{"meta":{"query_hash":"a1b0af3d833a","filters":{"venue":"Computer Optics"},"cohort_total":6,"direct_labels_cover":0,"predictions_cover":6,"exported":6,"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/a1b0af3d833a","api":"https://metacan.xera.ac/api/v1/cohort?venue=Computer+Optics"},"results":[{"id":"W2974269638","doi":"10.18287/2412-6179-2019-43-4-550-556","title":"Search for designs of nonpolarizing interference systems","year":2019,"lang":"en","type":"article","venue":"Computer Optics","topic":"Optical Coatings and Gratings","field":"Materials Science","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":"Ministry of Education and Science of the Russian Federation","keywords":"Interference (communication); Reflection (computer programming); Transmission (telecommunications); Wavelength; Range (aeronautics); Optics; Energy (signal processing); Reflection coefficient; Quarter (Canadian coin); Spectral shape analysis; Computer science; Physics; Mathematics; Telecommunications; Materials science; Spectral line; Statistics; Quantum mechanics","score_opus":0.055008975508119104,"score_gpt":0.28540152694985743,"score_spread":0.2303925514417383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974269638","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":[0.61986464,0.00002103585,0.37890562,0.00002843396,0.0005333479,0.00024138084,0.000008679991,0.000022844355,0.00037402552],"genre_scores_gemma":[0.8322467,7.442361e-7,0.16746578,0.000041384923,0.00010110073,0.0000052345163,0.0000021501996,0.0000099481385,0.00012695747],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991507,0.000028599505,0.00025801302,0.0001984808,0.00014203628,0.00022217199],"domain_scores_gemma":[0.9991559,0.00028610384,0.00008031666,0.0002110727,0.00021396126,0.00005267589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003995156,0.00008784702,0.0002051634,0.00003508911,0.00004167987,0.000119489734,0.00027577483,0.00004958912,0.000019652934],"category_scores_gemma":[0.000021121987,0.000074391675,0.000043843196,0.00006973018,0.000043740547,0.00009791864,0.00014499707,0.00006758224,0.00008854554],"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":[0.000021110613,0.00004443408,0.0013092335,0.0004153002,0.00000682359,7.3321274e-7,0.00051495514,0.002511687,0.93718606,0.057130262,0.00013326204,0.0007261353],"study_design_scores_gemma":[0.0005026135,0.0010680627,0.00025438925,0.00032159215,0.000015133894,0.0000067745964,0.0001408546,0.62106276,0.37580618,0.0002941751,0.00027492424,0.0002525306],"about_ca_topic_score_codex":0.000012969288,"about_ca_topic_score_gemma":3.1205576e-7,"teacher_disagreement_score":0.6185511,"about_ca_system_score_codex":0.000015064723,"about_ca_system_score_gemma":0.000034147357,"threshold_uncertainty_score":0.30336046},"labels":[],"label_agreement":null},{"id":"W2974422157","doi":"10.18287/2412-6179-2019-43-4-632-646","title":"Analysis of a robust edge detection system in different color spaces using color and depth images","year":2019,"lang":"en","type":"article","venue":"Computer Optics","topic":"Industrial Vision Systems and Defect Detection","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":"Fields Institute for Research in Mathematical Sciences","funders":"","keywords":"Artificial intelligence; Edge detection; Computer vision; Computer science; Robustness (evolution); Color image; Image gradient; Color space; Pattern recognition (psychology); Image processing; Mathematics; Image (mathematics)","score_opus":0.020043368869894995,"score_gpt":0.21855528869999802,"score_spread":0.19851191983010302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974422157","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":[0.8616033,0.000063995365,0.13721974,8.836226e-7,0.00075092097,0.00021463804,0.000004356349,0.0000638682,0.00007827386],"genre_scores_gemma":[0.99850506,0.0000050805006,0.0013504593,0.0000014073438,0.00011128286,0.000003994987,0.0000016667061,0.000013400064,0.000007673825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993025,0.000037245063,0.0002814195,0.00014592381,0.000104529,0.0001283592],"domain_scores_gemma":[0.9996446,0.000064018925,0.0000700994,0.00013977959,0.000046072637,0.000035424266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013142319,0.00012313214,0.000394508,0.0003945736,0.000024516206,0.00005518832,0.0000466598,0.00011464209,0.000001865393],"category_scores_gemma":[0.000004026988,0.00010725982,0.00006900844,0.00047862023,0.000011790978,0.00006888449,0.000034941466,0.00009501944,0.0000022619968],"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":[0.00002240371,0.000017050561,0.011872729,0.0002637833,0.00027352225,0.000003744955,0.00018334764,0.9616634,0.02158677,0.000024740246,0.0000068384843,0.0040816152],"study_design_scores_gemma":[0.00035266532,0.00009092084,0.019227844,0.000092952854,0.00014191537,0.0000066050284,0.00013945594,0.971502,0.008315514,3.7254546e-7,0.000023136185,0.00010660738],"about_ca_topic_score_codex":0.000052928488,"about_ca_topic_score_gemma":0.0000512377,"teacher_disagreement_score":0.1369017,"about_ca_system_score_codex":0.00011697865,"about_ca_system_score_gemma":0.0000052896767,"threshold_uncertainty_score":0.43739283},"labels":[],"label_agreement":null},{"id":"W3190415999","doi":"10.18287/2412-6179-co-814","title":"Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network","year":2021,"lang":"en","type":"article","venue":"Computer Optics","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":7,"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":"Synthetic aperture radar; Computer science; Artificial intelligence; Speckle noise; Ground truth; Residual; Remote sensing; Convolutional neural network; Radar imaging; Change detection; Inverse synthetic aperture radar; Speckle pattern; Metric (unit); Radar; Computer vision; Pattern recognition (psychology); Geology; Algorithm; Telecommunications; Engineering","score_opus":0.041129299543974485,"score_gpt":0.25999354570929906,"score_spread":0.21886424616532457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190415999","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":[0.8764246,0.0024186056,0.11992607,0.00028997406,0.00050625246,0.00020627661,9.2283324e-7,0.000052318384,0.00017496813],"genre_scores_gemma":[0.9753451,0.00005640148,0.024360644,0.000033238735,0.00017589219,9.195116e-7,0.0000036766712,0.00002151424,0.0000026123878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992291,0.0001587814,0.00015314654,0.00012064214,0.00021518464,0.00012316588],"domain_scores_gemma":[0.99960935,0.000091089554,0.000016615384,0.00014675829,0.00011997359,0.000016205486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042830547,0.00008096275,0.000105272986,0.000031174324,0.000041181538,0.000033425793,0.00004868355,0.000040497485,0.0000020440657],"category_scores_gemma":[0.000053685148,0.00007521827,0.000021709073,0.00018853712,0.000031296182,0.00007085169,0.000030155517,0.00016629159,4.937862e-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":[0.0000020203988,0.000013515937,0.0024529232,0.00009306851,0.000026828318,0.000008067265,0.0016299477,0.8035364,0.0319356,0.000098516815,0.00002433808,0.16017875],"study_design_scores_gemma":[0.0001749768,0.0000023490054,0.01285015,0.00020315404,0.000048637023,0.000026295927,0.000025537594,0.98518497,0.0012022392,0.000051910174,0.00015407376,0.00007571872],"about_ca_topic_score_codex":0.0000021463609,"about_ca_topic_score_gemma":0.000005960143,"teacher_disagreement_score":0.18164852,"about_ca_system_score_codex":0.00006424269,"about_ca_system_score_gemma":0.000027748689,"threshold_uncertainty_score":0.3067312},"labels":[],"label_agreement":null},{"id":"W4286437749","doi":"10.18287/2412-6179-co-1035","title":"Neural network regularization in the problem of few-view computed tomography","year":2022,"lang":"en","type":"article","venue":"Computer Optics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Russian Foundation for Basic Research","keywords":"Algebraic Reconstruction Technique; Iterative reconstruction; Projection (relational algebra); Tomographic reconstruction; Artificial intelligence; Tomography; Regularization (linguistics); Artificial neural network; Mathematics; Algorithm; Computation; Algebraic number; Computer science; Computer vision; Mathematical analysis","score_opus":0.020586853887958948,"score_gpt":0.27269818416453123,"score_spread":0.2521113302765723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286437749","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":[0.05467144,0.0007032519,0.8989131,0.04055138,0.00025576423,0.0026445528,0.000011400074,0.00030974572,0.0019393881],"genre_scores_gemma":[0.4357656,0.000026431408,0.55915195,0.004356193,0.00034540455,0.00012608823,0.00014301068,0.000016839154,0.000068481146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991877,0.00008059508,0.00024760276,0.00012881286,0.00022554328,0.00012972194],"domain_scores_gemma":[0.99951404,0.000050701314,0.000076447956,0.00027834103,0.000045694982,0.00003475431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031709016,0.00006773601,0.00015693145,0.000057458332,0.00008052826,0.0000109583425,0.0002132561,0.000021412658,0.000015272724],"category_scores_gemma":[0.0000023431398,0.000051470597,0.00005580769,0.0007346074,0.00005740222,0.000016308102,0.00014625091,0.00024172444,4.945464e-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":[0.00008852303,0.002649982,0.02591069,0.00079704676,0.00014161147,0.00012408437,0.0023113026,0.06852078,0.00055902376,0.34521702,0.31482598,0.23885396],"study_design_scores_gemma":[0.0006002899,0.00037750878,0.007963018,0.00012133189,0.000057216068,0.00012116699,0.000024853929,0.90933067,0.00004279524,0.008860444,0.07238077,0.00011990696],"about_ca_topic_score_codex":0.0000030834337,"about_ca_topic_score_gemma":2.644752e-7,"teacher_disagreement_score":0.84080994,"about_ca_system_score_codex":0.0000133272415,"about_ca_system_score_gemma":0.000023415729,"threshold_uncertainty_score":0.209891},"labels":[],"label_agreement":null},{"id":"W4320806923","doi":"10.18287/2412-6179-co-933","title":"High-performance digital image filtering architectures in the residue number system based on the Winograd method","year":2022,"lang":"en","type":"article","venue":"Computer Optics","topic":"Advanced Data Processing 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":"Ministry of Science and Higher Education of the Russian Federation; Centre de Recherches Mathématiques","keywords":"Field-programmable gate array; Computer science; Image processing; Digital image processing; Computer hardware; Residue number system; Digital image; Gate array; Filter (signal processing); Median filter; Embedded system; Image (mathematics); Computer engineering; Computer vision; Algorithm","score_opus":0.010067682054575356,"score_gpt":0.2341822714195747,"score_spread":0.22411458936499934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320806923","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":[0.08511101,0.000015572741,0.9127917,0.00016589777,0.00019408215,0.00020952613,0.000049683866,0.0005678076,0.00089469494],"genre_scores_gemma":[0.70608246,0.0000012654339,0.29354095,0.00017337367,0.00008163038,0.0000703799,0.00001873654,0.00002827093,0.000002946247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990971,0.000079683996,0.00019010875,0.00016904263,0.0002417501,0.00022232682],"domain_scores_gemma":[0.99904585,0.00033899918,0.00003542215,0.0005484143,0.0000130535855,0.000018261755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003387099,0.00016156124,0.0001328651,0.00006467479,0.00017625146,0.00016321076,0.0007059564,0.00002369097,0.0000057722714],"category_scores_gemma":[0.000011753046,0.00010888618,0.00003034857,0.00026600083,0.000033186083,0.00010399087,0.00021387852,0.00047391976,0.000006108774],"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":[0.00001058737,0.000019537283,0.00010182784,0.000116631585,0.000007832587,0.000033254455,0.00031596867,0.9822469,0.00009622683,0.0009665402,0.0011838565,0.014900839],"study_design_scores_gemma":[0.00011258012,0.000047510704,0.00038681275,0.000079913836,0.0000034420966,0.00005094329,0.000053092645,0.99552894,0.0019207068,0.0002495193,0.0014095951,0.00015691928],"about_ca_topic_score_codex":0.0000022371828,"about_ca_topic_score_gemma":4.236155e-7,"teacher_disagreement_score":0.62097144,"about_ca_system_score_codex":0.00008682635,"about_ca_system_score_gemma":0.000010277473,"threshold_uncertainty_score":0.44402498},"labels":[],"label_agreement":null},{"id":"W4323927968","doi":"10.18287/2412-6179-co-1145","title":"New method for detecting and removing random-valued impulse noise from images","year":2023,"lang":"en","type":"article","venue":"Computer Optics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Higher Education of the Russian Federation; Russian Science Foundation; Centre de Recherches Mathématiques","keywords":"Impulse noise; Pixel; Artificial intelligence; Impulse (physics); Median filter; Brightness; Mathematics; Computer vision; Gradient noise; Computer science; Noise (video); Value noise; Salt-and-pepper noise; Image noise; Detector; Pattern recognition (psychology); Image processing; Image (mathematics); Optics; Physics","score_opus":0.029262597480574856,"score_gpt":0.32272395413069843,"score_spread":0.2934613566501236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323927968","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":[0.004562192,0.00022314161,0.99291825,0.00040642064,0.0010827805,0.00026378297,0.000004583255,0.00045579328,0.00008303326],"genre_scores_gemma":[0.002151519,0.00002035933,0.9963213,0.00041109687,0.00073368417,0.0000072561274,0.0000049926593,0.000028057955,0.00032174226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982499,0.00022596851,0.00032128647,0.0005799324,0.00020420518,0.00041872388],"domain_scores_gemma":[0.99700344,0.0020690674,0.00010965628,0.00052962685,0.000114350034,0.00017384691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013741734,0.00021466399,0.00035347996,0.00017748775,0.00025873788,0.00062821386,0.0006433198,0.00008591317,0.0000021737042],"category_scores_gemma":[0.00026525953,0.00020517533,0.00012225116,0.0004628425,0.00002343414,0.00039411298,0.0006418786,0.0001654442,0.000016784408],"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":[0.000048512087,0.000008869354,0.00003196254,0.000031460924,0.000045470144,0.000056710127,0.0011320153,0.001362021,0.017824443,0.0008852837,0.0028813947,0.97569185],"study_design_scores_gemma":[0.002565925,0.00008413356,0.0005060906,0.00005262014,0.000030810137,0.000027136566,0.000014074604,0.96149355,0.016153151,0.018205434,0.0005966792,0.00027038157],"about_ca_topic_score_codex":0.00006506599,"about_ca_topic_score_gemma":0.0000015623173,"teacher_disagreement_score":0.9754215,"about_ca_system_score_codex":0.000021384736,"about_ca_system_score_gemma":0.000068712885,"threshold_uncertainty_score":0.8366807},"labels":[],"label_agreement":null}]}