948,0.00041669625,0.0000056238696,0.0011270883,0.00003680574],"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":[0.000029607487,0.00007697963,7.220971e-7,0.000011639758,0.0000104451465,0.000029495217,0.000014981658,0.65458053,0.0007206396,8.4153334e-7,0.0013789285,0.3431452],"study_design_scores_gemma":[0.00065973523,0.00028744375,0.000009494945,0.00007049162,0.00003861136,0.00003960399,0.0000151961785,0.9936949,0.0037083656,0.000006384396,0.0010550076,0.00041477606],"about_ca_topic_score_codex":0.000011097687,"about_ca_topic_score_gemma":0.000015480946,"teacher_disagreement_score":0.92976147,"about_ca_system_score_codex":0.000069810376,"about_ca_system_score_gemma":0.0000059840386,"threshold_uncertainty_score":0.99980843},"labels":[],"label_agreement":null},{"id":"W2103898652","doi":"","title":"An EM-CI based approach to fusion of IR and visual images","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Image fusion; Intersection (aeronautics); Infrared; Sensor fusion; Distortion (music); Image (mathematics); Optics; Physics","score_opus":0.012642092272321402,"score_gpt":0.28373798041897885,"score_spread":0.27109588814665747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103898652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18723394,0.0000069063935,0.7510523,0.00023370747,0.00016639462,0.00035171927,0.00006101409,0.00044210217,0.06045188],"genre_scores_gemma":[0.9755866,0.00004066707,0.023670672,0.00043744707,0.000027653292,0.000016800628,0.00018747614,0.0000072398707,0.000025433283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904895,0.000014655851,0.00035107773,0.00011192261,0.00035948516,0.00011391246],"domain_scores_gemma":[0.9993862,0.000016673997,0.00008710948,0.00013305298,0.00029150248,0.000085492255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012283277,0.00014477014,0.00012715619,0.00040486603,0.000045591172,0.00008602495,0.00018891857,0.00007048956,0.0001794862],"category_scores_gemma":[0.000047361507,0.00013849308,0.000026332054,0.00013209626,0.000019562905,0.0010146713,0.000030894826,0.00011736661,0.00003366466],"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":[0.00021336453,0.00017433133,0.00010134894,0.00006218901,0.000009123251,8.963523e-7,0.0010395023,0.014978562,0.29227632,0.012117939,0.0019982946,0.6770281],"study_design_scores_gemma":[0.00055395724,0.0005015851,0.007943663,0.00015314884,0.00000440499,0.0000046450805,0.00044794084,0.8342716,0.15244094,0.0010721418,0.0022960887,0.00030988303],"about_ca_topic_score_codex":0.000004799032,"about_ca_topic_score_gemma":5.5355065e-7,"teacher_disagreement_score":0.819293,"about_ca_system_score_codex":0.000049538416,"about_ca_system_score_gemma":0.000018329332,"threshold_uncertainty_score":0.56475836},"labels":[],"label_agreement":null},{"id":"W2104985664","doi":"10.5589/m11-026","title":"Crop-type identification potential of Radarsat-2 and MODIS images for the Canadian prairies","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Agriculture and Agri-Food Canada; Natural Resources Canada","funders":"","keywords":"Remote sensing; Synthetic aperture radar; Image resolution; Image fusion; Data set; Sensor fusion; Geography; Computer science; Contextual image classification; Moderate-resolution imaging spectroradiometer; Identification (biology); Artificial intelligence; Satellite; Image (mathematics); Engineering","score_opus":0.01674746290532333,"score_gpt":0.22193687659581068,"score_spread":0.20518941369048735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104985664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.089878276,0.0025831796,0.90490484,0.00067815126,0.00097479933,0.00030012275,0.00003786961,0.000031960757,0.000610771],"genre_scores_gemma":[0.8109457,0.00007362097,0.18882549,0.000028934466,0.00006323803,1.3207495e-8,0.0000012033593,0.000022713964,0.000039106013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935377,0.000015082036,0.00027850267,0.00006643524,0.00007765359,0.00020853314],"domain_scores_gemma":[0.99907345,0.000029397692,0.0001236645,0.00014345413,0.00036648603,0.00026354459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022947972,0.0000926384,0.00014640413,0.00026596503,0.00019402611,0.00004933811,0.000111156834,0.000056859604,0.000007056665],"category_scores_gemma":[0.00018656829,0.000080687954,0.000048225866,0.000114518014,0.00015740101,0.0001779539,0.0000046038244,0.00014476321,4.833005e-7],"study_design_candidate":"design_other","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":[0.000018458506,9.607983e-7,0.0000681795,0.000059800765,0.000075190335,0.000097541444,0.0016949029,0.00041263728,0.021331053,0.000054603377,0.0053081964,0.9708785],"study_design_scores_gemma":[0.0011676417,0.0003531014,0.031460583,0.00081439025,0.0005180747,0.0035352423,0.0025856085,0.13218172,0.68011475,0.018315213,0.12783715,0.0011165334],"about_ca_topic_score_codex":0.20717888,"about_ca_topic_score_gemma":0.56062216,"teacher_disagreement_score":0.96976197,"about_ca_system_score_codex":0.0001336575,"about_ca_system_score_gemma":0.00041366427,"threshold_uncertainty_score":0.7981006},"labels":[],"label_agreement":null},{"id":"W2105602976","doi":"10.1080/01431160701313826","title":"Comparison and improvement of wavelet‐based image fusion","year":2007,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of New Brunswick","funders":"","keywords":"Wavelet; Wavelet transform; Artificial intelligence; Image fusion; Stationary wavelet transform; Discrete wavelet transform; Wavelet packet decomposition; Second-generation wavelet transform; Pattern recognition (psychology); Orthogonal wavelet; Biorthogonal wavelet; Lifting scheme; Computer vision; Computer science; Biorthogonal system; Transformation (genetics); Fusion; Mathematics; Image (mathematics)","score_opus":0.010009726990960028,"score_gpt":0.301327414056103,"score_spread":0.291317687065143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105602976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3408512,0.00009779968,0.6581855,0.000065844455,0.00031082568,0.00003129609,0.0000011001847,0.000025468564,0.00043097715],"genre_scores_gemma":[0.60792637,0.000033140463,0.39191058,0.0000331793,0.0000815313,2.1298976e-9,7.752048e-7,0.000010024052,0.000004399866],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999018,0.000006206897,0.00049120694,0.000056646608,0.0003298565,0.00009807647],"domain_scores_gemma":[0.99916846,0.00006882053,0.0002351889,0.000065600696,0.000413453,0.00004848909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000327452,0.00008411772,0.00016388574,0.0002462746,0.000014361312,0.00001737319,0.00009522887,0.00003528819,0.000008770711],"category_scores_gemma":[0.00007386318,0.0000796353,0.00005367281,0.000054698812,0.000035878664,0.00012561276,0.00003170085,0.00016394061,5.5029227e-7],"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":[0.000026739812,0.000005184762,0.000016908767,0.000007981674,0.000014582794,0.000038882416,0.00004688111,0.00008621763,0.5378001,0.000002094806,0.00005774162,0.46189672],"study_design_scores_gemma":[0.0003701617,0.00007307143,0.00071422354,0.000205002,0.000008324404,0.00011050876,0.00008145835,0.09186732,0.9051635,0.00030984782,0.0010230922,0.00007348543],"about_ca_topic_score_codex":0.000009503193,"about_ca_topic_score_gemma":0.000004689191,"teacher_disagreement_score":0.46182323,"about_ca_system_score_codex":0.00009505745,"about_ca_system_score_gemma":0.00001180868,"threshold_uncertainty_score":0.3247433},"labels":[],"label_agreement":null},{"id":"W2106940366","doi":"10.1109/igarss.2003.1295352","title":"Multi- spectral image resolution refinement using stationary wavelet transform","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Panchromatic film; Image fusion; Wavelet; Wavelet transform; Artificial intelligence; Computer science; Image resolution; Computer vision; Pixel; Resolution (logic); Image (mathematics); Pattern recognition (psychology); Stationary wavelet transform; Multiresolution analysis; Image processing; Wavelet packet decomposition","score_opus":0.01643244415887908,"score_gpt":0.2667910657622425,"score_spread":0.2503586216033634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106940366","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021143533,0.00006093689,0.97196954,0.00012378277,0.00006709027,0.00021934103,0.000013720739,0.0010390616,0.0053629894],"genre_scores_gemma":[0.12043843,0.00007223386,0.8791959,0.000044702654,0.000032408047,0.000012593595,0.000021297697,0.00003061918,0.00015176395],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993205,0.000004473712,0.00020083031,0.0001286585,0.0001270617,0.00021846194],"domain_scores_gemma":[0.99976736,0.0000060307884,0.000014169052,0.00013495926,0.000031492153,0.000045986802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056175726,0.00012313495,0.00009181791,0.00008731164,0.000056319666,0.000014156179,0.000065821725,0.000039916406,0.0002239487],"category_scores_gemma":[0.0000071014474,0.000123326,0.000038553746,0.000120958786,0.000025725805,0.00031280227,0.000010983309,0.00010810819,0.00002380778],"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":[0.000009461959,0.000055572156,0.0000032751907,0.00004169995,0.000011773367,0.000022101567,0.0002067838,0.03671703,0.9523665,0.00081919896,0.0005166614,0.009229992],"study_design_scores_gemma":[0.0010038756,0.000058395,0.00058482954,0.000046986705,0.0000106497255,0.000026127907,0.00013331078,0.15260985,0.83908623,0.0034227145,0.0026615157,0.00035548618],"about_ca_topic_score_codex":0.00005153889,"about_ca_topic_score_gemma":0.000036463472,"teacher_disagreement_score":0.11589282,"about_ca_system_score_codex":0.00032035258,"about_ca_system_score_gemma":0.000016576736,"threshold_uncertainty_score":0.5029088},"labels":[],"label_agreement":null},{"id":"W2113063773","doi":"10.1109/crv.2011.40","title":"Human Visual System Based Framework for Concealed Weapon Detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"","keywords":"Fuse (electrical); Artificial intelligence; Computer science; Computer vision; Robustness (evolution); Frequency domain; Human visual system model; Image fusion; Fusion; Frame (networking); Frame rate; Object detection; Image (mathematics); Pattern recognition (psychology); Engineering; Telecommunications","score_opus":0.020999815848559287,"score_gpt":0.2762716663458427,"score_spread":0.2552718504972834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113063773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014791828,0.000015335572,0.9761551,0.0000014427518,0.00020714117,0.00037995327,0.0000027467356,0.0032432438,0.0052032303],"genre_scores_gemma":[0.789361,5.3566146e-7,0.2103142,0.00002109194,0.000054459775,0.00014578087,0.0000020608877,0.00003589222,0.00006496877],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994649,0.0000101593,0.00016996622,0.00012576807,0.00006489575,0.00016431084],"domain_scores_gemma":[0.9996868,0.000039828737,0.000026974103,0.00015904634,0.00004300273,0.00004434969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007289023,0.000113392256,0.00012586976,0.00006895228,0.00006788059,0.000010358611,0.00007802839,0.00011129846,0.00013505953],"category_scores_gemma":[0.000023590552,0.000110818546,0.00005090645,0.00008093034,0.000015881795,0.00009064384,0.000009846361,0.00009974024,0.000015754767],"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":[0.00007316339,0.00007636839,0.000112935646,0.0009114833,0.000049593793,0.000009445088,0.0002788929,0.0003180218,0.9159578,0.03217992,0.00074249425,0.049289852],"study_design_scores_gemma":[0.00020593568,0.00014362232,0.00013071095,0.00007274631,0.000009323524,0.0000016522613,0.00015841315,0.08724916,0.91004056,0.0014343108,0.00037488266,0.00017865673],"about_ca_topic_score_codex":0.000009479293,"about_ca_topic_score_gemma":0.0000065588124,"teacher_disagreement_score":0.77456915,"about_ca_system_score_codex":0.000077745244,"about_ca_system_score_gemma":0.0000034487596,"threshold_uncertainty_score":0.45190492},"labels":[],"label_agreement":null},{"id":"W2113318397","doi":"10.1109/tip.2010.2090530","title":"A Maximum Likelihood Approach to Joint Image Registration and Fusion","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":47,"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; University of Calgary","funders":"","keywords":"Image registration; Artificial intelligence; Image fusion; Computer science; Mean squared error; Computer vision; Sensor fusion; Pattern recognition (psychology); Maximization; Image (mathematics); Joint (building); Mathematics; Statistics; Mathematical optimization","score_opus":0.009470207990330928,"score_gpt":0.23585884896061102,"score_spread":0.22638864097028008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113318397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016109625,0.000041785675,0.9756041,0.00013431793,0.00020623193,0.00039079098,0.000012841542,0.0012630572,0.0062372154],"genre_scores_gemma":[0.56683016,0.000030393663,0.4327752,0.000068216825,0.000046783414,0.00010993814,0.0000025859542,0.00006009129,0.00007663312],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987683,0.000013954184,0.00030551333,0.00037426868,0.00021683767,0.00032110186],"domain_scores_gemma":[0.99933887,0.000016568258,0.000048617523,0.00030232468,0.000118876305,0.00017476773],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017063315,0.00026159533,0.0001978809,0.00026974035,0.00027587978,0.00022650436,0.00012299069,0.00012412976,0.00004459151],"category_scores_gemma":[0.000015127017,0.00026988608,0.000052839187,0.00034049264,0.00008703426,0.00082848035,0.00000290837,0.0006261104,0.00003242832],"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":[0.000010563794,0.00006185001,2.5592973e-7,0.00012713792,0.000003413192,0.0000026332755,0.00026177443,0.00021161513,0.76456565,0.0000024177366,0.00018718808,0.23456548],"study_design_scores_gemma":[0.00028136492,0.000059018406,0.00003715784,0.00009591231,0.000023323546,0.00007867581,0.00014462507,0.05481039,0.9426099,0.0009819284,0.00049199874,0.00038567703],"about_ca_topic_score_codex":0.000007492419,"about_ca_topic_score_gemma":0.000010678995,"teacher_disagreement_score":0.5507205,"about_ca_system_score_codex":0.000047463887,"about_ca_system_score_gemma":0.000029532532,"threshold_uncertainty_score":0.9999753},"labels":[],"label_agreement":null},{"id":"W2116037602","doi":"10.1109/dfua.2003.1219966","title":"High resolution image fusion based on wavelet and IHS transformations","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of New Brunswick","funders":"","keywords":"Image fusion; Wavelet transform; Artificial intelligence; Computer science; Computer vision; Wavelet; Fusion; Image (mathematics); Sensor fusion; Image resolution; Stationary wavelet transform; Pattern recognition (psychology); Discrete wavelet transform","score_opus":0.0046723196363916576,"score_gpt":0.20503729555398748,"score_spread":0.2003649759175958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116037602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008808961,0.000025272482,0.93730265,0.00011701061,0.000054909546,0.00017672816,0.000008932843,0.0008364647,0.052669078],"genre_scores_gemma":[0.71160495,0.00009873436,0.2879197,0.00014133823,0.00000944194,0.000023482515,0.000015213318,0.000021225513,0.00016588095],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955666,0.000015836282,0.00012558112,0.0000879114,0.00008800128,0.00012599266],"domain_scores_gemma":[0.99976104,0.000029769044,0.0000089886435,0.00013690337,0.000021447117,0.00004186691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077221026,0.00009370539,0.00007452245,0.00009497918,0.00006495945,0.000016358506,0.000031300264,0.000045697758,0.00033329162],"category_scores_gemma":[0.000027095184,0.00008607345,0.000018765413,0.000108009815,0.00001899854,0.00019566249,0.0000032986666,0.00009229942,0.000023018103],"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":[0.000041739844,0.00014731636,0.000023517721,0.00022760649,0.000013792535,0.000014928884,0.000308017,0.023418544,0.79233366,0.043506704,0.041787174,0.09817697],"study_design_scores_gemma":[0.0005669833,0.00008809129,0.00030400287,0.000039940667,0.000006963599,0.000005749205,0.000037838505,0.25525934,0.7201791,0.0028905668,0.020351136,0.0002703192],"about_ca_topic_score_codex":0.0000042794263,"about_ca_topic_score_gemma":0.0000034085442,"teacher_disagreement_score":0.70279604,"about_ca_system_score_codex":0.000035681525,"about_ca_system_score_gemma":0.000004719966,"threshold_uncertainty_score":0.36493096},"labels":[],"label_agreement":null},{"id":"W2123661622","doi":"10.1109/ccece.2008.4564752","title":"The 2-directional wavelet transform: Theory and implementation","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Wavelet transform; Stationary wavelet transform; Second-generation wavelet transform; Wavelet; Harmonic wavelet transform; Contourlet; Mathematics; Discrete wavelet transform; High-pass filter; Filter bank; Artificial intelligence; Wavelet packet decomposition; Pattern recognition (psychology); Algorithm; Filter (signal processing); Computer science; Computer vision; Low-pass filter","score_opus":0.008431111176512069,"score_gpt":0.20589629262702253,"score_spread":0.19746518145051045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123661622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27505004,0.0011562032,0.71151006,0.0016069543,0.00047805643,0.0012223,0.00003656288,0.0017580972,0.007181679],"genre_scores_gemma":[0.99560064,0.0013982503,0.0026290147,0.00011224824,0.0000872142,0.0000712382,0.0000044836074,0.000025971409,0.00007093617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989316,0.0000065809286,0.00019932551,0.00025106163,0.00014256373,0.0004688591],"domain_scores_gemma":[0.99940753,0.0000810712,0.000020974228,0.000064024345,0.000117608826,0.0003087802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013568898,0.00023423898,0.00016948004,0.00018400584,0.00031678777,0.00015133711,0.00015077039,0.00008033387,0.000029149007],"category_scores_gemma":[0.000018038781,0.00020053652,0.000025602156,0.00020722064,0.00007601702,0.00021255473,0.000017977081,0.00033339547,0.00000301464],"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":[0.000028696564,0.000011202153,0.000435069,0.000066836685,0.00006604525,0.000019400479,0.0011194715,0.000104664054,0.0058392873,0.282757,0.0023604329,0.7071919],"study_design_scores_gemma":[0.0005854471,0.00039295442,0.0113316635,0.00010271513,0.000020940868,0.00038589942,0.00014255532,0.94380856,0.012635596,0.011954064,0.017694473,0.0009451201],"about_ca_topic_score_codex":0.00022684949,"about_ca_topic_score_gemma":0.00034856927,"teacher_disagreement_score":0.9437039,"about_ca_system_score_codex":0.00010263284,"about_ca_system_score_gemma":0.00009995358,"threshold_uncertainty_score":0.8177642},"labels":[],"label_agreement":null},{"id":"W2124952510","doi":"10.1109/tgrs.2008.2005639","title":"Fusion of Multispectral and Panchromatic Images Using a Restoration-Based Method","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":101,"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":"Panchromatic film; Multispectral image; Multispectral pattern recognition; Image fusion; Image resolution; Computer science; Remote sensing; Artificial intelligence; Computer vision; Geology; Image (mathematics)","score_opus":0.01805358677677881,"score_gpt":0.26857396983299614,"score_spread":0.2505203830562173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124952510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27401963,0.00004925385,0.72559136,0.000015281987,0.0000824723,0.00009170427,0.000003264829,0.00012468133,0.000022327844],"genre_scores_gemma":[0.468319,0.000111664456,0.531531,0.000012592919,0.0000054214006,5.8411093e-8,1.4151115e-7,0.000008570138,0.000011572898],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930745,0.000032093143,0.00018469726,0.00017937344,0.00014373628,0.00015264939],"domain_scores_gemma":[0.99963456,0.00009267617,0.00003931223,0.00013709073,0.00004163851,0.000054729382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115239665,0.00011933639,0.0001549501,0.00019891514,0.00028186687,0.00001420919,0.000034526092,0.000050673676,0.0000024651545],"category_scores_gemma":[0.00000957333,0.000112817215,0.000029944247,0.00027401492,0.00020626059,0.00019286035,9.862708e-7,0.00012660473,3.633419e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056189006,0.0000075569483,9.1258386e-7,0.000040220104,0.0000025226439,0.000009666481,0.00026007256,0.012642126,0.71514666,3.977515e-7,0.0000021486169,0.2718821],"study_design_scores_gemma":[0.00009735737,0.000028768922,0.00011319402,0.000092385686,0.0000076107854,0.00008692884,0.000032020365,0.59274185,0.4066659,0.000051144012,0.0000044603166,0.00007839437],"about_ca_topic_score_codex":0.0001780529,"about_ca_topic_score_gemma":0.000019357969,"teacher_disagreement_score":0.5800997,"about_ca_system_score_codex":0.000031969867,"about_ca_system_score_gemma":0.000023966619,"threshold_uncertainty_score":0.46005526},"labels":[],"label_agreement":null},{"id":"W2125058434","doi":"10.1007/s00034-015-0097-2","title":"Structuring of Contourlet Transform for Pipeline-Based Implementation","year":2015,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"McMaster University","funders":"","keywords":"Computer science; Contourlet; Pipeline (software); Quantization (signal processing); Field-programmable gate array; Computer hardware; Hardware architecture; Computer engineering; Artificial intelligence; Computer vision; Wavelet transform; Software; Wavelet","score_opus":0.028752429378169086,"score_gpt":0.28424824208001226,"score_spread":0.2554958127018432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125058434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026999218,0.0020748707,0.9696615,0.000007037751,0.000065365755,0.00046875843,0.000029523144,0.00018778174,0.0005059627],"genre_scores_gemma":[0.9978988,0.000004330711,0.0019019586,0.000007795264,0.00007683493,0.00005716974,0.000013906996,0.000026727226,0.000012442241],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992954,0.000008284058,0.00031032093,0.00011242038,0.0001239843,0.00014959468],"domain_scores_gemma":[0.9996301,0.000024562234,0.00007189412,0.000053489974,0.00015315524,0.000066800494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019983215,0.00010944922,0.00019438879,0.00007627049,0.00004670875,0.000043237265,0.00005325396,0.000045421224,0.000003105561],"category_scores_gemma":[0.0000072468715,0.00010409475,0.00002375761,0.00007007089,0.000021333815,0.00022378673,0.0000035309647,0.00004799624,1.3055532e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012970637,0.000008388212,0.00015351857,0.0030369677,0.000011729822,0.0000014752267,0.0010330961,0.012270571,0.13686842,0.00014162963,0.00033653807,0.8461247],"study_design_scores_gemma":[0.0023057081,0.00015927969,0.000065067,0.00061329454,0.000040283612,0.000017452046,0.002878145,0.43711478,0.55209994,0.0016420968,0.0026602403,0.00040373966],"about_ca_topic_score_codex":0.000014407641,"about_ca_topic_score_gemma":0.0000035374617,"teacher_disagreement_score":0.97089964,"about_ca_system_score_codex":0.00004101644,"about_ca_system_score_gemma":0.000043376684,"threshold_uncertainty_score":0.4244861},"labels":[],"label_agreement":null},{"id":"W2126340432","doi":"10.3390/rs5126346","title":"An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model","year":2013,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":96,"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":"U.S. Geological Survey; State Key Laboratory of Resources and Environmental Information System; National Natural Science Foundation of China; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; National Science Foundation","keywords":"Remote sensing; Reflectivity; Temporal resolution; Satellite; Fusion; Image resolution; Land cover; Computer science; Environmental science; Computer vision; Land use; Geology; Optics","score_opus":0.010553144957535164,"score_gpt":0.23681482333740403,"score_spread":0.22626167837986885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126340432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12947643,0.00002324796,0.8659096,0.000074583906,0.00005709385,0.00058194326,0.0000018549455,0.00063393527,0.0032413632],"genre_scores_gemma":[0.61446166,0.00002079171,0.3852523,0.00013391633,0.000055387016,3.5750847e-7,0.0000065010313,0.00004896522,0.00002008801],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881536,0.000063277555,0.00023958618,0.00037093862,0.00019204263,0.00031882134],"domain_scores_gemma":[0.99913263,0.00004930933,0.00007239963,0.0005117446,0.00013940159,0.000094483315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013184342,0.00027404557,0.00021566113,0.00009071275,0.00020271503,0.0000790072,0.000118550925,0.000117713906,0.000005182004],"category_scores_gemma":[0.000043150267,0.00021249897,0.00003897607,0.00015372706,0.00008058529,0.0003031407,0.000049302973,0.0003547586,0.0000055293476],"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":[0.00002695481,0.000007824297,1.5460078e-7,0.000014064842,0.0000021880617,0.0000016831143,0.00015212438,0.01603315,0.7555482,0.0000016553882,0.000054343833,0.2281577],"study_design_scores_gemma":[0.00020564694,0.00009692374,0.000018174323,0.00006356075,0.0000057486213,0.0000053252757,0.00006948756,0.8121191,0.18659766,0.0005943791,0.000008175641,0.00021580193],"about_ca_topic_score_codex":0.00013360377,"about_ca_topic_score_gemma":0.000016729235,"teacher_disagreement_score":0.79608595,"about_ca_system_score_codex":0.00009393954,"about_ca_system_score_gemma":0.000019335275,"threshold_uncertainty_score":0.8665456},"labels":[],"label_agreement":null},{"id":"W2126908014","doi":"10.1109/eit.2009.5189644","title":"Fingerprint image compression standard based on wave atoms decomposition and self organizing feature map","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Windsor","funders":"","keywords":"Artificial intelligence; Computer science; Vector quantization; Pattern recognition (psychology); Wavelet transform; Image compression; Data compression; Wavelet; Quantization (signal processing); Computer vision; Fingerprint (computing); Feature extraction; Image processing; Image (mathematics)","score_opus":0.0036397986778249135,"score_gpt":0.23128946761594968,"score_spread":0.22764966893812477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126908014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041544486,0.00011384948,0.9476923,0.0006556361,0.000088598455,0.00030194165,0.000011526236,0.0031758815,0.0064157895],"genre_scores_gemma":[0.6095139,0.000032133164,0.39006123,0.00029033882,0.000026371437,0.0000032782884,0.000012036555,0.00002421777,0.00003649416],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993393,0.000015902307,0.00012093025,0.00019083782,0.00015560552,0.00017743971],"domain_scores_gemma":[0.99961245,0.00003777363,0.000022528297,0.00020681205,0.000046379497,0.0000740445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000063439256,0.00017372475,0.00015854042,0.000091251895,0.0000821541,0.0000578951,0.000060154896,0.00008507834,0.00010732303],"category_scores_gemma":[0.000009738827,0.00015398573,0.000026367217,0.00009269975,0.000012967383,0.00016403644,0.000025275227,0.00021640575,0.000010296787],"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":[0.000068847585,0.00007782517,0.000047543912,0.00009102241,0.000009670299,0.000057503104,0.00017447144,0.0013614519,0.9268266,0.0003327081,0.020660883,0.050291467],"study_design_scores_gemma":[0.00039533793,0.00017632006,0.0012936146,0.00018725137,0.000008134661,0.000009162468,0.000017952832,0.14637904,0.84533995,0.00091944303,0.0049993475,0.000274443],"about_ca_topic_score_codex":7.0450164e-7,"about_ca_topic_score_gemma":5.600491e-7,"teacher_disagreement_score":0.5679694,"about_ca_system_score_codex":0.00010626673,"about_ca_system_score_gemma":0.00000584705,"threshold_uncertainty_score":0.6279356},"labels":[],"label_agreement":null},{"id":"W2128035609","doi":"10.1109/ccece.2008.4564491","title":"Mean shift point-mass level-of-detail","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Counterintuitive; Point (geometry); Metric (unit); Computer science; Segmentation; Measure (data warehouse); Image (mathematics); Image segmentation; Field (mathematics); Artificial intelligence; Homogeneous; Pattern recognition (psychology); Algorithm; Mathematics; Data mining; Geometry; Physics; Combinatorics","score_opus":0.03186372917017686,"score_gpt":0.2054821520585913,"score_spread":0.17361842288841445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128035609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21211214,0.00037136328,0.7769725,0.00039474733,0.0003271777,0.00059547386,0.000049881586,0.001407574,0.007769184],"genre_scores_gemma":[0.98568976,0.0003092352,0.013646244,0.0000948931,0.00009570981,0.00003597238,0.0000051532024,0.000052579508,0.00007044964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824345,0.000005019288,0.00038377792,0.00041334276,0.00024241532,0.0007119654],"domain_scores_gemma":[0.9989214,0.000044774646,0.00005385784,0.00015644435,0.00023182807,0.0005916913],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008587012,0.00040409903,0.0004571664,0.000519546,0.000109278626,0.00008570339,0.00038109158,0.00017813964,0.00007103673],"category_scores_gemma":[0.000035758287,0.00042576098,0.00006502806,0.00046642835,0.00008948582,0.00032088236,0.000039540115,0.00054100604,0.000013386156],"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":[0.000098612785,0.00017756448,0.012443046,0.0012624881,0.0004367757,0.00043267914,0.0076914546,0.0054343296,0.10366747,0.5601942,0.008934625,0.29922676],"study_design_scores_gemma":[0.0003684237,0.00034740902,0.0062984293,0.00024388483,0.000016503553,0.00009840798,0.000025018227,0.9655327,0.021854417,0.0023088846,0.002001918,0.000904004],"about_ca_topic_score_codex":0.000691703,"about_ca_topic_score_gemma":0.00035976307,"teacher_disagreement_score":0.9600984,"about_ca_system_score_codex":0.00014807898,"about_ca_system_score_gemma":0.00018691314,"threshold_uncertainty_score":0.9998194},"labels":[],"label_agreement":null},{"id":"W2130815094","doi":"10.1007/978-3-642-21593-3_11","title":"A Class of Image Metrics Based on the Structural Similarity Quality Index","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":30,"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":"Metric (unit); Computer science; Norm (philosophy); Similarity (geometry); Index (typography); Class (philosophy); Scalar (mathematics); Pattern recognition (psychology); Feature (linguistics); Algorithm; Image (mathematics); Structural similarity; Artificial intelligence; Data mining; Mathematics; Geometry","score_opus":0.025887249203038608,"score_gpt":0.27116588431453476,"score_spread":0.24527863511149614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130815094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028341968,0.00007094185,0.9922821,0.00008550163,0.00041869364,0.0003216752,0.000028191407,0.00022613542,0.0062833414],"genre_scores_gemma":[0.76763016,0.000015523347,0.23160376,0.00057926757,0.0000897434,0.000006288575,0.0000035693683,0.000045527944,0.000026141126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804175,0.00003404989,0.0004371746,0.0004885499,0.0006797073,0.00031878025],"domain_scores_gemma":[0.9977313,0.00081346824,0.00017953053,0.0010151593,0.00019729281,0.00006320169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00068582693,0.0003744764,0.00041951193,0.0005647792,0.00008664974,0.000057734087,0.0012413956,0.00025493946,0.000102730046],"category_scores_gemma":[0.00032990635,0.00027055835,0.00011368779,0.0005004114,0.0007616056,0.00015647702,0.000280971,0.0010201146,0.0000035863015],"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":[0.00004790089,0.000034940273,0.0003505342,0.00037428306,0.000027883174,0.000042652417,0.0005203873,0.18563879,0.0050948374,0.011737645,0.00014989419,0.7959803],"study_design_scores_gemma":[0.00013656926,0.00009370619,0.00076171674,0.00020940967,0.000007197357,0.0000035181504,1.5622034e-7,0.7948029,0.05204972,0.15103325,0.00040559724,0.0004962465],"about_ca_topic_score_codex":0.000023869437,"about_ca_topic_score_gemma":0.000029513523,"teacher_disagreement_score":0.795484,"about_ca_system_score_codex":0.00020194892,"about_ca_system_score_gemma":0.000095601245,"threshold_uncertainty_score":0.99997467},"labels":[],"label_agreement":null},{"id":"W2132854572","doi":"10.1109/icip.2009.5414473","title":"A generic fingerprint image compression technique based on wave atoms decomposition","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Windsor","funders":"","keywords":"Vector quantization; Computer science; Image compression; Wavelet transform; Artificial intelligence; Data compression; Quantization (signal processing); Wavelet; Fingerprint (computing); Pattern recognition (psychology); Computer vision; Entropy (arrow of time); Algorithm; Image (mathematics); Image processing; Physics","score_opus":0.007920384977523572,"score_gpt":0.2530095637912303,"score_spread":0.24508917881370673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132854572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005830389,0.000021702814,0.9648806,0.00011354254,0.00004687355,0.00039678795,0.0000032896737,0.0029573806,0.02574947],"genre_scores_gemma":[0.5681744,0.000014318988,0.43127808,0.00038620707,0.000023609435,0.000053482137,0.000012556077,0.000023224822,0.000034093766],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99918455,0.000021633163,0.00020565242,0.00020917055,0.00015943813,0.0002195586],"domain_scores_gemma":[0.99946797,0.000033277356,0.000028920289,0.00035883358,0.000037684422,0.0000733028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075904165,0.00019748941,0.00016553764,0.00016885567,0.000058260484,0.000029513618,0.00011750388,0.00009147124,0.00023192217],"category_scores_gemma":[0.000010686317,0.00017978113,0.00006445259,0.0001750186,0.000018520568,0.00013951062,0.000022208764,0.00020016417,0.0000375276],"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":[0.0000134722495,0.00005383874,0.000002867221,0.000012744404,0.0000016199572,0.000019975028,0.000008107549,0.004122419,0.94809824,0.0001530049,0.0031668723,0.044346817],"study_design_scores_gemma":[0.00011625117,0.00010130388,0.00033871137,0.00007571423,0.0000025290065,0.0000070845463,0.0000018109414,0.2699698,0.7268455,0.0012397738,0.0011179132,0.00018362257],"about_ca_topic_score_codex":0.0000020331563,"about_ca_topic_score_gemma":3.6429134e-7,"teacher_disagreement_score":0.5623441,"about_ca_system_score_codex":0.00011655584,"about_ca_system_score_gemma":0.0000064216533,"threshold_uncertainty_score":0.73312616},"labels":[],"label_agreement":null},{"id":"W2136689788","doi":"10.1007/978-3-319-10602-1_5","title":"Spectral Edge Image Fusion: Theory and Applications","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Simon Fraser University","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Structure tensor; Rendering (computer graphics); Artificial intelligence; Contrast (vision); Computer vision; Image fusion; Image (mathematics); Image gradient; Algorithm; Image processing; Edge detection","score_opus":0.005407876286158084,"score_gpt":0.22661171584089917,"score_spread":0.2212038395547411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136689788","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017511318,0.0006645048,0.98428595,0.000041931526,0.00024243406,0.0003461868,0.000005585377,0.00048029897,0.013915608],"genre_scores_gemma":[0.039932746,0.00052663434,0.95638996,0.0006801144,0.0013623283,0.00006411928,0.000011809849,0.00013280276,0.0008994548],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99872255,0.000011165079,0.0002224955,0.0005249212,0.00023124542,0.0002876065],"domain_scores_gemma":[0.99895483,0.00026719272,0.00005180228,0.0005773898,0.000059650025,0.0000891248],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036582092,0.00029998115,0.00026392195,0.00031535586,0.00011127239,0.000100104604,0.00055183884,0.0001633391,0.00006745641],"category_scores_gemma":[0.000030197058,0.00028302183,0.000042646734,0.00015237904,0.00061196607,0.00013173491,0.00029686853,0.0005139738,0.00002843181],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022554132,0.000003285505,0.0000025537577,0.000057045632,0.0000031110599,0.000009152639,0.00008762334,0.0028702547,0.004488867,0.02289652,0.000059176935,0.96952015],"study_design_scores_gemma":[0.00015424256,0.00006973817,0.00005605111,0.00027306326,0.000012248878,0.000086956185,1.890302e-7,0.07310932,0.025568599,0.87425846,0.025555527,0.00085560116],"about_ca_topic_score_codex":7.3877675e-7,"about_ca_topic_score_gemma":0.000005041611,"teacher_disagreement_score":0.9686645,"about_ca_system_score_codex":0.00009893087,"about_ca_system_score_gemma":0.000033102096,"threshold_uncertainty_score":0.9999622},"labels":[],"label_agreement":null},{"id":"W2140539344","doi":"10.1109/cse.2009.228","title":"Visible and IR Data Fusion Technique Using the Contourlet Transform","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Guelph","funders":"","keywords":"Artificial intelligence; Contourlet; Image fusion; Computer vision; Computer science; Pixel; Image resolution; Pattern recognition (psychology); Fusion; Image (mathematics); Noise (video); Sensitivity (control systems); Process (computing); Image segmentation; Wavelet transform","score_opus":0.02262796035498927,"score_gpt":0.2850728186380333,"score_spread":0.26244485828304404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140539344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028778736,0.00033919897,0.9826656,0.00032841344,0.000026866963,0.00044085784,0.000015438103,0.0010002922,0.012305457],"genre_scores_gemma":[0.64477545,0.0006117259,0.35397407,0.0003380829,0.00006363842,0.000013735542,0.000021242595,0.000033152828,0.00016889251],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944514,0.000008488391,0.00014227857,0.00015270036,0.00009086196,0.00016053313],"domain_scores_gemma":[0.9993952,0.000024783758,0.00001252755,0.00051382027,0.000016947426,0.000036700032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016643386,0.00011737013,0.00010842381,0.000047306752,0.00008236103,0.000034392255,0.00027692365,0.0000581603,0.0000697952],"category_scores_gemma":[0.000012004866,0.00007992825,0.000014304039,0.00012048073,0.000029657413,0.00036676344,0.000056591518,0.00014374588,0.00000156362],"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":[0.0000060070943,0.000013176426,0.0000068411714,0.000018692595,0.0000052456357,0.000007330499,0.000076153214,0.0001490201,0.8093561,0.0006412342,0.005826371,0.18389384],"study_design_scores_gemma":[0.0002560587,0.00007922183,0.00012434369,0.00007888791,0.000019337658,0.000121231686,0.000112506954,0.15290768,0.78672266,0.008260642,0.050950125,0.00036728568],"about_ca_topic_score_codex":0.00001510707,"about_ca_topic_score_gemma":0.000008630344,"teacher_disagreement_score":0.64189756,"about_ca_system_score_codex":0.00002196377,"about_ca_system_score_gemma":0.000005996592,"threshold_uncertainty_score":0.32593793},"labels":[],"label_agreement":null},{"id":"W2140657893","doi":"10.1109/naecon.2011.6183109","title":"LANDSAT satellite image fusion metric assessment","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Defence Research and Development Canada","funders":"","keywords":"Multispectral image; Image fusion; Computer science; Artificial intelligence; Remote sensing; Metric (unit); Computer vision; Satellite imagery; Satellite; Fusion; Entropy (arrow of time); Sensor fusion; Image (mathematics); Pattern recognition (psychology); Geography; Engineering","score_opus":0.014712028357008687,"score_gpt":0.2516657246976009,"score_spread":0.2369536963405922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140657893","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005838882,0.00021406054,0.5209174,0.000008367349,0.00012720982,0.0001322889,0.0000019090273,0.001844263,0.4709156],"genre_scores_gemma":[0.4588199,0.00092560024,0.5391718,0.00005501054,0.000028359087,0.000020264437,0.0000061136875,0.00003799551,0.00093495514],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994222,0.000009607624,0.00015127085,0.00012258276,0.00011603821,0.00017829206],"domain_scores_gemma":[0.9996372,0.000019588651,0.000015465746,0.00023580652,0.00003258475,0.000059343845],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008709083,0.00011746852,0.00011224875,0.00017055277,0.000026395517,0.000014191351,0.00011919215,0.000044931858,0.0018740353],"category_scores_gemma":[0.000010634306,0.00009866445,0.0000377982,0.0002761275,0.0000148162935,0.00021228334,0.000054828906,0.00011286373,0.00015835455],"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":[0.000011144068,0.00012105128,0.003920496,0.00010661445,0.000041229774,0.000076859564,0.00036507595,0.000028177654,0.4086506,0.0030552554,0.012901795,0.5707217],"study_design_scores_gemma":[0.0003339123,0.00009726003,0.031001978,0.000025246562,0.000017471268,0.0000140019565,0.000104277664,0.0060676914,0.91660774,0.00369177,0.041516077,0.00052258675],"about_ca_topic_score_codex":0.000013350954,"about_ca_topic_score_gemma":0.0000033132376,"teacher_disagreement_score":0.57019913,"about_ca_system_score_codex":0.000048087248,"about_ca_system_score_gemma":0.0000041954695,"threshold_uncertainty_score":0.9990384},"labels":[],"label_agreement":null},{"id":"W2142224063","doi":"10.1007/978-1-4419-5913-3_68","title":"A Fuzzy Approach for Contrast Enhancement of Mammography Breast Images","year":2010,"lang":"en","type":"article","venue":"Advances in experimental medicine and biology","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Toronto Metropolitan University","funders":"","keywords":"Mammography; Fuzzy logic; Contrast (vision); Artificial intelligence; Computer science; Contrast enhancement; Image (mathematics); Computer vision; Selection (genetic algorithm); Ambiguity; Pattern recognition (psychology); Breast cancer; Radiology; Medicine; Magnetic resonance imaging","score_opus":0.009495380761112738,"score_gpt":0.3065149565588295,"score_spread":0.29701957579771676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142224063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56530935,0.08389287,0.31801507,0.00020803444,0.0014716126,0.0021082372,0.0001228913,0.00039151334,0.028480448],"genre_scores_gemma":[0.9372295,0.0016027299,0.060774613,0.000051900508,0.000085700485,0.00020879917,0.000028866896,0.000012265247,0.0000056047975],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934375,0.00000909236,0.00024673186,0.00017959678,0.000037577553,0.00018326196],"domain_scores_gemma":[0.9997178,0.00006466077,0.0000435574,0.00011229832,0.000021535263,0.000040197527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000085513915,0.00013662082,0.00027138408,0.00009737501,0.000019455818,0.000001410999,0.000090380876,0.000063442785,0.000024738792],"category_scores_gemma":[0.000016093974,0.00010437975,0.000024902654,0.00009540589,0.00042890385,0.00011273333,0.000026621003,0.00011057146,1.4460764e-7],"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":[0.00003963602,0.000055131426,0.00091464305,0.00004809679,0.0000067764663,3.844611e-7,0.00012282045,0.0000023982527,0.95883936,0.0012443791,0.00007797182,0.038648434],"study_design_scores_gemma":[0.0011213523,0.0005385713,0.00017090747,0.000038540737,0.0000055160704,0.000026568432,0.0008416235,0.00019515908,0.98982966,0.002444924,0.004630568,0.0001566231],"about_ca_topic_score_codex":0.000008029339,"about_ca_topic_score_gemma":0.0000040467276,"teacher_disagreement_score":0.37192017,"about_ca_system_score_codex":0.000008156912,"about_ca_system_score_gemma":0.0000024790006,"threshold_uncertainty_score":0.42564827},"labels":[],"label_agreement":null},{"id":"W2144567021","doi":"10.1142/s0219691311004444","title":"AN IMAGE FUSION FRAMEWORK BASED ON HUMAN VISUAL SYSTEM IN FRAMELET DOMAIN","year":2011,"lang":"en","type":"article","venue":"International Journal of Wavelets Multiresolution and Information Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":28,"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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fuse (electrical); Image fusion; Artificial intelligence; Computer science; Fusion; Image (mathematics); Computer vision; Frequency domain; Pattern recognition (psychology); Fusion rules; Engineering","score_opus":0.012227460072606193,"score_gpt":0.29327327661138675,"score_spread":0.2810458165387806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144567021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32161406,0.000035095156,0.6753223,0.00003785688,0.0002870373,0.00011929673,0.000006760942,0.00016778594,0.0024098284],"genre_scores_gemma":[0.907958,0.00001911211,0.09173311,0.00018044008,0.00007679539,0.0000048673437,0.000013797391,0.000013008468,9.0458275e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986139,0.000034135825,0.0007122038,0.00007381541,0.0004235347,0.00014242825],"domain_scores_gemma":[0.99908936,0.000024777952,0.00033271822,0.00007469139,0.00039557114,0.00008288341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000370832,0.00014290307,0.00015760673,0.0007147268,0.00007996131,0.00012163915,0.00020535356,0.000115344235,0.00003668914],"category_scores_gemma":[0.00007382163,0.00013331111,0.000039304407,0.00013887418,0.00004585381,0.0036367786,0.000020628424,0.00034095751,0.0000066671296],"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":[0.000995156,0.0007143709,0.003718664,0.0007828493,0.000064573665,0.00018227553,0.017112976,0.014914795,0.07534715,0.00943179,0.00033562866,0.87639976],"study_design_scores_gemma":[0.0018437153,0.00037118848,0.021110484,0.0021171246,0.000008157734,0.00011992249,0.0020036758,0.9469043,0.023154624,0.0008756831,0.0011084143,0.00038271473],"about_ca_topic_score_codex":0.0000072987546,"about_ca_topic_score_gemma":0.0000010848788,"teacher_disagreement_score":0.9319895,"about_ca_system_score_codex":0.00023712411,"about_ca_system_score_gemma":0.00003021688,"threshold_uncertainty_score":0.54362696},"labels":[],"label_agreement":null},{"id":"W2144738839","doi":"10.4304/jnw.9.2.501-506","title":"An Image Fusion Method Based on NSCT and Dual-channel PCNN Model","year":2014,"lang":"en","type":"article","venue":"Journal of Networks","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer science; Dual (grammatical number); Artificial intelligence; Computer vision; Image (mathematics); Image fusion; Fusion; Channel (broadcasting); Pattern recognition (psychology); Telecommunications","score_opus":0.007480037986895567,"score_gpt":0.26632555880745745,"score_spread":0.2588455208205619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144738839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060408553,0.0001296614,0.9925508,0.000074249525,0.00015719069,0.00006199931,0.0000011856521,0.00012358338,0.0008604897],"genre_scores_gemma":[0.607121,0.00016418293,0.39209494,0.00024347322,0.00031516084,0.0000018715051,0.0000012861751,0.000041958225,0.000016128311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991529,0.00007271434,0.0003036057,0.000106230356,0.00017986607,0.00018470932],"domain_scores_gemma":[0.9993161,0.00012712572,0.00012190605,0.00019968495,0.00008917618,0.00014598679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006288678,0.00015102718,0.00024431414,0.00014228409,0.00005068379,0.00003770283,0.00010457105,0.00010100942,0.000014899766],"category_scores_gemma":[0.000044413762,0.00012790164,0.00006516112,0.0000884266,0.000019518555,0.00028075484,0.000020305031,0.0004254551,8.546017e-7],"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":[0.000034224962,0.00002093129,0.0000038035428,0.000009594823,0.0000049793944,0.000013005959,0.000026934886,0.94375795,0.014224419,0.000021047932,0.0023395577,0.039543573],"study_design_scores_gemma":[0.0003405666,0.00025578451,0.00007748488,0.00009579258,0.000014712775,0.000041813742,0.000007799383,0.9928647,0.0038660893,0.001938775,0.00036078366,0.0001356758],"about_ca_topic_score_codex":6.3381646e-7,"about_ca_topic_score_gemma":6.365362e-7,"teacher_disagreement_score":0.6010801,"about_ca_system_score_codex":0.000034826553,"about_ca_system_score_gemma":0.000007279312,"threshold_uncertainty_score":0.52156776},"labels":[],"label_agreement":null},{"id":"W2145433418","doi":"10.1109/icapr.2009.97","title":"Multimodal Medical Image Fusion Using Redundant Discrete Wavelet Transform","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":81,"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":"Image fusion; Multispectral image; Discrete wavelet transform; Computer science; Artificial intelligence; Wavelet transform; Entropy (arrow of time); Pattern recognition (psychology); Medical imaging; Wavelet; Fusion; Mutual information; Computer vision; Algorithm; Image (mathematics)","score_opus":0.006757784423125557,"score_gpt":0.2677301143472135,"score_spread":0.2609723299240879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145433418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022055916,0.00006468388,0.94744116,0.0004789813,0.000106435895,0.00022339371,0.000006479481,0.0017198253,0.027903112],"genre_scores_gemma":[0.5956977,0.00020261058,0.40350503,0.00023563443,0.00011056173,0.0000052776086,0.0000127009935,0.00004434984,0.00018613887],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988028,0.00001139377,0.0002945975,0.00019172014,0.0003618302,0.0003376867],"domain_scores_gemma":[0.9995274,0.000020462692,0.00001655465,0.00022687008,0.000030209796,0.00017849942],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013278467,0.0001974496,0.00020196603,0.000091294074,0.00007300016,0.000026920354,0.00018675959,0.00012929812,0.0010865504],"category_scores_gemma":[0.000029716897,0.00016557518,0.00007896289,0.00014835672,0.00005119212,0.00033872892,0.00002387269,0.00027039298,0.000023500623],"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":[0.000015818478,0.000032943324,0.0000024467095,0.000025202651,0.0000074868403,0.00008811717,0.00015906761,0.00019997422,0.6315388,0.0004022496,0.0014630116,0.36606485],"study_design_scores_gemma":[0.00042416944,0.000067911766,0.00013989928,0.00008511542,0.000008888236,0.00007256033,0.00004196819,0.5030655,0.49074078,0.0019512809,0.0030440632,0.00035787077],"about_ca_topic_score_codex":0.000018941768,"about_ca_topic_score_gemma":0.000006574906,"teacher_disagreement_score":0.5736418,"about_ca_system_score_codex":0.000090991525,"about_ca_system_score_gemma":0.000019199135,"threshold_uncertainty_score":0.9998266},"labels":[],"label_agreement":null},{"id":"W2146831490","doi":"10.1080/01431161.2013.810825","title":"A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":89,"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 British Columbia","keywords":"Oversampling; Computer science; Artificial intelligence; Smoothing; Remote sensing; Multispectral image; Tree (set theory); Pattern recognition (psychology); Cartography; Computer vision; Geography; Mathematics","score_opus":0.009457026919022348,"score_gpt":0.24087820235144167,"score_spread":0.23142117543241933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146831490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23048274,0.00030711363,0.7687216,0.0001580235,0.00007404767,0.00011952229,0.0000146147695,0.000060444123,0.00006193674],"genre_scores_gemma":[0.48941243,0.000039982988,0.5103909,0.000037751448,0.000079716825,2.1017219e-7,0.000009787837,0.000017031634,0.000012242005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908066,0.00002120875,0.00037895504,0.00013977142,0.00023148123,0.00014795052],"domain_scores_gemma":[0.999125,0.00014052624,0.00017361327,0.00008574204,0.00037362057,0.00010152111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015635205,0.00014620736,0.00026950033,0.00050823274,0.000049799728,0.00015223469,0.00010691314,0.000029801047,0.000009177268],"category_scores_gemma":[0.00015757697,0.0001373217,0.00013356884,0.00011340791,0.00004982699,0.0003682623,0.0000334217,0.0001415023,4.532007e-7],"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":[0.00005957131,0.000019950305,0.0004647392,0.00006983378,0.00074280024,0.000067349756,0.00024004508,0.008217247,0.35069326,0.0000027651004,0.0003060632,0.6391164],"study_design_scores_gemma":[0.0007101015,0.000021842603,0.0031433487,0.00015357135,0.000101100646,0.00013422617,0.00012614335,0.9639486,0.030743489,0.00032750604,0.00043745953,0.0001526028],"about_ca_topic_score_codex":0.00003741225,"about_ca_topic_score_gemma":0.0000037487082,"teacher_disagreement_score":0.95573133,"about_ca_system_score_codex":0.00006709233,"about_ca_system_score_gemma":0.000012394239,"threshold_uncertainty_score":0.55998164},"labels":[],"label_agreement":null},{"id":"W2146918015","doi":"10.1109/igarss.2002.1026575","title":"An advanced computational method to determine co-occurrence probability texture features","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Histogram; Computer science; Grey level; Artificial intelligence; Pattern recognition (psychology); Quantization (signal processing); Probability density function; Algorithm; Image (mathematics); Mathematics; Statistics","score_opus":0.01361799827743337,"score_gpt":0.324154081502447,"score_spread":0.31053608322501364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146918015","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055894796,0.000040258474,0.98738474,0.000028338734,0.000065065426,0.00042613287,0.00006210342,0.0011686338,0.0052352785],"genre_scores_gemma":[0.178471,0.0000020922555,0.8211198,0.00019834048,0.000011824877,0.00006035332,0.000039184786,0.000016588408,0.00008086269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908465,0.00006180785,0.00018885001,0.0002819294,0.00016273327,0.00022003027],"domain_scores_gemma":[0.99934644,0.00010252464,0.000019854742,0.00029498056,0.000090118025,0.00014610967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018147909,0.00017963433,0.00017013456,0.0000720954,0.000051965882,0.000027365812,0.00016427909,0.0000641531,0.00026760082],"category_scores_gemma":[0.00011756682,0.00016420252,0.00003405274,0.00020835474,0.000022493228,0.00025574586,0.000013810672,0.00016988027,0.000018777733],"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":[0.000020485233,0.00010413664,0.00029682813,0.00008372115,0.000011438591,0.000007893811,0.00023419634,0.47301185,0.07678714,0.0037091686,0.010164408,0.43556872],"study_design_scores_gemma":[0.0007619747,0.0004161727,0.0051783486,0.0000599049,0.000014946947,0.00010499047,0.00011139814,0.0691673,0.78343725,0.05663404,0.0827438,0.0013698544],"about_ca_topic_score_codex":0.0000011852565,"about_ca_topic_score_gemma":0.000005744688,"teacher_disagreement_score":0.70665014,"about_ca_system_score_codex":0.00005968476,"about_ca_system_score_gemma":0.000018098734,"threshold_uncertainty_score":0.6695984},"labels":[],"label_agreement":null},{"id":"W2148130171","doi":"10.1109/ccece.1997.608309","title":"Preserving internal contrast in image fusion using fuzzy sets theory","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Manitoba","funders":"","keywords":"Contrast (vision); Fuzzy logic; Image fusion; Fuzzy set; Artificial intelligence; Computer science; Fusion; Image (mathematics); Multimodality; Noise (video); Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.013649446250765087,"score_gpt":0.24881917367880546,"score_spread":0.23516972742804038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148130171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22950952,0.00050470716,0.6296972,0.000028825129,0.00016970596,0.00025520686,0.0000045683837,0.0011862285,0.13864405],"genre_scores_gemma":[0.9043788,0.00011603107,0.09487127,0.00006348055,0.000037878064,0.000009225483,0.0000011890472,0.000045231398,0.0004769008],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992352,0.000033133318,0.00022526745,0.00014675217,0.000108887034,0.00025077656],"domain_scores_gemma":[0.99963397,0.00006280719,0.000021537699,0.00020865559,0.000024178931,0.000048842838],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014634755,0.00013961601,0.00014350643,0.00014189271,0.000029027085,0.00003343042,0.00018064222,0.000057659207,0.0021929205],"category_scores_gemma":[0.000060527134,0.00013345093,0.00003443168,0.00013648704,0.000027538297,0.00041530296,0.0001153441,0.00020742379,0.00004237469],"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":[0.0000138490595,0.000047596517,0.0005925769,0.00006710356,0.000011293055,0.00009038143,0.0004675568,0.0019767398,0.9191322,0.0006819882,0.0032889731,0.07362974],"study_design_scores_gemma":[0.0005763186,0.000026068987,0.0008895245,0.00025708842,0.0000066264784,0.00004718435,0.00021316216,0.7598657,0.22500168,0.011811891,0.00087122765,0.0004335515],"about_ca_topic_score_codex":0.000028725693,"about_ca_topic_score_gemma":0.00002093602,"teacher_disagreement_score":0.757889,"about_ca_system_score_codex":0.000085632404,"about_ca_system_score_gemma":0.000001777848,"threshold_uncertainty_score":0.9987192},"labels":[],"label_agreement":null},{"id":"W2149598000","doi":"10.1109/jsen.2013.2282693","title":"A Fast Learning Algorithm for Blind Data Fusion Using a Novel $L_{2}$-Norm Estimation","year":2013,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Algorithm; Norm (philosophy); Artificial intelligence; Sensor fusion; Computer science; Computation; Fusion; Image fusion; Estimation; Image (mathematics); Engineering","score_opus":0.04526730584807036,"score_gpt":0.3039966343415861,"score_spread":0.2587293284935157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149598000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04592889,0.00008337251,0.9526089,0.000030587344,0.0005081835,0.00035473562,0.000036824895,0.00033491795,0.00011360732],"genre_scores_gemma":[0.03402427,0.000078120844,0.9650682,0.000023265715,0.0005255275,0.0000101782925,0.000035393772,0.000085912194,0.00014910361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987427,0.00002304662,0.00040791795,0.00021131226,0.00025017883,0.00036483913],"domain_scores_gemma":[0.9991221,0.000083862855,0.00014446524,0.00030480436,0.00020524021,0.00013954598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030338124,0.00020863654,0.00021454087,0.00022716091,0.0002899906,0.00020287711,0.0002993042,0.00010745894,0.0001422081],"category_scores_gemma":[0.00013739095,0.00020267548,0.00006304131,0.00017200784,0.000029885632,0.0011531974,0.00006663211,0.00055785954,0.000029872725],"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":[0.0000068908116,0.00002343372,0.0000075138037,0.000022963251,0.00002522114,0.0000056965914,0.0001500976,0.17166685,0.25639376,0.0000017577767,0.0024246052,0.5692712],"study_design_scores_gemma":[0.00063606777,0.00004452605,0.000017053539,0.00010575476,0.000023042032,0.000539962,0.000099796285,0.9697214,0.025625411,0.00036491608,0.0025868074,0.00023526612],"about_ca_topic_score_codex":0.0000110488645,"about_ca_topic_score_gemma":0.0000012626585,"teacher_disagreement_score":0.7980546,"about_ca_system_score_codex":0.00012624498,"about_ca_system_score_gemma":0.000026771953,"threshold_uncertainty_score":0.82648665},"labels":[],"label_agreement":null},{"id":"W2149604743","doi":"10.1007/s11760-007-0025-4","title":"Context enhancement through infrared vision: a modified fusion scheme","year":2007,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":22,"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":"Infrared; Scheme (mathematics); Context (archaeology); Fusion; Computer science; Computer vision; Artificial intelligence; Optics; Physics; Mathematics; Geography","score_opus":0.013255100699797047,"score_gpt":0.2862675198085921,"score_spread":0.27301241910879503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149604743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026314648,0.0036596137,0.9537592,0.00004225348,0.0000561937,0.00022788132,0.0000021507415,0.0006242897,0.015313756],"genre_scores_gemma":[0.8568287,0.00022264043,0.14208056,0.0003410552,0.00010474232,0.00002124179,0.000007930336,0.000046058816,0.00034709595],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987088,0.000013666047,0.0003766731,0.00028828756,0.0002308448,0.00038171525],"domain_scores_gemma":[0.99948627,0.00005796384,0.000070425114,0.00016084834,0.00012810841,0.00009636335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003207411,0.00024000379,0.00022913603,0.000093702845,0.00020088247,0.0001395445,0.00011966014,0.00009720692,0.00014696758],"category_scores_gemma":[0.000037782767,0.00022680197,0.00004069442,0.00023287379,0.00010331251,0.0011916652,0.00009193005,0.0002379942,0.000015714826],"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":[0.000033942768,0.000021464319,0.0000078083995,0.00014475605,0.000005781619,0.000024675115,0.0005339678,0.000009195236,0.6603399,0.000041828323,0.0009089604,0.33792773],"study_design_scores_gemma":[0.0005213729,0.0000932268,0.00005615702,0.0004181238,0.00001159998,0.000019556355,0.0004081852,0.016579244,0.96943676,0.0036542995,0.008425502,0.00037598732],"about_ca_topic_score_codex":0.0000059520535,"about_ca_topic_score_gemma":0.0000024716096,"teacher_disagreement_score":0.830514,"about_ca_system_score_codex":0.00005582121,"about_ca_system_score_gemma":0.00002023583,"threshold_uncertainty_score":0.9248716},"labels":[],"label_agreement":null},{"id":"W2150268108","doi":"10.1111/1467-8659.00450","title":"Wavelet Radiative Transfer and Surface Interaction","year":2000,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Radiance; Radiative transfer; Wavelet; Bidirectional reflectance distribution function; Smoothing; Computation; Surface (topology); Representation (politics); Computer science; Basis (linear algebra); Operator (biology); Basis function; Algorithm; Geometry; Mathematics; Mathematical analysis; Physics; Computer vision; Optics; Reflectivity","score_opus":0.00570571572279846,"score_gpt":0.20886719999638076,"score_spread":0.2031614842735823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150268108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20122868,0.00024021878,0.79632944,0.00012421745,0.00019983052,0.00014956131,0.000009432276,0.00069963693,0.0010189869],"genre_scores_gemma":[0.9783272,0.0010702668,0.020155426,0.00028367245,0.0000487224,0.000006295233,0.000010246874,0.000034807777,0.00006333788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994662,0.000013437716,0.0001282579,0.00014395469,0.0000673355,0.00018083537],"domain_scores_gemma":[0.9997541,0.00003588858,0.000005631296,0.00013297137,0.000018878318,0.000052559655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000038164235,0.0001283354,0.00011873096,0.000066345434,0.00005240784,0.000029391704,0.00007530642,0.000057903908,0.000086466556],"category_scores_gemma":[9.0252934e-7,0.00013239385,0.000040176226,0.0001439901,0.00004082378,0.00025189007,0.000014097394,0.00018643898,0.000012706052],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041244573,0.000062487576,0.00078305346,0.000096925745,0.00011255163,0.000027458884,0.0013201386,0.017734246,0.0028983357,0.008493669,0.022099016,0.94633085],"study_design_scores_gemma":[0.00045633072,0.00013416055,0.0015826771,0.0000640871,0.000011807413,0.000042143467,0.000029170464,0.87658596,0.019127823,0.005417894,0.09610237,0.00044558747],"about_ca_topic_score_codex":0.0000038619264,"about_ca_topic_score_gemma":0.0000049323507,"teacher_disagreement_score":0.9458853,"about_ca_system_score_codex":0.000015565407,"about_ca_system_score_gemma":0.0000022640386,"threshold_uncertainty_score":0.5398864},"labels":[],"label_agreement":null},{"id":"W2150734399","doi":"10.1109/tip.2009.2025923","title":"Complex Wavelet Structural Similarity: A New Image Similarity Index","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":661,"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":"Similarity (geometry); Artificial intelligence; Structural similarity; Wavelet; Preprocessor; Pattern recognition (psychology); Hausdorff distance; Mathematics; Image (mathematics); Wavelet transform; Image processing; Computer vision; Computer science","score_opus":0.016998846269429727,"score_gpt":0.27766934285260364,"score_spread":0.2606704965831739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150734399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038323244,0.00009851767,0.98992026,0.0005246091,0.00017875715,0.00033724908,0.000048677135,0.0026892752,0.0023703212],"genre_scores_gemma":[0.7199554,0.000036918795,0.2791209,0.00052299834,0.00009362485,0.000013874818,0.000008562532,0.00007322884,0.0001745148],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980697,0.000031507345,0.00046898655,0.00045742022,0.00037955042,0.00059286],"domain_scores_gemma":[0.9990551,0.000041415773,0.00007940834,0.00042789007,0.00015160188,0.00024462203],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000101437894,0.00047098627,0.00039909614,0.00030550148,0.00042218406,0.0002963696,0.00033833957,0.00018434926,0.00042000238],"category_scores_gemma":[0.000015105973,0.00050270074,0.00015159528,0.0005777337,0.00012588476,0.0014495588,0.000002761143,0.0009293139,0.000031003216],"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":[0.00005732996,0.00007404239,0.0000026037344,0.00013436454,0.000022463682,0.000043885648,0.0003658651,0.004850297,0.33815676,0.00000504298,0.002241473,0.6540459],"study_design_scores_gemma":[0.0011770799,0.000153816,0.0008542318,0.00020382072,0.000072538256,0.00011344185,0.000115222836,0.39594123,0.5921845,0.0064744023,0.0015824419,0.0011272992],"about_ca_topic_score_codex":0.000018677756,"about_ca_topic_score_gemma":0.00001759035,"teacher_disagreement_score":0.71612304,"about_ca_system_score_codex":0.00019946694,"about_ca_system_score_gemma":0.00008301045,"threshold_uncertainty_score":0.99974245},"labels":[],"label_agreement":null},{"id":"W2150830784","doi":"10.1117/1.jrs.9.096095","title":"Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images","year":2015,"lang":"en","type":"article","venue":"Journal of Applied Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":64,"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":"Yarmouk University; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Remote sensing; Spectroradiometer; Moderate-resolution imaging spectroradiometer; Mean squared error; Image resolution; Image fusion; Environmental science; Thematic Mapper; Spectral bands; Reflectivity; Geology; Satellite imagery; Satellite; Computer science; Mathematics; Image (mathematics); Physics; Artificial intelligence; Optics","score_opus":0.03014513357581948,"score_gpt":0.28544119398955714,"score_spread":0.25529606041373765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150830784","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17952071,0.00031431098,0.81896424,0.00008191143,0.00016896357,0.00028567787,0.0000086615155,0.0001004442,0.000555073],"genre_scores_gemma":[0.48830932,0.000055489734,0.5114357,0.000015283824,0.000121462974,5.9196122e-8,0.0000022183638,0.000045609362,0.000014859495],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981065,0.00003995764,0.00091026165,0.00017917668,0.00044006703,0.00032407674],"domain_scores_gemma":[0.99817127,0.000119338845,0.0006793482,0.0003013325,0.0006225014,0.00010622099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011806412,0.00025812082,0.00048591898,0.00016430939,0.0001369012,0.000052934414,0.00019844413,0.00012217899,9.0689946e-7],"category_scores_gemma":[0.00015340795,0.00020344928,0.00015010382,0.00026040923,0.000110349276,0.00038890357,0.00006797909,0.00040873498,6.1611684e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015965175,0.000006149609,0.0000010816693,0.000056182507,0.000016908129,0.000005423004,0.00047558572,0.28426367,0.70261455,0.0000042620727,0.00074429245,0.01165222],"study_design_scores_gemma":[0.00036209836,0.000032782784,0.0000026724608,0.00014674797,0.00003018389,0.000047072033,0.00022679805,0.5740682,0.42194253,0.0029709707,0.000041577863,0.00012839655],"about_ca_topic_score_codex":0.000032380165,"about_ca_topic_score_gemma":0.000019422201,"teacher_disagreement_score":0.30878863,"about_ca_system_score_codex":0.00026694004,"about_ca_system_score_gemma":0.00012862179,"threshold_uncertainty_score":0.82964206},"labels":[],"label_agreement":null},{"id":"W2154742968","doi":"","title":"PROBLEMS IN THE FUSION OF COMMERCIAL HIGH-RESOLUTION SATELLITE AS WELL AS LANDSAT 7 IMAGES AND INITIAL SOLUTIONS","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":176,"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 New Brunswick","funders":"","keywords":"Distortion (music); Image fusion; Satellite; Computer vision; Remote sensing; Fusion; Artificial intelligence; Computer science; Image resolution; Sensor fusion; High resolution; Image (mathematics); Geography; Engineering; Telecommunications","score_opus":0.018053652649459493,"score_gpt":0.2423540075279358,"score_spread":0.2243003548784763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154742968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4568955,0.0068319864,0.112484686,0.0027963642,0.00045203572,0.0022686287,0.000060159982,0.0018023204,0.41640833],"genre_scores_gemma":[0.9931242,0.0026463359,0.0037691211,0.0001129333,0.000044639342,0.000026741267,0.000010781063,0.000016488646,0.0002487732],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992866,0.00004584669,0.00022498674,0.00011351048,0.00013662755,0.00019246044],"domain_scores_gemma":[0.9996688,0.00007756818,0.000029021056,0.00016930173,0.000027722193,0.000027618185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015127019,0.00011520338,0.00013202657,0.00011007881,0.000069019065,0.000019230325,0.00011107379,0.00007112927,0.0005389906],"category_scores_gemma":[0.000029005836,0.00008778997,0.000025457517,0.00018004868,0.00007860647,0.00018647104,0.000058855545,0.00016913029,0.00008035708],"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":[0.00010623871,0.0009892571,0.0044680308,0.0006695721,0.000066683446,0.000082448176,0.0080951555,0.009799576,0.37447402,0.018059354,0.093318835,0.48987085],"study_design_scores_gemma":[0.0068083564,0.0019038118,0.089892745,0.00092502864,0.00015436376,0.00042231288,0.002259275,0.09876833,0.45093367,0.06357214,0.2816255,0.0027344632],"about_ca_topic_score_codex":0.000281409,"about_ca_topic_score_gemma":0.00014857603,"teacher_disagreement_score":0.5362287,"about_ca_system_score_codex":0.000023465249,"about_ca_system_score_gemma":0.0000029717567,"threshold_uncertainty_score":0.5901569},"labels":[],"label_agreement":null},{"id":"W2157969333","doi":"10.1109/igarss.2003.1293902","title":"An independent wavelet reconstruction implementation for image fusion","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 du Québec en Outaouais","funders":"","keywords":"Panchromatic film; Multispectral image; Image fusion; Artificial intelligence; Computer vision; Wavelet; Computer science; Image resolution; Multiresolution analysis; Wavelet transform; Iterative reconstruction; Image restoration; Image (mathematics); Pattern recognition (psychology); Image processing; Discrete wavelet transform","score_opus":0.007349381882344597,"score_gpt":0.28953559661982403,"score_spread":0.28218621473747946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157969333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15579523,0.0000067229707,0.8417114,0.000034218796,0.00015174832,0.0003721555,0.000012546826,0.00089287356,0.0010230764],"genre_scores_gemma":[0.5161681,0.000032933134,0.48352388,0.00003528401,0.000058416925,0.00008161316,0.00005339931,0.00002606378,0.000020306434],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947375,0.0000045356683,0.0001680933,0.00013131756,0.00007862343,0.00014367655],"domain_scores_gemma":[0.99973756,0.000006603754,0.000022456903,0.00014052584,0.000049820057,0.000043009488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074792784,0.00009059431,0.00007183415,0.00007982863,0.000054467673,0.000026338414,0.00006175122,0.000043682176,0.00030132168],"category_scores_gemma":[0.0000052058135,0.0000912817,0.000028124916,0.00006416053,0.000011517089,0.0005295499,0.000010203865,0.000055266697,0.000011228978],"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":[0.0000046885984,0.000010373907,0.000017344708,0.000014560954,0.0000035811402,8.3325017e-7,0.000068198104,0.00042419744,0.6301532,0.00067600724,0.00028530828,0.3683417],"study_design_scores_gemma":[0.00062156975,0.00009893802,0.00037306183,0.000008517773,0.0000049931855,0.00002452112,0.00043452863,0.0029652414,0.9822122,0.01247805,0.00062284205,0.0001555631],"about_ca_topic_score_codex":0.000023030489,"about_ca_topic_score_gemma":0.00006421586,"teacher_disagreement_score":0.36818615,"about_ca_system_score_codex":0.00012699564,"about_ca_system_score_gemma":0.000009421033,"threshold_uncertainty_score":0.37223595},"labels":[],"label_agreement":null},{"id":"W2158371632","doi":"10.1109/urs.2007.371781","title":"Applications of Wavelet Transforms in Image Fusion","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Panchromatic film; Multispectral image; Image fusion; Wavelet; Artificial intelligence; Image resolution; Computer science; Wavelet transform; Distortion (music); Computer vision; Pattern recognition (psychology); Image (mathematics); Remote sensing; Geography; Bandwidth (computing); Telecommunications","score_opus":0.003165159629771923,"score_gpt":0.2383555701616869,"score_spread":0.23519041053191497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158371632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012994602,0.000033138385,0.92478573,0.000012096062,0.000009316399,0.0002057261,0.0000023374835,0.0003056543,0.0616514],"genre_scores_gemma":[0.7976609,0.00009859855,0.20200394,0.000016147604,0.000010720966,0.00002795168,0.000004720769,0.000016002366,0.00016106409],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995785,0.0000012929453,0.00018807847,0.000059660022,0.000060663282,0.00011184659],"domain_scores_gemma":[0.9998065,0.000019748099,0.000009208122,0.00012336072,0.000018409071,0.000022727334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010727585,0.000054581236,0.00007564684,0.00012268742,0.0000075246967,0.0000018153825,0.00006817085,0.000035386478,0.00015221462],"category_scores_gemma":[0.000003783766,0.000048939062,0.000019601644,0.00022799145,0.000018215269,0.00009297786,0.000007956795,0.00006890443,0.000010342757],"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":[0.0000030434169,0.000022611337,0.00005628414,0.000036857047,0.0000012298844,0.0000015765222,0.00009112696,0.0000309483,0.6425293,0.00096232805,0.00022336158,0.3560413],"study_design_scores_gemma":[0.00012143547,0.00000991538,0.0010669935,0.000010117999,9.743223e-7,0.0000015694891,0.00007694937,0.0013055925,0.987187,0.0020465364,0.008092186,0.00008067651],"about_ca_topic_score_codex":0.000011249089,"about_ca_topic_score_gemma":0.000045499837,"teacher_disagreement_score":0.78466624,"about_ca_system_score_codex":0.000023597877,"about_ca_system_score_gemma":0.0000023037155,"threshold_uncertainty_score":0.1995677},"labels":[],"label_agreement":null},{"id":"W2159606561","doi":"10.1109/ccece.2013.6567721","title":"Multiresolution DCT decomposition for multifocus image fusion","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Concordia University","funders":"","keywords":"Image fusion; Artificial intelligence; Wavelet; Computer science; Computer vision; Domain (mathematical analysis); Discrete cosine transform; Image (mathematics); Wavelet transform; Fuse (electrical); Computational complexity theory; Pattern recognition (psychology); Image quality; Fusion; Mathematics; Algorithm; Engineering","score_opus":0.005644266682770902,"score_gpt":0.2552853148869273,"score_spread":0.2496410482041564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159606561","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043024264,0.00006302193,0.95004237,0.00007104547,0.00013399949,0.0008158712,0.0000069971843,0.0018704464,0.003971969],"genre_scores_gemma":[0.4699017,0.00003462825,0.5292919,0.00004024107,0.000052461837,0.0003267641,0.000029710644,0.000035176854,0.00028738237],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999407,0.0000073610313,0.0001709769,0.00013696255,0.00007283652,0.00020488375],"domain_scores_gemma":[0.9996205,0.000048166017,0.00001931874,0.00016595719,0.00009173572,0.000054317992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045889417,0.00012158036,0.00009871714,0.000075765405,0.00006869813,0.00003423473,0.00007910069,0.00006452912,0.00041479385],"category_scores_gemma":[0.000027586722,0.00011341733,0.000053293716,0.000067136156,0.00001805501,0.0004573881,0.000026750567,0.00006480735,0.00022503945],"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":[0.0000033767542,0.00001439212,0.0000079508345,0.000022830392,0.0000030394076,3.0208997e-7,0.000020580886,0.00029015122,0.8988254,0.0000613538,0.01578636,0.08496425],"study_design_scores_gemma":[0.0002934044,0.00003222823,0.0006353912,0.000015668216,0.0000037018076,0.0000024323906,0.000018776913,0.37745416,0.6171303,0.0011937482,0.0030590799,0.00016113753],"about_ca_topic_score_codex":0.00004359364,"about_ca_topic_score_gemma":0.0000067266456,"teacher_disagreement_score":0.42687744,"about_ca_system_score_codex":0.00007017608,"about_ca_system_score_gemma":0.000002388964,"threshold_uncertainty_score":0.46250245},"labels":[],"label_agreement":null},{"id":"W2159760851","doi":"10.1007/s10044-005-0020-8","title":"Concealed weapon detection and visualization in a synthesized image","year":2005,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Ottawa","funders":"Army Research Office","keywords":"Artificial intelligence; Computer vision; Computer science; Image fusion; Visualization; Pixel; Image (mathematics); Cluster analysis; Process (computing); Feature detection (computer vision); Pattern recognition (psychology); Image processing","score_opus":0.004138618208844329,"score_gpt":0.25241919397226115,"score_spread":0.2482805757634168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159760851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09582854,0.00015635403,0.9034508,0.000037742113,0.0000018001401,0.00015942857,0.000005896847,0.0001526483,0.00020678788],"genre_scores_gemma":[0.99647176,0.0005250661,0.0026735202,0.000033209813,0.000024271198,0.0002325403,0.000012549741,0.000010449894,0.000016609963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995687,0.000013183787,0.00015853692,0.00013839584,0.000045166482,0.0000760099],"domain_scores_gemma":[0.99978054,0.000030937546,0.000027485694,0.000112303445,0.00001802819,0.000030726173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006179493,0.00007234532,0.00012863273,0.00020991372,0.000040864503,0.00002767068,0.000031140185,0.000030718606,0.000028303815],"category_scores_gemma":[0.000005806297,0.0000752509,0.000024197734,0.00044578357,0.00002179807,0.00010441289,0.000012912747,0.000045445664,0.000004332817],"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":[0.0000015980933,0.000024260897,0.004357172,0.000022354528,0.00005052278,2.6106696e-7,0.00010199453,0.00038560847,0.20746279,0.000054219683,0.000008619318,0.7875306],"study_design_scores_gemma":[0.00035412217,0.000010949192,0.04913335,0.000016224096,0.0002583241,0.0000028581337,0.00014151946,0.7877989,0.1573455,0.00032069915,0.0043216236,0.00029594533],"about_ca_topic_score_codex":0.000028923243,"about_ca_topic_score_gemma":0.00037053783,"teacher_disagreement_score":0.9007773,"about_ca_system_score_codex":0.000023498176,"about_ca_system_score_gemma":0.0000010817129,"threshold_uncertainty_score":0.3068643},"labels":[],"label_agreement":null},{"id":"W2165174801","doi":"10.1109/icicic.2006.296","title":"Image Fusion Algorithm Assessment Based on Feature Measurement","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Image fusion; Phase congruency; Artificial intelligence; Metric (unit); Fusion; Feature (linguistics); Image (mathematics); Pattern recognition (psychology); Computer science; Algorithm; Image quality; Congruence (geometry); Ranging; Computer vision; Mathematics","score_opus":0.006594602894540994,"score_gpt":0.2356784122747116,"score_spread":0.2290838093801706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165174801","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012760503,0.000042824428,0.8942348,0.00021105258,0.00012874785,0.0002284504,0.000007128794,0.0016343755,0.10338504],"genre_scores_gemma":[0.06500787,0.000011596147,0.9339245,0.00019989202,0.00010884834,0.000056587112,0.000025116004,0.00005234394,0.0006132491],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989613,0.000015878331,0.00013937835,0.00018063498,0.00048885366,0.00021391414],"domain_scores_gemma":[0.9995361,0.000015126977,0.000019290115,0.00029365666,0.000091718604,0.00004410391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015268543,0.00018165854,0.0001268193,0.00010098965,0.0000523396,0.000034043125,0.000105200466,0.000069115384,0.0003614927],"category_scores_gemma":[0.0000068606614,0.00015653491,0.000054816115,0.00013361298,0.000014178276,0.00010145628,0.000021125717,0.00019673095,0.000037929643],"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":[0.0000058716987,0.00017525993,0.000042665066,0.00005180667,0.000008475875,0.00003877347,0.0000051414045,0.0063574966,0.5548453,0.00021615926,0.2568596,0.18139347],"study_design_scores_gemma":[0.00043817828,0.0000841152,0.0025290847,0.000061970386,0.000008138171,0.0000019699141,0.0000096556405,0.645166,0.31719542,0.0005586842,0.033601265,0.00034556453],"about_ca_topic_score_codex":0.000013927891,"about_ca_topic_score_gemma":0.0000075138355,"teacher_disagreement_score":0.6388085,"about_ca_system_score_codex":0.00028510406,"about_ca_system_score_gemma":0.0000153415,"threshold_uncertainty_score":0.6383308},"labels":[],"label_agreement":null},{"id":"W2175526744","doi":"10.1109/newcas.2011.5981217","title":"Hybrid discrete wavelet transform and Gabor filter banks processing for mammogram features extraction","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Université du Québec à Montréal","funders":"","keywords":"Gabor transform; Artificial intelligence; Pattern recognition (psychology); Discrete wavelet transform; Gabor wavelet; Computer science; Gabor filter; Wavelet transform; Wavelet; Kernel (algebra); Filter bank; Computer vision; Standard deviation; Mathematics; Feature extraction; Filter (signal processing); Stationary wavelet transform; Time–frequency analysis; Statistics","score_opus":0.01247027994307217,"score_gpt":0.2489323371960047,"score_spread":0.2364620572529325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175526744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057139522,0.0002667901,0.97958803,0.000054839897,0.000067687746,0.00045753235,0.000022042259,0.0010331243,0.012796015],"genre_scores_gemma":[0.6700827,0.00010956514,0.3286451,0.00004982239,0.000046054403,0.00013696736,0.000015379484,0.000047264417,0.00086714444],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943274,0.0000031005777,0.00013870149,0.00015596226,0.000064161366,0.00020533358],"domain_scores_gemma":[0.9997759,0.000017032971,0.000018082532,0.00010335373,0.00003240986,0.000053247528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051901152,0.0001446011,0.00011754711,0.00006384629,0.00006129876,0.000035283727,0.000059693975,0.00005269168,0.0001104367],"category_scores_gemma":[0.000009327697,0.00012082052,0.000038199774,0.00004205285,0.000028644748,0.0004549108,0.000008262557,0.00011931808,0.0000015402709],"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":[0.000042333686,0.000014488981,0.000012075537,0.00021537898,0.000011165516,0.000004392732,0.00030253173,0.0000060067136,0.03130222,0.00018988547,0.0035336718,0.96436584],"study_design_scores_gemma":[0.00032242955,0.00009691659,0.0011150579,0.000056894576,0.000022929693,0.00006124136,0.000076737546,0.012191646,0.9637368,0.006745222,0.015231068,0.00034306897],"about_ca_topic_score_codex":0.000007766826,"about_ca_topic_score_gemma":0.000008591847,"teacher_disagreement_score":0.96402276,"about_ca_system_score_codex":0.000018006067,"about_ca_system_score_gemma":0.0000041614235,"threshold_uncertainty_score":0.4926918},"labels":[],"label_agreement":null},{"id":"W2204666838","doi":"10.1007/978-3-540-72108-6_1","title":"An Online Colour 2D and 3D Image System for Disaster Management","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 New Brunswick","funders":"","keywords":"Visualization; Computer science; Satellite; Remote sensing; Emergency management; Scale (ratio); The Internet; Satellite imagery; Computer vision; Geography; Artificial intelligence; Cartography; World Wide Web; Engineering","score_opus":0.008424532192989631,"score_gpt":0.23211023668335815,"score_spread":0.2236857044903685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204666838","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043123378,0.0007694065,0.94233835,0.0000146671255,0.00016000326,0.0011067245,0.00019521084,0.0005527892,0.054431625],"genre_scores_gemma":[0.1685698,0.004829186,0.8190713,0.0013554435,0.0005441914,0.0003716214,0.0032448443,0.00042750305,0.0015861131],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990528,0.0000042120896,0.0004051539,0.00017969076,0.00014304079,0.00021508663],"domain_scores_gemma":[0.99950385,0.00004691699,0.00009116094,0.00022960144,0.000053604974,0.000074891206],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012079553,0.00032210702,0.00029143653,0.0006347355,0.000058331578,0.000071526774,0.00008476062,0.00026833778,0.000016033504],"category_scores_gemma":[0.0000040423347,0.00030959316,0.00007262325,0.00006812573,0.00006252219,0.00030872453,0.0000335482,0.00024341223,0.0000017653481],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000907983,0.000019711943,0.000045470082,0.005011205,0.0001288862,0.000017827848,0.0015933602,0.0027591675,0.00023778023,0.007962996,0.00033650117,0.9817963],"study_design_scores_gemma":[0.0057416977,0.0011510786,0.0010736547,0.0045031444,0.000629229,0.00023864787,0.0012332783,0.286014,0.0043559982,0.026841458,0.663204,0.0050138077],"about_ca_topic_score_codex":0.000002150334,"about_ca_topic_score_gemma":0.000056017547,"teacher_disagreement_score":0.9767825,"about_ca_system_score_codex":0.00004219131,"about_ca_system_score_gemma":0.0000029361393,"threshold_uncertainty_score":0.9999356},"labels":[],"label_agreement":null},{"id":"W2240835969","doi":"","title":"06 - Un opérateur newtonien pour le rehaussement de contraste","year":2000,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Convolution (computer science); Pixel; Contrast (vision); Grey level; Computer science; Artificial intelligence; Mathematics","score_opus":0.00704472420326329,"score_gpt":0.21655872127357,"score_spread":0.2095139970703067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2240835969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17734823,0.010098337,0.75651366,0.010066604,0.00045795325,0.0016356547,0.00019804481,0.0015650776,0.042116463],"genre_scores_gemma":[0.67943174,0.0025887843,0.26200816,0.0016066344,0.0011205169,0.00029193936,0.000089663095,0.0002755905,0.05258695],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99739045,0.00013543408,0.0006883965,0.00045000023,0.00036123,0.00097449694],"domain_scores_gemma":[0.99910736,0.000086098946,0.000088237364,0.00034147076,0.000067468296,0.00030938623],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00040945975,0.0004802574,0.00040089685,0.00010987541,0.00018667981,0.00011240055,0.0003732157,0.00018229902,0.025751827],"category_scores_gemma":[0.0000116469155,0.0005643824,0.0001711406,0.00023374555,0.00011941328,0.00043009495,0.00005251463,0.0003914245,0.0008512033],"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":[0.00008820661,0.0008189555,0.00031929213,0.00024549096,0.00028054172,0.00032703025,0.0011216219,0.050956126,0.27103123,0.0064219106,0.1106015,0.5577881],"study_design_scores_gemma":[0.0030829464,0.000361526,0.002526659,0.0004133901,0.0001423096,0.00009978384,0.00028982083,0.0557142,0.3056443,0.0059771934,0.6247373,0.0010105416],"about_ca_topic_score_codex":0.00033781904,"about_ca_topic_score_gemma":0.000013148958,"teacher_disagreement_score":0.55677754,"about_ca_system_score_codex":0.000380392,"about_ca_system_score_gemma":0.000159707,"threshold_uncertainty_score":0.99992675},"labels":[],"label_agreement":null},{"id":"W2248416044","doi":"10.1109/lgrs.2015.2492569","title":"An Adaptive Pansharpening Method by Using Weighted Least Squares Filter","year":2015,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":48,"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, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China; Alberta Innovates - Health Solutions","keywords":"Panchromatic film; Multispectral image; Artificial intelligence; Image fusion; Computer science; Computer vision; Filter (signal processing); Composite image filter; Pattern recognition (psychology); Image (mathematics)","score_opus":0.026967301890021098,"score_gpt":0.27829602195275627,"score_spread":0.25132872006273516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2248416044","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2578092,0.000085153166,0.7411465,0.00013345006,0.00029341484,0.000089537985,0.000006115388,0.00036824346,0.00006836498],"genre_scores_gemma":[0.1977525,0.000013360888,0.8011354,0.0009230618,0.00010646892,9.235108e-8,0.0000035727558,0.000038094055,0.000027410177],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986756,0.00007716544,0.00019416773,0.0003753378,0.0002626748,0.0004150855],"domain_scores_gemma":[0.99938786,0.000043269672,0.00005123097,0.00025301415,0.000060918075,0.00020368113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031680896,0.0002185686,0.00020094628,0.00014721103,0.0001737486,0.00011321092,0.00014690084,0.00007027911,0.000002134569],"category_scores_gemma":[0.000022263574,0.0002024662,0.000031368043,0.00029059857,0.00018101255,0.0006127159,0.000030026355,0.00022067709,0.0000037270172],"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":[0.000007729155,0.0000024486517,0.000006617344,0.0000061977094,0.000004142464,0.0000287743,0.00048203478,0.002250556,0.8121855,5.8521726e-7,0.0014367738,0.18358864],"study_design_scores_gemma":[0.00013992406,0.000047696954,0.000014481265,0.000081731836,0.000010321605,0.0001165513,0.0003307112,0.8597318,0.13828368,0.00011947029,0.0008256689,0.0002979766],"about_ca_topic_score_codex":0.00033262032,"about_ca_topic_score_gemma":0.0000060235784,"teacher_disagreement_score":0.85748124,"about_ca_system_score_codex":0.00008150826,"about_ca_system_score_gemma":0.000017237699,"threshold_uncertainty_score":0.82563317},"labels":[],"label_agreement":null},{"id":"W2274118206","doi":"10.1007/978-3-642-23678-5_53","title":"Contourlet-Based Texture Retrieval Using a Mixture of Generalized Gaussian Distributions","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contourlet; Histogram; Computer science; Pattern recognition (psychology); Artificial intelligence; Wavelet; Kullback–Leibler divergence; Gaussian; Probability density function; Probability distribution; Divergence (linguistics); Image texture; Texture (cosmology); Wavelet transform; Mathematics; Statistics; Image processing; Image (mathematics)","score_opus":0.01727781589384302,"score_gpt":0.24858097426819914,"score_spread":0.2313031583743561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274118206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017395493,0.00081111235,0.9965151,0.000042157873,0.0005660571,0.00036675745,0.00011510087,0.00032855547,0.0010811936],"genre_scores_gemma":[0.22903758,0.000059385748,0.7701421,0.00020785643,0.00031080403,0.0000031996221,0.00004873856,0.0000988222,0.00009152218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998025,0.000017668257,0.0004875763,0.00057184655,0.0004599383,0.00043796844],"domain_scores_gemma":[0.99858445,0.00011450865,0.00019251315,0.00077014783,0.00021716021,0.0001212454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023330374,0.0004756561,0.0005764721,0.00049495586,0.00010850171,0.000053320477,0.000817576,0.00046237817,0.00012655421],"category_scores_gemma":[0.0000830335,0.00044130336,0.00015196965,0.00042485454,0.00065906113,0.0001694786,0.00018144042,0.0007935196,0.0000033308397],"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":[0.00015329898,0.00012253292,0.000082888146,0.00083216303,0.00010918424,0.00034068993,0.00089379103,0.3701548,0.28284216,0.012702912,0.00037732985,0.33138826],"study_design_scores_gemma":[0.0006021019,0.00014469158,0.00004357415,0.0013494104,0.0000523854,0.000056135785,1.434839e-7,0.5501294,0.36464414,0.078307785,0.0034553122,0.0012148846],"about_ca_topic_score_codex":0.0000157916,"about_ca_topic_score_gemma":0.000018526564,"teacher_disagreement_score":0.33017337,"about_ca_system_score_codex":0.00031743333,"about_ca_system_score_gemma":0.00021094651,"threshold_uncertainty_score":0.9998039},"labels":[],"label_agreement":null},{"id":"W2282251981","doi":"10.3390/rs8020083","title":"Improved Pansharpening with Un-Mixing of Mixed MS Sub-Pixels near Boundaries between Vegetation and Non-Vegetation Objects","year":2016,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"York University","funders":"China Geological Survey; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Panchromatic film; Pixel; Multispectral image; Vegetation (pathology); Image resolution; Remote sensing; Computer science; Artificial intelligence; Geography","score_opus":0.006042294239494739,"score_gpt":0.20871224114491702,"score_spread":0.20266994690542228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282251981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49842712,0.000115648225,0.5009459,0.000012803048,0.00004249695,0.00013424904,0.0000012611252,0.00021579045,0.000104720886],"genre_scores_gemma":[0.76624393,0.000031398333,0.23361412,0.0000053465124,0.000045284105,7.1101354e-8,0.0000039788792,0.000050531584,0.0000053372587],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990381,0.000026834345,0.0003031188,0.00022236173,0.00015994188,0.0002496654],"domain_scores_gemma":[0.9992404,0.00023546362,0.00013742808,0.00019791436,0.00012925614,0.000059524522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016677313,0.00019625573,0.0002757229,0.000110389046,0.00017344388,0.000096074546,0.000046999037,0.00008778373,7.705812e-7],"category_scores_gemma":[0.00016334622,0.00015873408,0.000031126838,0.0001804455,0.00016815116,0.00041746628,0.000031499134,0.000117776486,0.0000018003655],"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":[0.0000061162036,9.809489e-7,0.00016922297,0.00011187466,0.000016509888,0.0000038053367,0.00051878,0.000105411615,0.6587484,0.0000014502058,0.0000012874493,0.34031615],"study_design_scores_gemma":[0.0004277745,0.00007061439,0.0051515563,0.0009049114,0.000034942772,0.00001660669,0.000037997117,0.12604196,0.8664942,0.00056513055,0.000029299388,0.00022502556],"about_ca_topic_score_codex":0.000030724947,"about_ca_topic_score_gemma":0.00004066057,"teacher_disagreement_score":0.3400911,"about_ca_system_score_codex":0.00006871672,"about_ca_system_score_gemma":0.00003698418,"threshold_uncertainty_score":0.6472988},"labels":[],"label_agreement":null},{"id":"W2349814937","doi":"","title":"Research of Image Fusion Based on Laplacian-Pyramid and HIS-Transform","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Panchromatic film; Computer science; Image fusion; Artificial intelligence; Pyramid (geometry); Computer vision; Image (mathematics); MATLAB; Fusion; Laplace operator; Decomposition; Pattern recognition (psychology); Mathematics","score_opus":0.008686862707116251,"score_gpt":0.29109686452114336,"score_spread":0.2824100018140271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2349814937","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015430799,0.00003510638,0.9756571,0.00024662056,0.000015373898,0.00075708947,0.000030467614,0.00041521547,0.0074122064],"genre_scores_gemma":[0.3427084,0.00004327331,0.6565861,0.00006752625,0.00007516144,0.00038063538,0.00002639777,0.000041073155,0.000071411014],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993058,0.000010752283,0.000174997,0.0001923198,0.00013324122,0.00018293214],"domain_scores_gemma":[0.9993834,0.000098890974,0.000017871234,0.00032924945,0.00010832383,0.00006224977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001727234,0.00010536882,0.00011148113,0.00022237921,0.00009745682,0.0000240184,0.00020207922,0.00007989833,0.000048455782],"category_scores_gemma":[0.0000010276806,0.000107389016,0.000029564131,0.000275346,0.00013092473,0.000076496566,0.00004416079,0.00039273017,0.00003111409],"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":[0.0000030666374,0.000042202435,0.000013575305,0.00005249108,0.0000020133618,3.9925314e-7,0.000043524626,0.000072869145,0.80982846,0.0005273806,0.0024162412,0.1869978],"study_design_scores_gemma":[0.00020473458,0.000035887042,0.0005100601,0.00002338614,0.000003112665,0.0000036253768,0.000009548588,0.018192416,0.70036787,0.0017892203,0.27872497,0.00013514287],"about_ca_topic_score_codex":0.000006934247,"about_ca_topic_score_gemma":0.000009778876,"teacher_disagreement_score":0.3272776,"about_ca_system_score_codex":0.000021731083,"about_ca_system_score_gemma":0.000012477817,"threshold_uncertainty_score":0.43791968},"labels":[],"label_agreement":null},{"id":"W2360017885","doi":"","title":"ADAPTIVE IMAGE FUSION ALGORITHM FOR INFRARED AND VISIBLE LIGHT IMAGES BASED ON DT-CWT","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Image fusion; Contrast (vision); Wavelet; Pattern recognition (psychology); Fusion; Image (mathematics); Infrared; Human visual system model; Algorithm; Optics; Physics","score_opus":0.006144975174042358,"score_gpt":0.24200835906713974,"score_spread":0.23586338389309738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2360017885","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029398876,0.00009937587,0.9552812,0.00005405734,0.00008772689,0.00049139804,0.000031954147,0.0011456795,0.042514626],"genre_scores_gemma":[0.018276367,0.000055336775,0.97969115,0.00024062206,0.00008692453,0.000057118184,0.000017110444,0.00007254596,0.0015028501],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908316,0.000007834616,0.00021439242,0.00024016923,0.00014399641,0.0003104219],"domain_scores_gemma":[0.9993654,0.00017476811,0.00002958211,0.00023736845,0.00009126528,0.00010161854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020111595,0.0002094504,0.00018078552,0.00021389482,0.00008015371,0.000035907786,0.00009690334,0.00009184085,0.00013504497],"category_scores_gemma":[0.000045463163,0.00018972074,0.00005158728,0.00016413961,0.000034357203,0.0002627247,0.0000401873,0.00013026319,0.0000137416155],"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":[0.00007245123,0.000060562328,0.000009649422,0.00005596956,0.000012532442,0.000026396669,0.00006634912,0.00017857924,0.35033494,0.00013748533,0.03154371,0.6175014],"study_design_scores_gemma":[0.0005454201,0.00023303904,0.00022500679,0.000046253845,0.00000707748,0.0000024436667,0.00007039427,0.21033725,0.7782431,0.0010715934,0.008953493,0.00026491383],"about_ca_topic_score_codex":0.000004512218,"about_ca_topic_score_gemma":0.0000026620687,"teacher_disagreement_score":0.6172365,"about_ca_system_score_codex":0.00005984117,"about_ca_system_score_gemma":0.0000083572395,"threshold_uncertainty_score":0.7736587},"labels":[],"label_agreement":null},{"id":"W2382850683","doi":"","title":"System of Generated Cloud Based on Perlin Noise","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Grayscale; Computer science; Cloud computing; Computer graphics (images); Noise (video); Computer vision; Artificial intelligence; Image (mathematics)","score_opus":0.007264719331161168,"score_gpt":0.20629550545149455,"score_spread":0.1990307861203334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2382850683","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011505192,0.00006752544,0.98558956,0.000017323131,0.000018227403,0.00046131382,0.000025269572,0.0010017476,0.0013138467],"genre_scores_gemma":[0.46936285,0.000013040091,0.5300025,0.00006831259,0.00011136921,0.0003249296,0.000035318397,0.000034283003,0.00004743413],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994036,0.000010860993,0.00022526475,0.00015924132,0.0000785942,0.00012243571],"domain_scores_gemma":[0.99949765,0.000027692862,0.000035662302,0.00032329437,0.00007161506,0.00004410687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031267304,0.00011956568,0.00014250784,0.00010412711,0.000069614754,0.0000055938285,0.00018425402,0.000051609248,0.000011705271],"category_scores_gemma":[2.321865e-7,0.00012513822,0.000048872742,0.00024701163,0.000033833243,0.000030110044,0.000021278523,0.00009114134,0.00006388489],"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":[0.0000075917856,0.00016246838,0.00012901996,0.00024364145,0.000021413138,0.0000064655455,0.00014412502,0.12421032,0.80529803,0.0008776222,0.019421723,0.04947755],"study_design_scores_gemma":[0.000254719,0.000024146406,0.00030249753,0.000043071857,0.0000066213292,0.00001942575,0.000006980954,0.32374743,0.56852984,0.000019509724,0.10684665,0.00019911212],"about_ca_topic_score_codex":0.000002846564,"about_ca_topic_score_gemma":2.7091133e-7,"teacher_disagreement_score":0.45785767,"about_ca_system_score_codex":0.000064291235,"about_ca_system_score_gemma":0.000014006431,"threshold_uncertainty_score":0.51029885},"labels":[],"label_agreement":null},{"id":"W2384554286","doi":"","title":"Application of Biorthogonal Wavelet Transform in Image Fusion","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer science; Image fusion; Artificial intelligence; Wavelet; Computer vision; Fusion; Image (mathematics); Wavelet transform; Biorthogonal wavelet; Operator (biology); Image quality; Fusion rules; Biorthogonal system; Multiresolution analysis; Pattern recognition (psychology); Discrete wavelet transform","score_opus":0.0020165946278884925,"score_gpt":0.20229619253188988,"score_spread":0.20027959790400138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2384554286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009679539,0.000110272085,0.98653626,0.000060853607,0.0000046384603,0.0007689954,0.000033008826,0.00037564387,0.002430764],"genre_scores_gemma":[0.34997267,0.000040445288,0.64897984,0.00002236439,0.00006791576,0.00072120357,0.00013656983,0.000032666365,0.000026338976],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992191,0.000005659719,0.0003538859,0.0001833421,0.00008021765,0.00015775503],"domain_scores_gemma":[0.99965113,0.000020319934,0.000039469745,0.00021931548,0.00004613618,0.000023627823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005556502,0.00012459751,0.00014311254,0.00017791668,0.000029601146,0.000009745895,0.00018029854,0.000064049396,0.000013946985],"category_scores_gemma":[1.0624109e-7,0.00013874419,0.000048174155,0.00039121043,0.000043110882,0.000111315334,0.000026130167,0.00010556535,0.000025645593],"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":[0.0000015048357,0.00006892848,0.0001217599,0.000046414298,0.0000019101833,3.1744216e-7,0.000023457795,0.00062629645,0.6853671,0.0012494172,0.0006601341,0.31183276],"study_design_scores_gemma":[0.0003994205,0.000014015254,0.008943028,0.000023723693,0.000007130424,0.000010015554,0.000008603812,0.03304001,0.73129255,0.011352743,0.21460249,0.0003062687],"about_ca_topic_score_codex":0.000037383026,"about_ca_topic_score_gemma":0.000028077164,"teacher_disagreement_score":0.34029314,"about_ca_system_score_codex":0.00005100397,"about_ca_system_score_gemma":0.000007414557,"threshold_uncertainty_score":0.5657824},"labels":[],"label_agreement":null},{"id":"W2386722160","doi":"","title":"An ImageFusion Algorithm Based on Max Comentropy and Gradient of Multiple Scales","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer science; Algorithm; Wavelet; Image fusion; Entropy (arrow of time); Fusion; Image (mathematics); Artificial intelligence","score_opus":0.002693656457158967,"score_gpt":0.217510861540782,"score_spread":0.21481720508362304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2386722160","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034155063,0.000020050556,0.96477485,0.00003612759,0.000017813041,0.00047875696,0.000051584568,0.0003758001,0.00008996282],"genre_scores_gemma":[0.23901197,0.000011389186,0.76063854,0.000056233825,0.000043247528,0.00017018738,0.000042889234,0.000021646774,0.000003899662],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944246,0.000007708163,0.00016597814,0.00018897648,0.00007067871,0.00012421091],"domain_scores_gemma":[0.99947965,0.00004396387,0.0000321203,0.0003269621,0.000044574852,0.00007272393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000044578806,0.000119157434,0.00011851114,0.0001082413,0.0000661025,0.000019573205,0.00015945405,0.00004818368,0.000012143912],"category_scores_gemma":[4.303376e-7,0.00011979496,0.00002704148,0.00011016361,0.0000689137,0.000068925925,0.00003475194,0.00014403374,0.000007326967],"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":[0.000001260542,0.000082332655,0.00017196676,0.000015377222,0.000002100682,2.7353644e-7,0.000019215175,0.00037715718,0.6309065,0.00010417636,0.00031509588,0.36800453],"study_design_scores_gemma":[0.00032569788,0.000050838706,0.0040850677,0.00001271122,0.0000051849347,0.000005242764,0.000005730188,0.5024241,0.43003675,0.00043271467,0.062442116,0.00017382027],"about_ca_topic_score_codex":0.0000047799854,"about_ca_topic_score_gemma":0.0000056683257,"teacher_disagreement_score":0.50204694,"about_ca_system_score_codex":0.00001489227,"about_ca_system_score_gemma":0.0000036622303,"threshold_uncertainty_score":0.48850965},"labels":[],"label_agreement":null},{"id":"W2391647160","doi":"","title":"Image fusion algorithm based on Bayesian compressive sensing","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"CAE (Canada)","funders":"","keywords":"Computer science; Compressed sensing; Bayesian probability; Image (mathematics); Image fusion; Algorithm; Domain (mathematical analysis); Sampling (signal processing); Fusion; Artificial intelligence; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.004052660022711194,"score_gpt":0.21350110855428434,"score_spread":0.20944844853157313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2391647160","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084154046,0.000011276778,0.95339,0.000099344354,0.00010313689,0.00027792348,0.0000034998793,0.0018351562,0.04343811],"genre_scores_gemma":[0.11507905,0.000007901463,0.88414794,0.0003731671,0.00005557799,0.000012968879,0.000010829666,0.00005339735,0.00025914639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992967,0.000016809565,0.00015204161,0.00016279056,0.00014800974,0.00022362862],"domain_scores_gemma":[0.99947554,0.00006295958,0.000020551053,0.00029125175,0.00006710873,0.00008260045],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000033291435,0.00016469623,0.00013529508,0.0001074677,0.00005714362,0.000048479233,0.000089376605,0.000059916718,0.0014044124],"category_scores_gemma":[0.000017658627,0.00014644994,0.00004207907,0.000110299305,0.000028838622,0.00021628286,0.000032255928,0.00015978361,0.0003041353],"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":[0.000001921043,0.000020625796,0.000004411339,0.00002262475,0.0000046372015,0.00002497167,0.000032684457,0.0025115558,0.3511092,0.000013244027,0.03062258,0.6156315],"study_design_scores_gemma":[0.00012421895,0.000022148075,0.00008357938,0.00003642841,0.0000018004172,0.0000025740694,0.000021866837,0.78778034,0.21033238,0.000356999,0.0010820769,0.00015555792],"about_ca_topic_score_codex":0.00003392488,"about_ca_topic_score_gemma":0.0000011299077,"teacher_disagreement_score":0.7852688,"about_ca_system_score_codex":0.000048822265,"about_ca_system_score_gemma":0.000004535099,"threshold_uncertainty_score":0.99950844},"labels":[],"label_agreement":null},{"id":"W2418999752","doi":"10.1142/s0218126616501231","title":"Multi-Exposure and Multi-Focus Image Fusion in Gradient Domain","year":2016,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":210,"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":"Chrominance; Luminance; Artificial intelligence; Focus (optics); Image fusion; Computer vision; Computer science; Fusion; Image gradient; Thresholding; Grayscale; Pixel; Image (mathematics); Mathematics; Color image; Image processing; Optics; Physics","score_opus":0.009080473626909997,"score_gpt":0.21728353796598757,"score_spread":0.20820306433907756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2418999752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39132124,0.0027188722,0.6052903,0.000040654384,0.00041788848,0.0001332794,0.0000031522702,0.00004823133,0.000026384605],"genre_scores_gemma":[0.9770349,0.0006919986,0.022140004,0.00000996795,0.00008246846,0.0000033347872,1.2023399e-7,0.000021016614,0.000016161388],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99906427,0.0000468232,0.00046075144,0.00011584165,0.00013776397,0.00017452693],"domain_scores_gemma":[0.9995102,0.00006147035,0.00014260133,0.000103789695,0.00006499879,0.00011694013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028671828,0.00014266765,0.0003014972,0.00021833403,0.000030663337,0.00004512002,0.00010038714,0.00006241963,0.0000015504598],"category_scores_gemma":[0.000014090965,0.00009665282,0.000040398736,0.000079695,0.000039212602,0.00028759867,0.000036227386,0.00013090111,9.430232e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013877103,0.00010310146,0.005772409,0.00024956302,0.00005449142,0.00038677026,0.0013513444,0.0005209659,0.7158952,0.00014652852,0.0006247237,0.27488104],"study_design_scores_gemma":[0.07532864,0.005388521,0.46842688,0.035282902,0.00021088182,0.02001711,0.0059354417,0.27200887,0.06942387,0.004142911,0.037400827,0.0064331354],"about_ca_topic_score_codex":0.000009641517,"about_ca_topic_score_gemma":0.0000057632597,"teacher_disagreement_score":0.6464713,"about_ca_system_score_codex":0.00007202685,"about_ca_system_score_gemma":0.00000788516,"threshold_uncertainty_score":0.39413875},"labels":[],"label_agreement":null},{"id":"W2480280476","doi":"10.21611/qirt.2016.156","title":"A novel data fusion method for infrared and visible images based on NSCT and Gaussian statistical estimation","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Gaussian; Artificial intelligence; Sensor fusion; Infrared; Fusion; Gaussian process; Pattern recognition (psychology); Estimation; Computer vision; Optics; Physics","score_opus":0.021683912337551296,"score_gpt":0.32624962668488144,"score_spread":0.30456571434733015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2480280476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007834336,0.0000187505,0.99737954,0.00025794524,0.000023622097,0.00028914522,0.0005553477,0.00037443044,0.0010228927],"genre_scores_gemma":[0.017215606,0.000029137815,0.9823644,0.00008277887,0.000013888828,0.00003178438,0.00007532148,0.000026380116,0.0001606756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993918,0.000011918186,0.00014207598,0.00023672695,0.000084043655,0.00013343553],"domain_scores_gemma":[0.9989998,0.0005676019,0.00001925029,0.00032562055,0.000022730625,0.0000649956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001983627,0.00011541439,0.00012374653,0.0000832117,0.00004283193,0.000033458815,0.000084338404,0.000045886445,0.000079532525],"category_scores_gemma":[0.00038536114,0.00007853081,0.0000070695082,0.00004767865,0.00003073862,0.00030575713,0.00007516044,0.00004110235,0.0000020981686],"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":[0.00004698775,0.000023361736,0.00001790514,0.00010480467,0.000005664361,0.000001243096,0.000010717801,0.0001828671,0.31636414,0.002001073,0.009104955,0.6721363],"study_design_scores_gemma":[0.00072183093,0.000097863085,0.00077527796,0.00007697912,0.000010433006,0.000003885543,0.000005409394,0.91619366,0.0752345,0.004772641,0.0019582096,0.00014931313],"about_ca_topic_score_codex":0.000005798328,"about_ca_topic_score_gemma":0.0000028636657,"teacher_disagreement_score":0.9160108,"about_ca_system_score_codex":0.000018661261,"about_ca_system_score_gemma":0.000009381204,"threshold_uncertainty_score":0.32023937},"labels":[],"label_agreement":null},{"id":"W2483378596","doi":"10.5539/cis.v9n3p37","title":"Refinement for Ocular Ultrasound Images Quality by Utilizing Combination of Enhancement Techniques","year":2016,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer science; Artificial intelligence; Speckle noise; Computer vision; Artifact (error); Preprocessor; Wavelet; Image quality; Noise (video); Ultrasound; Speckle pattern; Pattern recognition (psychology); Image (mathematics); Radiology; Medicine","score_opus":0.009905869058757745,"score_gpt":0.27749850321225966,"score_spread":0.26759263415350193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483378596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01227559,0.000031887936,0.98593175,0.00005362719,0.00006571726,0.00023787463,0.000017105147,0.00015748618,0.0012289332],"genre_scores_gemma":[0.81823194,0.00029975735,0.18129906,0.0000951701,0.000008303288,0.000043511067,0.0000080952805,0.0000032219755,0.0000109396115],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993217,0.0000053421327,0.0002977724,0.00007652622,0.00018714405,0.00011154444],"domain_scores_gemma":[0.9995024,0.00006020469,0.000079154925,0.00012650728,0.00020169633,0.00003000827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049347733,0.0000671397,0.00008511725,0.0001142967,0.00007017346,0.00004301477,0.00013006337,0.000018023638,0.0000069875196],"category_scores_gemma":[0.0000529115,0.000050147693,0.00001530411,0.00014030693,0.00013661945,0.002817365,0.00004548001,0.000019417466,0.0000011524415],"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":[0.0000023467342,0.000007901877,0.000033024724,0.000064436594,0.000001627651,6.7461428e-9,0.00016503857,0.000004224617,0.6338232,0.0029609872,0.0020032458,0.36093393],"study_design_scores_gemma":[0.0001569072,0.00006912882,0.00070712005,0.000048022997,0.0000010968074,6.8485775e-7,0.000018573812,0.0019237852,0.98274696,0.00044890883,0.0137948,0.00008403053],"about_ca_topic_score_codex":0.0000013240851,"about_ca_topic_score_gemma":4.6622567e-8,"teacher_disagreement_score":0.80595636,"about_ca_system_score_codex":0.000048051876,"about_ca_system_score_gemma":0.000010017138,"threshold_uncertainty_score":0.20449637},"labels":[],"label_agreement":null},{"id":"W2510415732","doi":"10.1109/icip.2016.7533130","title":"Wavelet-based texture-characteristic morphological component analysis for colour image enhancement","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Saskatchewan","funders":"","keywords":"Artificial intelligence; Computer vision; Luminance; Chromatic scale; Image texture; Computer science; Preprocessor; Channel (broadcasting); Pattern recognition (psychology); Image segmentation; Texture (cosmology); Segmentation; Image (mathematics); Mathematics","score_opus":0.011517668657908065,"score_gpt":0.247940383268466,"score_spread":0.23642271461055794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510415732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03351638,0.000021598988,0.96429133,0.00026472687,0.00006951936,0.00040387275,0.00007239013,0.0007517014,0.0006085021],"genre_scores_gemma":[0.8002935,0.000023249326,0.19856195,0.00014765326,0.00004094286,0.00025563233,0.00004396067,0.000024018203,0.0006091026],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900293,0.000016406853,0.00029305465,0.0002537527,0.00013368373,0.0003001539],"domain_scores_gemma":[0.99930644,0.00018178523,0.00004579168,0.0003037445,0.000076716315,0.000085505766],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011260862,0.00019251902,0.00030610635,0.00014775821,0.00004409235,0.000020135212,0.00015880917,0.00007328817,0.002215535],"category_scores_gemma":[0.000056620385,0.00012297828,0.0001616956,0.00018759335,0.000052093994,0.000079581536,0.000032598397,0.000059860875,0.00007822787],"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":[0.000023250823,0.000042512056,0.00005060036,0.000021857331,0.00008571458,0.000012391638,0.0000030889119,0.000042560947,0.9673576,0.00006996761,0.0029247585,0.029365752],"study_design_scores_gemma":[0.00063087605,0.00019179973,0.0036956395,0.000021826241,0.00012956584,0.0000022290246,0.0000037878826,0.045900986,0.9370034,0.00028808718,0.011760181,0.00037165024],"about_ca_topic_score_codex":0.0000023095845,"about_ca_topic_score_gemma":0.0000037197233,"teacher_disagreement_score":0.7667771,"about_ca_system_score_codex":0.00017040141,"about_ca_system_score_gemma":0.000008429945,"threshold_uncertainty_score":0.99869657},"labels":[],"label_agreement":null},{"id":"W2534152355","doi":"10.1109/tic-sth.2009.5444464","title":"Multiresolution region-based image fusion using the Contourlet Transform","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial intelligence; Contourlet; Image fusion; Computer science; Computer vision; Image resolution; Noise (video); Fusion; Pixel; Sensitivity (control systems); Segmentation; Image segmentation; Process (computing); Pattern recognition (psychology); Image (mathematics); Wavelet transform; Wavelet; Engineering","score_opus":0.013766789527704618,"score_gpt":0.25338171472234733,"score_spread":0.23961492519464272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2534152355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006471239,0.000109947825,0.9833671,0.0006653772,0.00006203473,0.00031486334,0.0000018629546,0.0013012714,0.0077063167],"genre_scores_gemma":[0.86747,0.000050053848,0.13182433,0.0004631729,0.00005865799,0.00000851621,0.0000050709673,0.000023676508,0.0000965083],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993644,0.000014894793,0.00017087699,0.000115836236,0.00012482954,0.00020921329],"domain_scores_gemma":[0.9996191,0.000035078298,0.000019806233,0.00024321247,0.00004247295,0.00004028325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008118322,0.00013518418,0.00010616294,0.00005966412,0.000117149524,0.000026360933,0.00012799945,0.000060532377,0.00006737365],"category_scores_gemma":[0.000016706257,0.000095598734,0.0000633758,0.00014344207,0.000038122434,0.00020097688,0.000005518963,0.00013810008,0.0000069158637],"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":[0.000021616837,0.000029919016,0.000007263853,0.000015246307,0.0000040665464,0.000014564146,0.00011501418,0.0060878894,0.85450256,0.00036581032,0.004201338,0.1346347],"study_design_scores_gemma":[0.0003217009,0.000038127015,0.0001515105,0.000035143625,0.0000081950275,0.00000958821,0.000047089205,0.59200144,0.40153068,0.0010462933,0.004632138,0.00017811828],"about_ca_topic_score_codex":0.0000242961,"about_ca_topic_score_gemma":0.000009179215,"teacher_disagreement_score":0.86099875,"about_ca_system_score_codex":0.000090577385,"about_ca_system_score_gemma":0.000009645335,"threshold_uncertainty_score":0.38984033},"labels":[],"label_agreement":null},{"id":"W2534734673","doi":"10.1109/icnnsp.2003.1281049","title":"Pixel visibility based multifocus image fusion","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Calgary","funders":"","keywords":"Visibility; Pixel; Computer vision; Artificial intelligence; Image fusion; Computer science; Focus (optics); Fusion; Image (mathematics); Optics; Physics","score_opus":0.0056492872340639134,"score_gpt":0.2291175989703506,"score_spread":0.2234683117362867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2534734673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027687503,0.000063000014,0.8998556,0.000018367386,0.00011141384,0.00020825426,0.0000038579133,0.0021482045,0.06990379],"genre_scores_gemma":[0.60761046,0.000011815655,0.39191067,0.000074380056,0.000010030743,0.000018815603,0.0000027942324,0.000030687825,0.00033031596],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992825,0.000026326079,0.00017968558,0.00018539462,0.00011812912,0.00020800257],"domain_scores_gemma":[0.99941754,0.000050248025,0.000013854336,0.00039723254,0.00004872169,0.000072391405],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015067187,0.00014098427,0.00012112719,0.00006315513,0.000042369473,0.000018685136,0.000093385235,0.000060305218,0.0017522252],"category_scores_gemma":[0.00014881295,0.00012804334,0.000051600455,0.00015039834,0.000030269819,0.00017391091,0.000017529512,0.00012632793,0.0001432706],"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":[0.000003841488,0.00004629655,0.00016603287,0.00003390148,0.0000028255583,0.0000051529214,0.00001699738,0.0004801688,0.9763027,0.00026006324,0.0037299423,0.018952074],"study_design_scores_gemma":[0.00026448732,0.000019026873,0.00038068407,0.000009522081,0.0000028876266,0.0000018897315,0.000016070067,0.03834023,0.93934095,0.0010940832,0.020318542,0.00021160513],"about_ca_topic_score_codex":0.0000055557516,"about_ca_topic_score_gemma":0.0000057029765,"teacher_disagreement_score":0.579923,"about_ca_system_score_codex":0.000062573185,"about_ca_system_score_gemma":0.000009871515,"threshold_uncertainty_score":0.9991603},"labels":[],"label_agreement":null},{"id":"W2549200261","doi":"","title":"MERGING OF HETEROGENEOUS DATA FOR EMERGENCY MAPPING: DATA INTEGRATION OR DATA FUSION?","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Geospatial analysis; Context (archaeology); Computer science; Damages; Data integration; Sensor fusion; Digital mapping; Data science; Spatial analysis; Geographic information system; Identification (biology); Data mining; Geography; Cartography; Remote sensing; Artificial intelligence","score_opus":0.16626792789887104,"score_gpt":0.33517959700558186,"score_spread":0.16891166910671082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549200261","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048914144,0.00072701764,0.99348426,0.00009427313,0.00030725036,0.000440458,0.002584635,0.0006637452,0.0012092458],"genre_scores_gemma":[0.11423176,0.004376382,0.8666137,0.00004260348,0.00021380048,0.000034971486,0.012623317,0.000116160285,0.0017473416],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856824,0.000013822973,0.00050043844,0.00052800716,0.00016883668,0.00022067258],"domain_scores_gemma":[0.9944331,0.00006552591,0.00007752615,0.005310822,0.00006088864,0.000052138235],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024368217,0.00017859534,0.00021758849,0.000111556685,0.000057584522,0.00001629996,0.002843801,0.00006212389,0.0022604964],"category_scores_gemma":[0.00042945668,0.00014759836,0.000018553395,0.00023434566,0.000020385758,0.0011500515,0.0020185034,0.00009606923,0.000015896638],"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":[0.000016151254,0.00008908489,0.00002273127,0.00023054685,0.000075825534,0.000006653173,0.000118237826,0.00049055525,0.09563773,0.000108343906,0.54576,0.35744414],"study_design_scores_gemma":[0.0001276566,0.000024163362,0.000003825438,0.000037821963,0.000019802275,0.000008827927,0.000071806215,0.8498114,0.019251715,0.00012153647,0.13033663,0.00018487578],"about_ca_topic_score_codex":0.000018757906,"about_ca_topic_score_gemma":0.00009885716,"teacher_disagreement_score":0.84932077,"about_ca_system_score_codex":0.000017180335,"about_ca_system_score_gemma":0.0000067484216,"threshold_uncertainty_score":0.99865156},"labels":[],"label_agreement":null},{"id":"W2562813894","doi":"10.1088/1361-6420/aa5489","title":"The pre-image problem for Laplacian Eigenmaps utilizing <i>L</i> <sub>1</sub> regularization with applications to data fusion","year":2016,"lang":"en","type":"article","venue":"Inverse Problems","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":13,"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":"Defense Threat Reduction Agency; University of Southern Mississippi; Ryerson University; U.S. Department of Energy","keywords":"Artificial intelligence; Mathematics; Laplace operator; Nonlinear dimensionality reduction; Feature (linguistics); Computer vision; Pattern recognition (psychology); Inpainting; Image (mathematics); Computer science; Dimensionality reduction","score_opus":0.018535560089182883,"score_gpt":0.24495584821381855,"score_spread":0.22642028812463566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562813894","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009356715,0.00008438882,0.9921085,0.00057064526,0.000054891465,0.003326232,0.0001809759,0.0010469345,0.001691723],"genre_scores_gemma":[0.15041634,0.0030545224,0.8329171,0.0005137369,0.00037801403,0.010347677,0.0006156593,0.00048602643,0.0012709361],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99861187,0.000022723483,0.00031711685,0.000438493,0.00021946101,0.00039034858],"domain_scores_gemma":[0.9983667,0.0001187027,0.00008964935,0.0011352354,0.0001712916,0.00011840015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027561045,0.00022365239,0.00015273038,0.00008670133,0.00037274286,0.00006794208,0.00060949015,0.00008394481,0.0000044609415],"category_scores_gemma":[0.000058205518,0.00014165147,0.000030736777,0.0003584072,0.000103361934,0.0006308387,0.00024383898,0.00010506291,0.000049810464],"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":[0.000034814148,0.000022834247,0.000020514339,0.00011587449,0.000022818316,9.244212e-7,0.0002050563,0.0007131455,0.84978074,0.0010502401,0.023800835,0.12423217],"study_design_scores_gemma":[0.00055782846,0.000105441395,0.000018296747,0.0002828083,0.000027401355,0.000013617619,0.0000657731,0.014687017,0.55075556,0.0057404665,0.42731994,0.000425849],"about_ca_topic_score_codex":0.0000036048164,"about_ca_topic_score_gemma":0.00012453969,"teacher_disagreement_score":0.4035191,"about_ca_system_score_codex":0.000099229204,"about_ca_system_score_gemma":0.00004251459,"threshold_uncertainty_score":0.577638},"labels":[],"label_agreement":null},{"id":"W2579362737","doi":"10.1117/1.jei.26.1.010501","title":"Multisensor image fusion approach utilizing hybrid pre-enhancement and double nonsubsampled contourlet transform","year":2017,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Laval","funders":"","keywords":"Contourlet; Image fusion; Artificial intelligence; Fusion; Computer science; Computer vision; Pattern recognition (psychology); Image (mathematics); Hybrid image; Domain (mathematical analysis); Contrast (vision); Wavelet transform; Mathematics; Wavelet","score_opus":0.008904467819232414,"score_gpt":0.26562352135396483,"score_spread":0.2567190535347324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579362737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20795387,0.0042123687,0.7765287,0.00034117364,0.00023572159,0.0005146663,0.0000061271126,0.00018931416,0.010018072],"genre_scores_gemma":[0.9587373,0.0019137212,0.03898633,0.00002945377,0.00012799197,0.000012821619,0.0000032320042,0.000057349356,0.00013177963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984146,0.000017804336,0.00051618926,0.00018673441,0.0002736413,0.0005910799],"domain_scores_gemma":[0.9990433,0.000034565237,0.00031882583,0.00033794632,0.00014842465,0.0001169543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004940965,0.00024840972,0.00038351922,0.00016385526,0.00033019122,0.00022181057,0.00035902188,0.000032895623,0.000071742616],"category_scores_gemma":[0.000042812702,0.00022941778,0.000115015435,0.00004119573,0.00009784421,0.0011838379,0.000059227223,0.00060850906,0.000003365365],"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":[0.00029550647,0.0000790097,0.00016623097,0.00017991851,0.000112171365,0.00008246939,0.00046819993,0.00025813244,0.85254174,0.0001544724,0.0019277483,0.14373438],"study_design_scores_gemma":[0.005146751,0.00019345329,0.0005083882,0.0003058743,0.00011617529,0.0011295945,0.00022200508,0.12726833,0.847868,0.0016452335,0.014995933,0.000600261],"about_ca_topic_score_codex":0.000018334664,"about_ca_topic_score_gemma":0.0000070147635,"teacher_disagreement_score":0.75078344,"about_ca_system_score_codex":0.00033180058,"about_ca_system_score_gemma":0.000068276386,"threshold_uncertainty_score":0.93553853},"labels":[],"label_agreement":null},{"id":"W2596392286","doi":"10.20944/preprints201703.0086.v1","title":"Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement","year":2017,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":59,"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; Changzhou Institute of Technology; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"RGB color model; Color space; Color histogram; Artificial intelligence; Computer vision; RGB color space; Color balance; Adaptive histogram equalization; Mathematics; Color image; Histogram equalization; Color depth; Color normalization; Computer science; Image processing; Image (mathematics)","score_opus":0.10354197780330451,"score_gpt":0.3478299222250583,"score_spread":0.2442879444217538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596392286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025807735,0.00014252472,0.9625746,0.00012585247,0.000893567,0.002858238,0.00012871956,0.0017514677,0.0057173087],"genre_scores_gemma":[0.92837435,0.00027496825,0.06725631,0.0000993606,0.0001788027,0.002007979,0.0006482135,0.00020167744,0.0009583592],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99734694,0.00007891114,0.000705791,0.0009702073,0.00035899717,0.0005391555],"domain_scores_gemma":[0.9970993,0.000116986346,0.00041664983,0.0017891028,0.00042837608,0.00014959906],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005047498,0.00062659796,0.0006339074,0.0002679015,0.00023057389,0.00007464202,0.000784738,0.0004886323,0.00069791445],"category_scores_gemma":[0.00030004868,0.00068556675,0.00028784946,0.00006673089,0.0001345799,0.00024441484,0.0008718206,0.00064273033,0.00022367055],"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":[0.0002957578,0.00028028918,0.0012196733,0.001203646,0.00020978364,0.00001441675,0.00054427475,0.0138969505,0.9679115,0.00025420843,0.001247698,0.012921797],"study_design_scores_gemma":[0.00082073273,0.000060632225,0.0014710424,0.0005886362,0.00009635945,0.0000010510116,0.000026541717,0.08933412,0.88947177,0.0043271314,0.0129510285,0.0008509325],"about_ca_topic_score_codex":0.000047472535,"about_ca_topic_score_gemma":0.000018038416,"teacher_disagreement_score":0.9025666,"about_ca_system_score_codex":0.0006497576,"about_ca_system_score_gemma":0.000090091446,"threshold_uncertainty_score":0.9995595},"labels":[],"label_agreement":null},{"id":"W2602365064","doi":"10.5555/arwg.3.1.m355l26453m22t42","title":"Spectral Information Analysis from Multisensor Image Fusion for Land Use/Land Cover Classification in a Tropical Area: A Case Study in Bogor, Indonesia","year":2011,"lang":"en","type":"article","venue":"Arab world geographer","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Panchromatic film; RGB color model; Principal component analysis; Land cover; Remote sensing; Image fusion; Computer science; Hue; Artificial intelligence; Pattern recognition (psychology); Geography; Land use; Multispectral image; Image (mathematics); Biology","score_opus":0.0210355391389757,"score_gpt":0.2522852994450192,"score_spread":0.2312497603060435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602365064","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9502084,0.000029522904,0.04837359,0.000008336442,0.000044456476,0.0009597264,0.000044156273,0.0002051107,0.00012665059],"genre_scores_gemma":[0.9803796,0.000024678635,0.019195085,0.000024972294,0.000016167814,0.0002530615,0.00007116197,0.000021243917,0.000014044968],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988326,0.00005066,0.0004884282,0.0002319551,0.0001357146,0.00026065193],"domain_scores_gemma":[0.99938333,0.000107120024,0.000082385246,0.00031291062,0.00005523172,0.000059012702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010028998,0.00020247881,0.0003085847,0.0011154754,0.000042193198,0.000057687812,0.00009630231,0.00007504934,0.00016697687],"category_scores_gemma":[0.00003193687,0.00019450346,0.000110700865,0.001428815,0.000032053817,0.0008721137,0.000028467135,0.00020772968,0.000009039597],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098170065,0.00017128253,0.9946627,0.0000093642,0.000071338814,0.00013511317,0.001093974,0.0006211823,0.00046463756,0.0000041852677,0.00005989546,0.0026081416],"study_design_scores_gemma":[0.0015583478,0.00004874028,0.9452475,0.000016909202,0.00009970952,0.000006152093,0.0003685208,0.051821843,0.00043267914,0.00010504126,0.00007370235,0.00022090743],"about_ca_topic_score_codex":0.0041845455,"about_ca_topic_score_gemma":0.031107292,"teacher_disagreement_score":0.051200658,"about_ca_system_score_codex":0.00006017659,"about_ca_system_score_gemma":0.0000057161274,"threshold_uncertainty_score":0.9865725},"labels":[],"label_agreement":null},{"id":"W2602941503","doi":"10.1117/3.2265064.ch6","title":"Justice in Rational Transmission","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Rationality; Transmission (telecommunications); Pixel; Theoretical computer science; Computer science; Wavelet; Action (physics); Constellation; Node (physics); Artificial intelligence; Mathematics; Telecommunications; Political science","score_opus":0.023630421617677602,"score_gpt":0.27154785272357956,"score_spread":0.24791743110590198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602941503","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.7343634e-7,0.00026765864,0.2295426,0.000026697999,0.000103985105,0.00010769633,0.000005575349,0.00036207854,0.76958346],"genre_scores_gemma":[0.00030193385,0.0013157068,0.051834803,0.000040413972,0.00012667061,0.000010668078,0.00003602803,0.00007608956,0.9462577],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994926,0.0000013040766,0.00016719807,0.00013044308,0.000116637806,0.000091828675],"domain_scores_gemma":[0.9996601,0.000022778915,0.000026321313,0.00023375827,0.00002171617,0.00003534852],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000038042246,0.00016662551,0.00015971181,0.000107823675,0.000032003776,0.000015976864,0.00014461302,0.0001990277,0.0023028967],"category_scores_gemma":[0.000008019668,0.00016284366,0.000041563737,0.0000032529085,0.000021568718,0.000112373426,0.000016448615,0.00027808268,0.000104848325],"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":[0.000017899622,0.000012671589,0.0000010139881,0.000642333,0.000027994978,0.00015101605,0.00009313304,0.001906209,0.008485693,0.21572,0.08783457,0.68510747],"study_design_scores_gemma":[0.00010914913,0.000008865715,0.0000074885197,0.00025553384,0.00001562947,0.000004930541,9.0405507e-7,0.0037727316,0.0029352428,0.035611246,0.9569966,0.0002816672],"about_ca_topic_score_codex":0.0000013630039,"about_ca_topic_score_gemma":0.00000755195,"teacher_disagreement_score":0.869162,"about_ca_system_score_codex":0.000051663243,"about_ca_system_score_gemma":0.000014193784,"threshold_uncertainty_score":0.9986091},"labels":[],"label_agreement":null},{"id":"W2611424723","doi":"10.1117/12.2266038","title":"Satellite image fusion by using a combination of IHS and HPM methods","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Image fusion; Computer science; Satellite; Computer vision; Image resolution; Artificial intelligence; Principal component analysis; Fusion; Satellite image; Distortion (music); Image processing; Hue; Remote sensing; Image (mathematics); Geography; Telecommunications; Engineering","score_opus":0.013957978464728885,"score_gpt":0.28448523487807026,"score_spread":0.27052725641334135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611424723","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934508,0.0003528052,0.0031545374,0.00036815836,0.00016852112,0.00046807772,0.000045182685,0.0001511523,0.0018407194],"genre_scores_gemma":[0.3115304,0.0007242624,0.6874149,0.000019086125,0.000098450495,0.0000536923,0.00000626751,0.000082906925,0.00007003193],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99831915,2.4145978e-8,0.00065071293,0.0002941313,0.00043786888,0.00029810934],"domain_scores_gemma":[0.99807936,0.00011815466,0.0004702569,0.00010094095,0.0011356324,0.00009567468],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00065549574,0.00030319553,0.00045498656,0.00010744097,0.00013451814,0.00013504231,0.0007946743,0.00019038601,0.00000764589],"category_scores_gemma":[0.0006925922,0.00027759367,0.0003098259,0.00013659899,0.0003601178,0.0010065585,0.0002670529,0.0002766803,2.792737e-7],"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":[0.000030296664,0.000050940478,0.00015938647,0.000649441,0.00014127298,5.0916714e-8,0.00014201005,0.00002949168,0.9503778,0.04304738,0.0007506427,0.0046212757],"study_design_scores_gemma":[0.0006495098,0.00013129505,0.00052230427,0.00034619786,0.00008978112,0.000009548122,0.00027372278,0.08864639,0.9045934,0.0031282662,0.0013259333,0.00028363738],"about_ca_topic_score_codex":0.000011402793,"about_ca_topic_score_gemma":6.029145e-8,"teacher_disagreement_score":0.68426037,"about_ca_system_score_codex":0.00011861683,"about_ca_system_score_gemma":0.00000997544,"threshold_uncertainty_score":0.99996763},"labels":[],"label_agreement":null},{"id":"W2617128359","doi":"10.1080/01431161.2017.1328145","title":"Improving UAV imaging quality by optical sensor fusion: an initial study","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Image resolution; Panchromatic film; Pixel; Artificial intelligence; Computer science; Computer vision; Image sensor; Remote sensing; Filter (signal processing); Image fusion; Noise (video); Image (mathematics); Geology","score_opus":0.02148270447447478,"score_gpt":0.359570464563805,"score_spread":0.3380877600893302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617128359","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.552438,0.00005690848,0.44431162,0.00024544058,0.0014776463,0.00008610601,0.0000055825312,0.000119159806,0.0012595358],"genre_scores_gemma":[0.8302168,0.000022378335,0.16888623,0.00006730536,0.00074950565,2.491181e-8,0.0000021729352,0.000036034773,0.000019549809],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982414,0.00006390758,0.000654478,0.00016845501,0.00066754303,0.00020417475],"domain_scores_gemma":[0.9983372,0.00007839903,0.000454559,0.00034750634,0.00064641703,0.00013591153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006678379,0.00018008993,0.00026432736,0.00016410144,0.0001847169,0.00038337128,0.00054283056,0.000050347775,0.000017861359],"category_scores_gemma":[0.00065018696,0.00017659484,0.00010216763,0.000028260236,0.0000784749,0.0010263401,0.00016446882,0.0004579319,0.0000040647988],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006625465,0.000042298736,0.00022576904,0.000008297952,0.000056757286,0.0007299163,0.00027805395,0.00018789302,0.1763342,0.000003711452,0.00014174338,0.8219251],"study_design_scores_gemma":[0.0047929324,0.0004811014,0.008585212,0.0007883522,0.00012416343,0.004845704,0.0038616047,0.59065425,0.37849322,0.0019633395,0.004063204,0.0013469442],"about_ca_topic_score_codex":0.000116915355,"about_ca_topic_score_gemma":0.00001891871,"teacher_disagreement_score":0.82057816,"about_ca_system_score_codex":0.00017778676,"about_ca_system_score_gemma":0.000030457153,"threshold_uncertainty_score":0.7201329},"labels":[],"label_agreement":null},{"id":"W2623307522","doi":"10.3390/ijgi6060168","title":"Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents","year":2017,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":47,"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; U.S. Geological Survey; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Panchromatic film; Normalized Difference Vegetation Index; Image resolution; Remote sensing; Vegetation (pathology); Multispectral image; Sharpening; Environmental science; Enhanced vegetation index; Canopy; Thematic Mapper; Leaf area index; Vegetation Index; Satellite imagery; Geography; Physics; Optics; Ecology","score_opus":0.006483482852973365,"score_gpt":0.257612838876635,"score_spread":0.25112935602366165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623307522","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9047386,0.000109867535,0.094227165,0.00015949624,0.00022615552,0.00014432867,0.00001355454,0.000021808624,0.0003590135],"genre_scores_gemma":[0.99683076,0.00017591032,0.0028935287,0.00002391225,0.000040149436,0.000006279888,0.000019590454,0.0000059041345,0.0000039623656],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991345,0.000009550906,0.00049718504,0.000042118925,0.00023047715,0.00008620751],"domain_scores_gemma":[0.999078,0.000025347508,0.00038283353,0.00007144251,0.00041335583,0.000029043416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023827913,0.000083385916,0.0001368497,0.00025289593,0.000054424985,0.000093942916,0.0001709584,0.000046575788,0.0000031434608],"category_scores_gemma":[0.00013705557,0.00007116286,0.000022814716,0.000020176984,0.000024105731,0.0035719315,0.00005766016,0.00012129178,0.0000020330103],"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":[0.00019361038,0.000028003948,0.059743233,0.00037738148,0.00013006007,0.000013742481,0.0046749534,0.006147506,0.3404734,0.0008225597,0.00006992482,0.58732563],"study_design_scores_gemma":[0.002473447,0.00007249822,0.2666315,0.0006846271,0.000025050645,0.0001519066,0.00040143525,0.17842242,0.54871583,0.001339205,0.00080114097,0.00028094763],"about_ca_topic_score_codex":0.00005257741,"about_ca_topic_score_gemma":0.00001467508,"teacher_disagreement_score":0.58704466,"about_ca_system_score_codex":0.00005674387,"about_ca_system_score_gemma":0.000006592404,"threshold_uncertainty_score":0.2901937},"labels":[],"label_agreement":null},{"id":"W2623913549","doi":"10.3390/rs9060596","title":"Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":330,"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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Panchromatic film; Sharpening; Multispectral image; Remote sensing; Spectral bands; Image resolution; Environmental science; Geology; Computer science; Artificial intelligence","score_opus":0.013344748143164721,"score_gpt":0.23516371316163062,"score_spread":0.2218189650184659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623913549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55200714,0.0001647739,0.443944,0.00009943346,0.000199037,0.00014332101,0.000010133389,0.00064797513,0.00278417],"genre_scores_gemma":[0.7023014,0.00001767176,0.29694036,0.000022604054,0.00012618944,3.397374e-8,0.00004243838,0.000086188375,0.00046313857],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983965,0.000048437287,0.00045244143,0.00036155494,0.00025436695,0.00048668578],"domain_scores_gemma":[0.9985588,0.00008780427,0.00019089987,0.0009292925,0.00015601466,0.000077192024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025347067,0.00030405528,0.0004062219,0.00012744138,0.00047059715,0.000116582385,0.00021790921,0.0001297824,0.00009165165],"category_scores_gemma":[0.00014897574,0.00029667467,0.00014080705,0.00004775715,0.00017569312,0.00045487192,0.00027395002,0.00023932575,0.00007544659],"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":[0.000022405731,0.0000032829503,0.00009217329,0.0000895252,0.000019161342,0.000055906876,0.00026898726,0.0019526022,0.990479,1.531551e-7,0.0010928328,0.0059239524],"study_design_scores_gemma":[0.00017787797,0.000003985766,0.00034182743,0.00023466296,0.000011793607,0.0000063227,0.00001848768,0.4318939,0.566229,0.0001055948,0.0007658382,0.00021075532],"about_ca_topic_score_codex":0.00016417097,"about_ca_topic_score_gemma":0.000013226193,"teacher_disagreement_score":0.42994127,"about_ca_system_score_codex":0.0001764324,"about_ca_system_score_gemma":0.00000970262,"threshold_uncertainty_score":0.99994856},"labels":[],"label_agreement":null},{"id":"W2752975621","doi":"10.5194/isprs-archives-xlii-2-w6-153-2017","title":"APPLICATION OF SENSOR FUSION TO IMPROVE UAV IMAGE CLASSIFICATION","year":2017,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Artificial intelligence; Panchromatic film; Computer vision; Computer science; Image fusion; Image sensor; Fusion; Image resolution; Image quality; Image (mathematics)","score_opus":0.014164226967413809,"score_gpt":0.26830539738631337,"score_spread":0.25414117041889955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752975621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011493138,0.000015237772,0.9741953,0.0025280388,0.0013584374,0.0009089602,0.0001374185,0.00008737817,0.0092761],"genre_scores_gemma":[0.9827469,0.00014254851,0.016481955,0.0003951054,0.00012055911,0.0000010218822,0.000030206316,0.000016138556,0.00006554152],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996173,0.0001406973,0.0014116572,0.00036902877,0.0014825019,0.00042308177],"domain_scores_gemma":[0.99630946,0.00056133413,0.0017453177,0.00082677585,0.00040776285,0.00014932288],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0011026374,0.00038843206,0.00041714942,0.00082340505,0.0013381505,0.0006567562,0.0020242445,0.00009669932,0.0000052830246],"category_scores_gemma":[0.0009334464,0.000262396,0.00029462358,0.00056125474,0.0025627906,0.00069849286,0.0010047052,0.00037779374,0.0000042455986],"study_design_candidate":"design_other","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":[0.00009224535,0.000013230167,0.00014172576,0.000055201373,0.00004364997,1.1921706e-7,0.0013875398,0.002304966,0.073193215,0.000009414948,0.0000443318,0.92271435],"study_design_scores_gemma":[0.00045295566,0.00010097511,0.0058842315,0.00025713234,0.000029255421,0.00003942819,0.00087557954,0.90773505,0.07718348,0.003824753,0.0033539087,0.00026322575],"about_ca_topic_score_codex":0.50996834,"about_ca_topic_score_gemma":0.057724852,"teacher_disagreement_score":0.9712538,"about_ca_system_score_codex":0.000055602686,"about_ca_system_score_gemma":0.00011368368,"threshold_uncertainty_score":0.99998283},"labels":[],"label_agreement":null},{"id":"W2753836765","doi":"10.1080/07038992.2017.1371583","title":"Influence of Pansharpening in Obtaining Accurate Vegetation Maps","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":14,"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":"Ministerio de Economía y Competitividad","keywords":"Panchromatic film; Thematic map; Multispectral image; Cartography; Computer science; Remote sensing; Geography; Vegetation (pathology); Image resolution; Context (archaeology); Artificial intelligence; Data mining","score_opus":0.015086232761827712,"score_gpt":0.2534104450525443,"score_spread":0.23832421229071657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753836765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93516463,0.00030577442,0.06329224,0.000054620406,0.00014224037,0.000050648,0.0000013921366,0.000016983797,0.00097148266],"genre_scores_gemma":[0.8657121,0.00003110771,0.13417669,0.000017993005,0.00003771459,7.765805e-9,3.821922e-7,0.000018859293,0.0000051333272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992269,0.000019087807,0.00037912428,0.00006429594,0.00009518265,0.00021537096],"domain_scores_gemma":[0.9991147,0.000035523386,0.00030346325,0.00020304692,0.00017936194,0.00016391178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028795333,0.00009413564,0.00020289703,0.0003554542,0.00010563927,0.000059829894,0.00019038099,0.00005532642,0.0000017611741],"category_scores_gemma":[0.00062185316,0.0001053231,0.000038688147,0.000086431624,0.000060727507,0.00055505417,0.000010180097,0.00028607142,0.0000012221658],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070386063,6.8050537e-7,0.001742993,0.00009051865,0.000017389983,0.00079502387,0.0018028745,0.14850803,0.04412508,0.000017047216,0.000056050987,0.80283725],"study_design_scores_gemma":[0.0016631581,0.00015793215,0.14628774,0.011365627,0.000058069792,0.0014743069,0.0010108829,0.4611009,0.3608192,0.009442652,0.005564147,0.0010553817],"about_ca_topic_score_codex":0.0015511601,"about_ca_topic_score_gemma":0.009437415,"teacher_disagreement_score":0.8017819,"about_ca_system_score_codex":0.00015357634,"about_ca_system_score_gemma":0.00017952952,"threshold_uncertainty_score":0.52662987},"labels":[],"label_agreement":null},{"id":"W2760438440","doi":"10.3390/rs9100972","title":"Haze Removal Based on a Fully Automated and Improved Haze Optimized Transformation for Landsat Imagery over Land","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":30,"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 Space Agency","keywords":"Haze; Remote sensing; Environmental science; Satellite; Satellite imagery; Computer science; Transformation (genetics); Meteorology; Geology; Geography","score_opus":0.009109866381172697,"score_gpt":0.2569647507543645,"score_spread":0.24785488437319178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760438440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10213143,0.000040497453,0.892615,0.00036142863,0.00019957402,0.0006325538,0.000021723905,0.0020749872,0.0019227975],"genre_scores_gemma":[0.4026047,0.0000410476,0.5969379,0.00012731063,0.00008870324,2.9039145e-7,0.00003244985,0.00007785949,0.00008978522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917996,0.000017757626,0.00023026511,0.00019972837,0.00011003687,0.00026223614],"domain_scores_gemma":[0.9993336,0.00006457761,0.00008616389,0.00038642486,0.00006181153,0.00006740016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019554335,0.00020489898,0.00025693772,0.00011061009,0.00024933057,0.00018157382,0.000075867276,0.00011520448,0.000003551232],"category_scores_gemma":[0.00016408363,0.00019785605,0.00007160529,0.000038035218,0.000042581432,0.0003184793,0.000016500675,0.0001376514,0.0000018902415],"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":[0.00047013228,0.000011036542,0.000003824874,0.00032913595,0.00003481169,0.000054535725,0.0002673851,0.011695444,0.48863897,0.000004217691,0.0011864697,0.49730405],"study_design_scores_gemma":[0.0017152707,0.00004154093,0.0001311706,0.00018198203,0.000020305222,0.000033407356,0.000007992351,0.94679147,0.04911792,0.00012077633,0.0016105536,0.00022763536],"about_ca_topic_score_codex":0.000029159064,"about_ca_topic_score_gemma":0.000009030792,"teacher_disagreement_score":0.935096,"about_ca_system_score_codex":0.00006335887,"about_ca_system_score_gemma":0.000015232189,"threshold_uncertainty_score":0.8068335},"labels":[],"label_agreement":null},{"id":"W2767512561","doi":"10.1016/j.inffus.2017.10.007","title":"Deep learning for pixel-level image fusion: Recent advances and future prospects","year":2017,"lang":"en","type":"article","venue":"Information Fusion","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":725,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Pixel; Artificial intelligence; Deep learning; Fusion; Image (mathematics); Image fusion; Computer vision","score_opus":0.008518233852569142,"score_gpt":0.2507800551967895,"score_spread":0.24226182134422033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767512561","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009709088,0.0024230392,0.9567035,0.0008232883,0.0012137068,0.001399783,0.00002267574,0.0014558901,0.026249027],"genre_scores_gemma":[0.24237147,0.15308194,0.59910613,0.000656087,0.002120602,0.00089529785,0.0006136535,0.00019360978,0.0009612336],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999196,0.000007123797,0.0002956024,0.0001109567,0.00017701529,0.00021328575],"domain_scores_gemma":[0.99922997,0.00002567292,0.00018938708,0.0002875106,0.00019474272,0.00007273606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015289844,0.00017257221,0.00015260545,0.00010402183,0.0006892459,0.00023044217,0.00017966598,0.00010247444,0.000097908865],"category_scores_gemma":[0.00018078313,0.00015929979,0.00003422797,0.000056722038,0.000045780653,0.0035853446,0.00012447432,0.00018666543,0.00003179061],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025700643,0.000004626505,0.000067098175,0.00018611355,0.0000030963881,4.891094e-7,0.0004938904,0.00024613924,0.0052780467,0.00017711383,0.00079630973,0.9927214],"study_design_scores_gemma":[0.00054708554,0.00007027185,0.0021783845,0.000057738347,0.0000061867004,0.000009887081,0.00030607835,0.040409807,0.013070206,0.0008768096,0.94221807,0.00024950146],"about_ca_topic_score_codex":0.0000011239099,"about_ca_topic_score_gemma":0.0000052198116,"teacher_disagreement_score":0.9924719,"about_ca_system_score_codex":0.000053350403,"about_ca_system_score_gemma":0.000008241322,"threshold_uncertainty_score":0.64960563},"labels":[],"label_agreement":null},{"id":"W2789326396","doi":"10.1007/s11042-018-5804-0","title":"Mammogram classification using contourlet features with forest optimization-based feature selection approach","year":2018,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Contourlet; Computer science; Artificial intelligence; Support vector machine; Feature selection; Mammography; Naive Bayes classifier; CAD; Breast cancer; Pattern recognition (psychology); Computer-aided diagnosis; Random forest; Feature (linguistics); Machine learning; Cancer; Medicine","score_opus":0.016654980882967706,"score_gpt":0.2485609823190822,"score_spread":0.23190600143611448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789326396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011428625,0.000093010734,0.99528813,0.00006835083,0.000019895044,0.00088622305,0.00004507467,0.0006885824,0.0017678491],"genre_scores_gemma":[0.2908253,0.000027448204,0.7078171,0.000049444734,0.00022722968,0.0006693934,0.0002730407,0.000042563854,0.00006853214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930704,0.000012358599,0.00013040514,0.00025355272,0.0001161511,0.00018047648],"domain_scores_gemma":[0.9994316,0.000051895702,0.00005681398,0.00021482701,0.00016396647,0.000080869766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004961504,0.00016964242,0.00012998814,0.000091792775,0.00023248888,0.000101209815,0.0000935445,0.0001220826,0.000015995965],"category_scores_gemma":[0.000015442567,0.00015017753,0.000021712007,0.00037338666,0.00014353533,0.00020077146,0.000011269658,0.00015889634,0.0000030341253],"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":[0.000053036656,0.00018818665,0.004106371,0.00013867157,0.000059230355,5.591972e-7,0.00019788159,0.8009758,0.039026722,0.0009694307,0.0039973385,0.1502868],"study_design_scores_gemma":[0.0002964396,0.00003407382,0.003781265,0.000016733238,0.000026343325,0.000008997793,0.000051010444,0.98415893,0.0044899746,0.000039879633,0.00689181,0.00020456413],"about_ca_topic_score_codex":0.00001028201,"about_ca_topic_score_gemma":0.00002518252,"teacher_disagreement_score":0.28968242,"about_ca_system_score_codex":0.0000540186,"about_ca_system_score_gemma":0.000021856014,"threshold_uncertainty_score":0.6124062},"labels":[],"label_agreement":null},{"id":"W2795156864","doi":"10.1117/3.2316455","title":"Multispectral Image Fusion and Colorization","year":2018,"lang":"en","type":"book","venue":"SPIE eBooks","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":false,"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","funders":"","keywords":"Multispectral image; Artificial intelligence; Computer vision; Fusion; Image fusion; Image (mathematics); Computer science; Remote sensing; Geography; Linguistics; Philosophy","score_opus":0.005083307293702679,"score_gpt":0.21228740058117274,"score_spread":0.20720409328747005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795156864","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009274022,0.00018210977,0.036562286,0.000003846513,0.00047671568,0.00055461813,0.0000398711,0.0018187332,0.9594344],"genre_scores_gemma":[0.0009943335,0.00015160495,0.06950311,0.00005531277,0.0008882772,0.00006497285,0.000101683974,0.00026811106,0.9279726],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991593,0.000004590207,0.0002170986,0.0002616019,0.0001441588,0.0002132979],"domain_scores_gemma":[0.99950635,0.0000208168,0.000058795464,0.0002794998,0.00006164854,0.00007287076],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000059022175,0.00029166212,0.00024806717,0.00014148696,0.000067628,0.00003969187,0.00012191497,0.0002921368,0.00015766262],"category_scores_gemma":[0.000019227227,0.00031235168,0.00004648969,0.00001628912,0.00014022313,0.00006694187,0.0000942094,0.00029572286,0.00008390345],"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":[0.000047595415,0.000015443886,0.000012410327,0.0009921264,0.00011389026,0.00015787037,0.0007225764,0.000012982121,0.321146,0.007931301,0.5045204,0.16432734],"study_design_scores_gemma":[0.0007887208,0.00036945913,0.00013410562,0.0007964024,0.0001052925,0.00005076994,0.000014938242,0.004171154,0.17652659,0.035183873,0.78012764,0.0017310374],"about_ca_topic_score_codex":0.0000013961642,"about_ca_topic_score_gemma":0.0000075549815,"teacher_disagreement_score":0.27560723,"about_ca_system_score_codex":0.0001492367,"about_ca_system_score_gemma":0.000033223434,"threshold_uncertainty_score":0.9999329},"labels":[],"label_agreement":null},{"id":"W2803113290","doi":"","title":"The Canadian CloudSat CALIPSO Validation Project: Evaluation of sensitivity and sub-pixel variability of CloudSat data products","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Sensitivity (control systems); Environmental science; Remote sensing; Measure (data warehouse); Computer science; Data mining; Geology; Engineering","score_opus":0.04887448108695515,"score_gpt":0.3109928710348061,"score_spread":0.26211838994785097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803113290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92743415,0.00013150628,0.06690032,0.00018011779,0.00022987752,0.0015876122,0.0001399373,0.00019037227,0.0032061294],"genre_scores_gemma":[0.9901835,0.000024781786,0.009652386,0.000007611739,0.000040735318,0.0000059912927,0.000058448484,0.000015532747,0.0000110601095],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986709,0.00018019139,0.00033791063,0.0002405091,0.0003658568,0.00020468187],"domain_scores_gemma":[0.9981717,0.00028467533,0.00007764052,0.0008620821,0.0005498276,0.000054114607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008895862,0.00011229116,0.00014698014,0.00007806312,0.00009280371,0.000015624775,0.00013610872,0.00007582031,0.000006863812],"category_scores_gemma":[0.0018424763,0.000089698595,0.000012756096,0.0002887648,0.00011991792,0.00023045241,0.00008519004,0.000114926006,6.053816e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055593722,0.00008556303,0.008309227,0.00047638448,0.00008855668,0.0000026597993,0.0008580272,0.0021985443,0.7268483,0.0024929966,0.0040630796,0.25452107],"study_design_scores_gemma":[0.00019607916,0.0000243993,0.024722744,0.000024312532,0.000057101686,0.000008380542,0.000099422105,0.09753106,0.87462187,0.0012737,0.0012702347,0.00017071133],"about_ca_topic_score_codex":0.016199041,"about_ca_topic_score_gemma":0.15041861,"teacher_disagreement_score":0.25435036,"about_ca_system_score_codex":0.000196485,"about_ca_system_score_gemma":0.00022589094,"threshold_uncertainty_score":0.99035215},"labels":[],"label_agreement":null},{"id":"W2804199492","doi":"10.1007/s12652-018-0866-4","title":"Pansharpening using a guided image filter based on dual-scale detail extraction","year":2018,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":9,"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":"Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China; Alberta Innovates - Health Solutions","keywords":"Panchromatic film; Multispectral image; Computer science; Artificial intelligence; Computer vision; Filter (signal processing); Image resolution; Image (mathematics); Composite image filter; Visibility; Image processing; Pattern recognition (psychology); Optics","score_opus":0.041840470209723854,"score_gpt":0.31809387039508225,"score_spread":0.2762534001853584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804199492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32811934,0.00008250134,0.67093605,0.000012286762,0.0002956118,0.00007886213,7.1410284e-7,0.00009205987,0.00038259596],"genre_scores_gemma":[0.79079854,0.00003189255,0.20865674,0.00011036902,0.00035815762,5.515757e-7,6.197618e-7,0.00002914716,0.000013951445],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986165,0.000037884052,0.00066962215,0.00015811769,0.00026771656,0.0002501141],"domain_scores_gemma":[0.9990104,0.00011648217,0.00029453202,0.00015923135,0.00033284578,0.00008649671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046109545,0.00018522659,0.0002689674,0.00029060274,0.00022255772,0.00013027406,0.00014881288,0.00005885511,0.00016199877],"category_scores_gemma":[0.00007711542,0.00017292325,0.0000953288,0.00016275357,0.00008360517,0.00035860698,0.000047319565,0.00035015098,0.00001122881],"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":[0.00017867496,0.00014164222,0.00047743207,0.00015733528,0.00006141162,0.00015468296,0.0023368779,0.058899887,0.8230044,0.00011019166,0.0012669206,0.11321053],"study_design_scores_gemma":[0.0001876547,0.00027837697,0.00016703147,0.0005978577,0.000024419842,0.00018620979,0.00021372583,0.6017362,0.39562738,0.0003966076,0.000393454,0.00019110857],"about_ca_topic_score_codex":0.000005045736,"about_ca_topic_score_gemma":0.0000018873819,"teacher_disagreement_score":0.5428363,"about_ca_system_score_codex":0.0001051029,"about_ca_system_score_gemma":0.000018688334,"threshold_uncertainty_score":0.7051605},"labels":[],"label_agreement":null},{"id":"W2805158007","doi":"10.3390/s18061887","title":"Multi-Focus Fusion Technique on Low-Cost Camera Images for Canola Phenotyping","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"Canada First Research Excellence Fund","keywords":"Artificial intelligence; Image fusion; Computer vision; Computer science; Focus (optics); Distortion (music); Canola; Filter (signal processing); Field (mathematics); Fusion; Similarity (geometry); Pixel; Image quality; Sensor fusion; Image (mathematics); Mathematics","score_opus":0.011814612949726258,"score_gpt":0.2666210517814442,"score_spread":0.25480643883171794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805158007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0633157,0.000094665316,0.92648095,0.000104087696,0.00046848078,0.002061655,0.00010634279,0.0032130517,0.004155056],"genre_scores_gemma":[0.6963507,0.00009192562,0.30176082,0.00014402886,0.0003683766,0.00037105152,0.000015494958,0.0001724852,0.0007251093],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989128,0.000020375766,0.00023691048,0.00029920295,0.00012732674,0.0004033742],"domain_scores_gemma":[0.99925375,0.000083332896,0.00004823965,0.00039848062,0.00013166401,0.000084542735],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012392929,0.00025620547,0.00021406008,0.00018112124,0.00015204647,0.000027833072,0.00017641683,0.00013456852,0.00006341032],"category_scores_gemma":[0.00014140694,0.00025336834,0.00007662201,0.00018258799,0.000092096714,0.00010418098,0.000045850225,0.00021213536,0.00008772656],"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":[0.000037651036,0.000033510358,0.000012321366,0.00005958779,0.000011151345,0.000008066466,0.00020133358,0.00086713955,0.8981434,0.000044516077,0.0051990557,0.095382266],"study_design_scores_gemma":[0.000315753,0.00006840915,0.0000949191,0.00015043326,0.0000071496147,0.0000068620175,0.000052740983,0.019999886,0.9623438,0.00021671476,0.016419187,0.000324124],"about_ca_topic_score_codex":0.00004265189,"about_ca_topic_score_gemma":0.0000220821,"teacher_disagreement_score":0.633035,"about_ca_system_score_codex":0.00013981329,"about_ca_system_score_gemma":0.000012408876,"threshold_uncertainty_score":0.99999183},"labels":[],"label_agreement":null},{"id":"W2806651786","doi":"10.3390/rs10060884","title":"Utilizing Pansharpening Technique to Produce Sub-Pixel Resolution Thematic Map from Coarse Remote Sensing Image","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Calgary","funders":"Fundamental Research Funds for the Central Universities; Tongji University; National Natural Science Foundation of China","keywords":"Shuttle Radar Topography Mission; Thematic map; Computer science; Pixel; Artificial intelligence; Image resolution; Resolution (logic); Image (mathematics); Computer vision; Remote sensing; Range (aeronautics); Maximum a posteriori estimation; A priori and a posteriori; Pattern recognition (psychology); Mathematics; Geology; Digital elevation model; Maximum likelihood; Geography; Cartography","score_opus":0.016791555118223143,"score_gpt":0.26086167267644433,"score_spread":0.2440701175582212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806651786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08392834,0.00017318277,0.9086693,0.00019306266,0.00045587815,0.0010526465,0.000009242983,0.003251095,0.002267242],"genre_scores_gemma":[0.25197843,0.00002657985,0.7470453,0.00015014371,0.0005234714,7.88897e-8,0.000016428477,0.00019787539,0.00006171112],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970951,0.00013903869,0.0007120508,0.0007862917,0.00042581014,0.0008416811],"domain_scores_gemma":[0.9979459,0.00017592926,0.00016383716,0.0011683982,0.0003303968,0.00021554624],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006486408,0.0005341308,0.0005495337,0.0003929019,0.0003654418,0.00015718667,0.00023765521,0.00023939116,0.000017192204],"category_scores_gemma":[0.00064705283,0.00059541117,0.0001253281,0.00066339044,0.00015142336,0.000518902,0.00024682682,0.00059387274,0.00023943736],"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":[0.000017229513,0.0000022510517,1.3929858e-7,0.00007068104,0.000014043292,0.00004764519,0.00049882376,0.0001069222,0.7021798,0.0000023056589,0.00059826573,0.29646188],"study_design_scores_gemma":[0.00009894951,0.000029375542,0.000003078526,0.0013530464,0.00002352564,0.000098575736,0.00015892091,0.44465894,0.54956514,0.0023687845,0.0012365837,0.00040508198],"about_ca_topic_score_codex":0.00022164409,"about_ca_topic_score_gemma":0.00007413488,"teacher_disagreement_score":0.44455203,"about_ca_system_score_codex":0.00042036062,"about_ca_system_score_gemma":0.00003729043,"threshold_uncertainty_score":0.9996497},"labels":[],"label_agreement":null},{"id":"W284653500","doi":"10.1117/12.2177535","title":"An infrared-visible image fusion scheme based on NSCT and compressed sensing","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Contourlet; Image fusion; Compressed sensing; Computer science; Artificial intelligence; Weighting; Sparse approximation; Fusion rules; Pyramid (geometry); Pattern recognition (psychology); Algorithm; Wavelet transform; Computer vision; Image (mathematics); Wavelet; Mathematics","score_opus":0.012060531187621086,"score_gpt":0.24302199073230085,"score_spread":0.23096145954467975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W284653500","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909429,0.00007151713,0.0025927923,0.00039392343,0.00016301371,0.0004967097,0.00003733708,0.0005100371,0.0047917743],"genre_scores_gemma":[0.31986585,0.000058021473,0.6794699,0.0001234216,0.00024776944,0.000050352737,0.000014983156,0.00011767136,0.00005205643],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980394,2.2330886e-8,0.0005465214,0.00036989857,0.0006671917,0.00037696774],"domain_scores_gemma":[0.9980495,0.00011100692,0.00018237066,0.00009723508,0.0013301747,0.00022968983],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005097775,0.00037051,0.0004047169,0.0001671139,0.00007251775,0.0001505192,0.0005786962,0.00019260697,0.000008048711],"category_scores_gemma":[0.0004491237,0.00033361994,0.00024672892,0.00030101248,0.0002013268,0.0008517978,0.00012356954,0.00037860795,0.0000013861211],"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":[0.000097013166,0.000079582074,0.00009735528,0.00039177403,0.00008264202,2.749843e-7,0.00014495593,0.0017421427,0.96643317,0.025655188,0.004455293,0.00082063716],"study_design_scores_gemma":[0.0008704269,0.00031330736,0.0001456361,0.00029406647,0.00003584899,0.000007895967,0.0004648465,0.59628594,0.39844626,0.001227416,0.0015917076,0.00031665748],"about_ca_topic_score_codex":0.0000049526643,"about_ca_topic_score_gemma":6.7814966e-8,"teacher_disagreement_score":0.6768771,"about_ca_system_score_codex":0.0001709676,"about_ca_system_score_gemma":0.000025037794,"threshold_uncertainty_score":0.9999116},"labels":[],"label_agreement":null},{"id":"W2889244413","doi":"10.3390/rs10091371","title":"A Prediction Smooth Method for Blending Landsat and Moderate Resolution Imagine Spectroradiometer Images","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada; Canadian Space Agency","keywords":"Remote sensing; Spectroradiometer; Normalized Difference Vegetation Index; Smoothing; Computer science; Environmental science; Image fusion; Image resolution; Satellite imagery; Moderate-resolution imaging spectroradiometer; Spectral bands; Satellite; Reflectivity; Artificial intelligence; Computer vision; Image (mathematics); Geology; Climate change","score_opus":0.015735518949969978,"score_gpt":0.2749017695710001,"score_spread":0.2591662506210301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889244413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010358803,0.00019830036,0.9866767,0.00010953974,0.00020025432,0.00024305853,0.0000108282065,0.00088144175,0.0013210576],"genre_scores_gemma":[0.12672348,0.00012384083,0.8725857,0.000037536076,0.0003863059,1.1523392e-7,0.00001031909,0.000054474378,0.00007820813],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992036,0.000027274575,0.00017852183,0.00023264166,0.00008689804,0.00027103012],"domain_scores_gemma":[0.99959147,0.000065299246,0.000035810688,0.000172544,0.00008269651,0.00005220896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024532707,0.00015379835,0.0001796569,0.00020177875,0.00013393688,0.000057518428,0.00003338815,0.000058594087,0.0000041900066],"category_scores_gemma":[0.00006466522,0.00015724765,0.000036083882,0.00015734833,0.000047293714,0.0002441796,0.00002696737,0.00011195159,0.0000027730446],"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":[0.00003076293,0.0000014024962,0.0000014853983,0.000043333406,0.000013872419,0.0000033339547,0.00011394879,0.0004733535,0.632402,0.000010782524,0.0007955377,0.36611018],"study_design_scores_gemma":[0.00019351224,0.000042451822,0.00002022081,0.00006186411,0.000017046455,0.000059631122,0.000012363711,0.69893414,0.29579595,0.00254504,0.0022069432,0.00011083119],"about_ca_topic_score_codex":0.00001530331,"about_ca_topic_score_gemma":0.0000039156885,"teacher_disagreement_score":0.6984608,"about_ca_system_score_codex":0.00010075208,"about_ca_system_score_gemma":0.00000557345,"threshold_uncertainty_score":0.6412373},"labels":[],"label_agreement":null},{"id":"W2892509018","doi":"10.1109/access.2018.2866867","title":"Algebraic Multi-Grid Based Multi-Focus Image Fusion Using Watershed Algorithm","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"China Scholarship Council; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Image fusion; Computer science; Focus (optics); Grid; Interpolation (computer graphics); Image segmentation; Block (permutation group theory); Algorithm; Artificial intelligence; Computer vision; Image (mathematics); Mathematics","score_opus":0.03560110686669439,"score_gpt":0.32249038889607207,"score_spread":0.2868892820293777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892509018","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060857125,0.0000805009,0.9355426,0.000022648795,0.0011416132,0.00039802692,0.000039889263,0.0017672881,0.00015035135],"genre_scores_gemma":[0.2638803,0.000026974034,0.7351048,0.00015743634,0.00055726076,0.000044368575,0.000020765498,0.0001489251,0.00005914579],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998389,0.000043155695,0.00037269987,0.00041179298,0.0002449955,0.0005383478],"domain_scores_gemma":[0.9989695,0.00004043456,0.00007589188,0.0005711912,0.00019645995,0.00014650101],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015578188,0.0003527251,0.0002989147,0.00026657546,0.00018569807,0.00017111242,0.0006320281,0.00015536006,0.0003565936],"category_scores_gemma":[0.000040971336,0.00033646703,0.00009504098,0.0003879208,0.00015176769,0.0010759082,0.00014641727,0.0002550554,0.00014081379],"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":[0.0000131524985,0.00009244218,0.0001272213,0.000058608857,0.000018996721,0.000056199176,0.00012272893,0.00082118745,0.91697633,7.405352e-7,0.0019203055,0.079792075],"study_design_scores_gemma":[0.0005812294,0.000020315765,0.0002196326,0.000044547938,0.000011089718,0.0000054373127,0.0000074997965,0.43782994,0.5603243,0.00004222475,0.0006359542,0.00027779953],"about_ca_topic_score_codex":0.00014837536,"about_ca_topic_score_gemma":0.000035371973,"teacher_disagreement_score":0.43700877,"about_ca_system_score_codex":0.00014857942,"about_ca_system_score_gemma":0.000023603014,"threshold_uncertainty_score":0.99990875},"labels":[],"label_agreement":null},{"id":"W2900103250","doi":"10.1002/cpe.5074","title":"Pansharpening multispectral remote‐sensing images with guided filter for monitoring impact of human behavior on environment","year":2018,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Department of Science and Technology of Sichuan Province; Alberta Innovates - Health Solutions","keywords":"Panchromatic film; Multispectral image; Computer science; Artificial intelligence; Image fusion; Computer vision; Image resolution; Remote sensing; Redundancy (engineering); Hyperspectral imaging; Composite image filter; Filter (signal processing); Image (mathematics)","score_opus":0.035723690027471125,"score_gpt":0.3739108459302976,"score_spread":0.3381871559028265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900103250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61536723,0.0001772592,0.38405028,0.0000073933,0.000045295372,0.00017648615,0.000003888112,0.000060261602,0.00011193752],"genre_scores_gemma":[0.90706325,0.00008910877,0.09277298,0.000007007801,0.00003802071,0.000008550568,0.000003575523,0.000012129019,0.0000053517465],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942696,0.000015980437,0.00016966661,0.00017434459,0.00008642207,0.00012664734],"domain_scores_gemma":[0.9995968,0.00012735755,0.000085760425,0.00008300412,0.00007064355,0.000036478326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000607335,0.000119136006,0.00011554121,0.00004451788,0.00013776928,0.00003942028,0.00003659205,0.000027375108,0.000007835218],"category_scores_gemma":[0.000052020998,0.00010509476,0.0000186211,0.000043565913,0.00011699986,0.00049655646,0.000017567754,0.00007461364,5.977653e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084573294,0.000036575984,0.00074155047,0.00004921208,0.00002443343,0.00000948377,0.0090413075,0.00089108583,0.25427198,0.000029636436,0.00007051141,0.7347496],"study_design_scores_gemma":[0.0014480557,0.002941968,0.013017118,0.00044832478,0.00008745634,0.00013996768,0.0035980302,0.1058118,0.87112015,0.00038477322,0.0003470847,0.0006552841],"about_ca_topic_score_codex":0.00002304856,"about_ca_topic_score_gemma":1.9218598e-7,"teacher_disagreement_score":0.7340943,"about_ca_system_score_codex":0.0000252442,"about_ca_system_score_gemma":0.000005700784,"threshold_uncertainty_score":0.42856398},"labels":[],"label_agreement":null},{"id":"W2903441379","doi":"10.1117/3.2316455.ch7","title":"Image Fusion Methods","year":2018,"lang":"en","type":"book-chapter","venue":"SPIE eBooks","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Okanagan University College; University of British Columbia","funders":"","keywords":"Image fusion; Artificial intelligence; Fusion; Pixel; Computer vision; Image (mathematics); Computer science; Wavelet; Pattern recognition (psychology); Principal component analysis; Fusion rules; Domain (mathematical analysis); Set (abstract data type); Mathematics","score_opus":0.01805478107737155,"score_gpt":0.29834735579159816,"score_spread":0.2802925747142266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903441379","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009988415,0.00031405993,0.16903688,0.0000044688345,0.0005515118,0.00029759656,0.000021596137,0.0020329542,0.82773095],"genre_scores_gemma":[0.0000076022775,0.00008287273,0.48340514,0.000055011482,0.00041651793,0.000021611995,0.000014091906,0.0002301359,0.51576704],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998801,0.000006805028,0.0003543035,0.00035048017,0.00019693728,0.00029050585],"domain_scores_gemma":[0.99891376,0.00004553752,0.00008810382,0.0007478404,0.000098039294,0.0001067173],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001916945,0.0004746104,0.00042266364,0.0002125765,0.00006488061,0.000035113466,0.00031434715,0.00043571662,0.0036635234],"category_scores_gemma":[0.000023540508,0.000484476,0.00017669243,0.000010135951,0.00017022435,0.00005739357,0.00020618361,0.00054609973,0.0008723757],"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":[0.000011001972,0.0000034866284,8.335217e-8,0.00022847626,0.00008040707,0.00008416742,0.0001557599,0.0000013510212,0.21175964,0.012536745,0.08049206,0.69464684],"study_design_scores_gemma":[0.0000843589,0.000052533636,5.238184e-7,0.00020751855,0.00003413175,0.000013233256,0.000002753154,0.00023528935,0.086065724,0.09843844,0.8143328,0.00053272495],"about_ca_topic_score_codex":0.0000013810385,"about_ca_topic_score_gemma":0.0000026457499,"teacher_disagreement_score":0.7338407,"about_ca_system_score_codex":0.00011061716,"about_ca_system_score_gemma":0.000019963814,"threshold_uncertainty_score":0.9999056},"labels":[],"label_agreement":null},{"id":"W2904059475","doi":"10.1155/2018/7836169","title":"Novel Registration and Fusion Algorithm for Multimodal Railway Images with Different Field of Views","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"National Key Research and Development Program of China; China Scholarship Council","keywords":"RANSAC; Contourlet; Image fusion; Artificial intelligence; Matching (statistics); Computer science; Fusion; Computer vision; Algorithm; Fusion rules; Pattern recognition (psychology); Energy (signal processing); Inverse; k-nearest neighbors algorithm; Image (mathematics); Image registration; Feature (linguistics); Field (mathematics); Mathematics; Wavelet transform","score_opus":0.008984197337772653,"score_gpt":0.2654370727363173,"score_spread":0.2564528753985446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904059475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21312886,0.00012556276,0.7863506,0.0000317799,0.00009987617,0.0001917997,0.000020725578,0.00002834558,0.000022409953],"genre_scores_gemma":[0.5073951,0.00023081742,0.4922477,0.000010052203,0.00007073453,0.0000078066505,0.000010474024,0.000015546619,0.000011765131],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992404,0.0000046464984,0.00042925225,0.000086165586,0.00014779624,0.00009174898],"domain_scores_gemma":[0.9992517,0.00006310716,0.00027671558,0.00007761505,0.00028955695,0.000041292275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007799291,0.00011630732,0.00021802509,0.00009319308,0.000032840107,0.0000069350713,0.000053424006,0.000046978264,0.000007776524],"category_scores_gemma":[0.000016904676,0.00009009985,0.000048849717,0.000070546776,0.00004284809,0.00040317953,0.0000011023521,0.000093523566,5.782137e-8],"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":[0.00020314926,0.000043188746,0.00011300924,0.000098550205,0.00001969082,0.000001929171,0.00048050948,0.0023130076,0.68279004,0.000026177297,0.00006463445,0.3138461],"study_design_scores_gemma":[0.002005598,0.0016095504,0.02263145,0.00029714167,0.000057332156,0.000018493693,0.00020988162,0.0047361227,0.9670172,0.00042620994,0.00082474493,0.00016628618],"about_ca_topic_score_codex":0.0000020424138,"about_ca_topic_score_gemma":0.00003582471,"teacher_disagreement_score":0.3136798,"about_ca_system_score_codex":0.000018136998,"about_ca_system_score_gemma":0.000009369639,"threshold_uncertainty_score":0.36741653},"labels":[],"label_agreement":null},{"id":"W2906271100","doi":"10.7202/1054302ar","title":"Renforcement de la qualité d’information de l’occupation du sol par l’intégration de données satellitaires optiques et radar en support à la modélisation hydrologique","year":2018,"lang":"fr","type":"article","venue":"Revue des sciences de l eau","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Institut National de la Recherche Scientifique; Centre de Géomatique du Québec","funders":"","keywords":"Humanities; Geography; Forestry; Art","score_opus":0.030002485717587235,"score_gpt":0.3260568656744002,"score_spread":0.29605437995681294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906271100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22138311,0.00027147116,0.76911044,0.00068635365,0.0001324461,0.00033980326,0.000018144532,0.00042212656,0.0076361024],"genre_scores_gemma":[0.5957498,0.0035985305,0.40006045,0.00022733431,0.00018983902,0.00007393963,0.000038176287,0.000017906288,0.000044012344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970939,0.0010494554,0.000617049,0.00028553847,0.0002861147,0.0006678999],"domain_scores_gemma":[0.9985464,0.0005879635,0.0002780672,0.00026036607,0.00016693333,0.00016023917],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0062999106,0.00028908165,0.00023875872,0.00028206885,0.0004922666,0.00038689206,0.00039418187,0.00041407524,0.00009774755],"category_scores_gemma":[0.0012179099,0.0003122329,0.00009256905,0.0003483771,0.0012439967,0.0029425856,0.00008902642,0.00032098865,0.000032844433],"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":[0.00013218929,0.00017507507,0.014972139,0.0013176639,0.00006009768,0.000039737628,0.11267426,0.40398943,0.08906237,0.21114221,0.0025656924,0.16386913],"study_design_scores_gemma":[0.00021913508,0.00065681286,0.008016173,0.00038920695,0.000040112198,0.00028570494,0.0011210546,0.73101854,0.1620389,0.08782158,0.007972249,0.00042054907],"about_ca_topic_score_codex":0.0020339233,"about_ca_topic_score_gemma":0.00051402603,"teacher_disagreement_score":0.3743667,"about_ca_system_score_codex":0.0014477739,"about_ca_system_score_gemma":0.0005052383,"threshold_uncertainty_score":0.999933},"labels":[],"label_agreement":null},{"id":"W2912581987","doi":"10.1109/lsp.2019.2895749","title":"Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":305,"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":"Artificial intelligence; Computer science; Pattern recognition (psychology); Component (thermodynamics); Sparse approximation; Image fusion; Convolutional neural network; Image (mathematics); Computer vision; Component analysis; Representation (politics); Pixel","score_opus":0.007747057653277735,"score_gpt":0.22586037780437043,"score_spread":0.2181133201510927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912581987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3636671,0.000049103448,0.63486946,0.00045660575,0.0000927065,0.00011503205,0.0000070269452,0.0005850602,0.00015786922],"genre_scores_gemma":[0.97143704,0.0000036290521,0.026289064,0.002062762,0.00009556103,0.000014320823,0.000052975673,0.000031011128,0.00001362047],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807787,0.000058827805,0.00034199224,0.00038001544,0.0007590505,0.0003822415],"domain_scores_gemma":[0.99936825,0.00009327746,0.00007889219,0.00021172558,0.00007418834,0.00017365135],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029848242,0.00025199904,0.00036770958,0.00027500253,0.00011190463,0.000049446007,0.00032227222,0.0001502324,0.0019543555],"category_scores_gemma":[0.00001486923,0.00023611718,0.00017323821,0.00055640447,0.00017887942,0.00024539907,0.000047756836,0.0004512199,0.00015413735],"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":[0.000028886572,0.000054122684,0.0017263697,0.00008204889,0.000056052406,0.0001288056,0.000018324821,0.04157112,0.9486531,0.0000014209511,0.0023634743,0.005316276],"study_design_scores_gemma":[0.00043681727,0.000031491472,0.004393434,0.000070370574,0.000092179565,0.000024574138,0.0000053906765,0.9151044,0.07900741,0.000059078793,0.00038373994,0.00039110982],"about_ca_topic_score_codex":0.000010657239,"about_ca_topic_score_gemma":0.0000013676649,"teacher_disagreement_score":0.8735333,"about_ca_system_score_codex":0.00014705303,"about_ca_system_score_gemma":0.000035187855,"threshold_uncertainty_score":0.998958},"labels":[],"label_agreement":null},{"id":"W2913358852","doi":"10.1117/3.2316455.ch9","title":"Image Fusion Metrics","year":2018,"lang":"en","type":"book-chapter","venue":"SPIE eBooks","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Okanagan University College; University of British Columbia","funders":"","keywords":"Image fusion; Fuse (electrical); Computer science; Fusion; Artificial intelligence; Image (mathematics); Distortion (music); Process (computing); Image quality; Quality (philosophy); Machine learning; Data mining; Computer vision; Pattern recognition (psychology); Engineering","score_opus":0.013855818073251456,"score_gpt":0.2340116285945164,"score_spread":0.22015581052126496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913358852","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000038648257,0.0004400023,0.025318086,0.0000042233446,0.00059058395,0.00033392958,0.000044135395,0.0021641033,0.9710663],"genre_scores_gemma":[0.00015401728,0.0002657074,0.094257176,0.00008726893,0.000721554,0.00002514533,0.00003476823,0.00037997548,0.9040744],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987557,0.0000020859804,0.0003365321,0.00032130376,0.00029876764,0.00028563075],"domain_scores_gemma":[0.9989706,0.000033294804,0.00008854321,0.0006747373,0.00012516024,0.00010762225],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000096029675,0.00044611504,0.00037328104,0.00041995815,0.00006266272,0.00003969764,0.0003165242,0.0004222388,0.0025191214],"category_scores_gemma":[0.000029207544,0.00046276528,0.00015829678,0.00002353336,0.00013606473,0.000060065813,0.00019169494,0.0005187265,0.0013151885],"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":[0.000022279562,0.000011276206,8.1955346e-7,0.0005655859,0.00017630194,0.00033436928,0.00023628453,0.0000039535603,0.08573042,0.028777953,0.3612605,0.52288026],"study_design_scores_gemma":[0.00011350932,0.000071343085,9.3763276e-7,0.00019858572,0.000040358056,0.000012395619,0.0000028476245,0.00025842132,0.030798925,0.052051987,0.9158298,0.0006208864],"about_ca_topic_score_codex":0.0000014636996,"about_ca_topic_score_gemma":0.0000035516846,"teacher_disagreement_score":0.5545693,"about_ca_system_score_codex":0.00014261615,"about_ca_system_score_gemma":0.000020376003,"threshold_uncertainty_score":0.9997824},"labels":[],"label_agreement":null},{"id":"W2947946094","doi":"10.3390/s19112443","title":"An Improved Spatiotemporal Fusion Approach Based on Multiple Endmember Spectral Mixture Analysis","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Ministry of Transportation of Ontario; Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; Central South University; National Natural Science Foundation of China","keywords":"Endmember; Pixel; Fusion; Remote sensing; Sensor fusion; Image resolution; Land cover; Image fusion; Pattern recognition (psychology); Computer science; Multispectral image; Artificial intelligence; Image (mathematics); Geography; Land use; Engineering","score_opus":0.004657217370046493,"score_gpt":0.21518372651342993,"score_spread":0.21052650914338344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947946094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9325945,0.000011260028,0.059353694,0.000023215613,0.00012904493,0.00042365026,0.00003279089,0.0011583857,0.0062735],"genre_scores_gemma":[0.9175086,0.0000036848858,0.081787,0.00007492881,0.00006533477,0.000011450842,0.00015856927,0.000055661258,0.00033476567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989083,0.000040135183,0.00021106738,0.00035109313,0.00020411496,0.0002852858],"domain_scores_gemma":[0.99910825,0.000050785457,0.000046251724,0.0006439255,0.00004968767,0.00010110814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000980555,0.00023691478,0.00027920518,0.0002995242,0.000039433868,0.000029046541,0.00015155622,0.00014211056,0.00028336453],"category_scores_gemma":[0.00003613697,0.00021957791,0.00015530825,0.0005107015,0.000019260053,0.00012270408,0.000014569091,0.00026774555,0.00004679102],"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":[0.00005759056,0.00010856514,0.0071880277,0.00004640892,0.00007016572,0.000006929686,0.00015568844,0.7286663,0.26014456,0.000011605916,0.0003282189,0.0032159467],"study_design_scores_gemma":[0.00029319024,0.00009243311,0.0044506392,0.000006129911,0.00003985328,7.525376e-7,0.000050930812,0.8858235,0.108457714,0.000015060223,0.0005159209,0.00025389888],"about_ca_topic_score_codex":0.000044794808,"about_ca_topic_score_gemma":0.000021752661,"teacher_disagreement_score":0.15715718,"about_ca_system_score_codex":0.00007769341,"about_ca_system_score_gemma":0.000008307994,"threshold_uncertainty_score":0.89541274},"labels":[],"label_agreement":null},{"id":"W2950648852","doi":"10.1109/jsen.2019.2921803","title":"Coupled GAN With Relativistic Discriminators for Infrared and Visible Images Fusion","year":2019,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":119,"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, Okanagan Campus; University of British Columbia","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Infrared; Fusion; Physics; Materials science; Optics","score_opus":0.006040213357184855,"score_gpt":0.22571584138220574,"score_spread":0.21967562802502089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950648852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8115973,0.00015190677,0.18495113,0.000041517218,0.00031714528,0.00036987968,0.000010179431,0.00027296264,0.002288013],"genre_scores_gemma":[0.9677895,0.00019662445,0.02989315,0.000020876252,0.00008899316,0.000007896767,0.0000023823063,0.00007061139,0.0019300206],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991958,0.000017343693,0.0002209379,0.00014533597,0.00016535391,0.0002552363],"domain_scores_gemma":[0.9994318,0.00011791517,0.00007736029,0.00015817775,0.00009933725,0.0001153943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013483338,0.00017953229,0.00021760886,0.00015023134,0.000110131514,0.000075642034,0.00008260328,0.00006033636,0.000060715603],"category_scores_gemma":[0.000049352693,0.00014090781,0.000041722742,0.00010437638,0.000045538443,0.0003180088,0.000010679612,0.0002933102,0.000009834607],"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":[0.0003988581,0.00006516802,0.0036270923,0.00048769778,0.00014473489,0.00014883636,0.0012809454,0.032820597,0.9346451,0.00020899231,0.014238705,0.011933251],"study_design_scores_gemma":[0.005828724,0.0017773645,0.011503315,0.0012124605,0.00020698884,0.0022452823,0.0011713259,0.27607533,0.6731193,0.01567136,0.009364258,0.001824293],"about_ca_topic_score_codex":0.000001033564,"about_ca_topic_score_gemma":6.397057e-7,"teacher_disagreement_score":0.26152584,"about_ca_system_score_codex":0.00005471719,"about_ca_system_score_gemma":0.000014941088,"threshold_uncertainty_score":0.57460535},"labels":[],"label_agreement":null},{"id":"W2956075495","doi":"10.1117/3.2316455.ch10","title":"Image Fusion Evaluation","year":2018,"lang":"en","type":"book-chapter","venue":"SPIE eBooks","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Okanagan University College; University of British Columbia","funders":"","keywords":"Image fusion; Computer science; Artificial intelligence; Metric (unit); Usability; Fusion; Set (abstract data type); Process (computing); Computer vision; Visualization; Face (sociological concept); Image (mathematics); Pattern recognition (psychology); Engineering; Human–computer interaction","score_opus":0.019351369012684902,"score_gpt":0.2630435402598189,"score_spread":0.243692171247134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956075495","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013403397,0.00028934496,0.009364902,0.000005233623,0.00054623774,0.00064366456,0.000023596813,0.0014694772,0.9875235],"genre_scores_gemma":[0.0008355704,0.00014160688,0.058519673,0.00009604956,0.0012288657,0.000121955956,0.00012662448,0.00045221264,0.93847746],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987626,0.000004247715,0.0002995642,0.00029533764,0.0004270526,0.0002112137],"domain_scores_gemma":[0.9990335,0.000017243614,0.00008535502,0.0005788864,0.000215337,0.00006969579],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00021014868,0.00035998313,0.00026519407,0.00018590927,0.000059901362,0.000031852167,0.0002022414,0.0003329174,0.005533247],"category_scores_gemma":[0.000023635514,0.000379826,0.00011236628,0.000008346268,0.000105632294,0.00006659752,0.00010293001,0.00034850222,0.0012479051],"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":[0.000017295644,0.0000058689056,2.7331924e-7,0.00023697581,0.000087465545,0.00004210753,0.00025169752,0.00001283553,0.11747659,0.007892567,0.13801081,0.7359655],"study_design_scores_gemma":[0.00029699734,0.00010723501,0.0000038885332,0.00048270077,0.00012228607,0.000014015466,0.0000058036453,0.004180637,0.044281706,0.1790344,0.77057785,0.00089250214],"about_ca_topic_score_codex":0.0000011510748,"about_ca_topic_score_gemma":0.0000061100163,"teacher_disagreement_score":0.73507303,"about_ca_system_score_codex":0.00019668425,"about_ca_system_score_gemma":0.000034597953,"threshold_uncertainty_score":0.99986535},"labels":[],"label_agreement":null},{"id":"W2962831904","doi":"10.1109/access.2019.2930225","title":"Multimodal Neuroimaging Fusion in Nonsubsampled Shearlet Domain Using Location-Scale Distribution by Maximizing the High Frequency Subband Energy","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shearlet; Computer science; Neuroimaging; Artificial intelligence; Pattern recognition (psychology); Estimator; Maximum a posteriori estimation; Gaussian; Brain atlas; Fusion; Mathematics; Statistics; Wavelet; Maximum likelihood; Psychology","score_opus":0.008688939392044466,"score_gpt":0.24607446123514565,"score_spread":0.2373855218431012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962831904","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5617352,0.00014829244,0.43723974,0.000059493515,0.00029793748,0.00019511534,0.000029727007,0.00021222449,0.00008224157],"genre_scores_gemma":[0.99366325,0.00008102066,0.0058896123,0.00009289209,0.00006555745,0.000041923056,0.000100364115,0.00005362489,0.000011730636],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871397,0.00006789432,0.00033834015,0.00030343683,0.00021875468,0.00035762994],"domain_scores_gemma":[0.9992727,0.00010946155,0.00007537813,0.00041972945,0.00007283316,0.000049902796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017559496,0.00021534528,0.00020666192,0.000098907745,0.00011864582,0.00013901736,0.00043723796,0.000084949555,0.00005923747],"category_scores_gemma":[0.000022374232,0.00019233052,0.000038016664,0.00055095885,0.000047385933,0.0008544634,0.000086091044,0.00025917287,0.000007594416],"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":[0.000019354657,0.00004582461,0.019263553,0.00006621993,0.000008502904,0.000009806759,0.00015306978,0.052956633,0.91897774,0.00013255634,0.0006950395,0.007671704],"study_design_scores_gemma":[0.0016338422,0.000032407992,0.03041326,0.0003285518,0.000023230705,0.000031924414,0.000108212276,0.3836383,0.576462,0.004628638,0.0016908959,0.0010087601],"about_ca_topic_score_codex":0.0018866005,"about_ca_topic_score_gemma":0.00013726458,"teacher_disagreement_score":0.43192804,"about_ca_system_score_codex":0.00022223458,"about_ca_system_score_gemma":0.000020090565,"threshold_uncertainty_score":0.7843011},"labels":[],"label_agreement":null},{"id":"W2973555408","doi":"10.1109/tip.2019.2940678","title":"Perceptual Evaluation for Multi-Exposure Image Fusion of Dynamic Scenes","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Ghosting; Artificial intelligence; High dynamic range; Computer science; Computer vision; Image quality; High-dynamic-range imaging; Dynamic range; Image fusion; Image (mathematics); Pattern recognition (psychology)","score_opus":0.018495462001573332,"score_gpt":0.30644451035975495,"score_spread":0.28794904835818164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973555408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103476,0.00021682071,0.8943499,0.000016289845,0.00026608794,0.00089541887,0.000047533602,0.00053253537,0.0001994476],"genre_scores_gemma":[0.7222592,0.00005283686,0.27729756,0.0000123688,0.000012642856,0.00014622614,0.000010050962,0.00006750599,0.00014157656],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998746,0.000029602195,0.00036950488,0.00029719804,0.00030388616,0.000253813],"domain_scores_gemma":[0.9991376,0.00005037218,0.00008668508,0.00026725233,0.00040884395,0.000049271726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024978636,0.000234072,0.00025202738,0.00025203737,0.00013399571,0.000047638023,0.00015226076,0.00011449643,0.0002469244],"category_scores_gemma":[0.000016549839,0.0002421471,0.00012147196,0.00025322213,0.000073206196,0.00077413727,0.0000016151063,0.00021799121,0.00003080973],"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":[0.000029810266,0.000073504074,0.0000029045552,0.00036969394,0.000008508735,2.761018e-7,0.0003232157,0.0059364913,0.691183,2.9274233e-7,0.000014555921,0.3020578],"study_design_scores_gemma":[0.0007224273,0.00009563624,0.00008530346,0.00018591253,0.000043766307,0.0000038484322,0.00032548865,0.56719863,0.43107477,0.000046743906,0.00002407892,0.00019339721],"about_ca_topic_score_codex":0.0000032178486,"about_ca_topic_score_gemma":0.000008310014,"teacher_disagreement_score":0.61878324,"about_ca_system_score_codex":0.00014874853,"about_ca_system_score_gemma":0.00005384139,"threshold_uncertainty_score":0.9874472},"labels":[],"label_agreement":null},{"id":"W2981323410","doi":"10.4095/219786","title":"Satellite image fusion with multi-scale wavelet analysis: Preserving Spatial Information and Minimizing Artifacts (PSIMA)","year":2001,"lang":"en","type":"report","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Natural Resources Canada","funders":"","keywords":"Wavelet; Satellite; Image fusion; Scale (ratio); Computer science; Fusion; Computer vision; Artificial intelligence; Satellite image; Remote sensing; Image (mathematics); Spatial analysis; Geology; Geography; Cartography; Engineering","score_opus":0.014326370729709922,"score_gpt":0.2537354318704471,"score_spread":0.23940906114073718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981323410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003125052,0.00076260907,0.9290376,0.000016806516,0.00012154709,0.0006411401,0.000044199853,0.0014888339,0.064762235],"genre_scores_gemma":[0.036818836,0.040604085,0.91515106,0.000066703265,0.00029388635,0.00019020264,0.0016728084,0.00025463905,0.00494777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761635,0.000027035472,0.0008160055,0.00036182057,0.0007323878,0.00044638006],"domain_scores_gemma":[0.9984297,0.000056188936,0.00030043625,0.000595899,0.00045180545,0.00016598111],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032950766,0.0005393068,0.00073171785,0.0008551726,0.00010434532,0.00021901687,0.00020332087,0.0003532095,0.00034039968],"category_scores_gemma":[0.000096332435,0.0004591699,0.0001322728,0.00064119766,0.00005841745,0.0013358124,0.00021163188,0.0005051881,0.00002213186],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054197575,0.000065119835,0.002478927,0.0018978374,0.0009321003,0.00009732968,0.0008359615,0.0036620824,0.014296,8.476838e-7,0.0036511591,0.97202843],"study_design_scores_gemma":[0.0012940814,0.00019596069,0.023334296,0.001579792,0.0020198605,0.0001936031,0.00055214163,0.5964557,0.082468055,0.000048488284,0.2888381,0.0030199443],"about_ca_topic_score_codex":0.00086378225,"about_ca_topic_score_gemma":0.0017316527,"teacher_disagreement_score":0.9690085,"about_ca_system_score_codex":0.00022896718,"about_ca_system_score_gemma":0.00008422497,"threshold_uncertainty_score":0.999786},"labels":[],"label_agreement":null},{"id":"W2981442431","doi":"10.4095/219921","title":"A Method Based on Local Variance for Quality Assessment of Multiresolution Image Fusion","year":2002,"lang":"en","type":"report","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Natural Resources Canada","funders":"","keywords":"Image fusion; Variance (accounting); Artificial intelligence; Fusion; Computer science; Quality (philosophy); Computer vision; Image (mathematics); Image quality; Pattern recognition (psychology); Data mining; Physics; Business","score_opus":0.0659067860012735,"score_gpt":0.41971910660733647,"score_spread":0.35381232060606294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981442431","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000041604244,0.00009112806,0.91061705,0.000021723268,0.0003121115,0.0010348021,0.00022672254,0.00084462325,0.08684767],"genre_scores_gemma":[0.00619124,0.00035549904,0.9907536,0.0000340302,0.00011098494,0.00035924162,0.00018398823,0.00011964084,0.0018917695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997436,0.00011286603,0.00088253623,0.00045918374,0.0008033886,0.000306061],"domain_scores_gemma":[0.99780804,0.00054086326,0.0003190345,0.00071043987,0.00054767873,0.00007395028],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015170743,0.00040432173,0.0007685685,0.0002462362,0.000048794343,0.000014905156,0.00021101955,0.00042769898,0.00043326156],"category_scores_gemma":[0.00030380135,0.0003834831,0.00029246288,0.00017611662,0.000050508654,0.000100513746,0.000052998665,0.00042349167,0.0000033961421],"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":[0.00007428794,0.0005399115,0.00002300094,0.0062630395,0.00011347496,0.00001613014,0.000024642935,0.09090789,0.07246834,0.0006276336,0.08344394,0.7454977],"study_design_scores_gemma":[0.00043977232,0.00014944361,0.00036350227,0.00041004393,0.000043983866,0.0000028463678,0.000008336354,0.9225928,0.033290934,0.00019439073,0.042065956,0.00043796215],"about_ca_topic_score_codex":0.00016646636,"about_ca_topic_score_gemma":0.00001528182,"teacher_disagreement_score":0.83168495,"about_ca_system_score_codex":0.0010088196,"about_ca_system_score_gemma":0.00017537989,"threshold_uncertainty_score":0.9998617},"labels":[],"label_agreement":null},{"id":"W2997051825","doi":"10.1016/j.infrared.2019.103171","title":"Image fusion based on guided filter and online robust dictionary learning","year":2019,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":20,"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":"Key Laboratory of High Performance Computing and Stochastic Information Processing; National University of Defense Technology; China Scholarship Council; University of Toronto","keywords":"Sparse approximation; Computer science; Image fusion; Artificial intelligence; Pattern recognition (psychology); K-SVD; Filter (signal processing); Image (mathematics); Enhanced Data Rates for GSM Evolution; Fusion; Cluster analysis; Composite image filter; Computer vision","score_opus":0.008983689015302854,"score_gpt":0.2268942034650017,"score_spread":0.21791051444969883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997051825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4218553,0.00021220335,0.5481134,0.0005866225,0.00040865983,0.00080362504,0.0000684364,0.009721559,0.018230217],"genre_scores_gemma":[0.8376791,0.0001314057,0.16083384,0.00029371394,0.00011230493,0.00006872825,0.00015593767,0.00013610064,0.0005888725],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991003,0.000015604846,0.00020940528,0.00028640547,0.00013115491,0.00025711805],"domain_scores_gemma":[0.999343,0.000060139344,0.000054728687,0.0004361479,0.00006945285,0.000036580295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000043019598,0.0002208894,0.00022803534,0.0002521258,0.0000675253,0.00001624201,0.00018031315,0.00021315702,0.00014534182],"category_scores_gemma":[0.000043022992,0.00023322669,0.00004175352,0.00038522162,0.00009113382,0.00018442127,0.00013034673,0.00061237,0.00007933291],"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":[0.00003386713,0.00015313586,0.0029120725,0.00014698581,0.000031980308,0.00003178639,0.00006754648,0.08433102,0.8104952,0.0009378251,0.0067310273,0.09412754],"study_design_scores_gemma":[0.0009568975,0.00036789922,0.0013657175,0.00021109032,0.00001650826,0.000018025146,0.00009380034,0.81724,0.14170974,0.020385839,0.017020017,0.00061445276],"about_ca_topic_score_codex":0.0000018836278,"about_ca_topic_score_gemma":2.999854e-7,"teacher_disagreement_score":0.73290896,"about_ca_system_score_codex":0.00006858835,"about_ca_system_score_gemma":0.000012143444,"threshold_uncertainty_score":0.95107085},"labels":[],"label_agreement":null},{"id":"W3001892559","doi":"10.1109/jstars.2020.2965190","title":"Quantifying the Effect of Registration Error on Spatio-Temporal Fusion","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":30,"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":"Tongji University; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Fusion; Image resolution; Temporal resolution; Sensor fusion; Computer science; Remote sensing; Image registration; Artificial intelligence; Satellite; Image fusion; Computer vision; Pattern recognition (psychology); Geography; Image (mathematics)","score_opus":0.036085975368529706,"score_gpt":0.26167579549285974,"score_spread":0.22558982012433004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001892559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8747659,0.000067908775,0.12402691,0.00057318155,0.00011297572,0.000196655,0.0000012005302,0.000046315447,0.00020898593],"genre_scores_gemma":[0.9356118,0.00011449482,0.0640327,0.000070088325,0.00014864816,7.7361065e-8,0.000003096922,0.000014435452,0.000004670632],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912965,0.000042712934,0.00043938478,0.000082833816,0.00020313525,0.0001023047],"domain_scores_gemma":[0.9994082,0.0001263753,0.00020718752,0.000092977796,0.00012895235,0.0000363209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025608484,0.000109626184,0.00021461133,0.00009204141,0.00007018674,0.000021913153,0.00006251461,0.00006874037,0.0000016379956],"category_scores_gemma":[0.00014580593,0.0000808119,0.000031801235,0.00044692043,0.000031890413,0.00007297748,0.000008471,0.00037377048,2.2110333e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002095643,0.000009801248,0.0009370166,0.00024337668,0.00003685017,0.000019318299,0.00092742726,0.06241745,0.5956079,0.00015028326,0.00037551133,0.3390655],"study_design_scores_gemma":[0.00080616877,0.00044119844,0.009842781,0.00038850738,0.00003184084,0.00003243861,0.00009124891,0.53037685,0.45518643,0.0003286836,0.00228191,0.00019195279],"about_ca_topic_score_codex":0.000011171538,"about_ca_topic_score_gemma":0.000063307125,"teacher_disagreement_score":0.46795937,"about_ca_system_score_codex":0.000023328712,"about_ca_system_score_gemma":0.000023787623,"threshold_uncertainty_score":0.32954136},"labels":[],"label_agreement":null},{"id":"W3002017077","doi":"10.1364/oe.381609","title":"Multispectral imaging via nanostructured random broadband filtering","year":2020,"lang":"en","type":"article","venue":"Optics Express","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multispectral image; Spectral imaging; Broadband; Computer science; Optics; Image resolution; Full spectral imaging; Spectral bands; Spectral resolution; Remote sensing; Hyperspectral imaging; Filter (signal processing); Computer vision; Artificial intelligence; Telecommunications; Physics; Spectral line; Geology","score_opus":0.0067473075912141585,"score_gpt":0.21585440045957055,"score_spread":0.20910709286835638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002017077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015014198,0.00037484383,0.9791849,0.000088249035,0.00032657236,0.00023726653,0.000019561752,0.0014766396,0.0032777546],"genre_scores_gemma":[0.781395,0.00007194006,0.21810025,0.000123972,0.00016959953,0.00002042016,0.000009685118,0.00007009671,0.000039018003],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915797,0.000011502494,0.00021030554,0.00020630058,0.00013307709,0.00028087525],"domain_scores_gemma":[0.99957097,0.000030152762,0.000028893983,0.0002114866,0.000028963303,0.0001295603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000027489994,0.00019489916,0.00020622615,0.00003792526,0.000054919776,0.00006095065,0.00021738268,0.000046443038,0.00010250954],"category_scores_gemma":[0.000046545938,0.00020630837,0.00005865226,0.00009644953,0.00003485998,0.00022609383,0.00008483232,0.00020807136,0.000015365087],"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":[0.000022878183,0.0000032786563,0.000029193305,0.000049064467,0.0000105845165,0.000035795194,0.00039404823,0.0076144044,0.98061204,0.000022327034,0.0012412114,0.009965182],"study_design_scores_gemma":[0.0007464798,0.0000106620855,0.000032374774,0.000026227191,0.000008222184,0.000013412866,0.00002645487,0.27272728,0.72162884,0.0002077991,0.0043258714,0.0002463694],"about_ca_topic_score_codex":0.0000026160194,"about_ca_topic_score_gemma":3.540938e-7,"teacher_disagreement_score":0.76638085,"about_ca_system_score_codex":0.000026027068,"about_ca_system_score_gemma":0.0000037679838,"threshold_uncertainty_score":0.8413011},"labels":[],"label_agreement":null},{"id":"W3011768656","doi":"10.1109/tip.2020.2977573","title":"DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1271,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Discriminator; Artificial intelligence; Dual (grammatical number); Image fusion; Computer science; Generative adversarial network; Adversarial system; Image (mathematics); Pattern recognition (psychology); Computer vision; Fusion; Image processing; Telecommunications","score_opus":0.028488653914707975,"score_gpt":0.28278023214236814,"score_spread":0.2542915782276602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011768656","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006617384,0.00015382266,0.9958939,0.00035141304,0.00039896672,0.00073746283,0.00029509026,0.0013618516,0.00014577855],"genre_scores_gemma":[0.29127884,0.00004437896,0.70714444,0.000399346,0.00047867157,0.0003913164,0.00007719619,0.000111875976,0.00007391768],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845374,0.000037698806,0.00039790696,0.00043305237,0.0002460854,0.000431506],"domain_scores_gemma":[0.9992844,0.00006665887,0.00008992103,0.00014559104,0.00023963903,0.00017380965],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000102376005,0.0003407787,0.00028561498,0.00010612444,0.00056995044,0.00012594931,0.00013353744,0.00014130423,0.00012932657],"category_scores_gemma":[0.00003145479,0.00036783636,0.00015648219,0.00034250043,0.00013049893,0.0009921767,0.000003332774,0.000400266,0.000035140925],"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":[0.00032553336,0.00015898734,5.663118e-7,0.00044266105,0.00005594954,0.000021920816,0.0012678022,0.19400421,0.7598936,0.00002835223,0.00693203,0.036868382],"study_design_scores_gemma":[0.0010599445,0.00010642901,0.0000060727057,0.000081083344,0.000057850815,0.000009026931,0.00013959095,0.6796135,0.3176075,0.00026126485,0.0007218791,0.0003358981],"about_ca_topic_score_codex":0.000002790167,"about_ca_topic_score_gemma":0.000005774637,"teacher_disagreement_score":0.48560926,"about_ca_system_score_codex":0.00015353087,"about_ca_system_score_gemma":0.000067744506,"threshold_uncertainty_score":0.99987733},"labels":[],"label_agreement":null},{"id":"W3026329137","doi":"10.1117/1.oe.59.5.053103","title":"Enhanced situation awareness through CNN-based deep multimodal image fusion","year":2020,"lang":"en","type":"article","venue":"Optical Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":50,"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":"Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Image fusion; Task (project management); Computer vision; Key (lock); Information fusion; Situation awareness; Fusion; Image (mathematics); Pattern recognition (psychology); Computer security","score_opus":0.009320575498191765,"score_gpt":0.23812035488687186,"score_spread":0.2287997793886801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026329137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038194142,0.00007392523,0.9573362,0.00015445793,0.00016128786,0.00022583293,0.000005544989,0.0025321383,0.0013164771],"genre_scores_gemma":[0.67224276,0.000018497156,0.3273845,0.0000929126,0.0001158382,0.000044792407,0.000025407637,0.00007164974,0.0000036375388],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881744,0.0000063726625,0.00028971347,0.00028770923,0.00023526853,0.00036349986],"domain_scores_gemma":[0.9994212,0.00010128392,0.000021693286,0.00022068492,0.00006394233,0.00017121501],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000050047263,0.00027442773,0.00025688272,0.00005207161,0.000049560378,0.00004408034,0.0001842381,0.0001274218,0.00015224953],"category_scores_gemma":[0.00027227082,0.0002993441,0.00008177803,0.0003000341,0.000027340197,0.000372687,0.00005690711,0.00030668097,0.00010003259],"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":[0.0000074065406,0.000009714587,0.0000016740182,0.00008943424,0.000005327867,0.000009269054,0.000101886,0.38666555,0.6079522,0.00017492083,0.000025874326,0.0049567265],"study_design_scores_gemma":[0.00021114487,0.000025126768,0.00003952111,0.000028596463,0.0000065264667,6.652948e-7,0.000010339348,0.55675733,0.4423701,0.000022898816,0.00032498952,0.0002027603],"about_ca_topic_score_codex":0.0000026529653,"about_ca_topic_score_gemma":5.92399e-7,"teacher_disagreement_score":0.63404864,"about_ca_system_score_codex":0.00008658392,"about_ca_system_score_gemma":0.00001434018,"threshold_uncertainty_score":0.9999459},"labels":[],"label_agreement":null},{"id":"W3027439202","doi":"10.3390/rs12101674","title":"Two-Path Network with Feedback Connections for Pan-Sharpening in Remote Sensing","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Université du Québec à Chicoutimi","funders":"National Natural Science Foundation of China","keywords":"Sharpening; Computer science; Panchromatic film; Artificial intelligence; Leverage (statistics); Computer vision; Remote sensing; Image resolution; Pattern recognition (psychology); Geology","score_opus":0.016389548259354784,"score_gpt":0.24664580828997784,"score_spread":0.23025626003062305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027439202","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03492362,0.0001706176,0.9603131,0.00037337845,0.00018719077,0.0005403574,0.0000039524975,0.0014233958,0.0020644304],"genre_scores_gemma":[0.34530926,0.00003595735,0.6537939,0.0003208015,0.000390575,2.0338037e-8,0.000010478332,0.000116982024,0.000022018514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984343,0.0000431397,0.00037879066,0.00040116845,0.0001581839,0.00058440765],"domain_scores_gemma":[0.99919945,0.00020882525,0.00008411374,0.0002789358,0.00009871915,0.00012995709],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017468038,0.00030292012,0.00039765233,0.00009958377,0.00017042905,0.000069585374,0.00008476919,0.000100064986,0.0000042069396],"category_scores_gemma":[0.00017237924,0.00032358884,0.00007839764,0.00064614974,0.000042258613,0.00019137327,0.000055110067,0.00042292706,0.000011175729],"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":[0.00008191109,0.0000021025642,0.000005113794,0.00012538333,0.00003434521,0.00013722046,0.000634292,0.2094176,0.08428378,0.000022525648,0.0008197778,0.70443594],"study_design_scores_gemma":[0.0006467011,0.000059096423,0.00001279225,0.0006190001,0.000022883361,0.00011451119,0.00021616707,0.9731135,0.02044548,0.0008310671,0.0035141176,0.00040471877],"about_ca_topic_score_codex":0.000088513945,"about_ca_topic_score_gemma":0.00013358828,"teacher_disagreement_score":0.7636959,"about_ca_system_score_codex":0.00014954156,"about_ca_system_score_gemma":0.000028023185,"threshold_uncertainty_score":0.9999216},"labels":[],"label_agreement":null},{"id":"W3029559111","doi":"10.1145/3339825.3391861","title":"Hyperspectral reconstruction from RGB images for vein visualization","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Simon Fraser University","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Computer vision; Visualization; RGB color model; Leverage (statistics)","score_opus":0.012360933838154602,"score_gpt":0.24523111532957145,"score_spread":0.23287018149141686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029559111","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011304693,0.00007807478,0.98287433,0.00022792451,0.000120517754,0.00017754395,0.000026916772,0.0017230053,0.0034669708],"genre_scores_gemma":[0.33241028,0.000116288975,0.6664975,0.00029522154,0.00034417183,0.00005134186,0.0000686123,0.000056563153,0.00016003069],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996299,0.0000044999383,0.00011184637,0.00011856756,0.000044143668,0.00009099856],"domain_scores_gemma":[0.9998278,0.000023504943,0.00001371931,0.000064854714,0.000029596049,0.000040514875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000015369395,0.00007484427,0.00008254566,0.000024181882,0.00002337363,0.000019609326,0.00005041759,0.00003740864,0.000294852],"category_scores_gemma":[0.000050701492,0.00007764669,0.000030455021,0.00007947299,0.000011969319,0.00021357933,0.000008894681,0.000042081436,0.000019119008],"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":[0.000012807683,0.0000054186244,0.00007257912,0.000023693881,0.0000133418835,7.149621e-7,0.00015307797,0.0006288728,0.90199363,0.0009758661,0.021305814,0.074814156],"study_design_scores_gemma":[0.00016785612,0.000035972425,0.00006594798,0.0000067957058,0.000006214647,0.0000012501206,0.00012784573,0.08298191,0.90860647,0.002496005,0.005379981,0.00012376098],"about_ca_topic_score_codex":0.0000058607357,"about_ca_topic_score_gemma":0.0000010389094,"teacher_disagreement_score":0.32110557,"about_ca_system_score_codex":0.000026968779,"about_ca_system_score_gemma":0.0000034454144,"threshold_uncertainty_score":0.32284227},"labels":[],"label_agreement":null},{"id":"W3035441328","doi":"10.1109/tip.2020.2999855","title":"MEF-GAN: Multi-Exposure Image Fusion via Generative Adversarial Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":269,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Hubei Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Discriminator; Computer science; Artificial intelligence; Generator (circuit theory); Ground truth; Image (mathematics); Code (set theory); Representation (politics); Distortion (music); Pattern recognition (psychology); Computer vision; Luminance; Fusion rules; Adversarial system; Image fusion; Power (physics); Detector","score_opus":0.012473978891396068,"score_gpt":0.24294201802836385,"score_spread":0.23046803913696778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035441328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006793462,0.0003709168,0.995023,0.00024154824,0.00045649466,0.0004112017,0.000028253658,0.0022925902,0.000496661],"genre_scores_gemma":[0.7127944,0.0001425882,0.2861301,0.00036900464,0.0002778054,0.00008393497,0.000009687074,0.00013265146,0.000059778267],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982999,0.000052171865,0.00044034355,0.00048719038,0.0002752482,0.00044516262],"domain_scores_gemma":[0.9992264,0.00004254948,0.00008498726,0.00024582472,0.00017610956,0.00022414033],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000092064336,0.00042487844,0.00034497966,0.00013586211,0.00040922206,0.00016207721,0.00025261161,0.00019267116,0.0002329234],"category_scores_gemma":[0.000014288494,0.00044412617,0.00014600788,0.00055010855,0.00011950815,0.0011451215,0.0000039475804,0.0008155978,0.00006774588],"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":[0.00007665569,0.00008183822,8.688401e-7,0.00010966269,0.000030511777,0.0000372943,0.00087757874,0.15720123,0.64100426,3.6623612e-7,0.00043292853,0.2001468],"study_design_scores_gemma":[0.0005745672,0.00008065336,0.0000035173787,0.0000589683,0.000036558213,0.000009102556,0.00009008704,0.56220305,0.4363379,0.000015254866,0.0002680974,0.00032223252],"about_ca_topic_score_codex":0.0000057282973,"about_ca_topic_score_gemma":0.0000075160033,"teacher_disagreement_score":0.7121151,"about_ca_system_score_codex":0.00011974466,"about_ca_system_score_gemma":0.00003902272,"threshold_uncertainty_score":0.99980104},"labels":[],"label_agreement":null},{"id":"W3036842770","doi":"10.1109/jstars.2020.3002780","title":"A Variational Approach for Fusion of Panchromatic and Multispectral Images Using a New Spatial–Spectral Consistency Term","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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, Okanagan Campus; University of British Columbia","funders":"","keywords":"Panchromatic film; Multispectral image; Sharpening; Computer science; Image fusion; Image resolution; Artificial intelligence; Computer vision; Pixel; Image (mathematics)","score_opus":0.028471068606527712,"score_gpt":0.2375915770585896,"score_spread":0.2091205084520619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036842770","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3068572,0.00014143168,0.6926013,0.00008224916,0.00003879891,0.00020305409,0.0000045139136,0.000027783115,0.000043637392],"genre_scores_gemma":[0.38763258,0.00010850661,0.6120724,0.000024922625,0.00014371873,7.726445e-8,0.0000026724151,0.000012655175,0.0000024680712],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907154,0.000015342495,0.0005095433,0.00011590185,0.0001436199,0.00014403135],"domain_scores_gemma":[0.9994235,0.00007393114,0.00019343224,0.000060110793,0.0001729936,0.00007599901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000106407206,0.00012721328,0.00029987082,0.00012665123,0.000063714935,0.000027046532,0.00004433475,0.000074558375,0.0000015407304],"category_scores_gemma":[0.00009525132,0.00012487842,0.000034351135,0.00028473834,0.000041259373,0.00008742719,0.000012003248,0.0002165993,1.9140105e-8],"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":[0.00003781602,0.0000146002585,0.00020909886,0.00023949626,0.000039076545,0.000004308091,0.00074281293,0.009898641,0.8868836,0.00012714151,0.000022640615,0.10178075],"study_design_scores_gemma":[0.0009629492,0.000076165015,0.013384431,0.00014200572,0.00004537971,0.00007919522,0.000065924716,0.92509025,0.05805963,0.0019067823,0.000029242885,0.00015805083],"about_ca_topic_score_codex":0.00002833259,"about_ca_topic_score_gemma":0.000017240038,"teacher_disagreement_score":0.9151916,"about_ca_system_score_codex":0.000031954554,"about_ca_system_score_gemma":0.000092514776,"threshold_uncertainty_score":0.50923944},"labels":[],"label_agreement":null},{"id":"W3040323135","doi":"10.1109/tgrs.2020.3004353","title":"Super-Resolution Mapping Based on Spatial–Spectral Correlation for Spectral Imagery","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":175,"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":"Zhejiang University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Subpixel rendering; Hyperspectral imaging; Pixel; Full spectral imaging; Spectral imaging; Spatial correlation; Nonlinear system; Image resolution; Spectral resolution; Remote sensing; Land cover; Imaging spectroscopy; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Spectral line; Physics; Geology; Statistics","score_opus":0.015591971814397896,"score_gpt":0.22529009887284876,"score_spread":0.20969812705845087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040323135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012003488,0.0000129166965,0.9856316,0.00074353605,0.00046292713,0.00032786632,0.00001501021,0.0005894066,0.00021324022],"genre_scores_gemma":[0.7034884,0.0000267962,0.29602748,0.0003207377,0.000074749085,5.251288e-7,0.000002803504,0.000024993034,0.000033533128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897456,0.000019833386,0.0002022492,0.00032048396,0.00018243073,0.0003004177],"domain_scores_gemma":[0.9995751,0.00010905775,0.000030287483,0.00013523428,0.000039099185,0.00011124668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010568849,0.00017750695,0.0001523404,0.00017461779,0.00032406775,0.00005661115,0.0000594383,0.00007629341,0.0000084600015],"category_scores_gemma":[0.000021863247,0.00018188445,0.000079819605,0.00031768304,0.00009386669,0.00023401454,8.205151e-7,0.00025640367,0.0000057570055],"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":[0.000053643264,0.00001339074,5.90894e-7,0.0000480171,0.0000046375776,0.000007945743,0.00033359168,0.2100278,0.16982682,0.0000047362732,0.00006860322,0.61961025],"study_design_scores_gemma":[0.00022793404,0.00013464184,0.00006664756,0.000072558876,0.000010221041,0.000009236196,0.00007314891,0.8719256,0.12691653,0.00010264237,0.00027745552,0.00018339924],"about_ca_topic_score_codex":0.000044816024,"about_ca_topic_score_gemma":0.000018405064,"teacher_disagreement_score":0.69148487,"about_ca_system_score_codex":0.00008343311,"about_ca_system_score_gemma":0.000022975802,"threshold_uncertainty_score":0.7417033},"labels":[],"label_agreement":null},{"id":"W3046767631","doi":"10.1007/978-3-030-54407-2_36","title":"Synthetic Aerial Image Generation and Runway Segmentation","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Runway; Computer science; Computer vision; Takeoff; Artificial intelligence; Adverse weather; Intersection (aeronautics); SAFER; Segmentation; Orientation (vector space); Point (geometry); Aerospace engineering; Engineering; Geology; Computer security; Cartography","score_opus":0.010452577282404537,"score_gpt":0.22930495247297264,"score_spread":0.2188523751905681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046767631","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016268225,0.00020829565,0.99725413,0.00009463953,0.00063202303,0.00028978076,0.0000067164124,0.00029952877,0.001052189],"genre_scores_gemma":[0.06865701,0.00017106082,0.92996943,0.00031399148,0.00075831934,0.000015248998,0.000018167017,0.000058052607,0.00003873187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988978,0.000007068061,0.00020859225,0.00044775035,0.00027254634,0.00016623373],"domain_scores_gemma":[0.9995575,0.000050832692,0.000049258655,0.00022972714,0.000050794082,0.000061879226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116432355,0.00023529743,0.00019909584,0.0001941266,0.00007422571,0.00014388096,0.00022903013,0.00013076527,0.00003256621],"category_scores_gemma":[0.000035070036,0.00023683535,0.000025903732,0.00010391017,0.0002179624,0.00026157722,0.00014352122,0.0003075224,0.000011397855],"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":[0.0000025816582,0.000002424245,0.0000012974909,0.00005078748,0.0000035853159,0.000020294754,0.00025795872,0.020761114,0.2186464,0.00035919386,0.000058469086,0.7598359],"study_design_scores_gemma":[0.000119107135,0.00006186204,0.000005685871,0.0001123861,0.0000072149164,0.000022654387,1.1194799e-7,0.7469979,0.23616013,0.0155896805,0.00055172585,0.00037155757],"about_ca_topic_score_codex":0.00000165107,"about_ca_topic_score_gemma":0.0000066088933,"teacher_disagreement_score":0.7594643,"about_ca_system_score_codex":0.00012169909,"about_ca_system_score_gemma":0.00003382214,"threshold_uncertainty_score":0.96578646},"labels":[],"label_agreement":null},{"id":"W3048458171","doi":"10.3390/rs12162595","title":"Reconstruction of Cloud-free Sentinel-2 Image Time-series Using an Extended Spatiotemporal Image Fusion Approach","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Natural Resources Canada","funders":"","keywords":"Pixel; Multispectral image; Image fusion; Remote sensing; Cloud computing; Computer science; Satellite; Image resolution; Computer vision; Artificial intelligence; Image (mathematics); Environmental science; Geology; Physics","score_opus":0.016308518498328206,"score_gpt":0.2359446690101093,"score_spread":0.2196361505117811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048458171","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3221664,0.0000480966,0.67401797,0.0000578,0.00012832084,0.00020802408,0.000008727795,0.0009498956,0.0024147527],"genre_scores_gemma":[0.10953602,0.000039211922,0.88995594,0.000028840526,0.00029565804,2.6507346e-8,0.000029891697,0.00009564834,0.00001876072],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986083,0.000074657306,0.00046991077,0.0003397309,0.00022889713,0.0002784955],"domain_scores_gemma":[0.99905574,0.000018998355,0.00016229268,0.00048470654,0.00015638836,0.0001218745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014886702,0.0002644102,0.00037090128,0.00012379744,0.00009867023,0.00005064408,0.0001515217,0.00012403083,0.000029075696],"category_scores_gemma":[0.00015515836,0.0002981625,0.00008725748,0.00034301227,0.00013658025,0.0008005633,0.00013004015,0.0002593082,0.000008225787],"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":[0.00002943513,0.000007818429,0.0000019684298,0.00013517111,0.00001101713,0.000015746806,0.00024196447,0.0009881636,0.9192374,0.000003052169,0.00015826918,0.079169996],"study_design_scores_gemma":[0.000180777,0.000024128009,0.000013808976,0.00006936715,0.000016764641,0.00012398894,0.00013585259,0.6047535,0.39397836,0.00046352527,0.000040669776,0.00019924501],"about_ca_topic_score_codex":0.000027600385,"about_ca_topic_score_gemma":0.0000011414256,"teacher_disagreement_score":0.60376537,"about_ca_system_score_codex":0.00007074316,"about_ca_system_score_gemma":0.000022957605,"threshold_uncertainty_score":0.9999471},"labels":[],"label_agreement":null},{"id":"W3048870524","doi":"10.3390/app10165583","title":"Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"National Key Research and Development Program of China; China Scholarship Council; University of Toronto; National University of Defense Technology; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Artificial intelligence; Image resolution; Pattern recognition (psychology); Computer science; Spatial analysis; Regularization (linguistics); Computer vision; Cluster analysis; Remote sensing; Geography","score_opus":0.013824545329672729,"score_gpt":0.21676335398695076,"score_spread":0.20293880865727804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048870524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016151933,0.000013764256,0.9627668,0.00048624325,0.000087582215,0.00022650014,0.000004991694,0.0009963915,0.019265795],"genre_scores_gemma":[0.8476789,0.0000041550793,0.15185651,0.00032116877,0.00009727191,0.0000132483765,0.000009993843,0.000013422432,0.0000053550475],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989962,0.000014176358,0.00013652624,0.00029937585,0.00031149437,0.00024224576],"domain_scores_gemma":[0.99971694,0.000035470624,0.000026687261,0.00012504519,0.00001661707,0.00007923441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016006445,0.00013153716,0.00011573884,0.000079002115,0.00020996253,0.000065259985,0.00023529965,0.000049117538,0.00006934902],"category_scores_gemma":[0.000039348917,0.00012965505,0.000031030148,0.00046223798,0.00017201915,0.00021028909,0.000033584623,0.00013310861,0.000036634356],"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":[0.000020972804,0.00002252761,0.00006992587,0.00002181318,0.000002375929,0.000003729357,0.00021138831,0.04393017,0.94078475,0.007218268,0.0007580899,0.006955989],"study_design_scores_gemma":[0.0001856,0.00009972237,0.00050049677,0.000011051639,0.0000049953323,5.7003956e-7,0.00009338197,0.6613065,0.33575234,0.001300046,0.0005332799,0.00021199754],"about_ca_topic_score_codex":0.000010322107,"about_ca_topic_score_gemma":0.0000050993967,"teacher_disagreement_score":0.83152694,"about_ca_system_score_codex":0.000057479567,"about_ca_system_score_gemma":0.000015200169,"threshold_uncertainty_score":0.528718},"labels":[],"label_agreement":null},{"id":"W3083923056","doi":"10.1109/tim.2020.3022438","title":"SEDRFuse: A Symmetric Encoder–Decoder With Residual Block Network for Infrared and Visible Image Fusion","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":240,"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","funders":"","keywords":"Computer science; Residual; Artificial intelligence; Block (permutation group theory); Image fusion; Computer vision; Compensation (psychology); Fuse (electrical); Encoder; Fusion; Feature extraction; Pixel; Feature (linguistics); Pattern recognition (psychology); Image (mathematics); Algorithm; Mathematics; Engineering","score_opus":0.02320607446375438,"score_gpt":0.23449294528374923,"score_spread":0.21128687081999484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083923056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029347533,0.00013162477,0.96822464,0.00033843907,0.00013221214,0.0008737964,0.00003478433,0.0004134047,0.0005035501],"genre_scores_gemma":[0.9139143,0.00058623165,0.084672354,0.00043131126,0.000051498453,0.00025464897,0.0000065374593,0.00004615269,0.000036955567],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901676,0.000021295948,0.00022847224,0.00024044048,0.00029907955,0.00019394173],"domain_scores_gemma":[0.99959224,0.000029635994,0.000041264815,0.000100677105,0.000103290586,0.00013288566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012520877,0.00018399105,0.0001601744,0.00012293698,0.00018727528,0.000059928145,0.000045186738,0.000053605265,0.000029787749],"category_scores_gemma":[0.0000064857477,0.00017361196,0.000025034733,0.00029704696,0.00003460178,0.00024862928,0.0000013708117,0.00013326596,0.0000024350131],"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":[0.0013435413,0.00029005826,0.00017230562,0.0010833956,0.00044232435,0.000007989498,0.0036086696,0.079421416,0.39148304,0.00018937906,0.010140617,0.5118173],"study_design_scores_gemma":[0.006259327,0.001750425,0.00063193764,0.00028847918,0.00022178859,0.000025200045,0.0010483509,0.05780945,0.924949,0.00039736283,0.00580939,0.0008092962],"about_ca_topic_score_codex":0.000004751146,"about_ca_topic_score_gemma":0.000022950753,"teacher_disagreement_score":0.8845668,"about_ca_system_score_codex":0.000079412624,"about_ca_system_score_gemma":0.000023388027,"threshold_uncertainty_score":0.707969},"labels":[],"label_agreement":null},{"id":"W3084095034","doi":"10.1109/access.2020.3022208","title":"Hardware-Friendly Laplacian-Based Multi-Focus Image Fusion in DCT Domain for Visual Sensor Network","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Indian Institute of Technology Kharagpur; Sleep Research Society Foundation; Shastri Indo-Canadian Institute","keywords":"Discrete cosine transform; Computer science; Focus (optics); Image fusion; Computer vision; Artificial intelligence; Laplacian matrix; Algorithm; Theoretical computer science; Image (mathematics); Graph","score_opus":0.021230366536742373,"score_gpt":0.31029505942715335,"score_spread":0.289064692890411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084095034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08414399,0.00012654172,0.9126555,0.0002776294,0.0003476026,0.00086009136,0.000057873483,0.0012192756,0.00031152228],"genre_scores_gemma":[0.759254,0.000035967983,0.23899348,0.0006353081,0.00053787703,0.0003150683,0.000035272235,0.0001590006,0.000034030385],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985137,0.000041400403,0.00038269485,0.00037547108,0.00017958951,0.000507142],"domain_scores_gemma":[0.99932396,0.00015517494,0.00006781094,0.00023506554,0.000070517606,0.00014744069],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015109469,0.00028356758,0.00033936038,0.00010477183,0.00008198589,0.00010462436,0.00042119945,0.00013358936,0.000072335504],"category_scores_gemma":[0.00010386324,0.00029845754,0.00010132006,0.00048532145,0.000041482366,0.00048539438,0.000072084724,0.0002691121,0.00003120181],"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":[0.0006921329,0.00024396842,0.0028279338,0.0007676777,0.000047747406,0.00036997054,0.000551096,0.24140699,0.64417285,0.000048688442,0.055564363,0.05330656],"study_design_scores_gemma":[0.0026698732,0.00017467764,0.00082580483,0.00013854007,0.000015064545,0.000002433227,0.000047784055,0.44677722,0.5321214,0.0003709225,0.01613856,0.00071770535],"about_ca_topic_score_codex":0.000020374038,"about_ca_topic_score_gemma":0.000053919284,"teacher_disagreement_score":0.67511,"about_ca_system_score_codex":0.000090467176,"about_ca_system_score_gemma":0.000025026508,"threshold_uncertainty_score":0.9999468},"labels":[],"label_agreement":null},{"id":"W3087972099","doi":"10.18280/rces.070303","title":"Fast and Efficient Algorithm for Contrast Enhancement of Color Images","year":2020,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of Mosul","keywords":"Contrast (vision); Contrast enhancement; Computer science; Artificial intelligence; Computer vision; Image enhancement; Algorithm; Color contrast; Image (mathematics); Medicine","score_opus":0.010958880272269171,"score_gpt":0.2632059274864936,"score_spread":0.2522470472142244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087972099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038640999,0.17540877,0.82340616,0.00008124585,0.00009377207,0.00045868373,0.000022716447,0.00013387743,0.0000083487075],"genre_scores_gemma":[0.014295554,0.14348203,0.841821,0.0001248153,0.00010157027,0.00013280222,0.000003864992,0.000035396908,0.000002964463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999272,0.000005470118,0.00037321274,0.00012644907,0.000094591276,0.00012825897],"domain_scores_gemma":[0.99955916,0.000116388364,0.000061987535,0.00009382127,0.00013203164,0.000036594458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009364893,0.00015883351,0.0005878986,0.00003290452,0.000012661968,0.0000031064565,0.00008415897,0.000015971598,0.0000019841796],"category_scores_gemma":[0.00004802785,0.00014229854,0.00006911816,0.000102388534,0.000035454057,0.000030764302,0.00008006546,0.00004900635,4.2943327e-7],"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":[0.0000046495625,0.00004325964,0.0000041802355,0.090391204,0.0005259363,0.0000030848616,0.0005279701,0.05318014,0.032072667,0.00017041824,0.006897429,0.81617904],"study_design_scores_gemma":[0.00035411262,0.00027756725,0.00004198427,0.00890927,0.000083267536,0.0000028054,0.000019680314,0.82099444,0.16097832,0.0000060109332,0.008073764,0.00025876853],"about_ca_topic_score_codex":1.2107616e-7,"about_ca_topic_score_gemma":1.0814756e-8,"teacher_disagreement_score":0.8159203,"about_ca_system_score_codex":0.000016540338,"about_ca_system_score_gemma":0.0000033618983,"threshold_uncertainty_score":0.5802766},"labels":[],"label_agreement":null},{"id":"W3088064622","doi":"10.1117/12.2579480","title":"SAR and optical image fusion for urban infrastructure detection and monitoring","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Image fusion; Computer science; Remote sensing; Computer vision; Synthetic aperture radar; Image (mathematics); Artificial intelligence; Geology","score_opus":0.00585955809055888,"score_gpt":0.2231208622083578,"score_spread":0.21726130411779893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088064622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22563463,0.00014548877,0.772799,0.00007050363,0.00006247773,0.00018149828,0.0000024142994,0.0006357505,0.000468202],"genre_scores_gemma":[0.76440966,0.00009672724,0.23529963,0.000024106646,0.00013120347,0.000007498498,6.7382103e-7,0.00001941694,0.000011085963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996566,0.0000023272853,0.00008203608,0.00011869096,0.000042769072,0.00009758487],"domain_scores_gemma":[0.9998237,0.000025056852,0.0000075553526,0.00004949586,0.000018089942,0.00007609356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000019164065,0.000084872554,0.00008193211,0.000022895163,0.00004169443,0.000027281725,0.000026553125,0.000052086798,0.000010797361],"category_scores_gemma":[0.00004900683,0.000078846206,0.000011830533,0.000047293117,0.000019651097,0.00016096355,0.00004028427,0.00009113287,7.2982397e-7],"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":[0.000009465703,7.9423967e-7,0.00020069587,0.00006238005,0.0000027330802,8.9290734e-7,0.00009451535,0.000040521336,0.8996022,0.000030363855,0.00032374813,0.09963173],"study_design_scores_gemma":[0.00020657778,0.00007834263,0.0022885392,0.000011194002,0.000006842845,0.0000061652017,0.00008453093,0.0523775,0.94095355,0.00046264014,0.0033954703,0.00012862982],"about_ca_topic_score_codex":6.4015984e-7,"about_ca_topic_score_gemma":2.0741469e-7,"teacher_disagreement_score":0.538775,"about_ca_system_score_codex":0.000012787667,"about_ca_system_score_gemma":0.000001261701,"threshold_uncertainty_score":0.3215255},"labels":[],"label_agreement":null},{"id":"W3094223337","doi":"10.18280/ria.340406","title":"A Complex Diffusion Based Modified Fuzzy C- Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Speckle noise; Speckle pattern; Ultrasound; Breast cancer; Artificial intelligence; Noise (video); Computer vision; Segmentation; Fuzzy logic; Computer science; Image segmentation; Diffusion; Pattern recognition (psychology); Image (mathematics); Cancer; Medicine; Radiology; Physics; Internal medicine","score_opus":0.0405421607404255,"score_gpt":0.28313349938116084,"score_spread":0.24259133864073534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094223337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037340783,0.000050262177,0.96057093,0.00006157508,0.000047124428,0.001324243,0.00028878346,0.000105886415,0.00021042621],"genre_scores_gemma":[0.8870498,0.00005930668,0.112422235,0.000017986395,0.000035304824,0.00033155445,0.000036589347,0.000031590695,0.0000156293],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889916,0.000021444303,0.0005083163,0.00025893384,0.000115545176,0.00019661617],"domain_scores_gemma":[0.9992412,0.00022252559,0.00013128201,0.0001952607,0.00015773707,0.000052042175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001421759,0.00014913573,0.0002658453,0.00010574719,0.00003912668,0.000011066786,0.00017645175,0.00006134182,0.000045314402],"category_scores_gemma":[0.00012836086,0.0001615228,0.00009614852,0.00037428155,0.00006935987,0.00016209451,0.000020338197,0.00008648266,9.627735e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105450905,0.00005951523,0.00004701662,0.00058003713,0.000004195358,1.2194036e-7,0.00045244637,0.36144313,0.6236224,0.000036149322,0.000051062852,0.013598433],"study_design_scores_gemma":[0.000085817184,0.000050872437,0.000075510536,0.000042709922,0.000007222153,7.2251805e-7,0.00032939657,0.51124924,0.48791012,0.00015048934,0.000019015653,0.00007886295],"about_ca_topic_score_codex":0.00005769748,"about_ca_topic_score_gemma":0.000026872129,"teacher_disagreement_score":0.84970903,"about_ca_system_score_codex":0.00006949177,"about_ca_system_score_gemma":0.000015198337,"threshold_uncertainty_score":0.65867084},"labels":[],"label_agreement":null},{"id":"W3094960234","doi":"10.1007/s11042-020-09952-w","title":"Hyperspectral image super-resolution through clustering-based sparse representation","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Multispectral image; Image resolution; Sparse approximation; Pattern recognition (psychology); Computer vision; Full spectral imaging; Image (mathematics); Cluster analysis; Remote sensing; Geography","score_opus":0.03696518461276859,"score_gpt":0.2802509897638705,"score_spread":0.24328580515110187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094960234","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027890934,0.00017504541,0.9925734,0.0010935806,0.00002551029,0.00061672815,0.000089090296,0.0009259093,0.0017116604],"genre_scores_gemma":[0.39859742,0.00025577293,0.5992163,0.00043817516,0.00030904976,0.00085440185,0.00024320444,0.00005370534,0.000032020867],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993267,0.00001027567,0.00017718329,0.00023086616,0.000094226205,0.00016072493],"domain_scores_gemma":[0.9995935,0.000060900238,0.000024830451,0.00019148232,0.000037788763,0.00009152921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000024939045,0.00012517092,0.00012186509,0.000023673387,0.00008685487,0.00005309581,0.00009435293,0.00005330209,0.000069837595],"category_scores_gemma":[0.000040337865,0.00013442096,0.000036107274,0.00018779654,0.000068019566,0.00031571658,0.000030750103,0.00012436473,0.00005587105],"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":[0.000027881784,0.0000808844,0.00019273463,0.00014450306,0.000023353685,0.0000058877094,0.0014404262,0.027169656,0.8353581,0.0006045303,0.0075209113,0.12743115],"study_design_scores_gemma":[0.00047198642,0.00002975702,0.00076067966,0.0000113605065,0.000018612729,0.0000028263923,0.00021022024,0.8532757,0.10904523,0.00034394328,0.035571743,0.00025795167],"about_ca_topic_score_codex":0.000012678313,"about_ca_topic_score_gemma":0.0000071442737,"teacher_disagreement_score":0.826106,"about_ca_system_score_codex":0.000029477802,"about_ca_system_score_gemma":0.0000080098525,"threshold_uncertainty_score":0.54815274},"labels":[],"label_agreement":null},{"id":"W3100853521","doi":"10.1080/19479832.2020.1845244","title":"A context-driven pansharpening method using superpixel based texture analysis","year":2020,"lang":"en","type":"article","venue":"International Journal of Image and Data Fusion","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Université de Moncton","funders":"","keywords":"Computer science; Weighting; Artificial intelligence; Pattern recognition (psychology); Pyramid (geometry); Context (archaeology); Cluster analysis; Image resolution; Image (mathematics); Mathematics","score_opus":0.04092120648154868,"score_gpt":0.3476057484989616,"score_spread":0.3066845420174129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3100853521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014700637,0.0004499575,0.98359036,0.00067073625,0.00011580135,0.000050487575,0.00029193587,0.000059564103,0.000070496826],"genre_scores_gemma":[0.52514786,0.00019062069,0.47360548,0.00066008297,0.00023027221,4.888699e-7,0.00014295267,0.00001922836,0.0000030363988],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892414,0.000043063475,0.00040029144,0.00017447188,0.00035409036,0.00010392149],"domain_scores_gemma":[0.999147,0.0001027991,0.00015075975,0.00018994184,0.0002918854,0.000117626136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023235378,0.00013068791,0.00026278605,0.00026748911,0.000038637732,0.000111478636,0.00064036506,0.00004915164,0.00027563906],"category_scores_gemma":[0.00024784598,0.000114303955,0.00009295522,0.00024120891,0.000025559884,0.0009554244,0.0002754717,0.0002537697,0.0000024333958],"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":[0.00017387662,0.000037866543,0.001828914,0.000035947,0.0007727749,0.00045761658,0.0004546399,0.013007679,0.8254256,0.000022609187,0.0060788854,0.1517036],"study_design_scores_gemma":[0.00051901175,0.000037946873,0.00032983068,0.0000776629,0.00022920845,0.00008191134,0.00016408491,0.9702611,0.020222835,0.000041426334,0.007891695,0.00014329015],"about_ca_topic_score_codex":0.000013856207,"about_ca_topic_score_gemma":0.0000048900565,"teacher_disagreement_score":0.9572534,"about_ca_system_score_codex":0.000035230605,"about_ca_system_score_gemma":0.000028045853,"threshold_uncertainty_score":0.46611798},"labels":[],"label_agreement":null},{"id":"W3111028227","doi":"10.18280/ts.370502","title":"Image Fusion of Noisy Images Based on Simultaneous Empirical Wavelet Transform","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Image fusion; Artificial intelligence; Computer vision; Thresholding; Computer science; Noise (video); Noise reduction; Image processing; Image (mathematics); Wavelet transform; Fusion; Top-hat transform; Pattern recognition (psychology); Wavelet; Digital image processing","score_opus":0.01325819118007844,"score_gpt":0.24824617116159076,"score_spread":0.23498797998151233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111028227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03874959,0.00004499943,0.9508053,0.0019316274,0.000058998558,0.0006985944,0.00017612378,0.0012326038,0.0063021705],"genre_scores_gemma":[0.9576599,0.000024031318,0.041172054,0.00092424575,0.00008906087,0.000026614422,0.00003453092,0.000056900393,0.000012674478],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986583,0.000026028389,0.00040972154,0.00024740645,0.00038517153,0.000273385],"domain_scores_gemma":[0.99941796,0.00016771893,0.000044791264,0.00015889584,0.00006232009,0.00014828704],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009100452,0.00025429678,0.0002902592,0.00009049606,0.000042407817,0.000017689665,0.00020530971,0.00007273035,0.0012995866],"category_scores_gemma":[0.00004423791,0.000237559,0.00012583661,0.00021057818,0.000063025414,0.00012542676,0.000019500916,0.00022011099,0.000034819455],"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":[0.00023746224,0.0001739164,0.000027388794,0.00028644459,0.000021907,0.0001144603,0.0004602883,0.034652483,0.8955096,0.000009859857,0.008045576,0.06046061],"study_design_scores_gemma":[0.000688472,0.0004936036,0.000077142045,0.00004437982,0.000016074566,0.0000016162893,0.00002838982,0.51081127,0.483154,0.00004237504,0.004420661,0.00022201108],"about_ca_topic_score_codex":0.000001689862,"about_ca_topic_score_gemma":6.292002e-7,"teacher_disagreement_score":0.9189103,"about_ca_system_score_codex":0.00005463214,"about_ca_system_score_gemma":0.0000175942,"threshold_uncertainty_score":0.99961334},"labels":[],"label_agreement":null},{"id":"W3111131600","doi":"10.1109/tgrs.2020.3039046","title":"Hyperspectral Image Fusion and Multitemporal Image Fusion by Joint Sparsity","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Image fusion; Artificial intelligence; Computer science; Panchromatic film; Multispectral image; Computer vision; Hyperspectral imaging; Deblurring; Pattern recognition (psychology); Fusion; Regularization (linguistics); Image restoration; Image (mathematics); Image processing","score_opus":0.013366104149338117,"score_gpt":0.2223007161521802,"score_spread":0.20893461200284208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111131600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2517247,0.00007523288,0.7467916,0.0004829458,0.00014277428,0.00014992262,0.00001555999,0.00042850073,0.0001887662],"genre_scores_gemma":[0.6302543,0.0009116799,0.36843175,0.00025804384,0.000036670397,1.3467876e-7,0.0000019002616,0.000030148192,0.00007532723],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988925,0.000025011619,0.00019658108,0.0003942487,0.00019915728,0.00029248683],"domain_scores_gemma":[0.99953634,0.000025648049,0.000033887227,0.00015244591,0.000042160926,0.00020950906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103101724,0.00021094109,0.00019936753,0.00010369819,0.00038139656,0.00008822395,0.000065980494,0.00008179715,0.00001187686],"category_scores_gemma":[0.00001119169,0.0002013897,0.000050948278,0.0002825025,0.0002497601,0.0004380255,0.000006206275,0.00033485176,0.000009234755],"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":[0.00001045329,0.000007581655,2.9548735e-7,0.000022946626,0.0000021569235,0.000017277313,0.00033932418,0.000068154965,0.78690547,2.3702653e-7,0.00012915023,0.21249695],"study_design_scores_gemma":[0.00022562334,0.00008433648,0.00006257258,0.000060436432,0.000011695129,0.000051973682,0.00023509297,0.43218642,0.566505,0.000048935486,0.00029674766,0.00023113802],"about_ca_topic_score_codex":0.00014210473,"about_ca_topic_score_gemma":0.000018091347,"teacher_disagreement_score":0.43211827,"about_ca_system_score_codex":0.000041655014,"about_ca_system_score_gemma":0.000011087358,"threshold_uncertainty_score":0.82124335},"labels":[],"label_agreement":null},{"id":"W3115934669","doi":"10.1109/jstars.2020.3046838","title":"Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":26,"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":"Key Science and Technology Program of Shaanxi Province; National Natural Science Foundation of China; Royal Society","keywords":"Computer science; Artificial intelligence; Mixture model; Gaussian process; Pattern recognition (psychology); Change detection; Gaussian; Multiresolution analysis; Computer vision; Wavelet; Wavelet transform; Physics","score_opus":0.04549166980485922,"score_gpt":0.25526819374074317,"score_spread":0.20977652393588395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115934669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46410254,0.00025309477,0.5351605,0.00017092659,0.000059909667,0.00016669967,0.0000023065613,0.00004799265,0.000036052985],"genre_scores_gemma":[0.6707465,0.00076900545,0.32818505,0.00013805878,0.00013033625,2.4299808e-7,0.0000026115822,0.000020340522,0.000007821927],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902904,0.00002108321,0.00044562068,0.00014681245,0.00017342789,0.00018401937],"domain_scores_gemma":[0.99953324,0.000021848638,0.0001283306,0.00007291804,0.00015372844,0.00008991181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011411233,0.00015227654,0.00022270529,0.00011841389,0.00010725607,0.000034072124,0.00004436024,0.00011088567,0.0000018714383],"category_scores_gemma":[0.000043413496,0.00015373042,0.0000218967,0.00041792405,0.000032054537,0.00018540659,0.0000163328,0.00032293302,1.7803413e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012448539,0.0000070440515,0.000020214013,0.000044854358,0.000009515183,0.0000030252897,0.00058853667,0.0036337378,0.86680543,0.0000028133984,0.000029220448,0.12884316],"study_design_scores_gemma":[0.00042475975,0.000032416425,0.0006395779,0.00008580842,0.000014788653,0.000020884536,0.000043052936,0.79372513,0.20443621,0.00014839934,0.00029515402,0.00013380349],"about_ca_topic_score_codex":0.000026872862,"about_ca_topic_score_gemma":0.000035402838,"teacher_disagreement_score":0.7900914,"about_ca_system_score_codex":0.000068104106,"about_ca_system_score_gemma":0.00002096671,"threshold_uncertainty_score":0.6268944},"labels":[],"label_agreement":null},{"id":"W3118300304","doi":"10.3389/fnins.2020.615435","title":"LatLRR-FCNs: Latent Low-Rank Representation With Fully Convolutional Networks for Medical Image Fusion","year":2021,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Toronto Rehabilitation Institute; University Health Network","funders":"National Key Research and Development Program of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Weighting; Artificial intelligence; Modality (human–computer interaction); Computer science; Fusion rules; Pattern recognition (psychology); Rank (graph theory); Image fusion; Representation (politics); Medical imaging; Pyramid (geometry); Image (mathematics); Computer vision; Mathematics; Medicine; Radiology","score_opus":0.007179570351702232,"score_gpt":0.24273361337857355,"score_spread":0.23555404302687133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118300304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013290342,0.00027882477,0.9839641,0.00023872298,0.0013790916,0.00028898948,0.000009281343,0.00026555575,0.0002851126],"genre_scores_gemma":[0.5545743,0.0018115155,0.4409581,0.0012354731,0.000256297,0.0003139792,0.00007026072,0.0000984812,0.0006815374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985506,0.00003519922,0.00024999876,0.00040484755,0.0004271083,0.00033222922],"domain_scores_gemma":[0.9994586,0.000057070807,0.000040232855,0.00024194324,0.00009340337,0.00010876289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018787936,0.00013784056,0.00017372996,0.00010282387,0.00009272792,0.0000461299,0.00024247664,0.00008024486,0.000026205656],"category_scores_gemma":[0.00036250238,0.00013244744,0.000040371746,0.00057886547,0.00020840301,0.0003736576,0.00007826293,0.00023388803,0.0000010059558],"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":[0.00040584287,0.00042667368,0.037162244,0.00034729778,0.000019407442,0.0013894078,0.00037084403,0.3626919,0.35379547,0.0010811879,0.16051841,0.08179131],"study_design_scores_gemma":[0.0006169115,0.000059582864,0.0077868374,0.00010944525,0.0000049382343,0.000049501577,0.000038568858,0.9690032,0.01851029,0.0006143748,0.0029844996,0.00022183081],"about_ca_topic_score_codex":0.0000024149995,"about_ca_topic_score_gemma":0.0000067200426,"teacher_disagreement_score":0.6063113,"about_ca_system_score_codex":0.000080467034,"about_ca_system_score_gemma":0.00007503061,"threshold_uncertainty_score":0.540105},"labels":[],"label_agreement":null},{"id":"W3129675108","doi":"10.1007/s11831-020-09518-x","title":"Review of Various Image Fusion Algorithms and Image Fusion Performance Metric","year":2021,"lang":"en","type":"article","venue":"Archives of Computational Methods in Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Image fusion; Artificial intelligence; Computer vision; Computer science; Fusion; Process (computing); Metric (unit); Transformation (genetics); Focus (optics); Sensor fusion; Domain (mathematical analysis); Photography; Image (mathematics); Composite image filter; Image processing; Image registration; Mathematics; Optics; Engineering; Physics","score_opus":0.010401226295442769,"score_gpt":0.31507305340732594,"score_spread":0.3046718271118832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129675108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007487127,0.022342548,0.9691823,0.000018728922,0.00009055031,0.00015255433,0.000011091347,0.00010424261,0.00061083224],"genre_scores_gemma":[0.012860674,0.02755096,0.9594786,0.000019996027,0.000016696584,0.000014783239,0.000020928443,0.000032049837,0.0000052799173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988756,0.000082260696,0.00052356743,0.00018867296,0.0001662493,0.00016365558],"domain_scores_gemma":[0.9985979,0.0010169109,0.00008041666,0.00017136926,0.00008504641,0.000048336642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037551302,0.00016788537,0.00039111232,0.00040659762,0.00001901022,0.000006835116,0.00012587929,0.000033888515,0.000020041902],"category_scores_gemma":[0.0004663455,0.0001847514,0.0000658418,0.00062266557,0.000054173866,0.00018629199,0.00015349287,0.00020895537,4.5616957e-7],"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":[0.0000051696147,0.000033167773,0.00005610039,0.009964919,0.000021800297,0.000010613632,0.0001517827,0.18712078,0.32227758,0.00013601838,0.000018406568,0.48020366],"study_design_scores_gemma":[0.00022393426,0.00003257113,0.004826944,0.0041852063,0.000013665673,0.00003666064,0.000012615313,0.8152033,0.17288938,0.002198538,0.00018991758,0.00018724828],"about_ca_topic_score_codex":0.000002489776,"about_ca_topic_score_gemma":1.2287671e-7,"teacher_disagreement_score":0.6280826,"about_ca_system_score_codex":0.000019542795,"about_ca_system_score_gemma":0.000022798646,"threshold_uncertainty_score":0.75339437},"labels":[],"label_agreement":null},{"id":"W3133758652","doi":"10.3390/rs13050849","title":"Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Laval","funders":"Agence Nationale de la Recherche; Centre National d’Etudes Spatiales; Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Computer science; Panchromatic film; Markov chain; Multispectral image; Quadtree; Probabilistic logic; Artificial intelligence; Graphical model; Markov model; Multiresolution analysis; Pattern recognition (psychology); Data mining; Algorithm; Machine learning; Wavelet","score_opus":0.017986722580863725,"score_gpt":0.2828300372615704,"score_spread":0.2648433146807067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133758652","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3077339,0.00065700477,0.69018066,0.00018304026,0.00018299733,0.00016719472,0.0000042608085,0.0005408201,0.00035014376],"genre_scores_gemma":[0.27154145,0.0013681883,0.72675633,0.00007448799,0.0001556957,1.168593e-8,0.000012303088,0.00007361155,0.000017939488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815184,0.00011965193,0.00043694588,0.000586448,0.00027831018,0.0004268087],"domain_scores_gemma":[0.9984457,0.00066159305,0.00008209845,0.00048128408,0.00018941388,0.00013993646],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019170728,0.00033136248,0.0003720468,0.00012799082,0.0002582204,0.00014436724,0.00004838375,0.00030741945,0.0000039231086],"category_scores_gemma":[0.001141694,0.00036461453,0.00006518599,0.00030755086,0.00017461168,0.00029613747,0.00013262685,0.0005815445,0.000005809932],"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":[0.000025200947,0.0000023269376,0.0000016753859,0.000044638477,0.000010253129,0.00014235244,0.0002928701,0.00012709328,0.34715346,0.000015045328,0.00008868427,0.6520964],"study_design_scores_gemma":[0.00035492663,0.000017789738,0.0006020345,0.00074425794,0.000030709885,0.00060113356,0.00021455837,0.91119075,0.071120046,0.01417622,0.0005846825,0.0003629125],"about_ca_topic_score_codex":0.000058907706,"about_ca_topic_score_gemma":0.00007184648,"teacher_disagreement_score":0.9110636,"about_ca_system_score_codex":0.0001842953,"about_ca_system_score_gemma":0.000028174667,"threshold_uncertainty_score":0.99988055},"labels":[],"label_agreement":null},{"id":"W3147951545","doi":"10.1109/jstars.2021.3070786","title":"Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":67,"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":"Shenzhen Technology Development Program; Government of Canada; Compute Canada","keywords":"Jaccard index; Cloud computing; Computer science; Shadow (psychology); Segmentation; Image segmentation; Function (biology); Parametric statistics; Computer vision; Remote sensing; Artificial intelligence; Geology; Pattern recognition (psychology); Mathematics","score_opus":0.019201664396249547,"score_gpt":0.24307623715124269,"score_spread":0.22387457275499315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147951545","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42713726,0.0002560652,0.5719197,0.00010059791,0.00034685075,0.00017248419,0.0000029875964,0.000040498282,0.000023576298],"genre_scores_gemma":[0.3576324,0.0011584405,0.64057493,0.000113783004,0.00044328882,9.690751e-8,0.00001869529,0.00003105203,0.000027280936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885464,0.00004184162,0.00053762004,0.00019387653,0.00017767024,0.00019433572],"domain_scores_gemma":[0.99903464,0.0001757831,0.00020410398,0.00010439776,0.00041331653,0.000067729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026456133,0.0001706526,0.00027977617,0.0002858864,0.0001487849,0.00010495478,0.000025203346,0.00011803825,0.0000013509502],"category_scores_gemma":[0.00011804783,0.00018491888,0.00003308091,0.0006531365,0.000042137195,0.00021487266,0.000016698701,0.00029958057,1.1142188e-7],"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":[0.000057212754,0.0000057309026,0.00005432037,0.00011389935,0.000038927727,0.00001519688,0.00018939406,0.00212967,0.35803697,0.000017724027,0.00007661293,0.63926435],"study_design_scores_gemma":[0.0023884175,0.00016086677,0.0122553585,0.0004089683,0.00015569234,0.00062877,0.00043721416,0.71117586,0.254894,0.016081052,0.0009325449,0.00048127054],"about_ca_topic_score_codex":0.00001628988,"about_ca_topic_score_gemma":0.00006398467,"teacher_disagreement_score":0.7090462,"about_ca_system_score_codex":0.00007849379,"about_ca_system_score_gemma":0.000043492644,"threshold_uncertainty_score":0.7540773},"labels":[],"label_agreement":null},{"id":"W3150070458","doi":"10.1109/icce.1996.517288","title":"Image Indexing Using Moments and Wavelets","year":2005,"lang":"en","type":"article","venue":"International Conference on Consumer Electronics","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"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":"Wavelet; Search engine indexing; Computer science; Artificial intelligence; Image (mathematics); Computer vision; Pattern recognition (psychology)","score_opus":0.023038155170329352,"score_gpt":0.2985466479184407,"score_spread":0.27550849274811134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150070458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70726067,0.0017063805,0.22116525,0.0012339769,0.0005701362,0.0005098807,0.000056407498,0.0013430067,0.06615427],"genre_scores_gemma":[0.9712953,0.0010641498,0.027187407,0.00015102421,0.00005521371,0.000013077328,0.0000099244835,0.000030496589,0.00019339284],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991703,0.00001079973,0.00018469426,0.0001835807,0.00020145896,0.00024919174],"domain_scores_gemma":[0.9996454,0.00002601872,0.00003863767,0.00012535509,0.0001100417,0.00005451916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006447018,0.00015633933,0.000114299444,0.00013626128,0.00004604692,0.00007459492,0.00017303256,0.00005699869,0.00028075004],"category_scores_gemma":[0.000026281372,0.00017088854,0.00002328395,0.000057826273,0.00004835839,0.00029327685,0.000047553618,0.00027130567,0.000034879333],"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":[0.00004213565,0.00007289402,0.0004359416,0.000024815097,0.0001752042,0.000017937708,0.00018102599,0.0008058086,0.651244,0.03759429,0.00093281036,0.30847314],"study_design_scores_gemma":[0.0005604853,0.000051800504,0.0001652585,0.0000941256,0.000014330748,0.00005576976,0.000029224548,0.81083965,0.1412143,0.004715922,0.041808113,0.00045105146],"about_ca_topic_score_codex":0.0000035770001,"about_ca_topic_score_gemma":0.000010596319,"teacher_disagreement_score":0.8100338,"about_ca_system_score_codex":0.0002439591,"about_ca_system_score_gemma":0.00004103765,"threshold_uncertainty_score":0.69686323},"labels":[],"label_agreement":null},{"id":"W3152766032","doi":"10.1016/j.heliyon.2021.e06839","title":"A fuzzy inference method for image fusion/refinement of CT images from incomplete data","year":2021,"lang":"en","type":"article","venue":"Heliyon","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Missing data; A priori and a posteriori; Pixel; Computer science; Inference; Image (mathematics); Fuzzy logic; Pattern recognition (psychology); Iterative reconstruction; Mathematics; Data mining; Machine learning","score_opus":0.03926571209915223,"score_gpt":0.3386011299236071,"score_spread":0.29933541782445483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152766032","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035896837,0.0028416223,0.98848575,0.00012326147,0.00015824908,0.0002600813,0.0017095405,0.0003869542,0.0024448384],"genre_scores_gemma":[0.010692299,0.002038836,0.9862324,0.00009495099,0.00007634424,0.000060562626,0.00064707483,0.0000468386,0.00011066504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885845,0.000039684797,0.00038625638,0.00033717215,0.00016849034,0.00020992818],"domain_scores_gemma":[0.9983565,0.00032937364,0.000072852206,0.001035056,0.00015220125,0.000053998505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019042463,0.00017196103,0.00029726606,0.00006702445,0.00004017226,0.000026321726,0.00039893482,0.000035277688,0.0003312785],"category_scores_gemma":[0.00041687177,0.00017527938,0.000054276225,0.00016984496,0.000027511493,0.00024335037,0.00051679916,0.00012514344,0.000015985666],"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":[0.000008571077,0.000031162093,0.000036086185,0.00038729116,0.00002633588,0.000020591837,0.000041770993,0.00013579836,0.9320791,0.00011765561,0.0032346824,0.06388096],"study_design_scores_gemma":[0.0004096104,0.000031093907,0.0004074268,0.00032702286,0.000032944045,0.0000057087054,0.000064090316,0.008435713,0.9507912,0.003738193,0.03551624,0.00024075276],"about_ca_topic_score_codex":0.000043104086,"about_ca_topic_score_gemma":0.000044953325,"teacher_disagreement_score":0.06364021,"about_ca_system_score_codex":0.000032001877,"about_ca_system_score_gemma":0.00003319431,"threshold_uncertainty_score":0.7147685},"labels":[],"label_agreement":null},{"id":"W3162511426","doi":"10.18280/ts.380201","title":"Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":10,"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 fusion; Focus (optics); Artificial intelligence; Computer science; Image (mathematics); Top-hat transform; Algorithm; Curvelet; Fusion; Computer vision; Scale (ratio); Fusion rules; Pattern recognition (psychology); Feature detection (computer vision); Image processing; Wavelet transform; Wavelet","score_opus":0.00946067967553246,"score_gpt":0.2325194207745145,"score_spread":0.22305874109898205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162511426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029512753,0.00085985544,0.9935592,0.000108294924,0.00007976238,0.0005674004,0.00015798601,0.0010443439,0.0006719281],"genre_scores_gemma":[0.055505853,0.00040359917,0.9427678,0.00007801414,0.000056846005,0.00021452116,0.00015446148,0.00012612336,0.00069278415],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99824643,0.00004637568,0.00044695684,0.00041818846,0.00037646148,0.00046560107],"domain_scores_gemma":[0.9992998,0.00005388553,0.000052574283,0.00027847878,0.00013595934,0.0001792873],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015763963,0.00038788829,0.00041491652,0.000090962174,0.00011726209,0.00007193677,0.0002032556,0.00009408527,0.0014086099],"category_scores_gemma":[0.000010700277,0.00034529765,0.00012635993,0.0002700048,0.000080085585,0.00033813997,0.000037792655,0.0002849085,0.000034202654],"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":[0.000070688824,0.0004386608,0.000011122875,0.00009163196,0.00010487973,0.00016677254,0.00038264995,0.0036504017,0.93320036,0.0000035148785,0.0022622268,0.059617113],"study_design_scores_gemma":[0.0044588638,0.00012417168,0.000088899695,0.000083846935,0.000094261566,0.000035782876,0.00015100825,0.29051313,0.6997207,0.00002522838,0.004179664,0.0005244365],"about_ca_topic_score_codex":0.00001693893,"about_ca_topic_score_gemma":0.00002632244,"teacher_disagreement_score":0.28686273,"about_ca_system_score_codex":0.000107655156,"about_ca_system_score_gemma":0.000028165565,"threshold_uncertainty_score":0.9998999},"labels":[],"label_agreement":null},{"id":"W3163843793","doi":"10.1007/s11554-021-01111-0","title":"Embedded real-time infrared and visible image fusion for UAV surveillance","year":2021,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":23,"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":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision; Frame rate; Visibility; Field-programmable gate array; Frame (networking); Image fusion; Object detection; Infrared; Fusion; Task (project management); Image (mathematics); Computer hardware; Pattern recognition (psychology); Engineering; Optics","score_opus":0.006201617928378585,"score_gpt":0.26229733876024,"score_spread":0.2560957208318614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163843793","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2784594,0.007435891,0.6575478,0.00073264085,0.0005239314,0.001179756,0.00013846751,0.0021413409,0.05184079],"genre_scores_gemma":[0.015872527,0.005395183,0.9749811,0.000061396124,0.00043215274,0.000020493051,0.000032457352,0.0002202611,0.0029843957],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99789125,0.00006885241,0.000882333,0.00030640283,0.0003779522,0.0004731989],"domain_scores_gemma":[0.99760354,0.00023305051,0.00048151557,0.00029746792,0.0011674513,0.00021700824],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067757093,0.00034830836,0.00070938544,0.00026871945,0.00019512301,0.0003402508,0.00024368124,0.00014742011,0.00021940551],"category_scores_gemma":[0.0005566907,0.00033815132,0.00016335717,0.00044822288,0.0001041181,0.0015960294,0.00011822206,0.00033678606,0.00001584477],"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":[0.00007420953,0.000037413098,0.000030025876,0.00047892387,0.00002669251,0.00014368427,0.00018896669,0.000073940995,0.94926995,0.0000015267552,0.011556871,0.038117785],"study_design_scores_gemma":[0.0020840308,0.00024772456,0.0007623121,0.0012564729,0.000085240776,0.0011039057,0.00024674294,0.06849706,0.91822696,0.0032554546,0.0033989358,0.000835191],"about_ca_topic_score_codex":0.0000022185905,"about_ca_topic_score_gemma":6.593837e-7,"teacher_disagreement_score":0.31743336,"about_ca_system_score_codex":0.0001295033,"about_ca_system_score_gemma":0.00019233646,"threshold_uncertainty_score":0.9999071},"labels":[],"label_agreement":null},{"id":"W3171348742","doi":"10.1109/ner49283.2021.9441386","title":"SSVEP Harmonic Fusion for Improved Visual Field Reconstruction with CNN","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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; Artificial intelligence; Fusion; Computer vision; Field (mathematics); Visualization; Mathematics","score_opus":0.005543853738925121,"score_gpt":0.23452006513373438,"score_spread":0.22897621139480925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171348742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04264135,0.00008532962,0.95164645,0.00017441726,0.00021173856,0.00023002182,0.0000029477305,0.00090470514,0.004103037],"genre_scores_gemma":[0.32114282,0.00025850974,0.67448044,0.0003486997,0.0001529047,0.00014427825,0.000028450484,0.000069131536,0.003374753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952954,0.0000047409194,0.00012306274,0.0001503094,0.000043277843,0.00014904967],"domain_scores_gemma":[0.99968,0.000049146623,0.000016957727,0.00013676488,0.000081321625,0.000035833775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031770494,0.00009781538,0.00010571625,0.000036958696,0.000044252694,0.000021311042,0.000041217478,0.0000630421,0.00043129077],"category_scores_gemma":[0.000034875702,0.00008552004,0.000035042896,0.00010421727,0.000011149359,0.00015663829,0.000021666205,0.00009354648,0.0000058802902],"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":[0.000024221818,0.000010109631,0.000019704272,0.000034180954,0.000011554463,0.0000023722237,0.000015381016,0.00004228643,0.6092411,0.00008379904,0.0019118937,0.38860336],"study_design_scores_gemma":[0.00030131073,0.00012723503,0.000025235071,0.000026870448,0.0000077820005,0.000038127746,0.000057796264,0.035169244,0.9564357,0.0005929185,0.0070746774,0.0001431263],"about_ca_topic_score_codex":0.0000035145304,"about_ca_topic_score_gemma":0.000019890284,"teacher_disagreement_score":0.38846022,"about_ca_system_score_codex":0.00003355714,"about_ca_system_score_gemma":0.000017338321,"threshold_uncertainty_score":0.47223315},"labels":[],"label_agreement":null},{"id":"W3173072849","doi":"","title":"Change Detection in Ottawa City Synthetic Aperture Radar Remote Sensing Images based on DWT Fuzzy C Means Clustering","year":2021,"lang":"en","type":"article","venue":"Annals of the Romanian Society for Cell Biology","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Image fusion; Artificial intelligence; Remote sensing; Synthetic aperture radar; Computer science; Computer vision; Wavelet; Fuzzy logic; Sensitivity (control systems); Ground truth; Wavelet transform; Discrete wavelet transform; Cluster analysis; Image (mathematics); Pattern recognition (psychology); Geography; Engineering","score_opus":0.02640861503562019,"score_gpt":0.26405322672950676,"score_spread":0.23764461169388656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173072849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094108924,0.003314122,0.8880026,0.005565792,0.0010974861,0.0017162471,0.0002855252,0.0008764826,0.0050328216],"genre_scores_gemma":[0.9384791,0.00036121724,0.05950682,0.0014331872,0.000076974145,0.0000074449936,0.00001720167,0.000054880657,0.00006315413],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990098,0.00007385482,0.00025786794,0.0002638945,0.00007544508,0.00031915106],"domain_scores_gemma":[0.9991751,0.00023930585,0.00009021786,0.0003881432,0.000078165256,0.000029121058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026181893,0.00019355203,0.0002716539,0.00004373037,0.00009573585,0.000009417215,0.0001672427,0.00022237528,0.000008231986],"category_scores_gemma":[0.00011734205,0.00016750053,0.00040959037,0.00023643627,0.00007423686,0.000058527276,0.000075437245,0.0002488686,0.0000013430657],"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":[0.000020040678,0.000026682328,0.000012424474,0.00025468974,0.000033469987,0.0000025870493,0.00027477002,0.0006684363,0.9212766,0.000007106743,0.00038291,0.077040255],"study_design_scores_gemma":[0.0002360839,0.00008552711,0.000113508366,0.00013984481,0.000014602089,0.000004616023,0.00012689267,0.1320003,0.85404587,0.0006656733,0.012376124,0.00019095631],"about_ca_topic_score_codex":0.00003818565,"about_ca_topic_score_gemma":0.00009143779,"teacher_disagreement_score":0.8443702,"about_ca_system_score_codex":0.000054477106,"about_ca_system_score_gemma":0.000012066589,"threshold_uncertainty_score":0.68304735},"labels":[],"label_agreement":null},{"id":"W3182688976","doi":"10.1109/tmm.2021.3096088","title":"Infrared and Visible Image Fusion Based on Deep Decomposition Network and Saliency Analysis","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":49,"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, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Image fusion; Pattern recognition (psychology); Fusion; Fuse (electrical); Merge (version control); Decomposition; Computer vision; Residual; Image texture; Autoencoder; Image (mathematics); Image processing; Deep learning; Algorithm","score_opus":0.004761814742510791,"score_gpt":0.24114036950292658,"score_spread":0.2363785547604158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182688976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018166987,0.00015101192,0.9800181,0.000042241158,0.00018138206,0.00015508241,0.000029851768,0.000478026,0.0007773196],"genre_scores_gemma":[0.74801123,0.00053400605,0.25105283,0.00013836705,0.000038788665,0.000052652795,0.00003801822,0.000040205054,0.00009390518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990892,0.000046368412,0.00020889581,0.00028619287,0.00015884402,0.00021049645],"domain_scores_gemma":[0.99932855,0.00019604834,0.000027140206,0.00026848793,0.00005929542,0.000120503835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007401083,0.00018830429,0.00022319361,0.0002737955,0.00015655086,0.00004906857,0.000050088554,0.00010221967,0.00029138546],"category_scores_gemma":[0.000008185818,0.00020397434,0.000082349776,0.0006882051,0.000048184887,0.00018703884,0.0000017131978,0.0002339505,0.00001188249],"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":[0.000072453855,0.00017427145,0.00010675917,0.000062032144,0.00014937174,0.000048171707,0.00023444707,0.6761241,0.12205669,0.000003441065,0.00028958605,0.20067865],"study_design_scores_gemma":[0.0004315873,0.000060370934,0.0012088877,0.000043434706,0.00015921795,0.0000039851966,0.00002272211,0.8305997,0.16701953,0.00012709953,0.00011826637,0.00020514357],"about_ca_topic_score_codex":0.000005688373,"about_ca_topic_score_gemma":0.00004268046,"teacher_disagreement_score":0.7298443,"about_ca_system_score_codex":0.000049942977,"about_ca_system_score_gemma":0.000010613033,"threshold_uncertainty_score":0.8317832},"labels":[],"label_agreement":null},{"id":"W3186774183","doi":"10.18280/ts.380308","title":"A DWT Based Novel Multimodal Image Fusion Method","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Image fusion; Artificial intelligence; Computer vision; Computer science; Enhanced Data Rates for GSM Evolution; Image (mathematics); Fusion; Filter (signal processing); Contrast (vision); Fusion rules; Image processing; Pattern recognition (psychology)","score_opus":0.011253578592050978,"score_gpt":0.2620904782553479,"score_spread":0.2508368996632969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186774183","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006990753,0.00008493261,0.9888196,0.00013534306,0.00011223298,0.00021873288,0.00004776954,0.00092868804,0.002661956],"genre_scores_gemma":[0.2227334,0.0000124021435,0.77659816,0.00028273888,0.000101694284,0.00006512229,0.000055503333,0.00005505945,0.00009591862],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988046,0.00003897554,0.00029741073,0.00027749117,0.00028039858,0.000301108],"domain_scores_gemma":[0.9994181,0.00009811337,0.00003438449,0.00024285095,0.0001055676,0.00010099209],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020274756,0.00021877626,0.00021090041,0.00009410333,0.00006912499,0.000044509954,0.0001448564,0.00007094543,0.0029784273],"category_scores_gemma":[0.000033138123,0.00022853827,0.00010873984,0.00022352023,0.00002486713,0.00019364782,0.000056978817,0.00019040541,0.000040810715],"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":[0.000012582185,0.00011172241,0.000015339027,0.000051408446,0.000016186224,0.00004885472,0.00006693885,0.0072735967,0.9595847,0.00009564921,0.0015640128,0.031158999],"study_design_scores_gemma":[0.0007797722,0.00003180125,0.000344696,0.000038056238,0.000015153318,0.000010287869,0.000032982673,0.34409067,0.6463989,0.00010212261,0.007930434,0.0002251453],"about_ca_topic_score_codex":0.000006517025,"about_ca_topic_score_gemma":0.000004671142,"teacher_disagreement_score":0.33681706,"about_ca_system_score_codex":0.00008731655,"about_ca_system_score_gemma":0.00003278947,"threshold_uncertainty_score":0.997933},"labels":[],"label_agreement":null},{"id":"W3187067390","doi":"10.18280/ts.380328","title":"Contrast Enhancement of Poor-Quality Satellite Images Through Morphological Operations","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Contrast (vision); Satellite; Artificial intelligence; Pixel; Computer science; Computer vision; Brightness; Contrast enhancement; Remote sensing; Wavelet; Geography","score_opus":0.02429127397363457,"score_gpt":0.28840398824109187,"score_spread":0.2641127142674573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187067390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14135496,0.00111016,0.85016876,0.0002453602,0.000101175116,0.00030681698,0.00009556733,0.00034879154,0.006268415],"genre_scores_gemma":[0.9166248,0.0006792839,0.08206806,0.00025637186,0.00005587278,0.00006775683,0.00007464693,0.000019821708,0.0001533784],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986785,0.00006851454,0.0005316286,0.00022708568,0.00025587447,0.00023837005],"domain_scores_gemma":[0.9994969,0.00006509753,0.00003910032,0.0002018573,0.00014493035,0.000052160318],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017627943,0.00017693973,0.00028042163,0.000029250286,0.000057921083,0.000028515902,0.0001277453,0.00005812132,0.003166756],"category_scores_gemma":[0.00003036645,0.00016845514,0.000089209076,0.00014939679,0.0000850519,0.00021285028,0.000049832717,0.00013151302,0.00002314742],"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":[0.000011379859,0.0001541205,0.000080830876,0.000049008784,0.00004188475,0.00003341993,0.00015228194,0.0016965151,0.9864626,0.0017085591,0.00088961696,0.008719816],"study_design_scores_gemma":[0.00042648293,0.000070072536,0.0014093231,0.000033372227,0.000016735676,0.000010317505,0.0001256429,0.00082526205,0.9917005,0.00056986767,0.0046188016,0.00019366453],"about_ca_topic_score_codex":0.000009256133,"about_ca_topic_score_gemma":0.0000064322867,"teacher_disagreement_score":0.77526987,"about_ca_system_score_codex":0.000057780162,"about_ca_system_score_gemma":0.000022173233,"threshold_uncertainty_score":0.9977445},"labels":[],"label_agreement":null},{"id":"W3188514889","doi":"10.1155/2021/5439935","title":"The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Taishan Scholar Project of Shandong Province; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Shearlet; Focus (optics); Computer science; Artificial intelligence; Image fusion; Adversarial system; Generative grammar; Contourlet; Wavelet; Image (mathematics); Representation (politics); Pyramid (geometry); Process (computing); Pattern recognition (psychology); Domain (mathematical analysis); Computer vision; Wavelet transform; Mathematics","score_opus":0.01108381991969944,"score_gpt":0.2457575043527111,"score_spread":0.23467368443301165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188514889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10580813,0.0019282522,0.88760924,0.001999734,0.0013560866,0.00065445394,0.00010379721,0.00017594911,0.00036432693],"genre_scores_gemma":[0.88900477,0.0005951718,0.10998979,0.000108816326,0.00018740816,0.000008424438,0.000037935115,0.000036237205,0.000031430965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869066,0.00010153719,0.0005549938,0.00011215759,0.0003548928,0.0001857533],"domain_scores_gemma":[0.998577,0.00040005537,0.00031605724,0.0002069577,0.0004525787,0.000047362617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000257615,0.00017143422,0.0002539306,0.000053358912,0.00017348788,0.00002173377,0.00017118339,0.00006297249,0.000048877493],"category_scores_gemma":[0.00011355765,0.00010678071,0.000159943,0.0003349543,0.00007148225,0.00020671064,0.00000352327,0.00040341227,6.393071e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022446216,0.000039664792,0.000051055707,0.0000131136585,0.00003397797,0.000030890493,0.00022518815,0.7026356,0.28483316,0.00013023986,0.001280394,0.010502261],"study_design_scores_gemma":[0.0028586076,0.00035889645,0.091680475,0.00038976865,0.00008962771,0.000014181207,0.0007036573,0.017156322,0.88025045,0.0014668127,0.004730549,0.00030063902],"about_ca_topic_score_codex":0.0000021578744,"about_ca_topic_score_gemma":0.000041502204,"teacher_disagreement_score":0.7831966,"about_ca_system_score_codex":0.0000569846,"about_ca_system_score_gemma":0.000050039296,"threshold_uncertainty_score":0.43543908},"labels":[],"label_agreement":null},{"id":"W3188896455","doi":"10.3390/s21165394","title":"TaijiGNN: A New Cycle-Consistent Generative Neural Network for High-Quality Bidirectional Transformation between RGB and Multispectral Domains","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","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; Ericsson (Canada); École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial neural network; Multispectral image; RGB color model; Convolutional neural network; Generator (circuit theory); Image translation; Artificial intelligence; Transformation (genetics); Domain (mathematical analysis); Process (computing); Pattern recognition (psychology); Polarity (international relations); Translation (biology); Computer vision; Image (mathematics); Mathematics","score_opus":0.01992038593944519,"score_gpt":0.27433744674136945,"score_spread":0.2544170608019243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188896455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76272213,0.0002087038,0.23526269,0.0003887556,0.00022755844,0.00033744753,0.00010764942,0.0004845363,0.00026053208],"genre_scores_gemma":[0.82732624,0.00008226172,0.17158864,0.00007423012,0.00052836485,0.000031635896,0.00012829922,0.000034627283,0.00020571008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990917,0.000052097846,0.00028646158,0.00019776777,0.00011845344,0.00025351372],"domain_scores_gemma":[0.9995307,0.00015017102,0.000037469687,0.000120591314,0.00006154249,0.00009956854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009450751,0.00016291995,0.00022734707,0.00004015525,0.00012299702,0.00003488579,0.00003724598,0.00008026438,0.000025459938],"category_scores_gemma":[0.00004096625,0.00017204224,0.00007491811,0.00014219742,0.000034364468,0.00015129254,0.000012051155,0.00013373452,0.0000023434843],"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":[0.00017961439,0.00008565481,0.003813024,0.00040044478,0.00057479955,0.000049218845,0.004260421,0.5139795,0.15491918,0.01580625,0.009655842,0.29627606],"study_design_scores_gemma":[0.003851769,0.00022088982,0.074270755,0.00011398352,0.00018554175,0.0001006698,0.00060149404,0.15716366,0.71199185,0.03492838,0.015045667,0.0015253267],"about_ca_topic_score_codex":0.000036196605,"about_ca_topic_score_gemma":0.00009434248,"teacher_disagreement_score":0.5570727,"about_ca_system_score_codex":0.00007521719,"about_ca_system_score_gemma":0.000020647782,"threshold_uncertainty_score":0.7015679},"labels":[],"label_agreement":null},{"id":"W3204847229","doi":"10.18280/ts.380414","title":"Three-Dimensional Image Quality Evaluation and Optimization Based on Convolutional Neural Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Convolutional neural network; Computer science; Artificial intelligence; Preprocessor; Image quality; Computer vision; Quality (philosophy); Process (computing); Image (mathematics); Artificial neural network; Pattern recognition (psychology); Key (lock)","score_opus":0.02282841638954053,"score_gpt":0.2785479039020756,"score_spread":0.2557194875125351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204847229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024230361,0.00023093639,0.97378886,0.00023123751,0.00015630752,0.00034706623,0.00003118761,0.00034662988,0.0006373956],"genre_scores_gemma":[0.7886921,0.000007019311,0.21017462,0.00045161365,0.00019346598,0.000081502425,0.0003598814,0.00003065131,0.000009132616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870527,0.00008965814,0.00028247057,0.0002364591,0.00049050606,0.00019561514],"domain_scores_gemma":[0.9993993,0.00013874525,0.000046751993,0.00013423493,0.00021603005,0.00006490801],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046707306,0.00015626199,0.00014146189,0.000045957753,0.00010651795,0.00003713433,0.000049466107,0.000052846764,0.0019760323],"category_scores_gemma":[0.00004952701,0.00016694976,0.000044262975,0.00014625606,0.00004600826,0.0001764711,0.00002433208,0.00012434289,0.000005608199],"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":[0.00003123587,0.00004043809,0.00025522252,0.000017902259,0.000009966159,0.000004721429,0.000007061903,0.9857818,0.007270807,0.00025732076,0.0019627695,0.0043607554],"study_design_scores_gemma":[0.00074734166,0.00004767617,0.007153795,0.000033034954,0.000019495486,0.000003877435,0.0000040840496,0.98761344,0.0032971187,0.0007801708,0.00012467054,0.00017529256],"about_ca_topic_score_codex":0.0000029697826,"about_ca_topic_score_gemma":0.0000086378395,"teacher_disagreement_score":0.76446176,"about_ca_system_score_codex":0.00010397672,"about_ca_system_score_gemma":0.0000456832,"threshold_uncertainty_score":0.9989363},"labels":[],"label_agreement":null},{"id":"W3208059427","doi":"10.3390/rs13214274","title":"A Fusion Method of Optical Image and SAR Image Based on Dense-UGAN and Gram–Schmidt Transformation","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":44,"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 Science and Technology Major Project; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Speckle pattern; Computer vision; Speckle noise; Transformation (genetics); Image fusion; Distortion (music); Discriminator; Image (mathematics); Noise (video); Synthetic aperture radar; Fusion; Pattern recognition (psychology); Telecommunications","score_opus":0.007637074928292203,"score_gpt":0.2621173640466202,"score_spread":0.25448028911832804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208059427","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11885455,0.00006342012,0.8779942,0.00014970003,0.00003084313,0.00011999707,0.0000031109143,0.00021857972,0.0025656123],"genre_scores_gemma":[0.25562492,0.000103450715,0.7441621,0.000054600267,0.00001307179,1.3241579e-8,0.000007226851,0.000027090025,0.0000075334797],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921674,0.000057340985,0.0002319658,0.00017906661,0.00014887296,0.0001660198],"domain_scores_gemma":[0.99947876,0.00014644943,0.000030699743,0.0001843254,0.000090300775,0.00006947294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001874113,0.00014753701,0.00022069101,0.00010749765,0.00005501531,0.00003481603,0.000023920089,0.00008089114,0.000008159567],"category_scores_gemma":[0.00012542633,0.00015348087,0.00004036993,0.0001552342,0.000051383977,0.00016206849,0.000021969985,0.00017834023,0.0000016433664],"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":[0.000010784363,0.0000034502993,3.3732815e-7,0.000107545995,0.0000030830681,0.00003282941,0.00012677138,0.00019431823,0.6674075,0.000014570333,0.000011490791,0.33208728],"study_design_scores_gemma":[0.0001826993,0.00002050924,0.00005098325,0.00012988863,0.000012129249,0.00006560313,0.00006238211,0.49849027,0.5004325,0.00034931648,0.00011446196,0.000089236186],"about_ca_topic_score_codex":0.000007837487,"about_ca_topic_score_gemma":0.0000027431006,"teacher_disagreement_score":0.49829596,"about_ca_system_score_codex":0.0000337038,"about_ca_system_score_gemma":0.000012166743,"threshold_uncertainty_score":0.62587684},"labels":[],"label_agreement":null},{"id":"W3214451643","doi":"10.1016/j.neunet.2021.11.014","title":"Dilated projection correction network based on autoencoder for hyperspectral image super-resolution","year":2021,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Artificial intelligence; Computer science; Autoencoder; Projection (relational algebra); Pattern recognition (psychology); Computer vision; Distortion (music); Image resolution; Embedding; Image (mathematics); Representation (politics); Artificial neural network; Algorithm","score_opus":0.008669699704781859,"score_gpt":0.23471284034395148,"score_spread":0.22604314063916964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214451643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041679544,0.00023582552,0.9880152,0.00016968265,0.0034528563,0.0005781491,0.00000792568,0.0020498333,0.0013226221],"genre_scores_gemma":[0.87525445,0.00015639974,0.11963589,0.00070811185,0.002335283,0.00042341487,0.00047865213,0.00021162056,0.00079615845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873984,0.000053926484,0.00024633706,0.00033623894,0.00014663089,0.00047703562],"domain_scores_gemma":[0.99935883,0.00012862287,0.0000399622,0.0002639283,0.00014008509,0.00006855722],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000102084035,0.0002354182,0.00020211344,0.00005465105,0.00018272268,0.00006393206,0.00008172723,0.00018183191,0.00008904823],"category_scores_gemma":[0.000066500885,0.0002487526,0.00012999405,0.00047797372,0.000030401648,0.0002327753,0.000018256042,0.0004065851,0.0000047451986],"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":[0.000070115704,0.000028483417,0.00004476199,0.000013666846,0.000007961982,0.000009829249,0.000010870025,0.9400791,0.005921351,0.000021187738,0.038194157,0.015598517],"study_design_scores_gemma":[0.00036790734,0.00015104217,0.00033026462,0.00005052161,0.000019552248,0.000019217972,0.000012529359,0.98589545,0.009819326,0.00010506898,0.002977444,0.00025168186],"about_ca_topic_score_codex":0.000006079044,"about_ca_topic_score_gemma":0.000024384803,"teacher_disagreement_score":0.87108654,"about_ca_system_score_codex":0.00019287306,"about_ca_system_score_gemma":0.000017765062,"threshold_uncertainty_score":0.9999965},"labels":[],"label_agreement":null},{"id":"W3217417870","doi":"10.1109/tgrs.2021.3131477","title":"Multiresolution Analysis Based on Dual-Scale Regression for Pansharpening","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":48,"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":"Foundation of the Graduate Innovation Center, Nanjing University of Aeronautics and Astronautics; Nanjing University of Aeronautics and Astronautics; Fundamental Research Funds for the Central Universities; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Panchromatic film; Computer science; Multiresolution analysis; Robustness (evolution); Multispectral image; Scale (ratio); Regression; Regression analysis; Artificial intelligence; Image resolution; Pattern recognition (psychology); Data mining; Wavelet; Wavelet transform; Mathematics; Statistics; Machine learning; Cartography; Discrete wavelet transform; Geography","score_opus":0.012669860588605268,"score_gpt":0.25725553496551584,"score_spread":0.24458567437691056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217417870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021982782,0.000038032926,0.9770005,0.000104126986,0.000258581,0.00013480874,0.000018291825,0.00032276614,0.00014010783],"genre_scores_gemma":[0.5632738,0.00008226801,0.4362521,0.000102643644,0.000019215053,6.023283e-7,0.000004932241,0.000017612494,0.00024683162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990318,0.000027825345,0.00017657578,0.00033480083,0.00018833083,0.0002406664],"domain_scores_gemma":[0.99942905,0.00012801567,0.000031204116,0.00025376404,0.00008104006,0.00007691911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013609792,0.00014948456,0.00018102342,0.00030476475,0.0004078652,0.000053845917,0.000040139734,0.0000800741,0.0000088619945],"category_scores_gemma":[0.000014135997,0.00013752191,0.00012276188,0.0008098774,0.00006596573,0.00014025452,0.0000010685346,0.00017036957,0.0000019460847],"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":[0.00001816859,0.000016497163,6.8789444e-7,0.000027396527,0.000014304372,0.000012129335,0.00012331459,0.11048154,0.14108157,4.5244994e-7,0.000017903398,0.748206],"study_design_scores_gemma":[0.00013083318,0.000028621018,0.000057655656,0.00012342271,0.000053820753,0.000007611057,0.00005703737,0.6761149,0.3231172,0.0000228143,0.00017097303,0.00011511697],"about_ca_topic_score_codex":0.000026844602,"about_ca_topic_score_gemma":0.00007349778,"teacher_disagreement_score":0.7480909,"about_ca_system_score_codex":0.000059761303,"about_ca_system_score_gemma":0.000019969304,"threshold_uncertainty_score":0.56079805},"labels":[],"label_agreement":null},{"id":"W4206092953","doi":"10.1007/978-3-030-90436-4_20","title":"Hyperspectral Video Super-Resolution Using Beta Process and Bayesian Dictionary Learning","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Concordia University","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Image resolution; Pattern recognition (psychology); Coding (social sciences); Frame (networking); Computer vision; Bayesian probability; Dictionary learning; Sparse approximation; Mathematics; Telecommunications; Statistics","score_opus":0.00977172602150067,"score_gpt":0.2406529710151301,"score_spread":0.23088124499362944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206092953","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00126894,0.0018499313,0.99478287,0.00006395561,0.00037054534,0.00017006072,0.0000029119105,0.00041979545,0.0010709699],"genre_scores_gemma":[0.34003302,0.00031791165,0.6587284,0.00013414254,0.000499896,0.000007624111,0.000014308065,0.00009999921,0.00016465133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838465,0.000012777422,0.0002482231,0.0006332715,0.00036857266,0.00035253033],"domain_scores_gemma":[0.9993855,0.000094708135,0.000053449086,0.00026773562,0.00011327469,0.00008535673],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017130765,0.0003222034,0.00029656402,0.00039877844,0.00021041498,0.00012998332,0.0002954399,0.00021219996,0.000036472913],"category_scores_gemma":[0.0000434137,0.00034381967,0.00004840836,0.00027180393,0.00033269255,0.00039010282,0.00020586907,0.00084592775,0.0000013896608],"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":[0.0000037934476,0.000008035282,0.0001397376,0.00013883898,0.000011704085,0.00009470967,0.00055216823,0.75238925,0.012779215,0.0004856898,0.0000054125526,0.23339146],"study_design_scores_gemma":[0.000089383146,0.000048327962,0.00006223478,0.0005207206,0.0000114480645,0.00019566619,0.0000014540948,0.9742783,0.01236082,0.011562965,0.00040274602,0.0004659876],"about_ca_topic_score_codex":0.0000064964497,"about_ca_topic_score_gemma":0.000011588527,"teacher_disagreement_score":0.3387641,"about_ca_system_score_codex":0.00028136943,"about_ca_system_score_gemma":0.00010306924,"threshold_uncertainty_score":0.99990135},"labels":[],"label_agreement":null},{"id":"W4210960576","doi":"10.32920/ryerson.14655834.v1","title":"Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial intelligence; Image fusion; Sparse approximation; Pixel; Pattern recognition (psychology); Computer science; Noise reduction; Computer vision; Image (mathematics); Fusion","score_opus":0.012088550293684145,"score_gpt":0.2610911897177114,"score_spread":0.24900263942402726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210960576","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023302352,0.0006593385,0.9614364,0.00006541346,0.0003131444,0.0013914683,0.00006395054,0.00873265,0.004035275],"genre_scores_gemma":[0.21739312,0.0003584353,0.78135264,0.0002051985,0.00007487803,0.0001501814,0.0002140889,0.00020082018,0.000050670125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972032,0.000120135584,0.0006664473,0.0010605024,0.00041386377,0.0005358869],"domain_scores_gemma":[0.9979332,0.00014367868,0.00015700757,0.00129464,0.0002500777,0.00022141416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029904948,0.0008891513,0.0008144711,0.00087816094,0.00013898683,0.00041333234,0.00043629267,0.00033646403,0.00023892976],"category_scores_gemma":[0.000086808956,0.0009095251,0.00030161306,0.000428208,0.00017775777,0.00031943648,0.00097561534,0.0013467754,0.000007581671],"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":[0.00005357743,0.00043046716,0.00010228981,0.0013316985,0.00008347873,0.00096989813,0.00012493468,0.0018504086,0.8695944,0.000023174725,0.002610116,0.12282558],"study_design_scores_gemma":[0.0004701985,0.000072127594,0.0015827192,0.0013473709,0.00004812816,0.000030126685,0.00006142401,0.2411082,0.7535558,0.00030754204,0.00035040395,0.0010660101],"about_ca_topic_score_codex":0.00006812101,"about_ca_topic_score_gemma":0.000025164434,"teacher_disagreement_score":0.23925778,"about_ca_system_score_codex":0.0001607492,"about_ca_system_score_gemma":0.000051095038,"threshold_uncertainty_score":0.9993355},"labels":[],"label_agreement":null},{"id":"W4211214289","doi":"10.32920/ryerson.14655834","title":"Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial intelligence; Image fusion; Sparse approximation; Pixel; Pattern recognition (psychology); Computer science; Noise reduction; Computer vision; Image (mathematics); Fusion","score_opus":0.012088550293684145,"score_gpt":0.2610911897177114,"score_spread":0.24900263942402726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211214289","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023302352,0.0006593385,0.9614364,0.00006541346,0.0003131444,0.0013914683,0.00006395054,0.00873265,0.004035275],"genre_scores_gemma":[0.21739312,0.0003584353,0.78135264,0.0002051985,0.00007487803,0.0001501814,0.0002140889,0.00020082018,0.000050670125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972032,0.000120135584,0.0006664473,0.0010605024,0.00041386377,0.0005358869],"domain_scores_gemma":[0.9979332,0.00014367868,0.00015700757,0.00129464,0.0002500777,0.00022141416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029904948,0.0008891513,0.0008144711,0.00087816094,0.00013898683,0.00041333234,0.00043629267,0.00033646403,0.00023892976],"category_scores_gemma":[0.000086808956,0.0009095251,0.00030161306,0.000428208,0.00017775777,0.00031943648,0.00097561534,0.0013467754,0.000007581671],"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":[0.00005357743,0.00043046716,0.00010228981,0.0013316985,0.00008347873,0.00096989813,0.00012493468,0.0018504086,0.8695944,0.000023174725,0.002610116,0.12282558],"study_design_scores_gemma":[0.0004701985,0.000072127594,0.0015827192,0.0013473709,0.00004812816,0.000030126685,0.00006142401,0.2411082,0.7535558,0.00030754204,0.00035040395,0.0010660101],"about_ca_topic_score_codex":0.00006812101,"about_ca_topic_score_gemma":0.000025164434,"teacher_disagreement_score":0.23925778,"about_ca_system_score_codex":0.0001607492,"about_ca_system_score_gemma":0.000051095038,"threshold_uncertainty_score":0.9993355},"labels":[],"label_agreement":null},{"id":"W4214708938","doi":"10.1007/s11263-022-01584-x","title":"Atmospheric Turbulence Removal in Long-Range Imaging Using a Data-Driven-Based Approach","year":2022,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":12,"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":"Artificial intelligence; Computer science; Autoencoder; Fuse (electrical); Frame (networking); Computer vision; Pattern recognition (psychology); Cluster analysis; Distortion (music); Convolutional neural network; Set (abstract data type); Encoder; Deep learning","score_opus":0.015081879340786208,"score_gpt":0.2882478256707828,"score_spread":0.2731659463299966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214708938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081839494,0.0004502174,0.91636944,0.00008542303,0.0010183728,0.0000765272,0.0000128506235,0.0000724216,0.00007526291],"genre_scores_gemma":[0.54802734,0.00001974273,0.4515981,0.00011973429,0.00019445356,0.0000013984217,0.000015559717,0.000021029047,0.0000026349123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984839,0.00006659507,0.00046709712,0.00017021528,0.00067021704,0.00014196905],"domain_scores_gemma":[0.99933076,0.000057531677,0.0001858307,0.0002324771,0.00014866852,0.000044756773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034873487,0.00012704787,0.00017770482,0.00015303836,0.000043413926,0.000064729924,0.001140049,0.000018611086,0.00006324595],"category_scores_gemma":[0.00001449981,0.00013143239,0.0000644163,0.00021697996,0.000023222348,0.000604308,0.00046307256,0.00040450215,0.0000010847807],"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":[0.000041934454,0.00008634811,0.0008063265,0.0000097996535,0.000017007718,0.0007895307,0.0000595732,0.91992736,0.0012529898,0.000008087023,0.0006919846,0.07630904],"study_design_scores_gemma":[0.0005417108,0.00004785753,0.0011859074,0.000113925664,0.0000052342484,0.0011974516,0.000013136037,0.9938639,0.00012435687,0.00009924441,0.0026812926,0.00012597763],"about_ca_topic_score_codex":0.000008166215,"about_ca_topic_score_gemma":5.9759105e-7,"teacher_disagreement_score":0.46618783,"about_ca_system_score_codex":0.00037430582,"about_ca_system_score_gemma":0.00004764742,"threshold_uncertainty_score":0.53596574},"labels":[],"label_agreement":null},{"id":"W4214826761","doi":"10.3390/photonics9030150","title":"DDGANSE: Dual-Discriminator GAN with a Squeeze-and-Excitation Module for Infrared and Visible Image Fusion","year":2022,"lang":"en","type":"article","venue":"Photonics","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Dalhousie University","funders":"","keywords":"Discriminator; Computer science; Artificial intelligence; Infrared; Contrast (vision); Computer vision; Generator (circuit theory); Image fusion; Visible spectrum; Texture (cosmology); Pattern recognition (psychology); Fusion; Image (mathematics); Optics; Physics; Telecommunications; Power (physics)","score_opus":0.006080775218867593,"score_gpt":0.2223083517072089,"score_spread":0.2162275764883413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214826761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83741605,0.00080268667,0.15655409,0.00013407232,0.00012337798,0.0013743981,0.0001840351,0.0009944364,0.002416857],"genre_scores_gemma":[0.8500407,0.00029226977,0.14832203,0.00012238209,0.000015818832,0.0007472206,0.000075729404,0.00009528537,0.0002885852],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932164,0.000013744315,0.00013974342,0.00020241045,0.00013956096,0.00018289966],"domain_scores_gemma":[0.9996326,0.000049911374,0.000037016373,0.00017381573,0.000049399245,0.000057232726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011135319,0.00013823873,0.00013989615,0.00009590834,0.00019047999,0.000036751702,0.000061033534,0.000030840714,0.000057018373],"category_scores_gemma":[0.000031415522,0.00013972857,0.00002048474,0.00014005303,0.00003453012,0.00021371528,0.00007554113,0.00014664355,6.9883265e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004145072,0.00013782638,0.0001521238,0.0008568477,0.00006191435,0.000046010144,0.0047082645,0.0065916725,0.9514287,0.001968724,0.009099088,0.024534324],"study_design_scores_gemma":[0.0021232024,0.00080862176,0.0008566327,0.000060987015,0.00006315513,0.000098113385,0.0011828099,0.6230179,0.3168244,0.013070414,0.04116111,0.0007326363],"about_ca_topic_score_codex":0.000008119302,"about_ca_topic_score_gemma":0.000005900556,"teacher_disagreement_score":0.6346043,"about_ca_system_score_codex":0.00007266889,"about_ca_system_score_gemma":0.00001713481,"threshold_uncertainty_score":0.56979656},"labels":[],"label_agreement":null},{"id":"W4220959191","doi":"10.1016/j.displa.2022.102188","title":"Quality assessment of multi-exposure image fusion by synthesizing local and global intermediate references","year":2022,"lang":"en","type":"article","venue":"Displays","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of Waterloo","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Image quality; Domain (mathematical analysis); Artificial intelligence; Computer science; Perception; Image fusion; Human visual system model; Image (mathematics); Quality (philosophy); Fuse (electrical); Consistency (knowledge bases); Quality Score; Property (philosophy); Perspective (graphical); Computer vision; Pattern recognition (psychology); Fusion; Scale (ratio); Mathematics; Psychology; Engineering; Geography","score_opus":0.015207012139420073,"score_gpt":0.3088511164267696,"score_spread":0.29364410428734955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220959191","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55674934,0.0005149009,0.44034252,0.000018538989,0.00013169266,0.0001622872,0.0004395875,0.0002565782,0.0013845329],"genre_scores_gemma":[0.9862034,0.000115171366,0.013535935,0.000018760891,0.0000062822337,0.00004448894,0.000044435255,0.000014006252,0.000017526925],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99912256,0.00007277613,0.00026432835,0.00017507185,0.00020940436,0.00015588735],"domain_scores_gemma":[0.99962807,0.00006086606,0.00006615125,0.00017237425,0.000023135932,0.00004941216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023262188,0.00012538784,0.00019658002,0.000029913646,0.000081010614,0.00001300638,0.00015387767,0.000033191594,0.00010035434],"category_scores_gemma":[0.000025011794,0.00012427972,0.000034297063,0.00010501994,0.000078094046,0.0001215778,0.00022900464,0.00017073394,8.8398093e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005381418,0.00031312238,0.042247176,0.00060880615,0.00006487834,0.00003045834,0.0005975455,0.0017526117,0.7447725,0.0012702396,0.0030242254,0.20526466],"study_design_scores_gemma":[0.0027858773,0.0009844052,0.43137825,0.00039385067,0.00008991629,0.00005200853,0.008965527,0.33551642,0.20756727,0.0026917425,0.0075324,0.0020423457],"about_ca_topic_score_codex":0.000041149466,"about_ca_topic_score_gemma":0.000014962324,"teacher_disagreement_score":0.5372052,"about_ca_system_score_codex":0.00011725354,"about_ca_system_score_gemma":0.00001129821,"threshold_uncertainty_score":0.50679797},"labels":[],"label_agreement":null},{"id":"W4221117076","doi":"10.3389/fmed.2022.792390","title":"PET and CT Image Fusion of Lung Cancer With Siamese Pyramid Fusion Network","year":2022,"lang":"en","type":"article","venue":"Frontiers in Medicine","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Shanxi Province; National Natural Science Foundation of China","keywords":"Image fusion; Artificial intelligence; Fusion; Computer science; Computer vision; Pattern recognition (psychology); Pyramid (geometry); Image (mathematics); Mathematics","score_opus":0.0030910465992993667,"score_gpt":0.232240902528594,"score_spread":0.22914985592929463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221117076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48832855,0.04036958,0.4595197,0.001088888,0.0029743286,0.0018687522,0.000048089885,0.0008592613,0.004942834],"genre_scores_gemma":[0.8254321,0.00867443,0.16417539,0.00030336156,0.0003179189,0.0004312418,0.000048787948,0.00012606605,0.0004906724],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989977,0.00003748063,0.00024976558,0.00019101158,0.00028911786,0.00023495751],"domain_scores_gemma":[0.99960834,0.000025408255,0.00006613853,0.00021536503,0.000026479405,0.000058300233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002617103,0.00015557763,0.0003754359,0.0001836234,0.00007295713,0.0000027699077,0.00013901186,0.000015400326,0.00033883762],"category_scores_gemma":[0.000025140645,0.0001299031,0.000015447293,0.0004505075,0.00016041123,0.00009346777,0.000121654404,0.00032586732,9.998311e-8],"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":[0.00045702705,0.00007151518,0.08772738,0.0007707234,0.00009877407,0.0006692148,0.0020511528,0.053830177,0.047307298,0.00005779785,0.6915661,0.11539285],"study_design_scores_gemma":[0.008562301,0.0016058746,0.025871024,0.0030003958,0.00028217526,0.0003130662,0.006440356,0.8241328,0.012513478,0.0035162491,0.11215785,0.0016044208],"about_ca_topic_score_codex":0.000085220534,"about_ca_topic_score_gemma":0.000014965557,"teacher_disagreement_score":0.77030265,"about_ca_system_score_codex":0.00014356506,"about_ca_system_score_gemma":0.000015597048,"threshold_uncertainty_score":0.5297295},"labels":[],"label_agreement":null},{"id":"W4230415802","doi":"10.32920/ryerson.14646054.v1","title":"Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial intelligence; Image fusion; Sparse approximation; Pixel; Pattern recognition (psychology); Computer science; Noise reduction; Computer vision; Image (mathematics); Fusion","score_opus":0.012088550293684145,"score_gpt":0.2610911897177114,"score_spread":0.24900263942402726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230415802","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023302352,0.0006593385,0.9614364,0.00006541346,0.0003131444,0.0013914683,0.00006395054,0.00873265,0.004035275],"genre_scores_gemma":[0.21739312,0.0003584353,0.78135264,0.0002051985,0.00007487803,0.0001501814,0.0002140889,0.00020082018,0.000050670125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972032,0.000120135584,0.0006664473,0.0010605024,0.00041386377,0.0005358869],"domain_scores_gemma":[0.9979332,0.00014367868,0.00015700757,0.00129464,0.0002500777,0.00022141416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029904948,0.0008891513,0.0008144711,0.00087816094,0.00013898683,0.00041333234,0.00043629267,0.00033646403,0.00023892976],"category_scores_gemma":[0.000086808956,0.0009095251,0.00030161306,0.000428208,0.00017775777,0.00031943648,0.00097561534,0.0013467754,0.000007581671],"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":[0.00005357743,0.00043046716,0.00010228981,0.0013316985,0.00008347873,0.00096989813,0.00012493468,0.0018504086,0.8695944,0.000023174725,0.002610116,0.12282558],"study_design_scores_gemma":[0.0004701985,0.000072127594,0.0015827192,0.0013473709,0.00004812816,0.000030126685,0.00006142401,0.2411082,0.7535558,0.00030754204,0.00035040395,0.0010660101],"about_ca_topic_score_codex":0.00006812101,"about_ca_topic_score_gemma":0.000025164434,"teacher_disagreement_score":0.23925778,"about_ca_system_score_codex":0.0001607492,"about_ca_system_score_gemma":0.000051095038,"threshold_uncertainty_score":0.9993355},"labels":[],"label_agreement":null},{"id":"W4231775717","doi":"10.32920/ryerson.14644716.v1","title":"The generation of digital elevation models using open source images","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Toronto Metropolitan University","funders":"Ministry of Economy, Trade and Industry; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Digital elevation model; Remote sensing; Advanced Spaceborne Thermal Emission and Reflection Radiometer; Elevation (ballistics); Satellite; Ranging; Radiometry; Geology; Geography; Cartography; Geodesy; Mathematics","score_opus":0.0648249513114055,"score_gpt":0.2972312071020001,"score_spread":0.23240625579059457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231775717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022202313,0.00033810394,0.97143734,0.000023530865,0.00014384216,0.00038407845,0.000015611908,0.0002825756,0.005172601],"genre_scores_gemma":[0.80022347,0.00040097817,0.19820344,0.000012182227,0.00009603331,0.000051111616,0.00015070855,0.00006706331,0.00079499127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992144,0.00001621453,0.0003170602,0.00018914902,0.00014944423,0.00011371245],"domain_scores_gemma":[0.9992393,0.00003518924,0.00009402578,0.00044862748,0.00016289724,0.000019920715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011556023,0.000156062,0.00018372077,0.00004379163,0.000065897286,0.00057458214,0.00038272073,0.00010831497,0.000020817646],"category_scores_gemma":[0.000045507968,0.0001286342,0.000056815054,0.00007479131,0.000027365788,0.0006866015,0.0009600194,0.00020954783,5.342626e-7],"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":[0.0000012934588,0.000007053895,0.0000066004172,0.00004698806,0.000022947122,5.295696e-7,0.000075974494,0.86002356,0.1091207,0.00017684665,0.00064991263,0.029867616],"study_design_scores_gemma":[0.00003035226,0.0000026635146,0.0000031886784,0.00005289106,0.0000069196653,0.0000014617879,0.000069525755,0.7422133,0.25442213,0.0029439186,0.00013484513,0.00011878422],"about_ca_topic_score_codex":0.00003718027,"about_ca_topic_score_gemma":0.000007198349,"teacher_disagreement_score":0.77802116,"about_ca_system_score_codex":0.00007899081,"about_ca_system_score_gemma":0.000042405256,"threshold_uncertainty_score":0.5540709},"labels":[],"label_agreement":null},{"id":"W4239218748","doi":"10.23919/fusion49465.2021.9626972","title":"A New Image Fusion Method for Ship Target Enhancement in Spaceborne and Airborne SAR Collaboration","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Toronto Metropolitan University","funders":"National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Synthetic aperture radar; Clutter; Remote sensing; Computer science; Image fusion; Computer vision; Artificial intelligence; Radar imaging; Sensor fusion; Inverse synthetic aperture radar; Radar; Geology; Image (mathematics); Telecommunications","score_opus":0.016554757408897565,"score_gpt":0.300178053640566,"score_spread":0.28362329623166843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239218748","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012733004,0.00007507098,0.95867306,0.003272949,0.0016937752,0.0010930987,0.0002081611,0.0002680193,0.021982847],"genre_scores_gemma":[0.12101458,0.002913908,0.86864805,0.0014483994,0.00041727052,0.00039002023,0.001998343,0.00008028048,0.0030891593],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974428,0.00009136234,0.000986424,0.00040501315,0.0007080252,0.0003663897],"domain_scores_gemma":[0.9978816,0.00017343744,0.00028408086,0.00036509204,0.0010979573,0.00019783157],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005365182,0.00039857667,0.0003930333,0.0005865382,0.00016743917,0.00033239526,0.0002968082,0.00022423947,0.0051468024],"category_scores_gemma":[0.00041949,0.00041870537,0.00009023228,0.00060609216,0.000034783337,0.0018669965,0.00017149176,0.0003616008,0.00017329874],"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":[0.0007232312,0.00020293151,0.00008146648,0.00029268867,0.00007759851,0.000027696211,0.0027052471,0.0043442696,0.48534796,0.04300534,0.048546918,0.41464466],"study_design_scores_gemma":[0.0030061714,0.00028182074,0.00066721305,0.0006104164,0.000021930442,0.00002205901,0.0014807907,0.47617874,0.40642503,0.009937518,0.1005323,0.0008359969],"about_ca_topic_score_codex":0.00004025042,"about_ca_topic_score_gemma":0.00009241649,"teacher_disagreement_score":0.47183448,"about_ca_system_score_codex":0.00034584387,"about_ca_system_score_gemma":0.00028586452,"threshold_uncertainty_score":0.9998265},"labels":[],"label_agreement":null},{"id":"W4243652034","doi":"10.37190/oa210212","title":"Light field camera all-in-focus image acquisition based on angular information","year":2021,"lang":"en","type":"article","venue":"Optica Applicata","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Simon Fraser University","funders":"China Scholarship Council; Shanxi Scholarship Council of China; National Natural Science Foundation of China","keywords":"Computer vision; Focus (optics); Artificial intelligence; Computer science; Light field; Image fusion; Pixel; Field (mathematics); Image quality; Image (mathematics); Optics; Physics; Mathematics","score_opus":0.0045599906361519045,"score_gpt":0.23308139556816715,"score_spread":0.22852140493201525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243652034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045300694,0.00010856571,0.9240991,0.0022676704,0.00006422024,0.00044092513,0.000028287299,0.00085153384,0.06760965],"genre_scores_gemma":[0.77414155,0.00011758002,0.22288504,0.0020753504,0.00007861678,0.0003563487,0.00024268914,0.00005011421,0.00005271798],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992697,0.00000959048,0.00021732971,0.00015350341,0.00014933471,0.00020053923],"domain_scores_gemma":[0.9992997,0.00005505211,0.000025411939,0.0005158787,0.000046599394,0.000057375648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006594277,0.00013356993,0.00013217673,0.00009497159,0.000025534426,0.000050954288,0.00014242767,0.00009077033,0.0002877315],"category_scores_gemma":[0.0000372092,0.00014631716,0.000032056334,0.00023200481,0.000009320692,0.00033904266,0.000044490356,0.00018255162,0.00022871765],"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":[0.00003488505,0.00011637141,0.000023643654,0.00014688172,0.000015810341,0.00008814008,0.00013784421,0.0026540044,0.8629745,0.0035247486,0.02531271,0.10497044],"study_design_scores_gemma":[0.00024461094,0.000038246595,0.00009445065,0.000040350962,0.0000067849805,0.0000051273464,0.000022837265,0.039281815,0.9155073,0.0006021199,0.043960672,0.00019568033],"about_ca_topic_score_codex":0.0000047012054,"about_ca_topic_score_gemma":0.0000020433204,"teacher_disagreement_score":0.7696115,"about_ca_system_score_codex":0.00006955131,"about_ca_system_score_gemma":0.0000124577655,"threshold_uncertainty_score":0.5966641},"labels":[],"label_agreement":null},{"id":"W4247409111","doi":"10.1117/3.2316455.ch3","title":"Biological Vision","year":2018,"lang":"en","type":"book-chapter","venue":"SPIE eBooks","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Okanagan University College; University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Image fusion; Computer vision; Fusion; Image (mathematics)","score_opus":0.019061068459425744,"score_gpt":0.24843625660076313,"score_spread":0.2293751881413374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247409111","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001249983,0.00025855436,0.007499909,0.000004338738,0.0003736529,0.00023325831,0.000021122703,0.00200259,0.98948157],"genre_scores_gemma":[0.0014691023,0.0001898706,0.025779018,0.00012785621,0.0008106156,0.00002668081,0.000034893033,0.00021851825,0.97134346],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991614,0.0000018359003,0.00023461194,0.00026521063,0.00013229305,0.00020460218],"domain_scores_gemma":[0.99939334,0.00002181481,0.00004645306,0.00042229172,0.00004445255,0.0000716342],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000058635153,0.00032062727,0.00028182063,0.0001082275,0.000039625706,0.00001862355,0.00021870778,0.00045939852,0.002308321],"category_scores_gemma":[0.000010212381,0.000280342,0.000113394424,0.0000050471567,0.00015575175,0.000024663066,0.00012504385,0.00036879067,0.001138657],"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":[0.000031346917,0.00000976211,0.0000016298123,0.00016036954,0.000118393276,0.00019997609,0.00012516128,0.0000072467333,0.052882403,0.0938602,0.22284313,0.6297604],"study_design_scores_gemma":[0.00006112594,0.00013387659,0.0000024921194,0.00015584081,0.000008401071,0.000008917154,0.0000010010809,0.000045773115,0.010616158,0.09293701,0.89566016,0.00036925884],"about_ca_topic_score_codex":3.7283397e-7,"about_ca_topic_score_gemma":0.000001343759,"teacher_disagreement_score":0.67281705,"about_ca_system_score_codex":0.00006365633,"about_ca_system_score_gemma":0.00000854653,"threshold_uncertainty_score":0.9999649},"labels":[],"label_agreement":null},{"id":"W4249110488","doi":"10.1016/s0030-4018(04)00457-2","title":"Recognition invariant under unknown affine transformations of intensity","year":2004,"lang":"en","type":"article","venue":"Optics Communications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1,"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 Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Invariant (physics); Affine transformation; Multiplicative function; Intensity (physics); Orthonormal basis; Computer science; Nonlinear system; Filter (signal processing); Artificial intelligence; Pattern recognition (psychology); Optics; Mathematics; Computer vision; Algorithm; Physics; Mathematical analysis; Geometry","score_opus":0.034997478062195594,"score_gpt":0.26102436760353154,"score_spread":0.22602688954133593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249110488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066831373,0.0001904366,0.9158545,0.0012868933,0.000050417544,0.0002469212,0.000050323863,0.00052202115,0.07511536],"genre_scores_gemma":[0.65417725,0.0015741586,0.34401846,0.000043876295,0.0000057595103,0.00002744015,0.00011766344,0.000015315529,0.000020102492],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950486,0.000013249837,0.00026590028,0.000052800384,0.00006865769,0.000094541305],"domain_scores_gemma":[0.9988898,0.000054777436,0.00003850269,0.00080306106,0.00017920206,0.000034648667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008158227,0.00008102299,0.00011468286,0.00009615872,0.00009459028,0.000011156164,0.00032630717,0.000052718486,0.000028636017],"category_scores_gemma":[0.000038852664,0.000090325164,0.000039776165,0.00024751696,0.00010040848,0.00021565972,0.00007490518,0.00019255134,0.000025082923],"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":[0.000019357592,0.00087371015,0.000013533717,0.00019652495,0.00018571602,0.0000017187807,0.0037313846,0.13069646,0.2170656,0.58683604,0.0006023317,0.05977762],"study_design_scores_gemma":[0.0019180508,0.00018539121,0.0011942729,0.0006182029,0.00019665883,0.00005597543,0.0013447141,0.15070967,0.45584217,0.37363255,0.0132307075,0.0010716398],"about_ca_topic_score_codex":0.000015169799,"about_ca_topic_score_gemma":0.00008237245,"teacher_disagreement_score":0.6474941,"about_ca_system_score_codex":0.00007011382,"about_ca_system_score_gemma":0.000021346737,"threshold_uncertainty_score":0.36833534},"labels":[],"label_agreement":null},{"id":"W4253227157","doi":"10.32920/ryerson.14646054","title":"Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial intelligence; Image fusion; Sparse approximation; Pixel; Pattern recognition (psychology); Computer science; Noise reduction; Computer vision; Image (mathematics)","score_opus":0.012088550293684145,"score_gpt":0.2610911897177114,"score_spread":0.24900263942402726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253227157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023302352,0.0006593385,0.9614364,0.00006541346,0.0003131444,0.0013914683,0.00006395054,0.00873265,0.004035275],"genre_scores_gemma":[0.21739312,0.0003584353,0.78135264,0.0002051985,0.00007487803,0.0001501814,0.0002140889,0.00020082018,0.000050670125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972032,0.000120135584,0.0006664473,0.0010605024,0.00041386377,0.0005358869],"domain_scores_gemma":[0.9979332,0.00014367868,0.00015700757,0.00129464,0.0002500777,0.00022141416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029904948,0.0008891513,0.0008144711,0.00087816094,0.00013898683,0.00041333234,0.00043629267,0.00033646403,0.00023892976],"category_scores_gemma":[0.000086808956,0.0009095251,0.00030161306,0.000428208,0.00017775777,0.00031943648,0.00097561534,0.0013467754,0.000007581671],"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":[0.00005357743,0.00043046716,0.00010228981,0.0013316985,0.00008347873,0.00096989813,0.00012493468,0.0018504086,0.8695944,0.000023174725,0.002610116,0.12282558],"study_design_scores_gemma":[0.0004701985,0.000072127594,0.0015827192,0.0013473709,0.00004812816,0.000030126685,0.00006142401,0.2411082,0.7535558,0.00030754204,0.00035040395,0.0010660101],"about_ca_topic_score_codex":0.00006812101,"about_ca_topic_score_gemma":0.000025164434,"teacher_disagreement_score":0.23925778,"about_ca_system_score_codex":0.0001607492,"about_ca_system_score_gemma":0.000051095038,"threshold_uncertainty_score":0.9993355},"labels":[],"label_agreement":null},{"id":"W4283815286","doi":"10.1609/aaai.v36i3.20267","title":"RETRACTED: Pan-Sharpening with Customized Transformer and Invertible Neural Network","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":117,"is_retracted":true,"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":"Sharpening; Panchromatic film; Computer science; Artificial intelligence; Multispectral image; Convolutional neural network; Transformer; Artificial neural network; Pattern recognition (psychology); Computer vision; Engineering","score_opus":0.030376311306991956,"score_gpt":0.24445493062226537,"score_spread":0.21407861931527342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283815286","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96265715,0.00017237694,0.0138873095,0.0010197066,0.0004261048,0.0012215914,0.00002085191,0.0007874968,0.01980743],"genre_scores_gemma":[0.99648094,0.0000768379,0.0030275886,0.00012180488,0.000044100085,0.00009845877,0.0000011575069,0.00003626407,0.000112869035],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866927,0.0000120684335,0.00036563375,0.00026358382,0.00036331726,0.00032615016],"domain_scores_gemma":[0.99948025,0.00005537863,0.00012576776,0.00013021105,0.0001490541,0.00005934718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028509047,0.00021237621,0.00025884237,0.00008823329,0.00028687992,0.00006896262,0.00047540705,0.000054191867,0.0002515069],"category_scores_gemma":[0.00005795777,0.00016408645,0.000052788015,0.00054387643,0.00017197205,0.00028283044,0.000105042614,0.0006848108,0.000004632058],"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":[0.0013724815,0.00013910579,0.0012936482,0.0002301484,0.000080923004,0.000004603483,0.0030595628,0.017086446,0.6081794,0.09035713,0.0020547383,0.27614185],"study_design_scores_gemma":[0.00010375656,0.00038579034,0.00016864363,0.00018516537,0.000035930763,0.000028044142,0.001314424,0.19220962,0.7730747,0.031409774,0.00064539065,0.000438792],"about_ca_topic_score_codex":0.000017164066,"about_ca_topic_score_gemma":0.0000066233156,"teacher_disagreement_score":0.27570304,"about_ca_system_score_codex":0.000051275867,"about_ca_system_score_gemma":0.000022694265,"threshold_uncertainty_score":0.6691251},"labels":[],"label_agreement":null},{"id":"W4285891671","doi":"10.36227/techrxiv.20329914.v1","title":"BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"IONICS Mass Spectrometry (Canada)","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Artificial intelligence; Hyperspectral imaging; Computer science; Regularization (linguistics); RGB color model; Pattern recognition (psychology); Fusion; Artificial neural network; Computer vision; Image (mathematics); Encoding (memory); Degradation (telecommunications)","score_opus":0.015337676260545564,"score_gpt":0.2483939937039941,"score_spread":0.23305631744344854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285891671","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75844246,0.007389043,0.108903825,0.000469161,0.0013721796,0.0022899224,0.0004625275,0.005115054,0.115555845],"genre_scores_gemma":[0.41014457,0.0065297405,0.5803636,0.00009433625,0.0002332138,0.00020669034,0.00024964107,0.00031059675,0.0018676277],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982187,0.000046304216,0.0005158141,0.00053228595,0.00036523837,0.0003216271],"domain_scores_gemma":[0.99884504,0.00007604456,0.000107429805,0.0007414569,0.00012163496,0.000108364075],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017616556,0.00044764404,0.0005931888,0.00034598142,0.00008920576,0.000060323324,0.0004122367,0.00023073252,0.004103528],"category_scores_gemma":[0.000059327416,0.0004585962,0.00015020436,0.0002286358,0.00014188775,0.00017797308,0.0017709828,0.0008252787,0.0000063324696],"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":[0.00003972728,0.000106346655,0.000055264314,0.00056919287,0.000036824993,0.000038334874,0.00031587243,0.0010313035,0.98100656,0.00008062522,0.0037293027,0.012990622],"study_design_scores_gemma":[0.0010328359,0.0002319268,0.0006169427,0.0002616135,0.00009067067,0.000039934785,0.0009845917,0.00843009,0.9782703,0.004565691,0.0042731096,0.0012023095],"about_ca_topic_score_codex":0.000053303276,"about_ca_topic_score_gemma":0.0000041091557,"teacher_disagreement_score":0.47145978,"about_ca_system_score_codex":0.00014386744,"about_ca_system_score_gemma":0.000034918867,"threshold_uncertainty_score":0.99978656},"labels":[],"label_agreement":null},{"id":"W4285891871","doi":"10.36227/techrxiv.20329914","title":"BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"IONICS Mass Spectrometry (Canada)","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Artificial intelligence; Regularization (linguistics); Computer science; RGB color model; Hyperspectral imaging; Pattern recognition (psychology); Fusion; Image (mathematics); Artificial neural network; Computer vision; Degradation (telecommunications); Representation (politics)","score_opus":0.015337676260545564,"score_gpt":0.2483939937039941,"score_spread":0.23305631744344854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285891871","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75844246,0.007389043,0.108903825,0.000469161,0.0013721796,0.0022899224,0.0004625275,0.005115054,0.115555845],"genre_scores_gemma":[0.41014457,0.0065297405,0.5803636,0.00009433625,0.0002332138,0.00020669034,0.00024964107,0.00031059675,0.0018676277],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982187,0.000046304216,0.0005158141,0.00053228595,0.00036523837,0.0003216271],"domain_scores_gemma":[0.99884504,0.00007604456,0.000107429805,0.0007414569,0.00012163496,0.000108364075],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017616556,0.00044764404,0.0005931888,0.00034598142,0.00008920576,0.000060323324,0.0004122367,0.00023073252,0.004103528],"category_scores_gemma":[0.000059327416,0.0004585962,0.00015020436,0.0002286358,0.00014188775,0.00017797308,0.0017709828,0.0008252787,0.0000063324696],"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":[0.00003972728,0.000106346655,0.000055264314,0.00056919287,0.000036824993,0.000038334874,0.00031587243,0.0010313035,0.98100656,0.00008062522,0.0037293027,0.012990622],"study_design_scores_gemma":[0.0010328359,0.0002319268,0.0006169427,0.0002616135,0.00009067067,0.000039934785,0.0009845917,0.00843009,0.9782703,0.004565691,0.0042731096,0.0012023095],"about_ca_topic_score_codex":0.000053303276,"about_ca_topic_score_gemma":0.0000041091557,"teacher_disagreement_score":0.47145978,"about_ca_system_score_codex":0.00014386744,"about_ca_system_score_gemma":0.000034918867,"threshold_uncertainty_score":0.99978656},"labels":[],"label_agreement":null},{"id":"W4298009700","doi":"10.18280/ts.390432","title":"An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Direction Générale de la Recherche Scientifique et du Développement Technologique","keywords":"Shearlet; Contourlet; Image fusion; Artificial intelligence; Biorthogonal system; Image (mathematics); Computer science; Algorithm; Fusion; Curvelet; Pattern recognition (psychology); Pyramid (geometry); Computer vision; Mathematics; Wavelet; Wavelet transform","score_opus":0.01085336023047823,"score_gpt":0.24160193997192214,"score_spread":0.23074857974144392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298009700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017784322,0.000057873254,0.97872734,0.0001901001,0.00011520422,0.0014509804,0.00080819754,0.00040266666,0.00046330033],"genre_scores_gemma":[0.9632265,0.000005226246,0.033936124,0.0008086528,0.00012149714,0.0016346539,0.00020605385,0.000048619626,0.000012659118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988494,0.00014844936,0.00019977764,0.00024832872,0.0003243885,0.00022969038],"domain_scores_gemma":[0.99952656,0.00015360606,0.000034968685,0.00020598702,0.000030393307,0.000048462258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020938835,0.00018126967,0.00013540025,0.00012836653,0.00022005555,0.000034221375,0.00027710252,0.00001715279,0.0004445893],"category_scores_gemma":[0.000009366111,0.00015202971,0.000067034634,0.00019633412,0.000030923027,0.0001608935,0.000030191832,0.0001894713,0.0000020341233],"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":[0.0005909787,0.001441777,0.000053649488,0.000048329155,0.00002370129,0.00012187745,0.0016455873,0.6248675,0.16631377,0.0009327137,0.014121614,0.1898385],"study_design_scores_gemma":[0.00079241366,0.0013718612,0.0006976421,0.000010340596,0.000014019884,0.0000014603845,0.0005208855,0.9735395,0.020093584,0.0004600404,0.0022788506,0.00021941145],"about_ca_topic_score_codex":0.000005122019,"about_ca_topic_score_gemma":0.0000036556012,"teacher_disagreement_score":0.9454422,"about_ca_system_score_codex":0.00011394853,"about_ca_system_score_gemma":0.000019045201,"threshold_uncertainty_score":0.6199591},"labels":[],"label_agreement":null},{"id":"W4299468970","doi":"","title":"A CRITICAL COMPARISON OF PANSHARPENING ALGORITHMS","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Impact","funders":"","keywords":"Computer science; Algorithm","score_opus":0.053024170316582714,"score_gpt":0.3587439635383925,"score_spread":0.30571979322180975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299468970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029593918,0.0003017572,0.9666017,0.000026909229,0.00010502407,0.00004969446,0.0000024441995,0.0007834758,0.029169593],"genre_scores_gemma":[0.6804059,0.000002159761,0.3194845,0.000009696052,0.000017732998,0.0000049718988,0.0000012009796,0.00001153924,0.000062271276],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995502,0.0000062194313,0.0001606061,0.0000610009,0.00011251267,0.0001094637],"domain_scores_gemma":[0.99968535,0.00004714064,0.000008076579,0.0001145508,0.000073948846,0.00007093882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060611572,0.00006066261,0.00013215542,0.000039252864,0.00000784458,0.0000068072773,0.00007738001,0.000032554777,0.000110154404],"category_scores_gemma":[0.00014170747,0.00005717435,0.000018957977,0.00007733231,0.000031153766,0.0001228615,0.00002829897,0.000080353624,0.000024940022],"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":[0.00003819529,0.00035265717,0.011761203,0.0004246939,0.00006662132,0.000040985826,0.004369964,0.015222892,0.15769841,0.02497333,0.21406579,0.57098526],"study_design_scores_gemma":[0.00017735446,0.00007977972,0.00011385583,0.00003161803,0.000006278672,0.0000057277357,0.0005423174,0.3396332,0.64959186,0.0030847534,0.0065583065,0.00017493677],"about_ca_topic_score_codex":0.000004363852,"about_ca_topic_score_gemma":9.860266e-7,"teacher_disagreement_score":0.67744654,"about_ca_system_score_codex":0.000018685154,"about_ca_system_score_gemma":0.000005589811,"threshold_uncertainty_score":0.23315023},"labels":[],"label_agreement":null},{"id":"W4301403021","doi":"","title":"Tetrolet shrinkage with anisotropic total variation minimization for image approximation","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Shrinkage; Minification; Variation (astronomy); Anisotropy; Image (mathematics); Mathematics; Computer science; Mathematical optimization; Statistics; Computer vision; Optics; Physics","score_opus":0.003787574510693012,"score_gpt":0.20799574442731136,"score_spread":0.20420816991661836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301403021","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008390556,0.000012726708,0.9845191,0.00008287164,0.00003559329,0.0004539502,0.000005977622,0.0011791214,0.0053200675],"genre_scores_gemma":[0.41833943,0.00000846539,0.5812564,0.000048319034,0.000045894285,0.000048149803,0.00005118657,0.000019437824,0.00018272124],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994684,0.0000061144974,0.00015066947,0.00013388062,0.000097800235,0.00014314416],"domain_scores_gemma":[0.99970686,0.000018699986,0.00003186075,0.00014515188,0.00006842709,0.000028978793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003593792,0.00011602587,0.000101377875,0.00008004299,0.00004323361,0.000038853424,0.000052619875,0.00004876785,0.00005258794],"category_scores_gemma":[0.000023600456,0.000102954415,0.000022877903,0.00013087815,0.0000085330585,0.00046544618,0.0000048258003,0.000048230835,0.0000065899435],"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":[0.00009909701,0.00014705745,0.00006120788,0.00015755268,0.000033534023,0.0000051589896,0.00043538434,0.040063955,0.8293617,0.01658567,0.007239963,0.10580972],"study_design_scores_gemma":[0.0012078309,0.0005099744,0.0067236307,0.000030207284,0.000026367346,0.000016982496,0.000050821123,0.7484486,0.23705958,0.0044119135,0.0010836383,0.0004304212],"about_ca_topic_score_codex":9.302288e-7,"about_ca_topic_score_gemma":7.6920287e-7,"teacher_disagreement_score":0.7083847,"about_ca_system_score_codex":0.000056795274,"about_ca_system_score_gemma":0.0000056656927,"threshold_uncertainty_score":0.4198359},"labels":[],"label_agreement":null},{"id":"W4309909100","doi":"","title":"Multiple spectrum vision for wildland fires","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Image Fusion Techniques","field":"Engineering","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; Remote sensing; Spectrum (functional analysis); Environmental science; Geography; Physics","score_opus":0.009441650400649544,"score_gpt":0.23611124764697627,"score_spread":0.22666959724632674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309909100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036074392,0.0009981686,0.9427311,0.0019483486,0.00046490112,0.0010134078,0.00020176625,0.0018589062,0.014709019],"genre_scores_gemma":[0.58892,0.0011479154,0.40453827,0.000056664103,0.00011053394,0.00031953995,0.0007566996,0.00017476795,0.003975638],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976501,0.00061643886,0.00046604304,0.0006159489,0.00026076054,0.00039073976],"domain_scores_gemma":[0.9958532,0.0011170717,0.00020970863,0.0018462988,0.00083418604,0.00013952744],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017613833,0.0003817945,0.00037626486,0.00019102152,0.00024100901,0.0002469327,0.00095864263,0.0003223183,0.000112573645],"category_scores_gemma":[0.0011799358,0.00041607523,0.00020564653,0.0001808807,0.00017923255,0.00015950874,0.0008612561,0.00047378673,0.00003765302],"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":[0.00019939712,0.0019452968,0.00639728,0.0047941306,0.0006872091,0.000025802467,0.01866733,0.0068418295,0.15604451,0.022500735,0.27915192,0.50274456],"study_design_scores_gemma":[0.00080193917,0.0000014539909,0.0017922074,0.0027702702,0.00004658042,0.0000077187515,0.000030218713,0.3693884,0.46431768,0.02247661,0.1373988,0.0009681223],"about_ca_topic_score_codex":0.00016254824,"about_ca_topic_score_gemma":0.0007337531,"teacher_disagreement_score":0.5528456,"about_ca_system_score_codex":0.00014628726,"about_ca_system_score_gemma":0.000062762825,"threshold_uncertainty_score":0.9998291},"labels":[],"label_agreement":null},{"id":"W4311378100","doi":"10.3390/s22249780","title":"Enhancement and Restoration of Scratched Murals Based on Hyperspectral Imaging—A Case Study of Murals in the Baoguang Hall of Qutan Temple, Qinghai, China","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Mural; Principal component analysis; Artificial intelligence; Computer vision; Smoothing; Computer science; Hyperspectral imaging; Translation (biology); Image restoration; Gaussian blur; Pattern recognition (psychology); Image processing; Image (mathematics); Art","score_opus":0.01547480963197056,"score_gpt":0.2712914240699328,"score_spread":0.2558166144379622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311378100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985829,0.000084234445,0.0002604762,0.000074802614,0.000034857065,0.0005972888,0.00001688553,0.00004600816,0.0003025715],"genre_scores_gemma":[0.9977955,0.000007349923,0.002094987,0.000019251233,0.000007032386,0.00003889813,0.000003751822,0.000018338555,0.000014861794],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989425,0.00016371252,0.0003667825,0.00013196058,0.00026652202,0.0001285368],"domain_scores_gemma":[0.99943715,0.00010009622,0.00012240814,0.00029054645,0.00003257627,0.000017204642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039849384,0.00012073047,0.00022590937,0.00019022886,0.000052797666,0.000005311942,0.00010641455,0.000015007127,0.000029414361],"category_scores_gemma":[0.000050261344,0.0001047442,0.000030919808,0.00028382827,0.000039590046,0.00006115271,0.00003358387,0.00015600924,1.06750235e-7],"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":[0.00010166888,0.00069627626,0.0040173796,0.00015850739,0.000027138245,0.0003716484,0.02967896,0.15224113,0.80847865,0.000059868653,0.00026337383,0.0039053932],"study_design_scores_gemma":[0.0024524368,0.002033294,0.008918756,0.00013359929,0.000057310073,0.000141289,0.15195973,0.15648225,0.6769963,0.00031454777,0.000048547354,0.00046194505],"about_ca_topic_score_codex":0.0009829317,"about_ca_topic_score_gemma":0.0001270948,"teacher_disagreement_score":0.13148236,"about_ca_system_score_codex":0.000070200826,"about_ca_system_score_gemma":0.000010172206,"threshold_uncertainty_score":0.42713442},"labels":[],"label_agreement":null},{"id":"W4312223815","doi":"10.3390/rs15010144","title":"DCFusion: Dual-Headed Fusion Strategy and Contextual Information Awareness for Infrared and Visible Remote Sensing Image","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Royal Military College of Canada","funders":"Sichuan University; Department of Science and Technology of Sichuan Province","keywords":"Computer science; Fuse (electrical); Image fusion; Artificial intelligence; Dual (grammatical number); Pooling; Fusion; Sensor fusion; Encoder; Computer vision; Information fusion; Image (mathematics); Pattern recognition (psychology)","score_opus":0.01351541416077599,"score_gpt":0.2551166323132736,"score_spread":0.2416012181524976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312223815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27757412,0.0002500888,0.7188038,0.000087550136,0.0002194177,0.00060082483,0.000028299477,0.0007643539,0.0016715188],"genre_scores_gemma":[0.5424217,0.00029185138,0.45642057,0.00028129673,0.00013620754,1.4336194e-7,0.00013973712,0.00010591469,0.00020260888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865454,0.0000621666,0.0004336519,0.00025298807,0.00024380909,0.00035286922],"domain_scores_gemma":[0.99922454,0.00014588772,0.00011640053,0.00026410114,0.00014524728,0.00010381578],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003204669,0.00026249784,0.00031149635,0.00025950646,0.00058432494,0.00014672727,0.000051288414,0.000097695425,0.000012346678],"category_scores_gemma":[0.00012286926,0.00030254654,0.00004684728,0.00026079215,0.00006643482,0.00068471103,0.00022324215,0.00030907447,0.0000018083689],"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":[0.000050417453,0.0000016417595,4.64622e-7,0.00013654954,0.000009394641,0.000020199397,0.0006957538,0.0015307992,0.25083873,0.000012746099,0.0004952165,0.7462081],"study_design_scores_gemma":[0.0007799739,0.00010019903,0.000033848253,0.00012664469,0.000019596815,0.00041277165,0.001530846,0.9393196,0.04532357,0.0023607526,0.009623973,0.00036824204],"about_ca_topic_score_codex":0.000107872125,"about_ca_topic_score_gemma":0.00001367494,"teacher_disagreement_score":0.9377888,"about_ca_system_score_codex":0.00013570281,"about_ca_system_score_gemma":0.0000396215,"threshold_uncertainty_score":0.99994266},"labels":[],"label_agreement":null},{"id":"W43127747","doi":"","title":"Visual remote sensing image fusion using local correlation moment","year":2004,"lang":"en","type":"article","venue":"Chinese Optics Letters","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Panchromatic film; Multispectral image; Image fusion; Moment (physics); Artificial intelligence; Computer science; Discrete wavelet transform; Wavelet; Standard deviation; Resolution (logic); Fusion; Optics; Computer vision; Image (mathematics); Image resolution; Wavelet transform; Pattern recognition (psychology); Physics; Mathematics; Statistics","score_opus":0.005428093999952154,"score_gpt":0.2481603175598042,"score_spread":0.24273222355985205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W43127747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4030914,0.000020678452,0.5957689,0.00015704341,0.00025696508,0.00011368664,0.0000011416597,0.00043356343,0.00015659853],"genre_scores_gemma":[0.5260624,0.000020153158,0.4734259,0.0002819731,0.00012694443,3.1347176e-7,0.0000143464995,0.00006293193,0.0000050054055],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902815,0.00001252265,0.0002644417,0.00020231628,0.00021698402,0.0002755812],"domain_scores_gemma":[0.99958605,0.00002221603,0.000048671747,0.00022226515,0.0000428134,0.000077967976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008291613,0.00024018575,0.00018242502,0.00015436573,0.000102062826,0.000045713998,0.00008211759,0.00007664693,0.000009312291],"category_scores_gemma":[0.000026158474,0.00023548615,0.0000630787,0.00026403266,0.00007342942,0.00027918766,0.000066982306,0.00025664814,0.000026549838],"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":[0.0000050119556,0.0000058943547,0.000008126491,0.000023048751,0.0000065330314,0.00005073389,0.00012179186,0.34941787,0.6282524,0.000009262292,0.0000407664,0.022058586],"study_design_scores_gemma":[0.00038185602,0.00001848233,0.000114747134,0.000103693696,0.000012886455,0.00005651685,0.000023818486,0.9288004,0.069266714,0.000865211,0.00006031482,0.0002953401],"about_ca_topic_score_codex":0.000022204404,"about_ca_topic_score_gemma":0.0000024998517,"teacher_disagreement_score":0.57938254,"about_ca_system_score_codex":0.00041812006,"about_ca_system_score_gemma":0.000011152982,"threshold_uncertainty_score":0.96028465},"labels":[],"label_agreement":null},{"id":"W4312832599","doi":"10.1109/jstars.2022.3218367","title":"Multispectral Pansharpening Based on Multisequence Convolutional Recurrent Neural Network","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Henan University; Nanjing University of Aeronautics and Astronautics; Nanjing University; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Panchromatic film; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Feature extraction; Multispectral image; Feature (linguistics); Deep learning","score_opus":0.021121387956259126,"score_gpt":0.23334575987852713,"score_spread":0.212224371922268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312832599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7846907,0.00028595922,0.21316178,0.00028665972,0.0008655113,0.00030527514,0.000012934743,0.00015780487,0.00023339571],"genre_scores_gemma":[0.73955107,0.000052350006,0.26004106,0.00010771372,0.00021021298,2.7755485e-7,0.000006893972,0.000019306226,0.0000110947785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882567,0.000050915223,0.00043840415,0.00013167618,0.00030799865,0.0002453143],"domain_scores_gemma":[0.9994409,0.000117874726,0.00014397931,0.000101911275,0.00013694796,0.000058402624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024862602,0.00013999245,0.00020685827,0.00018549476,0.0002318656,0.000027460956,0.00008843074,0.000046556863,0.000012362399],"category_scores_gemma":[0.00004829234,0.00014954468,0.000037793845,0.00057162234,0.000034699024,0.00007325381,0.00001902873,0.00081819325,1.8821696e-7],"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":[0.000046080513,0.000014747011,0.00018288511,0.000015212051,0.000009785675,0.000037681948,0.000086863416,0.88502216,0.04254038,0.000070427435,0.0001377355,0.071836025],"study_design_scores_gemma":[0.00051908236,0.00007890193,0.012183014,0.000083721636,0.000008038736,0.00007505659,0.000026237927,0.9817186,0.003585366,0.00051713537,0.0010434159,0.00016145188],"about_ca_topic_score_codex":0.000007884338,"about_ca_topic_score_gemma":0.000022833428,"teacher_disagreement_score":0.0966964,"about_ca_system_score_codex":0.00016035922,"about_ca_system_score_gemma":0.00006656721,"threshold_uncertainty_score":0.6098255},"labels":[],"label_agreement":null},{"id":"W4313576996","doi":"10.3390/s23020599","title":"Infrared and Visible Image Fusion Technology and Application: A Review","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":168,"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 Key Research and Development Program of China; Chongqing Municipal Education Commission; Beijing Academy of Agricultural and Forestry Sciences; Natural Science Foundation of Chongqing","keywords":"Artificial intelligence; Computer vision; Image fusion; Computer science; Pixel; Image sensor; Night vision; Infrared; Fusion; Visible spectrum; Noise (video); Interference (communication); Image (mathematics); Materials science; Optics; Telecommunications; Optoelectronics; Physics","score_opus":0.014659769362532712,"score_gpt":0.31092699105458127,"score_spread":0.29626722169204855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313576996","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001384538,0.9938319,0.0010958893,0.000040631177,0.00003882008,0.0012024344,0.0000269742,0.0021905391,0.0015714788],"genre_scores_gemma":[8.93727e-8,0.9906792,0.007902808,0.000021038715,0.00003573436,0.00033359614,0.000038240352,0.00011775279,0.0008715235],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99891263,0.000025380394,0.00042008568,0.00034866252,0.00009124679,0.00020196263],"domain_scores_gemma":[0.9992407,0.00007132237,0.00010240802,0.00048802834,0.000037580954,0.000059945713],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011989073,0.0003278797,0.0010326632,0.0004634468,0.000050547704,0.000018821409,0.00014546812,0.00028354904,0.000018850607],"category_scores_gemma":[0.00010461167,0.0002960587,0.00007194057,0.0010131731,0.00009531602,0.000057748402,0.00019380623,0.00036707768,0.00011874455],"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":[1.378519e-7,0.0000015824056,6.586343e-8,0.05686176,0.000015212899,0.000011039454,0.0000035614514,3.3471144e-7,0.000016412781,0.000089585454,0.002633369,0.9403669],"study_design_scores_gemma":[0.00003245029,0.000010396132,2.0111007e-7,0.02729694,0.00012445277,0.00009638466,0.000004555322,0.00013585275,0.000026469012,0.0010697347,0.9709282,0.0002743612],"about_ca_topic_score_codex":7.3910024e-7,"about_ca_topic_score_gemma":4.9707734e-7,"teacher_disagreement_score":0.96829486,"about_ca_system_score_codex":0.00003938644,"about_ca_system_score_gemma":0.000013900637,"threshold_uncertainty_score":0.99994916},"labels":[],"label_agreement":null},{"id":"W4313609050","doi":"10.3389/fnins.2022.1128938","title":"Editorial: Multimodal brain image fusion: Methods, evaluations, and applications","year":2023,"lang":"en","type":"editorial","venue":"Frontiers in Neuroscience","topic":"Advanced Image Fusion Techniques","field":"Engineering","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, Okanagan Campus; University of British Columbia","funders":"","keywords":"Image fusion; Computer science; Artificial intelligence; Image (mathematics); Computer vision","score_opus":0.009156708169756715,"score_gpt":0.34194655200635554,"score_spread":0.3327898438365988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313609050","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.7253545e-7,0.0002546986,0.44391954,0.000034281493,0.5544251,0.000463329,0.000101537655,0.0006991334,0.00010185658],"genre_scores_gemma":[7.8097054e-7,0.0021294581,0.29536662,0.000024301607,0.7012261,0.00070240855,0.000059540293,0.0001128483,0.0003779189],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971572,0.00017886274,0.00047621387,0.00084494764,0.0008680681,0.0004746928],"domain_scores_gemma":[0.99786854,0.0010760424,0.00011515688,0.00063592935,0.00016319448,0.00014116024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013133568,0.00037478446,0.00044122778,0.00057084503,0.00018102236,0.00014889623,0.000763555,0.0005184599,0.0000043760383],"category_scores_gemma":[0.00605973,0.00042052154,0.00005941228,0.0011500315,0.00034356597,0.00041904437,0.00030865317,0.0011554785,0.000009968249],"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":[0.0000030741535,0.000010562292,0.0000039825863,0.00007248909,0.000001362526,0.000003271632,0.000031330404,0.00026663588,0.010519458,0.000004001773,0.96043766,0.028646158],"study_design_scores_gemma":[0.0001921674,0.000024572872,0.000023112518,0.000055081753,0.000010669435,5.782369e-7,0.00002305987,0.024729593,0.0006041735,0.0015700761,0.972398,0.0003689067],"about_ca_topic_score_codex":0.000012085963,"about_ca_topic_score_gemma":0.0000039696984,"teacher_disagreement_score":0.14855294,"about_ca_system_score_codex":0.00019632121,"about_ca_system_score_gemma":0.00013969328,"threshold_uncertainty_score":0.99982464},"labels":[],"label_agreement":null},{"id":"W4323064998","doi":"10.1109/tgrs.2023.3252001","title":"Multiresolution Analysis Pansharpening Based on Variation Factor for Multispectral and Panchromatic Images From Different Times","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Calgary","funders":"National Key Research and Development Program of China; Nanjing University of Aeronautics and Astronautics; Society of Hong Kong Scholars; Research Grants Council, University Grants Committee; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Panchromatic film; Multispectral image; Computer science; Robustness (evolution); Multiresolution analysis; Artificial intelligence; Image resolution; Image fusion; Computer vision; Pattern recognition (psychology); Wavelet transform; Wavelet; Image (mathematics); Discrete wavelet transform","score_opus":0.012199553112765051,"score_gpt":0.2447032696331601,"score_spread":0.23250371652039506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323064998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22861154,0.000013218655,0.77045,0.000044518412,0.00013651783,0.00019288747,0.000093647825,0.00045158074,0.000006094383],"genre_scores_gemma":[0.855713,0.00007665566,0.14410543,0.00002222847,0.000016326054,0.0000011617477,0.000009274318,0.000016926051,0.000038972885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999107,0.000025532743,0.00017479187,0.00030541787,0.00015789538,0.0002293837],"domain_scores_gemma":[0.99941826,0.00029961567,0.0000367014,0.00015359111,0.000027108337,0.000064752414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076902135,0.0001666417,0.00019603696,0.00042204952,0.00031600328,0.000074252064,0.000042172844,0.0000638501,0.000008544218],"category_scores_gemma":[0.00001734985,0.00014918535,0.000072965275,0.0004547187,0.000060341925,0.00016611416,0.000001228928,0.00012005887,0.000002371587],"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":[0.000020000645,0.000013982564,0.0000043030373,0.0000364385,0.00004806034,0.0000034071231,0.00060650666,0.06518167,0.24573901,4.6094635e-7,0.0000053672265,0.6883408],"study_design_scores_gemma":[0.00021453122,0.000047103218,0.0088753,0.00007745208,0.00008440518,8.509219e-7,0.00004848419,0.881346,0.10905952,0.00009065162,0.0000044948847,0.00015123632],"about_ca_topic_score_codex":0.0001611773,"about_ca_topic_score_gemma":0.000068387,"teacher_disagreement_score":0.8161643,"about_ca_system_score_codex":0.000054852044,"about_ca_system_score_gemma":0.000005818566,"threshold_uncertainty_score":0.6083602},"labels":[],"label_agreement":null},{"id":"W4327785494","doi":"10.1109/tgrs.2023.3258666","title":"SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":311,"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":"National Natural Science Foundation of China","keywords":"Computer science; Computation; Object detection; Artificial intelligence; Pixel; Feature (linguistics); Image resolution; Pattern recognition (psychology); Inference; Object (grammar); FLOPS; Computer vision; Deep learning; Artificial neural network; Algorithm","score_opus":0.014182825586978868,"score_gpt":0.24808520629001457,"score_spread":0.2339023807030357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327785494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28059494,0.000021510157,0.7173581,0.000057108067,0.00060771237,0.00017536142,0.0000043490368,0.0010260375,0.00015485492],"genre_scores_gemma":[0.8405659,0.00036283606,0.15877366,0.000046375353,0.00004018047,1.04974156e-7,0.0000020390942,0.00004417208,0.00016469306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831426,0.000079041616,0.0003377166,0.0004655593,0.00027645435,0.0005269453],"domain_scores_gemma":[0.99937904,0.00013898565,0.00003161549,0.0002876815,0.000064867505,0.00009779663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040533397,0.00024914363,0.00023365144,0.0008272659,0.00042331975,0.000079859914,0.000070240356,0.0001710378,0.0000032884973],"category_scores_gemma":[0.000039127466,0.0002603296,0.00007970917,0.0015604078,0.00018482427,0.00045182486,0.000004088352,0.00050111505,0.000024380986],"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":[0.000011803517,0.0000030157971,1.5301897e-7,0.000013652692,0.0000024121061,0.000045673925,0.00017951785,0.0057474053,0.3580137,4.4387836e-8,0.000005159393,0.63597745],"study_design_scores_gemma":[0.00018835737,0.000032704134,0.00041793677,0.00016025138,0.000008294794,0.00020405822,0.0002143522,0.74032277,0.2580511,0.00010090815,0.00007280849,0.00022646313],"about_ca_topic_score_codex":0.0008520405,"about_ca_topic_score_gemma":0.00066931633,"teacher_disagreement_score":0.7345754,"about_ca_system_score_codex":0.00019842894,"about_ca_system_score_gemma":0.000025988213,"threshold_uncertainty_score":0.9999849},"labels":[],"label_agreement":null},{"id":"W4327969903","doi":"10.3390/rs15061666","title":"Multi-Scale and Multi-Stream Fusion Network for Pansharpening","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Panchromatic film; Computer science; Image fusion; Scale (ratio); Remote sensing; Artificial intelligence; Block (permutation group theory); Channelized; Image resolution; Process (computing); Fusion; Fidelity; Pattern recognition (psychology); Image (mathematics); Computer vision; Geology; Cartography; Geography; Telecommunications; Mathematics","score_opus":0.02561547875945507,"score_gpt":0.2774003137511397,"score_spread":0.25178483499168464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327969903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061479133,0.00029058327,0.93506235,0.00002762787,0.00025011014,0.0004141902,0.00000803586,0.002375553,0.000092440736],"genre_scores_gemma":[0.046692792,0.00029490274,0.95237553,0.000035986042,0.00017074932,2.6984569e-7,0.000028936765,0.00008886202,0.00031194347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990987,0.000014652027,0.0001928689,0.00023117925,0.00008033671,0.00038227148],"domain_scores_gemma":[0.9995448,0.000115038754,0.00003157396,0.00019321851,0.000041537696,0.00007384997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015927157,0.0001689176,0.0001906141,0.00009001188,0.00016560218,0.000035540623,0.00005096102,0.000094115,0.000002438271],"category_scores_gemma":[0.000069364,0.0001810863,0.000050353898,0.0002578712,0.000026427417,0.00010034801,0.000073057075,0.00013977208,0.000014756511],"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":[0.0000055108544,0.0000020085506,0.000029444713,0.0000687001,0.00000771858,0.000014167396,0.00019795918,0.013765177,0.28042915,6.2645427e-7,0.0010328065,0.70444673],"study_design_scores_gemma":[0.00033284823,0.000012302776,0.00022416888,0.00019391738,0.000009446745,0.000017732977,0.00007494129,0.96745735,0.027698947,0.00013351203,0.0036420035,0.00020282666],"about_ca_topic_score_codex":0.000017464981,"about_ca_topic_score_gemma":0.000040272032,"teacher_disagreement_score":0.9536922,"about_ca_system_score_codex":0.000037010457,"about_ca_system_score_gemma":0.0000045537176,"threshold_uncertainty_score":0.7384485},"labels":[],"label_agreement":null},{"id":"W4367053587","doi":"10.1002/cpe.7712","title":"Multiscale aggregation and illumination‐aware attention network for infrared and visible image fusion","year":2023,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Royal Military College of Canada","funders":"","keywords":"Computer science; Discriminative model; Artificial intelligence; Image fusion; Computer vision; Salient; Channel (broadcasting); Feature (linguistics); Fusion; Image (mathematics); Feature extraction; Focus (optics); Pattern recognition (psychology); Fusion rules; Optics","score_opus":0.014053380304732473,"score_gpt":0.31230037967996427,"score_spread":0.2982469993752318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367053587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40677512,0.0015393287,0.5903866,0.00018475018,0.00019050566,0.00038904644,0.000010554907,0.00034727875,0.0001767705],"genre_scores_gemma":[0.9670672,0.004614389,0.02790186,0.00006404814,0.000053797685,0.00013744991,0.000074177005,0.000015064053,0.00007200448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937725,0.000026343685,0.00017353141,0.00021163006,0.000080991835,0.00013025015],"domain_scores_gemma":[0.99940735,0.00030218042,0.00007129923,0.0000543389,0.0001109355,0.000053888507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014739536,0.0001059115,0.0001009082,0.000067474524,0.00025631706,0.00010332261,0.000025837002,0.00005020841,0.000003441689],"category_scores_gemma":[0.00019214443,0.0001135342,0.000009597181,0.00019081026,0.0000903597,0.0013015418,0.000048408427,0.0000694133,0.0000014460362],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036439556,0.000016879743,0.00069273927,0.00025675911,0.000010301669,0.000004396618,0.007264114,0.0006364223,0.008677393,0.000637345,0.0020689324,0.9796983],"study_design_scores_gemma":[0.00090854755,0.0001624608,0.010654063,0.00019407294,0.00002978103,0.000045454875,0.0059366915,0.96488035,0.002659541,0.005296788,0.0088679865,0.00036427262],"about_ca_topic_score_codex":0.0000040738605,"about_ca_topic_score_gemma":0.0000011562574,"teacher_disagreement_score":0.979334,"about_ca_system_score_codex":0.000008551999,"about_ca_system_score_gemma":0.0000055103187,"threshold_uncertainty_score":0.46297905},"labels":[],"label_agreement":null},{"id":"W4377232614","doi":"10.1109/jstars.2023.3278296","title":"Cross Spectral and Spatial Scale Non-local Attention-Based Unsupervised Pansharpening Network","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":7,"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":"Baidu; Ministry of Natural Resources","keywords":"Panchromatic film; Artificial intelligence; Multispectral image; Computer science; Pattern recognition (psychology); Feature (linguistics); Image resolution; Image fusion; Feature extraction; Fuse (electrical); Scale (ratio); Feature learning; Fusion; Convolutional neural network; Computer vision; Image (mathematics); Geography","score_opus":0.013387052109013937,"score_gpt":0.23511937292811227,"score_spread":0.22173232081909833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377232614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59498066,0.000042407395,0.40448102,0.00006660655,0.00015941418,0.000097048534,0.0000018171036,0.0000939338,0.00007707736],"genre_scores_gemma":[0.84436893,0.00015637145,0.15501243,0.000042908643,0.0003497435,1.7135618e-7,0.000008075506,0.00002905943,0.000032286476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989015,0.000019535071,0.0004617906,0.00013918692,0.00019407325,0.00028391523],"domain_scores_gemma":[0.99946123,0.00008642465,0.00009633804,0.00010059949,0.00017852357,0.00007686639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026354726,0.00014975783,0.00025127633,0.00020676527,0.00015508449,0.00007721875,0.000060161285,0.00011070025,0.0000039186357],"category_scores_gemma":[0.00002675458,0.0001526436,0.000035052904,0.00078521634,0.000070839356,0.000103566614,0.0000163029,0.00042869893,7.222137e-7],"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":[0.000058462574,0.000013930019,0.008481097,0.00014223918,0.00004299188,0.00007905044,0.00031917682,0.3118903,0.1618302,0.000017113685,0.00018166732,0.51694375],"study_design_scores_gemma":[0.0006213056,0.00003058631,0.18998674,0.00019494104,0.000012522462,0.000029685343,0.0000381007,0.7960818,0.012002802,0.0006004504,0.00023940383,0.00016169705],"about_ca_topic_score_codex":0.000040924464,"about_ca_topic_score_gemma":0.00026488485,"teacher_disagreement_score":0.51678205,"about_ca_system_score_codex":0.000042679032,"about_ca_system_score_gemma":0.00004869587,"threshold_uncertainty_score":0.6224625},"labels":[],"label_agreement":null},{"id":"W4379032427","doi":"10.1504/ijnt.2023.131125","title":"Dimension adaptive hybrid recovery with collaborative group sparse representation based compressive sensing for colour images","year":2023,"lang":"en","type":"article","venue":"International Journal of Nanotechnology","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Consortium canadien en neurodégénérescence associée au vieillissement","keywords":"Dimension (graph theory); Compressed sensing; Sparse approximation; Group (periodic table); Representation (politics); Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Materials science; Mathematics; Combinatorics; Physics","score_opus":0.012594680407111574,"score_gpt":0.27133382515386123,"score_spread":0.25873914474674964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379032427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17656173,0.00010342959,0.82056683,0.0010807253,0.00073130056,0.00033069006,0.000098337616,0.00046082796,0.00006614029],"genre_scores_gemma":[0.83842045,0.00017570234,0.16110134,0.000079569414,0.00009757449,0.0000177957,0.000034217508,0.000041676216,0.000031663887],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99892735,0.00003736545,0.0003892212,0.00016406475,0.0003011392,0.00018085817],"domain_scores_gemma":[0.997992,0.00038399195,0.00035601662,0.00013185808,0.0011001945,0.000035954938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001641195,0.00015569586,0.0002665288,0.0007267047,0.000052031166,0.00003066882,0.00025754326,0.00009811058,0.000008884692],"category_scores_gemma":[0.00028905226,0.00014040328,0.000074369505,0.0003744015,0.000108342465,0.00034419217,0.000055610617,0.00025213725,0.0000052915775],"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":[0.0020251367,0.00006773418,0.000090529385,0.000020107358,0.0004514144,0.0012543072,0.00011885086,0.14103462,0.7469441,0.00042981113,0.018264472,0.08929889],"study_design_scores_gemma":[0.0017607587,0.00079991453,0.00015256088,0.00025697294,0.00003440281,0.00033048005,0.00040558862,0.07641444,0.91078544,0.005902934,0.0029437323,0.00021274328],"about_ca_topic_score_codex":0.0000034640439,"about_ca_topic_score_gemma":0.000005707546,"teacher_disagreement_score":0.66185874,"about_ca_system_score_codex":0.00019580168,"about_ca_system_score_gemma":0.000053647986,"threshold_uncertainty_score":0.572548},"labels":[],"label_agreement":null},{"id":"W4381166131","doi":"10.3390/rs15123173","title":"Forest Fire Monitoring Method Based on UAV Visual and Infrared Image Fusion","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":33,"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 Energy, Northern Development and Mines","funders":"National Natural Science Foundation of China","keywords":"Remote sensing; ALARM; Constant false alarm rate; Computer science; Environmental science; Image fusion; Warning system; Aerial photography; False alarm; Sensor fusion; Artificial intelligence; Computer vision; Image (mathematics); Geography","score_opus":0.011797760989090056,"score_gpt":0.29758297979990256,"score_spread":0.2857852188108125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381166131","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40560988,0.000050336905,0.5891628,0.000053276835,0.00028765274,0.00018735956,0.0000023401383,0.0027525597,0.0018937732],"genre_scores_gemma":[0.40398124,0.00012277596,0.59542954,0.000036676,0.00018510906,8.2186176e-8,0.00001043571,0.000099502926,0.00013465704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990824,0.000039290808,0.00017927027,0.00022878603,0.00018218879,0.00028804826],"domain_scores_gemma":[0.9994167,0.00020857393,0.000030198513,0.00022586282,0.000040503197,0.00007814156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019524312,0.00018673402,0.0001823843,0.00019390843,0.0001198779,0.0000515257,0.000049506118,0.00009169133,0.000003911068],"category_scores_gemma":[0.00016251252,0.00019838508,0.000042878022,0.00037616276,0.000022509032,0.0001327576,0.00006558898,0.0002220683,0.000023814511],"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":[0.000009287722,0.000002028174,0.000018074219,0.000060023744,0.0000040032705,0.00008492019,0.000082438106,0.012283707,0.35379484,7.968771e-7,0.00027498155,0.6333849],"study_design_scores_gemma":[0.00012611222,0.000026579914,0.0005067659,0.00024307423,0.0000053993454,0.000008883579,0.000049388047,0.8077677,0.19026019,0.00036904906,0.0004692828,0.0001675907],"about_ca_topic_score_codex":0.000016782153,"about_ca_topic_score_gemma":0.0000013031187,"teacher_disagreement_score":0.79548395,"about_ca_system_score_codex":0.000071300004,"about_ca_system_score_gemma":0.0000079860765,"threshold_uncertainty_score":0.8089909},"labels":[],"label_agreement":null},{"id":"W4381735870","doi":"10.1109/jstars.2023.3288143","title":"An Effective Multimodel Fusion Method for SAR and Optical Remote Sensing Images","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":29,"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":"State Key Laboratory of Remote Sensing Science; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Ministry of Natural Resources of the People's Republic of China; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; Ministry of Natural Resources","keywords":"Synthetic aperture radar; Remote sensing; Computer science; Image fusion; Artificial intelligence; Fusion; Computer vision; Sensor fusion; Image resolution; Pattern recognition (psychology); Image (mathematics); Geology","score_opus":0.019206763787964286,"score_gpt":0.28843920384318567,"score_spread":0.2692324400552214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381735870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28880772,0.000064999156,0.7104325,0.00010430442,0.00010874936,0.0002839368,0.0000033238443,0.00013545228,0.00005897176],"genre_scores_gemma":[0.1182113,0.00039853566,0.8811233,0.000045755834,0.00016260092,5.265502e-8,0.0000053574195,0.00003806061,0.000015063799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988813,0.000047983372,0.0004494015,0.0001915747,0.00016976302,0.00025998137],"domain_scores_gemma":[0.998977,0.00035393581,0.0001106801,0.00013402442,0.00034352188,0.00008082943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005754472,0.00017956636,0.0003303814,0.00034095903,0.00015158649,0.000060982296,0.000051709474,0.00014526195,3.8273396e-7],"category_scores_gemma":[0.00018378053,0.00017348121,0.00003653531,0.00058141525,0.000044343513,0.00016126083,0.000020462685,0.000416105,2.0469695e-7],"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":[0.000019881212,0.0000027242822,0.0000025251013,0.000040172457,0.000011756074,0.000010717607,0.00017258308,0.011977728,0.39840236,0.000014175994,0.000021639822,0.58932376],"study_design_scores_gemma":[0.00052191265,0.00006427716,0.0025509112,0.0001537656,0.000023895385,0.00008828177,0.00008663971,0.86447585,0.1273956,0.0041061835,0.00036619947,0.00016649156],"about_ca_topic_score_codex":0.000020290716,"about_ca_topic_score_gemma":0.000029178318,"teacher_disagreement_score":0.8524981,"about_ca_system_score_codex":0.000050423816,"about_ca_system_score_gemma":0.000027630073,"threshold_uncertainty_score":0.70743585},"labels":[],"label_agreement":null},{"id":"W4383535562","doi":"10.54254/2755-2721/4/20230425","title":"Research on RGB image optimization technology based on cluster analysis and improved Hibbard algorithm","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Demosaicing; Zipper; Diagonal; Artificial intelligence; Interpolation (computer graphics); Cluster analysis; Algorithm; RGB color model; Enhanced Data Rates for GSM Evolution; Computer science; Image (mathematics); Stairstep interpolation; Mathematics; Image gradient; Image processing; Pattern recognition (psychology); Computer vision; Color image; Linear interpolation","score_opus":0.007007596602077992,"score_gpt":0.2582114828595029,"score_spread":0.2512038862574249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007843059,0.000016885706,0.99028075,0.00014335748,0.000027321807,0.00019069771,0.000012358865,0.0011324723,0.00035311552],"genre_scores_gemma":[0.5815795,0.000026747668,0.4180557,0.00005022437,0.00003174092,0.00011659686,0.0000873197,0.000039755465,0.000012417803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991854,0.0000066819357,0.00014825448,0.00024632807,0.00019159156,0.00022174628],"domain_scores_gemma":[0.9995321,0.00022025286,0.000013960845,0.0001228211,0.000057026446,0.000053862394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017777151,0.00014183258,0.00015742442,0.001410201,0.00008084754,0.000044327935,0.00006816485,0.00008567304,0.000008440323],"category_scores_gemma":[0.000018438648,0.00015201741,0.000024041747,0.001587839,0.000038124115,0.00005224136,0.00005000405,0.00024662848,0.00000902097],"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":[0.0000054749858,0.0000063400316,0.0000027807735,0.00002240925,0.000031451727,0.0000025519519,0.00001564791,0.9724418,0.002172588,0.0006302795,0.000067884015,0.024600808],"study_design_scores_gemma":[0.00025901367,0.000035888053,0.00037984666,0.000014760089,0.000013485001,9.668825e-7,0.000029174984,0.9958801,0.002379685,0.000746047,0.00011527498,0.00014575975],"about_ca_topic_score_codex":0.0000010206018,"about_ca_topic_score_gemma":1.5268657e-7,"teacher_disagreement_score":0.5737364,"about_ca_system_score_codex":0.000043216463,"about_ca_system_score_gemma":0.000006582928,"threshold_uncertainty_score":0.619909},"labels":[],"label_agreement":null},{"id":"W4384575354","doi":"10.23952/jnva.7.2023.4.05","title":"Multitemporal image cloud removal using group sparsity and nonconvex low-rank approximation","year":2023,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":8,"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":"Rank (graph theory); Cloud computing; Image (mathematics); Group (periodic table); Mathematics; Computer science; Artificial intelligence; Combinatorics; Physics","score_opus":0.013593355995992007,"score_gpt":0.26100157104364724,"score_spread":0.24740821504765523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384575354","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5080531,0.00009635143,0.4915481,0.000059691876,0.0000745767,0.0000421339,0.000026067562,0.00006103855,0.00003893788],"genre_scores_gemma":[0.4358949,0.00040127613,0.56300575,0.000034892066,0.0005308375,9.668643e-7,0.00006191274,0.000022297987,0.00004716023],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991081,0.000032723474,0.0003892757,0.000105616564,0.00025050578,0.00011382523],"domain_scores_gemma":[0.99939305,0.00010501951,0.00017612262,0.00007375948,0.00017390889,0.000078159756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046785263,0.0001097781,0.00027828926,0.00051580695,0.00008299342,0.000050574712,0.00006344906,0.000060642564,0.000022625276],"category_scores_gemma":[0.000098069715,0.00010066832,0.00012244261,0.0007758039,0.000030861192,0.00041026442,0.000037528138,0.00015676624,0.0000023519308],"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":[0.00030890273,0.0005494131,0.061888795,0.00089011353,0.007916871,0.00074046076,0.0030041812,0.35511482,0.5338778,0.001711525,0.0029787791,0.031018369],"study_design_scores_gemma":[0.00031473438,0.000020685038,0.017285159,0.000019140625,0.00032593854,0.000055071887,0.00005514001,0.979927,0.0008266506,0.0007207935,0.00036128002,0.000088388064],"about_ca_topic_score_codex":0.000012719507,"about_ca_topic_score_gemma":0.0000043739324,"teacher_disagreement_score":0.6248122,"about_ca_system_score_codex":0.000040079376,"about_ca_system_score_gemma":0.000015050786,"threshold_uncertainty_score":0.41051352},"labels":[],"label_agreement":null},{"id":"W4386159375","doi":"10.23919/fusion52260.2023.10224104","title":"MobileFuse: Multimodal Image Fusion at the Edge","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Defence Research and Development Canada; National Research Council Canada; Thales (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Image fusion; Enhanced Data Rates for GSM Evolution; Computer vision; Ground truth; Focus (optics); Process (computing); Sensor fusion; Artificial neural network; Image (mathematics)","score_opus":0.007270435979230472,"score_gpt":0.24755915761298863,"score_spread":0.24028872163375817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386159375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6064849,0.00040131056,0.19166018,0.0010178359,0.0012345285,0.001473255,0.00005354055,0.029017985,0.16865647],"genre_scores_gemma":[0.88159597,0.0014247285,0.04569557,0.00045016577,0.0003699513,0.00048282655,0.00010226146,0.0002734927,0.06960503],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993712,0.000011025263,0.00012737993,0.00013307281,0.00012790371,0.00022945278],"domain_scores_gemma":[0.9994986,0.00007675945,0.000010915715,0.00034875897,0.000024032466,0.00004090553],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00010145148,0.00011692696,0.00008511225,0.000060574777,0.000107704625,0.000018804576,0.0001735098,0.00004655022,0.0015274042],"category_scores_gemma":[0.000034522254,0.00007796179,0.00004644569,0.00028583728,0.000046434932,0.00012313381,0.0002166941,0.00011986784,0.0022732327],"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":[0.0000033290996,0.000006616634,0.00004391226,0.000018314175,0.0000053276312,0.00001501086,0.00012371244,0.0014416685,0.69785845,0.00006614825,0.2592069,0.041210625],"study_design_scores_gemma":[0.00021993324,0.00001984287,0.001516005,0.000014099234,0.0000049358355,0.000008976734,0.00011972746,0.10908991,0.7531801,0.0005836041,0.13497987,0.00026298483],"about_ca_topic_score_codex":0.000008308775,"about_ca_topic_score_gemma":0.00001502952,"teacher_disagreement_score":0.27511108,"about_ca_system_score_codex":0.00005561176,"about_ca_system_score_gemma":0.0000031690677,"threshold_uncertainty_score":0.99938536},"labels":[],"label_agreement":null},{"id":"W4386176509","doi":"10.2316/j.2023.201-0383","title":"A RESEARCH OF TREE IMAGE MARKOV RANDOM FIELD SEGMENTATION METHOD BASED ON GENETIC ALGORITHM, 166-171.","year":2023,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Markov random field; Markov chain; Computer science; Artificial intelligence; Image segmentation; Tree (set theory); Segmentation; Field (mathematics); Image (mathematics); Pattern recognition (psychology); Algorithm; Mathematics; Machine learning; Combinatorics","score_opus":0.011796357378560282,"score_gpt":0.3092014043430287,"score_spread":0.2974050469644684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386176509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016372807,0.0010211022,0.9950928,0.00008439627,0.00017726717,0.00094934,0.0000281297,0.00034058947,0.0006690927],"genre_scores_gemma":[0.9322368,0.00026366496,0.06606535,0.000039326747,0.00013312187,0.0008047025,0.000011651941,0.00006367096,0.00038169321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985064,0.00026828604,0.00032896388,0.00022163881,0.00034661405,0.00032807005],"domain_scores_gemma":[0.99874234,0.0007694909,0.000050828967,0.00028850703,0.00008822163,0.00006059537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012217016,0.00014603733,0.00033434984,0.0003196555,0.00006592124,0.000037208003,0.00012198558,0.00009134969,0.000038469916],"category_scores_gemma":[0.00007156631,0.00013145737,0.00006641178,0.00029738728,0.000020890655,0.00006722449,0.000022021608,0.00019326313,0.000012347368],"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":[0.00028026654,0.000037458816,0.00004549926,0.00057920045,0.000112039925,0.00004118366,0.00015720712,0.02108773,0.20649163,0.00023539549,0.0039247773,0.7670076],"study_design_scores_gemma":[0.0032868937,0.00026330826,0.000119372606,0.00011815516,0.000021181451,0.0000039265406,0.00030111097,0.95954794,0.03549218,0.0002727475,0.00044455484,0.00012863672],"about_ca_topic_score_codex":0.000095692085,"about_ca_topic_score_gemma":0.0000051220213,"teacher_disagreement_score":0.9384602,"about_ca_system_score_codex":0.0000702872,"about_ca_system_score_gemma":0.000023503548,"threshold_uncertainty_score":0.5360676},"labels":[],"label_agreement":null},{"id":"W4386457042","doi":"10.3390/rs15184370","title":"Multiband Image Fusion via Regularization on a Riemannian Submanifold","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Panchromatic film; Multispectral image; Submanifold; Regularization (linguistics); Image fusion; Subspace topology; Computer science; Hyperspectral imaging; Artificial intelligence; Fusion; Computer vision; Mathematics; Image (mathematics); Pattern recognition (psychology); Mathematical analysis","score_opus":0.008547703866065525,"score_gpt":0.23403011142726002,"score_spread":0.2254824075611945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386457042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12592793,0.00002646883,0.8629794,0.00012567485,0.00030809254,0.00027618918,0.00000268293,0.004458598,0.0058949757],"genre_scores_gemma":[0.81811345,0.00018920918,0.17970045,0.00013728211,0.000260338,8.310078e-8,0.00007330862,0.0001964816,0.0013294087],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990691,0.00002561455,0.00019785453,0.00022642959,0.00019542796,0.0002855967],"domain_scores_gemma":[0.99946994,0.000044480086,0.000035443256,0.0003328963,0.00005418093,0.00006306888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001325408,0.00017558553,0.00015339612,0.00024273442,0.00011612933,0.000039818457,0.000064419706,0.00009658225,0.000010894602],"category_scores_gemma":[0.00007733009,0.00018830832,0.000050092334,0.00050172,0.000024238905,0.00014581776,0.000045158566,0.00017469244,0.00022370089],"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":[0.000006098267,0.0000022322274,6.7509785e-7,0.0000339742,0.000004552368,0.00008122588,0.00014286206,0.002950284,0.7330502,0.000009934806,0.0010258781,0.26269206],"study_design_scores_gemma":[0.00012974798,0.000016943817,0.000101165846,0.0001244532,0.0000049276973,0.00001755542,0.000019328494,0.70696235,0.2894288,0.0013527663,0.0016693905,0.0001725793],"about_ca_topic_score_codex":0.00001166947,"about_ca_topic_score_gemma":0.000005682924,"teacher_disagreement_score":0.70401204,"about_ca_system_score_codex":0.00008702521,"about_ca_system_score_gemma":0.0000046531536,"threshold_uncertainty_score":0.76789904},"labels":[],"label_agreement":null},{"id":"W4386596898","doi":"10.1109/icip49359.2023.10222863","title":"A Visible and Infrared Image Fusion Framework Based on Dual-Path Encoder-Decoder and Multi-Scale Discrete Wavelet Transform","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Discrete wavelet transform; Encoder; Pattern recognition (psychology); Wavelet transform; Image fusion; Fusion; Wavelet; Scale (ratio); Fusion rules; Computer vision; Image (mathematics)","score_opus":0.008750482617489914,"score_gpt":0.2579288072868773,"score_spread":0.24917832466938736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386596898","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029853417,0.00007381247,0.9598417,0.00029762692,0.00007995451,0.00051116484,0.0000614761,0.0025389271,0.0067419037],"genre_scores_gemma":[0.22914353,0.0010131182,0.76766086,0.00036420437,0.00004614807,0.00013016736,0.000049584894,0.00013421268,0.0014581577],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998857,0.000017733793,0.0002494473,0.00033128064,0.00019457226,0.00034991867],"domain_scores_gemma":[0.99932855,0.00015969315,0.000021195885,0.0003179261,0.000029477453,0.00014316181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014379201,0.00026503898,0.00024755643,0.0002216187,0.0001131724,0.00007405057,0.000081928265,0.00016491489,0.00022053305],"category_scores_gemma":[0.000058751615,0.000231598,0.000047054837,0.0003466944,0.000068467925,0.00029528872,0.00005941832,0.00029818877,0.000031563566],"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":[0.00025155948,0.0002608275,0.0012000656,0.0016774772,0.00007759198,0.00030725214,0.0057452656,0.005488487,0.65201074,0.000604171,0.027719814,0.30465677],"study_design_scores_gemma":[0.00073621474,0.00012655728,0.0021625874,0.00022118607,0.000010986031,0.0000051083475,0.0001945184,0.8978535,0.09329163,0.0032792275,0.0017240066,0.00039450824],"about_ca_topic_score_codex":0.000009587438,"about_ca_topic_score_gemma":0.000014151602,"teacher_disagreement_score":0.892365,"about_ca_system_score_codex":0.000030140985,"about_ca_system_score_gemma":0.000009100136,"threshold_uncertainty_score":0.94442916},"labels":[],"label_agreement":null},{"id":"W4387350506","doi":"10.1109/tgrs.2023.3321729","title":"SSMU-Net: A Style Separation and Mode Unification Network for Multimodal Remote Sensing Image Classification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":17,"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":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; Panchromatic film; Unification; Exploit; Hyperspectral imaging; Artificial intelligence; Multispectral image; Data mining; Mode (computer interface); Pattern recognition (psychology)","score_opus":0.01907566019084118,"score_gpt":0.29170803032205006,"score_spread":0.2726323701312089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387350506","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052510187,0.000022551743,0.9455777,0.0002165596,0.00031481017,0.00044186533,0.000015983624,0.00080539327,0.00009494361],"genre_scores_gemma":[0.43852717,0.00043480328,0.56071675,0.00004187051,0.000061201936,3.263945e-7,0.000011742433,0.00003370803,0.00017241831],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881274,0.000034693327,0.0002574837,0.00039223718,0.00016452583,0.00033831855],"domain_scores_gemma":[0.9993798,0.00013389127,0.000060540933,0.00024112148,0.00009823174,0.00008639787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028307058,0.00018736601,0.0001620706,0.00023121365,0.0005793579,0.000107535,0.00005196459,0.00011322006,8.437232e-7],"category_scores_gemma":[0.000017649832,0.00019717442,0.000043075674,0.0006103997,0.00014654365,0.00038408086,0.0000021742742,0.0001866649,0.0000060672105],"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":[0.000013101409,0.0000022481365,5.85984e-8,0.000024006124,0.000004067004,0.0000015063293,0.00032082055,0.018228363,0.24708533,0.0000032667324,0.00005753859,0.7342597],"study_design_scores_gemma":[0.0001865926,0.000037703507,0.00012745916,0.00009768348,0.000019150779,0.000026596714,0.00020491093,0.95321786,0.04461274,0.00087393867,0.0003862353,0.00020915088],"about_ca_topic_score_codex":0.00011228692,"about_ca_topic_score_gemma":0.00009150006,"teacher_disagreement_score":0.93498945,"about_ca_system_score_codex":0.00006890273,"about_ca_system_score_gemma":0.000019159339,"threshold_uncertainty_score":0.80405396},"labels":[],"label_agreement":null},{"id":"W4387885618","doi":"10.1109/tgrs.2023.3324993","title":"BrGAN: Blur Resist Generative Adversarial Network With Multiple Joint Dilated Residual Convolutions for Chlorophyll Color Image Restoration","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Huaqiao University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Residual; Joint (building); Computer vision; Image restoration; Convolution (computer science); Convolutional neural network; Pattern recognition (psychology); Image (mathematics); Image processing; Artificial neural network; Algorithm","score_opus":0.014697982186602563,"score_gpt":0.22988583710801247,"score_spread":0.2151878549214099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387885618","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066605136,0.000014953311,0.9311219,0.00022669259,0.0005634875,0.0005417319,0.00005084423,0.0008167493,0.000058499012],"genre_scores_gemma":[0.48994982,0.0001572409,0.509011,0.0000685226,0.00020252416,0.000004487422,0.000019813464,0.000053914733,0.0005326693],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987974,0.000035560668,0.00024098238,0.00035087098,0.00019551978,0.0003796637],"domain_scores_gemma":[0.9993599,0.00014087421,0.000053381948,0.000207871,0.00014729046,0.000090695095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019962234,0.00019665099,0.0001943197,0.00019941638,0.0008990766,0.00008075574,0.00006188588,0.00009763307,0.000002631073],"category_scores_gemma":[0.000029050783,0.00017935422,0.000046200337,0.0007798819,0.0002643877,0.00033262532,0.0000026287382,0.00021400812,0.000007796443],"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":[0.00031807672,0.000021686166,5.17649e-7,0.000051757306,0.0000393205,0.000035306315,0.00082429464,0.26945305,0.6492406,0.000013733059,0.0015804415,0.07842123],"study_design_scores_gemma":[0.00053724286,0.00019576578,0.0001235433,0.000101128695,0.000029169858,0.000019449682,0.00019115585,0.8162205,0.18165249,0.00017374953,0.00051641994,0.00023933563],"about_ca_topic_score_codex":0.00010987187,"about_ca_topic_score_gemma":0.0005109077,"teacher_disagreement_score":0.5467675,"about_ca_system_score_codex":0.000100693316,"about_ca_system_score_gemma":0.000053139272,"threshold_uncertainty_score":0.73138523},"labels":[],"label_agreement":null},{"id":"W4387917214","doi":"10.2139/ssrn.4611611","title":"IR and Visible Image Fusion in the Gradient Domain Using Two-Scale Image Decomposition and Saliency Detection","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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; University of Victoria","funders":"","keywords":"Image fusion; Image (mathematics); Artificial intelligence; Scale (ratio); Fusion; Computer vision; Decomposition; Pattern recognition (psychology); Domain (mathematical analysis); Computer science; Mathematics; Chemistry; Geography; Cartography; Mathematical analysis","score_opus":0.006915531796662105,"score_gpt":0.2722252300388837,"score_spread":0.2653096982422216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387917214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6127603,0.0016471359,0.38474473,0.00007596302,0.00018087207,0.00032004947,0.000004699228,0.00018989509,0.00007634841],"genre_scores_gemma":[0.9639147,0.01739287,0.018310277,0.000017750368,0.00020693663,0.000039556624,0.000011516136,0.00009460536,0.000011787739],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99763876,0.00015763118,0.00042723725,0.0003396916,0.00025551472,0.0011811652],"domain_scores_gemma":[0.9994289,0.00005798918,0.0001481252,0.00025162598,0.000051117015,0.0000622147],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0016497091,0.00031684947,0.00028236915,0.00041923413,0.00025539743,0.00020414064,0.00023069601,0.00015786459,0.0000034889413],"category_scores_gemma":[0.000022173948,0.00027483157,0.00008095816,0.0002470917,0.00007504539,0.00032384205,0.00024187539,0.0032319145,0.0000027153094],"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":[0.000060749317,0.00005131319,0.00036451357,0.00020844571,0.00006212654,0.00005760977,0.0011041557,0.002971775,0.93898445,0.00067426905,0.000032647982,0.05542796],"study_design_scores_gemma":[0.0013495614,0.0003432163,0.004161426,0.0007055432,0.000113186376,0.0034064616,0.003285531,0.07200324,0.037593585,0.8759745,0.000083696,0.0009800706],"about_ca_topic_score_codex":0.00020294281,"about_ca_topic_score_gemma":0.002073343,"teacher_disagreement_score":0.90139085,"about_ca_system_score_codex":0.0011856804,"about_ca_system_score_gemma":0.00016041743,"threshold_uncertainty_score":0.9999704},"labels":[],"label_agreement":null},{"id":"W4388023589","doi":"10.18280/ts.400531","title":"Enhancing the Quality of Compressed Breast Ultrasound Imagery through Application of Wavelet Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Convolutional neural network; Computer science; Artificial intelligence; Wavelet; Breast ultrasound; Pattern recognition (psychology); Ultrasound; Computer vision; Medicine; Radiology; Mammography; Internal medicine; Breast cancer","score_opus":0.016733581427139745,"score_gpt":0.27068180625188876,"score_spread":0.253948224824749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388023589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14862983,0.000083223495,0.85021013,0.00005457952,0.000061819366,0.00033804466,0.000098180826,0.0003417537,0.00018245587],"genre_scores_gemma":[0.9962824,0.00003375556,0.0033304354,0.00003802888,0.000095308096,0.00007591358,0.00011338909,0.000022270971,0.000008519727],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987178,0.00006247144,0.0005731435,0.00014678984,0.00029240755,0.00020738279],"domain_scores_gemma":[0.99903977,0.000465537,0.0001495059,0.00021800112,0.00010159521,0.000025563462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041003374,0.00013788864,0.00022386319,0.000043796015,0.00006321882,0.000009686459,0.00020146517,0.000046327343,0.00011577641],"category_scores_gemma":[0.000018724777,0.00011690086,0.000085004714,0.0003047858,0.00014108486,0.00015885195,0.00003968319,0.00014065679,0.0000033134193],"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":[0.0000332034,0.000044257282,0.000524251,0.00012790436,0.000043536947,5.900365e-7,0.00024990196,0.10699088,0.8851031,0.0011178382,0.0010938086,0.004670722],"study_design_scores_gemma":[0.00056258705,0.000040874776,0.056681786,0.000054391374,0.000026705058,0.000011724086,0.00026520828,0.6651427,0.27503946,0.0016217697,0.00028223373,0.00027052156],"about_ca_topic_score_codex":0.000041772644,"about_ca_topic_score_gemma":0.0000074502427,"teacher_disagreement_score":0.84765255,"about_ca_system_score_codex":0.000034555826,"about_ca_system_score_gemma":0.000009332091,"threshold_uncertainty_score":0.47670788},"labels":[],"label_agreement":null},{"id":"W4388119863","doi":"10.1109/iceogi57454.2023.10292972","title":"Pansharpening Methods Based on a Redundant Contourlet Transform","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 en Outaouais","funders":"","keywords":"Panchromatic film; Contourlet; Multispectral image; Computer science; Image fusion; Artificial intelligence; Wavelet transform; Multiresolution analysis; Wavelet; Computer vision; Distortion (music); Image resolution; Pattern recognition (psychology); Image (mathematics); Discrete wavelet transform","score_opus":0.020364170502882546,"score_gpt":0.3335369519969495,"score_spread":0.31317278149406697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388119863","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041004017,0.000020281432,0.9217483,0.00025446885,0.00011761795,0.00019766898,0.0000081354265,0.005775157,0.071468376],"genre_scores_gemma":[0.20246454,0.000053694377,0.7945247,0.0003919295,0.000052867857,0.00014538634,0.000025297919,0.000100096055,0.0022414674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992845,0.00002458754,0.00016973811,0.0001469929,0.00012145212,0.0002527053],"domain_scores_gemma":[0.9995136,0.00017674352,0.000010070654,0.00021997408,0.0000177291,0.00006186254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029889873,0.00013532054,0.00015383265,0.00017361721,0.00004471296,0.00002108116,0.000120348064,0.000054006276,0.0005449815],"category_scores_gemma":[0.00005393031,0.00012059407,0.000063466316,0.00035138577,0.000016360695,0.0000935203,0.0000103854545,0.00015520793,0.00012597657],"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":[0.000023722238,0.000016606557,0.000011377333,0.00008636479,0.00001848419,0.00004905882,0.00021825943,0.026809376,0.2598304,0.0009455552,0.028547594,0.68344325],"study_design_scores_gemma":[0.00020306067,0.000043319284,0.00005870129,0.00003836654,0.000003909461,0.0000013878189,0.00004711174,0.48756486,0.47043246,0.0010653214,0.040369663,0.00017181788],"about_ca_topic_score_codex":0.00000550732,"about_ca_topic_score_gemma":0.0000029667362,"teacher_disagreement_score":0.6832714,"about_ca_system_score_codex":0.000045648605,"about_ca_system_score_gemma":0.000008042375,"threshold_uncertainty_score":0.5967166},"labels":[],"label_agreement":null},{"id":"W4388672333","doi":"10.3390/math11224644","title":"High-Speed Wavelet Image Processing Using the Winograd Method with Downsampling","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Council on grants of the President of the Russian Federation; Russian Science Foundation; Centre de Recherches Mathématiques","keywords":"Upsampling; Wavelet; Computer science; Decimation; Image processing; Computational complexity theory; Modular design; Digital image processing; Discrete wavelet transform; Wavelet transform; Computer vision; Artificial intelligence; Computer engineering; Computational science; Filter (signal processing); Algorithm; Image (mathematics)","score_opus":0.028285762237517442,"score_gpt":0.3045546250986232,"score_spread":0.27626886286110575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388672333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029630825,0.00003754735,0.9673742,0.00006137546,0.000048071626,0.0002759414,0.0000060999555,0.0020439783,0.00052195677],"genre_scores_gemma":[0.028425196,0.000017207793,0.971227,0.000027122023,0.000065869535,0.000018197386,0.000005794685,0.00010458259,0.000109041604],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990104,0.000016937656,0.00026354767,0.00014848424,0.00024286994,0.0003177908],"domain_scores_gemma":[0.99929965,0.00015452436,0.00007378424,0.0003630752,0.00006831982,0.00004064142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034510173,0.00020207705,0.00023435433,0.0001145996,0.0001700494,0.000106830325,0.00022691669,0.000051326304,0.000023839613],"category_scores_gemma":[0.00006154785,0.00013201348,0.00003900611,0.0006922976,0.00005616128,0.00020127672,0.00007361635,0.00021261521,0.00003080275],"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":[0.000013758658,0.00009365536,0.000024028323,0.0030042229,0.000148434,0.00012465494,0.010852029,0.1347373,0.74970424,0.0026607611,0.0030156597,0.09562126],"study_design_scores_gemma":[0.00014202466,0.000014266415,0.000014094084,0.00025115572,0.00003788593,0.000054213044,0.0008736386,0.8942067,0.09496214,0.008773685,0.0004096811,0.00026048135],"about_ca_topic_score_codex":0.0000039757133,"about_ca_topic_score_gemma":0.0000011255731,"teacher_disagreement_score":0.75946945,"about_ca_system_score_codex":0.000048319514,"about_ca_system_score_gemma":0.000014056884,"threshold_uncertainty_score":0.5383353},"labels":[],"label_agreement":null},{"id":"W4389429244","doi":"10.1186/s12880-023-01160-w","title":"MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network","year":2023,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":58,"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; Hôtel-Dieu de Québec","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Medical imaging; Discriminator; Generator (circuit theory); Segmentation; Deep learning; Magnetic resonance imaging; Similarity (geometry); Computer vision; Image (mathematics); Radiology; Medicine","score_opus":0.017761544924166747,"score_gpt":0.29587620855559194,"score_spread":0.2781146636314252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389429244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06633003,0.00030713482,0.92597395,0.00027084147,0.0017875627,0.0003935059,0.000008560879,0.0038957682,0.0010326566],"genre_scores_gemma":[0.3345112,0.0011107218,0.65285975,0.001960427,0.008253028,0.00017119193,0.0004812549,0.0005478463,0.00010459116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99543613,0.0003039702,0.00072921236,0.00062303187,0.0018836985,0.0010239417],"domain_scores_gemma":[0.9976567,0.00054570666,0.000072330535,0.00053784554,0.00013416797,0.0010532644],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014049595,0.00044842748,0.00051215815,0.00025974528,0.00037184556,0.00008473822,0.00071158336,0.00031717456,0.0041460963],"category_scores_gemma":[0.0014973478,0.0004253038,0.00015239265,0.0009175769,0.0003283654,0.00071044354,0.00054429466,0.0008641806,0.00019462545],"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":[0.00025744227,0.00048947363,0.006191705,0.0006070447,0.00017640855,0.005548199,0.0032225815,0.10051815,0.2391327,0.0006448341,0.04629541,0.596916],"study_design_scores_gemma":[0.0011708055,0.000017785622,0.0004505554,0.0002770132,0.000024264627,0.00008152151,0.00026926072,0.9904921,0.0051631555,0.00060874893,0.00095235766,0.0004924401],"about_ca_topic_score_codex":0.0000656442,"about_ca_topic_score_gemma":0.00013479765,"teacher_disagreement_score":0.88997394,"about_ca_system_score_codex":0.00016340916,"about_ca_system_score_gemma":0.00030471865,"threshold_uncertainty_score":0.9998199},"labels":[],"label_agreement":null},{"id":"W4390444741","doi":"10.18280/ts.400633","title":"Exploring the Application of Deep Learning in Multi-View Image Fusion in Complex Environments","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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; Fusion; Computer science; Image fusion; Image (mathematics); Computer vision; Philosophy","score_opus":0.058167559112700946,"score_gpt":0.2729261337897568,"score_spread":0.21475857467705584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390444741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51671815,0.00007186027,0.48217866,0.000037977865,0.000020508203,0.00054347754,0.0000024859696,0.00028695905,0.00013990642],"genre_scores_gemma":[0.9925508,0.00046159636,0.0065655503,0.000011229149,0.000012037755,0.0003327091,0.00003090171,0.000025603389,0.00000961095],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915653,0.000042429958,0.00031524838,0.00013786259,0.00016186126,0.00018605756],"domain_scores_gemma":[0.9997585,0.000053227122,0.00004220809,0.00012067583,0.000005311231,0.000020113162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002900164,0.00010898495,0.000136351,0.00015769439,0.000033248685,0.0000069085854,0.0001418663,0.000019799505,0.00007795996],"category_scores_gemma":[0.000010979146,0.00009857301,0.00002793201,0.00034291775,0.000029501633,0.00018324019,0.000063254905,0.00016450066,0.000034657805],"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":[0.0000058389583,0.000040887313,0.0017498895,0.000058097125,0.0000036599215,0.0000040504165,0.00081712275,0.08083206,0.69149995,0.000024765166,0.000013747673,0.22494994],"study_design_scores_gemma":[0.00060366205,0.00003149558,0.21806233,0.00006707156,0.0000041439694,6.732851e-7,0.00046244168,0.7225241,0.054695293,0.000079936035,0.0033000354,0.00016881737],"about_ca_topic_score_codex":0.000017732775,"about_ca_topic_score_gemma":0.00002721613,"teacher_disagreement_score":0.64169204,"about_ca_system_score_codex":0.00007728992,"about_ca_system_score_gemma":0.0000016097622,"threshold_uncertainty_score":0.40196905},"labels":[],"label_agreement":null},{"id":"W4391307131","doi":"10.1109/smc53992.2023.10394151","title":"Visible-Infrared Features Fusion Based Object Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial intelligence; Computer science; Object detection; Feature (linguistics); Infrared; Computer vision; Pattern recognition (psychology); Fusion; Feature extraction; Object (grammar); Image fusion; Image (mathematics); Optics","score_opus":0.006704048362923323,"score_gpt":0.2393959168881021,"score_spread":0.23269186852517879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391307131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029868253,0.00010628439,0.8695578,0.00008021487,0.00064334634,0.00043514313,0.000008849901,0.025681851,0.073618256],"genre_scores_gemma":[0.9687034,0.00010271322,0.025474893,0.00013367806,0.00009011592,0.00007680515,0.000026605943,0.00007540709,0.0053164186],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993843,0.000011782015,0.00011890617,0.00014267454,0.00014017119,0.00020219175],"domain_scores_gemma":[0.9996329,0.000050625214,0.000012401347,0.00022934459,0.000029241284,0.000045483135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079836725,0.00012243046,0.00009755254,0.00025939374,0.000066252585,0.00002609256,0.00009096568,0.00008200482,0.00030580827],"category_scores_gemma":[0.00004966527,0.00011418685,0.00004873258,0.0006279552,0.000011013897,0.00013659782,0.000032456726,0.00013414034,0.00022579684],"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":[0.000010737257,0.0000062824865,0.000023898243,0.000047255824,0.000005718985,0.0000138798305,0.000034383116,0.008247631,0.8380697,0.00002691697,0.033991463,0.11952214],"study_design_scores_gemma":[0.00014900125,0.000031803113,0.002027764,0.000019247798,0.0000029937742,0.0000026561308,0.000029426721,0.08921228,0.89711136,0.0007427627,0.01050819,0.00016249726],"about_ca_topic_score_codex":0.000008762434,"about_ca_topic_score_gemma":0.000018386732,"teacher_disagreement_score":0.9388351,"about_ca_system_score_codex":0.00004936997,"about_ca_system_score_gemma":0.000006339808,"threshold_uncertainty_score":0.46564046},"labels":[],"label_agreement":null},{"id":"W4392194277","doi":"10.52058/2695-1592-2024-2(33)-449-459","title":"TENDENCIES IN THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE PROCESSING OF PHOTO MATERIALS","year":2024,"lang":"en","type":"article","venue":"Věda a perspektivy","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Centennial College","funders":"","keywords":"Computer science; Artificial intelligence; Image processing; Process (computing); Machine learning; Artificial neural network; Retraining; Adaptation (eye); Deep learning; Data processing; Image (mathematics); Database","score_opus":0.018474887421089558,"score_gpt":0.29685750915344106,"score_spread":0.2783826217323515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392194277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63412815,0.003176768,0.35820222,0.00043675426,0.000118316246,0.0011335012,0.000030060308,0.00027290918,0.0025013054],"genre_scores_gemma":[0.9964737,0.000074946554,0.0032810064,0.000015970618,0.000026473685,0.00011136408,0.0000019736456,0.000010978755,0.000003582174],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935323,0.000033913115,0.0002670296,0.000102394646,0.00014577302,0.00009767866],"domain_scores_gemma":[0.9996915,0.00008058773,0.000028254859,0.00016975898,0.000025199259,0.000004704734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043605166,0.00007537648,0.00010899399,0.00012450255,0.000012496345,0.000027564869,0.0002422751,0.000034336656,0.00002286329],"category_scores_gemma":[0.000043617176,0.000046867684,0.000020898236,0.00053539063,0.0000682422,0.00011008705,0.00001761938,0.00010614859,0.0000039234787],"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":[0.000007721475,0.000034648077,0.00002001651,0.00031538313,0.000002758925,0.0000042979996,0.016849883,0.0005052017,0.8756196,0.005652972,0.000052263877,0.100935236],"study_design_scores_gemma":[0.000016110298,0.000026082465,0.00034384328,0.000221035,0.0000067809106,0.000007773273,0.013244967,0.02399068,0.9495903,0.011895444,0.0005584704,0.00009850505],"about_ca_topic_score_codex":0.00007783771,"about_ca_topic_score_gemma":0.000041434625,"teacher_disagreement_score":0.36234555,"about_ca_system_score_codex":0.000029595853,"about_ca_system_score_gemma":0.000015336082,"threshold_uncertainty_score":0.19112086},"labels":[],"label_agreement":null},{"id":"W4392348711","doi":"10.18280/ts.410146","title":"Enhancing Intrinsic Image Decomposition with Transformer and Laplacian Pyramid Network","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Computer science; Transformer; Pyramid (geometry); Artificial intelligence; Computer vision; Segmentation; Laplace operator; Benchmark (surveying); Feature extraction; Pattern recognition (psychology); Mathematics; Engineering; Geography; Cartography","score_opus":0.0032692925799902186,"score_gpt":0.21527676884730765,"score_spread":0.21200747626731742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392348711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08741651,0.0007745912,0.90807253,0.00008584542,0.00009182164,0.0002987503,0.0000090740905,0.0012117119,0.00203917],"genre_scores_gemma":[0.9613723,0.00015473584,0.03806614,0.000066933055,0.00019133811,0.000053348336,0.00001879193,0.000049344126,0.00002707774],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992333,0.000010963742,0.00018917222,0.00018237856,0.00013336705,0.0002508298],"domain_scores_gemma":[0.9997997,0.00003587154,0.0000106014095,0.000070803486,0.000017687462,0.00006528679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011025429,0.00017067352,0.00013423039,0.00008147029,0.000064905245,0.000096550015,0.000052440406,0.000039460803,0.00025531478],"category_scores_gemma":[8.674097e-7,0.0001497822,0.000029646802,0.00017023292,0.000045325,0.00036524,0.000007528055,0.00016269789,0.000015640779],"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":[0.00008031025,0.000032293745,0.00006539403,0.000576798,0.00013025034,0.00016913803,0.0010161792,0.0058640973,0.76928586,0.0014534713,0.0023661163,0.21896012],"study_design_scores_gemma":[0.0010558187,0.0006454869,0.0014260722,0.0013794096,0.0001369435,0.00019472804,0.00019189244,0.06685175,0.90031,0.0028859132,0.023873325,0.001048687],"about_ca_topic_score_codex":0.0000025425957,"about_ca_topic_score_gemma":0.00002319402,"teacher_disagreement_score":0.8739558,"about_ca_system_score_codex":0.00005859026,"about_ca_system_score_gemma":0.000009599476,"threshold_uncertainty_score":0.61079407},"labels":[],"label_agreement":null},{"id":"W4392969647","doi":"10.1109/tip.2024.3374072","title":"MM-Net: A MixFormer-Based Multi-Scale Network for Anatomical and Functional Image Fusion","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 British Columbia","funders":"National Natural Science Foundation of China","keywords":"Image fusion; Computer science; Artificial intelligence; Fusion; Pattern recognition (psychology); Feature (linguistics); Fusion rules; Source code; Generalization; Benchmark (surveying); Image (mathematics); Domain (mathematical analysis); Scale (ratio); Mathematics","score_opus":0.012830251027779809,"score_gpt":0.2642581048594617,"score_spread":0.2514278538316819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392969647","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031940432,0.0010314726,0.9926029,0.0001311079,0.0004854898,0.00042944596,0.00007958647,0.0018972991,0.00014864099],"genre_scores_gemma":[0.42639223,0.00010608285,0.5724426,0.00012049283,0.00017011155,0.00034657278,0.00001911552,0.00014284551,0.00025993836],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987231,0.000011914713,0.00030163061,0.00040297623,0.00017458652,0.00038582803],"domain_scores_gemma":[0.99948126,0.00012774167,0.000026739179,0.00015513193,0.0000942733,0.00011485532],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014644329,0.00028547403,0.00021493557,0.00022859337,0.000340959,0.00023964475,0.000079876416,0.00013101786,0.000111178546],"category_scores_gemma":[0.000006375793,0.00028445123,0.00012212775,0.0003800751,0.0001272158,0.0006994467,0.0000018243195,0.00039753836,0.00001783236],"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":[0.00012372242,0.00011383126,0.0000031563297,0.0011352636,0.00003206118,0.000013533484,0.00014425893,0.022567643,0.5342384,0.000003639776,0.002634624,0.43898988],"study_design_scores_gemma":[0.00046528436,0.000053055795,0.000026687267,0.0003427835,0.000048794733,0.000019871457,0.000033765493,0.78050417,0.21600941,0.00023946172,0.0019634776,0.00029320965],"about_ca_topic_score_codex":0.0000026627183,"about_ca_topic_score_gemma":0.0000115670655,"teacher_disagreement_score":0.75793654,"about_ca_system_score_codex":0.000115126655,"about_ca_system_score_gemma":0.000060940383,"threshold_uncertainty_score":0.9999608},"labels":[],"label_agreement":null},{"id":"W4393867779","doi":"10.21203/rs.3.rs-4177942/v1","title":"Enhancing the Quality of 2D and 3D Image Using Block Matching 3D and Fuzzy Hybrid Model Based on Random Noise Reduction","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Western University","funders":"","keywords":"Fuzzy logic; Block (permutation group theory); Noise reduction; Reduction (mathematics); Noise (video); Quality (philosophy); Computer science; Image (mathematics); Matching (statistics); Random noise; Image quality; Artificial intelligence; Pattern recognition (psychology); Mathematics; Computer vision; Algorithm; Statistics; Physics","score_opus":0.04138934922562703,"score_gpt":0.38357733569883784,"score_spread":0.3421879864732108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393867779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69573605,0.0018672352,0.29893944,0.00013737212,0.0001468224,0.0014563766,0.0001777082,0.00045928344,0.0010797213],"genre_scores_gemma":[0.9366265,0.0006231034,0.06234707,0.0000057507023,0.00012094377,0.0001118236,0.000017801884,0.00010018622,0.00004684254],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972745,0.00039228628,0.0005466802,0.00055807474,0.0008123918,0.00041603378],"domain_scores_gemma":[0.9982568,0.00055896625,0.00008972681,0.00072424737,0.0002658227,0.000104447536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003180063,0.00031141864,0.00047296134,0.0004686774,0.00021176998,0.00019066528,0.00022659657,0.00016192324,0.000010154953],"category_scores_gemma":[0.00040654215,0.00025816448,0.00010005255,0.00020733674,0.00022780058,0.0001201588,0.0008655078,0.0021011534,0.0000022636457],"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":[0.00018281995,0.000028954491,0.000003874116,0.0068043205,0.0000365504,0.000014604826,0.00077640364,0.4422069,0.5425035,0.000058657064,0.00012631937,0.007257142],"study_design_scores_gemma":[0.00035017743,0.000030830397,0.000021497393,0.0024189064,0.000022565579,0.000010018376,0.00027037683,0.8363209,0.14790422,0.012395971,0.000012366545,0.00024220532],"about_ca_topic_score_codex":0.00026151558,"about_ca_topic_score_gemma":0.000011809985,"teacher_disagreement_score":0.39459926,"about_ca_system_score_codex":0.000257659,"about_ca_system_score_gemma":0.000134536,"threshold_uncertainty_score":0.99998707},"labels":[],"label_agreement":null},{"id":"W4394690114","doi":"10.1098/rsos.231762","title":"Advancing multimodal medical image fusion: an adaptive image decomposition approach based on multilevel Guided filtering","year":2024,"lang":"en","type":"article","venue":"Royal Society Open Science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Saskatchewan","funders":"","keywords":"Image fusion; Artificial intelligence; Computer science; Medical imaging; Contrast (vision); Computer vision; Pattern recognition (psychology); Component (thermodynamics); Fusion rules; Image (mathematics); Physics","score_opus":0.016128951525574507,"score_gpt":0.3307294954187583,"score_spread":0.31460054389318376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394690114","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009378149,0.000039485738,0.9757194,0.0001487882,0.0003023349,0.00065125484,0.0000342738,0.0012412079,0.0124851065],"genre_scores_gemma":[0.35786888,0.000011048193,0.64147985,0.00032364245,0.00008648411,0.00009806909,0.00001968371,0.000048594047,0.00006371849],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700475,0.000044604036,0.00037448396,0.00087444205,0.0010649234,0.0006367726],"domain_scores_gemma":[0.99870276,0.0001339224,0.00004031867,0.0005352994,0.00018846296,0.00039921305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016389504,0.000315705,0.00026914768,0.00009673514,0.0005177546,0.0006839793,0.0017103782,0.00012944697,0.00042367182],"category_scores_gemma":[0.0001801112,0.0002949008,0.00013901335,0.0006098475,0.00046117653,0.0019268658,0.0007205601,0.00053708727,0.000048364265],"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":[0.000044364482,0.00027627294,0.0000112741545,0.00025429338,0.00002126771,0.000114144415,0.0017920026,0.12983367,0.7268191,0.00028593425,0.014114366,0.1264333],"study_design_scores_gemma":[0.00028283134,0.000099350305,0.0002066807,0.00026710235,0.0000061072246,0.000011212618,0.00026229394,0.9186267,0.07949153,0.00007300976,0.00031822213,0.00035494994],"about_ca_topic_score_codex":0.00008291358,"about_ca_topic_score_gemma":0.0000033651402,"teacher_disagreement_score":0.788793,"about_ca_system_score_codex":0.00052128854,"about_ca_system_score_gemma":0.00025042036,"threshold_uncertainty_score":0.9999503},"labels":[],"label_agreement":null},{"id":"W4394892271","doi":"10.1007/s00521-024-09729-4","title":"End-to-end dynamic residual focal transformer network for multimodal medical image fusion","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Alberta","funders":"Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Residual; Encoder; Convolutional neural network; Image fusion; Deep learning; Transformer; Feature extraction; Pattern recognition (psychology); Image (mathematics); Algorithm; Engineering","score_opus":0.006547348836103625,"score_gpt":0.2889312222107686,"score_spread":0.282383873374665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394892271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02282036,0.0004884832,0.9731876,0.0008785426,0.00012760426,0.00062933634,0.000043373533,0.0013768988,0.00044779334],"genre_scores_gemma":[0.91153866,0.000070665534,0.08739532,0.00016010817,0.00046126993,0.00020760989,0.000063670326,0.00005055322,0.000052159663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905735,0.00001126598,0.00022832786,0.00027677373,0.0001500471,0.00027623735],"domain_scores_gemma":[0.99943584,0.00025302806,0.000010855343,0.0001361726,0.000027203876,0.00013686999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001466359,0.00014611865,0.00013701287,0.000063252985,0.00019981244,0.00006221783,0.00012966468,0.000081463004,0.000028197133],"category_scores_gemma":[0.000013121604,0.0001386754,0.000049998165,0.00022229587,0.00005311763,0.00007120804,0.000039093997,0.000255515,0.000011530587],"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":[0.0000068583295,0.0000132303385,0.000008113862,0.00013711482,0.000011140467,0.0000036574143,0.00011915641,0.006701832,0.023767462,0.0014659892,0.0038462149,0.9639192],"study_design_scores_gemma":[0.00013763906,0.000043488508,0.00023700565,0.000084378524,0.000015493788,0.000028113685,0.000018264845,0.96317315,0.0026196581,0.0011808673,0.03227042,0.0001914972],"about_ca_topic_score_codex":0.000005656392,"about_ca_topic_score_gemma":0.000012265684,"teacher_disagreement_score":0.9637277,"about_ca_system_score_codex":0.000030568794,"about_ca_system_score_gemma":0.000014230124,"threshold_uncertainty_score":0.56550187},"labels":[],"label_agreement":null},{"id":"W4395053065","doi":"10.1055/s-0044-1783007","title":"Autonomous Artificial Intelligence versus AI Assisted Human optical diagnosis of colorectal polyps: A randomized controlled trial","year":2024,"lang":"en","type":"article","venue":"Endoscopy","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"McGill University; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Medicine; Randomized controlled trial; Artificial intelligence; Colonoscopy; Surgery; Colorectal cancer; Internal medicine; Computer science; Cancer","score_opus":0.02032904445573086,"score_gpt":0.3214973807354374,"score_spread":0.3011683362797065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395053065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51734895,0.0028276439,0.44543135,0.000268165,0.0078525245,0.008394793,0.000054538807,0.004950081,0.012871951],"genre_scores_gemma":[0.97901535,0.000106107625,0.018505882,0.000011338105,0.00023429655,0.0020049189,0.000012030864,0.000060413207,0.000049684353],"study_design_codex":"randomized_trial","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981012,0.0001249467,0.000929448,0.00027881106,0.00026198477,0.00030362332],"domain_scores_gemma":[0.99748987,0.0020719946,0.00006934611,0.00022320531,0.000054959433,0.00009063396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050767907,0.00027262903,0.0012099482,0.00025496245,0.000055242992,0.00008230596,0.00020650198,0.0001452344,0.00055661635],"category_scores_gemma":[0.0009063045,0.0002336442,0.00046524417,0.00028562438,0.00022414919,0.00015499626,0.000051026407,0.000357725,0.000047562287],"study_design_candidate":"randomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.908342,0.00019807936,0.0000016186141,0.000091457645,0.0005260364,0.000070824346,0.00023354367,0.00056251214,0.04377412,0.028781073,0.00051941484,0.016899312],"study_design_scores_gemma":[0.39673337,0.0005412029,0.0000015537263,0.00013043603,0.00023742094,0.000001916583,0.0000359715,0.038692523,0.5589992,0.0041499157,0.00019428594,0.00028224502],"about_ca_topic_score_codex":0.000014670684,"about_ca_topic_score_gemma":0.0000073867036,"teacher_disagreement_score":0.51522505,"about_ca_system_score_codex":0.0001326334,"about_ca_system_score_gemma":0.00006604163,"threshold_uncertainty_score":0.9527734},"labels":[],"label_agreement":null},{"id":"W4399039901","doi":"10.1109/tgrs.2024.3405848","title":"Low-Rank Tensor Completion Pansharpening Based on Haze Correction","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of Calgary","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Panchromatic film; Haze; Multispectral image; Computer science; Artificial intelligence; Image resolution; Tensor (intrinsic definition); Rank (graph theory); Computer vision; Remote sensing; Pattern recognition (psychology); Mathematics; Physics; Geology","score_opus":0.010336035455153969,"score_gpt":0.235795878800185,"score_spread":0.22545984334503102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399039901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010210511,0.00003791355,0.98532474,0.00015595874,0.0021486117,0.00016252114,0.000008145322,0.00133808,0.0006135059],"genre_scores_gemma":[0.9288866,0.00013689628,0.07028315,0.00016655131,0.000057082863,5.073944e-7,0.0000021679207,0.000039508763,0.00042752974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899,0.000025188012,0.00017584286,0.00033548413,0.00022625946,0.00024721428],"domain_scores_gemma":[0.9995887,0.000107999724,0.000015933296,0.00017632275,0.00003764985,0.00007339438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014689029,0.00018068853,0.00013707996,0.00035132768,0.00032238697,0.00012835485,0.00005262141,0.00007862831,0.000019935009],"category_scores_gemma":[0.0000087801245,0.00016891884,0.00006314611,0.000497893,0.00010230571,0.0002837497,7.4554595e-7,0.00036824573,0.00003125934],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010668262,0.00000809975,6.947746e-8,0.000043482334,0.0000032928501,0.000019225214,0.00010103348,0.05063025,0.058385555,0.0000012205426,0.00010901191,0.8906881],"study_design_scores_gemma":[0.00007365848,0.00007031085,0.00003068262,0.0005699656,0.000011253671,0.000050481558,0.00004263045,0.82837856,0.1696294,0.0000367787,0.0009393794,0.0001669126],"about_ca_topic_score_codex":0.000048640668,"about_ca_topic_score_gemma":0.000018426726,"teacher_disagreement_score":0.9186761,"about_ca_system_score_codex":0.000098464356,"about_ca_system_score_gemma":0.00001861082,"threshold_uncertainty_score":0.688831},"labels":[],"label_agreement":null},{"id":"W4399728165","doi":"10.1109/tnnls.2024.3409563","title":"VOGTNet: Variational Optimization-Guided Two-Stage Network for Multispectral and Panchromatic Image Fusion","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Calgary","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Panchromatic film; Multispectral image; Artificial intelligence; Computer science; Robustness (evolution); Image resolution; Computer vision; Noise (video); Image fusion; Pattern recognition (psychology); Image (mathematics)","score_opus":0.009460183352951816,"score_gpt":0.24698004351621064,"score_spread":0.23751986016325882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399728165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004484081,0.002277576,0.990008,0.000037961367,0.0013045017,0.00061656424,0.000020645904,0.0011856408,0.000065030545],"genre_scores_gemma":[0.9683223,0.00053525297,0.029735878,0.000024979317,0.0004349806,0.00021236589,0.000024710638,0.00009447617,0.00061510527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880475,0.00008874175,0.0003509027,0.00031190185,0.00012816173,0.00031551425],"domain_scores_gemma":[0.999273,0.0004299495,0.000045542183,0.00011668728,0.00004241628,0.000092398914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023969288,0.00024694382,0.00025071594,0.00011281894,0.000392499,0.00027120355,0.000058641363,0.00010812544,0.00003744454],"category_scores_gemma":[0.000007657331,0.00023697587,0.0000691854,0.00023099789,0.0000426627,0.00029794604,0.0000019166787,0.00049359497,0.0000017028717],"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":[0.00001262454,0.000007365911,0.0000065227605,0.00021653326,0.000038431746,0.0000064107885,0.000086702945,0.9900297,0.0006780585,0.00008962907,0.00040008986,0.008427921],"study_design_scores_gemma":[0.00033441026,0.000098091485,0.000021593884,0.00026081956,0.000039907572,0.000051938732,0.000038080587,0.9980324,0.00009426495,0.000018240862,0.0007645255,0.00024575676],"about_ca_topic_score_codex":0.000028130517,"about_ca_topic_score_gemma":0.0000072064586,"teacher_disagreement_score":0.96383816,"about_ca_system_score_codex":0.000049173723,"about_ca_system_score_gemma":0.0000075182306,"threshold_uncertainty_score":0.96635956},"labels":[],"label_agreement":null},{"id":"W4399767093","doi":"10.1109/jsen.2024.3411588","title":"Multifocus Image Fusion With Complex Sparse Representation","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Image fusion; Sparse approximation; Computer science; Focus (optics); Artificial intelligence; Representation (politics); Fusion; Computer vision; Image (mathematics); Pattern recognition (psychology); Physics; Optics","score_opus":0.0221588270988256,"score_gpt":0.2853927492169346,"score_spread":0.263233922118109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399767093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47729582,0.0006037872,0.5103467,0.0002566846,0.001294698,0.00028153724,0.000017111872,0.0022065646,0.0076970872],"genre_scores_gemma":[0.877913,0.00062058785,0.12012149,0.00003180006,0.0005529617,0.00000613291,0.000005653709,0.000103463804,0.00064490753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990366,0.000035920522,0.00025506993,0.00016513925,0.00026698323,0.00024025666],"domain_scores_gemma":[0.99952805,0.000055689994,0.00003616857,0.00017894505,0.000088821114,0.00011232902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011898753,0.00016829542,0.00015171427,0.00021320954,0.00010192962,0.00018925569,0.000104258375,0.000050589602,0.00038232867],"category_scores_gemma":[0.000021376765,0.00013513942,0.00006624022,0.00025706398,0.000051528183,0.00045542838,0.000013158685,0.00046338694,0.00013775387],"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":[0.000026971398,0.000013108971,0.000030223408,0.000044092307,0.00003690623,0.0010458376,0.00035623624,0.018151887,0.90073335,0.000011819532,0.04369227,0.035857294],"study_design_scores_gemma":[0.0005556781,0.00012506041,0.0007383369,0.0004551725,0.000047879737,0.003867029,0.00026391272,0.28957763,0.6737663,0.00088262424,0.029206755,0.00051359955],"about_ca_topic_score_codex":0.0000053622757,"about_ca_topic_score_gemma":0.0000037816526,"teacher_disagreement_score":0.4006172,"about_ca_system_score_codex":0.00009680874,"about_ca_system_score_gemma":0.000014644064,"threshold_uncertainty_score":0.55108255},"labels":[],"label_agreement":null},{"id":"W4399809570","doi":"10.1007/s10489-024-05580-1","title":"Dual-branch and triple-attention network for pan-sharpening","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Royal Military College of Canada","funders":"Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Computer science; Sharpening; Panchromatic film; Image resolution; Distortion (music); Channel (broadcasting); Artificial intelligence; Face (sociological concept); Spatial analysis; Computer vision; Telecommunications; Remote sensing","score_opus":0.013166698609443823,"score_gpt":0.2618627931393542,"score_spread":0.24869609452991034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399809570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027395368,0.0024195455,0.9895664,0.000027245944,0.0004012889,0.00049075304,0.000009115746,0.0013149864,0.0030311344],"genre_scores_gemma":[0.9194289,0.00075768423,0.07876069,0.000050639675,0.0002590493,0.00034926273,0.000020467176,0.000060766313,0.00031254327],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991909,0.0000037253226,0.00022316418,0.0002475085,0.000081300415,0.00025335114],"domain_scores_gemma":[0.99963963,0.00013778043,0.000015530277,0.00014683616,0.000019434332,0.000040791478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015602545,0.0001487252,0.00013767295,0.000063318636,0.0000660017,0.00007863778,0.00008266771,0.000068907495,0.000050252303],"category_scores_gemma":[0.000016458805,0.00015121489,0.000039570623,0.000227542,0.00003450653,0.00013063467,0.000038493,0.0001534019,0.000050636696],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012673591,0.0000050784943,0.000012712084,0.00029586835,0.00002750246,0.0000037623647,0.00015880469,0.002765114,0.08572517,0.03274067,0.008880125,0.8693725],"study_design_scores_gemma":[0.0001368254,0.000091773836,0.00019534388,0.00046644462,0.000056716985,0.000034794593,0.00016502653,0.23984286,0.46276423,0.17029463,0.12508927,0.0008620701],"about_ca_topic_score_codex":0.0000015667042,"about_ca_topic_score_gemma":0.0000016852812,"teacher_disagreement_score":0.91668934,"about_ca_system_score_codex":0.000030048624,"about_ca_system_score_gemma":0.0000060056623,"threshold_uncertainty_score":0.6166364},"labels":[],"label_agreement":null},{"id":"W4399849938","doi":"10.1109/tce.2024.3417489","title":"Lightweight Infrared and Visible Image Fusion Technique: Guided Gradient Optimization Driven","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"École de Technologie Supérieure","funders":"","keywords":"Image fusion; Computer vision; Infrared; Artificial intelligence; Computer science; Fusion; Image (mathematics); Optics; Physics","score_opus":0.006239448380616483,"score_gpt":0.23715350605599922,"score_spread":0.23091405767538273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399849938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014249055,0.0027716733,0.9906575,0.00009312273,0.00032604107,0.00061838684,0.000031110783,0.0028219458,0.0012552862],"genre_scores_gemma":[0.6236873,0.037337393,0.33532736,0.0001416124,0.00007313146,0.0010797394,0.000047871374,0.0003926326,0.0019129488],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987153,0.00003169025,0.00031958034,0.0003593874,0.00018580085,0.00038825112],"domain_scores_gemma":[0.9994061,0.00007136249,0.000024408006,0.00032488472,0.00007294941,0.00010031326],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010271755,0.00028859376,0.00020839578,0.00042731172,0.00016151433,0.00011532179,0.0001284753,0.00016493039,0.0002112706],"category_scores_gemma":[0.0000044566723,0.000293832,0.00008427009,0.0005364976,0.00007109346,0.0004135549,0.0000028254738,0.0005399896,0.00003436023],"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":[0.00004450526,0.00014608807,0.0000022331546,0.00039134157,0.00023604684,0.000057577196,0.00031277214,0.16406521,0.6658487,0.0005124509,0.0110716475,0.15731147],"study_design_scores_gemma":[0.00018021744,0.00011333091,9.445993e-7,0.00013396445,0.00006541948,0.000069402486,0.000007667479,0.33299598,0.6451641,0.0005366068,0.020403568,0.0003287928],"about_ca_topic_score_codex":0.0000037087177,"about_ca_topic_score_gemma":0.0000089509185,"teacher_disagreement_score":0.6553302,"about_ca_system_score_codex":0.00027652155,"about_ca_system_score_gemma":0.00006397222,"threshold_uncertainty_score":0.99995136},"labels":[],"label_agreement":null},{"id":"W4400086219","doi":"10.3390/rs16132349","title":"Variational-Based Spatial–Temporal Approximation of Images in Remote Sensing","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer science; Remote sensing; Mean squared error; Poisson distribution; Pixel; Metric (unit); Satellite; Image resolution; Artificial intelligence; Algorithm; Pattern recognition (psychology); Mathematics; Statistics; Geography","score_opus":0.009292646416853751,"score_gpt":0.2460023422888687,"score_spread":0.23670969587201493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400086219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015803607,0.00027077465,0.98075086,0.00014083419,0.00027310377,0.00020969413,0.0000058312007,0.00089672505,0.0016485589],"genre_scores_gemma":[0.5115857,0.000015946012,0.48823565,0.000019203973,0.00006205747,3.990806e-9,0.000019304893,0.000043606575,0.00001851962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988363,0.000053398482,0.00042476135,0.00023484512,0.00023236808,0.00021836258],"domain_scores_gemma":[0.99943924,0.00014803003,0.000056331704,0.00023897586,0.000080284546,0.000037136622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030026972,0.00018238346,0.00023571984,0.00040470465,0.000030365687,0.000045925546,0.00005119881,0.00010058808,0.000008505678],"category_scores_gemma":[0.00012732024,0.00020464408,0.00006773386,0.00046480712,0.00003582456,0.000184209,0.000023807495,0.00024421458,0.00000916983],"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":[0.0000060642874,0.000001993965,0.000001572387,0.00027211598,0.000007914144,0.000060851304,0.00013502502,0.027104577,0.25624287,0.000013187517,0.00008976459,0.71606404],"study_design_scores_gemma":[0.00010290553,0.000007941399,0.00003698938,0.0006413282,0.000008185613,0.000019053576,0.0000150845335,0.7794053,0.21606481,0.003222043,0.00032508004,0.00015131029],"about_ca_topic_score_codex":0.00049114646,"about_ca_topic_score_gemma":0.00006254013,"teacher_disagreement_score":0.7523007,"about_ca_system_score_codex":0.00018959129,"about_ca_system_score_gemma":0.000049830625,"threshold_uncertainty_score":0.8345144},"labels":[],"label_agreement":null},{"id":"W4400086229","doi":"10.3390/rs16132352","title":"Reconstructing Snow-Free Sentinel-2 Satellite Imagery: A Generative Adversarial Network (GAN) Approach","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Remote sensing; Satellite; Generative grammar; Snow; Adversarial system; Generative adversarial network; Satellite imagery; Environmental science; Computer science; Geology; Meteorology; Artificial intelligence; Deep learning; Geography; Astronomy; Physics","score_opus":0.010796408714491734,"score_gpt":0.22734856925926228,"score_spread":0.21655216054477056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400086229","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008914381,0.0022958072,0.93521297,0.000078047146,0.0015858186,0.0002782918,0.000005759089,0.0035473073,0.04808161],"genre_scores_gemma":[0.08429219,0.00048398844,0.9121514,0.0000847859,0.002450676,1.1111808e-7,0.000030647298,0.00017852013,0.00032769155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827933,0.00007275862,0.00040920515,0.00046653763,0.00021154078,0.00056060206],"domain_scores_gemma":[0.99911225,0.00017245623,0.00004907176,0.00050400465,0.00006619683,0.00009600169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030832936,0.00033853165,0.00033638274,0.00015585423,0.0001552485,0.00021405407,0.00015044556,0.00015082279,0.000016521262],"category_scores_gemma":[0.00015853712,0.0003570026,0.00013550514,0.00057974964,0.00008104019,0.0003395995,0.00011503634,0.0005594382,0.000035970137],"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":[0.000012134859,0.0000028609072,0.0000033710642,0.00017549418,0.00010789854,0.00021709672,0.00059516885,0.02210688,0.09210313,0.00013385448,0.0049374006,0.8796047],"study_design_scores_gemma":[0.00015367208,0.0000065278214,0.0000021993092,0.00041483768,0.000036806243,0.00036864355,0.00012444648,0.9488515,0.038100414,0.0040372405,0.0075038895,0.00039981757],"about_ca_topic_score_codex":0.000009610962,"about_ca_topic_score_gemma":0.0000022566896,"teacher_disagreement_score":0.92674464,"about_ca_system_score_codex":0.00016729996,"about_ca_system_score_gemma":0.00003433457,"threshold_uncertainty_score":0.9998882},"labels":[],"label_agreement":null},{"id":"W4400443154","doi":"10.2139/ssrn.4890197","title":"Slrcnn: Integrating Local Sparse Low-Rank and Cnn Denoiser for Hyperspectral and Multispectral Image Fusion","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Multispectral image; Hyperspectral imaging; Rank (graph theory); Artificial intelligence; Computer science; Image fusion; Pattern recognition (psychology); Image (mathematics); Computer vision; Remote sensing; Mathematics; Geology","score_opus":0.005164601511075322,"score_gpt":0.24224601352409345,"score_spread":0.23708141201301813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400443154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.243525,0.031492352,0.7218771,0.00028397836,0.00069907936,0.0008440807,0.00004279728,0.00079087575,0.00044472053],"genre_scores_gemma":[0.899612,0.025504313,0.07317396,0.000036598205,0.0008540172,0.000100151905,0.00002543391,0.0002783072,0.00041520217],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963776,0.00004407925,0.00057293894,0.00059842924,0.0002500378,0.0021568986],"domain_scores_gemma":[0.99919796,0.000090864385,0.0001293688,0.00027598164,0.00011606766,0.00018977899],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008289927,0.0006289449,0.0005788398,0.0003368691,0.00020285421,0.00030695277,0.00029821336,0.00038308895,0.000022091499],"category_scores_gemma":[0.00009045758,0.0005837272,0.00022714332,0.000120111246,0.00016482922,0.00021881405,0.0004292986,0.0066916933,0.0000074550085],"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":[0.00045151165,0.0001563214,0.00013778594,0.0029937017,0.0013303937,0.00025572788,0.002423178,0.0034443056,0.34745386,0.0484931,0.002025745,0.5908344],"study_design_scores_gemma":[0.0015101136,0.00057126285,0.0000703223,0.0015139065,0.00036617418,0.002424594,0.0030462486,0.18985549,0.035087474,0.7634346,0.00049845997,0.0016213679],"about_ca_topic_score_codex":0.000038142207,"about_ca_topic_score_gemma":0.0003438025,"teacher_disagreement_score":0.7149415,"about_ca_system_score_codex":0.0014161335,"about_ca_system_score_gemma":0.0005354096,"threshold_uncertainty_score":0.9996614},"labels":[],"label_agreement":null},{"id":"W4401267147","doi":"10.1016/j.heliyon.2024.e35249","title":"Specific spectral sub-images for machine learning evaluation of optical differences between carbon ion and X ray radiation effects","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":5,"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":"Ontario Ministry of Research and Innovation; European Regional Development Fund; Institutul National de Cercetare-Dezvoltare pentru Fizica si Inginerie Nucleara 'Horia Hulubei'; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Commissariat à l'Énergie Atomique et aux Énergies Alternatives; Ministerul Cercetării, Inovării şi Digitalizării","keywords":"Radiation; Carbon fibers; Ion; Optics; Materials science; Computer science; Artificial intelligence; Physics","score_opus":0.01617918911698792,"score_gpt":0.2706806696751242,"score_spread":0.2545014805581363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401267147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8859054,0.024637071,0.08783308,0.000029278806,0.0002189196,0.00052843743,0.000010151211,0.0004408391,0.00039682887],"genre_scores_gemma":[0.99189514,0.0034375815,0.0043325014,0.0000010744953,0.00020112259,0.000053022944,0.00003319432,0.000030933053,0.000015414109],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924505,0.000049001086,0.00017697546,0.00017371605,0.00021488035,0.00014036657],"domain_scores_gemma":[0.99951565,0.00030455674,0.000023536308,0.000079623285,0.0000417702,0.000034836798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003524892,0.00012268384,0.00017565688,0.000140356,0.000028394497,0.000034291483,0.00004105368,0.00006787164,0.0000083146615],"category_scores_gemma":[0.00011865258,0.00011467185,0.000035817313,0.00011321874,0.00002743024,0.000114818875,0.000014080303,0.00015401376,0.000001563172],"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":[0.000011598556,0.000007588192,0.0026492886,0.0013528571,0.000025396432,0.0000014789309,0.00016128266,0.0011814922,0.66715944,0.00012047596,0.000032158678,0.3272969],"study_design_scores_gemma":[0.0002789385,0.00016754188,0.038245745,0.00039875766,0.00007254244,0.0000014534185,0.00000880832,0.1300237,0.8296277,0.0007843809,0.00024399086,0.00014642932],"about_ca_topic_score_codex":0.000001807114,"about_ca_topic_score_gemma":8.410107e-7,"teacher_disagreement_score":0.3271505,"about_ca_system_score_codex":0.000072337345,"about_ca_system_score_gemma":0.000008520609,"threshold_uncertainty_score":0.46761823},"labels":[],"label_agreement":null},{"id":"W4401872285","doi":"10.1016/j.optcom.2024.131024","title":"WaveFusionNet: Infrared and visible image fusion based on multi-scale feature encoder–decoder and discrete wavelet decomposition","year":2024,"lang":"en","type":"article","venue":"Optics Communications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"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":"Wavelet; Encoder; Image fusion; Optics; Computer science; Feature (linguistics); Infrared; Scale (ratio); Artificial intelligence; Decomposition; Computer vision; Image (mathematics); Pattern recognition (psychology); Physics","score_opus":0.010754086860266876,"score_gpt":0.288722556710692,"score_spread":0.27796846985042517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401872285","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00952281,0.0035625333,0.955901,0.0030959437,0.00014182487,0.00071517326,0.00021704877,0.0017409197,0.025102736],"genre_scores_gemma":[0.20549107,0.0042964593,0.78935254,0.00012349593,0.00001875685,0.00007446312,0.00023638584,0.00006362409,0.0003432381],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925154,0.0000478027,0.000193497,0.00021790747,0.00011534598,0.00017388514],"domain_scores_gemma":[0.99857783,0.000253226,0.000024664163,0.0009924774,0.00005777563,0.00009403402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011641146,0.00019575142,0.00015323049,0.00018931845,0.00027862302,0.00018097069,0.0002586044,0.000120439196,0.000030163572],"category_scores_gemma":[0.00003205195,0.00019107891,0.000038558104,0.00022393027,0.00013231827,0.00027861688,0.00028094332,0.00042544142,0.000013081505],"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":[0.000047643156,0.00036997904,0.00013558925,0.0007655604,0.00009086651,0.000029089368,0.0027872392,0.0052810684,0.86536133,0.007953719,0.009669449,0.10750849],"study_design_scores_gemma":[0.0002204126,0.000043103668,0.0005602157,0.000269497,0.00002329705,0.00000829716,0.0000698251,0.9819199,0.009533027,0.0009858162,0.0061404943,0.00022612051],"about_ca_topic_score_codex":0.0000037295129,"about_ca_topic_score_gemma":0.000023227227,"teacher_disagreement_score":0.97663885,"about_ca_system_score_codex":0.000053291995,"about_ca_system_score_gemma":0.00001399079,"threshold_uncertainty_score":0.7791972},"labels":[],"label_agreement":null},{"id":"W4402210587","doi":"10.2139/ssrn.4946996","title":"Dadigan: A Dual Attention Blocks-Based Disentangled Iterative Generative Adversarial Network for Cloud and Shadow Removal on Sar and Optical Images","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Shadow (psychology); Dual (grammatical number); Generative grammar; Generative adversarial network; Computer science; Cloud computing; Adversarial system; Artificial intelligence; Computer vision; Image (mathematics); Psychology; Art","score_opus":0.006666634280787794,"score_gpt":0.2506754274590271,"score_spread":0.2440087931782393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402210587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18525127,0.017617323,0.78932333,0.0014278763,0.0027172824,0.0020494661,0.00028550767,0.0007521223,0.0005758359],"genre_scores_gemma":[0.8860031,0.007514766,0.09634177,0.00018212167,0.00813883,0.00018676446,0.00029207484,0.0003583835,0.0009822021],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99708396,0.000086273845,0.0004827824,0.00058341597,0.00027895163,0.0014846099],"domain_scores_gemma":[0.9992116,0.00016651119,0.00013964149,0.00021572883,0.000114936156,0.00015155393],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007449688,0.00054634345,0.00053760374,0.00019643157,0.00023340441,0.00029637342,0.0001510743,0.00031631184,0.000009786956],"category_scores_gemma":[0.00008377779,0.000507318,0.00021620927,0.00011422834,0.00012403913,0.00011418708,0.00022683346,0.0037958887,0.00000223272],"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":[0.00812063,0.000696,0.00030947838,0.0024890609,0.008928506,0.0014306438,0.0022489368,0.21869697,0.10950098,0.31096852,0.0359257,0.30068457],"study_design_scores_gemma":[0.0033855643,0.0017887291,0.00006387486,0.0014062172,0.00074555364,0.0012236313,0.00038494414,0.18489067,0.012974127,0.7900614,0.0015499551,0.0015253283],"about_ca_topic_score_codex":0.0000041474837,"about_ca_topic_score_gemma":0.000049863927,"teacher_disagreement_score":0.70075184,"about_ca_system_score_codex":0.0010023428,"about_ca_system_score_gemma":0.00051904283,"threshold_uncertainty_score":0.99973786},"labels":[],"label_agreement":null},{"id":"W4402265358","doi":"10.1109/igarss53475.2024.10642314","title":"Synchronizing Spatiotemporal Reflectance Fusion via Dual Bayesian Nonparametric Inference and Explicit Downsampling","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Upsampling; Synchronizing; Computer science; Inference; Bayesian inference; Dual (grammatical number); Artificial intelligence; Reflectivity; Bayesian probability; Fusion; Nonparametric statistics; Mathematics; Econometrics; Image (mathematics); Optics; Physics; Telecommunications","score_opus":0.012530951211547878,"score_gpt":0.27486549900434987,"score_spread":0.26233454779280196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402265358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048583444,0.0020799201,0.9424811,0.000048258873,0.00020191714,0.00018381124,0.00000235429,0.0025347292,0.0038844731],"genre_scores_gemma":[0.88872045,0.0005346092,0.11043579,0.000032696513,0.00008003945,0.000028260794,0.0000048570046,0.00004830959,0.00011497568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897164,0.000011884687,0.000264481,0.0003177145,0.00015629108,0.00027799094],"domain_scores_gemma":[0.99947256,0.00016870249,0.000019000665,0.00022054306,0.000032557182,0.000086614535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010050825,0.0002108023,0.0001799364,0.00033373752,0.00007674331,0.00013288479,0.000089496105,0.00009797439,0.00012579655],"category_scores_gemma":[0.00006531901,0.00020059438,0.00003276685,0.0007132318,0.000026890864,0.00047558098,0.00008485313,0.000287692,0.00002892884],"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":[0.000008824458,0.000017738335,0.00045039694,0.00057870697,0.000031023483,0.0001670598,0.00044811607,0.0026844398,0.32793987,0.0028929894,0.0009393612,0.6638415],"study_design_scores_gemma":[0.00010372297,0.00006821928,0.00022878348,0.00032764062,0.000011684734,0.00007302015,0.000038167174,0.86023986,0.13212493,0.004482032,0.0018344463,0.00046751712],"about_ca_topic_score_codex":0.000026722588,"about_ca_topic_score_gemma":0.000017401524,"teacher_disagreement_score":0.8575554,"about_ca_system_score_codex":0.00011247519,"about_ca_system_score_gemma":0.000016792712,"threshold_uncertainty_score":0.81800014},"labels":[],"label_agreement":null},{"id":"W4402365431","doi":"10.1016/j.jag.2024.104130","title":"NR-IQA for UAV hyperspectral image based on distortion constructing, feature screening, and machine learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Hyperspectral imaging; Distortion (music); Feature (linguistics); Artificial intelligence; Image (mathematics); Computer science; Computer vision; Pattern recognition (psychology); Geography; Remote sensing; Cartography; Telecommunications; Linguistics","score_opus":0.009363322418488522,"score_gpt":0.23121955446177359,"score_spread":0.22185623204328506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402365431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09560778,0.00019795446,0.90079004,0.00078779634,0.00041653466,0.00022893374,0.00003198718,0.00023026012,0.001708696],"genre_scores_gemma":[0.8114735,0.00020991609,0.18757759,0.00025441116,0.00021670279,0.000011148345,0.00015416309,0.000022432192,0.0000801771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928,0.0000054314837,0.00030632113,0.00007547033,0.00024454133,0.00008825848],"domain_scores_gemma":[0.9995058,0.00008525628,0.00013902159,0.00003904935,0.00018622281,0.000044637312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002217055,0.00011333387,0.00010951987,0.00025654407,0.00005615327,0.00017699956,0.00006284991,0.000057714064,0.000022109853],"category_scores_gemma":[0.00006492815,0.00010513476,0.000045689438,0.00007720547,0.000028057393,0.0007417694,0.000010414269,0.0002505608,0.0000017586615],"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":[0.000547394,0.000022985809,0.00084413006,0.0003279796,0.00014600945,0.000007903552,0.00143239,0.05110966,0.026803922,0.011686654,0.0021454934,0.90492547],"study_design_scores_gemma":[0.00094829453,0.00012716303,0.0029468373,0.00020438952,0.000022077433,0.000058018162,0.00023303374,0.92099506,0.02029034,0.001132302,0.05286417,0.0001783351],"about_ca_topic_score_codex":0.0000011959764,"about_ca_topic_score_gemma":0.0000016882641,"teacher_disagreement_score":0.9047471,"about_ca_system_score_codex":0.000041133597,"about_ca_system_score_gemma":0.000015399504,"threshold_uncertainty_score":0.42872712},"labels":[],"label_agreement":null},{"id":"W4402508004","doi":"10.1016/j.eja.2024.127333","title":"SSGAN: Cloud removal in satellite images using spatiospectral generative adversarial network","year":2024,"lang":"en","type":"article","venue":"European Journal of Agronomy","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Ottawa","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Satellite; Generative grammar; Adversarial system; Cloud computing; Generative adversarial network; Satellite image; Computer science; Environmental science; Remote sensing; Artificial intelligence; Image (mathematics); Geology; Engineering","score_opus":0.010824443396091935,"score_gpt":0.22879384670381647,"score_spread":0.21796940330772455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402508004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17313655,0.01763424,0.769227,0.00009779314,0.0027795811,0.00018938903,0.000004988255,0.00032589398,0.03660451],"genre_scores_gemma":[0.56567013,0.0005584353,0.42946547,0.00003602496,0.0039881268,5.126088e-7,0.0000026958296,0.000104091,0.00017451266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.998797,0.00016686255,0.00052224915,0.00012905811,0.00013041109,0.00025444725],"domain_scores_gemma":[0.99962705,0.000059282083,0.000086632775,0.00011208854,0.000038063332,0.000076880475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058557506,0.00017311,0.00021677873,0.00017143316,0.000034631146,0.00010032061,0.00017738249,0.00002181888,0.00005834629],"category_scores_gemma":[0.000024489707,0.00016184311,0.000111049936,0.00024858883,0.00004045163,0.00047865376,0.000034150118,0.00046351974,0.000027364504],"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":[0.000104834086,0.0000518828,0.000539992,0.00014351911,0.00039184294,0.015570476,0.0038311922,0.68139523,0.08117003,0.0016642007,0.03530652,0.17983025],"study_design_scores_gemma":[0.0035365939,0.0008959081,0.012570368,0.0041797836,0.0003241007,0.004752674,0.00069933216,0.07918572,0.17531781,0.0074374247,0.70912313,0.0019771217],"about_ca_topic_score_codex":0.000001379474,"about_ca_topic_score_gemma":0.0000010747178,"teacher_disagreement_score":0.6738166,"about_ca_system_score_codex":0.00017288388,"about_ca_system_score_gemma":0.000037362646,"threshold_uncertainty_score":0.6599771},"labels":[],"label_agreement":null},{"id":"W4402742276","doi":"10.1109/lgrs.2024.3465890","title":"Joint Weighted Schatten- <i>p</i> Norm and Spatial Smoothness Regularization for Hyperspectral and Multispectral Image Fusion With Spectral Variability","year":2024,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Multispectral image; Regularization (linguistics); Mathematics; Image fusion; Norm (philosophy); Artificial intelligence; Computer science; Remote sensing; Pattern recognition (psychology); Computer vision; Geology; Image (mathematics); Political science","score_opus":0.00574409848803985,"score_gpt":0.2078210107633436,"score_spread":0.20207691227530375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402742276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45261535,0.000063768326,0.54589576,0.0006367573,0.00021735593,0.00024229115,0.0000063129382,0.00029461342,0.000027811673],"genre_scores_gemma":[0.5766142,0.00015660074,0.42272335,0.00024457576,0.00018046856,6.9675724e-7,0.0000061985866,0.000042799475,0.000031098287],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870676,0.000024365461,0.00019221564,0.0005307151,0.00017071812,0.00037522538],"domain_scores_gemma":[0.99959046,0.000065387336,0.000029348115,0.00017210051,0.000039897157,0.00010280292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026386118,0.00023483863,0.00020923656,0.00015174178,0.00022377048,0.00022924761,0.0000504736,0.0000730575,0.0000011982261],"category_scores_gemma":[0.000024473278,0.0001932485,0.00003051835,0.00023281893,0.00045436047,0.0004183565,0.000027626767,0.00020672273,4.299943e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019006211,0.0000036213976,0.000015756714,0.00013903326,0.000007308244,0.000035673587,0.0003576681,0.00003750458,0.9005522,0.000028027373,0.000052911942,0.09875127],"study_design_scores_gemma":[0.0003501991,0.00009623207,0.0013478908,0.00028301126,0.000034392175,0.00028426168,0.00006289808,0.7806702,0.21515861,0.0012324469,0.00010745136,0.00037236023],"about_ca_topic_score_codex":0.00015310833,"about_ca_topic_score_gemma":0.000047884532,"teacher_disagreement_score":0.78063273,"about_ca_system_score_codex":0.0000672555,"about_ca_system_score_gemma":0.00001833936,"threshold_uncertainty_score":0.7880445},"labels":[],"label_agreement":null},{"id":"W4402916565","doi":"10.1109/jstars.2024.3469974","title":"Landsat-8 and Sentinel-2 Image Fusion Based on Multiscale Smoothing-Sharpening Filter","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Nanjing University of Aeronautics and Astronautics; Division of Graduate Education; Nanjing University; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Sharpening; Image fusion; Smoothing; Filter (signal processing); Computer science; Scale (ratio); Computer vision; Artificial intelligence; Fusion; Image (mathematics); Physics","score_opus":0.013187904690108361,"score_gpt":0.23258042859906225,"score_spread":0.2193925239089539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6373515,0.00026474454,0.36045942,0.00044111547,0.0002726438,0.00014974129,0.0000039280153,0.00019307878,0.0008637883],"genre_scores_gemma":[0.6403007,0.00033587572,0.35885468,0.00015822459,0.00024628718,1.3397893e-7,0.0000050462386,0.000038215017,0.00006086988],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915963,0.000018260564,0.0003497877,0.00013708565,0.00017183607,0.00016336679],"domain_scores_gemma":[0.99956566,0.00011950845,0.000057629128,0.00009190596,0.00011118725,0.000054109987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020314779,0.00014128204,0.00019152787,0.00029626812,0.00007799473,0.0001113473,0.000041372383,0.00008940185,0.0000059049084],"category_scores_gemma":[0.000050488263,0.00012690754,0.000027824282,0.0003802734,0.000029695933,0.00013272175,0.000013942808,0.0005089082,6.0212324e-7],"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":[0.00002604887,0.000012498496,0.00013314644,0.0002146263,0.000021974774,0.00012596347,0.00043712935,0.008803974,0.6649826,0.00003583831,0.00052990403,0.32467628],"study_design_scores_gemma":[0.0003679663,0.000026276057,0.003977219,0.0006451693,0.000015019118,0.0000612552,0.00002591357,0.9488234,0.04224862,0.00046399984,0.003183999,0.00016119637],"about_ca_topic_score_codex":0.0000061254127,"about_ca_topic_score_gemma":0.000014791509,"teacher_disagreement_score":0.94001937,"about_ca_system_score_codex":0.000033860433,"about_ca_system_score_gemma":0.00002672865,"threshold_uncertainty_score":0.51751393},"labels":[],"label_agreement":null},{"id":"W4403147773","doi":"10.1016/j.eswa.2024.125472","title":"SFINet: A semantic feature interactive learning network for full-time infrared and visible image fusion","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Royal Military College of Canada","funders":"National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Artificial intelligence; Semantic feature; Fusion; Infrared; Image (mathematics); Computer vision; Pattern recognition (psychology)","score_opus":0.0037517997993182636,"score_gpt":0.24175441309757142,"score_spread":0.23800261329825317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403147773","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005775194,0.0076113236,0.98552763,0.00012876073,0.00010208305,0.0018175767,0.000021475325,0.0019797508,0.002233851],"genre_scores_gemma":[0.46905607,0.0013997034,0.4755546,0.00013241902,0.0025373914,0.03044749,0.00052846124,0.0006288112,0.019715063],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991832,0.000017986138,0.00017946586,0.00029649146,0.0000970067,0.00022589116],"domain_scores_gemma":[0.9993683,0.00020843363,0.000038344024,0.00023997914,0.00007520899,0.00006971988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008453151,0.00019516387,0.0002106648,0.00010304309,0.00016939086,0.00016255095,0.00009755098,0.000082933904,0.00001886161],"category_scores_gemma":[0.000016411574,0.000165605,0.000034780755,0.00032969232,0.000032201267,0.00026274976,0.000040266266,0.00021989012,0.000037735477],"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":[0.0000975365,0.00004590255,0.00004070179,0.0014750501,0.00024386727,0.00001688139,0.0031675072,0.022057636,0.63982546,0.0018975522,0.30554166,0.025590219],"study_design_scores_gemma":[0.00020476115,0.000107371896,0.000020205845,0.00076286925,0.000021920365,0.000097994285,0.0004132031,0.47105956,0.0034400648,0.00029192035,0.52323437,0.00034577213],"about_ca_topic_score_codex":0.000009289006,"about_ca_topic_score_gemma":0.000002490088,"teacher_disagreement_score":0.6363854,"about_ca_system_score_codex":0.0000638421,"about_ca_system_score_gemma":0.00001495306,"threshold_uncertainty_score":0.6753176},"labels":[],"label_agreement":null},{"id":"W4403687112","doi":"10.1016/j.jag.2024.104227","title":"SLRCNN: Integrating sparse and low-rank with a CNN denoiser for hyperspectral and multispectral image fusion","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Multispectral image; Hyperspectral imaging; Artificial intelligence; Rank (graph theory); Image fusion; Pattern recognition (psychology); Computer science; Remote sensing; Image (mathematics); Computer vision; Geography; Mathematics","score_opus":0.006567199867781173,"score_gpt":0.22503605933225984,"score_spread":0.21846885946447867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403687112","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7570221,0.00021778753,0.2412906,0.00030602104,0.00017152193,0.00024718998,0.000013178386,0.0001144049,0.00061719364],"genre_scores_gemma":[0.8553053,0.000515245,0.14381213,0.00013084584,0.000145549,0.000013581944,0.000030071484,0.000018271343,0.000029011746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992469,0.0000036262018,0.0003558272,0.00008548039,0.00020407033,0.00010410155],"domain_scores_gemma":[0.9995307,0.00006573009,0.00010301638,0.000040783056,0.00020329324,0.00005648262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018137571,0.00012678077,0.00012733275,0.00021156622,0.000051445786,0.00023674175,0.000059981405,0.000048222817,0.000014132945],"category_scores_gemma":[0.0000264789,0.00010324297,0.000026046377,0.00007933387,0.00003936288,0.0012596272,0.000018399507,0.0001634654,0.0000015327357],"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":[0.0009839709,0.000046232653,0.0008087163,0.0007864509,0.00031840013,0.000033861445,0.009537957,0.006428111,0.2917394,0.030487105,0.0010870236,0.65774274],"study_design_scores_gemma":[0.0027161508,0.0003318827,0.01851039,0.0007808311,0.000065881104,0.000435637,0.0017922503,0.90110385,0.06141735,0.0050125816,0.0073501836,0.0004830007],"about_ca_topic_score_codex":0.000002824193,"about_ca_topic_score_gemma":0.000009115691,"teacher_disagreement_score":0.89467573,"about_ca_system_score_codex":0.000033388456,"about_ca_system_score_gemma":0.000019638885,"threshold_uncertainty_score":0.4210126},"labels":[],"label_agreement":null},{"id":"W4403779027","doi":"10.1007/978-3-031-72698-9_22","title":"Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Huawei Technologies (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Correlation; Spatial correlation; Algorithm; Linear correlation; Artificial intelligence; Statistical physics; Pattern recognition (psychology); Physics; Statistics; Mathematics; Telecommunications; Geometry","score_opus":0.01011856243330203,"score_gpt":0.24370538579066742,"score_spread":0.2335868233573654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403779027","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001798534,0.0010927073,0.9934334,0.00017703153,0.001901223,0.0006865946,0.00001470393,0.0009192871,0.001595186],"genre_scores_gemma":[0.3671303,0.00037493184,0.62803066,0.00006168311,0.0017326756,0.00006275891,0.0000699341,0.00018855814,0.0023484784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763435,0.00000976376,0.00043185445,0.0008774128,0.0005211589,0.0005254745],"domain_scores_gemma":[0.9990937,0.00018643324,0.00009848064,0.0003488331,0.0001801206,0.00009243194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032581144,0.00047356752,0.00037055675,0.0005183782,0.00020039895,0.0001628778,0.0004938659,0.00035846882,0.0000661308],"category_scores_gemma":[0.00006316409,0.0004897959,0.000153117,0.00027823987,0.0002760835,0.00043527666,0.00019395206,0.0012070733,0.000048606853],"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":[0.000013549845,0.0000067175465,0.00004745093,0.00016018125,0.000014177841,0.000041022722,0.00026118217,0.87573826,0.0026901374,0.0018673204,0.00005545315,0.119104534],"study_design_scores_gemma":[0.00014553894,0.00020753889,0.000086201464,0.00081463315,0.000024108072,0.000047361827,7.871189e-7,0.95848566,0.0047962503,0.03233674,0.0025270667,0.00052810897],"about_ca_topic_score_codex":0.00001946602,"about_ca_topic_score_gemma":0.00018508693,"teacher_disagreement_score":0.36695045,"about_ca_system_score_codex":0.00060880475,"about_ca_system_score_gemma":0.00009011717,"threshold_uncertainty_score":0.9997554},"labels":[],"label_agreement":null},{"id":"W4404399962","doi":"10.1007/978-981-96-0026-7_10","title":"EBcGAN: An Edge-Based Conditional Generative Adversarial Network for Image Fusion","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Toronto","funders":"","keywords":"Computer science; Adversarial system; Generative grammar; Image (mathematics); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Generative adversarial network; Image fusion; Computer vision; Fusion; Theoretical computer science; Linguistics","score_opus":0.010686385399949863,"score_gpt":0.2520217452880604,"score_spread":0.2413353598881105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404399962","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016438546,0.00032581534,0.9942304,0.00011549621,0.002573111,0.00066442526,0.00013372116,0.0006533833,0.0012872177],"genre_scores_gemma":[0.007828133,0.000020631474,0.9875919,0.0006433484,0.0032360281,0.00006255532,0.00026645453,0.000111672816,0.00023924312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980549,0.000009649016,0.00032889293,0.00077185006,0.00040072593,0.00043393634],"domain_scores_gemma":[0.9989244,0.00027262283,0.00006885817,0.00043944243,0.00018547243,0.00010922731],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027909962,0.0004366724,0.00035289535,0.00035812805,0.00017621275,0.00019086532,0.0005861562,0.00027844444,0.0000968269],"category_scores_gemma":[0.000034197456,0.00042245892,0.000117464755,0.00022235213,0.00043168096,0.00032300258,0.00017545489,0.0005858736,0.000020537702],"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":[0.000021209195,0.000011354694,7.380126e-7,0.00010416098,0.000012602371,0.00005784384,0.000113207934,0.8883595,0.007880323,0.005985684,0.0013577917,0.096095614],"study_design_scores_gemma":[0.00018465125,0.00013453794,0.000002121909,0.00023344182,0.000011513489,0.000008883924,7.068826e-8,0.7205898,0.018986413,0.25367418,0.0057585123,0.00041584318],"about_ca_topic_score_codex":0.0000017758473,"about_ca_topic_score_gemma":0.000024720004,"teacher_disagreement_score":0.2476885,"about_ca_system_score_codex":0.0003057326,"about_ca_system_score_gemma":0.00018249496,"threshold_uncertainty_score":0.99982274},"labels":[],"label_agreement":null},{"id":"W4404423334","doi":"10.18280/ts.410503","title":"Pyramid Model of Parallel Fusion Attention Mechanism Based on Channel-Coordinates and Its Application in Medical Images","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Pyramid (geometry); Computer vision; Mechanism (biology); Channel (broadcasting); Computer science; Artificial intelligence; Fusion; Mathematics; Telecommunications; Geometry; Physics","score_opus":0.009436836604066526,"score_gpt":0.24054980721797214,"score_spread":0.23111297061390562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404423334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08433403,0.00025675254,0.91398215,0.00031083013,0.00003767483,0.00042094616,0.000021383763,0.00040125212,0.0002349998],"genre_scores_gemma":[0.9960516,0.00018641354,0.0034421487,0.000047808462,0.000026562373,0.00016585387,0.00003032737,0.000028627617,0.000020627207],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903613,0.000017190598,0.00027215958,0.0002150336,0.00030825837,0.00015120511],"domain_scores_gemma":[0.9997655,0.000044117973,0.000024532514,0.00008498031,0.000026494348,0.000054336862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002670776,0.00014367467,0.00014786255,0.00021924908,0.000021487502,0.000013763442,0.00009047273,0.000085738546,0.00007974289],"category_scores_gemma":[0.00001046866,0.0001361915,0.00003630498,0.0001433675,0.00002150763,0.00014040332,0.000027373366,0.00014659757,0.0000051326647],"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":[0.000040037714,0.000116510215,0.000012967539,0.00051343127,0.000012640562,0.000013338099,0.0001224652,0.13406245,0.8167935,0.0060213953,0.000188908,0.042102344],"study_design_scores_gemma":[0.00027998883,0.000065481545,0.000089489426,0.00022874068,0.0000071459594,0.0000011730773,0.000012293857,0.8640442,0.13000272,0.00513849,0.000020533695,0.00010977002],"about_ca_topic_score_codex":0.0000043728382,"about_ca_topic_score_gemma":0.0000027853737,"teacher_disagreement_score":0.9117176,"about_ca_system_score_codex":0.00004990547,"about_ca_system_score_gemma":0.000012689688,"threshold_uncertainty_score":0.55537283},"labels":[],"label_agreement":null},{"id":"W4404608881","doi":"10.23919/eusipco63174.2024.10715320","title":"A Variational Approach for Robust Online Fusion of Multiresolution Multispectral Images","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Division of Electrical, Communications and Cyber Systems; Agence Nationale de la Recherche; National Science Foundation","keywords":"Multispectral image; Fusion; Artificial intelligence; Computer vision; Computer science; Image fusion; Multiresolution analysis; Sensor fusion; Pattern recognition (psychology); Image (mathematics); Wavelet; Wavelet transform","score_opus":0.017179421896748724,"score_gpt":0.26011393052652043,"score_spread":0.2429345086297717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404608881","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008806331,0.0003967363,0.9953781,0.000030803774,0.00011595305,0.00033154426,0.0001570231,0.0012002314,0.0015089802],"genre_scores_gemma":[0.14232485,0.00006077384,0.8568534,0.0000066564635,0.00009758951,0.000053775475,0.00018136355,0.000030132993,0.00039144987],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994051,0.000005991895,0.00019862247,0.00015226609,0.00010353322,0.0001344755],"domain_scores_gemma":[0.99973637,0.000067004905,0.000013852214,0.000104923885,0.000052022566,0.000025853373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077721925,0.00010331919,0.00011090309,0.000118298645,0.000023678467,0.000014669342,0.00007166863,0.00005808511,0.00006199304],"category_scores_gemma":[0.00003673795,0.000093120776,0.000066939465,0.00013960763,0.000021110562,0.00013510771,0.000023238128,0.000086341824,0.0000017889406],"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":[0.000026560354,0.00018047036,0.000027128266,0.0007691516,0.000051358355,0.0000022625065,0.00015438582,0.23495671,0.6292887,0.0063184337,0.008585518,0.119639345],"study_design_scores_gemma":[0.0001486605,0.000026453517,0.00032655755,0.000028717743,0.000009195517,0.0000036520787,0.000024822735,0.93735003,0.060869314,0.0004028205,0.0007084723,0.00010129449],"about_ca_topic_score_codex":0.000013445471,"about_ca_topic_score_gemma":0.000004051082,"teacher_disagreement_score":0.70239335,"about_ca_system_score_codex":0.000054758773,"about_ca_system_score_gemma":0.000011347668,"threshold_uncertainty_score":0.3797355},"labels":[],"label_agreement":null},{"id":"W4405303762","doi":"10.1109/tip.2024.3512365","title":"VDMUFusion: A Versatile Diffusion Model-Based Unsupervised Framework for Image Fusion","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Image fusion; Artificial intelligence; Image processing; Computer vision; Image (mathematics); Diffusion; Pattern recognition (psychology)","score_opus":0.012938292560673774,"score_gpt":0.2768048105106809,"score_spread":0.26386651795000715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405303762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035210007,0.00056736375,0.98962295,0.0002434324,0.0006036139,0.0007275421,0.00011255112,0.004136328,0.00046520325],"genre_scores_gemma":[0.5096131,0.000114878785,0.48934755,0.0001304188,0.000081643775,0.00037608788,0.000013194958,0.00017596612,0.00014716308],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814016,0.000022564038,0.00042756408,0.00056924723,0.00034572638,0.0004947665],"domain_scores_gemma":[0.9990125,0.00022920556,0.000039100352,0.00040577748,0.00016280204,0.00015067328],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016120933,0.00044067772,0.00030994535,0.00045746434,0.0004686945,0.0003523082,0.00025369143,0.0002485581,0.00023975248],"category_scores_gemma":[0.000023207183,0.00043762295,0.00024486982,0.00066968164,0.0001093532,0.0009245017,0.0000036477154,0.00066726404,0.00005489217],"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":[0.00009461475,0.00012628584,1.5253157e-7,0.0014502297,0.000017195409,0.000027179947,0.00047788562,0.06596777,0.5940984,0.000025927582,0.000623702,0.33709067],"study_design_scores_gemma":[0.000284897,0.000057215944,5.4866774e-7,0.0008951861,0.000046904926,0.000005415199,0.00006025171,0.6552439,0.34004554,0.002667163,0.0003662222,0.00032674853],"about_ca_topic_score_codex":0.0000024008402,"about_ca_topic_score_gemma":0.000002057139,"teacher_disagreement_score":0.58927613,"about_ca_system_score_codex":0.00022737314,"about_ca_system_score_gemma":0.00011786901,"threshold_uncertainty_score":0.99980754},"labels":[],"label_agreement":null},{"id":"W4405469221","doi":"10.1190/image2024-4094828.1","title":"CNN for image super-resolution of airborne magnetic data in Ontario, Canada","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1,"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":"Image resolution; Computer science; Computer vision; Remote sensing; Artificial intelligence; Resolution (logic); Image (mathematics); Geology","score_opus":0.01302217163675819,"score_gpt":0.23362157253984,"score_spread":0.2205994009030818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405469221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04225365,0.0028228757,0.9279141,0.00038617925,0.00078005233,0.0010589412,0.0004445468,0.0011784006,0.023161272],"genre_scores_gemma":[0.7795096,0.00005042944,0.21669255,0.00004021107,0.000029790659,0.000054159318,0.00022150385,0.000048933067,0.0033528472],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994304,0.0000036453027,0.00018121643,0.00015248035,0.00008471839,0.00014751118],"domain_scores_gemma":[0.9995572,0.000049121663,0.0000049738264,0.00034492373,0.000019119527,0.000024677294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007550439,0.000080682956,0.00010341797,0.000067705965,0.0000075435737,0.000010680399,0.00018684559,0.000028090217,0.00053477427],"category_scores_gemma":[0.00002937194,0.00007982646,0.000012964485,0.00010508758,0.000013811138,0.00020692065,0.00006999784,0.00007779179,0.0000023790358],"study_design_candidate":"not_applicable","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":[0.000027819398,0.000051171926,0.00041679636,0.0012670744,0.000034422756,0.000121205565,0.00030402874,0.0026799545,0.19961956,0.0023392362,0.6671958,0.12594295],"study_design_scores_gemma":[0.0003463028,0.00009567157,0.0052333125,0.00021497678,0.000022950608,0.000010668122,0.000091511436,0.549638,0.10704636,0.001428753,0.33545873,0.00041279732],"about_ca_topic_score_codex":0.8886958,"about_ca_topic_score_gemma":0.99440247,"teacher_disagreement_score":0.73725593,"about_ca_system_score_codex":0.00026933593,"about_ca_system_score_gemma":0.00017289964,"threshold_uncertainty_score":0.58554035},"labels":[],"label_agreement":null},{"id":"W4406297599","doi":"10.23977/acss.2024.080708","title":"A Frequency Decomposition and Gaussian-Based Enhancement Network for Infrared and Visible Image Fusion","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Image fusion; Fusion; Decomposition; Infrared; Gaussian; Image (mathematics); Artificial intelligence; Image enhancement; Computer science; Computer vision; Physics; Optics; Chemistry","score_opus":0.006996983007037591,"score_gpt":0.2704261604808386,"score_spread":0.263429177473801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406297599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015352951,0.06897614,0.9142034,0.000020837797,0.00047912548,0.0005637554,0.00001386311,0.00023930664,0.0001506351],"genre_scores_gemma":[0.7027975,0.0054962966,0.29099697,0.000059402875,0.0002908853,0.00028157094,0.000021699087,0.00003883128,0.000016870845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908316,0.000029963901,0.0003056032,0.00028578046,0.00008381988,0.00021168136],"domain_scores_gemma":[0.9996004,0.00017749741,0.000032305223,0.000106837884,0.00002684812,0.000056111898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020390772,0.00017453059,0.00024206936,0.00010845723,0.00006613318,0.00017178485,0.000051231826,0.000056008186,0.000004419843],"category_scores_gemma":[0.0000030928652,0.000159541,0.000021457861,0.00013070967,0.00003559471,0.0004583589,0.000032564712,0.000085292595,6.69407e-7],"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":[0.000082462604,0.00008091847,0.0014704843,0.011562162,0.00008730287,0.00012766586,0.00080835103,0.13248478,0.18221307,0.0064042015,0.0040617697,0.6606168],"study_design_scores_gemma":[0.00032046405,0.00018634505,0.00008614888,0.0017066643,0.000008065602,0.000011762416,0.000013697391,0.98083496,0.0052281823,0.0074092587,0.0039545903,0.00023988065],"about_ca_topic_score_codex":0.0000051595134,"about_ca_topic_score_gemma":0.000005167554,"teacher_disagreement_score":0.84835017,"about_ca_system_score_codex":0.000031806445,"about_ca_system_score_gemma":0.000007354136,"threshold_uncertainty_score":0.6505893},"labels":[],"label_agreement":null},{"id":"W4407628149","doi":"10.1109/lacap63752.2024.10876249","title":"True Rays Model Integration in OMNeT++ for IIoT Network Planning in Underground Mines","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer science; Mining engineering; Engineering","score_opus":0.021508773798121415,"score_gpt":0.29210666984104267,"score_spread":0.2705978960429212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407628149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006459894,0.0011420654,0.98721004,0.00005696993,0.00016018594,0.00035248813,0.0000022197548,0.00076385145,0.0038522757],"genre_scores_gemma":[0.80488056,0.00009353845,0.19375917,0.00005904494,0.00008737166,0.00021682144,0.000020614263,0.000047727015,0.0008351423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935704,0.000007093182,0.00023200786,0.00015132694,0.000053340078,0.00019917847],"domain_scores_gemma":[0.9997276,0.00013532423,0.000008204524,0.00009880485,0.000011123089,0.000018942485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013033394,0.000120677716,0.00014698594,0.00017241454,0.000013984961,0.000045446275,0.00007205023,0.00007432752,0.0000110769015],"category_scores_gemma":[0.000035375608,0.00011079603,0.000029927187,0.0002724507,0.0000083195355,0.0002786376,0.00001755953,0.00012800939,0.0000020019156],"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":[0.000008888721,0.000006251272,0.000036228426,0.00038251298,0.000004003403,0.0000069370026,0.00027223455,0.97220784,0.006268705,0.007074825,0.007841954,0.0058896267],"study_design_scores_gemma":[0.000100162884,0.000013239181,0.000018354009,0.00081888575,0.0000027173478,0.0000011650607,0.000074248856,0.95585686,0.004414634,0.038261596,0.00031081476,0.0001273282],"about_ca_topic_score_codex":0.0000110177525,"about_ca_topic_score_gemma":0.00032328904,"teacher_disagreement_score":0.79842067,"about_ca_system_score_codex":0.00011344742,"about_ca_system_score_gemma":0.00000999482,"threshold_uncertainty_score":0.4518131},"labels":[],"label_agreement":null},{"id":"W4407857683","doi":"10.53555/sfs.v10i2.3394","title":"Advanced Minimum Method for fusion of True Color Images","year":2023,"lang":"en","type":"article","venue":"Journal of Survey in Fisheries Sciences","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Computer vision; Artificial intelligence; Fusion; Computer science; Computer graphics (images)","score_opus":0.10661374073701281,"score_gpt":0.334712876373992,"score_spread":0.2280991356369792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407857683","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86217076,0.00037174986,0.13481043,0.0003133178,0.001050338,0.00031213826,0.00007968947,0.0001440887,0.0007474703],"genre_scores_gemma":[0.5417902,0.0009910502,0.45687142,0.000030430256,0.00005659137,0.000022127939,0.000004037619,0.00002889439,0.0002052778],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866927,0.00010715151,0.00058560504,0.00011661976,0.00029074238,0.00023060829],"domain_scores_gemma":[0.9982017,0.0011981183,0.00023262676,0.00009904306,0.00022380515,0.000044705725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034816284,0.000112088,0.00034837323,0.00037885923,0.00006038822,0.000029244353,0.0003777497,0.000051602274,0.000028542367],"category_scores_gemma":[0.0014068482,0.000091795926,0.0000750009,0.0011396279,0.00020327792,0.0006399525,0.000050311137,0.00012090534,7.908913e-7],"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":[0.00034418734,0.000071816605,0.072182335,0.00027682897,0.000028415534,0.000027262242,0.0010616092,0.036798257,0.7172046,0.0000454901,0.031326734,0.14063247],"study_design_scores_gemma":[0.0014644492,0.0016602578,0.24567132,0.00040406873,0.000016072821,0.000043145214,0.0023581046,0.027781555,0.7036439,0.0065882313,0.009805792,0.00056309305],"about_ca_topic_score_codex":0.000022097838,"about_ca_topic_score_gemma":0.00008369314,"teacher_disagreement_score":0.322061,"about_ca_system_score_codex":0.000037377395,"about_ca_system_score_gemma":0.000056304914,"threshold_uncertainty_score":0.37433293},"labels":[],"label_agreement":null},{"id":"W4408062808","doi":"10.5220/0013153900003905","title":"Single Hyperspectral Image Super-Resolution Utilizing Implicit Neural Representations","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Ontario Tech University","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Image (mathematics); Resolution (logic); Computer vision; Artificial neural network; Pattern recognition (psychology)","score_opus":0.013991956621554284,"score_gpt":0.2837422763380413,"score_spread":0.269750319716487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408062808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07947179,0.00030445948,0.6442298,0.0006140735,0.00031081025,0.00031312584,0.000009210555,0.0038084255,0.27093828],"genre_scores_gemma":[0.87825966,0.00003113338,0.119819924,0.00013284713,0.000041205283,0.000036906134,0.00001218232,0.000026962123,0.0016391777],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992664,0.000013163402,0.00019553673,0.00018891033,0.00008321137,0.00025280216],"domain_scores_gemma":[0.9995642,0.000046488847,0.00001097741,0.0002885735,0.00005064125,0.000039170016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000040510946,0.00012540797,0.00011594501,0.00015828044,0.000090959365,0.00005075816,0.00012840703,0.00004856416,0.00018590156],"category_scores_gemma":[0.00006171622,0.00013212628,0.00006139023,0.00033046328,0.000041752006,0.0003554457,0.000056354886,0.00014233383,0.000022169308],"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":[0.0000030081783,0.000020318246,0.00009263738,0.0000156124,0.000009215416,0.000004230715,0.0000551409,0.0009039199,0.9764383,0.004310224,0.01059236,0.007555075],"study_design_scores_gemma":[0.00024263086,0.000026820317,0.0017469487,0.000024701114,0.000017455704,0.000013026975,0.0006351841,0.11210174,0.8767057,0.003756398,0.0044777086,0.0002516854],"about_ca_topic_score_codex":0.000046004614,"about_ca_topic_score_gemma":0.000019715751,"teacher_disagreement_score":0.7987879,"about_ca_system_score_codex":0.00011780587,"about_ca_system_score_gemma":0.000007842117,"threshold_uncertainty_score":0.53879535},"labels":[],"label_agreement":null},{"id":"W4409366430","doi":"10.1609/aaai.v39i8.32898","title":"Multi-axis Prompt and Multi-dimension Fusion Network for All-in-one Weather-degraded Image Restoration","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Dimension (graph theory); Image (mathematics); Fusion; Image fusion; Artificial intelligence; Computer science; Computer vision; Mathematics","score_opus":0.08361940241962,"score_gpt":0.3249781678598214,"score_spread":0.2413587654402014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409366430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49094656,0.0003598166,0.5001827,0.0014650344,0.0006251302,0.004574531,0.000020309086,0.00065475894,0.0011711332],"genre_scores_gemma":[0.8553688,0.00019504456,0.14391637,0.00006371242,0.000030764928,0.00022555277,0.0000023363534,0.000026035015,0.00017141669],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986605,0.000011359979,0.0005123498,0.00034963916,0.00016487534,0.00030127913],"domain_scores_gemma":[0.99922264,0.00008163997,0.00014140233,0.00017079157,0.00033960625,0.000043934557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036518453,0.00022905102,0.00027847738,0.00015723445,0.00012311325,0.0000723501,0.00033845843,0.00012943985,0.0000138895475],"category_scores_gemma":[0.00036630087,0.00019753177,0.000060162325,0.000405451,0.00015079223,0.0002640654,0.00013272725,0.0002639149,0.0000050543663],"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":[0.00010797881,0.00012378364,0.00023021932,0.00017461569,0.000010642557,1.9832399e-7,0.00044904495,0.0003541243,0.9296705,0.01470576,0.00023055793,0.053942554],"study_design_scores_gemma":[0.00008488671,0.00008041669,0.0004994752,0.0005367152,0.000013889136,4.7622794e-7,0.00019841982,0.30016613,0.67739075,0.02075865,0.00010561049,0.00016459166],"about_ca_topic_score_codex":0.00002315127,"about_ca_topic_score_gemma":0.00004310646,"teacher_disagreement_score":0.3644222,"about_ca_system_score_codex":0.000076516946,"about_ca_system_score_gemma":0.000023533816,"threshold_uncertainty_score":0.8055112},"labels":[],"label_agreement":null},{"id":"W4409791018","doi":"10.61091/jcmcc127a-328","title":"Research on the picture hierarchy enhancement technique based on probability distribution in oil paintings","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Hierarchy; Painting; Oil painting; Distribution (mathematics); Mathematics; Statistics; Environmental science; Artificial intelligence; Computer science; Art; Visual arts; Economics; Mathematical analysis","score_opus":0.01565484761043209,"score_gpt":0.2919760243241131,"score_spread":0.27632117671368106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409791018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73624027,0.00015459703,0.24451052,0.001455887,0.008063049,0.0014740782,0.000006532433,0.00025356858,0.007841527],"genre_scores_gemma":[0.99497277,0.000027307078,0.0046584955,0.000042398562,0.00023331844,0.00003420765,0.0000016079971,0.000023143359,0.0000067531505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99752957,0.00030277722,0.0008898893,0.00019805226,0.0007173878,0.00036232083],"domain_scores_gemma":[0.99661285,0.0022529878,0.000246239,0.00035146184,0.00046709058,0.00006938443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00549488,0.0002465294,0.0004641221,0.00033150142,0.0002545562,0.0001313483,0.00043941842,0.00017934486,0.0000066610787],"category_scores_gemma":[0.0013688089,0.00018274835,0.000102619415,0.0007981451,0.00012279824,0.00008161615,0.00013987899,0.0015126689,8.7682895e-7],"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":[0.0001963298,0.00097321835,0.00004916508,0.0006843285,0.0000354249,0.000016814098,0.00033866745,0.0019500175,0.0044985362,0.98031646,0.0014890388,0.009452],"study_design_scores_gemma":[0.0015793734,0.0007533722,0.00004748297,0.0025150476,0.000014127779,0.0000039019524,0.00020428869,0.04035199,0.04940092,0.9032577,0.0016470602,0.0002246878],"about_ca_topic_score_codex":0.0000020135217,"about_ca_topic_score_gemma":1.93813e-7,"teacher_disagreement_score":0.25873253,"about_ca_system_score_codex":0.00046711692,"about_ca_system_score_gemma":0.00010688596,"threshold_uncertainty_score":0.74522614},"labels":[],"label_agreement":null},{"id":"W4409795690","doi":"10.61091/jcmcc127b-430","title":"Infrared visible image fusion algorithm based on double branching and resultant decomposition","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Image fusion; Fusion; Infrared; Branching (polymer chemistry); Image (mathematics); Decomposition; Algorithm; Computer vision; Computer science; Artificial intelligence; Physics; Materials science; Optics; Chemistry","score_opus":0.0053359377627011035,"score_gpt":0.2593823728429357,"score_spread":0.2540464350802346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409795690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24514352,0.0005803598,0.73071796,0.00014155198,0.013057397,0.00066933426,0.0000065263666,0.00040137907,0.009281971],"genre_scores_gemma":[0.83796054,0.00011755386,0.16130377,0.00004786028,0.00049898843,0.0000038833123,0.000003065227,0.00005305922,0.000011278735],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981942,0.000052338037,0.0009024019,0.00018736554,0.00040005834,0.00026359412],"domain_scores_gemma":[0.99842376,0.00054005167,0.00036027003,0.0002201614,0.00033160602,0.00012417183],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008705821,0.000291276,0.00059209345,0.00040946843,0.00026763612,0.000252013,0.00021676313,0.000155273,0.000004264713],"category_scores_gemma":[0.00017116804,0.00027658985,0.000096148935,0.00036831773,0.000058886515,0.00028484996,0.00013544646,0.00054786826,7.34963e-7],"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":[0.0016369274,0.002257397,0.00012153395,0.0032077632,0.0004344807,0.00024848222,0.0022642955,0.0065254876,0.12811223,0.6949298,0.0055171894,0.15474445],"study_design_scores_gemma":[0.0080968365,0.0005690972,0.00005289682,0.0017987855,0.00007592498,0.00003533076,0.000109604945,0.4688986,0.038589105,0.48069713,0.0006994387,0.000377273],"about_ca_topic_score_codex":0.0000023188963,"about_ca_topic_score_gemma":6.7545095e-8,"teacher_disagreement_score":0.592817,"about_ca_system_score_codex":0.00011950544,"about_ca_system_score_gemma":0.000057575537,"threshold_uncertainty_score":0.99996865},"labels":[],"label_agreement":null},{"id":"W4410946253","doi":"10.2139/ssrn.5279683","title":"Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Multispectral image; Computer science; Artificial intelligence; Artificial neural network; Fusion; Computer vision; Multiresolution analysis; Pattern recognition (psychology); Wavelet","score_opus":0.006974064374630458,"score_gpt":0.22846189009086756,"score_spread":0.2214878257162371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410946253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008711007,0.009892206,0.9795647,0.000099440185,0.00051078206,0.00056680175,0.000029052715,0.0004155905,0.00021040149],"genre_scores_gemma":[0.9638138,0.018071583,0.01713866,0.00001565423,0.000345501,0.00005520866,0.000089971625,0.00008908959,0.00038055997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969005,0.000094361974,0.00060729927,0.0003834582,0.000331806,0.0016825788],"domain_scores_gemma":[0.99851805,0.00007126191,0.00038150864,0.00044768237,0.00049649546,0.00008501764],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00047185406,0.00047974085,0.0005161979,0.000358575,0.00015306665,0.000049819722,0.0005341113,0.00035708147,0.00001589378],"category_scores_gemma":[0.00005902166,0.0004466087,0.00017131318,0.0003562101,0.00010228764,0.00022883898,0.00021075663,0.0055464995,0.0000010302281],"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":[0.00009490663,0.000040794417,0.00014547851,0.00014353222,0.00017201036,0.0000070484334,0.00007754247,0.9662117,0.0003403066,0.0007624742,0.00027791527,0.03172629],"study_design_scores_gemma":[0.0010168912,0.0004008165,0.00051149155,0.0014800172,0.00020556309,0.00028319238,0.00061719626,0.96829236,0.00689011,0.019387648,0.00007015752,0.0008445625],"about_ca_topic_score_codex":0.00010879304,"about_ca_topic_score_gemma":0.00053222955,"teacher_disagreement_score":0.96242607,"about_ca_system_score_codex":0.0017888875,"about_ca_system_score_gemma":0.00088932033,"threshold_uncertainty_score":0.9997986},"labels":[],"label_agreement":null},{"id":"W4411360106","doi":"10.1007/s11263-025-02507-2","title":"OCCO: LVM-Guided Infrared and Visible Image Fusion Framework Based on Object-Aware and Contextual Contrastive Learning","year":2025,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for Central Universities of the Central South University; National Natural Science Foundation of China","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Computer vision; Object (grammar); Image fusion; Fusion; Image (mathematics)","score_opus":0.00443597581090155,"score_gpt":0.28300803308960126,"score_spread":0.2785720572786997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411360106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058240682,0.00019603419,0.93944556,0.00032055628,0.0008599458,0.00010430947,0.000004616681,0.00011571827,0.000712603],"genre_scores_gemma":[0.924377,0.00020490974,0.07462693,0.0005275082,0.00018169067,0.0000019861457,0.0000050000417,0.000018820952,0.000056105124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890924,0.000057170586,0.00042255796,0.00015177109,0.00033584874,0.0001234325],"domain_scores_gemma":[0.9987262,0.0004986737,0.00015252463,0.000084727704,0.00046702212,0.00007083762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021358095,0.00016958924,0.0002437136,0.0004508805,0.000064686596,0.00015457031,0.00020119917,0.00009575666,0.00003753345],"category_scores_gemma":[0.0001627288,0.00015416827,0.00006194488,0.000107953856,0.00005818482,0.00035048602,0.00011997591,0.0005004285,0.000002275745],"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":[0.0012694021,0.00028165092,0.0028508038,0.00014534306,0.00041668533,0.00063384196,0.00094867416,0.0704782,0.06444343,0.0020599298,0.032188904,0.8242831],"study_design_scores_gemma":[0.0029355423,0.00096159766,0.015333221,0.003578255,0.000028513885,0.00013784511,0.000098868564,0.9378675,0.022053903,0.0059309467,0.010715995,0.00035776803],"about_ca_topic_score_codex":0.0000016805562,"about_ca_topic_score_gemma":2.9940006e-7,"teacher_disagreement_score":0.8673893,"about_ca_system_score_codex":0.00009017753,"about_ca_system_score_gemma":0.000030467349,"threshold_uncertainty_score":0.62867993},"labels":[],"label_agreement":null},{"id":"W4411793007","doi":"10.18280/ts.420339","title":"Adaptive Enhancement Strategy for Multimodal Image Fusion in Behavior Monitoring for Remote Education Environments","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Image fusion; Computer science; Computer vision; Image (mathematics); Artificial intelligence; Fusion; Remote sensing; Geology","score_opus":0.016583406741911326,"score_gpt":0.2981803055263476,"score_spread":0.2815968987844363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411793007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12427344,0.00014285321,0.87244755,0.000019970195,0.00027184896,0.002374164,0.000030386745,0.000118371005,0.00032144232],"genre_scores_gemma":[0.81305677,0.000067034685,0.18473285,0.000018077433,0.000093718714,0.001614259,0.000051633848,0.000031379972,0.0003342639],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896294,0.000013311748,0.00035640667,0.00026171832,0.00012873767,0.00027689023],"domain_scores_gemma":[0.99968797,0.000053494663,0.000048415524,0.00013643924,0.000032327258,0.000041371663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014007252,0.0001959631,0.00016803585,0.000166835,0.00007081299,0.000024295545,0.00012770908,0.000066886816,0.000057752888],"category_scores_gemma":[0.000009374375,0.00022198122,0.0000645735,0.00009140425,0.000020797303,0.00021312361,0.00002869285,0.000095571224,0.000002824321],"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":[0.0000773419,0.00021052061,0.000109275505,0.00007165039,0.000012894687,6.799907e-7,0.00009207901,0.0021254045,0.6060533,0.00006551058,0.00026866287,0.39091268],"study_design_scores_gemma":[0.0015395478,0.00028597759,0.0054683774,0.0002505572,0.000046451645,3.8241188e-7,0.00036465045,0.07865269,0.9099854,0.0010008741,0.0020903163,0.00031473688],"about_ca_topic_score_codex":0.00001508328,"about_ca_topic_score_gemma":0.0000044627145,"teacher_disagreement_score":0.68878335,"about_ca_system_score_codex":0.0003808139,"about_ca_system_score_gemma":0.000035841626,"threshold_uncertainty_score":0.9052131},"labels":[],"label_agreement":null},{"id":"W4412105550","doi":"10.1016/j.inffus.2025.103487","title":"DADIGAN: A dual attention blocks-based disentangled iterative Generative Adversarial Network for cloud and shadow removal on SAR and optical images","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":30,"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":"Basic and Applied Basic Research Foundation of Guangdong Province; Shenzhen Science and Technology Innovation Program; General Research Fund of Shanghai Normal University; Jiangsu Association for Science and Technology; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China; State Key Laboratory of Integrated Services Networks; Natural Science Foundation of Jiangsu Province","keywords":"Computer science; Dual (grammatical number); Shadow (psychology); Cloud computing; Generative adversarial network; Generative grammar; Adversarial system; Artificial intelligence; Computer vision; Image (mathematics)","score_opus":0.00511375871567917,"score_gpt":0.2366062601098791,"score_spread":0.2314925013941999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412105550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15877832,0.000114045855,0.834578,0.00052664324,0.0006650935,0.0012239054,0.00011110194,0.00048143725,0.0035214701],"genre_scores_gemma":[0.78325397,0.00016534947,0.21351574,0.0012313061,0.0005266282,0.0001685367,0.0008071258,0.000035543908,0.00029579957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992095,0.000020739573,0.00032164913,0.00013461117,0.00013403494,0.00017944965],"domain_scores_gemma":[0.99952847,0.00013195211,0.00006347502,0.00011842426,0.00010280422,0.0000548518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014251079,0.0001782046,0.00017323103,0.00014804215,0.00020388732,0.0001105273,0.00004414077,0.0001031437,0.000014200933],"category_scores_gemma":[0.0000954709,0.00016777474,0.0000411884,0.00015601167,0.000057189256,0.00062938186,0.000050948223,0.0001263301,0.0000032243577],"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":[0.003946382,0.00019866099,0.00077538204,0.0010220639,0.00025908867,0.000030484607,0.0034883434,0.105688564,0.12332587,0.037124835,0.1415039,0.5826364],"study_design_scores_gemma":[0.004750767,0.00047693902,0.0019009653,0.00052231096,0.00008119595,0.000017070695,0.00032921246,0.8674356,0.08824738,0.0035340532,0.03211792,0.0005865985],"about_ca_topic_score_codex":0.000002181978,"about_ca_topic_score_gemma":0.00000253536,"teacher_disagreement_score":0.761747,"about_ca_system_score_codex":0.00007417535,"about_ca_system_score_gemma":0.000017752285,"threshold_uncertainty_score":0.6841655},"labels":[],"label_agreement":null},{"id":"W4413267779","doi":"10.1109/jiot.2025.3590733","title":"CLIP-Optimized Multimodal Image Enhancement via ISP-CNN Fusion for Coal Mine IoVT Under Uneven Illumination","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Carleton University; University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China-Shandong Joint Fund; National Natural Science Foundation of China","keywords":"Computer science; Fusion; Image fusion; Artificial intelligence; Computer vision; Coal mining; Image (mathematics); Coal; Image enhancement; Pattern recognition (psychology); Engineering","score_opus":0.007338107102263171,"score_gpt":0.2724401772012136,"score_spread":0.2651020700989504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413267779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08118733,0.00015062357,0.9155084,0.00023659074,0.0013597992,0.00036076314,0.0000054738935,0.00016493318,0.0010260682],"genre_scores_gemma":[0.6610692,0.00016189124,0.3356221,0.00020499284,0.00012933786,0.0000371752,0.000010908829,0.00004479686,0.0027195832],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984824,0.000034666366,0.00072508346,0.00019695397,0.0002709061,0.00029000614],"domain_scores_gemma":[0.99904096,0.00012742647,0.000259599,0.00018579354,0.0003138366,0.000072387986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004562305,0.00023886687,0.00035813704,0.00032061743,0.00006501958,0.00006349544,0.00035846102,0.00012896305,0.00022020451],"category_scores_gemma":[0.000101253136,0.0002294844,0.00020463995,0.00011423775,0.00007279,0.0005580717,0.00007361463,0.00039240968,0.000008104016],"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":[0.00028511882,0.000113432005,0.000010901455,0.00015758202,0.00013398286,0.000009148011,0.00067769643,0.004934995,0.94696254,0.000049310198,0.016044814,0.030620497],"study_design_scores_gemma":[0.0014645511,0.00014942761,0.000033327677,0.00037117815,0.000044329507,0.000032086184,0.00007342816,0.24018866,0.75321525,0.002250737,0.0019841762,0.00019284051],"about_ca_topic_score_codex":0.000024525589,"about_ca_topic_score_gemma":0.000002471451,"teacher_disagreement_score":0.5798863,"about_ca_system_score_codex":0.00028176382,"about_ca_system_score_gemma":0.000024156168,"threshold_uncertainty_score":0.9358102},"labels":[],"label_agreement":null},{"id":"W4413380256","doi":"10.18280/ts.420407","title":"Multi-Objective Image Fusion for Brain Tumor Detection Using Improved Weighted Quantum Firefly Optimization and StyleGAN-MAE-SwinViT","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 protocol; Firefly algorithm; Image fusion; Quantum; Fusion; Image (mathematics); Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Algorithm; Biology; Physics","score_opus":0.008710781437519131,"score_gpt":0.2516294715785206,"score_spread":0.24291869014100148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413380256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122887485,0.0001015456,0.8750036,0.000053454634,0.00014784835,0.0011347424,0.000035744262,0.0005972607,0.00003835792],"genre_scores_gemma":[0.71842545,0.000023011833,0.28102633,0.00013892795,0.00006606712,0.00018799822,0.000031855503,0.000056804147,0.000043584783],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988501,0.000041247677,0.00036772562,0.000336001,0.000117070405,0.0002878359],"domain_scores_gemma":[0.99945337,0.00012789447,0.00008378936,0.00014045747,0.00013472776,0.00005974477],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022207855,0.00026105222,0.00023492664,0.00026296367,0.0002172323,0.000072406394,0.000093237024,0.00008641382,0.00005159448],"category_scores_gemma":[0.0000629631,0.0002742064,0.00006812774,0.00027240897,0.000047111902,0.00042738207,0.000048859856,0.00014698014,8.082515e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008747229,0.000052979038,0.0000125541665,0.00013924943,0.000035888825,0.0000022773077,0.0001329381,0.014748535,0.9743897,0.000023722389,0.00010302482,0.010271659],"study_design_scores_gemma":[0.0010024151,0.00009783549,0.00013462828,0.00007068391,0.000030490064,0.000002500906,0.00008175648,0.6788182,0.31925076,0.0001641998,0.0001617934,0.00018471426],"about_ca_topic_score_codex":0.000022436709,"about_ca_topic_score_gemma":0.000017489414,"teacher_disagreement_score":0.6640697,"about_ca_system_score_codex":0.00021252006,"about_ca_system_score_gemma":0.0000235197,"threshold_uncertainty_score":0.99997103},"labels":[],"label_agreement":null},{"id":"W4413470504","doi":"10.1007/978-3-031-84869-8_12","title":"A Gradient-Based Optimization Approach to Image Fusion Using Structural Similarity","year":2025,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Ontario Tech University","funders":"","keywords":"Artificial intelligence; Similarity (geometry); Image fusion; Image (mathematics); Computer science; Structural similarity; Fusion; Computer vision; Pattern recognition (psychology); Philosophy","score_opus":0.015191854931126772,"score_gpt":0.2542995307235127,"score_spread":0.23910767579238593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413470504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006931005,0.00006324647,0.8917238,0.000006549268,0.00017669395,0.0011712508,0.00029218924,0.0006030287,0.1058939],"genre_scores_gemma":[0.00018559882,0.000081909624,0.995437,0.000040906994,0.000066007786,0.00005422465,0.00009168963,0.00022957724,0.00381306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752975,0.0000037825719,0.0009230065,0.0005503139,0.00051704847,0.00047611463],"domain_scores_gemma":[0.99874717,0.00009144683,0.00027365488,0.00033327696,0.00041567016,0.00013878652],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025381768,0.0007126179,0.0007697399,0.00078095536,0.00011197394,0.00016479827,0.00043589988,0.0003881641,0.0000688963],"category_scores_gemma":[0.000299521,0.0008026846,0.00008714435,0.00018689102,0.000092817245,0.00018111161,0.00024791088,0.00078123703,0.0000054809307],"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":[0.000064830645,0.00023481362,0.000037087826,0.027798997,0.0001965845,0.000078218385,0.003060147,0.377335,0.0070884265,0.5644889,0.0076041683,0.012012808],"study_design_scores_gemma":[0.00021849445,0.000026782458,0.000002714728,0.0012416646,0.00009335416,0.000006007305,0.00004374142,0.9318746,0.001173074,0.06399155,0.0005755269,0.0007524934],"about_ca_topic_score_codex":0.000004252017,"about_ca_topic_score_gemma":0.0000025023157,"teacher_disagreement_score":0.5545396,"about_ca_system_score_codex":0.00063129864,"about_ca_system_score_gemma":0.000067619614,"threshold_uncertainty_score":0.9994424},"labels":[],"label_agreement":null},{"id":"W4413478742","doi":"10.1017/eds.2025.10015","title":"Discrete variational autoencoders for synthetic nighttime visible satellite imagery","year":2025,"lang":"en","type":"article","venue":"Environmental Data Science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satellite; Remote sensing; Satellite imagery; Computer science; Artificial intelligence; Computer vision; Meteorology; Geography; Physics; Astronomy","score_opus":0.008286408837300545,"score_gpt":0.25665568870885797,"score_spread":0.2483692798715574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413478742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014331578,0.00033169775,0.9919171,0.00013971888,0.00026870865,0.00034069875,0.0008365053,0.00029092116,0.0044414955],"genre_scores_gemma":[0.45432574,0.00054812763,0.54306656,0.00022416281,0.00003947122,0.00008472259,0.0005743346,0.000032852986,0.0011040168],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988332,0.000007951466,0.00018023033,0.00044388248,0.00025102694,0.00028372547],"domain_scores_gemma":[0.99904007,0.00008689004,0.000024745574,0.0007853427,0.000003803291,0.00005916998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003325074,0.0001274547,0.00010104375,0.00012991905,0.00020545567,0.00007231079,0.00093957945,0.000027655915,0.00014310057],"category_scores_gemma":[0.000069878886,0.00012648267,0.000023565914,0.00023551032,0.0004267101,0.0014451679,0.00046639875,0.000080213336,0.00005755325],"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":[0.000016323518,0.000080441045,0.00051312434,0.000070175105,0.000024844125,0.0000038013568,0.00011375052,0.010723799,0.9025307,0.0032919727,0.007595447,0.075035624],"study_design_scores_gemma":[0.00031274933,0.00003570914,0.013797371,0.00007615557,0.000028914861,0.0000070472283,0.000093290895,0.71714294,0.17530991,0.0056748865,0.087015636,0.0005054105],"about_ca_topic_score_codex":0.0000020579707,"about_ca_topic_score_gemma":5.54211e-7,"teacher_disagreement_score":0.7272208,"about_ca_system_score_codex":0.0001681099,"about_ca_system_score_gemma":0.000030486808,"threshold_uncertainty_score":0.51578134},"labels":[],"label_agreement":null},{"id":"W4413636399","doi":"10.1016/j.optlaseng.2025.109287","title":"TSFI-Fusion: A dual-branch decoupled infrared and visible image fusion network based on transformer and spatial-frequency interaction","year":2025,"lang":"en","type":"article","venue":"Optics and Lasers in Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Impact","funders":"Natural Science Foundation of Hunan Province; Natural Science Foundation of Jiangsu Province","keywords":"Fusion; Transformer; Infrared; Computer science; Spatial frequency; Image fusion; Dual (grammatical number); Artificial intelligence; Computer vision; Optics; Image (mathematics); Physics; Voltage","score_opus":0.002969978682205887,"score_gpt":0.21424291095878703,"score_spread":0.21127293227658114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413636399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30455485,0.00076140737,0.68898,0.00008697901,0.00041160348,0.0005163447,0.000010477493,0.0004015308,0.004276838],"genre_scores_gemma":[0.9537635,0.0020729338,0.0439018,0.000060916333,0.000044481974,0.000065427135,0.000013794905,0.000041248557,0.00003590104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999208,0.000008220739,0.00024524753,0.00021556673,0.00008448315,0.00023852996],"domain_scores_gemma":[0.99962056,0.00012140024,0.000019856512,0.00014771559,0.000023122055,0.000067328845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012573891,0.00021481111,0.00021697792,0.0002549968,0.000060840306,0.0000620295,0.000042323125,0.000105851046,0.000020909913],"category_scores_gemma":[0.000045850113,0.00022860199,0.000022919554,0.00026272153,0.000024493409,0.00019603822,0.00002605701,0.00026848676,4.6759848e-7],"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":[0.0001258773,0.00006760703,0.0018879183,0.0013936826,0.000045256915,0.000065469525,0.0003484587,0.6558466,0.2164715,0.0007401928,0.00034117408,0.12266624],"study_design_scores_gemma":[0.0007075837,0.000068383255,0.002242316,0.00075407786,0.000014513266,0.000004853491,0.000028241679,0.9822988,0.012434831,0.00045138478,0.0007426108,0.00025238618],"about_ca_topic_score_codex":0.00002305709,"about_ca_topic_score_gemma":0.00004852519,"teacher_disagreement_score":0.64920866,"about_ca_system_score_codex":0.000041412102,"about_ca_system_score_gemma":0.000009460931,"threshold_uncertainty_score":0.9322118},"labels":[],"label_agreement":null},{"id":"W4413784308","doi":"10.2197/ipsjtbio.18.14","title":"Convolutional Neural Network with Packing Mechanism and Color Transfer Post Processing for Extended Depth of field Cervical Cytology Images","year":2025,"lang":"en","type":"article","venue":"IPSJ Transactions on Bioinformatics","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Institute of Genetics; University of Tokyo","keywords":"Convolutional neural network; Computer science; Field (mathematics); Artificial intelligence; Mathematics","score_opus":0.006537596589828631,"score_gpt":0.23573145616170357,"score_spread":0.22919385957187494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413784308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058291517,0.000066081375,0.99254274,0.00025036308,0.000088616136,0.0004338708,0.000058569178,0.00025597346,0.00047464116],"genre_scores_gemma":[0.80399406,0.00004877472,0.19554903,0.00027729166,0.0000096782005,0.00007453689,0.000009678627,0.000015113174,0.000021843345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924225,0.000008544622,0.0003275323,0.00009356424,0.000093031755,0.0002350893],"domain_scores_gemma":[0.99952394,0.00017645958,0.000029549641,0.0001110056,0.00011732413,0.000041733383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000069148206,0.00015649153,0.0002199858,0.00013181345,0.00014327513,0.000022432909,0.00008196946,0.00011431449,0.000015090273],"category_scores_gemma":[0.00000947944,0.00013798825,0.000049327344,0.00018896678,0.00007544449,0.0002539828,0.0000027420083,0.00018954798,4.0120284e-7],"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":[0.001938287,0.00034556634,0.00010727393,0.006000424,0.00047362663,0.000008743999,0.0021649746,0.18077312,0.020715747,0.023005595,0.0007641099,0.7637025],"study_design_scores_gemma":[0.001350983,0.0007154254,0.0005017798,0.00033836695,0.00012528172,0.000048610083,0.0005623735,0.8791696,0.11317698,0.0033707512,0.0003182732,0.00032156755],"about_ca_topic_score_codex":0.000002186557,"about_ca_topic_score_gemma":0.000029173201,"teacher_disagreement_score":0.7981649,"about_ca_system_score_codex":0.000030532683,"about_ca_system_score_gemma":0.000040033752,"threshold_uncertainty_score":0.56269974},"labels":[],"label_agreement":null},{"id":"W4413785606","doi":"10.1109/jstars.2025.3604011","title":"Robustness Comparison of Spatiotemporal Fusion Models With High Spatial Resolution Satellite Images","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Tellabs (Canada)","funders":"","keywords":"Robustness (evolution); Computer science; Fusion; Image resolution; Satellite; Sensor fusion; Computer vision; Artificial intelligence; Image fusion; Remote sensing; Geology; Physics","score_opus":0.018387020525107232,"score_gpt":0.24281736406219842,"score_spread":0.22443034353709118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413785606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37911806,0.00019856257,0.6201686,0.00007539729,0.000073737916,0.00010824165,0.0000015989323,0.000035477926,0.00022030092],"genre_scores_gemma":[0.6203961,0.00034896802,0.3791674,0.00000872554,0.00004580805,6.599842e-8,0.0000037893994,0.000010076823,0.00001908128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893314,0.000026812646,0.0005860919,0.000107608386,0.00020187181,0.000144495],"domain_scores_gemma":[0.9991481,0.000052739266,0.0002163949,0.00012126957,0.00043047155,0.00003104123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016147045,0.00013325438,0.00032657865,0.00031650142,0.00006972203,0.000024073648,0.00006226584,0.00010513333,9.192231e-7],"category_scores_gemma":[0.000023113322,0.000120426615,0.000022093458,0.00061682705,0.00005576129,0.00014594916,0.000014911127,0.00035022676,2.9063873e-8],"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":[0.00007887973,0.000021695358,0.00027341142,0.00010520097,0.000025440864,0.000005041969,0.00016342913,0.62186384,0.11741358,0.00020831976,0.000051338568,0.2597898],"study_design_scores_gemma":[0.00053125113,0.000055288194,0.010765336,0.00046399792,0.000025601285,0.000010670133,0.000066911365,0.8038041,0.18224646,0.0017289525,0.00017191791,0.00012953638],"about_ca_topic_score_codex":0.000097427575,"about_ca_topic_score_gemma":0.00029357444,"teacher_disagreement_score":0.25966024,"about_ca_system_score_codex":0.000056132627,"about_ca_system_score_gemma":0.00006904958,"threshold_uncertainty_score":0.49108547},"labels":[],"label_agreement":null},{"id":"W4414015745","doi":"10.11159/mvml25.106","title":"Pansharpening for Incompletely Overlapping Image-pairs via Dictionary Extension","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Beijing Institute of Technology Research Fund Program for Young Scholars; Beijing Institute of Technology","keywords":"Extension (predicate logic); Computer science; Image (mathematics); Artificial intelligence; Pattern recognition (psychology); Computer vision; Programming language","score_opus":0.005723794422137468,"score_gpt":0.21447870846296976,"score_spread":0.2087549140408323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22234608,0.0022200162,0.76771694,0.00028841104,0.003725731,0.0014069993,0.0000071796044,0.0012131336,0.0010755182],"genre_scores_gemma":[0.9885325,0.000032863663,0.011121888,0.000033520184,0.00007013368,0.000047373163,1.5705417e-7,0.000013264852,0.00014830533],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908936,0.0000020124128,0.00022557456,0.00026454873,0.00017599869,0.00024247901],"domain_scores_gemma":[0.9995478,0.000106204614,0.000050238814,0.00008453829,0.0001549587,0.000056250887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024789144,0.00014721572,0.00021562695,0.00032822372,0.00021209943,0.000121612524,0.0002539879,0.000029570716,1.9467613e-7],"category_scores_gemma":[0.000050254737,0.00011481077,0.00003562615,0.0007764175,0.00010333295,0.00022796365,0.00012545251,0.00016106799,8.4981714e-8],"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":[0.000059278966,0.00005203642,0.0011757149,0.0018604037,0.00007262279,0.0000017034471,0.00011913363,0.03278676,0.7945862,0.07661461,0.0054972544,0.08717431],"study_design_scores_gemma":[0.0001388844,0.000048549307,0.0021318262,0.0005726151,0.0000072072376,0.0000126412815,0.000003804172,0.979523,0.015851576,0.00019444394,0.0013884365,0.00012699538],"about_ca_topic_score_codex":0.0000031576205,"about_ca_topic_score_gemma":9.77139e-8,"teacher_disagreement_score":0.9467363,"about_ca_system_score_codex":0.0000494128,"about_ca_system_score_gemma":0.000010300607,"threshold_uncertainty_score":0.46818474},"labels":[],"label_agreement":null},{"id":"W4414221862","doi":"10.1109/jstars.2025.3608126","title":"RADiffSR: A Diffusion Model for Remote Sensing Image Super-Resolution Fusing Residual Attention and Cross-Scale Dynamic Gating","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":2,"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":"National Natural Science Foundation of China","keywords":"Feature (linguistics); Multispectral image; Block (permutation group theory); Kernel (algebra); Feature extraction; Pattern recognition (psychology); Channel (broadcasting); Footprint; Land cover; Distortion (music)","score_opus":0.013282361286338315,"score_gpt":0.26278643694250386,"score_spread":0.24950407565616556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414221862","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48056418,0.00011098922,0.51880515,0.000115773924,0.00010564025,0.00017943485,0.000003061665,0.000058272442,0.000057487796],"genre_scores_gemma":[0.32376668,0.0003592835,0.675623,0.000043822398,0.00008661998,5.985427e-8,0.000007923716,0.000027130163,0.00008545312],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985799,0.000030415562,0.00069765176,0.000217276,0.00018551647,0.00028921754],"domain_scores_gemma":[0.99907947,0.00011545114,0.00018241585,0.00014239365,0.0004247069,0.000055579098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003867345,0.00020362802,0.00032335223,0.00038602427,0.00032123472,0.00013369916,0.00005549232,0.00017197535,3.2598265e-7],"category_scores_gemma":[0.00013327406,0.00021520336,0.000047632988,0.00048912416,0.000082395076,0.00023593198,0.000033105054,0.00045710363,5.840735e-8],"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":[0.000036979163,0.000005669482,0.00003906396,0.00013536721,0.000015157731,0.000004084021,0.00027743191,0.019124756,0.7030616,0.000019429583,0.000020895604,0.27725962],"study_design_scores_gemma":[0.0008126932,0.00002089441,0.0058466773,0.0005662968,0.0000344586,0.000059577033,0.00008526258,0.9726879,0.01493451,0.0046887496,0.00007824998,0.00018475407],"about_ca_topic_score_codex":0.000029129638,"about_ca_topic_score_gemma":0.00024508528,"teacher_disagreement_score":0.9535631,"about_ca_system_score_codex":0.00014692788,"about_ca_system_score_gemma":0.00006503894,"threshold_uncertainty_score":0.87757385},"labels":[],"label_agreement":null},{"id":"W4414304012","doi":"10.21203/rs.3.rs-7291212/v1","title":"A multi-scale end-to-end visible and infrared image enhancement fusion method","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Image fusion; Fusion; Autoencoder; Encoder; Image quality; Image (mathematics); Merge (version control); Decoding methods","score_opus":0.03704502299717912,"score_gpt":0.4185972370664641,"score_spread":0.381552214069285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414304012","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026360117,0.0017127129,0.9782681,0.0002107152,0.0002315911,0.002722186,0.0003938291,0.0010100111,0.012814827],"genre_scores_gemma":[0.006387022,0.0034735035,0.9801307,0.0000468945,0.00011550458,0.0015504312,0.00015575916,0.000100179815,0.008040034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964147,0.00036009742,0.00049184327,0.0009159854,0.00096139894,0.0008559272],"domain_scores_gemma":[0.997298,0.00042613156,0.00004772766,0.0012025257,0.0006872899,0.00033833896],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016386788,0.0004455481,0.00056381105,0.0011756109,0.00021287723,0.0002286198,0.0006385303,0.00038924188,0.0007861078],"category_scores_gemma":[0.0005715658,0.00046488104,0.000110267625,0.00086928596,0.00010608241,0.00017534995,0.0038721487,0.0019934732,0.00007705425],"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":[0.00007681257,0.00017584204,0.000077717734,0.005374223,0.00008321476,0.00006642165,0.0016744842,0.0015601816,0.7091537,0.00005146146,0.021551056,0.2601549],"study_design_scores_gemma":[0.00071829825,0.00024232958,0.0011158219,0.003630544,0.000024946303,0.000004818538,0.0005101179,0.09830593,0.8236961,0.003054614,0.06775279,0.00094367994],"about_ca_topic_score_codex":0.00017732107,"about_ca_topic_score_gemma":0.00005969307,"teacher_disagreement_score":0.2592112,"about_ca_system_score_codex":0.0005078527,"about_ca_system_score_gemma":0.00018952027,"threshold_uncertainty_score":0.9997803},"labels":[],"label_agreement":null},{"id":"W4414901711","doi":"10.1109/iccv51701.2025.00775","title":"Revisiting Image Fusion for Multi-Illuminant White-Balance Correction","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Image fusion; Fusion; Artificial neural network; Limiting; Image (mathematics); Key (lock); Pattern recognition (psychology); Color correction","score_opus":0.009006876516943419,"score_gpt":0.27738890499170343,"score_spread":0.26838202847476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414901711","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017430247,0.00017059909,0.98336387,0.00007596758,0.00068204413,0.00038851815,0.0000043566683,0.0015521125,0.012019518],"genre_scores_gemma":[0.12330652,0.00029107535,0.8558756,0.00019431513,0.0001300152,0.00021919278,0.000022189284,0.00005517981,0.019905893],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993662,0.000007685131,0.00020657344,0.00017249938,0.000053726777,0.00019328862],"domain_scores_gemma":[0.9996224,0.000053194017,0.000026190914,0.00018734077,0.000086057385,0.000024837314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011333425,0.00012396464,0.00014467706,0.000100223384,0.00008758211,0.000026600037,0.000090312555,0.00005707368,0.000062210165],"category_scores_gemma":[0.00015970207,0.00011915512,0.00005366947,0.00019785286,0.000017691556,0.00017240454,0.000039848943,0.000100554185,0.000012721579],"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":[0.000018840637,0.000023490555,0.0007954184,0.00035305053,0.00001178409,0.0000030516885,0.00007707433,0.0006895572,0.5719324,0.00030837627,0.054150388,0.3716366],"study_design_scores_gemma":[0.00030744678,0.000014784837,0.00080641446,0.00023523562,0.000008373018,0.0000026142775,0.00010632462,0.56757593,0.40961072,0.00011833181,0.021031117,0.0001827305],"about_ca_topic_score_codex":0.0000045600063,"about_ca_topic_score_gemma":0.0000039805996,"teacher_disagreement_score":0.56688637,"about_ca_system_score_codex":0.00008139561,"about_ca_system_score_gemma":0.0000073307365,"threshold_uncertainty_score":0.4859005},"labels":[],"label_agreement":null},{"id":"W4415363631","doi":"10.1002/cjce.70117","title":"A novel <scp>VMD</scp> ‐ <scp>LassoNet</scp> ‐ <scp>iTransformer</scp> framework with enhanced feature fusion for dynamic <scp>NOx</scp> forecasting in flexible utility boilers","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Boiler (water heating); Artificial neural network; Feature selection; Thresholding; Transformer; NOx; Feature extraction","score_opus":0.00827347252726865,"score_gpt":0.21832024950094212,"score_spread":0.21004677697367347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415363631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.520654,0.0021640416,0.47283864,0.0001321042,0.0006454488,0.0009253521,0.00012422985,0.00042092058,0.0020952506],"genre_scores_gemma":[0.92645186,0.00012427455,0.0715793,0.0002224133,0.00031703824,0.00015059259,0.000043226617,0.00029035876,0.00082091],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953444,0.00003977282,0.0012302805,0.00063771807,0.0006460119,0.0021017957],"domain_scores_gemma":[0.99351233,0.003952119,0.00037161156,0.0007093412,0.00042437486,0.0010302293],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00094563025,0.0009942382,0.0011934864,0.0010194875,0.0002973505,0.00025109435,0.0012811847,0.0008814083,0.0000058014975],"category_scores_gemma":[0.0075042197,0.0008972601,0.0004389992,0.0018709446,0.00021822848,0.00065222976,0.000098150165,0.0033626761,0.000004004686],"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":[0.000020109044,0.00010823957,0.0003915876,0.0016902622,0.00051303586,0.00014749292,0.0047558676,0.22382867,0.74851716,0.0005388582,0.012676224,0.0068125073],"study_design_scores_gemma":[0.0024302893,0.00021986263,0.0006983285,0.004013212,0.0002464425,0.00031507682,0.0013991263,0.23988855,0.72298115,0.002427118,0.025158113,0.00022272769],"about_ca_topic_score_codex":0.00025425694,"about_ca_topic_score_gemma":0.0008517285,"teacher_disagreement_score":0.40579787,"about_ca_system_score_codex":0.0014369733,"about_ca_system_score_gemma":0.00072013296,"threshold_uncertainty_score":0.9993478},"labels":[],"label_agreement":null},{"id":"W4415458164","doi":"10.3390/s25216506","title":"A2G-SRNet: An Adaptive Attention-Guided Transformer and Super-Resolution Network for Enhanced Aircraft Detection in Satellite Imagery","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Northwestern Polytechnical University; National Natural Science Foundation of China; Northwestern University","keywords":"Upsampling; Clutter; Satellite imagery; Pipeline (software); Cluster analysis; Deep learning; Key (lock); Scale (ratio); Feature extraction","score_opus":0.009704940242661407,"score_gpt":0.25182750702896356,"score_spread":0.24212256678630215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415458164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4870771,0.0004174578,0.51044613,0.00003105778,0.00017939905,0.00055516796,0.000008556417,0.0004056682,0.0008794596],"genre_scores_gemma":[0.9721979,0.00056129816,0.0266702,0.000035187015,0.000066672685,0.00012019299,0.000019952005,0.000035650042,0.000292933],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990512,0.00003616287,0.0002831255,0.00025245806,0.000071019254,0.0003060438],"domain_scores_gemma":[0.99965775,0.000070025315,0.000021603253,0.00014797422,0.000059992875,0.00004268003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015780807,0.00017083964,0.0001903868,0.00017660751,0.0000774194,0.000020377012,0.00005153269,0.00011629907,0.0000067715896],"category_scores_gemma":[0.000026295493,0.0001904942,0.000055417455,0.00030012347,0.000044976827,0.00026340573,0.0000071036843,0.00014849495,0.0000025952359],"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":[0.00017914295,0.000028974544,0.00015467123,0.000121458885,0.000032419124,0.0000030850022,0.0003448244,0.061733145,0.6907127,0.00016035442,0.00019148098,0.24633776],"study_design_scores_gemma":[0.001046216,0.0001937406,0.011362952,0.0002358247,0.000039144335,0.0000051093702,0.00036114454,0.24562259,0.7316136,0.004899717,0.004119923,0.00050009874],"about_ca_topic_score_codex":0.00003714796,"about_ca_topic_score_gemma":0.0003924519,"teacher_disagreement_score":0.48512083,"about_ca_system_score_codex":0.000113556816,"about_ca_system_score_gemma":0.000009030811,"threshold_uncertainty_score":0.7768128},"labels":[],"label_agreement":null},{"id":"W4415644281","doi":"10.1080/01969722.2025.2573330","title":"Enhancing Medical Diagnosis through Multimodal Image Fusion: A Novel Approach Using Modified Swin-Based Cross Attention Fusion","year":2025,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Medical diagnosis; Image fusion; Medical imaging; Image (mathematics); Fusion; Pattern recognition (psychology)","score_opus":0.017775713452138176,"score_gpt":0.29235679000763304,"score_spread":0.27458107655549485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415644281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14945924,0.0009630302,0.84210885,0.000028231005,0.00114224,0.0008503331,0.000026065507,0.0010256927,0.0043963394],"genre_scores_gemma":[0.89048964,0.00011758975,0.1083432,0.00006950596,0.00023932756,0.00031438816,0.000056557055,0.000102327765,0.00026745937],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970243,0.0001032978,0.000948202,0.00059474516,0.0007487999,0.00058066385],"domain_scores_gemma":[0.99849397,0.00026060364,0.0001546324,0.0006831873,0.00024796536,0.00015962418],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061957864,0.00045113193,0.000559624,0.00021427879,0.0002484663,0.00021014057,0.00046729113,0.0004609616,0.000047724923],"category_scores_gemma":[0.00032929165,0.00045826123,0.00018111852,0.00057238265,0.00016005585,0.00027239387,0.00022265721,0.0005022732,0.000016927643],"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":[0.00005015597,0.0005479788,0.0021187093,0.0024699871,0.00010997843,0.000054552587,0.00054448395,0.17695953,0.8081922,0.0012262585,0.0014489584,0.0062772227],"study_design_scores_gemma":[0.0011173153,0.000024250194,0.00044908156,0.0012847311,0.00004886224,0.000017410946,0.00021611292,0.921356,0.073952705,0.00007243883,0.0009897855,0.0004712825],"about_ca_topic_score_codex":0.0003491891,"about_ca_topic_score_gemma":0.000013093031,"teacher_disagreement_score":0.7443965,"about_ca_system_score_codex":0.0003251149,"about_ca_system_score_gemma":0.000120794684,"threshold_uncertainty_score":0.9997869},"labels":[],"label_agreement":null},{"id":"W4415822344","doi":"10.1109/iccds64403.2025.11209173","title":"Improving Object Detection in Low Light Surveillance Using Hybrid Image Fusion Techniques Over Traditional Filtering Methods","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Object detection; Image fusion; Merge (version control); Precision and recall; Fusion; Pattern recognition (psychology); Visibility; Noise reduction; Image processing","score_opus":0.01174207885000975,"score_gpt":0.29579903954469483,"score_spread":0.28405696069468506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415822344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033073146,0.0006959156,0.9565156,0.000052526826,0.001123547,0.0011417746,0.000052324904,0.0019114623,0.005433679],"genre_scores_gemma":[0.42560425,0.00037985222,0.57332206,0.000082829014,0.00018414998,0.00011698743,0.000014666309,0.00011850375,0.00017668211],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99565744,0.00044490842,0.001450967,0.0011023638,0.0003899915,0.00095432997],"domain_scores_gemma":[0.99813443,0.00046957625,0.00027552337,0.0007884496,0.00020071409,0.00013132086],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016361839,0.00083759427,0.0008835518,0.0015780157,0.0003306932,0.00027449403,0.0004524217,0.00037471377,0.00064775324],"category_scores_gemma":[0.00054065965,0.0009764905,0.00030382557,0.0015343871,0.00011900284,0.0015365408,0.0004732089,0.0011554959,0.000006302974],"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":[0.00004794993,0.000069367066,0.00004541254,0.0005542618,0.000022873346,0.000036350422,0.00003919552,0.0004483648,0.73897845,0.000047758585,0.00008048979,0.25962955],"study_design_scores_gemma":[0.00031222746,0.000057838653,0.0006385536,0.0008490837,0.000016777385,0.000036841182,0.000034363256,0.32462728,0.6709702,0.0013940071,0.00043935425,0.00062349526],"about_ca_topic_score_codex":0.00045024286,"about_ca_topic_score_gemma":0.00011762156,"teacher_disagreement_score":0.3925311,"about_ca_system_score_codex":0.001637852,"about_ca_system_score_gemma":0.00014848157,"threshold_uncertainty_score":0.99926853},"labels":[],"label_agreement":null},{"id":"W4415847984","doi":"10.1177/00368504251375188","title":"TVNet: Multimodal medical image fusion by dual-branch network with vision transformer and one-shot aggregation","year":2025,"lang":"en","type":"article","venue":"Science Progress","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"National Key Research and Development Program of China Stem Cell and Translational Research; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Fuse (electrical); Convolutional neural network; Image fusion; Information loss; Transformer; Fusion; Source code; Information fusion; Convolution (computer science)","score_opus":0.005387264174263814,"score_gpt":0.2815217135685201,"score_spread":0.2761344493942563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415847984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42245445,0.0035076025,0.5640345,0.0015295264,0.0003955807,0.0012219799,0.000011387498,0.0013444792,0.0055004577],"genre_scores_gemma":[0.94443303,0.00036868252,0.05490544,0.00009234466,0.000040656163,0.00007239625,0.0000083944315,0.00002012291,0.000058943388],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983818,0.000016386615,0.00020426803,0.00035785543,0.0006350235,0.0004046528],"domain_scores_gemma":[0.9994964,0.000033103905,0.00003043763,0.00020010775,0.0000958604,0.00014406412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004900369,0.00016332536,0.00015897412,0.00012889357,0.00026762727,0.000115026465,0.00022806876,0.00008738662,0.000054351873],"category_scores_gemma":[0.000031658215,0.00013235453,0.00001739235,0.0009131884,0.00095013966,0.0007811624,0.000057394085,0.0002150915,0.0000031668108],"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":[0.000044739514,0.00006268457,0.0013719709,0.00011763487,0.0000072849557,0.000008718588,0.00022388448,0.00017796611,0.1210171,0.00027601435,0.0012228593,0.87546915],"study_design_scores_gemma":[0.0018108878,0.00044041307,0.008722241,0.0017502392,0.00003397049,0.000038155726,0.00009313747,0.19494943,0.78341126,0.0027905118,0.0051280786,0.000831649],"about_ca_topic_score_codex":0.000004963282,"about_ca_topic_score_gemma":0.000011636053,"teacher_disagreement_score":0.8746375,"about_ca_system_score_codex":0.00005188683,"about_ca_system_score_gemma":0.00007933209,"threshold_uncertainty_score":0.5397261},"labels":[],"label_agreement":null},{"id":"W4416611550","doi":"10.1109/iccv51701.2025.00404","title":"PAN-Crafter: Learning Modality-Consistent Alignment for Pan-Sharpening","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Robustness (evolution); Inference; Fuse (electrical); Deep learning; Benchmark (surveying); Feature extraction; Generalization","score_opus":0.008769058241800819,"score_gpt":0.25873307238894566,"score_spread":0.24996401414714484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416611550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006828248,0.00028228288,0.9694149,0.00031029506,0.00020594145,0.0005258168,0.0000058174546,0.0019808633,0.020445837],"genre_scores_gemma":[0.85437113,0.00009188796,0.13508704,0.00029595447,0.00003773632,0.00039641696,0.000016649557,0.00004298132,0.009660189],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990516,0.000017684497,0.00029577993,0.0002301171,0.00010727671,0.0002975532],"domain_scores_gemma":[0.99952114,0.00012431314,0.00002668597,0.00023893542,0.000049463855,0.000039485116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016411659,0.00017748159,0.00020925477,0.00011240124,0.00010119196,0.000046880425,0.00014307769,0.0000681392,0.00009012125],"category_scores_gemma":[0.000079063655,0.00017638509,0.000101453035,0.00013064606,0.000020236936,0.00013235315,0.00008998183,0.00015592785,0.00001741584],"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":[0.00005500599,0.000091108224,0.0017905546,0.00076632015,0.0002672995,0.000012869488,0.00041207968,0.01863993,0.3415577,0.018232659,0.031405732,0.58676875],"study_design_scores_gemma":[0.0007931255,0.00008928436,0.000688421,0.00025213376,0.00005595648,0.000003911562,0.0004572153,0.11133345,0.64667237,0.010327834,0.22870584,0.0006204621],"about_ca_topic_score_codex":0.000012199218,"about_ca_topic_score_gemma":0.0000054764923,"teacher_disagreement_score":0.8475429,"about_ca_system_score_codex":0.0001357082,"about_ca_system_score_gemma":0.000013208952,"threshold_uncertainty_score":0.7192775},"labels":[],"label_agreement":null},{"id":"W4416683839","doi":"10.18280/mmep.121024","title":"A Hybrid Multiscale and CNN-Based Fusion Framework for Enhanced SAR–MS Image Integration","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Image fusion; Image (mathematics); Fusion; Image processing; Pattern recognition (psychology); Image segmentation","score_opus":0.012518388519848664,"score_gpt":0.2474042484563719,"score_spread":0.23488585993652325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416683839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011913483,0.0017112275,0.9832472,0.00020510955,0.00023878558,0.0015140377,0.000030897765,0.0008829943,0.00025625667],"genre_scores_gemma":[0.3868617,0.00046182243,0.6121686,0.00002366076,0.000046276717,0.00022652105,0.000012329801,0.00008820108,0.00011089548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975907,0.000020759584,0.0008702287,0.0006660687,0.00020659811,0.00064562843],"domain_scores_gemma":[0.9981898,0.0009426066,0.00009571281,0.0004346215,0.00013776157,0.0001994696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004352729,0.00063701766,0.00074795756,0.000326239,0.00021132306,0.0002564956,0.00017870216,0.00034033752,0.000027615335],"category_scores_gemma":[0.0004353267,0.00064180704,0.00014043335,0.0002446528,0.00010833054,0.00025845485,0.00010726088,0.0006759202,0.0000054478064],"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":[0.00004188383,0.00011926558,5.776614e-7,0.008321681,0.000057151086,0.0000021003427,0.00060571724,0.8640647,0.075385585,0.0274146,0.000042684875,0.023944063],"study_design_scores_gemma":[0.00046419524,0.00008659146,8.4265884e-7,0.0054002926,0.000081267855,0.0000030798117,0.000022823495,0.7402696,0.108957134,0.1440609,0.00020384291,0.000449438],"about_ca_topic_score_codex":0.0000066971115,"about_ca_topic_score_gemma":2.973934e-7,"teacher_disagreement_score":0.37494823,"about_ca_system_score_codex":0.000103955776,"about_ca_system_score_gemma":0.000024297577,"threshold_uncertainty_score":0.99960333},"labels":[],"label_agreement":null},{"id":"W4416727754","doi":"10.1109/igarss55030.2025.11243499","title":"A novel U-shape Swin-Gan based Optical to SAR image transfer method","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"University of Calgary","funders":"Science and Engineering Research Council","keywords":"Upsampling; Synthetic aperture radar; Encoder; Radar imaging; Feature (linguistics); Image resolution; Generative adversarial network; Image fusion; Transformer","score_opus":0.011306310604696316,"score_gpt":0.3060470604901878,"score_spread":0.2947407498854915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416727754","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030020217,0.00012431988,0.8993866,0.0030934038,0.00040520035,0.0012574319,0.00006099554,0.001693752,0.093678124],"genre_scores_gemma":[0.06394474,0.000031279775,0.92898756,0.003620635,0.00008580916,0.00009102861,0.000007774579,0.00014843438,0.0030827555],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99646956,0.000072729614,0.0009963377,0.0009982032,0.00042991736,0.0010332783],"domain_scores_gemma":[0.99770594,0.0004679353,0.000019215653,0.0010774053,0.0002610399,0.0004684708],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006019224,0.000770337,0.00085224956,0.00071163615,0.00015627792,0.00017915055,0.00069989474,0.00039646783,0.006051065],"category_scores_gemma":[0.00045027325,0.00081723486,0.00038340123,0.0015275058,0.00010002101,0.00039573456,0.0001224671,0.00081089785,0.00028289776],"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":[0.000068434914,0.00023444548,0.0000022033798,0.00028241184,0.00007079671,0.000026165659,0.00009281371,0.003234711,0.8649852,0.0072583426,0.002344618,0.12139984],"study_design_scores_gemma":[0.0008721842,0.0001297135,0.00011251495,0.00025748755,0.00009972258,0.000005431779,0.00006375806,0.27595505,0.6998852,0.0003567155,0.021624893,0.00063733355],"about_ca_topic_score_codex":0.00004148842,"about_ca_topic_score_gemma":0.000015328227,"teacher_disagreement_score":0.27272034,"about_ca_system_score_codex":0.00031774872,"about_ca_system_score_gemma":0.000189643,"threshold_uncertainty_score":0.99942786},"labels":[],"label_agreement":null},{"id":"W4416965639","doi":"10.1109/tip.2025.3636676","title":"Heterospectral Structure Compensation Sampling for Hyperspectral Fusion Computational Imaging","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Multispectral image; Full spectral imaging; Pattern recognition (psychology); Interpolation (computer graphics); Residual; Sampling (signal processing); Fusion; Computational complexity theory","score_opus":0.00978297910938482,"score_gpt":0.27897666914276964,"score_spread":0.26919369003338484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416965639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010275714,0.00021035825,0.9869974,0.00016395646,0.0004025905,0.00041809492,0.000074031144,0.0011957351,0.00026210878],"genre_scores_gemma":[0.6500309,0.000011207507,0.3496758,0.000104049235,0.000035931083,0.000045354598,0.000015003058,0.000037584345,0.0000441574],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888283,0.000012839734,0.00031435065,0.00031156364,0.00016473135,0.00031366065],"domain_scores_gemma":[0.99952364,0.00008645925,0.000048683865,0.00014575543,0.00014710899,0.000048348957],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006802332,0.00024822098,0.00020365928,0.00031898526,0.00045701815,0.00017499081,0.00013708208,0.00006940645,0.000044577042],"category_scores_gemma":[0.0000071604495,0.00027548443,0.00010162745,0.00034014546,0.00007086901,0.00057973276,0.0000015058711,0.00031457993,0.0000026202733],"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":[0.00004143162,0.00003838323,0.000007544268,0.00023044467,0.000018712846,0.000001349738,0.00020580993,0.24501012,0.48823333,0.000057308436,0.00007662362,0.26607895],"study_design_scores_gemma":[0.0005428891,0.000020397283,0.00009583734,0.00022536473,0.00003420013,0.000011978339,0.00011549727,0.54049355,0.45066088,0.007424741,0.00010869312,0.00026597094],"about_ca_topic_score_codex":0.0000026710072,"about_ca_topic_score_gemma":0.000004692242,"teacher_disagreement_score":0.6397552,"about_ca_system_score_codex":0.0002383836,"about_ca_system_score_gemma":0.00004605189,"threshold_uncertainty_score":0.9999697},"labels":[],"label_agreement":null},{"id":"W6887081878","doi":"10.15468/dl.p9ebfw","title":"Occurrence Download","year":2020,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Range (aeronautics); Anas; Download; Accipitridae","score_opus":0.010138799092735315,"score_gpt":0.20709789894880737,"score_spread":0.19695909985607205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6887081878","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013707926,0.000021442938,0.00046518096,0.000041505034,0.00039974906,0.00028390795,0.9976006,0.0010050661,0.00016885284],"genre_scores_gemma":[0.00000458418,0.00022327711,0.000031615287,0.00032826274,0.0000016257628,0.000005272317,0.9994053,2.5925422e-8,2.3584022e-8],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989088,0.000018970477,0.0003441783,0.00015804621,0.00033024666,0.00023973727],"domain_scores_gemma":[0.999209,0.000010622501,0.000106889296,0.0003872585,0.00012125943,0.00016500366],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000068696645,0.00030202878,0.0002716043,0.00006707147,0.00010885876,0.000086264015,0.00047957504,0.0002890765,0.001004167],"category_scores_gemma":[0.0001017381,0.00034055873,0.000110684814,0.00029739155,0.000089954265,0.0009893395,0.00025412845,0.00038140593,0.24511406],"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":[0.000010483745,0.0000058265655,0.000023893,0.00025227535,0.00001621328,0.00000279081,0.000017771876,0.00007750017,4.6893948e-7,2.041296e-8,0.9963685,0.0032242562],"study_design_scores_gemma":[0.00013224615,0.000019079556,0.0000067243886,0.0000029317662,0.000022209446,0.0000029008177,0.000031812055,0.0000025176685,0.00006172945,0.0000012049778,0.99939984,0.0003168114],"about_ca_topic_score_codex":0.000032856184,"about_ca_topic_score_gemma":0.0000019434735,"teacher_disagreement_score":0.2441099,"about_ca_system_score_codex":0.0003074932,"about_ca_system_score_gemma":0.00003918554,"threshold_uncertainty_score":0.99990904},"labels":[],"label_agreement":null},{"id":"W6889673405","doi":"10.25919/cnda-sw76","title":"Parkes observations for project P885 semester 2021OCTS_14","year":2021,"lang":"en","type":"dataset","venue":"CSIRO","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Swift; Complement (music); Magnetar; Track (disk drive); Cadence","score_opus":0.045938742191172985,"score_gpt":0.298874074122709,"score_spread":0.252935331931536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889673405","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000846589,0.00078920566,0.01851391,0.00004183831,0.00062136236,0.0007363625,0.9786731,0.0004915076,0.00012426669],"genre_scores_gemma":[4.5572943e-7,0.001053272,0.03487186,0.00016856696,0.0003358587,0.00080357905,0.96171725,0.00007306143,0.0009760801],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99895436,0.000018484912,0.00029517128,0.0003068748,0.00013788756,0.00028721],"domain_scores_gemma":[0.9989286,0.0001333942,0.00006058861,0.0007236311,0.00011007069,0.00004368337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007238157,0.00030722638,0.00034068947,0.00013865855,0.00006633045,0.00006927002,0.00030057336,0.00025968603,0.000015811785],"category_scores_gemma":[0.00021580012,0.000318169,0.0001274948,0.00022525566,0.0000273058,0.00015899152,0.00009644372,0.00028448933,0.0000028773493],"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":[0.0000017759797,0.000016273,0.0000014069847,0.0005194977,0.000030777992,0.000011301901,0.000008104385,0.00004726081,0.00041629572,0.000004669698,0.99799824,0.0009443755],"study_design_scores_gemma":[0.000111094094,0.000019904544,0.000014308334,0.00018286037,0.000050055125,0.0000057637058,0.000015960322,0.00027611945,0.0015374715,0.00015509328,0.99728084,0.0003505238],"about_ca_topic_score_codex":0.000010424786,"about_ca_topic_score_gemma":0.00004939831,"teacher_disagreement_score":0.016955815,"about_ca_system_score_codex":0.000074477284,"about_ca_system_score_gemma":0.000069806825,"threshold_uncertainty_score":0.99992704},"labels":[],"label_agreement":null},{"id":"W6911009487","doi":"10.5167/uzh-126404","title":"2016 IEEE GRSS data fusion contest: very high temporal resolution from space technical committees","year":2016,"lang":"en","type":"article","venue":"Zurich Open Repository and Archive (University of Zurich)","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Sensor fusion; Image fusion; Fusion; Image resolution; High resolution; CONTEST; Space (punctuation)","score_opus":0.014632020311529876,"score_gpt":0.20975513837043588,"score_spread":0.195123118058906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911009487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2904844,0.0005706126,0.6971705,0.00058712286,0.00039856407,0.0007698389,0.0009746842,0.0006768136,0.008367437],"genre_scores_gemma":[0.8676136,0.0007878076,0.12981948,0.000019705576,0.000090730915,0.000001416175,0.00012839085,0.000033194865,0.0015056413],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986924,0.00022706632,0.00019892918,0.00046495855,0.00019053133,0.00022612205],"domain_scores_gemma":[0.99823093,0.00056420465,0.00013785133,0.0008765681,0.000054099928,0.00013634242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031478237,0.00020221794,0.00033922258,0.00011720548,0.0003221939,0.000037950977,0.0011755741,0.00010853468,0.00004916115],"category_scores_gemma":[0.00004203489,0.00018613948,0.00004518735,0.000130102,0.00032184352,0.0011509756,0.0014530072,0.00019604499,0.000015821062],"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":[0.00034770864,0.0001380968,0.0046705483,0.000050583723,0.000070169524,0.00019559883,0.00024666515,0.000029029909,0.79133755,0.00051174365,0.19346738,0.008934939],"study_design_scores_gemma":[0.010929362,0.0016723641,0.31472495,0.0043655126,0.0006156338,0.00030048785,0.0023149075,0.011501579,0.19861475,0.026351478,0.424605,0.0040039876],"about_ca_topic_score_codex":0.001193601,"about_ca_topic_score_gemma":0.00017872393,"teacher_disagreement_score":0.5927228,"about_ca_system_score_codex":0.000058942154,"about_ca_system_score_gemma":0.00003565417,"threshold_uncertainty_score":0.7590548},"labels":[],"label_agreement":null},{"id":"W6931733253","doi":"10.5281/zenodo.7390487","title":"Booklet of presentations from the PaNOSC Closing Event, 29-30 November 2022","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Canarie","funders":"","keywords":"Closing (real estate); Event (particle physics); Key (lock); MEDLINE","score_opus":0.02129738861713917,"score_gpt":0.2463102345233409,"score_spread":0.22501284590620171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931733253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10365663,0.0019106736,0.4681961,0.003053163,0.00097150094,0.0035044926,0.0063953404,0.0093998825,0.4029122],"genre_scores_gemma":[0.9942132,0.00014699741,0.0014052889,0.00020413069,0.00010989438,5.4110166e-7,0.0015633911,0.0014392574,0.0009172558],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987593,0.00022372381,0.00024051827,0.00020086905,0.00036783275,0.0002077518],"domain_scores_gemma":[0.9992222,0.000058414225,0.00007275506,0.0004439512,0.00014842008,0.000054265583],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027621948,0.000103287486,0.000105527906,0.00009672626,0.0014250593,0.00012467547,0.0007998439,0.00002215499,0.045145042],"category_scores_gemma":[0.00020563246,0.0001038461,0.00004868805,0.0004297512,0.00008700246,0.00016592095,0.001191237,0.00034995965,0.0005546955],"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":[0.000023711817,0.00009609047,0.00001951408,0.000028931472,0.00006135322,0.000008007529,0.0017534809,0.005606548,0.110518664,0.0012654489,0.8271802,0.053438026],"study_design_scores_gemma":[0.0002137945,0.000060576793,0.0004943653,0.000010255499,0.000012186519,0.00001595669,0.00059203303,0.0036793335,0.009687746,0.0005915917,0.98451865,0.00012354221],"about_ca_topic_score_codex":0.000028412114,"about_ca_topic_score_gemma":4.0720522e-7,"teacher_disagreement_score":0.89055663,"about_ca_system_score_codex":0.00010496847,"about_ca_system_score_gemma":0.000002311043,"threshold_uncertainty_score":0.99987495},"labels":[],"label_agreement":null},{"id":"W7000205059","doi":"","title":"Enbridge Inc. Reports First Quarter 2018 Results\\n \\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n","year":2018,"lang":"en","type":"other","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Quarter (Canadian coin); Government (linguistics); Period (music); Subject (documents)","score_opus":0.006297350752331708,"score_gpt":0.2235614740517255,"score_spread":0.2172641232993938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000205059","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000087404886,0.01519373,0.011134823,0.0011201286,0.030909833,0.012663103,0.00775852,0.087645434,0.83348703],"genre_scores_gemma":[0.006504125,0.026758814,0.037756342,0.0027095007,0.024245603,0.007068017,0.014888034,0.043732602,0.836337],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9364074,0.0018829777,0.017395606,0.01628313,0.011079846,0.016951067],"domain_scores_gemma":[0.9549355,0.0022357984,0.0075150914,0.024065452,0.0024603119,0.008787856],"candidate_categories":["metaepi_narrow","metaepi_broad","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.0061222115,0.020379396,0.015793571,0.017879713,0.0034513632,0.003639693,0.013627023,0.014984293,0.05976606],"category_scores_gemma":[0.0021399725,0.021970516,0.006231287,0.0139769055,0.009467431,0.004142641,0.0050376053,0.009641845,0.081246056],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007479783,0.0017479428,0.00028394928,0.0033609057,0.0045400755,0.0042420505,0.00045935935,0.0005123172,0.00071496045,0.0013140275,0.97578144,0.0062949797],"study_design_scores_gemma":[0.008621374,0.0021040454,0.00011826511,0.004473104,0.0025678568,0.004398085,0.00063251634,0.005079509,0.00475575,0.002667483,0.942164,0.022418018],"about_ca_topic_score_codex":0.0013829678,"about_ca_topic_score_gemma":0.0054013967,"teacher_disagreement_score":0.04391283,"about_ca_system_score_codex":0.0052165654,"about_ca_system_score_gemma":0.0021176662,"threshold_uncertainty_score":0.998818},"labels":[],"label_agreement":null},{"id":"W7099432706","doi":"","title":"Behavioral Ecology of Subterranean Termites and Implications","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Coptotermes; Rhinotermitidae; Foraging; Reticulitermes; Forage; Termitidae","score_opus":0.014243608094024742,"score_gpt":0.26352163240567594,"score_spread":0.2492780243116512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099432706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89972436,0.000038232418,0.09531583,0.0001958776,0.00002661515,0.00009800721,0.000014142872,0.00047232173,0.0041146027],"genre_scores_gemma":[0.9802487,0.000060803373,0.019414159,0.000010395057,0.00000512353,0.000014274192,6.4718597e-7,0.000007931312,0.00023796948],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978906,0.000002514593,0.00008003063,0.000051003088,0.000014640912,0.000062725034],"domain_scores_gemma":[0.9998225,0.000027139382,0.000009063178,0.0001077735,0.000014080827,0.000019458614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000013762026,0.000038475122,0.00005695836,0.000035063935,0.000008741551,0.0000015077482,0.000040564177,0.000023549472,0.00017579935],"category_scores_gemma":[0.0000053806034,0.000026277037,0.000009329473,0.000024122344,0.000041676285,0.00006461079,0.000018853418,0.000014938882,0.000004078352],"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":[6.1105965e-7,0.000008957545,0.015174951,0.0000053851554,0.0000017185216,2.9110518e-7,0.00001969506,6.11916e-7,0.83659756,0.0005989577,0.00077400374,0.14681727],"study_design_scores_gemma":[0.00019332403,0.000076223594,0.16948943,0.000012150956,0.000007321485,0.000015813384,0.000013981658,0.00006900795,0.8228872,0.0042529986,0.0028526997,0.00012989706],"about_ca_topic_score_codex":0.000002246524,"about_ca_topic_score_gemma":0.000011004721,"teacher_disagreement_score":0.15431447,"about_ca_system_score_codex":0.000007724181,"about_ca_system_score_gemma":0.0000015542136,"threshold_uncertainty_score":0.19248796},"labels":[],"label_agreement":null},{"id":"W7100087793","doi":"","title":"UNIVERSITY OF CALGARY Quality Assessment of Ikonos and Quickbird Fused Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Panchromatic film; Multispectral image; Wavelet; Image resolution; Image fusion; Wavelet transform; Sensor fusion; Spatial analysis; Principal component analysis","score_opus":0.011538058423889561,"score_gpt":0.26856237627156226,"score_spread":0.2570243178476727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100087793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23552348,0.00010650693,0.72829914,0.00005114896,0.000016148746,0.00013034882,0.000010749943,0.00036858747,0.03549387],"genre_scores_gemma":[0.74311846,0.00016919945,0.25646788,0.000008146472,0.000004152904,2.5947054e-7,0.0000012492329,0.000006298556,0.00022431821],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996225,0.00001746418,0.00013633033,0.000076221215,0.00007581237,0.000071644456],"domain_scores_gemma":[0.9997058,0.00004419969,0.000035123932,0.00014714767,0.000037151523,0.000030574676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098668825,0.00006085513,0.00014734126,0.00004113393,0.0000130209655,0.0000017186484,0.00006653243,0.000032239703,0.00019149123],"category_scores_gemma":[0.000008697263,0.00006413258,0.000024797764,0.000053000294,0.000057810663,0.0001294006,0.000047351645,0.000058492195,9.706855e-7],"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":[0.000017874343,0.00010909519,0.011316981,0.00032557224,0.000045300098,0.000003525497,0.00024531287,0.00067495654,0.789276,0.0038325698,0.0031343396,0.19101846],"study_design_scores_gemma":[0.00085317245,0.00008557152,0.18093304,0.000046632238,0.000021787899,0.000002155526,0.00048623089,0.024646614,0.7864469,0.0004528681,0.0057120286,0.00031295765],"about_ca_topic_score_codex":0.00011791459,"about_ca_topic_score_gemma":0.000039303108,"teacher_disagreement_score":0.507595,"about_ca_system_score_codex":0.0000308306,"about_ca_system_score_gemma":0.000009782051,"threshold_uncertainty_score":0.2615251},"labels":[],"label_agreement":null},{"id":"W7116371784","doi":"10.1016/j.sigpro.2025.110451","title":"Imaging coupled filtering: A unified multi-channel framework for multimodal medical image registration and fusion","year":2025,"lang":"en","type":"article","venue":"Signal Processing","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"École de Technologie Supérieure","funders":"National Natural Science Foundation of China","keywords":"Image fusion; Medical imaging; Feature (linguistics); Pattern recognition (psychology); Benchmark (surveying); Smoothing; Fusion; Feature extraction","score_opus":0.012732848034940484,"score_gpt":0.2971015682368053,"score_spread":0.2843687202018648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116371784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028527812,0.0008125357,0.9944931,0.0002783776,0.00009287583,0.00036530662,0.0000052985156,0.000825313,0.00027440177],"genre_scores_gemma":[0.68252397,0.000044109667,0.3170584,0.0001269208,0.000065201304,0.00007762322,0.000013649633,0.000034910252,0.00005518727],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891514,0.000013737026,0.0003151923,0.000288653,0.0001911846,0.00027611846],"domain_scores_gemma":[0.9994579,0.00014307113,0.00006305185,0.00012699745,0.00012078249,0.000088202185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025881428,0.00020155196,0.00020243308,0.00013867425,0.00021610636,0.00012356315,0.00014208013,0.00014051174,0.00002669593],"category_scores_gemma":[0.00025013238,0.00020726897,0.000038964714,0.00019589027,0.00009778453,0.00036791476,0.000071100265,0.00029554364,9.582094e-7],"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":[0.00014574139,0.00009475764,0.000062821884,0.0021758436,0.000027540185,0.000040430237,0.00075969595,0.0023736148,0.59539175,0.00078532915,0.00054617925,0.39759633],"study_design_scores_gemma":[0.00055517006,0.000014939983,0.0000744615,0.000872404,0.000013781036,0.000008089193,0.00016122106,0.9381311,0.052196395,0.007571544,0.00019288129,0.00020803374],"about_ca_topic_score_codex":0.000007702165,"about_ca_topic_score_gemma":0.0000036985575,"teacher_disagreement_score":0.93575746,"about_ca_system_score_codex":0.000055961063,"about_ca_system_score_gemma":0.00006376273,"threshold_uncertainty_score":0.8452183},"labels":[],"label_agreement":null}]}