{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":7,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":7,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"e3535f4a0108","filters":{"venue":"2022 IEEE International Conference on Image Processing (ICIP)"}},"results":[{"id":"W4308236992","doi":"10.1109/icip46576.2022.9897254","title":"Predicting Soil Properties from Hyperspectral Satellite Images","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Helmholtz Association","keywords":"Hyperspectral imaging; Artificial intelligence; Computer science; Artificial neural network; Satellite; Remote sensing; Fulvic acid; Pattern recognition (psychology); Geology; Chemistry; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0529398048948115,"gpt":0.2680628145019913,"spread":0.2151230096071798,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002566915,0.0003181965,0.0002452134,0.0002520116,0.0003474168,0.0005746351,0.0006329694,0.0000593478,0.0006802428],"category_scores_gemma":[0.0001080735,0.0003362662,0.00009078516,0.0002413674,0.0001412879,0.0006266677,0.0001074031,0.0007504786,0.00009917026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005226245,"about_ca_system_score_gemma":0.0001303902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009109629,"about_ca_topic_score_gemma":0.0000152361,"domain_scores_codex":[0.9976826,0.00008669282,0.000467598,0.0005334018,0.0008475559,0.0003821191],"domain_scores_gemma":[0.9991015,0.00005219063,0.0001677217,0.0003052899,0.000284429,0.00008883573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008691367,0.0000918614,0.0003923072,0.00005281286,0.00008348544,0.0000476076,0.001800823,0.006586473,0.9488728,0.0001382004,0.001267493,0.04057924],"study_design_scores_gemma":[0.0004851927,0.00007349066,0.001328591,0.000191081,0.00003735675,0.00003993722,0.002485272,0.8241835,0.1679489,0.0009267771,0.00174921,0.0005506176],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8989657,0.00140473,0.01071145,0.003315371,0.005942628,0.0004770472,0.0002170834,0.001825872,0.07714009],"genre_scores_gemma":[0.994288,0.0001327175,0.002993471,0.0001669526,0.0004364726,0.00007153099,0.0001254552,0.00008889198,0.001696568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.817597,"threshold_uncertainty_score":0.9999089,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4308235886","doi":"10.1109/icip46576.2022.9897527","title":"Automatic Inspection of Cultural Monuments Using Deep and Tensor-Based Learning on Hyperspectral Imagery","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Hyperspectral imaging; Robustness (evolution); Artificial intelligence; Deep learning; Computer science; Cultural heritage; Perspective (graphical); Tensor (intrinsic definition); Pattern recognition (psychology); Computer vision; Machine learning; Archaeology; Mathematics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.05082531560776408,"gpt":0.303273541000542,"spread":0.2524482253927779,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002192915,0.0002363188,0.0002247642,0.0003642974,0.000324484,0.0002236341,0.0002203748,0.0000469025,0.0001901253],"category_scores_gemma":[0.0001196402,0.0002524069,0.00006411715,0.0002608062,0.0001079895,0.0003945378,0.00003937059,0.0005599276,0.000009621121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006789935,"about_ca_system_score_gemma":0.00007109585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002520951,"about_ca_topic_score_gemma":0.000001945288,"domain_scores_codex":[0.9983084,0.00008623436,0.0003902173,0.0003480663,0.0006310102,0.0002360613],"domain_scores_gemma":[0.9992557,0.00005205011,0.0002296718,0.0001499833,0.0002547245,0.00005791161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009669902,0.000152289,0.0005027411,0.000152005,0.00006685964,0.00002882751,0.00143148,0.1581069,0.8101259,0.0001624114,0.0001734321,0.02900044],"study_design_scores_gemma":[0.0004363613,0.0001132681,0.001138225,0.0001474735,0.00002506185,0.00003360678,0.001265172,0.972977,0.02343999,0.0001317851,0.00005190944,0.000240145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821145,0.00006486622,0.01284541,0.0003023394,0.0008664039,0.000182081,0.00001106458,0.0004143608,0.003199003],"genre_scores_gemma":[0.9918697,0.00001478413,0.007690084,0.00006123373,0.00008162365,0.00001959748,0.00003980402,0.00004845963,0.0001747107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8148701,"threshold_uncertainty_score":0.9999928,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4308234157","doi":"10.1109/icip46576.2022.9897832","title":"Self-Superflow: Self-Supervised Scene Flow Prediction in Stereo Sequences","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"CODE","keywords":"Computer science; Ground truth; Artificial intelligence; Benchmark (surveying); Generalization; Flow (mathematics); Convergence (economics); Representation (politics); Artificial neural network; Deep learning; Machine learning; Test data; Supervised learning; Computer vision; Pattern recognition (psychology); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0385931564556103,"gpt":0.3103629937535771,"spread":0.2717698372979668,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006166924,0.0003089159,0.0002669583,0.0005491245,0.0005205754,0.0007092893,0.001922054,0.00004993764,0.0008242275],"category_scores_gemma":[0.00005867495,0.0003198335,0.00008634952,0.0007644682,0.00006344631,0.002245971,0.0005071586,0.0007365136,0.00007763837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000560123,"about_ca_system_score_gemma":0.0004465758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002729581,"about_ca_topic_score_gemma":0.00000918099,"domain_scores_codex":[0.9965862,0.0002007701,0.0005800643,0.0009065311,0.001264713,0.0004617047],"domain_scores_gemma":[0.9988381,0.00006545724,0.0002191867,0.0004066195,0.0003436865,0.0001269577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002286329,0.001975979,0.003005669,0.0002170919,0.0001222264,0.0004651189,0.01426346,0.005006333,0.05586731,0.008127745,0.003136982,0.9075835],"study_design_scores_gemma":[0.000836602,0.0001656074,0.0002792792,0.0001062682,0.000006787307,0.00006024452,0.0006129839,0.9894173,0.001619172,0.002578982,0.003985024,0.0003317423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06465599,0.0006465305,0.8400185,0.01531146,0.01201121,0.001342519,0.0001572657,0.002862107,0.06299449],"genre_scores_gemma":[0.7430146,0.00009507744,0.253686,0.001569134,0.0002091506,0.0002381689,0.0000474517,0.00003501503,0.001105348],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.984411,"threshold_uncertainty_score":0.9999254,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4308237016","doi":"10.1109/icip46576.2022.9898010","title":"Back to Old Constraints to Jointly Supervise Learning Depth, Camera Motion and Optical Flow in a Monocular Video","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Optical flow; Artificial intelligence; Computer vision; Constraint (computer-aided design); Monocular; Computer science; Motion (physics); Interpretation (philosophy); Structure from motion; Deep learning; Brightness; Motion estimation; Image (mathematics); Mathematics; Optics; Physics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.04102845790298629,"gpt":0.3200933049682664,"spread":0.2790648470652801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004767345,0.0002224865,0.0002292126,0.0004701687,0.0002823743,0.000607285,0.0007767894,0.00003105668,0.0006714546],"category_scores_gemma":[0.0002418803,0.0002437643,0.00004675158,0.0005072967,0.00009204329,0.000826754,0.0005834182,0.0005966324,0.0001235888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002346935,"about_ca_system_score_gemma":0.0001177629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003089648,"about_ca_topic_score_gemma":0.00001393726,"domain_scores_codex":[0.9976736,0.0001222041,0.0003898114,0.0007689507,0.0006752144,0.0003702023],"domain_scores_gemma":[0.9991635,0.000060345,0.0000955399,0.0002186864,0.0002405715,0.0002213403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008572735,0.0001729524,0.0008271024,0.00002760937,0.00001335041,0.0001163749,0.003549374,0.01174617,0.04880181,0.004541845,0.0003356123,0.9297821],"study_design_scores_gemma":[0.0006004158,0.0002027043,0.000962358,0.0001597767,0.000003307916,0.0000699104,0.0008202928,0.9912234,0.003127027,0.001252551,0.001233652,0.0003446227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03880807,0.00003650053,0.9439793,0.01004856,0.0006605843,0.000309254,0.000007739289,0.00009168134,0.006058322],"genre_scores_gemma":[0.8416342,0.000009338018,0.1545719,0.00276892,0.00005092794,0.00009367921,0.000009861787,0.00001967301,0.0008414485],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9794772,"threshold_uncertainty_score":0.9940418,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4308235812","doi":"10.1109/icip46576.2022.9897572","title":"Interpretable Concept-Based Prototypical Networks for Few-Shot Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Black box; Computer science; Artificial intelligence; Metric (unit); Set (abstract data type); Task (project management); Machine learning; Shot (pellet)","retraction":null,"screen_n_in":null,"score":{"opus":0.0627864200723764,"gpt":0.333805206212112,"spread":0.2710187861397356,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007770162,0.0002809196,0.0002720886,0.0002655916,0.0009342293,0.0009749439,0.001618048,0.00006640128,0.001427361],"category_scores_gemma":[0.0002400668,0.0002986441,0.0001495477,0.0004037229,0.0001232605,0.0006818468,0.000278421,0.0009837549,0.00002444283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002582182,"about_ca_system_score_gemma":0.0004174338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001133714,"about_ca_topic_score_gemma":0.000002744946,"domain_scores_codex":[0.9971563,0.0002337333,0.0004859098,0.0007703106,0.0008608209,0.0004929559],"domain_scores_gemma":[0.9984959,0.0002346428,0.0003903203,0.0002788886,0.0004621603,0.0001381107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001644234,0.001080283,0.0006186527,0.0001368534,0.0001877203,0.0001186517,0.00638522,0.460131,0.02123263,0.1680626,0.01082878,0.3295733],"study_design_scores_gemma":[0.0009865016,0.0004147337,0.00004520426,0.00007887745,0.000008650717,0.00001428882,0.0004763957,0.9732968,0.0007983455,0.001508655,0.02202171,0.0003499058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008377911,0.0000668373,0.9822214,0.00282533,0.001804961,0.0006224088,0.00001092748,0.0003458654,0.01126453],"genre_scores_gemma":[0.9647632,0.000003771211,0.02656097,0.002048957,0.0002061621,0.001086937,0.00009246522,0.00003761695,0.005199942],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9639254,"threshold_uncertainty_score":0.9999466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4308233806","doi":"10.1109/icip46576.2022.9897673","title":"ICIP 2022 Organizing Committee","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Diverse Perspectives in Modern Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.09495798603910299,"gpt":0.3038665532653487,"spread":0.2089085672262457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000637493,0.0002872629,0.0004164488,0.0004738413,0.0008532953,0.0004098186,0.001096359,0.00005026338,0.01185307],"category_scores_gemma":[0.0002173987,0.0003639852,0.0001263431,0.0003963113,0.0001946033,0.0005155689,0.0006168437,0.0007176782,0.000745327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007593175,"about_ca_system_score_gemma":0.00009164819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001259187,"about_ca_topic_score_gemma":0.00001541602,"domain_scores_codex":[0.9978099,0.00004689866,0.0006035387,0.0008013067,0.000331069,0.000407263],"domain_scores_gemma":[0.998744,0.00006549555,0.0005416766,0.0003271756,0.000246278,0.00007540404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004184046,0.001638973,0.02180834,0.0001413586,0.0008357927,0.0002464884,0.02071478,0.001172274,0.006081293,0.8534517,0.07745957,0.01603103],"study_design_scores_gemma":[0.005730358,0.001039585,0.01097471,0.0002982255,0.0001091807,0.0001457728,0.04935234,0.2242598,0.002191695,0.4288574,0.2727965,0.004244368],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08838066,0.002685142,0.01789427,0.01342514,0.01307575,0.0006049335,0.0009725387,0.0004409202,0.8625206],"genre_scores_gemma":[0.9811629,0.0001671352,0.00120368,0.001152175,0.0002814615,0.0001466431,0.00004903756,0.00005709172,0.01577985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8927823,"threshold_uncertainty_score":0.9998812,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3205781905","doi":"10.1109/icip46576.2022.9897680","title":"Deep Image Debanding","year":2022,"lang":"en","type":"preprint","venue":"2022 IEEE International Conference on Image Processing (ICIP)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Artifact (error); Deep learning; Artificial intelligence; Computer science; Construct (python library); Image (mathematics); Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.06057705323253225,"gpt":0.3542322993571265,"spread":0.2936552461245942,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009466565,0.0007454489,0.0005789693,0.0008584559,0.0005386358,0.003181555,0.006259276,0.0002258525,0.002461987],"category_scores_gemma":[0.0002224807,0.0008116527,0.0002718646,0.0004456123,0.0002123828,0.001664433,0.003856519,0.002255497,0.0001702923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000955474,"about_ca_system_score_gemma":0.0007172668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006120635,"about_ca_topic_score_gemma":0.000009324175,"domain_scores_codex":[0.9943264,0.0002225838,0.0009029225,0.001824474,0.002007228,0.0007164403],"domain_scores_gemma":[0.9966663,0.0001038226,0.0009531442,0.001231999,0.0008811037,0.0001635885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004247763,0.002549906,0.0003853705,0.001821012,0.0008504462,0.002398012,0.01104243,0.001392193,0.3033224,0.1322141,0.06641276,0.4771865],"study_design_scores_gemma":[0.0007411797,0.0002672366,0.0001317672,0.001143111,0.00005995422,0.00006716502,0.0003511828,0.8528514,0.07451932,0.06140835,0.006375183,0.002084166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007521567,0.0002010237,0.899748,0.003284352,0.005841647,0.0005485357,0.00003334342,0.001222153,0.08836877],"genre_scores_gemma":[0.6233538,0.0004456964,0.3596271,0.002388598,0.0009986922,0.001687194,0.0003814897,0.0001827108,0.01093469],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8514592,"threshold_uncertainty_score":0.9994335,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}