{"meta":{"query_hash":"347ee4cf1f29","filters":{"topic":"Advanced Neural Network Applications"},"cohort_total":1336,"direct_labels_cover":0,"predictions_cover":1336,"exported":1336,"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/347ee4cf1f29","api":"https://metacan.xera.ac/api/v1/cohort?topic=Advanced+Neural+Network+Applications"},"results":[{"id":"W1498573925","doi":"","title":"Alpha-Beta Divergences Discover Micro and Macro Structures in Data","year":2015,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Divergence (linguistics); Visualization; Curse of dimensionality; Code (set theory); Macro; Grid; Dimensionality reduction; Data mining; Artificial intelligence; Theoretical computer science; Machine learning; Mathematics","score_opus":0.10355522355114569,"score_gpt":0.3555235215195316,"score_spread":0.25196829796838593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498573925","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":[0.3966115,0.0010893136,0.53175956,0.03938197,0.0020004287,0.0006597716,0.00016789815,0.0004626233,0.027866954],"genre_scores_gemma":[0.98519516,0.000106721025,0.013749366,0.00026175767,0.00007431246,0.000007648327,0.000085438536,0.0000069780544,0.0005126122],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987705,0.00007302746,0.00018485611,0.0005019458,0.0003045212,0.00016518513],"domain_scores_gemma":[0.99925655,0.00009459283,0.00010642535,0.00038571452,0.000072448085,0.00008427431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018880778,0.00013140753,0.00011343161,0.00011018802,0.000071747265,0.00020191279,0.0015656105,0.00003191978,0.000050125145],"category_scores_gemma":[0.0001024211,0.00011538538,0.000013120323,0.00016034057,0.000057794063,0.0007243117,0.0010435845,0.0003329847,0.000027990745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056301666,0.00006651987,0.109865665,0.000005824758,0.000035163546,0.000040456624,0.0008427181,0.014673553,0.001960789,0.78518206,0.00082069257,0.08645025],"study_design_scores_gemma":[0.0006284204,0.00007112574,0.028611558,0.000037999453,0.0000037823243,0.000030953586,0.00008919864,0.88821834,0.00037192102,0.06176902,0.01985446,0.0003132069],"about_ca_topic_score_codex":0.0001541283,"about_ca_topic_score_gemma":0.0001815113,"teacher_disagreement_score":0.8735448,"about_ca_system_score_codex":0.000033035525,"about_ca_system_score_gemma":0.000049095783,"threshold_uncertainty_score":0.47052792},"labels":[],"label_agreement":null},{"id":"W1499038423","doi":"10.1007/11866565_99","title":"Spinal Crawlers: Deformable Organisms for Spinal Cord Segmentation and Analysis","year":2006,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Segmentation; Computer science; Spinal cord; Artificial intelligence; Computer vision; Polygon mesh; Magnetic resonance imaging; Image segmentation; Anatomy; Pattern recognition (psychology); Medicine; Neuroscience; Biology; Radiology","score_opus":0.016860543094133,"score_gpt":0.2951380919553125,"score_spread":0.2782775488611795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499038423","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":[0.05568383,0.000111204856,0.9428816,0.00073706784,0.00016961344,0.00031716263,0.0000016602046,0.0000916838,0.000006217134],"genre_scores_gemma":[0.52265084,0.0000025867023,0.47696257,0.0002887034,0.000067092136,0.000021319436,0.000002677291,0.0000033162003,8.748189e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823946,0.000019393647,0.00026384005,0.00073477445,0.0003037355,0.00043881292],"domain_scores_gemma":[0.9991059,0.000112599235,0.00012285598,0.00045953426,0.00012221989,0.000076905504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030875346,0.00016402322,0.000187867,0.00038604927,0.00037013862,0.0003441515,0.00085508724,0.00004336177,0.0000014937449],"category_scores_gemma":[0.000021098784,0.000149622,0.00005074286,0.0038278564,0.00022925455,0.0008902025,0.0002901048,0.000111571244,0.0000029944374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034874654,0.000051381812,0.0040436164,0.000013838975,0.000010879343,0.0000037917027,0.00008735836,0.2784929,0.008347905,0.004789511,0.000008952867,0.704115],"study_design_scores_gemma":[0.00024605004,0.00036211533,0.019053724,0.000011771048,0.000017116481,0.000019864323,4.7903507e-7,0.9140079,0.019122515,0.04691327,0.00004545059,0.00019976521],"about_ca_topic_score_codex":0.000036401227,"about_ca_topic_score_gemma":0.000088309724,"teacher_disagreement_score":0.70391524,"about_ca_system_score_codex":0.00011078113,"about_ca_system_score_gemma":0.00007246758,"threshold_uncertainty_score":0.61014086},"labels":[],"label_agreement":null},{"id":"W1505922856","doi":"10.1007/978-3-540-31988-7_19","title":"Coarse-to-Fine Object Recognition Using Shock Graphs","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Traverse; Artificial intelligence; Adjacency list; Segmentation; Graph; Pattern recognition (psychology); Theoretical computer science; Computer vision; Algorithm","score_opus":0.03624572823462184,"score_gpt":0.27707363563325077,"score_spread":0.24082790739862892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1505922856","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":[0.00032308055,0.0002283293,0.99484366,0.0009091452,0.0010610782,0.00074873545,0.000011190538,0.00031082952,0.0015639608],"genre_scores_gemma":[0.025030501,0.000041564992,0.9710492,0.0028468608,0.0007400196,0.000025656891,0.000011242425,0.000055644272,0.0001993432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568725,0.000032835953,0.00060707505,0.001958854,0.00089240604,0.0008215893],"domain_scores_gemma":[0.9970241,0.00040988385,0.00031715046,0.0016491269,0.00030461414,0.00029511738],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045202894,0.00060214207,0.0005167208,0.001097323,0.00040214293,0.00037546846,0.0030786635,0.00027685505,0.000035826502],"category_scores_gemma":[0.000063555904,0.0006079234,0.00014933747,0.0016641107,0.00040964698,0.0007769701,0.0013438874,0.0008285865,0.00016058459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038972807,0.000019108214,0.0000055677897,0.000008105421,0.0000048862157,0.00002194704,0.0001429975,0.14047837,0.00048677696,0.0040845405,0.000020548727,0.8547233],"study_design_scores_gemma":[0.00021301788,0.00013697441,0.000028100081,0.00034763038,0.000012307908,0.00015563778,6.782805e-8,0.7369381,0.0021898937,0.25597653,0.0030533818,0.00094833213],"about_ca_topic_score_codex":0.0000114226195,"about_ca_topic_score_gemma":0.00013814893,"teacher_disagreement_score":0.8537749,"about_ca_system_score_codex":0.00044153177,"about_ca_system_score_gemma":0.0003255531,"threshold_uncertainty_score":0.9996372},"labels":[],"label_agreement":null},{"id":"W1531103298","doi":"10.48550/arxiv.1409.2752","title":"Winner-Take-All Autoencoders","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":174,"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 Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Natural language processing","score_opus":0.07883050987738648,"score_gpt":0.20151051775245682,"score_spread":0.12268000787507034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531103298","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":[0.015768345,0.000039935494,0.9762499,0.00062403013,0.0005438222,0.00034358996,0.0000068986405,0.00076879933,0.005654689],"genre_scores_gemma":[0.97379297,0.00012601711,0.022736967,0.0006729514,0.00014649876,0.0000035971261,0.000021639655,0.000028639122,0.0024707352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755245,0.000117706666,0.00022216147,0.0015043147,0.00010867282,0.00049467786],"domain_scores_gemma":[0.9968166,0.00015881554,0.00031865528,0.0023301444,0.0001233974,0.00025242523],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015112509,0.0003727423,0.00033835668,0.00019336908,0.00018360188,0.000109134584,0.0032325895,0.0002814265,0.000021804684],"category_scores_gemma":[0.000024051267,0.0004441928,0.00021777308,0.00062781334,0.00014377336,0.00036169591,0.0024179518,0.0007274667,0.00036807623],"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":[0.00000426773,0.00003535588,0.00013957736,0.00001849573,0.000033476765,0.000052024505,0.000058473022,0.70466685,0.000020014651,0.29200342,0.001982862,0.0009852012],"study_design_scores_gemma":[0.00021265226,0.00002437842,0.00025198358,0.000030673455,0.000035402456,0.000004972419,0.0000084869325,0.8322447,0.000048538142,0.14953741,0.017139586,0.00046124082],"about_ca_topic_score_codex":0.00003481699,"about_ca_topic_score_gemma":0.000025846837,"teacher_disagreement_score":0.9580246,"about_ca_system_score_codex":0.00020344577,"about_ca_system_score_gemma":0.00011748401,"threshold_uncertainty_score":0.999801},"labels":[],"label_agreement":null},{"id":"W1599513353","doi":"10.1007/11566465_47","title":"Segmentation of Focal Cortical Dysplasia Lesions Using a Feature-Based Level Set","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Cortical dysplasia; Computer science; Segmentation; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Set (abstract data type); Epilepsy; Cortex (anatomy); Level set (data structures); Similarity (geometry); Magnetic resonance imaging; Medicine; Radiology; Neuroscience; Image (mathematics); Psychology","score_opus":0.04695083398903657,"score_gpt":0.3218166831586094,"score_spread":0.2748658491695728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1599513353","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":[0.063139796,0.000039991755,0.93457675,0.0017270667,0.0001923572,0.00025132918,0.0000035417165,0.00006534103,0.000003831284],"genre_scores_gemma":[0.50721824,8.736349e-7,0.49217638,0.00053014304,0.000062869854,0.000005824737,0.0000012753615,0.000004104874,2.6664407e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980647,0.00006843018,0.00028934935,0.0006449671,0.0005165064,0.00041603326],"domain_scores_gemma":[0.99854696,0.00041724916,0.00013294232,0.000651804,0.00013021196,0.00012085147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032937544,0.0001708556,0.00018178027,0.000262081,0.00025034614,0.00009836163,0.0012033782,0.00007159974,0.0000024092835],"category_scores_gemma":[0.00008502564,0.00015738778,0.000049193277,0.002220659,0.0003759774,0.00048220187,0.0003365747,0.00027732985,0.0000048962083],"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":[0.0000048897546,0.000056568097,0.0007225908,0.0000047088383,0.0000013811726,0.0000026693322,0.0001982593,0.7462009,0.0316323,0.0009044776,0.0000087120325,0.22026254],"study_design_scores_gemma":[0.0002602874,0.0000646004,0.0033991095,0.000032426244,0.000002862386,0.000026048394,3.627223e-7,0.9051421,0.088738106,0.0021560853,0.000027269196,0.00015070994],"about_ca_topic_score_codex":0.0000086639675,"about_ca_topic_score_gemma":0.000048745045,"teacher_disagreement_score":0.44407848,"about_ca_system_score_codex":0.00016660178,"about_ca_system_score_gemma":0.00028226752,"threshold_uncertainty_score":0.64180875},"labels":[],"label_agreement":null},{"id":"W1787224781","doi":"10.1371/journal.pone.0130140","title":"On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4586,"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":"Technische Universität Berlin; Bundesministerium für Wirtschaft und Technologie; Bundesministerium für Bildung und Forschung; Banting and Best Diabetes Centre, University of Toronto; National Research Foundation of Korea; Deutsche Forschungsgemeinschaft; National Research Foundation","keywords":"MNIST database; Computer science; Artificial intelligence; Pixel; Pascal (unit); Machine learning; Pattern recognition (psychology); Classifier (UML); Contextual image classification; Artificial neural network; Data mining; Image (mathematics)","score_opus":0.12538110018465173,"score_gpt":0.3043329874426111,"score_spread":0.17895188725795938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1787224781","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":[0.020371545,0.000055856308,0.9725996,0.0046716994,0.00009602005,0.0011566498,0.000047555735,0.00024637443,0.00075464597],"genre_scores_gemma":[0.3860932,0.000092031034,0.60766894,0.001123545,0.00024308244,0.0015737716,0.00012084622,0.000045842524,0.0030387612],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983234,0.000034904588,0.00030180713,0.00052723184,0.00050723826,0.000305408],"domain_scores_gemma":[0.9976201,0.0007837893,0.00015723494,0.000821734,0.0003840903,0.00023306308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017829651,0.00016572823,0.00018952668,0.000095471216,0.0002414553,0.000061937746,0.0006619091,0.000075346616,0.0000039964984],"category_scores_gemma":[0.00065175525,0.0001593511,0.00004659176,0.0005622804,0.00004338668,0.000539917,0.000109414505,0.00017323207,0.00025271715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035296514,0.012178076,0.0002777913,0.00009105544,0.00027073844,0.000010373041,0.00167149,0.021989211,0.19914307,0.32221127,0.35579765,0.08600631],"study_design_scores_gemma":[0.0016093779,0.0005325575,0.00009214485,0.00018138693,0.000050030863,0.000002815355,0.00002609668,0.8637451,0.041000374,0.05662578,0.035590738,0.0005435817],"about_ca_topic_score_codex":0.0000015161869,"about_ca_topic_score_gemma":0.000008030193,"teacher_disagreement_score":0.8417559,"about_ca_system_score_codex":0.000115373055,"about_ca_system_score_gemma":0.00008628951,"threshold_uncertainty_score":0.6498149},"labels":[],"label_agreement":null},{"id":"W1902934009","doi":"10.48550/arxiv.1511.00363","title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","year":2015,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1835,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Canadian Institute for Advanced Research; Polytechnique Montréal","funders":"","keywords":"MNIST database; Computer science; Dropout (neural networks); Artificial neural network; Deep neural networks; Binary number; Invariant (physics); Computation; Range (aeronautics); Artificial intelligence; Permutation (music); Computer engineering; Simple (philosophy); Deep learning; Algorithm; Machine learning; Arithmetic; Mathematics","score_opus":0.01877200430149616,"score_gpt":0.23914994251252858,"score_spread":0.2203779382110324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902934009","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":[0.08002909,0.0020643212,0.9065148,0.005207263,0.0003361989,0.0023322625,0.000025003625,0.0031465944,0.00034443845],"genre_scores_gemma":[0.6785156,0.00012981526,0.31690684,0.0006330397,0.00045362502,0.0028807565,0.0001046684,0.00014622055,0.00022939174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947062,0.00028424585,0.00089854776,0.0018316898,0.0007381646,0.0015411313],"domain_scores_gemma":[0.99439883,0.00022023726,0.00084411027,0.0032959266,0.00042897125,0.00081192533],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00053050666,0.0010079489,0.000869527,0.00077188696,0.0008309062,0.0006567327,0.0031923524,0.0006865101,0.000008354886],"category_scores_gemma":[0.000085261025,0.00094651035,0.0002708682,0.0016889243,0.0002268296,0.0010175005,0.0029071586,0.0023184672,0.000012272891],"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":[0.00007127074,0.00018729943,0.001136006,0.00006386609,0.00008295566,0.00030714725,0.0008695356,0.9576534,0.00038923125,0.025137074,0.00032764953,0.013774577],"study_design_scores_gemma":[0.0005279299,0.00020224828,0.003975621,0.00017204799,0.00005673878,0.0005570336,0.00006874785,0.9789689,0.0004252766,0.013679446,0.00025475002,0.0011112171],"about_ca_topic_score_codex":0.00047682543,"about_ca_topic_score_gemma":0.0011274377,"teacher_disagreement_score":0.59848654,"about_ca_system_score_codex":0.0009348929,"about_ca_system_score_gemma":0.0005726715,"threshold_uncertainty_score":0.99998325},"labels":[],"label_agreement":null},{"id":"W190710300","doi":"10.1007/978-3-642-40811-3_64","title":"Right Ventricle Segmentation with Probability Product Kernel Constraints","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Computer science; Graphics processing unit; Segmentation; Kernel (algebra); Algorithm; Artificial intelligence; Parallel computing; Mathematics","score_opus":0.011884045779261387,"score_gpt":0.24675830111330033,"score_spread":0.23487425533403894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W190710300","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":[0.09179178,0.000036410464,0.9051254,0.0018318739,0.00015985558,0.0008744703,4.940307e-7,0.0001508599,0.000028866041],"genre_scores_gemma":[0.54385924,0.000001055259,0.45573157,0.00029438568,0.00004503102,0.000064221866,6.0934804e-7,0.0000034290185,4.8271016e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784964,0.00005021129,0.00023100003,0.00094691524,0.0004463267,0.00047588858],"domain_scores_gemma":[0.99852943,0.00018554978,0.00011048797,0.0008491012,0.00020396282,0.00012147687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028356636,0.00017341893,0.0001419034,0.00013769256,0.00023559139,0.00030104877,0.001297301,0.000028870374,0.000019684863],"category_scores_gemma":[0.000050880863,0.00013068902,0.000021935337,0.0024229088,0.00069470203,0.0013451908,0.00031503648,0.00019806692,0.000050355666],"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":[0.0000021514882,0.000100073055,0.006532921,0.000008134539,0.0000023375576,0.00000543338,0.00036006648,0.08236832,0.0069232956,0.0020349128,0.00001039724,0.901652],"study_design_scores_gemma":[0.00041240544,0.00014315615,0.026938764,0.000037797363,0.0000023780838,0.00009400303,7.748969e-7,0.7243256,0.11028337,0.1373134,0.00005618641,0.00039220808],"about_ca_topic_score_codex":0.000021947495,"about_ca_topic_score_gemma":0.000017782539,"teacher_disagreement_score":0.9012597,"about_ca_system_score_codex":0.00014995968,"about_ca_system_score_gemma":0.00015812465,"threshold_uncertainty_score":0.5329343},"labels":[],"label_agreement":null},{"id":"W1945443124","doi":"10.1109/cvpr.2015.7299060","title":"Weakly supervised localization of novel objects using appearance transfer","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba; Nvidia","keywords":"Artificial intelligence; Novelty; Object (grammar); Computer science; Computer vision; Image (mathematics); Set (abstract data type); Object detection; Novelty detection; Pattern recognition (psychology); Active appearance model","score_opus":0.07522269434819795,"score_gpt":0.28842854644336585,"score_spread":0.2132058520951679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1945443124","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":[0.012085123,0.0000737136,0.98619926,0.00014779277,0.000075222495,0.00016807983,8.5358795e-7,0.00011080229,0.0011391282],"genre_scores_gemma":[0.8487483,0.000003843605,0.15094587,0.00022094735,0.000024065363,0.0000057487723,9.873935e-7,0.0000068237255,0.000043367658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992342,0.000015997955,0.00018201716,0.00022376396,0.00019569007,0.0001483208],"domain_scores_gemma":[0.9993715,0.000018250374,0.000024993438,0.0003633089,0.00013954433,0.00008238366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008324878,0.000080324324,0.00010674335,0.000044644807,0.000041187723,0.000018572036,0.0003754469,0.000032677257,0.0000023033772],"category_scores_gemma":[0.000011285727,0.000074569114,0.000027629487,0.0006433972,0.0000376739,0.00040536356,0.00005155048,0.000044036115,0.000007247501],"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":[0.000015392936,0.00024106735,0.00040129502,0.000028301925,0.000011002727,0.0000012633906,0.0012803604,0.7205059,0.08693369,0.18089584,0.00017840537,0.009507502],"study_design_scores_gemma":[0.00037552178,0.000023549663,0.00004470541,0.000015177715,0.0000025835627,0.00000566672,0.000024719911,0.9724099,0.025376413,0.001264668,0.00036144385,0.00009565806],"about_ca_topic_score_codex":0.000022261798,"about_ca_topic_score_gemma":0.0000107852975,"teacher_disagreement_score":0.83666325,"about_ca_system_score_codex":0.000024833384,"about_ca_system_score_gemma":0.000059035225,"threshold_uncertainty_score":0.30408403},"labels":[],"label_agreement":null},{"id":"W1952506714","doi":"10.1109/cvpr.2015.7299102","title":"segDeepM: Exploiting segmentation and context in deep neural networks for object detection","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":104,"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":"Nvidia","keywords":"Computer science; Artificial intelligence; Segmentation; Pascal (unit); Object detection; Convolutional neural network; Inference; Pattern recognition (psychology); Markov random field; Exploit; Object (grammar); Image segmentation; Context (archaeology); Computer vision","score_opus":0.03750173293701438,"score_gpt":0.2831155187903668,"score_spread":0.24561378585335242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1952506714","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":[0.037757486,0.00019256747,0.960944,0.0002627095,0.00013561703,0.0004491169,2.0416397e-7,0.00013007087,0.00012821039],"genre_scores_gemma":[0.9480596,0.000008519453,0.05121572,0.0003958285,0.00007417322,0.00021311454,0.0000027289839,0.00000761204,0.000022674436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923605,0.0000331089,0.00017562928,0.00027649023,0.00008028275,0.00019842057],"domain_scores_gemma":[0.99947834,0.00016356623,0.00006541005,0.00015783575,0.00005697597,0.00007786877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016184991,0.000086915745,0.00008964425,0.000056673965,0.00008195388,0.00007055312,0.00014416833,0.000034865043,6.107029e-7],"category_scores_gemma":[0.0000329333,0.00008520414,0.00001753566,0.000292244,0.00001816651,0.0006206416,0.000075826545,0.00007469478,0.0000016292416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001443732,0.0000134565025,0.00062674185,0.0000032323828,0.00000228024,8.621335e-7,0.00044878921,0.056153752,0.00065834564,0.0050637675,0.00003594378,0.9369784],"study_design_scores_gemma":[0.0005370528,0.0000680808,0.00039850947,0.000002639376,0.0000017562734,0.000011346324,0.00033670466,0.9929936,0.0017822555,0.0036735318,0.00009523262,0.00009929301],"about_ca_topic_score_codex":0.00002389122,"about_ca_topic_score_gemma":0.00039001336,"teacher_disagreement_score":0.9368791,"about_ca_system_score_codex":0.000056600602,"about_ca_system_score_gemma":0.000006615939,"threshold_uncertainty_score":0.3474524},"labels":[],"label_agreement":null},{"id":"W1981212635","doi":"10.1109/iscas.2008.4541986","title":"Graph cut video object segmentation using histogram of oriented gradients","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Segmentation; Histogram; Computer science; Computer vision; Cut; Luminance; Image segmentation; Segmentation-based object categorization; Graph; Scale-space segmentation; Pattern recognition (psychology); Image (mathematics); Theoretical computer science","score_opus":0.031584551319039415,"score_gpt":0.2822583373129719,"score_spread":0.2506737859939325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981212635","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":[0.262478,0.000026307931,0.7365862,0.00003753414,0.00011768939,0.00017136133,8.1913026e-7,0.000119151824,0.00046291456],"genre_scores_gemma":[0.70309514,0.000025463369,0.2966094,0.000098618206,0.000015228579,0.00001467843,0.000004076381,0.000006011553,0.00013138089],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990837,0.000026729058,0.00023153966,0.0002725805,0.00020897109,0.00017648951],"domain_scores_gemma":[0.9992771,0.000043262517,0.00013284173,0.00039767058,0.00008829956,0.00006082344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004382865,0.000092777096,0.00011112749,0.00011455372,0.00016584729,0.0000073420306,0.00031512635,0.000024550573,0.000007532696],"category_scores_gemma":[0.000009347971,0.00008809824,0.000054780354,0.0009778976,0.00007727458,0.00037871583,0.000090905516,0.00005467103,0.000008829203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008317942,0.0019088985,0.1098892,0.000090765156,0.00019479502,0.00007375762,0.007344147,0.06033235,0.33245337,0.3585517,0.00816848,0.12090934],"study_design_scores_gemma":[0.0033002608,0.00062092475,0.042845696,0.00008791185,0.00006730537,0.00040016862,0.00020291506,0.6041283,0.30485222,0.032056574,0.009856419,0.00158128],"about_ca_topic_score_codex":0.000071802075,"about_ca_topic_score_gemma":0.000015198235,"teacher_disagreement_score":0.54379594,"about_ca_system_score_codex":0.000057808506,"about_ca_system_score_gemma":0.000026770565,"threshold_uncertainty_score":0.3592542},"labels":[],"label_agreement":null},{"id":"W1992286510","doi":"10.1145/1508128.1508140","title":"A high-performance FPGA architecture for restricted boltzmann machines","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":37,"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":"Field-programmable gate array; Computer science; Architecture; Computer architecture; Parallel computing; Boltzmann machine; Embedded system; Computational science; Artificial intelligence; Artificial neural network","score_opus":0.011865502889778152,"score_gpt":0.25422324404777147,"score_spread":0.24235774115799333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992286510","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":[0.032450028,0.00004515313,0.9569921,0.008509452,0.000094654424,0.00041329153,0.0000026378805,0.00052040943,0.00097228505],"genre_scores_gemma":[0.5829889,0.00001704846,0.4142469,0.0017496858,0.00012998383,0.000062894775,0.000007295823,0.000006115668,0.0007911513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990726,0.000011006088,0.00016785815,0.0003447703,0.00012300945,0.00028074265],"domain_scores_gemma":[0.9991562,0.000096599484,0.000059664573,0.00056197407,0.000053477932,0.00007209732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000036529473,0.00013041629,0.000118452124,0.00007230122,0.00017476571,0.000052171737,0.0007310496,0.00003993987,0.0000038915573],"category_scores_gemma":[0.000018101664,0.0001029191,0.000037940885,0.0005624674,0.000018094484,0.00025494175,0.000062490704,0.00010990834,0.00001981361],"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":[0.000020606143,0.00006331915,0.000039652732,0.000006684249,0.0000052331848,0.0000014315415,0.00005366377,0.022407344,0.0040386915,0.1610576,0.008622302,0.80368346],"study_design_scores_gemma":[0.0014335532,0.00086792797,0.039080035,0.000028734587,0.00001346306,0.000059522492,0.000002510743,0.5624485,0.028344952,0.28343436,0.08345002,0.00083642115],"about_ca_topic_score_codex":0.0000032621226,"about_ca_topic_score_gemma":0.0000043257082,"teacher_disagreement_score":0.802847,"about_ca_system_score_codex":0.000015754777,"about_ca_system_score_gemma":0.000016945181,"threshold_uncertainty_score":0.4196919},"labels":[],"label_agreement":null},{"id":"W1994539652","doi":"10.1118/1.1568975","title":"Semiautomatic three-dimensional segmentation of the prostate using two-dimensional ultrasound images","year":2003,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials","funders":"","keywords":"Segmentation; Boundary (topology); Prostate; Image segmentation; Ultrasound; Artificial intelligence; Computer science; Computer vision; Mathematics; Physics; Medicine; Mathematical analysis; Acoustics","score_opus":0.01716768076588344,"score_gpt":0.28185277795299585,"score_spread":0.2646850971871124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994539652","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":[0.21568033,0.00006430333,0.78290147,0.0005358781,0.00025675914,0.00034776962,0.0000049561568,0.00007120078,0.00013732398],"genre_scores_gemma":[0.938566,0.0000011153093,0.06073197,0.0005766799,0.0000651319,0.000018024035,0.0000042814313,0.000011037516,0.000025710939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99836975,0.00008380147,0.000267788,0.00024883793,0.0008271004,0.00020271412],"domain_scores_gemma":[0.99884254,0.00039735113,0.00017094605,0.00041049416,0.000085567226,0.00009311895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020954356,0.000119429955,0.00013671235,0.000014472603,0.00018056639,0.000018355508,0.00040846827,0.00003377989,0.00002165638],"category_scores_gemma":[0.00012437419,0.00008384406,0.00006404768,0.0005006567,0.00020580449,0.00022795613,0.00013477412,0.00018676867,0.000010992741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017187527,0.0012293825,0.0074981474,0.00014218403,0.00013958059,0.000021811655,0.0006365574,0.5425709,0.22734751,0.12614751,0.0034428556,0.090806395],"study_design_scores_gemma":[0.0007733883,0.00003886869,0.0019045012,0.00014996012,0.000030395913,0.000098309094,0.0000060590664,0.28437096,0.3618933,0.35038656,0.00004252472,0.00030518314],"about_ca_topic_score_codex":0.000007010865,"about_ca_topic_score_gemma":0.0000025725456,"teacher_disagreement_score":0.7228857,"about_ca_system_score_codex":0.000043152337,"about_ca_system_score_gemma":0.00018111109,"threshold_uncertainty_score":0.34190616},"labels":[],"label_agreement":null},{"id":"W1994880983","doi":"10.1118/1.1833131","title":"Sliding slice: A novel approach for high accuracy and automatic 3D localization of seeds from CT scans","year":2004,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec","funders":"","keywords":"Imaging phantom; Orientation (vector space); Position (finance); Computer science; Artificial intelligence; Computer vision; Iterative reconstruction; Reconstruction algorithm; 3D reconstruction; Algorithm; Mathematics; Nuclear medicine; Medicine; Geometry","score_opus":0.023513478068445875,"score_gpt":0.27723412651141116,"score_spread":0.25372064844296527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994880983","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":[0.0076694107,0.000035374425,0.99121034,0.00055582257,0.000059072383,0.00030154162,0.000012125316,0.0001058405,0.000050451188],"genre_scores_gemma":[0.77095234,0.000010602153,0.22846243,0.0003269848,0.00014124291,0.00005791242,0.00003569892,0.000009843453,0.0000029246867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989445,0.000011644543,0.00023219988,0.0002910842,0.0003482407,0.00017233215],"domain_scores_gemma":[0.9990483,0.00033182124,0.00013317888,0.00031619385,0.00005507249,0.00011544302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010142503,0.00010723981,0.00018272782,0.000020180696,0.00010461302,0.000025772919,0.00042636,0.00003888135,0.0000017971778],"category_scores_gemma":[0.00014760504,0.000097274395,0.000027745658,0.0004507416,0.000077898396,0.00027734594,0.00014901964,0.00010154919,0.0000013423336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069908037,0.0007491443,0.00018888336,0.00020392021,0.000059376405,0.0000021140322,0.0012131056,0.08130561,0.0019834791,0.42485777,0.00025520718,0.4891744],"study_design_scores_gemma":[0.00078992906,0.000030294033,0.0001443703,0.000056290402,0.000015444046,0.0000033081583,0.000010290488,0.9235985,0.0046417173,0.07046248,0.00012698944,0.000120378885],"about_ca_topic_score_codex":0.000050006875,"about_ca_topic_score_gemma":0.0000019413844,"teacher_disagreement_score":0.8422929,"about_ca_system_score_codex":0.00003504199,"about_ca_system_score_gemma":0.0000885608,"threshold_uncertainty_score":0.39667347},"labels":[],"label_agreement":null},{"id":"W1998656041","doi":"10.1109/icassp.2014.6854680","title":"Sequence training of multiple deep neural networks for better performance and faster training speed","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Speedup; Training (meteorology); Discriminative model; Sequence (biology); Deep neural networks; Artificial neural network; Artificial intelligence; Frame (networking); Pattern recognition (psychology); Speech recognition; Parallel computing","score_opus":0.05802256397119777,"score_gpt":0.27022912532915555,"score_spread":0.2122065613579578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998656041","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":[0.20767103,0.000013171187,0.7911798,0.0004576074,0.00007644359,0.00022105798,7.253224e-7,0.00007922935,0.00030094603],"genre_scores_gemma":[0.79856676,0.000003196591,0.20043024,0.0008155381,0.000104589075,0.000025729032,0.000002469067,0.0000104622495,0.000041020674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889916,0.000024861012,0.00025924735,0.0003615055,0.00010653878,0.0003486628],"domain_scores_gemma":[0.99896,0.00044903226,0.00011541861,0.00033718572,0.00005388119,0.000084477026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001925827,0.00013839117,0.00019614561,0.00004428352,0.00013432307,0.00004367462,0.0004147834,0.00004632986,0.00000285053],"category_scores_gemma":[0.000032366348,0.00012265328,0.000042207485,0.00019553375,0.00009065199,0.0004936738,0.00011426484,0.0001103569,0.0000010376203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010093417,0.000009915557,0.0021528662,0.00001834616,0.0000071379905,2.8485155e-7,0.0015091148,0.07640937,0.0027180247,0.0052752537,0.000034138833,0.91185546],"study_design_scores_gemma":[0.00036670387,0.000090120804,0.0019263047,0.000010444067,0.000003794021,0.000013507678,0.000046418678,0.99566704,0.0004943208,0.00073238974,0.00050177146,0.00014716614],"about_ca_topic_score_codex":0.0000015288019,"about_ca_topic_score_gemma":0.000008083918,"teacher_disagreement_score":0.9192577,"about_ca_system_score_codex":0.000008482124,"about_ca_system_score_gemma":0.0000066246016,"threshold_uncertainty_score":0.5001655},"labels":[],"label_agreement":null},{"id":"W1998977702","doi":"10.1109/wacv.2013.6475031","title":"OpenVL: A task-based abstraction for developer-friendly computer vision","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Flexibility (engineering); Abstraction; Task (project management); Human–computer interaction; Interface (matter); Variety (cybernetics); Field (mathematics); User interface; Artificial intelligence; Programming language; Systems engineering; Engineering","score_opus":0.01537047314402995,"score_gpt":0.2811965793740053,"score_spread":0.2658261062299753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998977702","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":[0.0033396473,0.000006246091,0.9898992,0.0043843,0.00019300732,0.00087012287,0.0000011616986,0.00031076127,0.0009955403],"genre_scores_gemma":[0.29736507,0.0000011826772,0.7004663,0.0013842065,0.0000719354,0.0003441197,0.000008709212,0.000009497135,0.00034900525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989752,0.000014806411,0.00021125651,0.00041000484,0.0001379084,0.00025082927],"domain_scores_gemma":[0.9990142,0.00018883665,0.000077825025,0.00046196487,0.00016245138,0.00009474648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079512196,0.00012146208,0.000103537204,0.000055610217,0.0001866759,0.00019663104,0.0005866584,0.000046796587,0.000052514108],"category_scores_gemma":[0.0000080356795,0.00010136198,0.000047046484,0.0002793424,0.000023706783,0.0009564736,0.0001227164,0.00007328681,0.0005250199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011690058,0.0001926741,0.00005272546,0.000017581957,0.000010828503,0.00000160811,0.000045675064,0.018766982,0.012034053,0.07121016,0.12670644,0.7709496],"study_design_scores_gemma":[0.00041799754,0.00017239318,0.0038736255,0.000009158961,0.0000022027164,0.000007185298,0.0000031675127,0.8777966,0.0044852016,0.0047383322,0.108259365,0.00023475991],"about_ca_topic_score_codex":0.000019482082,"about_ca_topic_score_gemma":0.00000941534,"teacher_disagreement_score":0.85902965,"about_ca_system_score_codex":0.000039627797,"about_ca_system_score_gemma":0.000040866642,"threshold_uncertainty_score":0.67482436},"labels":[],"label_agreement":null},{"id":"W2001138721","doi":"10.1016/j.patcog.2012.11.002","title":"Adaptive shape prior in graph cut image segmentation","year":2012,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; University of Alberta","funders":"","keywords":"Prior probability; Cut; Artificial intelligence; Pixel; Segmentation; Image segmentation; Computer vision; Smoothing; Pattern recognition (psychology); Computer science; Image (mathematics); Graph; Segmentation-based object categorization; Mathematics; Scale-space segmentation; Bayesian probability","score_opus":0.04126264482323297,"score_gpt":0.2853842314343166,"score_spread":0.24412158661108366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001138721","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":[0.2115884,0.000039456107,0.7869806,0.00029948505,0.00015327778,0.0003364545,0.0000083488485,0.00011041968,0.00048351526],"genre_scores_gemma":[0.9437831,0.00003733917,0.05531319,0.0004692858,0.00011542576,0.00021523212,0.000046090136,0.000010325888,0.000010014911],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99911004,0.00006379042,0.00018367189,0.0002368649,0.00013693592,0.00026869492],"domain_scores_gemma":[0.9995233,0.00007633349,0.000097461,0.00018716845,0.00004632931,0.00006941259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001244208,0.00010279994,0.00008170004,0.00011533694,0.00006655717,0.00003600795,0.00018680442,0.00003609867,0.00005345119],"category_scores_gemma":[0.000009224212,0.00010871207,0.00003236097,0.00038255434,0.000020256022,0.0013014397,0.00006977223,0.00011453913,0.0004898776],"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":[0.0000040071313,0.00009498985,0.004244789,0.000005680341,0.0000034977884,0.0000017654163,0.000447342,0.0000097626,0.003230904,0.000088633366,0.000098200915,0.99177045],"study_design_scores_gemma":[0.003968715,0.0004004403,0.5082175,0.00035115142,0.00006309481,0.00014839819,0.00072342117,0.22455832,0.15789495,0.10063057,0.00075656135,0.0022868505],"about_ca_topic_score_codex":0.000012672208,"about_ca_topic_score_gemma":0.000024941945,"teacher_disagreement_score":0.9894836,"about_ca_system_score_codex":0.00005567701,"about_ca_system_score_gemma":0.0000066636794,"threshold_uncertainty_score":0.6296549},"labels":[],"label_agreement":null},{"id":"W2010132303","doi":"10.1109/cvpr.2011.5995720","title":"Learning to share visual appearance for multiclass object detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":339,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Object (grammar); Hierarchy; Object detection; Task (project management); Cognitive neuroscience of visual object recognition; Hierarchical database model; Pattern recognition (psychology); Object model; Class (philosophy); Machine learning; Statistical model; Computer vision; Data mining","score_opus":0.03498099483991979,"score_gpt":0.2934471307625395,"score_spread":0.2584661359226197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010132303","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":[0.019698296,0.000010246214,0.97838897,0.00011217579,0.00009356963,0.0004223615,3.981532e-7,0.00039280453,0.00088120654],"genre_scores_gemma":[0.83018595,0.0000011747654,0.16882898,0.00022898313,0.000046801128,0.00021613954,5.008701e-7,0.000008630265,0.00048286177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922943,0.000016488655,0.00011519506,0.00033310964,0.00008242767,0.0002233583],"domain_scores_gemma":[0.9995564,0.000039532788,0.0000394345,0.00021563515,0.000069311245,0.00007966639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007169489,0.00008304602,0.00007292199,0.000047449685,0.0001810452,0.00003161835,0.00033874274,0.00002854475,0.000005177104],"category_scores_gemma":[0.0000488985,0.00007911659,0.000036887577,0.00034629003,0.000009938779,0.00027305307,0.00011370202,0.00008043493,0.0001099156],"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":[0.0000480426,0.000094584786,0.00035470218,0.000018974883,0.000011785936,0.0000016342045,0.002176428,0.006309577,0.058540598,0.012857333,0.00021054191,0.9193758],"study_design_scores_gemma":[0.0003491887,0.000496338,0.0046066553,0.000017935867,0.0000036985693,0.0000078574485,0.000095223775,0.8105429,0.17033452,0.002396804,0.010827949,0.0003209263],"about_ca_topic_score_codex":0.000010073031,"about_ca_topic_score_gemma":0.000041520336,"teacher_disagreement_score":0.91905487,"about_ca_system_score_codex":0.000025565498,"about_ca_system_score_gemma":0.00000803443,"threshold_uncertainty_score":0.32262808},"labels":[],"label_agreement":null},{"id":"W2043230163","doi":"10.1109/embc.2013.6610424","title":"Cardiac right ventricular segmentation via point correspondence","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian VIGOUR Centre; University of Alberta","funders":"Servier","keywords":"Segmentation; Artificial intelligence; Computer vision; Image segmentation; Point (geometry); Computer science; Ventricle; Sequence (biology); Cardiac magnetic resonance; Pattern recognition (psychology); Set (abstract data type); Frame (networking); Magnetic resonance imaging; Training set; Level set (data structures); Mathematics; Geometry; Medicine; Radiology; Cardiology","score_opus":0.007217036588538978,"score_gpt":0.2347901394097866,"score_spread":0.22757310282124762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043230163","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":[0.008000361,0.00009172048,0.98790497,0.0013589656,0.0002231853,0.00043131184,3.9852287e-7,0.00021691628,0.0017721489],"genre_scores_gemma":[0.69172704,0.000025679763,0.30345917,0.0012281182,0.00011043105,0.00040937442,0.000008424508,0.000011710351,0.003020024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991614,0.000038739054,0.000141194,0.00027913068,0.00018842117,0.0001910643],"domain_scores_gemma":[0.9992445,0.00007960461,0.00005533903,0.00046253545,0.00007269593,0.000085318396],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006116645,0.00008617645,0.00008072966,0.000044259978,0.00010495691,0.000082992105,0.00043759908,0.00002494543,0.00025125535],"category_scores_gemma":[0.0000067981273,0.000071274575,0.000044812703,0.00043637792,0.000020668876,0.00080700207,0.00013440661,0.00006381753,0.0019472084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061577834,0.00025459792,0.0029725635,0.000017121847,0.00006121867,0.000073723466,0.00043124074,0.0071599516,0.15105338,0.3365572,0.1262358,0.37517703],"study_design_scores_gemma":[0.00058760575,0.00015048121,0.02192592,0.000019468544,0.000020833473,0.00010280797,0.00006227786,0.5463616,0.22971494,0.15820108,0.041838437,0.0010144853],"about_ca_topic_score_codex":0.000024743022,"about_ca_topic_score_gemma":8.240528e-7,"teacher_disagreement_score":0.6844458,"about_ca_system_score_codex":0.000044546174,"about_ca_system_score_gemma":0.000011513791,"threshold_uncertainty_score":0.9988299},"labels":[],"label_agreement":null},{"id":"W2055572673","doi":"10.1109/icip.2010.5653335","title":"Adaptive shape prior in graph cut segmentation","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cut; Prior probability; Segmentation; Artificial intelligence; Computer science; Image segmentation; Graph; Shape analysis (program analysis); Pattern recognition (psychology); Active shape model; Minimum cut; Computer vision; Mathematics; Algorithm; Bayesian probability; Theoretical computer science","score_opus":0.01849045520512834,"score_gpt":0.275912738270609,"score_spread":0.2574222830654807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055572673","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":[0.118557654,0.0000061163637,0.875795,0.0012188223,0.00014573563,0.00028013226,4.7281586e-7,0.00016470603,0.003831356],"genre_scores_gemma":[0.711864,0.000004169373,0.28756538,0.00032375846,0.00002364892,0.00005347532,9.621913e-7,0.0000033133483,0.00016130395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942017,0.000011230367,0.00011069089,0.0002297798,0.000094091585,0.00013402315],"domain_scores_gemma":[0.9995593,0.000060366354,0.000036063848,0.0002774885,0.000025189964,0.00004159471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005468479,0.000060396083,0.00005135619,0.00006520311,0.00005042964,0.000027239088,0.0003517976,0.000026981394,0.00003851423],"category_scores_gemma":[0.000006386387,0.00005488665,0.000017299351,0.00047076232,0.00002373523,0.00040428032,0.00008665885,0.00013930208,0.00007478751],"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":[0.0000050188655,0.000086977045,0.0016573329,0.0000015954602,0.000003330189,0.000004387102,0.00037330503,0.00064064615,0.046426702,0.58234936,0.00063002534,0.3678213],"study_design_scores_gemma":[0.000833201,0.00010288506,0.049326696,0.000008668857,0.0000034387701,0.000021881546,0.00009342069,0.70396966,0.04605609,0.19650571,0.0025955483,0.00048279684],"about_ca_topic_score_codex":0.0000100089155,"about_ca_topic_score_gemma":0.0002941455,"teacher_disagreement_score":0.703329,"about_ca_system_score_codex":0.000010905093,"about_ca_system_score_gemma":0.000014163788,"threshold_uncertainty_score":0.22382125},"labels":[],"label_agreement":null},{"id":"W2063369767","doi":"10.1109/icpr.2010.1043","title":"Applying Error-Correcting Output Coding to Enhance Convolutional Neural Network for Target Detection and Pattern Recognition","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Computer science; Convolutional code; Coding (social sciences); Error detection and correction; Hamming distance; Pattern recognition (psychology); Artificial neural network; Hamming code; Artificial intelligence; Speech recognition; Time delay neural network; Neural coding; Decoding methods; Algorithm; Block code; Mathematics","score_opus":0.028509526726824837,"score_gpt":0.28683861411385436,"score_spread":0.25832908738702953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063369767","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":[0.08238684,0.000012354777,0.91476023,0.0005600396,0.0009843789,0.0009251981,0.0000043421387,0.00026106296,0.00010552894],"genre_scores_gemma":[0.73933494,0.0000012694495,0.25847286,0.00079933845,0.00047166707,0.0008358177,0.000007367759,0.0000115852845,0.00006516358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987505,0.000022759423,0.00022826978,0.00051168754,0.00012426624,0.00036251187],"domain_scores_gemma":[0.99905586,0.00036055778,0.00010896997,0.00023116481,0.0001197793,0.00012366447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023269195,0.00014089666,0.00012253555,0.000057283018,0.000514792,0.000108369895,0.00023497811,0.000062075334,0.0000071716363],"category_scores_gemma":[0.0000824116,0.00014804785,0.000038571754,0.0003143488,0.000025856265,0.00044762038,0.00014459644,0.00023248221,0.000018583165],"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":[0.000008830871,0.000011196175,0.00060270296,0.000008194614,0.000004330971,3.6841308e-7,0.000070730974,0.0045985985,0.018539,0.0004797271,0.00035029362,0.975326],"study_design_scores_gemma":[0.0001664593,0.000070804126,0.0015875017,0.000017659479,0.00000546619,0.000051095376,0.000024562105,0.96550804,0.01984139,0.008352946,0.0040609455,0.0003131188],"about_ca_topic_score_codex":0.00001490157,"about_ca_topic_score_gemma":0.00017983996,"teacher_disagreement_score":0.9750129,"about_ca_system_score_codex":0.000025811394,"about_ca_system_score_gemma":0.000011923702,"threshold_uncertainty_score":0.6037216},"labels":[],"label_agreement":null},{"id":"W2079377097","doi":"10.1109/globalsip.2014.7032076","title":"3D-to-2D mapping for user interactive segmentation of human leg muscles from MRI data","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Computer vision; Artificial intelligence; Image segmentation; Pattern recognition (psychology)","score_opus":0.06227083865890803,"score_gpt":0.33917456453155576,"score_spread":0.2769037258726477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079377097","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":[0.019688841,0.000003856936,0.97859406,0.00067393045,0.00008268761,0.00036984755,0.000026578067,0.00007481306,0.00048540477],"genre_scores_gemma":[0.36230388,0.0000013612939,0.63691854,0.00042905725,0.00007732397,0.000051547147,0.00009965737,0.0000063184775,0.00011234376],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991547,0.000026848444,0.00019809588,0.00040232082,0.00009743615,0.00012060683],"domain_scores_gemma":[0.9985381,0.00033633236,0.00011474373,0.00090441684,0.00006309258,0.000043356806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010205721,0.00007762192,0.000108415945,0.000052208325,0.00008875143,0.00004073053,0.0009969652,0.000019265448,0.000012918388],"category_scores_gemma":[0.000027592429,0.00007231873,0.000019594809,0.00017422145,0.000016620479,0.0007007552,0.0005067229,0.000042892956,0.000018006735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018455456,0.00016187513,0.0006596931,0.000022435093,0.00007025379,3.1125938e-7,0.002450689,0.004500052,0.49382865,0.107146755,0.018520717,0.3726201],"study_design_scores_gemma":[0.000985699,0.00020429766,0.009984362,0.00008707025,0.000024597475,0.0000010197627,0.00041460345,0.7561669,0.12958486,0.039676093,0.062354684,0.00051580777],"about_ca_topic_score_codex":0.000058750862,"about_ca_topic_score_gemma":0.00008866409,"teacher_disagreement_score":0.75166684,"about_ca_system_score_codex":0.000018808336,"about_ca_system_score_gemma":0.0000067176115,"threshold_uncertainty_score":0.29490724},"labels":[],"label_agreement":null},{"id":"W2092781340","doi":"10.1109/mlsp.2015.7324361","title":"Scalable multi-neighborhood learning for convolutional networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scalability; Scale (ratio); Artificial intelligence; Image (mathematics); Convolutional neural network; Feature (linguistics); Pattern recognition (psychology); Deep learning; Machine learning","score_opus":0.050690438563742404,"score_gpt":0.29268414190003555,"score_spread":0.24199370333629316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092781340","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":[0.00009012059,0.00014899614,0.99604505,0.0008419518,0.00018796361,0.00026542027,4.3904765e-7,0.00035817744,0.0020618502],"genre_scores_gemma":[0.50974274,0.000006110333,0.48569527,0.00040246738,0.0001594257,0.0001440406,0.000010162289,0.000010252077,0.0038295458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991093,0.00002400642,0.00014606182,0.0003051567,0.00012562152,0.00028983023],"domain_scores_gemma":[0.9991786,0.00016444012,0.000056820147,0.0002620745,0.0001754588,0.00016259447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016659651,0.0000923465,0.000094767776,0.000029849987,0.00017048667,0.000056982128,0.00044543712,0.000045647448,0.0000071646314],"category_scores_gemma":[0.00006636242,0.00008529028,0.00004004069,0.0002943493,0.000032469332,0.00034972644,0.00015936239,0.00011821847,0.00006537123],"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":[0.000006179328,0.000061103245,0.0016293378,0.000001865869,0.000008394823,6.26136e-7,0.000043124764,0.5230471,0.000060521994,0.4489797,0.010924737,0.015237334],"study_design_scores_gemma":[0.0005011941,0.00004216329,0.00030842182,0.0000023075809,0.000001942186,0.0000055614682,0.000010105891,0.96176654,0.00007136788,0.008311527,0.028864691,0.00011419641],"about_ca_topic_score_codex":0.00000319567,"about_ca_topic_score_gemma":0.000004172246,"teacher_disagreement_score":0.5103498,"about_ca_system_score_codex":0.000045739765,"about_ca_system_score_gemma":0.000054030446,"threshold_uncertainty_score":0.34780368},"labels":[],"label_agreement":null},{"id":"W2111263954","doi":"10.1017/s1431927611000456","title":"An Open-Source Engine for the Processing of Electron Backscatter Patterns: EBSD-Image","year":2011,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; McGill University","funders":"","keywords":"Electron backscatter diffraction; Materials science; Backscatter (email); Optics; Diffraction; Computer science; Physics; Telecommunications","score_opus":0.018718780891994746,"score_gpt":0.2934438939592494,"score_spread":0.27472511306725467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111263954","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":[0.07202317,0.00075955054,0.9264784,0.00030533504,0.000015096055,0.0003366664,0.000009536757,0.00003661242,0.000035654397],"genre_scores_gemma":[0.8059126,0.00011079214,0.19312131,0.00049901917,0.000031165615,0.00009877744,0.000010954846,0.000018485034,0.00019687558],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989619,0.000033191933,0.0002502309,0.00042784613,0.00006474859,0.00026207347],"domain_scores_gemma":[0.9989487,0.00006869974,0.0001736154,0.00065617706,0.00009839588,0.0000544472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021024763,0.00014645494,0.00021660724,0.00006828022,0.00027336355,0.00016868944,0.0013314083,0.000037889422,0.000012136069],"category_scores_gemma":[0.0000052349114,0.000107522894,0.000060638267,0.00039616885,0.00008305908,0.0005612325,0.00023877948,0.0000900233,0.0000027672145],"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":[0.000035972575,0.0001602329,0.00081030757,0.000044887485,0.00009216266,5.015052e-7,0.0016912408,0.00009825859,0.9046596,0.00034602542,0.00035841338,0.091702394],"study_design_scores_gemma":[0.00036517283,0.00014894764,0.0014676836,0.000025888525,0.0001745497,0.000010121285,0.0001079368,0.025234269,0.9671489,0.0007617951,0.0043065916,0.00024813457],"about_ca_topic_score_codex":0.00007689024,"about_ca_topic_score_gemma":0.000044714197,"teacher_disagreement_score":0.73388946,"about_ca_system_score_codex":0.000014860701,"about_ca_system_score_gemma":0.000023770706,"threshold_uncertainty_score":0.43846563},"labels":[],"label_agreement":null},{"id":"W2113137767","doi":"10.1109/tpami.2004.60","title":"An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4600,"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":"University of Tsukuba","keywords":"Algorithm; Computer science; Maximum flow problem; Minimum cut; Benchmark (surveying); Maximum cut; Minification; Time complexity; Image segmentation; Segmentation; Running time; Cut; Energy minimization; Artificial intelligence; Graph; Mathematics; Theoretical computer science; Mathematical optimization","score_opus":0.02598367102043963,"score_gpt":0.331384632381408,"score_spread":0.30540096136096834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113137767","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":[0.008043497,0.00011646952,0.9913795,0.00016150565,0.000074668766,0.00015495198,0.00002766388,0.000037044945,0.000004675991],"genre_scores_gemma":[0.94378173,0.000085767824,0.05590122,0.000106125546,0.0000117393765,0.00007395626,0.000019654142,0.000009324476,0.00001046859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986926,0.000041027808,0.00044713143,0.00047259493,0.00017742411,0.00016922527],"domain_scores_gemma":[0.99923956,0.00008761034,0.00013070258,0.00039957833,0.00005831368,0.00008424903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000718047,0.00016252794,0.0002946059,0.00040027653,0.00012039492,0.000042735886,0.0003440121,0.000054892444,0.000014517069],"category_scores_gemma":[0.0000012613012,0.00015648501,0.00012967501,0.0010123354,0.000052572675,0.00030520928,0.0000040971554,0.00009289557,0.0000012601542],"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":[0.0000115068815,0.00047140496,0.00008104582,0.000003418921,0.000031159612,5.118e-7,0.00035926845,0.6051345,0.0023800281,0.00019580168,0.0000011123926,0.39133027],"study_design_scores_gemma":[0.00011973241,0.00021330235,0.000090918045,0.000009457716,0.000037651844,0.0000011091225,0.000047297242,0.6002309,0.39879444,0.00033928343,0.000015850319,0.00010009806],"about_ca_topic_score_codex":0.0004388148,"about_ca_topic_score_gemma":0.0019772283,"teacher_disagreement_score":0.93573827,"about_ca_system_score_codex":0.000048138383,"about_ca_system_score_gemma":0.000013303318,"threshold_uncertainty_score":0.6381273},"labels":[],"label_agreement":null},{"id":"W2118579862","doi":"10.1109/crv.2006.63","title":"Semiautomatic Segmentation with Compact Shapre Prior","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Computer science; Scale-space segmentation; Segmentation-based object categorization; Image segmentation; Artificial intelligence; Computer vision; Object (grammar); Graph; Minimum spanning tree-based segmentation; Cut; Pattern recognition (psychology); Theoretical computer science","score_opus":0.01059305989003569,"score_gpt":0.2529571231435529,"score_spread":0.2423640632535172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118579862","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":[0.044576257,0.000015270127,0.94309115,0.0010476481,0.00001747488,0.00024540885,4.8321976e-7,0.00046307605,0.010543221],"genre_scores_gemma":[0.74057543,8.042964e-7,0.25822255,0.0002320467,0.000028159804,0.000018000013,0.0000047317594,0.000005450318,0.00091284333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993867,0.000012618587,0.00011522555,0.00019779014,0.00014588558,0.00014175377],"domain_scores_gemma":[0.99952126,0.000054094202,0.000057297937,0.00030663415,0.000027076905,0.00003363359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000028910865,0.00007858607,0.00006819015,0.000030567233,0.00009634543,0.00006403486,0.00027738808,0.000014600786,0.000018077366],"category_scores_gemma":[0.0000011468514,0.000057934936,0.000014586131,0.00033451748,0.000020445394,0.00038726596,0.000030876625,0.00004426138,0.000074736476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013799699,0.00036339078,0.0112811,0.000043944383,0.000029128518,0.000022498863,0.00043125558,0.11958743,0.028052907,0.72351134,0.030066155,0.08659706],"study_design_scores_gemma":[0.00096755783,0.00014868486,0.07035737,0.00003539681,0.000013575025,0.00008731267,0.00003584913,0.8646935,0.035287917,0.023999471,0.0038504296,0.0005228918],"about_ca_topic_score_codex":0.00002064386,"about_ca_topic_score_gemma":0.000021205207,"teacher_disagreement_score":0.7451061,"about_ca_system_score_codex":0.00003371247,"about_ca_system_score_gemma":0.000015449408,"threshold_uncertainty_score":0.2362518},"labels":[],"label_agreement":null},{"id":"W2125215748","doi":"10.1109/cvpr.2014.119","title":"The Role of Context for Object Detection and Semantic Segmentation in the Wild","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1475,"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":"Pascal (unit); Computer science; Segmentation; Object detection; Artificial intelligence; Exploit; Context model; Context (archaeology); Pixel; Image segmentation; Object (grammar); Pattern recognition (psychology); Machine learning; Computer vision; Geography","score_opus":0.009141096907340865,"score_gpt":0.2494392302303552,"score_spread":0.24029813332301433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125215748","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":[0.039802294,0.000083438616,0.9586414,0.0007866968,0.000022968099,0.00034485504,1.3989505e-7,0.000015250252,0.00030296695],"genre_scores_gemma":[0.99470747,0.000013733378,0.004967025,0.000186534,0.000013667045,0.00009013199,1.9787497e-7,0.0000015879991,0.000019631825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967575,0.000036194815,0.000084089035,0.00008802234,0.00005223079,0.00006368895],"domain_scores_gemma":[0.9992656,0.0005037026,0.00004237509,0.00016391276,0.000017600936,0.0000068055515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020089914,0.000030517414,0.000033967808,0.000012606302,0.00010879422,0.00002666861,0.00017200511,0.0000095279875,1.4063455e-7],"category_scores_gemma":[0.000020229412,0.000016688873,0.00001073677,0.00012078103,0.000023912287,0.0000981886,0.000022703674,0.000026390084,7.395716e-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":[0.0000037321695,0.000007216989,0.00029785096,0.0000020764603,0.0000013611112,8.135013e-9,0.00034315913,0.000200524,0.007931884,0.08914988,0.00001810637,0.9020442],"study_design_scores_gemma":[0.0002721574,0.000103846374,0.005734679,0.0000044651824,0.000003328312,0.0000041232875,0.00053033285,0.8645959,0.03509599,0.08822099,0.005377727,0.0000564857],"about_ca_topic_score_codex":0.000017326076,"about_ca_topic_score_gemma":0.00032066417,"teacher_disagreement_score":0.9549052,"about_ca_system_score_codex":0.00000490776,"about_ca_system_score_gemma":0.0000026512666,"threshold_uncertainty_score":0.083676815},"labels":[],"label_agreement":null},{"id":"W2133110153","doi":"10.1109/iembs.2008.4649288","title":"Detection of brachytherapy seeds using 3D ultrasound","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Brachytherapy; Ultrasound; Imaging phantom; Prostate brachytherapy; 3D ultrasound; Prostate; Radiology; Computer science; Medical physics; Medicine; Radiation therapy","score_opus":0.031119815744477097,"score_gpt":0.2710109490643811,"score_spread":0.23989113331990403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133110153","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":[0.23984315,0.000042831318,0.75927037,0.00002718596,0.00004947594,0.00006424006,1.7650694e-7,0.00008848249,0.0006140785],"genre_scores_gemma":[0.81648844,0.000032363103,0.18323416,0.00010194119,0.000031613563,0.0000036353833,1.951591e-7,0.0000040103196,0.000103636856],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949205,0.000014701432,0.00012442155,0.00015667867,0.000103803846,0.00010832347],"domain_scores_gemma":[0.99947107,0.000081093094,0.000062513885,0.00031108563,0.000044417895,0.000029846024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003365951,0.00005665164,0.00006963784,0.00004191815,0.000119178316,0.000007307171,0.00022306624,0.000023777451,0.000007766617],"category_scores_gemma":[0.0000060109437,0.00005239509,0.000026567623,0.00046162592,0.000043154974,0.00028836218,0.000029425466,0.000049093753,0.000008325768],"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":[0.0000035198796,0.00004552727,0.0010766345,0.000002589356,0.000007552143,0.0000017855356,0.00020616764,0.008741617,0.95223635,0.008532948,0.000030487343,0.029114794],"study_design_scores_gemma":[0.00037004414,0.00011092307,0.013208888,0.000006893114,0.000004388768,0.00058646564,0.000012115055,0.2710148,0.70504236,0.005452577,0.0038949985,0.00029553968],"about_ca_topic_score_codex":0.000017990984,"about_ca_topic_score_gemma":0.000006152764,"teacher_disagreement_score":0.5766453,"about_ca_system_score_codex":0.000018902156,"about_ca_system_score_gemma":0.000018846464,"threshold_uncertainty_score":0.21366099},"labels":[],"label_agreement":null},{"id":"W2136836265","doi":"","title":"Adaptive dropout for training deep neural networks","year":2013,"lang":"en","type":"article","venue":"neural information processing systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":280,"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":"Dropout (neural networks); MNIST database; Computer science; Artificial intelligence; Artificial neural network; Feature (linguistics); Boltzmann machine; Deep belief network; Restricted Boltzmann machine; Stochastic gradient descent; Pattern recognition (psychology); Convolutional neural network; Machine learning; Gradient descent; Deep learning; Backpropagation","score_opus":0.031988761766423526,"score_gpt":0.2623541483614051,"score_spread":0.23036538659498157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136836265","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":[0.0016844113,0.00024471484,0.99393046,0.0006971702,0.0006547029,0.0014109603,0.0000025943907,0.00060214713,0.00077284186],"genre_scores_gemma":[0.97862005,0.0000024139981,0.018892879,0.0009002767,0.00027407255,0.0011788458,0.000029702102,0.000016759055,0.00008499255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981577,0.000043467135,0.0007097769,0.00027602797,0.0002867118,0.0005263464],"domain_scores_gemma":[0.998305,0.00015306316,0.0005425934,0.0003658377,0.00047834465,0.00015516751],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00017423028,0.00024096631,0.00025136434,0.0001332559,0.0005361879,0.0010992214,0.00073744124,0.00010333191,0.000003246236],"category_scores_gemma":[0.000043985707,0.00021444114,0.000076491284,0.00059511507,0.000054973672,0.008921999,0.00010233806,0.00021328402,0.000048869904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064519004,0.000009153422,0.000027304031,0.00006638201,0.000006723537,2.7547082e-7,0.0016900268,0.40831125,0.00003837342,0.010291061,0.000878065,0.578675],"study_design_scores_gemma":[0.00028965404,0.00006830336,0.00008848021,0.000029630915,0.0000048448437,0.00004508556,0.00052256096,0.9957029,0.00001889275,0.00066669815,0.0023162502,0.00024665598],"about_ca_topic_score_codex":0.000017565986,"about_ca_topic_score_gemma":0.0000016098148,"teacher_disagreement_score":0.9769356,"about_ca_system_score_codex":0.00007187587,"about_ca_system_score_gemma":0.000035590958,"threshold_uncertainty_score":0.9999377},"labels":[],"label_agreement":null},{"id":"W2142775761","doi":"10.1109/cvpr.2013.224","title":"Fast Trust Region for Segmentation","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":40,"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":"","keywords":"Bhattacharyya distance; Trust region; Mathematical optimization; Computer science; Divergence (linguistics); Constraint (computer-aided design); Segmentation; Gradient descent; Algorithm; Nonlinear system; RADIUS; Mathematics; Artificial intelligence; Artificial neural network; Geometry; Physics","score_opus":0.025399350588664073,"score_gpt":0.2724158943882206,"score_spread":0.24701654379955654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142775761","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":[0.0020961764,0.000005919395,0.991383,0.0034432893,0.000058113736,0.0004957038,1.7652387e-7,0.00015889753,0.0023587341],"genre_scores_gemma":[0.26730472,0.000007041697,0.7257477,0.0012204556,0.000077045,0.0007254879,0.0000050566946,0.000006492402,0.0049059964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999595,0.0000051297657,0.00007950221,0.0001624797,0.000049900937,0.00010802761],"domain_scores_gemma":[0.99960876,0.00004647612,0.000032885524,0.00023055385,0.000046551937,0.00003475788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000016108206,0.000044034237,0.0000362965,0.00001975179,0.00007580492,0.00005313421,0.0002314092,0.000014460216,0.000014370844],"category_scores_gemma":[0.0000041192834,0.00003755466,0.000019460564,0.00013868579,0.000009830568,0.0005010025,0.00003920987,0.000019751576,0.00012921153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011505372,0.000027431226,0.00020419942,0.0000044653457,0.000003297945,1.8143005e-7,0.0001098054,0.0008181611,0.006706886,0.38112983,0.045010958,0.56598365],"study_design_scores_gemma":[0.0005974272,0.00010944191,0.00347711,0.000005511959,0.000003862235,0.000015766953,0.000084068866,0.77219814,0.026641807,0.17086884,0.025692564,0.00030543763],"about_ca_topic_score_codex":0.000005162367,"about_ca_topic_score_gemma":0.0000016110101,"teacher_disagreement_score":0.77138,"about_ca_system_score_codex":0.000015267467,"about_ca_system_score_gemma":0.0000048975216,"threshold_uncertainty_score":0.1660796},"labels":[],"label_agreement":null},{"id":"W2145276819","doi":"","title":"Transfer Learning by Borrowing Examples for Multiclass Object Detection","year":2011,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":114,"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":"National Science Foundation","keywords":"Object (grammar); Computer science; Object detection; Class (philosophy); Artificial intelligence; Transfer of learning; Detector; Machine learning; Training set; Transfer (computing); Pattern recognition (psychology)","score_opus":0.026507997466297967,"score_gpt":0.24658050683136706,"score_spread":0.22007250936506909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145276819","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":[0.09940033,0.00029325075,0.8966203,0.0011375927,0.00033236475,0.0006701501,0.00001659122,0.0011659272,0.00036349276],"genre_scores_gemma":[0.8033844,0.000052392294,0.19602306,0.00006622408,0.000026531638,0.0002870417,0.000006939233,0.000028681487,0.00012469957],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980964,0.000028412294,0.00042183226,0.0007072311,0.00020883803,0.0005372747],"domain_scores_gemma":[0.9987063,0.00007702641,0.00016437216,0.0008161663,0.00015163345,0.000084537],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018805033,0.00029281242,0.00035073445,0.00042860876,0.00038376113,0.000029087858,0.0011914023,0.0003373061,0.0000029327666],"category_scores_gemma":[0.00018208419,0.0003020659,0.00014503852,0.0011623895,0.00033391058,0.0010677739,0.00018626417,0.00045181427,0.000008883485],"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":[0.000055261644,0.00027241308,0.0016591122,0.00008185526,0.00015649365,0.000010521731,0.00081726373,0.0017233075,0.55938166,0.080911286,0.0005457299,0.3543851],"study_design_scores_gemma":[0.0010188052,0.0003687979,0.0002653574,0.000056205885,0.000048206417,0.000025156141,0.000104545674,0.016179444,0.7500273,0.0056573353,0.22571988,0.00052894966],"about_ca_topic_score_codex":0.000031449166,"about_ca_topic_score_gemma":0.00008559976,"teacher_disagreement_score":0.7039841,"about_ca_system_score_codex":0.00008694156,"about_ca_system_score_gemma":0.000046310564,"threshold_uncertainty_score":0.99994314},"labels":[],"label_agreement":null},{"id":"W2157414412","doi":"10.1109/igarss.2008.4779761","title":"Road Vehicle Detection and Classification from Very-High-Resolution Color Digital Orthoimagery based on Object-Oriented Method","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Orthophoto; Artificial intelligence; Computer vision; Computer science; Image segmentation; Pixel; Object detection; Segmentation; Feature extraction; Classifier (UML); Pattern recognition (psychology); Edge detection; Feature (linguistics); Contextual image classification; Image resolution; Image processing; Image (mathematics)","score_opus":0.023159018861299525,"score_gpt":0.2600345881580222,"score_spread":0.23687556929672268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157414412","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":[0.21226826,0.000012591937,0.7861262,0.00038467153,0.00011140516,0.00023986642,0.000008735766,0.00032725066,0.00052100624],"genre_scores_gemma":[0.8475243,0.000007944654,0.15190344,0.00028022454,0.0000601253,0.000074406285,0.000027144146,0.00001064091,0.00011176883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861896,0.00008157101,0.00023165437,0.0005963867,0.00025659232,0.0002148348],"domain_scores_gemma":[0.9987989,0.0003553116,0.00012056759,0.0005344685,0.000088431254,0.00010232797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009283846,0.00015288731,0.0001381513,0.00010272281,0.0004285665,0.00010942807,0.00020903762,0.00007878871,0.000006887148],"category_scores_gemma":[0.000069540845,0.00014538445,0.00004230958,0.00061193376,0.00008417221,0.0008105476,0.00007841333,0.00014704141,0.000057613815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003137297,0.00063191354,0.0038151946,0.0000093917115,0.000037462803,0.000025030826,0.00028533614,0.034228288,0.17150395,0.028347833,0.00079177204,0.7600101],"study_design_scores_gemma":[0.00040030014,0.00010823291,0.08944958,0.000004924344,0.000004514193,0.000006526944,0.000007946839,0.8971931,0.010721997,0.0007956709,0.0011504793,0.00015677432],"about_ca_topic_score_codex":0.00006644601,"about_ca_topic_score_gemma":0.000023346667,"teacher_disagreement_score":0.86296475,"about_ca_system_score_codex":0.0000898061,"about_ca_system_score_gemma":0.00004394389,"threshold_uncertainty_score":0.5928605},"labels":[],"label_agreement":null},{"id":"W2161017703","doi":"10.1109/icma.2011.5985611","title":"Object detection by parts using appearance, structural and shape features","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"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":"Alberta Innovates; China Scholarship Council","keywords":"Artificial intelligence; Object detection; Computer vision; Computer science; Classifier (UML); Pattern recognition (psychology); Viola–Jones object detection framework; Support vector machine; Object (grammar); Segmentation; Detector; Orientation (vector space); A priori and a posteriori; Feature extraction; Mathematics; Face detection","score_opus":0.024435365980102312,"score_gpt":0.25329558499162025,"score_spread":0.22886021901151793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161017703","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":[0.28900173,0.0004487363,0.70886636,0.000041990046,0.00012057566,0.00015848839,8.718084e-7,0.0002197041,0.0011415712],"genre_scores_gemma":[0.9502678,0.000012729413,0.04943634,0.00015738443,0.000029610368,0.000007551123,3.2473181e-7,0.0000052693454,0.00008300239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993872,0.000016404289,0.00008482987,0.0002684974,0.00007866834,0.00016439622],"domain_scores_gemma":[0.99964607,0.000012786594,0.000041894218,0.00022299775,0.000020498253,0.000055751916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002908149,0.00008695503,0.00006852775,0.000027337026,0.0001659069,0.000043556487,0.00019756604,0.00003250991,0.000008624179],"category_scores_gemma":[0.000003714182,0.000073322524,0.00001624225,0.00019831792,0.000040496347,0.00040310164,0.000096172625,0.000078981066,0.000004312159],"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":[0.000024823434,0.000028237431,0.0023825832,0.000016228083,0.00002355472,0.0000058062087,0.0010175112,0.00037086807,0.11062775,0.03201596,0.00059263175,0.85289407],"study_design_scores_gemma":[0.0004887321,0.00013319228,0.054886702,0.000021799186,0.000014307128,0.0002999444,0.000039483537,0.70402277,0.20189793,0.036026765,0.0014824688,0.00068589346],"about_ca_topic_score_codex":0.00003225,"about_ca_topic_score_gemma":0.000021911923,"teacher_disagreement_score":0.85220814,"about_ca_system_score_codex":0.000011537948,"about_ca_system_score_gemma":0.0000044157246,"threshold_uncertainty_score":0.2990006},"labels":[],"label_agreement":null},{"id":"W2161893161","doi":"","title":"3D Object Recognition with Deep Belief Nets","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":304,"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":"Artificial intelligence; Computer science; Discriminative model; Pattern recognition (psychology); Deep belief network; Cognitive neuroscience of visual object recognition; Generative model; Convolutional neural network; Task (project management); Viewpoints; Deep learning; Boltzmann machine; Object (grammar); Set (abstract data type); Test set; Test data; Support vector machine; Generative grammar","score_opus":0.015367772060032918,"score_gpt":0.24197441010463433,"score_spread":0.2266066380446014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161893161","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":[0.002444847,0.000032321772,0.9679756,0.002310889,0.000027219823,0.00015822139,2.6268646e-7,0.0003608833,0.02668974],"genre_scores_gemma":[0.39335164,0.000020039286,0.60242003,0.0037409302,0.000053852305,0.000021374542,0.0000052201135,0.0000048757083,0.00038203818],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932927,0.000012900209,0.0000925892,0.00026619175,0.000119972516,0.00017907418],"domain_scores_gemma":[0.9994588,0.000038347913,0.00003918381,0.000354418,0.000048636346,0.00006064181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034782348,0.00008118747,0.000064865126,0.00003422889,0.000097541146,0.00004665872,0.00029797788,0.000022776936,0.000021580638],"category_scores_gemma":[0.000004543446,0.00006300333,0.000015411197,0.0004178321,0.000014837945,0.00038266613,0.00003044465,0.000071809,0.00017748101],"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":[0.0000043303157,0.00004981877,0.00002567432,6.251153e-7,0.000002554397,0.0000052550267,0.000059291295,0.00096148165,0.0004447374,0.019613614,0.00080425595,0.97802836],"study_design_scores_gemma":[0.0020560746,0.0022862176,0.02572079,0.00007563864,0.000029961579,0.0004459841,0.000039176153,0.47163707,0.02690945,0.4115637,0.057431772,0.001804172],"about_ca_topic_score_codex":0.0000023166194,"about_ca_topic_score_gemma":0.000018429628,"teacher_disagreement_score":0.9762242,"about_ca_system_score_codex":0.000016254182,"about_ca_system_score_gemma":0.000012092564,"threshold_uncertainty_score":0.25692013},"labels":[],"label_agreement":null},{"id":"W2163605009","doi":"10.1145/3065386","title":"ImageNet classification with deep convolutional neural networks","year":2017,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":75709,"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":"Softmax function; Convolutional neural network; Computer science; Pooling; Dropout (neural networks); Artificial intelligence; Convolution (computer science); Regularization (linguistics); Pattern recognition (psychology); Deep neural networks; Word error rate; Normalization (sociology); Artificial neural network; Machine learning","score_opus":0.06443397073163805,"score_gpt":0.3127342939106008,"score_spread":0.24830032317896278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163605009","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":[0.009667458,0.0013496651,0.76125056,0.22041427,0.00027499878,0.0008586328,0.000010152589,0.00019883666,0.005975419],"genre_scores_gemma":[0.7423923,0.00009058791,0.25719917,0.0001496086,0.000024474464,0.00007146742,0.0000065828312,0.0000069893817,0.000058798832],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991522,0.00009591767,0.00021243395,0.00021451426,0.00016605684,0.00015890248],"domain_scores_gemma":[0.94542515,0.00024484046,0.00046604147,0.053629816,0.00019088745,0.000043252887],"candidate_categories":["sts","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00014761249,0.000099980134,0.00010738574,0.000027031163,0.0015625047,0.0001133395,0.05644206,0.0000384942,0.000002328779],"category_scores_gemma":[0.0009398077,0.000072492214,0.000057730227,0.00020244136,0.0007417361,0.00048696875,0.019231547,0.0002167907,0.000007349086],"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":[0.000022282029,0.00029146895,0.030789407,0.00000944363,0.00007324555,3.9361115e-7,0.00023502928,0.03797023,0.0024924558,0.87030953,0.010589233,0.047217295],"study_design_scores_gemma":[0.0001937397,0.00001533016,0.19956376,0.000013420397,0.000013182779,0.000011136793,0.000010539753,0.7456469,0.00019036185,0.05181773,0.0024121318,0.00011178029],"about_ca_topic_score_codex":0.0000149230245,"about_ca_topic_score_gemma":0.00007284257,"teacher_disagreement_score":0.8184918,"about_ca_system_score_codex":0.000032755444,"about_ca_system_score_gemma":0.000037589478,"threshold_uncertainty_score":0.9997373},"labels":[],"label_agreement":null},{"id":"W2165720259","doi":"10.1109/cvpr.2009.5206577","title":"Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":186,"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":"Pattern recognition (psychology); Restricted Boltzmann machine; Artificial intelligence; Discriminative model; Computer science; Feature extraction; Boltzmann machine; Feature learning; Classifier (UML); Convolutional neural network; Feature (linguistics); Invariant (physics); Deep learning; Cognitive neuroscience of visual object recognition; Mathematics","score_opus":0.036909895567094717,"score_gpt":0.29189500764385595,"score_spread":0.2549851120767612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165720259","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":[0.03771712,0.000050903753,0.95844567,0.0027805802,0.0002620968,0.00041395068,0.00005187689,0.00013522847,0.00014257658],"genre_scores_gemma":[0.94991314,0.00009287649,0.048506085,0.0010120354,0.00020545273,0.000025483345,0.00016515184,0.000009714493,0.00007006447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986329,0.00009902993,0.00028937677,0.00051354466,0.0002220324,0.00024311015],"domain_scores_gemma":[0.99893874,0.00023222758,0.00023237357,0.00024782075,0.00023897062,0.00010985024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011561721,0.00021393456,0.00023969458,0.00015406882,0.00019635235,0.00011988244,0.00029787884,0.00011542285,0.000012577221],"category_scores_gemma":[0.0000142502295,0.0001907942,0.00006924074,0.00023978506,0.000040409723,0.0003065351,0.000045341305,0.0002604705,0.000015863408],"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":[0.000052270574,0.00013361814,0.00008899242,0.000020300438,0.000009894193,0.0000045581596,0.00009976291,0.00040610426,0.002336621,0.0058004768,0.0022018843,0.9888455],"study_design_scores_gemma":[0.0012567242,0.0018436459,0.031255636,0.00030478663,0.000014596137,0.000024080997,0.00000415945,0.925451,0.0011920409,0.036955733,0.0013018949,0.00039572813],"about_ca_topic_score_codex":0.0000041991025,"about_ca_topic_score_gemma":0.000003948332,"teacher_disagreement_score":0.9884498,"about_ca_system_score_codex":0.000017858867,"about_ca_system_score_gemma":0.000034304463,"threshold_uncertainty_score":0.77803624},"labels":[],"label_agreement":null},{"id":"W2172943111","doi":"10.1016/j.tcs.2013.09.026","title":"Accelerated training of max-margin Markov networks with kernels","year":2013,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Google; University of Chicago; National Science Foundation","keywords":"Margin (machine learning); Computation; Markov chain; Convergence (economics); Projection (relational algebra); Computer science; Mathematical optimization; Euclidean geometry; Mathematics; Algorithm; Machine learning","score_opus":0.01849413291799128,"score_gpt":0.24994403962652262,"score_spread":0.23144990670853133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172943111","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":[0.04021905,0.000024658619,0.95560384,0.0008302669,0.00013619784,0.00038977552,5.282099e-7,0.00021300536,0.002582701],"genre_scores_gemma":[0.68163556,0.00000457402,0.3179065,0.00034455847,0.00005838536,0.000030696734,6.055434e-7,0.000008808427,0.000010320761],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975718,0.000075299504,0.00034122157,0.0007459358,0.00056027464,0.0007054505],"domain_scores_gemma":[0.997811,0.00042139352,0.00014193695,0.0009907308,0.00033058517,0.00030431172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004989906,0.00022260986,0.0002822373,0.0001313363,0.00028520005,0.00029406298,0.0027416178,0.000050891995,0.00008179841],"category_scores_gemma":[0.000030293562,0.00015927543,0.000045168697,0.0023760793,0.0026427987,0.0010677725,0.00076968595,0.00024618127,0.000041401996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006971967,0.00003745718,0.000051447518,0.0000030364758,0.0000048226625,0.0000036827455,0.00029705992,0.01348196,0.0006899945,0.84262365,0.000059681686,0.14274023],"study_design_scores_gemma":[0.00020573799,0.0002178443,0.0019760758,0.000034407898,0.0000033883957,0.000038901562,0.0000081895205,0.9376183,0.0020664234,0.0575531,0.000045477133,0.00023215346],"about_ca_topic_score_codex":0.000003952908,"about_ca_topic_score_gemma":3.6366475e-7,"teacher_disagreement_score":0.92413634,"about_ca_system_score_codex":0.00003259715,"about_ca_system_score_gemma":0.0000997311,"threshold_uncertainty_score":0.97375023},"labels":[],"label_agreement":null},{"id":"W2198190323","doi":"10.48550/arxiv.1510.03009","title":"Neural Networks with Few Multiplications","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"MNIST database; Computer science; Artificial neural network; Multiplication (music); Gradient descent; Computation; Point (geometry); Artificial intelligence; Layer (electronics); Stochastic gradient descent; Binary number; Sign (mathematics); Deep neural networks; Algorithm; Training (meteorology); Backpropagation; Arithmetic; Pattern recognition (psychology); Mathematics","score_opus":0.08012054912875996,"score_gpt":0.20369874021044435,"score_spread":0.12357819108168439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2198190323","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":[0.024331173,0.00010686151,0.9722124,0.0004316624,0.00021700868,0.0005761709,0.000010091806,0.00064300577,0.0014716671],"genre_scores_gemma":[0.983613,0.00008286302,0.014937656,0.00019028496,0.00013737689,0.00001193273,0.000041608655,0.000030143068,0.00095509994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997856,0.00009115661,0.00018819743,0.0013295196,0.00010173673,0.0004333643],"domain_scores_gemma":[0.9966123,0.00012878604,0.00029932047,0.002314422,0.00032149165,0.000323671],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010913206,0.000356537,0.0002945691,0.00014413953,0.0002386088,0.000119341326,0.002491061,0.00021971203,0.0000058530823],"category_scores_gemma":[0.000013441889,0.0003689989,0.00011171908,0.0011318038,0.00016515772,0.00043993202,0.002009243,0.00077896664,0.000056341163],"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":[0.000011500194,0.000048491285,0.0007654321,0.000006435055,0.000026520389,0.000046511574,0.00003703437,0.9207521,0.000002314079,0.076619,0.00072336284,0.00096127903],"study_design_scores_gemma":[0.00032681206,0.00003714513,0.0006581936,0.000023234597,0.000040498326,0.000012408589,0.000012954845,0.9779544,0.0000068221134,0.019305937,0.0011981968,0.0004233849],"about_ca_topic_score_codex":0.00003878117,"about_ca_topic_score_gemma":0.000071363094,"teacher_disagreement_score":0.95928186,"about_ca_system_score_codex":0.00021087211,"about_ca_system_score_gemma":0.00014544633,"threshold_uncertainty_score":0.9998762},"labels":[],"label_agreement":null},{"id":"W2200201590","doi":"10.1109/iccv.2015.14","title":"Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boosting (machine learning); Object detection; Artificial intelligence; Detector; Computer science; Pattern recognition (psychology); Classifier (UML); Computation; Generalization; Pedestrian detection; Feature extraction; Haar-like features; Haar; Discriminative model; Feature selection; Cognitive neuroscience of visual object recognition; Computer vision; Machine learning; Pedestrian; Mathematics; Algorithm; Engineering; Face detection","score_opus":0.03492062925447216,"score_gpt":0.3052797443843548,"score_spread":0.27035911512988264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2200201590","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":[0.0910925,0.0001079391,0.90778,0.00007005036,0.00013632598,0.00013797152,0.0000016835097,0.00018745425,0.00048611345],"genre_scores_gemma":[0.93115354,0.000013054112,0.068584554,0.0001395037,0.000046735026,0.000010276266,0.0000047779927,0.0000035656499,0.00004397769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925065,0.000043422933,0.00010684432,0.00029159468,0.00015204634,0.00015543535],"domain_scores_gemma":[0.9994861,0.00003803454,0.000058758495,0.00024910283,0.00007459672,0.00009340525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000112890295,0.000086963744,0.00007300937,0.00005596857,0.00016187709,0.000083516534,0.00021228561,0.000033807206,0.0000010591424],"category_scores_gemma":[0.00003929872,0.000077612014,0.000012545768,0.00046370365,0.0000352044,0.000397442,0.000075735625,0.000076007,0.000005843097],"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":[0.000039970575,0.0002507046,0.0087998025,0.00004580029,0.000026380256,0.000009590876,0.0029271613,0.36693326,0.213781,0.10187909,0.0045689964,0.30073822],"study_design_scores_gemma":[0.00046878733,0.000097037366,0.0068276143,0.00000989406,0.0000062497725,0.00010548452,0.000045882094,0.93254304,0.053759135,0.0040209354,0.0017666803,0.00034925176],"about_ca_topic_score_codex":0.000012131551,"about_ca_topic_score_gemma":0.0000101065125,"teacher_disagreement_score":0.84006107,"about_ca_system_score_codex":0.000040506093,"about_ca_system_score_gemma":0.00003154696,"threshold_uncertainty_score":0.3164926},"labels":[],"label_agreement":null},{"id":"W2252795377","doi":"10.1007/s11227-016-1633-y","title":"GPU-enabled back-propagation artificial neural network for digit recognition in parallel","year":2016,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Lakehead University","funders":"Fundo para o Desenvolvimento das Ciências e da Tecnologia; Universidade de Macau","keywords":"Computer science; CUDA; Programmer; Artificial neural network; Graphics processing unit; Graphics; Parallel computing; Code (set theory); Central processing unit; General-purpose computing on graphics processing units; GPU cluster; Backpropagation; Computer architecture; Computer hardware; Computer engineering; Artificial intelligence; Embedded system; Computer graphics (images); Programming language","score_opus":0.047587179519779164,"score_gpt":0.26928656411199237,"score_spread":0.2216993845922132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252795377","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":[0.2795927,0.000055195866,0.7160042,0.003783813,0.00026339904,0.00023869323,9.891967e-7,0.0000221952,0.000038821054],"genre_scores_gemma":[0.9157739,0.00003438236,0.08292557,0.00026482035,0.0009611851,0.000006116688,0.0000013942872,0.000013035188,0.00001955615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852747,0.00015371552,0.00062438264,0.00015737448,0.00019063284,0.0003464365],"domain_scores_gemma":[0.9981397,0.0010638259,0.00031711024,0.00022767075,0.00019195955,0.00005978004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000979655,0.00011839757,0.00018804234,0.000075470736,0.00019915776,0.00006574369,0.0006344893,0.00004162848,0.000005406116],"category_scores_gemma":[0.000108543834,0.00006869283,0.00008295693,0.0004134391,0.000043055978,0.000870509,0.00010587204,0.00017141645,0.00001931827],"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":[0.00016631649,0.00007320348,0.00076158065,0.000012516784,0.000019448134,0.0000059512718,0.0005098253,0.14490113,0.013251556,0.0042877425,0.001420049,0.8345907],"study_design_scores_gemma":[0.0025020684,0.00054975477,0.005171114,0.00060940394,0.000041987612,0.0004606061,0.00014009382,0.67333555,0.007144644,0.3075045,0.001959239,0.0005809969],"about_ca_topic_score_codex":0.0000032649561,"about_ca_topic_score_gemma":0.00001125904,"teacher_disagreement_score":0.8340097,"about_ca_system_score_codex":0.00007016856,"about_ca_system_score_gemma":0.000037181588,"threshold_uncertainty_score":0.28012124},"labels":[],"label_agreement":null},{"id":"W2253218192","doi":"10.48550/arxiv.1511.08498","title":"Iterative Instance Segmentation","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Naval Research; Multidisciplinary University Research Initiative; Nvidia","keywords":"Contiguity; Inference; Computer science; Prior probability; Segmentation; A priori and a posteriori; Sequence (biology); Artificial intelligence; Smoothness; Task (project management); Structured prediction; Algorithm; Pattern recognition (psychology); Machine learning; Bayesian probability; Mathematics","score_opus":0.09684413545597585,"score_gpt":0.22245494968815352,"score_spread":0.12561081423217768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2253218192","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":[0.045653455,0.00007485611,0.94921327,0.00017515593,0.0003365498,0.00038675565,0.000015046355,0.00030787045,0.0038370348],"genre_scores_gemma":[0.97821474,0.00010806784,0.019795375,0.00017592199,0.00007559482,0.0000049617806,0.000033382574,0.000013250994,0.0015787018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850416,0.000093155824,0.00015233201,0.0009302837,0.00008561621,0.0002344773],"domain_scores_gemma":[0.9982577,0.000064324326,0.00024898603,0.0010415077,0.00023795123,0.00014952557],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000102531,0.00023128733,0.00019704876,0.000120638106,0.00012980233,0.00009468703,0.0013459653,0.00013615827,0.0000075902603],"category_scores_gemma":[0.000014122121,0.00027541106,0.000077766905,0.0006676082,0.00007530087,0.00064270187,0.0013234816,0.00039774535,0.00012638443],"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":[0.000012314097,0.00005269213,0.0003166675,0.000016558546,0.000030198453,0.000072776755,0.0004281022,0.5941496,0.00009674273,0.40116388,0.0016063007,0.002054214],"study_design_scores_gemma":[0.00038369442,0.000035374025,0.0002033331,0.000045386107,0.000022394232,0.0000036612005,0.000057356086,0.6889052,0.00044024977,0.30728763,0.0021687783,0.00044696065],"about_ca_topic_score_codex":0.000011163845,"about_ca_topic_score_gemma":0.000020821022,"teacher_disagreement_score":0.9325613,"about_ca_system_score_codex":0.00034124847,"about_ca_system_score_gemma":0.0001534131,"threshold_uncertainty_score":0.9999698},"labels":[],"label_agreement":null},{"id":"W2279398222","doi":"10.1109/cvprw.2016.141","title":"Efficient Deep Feature Learning and Extraction via StochasticNets","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Feature extraction; Feature (linguistics); Deep neural networks; Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.011234836967210178,"score_gpt":0.27995919471231506,"score_spread":0.2687243577451049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279398222","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":[0.0012218409,0.0004214882,0.99316585,0.0019987712,0.0003940929,0.00036564024,8.4523157e-7,0.00043185608,0.0019996252],"genre_scores_gemma":[0.7880982,0.000064572145,0.20925814,0.0001226914,0.00025881684,0.00012931717,0.0000058983455,0.000023806797,0.0020385673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985873,0.000045027493,0.00015350078,0.0007524396,0.00020810378,0.00025361008],"domain_scores_gemma":[0.9989111,0.00020364445,0.00018231841,0.00051192864,0.0000770784,0.00011397277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009911175,0.00021944741,0.00017122898,0.000083380124,0.0001869092,0.000111662834,0.0004451034,0.00019209835,0.0000122097545],"category_scores_gemma":[0.000028572264,0.00017098966,0.000047092657,0.00012734572,0.000044881002,0.00006900174,0.0012557039,0.0006664766,0.000050642622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010800571,0.000075533535,0.00010662093,0.00006060434,0.000034609635,0.000013427322,0.00027484776,0.36410594,0.0071218605,0.0506998,0.00065895455,0.576837],"study_design_scores_gemma":[0.000119551376,0.000025306981,0.00057592033,0.000053214644,0.000009017254,0.00004917579,0.000004723773,0.97557104,0.00035161778,0.01871985,0.004211598,0.0003089998],"about_ca_topic_score_codex":0.0000020894088,"about_ca_topic_score_gemma":0.0000034245115,"teacher_disagreement_score":0.7868763,"about_ca_system_score_codex":0.00006315436,"about_ca_system_score_gemma":0.000023806639,"threshold_uncertainty_score":0.69727564},"labels":[],"label_agreement":null},{"id":"W2288122362","doi":"10.1109/cvpr.2016.314","title":"Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1318,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia; Microsoft Research","keywords":"Pooling; Computer science; Object detection; Exploit; Pascal (unit); Artificial intelligence; Intuition; Abstraction; Artificial neural network; Context (archaeology); Machine learning; Pattern recognition (psychology); Geography","score_opus":0.015087989654314534,"score_gpt":0.24234694661335793,"score_spread":0.22725895695904338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288122362","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":[0.23877615,0.000269334,0.75896823,0.0009564169,0.00010201774,0.00027006134,2.4819673e-7,0.00018319524,0.00047435664],"genre_scores_gemma":[0.98368996,0.000048595957,0.015510167,0.00058152527,0.00007294332,0.000041656487,2.3421201e-7,0.000013448041,0.000041452648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986943,0.000051662588,0.00023823878,0.0004929608,0.00012521767,0.0003975993],"domain_scores_gemma":[0.99890536,0.00045244602,0.00010039334,0.00038375167,0.00004336155,0.00011471131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001240016,0.00016403105,0.00016049916,0.00008058101,0.00011525195,0.00007200996,0.000312616,0.000043813834,0.0000029250293],"category_scores_gemma":[0.00003684058,0.00010381993,0.000019527919,0.00044013766,0.000058683898,0.0005442859,0.00023137065,0.00016974066,0.000004680006],"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":[0.000017807519,0.000017838764,0.008667635,0.0000033872511,0.000004943991,0.000017651753,0.0002749368,0.0063993996,0.0005431308,0.0060358047,0.000026623871,0.97799087],"study_design_scores_gemma":[0.001668371,0.00026992537,0.021889612,0.0002308977,0.00000664259,0.0001833075,0.00015130575,0.96784776,0.0027722209,0.003934751,0.00040612073,0.0006390854],"about_ca_topic_score_codex":0.000031884272,"about_ca_topic_score_gemma":0.0011486666,"teacher_disagreement_score":0.9773517,"about_ca_system_score_codex":0.000045973367,"about_ca_system_score_gemma":0.00001794946,"threshold_uncertainty_score":0.42336538},"labels":[],"label_agreement":null},{"id":"W2300770211","doi":"10.48550/arxiv.1603.05691","title":"Do Deep Convolutional Nets Really Need to be Deep and Convolutional?","year":2016,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Deep learning; Computer science; Convolution (computer science); Convolutional neural network; Artificial intelligence; Machine learning; Artificial neural network","score_opus":0.015654374398132203,"score_gpt":0.2514249562188843,"score_spread":0.23577058182075208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300770211","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":[0.004098133,0.0027840566,0.9347656,0.053499587,0.0003773451,0.001985774,0.0001640549,0.0015564173,0.0007690582],"genre_scores_gemma":[0.35265532,0.0007364652,0.62741005,0.013850571,0.00052562053,0.0035838792,0.00012439844,0.00013320975,0.0009804913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949524,0.00021084555,0.0008996404,0.0018647222,0.00082869234,0.0012437223],"domain_scores_gemma":[0.9948824,0.0004999816,0.0005560444,0.00261671,0.0004747766,0.00097010296],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062015635,0.0008098664,0.00072833546,0.00067911687,0.00051935983,0.00049537205,0.0026232945,0.00072728185,0.000031550557],"category_scores_gemma":[0.0002543315,0.0007955382,0.00024751032,0.0007627547,0.00027359315,0.0005615245,0.004446404,0.0010273578,0.00005764158],"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":[0.00008457491,0.00019824726,0.003305654,0.00005779797,0.00012629131,0.00005626155,0.00038832214,0.019921595,0.0036867585,0.91386694,0.014737095,0.043570463],"study_design_scores_gemma":[0.001064818,0.00021054868,0.043465436,0.0002873073,0.00007028866,0.00042017532,0.000037810692,0.5534308,0.001341843,0.35179192,0.045640267,0.0022387851],"about_ca_topic_score_codex":0.00069073075,"about_ca_topic_score_gemma":0.0011466438,"teacher_disagreement_score":0.562075,"about_ca_system_score_codex":0.0009951459,"about_ca_system_score_gemma":0.0005889654,"threshold_uncertainty_score":0.99944955},"labels":[],"label_agreement":null},{"id":"W2313247470","doi":"10.1109/glocomw.2013.6855710","title":"Traffic sign detection, recognition and transmission system for smart vehicles","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Support vector machine; Traffic sign recognition; Histogram; Transmission (telecommunications); Histogram of oriented gradients; Set (abstract data type); Advanced driver assistance systems; Artificial intelligence; Intelligent transportation system; Traffic sign; Pattern recognition (psychology); Real-time computing; Computer vision; Sign (mathematics); Engineering; Image (mathematics)","score_opus":0.02463915065996538,"score_gpt":0.22769888043997807,"score_spread":0.2030597297800127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313247470","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":[0.067851335,0.000045613266,0.9303595,0.0005643793,0.000049560207,0.00061689183,8.3552106e-7,0.00035568798,0.0001562067],"genre_scores_gemma":[0.90353453,0.000011412839,0.09584458,0.00008237862,0.000034036042,0.0003751719,0.000002353524,0.0000066030175,0.00010894919],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994077,0.00001965712,0.00013267023,0.00024631334,0.00006226474,0.00013139886],"domain_scores_gemma":[0.99954456,0.00012357299,0.000035460638,0.0001484686,0.00007498189,0.00007292878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000625686,0.00007192592,0.000069373484,0.000036493104,0.00017071619,0.00007134365,0.00012925704,0.000037395726,0.0000047741723],"category_scores_gemma":[0.0000040217656,0.00006030944,0.000024413366,0.00015948551,0.000015572183,0.00040726835,0.000015484922,0.00003855096,0.00005230832],"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":[0.0000020293655,0.000009312616,0.000001659144,0.000020481437,0.0000019660522,7.7276674e-8,0.000042514566,0.0001427581,0.019011257,0.0005862395,0.00021846952,0.97996324],"study_design_scores_gemma":[0.0005752117,0.00017437706,0.000655008,0.000044703786,0.0000105780555,0.00004152453,0.00008784863,0.88697267,0.09119218,0.013805265,0.0061530015,0.0002876504],"about_ca_topic_score_codex":0.0000067048704,"about_ca_topic_score_gemma":0.0000034746254,"teacher_disagreement_score":0.9796756,"about_ca_system_score_codex":0.000017352733,"about_ca_system_score_gemma":0.0000058863975,"threshold_uncertainty_score":0.24593475},"labels":[],"label_agreement":null},{"id":"W2330958039","doi":"10.1145/2925426.2926294","title":"Proteus","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":81,"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; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Memory footprint; Implementation; Representation (politics); Flexibility (engineering); Chip; Proteus; Computer engineering; Layer (electronics); Parallel computing; Computer architecture; Computer hardware; Programming language","score_opus":0.016808969538139082,"score_gpt":0.25513227797414517,"score_spread":0.23832330843600608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330958039","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":[0.00040135026,0.0000052145497,0.97635484,0.00902234,0.000027637137,0.00008561096,9.315426e-8,0.00023699757,0.013865946],"genre_scores_gemma":[0.62924707,0.000007103276,0.36095002,0.0006092223,0.000044731423,0.000076880264,3.9757364e-8,0.0000031461727,0.009061821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997021,0.0000043894333,0.00004109595,0.00012071046,0.00004779484,0.000083898856],"domain_scores_gemma":[0.9996064,0.00003414687,0.000011651082,0.00030529386,0.000013456412,0.000029083689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000018779147,0.0000261613,0.000021449137,0.000010101619,0.000026845293,0.000009425085,0.00032420084,0.000007606817,0.000026008447],"category_scores_gemma":[0.0000066034327,0.00001358731,0.000009203619,0.00011018508,0.000011230553,0.00020438565,0.00008431082,0.000010881809,0.0004924458],"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":[1.5376057e-7,0.00000416317,0.000050626768,1.4380375e-7,4.1058195e-7,4.202709e-7,0.00000308593,0.0000016797311,0.008167587,0.6783297,0.002296549,0.31114545],"study_design_scores_gemma":[0.00031222866,0.000044157303,0.002534682,0.0000091220545,6.7618015e-7,0.000019600053,8.645573e-7,0.0034362,0.09392829,0.41848022,0.48100588,0.00022807502],"about_ca_topic_score_codex":3.9916787e-7,"about_ca_topic_score_gemma":6.5135185e-7,"teacher_disagreement_score":0.6288457,"about_ca_system_score_codex":0.0000070472515,"about_ca_system_score_gemma":0.0000053148683,"threshold_uncertainty_score":0.6329558},"labels":[],"label_agreement":null},{"id":"W2343086953","doi":"10.48550/arxiv.1604.08859","title":"The Z-loss: a shift and scale invariant classification loss belonging to the Spherical Family","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Softmax function; Differentiable function; Computer science; Function (biology); Algorithm; Computation; Language model; Task (project management); Word (group theory); Scale (ratio); Artificial intelligence; Artificial neural network; Mathematics; Engineering","score_opus":0.05429059800473403,"score_gpt":0.20437235610599114,"score_spread":0.15008175810125712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343086953","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":[0.088444084,0.00007816589,0.89991605,0.009924168,0.00022343267,0.0005458899,0.000007914954,0.00015202179,0.0007082617],"genre_scores_gemma":[0.99543864,0.00036647604,0.0031076293,0.00043167316,0.00012969559,0.000013603989,0.000002169026,0.000015420377,0.0004946912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99818623,0.00015276863,0.00019027041,0.0009872696,0.00011318994,0.0003702434],"domain_scores_gemma":[0.9973572,0.00046906964,0.00019601216,0.0017118264,0.00009019762,0.00017573306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028222796,0.00023559388,0.00017617008,0.00005173873,0.00074947317,0.00021930285,0.0023780374,0.00012820575,0.000001961284],"category_scores_gemma":[0.000033159085,0.00015423344,0.00008295629,0.0006401391,0.00027180713,0.00024845873,0.002594365,0.00041524545,0.00009130454],"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":[0.000036811754,0.000044876506,0.0035601107,0.000013155557,0.000046565143,0.000050415834,0.0004607706,0.026740786,0.0002355222,0.9556477,0.000929614,0.01223369],"study_design_scores_gemma":[0.0005139629,0.0000647131,0.09971443,0.00015939561,0.000073424606,0.000020107216,0.000177291,0.4255457,0.000118733806,0.4458012,0.026902223,0.0009088101],"about_ca_topic_score_codex":0.000029742163,"about_ca_topic_score_gemma":0.00010297412,"teacher_disagreement_score":0.9069946,"about_ca_system_score_codex":0.00014679965,"about_ca_system_score_gemma":0.0000977049,"threshold_uncertainty_score":0.6289457},"labels":[],"label_agreement":null},{"id":"W2343861259","doi":"10.1080/21681163.2016.1148636","title":"Convolutional networks for kidney segmentation in contrast-enhanced CT scans","year":2016,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Segmentation; Computer science; Artificial intelligence; Voxel; Convolutional neural network; Contrast (vision); Pattern recognition (psychology); Data set; Computer vision; Image segmentation","score_opus":0.014176216789631765,"score_gpt":0.3316432606766952,"score_spread":0.3174670438870634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343861259","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":[0.00045442948,0.000133769,0.9972356,0.0008183636,0.000766811,0.0004198761,0.000009232824,0.00016055201,0.0000013446652],"genre_scores_gemma":[0.12243157,0.00013495304,0.8765894,0.0004374599,0.00017004178,0.00015993044,0.00004866531,0.000022816444,0.000005134532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838907,0.00009289821,0.00045378532,0.00052500627,0.00016254494,0.0003766887],"domain_scores_gemma":[0.9989365,0.00052236306,0.00010521279,0.00019691311,0.00007059505,0.00016841231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007801794,0.00018939254,0.00022478403,0.00040573618,0.00005869111,0.00006688667,0.00029390206,0.000056365887,0.0000021116361],"category_scores_gemma":[0.00014037652,0.00016184032,0.00003415125,0.0010061117,0.000049821298,0.00034591026,0.00016270106,0.00008583777,5.04536e-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":[0.000008309755,0.00008579557,0.000083688654,0.000046838435,0.00001076697,0.0000035168725,0.00006646682,0.0038495637,0.21331902,0.075786754,0.00014307055,0.7065962],"study_design_scores_gemma":[0.0011102246,0.000041913176,0.00020236794,0.00017590774,0.0000035923981,0.000011571293,0.0000023399984,0.9850236,0.0071536102,0.0041677644,0.0018892076,0.00021786385],"about_ca_topic_score_codex":0.0000040610876,"about_ca_topic_score_gemma":6.4440115e-7,"teacher_disagreement_score":0.98117405,"about_ca_system_score_codex":0.00014078162,"about_ca_system_score_gemma":0.000044936125,"threshold_uncertainty_score":0.6599657},"labels":[],"label_agreement":null},{"id":"W2415288898","doi":"10.1109/icmla.2017.00038","title":"Parametric Exponential Linear Unit for Deep Convolutional Neural Networks","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Nvidia","keywords":"MNIST database; Convolutional neural network; Parametric statistics; Computer science; Artificial intelligence; Set (abstract data type); Pattern recognition (psychology); Deep learning; Flexibility (engineering); Residual neural network; Realization (probability); Exponential function; Artificial neural network; Mathematics","score_opus":0.06382478069720048,"score_gpt":0.3267726271152846,"score_spread":0.2629478464180841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2415288898","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":[0.0008575479,0.00052495307,0.99300617,0.0013183008,0.0020994018,0.0012174934,0.000021332859,0.00044127126,0.0005135106],"genre_scores_gemma":[0.562363,0.00011957473,0.43268934,0.00047924725,0.0018140404,0.0009766516,0.0002727009,0.00004505281,0.0012403358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974838,0.000056958583,0.00045508565,0.0010936069,0.00032437049,0.0005861689],"domain_scores_gemma":[0.99627197,0.0005348937,0.0005073761,0.002162635,0.00031764578,0.000205458],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021428757,0.00038182456,0.00039689336,0.0001924236,0.0006005939,0.00035013433,0.003384004,0.00035625408,0.00002333723],"category_scores_gemma":[0.00012767159,0.00037727467,0.00029306446,0.00028449023,0.00015010251,0.0003324761,0.0027006608,0.0007319613,0.000031550826],"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":[0.00001751355,0.00007123754,0.00014466143,0.000023626002,0.00004570246,0.0000039473916,0.000013288117,0.87244576,0.000008237289,0.09067562,0.002343014,0.0342074],"study_design_scores_gemma":[0.00031605797,0.00003391636,0.00062179187,0.000012132039,0.000021256043,0.000010605166,0.0000012003751,0.9614613,0.000036359106,0.029825294,0.007264028,0.00039607982],"about_ca_topic_score_codex":0.000021524998,"about_ca_topic_score_gemma":0.000019348514,"teacher_disagreement_score":0.5615055,"about_ca_system_score_codex":0.000063955245,"about_ca_system_score_gemma":0.0001078319,"threshold_uncertainty_score":0.9998679},"labels":[],"label_agreement":null},{"id":"W2436179633","doi":"10.1109/cvpr.2016.267","title":"Hedgehog Shape Priors for Multi-Object Segmentation","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Western University","funders":"National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Convexity; Segmentation; Computer science; Star (game theory); Cut; Medial axis; Constraint (computer-aided design); Mathematics; Artificial intelligence; Computer vision; Image segmentation; Geometry; Mathematical analysis","score_opus":0.048451713714332635,"score_gpt":0.32314872968090336,"score_spread":0.2746970159665707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2436179633","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":[0.0040764725,0.0000150740125,0.99304783,0.0017821051,0.00010329145,0.00045773317,0.0000021735973,0.00025783482,0.00025747917],"genre_scores_gemma":[0.15990694,0.000015322983,0.83661723,0.000545618,0.000057617246,0.00024125788,0.0000016381159,0.000008903243,0.0026054804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993266,0.000010972327,0.00012499877,0.000282339,0.00007931249,0.0001757922],"domain_scores_gemma":[0.999388,0.0001524799,0.000050009752,0.0003070553,0.000050438102,0.000052029187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060359383,0.00007385186,0.00006126761,0.000032342235,0.000093960974,0.000027479708,0.00036262846,0.000022464817,0.000022195087],"category_scores_gemma":[0.000022197686,0.000047420708,0.000036852274,0.00016154867,0.000020758001,0.0004093346,0.000080323654,0.00001856894,0.00010660236],"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":[0.0000049928394,0.00004829727,0.00030695874,0.000004926634,0.0000067282654,3.8032215e-7,0.00011138306,0.00005902568,0.07971666,0.088655226,0.0023836985,0.82870173],"study_design_scores_gemma":[0.003834021,0.0002807506,0.008847586,0.00004262989,0.000013606451,0.000016703932,0.000048491456,0.6347654,0.2790842,0.022948377,0.04928973,0.0008285072],"about_ca_topic_score_codex":9.2195154e-7,"about_ca_topic_score_gemma":0.0000061275705,"teacher_disagreement_score":0.82787323,"about_ca_system_score_codex":0.00003517625,"about_ca_system_score_gemma":0.00001606553,"threshold_uncertainty_score":0.19337605},"labels":[],"label_agreement":null},{"id":"W2468368736","doi":"10.1109/cvpr.2016.236","title":"Monocular 3D Object Detection for Autonomous Driving","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1080,"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":"Artificial intelligence; Computer science; Object detection; Computer vision; Monocular; Benchmark (surveying); Pipeline (software); Object (grammar); Segmentation; Viola–Jones object detection framework; Set (abstract data type); Image segmentation; Object-class detection; Domain (mathematical analysis); Encoding (memory); Pattern recognition (psychology); Face detection; Facial recognition system; Mathematics","score_opus":0.014237763556782173,"score_gpt":0.2534866007799436,"score_spread":0.23924883722316145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468368736","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":[0.0023214475,0.000014171154,0.99502707,0.0009377166,0.00012747096,0.00026351563,3.895413e-7,0.0004065308,0.00090167165],"genre_scores_gemma":[0.648886,0.000006054413,0.3492022,0.00013177772,0.00007335981,0.00016623906,1.683816e-7,0.000007419223,0.0015267608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934405,0.000010472485,0.000110516594,0.0002838184,0.00006251001,0.00018861896],"domain_scores_gemma":[0.9992993,0.0001703024,0.000043284763,0.0004001401,0.000039306487,0.000047692927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006863492,0.0000706876,0.00006335379,0.000036124562,0.00012814092,0.000031947016,0.0003275385,0.000027754058,0.0000065173417],"category_scores_gemma":[0.000028034177,0.000047990678,0.000042124753,0.00015611533,0.000015123394,0.00036801962,0.000087455315,0.000024532666,0.000057910434],"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":[9.1216396e-7,0.0000077683735,0.00003470859,0.000001020958,0.0000032212604,2.662582e-7,0.000012746376,0.00014360016,0.047534328,0.017762097,0.000110988854,0.93438834],"study_design_scores_gemma":[0.00060355547,0.00015004401,0.0012432288,0.000016339178,0.000007235789,0.000022611986,0.000003845269,0.4902638,0.31929234,0.055185106,0.13281004,0.0004018562],"about_ca_topic_score_codex":0.0000030757337,"about_ca_topic_score_gemma":0.000022078348,"teacher_disagreement_score":0.9339865,"about_ca_system_score_codex":0.00005280128,"about_ca_system_score_gemma":0.000013963663,"threshold_uncertainty_score":0.1957003},"labels":[],"label_agreement":null},{"id":"W2469312016","doi":"10.1109/cvpr.2016.316","title":"Reinforcement Learning for Visual Object Detection","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":177,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pascal (unit); Computer science; Reinforcement learning; Artificial intelligence; Visual search; Object detection; Set (abstract data type); Sliding window protocol; Object (grammar); Detector; Machine learning; Computer vision; Pattern recognition (psychology); Window (computing)","score_opus":0.017181269663352195,"score_gpt":0.2868582971584004,"score_spread":0.2696770274950482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469312016","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":[0.001550036,0.0000041936337,0.99619585,0.00054615136,0.00007909085,0.00020406157,3.566984e-8,0.00026491244,0.00115565],"genre_scores_gemma":[0.9685211,0.000007183052,0.027103506,0.00012596424,0.0000646884,0.00013091024,3.029709e-7,0.000004672741,0.004041639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948174,0.000009149099,0.0000965645,0.00018670542,0.000074413976,0.00015144525],"domain_scores_gemma":[0.9995875,0.00014504607,0.000041438652,0.0001529669,0.00003819831,0.0000348687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006540125,0.00005234825,0.000043658176,0.000029652652,0.00013290686,0.000021230917,0.00017335273,0.000017773391,0.000010654942],"category_scores_gemma":[0.00003102858,0.000034023633,0.000029220897,0.00013086092,0.000010460093,0.00027183947,0.000069902184,0.000026920477,0.000061826366],"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":[0.0000066916,0.00000802746,0.000035110876,0.0000020142618,0.0000041996664,1.4902945e-7,0.000024328832,0.005327945,0.08222092,0.04810589,0.0001956384,0.8640691],"study_design_scores_gemma":[0.0006335163,0.0004134137,0.000344749,0.000011078512,0.0000032678975,0.000006596297,0.000007957358,0.55672026,0.3449892,0.010591695,0.086043105,0.00023515048],"about_ca_topic_score_codex":0.0000013801416,"about_ca_topic_score_gemma":0.000004178342,"teacher_disagreement_score":0.96909237,"about_ca_system_score_codex":0.00003628265,"about_ca_system_score_gemma":0.000008360862,"threshold_uncertainty_score":0.13874435},"labels":[],"label_agreement":null},{"id":"W2473751517","doi":"10.5555/2959355.2959416","title":"Accelerating embedded deep learning using DeepX: demonstration abstract","year":2016,"lang":"en","type":"article","venue":"Information Processing in Sensor Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Deep learning; Computer science; Convolutional neural network; Artificial intelligence; Inference; Artificial neural network; Deep neural networks; Software; Mobile device; Computer architecture; Resource (disambiguation); Embedded system; Machine learning; Computer engineering; Operating system; Computer network","score_opus":0.025046129716374033,"score_gpt":0.2743456450988635,"score_spread":0.24929951538248946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473751517","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":[0.047412265,0.00007886305,0.9504501,0.00020652938,0.00012636532,0.000236793,3.22761e-7,0.00035855372,0.0011301886],"genre_scores_gemma":[0.83903587,0.000027990549,0.16056108,0.0002069626,0.000106602674,0.000026404023,0.0000065376216,0.000011673989,0.000016849674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820334,0.000049757367,0.0007539688,0.00026383542,0.0002765612,0.00045252644],"domain_scores_gemma":[0.99872017,0.00016492806,0.00055361906,0.00027383328,0.00020037463,0.00008708626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033310076,0.00020244112,0.0001718359,0.0002001519,0.00041451593,0.000439668,0.0004109719,0.00014612701,0.0000109362845],"category_scores_gemma":[0.00011874876,0.00016858708,0.00004063836,0.00084942207,0.000053918746,0.0070127975,0.000099065335,0.000324718,0.00003324875],"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":[0.0000046506298,0.0000074786667,0.0008457731,0.000009680102,0.0000013680608,0.0000012621394,0.0004233768,0.50518966,0.0007594136,0.00069024775,0.0000075621565,0.49205953],"study_design_scores_gemma":[0.00036077597,0.000013014498,0.0028788892,0.00015427299,0.0000028974125,0.00003451333,0.00008279575,0.99471647,0.00042030093,0.00073147373,0.00035363896,0.0002509715],"about_ca_topic_score_codex":0.000003555292,"about_ca_topic_score_gemma":0.0000073672177,"teacher_disagreement_score":0.79162365,"about_ca_system_score_codex":0.00015585702,"about_ca_system_score_gemma":0.00005975749,"threshold_uncertainty_score":0.6874782},"labels":[],"label_agreement":null},{"id":"W2483030871","doi":"10.1109/cvprw.2016.130","title":"Differential Geometry Boosts Convolutional Neural Networks for Object Detection","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Washington; Nvidia","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Object detection; Cognitive neuroscience of visual object recognition; Computer vision; Pattern recognition (psychology); Object (grammar); Abstraction; 3D single-object recognition; Feature extraction; RGB color model","score_opus":0.014604007265268204,"score_gpt":0.25044471470380714,"score_spread":0.23584070743853894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483030871","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":[0.015073293,0.00003086323,0.9825627,0.0009462235,0.00061777874,0.00033021433,0.0000042537563,0.00032251666,0.000112108464],"genre_scores_gemma":[0.9854347,0.000009143606,0.01303534,0.00024621317,0.0004042733,0.00016600794,0.0000033473077,0.000010759537,0.0006901821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998913,0.00002287445,0.00018464959,0.0003982686,0.00014364274,0.00033757195],"domain_scores_gemma":[0.9990108,0.00037447605,0.00007393136,0.00036568713,0.000083566854,0.00009153574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049264592,0.00012583644,0.000107725806,0.00007045452,0.00020962115,0.000046736448,0.00043268688,0.00006241362,0.000041051084],"category_scores_gemma":[0.000029830991,0.000085425556,0.00008779674,0.00029193162,0.000051554234,0.00041291915,0.0001486514,0.0000651937,0.000021679482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005616438,0.0000811114,0.0005847275,0.000005537495,0.000029530474,0.0000011376673,0.000011234238,0.0038143236,0.034698486,0.15054367,0.0024299466,0.80774415],"study_design_scores_gemma":[0.0007716537,0.00011639383,0.007208002,0.0000059529866,0.000006724944,0.000022324062,0.000001172456,0.966943,0.010555206,0.010894263,0.0032198916,0.00025537072],"about_ca_topic_score_codex":0.000002369266,"about_ca_topic_score_gemma":0.000014209566,"teacher_disagreement_score":0.9703614,"about_ca_system_score_codex":0.00006031913,"about_ca_system_score_gemma":0.000012416321,"threshold_uncertainty_score":0.34835532},"labels":[],"label_agreement":null},{"id":"W2483966489","doi":"10.1109/lca.2016.2597140","title":"Stripes: Bit-Serial Deep Neural Network Computing","year":2016,"lang":"en","type":"article","venue":"IEEE Computer Architecture Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Toronto","funders":"","keywords":"Computer science; Computation; Acceleration; Artificial neural network; Overhead (engineering); Representation (politics); Set (abstract data type); Computer engineering; Energy (signal processing); Algorithm; Deep neural networks; State (computer science); Power (physics); Hardware acceleration; Efficient energy use; Artificial intelligence; Parallel computing; Computer hardware; Statistics; Mathematics; Electrical engineering","score_opus":0.010094015552453076,"score_gpt":0.22624569193254623,"score_spread":0.21615167638009317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483966489","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":[0.06153609,0.000045608373,0.91431344,0.01974646,0.0030636562,0.00039781045,0.0000028734466,0.00085852516,0.000035522004],"genre_scores_gemma":[0.5604418,0.0000040067466,0.41692445,0.0165182,0.006020705,0.000025477495,0.0000035704331,0.000047438294,0.00001436033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967431,0.00021770381,0.00049781956,0.0010613695,0.0004097235,0.0010702783],"domain_scores_gemma":[0.9974842,0.00073292694,0.0002358051,0.0012334742,0.000053348555,0.00026021892],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001788189,0.00045073451,0.00037544026,0.00015040566,0.0003801905,0.00020290354,0.0021466117,0.0000948781,0.0000077394025],"category_scores_gemma":[0.000010426007,0.00032643258,0.00020431004,0.000765675,0.00017829948,0.0003732167,0.0005665246,0.00039763833,0.00008524889],"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":[0.000016681905,0.000023597782,0.00023555986,0.0000074161203,0.00003252955,0.000047495643,0.0002033964,0.49555826,0.010620956,0.0018794086,0.007524256,0.48385045],"study_design_scores_gemma":[0.0037694375,0.00042548112,0.0072710966,0.00026543473,0.0000426849,0.0007222998,0.0000028258753,0.9187754,0.004420516,0.019839948,0.041792583,0.0026723358],"about_ca_topic_score_codex":0.0000040695886,"about_ca_topic_score_gemma":0.00001140857,"teacher_disagreement_score":0.4989057,"about_ca_system_score_codex":0.00007950114,"about_ca_system_score_gemma":0.000020391322,"threshold_uncertainty_score":0.99991876},"labels":[],"label_agreement":null},{"id":"W2501524975","doi":"10.1007/978-3-319-41501-7_41","title":"Object Detection and Localization Using Deep Convolutional Networks with Softmax Activation and Multi-class Log Loss","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Western University","funders":"","keywords":"Softmax function; Computer science; Artificial intelligence; Minimum bounding box; Convolutional neural network; Pattern recognition (psychology); Region of interest; Computer vision; Deep learning; Object detection; Object (grammar); Contextual image classification; Image (mathematics)","score_opus":0.01630436585931179,"score_gpt":0.24118291951103518,"score_spread":0.22487855365172338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2501524975","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":[0.00030273647,0.0002560951,0.9983992,0.00014124115,0.00026095324,0.00045786583,0.0000016578849,0.00012047244,0.00005975295],"genre_scores_gemma":[0.6805479,0.00008129921,0.31852108,0.0004838578,0.000284879,0.00001496377,0.0000033649337,0.000030497518,0.000032173637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976128,0.000032381544,0.00030170966,0.0012141125,0.0004294924,0.0004095381],"domain_scores_gemma":[0.9983982,0.0004033803,0.0003110263,0.0005129385,0.0002463129,0.00012818459],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002450679,0.00037659562,0.00028285815,0.00035506123,0.0004969513,0.000250782,0.00054468575,0.00024935877,0.0000018046624],"category_scores_gemma":[0.00003318854,0.00030537703,0.000027928318,0.00049686036,0.000934518,0.0008980234,0.00048223304,0.00038966027,0.0000016474454],"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":[0.000013656945,0.000011500469,0.00027961453,0.000014978339,0.000008094227,0.000006515353,0.00009585296,0.50920486,0.0004993639,0.0056877527,3.650289e-7,0.4841774],"study_design_scores_gemma":[0.0003581752,0.000084804786,0.00056265824,0.00022243295,0.00000781857,0.00015244281,1.0773868e-7,0.9742432,0.0007812052,0.023111219,0.0000879877,0.0003879291],"about_ca_topic_score_codex":0.0000075247094,"about_ca_topic_score_gemma":0.00010203528,"teacher_disagreement_score":0.68024516,"about_ca_system_score_codex":0.00029837163,"about_ca_system_score_gemma":0.00014529207,"threshold_uncertainty_score":0.99993986},"labels":[],"label_agreement":null},{"id":"W2507296351","doi":"10.1007/s11263-018-1140-0","title":"Semantic Understanding of Scenes Through the ADE20K Dataset","year":2018,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1667,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Samsung; National Science Foundation","keywords":"Computer science; Normalization (sociology); Parsing; Segmentation; Artificial intelligence; Variety (cybernetics); Object (grammar); Pixel; Context (archaeology); Pattern recognition (psychology); Computer vision; Geography","score_opus":0.05449105503919781,"score_gpt":0.3589244139169077,"score_spread":0.3044333588777099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507296351","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":[0.0050516534,0.000073458286,0.98713475,0.0060670665,0.0014819101,0.00005839785,0.000011958852,0.0000124334065,0.000108362554],"genre_scores_gemma":[0.9090503,0.000057004687,0.089221634,0.0007234388,0.0009280971,5.3798857e-7,0.000007123556,0.000005475929,0.0000064233277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868083,0.000056897738,0.00043784638,0.00014100184,0.0005731184,0.00011030244],"domain_scores_gemma":[0.9984736,0.00025767702,0.0005117462,0.00031542778,0.00040601607,0.000035570265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002319145,0.00008713682,0.0001282172,0.0000981168,0.00009660288,0.00010872684,0.0019634175,0.00002498383,0.0000116632],"category_scores_gemma":[0.00001603799,0.00005751299,0.00007196005,0.00022139406,0.00014329025,0.00084481057,0.00044540563,0.00012980792,0.000013780051],"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":[0.00023645465,0.00046939286,0.0005345511,0.000019606967,0.0004257476,0.00012501454,0.002634394,0.014517467,0.006147951,0.66620445,0.16599676,0.1426882],"study_design_scores_gemma":[0.0023147583,0.0015230646,0.004013892,0.00072825927,0.000043665546,0.0018723573,0.00011656882,0.4714196,0.012367757,0.33586803,0.16930108,0.00043099004],"about_ca_topic_score_codex":0.0000038243465,"about_ca_topic_score_gemma":0.000002533511,"teacher_disagreement_score":0.9039986,"about_ca_system_score_codex":0.000063185005,"about_ca_system_score_gemma":0.000038296348,"threshold_uncertainty_score":0.36485508},"labels":[],"label_agreement":null},{"id":"W2515193474","doi":"10.1145/2964284.2967273","title":"Scalable Compression of Deep Neural Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Scalability; Artificial neural network; Upgrade; Distributed computing; Computer network; Artificial intelligence; Operating system","score_opus":0.014540087857561874,"score_gpt":0.2535821708241046,"score_spread":0.23904208296654275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515193474","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":[0.002883283,0.00007514911,0.99332875,0.001139439,0.00010410057,0.00009289367,2.6150866e-7,0.00014436847,0.0022317825],"genre_scores_gemma":[0.94226104,0.000014302483,0.057110224,0.00018931951,0.00003931688,0.000011542718,3.0922487e-7,0.0000047348976,0.00036922243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993086,0.000022744181,0.00015915287,0.00021453584,0.00011068821,0.0001842939],"domain_scores_gemma":[0.9991362,0.00016996331,0.00007078627,0.0005107301,0.000048379447,0.00006390725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045391214,0.000069439266,0.000093530114,0.00002595438,0.000054570417,0.000011504236,0.0005866743,0.000029001912,0.000044285513],"category_scores_gemma":[0.0000079292,0.000040296003,0.00003185102,0.00026097562,0.000048502938,0.00033280512,0.0002539649,0.000039423827,0.000021080112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000651185,0.000052836353,0.0015441406,0.0000029096043,0.0000046263194,0.0000014352543,0.000017124574,0.027021829,0.015881434,0.1507702,0.0019775606,0.8027194],"study_design_scores_gemma":[0.00018989427,0.00002549788,0.0014776211,0.000008574442,0.0000011763841,0.0000044840995,9.609915e-7,0.9838909,0.00730711,0.005666698,0.0013420371,0.0000850413],"about_ca_topic_score_codex":0.0000023615057,"about_ca_topic_score_gemma":0.000003108527,"teacher_disagreement_score":0.95686907,"about_ca_system_score_codex":0.000009938884,"about_ca_system_score_gemma":0.000003091469,"threshold_uncertainty_score":0.16432233},"labels":[],"label_agreement":null},{"id":"W2516141709","doi":"10.1145/3007787.3001138","title":"Cnvlutin","year":2016,"lang":"en","type":"article","venue":"ACM SIGARCH Computer Architecture News","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":639,"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; University of Toronto","funders":"","keywords":"Computer science; Multiplication (music); Operand; Computation; Overhead (engineering); Parallel computing; Efficient energy use; Fraction (chemistry); State (computer science); Algorithm; Arithmetic; Computer hardware; Mathematics","score_opus":0.015607003404162132,"score_gpt":0.2567286129807016,"score_spread":0.24112160957653947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516141709","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":[0.004555518,0.000061057406,0.9603595,0.03287324,0.0003742674,0.0003487736,0.0000036102772,0.00069666066,0.00072733004],"genre_scores_gemma":[0.20807455,0.000031411888,0.786678,0.00379519,0.00091362954,0.00008163513,0.0000023191412,0.000032670298,0.00039057212],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99758244,0.00013963073,0.00032804903,0.000881936,0.00039619918,0.0006717261],"domain_scores_gemma":[0.9957933,0.0011285427,0.000110086025,0.002625724,0.00007447973,0.0002678819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001439035,0.00028957927,0.00024531854,0.00016738569,0.00019680391,0.00009535169,0.0037489587,0.00007473055,0.00002611285],"category_scores_gemma":[0.000098046046,0.00018553014,0.00013165626,0.00060375786,0.00012734372,0.00030408666,0.0021257596,0.0002659776,0.00034350622],"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":[0.0000060674884,0.000030966377,0.00028393604,0.000003512244,0.000012632796,0.00001403719,0.00017780114,0.00054483523,0.0054287254,0.018917317,0.0070513627,0.9675288],"study_design_scores_gemma":[0.0014478512,0.00036290675,0.0063588317,0.000100553,0.000009266447,0.00026748676,0.000002038807,0.0076410477,0.0078235045,0.4057811,0.5692179,0.0009875116],"about_ca_topic_score_codex":0.0000070393266,"about_ca_topic_score_gemma":0.000022466602,"teacher_disagreement_score":0.9665413,"about_ca_system_score_codex":0.00005498826,"about_ca_system_score_gemma":0.000057895446,"threshold_uncertainty_score":0.7565699},"labels":[],"label_agreement":null},{"id":"W2517954747","doi":"10.1007/978-3-319-46976-8_19","title":"The Importance of Skip Connections in Biomedical Image Segmentation","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":991,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université de Montréal; Polytechnique Montréal","funders":"Mitacs","keywords":"Computer science; Upsampling; Path (computing); Residual; Segmentation; Artificial intelligence; Image (mathematics); Image segmentation; Pattern recognition (psychology); Computer vision; Algorithm; Computer network","score_opus":0.013733747546252716,"score_gpt":0.2756294804930322,"score_spread":0.2618957329467795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517954747","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":[0.00013446077,0.00025895695,0.99310243,0.004256748,0.0006706205,0.00044195045,0.00000681205,0.00006423876,0.0010638002],"genre_scores_gemma":[0.14374979,0.00043068433,0.8532297,0.0012858158,0.00058574567,0.00011250429,0.000008304872,0.000050894258,0.00054658507],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973464,0.000032902197,0.00062096276,0.00090214884,0.00064729253,0.00045028838],"domain_scores_gemma":[0.99688,0.0013051393,0.00039724546,0.0011556094,0.00016490908,0.00009709116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006161827,0.0002701791,0.00028764128,0.0003947631,0.00025305845,0.00012052009,0.0023399275,0.00014638712,0.000009169531],"category_scores_gemma":[0.0001137878,0.00018452833,0.000073722724,0.00088299165,0.0013694399,0.0004742933,0.000697298,0.00043223106,0.000018303032],"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":[0.000005659437,0.000035957557,0.00021125212,0.0000144482165,0.000006533518,0.000023044362,0.00024559366,0.0025150406,0.0027782847,0.18620652,0.00005185246,0.8079058],"study_design_scores_gemma":[0.00048703217,0.00015051232,0.000637396,0.00034221972,0.0000053858616,0.00006340582,5.356878e-7,0.17886804,0.0043834476,0.8112588,0.0032508152,0.00055243156],"about_ca_topic_score_codex":0.000003937726,"about_ca_topic_score_gemma":0.00015870742,"teacher_disagreement_score":0.8073534,"about_ca_system_score_codex":0.00025837455,"about_ca_system_score_gemma":0.00029142483,"threshold_uncertainty_score":0.7524846},"labels":[],"label_agreement":null},{"id":"W2531425418","doi":"10.48550/arxiv.1610.02915","title":"Deep Pyramidal Residual Networks","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science, ICT and Future Planning","keywords":"Residual; Computer science; Upsampling; Convolutional neural network; Benchmark (surveying); Feature (linguistics); Artificial intelligence; Code (set theory); Deep learning; Dimension (graph theory); Generalization; Pooling; Residual neural network; Pattern recognition (psychology); Network architecture; Artificial neural network; Image (mathematics); Algorithm; Cartography; Mathematics; Geography; Set (abstract data type)","score_opus":0.05151344467729533,"score_gpt":0.19393130417617677,"score_spread":0.14241785949888144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531425418","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":[0.006612959,0.0001225368,0.98757946,0.0004640183,0.00060979085,0.0003447954,0.00000623335,0.00061032875,0.0036498948],"genre_scores_gemma":[0.9918732,0.0002783169,0.0051971744,0.00020990177,0.00041285364,0.000003990763,0.000012918042,0.00002975381,0.0019818794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975511,0.00011339735,0.00022428473,0.0014618096,0.0000940508,0.00055534893],"domain_scores_gemma":[0.99698526,0.00021882555,0.0002899565,0.0021282989,0.0001393958,0.00023825336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012965435,0.00036391884,0.00030700813,0.00015774813,0.00023913725,0.00010305852,0.0029799638,0.00035075616,0.000024962941],"category_scores_gemma":[0.000019270736,0.00037154515,0.00017366675,0.0006043332,0.00019318488,0.00042667898,0.0035449232,0.000677906,0.00018553664],"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":[0.000010511802,0.00002839493,0.00035765662,0.0000073392393,0.00002987273,0.00010669733,0.000021815938,0.5812041,0.0000108911845,0.41476882,0.00075135805,0.0027025542],"study_design_scores_gemma":[0.00026664455,0.00001999676,0.00063660997,0.0000445869,0.00002616497,0.00000601194,0.000005273962,0.7661918,0.00004251204,0.2310151,0.0012824305,0.00046290204],"about_ca_topic_score_codex":0.0000112731,"about_ca_topic_score_gemma":0.000028920884,"teacher_disagreement_score":0.98526025,"about_ca_system_score_codex":0.00021412442,"about_ca_system_score_gemma":0.00010220396,"threshold_uncertainty_score":0.99987364},"labels":[],"label_agreement":null},{"id":"W2534788641","doi":"10.1145/2966986.2967058","title":"Neural networks designing neural networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"MNIST database; Computer science; Artificial neural network; Artificial intelligence; Machine learning; Computational complexity theory; Contextual image classification; Design space exploration; Image (mathematics); Algorithm","score_opus":0.022049226012814074,"score_gpt":0.2554856277248758,"score_spread":0.23343640171206176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2534788641","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":[0.0014770235,0.00020378662,0.9919663,0.0036210518,0.0005371239,0.00027034798,3.8616443e-7,0.00087667874,0.0010472988],"genre_scores_gemma":[0.9423947,0.000039351617,0.054246206,0.0019182111,0.0004790675,0.0000722118,0.0000014253259,0.00002735493,0.00082148594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978941,0.00009567968,0.0003421999,0.0006846998,0.00021778523,0.0007654975],"domain_scores_gemma":[0.9980152,0.00054673135,0.00013040507,0.0009906127,0.000076835044,0.0002401665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016476384,0.0002583438,0.00020549294,0.000065401866,0.00028033202,0.00013738465,0.0013605804,0.00009755326,0.000045495],"category_scores_gemma":[0.000026398877,0.00017104266,0.000102395636,0.0006962977,0.00009516255,0.0010088308,0.00048655618,0.00021461894,0.000045648987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009524748,0.000029645562,0.0012235207,0.0000012137638,0.000011444263,0.000019182606,0.00002045297,0.4230107,0.000730637,0.053999245,0.0051666754,0.51577777],"study_design_scores_gemma":[0.00027253813,0.000048496815,0.00077982916,0.00000814236,0.000004146696,0.00004020032,0.0000021560172,0.9949537,0.00023367156,0.002063444,0.0013095498,0.0002841359],"about_ca_topic_score_codex":0.0000044438157,"about_ca_topic_score_gemma":0.000008031849,"teacher_disagreement_score":0.9409177,"about_ca_system_score_codex":0.00004913873,"about_ca_system_score_gemma":0.0000111489435,"threshold_uncertainty_score":0.6974917},"labels":[],"label_agreement":null},{"id":"W2547697372","doi":"10.1109/igarss.2016.7729396","title":"Object detection in pleiades images using deep features","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Effigis (Canada); Computer Research Institute of Montréal","funders":"","keywords":"Pleiades; Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Exploit; Object detection; Computer vision; Object (grammar); Deep neural networks; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology)","score_opus":0.01614770466482416,"score_gpt":0.27427686343637836,"score_spread":0.2581291587715542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547697372","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":[0.033524357,0.00006633538,0.96494377,0.0004679062,0.000066056906,0.00009767935,2.387576e-7,0.00015932578,0.00067436177],"genre_scores_gemma":[0.8816447,0.000017043158,0.11795681,0.00010178784,0.000040245075,0.000014111342,6.774659e-8,0.000004724237,0.00022047813],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993809,0.000024629107,0.000097220385,0.00024213849,0.00008242373,0.0001727232],"domain_scores_gemma":[0.99951774,0.00011475761,0.000033620992,0.0002811505,0.000021185553,0.000031531083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053024505,0.00006980795,0.000061818304,0.000076755125,0.000064127365,0.000034937057,0.00028363176,0.000028711089,0.0000073289684],"category_scores_gemma":[0.000023160466,0.000045402638,0.000020967756,0.00036869443,0.000025911737,0.00049223244,0.00010178598,0.000050128136,0.000020764595],"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":[0.0000036702477,0.000019246518,0.0007811907,0.00000195886,0.0000024280796,0.0000039489328,0.000056583125,0.00069635233,0.43538666,0.010573455,0.000076070886,0.55239844],"study_design_scores_gemma":[0.0004567818,0.00004413795,0.053194955,0.000031109572,0.0000028263025,0.00007163279,0.000016556014,0.03254385,0.86202765,0.050337814,0.00092849915,0.0003442258],"about_ca_topic_score_codex":0.000026582815,"about_ca_topic_score_gemma":0.00021819904,"teacher_disagreement_score":0.8481204,"about_ca_system_score_codex":0.000054963646,"about_ca_system_score_gemma":0.000009069882,"threshold_uncertainty_score":0.18514659},"labels":[],"label_agreement":null},{"id":"W2552126649","doi":"10.1109/icip.2017.8296851","title":"Convolutional gated recurrent networks for video segmentation","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Convolutional neural network; Artificial intelligence; Recurrent neural network; Baseline (sea); Categorical variable; Frame (networking); Pattern recognition (psychology); Network architecture; Deep learning; Computer vision; Machine learning; Artificial neural network","score_opus":0.04917659039787584,"score_gpt":0.3332314084279095,"score_spread":0.28405481803003363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552126649","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":[0.00009970208,0.00039085216,0.99328655,0.0018386123,0.0017170372,0.0014416454,0.000028312848,0.00035292064,0.00084438495],"genre_scores_gemma":[0.09978524,0.00026740247,0.8932761,0.0009475162,0.0010436506,0.002023389,0.0008149041,0.000041021904,0.0018007624],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829805,0.00003515971,0.00033690262,0.00080441346,0.00021085182,0.00031462824],"domain_scores_gemma":[0.99768674,0.0002008905,0.00048315653,0.0012664552,0.00025709948,0.00010566782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018040191,0.00024371725,0.00024042079,0.00006069204,0.00040051926,0.00028368115,0.0015702164,0.0001526457,0.00001460737],"category_scores_gemma":[0.000041794905,0.00024104066,0.00014027262,0.00008017664,0.000065476874,0.00027494962,0.0011837663,0.00033945942,0.000024418765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003653138,0.00014189845,0.00010907983,0.000085436484,0.00010022859,0.0000023870884,0.00006306664,0.3066835,0.0001477107,0.3902944,0.09297158,0.20936416],"study_design_scores_gemma":[0.0002680924,0.00002841144,0.00023781776,0.00004875394,0.000013851474,0.000003589341,0.0000013489674,0.9057505,0.000230684,0.07159767,0.021532979,0.0002862564],"about_ca_topic_score_codex":0.000008888815,"about_ca_topic_score_gemma":0.000016583244,"teacher_disagreement_score":0.59906703,"about_ca_system_score_codex":0.0001425128,"about_ca_system_score_gemma":0.00013048828,"threshold_uncertainty_score":0.98293525},"labels":[],"label_agreement":null},{"id":"W2554828302","doi":"10.1109/tpami.2016.2630686","title":"Local Submodularization for Binary Pairwise Energies","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; École de Technologie Supérieure; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Submodular set function; Pairwise comparison; Regularization (linguistics); Inpainting; Binary number; Computer science; Prior probability; Mathematics; Algorithm; Mathematical optimization; Artificial intelligence; Image (mathematics)","score_opus":0.01890100177105974,"score_gpt":0.2640982089617903,"score_spread":0.24519720719073057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554828302","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":[0.0013319182,0.000059258073,0.9967243,0.0014292547,0.00009827331,0.00017657837,0.000041596635,0.00013042592,0.000008379679],"genre_scores_gemma":[0.9916097,0.00038892272,0.007254904,0.00031114422,0.000016836299,0.00013025712,0.0000047836274,0.000011213573,0.0002722347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988017,0.00004359238,0.00027493061,0.00051477907,0.0001532886,0.00021168534],"domain_scores_gemma":[0.99897325,0.0002720925,0.00008501352,0.00049112155,0.000081359474,0.000097184304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011349957,0.00016941456,0.00019876286,0.00029978197,0.00021940088,0.00005092223,0.00035746998,0.000050711245,0.00003144607],"category_scores_gemma":[0.0000035274484,0.000120425735,0.00017945432,0.0008508043,0.0000886204,0.00030675597,0.0000057626926,0.000073792406,0.000014963139],"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":[0.000009075791,0.00007033351,0.00009510009,0.0000050071644,0.00010974658,0.0000011355086,0.000040368006,0.087719604,0.002307943,0.0010214524,0.000010484314,0.90860975],"study_design_scores_gemma":[0.00014431227,0.00016121952,0.00050516456,0.00001970042,0.00021334858,0.000004906239,0.000012148948,0.6530217,0.3421591,0.003058383,0.00040993025,0.00029012514],"about_ca_topic_score_codex":0.000067485926,"about_ca_topic_score_gemma":0.00025857025,"teacher_disagreement_score":0.99027777,"about_ca_system_score_codex":0.000035069865,"about_ca_system_score_gemma":0.000011824811,"threshold_uncertainty_score":0.4910819},"labels":[],"label_agreement":null},{"id":"W2555144715","doi":"10.1049/iet-cps.2016.0027","title":"ApesNet: a pixel‐wise efficient segmentation network for embedded devices","year":2016,"lang":"en","type":"article","venue":"IET Cyber-Physical Systems Theory & Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"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":"Air Force Research Laboratory; Canadian Institute for Advanced Research","keywords":"Segmentation; Pixel; Computer science; Artificial intelligence; Computer vision","score_opus":0.012246134194347868,"score_gpt":0.2742409701976298,"score_spread":0.2619948360032819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555144715","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":[0.0066548293,0.00024095492,0.9864469,0.0010993343,0.00020574886,0.0035880047,0.00006963216,0.0006112859,0.0010832661],"genre_scores_gemma":[0.9520008,0.000015077149,0.03157024,0.00042009822,0.0010553265,0.014286696,0.00003976371,0.00005634075,0.0005556687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973006,0.00025680385,0.0005261105,0.000939683,0.00036068185,0.00061610504],"domain_scores_gemma":[0.99600565,0.0017790075,0.00040552407,0.0013397508,0.00023474055,0.00023529363],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056173885,0.0003347392,0.00037022267,0.00006909496,0.00052767183,0.00017251114,0.0011443,0.00008784648,0.000006071438],"category_scores_gemma":[0.000032776086,0.0002474535,0.00020008552,0.00086969027,0.00014470547,0.00038581298,0.00020429895,0.00012363582,0.00034386263],"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":[0.000016503582,0.00021390003,0.00001694865,0.000023130957,0.000030363046,2.1322388e-7,0.00021289963,0.012895345,0.01701649,0.9344078,0.001157558,0.0340089],"study_design_scores_gemma":[0.0022547666,0.0002830205,0.0008416504,0.0002806167,0.00017627403,0.000031497388,0.00032231433,0.13431329,0.004249121,0.62094283,0.23462704,0.0016775744],"about_ca_topic_score_codex":0.000005021195,"about_ca_topic_score_gemma":0.0000050807207,"teacher_disagreement_score":0.9548767,"about_ca_system_score_codex":0.00014739374,"about_ca_system_score_gemma":0.00006514426,"threshold_uncertainty_score":0.9999978},"labels":[],"label_agreement":null},{"id":"W2560311620","doi":"10.1007/978-3-319-50835-1_22","title":"Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":903,"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":"Softmax function; Artificial intelligence; Computer science; Segmentation; Intersection (aeronautics); Pattern recognition (psychology); Object (grammar); Image segmentation; Artificial neural network; Deep learning; Object detection; Deep neural networks; Pixel; Computer vision; Cartography","score_opus":0.013842821977057669,"score_gpt":0.2666441925100097,"score_spread":0.25280137053295204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560311620","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":[0.00005842088,0.00018463812,0.9957886,0.00076289236,0.0016681643,0.00096921,0.000003861474,0.00015694897,0.00040728145],"genre_scores_gemma":[0.12932454,0.00004987841,0.86861634,0.0009936937,0.00071978976,0.00012576998,0.000012600466,0.000051486055,0.000105903426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968468,0.00004088312,0.0005625768,0.0014544551,0.0004204433,0.0006748707],"domain_scores_gemma":[0.9976555,0.00078653934,0.00035512072,0.00091429125,0.00016727997,0.00012123775],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052333693,0.00044752483,0.00038050246,0.0006743174,0.00025649197,0.0003320421,0.0019306443,0.00023030063,0.000008593292],"category_scores_gemma":[0.000043766195,0.0004042519,0.00012309397,0.0006549648,0.00035139566,0.001287548,0.0007610056,0.00055152003,0.000006588805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006976522,0.00001101522,0.000015826521,0.000009617093,0.0000029322453,0.000008492966,0.00019827319,0.35412887,0.00037313468,0.0067810533,0.0000063366183,0.6384575],"study_design_scores_gemma":[0.00039625022,0.000100501784,0.000042880703,0.00015555082,0.0000037958287,0.00002510334,2.8500415e-7,0.929703,0.00044935246,0.06858109,0.000119548495,0.00042264274],"about_ca_topic_score_codex":0.0000066157995,"about_ca_topic_score_gemma":0.00016109429,"teacher_disagreement_score":0.6380348,"about_ca_system_score_codex":0.00061376,"about_ca_system_score_gemma":0.000081957056,"threshold_uncertainty_score":0.9998409},"labels":[],"label_agreement":null},{"id":"W2562447241","doi":"10.18100/ijamec.271038","title":"Parallelization of a Hierarchical Graph-Based Image Segmentation using OpenMP","year":2016,"lang":"en","type":"article","venue":"International Journal of Applied Mathematics Electronics and Computers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Theoretical Astrophysics","keywords":"Computer science; Parallel computing; Image processing; Segmentation; Parallel processing; Image segmentation; Process (computing); Graph; Pixel; Artificial intelligence; Image (mathematics); Theoretical computer science","score_opus":0.012660656204208868,"score_gpt":0.27271323854519064,"score_spread":0.2600525823409818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562447241","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":[0.05814824,0.00004586776,0.94077504,0.0007370624,0.00009516434,0.00011647812,0.000001743448,0.000013049512,0.00006733371],"genre_scores_gemma":[0.4138778,0.00010861604,0.5858611,0.00009963008,0.000040101804,0.0000032291334,0.0000010475859,0.000007188997,0.0000013251197],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988375,0.000013945638,0.0004858973,0.00014176998,0.0003827014,0.00013820775],"domain_scores_gemma":[0.9986942,0.00022703128,0.0006000804,0.00014854681,0.00027116382,0.00005898777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021009783,0.00010393865,0.00017715489,0.00018512226,0.00003912096,0.00006142998,0.0006272931,0.000034220728,0.0000025528036],"category_scores_gemma":[0.000012080307,0.00007837158,0.00006357313,0.00015913052,0.000064243584,0.00025567302,0.000102435275,0.000093994095,7.1635543e-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":[0.00006210507,0.00020680451,0.000017911576,0.00002205666,0.00012395799,0.0000059928866,0.0002901184,0.008554546,0.27065012,0.6613289,0.00006986743,0.05866761],"study_design_scores_gemma":[0.002224857,0.00023770244,0.000069513706,0.00020249089,0.000035128283,0.00014352692,0.000028502423,0.4127093,0.08536896,0.498431,0.0002958889,0.00025313176],"about_ca_topic_score_codex":3.8599265e-7,"about_ca_topic_score_gemma":4.6458968e-7,"teacher_disagreement_score":0.40415475,"about_ca_system_score_codex":0.00007858383,"about_ca_system_score_gemma":0.000097397744,"threshold_uncertainty_score":0.31959003},"labels":[],"label_agreement":null},{"id":"W2581624817","doi":"10.48550/arxiv.1701.06538","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":268,"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 for Catastrophic Loss Reduction","keywords":"Computer science; Computation; Machine translation; Artificial neural network; Language model; Artificial intelligence; Layer (electronics); Translation (biology); Machine learning; Language translation; Algorithm","score_opus":0.0848082490162567,"score_gpt":0.21356524588881673,"score_spread":0.12875699687256004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581624817","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":[0.20651267,0.00006583061,0.7905393,0.0007792255,0.00032426752,0.00025860697,0.000003869408,0.0001620659,0.0013541451],"genre_scores_gemma":[0.99801415,0.00006456624,0.0006327114,0.00030936152,0.000087440225,0.0000010480369,0.0000023871717,0.000012413323,0.0008759386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879,0.00007448136,0.00014798403,0.00051395834,0.000075596,0.000397943],"domain_scores_gemma":[0.99742407,0.00014058026,0.00028282747,0.0019418218,0.000101280144,0.00010942581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013816738,0.00017322518,0.00017384581,0.000046129495,0.0008411005,0.00010602137,0.0027136612,0.00007824716,0.0000134322345],"category_scores_gemma":[0.000032205164,0.0001434971,0.00011500945,0.0003583329,0.0001897682,0.000684171,0.0006740626,0.00020402495,0.000022973321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004131392,0.00017703477,0.011743351,0.000007194967,0.000064531,0.00023355361,0.00030677323,0.4431343,0.00036587066,0.5373931,0.003236788,0.0032961757],"study_design_scores_gemma":[0.00041449998,0.00003306386,0.0047970144,0.00000911787,0.000018526358,0.000010090056,0.000029369412,0.98270243,0.00030702425,0.0053853593,0.006086052,0.00020744477],"about_ca_topic_score_codex":0.000033510005,"about_ca_topic_score_gemma":0.0000876814,"teacher_disagreement_score":0.79150146,"about_ca_system_score_codex":0.000029477136,"about_ca_system_score_gemma":0.000020272542,"threshold_uncertainty_score":0.646915},"labels":[],"label_agreement":null},{"id":"W2593267444","doi":"","title":"An Empirical Analysis of Deep Network Loss Surfaces","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Maxima and minima; Dimension (graph theory); Computer science; Regular polygon; Mathematical optimization; Artificial neural network; Stochastic neural network; Convex optimization; Function (biology); Stochastic optimization; Mathematics; Artificial intelligence; Geometry; Recurrent neural network; Combinatorics; Mathematical analysis","score_opus":0.07105419130914059,"score_gpt":0.24756931451530195,"score_spread":0.17651512320616136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593267444","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":[0.44335127,0.000010947768,0.5558857,0.00007969149,0.000040166095,0.000046645302,0.0000016166489,0.000055008997,0.00052894885],"genre_scores_gemma":[0.9929813,0.000034604254,0.006756738,0.00006234767,0.00003297785,2.8275707e-7,0.0000034214686,0.000005094226,0.00012319903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898535,0.000060259197,0.00012520442,0.0005176047,0.000063139705,0.00024845568],"domain_scores_gemma":[0.9975464,0.00010575536,0.00024963514,0.0018673958,0.00009439552,0.00013642893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012289263,0.00011055163,0.00022567243,0.000105807114,0.00044395626,0.000067197245,0.0021210052,0.000056874676,0.00001742969],"category_scores_gemma":[0.000014862995,0.00011928489,0.00012520068,0.0012100742,0.00019344913,0.0008417008,0.0003401602,0.000094821364,0.000017102788],"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":[0.0000067607084,0.000042430158,0.18027616,0.0000011565232,0.0000966912,0.00002514806,0.000040979565,0.7437633,0.000030979005,0.074843794,0.000037089838,0.0008354794],"study_design_scores_gemma":[0.00010764866,0.000027334421,0.16677722,0.000001868546,0.000119960394,5.856435e-7,0.000008364005,0.8217131,0.000055068085,0.010853543,0.00021818257,0.00011712721],"about_ca_topic_score_codex":0.00003195691,"about_ca_topic_score_gemma":0.00023722854,"teacher_disagreement_score":0.54963005,"about_ca_system_score_codex":0.000030096573,"about_ca_system_score_gemma":0.000018897359,"threshold_uncertainty_score":0.48642963},"labels":[],"label_agreement":null},{"id":"W2599571388","doi":"10.15353/vsnl.v2i1.84","title":"Automated Failure Detection in Computer Vision Systems","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"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 Waterloo","funders":"","keywords":"Computer science; Detector; Artificial intelligence; Convolutional neural network; Artificial neural network; Computer vision; Pixel; Convolution (computer science); Scale (ratio); Machine vision; Deep learning; Pattern recognition (psychology); Real-time computing; Telecommunications","score_opus":0.00705675858498899,"score_gpt":0.2724328980608549,"score_spread":0.2653761394758659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599571388","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":[0.044660863,0.00047889768,0.9518095,0.0018576052,0.0008877144,0.00017572202,0.0000014538922,0.00010948957,0.000018712875],"genre_scores_gemma":[0.98479277,0.000021619815,0.014870454,0.000070614755,0.00020890949,0.0000047707317,6.994003e-7,0.000009871087,0.000020312616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982277,0.00018573628,0.0007390591,0.00021481482,0.0004514808,0.0001812044],"domain_scores_gemma":[0.99836737,0.00042218785,0.0005533445,0.00016294836,0.00037777552,0.00011636368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005281975,0.00014649311,0.00027282865,0.00037704504,0.00010927578,0.0002799896,0.00030450663,0.00004156047,7.871775e-7],"category_scores_gemma":[0.00002299166,0.00009516935,0.000054757435,0.00039732634,0.000044595563,0.001264562,0.00008994125,0.00013094385,0.000013637718],"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":[0.00006010769,0.00017882077,0.0016956314,0.000095774674,0.00003934941,0.00009773906,0.00030029032,0.6035405,0.01438931,0.017979499,0.006555995,0.355067],"study_design_scores_gemma":[0.0008237891,0.00012819745,0.009231376,0.00052887795,0.0000031904265,0.0012064333,0.000020467292,0.98314834,0.00004272475,0.0013202399,0.0034190705,0.00012732],"about_ca_topic_score_codex":0.0000070977585,"about_ca_topic_score_gemma":0.0000010959186,"teacher_disagreement_score":0.9401319,"about_ca_system_score_codex":0.0001104675,"about_ca_system_score_gemma":0.000045811597,"threshold_uncertainty_score":0.38808933},"labels":[],"label_agreement":null},{"id":"W2600555690","doi":"10.15353/vsnl.v2i1.106","title":"StochasticNet in StochasticNet","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Computer science; Deep neural networks; Deep learning; Artificial neural network; Artificial intelligence; Convolutional neural network; Graph; Machine learning; Theoretical computer science","score_opus":0.009665562301139371,"score_gpt":0.2787130577608325,"score_spread":0.26904749545969314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600555690","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":[0.009381953,0.000542783,0.9863466,0.0032135919,0.00033789355,0.0000963117,0.0000016643041,0.000017909517,0.000061318024],"genre_scores_gemma":[0.97138774,0.000014859813,0.028323995,0.00012108434,0.000108419816,0.0000029680841,3.1912373e-7,0.000006085704,0.00003451932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988442,0.00006499204,0.00048481242,0.00013574955,0.0003309354,0.00013928654],"domain_scores_gemma":[0.99866265,0.00057915173,0.00032914846,0.00011609304,0.00021600092,0.00009696099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034401586,0.00009496598,0.0001847816,0.00023104843,0.000053827964,0.00009643987,0.00028359704,0.000016842545,0.0000018669043],"category_scores_gemma":[0.000047898484,0.000060374125,0.000033102853,0.000222166,0.000046290923,0.0006765662,0.00007735989,0.00008936688,0.000008022817],"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":[0.000041110838,0.00012366938,0.0018596917,0.000024739085,0.000019835954,0.000056405395,0.00037737793,0.7019814,0.0017887526,0.17763835,0.0046378253,0.111450806],"study_design_scores_gemma":[0.001331298,0.0001232691,0.010833452,0.00046497592,0.000004123254,0.00089824793,0.00004575617,0.9452466,0.000013333621,0.037209272,0.0036623613,0.0001673524],"about_ca_topic_score_codex":0.0000022370405,"about_ca_topic_score_gemma":2.251145e-7,"teacher_disagreement_score":0.9620058,"about_ca_system_score_codex":0.000049101687,"about_ca_system_score_gemma":0.00005355427,"threshold_uncertainty_score":0.24619853},"labels":[],"label_agreement":null},{"id":"W2605739168","doi":"10.1109/asap.2017.7995254","title":"Parallel Multi Channel convolution using General Matrix Multiplication","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Computer science; Kernel (algebra); Matrix multiplication; Convolution (computer science); Multiplication (music); Convolutional neural network; Parallel computing; Matrix (chemical analysis); Channel (broadcasting); Algorithm; Artificial intelligence; Artificial neural network; Mathematics","score_opus":0.07532438960000881,"score_gpt":0.35055939914316475,"score_spread":0.27523500954315594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605739168","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":[0.01295362,0.000049463102,0.98485327,0.0011381058,0.00017707088,0.00031317488,0.0000014385382,0.00023558985,0.00027829048],"genre_scores_gemma":[0.49676526,0.000016316057,0.50217956,0.00010311249,0.00010298079,0.000037802616,0.000002814638,0.0000070816777,0.0007850368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990255,0.000019753523,0.00017744102,0.0004008816,0.00012694462,0.0002494503],"domain_scores_gemma":[0.998285,0.000022867376,0.00021450073,0.0013062346,0.00008588927,0.00008550584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000862657,0.000117455216,0.00010248237,0.000040372244,0.00081589026,0.00019113693,0.0010691365,0.000055811506,0.0000041078924],"category_scores_gemma":[0.000027306525,0.00011232622,0.000045720823,0.00008301117,0.00006666243,0.00093321677,0.0003474283,0.000082159255,0.00009349337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019616788,0.00032607588,0.0030539876,0.000020325595,0.000037106187,0.000008250235,0.00034501025,0.28603262,0.09012945,0.5776925,0.0014932943,0.040841755],"study_design_scores_gemma":[0.0003440813,0.0000071640543,0.010506871,0.000003757336,0.0000028640575,0.000009422559,0.0000025780955,0.98328334,0.0015298275,0.0033419505,0.00082156126,0.00014660563],"about_ca_topic_score_codex":0.0001074413,"about_ca_topic_score_gemma":0.000024759642,"teacher_disagreement_score":0.6972507,"about_ca_system_score_codex":0.00005691218,"about_ca_system_score_gemma":0.000021490745,"threshold_uncertainty_score":0.6275251},"labels":[],"label_agreement":null},{"id":"W2606766152","doi":"10.23977/jaip.2016.11005","title":"BLSTM Recurrent Neural Network for Object Recognition","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Object (grammar); Representation (politics); Recurrent neural network; Fuse (electrical); Context (archaeology); Sequence (biology); Pattern recognition (psychology); Image (mathematics); Artificial neural network; Tree (set theory); Cognitive neuroscience of visual object recognition; Computer vision","score_opus":0.09685038027984123,"score_gpt":0.36577832241034786,"score_spread":0.26892794213050664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606766152","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":[0.0019435087,0.00022554728,0.9784831,0.017090557,0.001749546,0.0002865417,0.0000039890983,0.000043922875,0.00017331353],"genre_scores_gemma":[0.32334083,0.00076917425,0.6715561,0.0013234259,0.0028726298,0.00004785211,0.0000013391243,0.000026212318,0.00006240115],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979928,0.00017939246,0.0008537931,0.00027432284,0.0003389329,0.00036075473],"domain_scores_gemma":[0.9938442,0.003487683,0.001176802,0.00032979768,0.001011865,0.00014963631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012509471,0.00014882967,0.00021039457,0.00009967365,0.00020547761,0.00013513834,0.0007445979,0.00006214223,0.000019748148],"category_scores_gemma":[0.0024353086,0.0001049427,0.00016344525,0.00052978244,0.00006450514,0.0025723227,0.000089964335,0.0002472271,0.0000893526],"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":[0.00021104996,0.0001187777,0.0000074240843,0.0000034627762,0.000022341323,0.000010241961,0.00011240121,0.0019500875,0.001942843,0.023906302,0.0017503928,0.9699647],"study_design_scores_gemma":[0.0002414103,0.0022775729,0.000061098755,0.00030186752,0.00016223743,0.001114301,0.00029939137,0.06673808,0.06384546,0.7145958,0.14965181,0.0007110019],"about_ca_topic_score_codex":0.0000018576574,"about_ca_topic_score_gemma":0.000006315515,"teacher_disagreement_score":0.96925366,"about_ca_system_score_codex":0.0000902721,"about_ca_system_score_gemma":0.00009702393,"threshold_uncertainty_score":0.42794392},"labels":[],"label_agreement":null},{"id":"W2608362693","doi":"10.1109/icpr.2016.7900067","title":"Object figure-ground segmentation using zero-shot learning","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Zero (linguistics); Ground zero; Segmentation; Shot (pellet); Artificial intelligence; Object (grammar); Computer science; Computer vision; Figure–ground; Image segmentation; Physics; Linguistics; Materials science; Epistemology; Philosophy","score_opus":0.04637618592664908,"score_gpt":0.30812405416635397,"score_spread":0.26174786823970486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608362693","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":[0.050789565,0.000020047986,0.94574445,0.0004952238,0.000088160436,0.00012342239,2.4843968e-7,0.00026276533,0.00247612],"genre_scores_gemma":[0.8171894,0.000014151943,0.17990714,0.00023433352,0.000055864217,0.000015797012,0.0000010511023,0.000008711074,0.002573575],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991702,0.000039620983,0.00013924498,0.00029508607,0.00014934274,0.00020652433],"domain_scores_gemma":[0.9993959,0.00015001486,0.00007387086,0.00028500703,0.000040306935,0.000054885593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081618484,0.000089014546,0.00007138442,0.000046297206,0.00018577672,0.00006457706,0.00031368536,0.000027358008,0.00005338554],"category_scores_gemma":[0.000019394318,0.000061837825,0.000029878067,0.0002978694,0.000025216672,0.00072065694,0.000118012955,0.000061470826,0.00015341835],"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":[0.0000048937422,0.00003567814,0.0023406674,0.000004719249,0.000013901809,0.0000046248824,0.00023620462,0.008397454,0.6794296,0.07753748,0.0007236573,0.23127115],"study_design_scores_gemma":[0.0030086618,0.00044826386,0.015075259,0.00019686944,0.00004341093,0.00018196087,0.00018750736,0.45863995,0.35169637,0.12102399,0.047300827,0.002196912],"about_ca_topic_score_codex":0.000009781768,"about_ca_topic_score_gemma":0.0000030547578,"teacher_disagreement_score":0.7663998,"about_ca_system_score_codex":0.00007555298,"about_ca_system_score_gemma":0.000021296722,"threshold_uncertainty_score":0.2521673},"labels":[],"label_agreement":null},{"id":"W2612382424","doi":"10.1109/wacv.2017.87","title":"Deep Object Ranking for Template Matching","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Ranking (information retrieval); Artificial intelligence; Template matching; Matching (statistics); Object (grammar); Object detection; Pattern recognition (psychology); Computer vision; Image (mathematics); Mathematics","score_opus":0.03430910238187781,"score_gpt":0.31568366457659564,"score_spread":0.28137456219471785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612382424","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":[0.002235131,0.000016906302,0.99017304,0.0014741841,0.0001586062,0.0002274468,2.94627e-7,0.00018044632,0.005533971],"genre_scores_gemma":[0.56921595,0.0000036906263,0.42999318,0.00023984353,0.00006734465,0.00005332477,4.691273e-7,0.0000055911974,0.0004206087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938715,0.000005647237,0.0000996679,0.00024256577,0.00007360028,0.00019139894],"domain_scores_gemma":[0.99887115,0.00011881409,0.00009516468,0.0008474029,0.000028576913,0.00003892343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101604746,0.00006768906,0.00007542608,0.00001963806,0.000878747,0.00027614718,0.0011264327,0.00002057874,0.000004316042],"category_scores_gemma":[0.000023672303,0.000059350146,0.00004142673,0.000037720656,0.000020418798,0.0006069043,0.00024941063,0.00004986907,0.00003171422],"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":[0.000005414383,0.000018001989,0.00023937687,0.000010805366,0.00001208527,0.0000031481927,0.0002769242,0.004866296,0.005069982,0.7542492,0.0008037412,0.23444502],"study_design_scores_gemma":[0.0005500896,0.000029430144,0.0033021413,0.00001718202,0.0000052508467,0.000019401228,0.000011639479,0.51721984,0.0059412215,0.45653,0.01606706,0.0003067695],"about_ca_topic_score_codex":0.000010511831,"about_ca_topic_score_gemma":0.000032457392,"teacher_disagreement_score":0.56698084,"about_ca_system_score_codex":0.0000117928885,"about_ca_system_score_gemma":0.000008479969,"threshold_uncertainty_score":0.67587006},"labels":[],"label_agreement":null},{"id":"W2612864759","doi":"10.1145/3061639.3062259","title":"Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Nvidia; National Science Foundation","keywords":"Computer science; Floating point; Multiplier (economics); Inference; Artificial neural network; Adder; Latency (audio); Computer engineering; Deep neural networks; Binary number; Overhead (engineering); Computer hardware; Hardware acceleration; Deep learning; Algorithm; Field-programmable gate array; Artificial intelligence; Arithmetic","score_opus":0.0307591957431979,"score_gpt":0.2835629074287377,"score_spread":0.2528037116855398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612864759","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":[0.0010018876,0.000098584525,0.99561614,0.000840474,0.00030632468,0.00033400932,0.0000046650252,0.00032952338,0.0014684128],"genre_scores_gemma":[0.69858944,0.00002069981,0.3004586,0.0002595695,0.000096791504,0.000051008148,0.0000029716978,0.000015979595,0.0005049205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851197,0.00003787338,0.00032784935,0.00049996487,0.00023022344,0.00039213392],"domain_scores_gemma":[0.9955147,0.00029168956,0.00037319298,0.003512099,0.00016332117,0.00014500802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001280959,0.00019777044,0.0002445113,0.000044578694,0.0005596816,0.00020690492,0.0045300834,0.000084270425,0.00002537186],"category_scores_gemma":[0.00025897723,0.00017511434,0.000093161056,0.00017775474,0.00016921836,0.0010519993,0.0013286226,0.00019388509,0.000020014226],"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":[0.00004347913,0.00019707473,0.007473137,0.000032025502,0.00005388218,0.000033468805,0.00019814652,0.44466767,0.00091208937,0.098843,0.01187206,0.43567398],"study_design_scores_gemma":[0.00044881605,0.000044012813,0.007900085,0.000007783882,0.0000051826605,0.000009874213,0.000004766511,0.98310125,0.001162554,0.005809398,0.0012764998,0.00022977855],"about_ca_topic_score_codex":0.000021441716,"about_ca_topic_score_gemma":0.00005610291,"teacher_disagreement_score":0.69758755,"about_ca_system_score_codex":0.000021555716,"about_ca_system_score_gemma":0.000018069077,"threshold_uncertainty_score":0.84180975},"labels":[],"label_agreement":null},{"id":"W2613903212","doi":"10.1109/globalsip.2017.8309150","title":"A feature embedding strategy for high-level CNN representations from multiple convnets","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Embedding; Normalization (sociology); Convolutional neural network; Feature extraction; Principal component analysis; Feature (linguistics); Feature vector; Exploit; Contextual image classification; Image (mathematics)","score_opus":0.09048114161378658,"score_gpt":0.3638573489804966,"score_spread":0.27337620736671003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613903212","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":[0.0071111815,0.000026782407,0.9837156,0.0068077818,0.00023784488,0.00049931806,0.00007663067,0.00020222878,0.0013226406],"genre_scores_gemma":[0.60781044,0.0000050859626,0.3887537,0.00017177519,0.00013029728,0.00014824989,0.000031756696,0.000008341479,0.0029403416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989743,0.000014500652,0.00013037515,0.0005094787,0.00012557054,0.00024574067],"domain_scores_gemma":[0.99774736,0.00043762467,0.00017314257,0.0014455529,0.000102834245,0.000093465445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005118418,0.00012418641,0.00013350665,0.000028309352,0.0008361183,0.0004054655,0.0012690157,0.0000639567,0.00001641127],"category_scores_gemma":[0.0001317606,0.00011367373,0.000053245778,0.0000730083,0.00005481748,0.00082275074,0.00027772223,0.00010731588,0.000041532006],"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":[0.000028152275,0.00013296586,0.0029552493,0.000011070196,0.00008465456,0.000011924964,0.00034846299,0.01984108,0.02085111,0.7700732,0.08598038,0.09968175],"study_design_scores_gemma":[0.0015679343,0.000053317413,0.0535888,0.000019152605,0.000017198086,0.000004970887,0.00006681114,0.75645745,0.020133799,0.15625723,0.011380226,0.00045313634],"about_ca_topic_score_codex":0.00018811999,"about_ca_topic_score_gemma":0.00026616536,"teacher_disagreement_score":0.7366163,"about_ca_system_score_codex":0.000020688332,"about_ca_system_score_gemma":0.000031405674,"threshold_uncertainty_score":0.6430831},"labels":[],"label_agreement":null},{"id":"W2614475194","doi":"10.15353/vsnl.v1i1.42","title":"Stochastic Receptive Fields in Deep Convolutional Networks","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Receptive field; Convolutional neural network; Computer science; Conditional random field; Artificial intelligence; Algorithm","score_opus":0.01949999173153214,"score_gpt":0.27245027452478415,"score_spread":0.252950282793252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614475194","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":[0.009312839,0.00007519206,0.97857916,0.011349281,0.0003145637,0.00015142834,3.7635462e-7,0.000088610024,0.00012854046],"genre_scores_gemma":[0.9618927,0.0000036054546,0.032238394,0.00564945,0.0001319561,0.00003956795,0.000004847268,0.000006889402,0.000032572974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990451,0.000051414976,0.00016841367,0.00030533585,0.00019547825,0.00023426523],"domain_scores_gemma":[0.9993149,0.00016159235,0.000056531146,0.00031686763,0.000040530285,0.00010955009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013957161,0.000094263654,0.00009639896,0.000082723454,0.00005201934,0.00003479779,0.0004380417,0.000048955655,0.00000780735],"category_scores_gemma":[0.000035832065,0.00009158517,0.000027209346,0.00049389916,0.000050680064,0.00031649522,0.00016666962,0.00019718503,0.000081287886],"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":[0.000012293093,0.00003478387,0.00015999342,7.857449e-7,0.0000030698397,0.00000968828,0.00026562167,0.9540995,0.00027110573,0.012418238,0.016869092,0.015855836],"study_design_scores_gemma":[0.0003595461,0.00004355868,0.002184212,0.000013398852,0.0000011289155,0.000008293674,0.000012759163,0.9920435,0.000011121353,0.0036978181,0.001485345,0.00013932424],"about_ca_topic_score_codex":0.0000072552675,"about_ca_topic_score_gemma":0.000016774384,"teacher_disagreement_score":0.95257986,"about_ca_system_score_codex":0.0000869609,"about_ca_system_score_gemma":0.000018924602,"threshold_uncertainty_score":0.3734735},"labels":[],"label_agreement":null},{"id":"W2615084988","doi":"10.1145/3060403.3060435","title":"Evaluating Data Resilience in CNNs from an Approximate Memory Perspective","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Resilience (materials science); Error detection and correction; Computer engineering; Implementation; Volume (thermodynamics); Memory management; Parallel computing; Algorithm; Semiconductor memory; Artificial intelligence; Computer hardware","score_opus":0.1583669576298885,"score_gpt":0.44261312481235654,"score_spread":0.28424616718246803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615084988","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":[0.09942027,0.00007980784,0.8776397,0.0039563477,0.00015120469,0.0004480337,0.000017689885,0.00023866103,0.01804825],"genre_scores_gemma":[0.6510746,0.0000065598847,0.3484378,0.00018822386,0.00007349506,0.000026223026,0.000008242376,0.0000064008404,0.00017847278],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983729,0.00006602603,0.00016658033,0.00091735454,0.00023789698,0.00023923018],"domain_scores_gemma":[0.9945176,0.00013517677,0.00014798914,0.0050538154,0.00006789511,0.00007751826],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00039747168,0.000103684746,0.00011596991,0.000037080616,0.00042614163,0.0002765219,0.0053890455,0.000030678482,0.000016592252],"category_scores_gemma":[0.00028973565,0.00009571563,0.000012049157,0.00013204219,0.000101044025,0.0028022102,0.001505489,0.00015074883,0.00003867567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033866232,0.00044163057,0.005799896,0.0000074190525,0.000021389287,0.000053531538,0.008265641,0.01978188,0.022968933,0.44018736,0.00091336103,0.5015251],"study_design_scores_gemma":[0.00019155131,0.000023316237,0.01947103,0.000009404411,0.0000020118664,0.0000019012211,0.00035514645,0.9242212,0.0009485553,0.054602917,0.000034454526,0.00013847103],"about_ca_topic_score_codex":0.0010323504,"about_ca_topic_score_gemma":0.0010950746,"teacher_disagreement_score":0.90443933,"about_ca_system_score_codex":0.000059081834,"about_ca_system_score_gemma":0.000055688968,"threshold_uncertainty_score":0.99999225},"labels":[],"label_agreement":null},{"id":"W2618636670","doi":"10.1016/j.parco.2017.05.003","title":"Proteus: Exploiting precision variability in deep neural networks","year":2017,"lang":"en","type":"article","venue":"Parallel Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Memory footprint; Inference; Convolutional neural network; Artificial neural network; Proteus; Efficient energy use; Floating point; Computer engineering; Real-time computing; Algorithm; Artificial intelligence","score_opus":0.029956903295596044,"score_gpt":0.30042314322613595,"score_spread":0.2704662399305399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618636670","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":[0.041624006,0.000061286875,0.95503455,0.00068985875,0.00028486273,0.0006270588,1.7434643e-7,0.0002496583,0.0014285557],"genre_scores_gemma":[0.78866714,0.0000049038017,0.21094176,0.00010857617,0.00020814531,0.000045575478,0.0000013845012,0.000013170598,0.000009336859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765503,0.00022691899,0.0004944468,0.000788102,0.00023585756,0.00059964147],"domain_scores_gemma":[0.9970263,0.00066517293,0.00042208523,0.0016978701,0.00007401592,0.00011450636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012537807,0.00021294112,0.00026223456,0.00006114909,0.00097716,0.00044463738,0.0022870502,0.00008247695,0.0000032158325],"category_scores_gemma":[0.00054494163,0.00021480331,0.00007160731,0.00024298964,0.00007780045,0.00088151597,0.0015848835,0.0004514928,0.000014102962],"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":[0.00000650558,0.000044817814,0.01214735,0.000007192277,0.0000029148375,0.000010833302,0.00020701862,0.61398464,0.00003838084,0.008765785,0.000015829624,0.3647687],"study_design_scores_gemma":[0.00029502442,0.00001737163,0.033797495,0.00003404182,0.0000014441705,0.0000115193825,0.0000075930234,0.9537714,0.00002299795,0.011731482,0.000095346244,0.00021427883],"about_ca_topic_score_codex":0.000029699759,"about_ca_topic_score_gemma":0.000023664632,"teacher_disagreement_score":0.74704313,"about_ca_system_score_codex":0.000075379794,"about_ca_system_score_gemma":0.000015449927,"threshold_uncertainty_score":0.87594247},"labels":[],"label_agreement":null},{"id":"W2619093102","doi":"10.1007/s11042-017-4840-5","title":"Bi-linearly weighted fractional max pooling","year":2017,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"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":"Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology; Canadian Institute for Advanced Research","keywords":"Pooling; Computer science; Convolutional neural network; Reduction (mathematics); Flexibility (engineering); Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics; Statistics","score_opus":0.036041173580979566,"score_gpt":0.3005157181237911,"score_spread":0.2644745445428115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619093102","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":[0.0025887145,0.0001429996,0.9893697,0.0046745497,0.00009497972,0.00053740223,0.0000438738,0.00021465549,0.002333164],"genre_scores_gemma":[0.5112849,0.00030000662,0.48462564,0.00050887396,0.00088748673,0.001482464,0.000070939706,0.000024939027,0.0008147458],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989329,0.000011430422,0.0002042639,0.00045537928,0.00016408037,0.0002319454],"domain_scores_gemma":[0.99825305,0.00021725109,0.0001908941,0.0010841958,0.000094557654,0.00016002456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081656035,0.0001353235,0.0001285435,0.000046127596,0.0012963134,0.0005159164,0.0008787011,0.00006562332,0.00001875063],"category_scores_gemma":[0.00003938678,0.00012980161,0.000041810115,0.00013366892,0.0001380394,0.00090364565,0.0002868279,0.00017889641,0.00018329543],"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":[0.00000317072,0.00009728438,0.0013946816,0.0000065292247,0.000017076534,0.0000019319639,0.00007170723,0.00011893367,0.0031486142,0.13993993,0.0008168612,0.8543833],"study_design_scores_gemma":[0.000722446,0.000024551991,0.07290931,0.000017275417,0.000020020505,0.000031184183,0.000015278987,0.42920065,0.0020625787,0.03956861,0.4549222,0.0005059027],"about_ca_topic_score_codex":0.000018392937,"about_ca_topic_score_gemma":0.0000069498096,"teacher_disagreement_score":0.85387737,"about_ca_system_score_codex":0.000019312469,"about_ca_system_score_gemma":0.00003151023,"threshold_uncertainty_score":0.9970325},"labels":[],"label_agreement":null},{"id":"W2719597717","doi":"10.1145/3195970.3196072","title":"Loom","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":72,"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":"LOOM; Computer science; Granularity; Convolutional neural network; Energy (signal processing); Inference; Layer (electronics); Efficient energy use; Parallel computing; State (computer science); Real-time computing; Algorithm; Artificial intelligence; Computer hardware; Operating system; Mathematics; Statistics; Electrical engineering","score_opus":0.018721265917630014,"score_gpt":0.27724925649271337,"score_spread":0.25852799057508336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2719597717","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":[0.0009229679,0.000005681778,0.93468034,0.0014801931,0.00006976522,0.000031490374,4.3917275e-8,0.00021223369,0.06259728],"genre_scores_gemma":[0.61874425,0.0000016329245,0.37706262,0.0014651711,0.00013663888,0.0000064342926,1.2277877e-7,0.0000019500844,0.0025811843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999711,0.0000035019739,0.000038979462,0.00011897939,0.000044774777,0.00008277035],"domain_scores_gemma":[0.9996015,0.000016946098,0.000009916247,0.0003172935,0.00002575479,0.000028577984],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000017879103,0.000025368494,0.000021009337,0.000011125029,0.000056726396,0.00001878421,0.0003535323,0.000007867532,0.000046618294],"category_scores_gemma":[0.0000031786215,0.000020765665,0.000008057548,0.00020019125,0.000027799835,0.00014796802,0.00010008038,0.000018407223,0.0011145857],"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":[1.851018e-7,0.0000063172315,0.000051483257,1.4468029e-7,6.536184e-7,3.4029404e-7,0.000024373989,0.0000061417672,0.0009201442,0.91793835,0.011518387,0.06953349],"study_design_scores_gemma":[0.00013487371,0.000082312785,0.0024327505,0.0000020641155,9.565272e-7,0.000016861202,0.0000037539653,0.11868408,0.03712306,0.21779943,0.623526,0.00019390219],"about_ca_topic_score_codex":0.0000010614211,"about_ca_topic_score_gemma":0.0000034312393,"teacher_disagreement_score":0.7001389,"about_ca_system_score_codex":0.0000041916724,"about_ca_system_score_gemma":0.0000047625267,"threshold_uncertainty_score":0.9996632},"labels":[],"label_agreement":null},{"id":"W2732077503","doi":"","title":"Using Deep Learning to Automate Feature Modeling in Learning by Observation.","year":2017,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Deep learning; Feature learning; Machine learning","score_opus":0.16444875190367186,"score_gpt":0.41576943574958575,"score_spread":0.2513206838459139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732077503","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":[0.27434447,0.00022321568,0.7071754,0.017230846,0.0001215012,0.0004954804,8.637346e-7,0.00022161372,0.00018661884],"genre_scores_gemma":[0.9208952,0.0001800707,0.076551974,0.00043774763,0.0003011543,0.00012557111,0.0000048308175,0.000034357203,0.0014690567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974309,0.0002897727,0.00019606948,0.0005412246,0.00074363756,0.00079837797],"domain_scores_gemma":[0.9980598,0.00032591118,0.0000936806,0.0010844837,0.00029215246,0.00014395786],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002280435,0.00015446232,0.00016353613,0.000049638093,0.003986886,0.00081889733,0.0024972965,0.00010063793,0.000003319656],"category_scores_gemma":[0.0004891077,0.0001281174,0.00009032639,0.0008310296,0.00012807662,0.0009944676,0.001545702,0.0019292607,0.000037823913],"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":[0.000005795693,0.000013540169,0.001378824,0.00001102653,0.000011319748,0.000002660153,0.003981298,0.96413857,0.014118049,0.0020638425,0.0027483983,0.011526655],"study_design_scores_gemma":[0.00014888907,0.000021383397,0.0007266043,0.000036234756,0.0000015214557,0.0000025101558,0.00033006354,0.99018687,0.0004304394,0.0024169616,0.005555374,0.00014316446],"about_ca_topic_score_codex":0.00016333506,"about_ca_topic_score_gemma":0.000023547476,"teacher_disagreement_score":0.6465508,"about_ca_system_score_codex":0.00026611105,"about_ca_system_score_gemma":0.00008363706,"threshold_uncertainty_score":0.9973098},"labels":[],"label_agreement":null},{"id":"W2732463748","doi":"10.1109/lra.2017.2723926","title":"The Raincouver Scene Parsing Benchmark for Self-Driving in Adverse Weather and at Night","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parsing; Computer science; Benchmark (surveying); Benchmarking; Focus (optics); Artificial intelligence; Segmentation; Dusk; Task (project management); Pixel; Computer vision; Machine learning; Cartography; Geography; Engineering","score_opus":0.01155486080503709,"score_gpt":0.2505481348100118,"score_spread":0.23899327400497472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732463748","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":[0.23432037,0.00005373859,0.73962057,0.02505407,0.0003567041,0.00042617175,0.0000010037554,0.00009259467,0.00007476992],"genre_scores_gemma":[0.8724481,0.00006702778,0.12651409,0.0007872916,0.00007387184,0.00003365149,9.497893e-7,0.000009305257,0.00006574472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993552,0.000019746878,0.0001444436,0.0002144664,0.0000864572,0.0001796623],"domain_scores_gemma":[0.9992537,0.00019807395,0.0001408562,0.00034849203,0.000018389164,0.00004050672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001654975,0.000085626525,0.00008095881,0.00002970461,0.0009978543,0.00018786479,0.00025807336,0.000026796955,3.337084e-7],"category_scores_gemma":[0.00001886512,0.00006793874,0.000020076415,0.000047903974,0.00006907744,0.00037819365,0.000099883735,0.000053442116,0.0000013358273],"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":[0.00003629449,0.00018731006,0.054649282,0.00018200393,0.00014035916,0.000031552972,0.0067761512,0.5022342,0.059505448,0.08496367,0.025137708,0.26615602],"study_design_scores_gemma":[0.0003538638,0.000008863504,0.032180235,0.000032316435,0.0000057534166,0.000005092599,0.000006627278,0.9625971,0.00029148668,0.0014236403,0.0029700114,0.00012503196],"about_ca_topic_score_codex":0.0000023355612,"about_ca_topic_score_gemma":0.000037884583,"teacher_disagreement_score":0.6381277,"about_ca_system_score_codex":0.000047057514,"about_ca_system_score_gemma":0.000007358815,"threshold_uncertainty_score":0.76747894},"labels":[],"label_agreement":null},{"id":"W2737258237","doi":"10.1109/cvpr.2017.544","title":"Scene Parsing through ADE20K Dataset","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3165,"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; Samsung; National Science Foundation","keywords":"Parsing; Computer science; Object (grammar); Segmentation; Artificial intelligence; Benchmark (surveying); Market segmentation; Cascade; Computer vision; Key (lock); Image segmentation; Pattern recognition (psychology); Information retrieval; Cartography","score_opus":0.07541479895096069,"score_gpt":0.35608341277994154,"score_spread":0.28066861382898084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737258237","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":[0.00052590243,0.00003515004,0.98058844,0.0059436723,0.00014732801,0.00009800596,0.000025786167,0.00014284333,0.012492864],"genre_scores_gemma":[0.34129968,0.000043895387,0.6558799,0.002028639,0.00014572492,0.000018670884,0.000061892824,0.0000078937455,0.00051368773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993297,0.000008716119,0.0000915238,0.0002836009,0.000102370854,0.00018411492],"domain_scores_gemma":[0.99792343,0.000033853496,0.000079406374,0.0018997571,0.000016705582,0.00004682179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004719224,0.00006802432,0.00006647001,0.000008804498,0.00061378477,0.00024492087,0.0016065554,0.000020109537,0.000019228151],"category_scores_gemma":[0.0000211778,0.000059240127,0.000016194701,0.000055927427,0.000061456754,0.0013205209,0.00055856066,0.00006657337,0.00023875067],"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":[0.000002301842,0.00005346227,0.00042625415,0.000003991182,0.000007681687,0.000013675952,0.000079118654,0.0002427726,0.0013595895,0.70829386,0.1443693,0.14514801],"study_design_scores_gemma":[0.00045629963,0.000027953918,0.004985555,0.000017240738,0.000005164831,0.00003225947,0.000006965989,0.04974729,0.012572991,0.08471918,0.84706515,0.00036394797],"about_ca_topic_score_codex":0.000027212905,"about_ca_topic_score_gemma":0.000016680982,"teacher_disagreement_score":0.70269585,"about_ca_system_score_codex":0.000011130572,"about_ca_system_score_gemma":0.000011544282,"threshold_uncertainty_score":0.4720798},"labels":[],"label_agreement":null},{"id":"W2745815247","doi":"10.1109/cvpr.2017.642","title":"Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Submodular set function; Pairwise comparison; Computer science; Binary number; Context (archaeology); State (computer science); Extension (predicate logic); Algorithm; Function (biology); Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.04338587632187273,"score_gpt":0.3100333385755564,"score_spread":0.2666474622536837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745815247","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":[0.008337797,0.0001669932,0.9824734,0.0025173733,0.00012335072,0.00030413523,0.000003574493,0.00019231808,0.0058810934],"genre_scores_gemma":[0.7173519,0.000031780964,0.28080425,0.0003386777,0.0000601172,0.00009350566,6.0168617e-7,0.0000067537494,0.001312405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932176,0.000009724731,0.00009365067,0.00032112666,0.000071462986,0.00018226681],"domain_scores_gemma":[0.99898535,0.0001411229,0.00009309313,0.00069255754,0.000042207932,0.00004564331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006452465,0.00009306441,0.00009152947,0.000027327824,0.00056349504,0.00014057683,0.0006307777,0.000029955458,0.0000016996255],"category_scores_gemma":[0.00002694451,0.000081007616,0.000030045137,0.000042129646,0.000085948435,0.00071270537,0.00048891595,0.00004752323,0.0000054766124],"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":[0.000016354566,0.000027709892,0.00037875777,0.000008245722,0.0000120304585,0.000006681139,0.00016757437,0.0012420578,0.0010931713,0.7996234,0.004445766,0.19297823],"study_design_scores_gemma":[0.00046044218,0.00014375689,0.015253188,0.00003361856,0.000008662193,0.000015652378,0.00005693738,0.81137645,0.002969781,0.15244617,0.016873801,0.00036153718],"about_ca_topic_score_codex":0.000006426134,"about_ca_topic_score_gemma":0.000009183206,"teacher_disagreement_score":0.8101344,"about_ca_system_score_codex":0.000013016597,"about_ca_system_score_gemma":0.00001592463,"threshold_uncertainty_score":0.4334005},"labels":[],"label_agreement":null},{"id":"W2745993090","doi":"10.1109/tii.2017.2739340","title":"Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":200,"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; St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Big data; Deep learning; Overfitting; Artificial intelligence; Data modeling; Tensor (intrinsic definition); Machine learning; Computation; Pattern recognition (psychology); Data mining; Artificial neural network; Algorithm; Mathematics","score_opus":0.15446571080543314,"score_gpt":0.3252247230880227,"score_spread":0.17075901228258955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745993090","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":[0.0020214505,0.0000019990277,0.9960614,0.0005229307,0.0005880422,0.00045288584,0.00003744046,0.00005568133,0.00025818482],"genre_scores_gemma":[0.9029177,0.0000071817044,0.09659566,0.00017649487,0.00005912608,0.00004252324,0.00005271929,0.000009339949,0.0001392633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989023,0.000023029532,0.00046002684,0.00017901706,0.0002469218,0.00018870801],"domain_scores_gemma":[0.99833155,0.0003035037,0.0004707565,0.00074628583,0.00009376532,0.000054143096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028225713,0.00013659432,0.00018588651,0.00016635796,0.0003158332,0.00012762312,0.0012255999,0.00016983289,7.714551e-7],"category_scores_gemma":[0.000059789523,0.00013691079,0.0000504537,0.00015204684,0.0000790004,0.0014241217,0.000021215239,0.000608032,0.000006609267],"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":[0.000044430333,0.000056953522,0.00000912206,0.000008949513,0.000011842337,1.270427e-7,0.00070267107,0.82797545,0.0000131308525,0.001856265,0.00030403497,0.16901705],"study_design_scores_gemma":[0.0010704322,0.00010954683,0.000025324029,0.00006387794,0.000008867648,0.0000019932586,0.000035881876,0.99640524,0.0007197298,0.0011373403,0.00029847314,0.0001233014],"about_ca_topic_score_codex":0.000020305608,"about_ca_topic_score_gemma":0.000017566395,"teacher_disagreement_score":0.90089625,"about_ca_system_score_codex":0.000077922516,"about_ca_system_score_gemma":0.00009676415,"threshold_uncertainty_score":0.558306},"labels":[],"label_agreement":null},{"id":"W2747730426","doi":"10.1109/iccvw.2017.319","title":"STNet: Selective Tuning of Convolutional Networks for Object Localization","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Convolutional neural network; Benchmark (surveying); Abstraction; Artificial intelligence; Representation (politics); Covert; Class (philosophy); Feed forward; Visual processing; Machine learning; Pattern recognition (psychology)","score_opus":0.032935581449741916,"score_gpt":0.30619612494158865,"score_spread":0.27326054349184675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747730426","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":[0.000045607416,0.0002524122,0.9961545,0.00023365355,0.00040951668,0.0009132416,0.000016229329,0.0001609891,0.0018138605],"genre_scores_gemma":[0.7503231,0.00008723997,0.24808446,0.00012420588,0.00030944723,0.00041672002,0.0001343167,0.000023141956,0.000497346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857575,0.000039326438,0.00032822666,0.0006109978,0.00019182288,0.00025390607],"domain_scores_gemma":[0.9976013,0.0002779614,0.0005918902,0.0009689582,0.0004999487,0.000059954116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001895082,0.0002009105,0.00029295048,0.0000804225,0.00029611425,0.0000863155,0.0012887309,0.00020615847,0.000004331934],"category_scores_gemma":[0.00007563753,0.0002042179,0.00012288596,0.00014612202,0.00010302522,0.00021324171,0.0009476849,0.00027372153,0.0000020914815],"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":[0.000008356406,0.000026644124,0.00015366239,0.000031129737,0.000037726855,2.2353373e-7,0.00005797618,0.8309598,0.000046226913,0.15856938,0.0028889861,0.0072198673],"study_design_scores_gemma":[0.00015964785,0.000031382202,0.00039208523,0.000054985736,0.000012650401,0.0000020306763,0.0000023942684,0.9225896,0.0004644601,0.074727446,0.0013732599,0.00019003445],"about_ca_topic_score_codex":0.000024470923,"about_ca_topic_score_gemma":0.000021401229,"teacher_disagreement_score":0.7502775,"about_ca_system_score_codex":0.00009460514,"about_ca_system_score_gemma":0.0002146965,"threshold_uncertainty_score":0.8327764},"labels":[],"label_agreement":null},{"id":"W2752010668","doi":"10.1155/2017/2823617","title":"Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":178,"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; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Computer science; Frame (networking); Truck; Detector; Real-time computing; Computer vision; Frame rate; Task (project management); Artificial intelligence; Rotation (mathematics); Simulation; Automotive engineering; Engineering; Telecommunications","score_opus":0.00923285716901435,"score_gpt":0.24724321982989958,"score_spread":0.23801036266088524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752010668","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":[0.44020873,0.00006612452,0.5580995,0.0012259968,0.00023501097,0.00011237613,0.0000029023145,0.000020331365,0.000028985103],"genre_scores_gemma":[0.95217204,0.00006690908,0.047337316,0.00015198653,0.00021295568,0.000012398233,0.0000029437492,0.000012546651,0.000030896168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989593,0.000023921231,0.0003276518,0.00020146645,0.0003278078,0.0001598646],"domain_scores_gemma":[0.9980884,0.00009760399,0.00090818974,0.0005876325,0.00024551243,0.00007262345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001132795,0.00012947273,0.00016661797,0.000049558374,0.00043322064,0.00013635423,0.0007911922,0.000035943263,0.0000036048475],"category_scores_gemma":[0.0000137735415,0.000084695006,0.00008356927,0.00011783546,0.00008129473,0.002155429,0.000009045778,0.0002668509,0.0000059490144],"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":[0.00055197557,0.00017699065,0.0034488973,0.000029265602,0.00016471469,0.00019553042,0.004325918,0.36371526,0.27721307,0.0015658379,0.00013909885,0.34847343],"study_design_scores_gemma":[0.0024491106,0.00038144947,0.8941701,0.000178215,0.000115637944,0.000050173217,0.00032473626,0.0029449204,0.0861772,0.007716948,0.0050683115,0.0004232038],"about_ca_topic_score_codex":0.000010126299,"about_ca_topic_score_gemma":0.00022317492,"teacher_disagreement_score":0.8907212,"about_ca_system_score_codex":0.000037051104,"about_ca_system_score_gemma":0.00003850736,"threshold_uncertainty_score":0.3453762},"labels":[],"label_agreement":null},{"id":"W2753588254","doi":"10.1109/cvpr.2017.518","title":"Gated Feedback Refinement Network for Dense Image Labeling","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":237,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba; Nvidia","keywords":"Pascal (unit); Computer science; Parsing; Ambiguity; Encoder; Deep learning; Artificial intelligence; Data mining; Algorithm; Pattern recognition (psychology); Programming language","score_opus":0.033725729476701716,"score_gpt":0.3077822061983039,"score_spread":0.27405647672160216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753588254","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":[0.002061086,0.0000410317,0.9868851,0.0061498242,0.00023119083,0.00038942473,0.000001935363,0.00023573222,0.0040047197],"genre_scores_gemma":[0.06096713,0.000025889103,0.93518406,0.00079186994,0.00025331153,0.00008872983,0.0000037612147,0.000012562058,0.0026726825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990341,0.0000099351155,0.0001720139,0.00034651428,0.00010182253,0.0003356007],"domain_scores_gemma":[0.9983203,0.00009824087,0.00014500883,0.0012472497,0.000110773755,0.00007845491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015192803,0.00010506429,0.000107517575,0.000014261049,0.0008338119,0.00027816935,0.0011309603,0.00003010448,0.000015567852],"category_scores_gemma":[0.000050412204,0.00009334097,0.000045601533,0.00008857999,0.000042464744,0.0003869969,0.00036540884,0.00006359516,0.00008958194],"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":[0.00003323028,0.00011125552,0.00058726454,0.000025966661,0.000047908772,0.000010965408,0.000093042654,0.0130033,0.01159184,0.56884927,0.22305694,0.18258902],"study_design_scores_gemma":[0.0014466457,0.000117842734,0.0028490662,0.000047245405,0.000018868837,0.000012382023,0.000008674672,0.6323391,0.0100879995,0.07911925,0.27332783,0.000625076],"about_ca_topic_score_codex":0.000010083179,"about_ca_topic_score_gemma":0.000036475245,"teacher_disagreement_score":0.61933583,"about_ca_system_score_codex":0.000022470464,"about_ca_system_score_gemma":0.000017063958,"threshold_uncertainty_score":0.64130914},"labels":[],"label_agreement":null},{"id":"W2754500569","doi":"10.1109/icip.2017.8296366","title":"Object localization by optimizing convolutional neural network detection score using generic edge features","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Pascal (unit); Artificial intelligence; Computer science; Convolutional neural network; Object detection; Pattern recognition (psychology); Computer vision; Object (grammar); Parsing; Cognitive neuroscience of visual object recognition; Enhanced Data Rates for GSM Evolution; Edge detection; Artificial neural network; Viola–Jones object detection framework; Image (mathematics); Image processing","score_opus":0.03540216387383274,"score_gpt":0.2777612658215587,"score_spread":0.24235910194772595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754500569","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":[0.008569839,0.00035576453,0.98925024,0.00024412457,0.0006024337,0.00022306491,0.0000016513878,0.0002492918,0.0005036054],"genre_scores_gemma":[0.881905,0.000028986487,0.116818085,0.00047195377,0.0004662172,0.000025579602,0.000011023734,0.000016968,0.0002561504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987435,0.00004393846,0.00019155117,0.00045747467,0.00020896347,0.0003545884],"domain_scores_gemma":[0.99877805,0.000047513964,0.00023359433,0.00074885186,0.00009872525,0.0000932787],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000098741846,0.00016458353,0.00013110007,0.00003876304,0.0017574547,0.00038222366,0.0007924853,0.00007805684,0.00000619381],"category_scores_gemma":[0.00002702355,0.00015950443,0.000056374112,0.00026540985,0.000102847516,0.001033809,0.00032855992,0.00015092202,0.000008913426],"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":[0.000006987343,0.000019280786,0.0011656886,0.0000043258196,0.0000105635745,0.0000021942237,0.000022239688,0.95222825,0.009760394,0.0063405116,0.0052355467,0.025204021],"study_design_scores_gemma":[0.00016147518,0.000020959495,0.0029912111,0.000009732745,0.0000069011903,0.000042198666,0.0000026292773,0.98928475,0.0044041583,0.0017310429,0.0011444998,0.00020042912],"about_ca_topic_score_codex":0.000054111842,"about_ca_topic_score_gemma":0.00003725396,"teacher_disagreement_score":0.8733352,"about_ca_system_score_codex":0.000093725015,"about_ca_system_score_gemma":0.00003293986,"threshold_uncertainty_score":0.9995421},"labels":[],"label_agreement":null},{"id":"W2761909754","doi":"10.1145/3107614","title":"Automated Vehicle Detection and Classification","year":2017,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Categorization; Artificial intelligence; Open research; Scalability; Intelligent transportation system; Machine learning; Contextual image classification; Database; Image (mathematics)","score_opus":0.15472427463487923,"score_gpt":0.3917708695872088,"score_spread":0.23704659495232958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761909754","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":[0.000013290931,0.59437174,0.40359262,0.000042355434,0.000311755,0.00039304985,0.0000028014103,0.0011851548,0.00008721611],"genre_scores_gemma":[0.0030853688,0.97186804,0.024655312,0.000017862012,0.00019742368,0.00004245244,0.000030853218,0.000042806114,0.000059910504],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974032,0.0008257071,0.0004829087,0.00080218207,0.00017746672,0.0003085146],"domain_scores_gemma":[0.99562126,0.001037754,0.0008106111,0.0023436511,0.00009078232,0.0000959427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013262758,0.00031484116,0.00071584573,0.00013683787,0.00084742205,0.0005249962,0.00209324,0.00021359933,5.242374e-7],"category_scores_gemma":[0.00043176737,0.00029702994,0.00011948656,0.00042799456,0.00010058071,0.00028642407,0.0011390193,0.00037492404,0.000081044745],"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":[7.84507e-8,0.000008688851,0.0000075714274,0.00034822774,0.000014611854,0.0000016359764,0.00001043027,0.0000129446125,0.00000258311,0.00046843832,0.000055666886,0.9990691],"study_design_scores_gemma":[0.0000970474,0.000024920148,0.0029294782,0.0013182561,0.000053096523,0.000065207045,6.5180666e-7,0.3428957,0.0000023171651,0.00068828335,0.6514483,0.00047672162],"about_ca_topic_score_codex":0.000015271935,"about_ca_topic_score_gemma":0.0000131430525,"teacher_disagreement_score":0.9985924,"about_ca_system_score_codex":0.00008481143,"about_ca_system_score_gemma":0.00009431638,"threshold_uncertainty_score":0.9999482},"labels":[],"label_agreement":null},{"id":"W2766143712","doi":"10.1109/tcsi.2017.2757036","title":"An Architecture to Accelerate Convolution in Deep Neural Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Convolutional neural network; Computer science; Latency (audio); Convolution (computer science); Artificial intelligence; Contextual image classification; Process (computing); Deep neural networks; Deep learning; State (computer science); Artificial neural network; Implementation; Computational complexity theory; Pattern recognition (psychology); Low latency (capital markets); Machine learning; Computer engineering; Image (mathematics); Algorithm; Telecommunications","score_opus":0.02093066787534213,"score_gpt":0.2590709766850191,"score_spread":0.238140308809677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766143712","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":[0.03248957,0.0001113774,0.9648808,0.0007979363,0.0007004398,0.00060752156,0.0000044297176,0.00012217958,0.00028573166],"genre_scores_gemma":[0.99884474,0.000031880474,0.00045340217,0.0002938499,0.000091986236,0.00016241832,0.0000013208654,0.000019140452,0.000101278354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843425,0.00011919533,0.0002693389,0.0006088051,0.00019770347,0.00037072183],"domain_scores_gemma":[0.9983119,0.00005687393,0.00011600608,0.0011660734,0.00004413384,0.0003050352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001913802,0.00020726101,0.00023692795,0.00014217867,0.0008044563,0.00046029393,0.0007516089,0.00011262338,0.0000025578192],"category_scores_gemma":[0.000005141077,0.0001955822,0.000053408927,0.00022411208,0.00007953797,0.00049398,0.0000050979024,0.00028157703,0.0000050900817],"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":[0.0000061809005,0.000038544127,0.000044563807,0.000008853586,0.000009059905,0.000009279069,0.00025128445,0.77411187,0.0049601337,0.0016056426,0.000007848769,0.21894673],"study_design_scores_gemma":[0.00047234388,0.00016491006,0.0034337256,0.000053075404,0.00000932343,0.00007477767,0.000063824096,0.9938809,0.000414272,0.00015674064,0.0009314138,0.0003446852],"about_ca_topic_score_codex":0.0000709947,"about_ca_topic_score_gemma":0.00030651657,"teacher_disagreement_score":0.96635514,"about_ca_system_score_codex":0.00006068788,"about_ca_system_score_gemma":0.000017458731,"threshold_uncertainty_score":0.79756105},"labels":[],"label_agreement":null},{"id":"W2767801289","doi":"10.1007/978-3-319-75541-0_8","title":"GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Artificial intelligence; Sørensen–Dice coefficient; Segmentation; Computer science; Hausdorff distance; Pattern recognition (psychology); Preprocessor; Computer vision; Deep learning; Convolutional neural network; Fully automatic; Image segmentation","score_opus":0.0164794992640584,"score_gpt":0.266395270442625,"score_spread":0.24991577117856661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767801289","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":[0.0003414947,0.000112154215,0.99469334,0.0011841339,0.0008016701,0.0020785271,0.000013478412,0.00043277984,0.00034240648],"genre_scores_gemma":[0.0109888585,0.000023132847,0.98712564,0.00070457114,0.0005831203,0.00021910865,0.000038459897,0.000049498183,0.00026760405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634576,0.000033421056,0.00060025504,0.001543874,0.00088262116,0.00059408264],"domain_scores_gemma":[0.9967357,0.0006434403,0.00062269584,0.0014886372,0.0003484733,0.0001610574],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005217862,0.000524891,0.0005117229,0.00041405848,0.00044918864,0.00051178737,0.0020372816,0.00021048343,0.000026557318],"category_scores_gemma":[0.0000522692,0.0004530053,0.000112438225,0.0007049481,0.00061037944,0.0009183252,0.00038020045,0.00034419118,0.000048953094],"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":[0.000006205497,0.000027379954,0.000016146138,0.00012734247,0.00002327793,0.000007328895,0.00073595776,0.018791234,0.00024736332,0.008835666,0.00043052857,0.9707516],"study_design_scores_gemma":[0.0002602094,0.00034922734,0.00010238102,0.00031975866,0.000023126911,0.00002958722,3.2151735e-7,0.91025746,0.0010211571,0.08617377,0.00093797676,0.0005250167],"about_ca_topic_score_codex":0.0000032166324,"about_ca_topic_score_gemma":0.000032533964,"teacher_disagreement_score":0.9702265,"about_ca_system_score_codex":0.00038660344,"about_ca_system_score_gemma":0.000518299,"threshold_uncertainty_score":0.99979216},"labels":[],"label_agreement":null},{"id":"W2768797272","doi":"10.1145/3105925","title":"CaffePresso","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Embedded Computing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Nvidia","keywords":"Computer science; Field-programmable gate array; Embedded system; Class (philosophy); Digital signal processing; Deep learning; Kernel (algebra); Range (aeronautics); Inference; Computer architecture; Deep neural networks; Artificial neural network; Computer engineering; Computer hardware; Artificial intelligence","score_opus":0.03462678056749936,"score_gpt":0.3068834700590013,"score_spread":0.27225668949150195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768797272","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":[0.0053271907,0.00006686382,0.9884014,0.001090082,0.0016796413,0.00038798232,0.0000044182148,0.00067049003,0.0023719326],"genre_scores_gemma":[0.9476745,0.000011298621,0.05135121,0.000112278605,0.00017986582,0.00004983025,0.0000010648816,0.000022828712,0.00059708545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981747,0.00008133902,0.00034549553,0.0006562521,0.00032482756,0.00041733915],"domain_scores_gemma":[0.9945576,0.0003224141,0.0003216078,0.004552587,0.00010007674,0.00014569533],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00022776274,0.0002244423,0.00025369818,0.00010270156,0.0020861595,0.00071472506,0.0037996783,0.00009054175,0.000004009828],"category_scores_gemma":[0.000051693285,0.00022275963,0.00011319542,0.00018645493,0.000084677406,0.0005457716,0.00008336569,0.00031403807,0.00019625784],"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":[0.00001903508,0.00034554326,0.00017818318,0.00006790232,0.00014811117,0.000044806184,0.0015668087,0.58063614,0.0018041054,0.065415636,0.0014975652,0.34827617],"study_design_scores_gemma":[0.0010012772,0.00013832853,0.0010925446,0.00022293793,0.000028091452,0.00016172625,0.00014291423,0.9783536,0.0032418596,0.0055748676,0.009270224,0.0007716067],"about_ca_topic_score_codex":0.000053440486,"about_ca_topic_score_gemma":0.0000056691524,"teacher_disagreement_score":0.94234735,"about_ca_system_score_codex":0.00007298655,"about_ca_system_score_gemma":0.0000331076,"threshold_uncertainty_score":0.999213},"labels":[],"label_agreement":null},{"id":"W2769036406","doi":"10.1109/iccvw.2017.28","title":"Fusing Geometry and Appearance for Road Segmentation","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Mixture model; Pattern recognition (psychology); Computer vision; Image segmentation; Bayesian probability; Gaussian; Adaptation (eye); Sensor fusion","score_opus":0.03195800015579364,"score_gpt":0.32027409383761524,"score_spread":0.2883160936818216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769036406","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":[0.029370317,0.00007308007,0.9679953,0.0014232245,0.00007060871,0.00019726017,5.2431983e-7,0.0000598237,0.0008098963],"genre_scores_gemma":[0.71875924,0.000017141943,0.2806834,0.00019329361,0.000040454855,0.000032937067,4.6658238e-7,0.0000027898193,0.00027028008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999599,0.000003196432,0.00006212581,0.00018512396,0.000049713854,0.000100860976],"domain_scores_gemma":[0.9994773,0.000015642581,0.00006651212,0.0003872575,0.00002338546,0.000029859943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000054716493,0.000043104883,0.0000449428,0.000018566041,0.0004552158,0.00017969515,0.0003049175,0.000013682474,9.363223e-7],"category_scores_gemma":[0.000017188262,0.00003997961,0.000011654681,0.00004140863,0.000029936098,0.00056714826,0.00011470735,0.000022848633,0.0000055803307],"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":[0.0000023621292,0.00000958285,0.00078908505,0.000009016866,0.0000030689762,2.900544e-7,0.000051104977,0.00015914417,0.009826796,0.07535518,0.00023439189,0.91356],"study_design_scores_gemma":[0.0014393887,0.000097235665,0.1279126,0.000044379933,0.000010048497,0.000022766848,0.000039446906,0.73204696,0.042228315,0.0848117,0.010814297,0.0005328788],"about_ca_topic_score_codex":0.000005606987,"about_ca_topic_score_gemma":0.0000043938285,"teacher_disagreement_score":0.9130271,"about_ca_system_score_codex":0.0000074317823,"about_ca_system_score_gemma":0.0000044826243,"threshold_uncertainty_score":0.3501198},"labels":[],"label_agreement":null},{"id":"W2772417463","doi":"10.15353/vsnl.v3i1.169","title":"Effects of Spatial Transformer Location on Segmentation Performance of a Dense Transformer Network","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"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 Waterloo","funders":"","keywords":"Segmentation; Computer science; Transformer; Spatial contextual awareness; Artificial intelligence; Pixel; Spatial analysis; Reuse; Pattern recognition (psychology); Machine learning; Data mining; Voltage; Geography; Remote sensing; Engineering; Electrical engineering","score_opus":0.007615766324722921,"score_gpt":0.2725299803004689,"score_spread":0.26491421397574594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772417463","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":[0.2559127,0.00046088145,0.7426759,0.00033190325,0.0003321065,0.00021376893,8.2046733e-7,0.0000063529956,0.0000656026],"genre_scores_gemma":[0.9907554,0.00011569441,0.008937388,0.00004949778,0.000118252356,0.000004874215,0.0000012406177,0.000006989226,0.000010709501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864846,0.00006113169,0.00061482366,0.000117866926,0.00044190348,0.00011579972],"domain_scores_gemma":[0.99808836,0.00027239433,0.0010003247,0.00015335447,0.00042380168,0.000061768646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034882932,0.00011082976,0.0002615996,0.00011996776,0.00021587373,0.00008255731,0.00030344137,0.000025141971,8.071637e-7],"category_scores_gemma":[0.00002414882,0.00008918953,0.00006660257,0.0001122887,0.00008093435,0.00082999707,0.000012718522,0.000107036474,0.0000010888917],"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":[0.00023126493,0.0001853955,0.004630968,0.0005959048,0.00006093727,0.00000456703,0.0007913157,0.6599933,0.012445047,0.0059806486,0.00023336585,0.3148473],"study_design_scores_gemma":[0.0018544635,0.00063045684,0.14558,0.0015309409,0.00003713909,0.00012855619,0.000030686864,0.8383604,0.009559019,0.0019402795,0.00018703096,0.00016098883],"about_ca_topic_score_codex":0.000010869651,"about_ca_topic_score_gemma":5.789714e-7,"teacher_disagreement_score":0.73484266,"about_ca_system_score_codex":0.00002677202,"about_ca_system_score_gemma":0.000060128383,"threshold_uncertainty_score":0.36370435},"labels":[],"label_agreement":null},{"id":"W2774505043","doi":"10.3141/2645-13","title":"Automated Vehicle Recognition with Deep Convolutional Neural Networks","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Computer vision; Trailer; Feature (linguistics); Pattern recognition (psychology); Frame (networking); Frame rate; Object detection; Feature extraction; Cognitive neuroscience of visual object recognition; Support vector machine; Class (philosophy); Telecommunications","score_opus":0.08932865542654352,"score_gpt":0.37677212112922326,"score_spread":0.28744346570267976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774505043","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":[0.79830843,0.00016767283,0.18992397,0.009509414,0.00055842835,0.0011564383,0.000027554404,0.00017870317,0.00016940462],"genre_scores_gemma":[0.9842066,0.00030684442,0.014821655,0.00008392252,0.0002461331,0.00011883706,0.000019426125,0.000041669155,0.0001549063],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9935553,0.00079662475,0.0010265064,0.0005587074,0.0030823248,0.0009805511],"domain_scores_gemma":[0.99225163,0.0009038487,0.0008817264,0.0012440771,0.00428429,0.00043440587],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0024420572,0.00025916725,0.0003713143,0.00050550594,0.0025976181,0.00055672566,0.0033862283,0.00016777142,0.00006398645],"category_scores_gemma":[0.00015238435,0.00018869842,0.0002500327,0.0014366902,0.001064861,0.0023454963,0.0000295617,0.0022292652,0.000024405746],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046448247,0.0013626919,0.48388925,0.00031575732,0.0006734638,0.0009636055,0.0033184425,0.23328876,0.0054072035,0.041777674,0.015883837,0.20847447],"study_design_scores_gemma":[0.001562471,0.0005039639,0.8054656,0.0001666317,0.000026322094,0.0000033274973,0.00014443346,0.18397778,0.00043872304,0.0057480284,0.0017323561,0.00023038765],"about_ca_topic_score_codex":0.0016357003,"about_ca_topic_score_gemma":0.015329205,"teacher_disagreement_score":0.32157633,"about_ca_system_score_codex":0.00020648997,"about_ca_system_score_gemma":0.00036610037,"threshold_uncertainty_score":0.99870086},"labels":[],"label_agreement":null},{"id":"W2775243158","doi":"10.1109/mfi.2017.8170451","title":"Neural regularization jointly involving neurons and connections for robust image classification","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"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":"Fundação para a Ciência e a Tecnologia; Canadian Institute for Advanced Research","keywords":"Overfitting; Regularization (linguistics); Computer science; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Contextual image classification; Machine learning; Image (mathematics); Dropout (neural networks); Test data; Test set","score_opus":0.0625584126123619,"score_gpt":0.29257884331322276,"score_spread":0.23002043070086087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775243158","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":[0.0033764476,0.000020150554,0.98121744,0.013199328,0.00018158698,0.0004461053,0.000003163918,0.00022067943,0.001335118],"genre_scores_gemma":[0.6204126,0.00003342819,0.37781322,0.000354795,0.000114344766,0.00017468893,0.00001182822,0.000015637936,0.0010694391],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991481,0.000020972073,0.00016641656,0.0004044067,0.00008089696,0.00017922837],"domain_scores_gemma":[0.99859095,0.00013192598,0.0001802013,0.00089077116,0.00013127452,0.000074859214],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00009432498,0.00009943182,0.00008965405,0.000051834853,0.0013749688,0.00053938106,0.0005106182,0.000039356473,0.000002775246],"category_scores_gemma":[0.0002000851,0.000098379045,0.00003233652,0.0000982673,0.0001063416,0.0014424793,0.0001991206,0.00007745851,0.0000051923203],"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":[0.0000035501948,0.000030264468,0.0004634945,0.00001040089,0.000004985193,5.410749e-7,0.000062853236,0.00086906727,0.035395928,0.9374589,0.0018077622,0.023892293],"study_design_scores_gemma":[0.00021441863,0.000030912895,0.023508443,0.0000043690607,0.000006482657,0.000011395301,0.000015545655,0.95139724,0.0013854663,0.022021038,0.0012788493,0.00012581146],"about_ca_topic_score_codex":0.000007635426,"about_ca_topic_score_gemma":0.000035048426,"teacher_disagreement_score":0.9505282,"about_ca_system_score_codex":0.00001977116,"about_ca_system_score_gemma":0.000016735545,"threshold_uncertainty_score":0.9999251},"labels":[],"label_agreement":null},{"id":"W2775632911","doi":"10.15353/vsnl.v3i1.175","title":"Estimating Optimal Depth of VGG Net with Tree-Structured Parzen Estimators","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; University of Toronto","funders":"","keywords":"Estimator; Computer science; Computation; Convolutional neural network; Grid; Tree (set theory); Architecture; Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics; Statistics","score_opus":0.012086793988530622,"score_gpt":0.2977692761548934,"score_spread":0.2856824821663628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775632911","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":[0.10556163,0.00023049048,0.89300007,0.00067463575,0.00029533458,0.00010477686,0.0000016944755,0.000018379484,0.00011300236],"genre_scores_gemma":[0.644113,0.0000020379284,0.3557628,0.000026994006,0.00007937169,0.0000012488442,8.862474e-7,0.0000064967794,0.000007152617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867696,0.000047645342,0.0005274829,0.00015887758,0.00044969373,0.00013933869],"domain_scores_gemma":[0.99761903,0.00020109516,0.0014042308,0.00027270959,0.00038646912,0.000116489115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027879814,0.00013462106,0.0002971159,0.000121388446,0.00033035254,0.00040211953,0.00058745855,0.000021169737,7.427074e-7],"category_scores_gemma":[0.000062366744,0.00009638636,0.00004987349,0.00010112242,0.00012705021,0.0010879631,0.00011764549,0.00014297818,8.395757e-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":[0.000028977503,0.000030704407,0.010942853,0.000041497264,0.00003378413,0.000023329634,0.00023879061,0.92981184,0.00028211437,0.0069634835,0.00059254776,0.051010076],"study_design_scores_gemma":[0.0005972279,0.000098917095,0.056445256,0.00023687622,0.000011932293,0.00084907183,0.000031927757,0.9386606,0.00005270503,0.0027477446,0.00016397274,0.000103763865],"about_ca_topic_score_codex":0.000008703498,"about_ca_topic_score_gemma":8.542945e-7,"teacher_disagreement_score":0.5385514,"about_ca_system_score_codex":0.000022577167,"about_ca_system_score_gemma":0.000080832135,"threshold_uncertainty_score":0.39305216},"labels":[],"label_agreement":null},{"id":"W2776460497","doi":"10.1109/iccv.2017.62","title":"DualNet: Learn Complementary Features for Image Recognition","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":91,"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":"Youth Innovation Promotion Association; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Subnetwork; Artificial intelligence; Extractor; Classifier (UML); Pattern recognition (psychology); Convolutional neural network; Discriminative model; Feature extraction; Contextual image classification; Weighting; Feature (linguistics); Machine learning; Image (mathematics)","score_opus":0.056397756823158424,"score_gpt":0.33585147245131436,"score_spread":0.2794537156281559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776460497","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":[0.0013127846,0.000015699388,0.97743994,0.010012181,0.0001490717,0.0004215783,0.000017394024,0.0001699049,0.010461443],"genre_scores_gemma":[0.12844102,0.000018629387,0.86678725,0.0015382419,0.00023174235,0.00018602377,0.00005808102,0.000012226634,0.0027267637],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993874,0.000009334575,0.00009464206,0.0002569058,0.000078648976,0.0001730563],"domain_scores_gemma":[0.999049,0.00007850901,0.00009215386,0.00067530846,0.0000592047,0.00004581029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007206021,0.00007243139,0.00006820912,0.000020724932,0.0006660469,0.00021910535,0.0007621628,0.000018493178,0.000036819907],"category_scores_gemma":[0.00002482967,0.00006579989,0.0000388526,0.000033131382,0.000044889446,0.00065999624,0.00021925678,0.00005723942,0.00008257059],"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":[0.000011785733,0.000059777318,0.000117245436,0.000011435152,0.000015559785,0.0000032831665,0.0000844763,0.000022445342,0.007270332,0.09257658,0.13844089,0.7613862],"study_design_scores_gemma":[0.0019637817,0.0002528241,0.023946214,0.000027428847,0.000021510261,0.000051481802,0.00006060458,0.027843898,0.061174635,0.5867338,0.2971446,0.00077920523],"about_ca_topic_score_codex":0.000021032449,"about_ca_topic_score_gemma":0.000063459316,"teacher_disagreement_score":0.760607,"about_ca_system_score_codex":0.000012741956,"about_ca_system_score_gemma":0.000007752269,"threshold_uncertainty_score":0.5122762},"labels":[],"label_agreement":null},{"id":"W2776622059","doi":"10.1109/iccv.2017.556","title":"3D Graph Neural Networks for RGBD Semantic Segmentation","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":525,"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":"Unary operation; Computer science; Segmentation; Artificial intelligence; Point cloud; Pattern recognition (psychology); Graph; Representation (politics); Theoretical computer science; Mathematics","score_opus":0.02800042271662722,"score_gpt":0.3040027723263105,"score_spread":0.2760023496096833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776622059","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":[0.0021867338,0.00003029479,0.99381673,0.0020790345,0.00034210327,0.00044765673,8.478986e-7,0.00018337124,0.00091324525],"genre_scores_gemma":[0.76651436,0.000017194368,0.2318545,0.00064484787,0.00016548048,0.00013500561,0.0000060600078,0.00001005691,0.0006525155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919087,0.000011329845,0.00014262839,0.0003146235,0.00009585442,0.00024468784],"domain_scores_gemma":[0.9987601,0.00009285887,0.00014164859,0.00089240883,0.0000487821,0.00006416865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077665114,0.00009861248,0.00009062616,0.000030041354,0.00070414395,0.00027293395,0.0009515956,0.00003272416,0.0000054295438],"category_scores_gemma":[0.000015402216,0.00008849016,0.000055331962,0.00007874023,0.000047416008,0.000755722,0.00018616019,0.000062828294,0.000010746512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015687534,0.00008135717,0.0022096264,0.00001880459,0.000028308905,0.000005518781,0.0000849215,0.11069544,0.0014390004,0.28769863,0.010516556,0.5872061],"study_design_scores_gemma":[0.00024548222,0.00003206271,0.0025291897,0.0000029067858,0.0000051991356,0.0000047096037,0.0000027795581,0.9845051,0.00053472276,0.0111504905,0.0008649966,0.00012235942],"about_ca_topic_score_codex":0.000007700201,"about_ca_topic_score_gemma":0.000036216185,"teacher_disagreement_score":0.87380964,"about_ca_system_score_codex":0.00001195154,"about_ca_system_score_gemma":0.0000078215535,"threshold_uncertainty_score":0.5415777},"labels":[],"label_agreement":null},{"id":"W2777795072","doi":"10.1109/iccv.2017.378","title":"SGN: Sequential Grouping Networks for Instance Segmentation","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":299,"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":"Pascal (unit); Computer science; Pixel; Segmentation; Object (grammar); Image segmentation; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Line segment; Sequence (biology); Line (geometry); Computer vision; Mathematics","score_opus":0.04461782187604923,"score_gpt":0.32672943723873865,"score_spread":0.2821116153626894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2777795072","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":[0.0013919187,0.00003052329,0.99457633,0.0012224037,0.00038081792,0.0003758547,8.640251e-7,0.00015630349,0.0018649637],"genre_scores_gemma":[0.63839513,0.000018944156,0.36038706,0.00041491244,0.00020300837,0.00013536388,0.0000034810296,0.000006764133,0.00043535294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927884,0.000008707369,0.00013187768,0.00028831256,0.00008702519,0.00020524552],"domain_scores_gemma":[0.9989635,0.000054237866,0.00015227705,0.00073211064,0.00004987115,0.00004804412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008789895,0.000079196805,0.00007389667,0.000017651491,0.00075065234,0.00028476366,0.0008691281,0.000030445713,0.0000038510925],"category_scores_gemma":[0.000016449963,0.000076854645,0.000038783943,0.000060068767,0.000038629845,0.0010255615,0.00018896474,0.000054888424,0.000009278036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009101947,0.000025686197,0.00042827637,0.0000076260294,0.00001151967,0.0000016397165,0.000068536036,0.028854284,0.0025776364,0.7392978,0.002344064,0.22637384],"study_design_scores_gemma":[0.00038762484,0.0000265586,0.0014024805,0.0000092847295,0.0000035748858,0.00000382261,0.0000056935382,0.97312343,0.0025217624,0.01695435,0.005397142,0.00016428887],"about_ca_topic_score_codex":0.0000057173265,"about_ca_topic_score_gemma":0.00003047163,"teacher_disagreement_score":0.9442691,"about_ca_system_score_codex":0.0000321697,"about_ca_system_score_gemma":0.000012888194,"threshold_uncertainty_score":0.57734865},"labels":[],"label_agreement":null},{"id":"W2780149736","doi":"10.1109/iccv.2017.305","title":"Centered Weight Normalization in Accelerating Training of Deep Neural Networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":76,"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":"Beihang University; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Normalization (sociology); Artificial neural network; Convolutional neural network; Artificial intelligence; Scalability; Deep learning; Norm (philosophy); Deep neural networks; Perceptron; Curvature; Machine learning; Mathematical optimization; Mathematics","score_opus":0.05577867744953627,"score_gpt":0.2934820380910469,"score_spread":0.23770336064151062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2780149736","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":[0.02679167,0.000026560007,0.9687092,0.0004418173,0.00014401646,0.00013605882,1.987858e-7,0.00005899911,0.0036915375],"genre_scores_gemma":[0.9452975,0.000008900621,0.054459047,0.00012380246,0.00006337147,0.000014247738,0.0000023907978,0.000005920024,0.000024865249],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917156,0.000024571225,0.00025985908,0.00022708449,0.000098966404,0.00021795431],"domain_scores_gemma":[0.999033,0.000053752345,0.0002265935,0.00060467847,0.00004029381,0.00004168825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009900183,0.000083515064,0.00012199092,0.000046381745,0.0002221086,0.00011262138,0.00091277383,0.000035167544,0.000009668334],"category_scores_gemma":[0.00003077797,0.000078973244,0.000027955937,0.00015753119,0.000036356174,0.0009569505,0.00025308682,0.000095962976,0.0000012809767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006719206,0.00005040185,0.029946059,0.000007739449,0.000005810695,0.0000065170625,0.0009862406,0.2821574,0.00096218643,0.1273671,0.000040207844,0.55846363],"study_design_scores_gemma":[0.00020699187,0.000011185874,0.020263903,0.0000077400555,8.429042e-7,0.0000031658512,0.000013691317,0.9780045,0.00044653183,0.00091875246,0.000044801945,0.00007789781],"about_ca_topic_score_codex":0.000015037505,"about_ca_topic_score_gemma":0.00016098708,"teacher_disagreement_score":0.9185058,"about_ca_system_score_codex":0.00001461571,"about_ca_system_score_gemma":0.00000623334,"threshold_uncertainty_score":0.32204354},"labels":[],"label_agreement":null},{"id":"W2782995645","doi":"10.48550/arxiv.1801.02722","title":"End-to-end detection-segmentation network with ROI convolution","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Artificial intelligence; End-to-end principle; Code (set theory); Convolutional neural network; Pattern recognition (psychology); Task (project management); Convolution (computer science); Computer vision; Scale-space segmentation; Segmentation-based object categorization; Region of interest; Image segmentation; Deep learning; Object (grammar); Artificial neural network","score_opus":0.04461937111463344,"score_gpt":0.19570714320851812,"score_spread":0.1510877720938847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782995645","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":[0.07850857,0.000024402903,0.91889536,0.000121073266,0.0005082917,0.0006749092,0.0000074850063,0.00040006248,0.0008598201],"genre_scores_gemma":[0.97076976,0.00005311062,0.027918784,0.00019992967,0.0003499043,0.000013375371,0.000026788577,0.000024144309,0.0006441892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787396,0.00010726338,0.00019594615,0.0012607535,0.00013302166,0.00042905178],"domain_scores_gemma":[0.997902,0.00010802429,0.00030646738,0.0012275177,0.0002419649,0.00021402398],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014617639,0.0003113744,0.00023662289,0.0001789714,0.00039543235,0.000105659376,0.0011645823,0.00017915982,0.00003134619],"category_scores_gemma":[0.000009557268,0.00034814267,0.00008848875,0.0013298242,0.00013926886,0.0004655769,0.0010463181,0.0004013235,0.00023162641],"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":[0.000057512367,0.00004071163,0.001032584,0.000015363548,0.000059896996,0.000029243618,0.00009090098,0.9529059,0.00021363495,0.040817097,0.00056329416,0.0041738683],"study_design_scores_gemma":[0.00081517326,0.00037724178,0.006999304,0.00014799947,0.00014215104,0.00002312207,0.000048242087,0.9064944,0.0023781012,0.07672223,0.0047006384,0.0011514374],"about_ca_topic_score_codex":0.00006616187,"about_ca_topic_score_gemma":0.00029055626,"teacher_disagreement_score":0.8922612,"about_ca_system_score_codex":0.00038585844,"about_ca_system_score_gemma":0.00011543412,"threshold_uncertainty_score":0.99989706},"labels":[],"label_agreement":null},{"id":"W2783436923","doi":"10.1016/j.patcog.2018.12.009","title":"From superpixel to human shape modelling for carried object detection","year":2018,"lang":"en","type":"preprint","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"","keywords":"Codebook; Artificial intelligence; Computer science; Frame (networking); Pattern recognition (psychology); Computer vision; Object (grammar); Complement (music); Matching (statistics); Set (abstract data type); Object detection; Enhanced Data Rates for GSM Evolution; Scale (ratio); Mathematics; Geography","score_opus":0.08964945454133784,"score_gpt":0.31162089409145166,"score_spread":0.22197143955011384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783436923","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":[0.25529858,0.000018071987,0.7418231,0.00018389012,0.0007876357,0.0011915304,0.0002783091,0.0003458716,0.00007303949],"genre_scores_gemma":[0.8904663,0.000012723663,0.104384676,0.0004955374,0.0018814019,0.0018356794,0.0008540204,0.00005493903,0.000014754783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976756,0.00006642158,0.00042469945,0.0012317749,0.00023720523,0.00036431337],"domain_scores_gemma":[0.9982944,0.00015638101,0.00022841204,0.00084881106,0.00032535515,0.00014664403],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016031979,0.00032900219,0.00029213086,0.00017932766,0.00031748597,0.00023620362,0.00078812137,0.00025934997,0.000034088192],"category_scores_gemma":[0.000021869595,0.0003826439,0.00016566087,0.00019555731,0.000025241701,0.00024967518,0.0006205087,0.000348249,0.00022120934],"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":[0.000026500074,0.00007615584,0.000051983643,0.00009097633,0.000065110056,0.0000019304293,0.001074783,0.017591149,0.013821349,0.000019034578,0.0003644981,0.96681654],"study_design_scores_gemma":[0.00026912728,0.00011462233,0.00013368565,0.00020265499,0.00004455859,0.0000023805844,0.000015152258,0.8553284,0.033318166,0.109520316,0.00047730142,0.00057363766],"about_ca_topic_score_codex":0.00025386928,"about_ca_topic_score_gemma":0.00018053816,"teacher_disagreement_score":0.9662429,"about_ca_system_score_codex":0.00015447757,"about_ca_system_score_gemma":0.000030805822,"threshold_uncertainty_score":0.99986255},"labels":[],"label_agreement":null},{"id":"W2783633361","doi":"10.1016/j.patcog.2018.01.004","title":"Handwritten digit segmentation: Is it still necessary?","year":2018,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":40,"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":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Nvidia","keywords":"Segmentation; Computer science; Heuristics; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Heuristic; Set (abstract data type); Image segmentation; Numerical digit; Scale-space segmentation; Handwriting recognition; String (physics); Digit recognition; NIST; Artificial neural network; Speech recognition; Feature extraction; Mathematics","score_opus":0.049838475596756124,"score_gpt":0.3014594936773087,"score_spread":0.25162101808055254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783633361","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":[0.03771198,0.000017010656,0.9518933,0.0060401405,0.00027696486,0.00025786986,0.00002777263,0.0002010515,0.0035738922],"genre_scores_gemma":[0.9631815,0.000051131537,0.024591954,0.010903547,0.00061056734,0.00014038244,0.00009229425,0.00001727632,0.00041133165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989409,0.000029939862,0.0002034668,0.0004022629,0.00020053968,0.00022287926],"domain_scores_gemma":[0.99922776,0.00006464247,0.00010872768,0.0003584753,0.00016087119,0.00007953326],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005508871,0.00012273749,0.0000916125,0.000061519924,0.00015228568,0.00014348234,0.0003670696,0.000043045842,0.00035389588],"category_scores_gemma":[0.000009252793,0.00012434175,0.00003928084,0.00033099187,0.000053913267,0.00080114586,0.00011984396,0.00008324471,0.0026023334],"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":[0.0000028289749,0.000037585098,0.0008974956,0.0000069085654,0.000009030105,0.0000026784592,0.0005550293,0.0000024660872,0.00076467945,0.000026796104,0.01118798,0.9865065],"study_design_scores_gemma":[0.0040538157,0.0011737023,0.019379936,0.00047904492,0.00011457942,0.00027631043,0.00054158684,0.117854774,0.5572606,0.13134933,0.16457368,0.0029426625],"about_ca_topic_score_codex":0.0000101326905,"about_ca_topic_score_gemma":0.00001880846,"teacher_disagreement_score":0.98356384,"about_ca_system_score_codex":0.00003244643,"about_ca_system_score_gemma":0.000012260538,"threshold_uncertainty_score":0.99817425},"labels":[],"label_agreement":null},{"id":"W2785318946","doi":"10.48550/arxiv.1802.00565","title":"Detecting Zones And Threat On 3D Body In Security Airports Using Deep Learning Machine","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"","keywords":"Computer science; Computer security; Border Security; Deep learning; Artificial intelligence; Business; International trade","score_opus":0.050088849492446234,"score_gpt":0.21321697739269277,"score_spread":0.16312812790024653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785318946","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":[0.5851789,0.00006851013,0.4139538,0.000016485286,0.00011052897,0.00018939214,8.914349e-7,0.0001405541,0.00034088784],"genre_scores_gemma":[0.9901106,0.00016201325,0.0095228525,0.00003896935,0.00007672297,0.0000012168862,0.000004319071,0.000019811647,0.00006354318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808383,0.00013011404,0.00018953207,0.0011601386,0.000077601486,0.00035878166],"domain_scores_gemma":[0.99862576,0.00016777126,0.0002820019,0.00074253936,0.00006667718,0.00011526696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021368277,0.000288911,0.0002809449,0.0002648436,0.00035309413,0.00007482651,0.00068496773,0.00018919923,0.000006064578],"category_scores_gemma":[0.000038229577,0.00034365375,0.00006518692,0.00059826847,0.000111982525,0.0003178923,0.0018243577,0.0008715101,0.000010501745],"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":[0.000034128807,0.000087237786,0.053421162,0.00005822675,0.000029370683,0.00034886086,0.0004927109,0.91923624,0.000098584096,0.019169562,0.0000031036254,0.0070207887],"study_design_scores_gemma":[0.00020661762,0.000048801146,0.00306022,0.00010280552,0.000018294,0.000015684604,0.000022515776,0.94523156,0.00012919646,0.050785184,0.000055525652,0.0003235891],"about_ca_topic_score_codex":0.00014051866,"about_ca_topic_score_gemma":0.00019025618,"teacher_disagreement_score":0.4049316,"about_ca_system_score_codex":0.00018957013,"about_ca_system_score_gemma":0.000035059264,"threshold_uncertainty_score":0.99990153},"labels":[],"label_agreement":null},{"id":"W2785493516","doi":"10.1109/crv.2017.32","title":"A Structured Deep-Learning Based Approach for the Automated Segmentation of Human Leg Muscle from 3D MRI","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Convolutional neural network; Pattern recognition (psychology); Computer vision; Voxel; Deep learning; Image segmentation; Initialization; Parametric statistics; Mathematics","score_opus":0.0274390039629281,"score_gpt":0.3038909637683829,"score_spread":0.27645195980545484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785493516","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":[0.007953057,0.000016655784,0.99048644,0.0002989959,0.00004759135,0.00051043404,0.0000038994644,0.00030547008,0.0003774421],"genre_scores_gemma":[0.59689116,9.051164e-7,0.40286773,0.000058781417,0.000026756068,0.00008254119,0.000022549666,0.0000058180563,0.000043727225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992573,0.000031835167,0.0001631834,0.00027297193,0.0001349224,0.00013978424],"domain_scores_gemma":[0.99865097,0.00018981026,0.00026416883,0.00079961604,0.00006498234,0.000030444275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000099311175,0.000088788656,0.000100970756,0.000022776167,0.0008399694,0.00013535481,0.0009770103,0.00003447592,0.000012230648],"category_scores_gemma":[0.000027017275,0.000063487714,0.000044386852,0.00008176106,0.000063892236,0.00028854923,0.00012779265,0.00007689525,0.0000014246264],"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":[0.000019396974,0.000118838296,0.0012054407,0.00003039628,0.000070280854,5.907177e-7,0.0010814733,0.6062269,0.16734669,0.027816234,0.0008799997,0.19520375],"study_design_scores_gemma":[0.00037897227,0.000026317211,0.016345257,0.0000027180638,0.000009657329,1.7983047e-7,0.000038408813,0.97114253,0.010539442,0.0012255649,0.0002150045,0.000075973876],"about_ca_topic_score_codex":0.00006932542,"about_ca_topic_score_gemma":0.000033075794,"teacher_disagreement_score":0.5889381,"about_ca_system_score_codex":0.000017122413,"about_ca_system_score_gemma":0.000012625855,"threshold_uncertainty_score":0.646045},"labels":[],"label_agreement":null},{"id":"W2786263712","doi":"","title":"Kronecker-factored Curvature Approximations for Recurrent Neural Networks","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Toronto","funders":"","keywords":"Kronecker delta; Recurrent neural network; Parameterized complexity; Computer science; Algorithm; Covariance; Gaussian; Inverse; Artificial neural network; Applied mathematics; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.060654942516671785,"score_gpt":0.3641504852356096,"score_spread":0.3034955427189378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786263712","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":[0.0041929656,0.000015656362,0.9742188,0.009353999,0.0012574967,0.0005722903,0.000023152495,0.00029641986,0.010069221],"genre_scores_gemma":[0.95713454,0.00002181044,0.03917479,0.00029487436,0.0006466597,0.00041485718,0.00021007707,0.000020099593,0.0020823034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823016,0.000088150875,0.00035112837,0.00065456174,0.0003598285,0.00031615281],"domain_scores_gemma":[0.9978947,0.00040965967,0.00026402425,0.00051719835,0.0007961817,0.0001182371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014098003,0.00019722013,0.00014766134,0.00018276641,0.00055844407,0.00033208184,0.0010642566,0.000084091655,0.000166379],"category_scores_gemma":[0.00036400728,0.00019897973,0.00010883527,0.00045718582,0.00014018125,0.0006195631,0.00018248928,0.00041255658,0.000088526765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000657004,0.0001845354,0.0013398101,0.000004816306,0.00008321337,0.0000020424598,0.0011662261,0.054346666,0.00074938143,0.83758146,0.004697937,0.0997782],"study_design_scores_gemma":[0.00032799612,0.00019294389,0.0020177292,0.000020548927,0.000007820992,0.0000075572457,0.00007149953,0.96812284,0.00020734667,0.010158647,0.01865998,0.00020508732],"about_ca_topic_score_codex":0.0000063498223,"about_ca_topic_score_gemma":0.00001815146,"teacher_disagreement_score":0.95294154,"about_ca_system_score_codex":0.000078959354,"about_ca_system_score_gemma":0.000053594915,"threshold_uncertainty_score":0.8114158},"labels":[],"label_agreement":null},{"id":"W2786406308","doi":"","title":"Espresso: Efficient Forward Propagation for Binary Deep Neural Networks","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; CUDA; Convolutional neural network; Memory footprint; Parallel computing; Binary number; Deep learning; Artificial neural network; Matrix multiplication; Artificial intelligence; Operating system; Arithmetic","score_opus":0.0384290167936386,"score_gpt":0.3438460892558937,"score_spread":0.30541707246225513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786406308","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":[0.013045955,0.000011972543,0.9729536,0.005693659,0.0008672475,0.0006108079,0.0000051618376,0.00032999052,0.0064816023],"genre_scores_gemma":[0.980784,0.000008944166,0.016835574,0.00029186334,0.00049053127,0.00046945523,0.000088445806,0.000019697805,0.0010114899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824566,0.00008964004,0.0003332213,0.00063595775,0.0003863193,0.00030918457],"domain_scores_gemma":[0.99800444,0.00039898077,0.0002519253,0.00045345526,0.0007881204,0.00010305957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017340662,0.00017568792,0.00013077083,0.00018896915,0.0005511699,0.0002819451,0.0009481361,0.00006542169,0.00008407126],"category_scores_gemma":[0.00030606618,0.00017640645,0.00009498589,0.0003950857,0.00014158209,0.00038628068,0.00021230278,0.0002686082,0.00007888459],"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":[0.00005414596,0.00010009323,0.00048754652,0.0000029530836,0.00003063111,0.0000023668063,0.00056356576,0.6278361,0.0015772459,0.33255675,0.0004935177,0.03629505],"study_design_scores_gemma":[0.00035404981,0.00021268427,0.0018607627,0.000016682947,0.0000072954376,0.000008115101,0.00006966018,0.9901053,0.00059966976,0.0044984557,0.0020872108,0.00018013208],"about_ca_topic_score_codex":0.000008812057,"about_ca_topic_score_gemma":0.000009404709,"teacher_disagreement_score":0.96773803,"about_ca_system_score_codex":0.000079010424,"about_ca_system_score_gemma":0.00003617492,"threshold_uncertainty_score":0.71936464},"labels":[],"label_agreement":null},{"id":"W2786593326","doi":"10.1109/icip.2018.8451100","title":"Deep Feature Compression for Collaborative Object Detection","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Lossy compression; Computer science; Lossless compression; Feature (linguistics); Overhead (engineering); Data compression; Cloud computing; Focus (optics); Mobile device; Artificial intelligence; Compression (physics); Object detection; Real-time computing; Object (grammar); Computer vision; Computer engineering; Pattern recognition (psychology)","score_opus":0.022474779539808917,"score_gpt":0.29955622725362074,"score_spread":0.2770814477138118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786593326","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":[0.00020077468,0.00020045266,0.9919719,0.0010975713,0.0010500635,0.0016615972,0.000013717375,0.0005295324,0.0032743767],"genre_scores_gemma":[0.1027719,0.000058895384,0.89269495,0.00045046076,0.0007531678,0.0014363388,0.000058401696,0.00003282527,0.0017430837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843967,0.000059607704,0.00019335163,0.00085626286,0.00019047801,0.00026061558],"domain_scores_gemma":[0.9978923,0.00022786921,0.00025648094,0.0010375576,0.0004992943,0.00008647728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009739154,0.00026326982,0.00024763984,0.00008747989,0.0003112261,0.00017603108,0.0010459356,0.0003061494,0.000007599257],"category_scores_gemma":[0.00003557201,0.00022649326,0.00009871999,0.00043893725,0.000058276288,0.00020628265,0.0011573536,0.00036984336,0.000029514438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003083853,0.000447612,0.000055820215,0.00048660103,0.000324452,0.0000080488035,0.0018316937,0.08124622,0.029152565,0.13488333,0.2066472,0.54460806],"study_design_scores_gemma":[0.0003148071,0.00014551637,0.00013215833,0.000067906854,0.000018531728,0.000005789146,0.000032323795,0.81142205,0.06010797,0.072112486,0.0551472,0.0004932626],"about_ca_topic_score_codex":0.000003922824,"about_ca_topic_score_gemma":0.00013261555,"teacher_disagreement_score":0.73017585,"about_ca_system_score_codex":0.00015446237,"about_ca_system_score_gemma":0.00008315936,"threshold_uncertainty_score":0.92361265},"labels":[],"label_agreement":null},{"id":"W2786615446","doi":"10.48550/arxiv.1803.02021","title":"Understanding Short-Horizon Bias in Stochastic Meta-Optimization","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Horizon; Hyperparameter; Computer science; Benchmark (surveying); Time horizon; Artificial neural network; Optimization problem; Meta learning (computer science); Mathematical optimization; Machine learning; Algorithm; Mathematics; Economics","score_opus":0.4050751399677809,"score_gpt":0.24767573840222354,"score_spread":0.15739940156555735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786615446","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":[0.0046439664,0.00004345205,0.9932688,0.00011440999,0.00037328489,0.0004648725,0.000008966994,0.00028239976,0.00079983013],"genre_scores_gemma":[0.98315054,0.000081879,0.01641902,0.00004383596,0.0000765723,0.00000584771,0.000021466676,0.00002348634,0.00017733616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979099,0.000137742,0.00025814716,0.0012125614,0.00011161624,0.00037001926],"domain_scores_gemma":[0.99814785,0.00021759898,0.00020868743,0.0011958657,0.00010019571,0.00012980578],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023565069,0.00032516342,0.00039767267,0.00040727886,0.00016784381,0.000099718316,0.00143729,0.00022865421,0.000033957098],"category_scores_gemma":[0.000039308354,0.00036809212,0.00018836775,0.0013140347,0.00012710357,0.0004466977,0.0014361995,0.0004897943,0.000041512474],"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":[0.0000073011715,0.00003157696,0.00007152478,0.000007881702,0.0000814192,0.000027313401,0.000036635964,0.9013765,0.000003571837,0.098190404,0.000112233414,0.000053675296],"study_design_scores_gemma":[0.00013277482,0.00003902005,0.000025293024,0.000030737232,0.00016859786,0.0000021959488,0.000021069836,0.91492224,0.000017445374,0.0842874,0.000031602438,0.00032162998],"about_ca_topic_score_codex":0.000020621219,"about_ca_topic_score_gemma":0.00007156659,"teacher_disagreement_score":0.97850657,"about_ca_system_score_codex":0.0006539043,"about_ca_system_score_gemma":0.00010341708,"threshold_uncertainty_score":0.9998771},"labels":[],"label_agreement":null},{"id":"W2786618695","doi":"10.1109/fpt.2017.8280150","title":"FPGA-based training of convolutional neural networks with a reduced precision floating-point library","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"MNIST database; Adder; Convolutional neural network; Computer science; Field-programmable gate array; Floating point; Double-precision floating-point format; Lookup table; IEEE floating point; Single-precision floating-point format; Artificial neural network; Artificial intelligence; Bandwidth (computing); Computer hardware; Parallel computing; Algorithm; Latency (audio)","score_opus":0.033905541623292464,"score_gpt":0.2668299662184518,"score_spread":0.23292442459515933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786618695","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":[0.035084218,0.000030029663,0.9584365,0.0027434672,0.000117210046,0.0002755346,0.0000022737806,0.000224723,0.00308608],"genre_scores_gemma":[0.74003154,0.0000018149116,0.25945428,0.00025358584,0.0000802146,0.000019775103,0.0000057746793,0.000011610752,0.00014141056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987695,0.00003974983,0.00027196665,0.0004071094,0.00024051266,0.00027114083],"domain_scores_gemma":[0.9981553,0.0002524364,0.00033966196,0.0010765825,0.00006536818,0.00011062718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011007733,0.0001452433,0.0001864232,0.00005180015,0.00041925456,0.00015126231,0.0012385496,0.000049653056,0.000030624527],"category_scores_gemma":[0.000042008447,0.0001142165,0.00005753902,0.00018805859,0.0001705559,0.0011770271,0.00028444495,0.00016424508,0.0000032002556],"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":[0.00015643182,0.00014748616,0.0016531205,0.000014836258,0.000027675846,0.000015875552,0.00029737788,0.711177,0.0032864574,0.08292878,0.0023498586,0.1979451],"study_design_scores_gemma":[0.0004766889,0.000120912424,0.006126243,0.00004305112,0.000003948278,0.000011811417,0.00000966265,0.9862113,0.004448043,0.0021855417,0.00020814859,0.00015462177],"about_ca_topic_score_codex":0.000005559003,"about_ca_topic_score_gemma":0.00000395014,"teacher_disagreement_score":0.70494735,"about_ca_system_score_codex":0.0000124453845,"about_ca_system_score_gemma":0.000096049655,"threshold_uncertainty_score":0.46576136},"labels":[],"label_agreement":null},{"id":"W2787094505","doi":"10.1109/fpt.2017.8280142","title":"An FPGA-based processor for training convolutional neural networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Scalability; Convolutional neural network; Computation; Process (computing); Parallel computing; Speedup; Computer architecture; Embedded system; Artificial intelligence; Operating system; Programming language","score_opus":0.06517504108062533,"score_gpt":0.3329627680702296,"score_spread":0.2677877269896043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787094505","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":[0.0026454786,0.000017249318,0.9928249,0.0031780277,0.00017768909,0.0003668441,0.0000034074828,0.00026296158,0.0005234109],"genre_scores_gemma":[0.81333065,5.3226273e-7,0.185164,0.0009253085,0.00022746717,0.00020628223,0.000010460051,0.000009249707,0.0001260404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989855,0.000013177361,0.0001511011,0.000401974,0.0001230192,0.0003252274],"domain_scores_gemma":[0.9986255,0.00013121513,0.00014575015,0.0008595279,0.00011329751,0.00012470635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012596165,0.00011379596,0.000111673486,0.000025799418,0.0009300033,0.0002789743,0.0014583125,0.00004536343,0.0000070937913],"category_scores_gemma":[0.00004113958,0.00010379065,0.000050745533,0.00007349765,0.00009440091,0.00088692555,0.00007252806,0.000089456065,0.0000036027836],"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":[0.00003046493,0.000111170935,0.00063785166,0.000011425458,0.00000980368,0.0000025753347,0.00010358914,0.43702888,0.0005106193,0.31725335,0.001450381,0.24284989],"study_design_scores_gemma":[0.0003707381,0.000065481756,0.002068219,0.0000035015703,0.0000026122893,0.0000032094872,0.000004652922,0.98844683,0.00029081866,0.0069591943,0.0016415595,0.00014315602],"about_ca_topic_score_codex":0.0000028643105,"about_ca_topic_score_gemma":0.000018408302,"teacher_disagreement_score":0.81068516,"about_ca_system_score_codex":0.000017707103,"about_ca_system_score_gemma":0.00005843695,"threshold_uncertainty_score":0.71529275},"labels":[],"label_agreement":null},{"id":"W2787217782","doi":"","title":"Decoupling the Layers in Residual Networks","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Residual; Computer science; Algorithm; Parallelizable manifold; Decoupling (probability); Scaling; Perturbation (astronomy); Mathematical optimization; Mathematics; Geometry; Physics; Engineering","score_opus":0.050020908066392776,"score_gpt":0.3584311460717074,"score_spread":0.30841023800531464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787217782","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":[0.054731186,0.000022107306,0.84840524,0.028820576,0.00088170497,0.0003517649,0.000001770609,0.0002860162,0.06649962],"genre_scores_gemma":[0.99378306,0.00004172962,0.0040326114,0.0004586427,0.00032953618,0.00008836029,0.000010791137,0.00000905284,0.001246242],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987383,0.000092259674,0.00024741358,0.00039807038,0.00030444405,0.00021951397],"domain_scores_gemma":[0.9987151,0.00046026762,0.00012868328,0.0004126704,0.00023669015,0.00004655872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023059947,0.0001065311,0.00007862597,0.00013915298,0.00033559735,0.00022750096,0.0011177474,0.00004090983,0.00013247538],"category_scores_gemma":[0.00022845922,0.000089655034,0.000034426077,0.0004976898,0.000142491,0.00034882867,0.0002150106,0.00042259382,0.00015525629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001933785,0.000052303036,0.0122449305,5.950596e-7,0.000020983856,0.000008079525,0.0013916291,0.2369248,0.00038649526,0.7262277,0.0012079388,0.021515235],"study_design_scores_gemma":[0.00020176635,0.00004902522,0.023470566,0.000024800107,0.000001999971,0.000008942327,0.00023483408,0.95872176,0.00019894223,0.013522283,0.0034410397,0.00012403223],"about_ca_topic_score_codex":0.00004901836,"about_ca_topic_score_gemma":0.00012419058,"teacher_disagreement_score":0.93905187,"about_ca_system_score_codex":0.000059146445,"about_ca_system_score_gemma":0.000050213926,"threshold_uncertainty_score":0.3656026},"labels":[],"label_agreement":null},{"id":"W2787522986","doi":"","title":"Bit-Regularized Optimization of Neural Nets","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"MNIST database; Artificial neural network; Range (aeronautics); Computer science; Integer (computer science); Regularization (linguistics); Algorithm; Set (abstract data type); Function (biology); Mathematics; Artificial intelligence; Mathematical optimization","score_opus":0.016052343021133165,"score_gpt":0.2616291623522803,"score_spread":0.2455768193311471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787522986","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":[0.0031003454,0.00000934564,0.9918566,0.0012157154,0.00010609394,0.00011537734,3.1515208e-7,0.00015177627,0.003444473],"genre_scores_gemma":[0.42190424,0.0000027984483,0.5772992,0.00037181063,0.00004700189,0.000007399166,0.0000014118771,0.0000040567857,0.00036208495],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942213,0.000017995177,0.00014379404,0.00018769057,0.00010649881,0.00012187132],"domain_scores_gemma":[0.999309,0.00003740062,0.00006947561,0.00043683397,0.00010730699,0.000039962182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000041985782,0.000058613485,0.00007471318,0.0000406708,0.00006122379,0.000018802375,0.00043444324,0.00002354681,0.000047248053],"category_scores_gemma":[0.0000118723365,0.000050653063,0.000023927065,0.00047546794,0.00007246661,0.00028601216,0.00013637412,0.000030715855,0.000023720884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000178559,0.000118477954,0.00029769115,0.0000064134856,0.000015192011,0.0000014878129,0.00023913516,0.31342903,0.028469699,0.57308286,0.0054964772,0.07882565],"study_design_scores_gemma":[0.0001364804,0.00004533712,0.00024285461,0.0000017420102,0.0000013050831,0.000003422034,0.0000013126019,0.9844195,0.011194594,0.002837144,0.0010565433,0.000059735343],"about_ca_topic_score_codex":0.000003940954,"about_ca_topic_score_gemma":0.000002655117,"teacher_disagreement_score":0.6709905,"about_ca_system_score_codex":0.000006847375,"about_ca_system_score_gemma":0.000011118348,"threshold_uncertainty_score":0.2065572},"labels":[],"label_agreement":null},{"id":"W2788701429","doi":"10.48550/arxiv.1802.06488","title":"Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Object detection; Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Object (grammar); Stack (abstract data type); Artificial neural network; Feature (linguistics); Computer vision; Pattern recognition (psychology)","score_opus":0.06501005214834311,"score_gpt":0.21298833360981217,"score_spread":0.14797828146146907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788701429","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":[0.1583416,0.000031771044,0.8376418,0.000048010013,0.0011317512,0.0011207374,0.00002296411,0.00081770186,0.0008436709],"genre_scores_gemma":[0.98255795,0.000047183858,0.015283737,0.000097698154,0.0011172433,0.000036054837,0.00007391876,0.000055408607,0.0007307916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658716,0.00021634181,0.00041357335,0.0018273221,0.00016797775,0.00078761135],"domain_scores_gemma":[0.9967637,0.00044254397,0.00065752486,0.0013999023,0.00049116707,0.00024517765],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030482555,0.0005265298,0.000457039,0.00025808503,0.0007714739,0.00015470559,0.0014366885,0.00047237432,0.000027698958],"category_scores_gemma":[0.00007027522,0.0006576934,0.00037301797,0.0012038568,0.0002354304,0.0005698698,0.0011430891,0.0005819815,0.00013147912],"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":[0.00031426764,0.00016252323,0.00013632336,0.000063448046,0.00013764093,0.000029039455,0.00014096977,0.9707054,0.013106094,0.007355765,0.0005870641,0.0072614835],"study_design_scores_gemma":[0.0005057296,0.00027583257,0.0005052612,0.00004252222,0.000100869525,0.000026420274,0.000014856216,0.9550398,0.0030035635,0.03914312,0.000697043,0.0006449942],"about_ca_topic_score_codex":0.000050266284,"about_ca_topic_score_gemma":0.00024669804,"teacher_disagreement_score":0.82421637,"about_ca_system_score_codex":0.0006191434,"about_ca_system_score_gemma":0.00012522211,"threshold_uncertainty_score":0.9995874},"labels":[],"label_agreement":null},{"id":"W2789613043","doi":"10.1109/globalsip.2017.8309176","title":"Activation pruning of deep convolutional neural networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Pruning; Convolutional neural network; Implementation; Latency (audio); Deep neural networks; Constraint (computer-aided design); Artificial intelligence; Deep learning; Machine learning; Telecommunications","score_opus":0.025078401893581764,"score_gpt":0.28115620293426347,"score_spread":0.2560778010406817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789613043","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":[0.008856973,0.000014291676,0.9864121,0.0011126985,0.00010587191,0.000098087694,1.7383591e-7,0.00007235047,0.00332746],"genre_scores_gemma":[0.9419401,0.0000029353725,0.057719532,0.00013304995,0.000072110146,0.000012312153,0.0000017854679,0.0000035063679,0.0001146228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994248,0.000012559263,0.00013110663,0.0001790891,0.00011632661,0.00013612266],"domain_scores_gemma":[0.9989556,0.00008068589,0.0001988214,0.0006513094,0.00007482423,0.00003874902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006028931,0.00006082187,0.00007383862,0.000023050574,0.00034070716,0.0000614118,0.00079494156,0.000029258725,0.000011434441],"category_scores_gemma":[0.0000354664,0.00005644413,0.0000284868,0.0000780082,0.000074950345,0.0007672514,0.00024768864,0.000079083875,0.0000038649086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051211564,0.00003193637,0.0036644987,0.00000294532,0.000008424755,6.335136e-7,0.000043222706,0.15910974,0.002323018,0.7182547,0.00029059764,0.116265155],"study_design_scores_gemma":[0.00010141076,0.000011468109,0.031817254,0.0000031379125,0.0000010445075,0.0000023716295,0.000002215735,0.96143454,0.0013014985,0.005024415,0.00023988126,0.000060729748],"about_ca_topic_score_codex":0.0000092911,"about_ca_topic_score_gemma":0.0000058696914,"teacher_disagreement_score":0.9330832,"about_ca_system_score_codex":0.00001479087,"about_ca_system_score_gemma":0.000008920207,"threshold_uncertainty_score":0.26204786},"labels":[],"label_agreement":null},{"id":"W2791533555","doi":"10.1109/isbi.2018.8363651","title":"Isointense infant brain segmentation with a hyper-dense connected convolutional neural network","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Segmentation; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Deep learning; Image segmentation; White matter; Artificial neural network; Magnetic resonance imaging; Machine learning; Computer vision","score_opus":0.015775017759898268,"score_gpt":0.2567186635196439,"score_spread":0.24094364575974564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791533555","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":[0.124238454,0.000058882633,0.8687152,0.005000696,0.00022847074,0.00039661623,0.0000025330278,0.00046855706,0.00089053425],"genre_scores_gemma":[0.8097901,0.000005553459,0.18310699,0.0057816687,0.0004918302,0.00008988546,0.00001968875,0.00001811042,0.00069617835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986034,0.00007024548,0.00022801926,0.0004663319,0.00024658375,0.00038543384],"domain_scores_gemma":[0.9986815,0.0002809741,0.00011417262,0.0004923206,0.00030566842,0.00012537558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001143696,0.00016623331,0.00013853985,0.000050454008,0.0002999672,0.00008296144,0.00040786504,0.00003474713,0.0000669214],"category_scores_gemma":[0.00003508744,0.0001322519,0.0000338044,0.0007937686,0.00021057569,0.00048909156,0.0001572673,0.00013273112,0.00016538998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043080468,0.000265203,0.010559729,0.000016206151,0.00010022036,0.000037182806,0.00093242584,0.024370907,0.022488136,0.7001482,0.16920821,0.07144272],"study_design_scores_gemma":[0.0018539451,0.0008029605,0.022789953,0.000038558213,0.00001861633,0.00048077712,0.000054620035,0.92959523,0.002760322,0.013936141,0.026936363,0.00073248206],"about_ca_topic_score_codex":0.000017395085,"about_ca_topic_score_gemma":0.00012021247,"teacher_disagreement_score":0.9052244,"about_ca_system_score_codex":0.00004519694,"about_ca_system_score_gemma":0.000062661726,"threshold_uncertainty_score":0.5393076},"labels":[],"label_agreement":null},{"id":"W2792035262","doi":"10.1049/iet-ipr.2017.0232","title":"Effect of fusing features from multiple DCNN architectures in image classification","year":2018,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Windsor","funders":"","keywords":"Artificial intelligence; Computer science; Contextual image classification; Pattern recognition (psychology); Image (mathematics); Computer vision","score_opus":0.01241567911412153,"score_gpt":0.2999108926474271,"score_spread":0.28749521353330554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792035262","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":[0.3830454,0.00028710876,0.61536777,0.00030645618,0.00006605021,0.0002488598,0.0000043719238,0.00014850814,0.0005254466],"genre_scores_gemma":[0.76696604,0.000003126188,0.23277019,0.000065333035,0.0001336013,0.000031599702,0.000005860797,0.000014108848,0.000010116744],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986345,0.00011670764,0.00027753907,0.00048676695,0.0002098931,0.00027459508],"domain_scores_gemma":[0.9987371,0.00038956376,0.00022579673,0.0004782355,0.00011697092,0.000052311432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023034112,0.00017291772,0.00020859732,0.00013444647,0.00016531619,0.00015472576,0.0006293341,0.000059880287,0.0000037152433],"category_scores_gemma":[0.0002028309,0.00015112324,0.00004357841,0.0007056188,0.00023390779,0.00053188286,0.00016934356,0.0002128816,0.000013770063],"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":[0.000037409325,0.000030605424,0.0011394212,0.00005027829,0.0000028691454,0.0000030527344,0.0009412619,0.00008323279,0.6201502,0.000048719055,0.00006794643,0.377445],"study_design_scores_gemma":[0.00064413063,0.00011914654,0.028032934,0.00019956815,0.000010523023,0.000010409363,0.000032416356,0.2297624,0.73579234,0.0050155073,0.00011835092,0.0002622406],"about_ca_topic_score_codex":0.00004092747,"about_ca_topic_score_gemma":0.000043706747,"teacher_disagreement_score":0.38392064,"about_ca_system_score_codex":0.000038580067,"about_ca_system_score_gemma":0.0000387854,"threshold_uncertainty_score":0.6162627},"labels":[],"label_agreement":null},{"id":"W2793268137","doi":"10.1109/lgrs.2018.2795531","title":"Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":318,"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","keywords":"Upsampling; Computer science; Segmentation; Benchmark (surveying); Artificial intelligence; Digital surface; Convolutional neural network; Semantics (computer science); Computer vision; Convolution (computer science); Pattern recognition (psychology); Remote sensing; Image (mathematics); Artificial neural network; Geology; Lidar","score_opus":0.012294003614808154,"score_gpt":0.2364067822291,"score_spread":0.22411277861429185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793268137","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":[0.28482518,0.000015304717,0.71165705,0.002786237,0.0003436444,0.00029465725,0.0000024272717,0.00006598887,0.000009543706],"genre_scores_gemma":[0.5878493,0.000014610823,0.41065222,0.0012728688,0.00015712282,3.7588885e-7,0.0000057449856,0.000008650438,0.000039056566],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984809,0.000056931334,0.00025942203,0.00053278165,0.0002855583,0.00038439708],"domain_scores_gemma":[0.9988977,0.00020283448,0.00023802814,0.00036230852,0.00022043826,0.000078709534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023678794,0.00016439185,0.000185362,0.00011614345,0.00049396843,0.00007786923,0.00022825046,0.000051551764,2.6382708e-7],"category_scores_gemma":[0.000023170876,0.00014272625,0.00003775825,0.0005940375,0.0008074764,0.00041419858,0.000055421344,0.00010014463,0.0000017830538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003048044,0.000035328598,0.000057761557,0.000050822007,0.000030128198,0.000017562448,0.00043226784,0.017413875,0.8478363,0.0010757534,0.0026859106,0.13005947],"study_design_scores_gemma":[0.0007032636,0.00034927737,0.0035581049,0.00010155959,0.000018978282,0.00009742176,0.00001975124,0.9710386,0.022550203,0.0012229454,0.00010987,0.0002300643],"about_ca_topic_score_codex":0.00010431596,"about_ca_topic_score_gemma":0.000029739876,"teacher_disagreement_score":0.95362467,"about_ca_system_score_codex":0.00004672993,"about_ca_system_score_gemma":0.000047353555,"threshold_uncertainty_score":0.58202076},"labels":[],"label_agreement":null},{"id":"W2793939440","doi":"10.1109/itsc.2017.8317600","title":"Convolutional gated recurrent networks for video semantic segmentation in automated driving","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Leverage (statistics); Image segmentation; Computer vision; Convolutional neural network; Pattern recognition (psychology); Embedding","score_opus":0.02659198054418639,"score_gpt":0.31973688872927636,"score_spread":0.29314490818509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793939440","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":[0.013515213,0.000034708435,0.98373985,0.001156912,0.0003067766,0.00056531717,0.0000011708422,0.0004631665,0.00021689791],"genre_scores_gemma":[0.8912518,0.000012117003,0.10825175,0.00016019744,0.00005768594,0.00014120726,0.000016689712,0.0000075744274,0.00010099746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990274,0.000024099387,0.00023488552,0.00033787166,0.00011384569,0.00026190636],"domain_scores_gemma":[0.99901175,0.0001588517,0.00018471728,0.0005145825,0.000074523865,0.000055573008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015899446,0.000101650345,0.00011533488,0.00005533923,0.00040251203,0.00016830426,0.0006372743,0.000035941706,0.0000061541537],"category_scores_gemma":[0.000058965423,0.00009989391,0.000034620076,0.00014826212,0.000043136413,0.0006292088,0.00017669387,0.00008022728,0.000013415779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051457435,0.00043934822,0.03679226,0.000057227386,0.0000610275,0.000014988225,0.00028428767,0.3311281,0.0071353666,0.42004877,0.026835993,0.17715119],"study_design_scores_gemma":[0.00041572037,0.000021867969,0.030391134,0.000027702104,0.0000024284002,0.000003597906,0.0000032416306,0.96476215,0.00038937354,0.003305337,0.0005595554,0.00011787565],"about_ca_topic_score_codex":0.0000135458695,"about_ca_topic_score_gemma":0.0001297996,"teacher_disagreement_score":0.87773657,"about_ca_system_score_codex":0.00007679202,"about_ca_system_score_gemma":0.00002803719,"threshold_uncertainty_score":0.40735555},"labels":[],"label_agreement":null},{"id":"W2794772564","doi":"10.48550/arxiv.1803.09569","title":"On Regularized Losses for Weakly-supervised CNN Segmentation","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; University of Waterloo","funders":"","keywords":"Regularization (linguistics); Segmentation; Inference; Computer science; CRFS; Artificial intelligence; Minification; Graph; Machine learning; Pattern recognition (psychology); Algorithm; Conditional random field; Theoretical computer science","score_opus":0.07747897837039533,"score_gpt":0.22271277448184812,"score_spread":0.1452337961114528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794772564","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":[0.10998186,0.000011418282,0.88719827,0.0002754544,0.00043480977,0.0009918942,0.000023857876,0.0003316122,0.0007508324],"genre_scores_gemma":[0.91057277,0.00008405303,0.08616334,0.00036663265,0.00020725273,0.000025967809,0.00009492658,0.00003368213,0.0024513903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803585,0.00007985967,0.0002012801,0.0012545309,0.00009580594,0.00033269715],"domain_scores_gemma":[0.99759454,0.00030233755,0.00026826852,0.0014769494,0.000226004,0.00013190643],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012992368,0.00030251456,0.00026926945,0.00017770895,0.0002838878,0.000104554456,0.0016170284,0.00021297332,0.00002532526],"category_scores_gemma":[0.000038971466,0.00035011617,0.00019501355,0.00051256013,0.00011930676,0.00033965852,0.0008727855,0.00026425815,0.00013098565],"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":[0.00019152314,0.00021711475,0.00010259611,0.00010601416,0.00011517182,0.000029729794,0.00016742841,0.2306698,0.0025920204,0.75758934,0.0049430993,0.003276159],"study_design_scores_gemma":[0.0007347074,0.00012378536,0.00010069003,0.000060801576,0.000047330395,0.0000012695431,0.000015915346,0.62794703,0.0032783765,0.366517,0.00077294564,0.00040014889],"about_ca_topic_score_codex":0.000009333712,"about_ca_topic_score_gemma":0.000009407644,"teacher_disagreement_score":0.8010349,"about_ca_system_score_codex":0.00021251857,"about_ca_system_score_gemma":0.00010492819,"threshold_uncertainty_score":0.9998951},"labels":[],"label_agreement":null},{"id":"W2795276939","doi":"10.1109/cvpr.2018.00096","title":"Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":429,"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; Polygon (computer graphics); Annotation; Artificial intelligence; Encoder; Segmentation; Recurrent neural network; Generalization; Graph; Pattern recognition (psychology); Machine learning; Artificial neural network; Theoretical computer science","score_opus":0.012517016191024447,"score_gpt":0.29393861300240376,"score_spread":0.2814215968113793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795276939","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":[0.087070495,0.00000440264,0.91149485,0.00025166158,0.000053764066,0.00018618611,0.000012876609,0.000059048594,0.0008667366],"genre_scores_gemma":[0.82133925,7.1986e-7,0.178409,0.00014139712,0.000024977124,0.000017076905,0.00003082926,0.0000033307529,0.000033428798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993864,0.00001927947,0.00012728719,0.00021955778,0.00014641928,0.00010104194],"domain_scores_gemma":[0.99933285,0.00006649081,0.00011652662,0.00034249318,0.000109195644,0.000032423053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004501501,0.0000641158,0.000060196195,0.000053545904,0.000063993546,0.000019477708,0.00025240664,0.0000118810985,0.000014702741],"category_scores_gemma":[0.000007065738,0.000049218168,0.000009985582,0.00041830275,0.00006734598,0.0002331253,0.000082111845,0.00003465488,0.000039478608],"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":[0.00031805845,0.00095550483,0.0013995407,0.00003667304,0.000108681226,0.00000788505,0.0073314677,0.04952301,0.32263604,0.25677827,0.010658159,0.35024673],"study_design_scores_gemma":[0.00058893743,0.00052512594,0.005271584,0.000029822093,0.000010139831,0.000017088847,0.00018977582,0.5584095,0.43240315,0.0012967552,0.0010379957,0.00022010217],"about_ca_topic_score_codex":0.000013942945,"about_ca_topic_score_gemma":0.000017525857,"teacher_disagreement_score":0.7342687,"about_ca_system_score_codex":0.000023368297,"about_ca_system_score_gemma":0.000017756034,"threshold_uncertainty_score":0.20070586},"labels":[],"label_agreement":null},{"id":"W2796265726","doi":"10.1109/cvpr.2018.00257","title":"Practical Block-Wise Neural Network Architecture Generation","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":539,"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; Canadian Institute for Advanced Research","keywords":"Computer science; Block (permutation group theory); Pipeline (software); Artificial intelligence; Network architecture; Artificial neural network; Machine learning; Convolutional neural network; Computer network","score_opus":0.049244830874330174,"score_gpt":0.3224370522342925,"score_spread":0.2731922213599623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796265726","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":[0.0020891204,0.000099619974,0.9751765,0.017034495,0.0016835547,0.00069140294,0.0000036072786,0.0007214972,0.0025002188],"genre_scores_gemma":[0.10874515,0.000042296884,0.8808079,0.0027438176,0.0064317766,0.00022934629,0.000057085017,0.000037274396,0.00090540166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730444,0.00016243008,0.00041096803,0.0011952458,0.0003901655,0.0005367702],"domain_scores_gemma":[0.99718547,0.00019403847,0.00028299296,0.0019551131,0.00017576113,0.00020660856],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020329004,0.00038015906,0.00029525545,0.000069051195,0.00027929453,0.00035766893,0.0012983943,0.00031010414,0.000036142945],"category_scores_gemma":[0.00006059624,0.00033332754,0.00014059452,0.00038218303,0.00011123987,0.00023322809,0.0027556038,0.0010639018,0.00015027125],"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":[0.0000075297717,0.00007840765,0.000042366617,0.000016754673,0.000037305188,0.000022829701,0.00008871706,0.6079895,0.0002517792,0.06440818,0.30668092,0.020375697],"study_design_scores_gemma":[0.00010039572,0.00005075211,0.00010639343,0.000016537839,0.000019251025,0.000100346006,7.562663e-7,0.91937816,0.00039412038,0.046579573,0.03279653,0.0004571592],"about_ca_topic_score_codex":0.0000067620585,"about_ca_topic_score_gemma":0.000034627352,"teacher_disagreement_score":0.31138867,"about_ca_system_score_codex":0.00006409176,"about_ca_system_score_gemma":0.00015219969,"threshold_uncertainty_score":0.9999119},"labels":[],"label_agreement":null},{"id":"W2796502408","doi":"10.48550/arxiv.1804.06882","title":"Pelee: A Real-Time Object Detection System on Mobile Devices","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Western University","funders":"","keywords":"Computer science; Pascal (unit); Object detection; Convolutional neural network; Convolution (computer science); Mobile device; Deep learning; Detector; Architecture; Artificial intelligence; Real-time computing; Computational science; Parallel computing; Pattern recognition (psychology); Artificial neural network; Operating system; Telecommunications","score_opus":0.037270509591293424,"score_gpt":0.19828620764737406,"score_spread":0.16101569805608063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796502408","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":[0.3900086,0.000021435832,0.5993584,0.000020380241,0.0005109744,0.00082208874,0.000012485297,0.0012534723,0.007992183],"genre_scores_gemma":[0.9958283,0.00007863211,0.0026574214,0.000035127712,0.00022812498,0.000014669655,0.000010100056,0.000027287473,0.0011203889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770546,0.00015436119,0.00021519788,0.0014312662,0.00012195824,0.0003717557],"domain_scores_gemma":[0.99728405,0.00017069644,0.00038259814,0.0018298252,0.00017205233,0.00016076157],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016193849,0.00034776703,0.00032184395,0.00024607286,0.00031985957,0.000109194894,0.0017993345,0.00026630625,0.000013792904],"category_scores_gemma":[0.000009449225,0.0003940032,0.00017729648,0.0008496334,0.000097867465,0.0003238177,0.0012731209,0.000426174,0.0010351195],"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":[0.00014142433,0.0002747944,0.0004747732,0.0003740612,0.00021402435,0.00032594264,0.00036671755,0.9028448,0.0025948214,0.08384242,0.0008993362,0.007646887],"study_design_scores_gemma":[0.00033994444,0.00029066327,0.00074247865,0.0002574121,0.00007683391,0.000019399105,0.00006439989,0.9865191,0.003243058,0.0064060246,0.0013050075,0.0007356911],"about_ca_topic_score_codex":0.00006701836,"about_ca_topic_score_gemma":0.00004044442,"teacher_disagreement_score":0.60581964,"about_ca_system_score_codex":0.00050752825,"about_ca_system_score_gemma":0.000091224225,"threshold_uncertainty_score":0.99985117},"labels":[],"label_agreement":null},{"id":"W2798419898","doi":"10.1109/cvprw.2018.00032","title":"Semantic Binary Segmentation Using Convolutional Networks without Decoders","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Nvidia","keywords":"Segmentation; Computer science; Encoder; Artificial intelligence; Convolutional neural network; Computation; Pattern recognition (psychology); Scale-space segmentation; Binary number; Image segmentation; Encoding (memory); Feature (linguistics); Computer vision; Algorithm; Mathematics","score_opus":0.04271934472222172,"score_gpt":0.3196910141322704,"score_spread":0.2769716694100487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798419898","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":[0.019944005,0.00018443774,0.97687626,0.000397655,0.0010321506,0.0006275843,0.00000349806,0.000440563,0.00049384526],"genre_scores_gemma":[0.41189706,0.00006141865,0.5866598,0.0005352415,0.0004503345,0.00008351298,0.0000662609,0.000026222,0.0002201585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979423,0.00008187893,0.00038898303,0.00088876276,0.00031560758,0.0003824253],"domain_scores_gemma":[0.998315,0.00009404084,0.00031998006,0.0009657799,0.0001830212,0.00012220601],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001671693,0.00030161618,0.00025432426,0.00012685051,0.00028718857,0.000170285,0.0010463942,0.00021293153,0.000043347998],"category_scores_gemma":[0.000009397303,0.00030619837,0.00010329973,0.00034303486,0.00015210293,0.00036130546,0.001712151,0.0003980226,0.00005999392],"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":[0.0000067868236,0.000051372917,0.0020107436,0.00002731413,0.000050737082,0.0000042269253,0.000069875154,0.9821122,0.0004994978,0.009716138,0.0028320448,0.0026190523],"study_design_scores_gemma":[0.0001433062,0.000019071802,0.00093851535,0.000060707756,0.00002175815,0.000021196618,0.0000070204733,0.9831236,0.00014405041,0.015017711,0.000168121,0.00033491812],"about_ca_topic_score_codex":0.000031185195,"about_ca_topic_score_gemma":0.000015722206,"teacher_disagreement_score":0.39195305,"about_ca_system_score_codex":0.00021127675,"about_ca_system_score_gemma":0.00017155134,"threshold_uncertainty_score":0.999939},"labels":[],"label_agreement":null},{"id":"W2798769807","doi":"10.48550/arxiv.1805.01317","title":"SdcNet: A Computation-Efficient CNN for Object Recognition","year":2018,"lang":"en","type":"preprint","venue":"Spectrum Research Repository (Concordia University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"FLOPS; Computation; Computer science; Convolution (computer science); Convolutional neural network; Word error rate; Feature (linguistics); Cognitive neuroscience of visual object recognition; Object (grammar); Task (project management); Artificial intelligence; Pattern recognition (psychology); Volume (thermodynamics); Hyperparameter; Algorithm; Artificial neural network; Parallel computing","score_opus":0.061967472608125226,"score_gpt":0.3151904038340808,"score_spread":0.25322293122595557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798769807","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":[0.14127421,0.00011068337,0.8251157,0.0017825485,0.0017385884,0.003092723,0.000063537926,0.000674858,0.026147138],"genre_scores_gemma":[0.97791535,0.00006905761,0.0178395,0.00003497077,0.0011304023,0.00009484718,0.00011064446,0.000056916913,0.002748286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995509,0.0005697978,0.00038968687,0.0017148794,0.00081881776,0.000997783],"domain_scores_gemma":[0.9959035,0.00091768935,0.00035165844,0.0014334104,0.0010163557,0.00037741705],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00074249425,0.00037519605,0.0004151465,0.0011599165,0.0013569288,0.00043318357,0.0025191628,0.00034920845,0.000007861918],"category_scores_gemma":[0.00010901685,0.0004482387,0.00031407774,0.0017057831,0.0005186519,0.00030956152,0.002625564,0.0012087452,0.00012541134],"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":[0.005327328,0.0062160986,0.011756615,0.004023585,0.003514869,0.0041143955,0.00797646,0.29949662,0.023834752,0.23942827,0.14827168,0.24603935],"study_design_scores_gemma":[0.0027134733,0.002258341,0.01029199,0.00061531825,0.00014883337,0.00012243913,0.00036256568,0.73725224,0.03276736,0.14263074,0.06828308,0.002553601],"about_ca_topic_score_codex":0.00089069316,"about_ca_topic_score_gemma":0.0004685765,"teacher_disagreement_score":0.83664113,"about_ca_system_score_codex":0.0011271735,"about_ca_system_score_gemma":0.0011541881,"threshold_uncertainty_score":0.9999432},"labels":[],"label_agreement":null},{"id":"W2798873012","doi":"10.1109/cvpr.2018.00615","title":"LiDAR-Video Driving Dataset: Learning Driving Policies Effectively","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lidar; Computer science; Dashboard; Point cloud; Scale (ratio); Computer vision; Artificial intelligence; Remote sensing; Data science","score_opus":0.015860566996501763,"score_gpt":0.29174423575182096,"score_spread":0.2758836687553192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798873012","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":[0.038854282,0.000020429805,0.9530012,0.0011003459,0.00014302289,0.00019159952,0.0000026121616,0.00062104897,0.0060654655],"genre_scores_gemma":[0.92995715,0.000008854356,0.06850608,0.0005890782,0.00032502913,0.000015273914,0.000014179987,0.000016651717,0.0005676806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985971,0.000086497355,0.00018902906,0.00050234277,0.00020189144,0.0004231296],"domain_scores_gemma":[0.99853235,0.00046165526,0.000100208774,0.0007123748,0.00007274204,0.00012068675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000212136,0.00016257,0.00014349463,0.00009800809,0.0005928242,0.00020260701,0.0009938509,0.000042502423,0.00003959883],"category_scores_gemma":[0.00016073487,0.00014880433,0.00003711363,0.00066773803,0.00013018721,0.0008589382,0.00084938935,0.00022074024,0.0005203068],"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":[0.000008671643,0.0001946035,0.068558656,0.00002705352,0.00009007924,0.00002049648,0.0030191024,0.0033569767,0.08687675,0.42702153,0.053161606,0.3576645],"study_design_scores_gemma":[0.0006175621,0.000526763,0.11184769,0.0001260127,0.000021064028,0.00010116266,0.0001276916,0.23091611,0.07388614,0.014925386,0.5656001,0.0013043393],"about_ca_topic_score_codex":0.000040141225,"about_ca_topic_score_gemma":0.00008162994,"teacher_disagreement_score":0.8911029,"about_ca_system_score_codex":0.000047423666,"about_ca_system_score_gemma":0.000023228791,"threshold_uncertainty_score":0.66876644},"labels":[],"label_agreement":null},{"id":"W2799197246","doi":"10.1109/cvpr.2018.00821","title":"CLIP-Q: Deep Network Compression Learning by In-parallel Pruning-Quantization","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":230,"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":"Agence Nationale de la Recherche","keywords":"Quantization (signal processing); Computer science; Pruning; Deep learning; Artificial intelligence; Artificial neural network; Computation; Residual neural network; Deep neural networks; Uncompressed video; Pattern recognition (psychology); Machine learning; Algorithm; Object (grammar)","score_opus":0.012832625436351571,"score_gpt":0.27216519974922876,"score_spread":0.2593325743128772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799197246","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":[0.0046293363,0.00011870015,0.98842096,0.0005342825,0.00019156425,0.000198996,7.121607e-8,0.00032861377,0.00557749],"genre_scores_gemma":[0.769151,0.000054519296,0.22875676,0.0005609986,0.0002055597,0.000030297642,0.00001150357,0.000013938167,0.0012154735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876326,0.0000848715,0.00023959871,0.0003991506,0.00016969253,0.00034341673],"domain_scores_gemma":[0.9992623,0.000116662064,0.00011202693,0.00037007808,0.00006717307,0.000071806935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001534695,0.00011863564,0.00012058124,0.00004740079,0.00028389267,0.00007124472,0.0005667373,0.000058650767,0.000032789732],"category_scores_gemma":[0.000023471172,0.00011039715,0.000022026556,0.0008368984,0.000059011894,0.0004553469,0.0002768637,0.00018728475,0.0001415173],"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":[0.000027366063,0.00014587342,0.025182497,0.000008634805,0.000009707325,0.0000041259777,0.00061481615,0.5328249,0.004773066,0.2925436,0.041000962,0.10286451],"study_design_scores_gemma":[0.00022884652,0.00006566736,0.0023769871,0.00002060202,0.0000011811942,0.0000031173577,0.000008681003,0.942933,0.0003881759,0.0063815126,0.04742527,0.00016692838],"about_ca_topic_score_codex":0.000009539329,"about_ca_topic_score_gemma":0.000038354676,"teacher_disagreement_score":0.7645216,"about_ca_system_score_codex":0.000028078795,"about_ca_system_score_gemma":0.000011536165,"threshold_uncertainty_score":0.45018652},"labels":[],"label_agreement":null},{"id":"W2799290373","doi":"10.1109/isqed.2018.8357318","title":"Deep neural network acceleration framework under hardware uncertainty","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Inference; Speedup; Artificial neural network; Acceleration; Floating point; Hardware acceleration; Efficient energy use; Computer engineering; Deep neural networks; Energy (signal processing); Artificial intelligence; Machine learning; Computer hardware; Algorithm; Parallel computing","score_opus":0.030322970626166516,"score_gpt":0.29507787251900225,"score_spread":0.26475490189283574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799290373","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":[0.001949478,0.000060285012,0.98871136,0.004175087,0.0006022536,0.00023741982,3.4813087e-7,0.00056778686,0.0036959618],"genre_scores_gemma":[0.69925755,0.0000077799705,0.2937239,0.005447655,0.0011751151,0.000044401437,0.0000053815643,0.000012867664,0.00032533027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985506,0.000052123993,0.00022710783,0.00050589995,0.00022135196,0.00044290576],"domain_scores_gemma":[0.9985589,0.00020106383,0.000085866326,0.0008673516,0.00016041972,0.00012640144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009578248,0.00016555564,0.00012992867,0.00003351897,0.0004586937,0.0001990203,0.00089936086,0.00009493104,0.00017426274],"category_scores_gemma":[0.0000267269,0.00014508788,0.000053889096,0.0008656442,0.0000992201,0.0006164502,0.0003029474,0.00020805678,0.00034668393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005639244,0.00002094548,0.0001726356,0.0000013726875,0.000008068243,0.000001256605,0.000102167935,0.25653017,0.00007633367,0.6914005,0.006025403,0.04565549],"study_design_scores_gemma":[0.0000886365,0.00005833538,0.0015517282,0.0000060400675,0.0000032558305,0.0000076049723,0.000011513274,0.8204658,0.00022584925,0.16933271,0.008048998,0.00019952972],"about_ca_topic_score_codex":0.000008384306,"about_ca_topic_score_gemma":0.000087578526,"teacher_disagreement_score":0.69730806,"about_ca_system_score_codex":0.00005225003,"about_ca_system_score_gemma":0.000021843925,"threshold_uncertainty_score":0.59165114},"labels":[],"label_agreement":null},{"id":"W2799738340","doi":"10.1016/j.media.2019.02.009","title":"Constrained-CNN losses for weakly supervised segmentation","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":282,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Kahnawake Education Center","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Computer science; Segmentation; Iterated function; Mathematical optimization; Differentiable function; Artificial intelligence; Dual (grammatical number); Pixel; Machine learning; Mathematics","score_opus":0.011301270334979404,"score_gpt":0.29366071320176274,"score_spread":0.28235944286678333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799738340","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":[0.01911923,0.000040067433,0.9748856,0.004698119,0.000058050493,0.00029589748,0.000007144426,0.00012233858,0.0007735765],"genre_scores_gemma":[0.7087473,0.000053472035,0.28799394,0.0021746291,0.000118398246,0.00014613186,0.00011545665,0.000012007006,0.00063863286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985665,0.00004173299,0.0002776969,0.000422414,0.00044954888,0.00024208437],"domain_scores_gemma":[0.99867564,0.00041158154,0.000085852924,0.00052381336,0.0001273703,0.00017574953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024266695,0.00011272532,0.00024526045,0.0001536091,0.00008571257,0.000095251584,0.0006477513,0.000055927165,0.0005673956],"category_scores_gemma":[0.00014077467,0.00009848409,0.00020381801,0.0014250894,0.00008720926,0.0004222263,0.00010554189,0.000096449825,0.00021149944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004624775,0.00071386324,0.019543186,0.00016641094,0.002471037,0.000067333065,0.000680828,0.0044521727,0.09364945,0.08169131,0.011488763,0.7850294],"study_design_scores_gemma":[0.0006435411,0.00005836511,0.0014455513,0.0000100065745,0.00028829082,0.000004990115,0.000059225444,0.98479533,0.0056707864,0.0027709794,0.0040119784,0.00024093821],"about_ca_topic_score_codex":0.000009539893,"about_ca_topic_score_gemma":0.000009577391,"teacher_disagreement_score":0.98034316,"about_ca_system_score_codex":0.000025376805,"about_ca_system_score_gemma":0.00005594831,"threshold_uncertainty_score":0.6212584},"labels":[],"label_agreement":null},{"id":"W2800421099","doi":"10.1109/iscas.2018.8350945","title":"A Convolutional Accelerator for Neural Networks With Binary Weights","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Convolutional neural network; Computer science; Binary number; Latency (audio); Artificial neural network; CMOS; Computation; Computer engineering; Embedded system; Parallel computing; Computer architecture; Computer hardware; Artificial intelligence; Algorithm; Telecommunications; Arithmetic; Electrical engineering; Engineering; Mathematics","score_opus":0.021484098735345472,"score_gpt":0.26473872273998383,"score_spread":0.24325462400463838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800421099","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":[0.01276918,0.000038449656,0.9839164,0.0015345431,0.0001732496,0.0003810598,0.0000018286889,0.00027081833,0.00091445446],"genre_scores_gemma":[0.7276764,0.000001903163,0.26980668,0.0012960013,0.00051456795,0.00016837606,0.000005883262,0.000009992402,0.0005201921],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908257,0.000012787314,0.00012520656,0.00036706147,0.00011795494,0.00029440678],"domain_scores_gemma":[0.99916154,0.0001194367,0.000054334236,0.00039504067,0.00017319006,0.00009648062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000042797503,0.000115283416,0.00009344005,0.000036089186,0.00030181106,0.000043813376,0.00053812214,0.000036265297,0.000025809377],"category_scores_gemma":[0.0000033126153,0.00008344322,0.0000328937,0.00038460005,0.000125921,0.0004283461,0.00012012491,0.00006764871,0.000024754116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086554566,0.000098562086,0.00083033554,0.0000042562106,0.000025119052,0.0000037241941,0.00005692887,0.009101794,0.00042329167,0.95772016,0.019997433,0.011651855],"study_design_scores_gemma":[0.0003397193,0.00029028745,0.001502157,0.0000032748196,0.0000031539973,0.00001983885,0.0000021577175,0.9776545,0.0003662573,0.004698537,0.01496904,0.00015103807],"about_ca_topic_score_codex":0.000001745348,"about_ca_topic_score_gemma":0.000012266191,"teacher_disagreement_score":0.96855277,"about_ca_system_score_codex":0.000021947018,"about_ca_system_score_gemma":0.00003162864,"threshold_uncertainty_score":0.3402716},"labels":[],"label_agreement":null},{"id":"W2804682296","doi":"","title":"Spotlight: Optimizing Device Placement for Training Deep Neural Networks","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"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; Artificial neural network; Training (meteorology); Artificial intelligence","score_opus":0.06539351812044211,"score_gpt":0.3354566578305311,"score_spread":0.270063139710089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804682296","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":[0.0039765383,0.000031059077,0.98414755,0.0039384705,0.00073861773,0.0002696709,0.0000030624267,0.00029915725,0.0065958514],"genre_scores_gemma":[0.90344155,0.00001794919,0.0944009,0.0010112704,0.0005806795,0.00015311541,0.00003776797,0.000023073817,0.00033368688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983739,0.00006610631,0.00030624066,0.00055919745,0.0003097847,0.00038476582],"domain_scores_gemma":[0.99880844,0.0002837137,0.00021380286,0.0002788994,0.00030758628,0.00010755488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025693484,0.0002154832,0.00016909065,0.0001369606,0.00041725536,0.00025335856,0.0010950738,0.00005093175,0.00011645531],"category_scores_gemma":[0.00011547109,0.00021102278,0.000077261444,0.00023287398,0.000063673644,0.00047081956,0.00023019292,0.00040114947,0.00003996157],"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":[0.00007930863,0.000037231122,0.00027912526,0.0000033825856,0.00003975934,0.0000047722842,0.0008218033,0.4728996,0.0005599742,0.32268628,0.000060655024,0.2025281],"study_design_scores_gemma":[0.00040731908,0.00023437294,0.0001543893,0.000026220478,0.000004943297,0.000013757499,0.000072020346,0.98748326,0.00017710791,0.0019177524,0.009287211,0.00022162804],"about_ca_topic_score_codex":0.000007850728,"about_ca_topic_score_gemma":0.000028828697,"teacher_disagreement_score":0.899465,"about_ca_system_score_codex":0.00007996264,"about_ca_system_score_gemma":0.000025970825,"threshold_uncertainty_score":0.8605259},"labels":[],"label_agreement":null},{"id":"W2805649337","doi":"10.1109/tits.2018.2838117","title":"A Comprehensive Study of the Effect of Spatial Resolution and Color of Digital Images on Vehicle Classification","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Grayscale; Image resolution; Set (abstract data type); Digital image; Process (computing); Contextual image classification; Digital image processing; Dimension (graph theory); Image processing; Color image; Data set; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.029169832470293318,"score_gpt":0.2799554236711189,"score_spread":0.2507855912008256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805649337","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":[0.5090894,0.000011283281,0.4898195,0.000022809756,0.0002109383,0.00076342677,0.00004127007,0.000022096407,0.000019274328],"genre_scores_gemma":[0.9997045,0.000010179648,0.00010576341,0.0000043016744,0.000017998897,0.0001199206,0.0000028634622,0.0000097447955,0.000024765766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99850625,0.00013520708,0.0005994183,0.00029310837,0.00036025996,0.00010575694],"domain_scores_gemma":[0.9984892,0.00036612657,0.00038110296,0.0004592819,0.0002669424,0.000037322847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009111544,0.00013990432,0.00025180037,0.00012467262,0.0001261331,0.0000185333,0.00027592675,0.000052845993,0.0000017905448],"category_scores_gemma":[0.000003986871,0.00010597147,0.000079567006,0.00046510695,0.00018881778,0.00019359824,0.0000010830754,0.000106408785,0.0000035794933],"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":[0.0027579393,0.0039018365,0.015025705,0.0009646937,0.0005624989,0.0000028189968,0.016929718,0.56140786,0.29718935,0.0064815735,0.00008366072,0.09469236],"study_design_scores_gemma":[0.0015283532,0.007788581,0.07446008,0.00027550446,0.00013115627,0.0000037960606,0.0012264741,0.1285037,0.7856345,0.00007104884,0.00012339349,0.0002533947],"about_ca_topic_score_codex":0.00013022628,"about_ca_topic_score_gemma":0.00007446315,"teacher_disagreement_score":0.49061507,"about_ca_system_score_codex":0.000031729236,"about_ca_system_score_gemma":0.00001821268,"threshold_uncertainty_score":0.43213913},"labels":[],"label_agreement":null},{"id":"W2806586267","doi":"10.1007/978-3-319-93000-8_21","title":"Deep Convolutional-Shepard Interpolation Neural Networks for Image Classification Tasks","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"MNIST database; Computer science; Convolutional neural network; Deep learning; Artificial intelligence; Leverage (statistics); Contextual image classification; Artificial neural network; Interpolation (computer graphics); Deep neural networks; Pattern recognition (psychology); Architecture; Image (mathematics); Network architecture; Task (project management); Machine learning; Engineering","score_opus":0.025038890128839465,"score_gpt":0.27891772975235535,"score_spread":0.2538788396235159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806586267","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":[0.000019839059,0.0002622166,0.99459743,0.0012436237,0.0017755475,0.001110632,0.000008558976,0.0002648032,0.0007173239],"genre_scores_gemma":[0.15395129,0.000036980633,0.84223795,0.0015362478,0.0017295491,0.00014215901,0.00008103614,0.00005805989,0.00022670708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618393,0.000035852467,0.0006745249,0.0017817874,0.00060714385,0.0007167455],"domain_scores_gemma":[0.9963076,0.00081962993,0.0005434394,0.0015053218,0.00064310664,0.00018088277],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053948484,0.000523393,0.0004292392,0.000495827,0.0004866781,0.00046074754,0.0028407942,0.00034512827,0.000026402085],"category_scores_gemma":[0.000102283186,0.000521857,0.00017238932,0.0006700606,0.001036289,0.001047173,0.00081115396,0.00061416195,0.00004311179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003092181,0.000045724704,0.000037457252,0.000033121796,0.000019323075,0.000008339226,0.0003658954,0.22144832,0.00058549544,0.10029107,0.0005383353,0.676596],"study_design_scores_gemma":[0.00018853323,0.00011802547,0.000109024986,0.00006390023,0.000008787854,0.000030559335,1.2958428e-7,0.83264387,0.0000852245,0.16471975,0.0015955089,0.0004366752],"about_ca_topic_score_codex":0.0000028765542,"about_ca_topic_score_gemma":0.000051628096,"teacher_disagreement_score":0.6761593,"about_ca_system_score_codex":0.00038529147,"about_ca_system_score_gemma":0.00018740512,"threshold_uncertainty_score":0.9997233},"labels":[],"label_agreement":null},{"id":"W2806788401","doi":"10.1016/j.media.2018.05.010","title":"A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Artificial intelligence; Segmentation; Sørensen–Dice coefficient; Prostate biopsy; Deep learning; Convolutional neural network; Hausdorff distance; Ultrasound; Pattern recognition (psychology); Computer vision; Image segmentation; Prostate; Radiology; Medicine; Cancer","score_opus":0.012907864425345618,"score_gpt":0.30040093726861444,"score_spread":0.2874930728432688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806788401","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":[0.023001745,0.000018948549,0.9754201,0.00037631215,0.000014911385,0.00029265392,0.0000017391382,0.000111647496,0.000761947],"genre_scores_gemma":[0.21481967,0.000055614408,0.78358024,0.00021586336,0.00017921216,0.00028065403,0.00020288963,0.000015681802,0.00065019075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983132,0.000099720724,0.00035419513,0.0005135145,0.00040523734,0.00031416392],"domain_scores_gemma":[0.99894994,0.00026258308,0.00013375877,0.0003631082,0.0001359453,0.00015468715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054407644,0.00012413542,0.00024290633,0.00024255332,0.00017415638,0.0000982695,0.0005184821,0.00006548913,0.00008805086],"category_scores_gemma":[0.0003519768,0.00011060286,0.00010893413,0.0023668855,0.00017640127,0.00034544538,0.000106753265,0.00013815565,0.000040887888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019605829,0.001992801,0.05373443,0.00016997347,0.0025730168,0.00016861048,0.012977527,0.05576872,0.36529052,0.0047520017,0.009879477,0.49249685],"study_design_scores_gemma":[0.00040621066,0.000048777885,0.0016005534,0.0000041148396,0.00010485915,0.000011114344,0.0000319639,0.9952989,0.0015282322,0.0006990407,0.00012643624,0.00013979175],"about_ca_topic_score_codex":0.000059505783,"about_ca_topic_score_gemma":0.000045285607,"teacher_disagreement_score":0.9395302,"about_ca_system_score_codex":0.00005631808,"about_ca_system_score_gemma":0.000031414984,"threshold_uncertainty_score":0.45102537},"labels":[],"label_agreement":null},{"id":"W2808526226","doi":"10.3390/s18061900","title":"Human Part Segmentation in Depth Images with Annotated Part Positions","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pixel; Random walker algorithm; Artificial intelligence; Segmentation; Cut; Markov random field; Computer science; Random forest; Graph; Pattern recognition (psychology); Image segmentation; Computer vision; Range segmentation; Conditional random field; Scale-space segmentation","score_opus":0.01997575774372173,"score_gpt":0.2900268334659139,"score_spread":0.27005107572219217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808526226","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":[0.9410576,0.000016836668,0.051732283,0.0015401623,0.00010261241,0.00040129825,0.000007733762,0.0003096087,0.0048318543],"genre_scores_gemma":[0.9663393,0.000005921403,0.03261464,0.00028458453,0.00011824351,0.00005224108,0.000020352616,0.0000112148955,0.00055352336],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991516,0.000047427442,0.00015504708,0.00030034257,0.00012693413,0.00021868388],"domain_scores_gemma":[0.9994212,0.00002875413,0.00006804191,0.00035143315,0.00007521893,0.000055346653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005344267,0.00009648184,0.00008028002,0.000074477015,0.00019774445,0.000049416365,0.0002124117,0.000023406134,0.000013899634],"category_scores_gemma":[0.0000045100946,0.00008723991,0.000014946898,0.0006340956,0.00010758704,0.00026959283,0.000044776476,0.00008140261,0.00009361511],"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":[0.00016717066,0.002291309,0.06126715,0.000080212405,0.00024243366,0.00050908705,0.014277779,0.1300039,0.33169687,0.19571814,0.09666573,0.16708021],"study_design_scores_gemma":[0.0051079798,0.0023261749,0.2849409,0.00040700345,0.00007507925,0.00027496263,0.0009820556,0.112501405,0.50915277,0.028840935,0.052513156,0.0028775719],"about_ca_topic_score_codex":0.00001713067,"about_ca_topic_score_gemma":0.00023886589,"teacher_disagreement_score":0.22367373,"about_ca_system_score_codex":0.00003483761,"about_ca_system_score_gemma":0.000013291692,"threshold_uncertainty_score":0.35575402},"labels":[],"label_agreement":null},{"id":"W2809446072","doi":"10.1109/tii.2018.2849348","title":"Fast Semantic Segmentation for Scene Perception","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"College of Computing; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Encoder; Block (permutation group theory); Focus (optics); Pixel; Image segmentation","score_opus":0.059116617942322966,"score_gpt":0.3019179391523913,"score_spread":0.2428013212100683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809446072","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":[0.0090878485,6.9873323e-7,0.9878522,0.00043695606,0.0011863981,0.0008260109,0.000025137784,0.00023682708,0.00034788693],"genre_scores_gemma":[0.7819944,0.000013902776,0.21612372,0.00079899636,0.0004505323,0.00029478036,0.0000146024,0.000018192082,0.0002908998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988531,0.00002301391,0.00047581134,0.00015606628,0.00022695995,0.0002650685],"domain_scores_gemma":[0.9990332,0.000125253,0.00017604492,0.00041304974,0.00015955254,0.00009289324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015466471,0.00015467883,0.00013171899,0.00016292262,0.00049551716,0.00013042477,0.0003793613,0.00013687904,0.00002431507],"category_scores_gemma":[0.000007047741,0.00015340479,0.00008044787,0.00052613893,0.000081055405,0.0010360399,0.0000031071042,0.00021289919,0.00021539701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007026451,0.00015725783,0.0000046996092,0.000021297648,0.000037982492,1.8564326e-7,0.002387195,0.03571256,0.002601598,0.0017277026,0.0029792264,0.95430005],"study_design_scores_gemma":[0.0020796186,0.0007942785,0.000024851315,0.000060092538,0.00004328007,0.00001747455,0.00049983605,0.92783016,0.061013747,0.0011593298,0.006070289,0.0004070422],"about_ca_topic_score_codex":0.0000040577083,"about_ca_topic_score_gemma":0.0000064814835,"teacher_disagreement_score":0.953893,"about_ca_system_score_codex":0.00012272228,"about_ca_system_score_gemma":0.000059432918,"threshold_uncertainty_score":0.6255666},"labels":[],"label_agreement":null},{"id":"W2809652586","doi":"10.1109/access.2018.2848307","title":"WPNets and PWNets: From the Perspective of Channel Fusion","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Taishan Scholar Project of Shandong Province; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Convolution (computer science); Channel (broadcasting); Benchmark (surveying); Network architecture; Artificial neural network; Code (set theory); Perspective (graphical); Architecture; Artificial intelligence; Data mining; Theoretical computer science; Computer network; Programming language","score_opus":0.03608005885901672,"score_gpt":0.3237392153216412,"score_spread":0.28765915646262447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809652586","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":[0.29612392,0.0004635426,0.6952619,0.0050940337,0.00044540968,0.00030437447,0.000010041445,0.00008870262,0.0022080801],"genre_scores_gemma":[0.99700147,0.000059584574,0.0020609917,0.00056337705,0.0002759744,0.000014224347,4.1983407e-7,0.0000042631905,0.000019689121],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99939007,0.000024355611,0.000091219445,0.00024739478,0.0001399164,0.00010706644],"domain_scores_gemma":[0.99912167,0.000161003,0.00007833556,0.00043813523,0.00016993977,0.00003092238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006421018,0.00006381261,0.000071645976,0.000018243723,0.00013931993,0.000056071713,0.00092538184,0.000022735672,0.000006518786],"category_scores_gemma":[0.000022058222,0.00004347608,0.000015033187,0.00027999698,0.0001362525,0.00039459587,0.00030457642,0.000058995123,0.000010293205],"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":[0.00016847531,0.00055918255,0.008187276,0.000028898736,0.0002139204,0.000018035475,0.040974017,0.0020133925,0.09182588,0.45562193,0.08487619,0.3155128],"study_design_scores_gemma":[0.0007480432,0.00021383572,0.080445446,0.000073552306,0.000023520359,0.000011539807,0.0003495261,0.1038612,0.112156846,0.69414425,0.0075231916,0.00044903933],"about_ca_topic_score_codex":0.0002370219,"about_ca_topic_score_gemma":0.00011697854,"teacher_disagreement_score":0.70087755,"about_ca_system_score_codex":0.000016632463,"about_ca_system_score_gemma":0.000013473848,"threshold_uncertainty_score":0.17729032},"labels":[],"label_agreement":null},{"id":"W2883283076","doi":"10.1109/isca.2018.00070","title":"Gist: Efficient Data Encoding for Deep Neural Network Training","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":146,"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":"Microsoft Research; National Science Foundation","keywords":"Computer science; Memory footprint; Feature (linguistics); Encoding (memory); Deep learning; Bottleneck; Artificial neural network; Artificial intelligence; Embedded system","score_opus":0.11202259486123024,"score_gpt":0.3337931831143889,"score_spread":0.22177058825315865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883283076","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":[0.0010235747,0.00005833156,0.99182165,0.0012242617,0.0005206084,0.00034113572,0.000003565672,0.00032667845,0.00468018],"genre_scores_gemma":[0.5771526,0.0000016105149,0.42093894,0.000842842,0.0008742081,0.000038732873,0.000013143872,0.000010251353,0.00012765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985248,0.00001983316,0.00021373846,0.00061786966,0.00014176201,0.00048202224],"domain_scores_gemma":[0.9980487,0.00031111902,0.00008014904,0.0013899142,0.00006896533,0.00010110167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027537343,0.00012016032,0.000121738514,0.000028799952,0.00043490512,0.00010603731,0.0020161644,0.000031537977,0.000013803425],"category_scores_gemma":[0.000053543885,0.000107449654,0.00003288367,0.0004780306,0.000083057865,0.0003083551,0.00077001465,0.00007686591,0.00003626948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012934717,0.00004695776,0.000066905035,0.00000764189,0.000016513543,0.0000026905316,0.0008692012,0.13608408,0.00083931,0.42214432,0.01417752,0.4257319],"study_design_scores_gemma":[0.00013781882,0.000045542776,0.00007353712,0.0000043469754,0.0000037713912,0.000010055387,0.000018804858,0.9671843,0.00014839637,0.0038199918,0.028416254,0.0001371961],"about_ca_topic_score_codex":0.0000020326006,"about_ca_topic_score_gemma":0.000028464778,"teacher_disagreement_score":0.8311002,"about_ca_system_score_codex":0.000019663541,"about_ca_system_score_gemma":0.000020325795,"threshold_uncertainty_score":0.43816698},"labels":[],"label_agreement":null},{"id":"W2884013187","doi":"10.1109/tpami.2018.2855738","title":"Scattering Networks for Hybrid Representation Learning","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":81,"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 Montréal; Mila - Quebec Artificial Intelligence Institute; Computer Research Institute of Montréal","funders":"Vlaamse regering; KU Leuven; Natural Sciences and Engineering Research Council of Canada; Fonds Wetenschappelijk Onderzoek","keywords":"Artificial intelligence; Computer science; Scattering; Stability (learning theory); Unsupervised learning; Pattern recognition (psychology); Representation (politics); Convolutional neural network; Feature learning; Competitive learning; Supervised learning; Deep learning; Machine learning; Residual; Artificial neural network; Algorithm; Physics; Optics","score_opus":0.02717808744385188,"score_gpt":0.3037644277812501,"score_spread":0.27658634033739826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884013187","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":[0.002222799,0.000036028534,0.9967522,0.00044662264,0.00016285802,0.00019896659,0.000006763453,0.00013768648,0.00003609119],"genre_scores_gemma":[0.9894911,0.00016241263,0.009594673,0.00037538138,0.00008088232,0.00009537265,0.0000073312526,0.000012514812,0.0001803475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987163,0.000049796497,0.00029222705,0.0005667292,0.00013525817,0.00023968834],"domain_scores_gemma":[0.9990319,0.00022447633,0.000116070354,0.00043260984,0.00010281407,0.00009213432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014561634,0.00015978124,0.00020163406,0.0002624294,0.00046613923,0.00012391302,0.0003445371,0.000031700136,0.000035310226],"category_scores_gemma":[0.000004956769,0.00015421465,0.00016378528,0.00095060916,0.00008532486,0.00025906326,0.000007424347,0.00018766681,0.000016489825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071585137,0.000028635413,0.00023640117,0.0000029282837,0.0001079658,7.693613e-7,0.0000744446,0.27596286,0.00030895133,0.00007193341,0.000009431251,0.7231885],"study_design_scores_gemma":[0.000053368123,0.00010909215,0.00027135364,0.000008227836,0.00012676303,0.0000073137694,0.000011229812,0.9001752,0.09841392,0.0004664401,0.0001991871,0.0001578775],"about_ca_topic_score_codex":0.00011247302,"about_ca_topic_score_gemma":0.00023568596,"teacher_disagreement_score":0.98726827,"about_ca_system_score_codex":0.00002287631,"about_ca_system_score_gemma":0.0000058325213,"threshold_uncertainty_score":0.6288691},"labels":[],"label_agreement":null},{"id":"W2884081170","doi":"10.1109/itsc.2018.8569793","title":"Leveraging Pre-Trained 3D Object Detection Models for Fast Ground Truth Generation","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Minimum bounding box; Artificial intelligence; Annotation; Object detection; Object (grammar); Task (project management); Bounding overwatch; Ground truth; Segmentation; Point cloud; Scheme (mathematics); Orientation (vector space); Generalization; Computer vision; Machine learning; Image (mathematics)","score_opus":0.04700156863065653,"score_gpt":0.2829623081059814,"score_spread":0.23596073947532487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884081170","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":[0.018429711,0.000009713614,0.9791497,0.00020283293,0.00035045954,0.0004510806,0.0000011016408,0.00034449398,0.0010609367],"genre_scores_gemma":[0.7375153,0.000002289608,0.2612117,0.00022102702,0.00051736814,0.00014802993,0.0000045982224,0.000009576325,0.000370135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990084,0.000022200944,0.00017728885,0.0004305728,0.00012684148,0.00023465414],"domain_scores_gemma":[0.9992743,0.000083192834,0.00006963578,0.00039688457,0.00012296432,0.000053020834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000118290925,0.00011306988,0.00008695598,0.000061037696,0.00038028127,0.00012976617,0.00030812257,0.00004151421,0.000005196134],"category_scores_gemma":[0.0000143283605,0.000105882806,0.000040910876,0.00028115613,0.000033214917,0.00091627054,0.00007076167,0.000052196025,0.000012614758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024271469,0.000062874096,0.0000047129847,0.000010338035,0.000020829775,3.9098893e-7,0.0013734477,0.072044246,0.11538082,0.14903863,0.00050580123,0.66153365],"study_design_scores_gemma":[0.0002050728,0.00009309426,0.000070454786,0.0000021972005,0.0000036620213,0.000006545336,0.000008725842,0.9369468,0.031880457,0.030071136,0.00058424706,0.00012758224],"about_ca_topic_score_codex":0.000012527504,"about_ca_topic_score_gemma":0.00005980561,"teacher_disagreement_score":0.86490256,"about_ca_system_score_codex":0.000062437975,"about_ca_system_score_gemma":0.000022027078,"threshold_uncertainty_score":0.43177757},"labels":[],"label_agreement":null},{"id":"W2884442510","doi":"10.1007/978-3-030-00536-8_13","title":"RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fluid-attenuated inversion recovery; Segmentation; Artificial intelligence; Dropout (neural networks); Voxel; Convolutional neural network; Pattern recognition (psychology); Partial volume; Convolution (computer science); Artificial neural network; Magnetic resonance imaging; Machine learning; Radiology; Medicine","score_opus":0.024057024919331895,"score_gpt":0.28900056039835326,"score_spread":0.2649435354790214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884442510","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":[0.00023103462,0.0002501895,0.9948683,0.003030173,0.00033753322,0.00096251373,0.000010200957,0.00003326437,0.00027678502],"genre_scores_gemma":[0.0930645,0.000054294556,0.90602154,0.00046788843,0.0002416604,0.00008113593,0.000005485349,0.000022850769,0.00004064334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99701774,0.000104248815,0.0006853103,0.0009812374,0.0007917399,0.00041970244],"domain_scores_gemma":[0.9938748,0.0037786514,0.0007642952,0.0012476826,0.00027679058,0.00005781746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013525266,0.00031535872,0.0004110919,0.00049890135,0.00021354263,0.00009836226,0.0029519713,0.00017474672,0.0000046129308],"category_scores_gemma":[0.00037845908,0.00023878465,0.00010074943,0.00088057586,0.0009262931,0.00048407566,0.00049024005,0.00032254003,0.000002385525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060584494,0.00009708186,0.00007465464,0.0001841756,0.000009941959,0.000006878476,0.0037648238,0.134193,0.012065426,0.013998099,0.0005268569,0.83501846],"study_design_scores_gemma":[0.00022472508,0.00024217373,0.00029832713,0.0013666069,0.000011837986,0.00002528948,0.0000022979812,0.8263624,0.01877134,0.15185305,0.00047596561,0.0003660082],"about_ca_topic_score_codex":0.00001242497,"about_ca_topic_score_gemma":0.00005845069,"teacher_disagreement_score":0.8346525,"about_ca_system_score_codex":0.00014048806,"about_ca_system_score_gemma":0.00022870184,"threshold_uncertainty_score":0.9737356},"labels":[],"label_agreement":null},{"id":"W2885020160","doi":"10.3390/s18072258","title":"Engineering Vehicles Detection Based on Modified Faster R-CNN for Power Grid Surveillance","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Computer science; Pooling; Object detection; Feature engineering; Key (lock); Artificial intelligence; Intrusion detection system; Grid; Feature (linguistics); Real-time computing; Task (project management); Convolutional neural network; Deep learning; Warning system; False alarm; Pattern recognition (psychology); Engineering; Computer security","score_opus":0.01407233236089022,"score_gpt":0.23780081091139044,"score_spread":0.2237284785505002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885020160","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":[0.1718617,0.0000036609974,0.8262759,0.000472865,0.00048142212,0.00026546197,0.000004931082,0.00027099822,0.00036304715],"genre_scores_gemma":[0.97586656,6.8419956e-7,0.0234494,0.00027777857,0.0002428655,0.000043816777,0.0000017158279,0.000015827214,0.00010136295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918514,0.00001841265,0.00012154459,0.00032551194,0.00011710308,0.00023230439],"domain_scores_gemma":[0.9991681,0.00021177757,0.000048146558,0.00043984078,0.00007821793,0.000053915704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009303197,0.00011040369,0.00008974832,0.00006590193,0.00011427132,0.00003876362,0.0002443625,0.000041710406,0.0000022364638],"category_scores_gemma":[0.000046202986,0.00010677965,0.000044795906,0.0002583701,0.000026205993,0.000090754686,0.00003102783,0.000070863956,0.00006271633],"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":[0.000117155025,0.000083117935,0.00016485287,0.000023429047,0.000018445833,0.0000035867497,0.00023241104,0.8704969,0.060047995,0.0069003897,0.00097613176,0.060935576],"study_design_scores_gemma":[0.0002037509,0.000106128005,0.001628938,0.000006531802,9.0566573e-7,0.0000015545174,0.0000019483828,0.9440494,0.046322715,0.0002389914,0.0073054875,0.0001336311],"about_ca_topic_score_codex":0.0000015928041,"about_ca_topic_score_gemma":0.0000058443175,"teacher_disagreement_score":0.80400485,"about_ca_system_score_codex":0.000032741387,"about_ca_system_score_gemma":0.000007434563,"threshold_uncertainty_score":0.43543476},"labels":[],"label_agreement":null},{"id":"W2887902861","doi":"10.1109/isc2.2018.8656929","title":"Efficient Single-Shot Multibox Detector for Construction Site Monitoring","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Leverage (statistics); Computer science; Cluster analysis; Single shot; Detector; Data mining; Artificial intelligence; Real-time computing; Pascal (unit); Telecommunications","score_opus":0.057210633609180224,"score_gpt":0.30734333563700966,"score_spread":0.25013270202782945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887902861","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":[0.0938616,0.000059557435,0.9010456,0.00017787554,0.0027183336,0.0010971165,0.000017630451,0.0006404518,0.00038178824],"genre_scores_gemma":[0.4118829,0.0000045418283,0.58694303,0.000030047408,0.0007381535,0.00029113033,0.000008189062,0.000020288475,0.00008170214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979874,0.000028763257,0.00036324668,0.0009965921,0.0002345894,0.00038943958],"domain_scores_gemma":[0.9978788,0.00023418233,0.0002792246,0.0011853114,0.000297369,0.00012511305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012370433,0.00029451863,0.0002524283,0.00012357236,0.0002462361,0.00019673293,0.00090379326,0.00020234444,0.0000065855647],"category_scores_gemma":[0.000053774234,0.00029064453,0.000120750454,0.0002494989,0.00010934457,0.000077584395,0.0011824061,0.00026560636,0.000050438517],"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":[0.000054586024,0.00037527195,0.0012159668,0.0003061753,0.00009933134,0.0000047709545,0.00080377224,0.37328002,0.25751284,0.012291621,0.0008285143,0.3532271],"study_design_scores_gemma":[0.00037963816,0.00013143082,0.00046509536,0.000121976904,0.000024925637,0.000019252546,0.000014218076,0.7078667,0.28065178,0.00551724,0.0041067214,0.0007010085],"about_ca_topic_score_codex":0.000009613273,"about_ca_topic_score_gemma":0.000004882025,"teacher_disagreement_score":0.3525261,"about_ca_system_score_codex":0.00018359066,"about_ca_system_score_gemma":0.000052173622,"threshold_uncertainty_score":0.9999546},"labels":[],"label_agreement":null},{"id":"W2888056695","doi":"10.1007/978-3-030-27272-2_2","title":"NetScore: Towards Universal Metrics for Large-Scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage","year":2019,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Waterloo","funders":"","keywords":"Computer science; Metric (unit); Deep learning; Convolutional neural network; Artificial intelligence; Artificial neural network; Enhanced Data Rates for GSM Evolution; Set (abstract data type); Performance metric; Deep neural networks; Machine learning; Scale (ratio); Focus (optics); Data mining; Computer engineering; Engineering","score_opus":0.03065402445168162,"score_gpt":0.31476574556887554,"score_spread":0.2841117211171939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888056695","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":[0.007846547,0.0001565545,0.98658943,0.0015493477,0.0018937049,0.0017899995,0.00004590139,0.00011488405,0.000013602179],"genre_scores_gemma":[0.5235384,0.00002225834,0.4753271,0.0008025991,0.00017343507,0.0000882314,0.000030350639,0.000016521351,0.0000011109887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995264,0.000099594516,0.0007167352,0.0020623403,0.00079633866,0.0010609942],"domain_scores_gemma":[0.9930476,0.0032078286,0.00070436473,0.0021885098,0.0006519578,0.00019974433],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013414673,0.0005219618,0.00095062336,0.0013995599,0.00031967091,0.00031226405,0.003985081,0.00035444598,0.0000024398512],"category_scores_gemma":[0.00035268173,0.00048896763,0.00042307086,0.008183472,0.0002932968,0.00069397356,0.0026513769,0.00096657197,0.000001507076],"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":[0.000033394124,0.00010416921,0.00077879673,0.000056845794,0.000052451367,0.000001455998,0.00025550535,0.8411456,0.000014662967,0.0011283406,0.000014441433,0.15641439],"study_design_scores_gemma":[0.0004304787,0.00032805893,0.0023741517,0.000044892586,0.00015034564,0.0000041650515,0.0000011315673,0.9931544,0.00061343587,0.0022289979,0.00018344108,0.00048648284],"about_ca_topic_score_codex":0.0000107309625,"about_ca_topic_score_gemma":0.00008431907,"teacher_disagreement_score":0.5156918,"about_ca_system_score_codex":0.00033519528,"about_ca_system_score_gemma":0.0004608584,"threshold_uncertainty_score":0.9997562},"labels":[],"label_agreement":null},{"id":"W2890720247","doi":"","title":"Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization","year":2018,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Cross entropy; Minification; Reinforcement learning; Computation; Artificial neural network; Cross-entropy method; Entropy (arrow of time); Importance sampling; Deep neural networks; Machine learning; Monte Carlo method; Empirical risk minimization; Artificial intelligence; Mathematical optimization; Principle of maximum entropy; Algorithm; Optimization problem; Mathematics","score_opus":0.01255468471138568,"score_gpt":0.27848019964696974,"score_spread":0.2659255149355841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890720247","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":[0.017410602,0.00005609947,0.9797602,0.0009913323,0.0001256037,0.0006626521,0.000004914085,0.0003363274,0.0006522674],"genre_scores_gemma":[0.9520732,0.0000067466294,0.046524115,0.0008500677,0.0002386355,0.00012894707,0.000048487993,0.000011907597,0.00011785175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866885,0.000035246292,0.00045372805,0.00023761558,0.00033677422,0.00026776927],"domain_scores_gemma":[0.99834263,0.000026478832,0.0004562937,0.00026377084,0.0008116128,0.000099187],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000114570925,0.00017437087,0.00013289686,0.00019409526,0.000540847,0.0012021195,0.0003001377,0.000060436552,0.0000021917267],"category_scores_gemma":[0.00004519146,0.00014252975,0.000012780377,0.0008094755,0.000114833274,0.0066621453,0.00009885753,0.0000868075,0.000018092796],"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":[0.00009969536,0.000035789606,0.0011700084,0.0003171877,0.000011778864,8.1410957e-7,0.0030023176,0.9397981,0.00027581293,0.015010773,0.00017497304,0.04010275],"study_design_scores_gemma":[0.00046200727,0.00018700298,0.0005106956,0.000060633767,0.000004525751,0.000087006134,0.00009718146,0.9959209,0.0004955976,0.000023639495,0.0019705053,0.00018032854],"about_ca_topic_score_codex":0.000018253344,"about_ca_topic_score_gemma":0.0000019434174,"teacher_disagreement_score":0.93466264,"about_ca_system_score_codex":0.00008660879,"about_ca_system_score_gemma":0.00012683474,"threshold_uncertainty_score":0.9998347},"labels":[],"label_agreement":null},{"id":"W2890980329","doi":"10.1109/lgrs.2018.2866816","title":"Vehicle Detection in High-Resolution Images Using Superpixel Segmentation and CNN Iteration Strategy","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"National Natural Science Foundation of China","keywords":"Overfitting; Artificial intelligence; Computer science; Pattern recognition (psychology); Convolutional neural network; Segmentation; Image segmentation; Object detection; Feature extraction; Feature selection; Computer vision; Artificial neural network","score_opus":0.018121674805215458,"score_gpt":0.2589676283604987,"score_spread":0.24084595355528324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890980329","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":[0.5021291,0.000012172466,0.4969365,0.00067061774,0.00013908553,0.00007993481,2.7347173e-7,0.000029762256,0.0000025750535],"genre_scores_gemma":[0.83474463,0.000017254959,0.16447742,0.00064013456,0.00010981382,1.569817e-7,7.5923003e-7,0.0000045666625,0.000005266615],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989462,0.00006081676,0.00016691873,0.00042832945,0.00015194957,0.00024577184],"domain_scores_gemma":[0.9996193,0.000038583312,0.000072338626,0.00017325312,0.000045105833,0.000051387895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018252501,0.000105496016,0.00008355406,0.00014542333,0.00046468884,0.00027113795,0.00009313745,0.00003860888,1.8605989e-7],"category_scores_gemma":[0.000010356655,0.0001043171,0.00001085154,0.0005323568,0.0003103663,0.0010124026,0.000043098185,0.000093503404,0.0000019187376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027866172,0.0000024816572,0.00003042609,0.0000025200627,5.992457e-7,0.0000025619734,0.00024046886,0.0012105885,0.76921,0.000029435014,0.000003871333,0.22926426],"study_design_scores_gemma":[0.00017325433,0.000044250915,0.005475654,0.000026279571,0.000002896706,0.000052296684,0.000032170108,0.900481,0.09272186,0.0008570176,0.000010419532,0.00012294679],"about_ca_topic_score_codex":0.0005370218,"about_ca_topic_score_gemma":0.00017009694,"teacher_disagreement_score":0.89927036,"about_ca_system_score_codex":0.00006235176,"about_ca_system_score_gemma":0.000016890905,"threshold_uncertainty_score":0.42539278},"labels":[],"label_agreement":null},{"id":"W2891004411","doi":"10.48550/arxiv.1809.03193","title":"Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Object (grammar); Deep neural networks; Pattern recognition (psychology)","score_opus":0.044096216657719275,"score_gpt":0.21071694261871207,"score_spread":0.1666207259609928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891004411","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":[0.09158538,0.000988576,0.90587634,0.00009039798,0.0003981986,0.0004633431,0.0000026423447,0.000058026922,0.00053709623],"genre_scores_gemma":[0.99615216,0.0028345445,0.0007774033,0.000103707636,0.000087682296,0.0000071766513,0.000013354311,0.0000079699,0.000016016413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983775,0.00025707332,0.00026476724,0.0007105452,0.00009533397,0.0002947588],"domain_scores_gemma":[0.9985304,0.00025583513,0.00026330908,0.00081234786,0.000100143436,0.00003800076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026980505,0.00020067264,0.0002311887,0.00021343818,0.00009039304,0.000027090866,0.0015490405,0.00016069741,0.0000063650677],"category_scores_gemma":[0.000029520641,0.000195548,0.00008795147,0.0014201286,0.00020469712,0.00038684212,0.0007200601,0.0006175005,0.0000047228823],"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":[0.000028318353,0.000060950075,0.0019900093,0.000010590766,0.0000050168833,0.000047377245,0.00010917547,0.96922886,0.0000120465065,0.013016023,0.000007946295,0.015483662],"study_design_scores_gemma":[0.0002597326,0.000043566783,0.009872551,0.00002862255,0.000008192606,0.000005080842,0.000034848257,0.95304537,0.00003197307,0.035564452,0.00091945945,0.00018612412],"about_ca_topic_score_codex":0.000028808317,"about_ca_topic_score_gemma":0.0015303816,"teacher_disagreement_score":0.9050989,"about_ca_system_score_codex":0.00019858015,"about_ca_system_score_gemma":0.000040270985,"threshold_uncertainty_score":0.7974216},"labels":[],"label_agreement":null},{"id":"W2891365890","doi":"10.1016/j.neucom.2018.07.079","title":"AlphaMEX: A smarter global pooling method for convolutional neural networks","year":2018,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"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; Canadian Institute for Advanced Research","keywords":"Pooling; Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Normalization (sociology); Feature (linguistics); Artificial neural network; Activation function; Deep learning; Machine learning","score_opus":0.021617962673296093,"score_gpt":0.32056104948196207,"score_spread":0.298943086808666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891365890","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":[0.004222086,0.000055720993,0.99203664,0.0013169441,0.0009632197,0.0004887783,0.0000040311984,0.00049577805,0.00041682852],"genre_scores_gemma":[0.4992227,7.368032e-7,0.4963302,0.0029234022,0.0014435974,0.000041777635,0.0000041556327,0.000015349591,0.000018105216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978659,0.00009812069,0.00037130073,0.0007808504,0.00020922563,0.0006746131],"domain_scores_gemma":[0.9983816,0.000549801,0.00019125745,0.00050562364,0.00021972843,0.00015200181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026953468,0.00022645094,0.00021067208,0.000045172874,0.0006052091,0.00015481094,0.0009365685,0.000073069736,0.0000037677],"category_scores_gemma":[0.000058003363,0.0002348284,0.00012873068,0.00067013176,0.0000838526,0.00031711734,0.00046762495,0.00019049589,0.000018476883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004109695,0.00008697482,0.0013949852,0.000015671354,0.000034131117,0.000010877214,0.00013960629,0.23618579,0.00089001824,0.16126633,0.005234381,0.59470016],"study_design_scores_gemma":[0.00036982237,0.000106513835,0.0020089385,0.000008936067,0.000007750844,0.00010886055,0.000002745047,0.98269045,0.00011677132,0.0070676547,0.0072962767,0.00021524698],"about_ca_topic_score_codex":0.0000057729444,"about_ca_topic_score_gemma":0.0000048772754,"teacher_disagreement_score":0.74650466,"about_ca_system_score_codex":0.00006126141,"about_ca_system_score_gemma":0.000034369696,"threshold_uncertainty_score":0.95760244},"labels":[],"label_agreement":null},{"id":"W2891867110","doi":"10.1109/cvprw.2018.00222","title":"Learning Network Architectures of Deep CNNs Under Resource Constraints","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Computer science; Deep learning; Artificial intelligence; Resource (disambiguation); Distributed computing; Computer architecture; Computer network","score_opus":0.014426842101330453,"score_gpt":0.2604323902544467,"score_spread":0.24600554815311626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891867110","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":[0.009760518,0.00003473963,0.9578729,0.0005936437,0.00005115612,0.00010427537,1.7808583e-7,0.0002015935,0.03138099],"genre_scores_gemma":[0.8398868,0.0000016294852,0.15895484,0.0006105641,0.00018622461,0.000006088413,7.4044476e-7,0.000007071386,0.00034606253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906117,0.00006014554,0.00017973813,0.00027701614,0.00014668454,0.00027523964],"domain_scores_gemma":[0.99910325,0.00025203952,0.000097270815,0.000418851,0.000057392896,0.00007122701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011860743,0.00009365863,0.00011261328,0.00003726215,0.00017767018,0.000023139002,0.0005699413,0.00003654174,0.00008156134],"category_scores_gemma":[0.000026154741,0.00008103723,0.00003851347,0.00045799755,0.00037665956,0.000051528423,0.00024087251,0.00015406957,0.0000453093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008346948,0.000029339042,0.000874126,0.0000045271254,0.000022151495,0.0000016784779,0.0005736803,0.27415672,0.0014043876,0.41931704,0.0015901666,0.30201784],"study_design_scores_gemma":[0.00073999807,0.00066361873,0.012942751,0.000059839233,0.0000182656,0.0001124308,0.0002587238,0.60542154,0.012651179,0.3041974,0.062136106,0.00079815526],"about_ca_topic_score_codex":0.0000027256508,"about_ca_topic_score_gemma":0.000021246111,"teacher_disagreement_score":0.8301263,"about_ca_system_score_codex":0.000010434326,"about_ca_system_score_gemma":0.000015469108,"threshold_uncertainty_score":0.33046022},"labels":[],"label_agreement":null},{"id":"W2895255442","doi":"10.1109/wacv.2019.00040","title":"Multi-Scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Overfitting; Computer science; Reuse; Convolution (computer science); Feature (linguistics); Regularization (linguistics); Concatenation (mathematics); Artificial intelligence; Scale (ratio); Aggregate (composite); Convolutional neural network; Artificial neural network; Machine learning; Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.028684782040612748,"score_gpt":0.2889173167260632,"score_spread":0.26023253468545043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895255442","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":[0.0030681565,0.00048258257,0.9914637,0.0024442158,0.00048663619,0.0017412344,0.000020568265,0.00025912313,0.00003373605],"genre_scores_gemma":[0.16819724,0.000059675218,0.8287579,0.0002541374,0.00011863768,0.0003601794,0.00007199828,0.000020658848,0.0021595617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986339,0.000033937635,0.0001819485,0.0007862792,0.00013695093,0.0002269544],"domain_scores_gemma":[0.9979532,0.00025379335,0.00020606593,0.0013396263,0.00016592976,0.00008142624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121029734,0.00021741798,0.00021505404,0.000073665666,0.00011874976,0.0001173108,0.0007912638,0.00025542156,0.0000011548482],"category_scores_gemma":[0.00008630461,0.00020897729,0.00006277525,0.000116513336,0.000043223554,0.00018161596,0.0012747687,0.00029128767,0.00001730548],"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":[0.000086777254,0.0002976849,0.00056451606,0.0007273681,0.00013334794,0.0000032176029,0.0016901107,0.72836107,0.011552839,0.041256107,0.043798104,0.17152885],"study_design_scores_gemma":[0.00035847005,0.00002475616,0.0010688087,0.00006697629,0.000015754984,0.000009977339,0.0000050888502,0.98183876,0.0004635204,0.014908049,0.0009823054,0.00025754512],"about_ca_topic_score_codex":0.0000076548395,"about_ca_topic_score_gemma":0.000038229016,"teacher_disagreement_score":0.25347766,"about_ca_system_score_codex":0.000071840026,"about_ca_system_score_gemma":0.00006280182,"threshold_uncertainty_score":0.85218465},"labels":[],"label_agreement":null},{"id":"W2895260923","doi":"10.1007/978-3-030-01237-3_25","title":"Constraint-Aware Deep Neural Network Compression","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":59,"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":"Agence Nationale de la Recherche","keywords":"Computer science; Deep learning; Artificial neural network; Deep neural networks; Software deployment; Artificial intelligence; Simulated annealing; Compression (physics); Constraint (computer-aided design); Mathematical optimization; Machine learning; Engineering","score_opus":0.019210064656595204,"score_gpt":0.26223441128313996,"score_spread":0.24302434662654476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895260923","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":[0.000047255653,0.0004786932,0.99284446,0.0009516373,0.0022738776,0.00061139354,0.0000050544622,0.00041458695,0.002373019],"genre_scores_gemma":[0.16002043,0.00006701352,0.8309932,0.0052413745,0.0031169306,0.000040219456,0.000021916898,0.00008915557,0.0004097981],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99513173,0.00005185071,0.00066163525,0.002108951,0.00094699435,0.0010988263],"domain_scores_gemma":[0.99597657,0.0007160732,0.0004540196,0.0022120685,0.0003282244,0.00031303958],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046002853,0.0006983436,0.00061291695,0.0003781651,0.0006337446,0.00045746894,0.004860347,0.00038019769,0.00009484779],"category_scores_gemma":[0.000037776746,0.0006402271,0.00015974385,0.0009319649,0.001824413,0.00069892494,0.002477527,0.001104836,0.00015482845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057740426,0.000019278357,0.000031774107,0.000015558511,0.000008184481,0.000082707265,0.00019007888,0.29917252,0.000056374527,0.016799392,0.00023823787,0.6833801],"study_design_scores_gemma":[0.0001699896,0.0001086538,0.00005512047,0.00020762086,0.000005869894,0.00014450387,8.4189544e-8,0.780871,0.00018734057,0.21394166,0.0036975038,0.0006106495],"about_ca_topic_score_codex":0.0000035616754,"about_ca_topic_score_gemma":0.00005585709,"teacher_disagreement_score":0.6827695,"about_ca_system_score_codex":0.00023315578,"about_ca_system_score_gemma":0.00023484591,"threshold_uncertainty_score":0.9996049},"labels":[],"label_agreement":null},{"id":"W2895435696","doi":"10.1109/access.2018.2872698","title":"Understanding Mixup Training Methods","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":105,"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":"Taishan University; Shandong University; Amazon Web Services; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Machine learning; Artificial neural network; Artificial intelligence; Empirical risk minimization; Weighting; Generalization; Sample (material); Interpolation (computer graphics); Training set; Pattern recognition (psychology); Data mining; Image (mathematics); Mathematics","score_opus":0.4585232216924992,"score_gpt":0.4692555632666826,"score_spread":0.010732341574183402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895435696","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":[0.001369641,0.000019935513,0.9879366,0.00079017633,0.00070915796,0.00010490472,4.2113763e-7,0.00026164766,0.008807481],"genre_scores_gemma":[0.6332228,0.0000049558935,0.36570317,0.000657165,0.000300886,0.00001857181,2.7620945e-7,0.000008361778,0.00008379371],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990693,0.00006222772,0.00014360645,0.00033315545,0.0001221461,0.0002696102],"domain_scores_gemma":[0.9990671,0.00023778863,0.00007529063,0.0004994437,0.00004165773,0.00007867635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023746201,0.00009219494,0.00010281889,0.00006905394,0.00026693544,0.00021387819,0.0013927821,0.000035034755,0.000016830341],"category_scores_gemma":[0.000024340889,0.0000879747,0.000031416377,0.00072383304,0.000092798095,0.0009710767,0.0001919886,0.00009723637,0.000041787265],"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":[0.000007842975,0.00004129209,0.00026873453,0.0000105671315,0.00003340149,0.000010764243,0.0028495255,0.0012332019,0.019457249,0.5851484,0.010724076,0.38021493],"study_design_scores_gemma":[0.00037295985,0.00008175681,0.0005938952,0.000033573757,0.000011151791,0.000039122046,0.000105141546,0.1969499,0.07269453,0.6975534,0.03103544,0.0005291122],"about_ca_topic_score_codex":0.000003358304,"about_ca_topic_score_gemma":0.000009004946,"teacher_disagreement_score":0.63185316,"about_ca_system_score_codex":0.00006506857,"about_ca_system_score_gemma":0.000027799222,"threshold_uncertainty_score":0.35875043},"labels":[],"label_agreement":null},{"id":"W2895814028","doi":"10.1109/disa.2018.8490611","title":"Influence of Positive Additive Noise on Classification Performance of Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Agentúra na Podporu Výskumu a Vývoja; Canadian Institute for Advanced Research","keywords":"Convolutional neural network; MNIST database; Computer science; Noise (video); Artificial intelligence; Deep learning; Process (computing); Artificial neural network; Pattern recognition (psychology); Set (abstract data type); Speech recognition; Machine learning; Image (mathematics)","score_opus":0.014735763303317622,"score_gpt":0.2559942067152896,"score_spread":0.24125844341197197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895814028","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":[0.73346245,0.000008448875,0.26433858,0.00022004386,0.00004753881,0.00018967582,0.000010292541,0.00004675222,0.001676197],"genre_scores_gemma":[0.99186796,0.000014928424,0.007644149,0.00029477774,0.00007432108,0.000027053886,0.0000095931355,0.0000049422247,0.00006224725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999031,0.000035531895,0.00027175987,0.00028000856,0.00021180077,0.00016988892],"domain_scores_gemma":[0.99859077,0.00022340925,0.00024353189,0.00041070578,0.00048069685,0.000050912662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007470181,0.00010176372,0.00012510137,0.0000646941,0.000096290896,0.0000072931975,0.000487257,0.000044357137,0.000010958985],"category_scores_gemma":[0.000023712506,0.00009191873,0.000034506153,0.0005310312,0.00035127118,0.0004286476,0.000105551786,0.000101998405,0.000019214329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016016558,0.00023020658,0.0051029115,0.000010661985,0.000027131555,5.940128e-7,0.00027032784,0.36465457,0.017090684,0.5765868,0.0007887249,0.035077244],"study_design_scores_gemma":[0.000112701215,0.000306544,0.25315186,0.00001988689,0.0000025177303,0.0000026692012,0.000004297368,0.72882193,0.017092492,0.00036469538,0.000044746117,0.00007563412],"about_ca_topic_score_codex":0.0000054608536,"about_ca_topic_score_gemma":0.0000022849003,"teacher_disagreement_score":0.57622206,"about_ca_system_score_codex":0.000032585627,"about_ca_system_score_gemma":0.00003101485,"threshold_uncertainty_score":0.3748337},"labels":[],"label_agreement":null},{"id":"W2895871797","doi":"10.32920/22734398.v1","title":"A Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":67,"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":"Dice; Segmentation; Pyramid (geometry); Function (biology); Image (mathematics); Artificial intelligence; Image segmentation; Computer science; Pattern recognition (psychology); Mathematics; Statistics; Geometry; Biology","score_opus":0.05545238564034197,"score_gpt":0.3025661462564296,"score_spread":0.2471137606160876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895871797","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":[0.003282413,0.00000849429,0.9912757,0.0015593065,0.0007692671,0.0021292942,0.00002783388,0.00087987975,0.000067755405],"genre_scores_gemma":[0.12004034,0.000049024384,0.87192625,0.00038957832,0.00041828962,0.002952023,0.00089060277,0.00008329418,0.0032506115],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980091,0.00002171226,0.00033611138,0.0010370677,0.0002825369,0.00031350044],"domain_scores_gemma":[0.9984563,0.00013574379,0.00032799615,0.00077597017,0.00021884213,0.00008515907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017571799,0.00028671458,0.00021915647,0.00014203774,0.00021435182,0.00017985975,0.0005600581,0.00019167944,0.0000031486986],"category_scores_gemma":[0.000011972293,0.00025109033,0.00011833431,0.00038009527,0.000040025847,0.00041594336,0.0006373587,0.0002718697,0.000039277933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096987333,0.0008834392,0.0006832367,0.00094153156,0.0004900318,0.00000812298,0.0004127521,0.18849416,0.343602,0.14574909,0.012021865,0.30574387],"study_design_scores_gemma":[0.0016723754,0.00045937055,0.0042911046,0.00012000982,0.000090734575,0.000013570011,0.000052776024,0.9613199,0.003833862,0.026381817,0.0011046445,0.0006597945],"about_ca_topic_score_codex":0.000050073733,"about_ca_topic_score_gemma":0.00013810341,"teacher_disagreement_score":0.7728258,"about_ca_system_score_codex":0.00016905645,"about_ca_system_score_gemma":0.00008621342,"threshold_uncertainty_score":0.99999416},"labels":[],"label_agreement":null},{"id":"W2897767703","doi":"10.1109/tmi.2018.2876796","title":"Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Convolutional neural network; Segmentation; Computer science; Artificial intelligence; Prostate biopsy; Context (archaeology); Trajectory; Image segmentation; Biopsy; Magnetic resonance imaging; Computer vision; Prostate; Radiology; Medicine","score_opus":0.006064120172578948,"score_gpt":0.25384112157384153,"score_spread":0.24777700140126258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897767703","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":[0.008955862,0.000037121124,0.9856041,0.0041481587,0.00022594506,0.00072816346,0.0000033541203,0.0002743162,0.000022944387],"genre_scores_gemma":[0.9487151,0.000021388923,0.04875387,0.002008276,0.00009195682,0.00036510028,0.0000116979145,0.00001966818,0.000012943538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825436,0.000078011384,0.0003412013,0.00053191336,0.00045924526,0.00033526454],"domain_scores_gemma":[0.99910295,0.00012759038,0.00008975932,0.00030882185,0.00011614638,0.00025473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021129592,0.00018056066,0.00015084547,0.00022558452,0.00032421548,0.00008267577,0.00027809272,0.000053019354,0.000033227345],"category_scores_gemma":[0.000008682227,0.00016736695,0.000021607142,0.0012612342,0.00023679336,0.00057578186,0.000008032275,0.0002390359,0.000026447526],"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":[0.000057872225,0.0002300214,0.001386871,0.000025256259,0.000018120789,0.000016476148,0.0010674682,0.59985226,0.0010481843,0.00069316314,0.0003889785,0.39521533],"study_design_scores_gemma":[0.0005596628,0.000071153736,0.0009227122,0.00005926511,0.000008668743,0.00008024098,0.00006402901,0.9967317,0.00090022676,0.00029659356,0.00012169415,0.00018408148],"about_ca_topic_score_codex":0.00003775988,"about_ca_topic_score_gemma":0.000059992726,"teacher_disagreement_score":0.93975925,"about_ca_system_score_codex":0.00011127694,"about_ca_system_score_gemma":0.000056496894,"threshold_uncertainty_score":0.6825026},"labels":[],"label_agreement":null},{"id":"W2897818259","doi":"10.48550/arxiv.1810.05749","title":"Graph HyperNetworks for Neural Architecture Search","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Inference; Architecture; Artificial neural network; Graph; Network architecture; Network topology; Artificial intelligence; Task (project management); Random search; Topological graph theory; Machine learning; Theoretical computer science; Algorithm; Computer network; Engineering","score_opus":0.0792400169802236,"score_gpt":0.21734876363655037,"score_spread":0.13810874665632678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897818259","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":[0.0577599,0.00008264127,0.9392424,0.00043566103,0.00055440725,0.00087470317,0.000027110076,0.00042788623,0.0005952758],"genre_scores_gemma":[0.9741484,0.000087857676,0.023847116,0.0002682646,0.0004914653,0.0000120571085,0.000041390893,0.000036911046,0.0010665351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974279,0.00010613229,0.00019720456,0.0015410049,0.00010458846,0.0006231822],"domain_scores_gemma":[0.9972427,0.00028722093,0.00017737946,0.0018141572,0.00024386254,0.00023466923],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017058806,0.0003716961,0.00032299777,0.00023107004,0.0003302018,0.00011954842,0.0030131717,0.00031387148,0.000011470154],"category_scores_gemma":[0.000017376113,0.00041261432,0.00031365023,0.0008740821,0.00019785503,0.00021699045,0.0023899018,0.00081703777,0.00004269215],"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":[0.000033737986,0.000042687414,0.0002761056,0.000037946334,0.000044473465,0.00002764122,0.0000798404,0.868533,0.000018429713,0.1253596,0.0014556306,0.0040908847],"study_design_scores_gemma":[0.0002829052,0.0000693647,0.00022479748,0.00002985326,0.000031176816,0.000007710993,0.000007013505,0.7889608,0.00006755559,0.20759498,0.0023253176,0.00039852105],"about_ca_topic_score_codex":0.0000144885435,"about_ca_topic_score_gemma":0.000026477119,"teacher_disagreement_score":0.9163885,"about_ca_system_score_codex":0.00010634018,"about_ca_system_score_gemma":0.000101422425,"threshold_uncertainty_score":0.9998326},"labels":[],"label_agreement":null},{"id":"W2897824080","doi":"10.1109/icci-cc.2018.8482089","title":"Neighbouring Proximity - An Key Impact Factor of Deep Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Machine translation; Machine learning; Key (lock); Factor (programming language)","score_opus":0.02992072391712175,"score_gpt":0.3069934370199146,"score_spread":0.27707271310279286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897824080","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":[0.21594119,0.000017258693,0.7819815,0.000078769386,0.000027772214,0.0001079658,0.0000010229849,0.00019414209,0.0016503666],"genre_scores_gemma":[0.89370245,0.0000027417852,0.10605249,0.000046460536,0.000078811274,0.0000051700636,0.0000015756217,0.0000076162837,0.00010268381],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991834,0.0000381152,0.00015600964,0.0002673428,0.00013675037,0.00021835037],"domain_scores_gemma":[0.99920654,0.000054862347,0.00009980758,0.00045068056,0.0000797553,0.000108378146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056400626,0.00009982653,0.000111967034,0.000047816466,0.00013870838,0.000037774545,0.0005621715,0.000028407532,0.00012497374],"category_scores_gemma":[0.000020861613,0.000077661716,0.000041984404,0.00032163184,0.00005575478,0.00075196393,0.00019517947,0.00012345494,0.000030515681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004053826,0.0003936789,0.08419364,0.000025162823,0.0000564297,0.0000048573297,0.0032443278,0.009758988,0.1066853,0.111893184,0.000067061024,0.68363684],"study_design_scores_gemma":[0.0001474535,0.0003167043,0.038244456,0.0000040599984,0.0000020604298,0.000008303734,0.0000061767396,0.9323799,0.024138004,0.004073514,0.0005212232,0.00015812083],"about_ca_topic_score_codex":0.000053439086,"about_ca_topic_score_gemma":0.00006358262,"teacher_disagreement_score":0.92262095,"about_ca_system_score_codex":0.000026202266,"about_ca_system_score_gemma":0.000015466652,"threshold_uncertainty_score":0.3166953},"labels":[],"label_agreement":null},{"id":"W2898018479","doi":"10.1109/ijcnn.2018.8489442","title":"Reducing SqueezeNet Storage Size with Depthwise Separable Convolutions","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Nvidia","keywords":"Convolutional neural network; Computer science; Reduction (mathematics); Inference; Algorithm; Artificial intelligence; Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.01373501367424318,"score_gpt":0.2576138056193586,"score_spread":0.24387879194511541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898018479","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":[0.022106877,0.000035199617,0.95003194,0.0010844271,0.00011306584,0.00022080944,0.0000013839964,0.00050203816,0.025904275],"genre_scores_gemma":[0.7088696,0.0000050454973,0.28666297,0.0004706978,0.000132564,0.00004385228,9.269446e-7,0.000009587387,0.0038047575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989398,0.000025901329,0.00013895985,0.0004263,0.00015603339,0.00031302244],"domain_scores_gemma":[0.9987509,0.00014256497,0.00006266027,0.0008000236,0.00012295859,0.000120865225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007352049,0.00011831787,0.00010228739,0.000035232028,0.00034287397,0.000072235496,0.00056248903,0.000032531112,0.000100686484],"category_scores_gemma":[0.00002154895,0.000096234944,0.000023273049,0.0006708442,0.00014344613,0.00047593765,0.000150571,0.00009712577,0.00028159659],"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":[0.00005533551,0.00035870142,0.0012830475,0.000017404174,0.00007777363,0.000037833015,0.0012564083,0.040670685,0.02891672,0.79786193,0.10053015,0.028933994],"study_design_scores_gemma":[0.0015000917,0.0008003116,0.007822624,0.00007192173,0.000034040528,0.00026056307,0.00008053734,0.74311614,0.038234055,0.0278468,0.1789062,0.0013267309],"about_ca_topic_score_codex":0.000040115527,"about_ca_topic_score_gemma":0.00008657921,"teacher_disagreement_score":0.7700151,"about_ca_system_score_codex":0.000051542655,"about_ca_system_score_gemma":0.00006881388,"threshold_uncertainty_score":0.3924347},"labels":[],"label_agreement":null},{"id":"W2898663682","doi":"10.1155/2018/7075814","title":"A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":37,"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":"Xi'an Polytechnic University","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Traffic flow (computer networking); Object detection; Deep learning; Vehicle tracking system; Real-time computing; Computer vision; Intelligent transportation system; Simulation; Pattern recognition (psychology); Engineering; Transport engineering; Computer network; Kalman filter","score_opus":0.010236499999240325,"score_gpt":0.24287512179512344,"score_spread":0.23263862179588313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898663682","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":[0.45434973,0.00012201889,0.5452567,0.000030413661,0.00012836354,0.000070353744,9.028284e-7,0.000026971653,0.000014557616],"genre_scores_gemma":[0.7365297,0.000048049023,0.2632497,0.000017938375,0.0001302714,0.000004999691,0.0000055636615,0.000009237958,0.0000045585007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987443,0.00004457228,0.0005648807,0.00020612162,0.00029047133,0.00014966071],"domain_scores_gemma":[0.99856585,0.00010340274,0.00070256664,0.00018239018,0.00037088338,0.00007488679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001364504,0.00011131418,0.00020898212,0.00012141975,0.00010716228,0.000014638599,0.00032659702,0.000053886048,0.000004839146],"category_scores_gemma":[0.000029357609,0.00010646343,0.0001032359,0.0005059697,0.00005898109,0.0010062353,0.0000047860935,0.00023699358,0.0000032673884],"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":[0.00008668593,0.00007702542,0.00016902007,0.00000796044,0.000019495552,0.0000024335836,0.0021061406,0.74422,0.10560617,0.0002349531,0.0000021787505,0.14746793],"study_design_scores_gemma":[0.0032072419,0.0013714031,0.13928802,0.00010679743,0.000094578354,0.00002340047,0.0008036611,0.70450354,0.1427021,0.0054180287,0.002039975,0.00044125813],"about_ca_topic_score_codex":0.0000040498408,"about_ca_topic_score_gemma":0.000041737992,"teacher_disagreement_score":0.28217995,"about_ca_system_score_codex":0.000036781155,"about_ca_system_score_gemma":0.000022210857,"threshold_uncertainty_score":0.43414524},"labels":[],"label_agreement":null},{"id":"W2899246346","doi":"10.1109/tsmc.2018.2872891","title":"Feature Selection Based on Tensor Decomposition and Object Proposal for Night-Time Multiclass Vehicle Detection","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"National Natural Science Foundation of China; City University of Hong Kong","keywords":"Computer science; Histogram; Artificial intelligence; Feature (linguistics); False positive paradox; Object detection; Pattern recognition (psychology); Histogram of oriented gradients; Computer vision; Feature extraction; Local binary patterns; Image (mathematics)","score_opus":0.008965563727785557,"score_gpt":0.24225593701761222,"score_spread":0.23329037328982666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899246346","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":[0.034142103,0.000065820364,0.96214885,0.00038419061,0.0008919084,0.0018936374,0.000025947105,0.000333482,0.00011407794],"genre_scores_gemma":[0.9919897,0.000008954517,0.006046921,0.00007231533,0.00028008004,0.0005528348,0.000003384348,0.000037524926,0.0010083058],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982368,0.0001817997,0.00030010822,0.00067123567,0.00028350053,0.00032655985],"domain_scores_gemma":[0.9988012,0.00024118784,0.00017621342,0.0003866179,0.00023721578,0.00015756601],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002278174,0.00027844074,0.0002785808,0.00021317955,0.00081965886,0.0004137567,0.00018201701,0.00019528028,9.2203993e-7],"category_scores_gemma":[0.0000041652006,0.00025540264,0.00006529631,0.0003884707,0.000110149194,0.00022560963,0.0000030312185,0.00021071534,0.00002779187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013712235,0.0013754321,0.00018516493,0.0013236202,0.00036936623,0.00001278918,0.0017073347,0.3960439,0.498604,0.015667174,0.002359102,0.08098089],"study_design_scores_gemma":[0.0008541643,0.0011454909,0.00013915192,0.00014715048,0.000035930876,0.000084404724,0.00003824592,0.9685456,0.02706376,0.00007026087,0.0015905814,0.00028523762],"about_ca_topic_score_codex":0.000049987775,"about_ca_topic_score_gemma":0.000072819945,"teacher_disagreement_score":0.9578476,"about_ca_system_score_codex":0.00013755144,"about_ca_system_score_gemma":0.000045011624,"threshold_uncertainty_score":0.9999898},"labels":[],"label_agreement":null},{"id":"W2899724367","doi":"10.1007/978-3-030-03801-4_43","title":"Scale-Aware RPN for Vehicle Detection","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Bootstrapping (finance); Disjoint sets; Scale (ratio); Artificial intelligence; Data mining; Feature (linguistics); Machine learning","score_opus":0.018702706010345682,"score_gpt":0.2626382747578919,"score_spread":0.24393556874754624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899724367","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":[0.00012073796,0.00008697066,0.9962898,0.00074431545,0.0013510917,0.0007931565,0.000007709648,0.00029122157,0.00031501695],"genre_scores_gemma":[0.13490972,0.000023401837,0.8606418,0.0018372828,0.0017176828,0.0001194323,0.000007855386,0.000066893175,0.0006759804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967947,0.000015589389,0.00040479822,0.0016053327,0.00054957706,0.0006300263],"domain_scores_gemma":[0.9972729,0.00047898592,0.000264867,0.0014718337,0.00035124403,0.00016018777],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037640217,0.00041236787,0.00035426547,0.00041436395,0.00059025263,0.00041634945,0.0027518454,0.00027262623,0.000012601268],"category_scores_gemma":[0.000040934803,0.0003563554,0.00013195559,0.000656862,0.0007916427,0.0005906113,0.00087069185,0.0004551369,0.000061134284],"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":[0.0000059446006,0.000013569053,0.000006003949,0.000017897517,0.000003846632,0.0000035420835,0.00017596316,0.009921766,0.0006217567,0.0026528179,0.000038487695,0.9865384],"study_design_scores_gemma":[0.0001852959,0.0001909234,0.000029256165,0.000099920384,0.000005404237,0.000028102377,6.3106924e-8,0.6907198,0.012759438,0.28843847,0.007089016,0.00045430876],"about_ca_topic_score_codex":0.000004208257,"about_ca_topic_score_gemma":0.00014597239,"teacher_disagreement_score":0.9860841,"about_ca_system_score_codex":0.00028017827,"about_ca_system_score_gemma":0.00022942775,"threshold_uncertainty_score":0.99988884},"labels":[],"label_agreement":null},{"id":"W2899954960","doi":"10.1145/3240765.3240822","title":"Logic synthesis of binarized neural networks for efficient circuit implementation","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Computer science; Scalability; Field-programmable gate array; Artificial neural network; Multiplier (economics); Key (lock); Computer architecture; Computer engineering; Binary number; Realization (probability); Efficient energy use; Computer hardware; Artificial intelligence; Mathematics; Arithmetic","score_opus":0.036966059002579266,"score_gpt":0.31528046325395936,"score_spread":0.2783144042513801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899954960","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":[0.009385901,0.000016258333,0.9887129,0.0005940826,0.00016022424,0.000529051,0.0000029480652,0.0001242065,0.00047442716],"genre_scores_gemma":[0.9309112,0.0000026469206,0.068470374,0.0002852392,0.00009784234,0.0001898776,0.0000030016515,0.000007124782,0.000032697295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990664,0.00002802558,0.00025789897,0.00029740072,0.00010078018,0.0002494778],"domain_scores_gemma":[0.9988936,0.0003572928,0.0001420991,0.00041475453,0.0001433699,0.000048835693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014736371,0.00009368904,0.0001303444,0.000055354612,0.00014117835,0.000026952619,0.00049446145,0.00003019701,0.000033115073],"category_scores_gemma":[0.000025390664,0.00008044158,0.0000629643,0.00042645546,0.000070395494,0.000109161956,0.00011504999,0.000033633176,0.0000071292106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023594377,0.0001131363,0.00020513192,0.0000127953535,0.000021373755,3.6638332e-7,0.00014809727,0.038414884,0.0071058553,0.6364669,0.0016445594,0.3158433],"study_design_scores_gemma":[0.00024964125,0.00012834636,0.0005407906,0.000003024608,0.000008956817,0.0000026521836,0.000021715852,0.97856265,0.012453469,0.0075040897,0.0004169199,0.0001077181],"about_ca_topic_score_codex":0.0000070365422,"about_ca_topic_score_gemma":0.000009150333,"teacher_disagreement_score":0.9401478,"about_ca_system_score_codex":0.000023811499,"about_ca_system_score_gemma":0.00001307993,"threshold_uncertainty_score":0.32803124},"labels":[],"label_agreement":null},{"id":"W2900763636","doi":"10.1109/cvpr.2019.00932","title":"Structured Pruning of Neural Networks With Budget-Aware Regularization","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs; Compute Canada","keywords":"Computer science; Pruning; Regularization (linguistics); Artificial neural network; Inference; Deep neural networks; Artificial intelligence; Residual; Machine learning; Feature (linguistics); Dropout (neural networks); Algorithm","score_opus":0.011209154557177367,"score_gpt":0.2397131926062216,"score_spread":0.22850403804904423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900763636","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":[0.0026013122,0.00009345491,0.9948291,0.00045207882,0.00037075646,0.0007847971,0.000006088164,0.00029878985,0.0005636224],"genre_scores_gemma":[0.82945246,0.000016170003,0.16954297,0.00015998476,0.000103319326,0.00004528442,0.00010856408,0.000029647268,0.0005416207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822307,0.00006037135,0.00036061712,0.0007618297,0.0003128411,0.00028124766],"domain_scores_gemma":[0.9973995,0.00008475504,0.0005099203,0.0017007502,0.00023268255,0.00007236071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007431819,0.00029347616,0.00036045827,0.000100652884,0.000076908014,0.000093281975,0.0014064311,0.00023419835,0.0000094317],"category_scores_gemma":[0.000006135811,0.00023992656,0.000070827264,0.0004937802,0.00006610697,0.00025833474,0.0013413911,0.00055349094,0.0000021089033],"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":[0.0000066929274,0.000009449062,0.0005727276,0.00003492755,0.000019333995,0.0000012622162,0.000051891013,0.9710021,0.000058765017,0.021146245,0.00014519386,0.0069514057],"study_design_scores_gemma":[0.00016310516,0.000041199688,0.0019437503,0.00006284301,0.000014505856,0.0000096348085,0.000005801652,0.98974484,0.0005512856,0.007105285,0.00008283631,0.0002749189],"about_ca_topic_score_codex":0.0000075053968,"about_ca_topic_score_gemma":0.000012443527,"teacher_disagreement_score":0.8268511,"about_ca_system_score_codex":0.00004298057,"about_ca_system_score_gemma":0.00008250831,"threshold_uncertainty_score":0.9783921},"labels":[],"label_agreement":null},{"id":"W2901048985","doi":"","title":"Weakly Supervised Semantic Image Segmentation with Self-correcting Networks","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Pascal (unit); Computer science; Segmentation; Artificial intelligence; Minimum bounding box; Pattern recognition (psychology); Set (abstract data type); Bounding overwatch; Annotation; Convolutional neural network; Image segmentation; Machine learning; Image (mathematics)","score_opus":0.03445265751493123,"score_gpt":0.1938507124578322,"score_spread":0.15939805494290096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901048985","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":[0.15414783,0.000022621536,0.8431588,0.00008960452,0.00037955557,0.0005730111,0.0000028481497,0.0007574668,0.0008682782],"genre_scores_gemma":[0.9315866,0.00009330566,0.06758914,0.00012266492,0.0002160014,0.000006772528,0.000029579061,0.000037393045,0.00031850685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976374,0.00013404292,0.00022680267,0.0014055091,0.00011224688,0.00048401393],"domain_scores_gemma":[0.9975998,0.00016469043,0.00035945905,0.0014342703,0.000266302,0.00017546864],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016487461,0.00039374578,0.0003079465,0.00017539726,0.00037494372,0.00019644626,0.0016213267,0.0002001035,0.000018248236],"category_scores_gemma":[0.000009719731,0.00042479127,0.00011803202,0.0010916739,0.0001241613,0.0007179443,0.0014193992,0.00060927833,0.000095554766],"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":[0.000054099633,0.00017409316,0.0040658982,0.00009356047,0.00017494624,0.00022763493,0.00055427453,0.9794059,0.00023353192,0.011235701,0.0007831598,0.0029972212],"study_design_scores_gemma":[0.00039072018,0.00007732705,0.0005853625,0.00009779267,0.00009023351,0.000020903906,0.0000710573,0.9920615,0.00046692954,0.0055432767,0.00007204658,0.00052284775],"about_ca_topic_score_codex":0.00004297063,"about_ca_topic_score_gemma":0.000053494936,"teacher_disagreement_score":0.7774388,"about_ca_system_score_codex":0.00025530905,"about_ca_system_score_gemma":0.00011959213,"threshold_uncertainty_score":0.9998204},"labels":[],"label_agreement":null},{"id":"W2901323331","doi":"10.1109/wacv.2019.00119","title":"Recurrent Iterative Gating Networks for Semantic Segmentation","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Gating; Computer science; Segmentation; Artificial intelligence; Neuroscience","score_opus":0.035084086299117793,"score_gpt":0.32245335268954894,"score_spread":0.28736926639043114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901323331","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":[0.000522689,0.00021714882,0.9938953,0.00082765525,0.0012934407,0.0022615239,0.000008013692,0.00027784583,0.00069637515],"genre_scores_gemma":[0.25457218,0.00009950095,0.7418337,0.00062437955,0.00044141884,0.0013502585,0.00022382758,0.00003189318,0.0008228463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982813,0.00005339412,0.00037420355,0.0008161763,0.00017200847,0.00030292894],"domain_scores_gemma":[0.9982083,0.0003807518,0.00035835238,0.00082235364,0.00016844211,0.00006182734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015961283,0.0002555377,0.00025662562,0.0000665834,0.0001432106,0.00024959436,0.0008274045,0.00013294893,0.000007090772],"category_scores_gemma":[0.000021413725,0.00024126959,0.00012351392,0.0001978513,0.000018738834,0.00025447438,0.0009944035,0.00036377346,0.000027113992],"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":[0.0000072243033,0.000046889338,0.00009153926,0.00012270895,0.00003930301,5.542178e-7,0.00044878828,0.80912507,0.00014919427,0.056529265,0.004252863,0.12918662],"study_design_scores_gemma":[0.00016418408,0.000045093402,0.00006938389,0.00010791204,0.000011575767,0.0000016257561,0.000013803284,0.97944874,0.0005111335,0.018723305,0.00063150405,0.00027175393],"about_ca_topic_score_codex":0.0000029364483,"about_ca_topic_score_gemma":0.0000069479347,"teacher_disagreement_score":0.25404948,"about_ca_system_score_codex":0.00010663602,"about_ca_system_score_gemma":0.00005500437,"threshold_uncertainty_score":0.98386884},"labels":[],"label_agreement":null},{"id":"W2901481277","doi":"10.1016/j.media.2020.101751","title":"Graph refinement based airway extraction using mean-field networks and graph neural networks","year":2020,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Danmarks Frie Forskningsfond; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Computer science; Graph; Artificial neural network; Inference; Algorithm; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science","score_opus":0.023797524028233572,"score_gpt":0.30691302252036046,"score_spread":0.28311549849212686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901481277","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":[0.0015364229,0.0010832102,0.9857303,0.010512194,0.00039434197,0.00035977372,0.0000044138824,0.00029118,0.000088174165],"genre_scores_gemma":[0.8748075,0.0010102678,0.11631346,0.006592356,0.0008518578,0.00015232466,0.00020163642,0.000050597395,0.000020007508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99533325,0.0003166136,0.0009214519,0.001666013,0.0011172062,0.0006454373],"domain_scores_gemma":[0.99635357,0.00062323926,0.0005895724,0.0015005834,0.00018597701,0.00074704515],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061549264,0.0005558904,0.000923128,0.00053680106,0.0003541034,0.0003749355,0.0015052641,0.0005360987,0.0001580308],"category_scores_gemma":[0.00019096614,0.00052763504,0.0006905147,0.0035947459,0.00020323799,0.00035350304,0.0016478982,0.002075431,0.0000030279652],"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":[0.000020958918,0.00010291362,0.0005754852,0.00003780652,0.00067620043,0.00016268696,0.00004071357,0.944272,0.00010395698,0.000323984,0.0015363344,0.052146956],"study_design_scores_gemma":[0.00024008265,0.00004289176,0.0003802336,0.000050403985,0.0011096029,0.00000857302,0.000008088818,0.9961543,0.00008221608,0.0012431451,0.00021325109,0.00046721142],"about_ca_topic_score_codex":0.00024874476,"about_ca_topic_score_gemma":0.00022571134,"teacher_disagreement_score":0.87327105,"about_ca_system_score_codex":0.00008016422,"about_ca_system_score_gemma":0.00008767551,"threshold_uncertainty_score":0.99971753},"labels":[],"label_agreement":null},{"id":"W2901663942","doi":"10.23919/date.2019.8714765","title":"Learning to Skip Ineffectual Recurrent Computations in LSTMs","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Pruning; Computation; Inference; Recurrent neural network; Speedup; Set (abstract data type); Process (computing); Computational complexity theory; Parallel computing; Algorithm; Artificial intelligence; Artificial neural network; Programming language","score_opus":0.013198067942521595,"score_gpt":0.2819775062096999,"score_spread":0.2687794382671783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901663942","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":[0.21677576,0.000013156035,0.7767251,0.0018295456,0.0001567373,0.00042072675,2.1931731e-7,0.00020335104,0.003875375],"genre_scores_gemma":[0.91136175,0.0000033843357,0.08740254,0.00047274295,0.000023580047,0.000049672697,0.0000027137485,0.000005814766,0.00067782286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918395,0.00004506639,0.00014618234,0.00030667995,0.00012048186,0.00019760872],"domain_scores_gemma":[0.999339,0.0002570767,0.000030339694,0.0002710985,0.000030399638,0.00007207505],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009652084,0.000077854536,0.00009396283,0.000110637404,0.000047954556,0.000042113734,0.0003962701,0.000020436091,0.000026942198],"category_scores_gemma":[0.000028041339,0.00007390203,0.000021064556,0.00085871905,0.000007257151,0.00020636429,0.00022258716,0.00016139004,0.0012025974],"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":[0.0000051034526,0.00008643055,0.0056468486,0.0000076095835,0.0000034432662,0.0000023268299,0.0014715341,0.5806128,0.0011897087,0.14016816,0.0015608984,0.26924518],"study_design_scores_gemma":[0.00042084247,0.00029581896,0.020903531,0.000032074684,0.0000010706192,0.0000069423313,0.0000677073,0.9443473,0.00047161107,0.0050468743,0.02811526,0.0002909434],"about_ca_topic_score_codex":0.0000069688886,"about_ca_topic_score_gemma":0.000022353131,"teacher_disagreement_score":0.694586,"about_ca_system_score_codex":0.00004080666,"about_ca_system_score_gemma":0.00001556044,"threshold_uncertainty_score":0.9995751},"labels":[],"label_agreement":null},{"id":"W2902644086","doi":"","title":"Using Deep Learning to Automate Feature Modeling in Learning by Observation: A Preliminary Study.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Deep learning; Machine learning; Feature learning","score_opus":0.25705089330873315,"score_gpt":0.4028845113484676,"score_spread":0.14583361803973444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902644086","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":[0.17482066,0.00001964584,0.8208339,0.0027156929,0.0001384554,0.0005085924,0.0000016188426,0.00016200321,0.0007993845],"genre_scores_gemma":[0.9680873,0.000007866657,0.03132462,0.00017423068,0.00008149253,0.00009792861,0.00000678873,0.000015679056,0.00020409483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765575,0.00013948901,0.00041549624,0.0007334254,0.0006988121,0.00035705575],"domain_scores_gemma":[0.9984458,0.00018203983,0.00025934496,0.00045738844,0.00053677754,0.00011865753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056569633,0.0002210488,0.00020661556,0.00019761728,0.0011442073,0.0006295298,0.0013790366,0.00009406772,0.00001626401],"category_scores_gemma":[0.000830354,0.00024347285,0.000040745985,0.00048336372,0.000049603736,0.00089442596,0.00037683916,0.0006418398,0.00009658958],"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":[0.000035179106,0.00013630252,0.00076805864,0.0000027979474,0.000004792805,0.0000063141647,0.00093411066,0.854387,0.0023843045,0.06381081,0.00001238669,0.077517964],"study_design_scores_gemma":[0.000038567367,0.00019056897,0.001477049,0.00005397678,0.0000024973394,0.0000027623248,0.00037880076,0.97158283,0.0012263849,0.02471692,0.00008276554,0.0002468533],"about_ca_topic_score_codex":0.000058373025,"about_ca_topic_score_gemma":0.00011033944,"teacher_disagreement_score":0.79326665,"about_ca_system_score_codex":0.00018877666,"about_ca_system_score_gemma":0.00012166704,"threshold_uncertainty_score":0.99285346},"labels":[],"label_agreement":null},{"id":"W2902806643","doi":"10.48550/arxiv.1811.12608","title":"DeepFlux for Skeletons in the Wild","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Skeletonization; Artificial intelligence; Computer science; Computer vision; Pixel; Benchmark (surveying); Context (archaeology); Object detection; Segmentation; Pattern recognition (psychology); Object (grammar); Clutter; Point (geometry); Image segmentation; Representation (politics); Topological skeleton; Edge detection; Field (mathematics); Image (mathematics); Image processing; Mathematics; Geography; Cartography; Active shape model","score_opus":0.08713992578444996,"score_gpt":0.21916429766259896,"score_spread":0.132024371878149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902806643","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":[0.041299634,0.00002930333,0.95497024,0.00086800026,0.00023256097,0.00079668604,0.000011701508,0.0001315206,0.0016603377],"genre_scores_gemma":[0.9859168,0.00006540455,0.012769,0.0005097506,0.00015201165,0.00001844322,0.0000133812255,0.000013196047,0.0005420026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985127,0.000087128996,0.00015066835,0.00085728243,0.00006014739,0.0003321132],"domain_scores_gemma":[0.9977436,0.00035755028,0.00015378198,0.0015893577,0.000091875554,0.00006384648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021383735,0.00020394943,0.00018044074,0.00012903176,0.00020085418,0.000071966184,0.0031761914,0.00015378083,0.0000050410763],"category_scores_gemma":[0.000028953864,0.00019047037,0.00014266858,0.0007448356,0.00012827797,0.00021621365,0.0011068574,0.0003921868,0.00005628407],"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":[0.000013146764,0.00010890327,0.0007978972,0.000028585371,0.000023527058,0.00003636861,0.00033607113,0.12354042,0.000013898368,0.86791617,0.0052368664,0.0019481254],"study_design_scores_gemma":[0.00032354298,0.00004982025,0.0010382498,0.000036017234,0.000025805732,0.000004096214,0.000042315005,0.49443766,0.000045992158,0.48657236,0.017087335,0.00033679322],"about_ca_topic_score_codex":0.000024475426,"about_ca_topic_score_gemma":0.00012298004,"teacher_disagreement_score":0.94461715,"about_ca_system_score_codex":0.00010596407,"about_ca_system_score_gemma":0.00008892087,"threshold_uncertainty_score":0.7767156},"labels":[],"label_agreement":null},{"id":"W2903585023","doi":"10.1609/aaai.v33i01.33013412","title":"Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung","keywords":"Computer science; Artificial intelligence; Interpretability; Object detection; Object (grammar); Convolutional neural network; Cognitive neuroscience of visual object recognition; Generalization; Transfer of learning; Machine learning; Invariant (physics); Unsupervised learning; Pattern recognition (psychology)","score_opus":0.03753807711940282,"score_gpt":0.2539223336304387,"score_spread":0.21638425651103588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903585023","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":[0.29850793,0.00002580493,0.6928432,0.0024846732,0.0005801849,0.0013050224,0.0000036138379,0.00022839084,0.0040211705],"genre_scores_gemma":[0.99556595,0.000011469515,0.0038471827,0.000302767,0.0000876709,0.000059876747,8.608943e-7,0.000015716303,0.000108524895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980865,0.000021952397,0.0004339651,0.00059678167,0.00048495288,0.00037585874],"domain_scores_gemma":[0.99839705,0.00013422134,0.00037131814,0.00040638226,0.0005997326,0.00009126859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023587281,0.00024263251,0.00024026391,0.00009264709,0.00022415952,0.00017603599,0.001638676,0.000087031825,0.00005152497],"category_scores_gemma":[0.00007021213,0.00017314294,0.00008606212,0.0009712808,0.00021157684,0.00056337053,0.0003048131,0.00039240433,0.00007182622],"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":[0.00018990441,0.00012177218,0.0008317025,0.000021312502,0.00002174002,5.5527045e-7,0.00027350153,0.03407411,0.06778325,0.84191614,0.000019269064,0.05474675],"study_design_scores_gemma":[0.00005124718,0.0003083528,0.0009591773,0.00007108893,0.000008871953,0.000013421557,0.000058639514,0.83593214,0.13215156,0.030194573,0.000031764488,0.00021914675],"about_ca_topic_score_codex":0.00003225662,"about_ca_topic_score_gemma":0.000039566108,"teacher_disagreement_score":0.81172156,"about_ca_system_score_codex":0.000060161885,"about_ca_system_score_gemma":0.0000819387,"threshold_uncertainty_score":0.7060564},"labels":[],"label_agreement":null},{"id":"W2903688003","doi":"10.1145/3242898","title":"You Cannot Improve What You Do not Measure","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Application-specific integrated circuit; Field-programmable gate array; Computer science; Reconfigurability; Convolutional neural network; Digital signal processing; Embedded system; Computer architecture; Ranging; Block (permutation group theory); FPGA prototype; Computer hardware; Computer engineering; Artificial intelligence","score_opus":0.020777876234960652,"score_gpt":0.24979059229416795,"score_spread":0.22901271605920728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903688003","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":[0.015927903,0.0022280382,0.9571178,0.017740762,0.0026659344,0.0010290487,0.000024346835,0.0015025906,0.001763583],"genre_scores_gemma":[0.9926664,0.00046300443,0.0040959436,0.00033651953,0.00008865601,0.0004003375,0.0000014308189,0.000021584587,0.0019261087],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99813247,0.00006261797,0.00039411505,0.00077386,0.00018783381,0.00044912382],"domain_scores_gemma":[0.99760574,0.00013850752,0.00015723711,0.0017809909,0.00020795847,0.00010955034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002895825,0.00024855963,0.00031269266,0.00044882286,0.0006969423,0.00025874653,0.001167252,0.00037084517,0.000024839375],"category_scores_gemma":[0.000036356505,0.00023093008,0.000056919762,0.0011633246,0.0003206879,0.0008265608,0.000017407625,0.00048175466,0.0001712197],"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":[0.000033494194,0.0000989756,0.000038156566,0.000031848584,0.00009409254,0.00000906767,0.00026337465,0.00029659978,0.0171355,0.056361113,0.0003394409,0.92529833],"study_design_scores_gemma":[0.0041203247,0.0033986822,0.00028550232,0.0011602377,0.00020844335,0.0021708042,0.005598771,0.055270426,0.51548505,0.097194776,0.31170186,0.0034051328],"about_ca_topic_score_codex":0.000035360255,"about_ca_topic_score_gemma":0.00004068958,"teacher_disagreement_score":0.9767385,"about_ca_system_score_codex":0.00006392358,"about_ca_system_score_gemma":0.000051743857,"threshold_uncertainty_score":0.94170547},"labels":[],"label_agreement":null},{"id":"W2904327456","doi":"10.1109/micro.2018.00020","title":"Diffy: a Déjà vu-Free Differential Deep Neural Network Accelerator","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Toronto","funders":"","keywords":"Convolutional neural network; Computer science; Bandwidth (computing); Chip; Convolution (computer science); Computation; Artificial neural network; Energy (signal processing); Computer hardware; Computational science; Computer engineering; Artificial intelligence; Algorithm; Telecommunications; Physics","score_opus":0.020300579178026874,"score_gpt":0.26177212459262744,"score_spread":0.24147154541460056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904327456","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":[0.03362112,0.000053803604,0.9582126,0.0020837174,0.00086302747,0.00028103517,0.0000014127028,0.0006943256,0.0041889423],"genre_scores_gemma":[0.9096796,0.000007634771,0.08552084,0.0018911147,0.0020814636,0.0000729607,0.0000034741845,0.000022334425,0.0007205709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998198,0.00004938913,0.00026810603,0.0006019712,0.00026821572,0.00061434513],"domain_scores_gemma":[0.99791324,0.00011200136,0.00009459314,0.0015740906,0.00010179787,0.00020427714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000525593,0.00022123475,0.00019120623,0.000043297896,0.00040040104,0.00021192571,0.0022442453,0.000069738155,0.00023299298],"category_scores_gemma":[0.000021479556,0.00018522845,0.0000814325,0.0006954179,0.00013655373,0.0005173226,0.0011966133,0.00017620163,0.0002733691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052269213,0.00028538358,0.0020727245,0.0000107575825,0.00006464477,0.000021546079,0.0004130617,0.0018399599,0.0074968585,0.62884533,0.110526375,0.24837106],"study_design_scores_gemma":[0.0011137688,0.0002977884,0.0133448085,0.00001000501,0.000018702702,0.00004227142,0.000008876124,0.8700486,0.0043545393,0.07957539,0.030376751,0.0008084909],"about_ca_topic_score_codex":0.0000067386422,"about_ca_topic_score_gemma":0.00009271723,"teacher_disagreement_score":0.87605846,"about_ca_system_score_codex":0.000030420346,"about_ca_system_score_gemma":0.000017046557,"threshold_uncertainty_score":0.7553397},"labels":[],"label_agreement":null},{"id":"W2904535507","doi":"10.1609/aaai.v33i01.33013280","title":"Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Term (time); Recurrent neural network; Computer science; Sequence (biology); Mechanism (biology); Artificial intelligence; Identification (biology); Artificial neural network; Algorithm; Chemistry; Physics","score_opus":0.12234183720027215,"score_gpt":0.3209075503423701,"score_spread":0.19856571314209798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904535507","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":[0.24750818,0.000038264825,0.7468648,0.0013332089,0.0005528827,0.0012465795,0.000006476399,0.00010540068,0.0023442137],"genre_scores_gemma":[0.97426295,0.000047891634,0.025144042,0.000113871436,0.00008453166,0.000109716115,0.0000013702593,0.000016994763,0.00021864979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798894,0.0000090113235,0.000536863,0.00061374105,0.00043086827,0.00042059016],"domain_scores_gemma":[0.99777913,0.00016066426,0.00045763337,0.0004073245,0.0011096018,0.0000856597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031857716,0.00024944803,0.00026990328,0.00009675825,0.00025127566,0.00023285292,0.0022480027,0.00008663996,0.00001403916],"category_scores_gemma":[0.00015318554,0.00019427945,0.000109205204,0.0008740587,0.000113225215,0.00059449766,0.0004326508,0.00031337002,0.000054958236],"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":[0.000051154173,0.0000678707,0.00022314498,0.00008662284,0.000012037126,1.2803059e-7,0.00090893166,0.005660053,0.030485885,0.830523,0.000037260535,0.13194388],"study_design_scores_gemma":[0.000029383185,0.00016451106,0.000101200116,0.00025869065,0.000008482634,0.000002971969,0.00013889009,0.58770967,0.31767723,0.0936505,0.000033093787,0.0002253621],"about_ca_topic_score_codex":0.0000077635805,"about_ca_topic_score_gemma":0.0000063841535,"teacher_disagreement_score":0.73687255,"about_ca_system_score_codex":0.000057479614,"about_ca_system_score_gemma":0.00012643095,"threshold_uncertainty_score":0.79224855},"labels":[],"label_agreement":null},{"id":"W2905076052","doi":"10.1109/cvpr.2019.01298","title":"PointPillars: Fast Encoders for Object Detection From Point Clouds","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":241,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Encoder; Point cloud; Computer science; Object detection; Encoding (memory); Pipeline (software); Artificial intelligence; Computer vision; Representation (politics); Margin (machine learning); Object (grammar); Convolutional neural network; State (computer science); Point (geometry); Pattern recognition (psychology); Real-time computing; Algorithm; Machine learning; Mathematics","score_opus":0.020645774024622955,"score_gpt":0.26870073009794854,"score_spread":0.2480549560733256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905076052","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":[0.0031847034,0.00014749687,0.9891487,0.0012512045,0.0019325744,0.0017230583,0.00005746175,0.0007204159,0.001834437],"genre_scores_gemma":[0.5775457,0.00010886297,0.41923168,0.00096261635,0.0005701587,0.0006795607,0.00010340131,0.000054919532,0.0007431104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747926,0.000055285913,0.00043713243,0.0013404755,0.00027514165,0.00041268388],"domain_scores_gemma":[0.99694186,0.0004287123,0.00032165938,0.0020267873,0.00016541462,0.00011555191],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016567756,0.00037861412,0.00039028682,0.000114444316,0.00014487361,0.00023073828,0.0016096495,0.00031142624,0.000022346338],"category_scores_gemma":[0.000049329898,0.00036794262,0.00028857403,0.000247151,0.00003997034,0.00028027748,0.001635426,0.000502585,0.00017137369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001171994,0.0002613585,0.00013977353,0.00023934897,0.0003584658,0.0000066424877,0.0016087891,0.40290204,0.020672645,0.044675484,0.011963945,0.5170543],"study_design_scores_gemma":[0.00046121285,0.00009911146,0.00023267596,0.000063098785,0.00003032358,0.000006708236,0.00004860864,0.7539206,0.022338178,0.21199599,0.010094016,0.00070943794],"about_ca_topic_score_codex":0.0001492723,"about_ca_topic_score_gemma":0.0001571624,"teacher_disagreement_score":0.57436097,"about_ca_system_score_codex":0.000206342,"about_ca_system_score_gemma":0.00013262875,"threshold_uncertainty_score":0.9998773},"labels":[],"label_agreement":null},{"id":"W2905260191","doi":"10.1109/cvpr.2019.00458","title":"PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":242,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Ground truth; Segmentation; Piecewise; Benchmark (surveying); RGB color model; Plane (geometry); Ground plane; Consistency (knowledge bases); Image segmentation; Augmented reality; Image plane; Robotics; Deep learning; Pattern recognition (psychology); Image (mathematics); Robot; Mathematics; Geography; Geometry","score_opus":0.00936048908813515,"score_gpt":0.20679889689333664,"score_spread":0.1974384078052015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905260191","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":[0.3126519,0.000025179861,0.6819106,0.0002044623,0.00023188056,0.00014490834,0.00000267453,0.00022059861,0.0046078186],"genre_scores_gemma":[0.77209187,0.0000113630595,0.22740354,0.00012851904,0.000063623804,0.000009291314,0.0000044365183,0.0000050619396,0.00028228902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994239,0.000014533013,0.00009704992,0.0002886159,0.00006800012,0.00010786924],"domain_scores_gemma":[0.99953556,0.00008315011,0.000044997323,0.0002781262,0.000016381688,0.00004179128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000267974,0.00006788548,0.000070222195,0.00003349323,0.000055496774,0.0000646024,0.00013431764,0.000035179466,0.000053740267],"category_scores_gemma":[0.0000043662526,0.00006291025,0.000010386983,0.00012861582,0.000021109126,0.00046633455,0.00006804699,0.00007055189,0.00019261804],"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":[0.000008221151,0.000016627291,0.0005874418,0.0000028258223,0.000005424157,0.0000014477805,0.000055479777,0.0000809135,0.40354443,0.0015085371,0.00009395019,0.5940947],"study_design_scores_gemma":[0.0013883252,0.00040018145,0.015530126,0.000042595337,0.000015851843,0.00058333186,0.000093914154,0.6409528,0.27676928,0.04545217,0.017926222,0.00084519736],"about_ca_topic_score_codex":0.000042826778,"about_ca_topic_score_gemma":0.000047878584,"teacher_disagreement_score":0.6408719,"about_ca_system_score_codex":0.00001952377,"about_ca_system_score_gemma":0.0000043519995,"threshold_uncertainty_score":0.25654057},"labels":[],"label_agreement":null},{"id":"W2905515056","doi":"10.1109/iiswc.2018.8573527","title":"Memory Requirements for Convolutional Neural Network Hardware Accelerators","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Dataflow; Bandwidth (computing); Memory bandwidth; Computer architecture; Convolutional neural network; High memory; Memory management; Deep learning; Embedded system; Computer hardware; Parallel computing; Artificial intelligence; Semiconductor memory; Computer network","score_opus":0.055646973867678175,"score_gpt":0.31403041264627196,"score_spread":0.25838343877859377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905515056","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":[0.0031550855,0.000053627624,0.98968065,0.0016469908,0.000850436,0.0004990086,0.000004495495,0.00035439944,0.0037553192],"genre_scores_gemma":[0.67229927,0.0000043819705,0.3183545,0.004384338,0.0019516501,0.0002742268,0.000017166965,0.000019562747,0.002694934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987496,0.000021088277,0.00021296994,0.00042344074,0.00018339237,0.00040949957],"domain_scores_gemma":[0.9989992,0.00010866713,0.00007981211,0.0005000184,0.00020426062,0.00010809305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011629835,0.00012690965,0.000110405876,0.000029329125,0.00042511497,0.00007431655,0.00077604724,0.000040930314,0.00007453741],"category_scores_gemma":[0.00001946796,0.00011652018,0.0000593443,0.00038015068,0.00010016041,0.0005558828,0.00024836452,0.00006304533,0.00011094374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034842058,0.00008153014,0.0010113219,0.000010438757,0.000041145264,0.0000019745537,0.000105163905,0.006245706,0.0012296899,0.5250266,0.39119828,0.07501332],"study_design_scores_gemma":[0.0011162395,0.00033711636,0.0044586547,0.000016585196,0.000013315824,0.00001863369,0.000015510457,0.6925936,0.006653344,0.07668364,0.2174595,0.0006338958],"about_ca_topic_score_codex":0.0000021590329,"about_ca_topic_score_gemma":0.000010952114,"teacher_disagreement_score":0.68634784,"about_ca_system_score_codex":0.00004319487,"about_ca_system_score_gemma":0.00004013351,"threshold_uncertainty_score":0.4751555},"labels":[],"label_agreement":null},{"id":"W2906164531","doi":"10.15353/jcvis.v4i1.340","title":"MonolithNet: Training monolithic deep neural networks via a partitioned training strategy","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Training (meteorology); Artificial neural network; Computer science; Artificial intelligence; Residual; Deep neural networks; Stochastic gradient descent; Convergence (economics); Deep learning; Gradient descent; Machine learning; Algorithm; Geography","score_opus":0.029801870094672972,"score_gpt":0.29968007370889693,"score_spread":0.269878203614224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906164531","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":[0.025778014,0.0010779651,0.97118664,0.0009824979,0.00060748524,0.00013373772,8.7719354e-7,0.000062232095,0.00017053098],"genre_scores_gemma":[0.977879,0.000013409017,0.020956218,0.00037107198,0.0007445175,0.000005990933,0.0000027355875,0.000014657829,0.000012406943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814546,0.0001561793,0.00075590465,0.00023694002,0.00040068722,0.00030480602],"domain_scores_gemma":[0.99820095,0.00030299398,0.0006177802,0.0001674838,0.0004977106,0.00021307904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053514045,0.00018081587,0.00031445254,0.00019761144,0.0003484362,0.00037648506,0.00037508647,0.000042780885,0.0000036708527],"category_scores_gemma":[0.000026109848,0.00015341799,0.00008291951,0.00039290197,0.00012818402,0.0010734828,0.000069354675,0.00026945068,0.000004249786],"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":[0.000018778603,0.000029371506,0.00017264373,0.0000089481155,0.000023850222,0.000017079286,0.0019838165,0.7776146,0.00017595796,0.009887253,0.0002185965,0.20984913],"study_design_scores_gemma":[0.00046675792,0.00017115026,0.0034120672,0.000077780496,0.000009301381,0.0012044172,0.00041317896,0.97785366,0.000005725,0.015754081,0.00047812844,0.00015378013],"about_ca_topic_score_codex":0.0000038543685,"about_ca_topic_score_gemma":8.8361065e-7,"teacher_disagreement_score":0.952101,"about_ca_system_score_codex":0.00004138474,"about_ca_system_score_gemma":0.000062968276,"threshold_uncertainty_score":0.62562037},"labels":[],"label_agreement":null},{"id":"W2907353425","doi":"","title":"Toward the Detection of Overlapping Body Parts in Infrared Images","year":2011,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Computer vision; Infrared; Artificial intelligence; Computer science; Remote sensing; Geology; Optics; Physics","score_opus":0.03438094807403668,"score_gpt":0.24651638095836764,"score_spread":0.21213543288433095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907353425","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":[0.6359991,0.00047193663,0.3313418,0.0012768857,0.0003371441,0.0010902839,0.0000016896346,0.0005050393,0.028976137],"genre_scores_gemma":[0.9925279,0.00003628716,0.007182349,0.000077024626,0.0000426942,0.000076493656,1.4258046e-7,0.0000062781287,0.00005082375],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992859,0.0000049722184,0.00019254765,0.00022283438,0.00011791055,0.00017583245],"domain_scores_gemma":[0.9995579,0.00003889639,0.00012277935,0.0001591783,0.00009164382,0.000029571633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012230786,0.000083933664,0.000097120515,0.00006600272,0.000064413085,0.000030348207,0.0005370947,0.000033442302,0.0000025737038],"category_scores_gemma":[0.000056798195,0.00006560305,0.000029822835,0.0005763943,0.00006863679,0.00059575436,0.00017888147,0.00011827614,0.000007235238],"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":[0.0000632333,0.00022243707,0.025282402,0.00018230599,0.00003666078,0.000007530504,0.034426663,0.00007624462,0.5999481,0.19880559,0.0011882297,0.13976061],"study_design_scores_gemma":[0.0004404362,0.00017105992,0.18950482,0.00010736816,0.00001168089,0.000042600703,0.000805232,0.020107713,0.6662835,0.1166178,0.0055130157,0.00039480443],"about_ca_topic_score_codex":0.000015631618,"about_ca_topic_score_gemma":0.0000022679399,"teacher_disagreement_score":0.35652882,"about_ca_system_score_codex":0.000021338758,"about_ca_system_score_gemma":0.000010209775,"threshold_uncertainty_score":0.26752147},"labels":[],"label_agreement":null},{"id":"W2907361740","doi":"","title":"Quantization Error as a Metric for Dynamic Precision Scaling in Neural Net Training","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"MNIST database; Rounding; Quantization (signal processing); Computer science; Scaling; Artificial neural network; Algorithm; Round-off error; Floating point; Metric (unit); Artificial intelligence; Mathematics","score_opus":0.12360272874188372,"score_gpt":0.2618221371763817,"score_spread":0.13821940843449798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907361740","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":[0.28542632,0.000052301082,0.7130902,0.000096388765,0.00034060676,0.00068270834,0.000007777711,0.00017395739,0.00012977979],"genre_scores_gemma":[0.9726701,0.000060934537,0.026828794,0.00008122788,0.000065850414,0.000010551081,0.000045239754,0.000026577914,0.00021076394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778605,0.00010903197,0.00030347114,0.0012816627,0.00010196421,0.0004178017],"domain_scores_gemma":[0.9980579,0.0003909663,0.00034235665,0.00092340965,0.00016776532,0.00011760406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029475676,0.00028104166,0.00032122098,0.00067256694,0.0002111097,0.0000989091,0.0015553797,0.00023846397,0.00000550459],"category_scores_gemma":[0.00014471446,0.00033958533,0.00015258607,0.0021246348,0.00008255665,0.00050394895,0.0010238682,0.000384383,0.000026026855],"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":[0.00003921518,0.00004707507,0.0003274361,0.000029475032,0.000013780498,0.000019066607,0.00031878782,0.9584336,0.00005786217,0.02833285,0.000035668603,0.012345163],"study_design_scores_gemma":[0.00039908514,0.00005978242,0.00096701836,0.00007784854,0.000021576365,0.0000039257943,0.000051137464,0.9231713,0.00003492787,0.07476428,0.00014297344,0.00030614156],"about_ca_topic_score_codex":0.000031432373,"about_ca_topic_score_gemma":0.000080159945,"teacher_disagreement_score":0.68724376,"about_ca_system_score_codex":0.00026727616,"about_ca_system_score_gemma":0.00012523914,"threshold_uncertainty_score":0.99990565},"labels":[],"label_agreement":null},{"id":"W2907670343","doi":"10.48550/arxiv.1903.03088","title":"Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperparameter; Hyperparameter optimization; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Mathematical optimization; Mathematics; Support vector machine","score_opus":0.04801974787008525,"score_gpt":0.19288876717889644,"score_spread":0.1448690193088112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907670343","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":[0.34023032,0.000011966802,0.6591909,0.000014652569,0.00012472854,0.00025167307,0.000002235645,0.00011057993,0.00006295089],"genre_scores_gemma":[0.85178727,0.000015636075,0.14794347,0.00003775126,0.000017258359,8.231941e-7,0.0000031422708,0.000012211859,0.00018244021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989205,0.00011537182,0.00016565941,0.00048730464,0.00006733538,0.00024385525],"domain_scores_gemma":[0.9986079,0.00024872663,0.00020005801,0.0007243759,0.00012781189,0.00009117343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011509663,0.00014639841,0.00016942613,0.00018280763,0.00015745882,0.000030356226,0.00061033556,0.000079599646,0.000018113225],"category_scores_gemma":[0.000019881903,0.00016900428,0.0000791163,0.0014640299,0.000052470583,0.0007043511,0.0002166473,0.00013345816,0.00001816076],"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":[0.00005135289,0.000032552078,0.0015113822,0.0000042901647,0.00002098222,0.0000041416974,0.000044436074,0.9899935,0.0010624846,0.006861475,0.0000064424075,0.0004069499],"study_design_scores_gemma":[0.00036329043,0.0000672414,0.00027275624,0.000018279057,0.000028624816,0.000006929103,0.000052649277,0.9980452,0.0001633295,0.00067695,0.00013175236,0.00017299672],"about_ca_topic_score_codex":0.000008538767,"about_ca_topic_score_gemma":0.0000021099606,"teacher_disagreement_score":0.511557,"about_ca_system_score_codex":0.00011592511,"about_ca_system_score_gemma":0.000066674256,"threshold_uncertainty_score":0.6891794},"labels":[],"label_agreement":null},{"id":"W2907807896","doi":"10.1109/newcas.2018.8585517","title":"A Multi-Mode Accelerator for Pruned Deep Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Convolutional neural network; Computer science; Pruning; Computation; Speedup; Convolutional code; Parallel computing; Memory management; Artificial intelligence; Algorithm; Computer hardware; Semiconductor memory; Decoding methods","score_opus":0.03614531567958412,"score_gpt":0.3190718226373708,"score_spread":0.2829265069577867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907807896","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":[0.00225223,0.00003573118,0.99513716,0.0009472663,0.00032018006,0.0005816409,0.0000011523545,0.0004377906,0.00028682483],"genre_scores_gemma":[0.53682333,0.0000019820618,0.460895,0.0014713941,0.00032934177,0.00019280094,0.0000026552523,0.000011718557,0.0002717823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988365,0.000018254019,0.00019289622,0.00045800285,0.00009503195,0.00039929702],"domain_scores_gemma":[0.99889,0.00012802922,0.00006963611,0.00063763146,0.00015277986,0.00012194657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060035698,0.0001393967,0.00012348058,0.00003574406,0.00027562206,0.00007593228,0.000897935,0.000055594315,0.000015315629],"category_scores_gemma":[0.00002019355,0.0001200958,0.0000615,0.0003946459,0.00006831775,0.00045175845,0.00022273451,0.00008239862,0.000038566297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011998832,0.0005937039,0.0012180529,0.000023815379,0.00007946592,0.0000071336804,0.0010206021,0.0652983,0.01871384,0.3148434,0.019274924,0.5788068],"study_design_scores_gemma":[0.000361039,0.00007835457,0.0003913369,0.0000013320065,0.0000027636133,0.000004524302,0.000003705737,0.9912097,0.0013787792,0.0012982613,0.00511224,0.00015795513],"about_ca_topic_score_codex":0.000004743415,"about_ca_topic_score_gemma":0.00009721244,"teacher_disagreement_score":0.9259114,"about_ca_system_score_codex":0.000023002498,"about_ca_system_score_gemma":0.000012137637,"threshold_uncertainty_score":0.48973647},"labels":[],"label_agreement":null},{"id":"W2907812233","doi":"10.3390/electronics8010065","title":"A Uniform Architecture Design for Accelerating 2D and 3D CNNs on FPGAs","year":2019,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Toronto","funders":"National Key Research and Development Program of China; National Science and Technology Major Project; National Natural Science Foundation of China","keywords":"Computer science; Field-programmable gate array; Convolutional neural network; Hardware acceleration; Computation; Parallel computing; Chip; Design space exploration; Matrix (chemical analysis); Feature (linguistics); Computer architecture; Computational science; Computer hardware; Artificial intelligence; Embedded system; Algorithm","score_opus":0.022918513067385758,"score_gpt":0.2623772270853668,"score_spread":0.23945871401798105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907812233","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":[0.015777698,0.00046101792,0.9812585,0.0010420197,0.000045757715,0.0006925399,0.0000011289447,0.00012396243,0.00059735053],"genre_scores_gemma":[0.34923783,0.0001530086,0.6483015,0.0012675782,0.00012390895,0.0001826013,0.0000059727404,0.000030825693,0.0006967822],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990623,0.000019545008,0.0001095385,0.0003334608,0.00010008794,0.0003750751],"domain_scores_gemma":[0.99918675,0.00030050048,0.00006092688,0.00037508013,0.00002669118,0.000050029917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116731455,0.0001166774,0.00010546682,0.00004032961,0.00014711148,0.000071846414,0.00036430534,0.000042733238,0.0000026127539],"category_scores_gemma":[0.000016599899,0.00010442243,0.00002621751,0.00019340948,0.000012794497,0.0001368185,0.000073026145,0.00020665565,0.000016418348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003761448,0.000045698573,0.000025941385,0.000018946908,0.000018718722,8.548184e-7,0.00032810826,0.16250786,0.008742753,0.44460976,0.0006619118,0.38300183],"study_design_scores_gemma":[0.0006448339,0.0010577609,0.000055184428,0.0000201307,0.000007232697,0.000027688464,0.000006274339,0.76508266,0.0150825335,0.10578529,0.11188626,0.0003441588],"about_ca_topic_score_codex":2.717463e-7,"about_ca_topic_score_gemma":0.0000061519763,"teacher_disagreement_score":0.6025748,"about_ca_system_score_codex":0.000058321737,"about_ca_system_score_gemma":0.00006583353,"threshold_uncertainty_score":0.4258223},"labels":[],"label_agreement":null},{"id":"W2907982323","doi":"10.1109/newcas.2018.8585433","title":"Power Reduction in CNN Pooling Layers with a Preliminary Partial Computation Strategy","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Pooling; Reduction (mathematics); Computation; Computer science; Power (physics); Algorithm; Artificial intelligence; Mathematics; Physics","score_opus":0.019381807180260866,"score_gpt":0.2821256187576986,"score_spread":0.2627438115774377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907982323","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":[0.1910697,0.000008958837,0.80499786,0.0005806105,0.00008377643,0.00019187298,1.5181179e-7,0.00015301652,0.0029140536],"genre_scores_gemma":[0.9246238,0.0000015693945,0.07508609,0.00008393205,0.00008125769,0.00002642915,0.0000018859412,0.0000063712178,0.00008868218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917626,0.00003048191,0.0001531198,0.0003182251,0.00012761891,0.00019431715],"domain_scores_gemma":[0.99957526,0.000029749737,0.000061227605,0.00021814955,0.00006798448,0.000047623882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007548189,0.00008519266,0.00007149127,0.000070464055,0.000092247734,0.000049812625,0.00019891578,0.00003093956,0.000012923631],"category_scores_gemma":[0.000003618213,0.00007313187,0.000012312162,0.0005531933,0.000064569045,0.00053379644,0.000055803237,0.000085667,0.000047501693],"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":[0.00043586743,0.00048129036,0.0011313944,0.000014857965,0.000031143467,0.00003883086,0.0035793397,0.61691606,0.013802758,0.2112574,0.00308569,0.1492254],"study_design_scores_gemma":[0.0006409141,0.0012348616,0.008809716,0.000031989508,0.000004229299,0.00010561831,0.00023122704,0.9672636,0.009862997,0.010951474,0.0005375693,0.00032583257],"about_ca_topic_score_codex":0.000016812148,"about_ca_topic_score_gemma":0.000013813668,"teacher_disagreement_score":0.73355407,"about_ca_system_score_codex":0.00003627607,"about_ca_system_score_gemma":0.00003607285,"threshold_uncertainty_score":0.29822314},"labels":[],"label_agreement":null},{"id":"W2909175897","doi":"10.1109/icmla.2018.00092","title":"Deep Learning Convolutional Neural Networks with Dropout - A Parallel Approach","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Dropout (neural networks); MNIST database; Overfitting; Convolutional neural network; Computer science; SPARK (programming language); Artificial intelligence; Deep learning; Machine learning; Pattern recognition (psychology); Artificial neural network; Big data; Data mining","score_opus":0.015080262440543678,"score_gpt":0.2404485063408059,"score_spread":0.22536824390026222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909175897","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":[0.0010221725,0.00007020519,0.98711765,0.00041921478,0.00006451966,0.00019261676,8.682989e-8,0.00044251824,0.010671011],"genre_scores_gemma":[0.6583967,0.0000041440335,0.3400991,0.00043354498,0.00022918929,0.00005163701,0.0000056797876,0.000010155598,0.0007698559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987154,0.000049673174,0.0001549643,0.00047768315,0.00021429513,0.00038799242],"domain_scores_gemma":[0.99919057,0.00007864259,0.000080421385,0.0004147995,0.00011438515,0.000121201614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081146914,0.00015005773,0.00012226144,0.000039988226,0.00039225633,0.00008855318,0.00062686746,0.000047589183,0.000030951163],"category_scores_gemma":[0.000007789664,0.00011576136,0.000033836903,0.0005353423,0.00022766857,0.00039601765,0.00021147194,0.00024419953,0.00005916304],"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":[0.000018558554,0.00005270181,0.0020626518,0.0000017682903,0.000014943189,0.0000028481968,0.0001132422,0.7543323,0.000018737319,0.22777477,0.00058466254,0.01502284],"study_design_scores_gemma":[0.00024471915,0.00012607202,0.0013659463,0.000001928662,0.000003291641,0.00006580058,0.000016804524,0.99446005,0.000006043495,0.0010430582,0.0024919591,0.00017435549],"about_ca_topic_score_codex":0.0000067238966,"about_ca_topic_score_gemma":0.000012670349,"teacher_disagreement_score":0.6573745,"about_ca_system_score_codex":0.000028427328,"about_ca_system_score_gemma":0.00001817321,"threshold_uncertainty_score":0.47206113},"labels":[],"label_agreement":null},{"id":"W2909590401","doi":"10.1109/ism.2018.00025","title":"Deep Reinforcement Learning with Parameterized Action Space for Object Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Parameterized complexity; Discriminative model; Pascal (unit); Object detection; Markov decision process; Machine learning; Cognitive neuroscience of visual object recognition; Object (grammar); Contextual image classification; Pattern recognition (psychology); Markov process; Image (mathematics); Mathematics; Algorithm","score_opus":0.02654629509425886,"score_gpt":0.28506300558960124,"score_spread":0.25851671049534236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909590401","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":[0.006867005,0.0000029360604,0.9909791,0.00024785896,0.000101402366,0.00042925775,2.1790603e-8,0.0003290932,0.0010433528],"genre_scores_gemma":[0.76723796,0.00000410408,0.23155324,0.000103628,0.00009113998,0.00016909123,0.0000011990687,0.000007282002,0.0008323462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992928,0.000018381243,0.00010571381,0.00027539514,0.00011078336,0.0001969628],"domain_scores_gemma":[0.999396,0.00009179633,0.00009088156,0.0002767414,0.00009885772,0.000045703207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076405544,0.00008877965,0.00007523594,0.00004559181,0.000297226,0.00006411605,0.00017862664,0.000028036595,0.000009640805],"category_scores_gemma":[0.000022554721,0.000071153285,0.000026279908,0.00031847713,0.000035032637,0.00037731623,0.000049588794,0.00007026711,0.000033209257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000394666,0.000050033057,0.00009695975,0.000024236171,0.00005617847,0.0000010034975,0.0006491151,0.23389594,0.15715988,0.039108172,0.00020519634,0.5683586],"study_design_scores_gemma":[0.0003498123,0.0006739014,0.00009475793,0.000003996855,0.000004974823,0.000009321857,0.000025668678,0.86947924,0.11901881,0.0010239384,0.009197564,0.00011802272],"about_ca_topic_score_codex":0.000008858922,"about_ca_topic_score_gemma":0.00007884562,"teacher_disagreement_score":0.76037097,"about_ca_system_score_codex":0.000050215287,"about_ca_system_score_gemma":0.0000120540335,"threshold_uncertainty_score":0.29015467},"labels":[],"label_agreement":null},{"id":"W2909808664","doi":"10.48550/arxiv.1901.02064","title":"Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Dataflow; Joint (building); Quantization (signal processing); Computer science; Artificial neural network; Artificial intelligence; Deep neural networks; Computer vision; Engineering; Parallel computing; Structural engineering","score_opus":0.08036996745277143,"score_gpt":0.2082286006720327,"score_spread":0.12785863321926128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909808664","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":[0.042832687,0.000047670637,0.9556607,0.00015875869,0.0002619637,0.00082823465,0.000027846585,0.00011242306,0.0000697149],"genre_scores_gemma":[0.9759952,0.00006049626,0.02357156,0.00008190648,0.000052704785,0.0000062880063,0.00014576207,0.000017546316,0.0000685566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858284,0.000060893988,0.00022942165,0.0008385061,0.000057791174,0.00023057412],"domain_scores_gemma":[0.99786526,0.00030446076,0.00041267296,0.0011290676,0.00020163506,0.00008693027],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000962922,0.0002163664,0.00028677276,0.00018887778,0.00015192889,0.000048668975,0.0008210941,0.00017623497,0.0000028767108],"category_scores_gemma":[0.000025192285,0.0002514043,0.00011464167,0.0005117617,0.0000767635,0.0004006221,0.00072104926,0.00026129335,0.000002176953],"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":[0.000014947853,0.000036302776,0.0005256977,0.00004775931,0.000017434842,0.0000013506914,0.000017116497,0.83491194,0.000060002505,0.16357727,0.000043306107,0.0007468436],"study_design_scores_gemma":[0.00039005312,0.000039950177,0.0010213339,0.00003633363,0.000041351967,7.141236e-7,0.0000066061375,0.9751601,0.0002932689,0.02265789,0.00011966774,0.00023269719],"about_ca_topic_score_codex":0.000013461697,"about_ca_topic_score_gemma":0.000018636896,"teacher_disagreement_score":0.9331625,"about_ca_system_score_codex":0.00006397385,"about_ca_system_score_gemma":0.0000635767,"threshold_uncertainty_score":0.9999938},"labels":[],"label_agreement":null},{"id":"W2910094941","doi":"10.1016/j.media.2022.102680","title":"The Liver Tumor Segmentation Benchmark (LiTS)","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1164,"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 Montréal; Polytechnique Montréal","funders":"National Cancer Institute; National Institutes of Health; Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec; International Graduate School of Science and Engineering; Universität Zürich; Deutsche Forschungsgemeinschaft","keywords":"Benchmark (surveying); Segmentation; Computer science; Artificial intelligence; Medical physics; Medicine; Pattern recognition (psychology); Cartography","score_opus":0.007724142912540659,"score_gpt":0.2656009642944292,"score_spread":0.25787682138188855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910094941","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":[0.0033326293,0.00042217888,0.9870038,0.008218525,0.00008523368,0.00012536584,0.0000035241276,0.00009734214,0.00071141764],"genre_scores_gemma":[0.9018634,0.00043205102,0.08322024,0.010097031,0.00029107835,0.00095393014,0.00011257655,0.000022274484,0.0030074164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811983,0.00019290509,0.00022884492,0.00032169413,0.0009128981,0.00022379936],"domain_scores_gemma":[0.9987161,0.0004054899,0.00010131131,0.00059441035,0.000048415895,0.00013425502],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005617029,0.00007964259,0.00012117124,0.00009148031,0.0009141251,0.000094440475,0.0012538373,0.0000115741805,0.0010107311],"category_scores_gemma":[0.00012026652,0.00005859865,0.00014346541,0.002639343,0.00009728655,0.00021002852,0.0006511899,0.00023805976,0.000060766375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017393011,0.00039862035,0.0019816433,0.000008671672,0.0010952831,0.00050541625,0.0011981385,0.0092160525,0.0014138841,0.045800272,0.06759614,0.8707685],"study_design_scores_gemma":[0.00016220893,0.000030027644,0.0021739262,0.0000010138788,0.00019666702,0.000020954545,0.00014506202,0.94519377,0.00031377206,0.00284923,0.048756614,0.0001567828],"about_ca_topic_score_codex":0.00003417503,"about_ca_topic_score_gemma":0.00004581247,"teacher_disagreement_score":0.9359777,"about_ca_system_score_codex":0.000065360226,"about_ca_system_score_gemma":0.000045466968,"threshold_uncertainty_score":0.9999025},"labels":[],"label_agreement":null},{"id":"W2911065620","doi":"10.1145/3287624.3287713","title":"XOMA","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Research Foundation of Korea; Ulsan National Institute of Science and Technology","keywords":"Computer science; Application-specific integrated circuit; Energy consumption; Latency (audio); Embedded system; Chip; Computer architecture; Memory architecture; Efficient energy use; Parallel computing; Auxiliary memory; Inference; Computer hardware; Artificial intelligence","score_opus":0.010384528543950299,"score_gpt":0.24444711545554015,"score_spread":0.23406258691158985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911065620","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":[0.012753405,0.000010203941,0.89637077,0.0015154501,0.0000882701,0.000082135004,4.9168406e-8,0.00022194252,0.08895777],"genre_scores_gemma":[0.85168236,0.0000017739443,0.13993089,0.0008667799,0.000016110205,0.0000063891507,1.9000366e-7,0.0000019495278,0.0074935765],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997113,0.0000031845389,0.000037873247,0.00012244233,0.000049505405,0.00007574276],"domain_scores_gemma":[0.9995765,0.000025130601,0.000010247161,0.00035621197,0.000009684148,0.000022242351],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000015034805,0.000025635807,0.000026386524,0.000010840461,0.00001622306,0.00001590187,0.00033486186,0.000008066974,0.00006994722],"category_scores_gemma":[0.0000011741738,0.000021118462,0.000010896271,0.00015405071,0.0000038223343,0.00017170137,0.00009025022,0.00002422591,0.0025936724],"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":[1.2362749e-7,0.0000046591113,0.00031758723,4.0174874e-7,5.382928e-7,2.6529554e-7,0.00000825512,0.0001763172,0.0014563428,0.9684925,0.0017411545,0.027801832],"study_design_scores_gemma":[0.00038055287,0.00007807614,0.01544312,0.000004859452,0.0000011561953,0.000028435603,0.000009223793,0.3259384,0.01737412,0.16779296,0.47256634,0.00038277396],"about_ca_topic_score_codex":6.878908e-7,"about_ca_topic_score_gemma":4.646547e-7,"teacher_disagreement_score":0.83892894,"about_ca_system_score_codex":0.000004657842,"about_ca_system_score_gemma":0.0000032398193,"threshold_uncertainty_score":0.99818295},"labels":[],"label_agreement":null},{"id":"W2912083667","doi":"10.48550/arxiv.1901.07124","title":"Linearized Multi-Sampling for Differentiable Image Transformation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","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":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Bilinear interpolation; Differentiable function; Upsampling; Computer science; Image scaling; Pixel; Artificial neural network; Artificial intelligence; Image (mathematics); Hessian matrix; Interpolation (computer graphics); Sampling (signal processing); Deep neural networks; Transformation (genetics); Algorithm; Mathematics; Image processing; Computer vision; Applied mathematics; Filter (signal processing); Mathematical analysis","score_opus":0.12690133675885815,"score_gpt":0.23753796866686058,"score_spread":0.11063663190800244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912083667","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":[0.018467916,0.000021132682,0.9791615,0.00012569403,0.00032435346,0.0012533736,0.00004151215,0.0003434188,0.00026106855],"genre_scores_gemma":[0.8397553,0.0001001884,0.1584814,0.00006239781,0.000060717728,0.000012930068,0.00010647639,0.000023647353,0.0013969624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984469,0.000041043982,0.00022690596,0.00087619585,0.000061187384,0.00034781345],"domain_scores_gemma":[0.9982314,0.00018872225,0.00023645865,0.0010619326,0.00018075523,0.00010072742],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000112942755,0.00026827052,0.00029994955,0.00015017581,0.00019915422,0.00013267878,0.0013665985,0.00020565739,0.000008009763],"category_scores_gemma":[0.000017791137,0.00031154725,0.00023174698,0.00035075878,0.000044764307,0.0006132137,0.00061131397,0.00038213882,0.00008009184],"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":[0.000036617414,0.000116078474,0.00008600191,0.00018854399,0.000053895135,0.000003265321,0.00019699306,0.8912278,0.0012305537,0.10568828,0.00011117315,0.0010608069],"study_design_scores_gemma":[0.0009083798,0.000020706113,0.00012682371,0.000043251835,0.00004166534,8.081637e-7,0.000014590752,0.970402,0.0007639687,0.025935052,0.0014144015,0.00032837564],"about_ca_topic_score_codex":0.000013254942,"about_ca_topic_score_gemma":0.00000786871,"teacher_disagreement_score":0.82128733,"about_ca_system_score_codex":0.00012608856,"about_ca_system_score_gemma":0.00007946489,"threshold_uncertainty_score":0.99993366},"labels":[],"label_agreement":null},{"id":"W2912975123","doi":"10.1109/reconfig.2018.8641735","title":"An Efficient FPGA-based Overlay Inference Architecture for Fully Connected DNNs","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Field-programmable gate array; Benchmark (surveying); Overlay; Inference; Computer architecture; Layer (electronics); Artificial neural network; Embedded system; Computer engineering; Computer hardware; Parallel computing; Artificial intelligence","score_opus":0.01833323259648206,"score_gpt":0.3009307790181714,"score_spread":0.2825975464216893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912975123","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":[0.018110082,0.000006355579,0.97811836,0.0016920529,0.00014540144,0.00054155645,0.00000942684,0.00054998085,0.000826764],"genre_scores_gemma":[0.68374896,2.059683e-7,0.31457585,0.0013922906,0.000114353046,0.00009249686,0.000008053874,0.00000888126,0.00005888661],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987207,0.00003559503,0.00018050802,0.0005360734,0.00017134506,0.0003557638],"domain_scores_gemma":[0.99824804,0.00041579505,0.00007838889,0.0008760546,0.0002290597,0.00015268581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009455677,0.00015406465,0.00012628929,0.0000750927,0.0002501326,0.00009373522,0.00094580575,0.000052957083,0.000045543467],"category_scores_gemma":[0.00007281905,0.00012771926,0.00004934415,0.00052571035,0.0001140046,0.000116452946,0.00008857224,0.00009506928,0.00005013473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007201049,0.00041670084,0.000082526145,0.000018559056,0.000014961013,0.0000024438789,0.0005193039,0.22894733,0.038278315,0.5767243,0.0027192573,0.15220429],"study_design_scores_gemma":[0.00043582427,0.00044425478,0.0007591374,0.000008257595,0.0000038707904,0.0000028620836,0.00000517701,0.9265523,0.051031876,0.00776964,0.012742576,0.00024423553],"about_ca_topic_score_codex":0.0000072566872,"about_ca_topic_score_gemma":0.00008003758,"teacher_disagreement_score":0.69760495,"about_ca_system_score_codex":0.00002829055,"about_ca_system_score_gemma":0.000085118954,"threshold_uncertainty_score":0.5208241},"labels":[],"label_agreement":null},{"id":"W2913081068","doi":"10.1109/tnnls.2019.2892035","title":"Balanced Decoupled Spatial Convolution for CNNs","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"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":"Guangzhou Municipal Science and Technology Project; National Natural Science Foundation of China","keywords":"Redundancy (engineering); Convolution (computer science); Computer science; Decoupling (probability); Filter (signal processing); Algorithm; Spatial analysis; Channel (broadcasting); Spatial filter; Artificial intelligence; Mathematics; Computer vision; Engineering; Telecommunications","score_opus":0.01063734284848073,"score_gpt":0.233604971592646,"score_spread":0.22296762874416529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913081068","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":[0.035721727,0.00020266593,0.96066105,0.00020806828,0.0018508758,0.0009724177,0.0000033933413,0.00033003572,0.00004977568],"genre_scores_gemma":[0.997048,0.00006422013,0.001224102,0.00011937648,0.0002016709,0.00025709358,0.0000054399084,0.000026408148,0.0010536888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846536,0.00012823884,0.00030589278,0.0005453304,0.00016426362,0.00039093127],"domain_scores_gemma":[0.9987634,0.0005262622,0.00016008716,0.00035320583,0.00007865351,0.000118393364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020194016,0.00021042554,0.00027147983,0.000081312726,0.00050002924,0.00015839763,0.00027172285,0.00011599767,0.000006614929],"category_scores_gemma":[0.000004622227,0.0001991459,0.00009779606,0.00029041298,0.000035276982,0.00030627073,0.0000038072783,0.0004676018,0.00001844389],"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":[0.00003443798,0.000021976799,0.00011695575,0.000020526395,0.000014961703,6.2211353e-7,0.000039340932,0.9724434,0.00045757316,0.0007296772,0.000052218737,0.026068319],"study_design_scores_gemma":[0.0006726837,0.00029914165,0.0002889162,0.000042249838,0.000012124241,0.000023849367,0.000024070921,0.9955841,0.00006809364,0.000029691413,0.0027322138,0.00022288894],"about_ca_topic_score_codex":0.00005035324,"about_ca_topic_score_gemma":0.000018367893,"teacher_disagreement_score":0.9613263,"about_ca_system_score_codex":0.000042970536,"about_ca_system_score_gemma":0.0000132215155,"threshold_uncertainty_score":0.8120934},"labels":[],"label_agreement":null},{"id":"W2913282347","doi":"10.1109/bibm.2018.8621552","title":"Optimizing U-Net to Segment Left Ventricle from Magnetic Resonance Imaging","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Dice; Computer science; Convolutional neural network; Convolution (computer science); Artificial intelligence; Net (polyhedron); Volume (thermodynamics); Task (project management); Image segmentation; Pattern recognition (psychology); Medical imaging; Artificial neural network; Mathematics; Engineering; Statistics; Physics; Geometry","score_opus":0.012575245774945709,"score_gpt":0.2547224344551139,"score_spread":0.24214718868016818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913282347","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":[0.008142622,0.0008098586,0.98377305,0.0031973808,0.00019065532,0.00022325812,0.0000025882227,0.00028916594,0.0033714464],"genre_scores_gemma":[0.29444253,0.0000129614045,0.70137423,0.0031700488,0.00020395267,0.000022495911,0.000001331967,0.000010626091,0.000761833],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988392,0.00001989461,0.00016538509,0.00047887512,0.00017817707,0.0003184625],"domain_scores_gemma":[0.99900424,0.00007456355,0.000033550776,0.0006951079,0.000059411017,0.00013314534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006123002,0.00010741915,0.00008912998,0.000047094425,0.00016627791,0.00010260733,0.0007657404,0.000014609145,0.00015448955],"category_scores_gemma":[0.000012222538,0.00010475269,0.000024201472,0.00038818005,0.000037702135,0.00028198515,0.00045622332,0.000062454135,0.00070077326],"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":[0.000011292372,0.000077621684,0.0025166185,0.0000015813849,0.0000030062656,0.000019033088,0.0014057058,0.0018666759,0.014478662,0.018089632,0.024573876,0.9369563],"study_design_scores_gemma":[0.00053900346,0.00016894356,0.020376492,0.0000434078,0.000006720702,0.000018948644,0.000049062393,0.5724452,0.04065624,0.012679169,0.35241205,0.00060473656],"about_ca_topic_score_codex":0.00003734179,"about_ca_topic_score_gemma":0.000021592263,"teacher_disagreement_score":0.93635154,"about_ca_system_score_codex":0.00005063098,"about_ca_system_score_gemma":0.000015256174,"threshold_uncertainty_score":0.90072566},"labels":[],"label_agreement":null},{"id":"W2913720416","doi":"10.1007/978-3-030-11726-9_23","title":"3D U-Net for Brain Tumour Segmentation","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Artificial intelligence; Cross entropy; Dice; Convolutional neural network; Categorical variable; Artificial neural network; Pattern recognition (psychology); Metric (unit); Network architecture; Machine learning; Statistics","score_opus":0.019093234430721657,"score_gpt":0.27552831876517847,"score_spread":0.25643508433445683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913720416","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":[0.000018614333,0.00016573336,0.9931229,0.002735013,0.001239838,0.0013173788,0.000013150557,0.00018597413,0.0012014251],"genre_scores_gemma":[0.008611409,0.000021226413,0.98507875,0.0042263623,0.0005372737,0.00006475668,0.000025826303,0.00004557861,0.0013887972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673444,0.000020861718,0.00045153307,0.0015683939,0.000621918,0.0006028322],"domain_scores_gemma":[0.9969628,0.0011105195,0.00033800135,0.0012549906,0.00020839555,0.00012532926],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049532385,0.00043591912,0.00039801723,0.00044759564,0.00024749333,0.0003297723,0.002781995,0.0002058034,0.000010750921],"category_scores_gemma":[0.00006613897,0.00042391586,0.00011514844,0.00052420906,0.0002864906,0.00067027943,0.00079222023,0.00045548985,0.00008806966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053669614,0.000017164319,0.0000105072895,0.000036235804,0.000006320302,0.0000064288415,0.00021098542,0.12370597,0.0015669715,0.06472026,0.00029946366,0.8094143],"study_design_scores_gemma":[0.00032653523,0.00016385931,0.000040624684,0.00012498737,0.0000056520034,0.00002977209,7.128842e-8,0.76595116,0.0025034286,0.22248256,0.0077999174,0.00057143165],"about_ca_topic_score_codex":0.0000031726731,"about_ca_topic_score_gemma":0.000027835622,"teacher_disagreement_score":0.8088429,"about_ca_system_score_codex":0.000286745,"about_ca_system_score_gemma":0.0003968175,"threshold_uncertainty_score":0.99982125},"labels":[],"label_agreement":null},{"id":"W2914625323","doi":"10.4271/2019-01-1044","title":"On the Safety of Autonomous Driving: A Dynamic Deep Object Detection Approach","year":2019,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Computer vision; Object (grammar); Pattern recognition (psychology)","score_opus":0.008382430008211365,"score_gpt":0.23305139249113918,"score_spread":0.2246689624829278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914625323","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":[0.755454,0.0005578215,0.0105911605,0.017382327,0.0011459683,0.010320481,0.000056006073,0.011420239,0.19307202],"genre_scores_gemma":[0.9821701,0.000118498116,0.01536909,0.0013384265,0.00004914162,0.00052857026,0.000012827862,0.00008403059,0.00032930938],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9952969,0.00025435069,0.0010570519,0.0015664418,0.00095176266,0.000873513],"domain_scores_gemma":[0.99431145,0.0015546788,0.0004522547,0.0033296961,0.00011585571,0.00023603819],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00071040203,0.00068288064,0.00079207815,0.00020605959,0.00049795455,0.00010691488,0.0029825196,0.0005425797,0.000104743165],"category_scores_gemma":[0.0004535942,0.0005007942,0.0004784605,0.0017017721,0.00073738117,0.00056108605,0.00091270474,0.0014921844,0.00019594099],"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":[0.00011986963,0.00030853454,0.000029845369,0.000021329888,0.000028001017,0.0000037156829,0.000031124557,0.0010742276,0.8269892,0.15548566,0.00011876448,0.015789753],"study_design_scores_gemma":[0.00067475426,0.0018278885,0.96048343,0.00014087491,0.000045256376,0.0001291988,0.00006352149,0.00013407847,0.00067910075,0.024660483,0.010273701,0.0008877395],"about_ca_topic_score_codex":0.0000099584095,"about_ca_topic_score_gemma":0.008138452,"teacher_disagreement_score":0.96045357,"about_ca_system_score_codex":0.00044025885,"about_ca_system_score_gemma":0.000093715294,"threshold_uncertainty_score":0.99974436},"labels":[],"label_agreement":null},{"id":"W2914989766","doi":"10.1145/3293883.3302260","title":"High performance distributed deep learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Nvidia; National Science Foundation","keywords":"Computer science; Deep learning; Artificial intelligence; Distributed computing","score_opus":0.007943369618771309,"score_gpt":0.22160179450792653,"score_spread":0.2136584248891552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914989766","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":[0.2405721,0.000013005264,0.7565013,0.0003655961,0.00009182173,0.00009395275,2.1613504e-7,0.00031058796,0.0020514126],"genre_scores_gemma":[0.9385279,0.000016072603,0.05966716,0.00014946978,0.000024750394,0.000012560723,0.0000071977584,0.0000045105667,0.0015903974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993743,0.000012103826,0.00009459587,0.00022865126,0.000106352774,0.00018404877],"domain_scores_gemma":[0.99946755,0.00005432407,0.00003965419,0.00036429262,0.000031141946,0.000043063534],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000042846346,0.00006690905,0.00006963379,0.000020203819,0.00008778039,0.00003716133,0.0004632431,0.000021450156,0.00007253395],"category_scores_gemma":[0.000005595635,0.000058193353,0.000016575883,0.00034959274,0.0000110858655,0.0003763621,0.00016686124,0.00012168908,0.0011826863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051531656,0.000044701734,0.023499846,0.000013669961,0.0000101693295,0.000002006976,0.00009310806,0.25181714,0.0035693028,0.37624624,0.00054508017,0.3441536],"study_design_scores_gemma":[0.00019247268,0.000062484,0.02972652,0.0000047502795,0.0000011902928,0.000007670667,0.0000068686363,0.94502777,0.0041274326,0.0014765364,0.019188033,0.00017824685],"about_ca_topic_score_codex":0.0000022913598,"about_ca_topic_score_gemma":7.1707854e-7,"teacher_disagreement_score":0.6979558,"about_ca_system_score_codex":0.000020194566,"about_ca_system_score_gemma":0.000006188486,"threshold_uncertainty_score":0.999595},"labels":[],"label_agreement":null},{"id":"W2917055433","doi":"10.1109/jtehm.2019.2900628","title":"Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Translational Engineering in Health and Medicine","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Western University","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Segmentation; Encoder; Artificial intelligence; Computer vision; Pixel; Convolutional neural network; Deep learning; Image segmentation; End-to-end principle; Pattern recognition (psychology); Metric (unit)","score_opus":0.029943480971452142,"score_gpt":0.3066570326280813,"score_spread":0.27671355165662914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917055433","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":[0.06008962,0.0025031539,0.9329958,0.0025334007,0.0011423976,0.00070159714,0.00000606769,0.000023832841,0.0000041261146],"genre_scores_gemma":[0.5108825,0.00029085987,0.4873536,0.00052549393,0.0008725852,0.000032423995,0.000008364543,0.000023190578,0.000010997295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983356,0.00004161623,0.00075513695,0.0002037344,0.00031468988,0.00034923863],"domain_scores_gemma":[0.99857664,0.00059439975,0.00025985978,0.00014361004,0.00011231369,0.00031316176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008179334,0.00014959683,0.00042343113,0.00024683628,0.00007774904,0.00001573185,0.0001752746,0.000047253165,0.000004530548],"category_scores_gemma":[0.000044526336,0.00013662028,0.00007236853,0.00048932334,0.000015045008,0.00031039657,0.000010358364,0.00019175324,7.112905e-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":[0.000028244138,0.000018185918,0.000919072,0.00020831902,0.000034718018,8.4490495e-7,0.00080056186,0.9455635,0.0069546322,0.0009951141,0.00007111455,0.044405684],"study_design_scores_gemma":[0.0013362865,0.00032703322,0.0049037538,0.00029903135,0.000017519706,0.00002287742,0.000028737233,0.99070656,0.00013152898,0.00069911417,0.0013888977,0.00013865897],"about_ca_topic_score_codex":0.00001536054,"about_ca_topic_score_gemma":0.0000041916214,"teacher_disagreement_score":0.45079288,"about_ca_system_score_codex":0.000115774215,"about_ca_system_score_gemma":0.00018318114,"threshold_uncertainty_score":0.5571213},"labels":[],"label_agreement":null},{"id":"W2921371084","doi":"10.1109/cvprw.2019.00095","title":"AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Enhanced Data Rates for GSM Evolution; Task (project management); Artificial neural network; Macro; Architecture; Human–machine system; Network architecture; Computer architecture; Network planning and design; Edge device; Machine learning; Distributed computing; Computer engineering; Engineering; Systems engineering; Computer network","score_opus":0.03317335131644809,"score_gpt":0.30656944726375135,"score_spread":0.27339609594730324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921371084","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":[0.00036774232,0.0035185332,0.9882429,0.002116673,0.0006907207,0.0045172186,0.000018164576,0.00026210767,0.00026591],"genre_scores_gemma":[0.93893677,0.00010477694,0.059124447,0.00080627995,0.0002548319,0.0004940742,0.00004125693,0.00004448601,0.00019309003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975742,0.00018484432,0.00043696704,0.0010418125,0.0002502916,0.00051186455],"domain_scores_gemma":[0.9955046,0.0021655217,0.00038257937,0.0015500122,0.00025704034,0.00014020145],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040778017,0.00047653096,0.000479789,0.00007432783,0.0006361308,0.00036469876,0.001787477,0.00019125525,0.000008021635],"category_scores_gemma":[0.000024908286,0.0003241849,0.000121837125,0.00045429604,0.00018392419,0.0001201592,0.0011011291,0.000661746,0.000008251589],"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":[0.000017330733,0.000043090207,0.000031228134,0.000015617796,0.000045853885,0.000001058107,0.000175275,0.9752102,0.00004561644,0.008243621,0.0013501042,0.014821034],"study_design_scores_gemma":[0.00036143762,0.000109787594,0.00052237103,0.000016538532,0.00003761253,0.000009289298,0.000015842565,0.9941314,0.00014057927,0.003604751,0.00066847145,0.0003819563],"about_ca_topic_score_codex":0.000024052155,"about_ca_topic_score_gemma":0.000034865738,"teacher_disagreement_score":0.938569,"about_ca_system_score_codex":0.00009261238,"about_ca_system_score_gemma":0.000052729953,"threshold_uncertainty_score":0.999921},"labels":[],"label_agreement":null},{"id":"W2925104720","doi":"10.1109/sustech.2018.8671351","title":"Robotic Sorting of Used Button Cell Batteries: Utilizing Deep Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Conestoga College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sorting; Deep learning; Computer science; Artificial intelligence; Pixel; Convolutional neural network; Object detection; Image processing; Strengths and weaknesses; Embedded system; Computer vision; Image (mathematics); Pattern recognition (psychology)","score_opus":0.026883462139256017,"score_gpt":0.2724281837967684,"score_spread":0.2455447216575124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925104720","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":[0.05476371,0.000027742335,0.9373306,0.0001830887,0.00008893185,0.00009583133,4.357369e-8,0.00021594991,0.0072941273],"genre_scores_gemma":[0.83401823,0.000004425308,0.16539147,0.0001189642,0.000059795933,0.000006982634,5.500711e-7,0.000008886306,0.000390713],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899393,0.00003439238,0.0002651079,0.0002994915,0.00014750038,0.00025955233],"domain_scores_gemma":[0.9991161,0.0001634677,0.00017798693,0.00040440453,0.00008227937,0.00005572261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115041126,0.00009748647,0.00013292032,0.0000512455,0.00018720092,0.000038523223,0.00051258027,0.000030958076,0.00004581555],"category_scores_gemma":[0.000033280223,0.000092367576,0.000037510334,0.0004345717,0.0000819186,0.00032886807,0.0002608682,0.00011276153,0.000082028826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000138738715,0.00021090833,0.022832923,0.00012257772,0.000030866107,0.000008676543,0.0076856334,0.08439584,0.5673868,0.14172114,0.0004129486,0.17517777],"study_design_scores_gemma":[0.00023465395,0.00016957178,0.0015263176,0.000027885768,0.0000071708455,0.000006929288,0.00030728537,0.761652,0.23086849,0.0032679834,0.0016561585,0.00027557573],"about_ca_topic_score_codex":0.0000096698805,"about_ca_topic_score_gemma":0.0000150336855,"teacher_disagreement_score":0.7792545,"about_ca_system_score_codex":0.000016652635,"about_ca_system_score_gemma":0.000013117384,"threshold_uncertainty_score":0.37666404},"labels":[],"label_agreement":null},{"id":"W2925148167","doi":"10.1109/cvpr42600.2020.01164","title":"nuScenes: A Multimodal Dataset for Autonomous Driving","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":379,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Computer science; Lidar; Artificial intelligence; Benchmark (surveying); Computer vision; Object detection; Segmentation; Bounding overwatch; Suite; Radar; Field (mathematics); Minimum bounding box; Software deployment; Tracking (education); Image (mathematics); Remote sensing; Geography","score_opus":0.048178722162768986,"score_gpt":0.32028320489291034,"score_spread":0.2721044827301414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925148167","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":[0.00008017245,0.0000388311,0.98966473,0.0069797183,0.00038275018,0.0011565662,0.0008985904,0.00057423505,0.00022439672],"genre_scores_gemma":[0.048046157,0.000015040887,0.94635624,0.0016126331,0.00035064842,0.0008893699,0.002567455,0.00003052173,0.00013192296],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980288,0.000023719947,0.00032483568,0.0011391666,0.00015495764,0.00032848062],"domain_scores_gemma":[0.9977596,0.00025749463,0.00018665608,0.0015672472,0.000057047804,0.00017199139],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008033249,0.00027392135,0.00029082526,0.000050913786,0.000140905,0.0002076573,0.0024574646,0.00013744924,0.000010831457],"category_scores_gemma":[0.00005771949,0.00027144692,0.00011547337,0.00016782194,0.000032601794,0.00021307578,0.0035916076,0.00035629224,0.000083575804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019932431,0.0003102166,0.00016909315,0.00045329877,0.0001762901,0.000033740263,0.0006886371,0.08583282,0.0022380308,0.30814773,0.3911021,0.2108281],"study_design_scores_gemma":[0.0001618355,0.000020798605,0.00008942038,0.00002042825,0.0000104562405,0.0000039003926,0.0000034556272,0.86831653,0.00047803996,0.03544584,0.09510647,0.00034284245],"about_ca_topic_score_codex":0.000019272818,"about_ca_topic_score_gemma":0.000021727732,"teacher_disagreement_score":0.7824837,"about_ca_system_score_codex":0.00006958199,"about_ca_system_score_gemma":0.00014558881,"threshold_uncertainty_score":0.9999738},"labels":[],"label_agreement":null},{"id":"W2931118404","doi":"10.1145/3297858.3304041","title":"Bit-Tactical","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":107,"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; Scheduling (production processes); Multiplexer; Artificial neural network; Reuse; Dynamic programming; Inference; Hardware acceleration; Parallel computing; Computer hardware; Algorithm; Multiplexing; Artificial intelligence; Field-programmable gate array","score_opus":0.012637413759943143,"score_gpt":0.2636886611615793,"score_spread":0.25105124740163615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2931118404","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":[0.007822109,0.000007923282,0.91555095,0.002736149,0.00010894487,0.00009134489,6.6757366e-8,0.00022794266,0.07345455],"genre_scores_gemma":[0.8228394,0.0000024447356,0.17144662,0.0011493858,0.000027586244,0.000007632983,2.585624e-7,0.0000029424073,0.0045237388],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995916,0.0000060100006,0.000053369637,0.00016497537,0.00007523687,0.00010882129],"domain_scores_gemma":[0.99946284,0.00006504765,0.000012127869,0.0004060472,0.000013614948,0.000040329425],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000023428875,0.000035297202,0.000038336173,0.0000152121975,0.000021535832,0.00002481327,0.0003453745,0.000013943476,0.00011412356],"category_scores_gemma":[0.000004109611,0.00002857243,0.000015580077,0.00019948988,0.0000074594313,0.00023344661,0.000113737224,0.00004840959,0.0033269473],"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":[2.9475916e-7,0.000009619986,0.00040333843,5.1976855e-7,8.4000254e-7,4.9620843e-7,0.000006846918,0.000102322,0.0011866839,0.9849132,0.001507822,0.011868022],"study_design_scores_gemma":[0.00041776578,0.000103391925,0.009113847,0.000005174119,0.000002320062,0.000045567704,0.000010180466,0.37846068,0.013734013,0.13543117,0.462257,0.00041887775],"about_ca_topic_score_codex":8.587057e-7,"about_ca_topic_score_gemma":4.6833412e-7,"teacher_disagreement_score":0.849482,"about_ca_system_score_codex":0.000007606103,"about_ca_system_score_gemma":0.000007687877,"threshold_uncertainty_score":0.9974491},"labels":[],"label_agreement":null},{"id":"W2935850529","doi":"10.1016/j.jpdc.2019.04.008","title":"GPU accelerated t-distributed stochastic neighbor embedding","year":2019,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":82,"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":"Canadian Institute for Advanced Research; Nvidia","keywords":"Computer science; MNIST database; CUDA; Embedding; Dimensionality reduction; Curse of dimensionality; Visualization; Implementation; Parallel computing; Artificial intelligence; Machine learning; Artificial neural network; Theoretical computer science","score_opus":0.021153718403705203,"score_gpt":0.285349262357592,"score_spread":0.2641955439538868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935850529","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":[0.15706222,0.00033423307,0.8414325,0.0006101173,0.0002681525,0.00015414343,0.000016496624,0.00007443764,0.000047690595],"genre_scores_gemma":[0.96282834,0.000015881631,0.0368064,0.0001324481,0.0001625658,0.0000014224735,0.000027426653,0.0000109089015,0.000014622646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820375,0.000066366454,0.0006852734,0.00031671743,0.0003121097,0.0004157657],"domain_scores_gemma":[0.99804354,0.0003565998,0.0007176395,0.00031697133,0.00032913082,0.00023609246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028781622,0.00021836007,0.00040383657,0.00010868866,0.00021374063,0.00022468592,0.0007279668,0.00007871974,0.000012296055],"category_scores_gemma":[0.000083849234,0.0001904314,0.00010666884,0.00071564893,0.000039849532,0.00056287134,0.00031974431,0.00040609136,0.000019019988],"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":[0.000051629322,0.00009821923,0.001594729,0.000021596017,0.00007281969,0.000042015756,0.00013410169,0.9717996,0.0015742315,0.013657966,0.001369659,0.00958341],"study_design_scores_gemma":[0.0013323886,0.00016547821,0.0079006795,0.00010323812,0.000021204436,0.00040869936,0.00007978139,0.9844843,0.00007090926,0.0042016143,0.0009497439,0.00028197624],"about_ca_topic_score_codex":0.0000021702488,"about_ca_topic_score_gemma":3.1996152e-7,"teacher_disagreement_score":0.8057661,"about_ca_system_score_codex":0.00007423122,"about_ca_system_score_gemma":0.0000782247,"threshold_uncertainty_score":0.77655673},"labels":[],"label_agreement":null},{"id":"W2937085287","doi":"10.1016/j.neucom.2019.03.049","title":"Efficient multi-kernel DCNN with pixel dropout for stroke MRI segmentation","year":2019,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Saskatchewan","funders":"Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Segmentation; Dropout (neural networks); Pattern recognition (psychology); Benchmark (surveying); Kernel (algebra); Image segmentation; Computer vision; Machine learning; Mathematics","score_opus":0.015209357021066986,"score_gpt":0.2690721429839013,"score_spread":0.25386278596283435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937085287","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":[0.21854131,0.00001237126,0.7795606,0.00028844518,0.00019696071,0.0009956312,0.0000024575977,0.0002586394,0.00014357011],"genre_scores_gemma":[0.60833824,7.8921653e-7,0.3909396,0.000359249,0.000060125258,0.000053241973,0.0000034950303,0.000020501275,0.0002247737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984459,0.00003304603,0.00024072666,0.0006546934,0.00023844911,0.000387183],"domain_scores_gemma":[0.99886495,0.0002654347,0.00019290994,0.000497926,0.000095674026,0.000083090235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011225715,0.00017891229,0.00015821772,0.00007207194,0.00022409945,0.00010346523,0.00055836834,0.00003359689,0.0000019982215],"category_scores_gemma":[0.0000101218375,0.00016135722,0.000057644513,0.0003293689,0.000024269852,0.00012601922,0.00020670636,0.0001466633,0.000069747395],"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":[0.000019472674,0.00014162209,0.0030101324,0.000037535174,0.000013100299,0.0000026406015,0.00038589162,0.90236366,0.04952834,0.0064123464,0.00009247444,0.037992794],"study_design_scores_gemma":[0.0009344119,0.000133126,0.0016064616,0.000019488636,0.0000053944677,0.000014679776,0.000025544694,0.9918921,0.003940348,0.000064592525,0.0011766504,0.00018723209],"about_ca_topic_score_codex":0.0000019716394,"about_ca_topic_score_gemma":0.0000016414921,"teacher_disagreement_score":0.3897969,"about_ca_system_score_codex":0.000046411606,"about_ca_system_score_gemma":0.000030717343,"threshold_uncertainty_score":0.65799564},"labels":[],"label_agreement":null},{"id":"W2941524851","doi":"10.1109/tmi.2019.2930068","title":"Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Hausdorff distance; Segmentation; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Scale-space segmentation; Computer science; Image segmentation; Segmentation-based object categorization; Computer vision; Similarity (geometry); Boundary (topology); Mathematics; Image (mathematics)","score_opus":0.011412820554936151,"score_gpt":0.28072207762325563,"score_spread":0.2693092570683195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941524851","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":[0.0013201914,0.00030337088,0.97240645,0.023001088,0.0017187933,0.000821479,0.0000116753645,0.00025411526,0.00016282036],"genre_scores_gemma":[0.980858,0.00028101576,0.015107362,0.0027773837,0.00030215204,0.00050572894,0.000027073187,0.00004826826,0.000092966475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99534583,0.0003060327,0.0006917557,0.0011173018,0.0019056344,0.0006334634],"domain_scores_gemma":[0.9972121,0.0009291475,0.00026418793,0.0011262519,0.000121656936,0.00034667176],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00076952734,0.00042592277,0.0004008875,0.00018247351,0.0003558642,0.00021121909,0.0019343257,0.00024575717,0.00014600201],"category_scores_gemma":[0.00004063616,0.00031829494,0.00014376148,0.00074003986,0.0005716122,0.00049991626,0.000061029517,0.003534754,0.000026678712],"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":[0.000040519586,0.00017000252,0.00009388828,0.00004007402,0.000033118395,0.00012375283,0.00023174177,0.84575176,0.000034439225,0.00061181624,0.000476919,0.15239199],"study_design_scores_gemma":[0.00070065854,0.000019406656,0.00023896161,0.0004931783,0.00002218666,0.00015536035,0.000046130765,0.997116,0.00015673315,0.00053964875,0.00017055402,0.00034115824],"about_ca_topic_score_codex":0.00009067291,"about_ca_topic_score_gemma":0.00013429529,"teacher_disagreement_score":0.97953784,"about_ca_system_score_codex":0.00031919547,"about_ca_system_score_gemma":0.000500674,"threshold_uncertainty_score":0.9999269},"labels":[],"label_agreement":null},{"id":"W2942740757","doi":"10.1109/ivs.2019.8813809","title":"Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Lidar; Computer science; Point cloud; Benchmark (surveying); Artificial intelligence; Segmentation; Computer vision; Object detection; Code (set theory); Object (grammar); Point (geometry); Graphics; Computer graphics (images); Remote sensing; Geography; Set (abstract data type); Cartography","score_opus":0.021439989958462274,"score_gpt":0.2905628930708862,"score_spread":0.26912290311242393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942740757","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":[0.006694244,0.00010581425,0.9881208,0.0013935702,0.00023321547,0.0017295115,0.0010847419,0.00023807945,0.00040000852],"genre_scores_gemma":[0.2651093,0.00020073754,0.73029023,0.00043426015,0.00018298662,0.0008978716,0.0016211206,0.000046952722,0.0012165259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998281,0.00003751986,0.00025835025,0.0010152778,0.00013827233,0.000269592],"domain_scores_gemma":[0.99770075,0.00023942326,0.00014647776,0.0017488161,0.00006432301,0.00010019486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015150996,0.00023500969,0.00024481243,0.00006707152,0.000120735516,0.0002625894,0.0010847963,0.00014431553,0.000025020925],"category_scores_gemma":[0.000028143682,0.000223834,0.00006258032,0.00007984281,0.00006253158,0.0003689495,0.0019728076,0.00025273845,0.00013996827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010433274,0.0005717343,0.00021785984,0.0013172077,0.00012917693,0.000011338901,0.0012667294,0.030899642,0.045800433,0.039735846,0.17723057,0.70271516],"study_design_scores_gemma":[0.00037984224,0.000087709035,0.0014264453,0.00007253083,0.000039598723,0.000017567123,0.000015990341,0.9227391,0.0016941676,0.029181054,0.043711472,0.00063456135],"about_ca_topic_score_codex":0.000017432822,"about_ca_topic_score_gemma":0.000006473671,"teacher_disagreement_score":0.89183944,"about_ca_system_score_codex":0.000063418964,"about_ca_system_score_gemma":0.000060740782,"threshold_uncertainty_score":0.9127685},"labels":[],"label_agreement":null},{"id":"W2942772093","doi":"10.1109/paap.2018.00036","title":"Accelerating Backward Convolution of Convolutional Neural Networks on BWDSP","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto Scarborough","keywords":"Computer science; Convolutional neural network; Field-programmable gate array; Deep learning; Backpropagation; Reconfigurability; Artificial intelligence; Application-specific integrated circuit; Computer architecture; Artificial neural network; Computer engineering; Convolution (computer science); Embedded system; Computer hardware","score_opus":0.03675775471560357,"score_gpt":0.28085819401090645,"score_spread":0.24410043929530287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942772093","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":[0.021851925,0.00003269128,0.9700287,0.0007841123,0.0003546128,0.00018833636,0.0000014538722,0.00019140643,0.0065667606],"genre_scores_gemma":[0.94514114,0.0000045284355,0.053427998,0.0008221266,0.00035985836,0.000018985531,0.000004967576,0.000007431535,0.00021295091],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988438,0.000044014978,0.00027923248,0.00034857754,0.0002143456,0.00027001926],"domain_scores_gemma":[0.99893755,0.0001811355,0.00014122635,0.00046554898,0.0001994026,0.00007515212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114718925,0.00012160727,0.00013225617,0.00005449496,0.00019826507,0.000033690347,0.0005477406,0.000055033142,0.000067455054],"category_scores_gemma":[0.0000257791,0.00011034457,0.00005142326,0.0004610612,0.00017465425,0.00034859884,0.00018014642,0.0001312088,0.000066231725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019244062,0.00007539134,0.0008930269,0.0000035646158,0.000012092519,9.3534254e-7,0.000057514186,0.041133143,0.002212057,0.92057353,0.0056421985,0.029377278],"study_design_scores_gemma":[0.00019671176,0.00016586702,0.0040483354,0.000007797169,0.000002134175,0.000007087458,0.0000047717645,0.9887755,0.002279652,0.0033560563,0.0010338976,0.00012218834],"about_ca_topic_score_codex":0.0000072673993,"about_ca_topic_score_gemma":0.00000840997,"teacher_disagreement_score":0.9476424,"about_ca_system_score_codex":0.000038374714,"about_ca_system_score_gemma":0.000024257857,"threshold_uncertainty_score":0.4499721},"labels":[],"label_agreement":null},{"id":"W2943076207","doi":"10.1109/iscas.2019.8702332","title":"Bit-Slicing FPGA Accelerator for Quantized Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; IBM (Canada); Université de Montréal","funders":"","keywords":"Stratix; Computer science; Field-programmable gate array; Artificial neural network; Key (lock); Hardware acceleration; Computer hardware; Computer architecture; Artificial intelligence","score_opus":0.026613038887922116,"score_gpt":0.28422610387764236,"score_spread":0.25761306498972025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943076207","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":[0.037854392,0.000055329376,0.9581021,0.0012472158,0.00047191602,0.0007816797,8.5980935e-7,0.00043045884,0.001056016],"genre_scores_gemma":[0.89776826,0.000007008649,0.09892484,0.0019423456,0.0001745294,0.000120258475,0.000004434025,0.00001826755,0.0010400662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875534,0.000020870932,0.00022835573,0.00047193025,0.00012303343,0.0004004515],"domain_scores_gemma":[0.99872714,0.00030268964,0.000088748784,0.00071255723,0.000074754804,0.00009408699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010390999,0.0001467436,0.00018222154,0.0000500027,0.00013678394,0.00009031457,0.00083596277,0.000054626445,0.000030337747],"category_scores_gemma":[0.000011535005,0.00012610493,0.00008697844,0.0004695914,0.000016598417,0.0005300065,0.00021146901,0.00011843702,0.00008265326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052185813,0.00010297509,0.0027164156,0.000028034576,0.00003473174,0.000002564316,0.00013319612,0.19757934,0.00988388,0.66133857,0.009675834,0.11845226],"study_design_scores_gemma":[0.00045318354,0.000055944904,0.0004355777,0.0000041357976,0.0000027021072,0.0000045235374,0.0000061146916,0.988705,0.0013761263,0.0018371197,0.0069365436,0.00018305222],"about_ca_topic_score_codex":0.000005463076,"about_ca_topic_score_gemma":0.000006990678,"teacher_disagreement_score":0.8599139,"about_ca_system_score_codex":0.000021937967,"about_ca_system_score_gemma":0.000015145468,"threshold_uncertainty_score":0.514241},"labels":[],"label_agreement":null},{"id":"W2943305281","doi":"","title":"Fast AutoAugment.","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Generalization; Artificial intelligence; Matching (statistics); Image (mathematics); Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.06365303841731074,"score_gpt":0.19384114286965026,"score_spread":0.13018810445233953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943305281","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":[0.018063921,0.000055058837,0.9725402,0.000244632,0.00062902895,0.00042113013,0.000011675264,0.0004969941,0.0075373524],"genre_scores_gemma":[0.9827842,0.0001370325,0.008843055,0.00021793932,0.00008337744,0.0000023766868,0.000017540375,0.00001989098,0.007894594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812,0.00006114849,0.00015672366,0.0012252652,0.000083159284,0.00035371032],"domain_scores_gemma":[0.9973658,0.000089863875,0.00022645996,0.0020854909,0.000090633745,0.00014180398],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007903461,0.00027657402,0.00025462225,0.00014538944,0.00012558921,0.00008917031,0.002599284,0.00020022989,0.000029722505],"category_scores_gemma":[0.000007569981,0.00033150325,0.00016353463,0.0005878147,0.00006852047,0.00035322353,0.0031686,0.00055660994,0.00080201286],"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":[0.0000032981359,0.000034133052,0.00033796724,0.000018136983,0.000023062987,0.000039878345,0.000034955738,0.681739,0.000030203226,0.31553856,0.0009859839,0.0012148421],"study_design_scores_gemma":[0.00026121113,0.000022991751,0.0005894975,0.00004028004,0.00002517092,0.000003922633,0.000012038885,0.9046715,0.00011238081,0.08776407,0.0060514156,0.0004455091],"about_ca_topic_score_codex":0.000016588,"about_ca_topic_score_gemma":0.000007244032,"teacher_disagreement_score":0.96472025,"about_ca_system_score_codex":0.00019834455,"about_ca_system_score_gemma":0.00012373803,"threshold_uncertainty_score":0.999976},"labels":[],"label_agreement":null},{"id":"W2945490722","doi":"10.48550/arxiv.1905.05889","title":"DARNet: Deep Active Ray Network for Building Segmentation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Canadian Institute for Advanced Research; Nvidia","keywords":"Computer science; Artificial intelligence; Segmentation; Polygon (computer graphics); Convolutional neural network; Function (biology); Deep learning; Active contour model; Energy (signal processing); Computer vision; Intersection (aeronautics); Energy minimization; Image segmentation; Pattern recognition (psychology); Mathematics; Cartography; Frame (networking)","score_opus":0.05916007232131909,"score_gpt":0.22286685988720595,"score_spread":0.16370678756588686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945490722","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":[0.021625914,0.00006220827,0.97540545,0.00011470009,0.00068563287,0.001295303,0.00001902697,0.00026929242,0.00052245165],"genre_scores_gemma":[0.87566376,0.000117128104,0.12296305,0.000181691,0.00026272162,0.000018065963,0.00006734242,0.00002939021,0.0006968379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794316,0.00007802482,0.00019344567,0.0012538747,0.000079316196,0.00045218717],"domain_scores_gemma":[0.99780744,0.00036015655,0.000402196,0.0011522685,0.00015927569,0.00011863161],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013677022,0.000303336,0.00029569384,0.000118980664,0.00025876012,0.000091265254,0.0015784916,0.00021761583,0.000008259536],"category_scores_gemma":[0.00001687853,0.00037411507,0.00019373464,0.00061098026,0.000055878332,0.00051678607,0.0012793901,0.00040935967,0.000048652746],"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":[0.000025417936,0.00002124859,0.000187886,0.000025431025,0.000046091078,0.000005469139,0.00007101231,0.8524115,0.00010781309,0.14382385,0.0003704001,0.002903879],"study_design_scores_gemma":[0.00041584636,0.00003959751,0.00040723808,0.000044355675,0.0000521417,0.0000012090782,0.000026765134,0.8682265,0.00039544073,0.12858324,0.0014144427,0.000393205],"about_ca_topic_score_codex":0.000012569715,"about_ca_topic_score_gemma":0.000016657823,"teacher_disagreement_score":0.8540379,"about_ca_system_score_codex":0.00032160507,"about_ca_system_score_gemma":0.0001003701,"threshold_uncertainty_score":0.9998711},"labels":[],"label_agreement":null},{"id":"W2945574069","doi":"10.48550/arxiv.1905.07836","title":"Enabling Computer Vision Driven Assistive Devices for the Visually Impaired via Micro-architecture Design Exploration","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Architecture; Artificial intelligence; Object detection; Network architecture; Object (grammar); Deep learning; Set (abstract data type); Hyperparameter; Artificial neural network; Deep neural networks; Function (biology); Cognitive neuroscience of visual object recognition; Computer architecture; Human–computer interaction; Computer vision; Pattern recognition (psychology); Computer network","score_opus":0.08714445836693874,"score_gpt":0.2356318135845963,"score_spread":0.14848735521765757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945574069","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":[0.0057535768,0.000086118445,0.99059683,0.0006489607,0.0005160268,0.002080901,0.000013377576,0.00028522624,0.000018998422],"genre_scores_gemma":[0.8656233,0.00009520262,0.13347565,0.00030299154,0.0002403207,0.000023593731,0.00004340814,0.000033063858,0.0001624584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976931,0.00022384358,0.000255908,0.001314503,0.00011927144,0.0003933737],"domain_scores_gemma":[0.9965969,0.0011975646,0.00047840498,0.001316033,0.00030586997,0.00010527934],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027011937,0.00038972453,0.00032710488,0.00018784936,0.00050692924,0.0002468858,0.0022653253,0.00024683145,0.0000030579922],"category_scores_gemma":[0.000016382026,0.00034021644,0.00024605278,0.00060354563,0.000098845616,0.00063657854,0.0016697955,0.0005245968,0.00004276295],"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":[0.0000617689,0.000047438585,0.00006400614,0.000036826168,0.00007490074,0.000009544024,0.00021689702,0.98397547,0.0006890833,0.004832019,0.00033459126,0.009657461],"study_design_scores_gemma":[0.00036776322,0.00016031861,0.0006435723,0.00008838969,0.00007616265,0.0000040227064,0.0000285127,0.978054,0.0004411779,0.018652475,0.0010899289,0.00039366027],"about_ca_topic_score_codex":0.000013070199,"about_ca_topic_score_gemma":0.000028043094,"teacher_disagreement_score":0.8598697,"about_ca_system_score_codex":0.00020193044,"about_ca_system_score_gemma":0.00015348106,"threshold_uncertainty_score":0.999905},"labels":[],"label_agreement":null},{"id":"W2945889381","doi":"10.1007/978-3-030-18305-9_22","title":"Memory-Efficient Backpropagation for Recurrent Neural Networks","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Recurrent neural network; Backpropagation; Flexibility (engineering); Sequence (biology); Artificial neural network; Process (computing); Artificial intelligence; Long short term memory; Dependency (UML); Pattern recognition (psychology); Algorithm","score_opus":0.020315661036380633,"score_gpt":0.2632183953868266,"score_spread":0.242902734350446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945889381","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":[0.000031172913,0.00048310956,0.99204135,0.001079728,0.003628214,0.0018751215,0.0000074067866,0.00022077345,0.00063311873],"genre_scores_gemma":[0.2399727,0.000088357025,0.7533047,0.0031519597,0.0022317807,0.00020154673,0.00006271525,0.00014007668,0.00084616337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957894,0.000029660601,0.0006160109,0.0019807534,0.00075002376,0.00083415577],"domain_scores_gemma":[0.9964,0.0008113285,0.00045832602,0.0017969616,0.00035454967,0.00017886041],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059495907,0.00056956004,0.0005146674,0.00046242782,0.00034077876,0.00036968818,0.0031873882,0.0002840873,0.00000856538],"category_scores_gemma":[0.000060787264,0.0005302198,0.00019078964,0.00073038944,0.00039001455,0.00038583323,0.0010912882,0.00077954,0.000038378366],"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":[0.000004607481,0.000012736672,0.0000012212079,0.000012769373,0.000002320508,0.0000022101772,0.00004712451,0.54545045,0.000013819124,0.009936537,0.0000271997,0.44448903],"study_design_scores_gemma":[0.00026098138,0.00020356984,0.000021800683,0.00012841441,0.0000075172147,0.000023765173,5.5490144e-8,0.96290445,0.00031240756,0.03419693,0.0013809865,0.00055913],"about_ca_topic_score_codex":0.0000017105228,"about_ca_topic_score_gemma":0.000016616526,"teacher_disagreement_score":0.44392988,"about_ca_system_score_codex":0.00038170157,"about_ca_system_score_gemma":0.0002827975,"threshold_uncertainty_score":0.9997149},"labels":[],"label_agreement":null},{"id":"W2946589087","doi":"10.3390/rs11101206","title":"High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":211,"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; National Natural Science Foundation of China; China National Space Administration","keywords":"Computer science; Convolutional neural network; Synthetic aperture radar; Artificial intelligence; Deep learning; Grid; Pattern recognition (psychology); Computer vision; Feature extraction; Focus (optics); Remote sensing; Geology","score_opus":0.014437071382568232,"score_gpt":0.2401088034786429,"score_spread":0.2256717320960747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946589087","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":[0.2957218,0.000033623583,0.70096433,0.0013765391,0.000932706,0.00034971008,0.0000011936432,0.0002438666,0.0003762321],"genre_scores_gemma":[0.8901355,0.000002755592,0.108635575,0.00079678115,0.00036514353,3.037212e-8,0.000005446304,0.000015150627,0.000043613436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854016,0.00011753988,0.000226905,0.00048096117,0.0002477384,0.0003866929],"domain_scores_gemma":[0.99893236,0.00031686184,0.00010845694,0.0005209406,0.000055193086,0.00006618375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018517108,0.00015882625,0.00017235156,0.00011101614,0.00012744783,0.00006237843,0.00022532433,0.00006556258,0.0000033025567],"category_scores_gemma":[0.000030823816,0.00016591222,0.000056409768,0.0007473969,0.000034585573,0.00022587925,0.00007745905,0.0002893993,0.000101636644],"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":[0.000027874681,0.000009805692,0.00006225871,0.0000049444716,0.0000022643235,0.000013606801,0.000013908138,0.85959226,0.012885009,0.000620842,0.00010110752,0.1266661],"study_design_scores_gemma":[0.0003805611,0.000049144506,0.003947211,0.000050089777,0.0000020719322,0.00002231821,0.0000018488249,0.98821616,0.0037555834,0.0030213085,0.00038165197,0.00017205013],"about_ca_topic_score_codex":0.00004768122,"about_ca_topic_score_gemma":0.000024160096,"teacher_disagreement_score":0.5944137,"about_ca_system_score_codex":0.00014748561,"about_ca_system_score_gemma":0.000030867825,"threshold_uncertainty_score":0.67657036},"labels":[],"label_agreement":null},{"id":"W2946958331","doi":"10.1109/cvprw53098.2021.00268","title":"DeepShift: Towards Multiplication-Less Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Convolutional neural network; Multiplication (music); Inference; Computation; Latency (audio); Convolution (computer science); Parallel computing; Code (set theory); Algorithm; Artificial intelligence; Artificial neural network; Mathematics","score_opus":0.03497310263196076,"score_gpt":0.2897568323736205,"score_spread":0.25478372974165975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946958331","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":[0.0040590963,0.00085005723,0.98586786,0.004326219,0.0010923144,0.0006757284,0.000004081721,0.00095125963,0.0021734023],"genre_scores_gemma":[0.7211444,0.00021460127,0.2754865,0.001544979,0.0004362392,0.00059157464,0.0001295193,0.000039556147,0.00041267317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99686086,0.00011045544,0.0005487772,0.0015475547,0.00038495535,0.0005473801],"domain_scores_gemma":[0.9958465,0.00017890832,0.0003081692,0.003138287,0.00028531472,0.00024281336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014898642,0.00045064907,0.00043789513,0.00009434708,0.00021681511,0.0006216478,0.003174053,0.00036457967,0.000033107794],"category_scores_gemma":[0.00003761098,0.00045138178,0.00024684818,0.0006861411,0.000075623735,0.00040437735,0.0051794937,0.0010681426,0.000022907554],"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":[0.000002247551,0.000104041275,0.00021690992,0.000029708777,0.000039200564,0.000019241921,0.00019077076,0.6764438,0.00003772733,0.03524496,0.0011500819,0.2865213],"study_design_scores_gemma":[0.00012286764,0.000008012389,0.0028068284,0.000026524453,0.0000123616055,0.00001830908,0.000020411408,0.99080026,0.00022518587,0.0044778893,0.0009866316,0.0004946881],"about_ca_topic_score_codex":0.00008199567,"about_ca_topic_score_gemma":0.00010290201,"teacher_disagreement_score":0.71708524,"about_ca_system_score_codex":0.000115876464,"about_ca_system_score_gemma":0.00014845325,"threshold_uncertainty_score":0.99979377},"labels":[],"label_agreement":null},{"id":"W2947592526","doi":"10.1007/s11265-019-01453-w","title":"POLYBiNN: Binary Inference Engine for Neural Networks using Decision Trees","year":2019,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; MNIST database; Convolutional neural network; Field-programmable gate array; Inference; Artificial intelligence; Machine learning; Artificial neural network; Inference engine; Deep learning; Computer engineering; Embedded system","score_opus":0.028940824194038685,"score_gpt":0.300909515127282,"score_spread":0.2719686909332433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947592526","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":[0.15665169,0.0028974835,0.8394387,0.00008839358,0.000597383,0.0002745405,7.246227e-7,0.000039483602,0.000011598878],"genre_scores_gemma":[0.9545975,0.000013833307,0.044670258,0.00006248836,0.0005876967,0.0000073871506,5.597211e-7,0.000020319929,0.000039954033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818933,0.000049174327,0.0007569357,0.00026461596,0.00041018828,0.0003297754],"domain_scores_gemma":[0.9976654,0.0005047229,0.0009575229,0.00025223976,0.00048627806,0.00013383816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004859389,0.00018874471,0.0003835365,0.00020172552,0.0002802547,0.00026072055,0.00087232864,0.0000865286,0.0000019676136],"category_scores_gemma":[0.00003070053,0.00014947893,0.00011995527,0.00061833207,0.000028999772,0.001427727,0.00014032306,0.00027941505,0.0000020161583],"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":[0.00004235597,0.000031516174,0.0006681921,0.000048384478,0.000010471,0.0000072725807,0.00007976223,0.9359601,0.005067622,0.00036689904,0.000063039435,0.0576544],"study_design_scores_gemma":[0.0004611941,0.0002704774,0.0002585735,0.0005084003,0.000014284723,0.00027966846,0.000032282296,0.9969689,0.00012475459,0.0005489984,0.00036386424,0.00016855665],"about_ca_topic_score_codex":0.0000026708774,"about_ca_topic_score_gemma":3.10822e-7,"teacher_disagreement_score":0.7979458,"about_ca_system_score_codex":0.00009095026,"about_ca_system_score_gemma":0.00011422619,"threshold_uncertainty_score":0.6095574},"labels":[],"label_agreement":null},{"id":"W2948630381","doi":"10.5194/isprs-archives-xlii-2-w13-835-2019","title":"USING EDGECONV TO IMPROVE 3D OBJECT DETECTION FROM RGB-D DATA","year":2019,"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 Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Minimum bounding box; Artificial intelligence; Bounding overwatch; Point cloud; RGB color model; Computer vision; Computer science; Benchmark (surveying); Object (grammar); Object detection; Point (geometry); 3d model; Deep learning; Pattern recognition (psychology); Mathematics; Image (mathematics); Geography; Geometry; Cartography","score_opus":0.028328291028907725,"score_gpt":0.28006968412645555,"score_spread":0.25174139309754784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948630381","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":[0.014225739,0.000025230682,0.97493577,0.003051513,0.0029540763,0.0010118848,0.00017218175,0.00007485107,0.003548759],"genre_scores_gemma":[0.9694082,0.00007109049,0.028617263,0.001581934,0.00019945049,5.2208e-7,0.000045351066,0.00001285932,0.000063321604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995196,0.0002833051,0.0014281714,0.00071800605,0.0018010727,0.0005734724],"domain_scores_gemma":[0.99521434,0.001305015,0.0016780231,0.0012946719,0.00031116762,0.000196762],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012540416,0.00044401342,0.00044500767,0.0008507069,0.0012868363,0.0010707946,0.004203063,0.000094637835,0.0000068854024],"category_scores_gemma":[0.00060754275,0.00029752843,0.00026483904,0.0014334465,0.0014920683,0.0012085118,0.0031134165,0.00048123577,0.0000114177055],"study_design_candidate":"simulation_or_modeling","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":[0.00008317578,0.000014921576,0.000117477255,0.000018174578,0.000065071144,1.719256e-7,0.0016693703,0.010490739,0.01248056,0.000010874787,0.000021484822,0.975028],"study_design_scores_gemma":[0.0005846649,0.0001227674,0.0016513083,0.00021816889,0.000033042776,0.00007763243,0.00067116896,0.9696392,0.015498446,0.0067154663,0.004444543,0.00034363844],"about_ca_topic_score_codex":0.72271883,"about_ca_topic_score_gemma":0.1588757,"teacher_disagreement_score":0.97468436,"about_ca_system_score_codex":0.0000697894,"about_ca_system_score_gemma":0.0002647061,"threshold_uncertainty_score":0.9999662},"labels":[],"label_agreement":null},{"id":"W2948895131","doi":"10.1145/3299869.3320230","title":"SVQ","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Frame (networking); Object (grammar); Computer vision; Pattern recognition (psychology); Telecommunications","score_opus":0.010813197599329456,"score_gpt":0.246373208218304,"score_spread":0.23556001061897455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948895131","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":[0.006797032,0.000010055742,0.8967342,0.0013259014,0.00008382225,0.000076010336,4.5557112e-8,0.00019965247,0.09477329],"genre_scores_gemma":[0.8146598,0.000002612359,0.17404078,0.0010956076,0.000019102896,0.000007326413,2.308912e-7,0.0000022341499,0.010172315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997165,0.0000031233944,0.000037095386,0.0001203405,0.000048494192,0.000074448224],"domain_scores_gemma":[0.99958307,0.000024891171,0.000009965226,0.0003507147,0.000009578465,0.000021783462],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000015824233,0.000024951582,0.00002594102,0.000010532391,0.000015755613,0.000015556045,0.00032957896,0.000007806403,0.00007430486],"category_scores_gemma":[0.0000011773582,0.000020486534,0.000010577352,0.00015146939,0.0000037383772,0.0001686773,0.00008865376,0.000025117344,0.0026560808],"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":[1.165039e-7,0.000004718703,0.0003803523,4.1412105e-7,5.402466e-7,2.0851321e-7,0.000009368226,0.00025814367,0.0013674067,0.9743358,0.0020375033,0.021605426],"study_design_scores_gemma":[0.00030284192,0.000057190642,0.008204245,0.000003986164,9.648942e-7,0.000017654953,0.000007777571,0.27556995,0.012846768,0.1496134,0.55306107,0.00031414698],"about_ca_topic_score_codex":6.968076e-7,"about_ca_topic_score_gemma":4.546899e-7,"teacher_disagreement_score":0.8247224,"about_ca_system_score_codex":0.0000046367904,"about_ca_system_score_gemma":0.0000042558404,"threshold_uncertainty_score":0.9981205},"labels":[],"label_agreement":null},{"id":"W2949722522","doi":"10.48550/arxiv.1608.07365","title":"Scalable Compression of Deep Neural Networks","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Scalability; Artificial neural network; Upgrade; Distributed computing; Deep neural networks; Artificial intelligence; Operating system","score_opus":0.04759501308342212,"score_gpt":0.19939861585727028,"score_spread":0.15180360277384816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949722522","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":[0.019287566,0.00014698655,0.97777766,0.00013872732,0.00043892112,0.0003002271,0.0000066091375,0.00023388937,0.001669427],"genre_scores_gemma":[0.9955399,0.00012310469,0.003711611,0.00006726201,0.000099802244,0.0000020437608,0.000008423692,0.000017901486,0.0004299215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823797,0.00010975331,0.00024419837,0.0009547887,0.000083490835,0.0003698301],"domain_scores_gemma":[0.9974059,0.00020394157,0.00041265273,0.0016537536,0.00016110337,0.00016266355],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009177955,0.0002707191,0.00034017762,0.00013420395,0.00014102204,0.00003444863,0.0022951874,0.00023556253,0.000022339513],"category_scores_gemma":[0.000011251416,0.00025793412,0.00017440174,0.00055547216,0.00016283081,0.00036356368,0.0030044348,0.00043386864,0.0000235777],"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":[0.000015106318,0.000044876462,0.00091269915,0.000019575247,0.000019768593,0.00002556981,0.000016636366,0.88739485,0.00010801201,0.10607091,0.00020373228,0.005168271],"study_design_scores_gemma":[0.00025349302,0.000021762447,0.00047122646,0.000062351544,0.000019826692,0.000002669897,0.000003232234,0.9371728,0.00022523862,0.061307214,0.00020068616,0.0002595361],"about_ca_topic_score_codex":0.000012233133,"about_ca_topic_score_gemma":0.000007751657,"teacher_disagreement_score":0.9762524,"about_ca_system_score_codex":0.00009125178,"about_ca_system_score_gemma":0.000030774412,"threshold_uncertainty_score":0.9999873},"labels":[],"label_agreement":null},{"id":"W2949870694","doi":"10.1145/3307650.3322255","title":"Laconic deep learning inference acceleration","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":88,"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":"Inference; Computer science; Deep learning; Computation; Artificial intelligence; Acceleration; Hardware acceleration; Efficient energy use; Deep neural networks; Computer engineering; Energy (signal processing); Simple (philosophy); Artificial neural network; Machine learning; Theoretical computer science; Algorithm; Embedded system; Field-programmable gate array; Mathematics","score_opus":0.01844972929077181,"score_gpt":0.27830758148621526,"score_spread":0.25985785219544344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949870694","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":[0.036512453,0.00002230473,0.94492054,0.00045023733,0.0000677805,0.00012860332,2.5467626e-8,0.0002856688,0.017612372],"genre_scores_gemma":[0.940651,0.000016241718,0.056166567,0.00029679493,0.000022406071,0.000015852487,0.0000014381942,0.000003660624,0.002826013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994334,0.00001893239,0.00009266269,0.00022312585,0.000093633105,0.0001382235],"domain_scores_gemma":[0.9994917,0.00010529126,0.00004023682,0.00029072628,0.000037619906,0.000034458535],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000048490256,0.00005734554,0.00005518364,0.000027437032,0.00007082791,0.0000786831,0.00038284058,0.000023175891,0.00013014616],"category_scores_gemma":[0.000010565007,0.000052030737,0.000016777461,0.0002635595,0.000007661463,0.0005769334,0.0001382695,0.00011417904,0.0013204227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001020343,0.000015947135,0.005937783,0.0000032528878,0.0000030956937,5.2256905e-7,0.00013629688,0.04466131,0.009003019,0.7286129,0.00009013318,0.21153471],"study_design_scores_gemma":[0.00015470004,0.000050541163,0.00805083,0.0000039845736,9.970402e-7,0.0000033290776,0.000012413647,0.95584065,0.0032088778,0.01151651,0.020988671,0.00016852756],"about_ca_topic_score_codex":0.000002503628,"about_ca_topic_score_gemma":0.000006155082,"teacher_disagreement_score":0.9111793,"about_ca_system_score_codex":0.000018696575,"about_ca_system_score_gemma":0.000012784222,"threshold_uncertainty_score":0.9994572},"labels":[],"label_agreement":null},{"id":"W2950314391","doi":"10.48550/arxiv.1807.10972","title":"RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fluid-attenuated inversion recovery; Computer science; Segmentation; Artificial intelligence; Voxel; Dropout (neural networks); Convolutional neural network; Pattern recognition (psychology); Partial volume; Regression; Magnetic resonance imaging; Machine learning; Mathematics; Statistics; Radiology; Medicine","score_opus":0.06613020928999264,"score_gpt":0.24069336570650265,"score_spread":0.17456315641651002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950314391","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":[0.06374396,0.00005246443,0.9340808,0.00086669426,0.000123153,0.0007908106,0.000019930685,0.00003859416,0.00028359998],"genre_scores_gemma":[0.9448007,0.00010643874,0.054899316,0.0000439454,0.00004871751,0.000013822442,0.000010311311,0.000010792935,0.0000659898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983965,0.00026521235,0.000307706,0.00066934177,0.00013182044,0.00022939543],"domain_scores_gemma":[0.99669,0.0013509337,0.00067386514,0.0010566693,0.00018301971,0.000045530804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005029143,0.00018489273,0.00025564324,0.00021113308,0.00013584053,0.000025555117,0.0016610422,0.00013777553,0.0000031664285],"category_scores_gemma":[0.00017920996,0.0001686207,0.00011737518,0.0007186971,0.00021992369,0.00029795515,0.0005819027,0.00020931875,0.0000024546378],"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":[0.00033338528,0.0004053899,0.0025078168,0.0005067043,0.000065287975,0.000020415895,0.0022144644,0.8736196,0.011325236,0.09071975,0.004437937,0.013843981],"study_design_scores_gemma":[0.000326971,0.000092851005,0.002751788,0.0006131538,0.00005193434,0.0000030077808,0.00019030302,0.9276648,0.007584072,0.060253642,0.00019795631,0.00026949114],"about_ca_topic_score_codex":0.000048188856,"about_ca_topic_score_gemma":0.000035730198,"teacher_disagreement_score":0.8810567,"about_ca_system_score_codex":0.00010673688,"about_ca_system_score_gemma":0.00010197353,"threshold_uncertainty_score":0.6876153},"labels":[],"label_agreement":null},{"id":"W2950672966","doi":"10.48550/arxiv.1412.0623","title":"Material Recognition in the Wild with the Materials in Context Database","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Segmentation; Context (archaeology); Clutter; Pattern recognition (psychology); Conditional random field; Deep learning; Field (mathematics); Class (philosophy); Pixel; Task (project management); Machine learning; Mathematics; Geography","score_opus":0.0718704577482576,"score_gpt":0.1974831627793115,"score_spread":0.1256127050310539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950672966","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":[0.77997935,0.000008440722,0.21775152,0.00093646924,0.00021079674,0.000734974,0.000057701705,0.00006289814,0.000257866],"genre_scores_gemma":[0.9983603,0.00005934227,0.0007046294,0.0006103332,0.00009705081,0.000018443883,0.000090650974,0.000011421743,0.000047862854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983749,0.0004138363,0.00017619222,0.00069813576,0.000080288395,0.00025664503],"domain_scores_gemma":[0.99816746,0.00022865563,0.00022026901,0.0013031377,0.00004581473,0.00003468884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045032846,0.00021150659,0.0002120973,0.00011245978,0.00011067057,0.00014354069,0.0019901218,0.00010203417,0.000018430494],"category_scores_gemma":[0.000013625532,0.00014385533,0.0000330297,0.00052888226,0.00012768361,0.0003160192,0.0008611948,0.00038319756,0.000050666506],"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":[0.0012916399,0.0008513919,0.0025824176,0.00035089787,0.00013213136,0.0014776891,0.004738551,0.30631688,0.0044617224,0.65221465,0.005209979,0.020372028],"study_design_scores_gemma":[0.009757733,0.00063677237,0.021236256,0.0025503337,0.00037336914,0.00021394061,0.0030629549,0.41430384,0.017653517,0.50151455,0.023059793,0.0056369547],"about_ca_topic_score_codex":0.0002491663,"about_ca_topic_score_gemma":0.0009298079,"teacher_disagreement_score":0.21838093,"about_ca_system_score_codex":0.00007809421,"about_ca_system_score_gemma":0.000050613307,"threshold_uncertainty_score":0.586625},"labels":[],"label_agreement":null},{"id":"W2950728319","doi":"10.48550/arxiv.1903.03088","title":"Self-Tuning Networks: Bilevel Optimization of Hyperparameters using\\n Structured Best-Response Functions","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Toronto","funders":"","keywords":"Hyperparameter; Hyperparameter optimization; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Mathematical optimization; Algorithm; Mathematics; Support vector machine","score_opus":0.07563503165928508,"score_gpt":0.21034802624186477,"score_spread":0.13471299458257968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950728319","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":[0.1579321,0.0001223337,0.8382595,0.00004486131,0.001332334,0.0018987134,0.00005913071,0.0002490024,0.00010196915],"genre_scores_gemma":[0.81518793,0.00043978236,0.18342857,0.0000622883,0.00011772648,0.000006588951,0.00004065569,0.00007717508,0.00063926226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99454004,0.0008102734,0.0008995538,0.0025714869,0.000265607,0.00091307116],"domain_scores_gemma":[0.9925552,0.001068031,0.0017560427,0.0033761368,0.0008408094,0.0004037837],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005054307,0.00090406666,0.0009914563,0.0007816457,0.0006626144,0.00017778167,0.0029240623,0.0007565773,0.000074026604],"category_scores_gemma":[0.00011548674,0.0011562338,0.0005567719,0.0036928637,0.00043185303,0.0011958774,0.0026297148,0.0011807123,0.000052203868],"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":[0.0004999815,0.00023571891,0.0010131288,0.00007443001,0.0002626446,0.000027183863,0.00021681972,0.9924935,0.00033391334,0.0037766618,0.000023448489,0.0010425559],"study_design_scores_gemma":[0.0009936815,0.00024043398,0.00024488324,0.0002820039,0.00050980836,0.000025504845,0.00016466768,0.9946417,0.0001098385,0.0014775115,0.0003232725,0.0009866907],"about_ca_topic_score_codex":0.000055319328,"about_ca_topic_score_gemma":0.000009211645,"teacher_disagreement_score":0.6572558,"about_ca_system_score_codex":0.00078742194,"about_ca_system_score_gemma":0.0007710654,"threshold_uncertainty_score":0.99908876},"labels":[],"label_agreement":null},{"id":"W2950894517","doi":"","title":"Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"MNIST database; Computer science; Artificial neural network; Binary number; Multiplication (music); Kernel (algebra); Computation; Algorithm; Treebank; Artificial intelligence; Code (set theory); Byte; Deep neural networks; Arithmetic; Computer hardware; Mathematics","score_opus":0.056102934091099496,"score_gpt":0.20584661946035174,"score_spread":0.14974368536925226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950894517","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":[0.16291606,0.000111340974,0.8349369,0.00036744648,0.00038653397,0.0006319229,0.000006885244,0.00042162975,0.00022124026],"genre_scores_gemma":[0.9926036,0.00025766168,0.0062757265,0.00015502049,0.0002918411,0.000011132773,0.000023167266,0.00004633784,0.00033552703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690765,0.00020826339,0.00032276908,0.0017292474,0.00014787649,0.00068421627],"domain_scores_gemma":[0.9967804,0.00069983193,0.00049573183,0.0015132998,0.00018327363,0.0003274492],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016188897,0.0005472373,0.0005120802,0.0002427738,0.0005418722,0.00021776934,0.0016056526,0.00032848123,0.0000086648415],"category_scores_gemma":[0.000023216002,0.0004691365,0.00015375423,0.00097151287,0.00023066076,0.001024316,0.0016151913,0.0009124979,0.0000047861085],"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":[0.000093011484,0.00003968195,0.0007528321,0.0000109670345,0.00005158148,0.000070260154,0.00010534777,0.94012374,0.000015013889,0.047688372,0.00008305079,0.010966156],"study_design_scores_gemma":[0.0007942426,0.000077540026,0.0015406084,0.00015820765,0.000051901305,0.00002474082,0.000022874945,0.9778835,0.000012825798,0.018720914,0.00013030543,0.0005823483],"about_ca_topic_score_codex":0.000011210158,"about_ca_topic_score_gemma":0.0000329858,"teacher_disagreement_score":0.82968754,"about_ca_system_score_codex":0.00012673246,"about_ca_system_score_gemma":0.00007587397,"threshold_uncertainty_score":0.999776},"labels":[],"label_agreement":null},{"id":"W2950911758","doi":"10.48550/arxiv.1807.06072","title":"Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Minimum bounding box; Computer science; Annotation; Artificial intelligence; Task (project management); Segmentation; Object (grammar); Object detection; Bounding overwatch; Ground truth; Scheme (mathematics); Point cloud; Orientation (vector space); Generalization; Point (geometry); Computer vision; Machine learning; Image (mathematics)","score_opus":0.10931635456017118,"score_gpt":0.217894364831059,"score_spread":0.10857801027088783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950911758","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":[0.11055569,0.000019260322,0.88707966,0.0000396355,0.0007438309,0.0008209938,0.000013541112,0.0003948114,0.00033260396],"genre_scores_gemma":[0.96605587,0.000047437734,0.032651737,0.000069379355,0.00052687875,0.000023461445,0.00005003408,0.000030242953,0.00054497394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977873,0.000086931606,0.00023522694,0.0014112996,0.000095428084,0.00038382903],"domain_scores_gemma":[0.9980298,0.00014073156,0.00031766095,0.0011437825,0.00024696867,0.00012101079],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018605974,0.00032722406,0.00026011263,0.0002172195,0.0004742975,0.00017461104,0.001135749,0.00023721154,0.000004074835],"category_scores_gemma":[0.000021251,0.00040113847,0.00017932996,0.00049248047,0.000083702864,0.0008624108,0.0007702726,0.00031911556,0.000013844263],"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":[0.000024477324,0.00003521873,0.000008370238,0.000027218688,0.000036611335,0.000004646322,0.0002366739,0.961012,0.00069322507,0.032089442,0.000057793128,0.005774348],"study_design_scores_gemma":[0.00033518262,0.000059156064,0.00008024652,0.000021850614,0.000041974978,0.0000039997935,0.00001375178,0.8690011,0.0011369853,0.1288005,0.00015330217,0.00035198752],"about_ca_topic_score_codex":0.00003784841,"about_ca_topic_score_gemma":0.000057699333,"teacher_disagreement_score":0.85550016,"about_ca_system_score_codex":0.00035748238,"about_ca_system_score_gemma":0.00010968379,"threshold_uncertainty_score":0.9998441},"labels":[],"label_agreement":null},{"id":"W2950973902","doi":"10.48550/arxiv.1811.08933","title":"Analyzing Machine Learning Workloads Using a Detailed GPU Simulator","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"MNIST database; General-purpose computing on graphics processing units; Computer science; CUDA; Parallel computing; Partition (number theory); Deep learning; Speedup; Dram; Microarchitecture; Computer architecture; Artificial intelligence; Operating system; Graphics; Computer hardware","score_opus":0.07782706764600732,"score_gpt":0.22403763662508824,"score_spread":0.1462105689790809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950973902","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":[0.29656318,0.00013115461,0.7021761,0.00003365033,0.00021292204,0.0002332194,0.000003283162,0.00040456437,0.00024191613],"genre_scores_gemma":[0.9762667,0.00011869841,0.022639224,0.000055153567,0.00019458613,0.0000012005081,0.000012452612,0.000036611775,0.0006753821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743026,0.00018714703,0.0002778863,0.0014725431,0.00010497724,0.00052717916],"domain_scores_gemma":[0.9974161,0.00020147706,0.0004615116,0.0014842749,0.00020283285,0.00023380596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021252711,0.00040972652,0.00041254074,0.0003218361,0.00050814275,0.00016060696,0.0020481932,0.00026688192,0.000035993613],"category_scores_gemma":[0.00005297094,0.00049010775,0.00024356299,0.0014501484,0.00014610607,0.00046658018,0.003252999,0.0009970084,0.00010895854],"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":[0.0000129368755,0.000031052314,0.012122742,0.000017437827,0.00005953747,0.00007318635,0.00005352311,0.9777869,0.0001374339,0.009051815,0.00001683442,0.0006365853],"study_design_scores_gemma":[0.00026191358,0.000025772837,0.00033942604,0.00009502607,0.00007419697,0.0000051690613,0.0000079584315,0.98282945,0.00012993511,0.015082855,0.00066188467,0.0004863866],"about_ca_topic_score_codex":0.00004128763,"about_ca_topic_score_gemma":0.000033873894,"teacher_disagreement_score":0.67970353,"about_ca_system_score_codex":0.00032145422,"about_ca_system_score_gemma":0.00013394421,"threshold_uncertainty_score":0.9997551},"labels":[],"label_agreement":null},{"id":"W2951341166","doi":"10.48550/arxiv.1502.04275","title":"segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":52,"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":"Nvidia","keywords":"Segmentation; Artificial intelligence; Pascal (unit); Computer science; Inference; Convolutional neural network; Object detection; Markov random field; Pattern recognition (psychology); Exploit; Object (grammar); Context (archaeology); Image segmentation; Computer vision; Geography","score_opus":0.07836379713199872,"score_gpt":0.2190607252072207,"score_spread":0.140696928075222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951341166","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":[0.1786,0.00018812367,0.8199801,0.000045692275,0.00026388248,0.00071236544,0.00000299471,0.00014786945,0.000058970913],"genre_scores_gemma":[0.9952551,0.00010209632,0.004335058,0.0000992706,0.00010107746,0.000023954806,0.000020207308,0.000018558081,0.000044657856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843127,0.00009918348,0.00021240998,0.0008913214,0.000054992313,0.0003108474],"domain_scores_gemma":[0.9987869,0.00021586513,0.00025779288,0.00048027543,0.00013833407,0.00012079784],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021037512,0.00023359043,0.0002385507,0.00018269941,0.00015524462,0.00009310859,0.00052653713,0.00017947807,9.1276894e-7],"category_scores_gemma":[0.00003160736,0.00029434965,0.00007171326,0.0004974831,0.000057490928,0.00055131415,0.0007018149,0.0003880909,0.000002228413],"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":[0.000033536144,0.000020361556,0.00070345163,0.000022465905,0.000012218044,0.000014001761,0.00023293713,0.93323976,0.000059380156,0.0074029984,0.000012025733,0.058246866],"study_design_scores_gemma":[0.0006148921,0.000049278864,0.00033835418,0.00002322867,0.000019850617,0.0000054440034,0.00022478297,0.9728853,0.0001403384,0.025397567,0.000033425906,0.000267542],"about_ca_topic_score_codex":0.00006033713,"about_ca_topic_score_gemma":0.00048165908,"teacher_disagreement_score":0.8166551,"about_ca_system_score_codex":0.00027497663,"about_ca_system_score_gemma":0.000031890275,"threshold_uncertainty_score":0.9999509},"labels":[],"label_agreement":null},{"id":"W2951458036","doi":"10.1007/s12652-019-01357-4","title":"A hybrid GPU-FPGA based design methodology for enhancing machine learning applications performance","year":2019,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Bottleneck; Field-programmable gate array; Leverage (statistics); Graphics processing unit; Artificial neural network; Convolutional neural network; Computer engineering; CUDA; General-purpose computing on graphics processing units; Gate array; Deep learning; Exploit; Graphics; Artificial intelligence; Machine learning; Computer architecture; Embedded system; Parallel computing; Operating system","score_opus":0.08211388848623831,"score_gpt":0.320849648271659,"score_spread":0.23873575978542072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951458036","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":[0.039415494,0.0004316161,0.95919037,0.0001509042,0.00017897702,0.0005503059,4.8401193e-7,0.00005407326,0.00002775742],"genre_scores_gemma":[0.5594345,0.000066691704,0.44016406,0.00018363353,0.000094981006,0.000012334421,0.0000010697314,0.000010440932,0.000032317028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982491,0.00016037517,0.0007127105,0.00033955186,0.00021151196,0.0003267288],"domain_scores_gemma":[0.99674565,0.0017710126,0.0007662871,0.00027320976,0.00034323081,0.00010063421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015967122,0.000174409,0.00036327142,0.0002176377,0.0004112127,0.000111842506,0.0007183623,0.000039148705,0.000009694],"category_scores_gemma":[0.000094411655,0.00015980256,0.00011085893,0.00026466782,0.00004668539,0.0003423226,0.00015942223,0.00038914967,0.000012151498],"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":[0.00011196335,0.00011987213,0.00044428505,0.00013091926,0.000054280237,0.0000043496584,0.0008512506,0.5361342,0.022312181,0.022322122,0.000033937882,0.41748068],"study_design_scores_gemma":[0.0003129631,0.00060134847,0.00006037083,0.0001014223,0.000019852452,0.00011808394,0.000056264606,0.9191016,0.07042696,0.0061409394,0.00286756,0.00019264083],"about_ca_topic_score_codex":0.0000017810625,"about_ca_topic_score_gemma":9.636137e-7,"teacher_disagreement_score":0.520019,"about_ca_system_score_codex":0.000057116285,"about_ca_system_score_gemma":0.00007443699,"threshold_uncertainty_score":0.6516559},"labels":[],"label_agreement":null},{"id":"W2951503448","doi":"10.48550/arxiv.1902.06326","title":"PIXOR: Real-time 3D Object Detection from Point Clouds","year":2019,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmark (surveying); Computer science; Object detection; Computation; Point cloud; Artificial intelligence; Context (archaeology); Detector; Object (grammar); Representation (politics); Computer vision; Component (thermodynamics); Curse of dimensionality; Point (geometry); Pixel; Pattern recognition (psychology); Algorithm","score_opus":0.026296400476782077,"score_gpt":0.2639832371540921,"score_spread":0.23768683667731003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951503448","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":[0.5565871,0.00014509093,0.43692252,0.0005829398,0.0015363682,0.00075075496,0.000027842189,0.00096325594,0.0024841456],"genre_scores_gemma":[0.9624109,0.00030394524,0.03473479,0.0003622137,0.00079083035,0.00020402735,0.0000860892,0.00006515438,0.0010420098],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696374,0.00014656922,0.0005156207,0.0015296126,0.0003520181,0.0004924361],"domain_scores_gemma":[0.9958712,0.00031659447,0.00046689218,0.0030523774,0.00013524541,0.00015769627],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002050284,0.00045348392,0.00048312676,0.000115739436,0.00019125613,0.00013267025,0.0019268532,0.00037487797,0.0000672224],"category_scores_gemma":[0.000052748703,0.0004695136,0.00021457566,0.00042085408,0.00006599375,0.00034134876,0.0024472452,0.00091697066,0.0037128325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020820853,0.0008922914,0.078593664,0.00029507812,0.0008514144,0.00014768635,0.003206396,0.17677547,0.4873202,0.004169808,0.01193742,0.23560236],"study_design_scores_gemma":[0.0014787551,0.00037031094,0.31073475,0.00049231044,0.00020856672,0.000052254825,0.000031449195,0.516177,0.08512529,0.058149394,0.023438053,0.0037418543],"about_ca_topic_score_codex":0.0004143804,"about_ca_topic_score_gemma":0.00004791753,"teacher_disagreement_score":0.40582386,"about_ca_system_score_codex":0.00025495427,"about_ca_system_score_gemma":0.0001427813,"threshold_uncertainty_score":0.99977565},"labels":[],"label_agreement":null},{"id":"W2951517617","doi":"10.1109/cvpr.2019.00752","title":"Multi-Task Multi-Sensor Fusion for 3D Object Detection","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":761,"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":"Benchmark (surveying); Object detection; Computer science; Artificial intelligence; Task (project management); Exploit; Object (grammar); Computer vision; Sensor fusion; Pattern recognition (psychology); Engineering","score_opus":0.026495996199834015,"score_gpt":0.28487876646253024,"score_spread":0.25838277026269624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951517617","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":[0.014247467,0.000024243323,0.98359424,0.00019887678,0.00036607057,0.000966071,0.0000022799913,0.00039824424,0.00020249374],"genre_scores_gemma":[0.3147095,0.000007847878,0.68201774,0.0002613586,0.000046275843,0.00011457611,0.0000024310139,0.000011284202,0.002828999],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897456,0.000020743752,0.00016983309,0.00046815744,0.000112108726,0.0002545933],"domain_scores_gemma":[0.9990366,0.00015311645,0.00007537579,0.0005865291,0.000083962346,0.000064428714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008656884,0.00012119166,0.00011340089,0.00006073263,0.00014834672,0.00004601053,0.00039294714,0.000058841895,0.000014278394],"category_scores_gemma":[0.000025306785,0.000105255844,0.00006587497,0.0002991009,0.000014454544,0.0003256459,0.00014162422,0.00008570269,0.00040072808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020133144,0.00017072166,0.00041380094,0.000021069032,0.000010775082,8.2866103e-7,0.00016864923,0.0048701707,0.66595715,0.0021357865,0.00022175492,0.32600915],"study_design_scores_gemma":[0.00072487775,0.0000769383,0.002003138,0.000004631537,0.0000028023426,0.0000062315753,0.00001157041,0.95162946,0.03024618,0.00017235398,0.014952833,0.00016897703],"about_ca_topic_score_codex":0.000012772952,"about_ca_topic_score_gemma":0.00005930419,"teacher_disagreement_score":0.9467593,"about_ca_system_score_codex":0.000041743217,"about_ca_system_score_gemma":0.000013282768,"threshold_uncertainty_score":0.51506823},"labels":[],"label_agreement":null},{"id":"W2952857977","doi":"10.1109/dac.2018.8465915","title":"Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":88,"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":"Convolutional neural network; Computer science; LOOM; Granularity; Inference; Energy (signal processing); Parallel computing; Layer (electronics); Deep neural networks; Artificial neural network; Algorithm; Artificial intelligence; Mathematics; Statistics; Operating system","score_opus":0.0962652447524089,"score_gpt":0.3100672727499506,"score_spread":0.21380202799754172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952857977","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":[0.031308293,0.00002587893,0.9614086,0.0040580584,0.000807172,0.0011702558,0.0000074536683,0.0007857726,0.00042850059],"genre_scores_gemma":[0.82182676,0.000032172775,0.17502825,0.0017977038,0.0006478559,0.00034505394,0.000029586046,0.00003533982,0.0002572543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965181,0.00030742833,0.0007354688,0.0011035786,0.0005714102,0.00076402334],"domain_scores_gemma":[0.99609005,0.000953134,0.00045978883,0.0012193188,0.00078149315,0.00049622473],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055415236,0.00044138666,0.00036255372,0.0003100263,0.0009289665,0.0006174468,0.0014872223,0.00020778773,0.00021630747],"category_scores_gemma":[0.0005183295,0.00044238567,0.000067149325,0.0012232397,0.00023483585,0.002480007,0.0004891876,0.00033594848,0.00035742842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032786955,0.0004680595,0.0010393062,0.00006572155,0.00017595042,0.000016080614,0.0048092776,0.09401366,0.101023346,0.15764445,0.06965623,0.5707601],"study_design_scores_gemma":[0.00046942063,0.00022598343,0.004213995,0.00008860181,0.000012948117,0.00002533915,0.000024363118,0.9667928,0.009483896,0.016150214,0.0019957607,0.0005166472],"about_ca_topic_score_codex":0.000015713264,"about_ca_topic_score_gemma":0.000011213123,"teacher_disagreement_score":0.8727792,"about_ca_system_score_codex":0.00015568822,"about_ca_system_score_gemma":0.0002231726,"threshold_uncertainty_score":0.99980277},"labels":[],"label_agreement":null},{"id":"W2954060246","doi":"10.1109/dsw.2019.8755548","title":"Enhancing Medical Imaging Semantic Segmentation Using the Digital Annealer","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; CRFS; Segmentation; Artificial intelligence; Convolutional neural network; Conditional random field; Pixel; Image segmentation; Representation (politics); Pattern recognition (psychology); Computer vision","score_opus":0.012187458966501382,"score_gpt":0.2805901382365753,"score_spread":0.26840267927007394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954060246","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":[0.098415144,0.000030266372,0.89761895,0.0018748437,0.00015311888,0.00017222544,2.0897717e-7,0.00011703902,0.0016182223],"genre_scores_gemma":[0.97713584,0.0000032409496,0.02148089,0.0010546714,0.00006536371,0.00000627191,0.0000014818888,0.0000064101387,0.00024584186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991293,0.000016477097,0.00014779858,0.00022815324,0.00029698378,0.00018127338],"domain_scores_gemma":[0.99936616,0.00014469905,0.000050889234,0.00035776742,0.000026771913,0.000053693464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011008999,0.00007097283,0.000060731534,0.000025034866,0.00011646575,0.00016222954,0.00048509843,0.000015527294,0.000059489576],"category_scores_gemma":[0.000016259164,0.00004770994,0.000027030688,0.0002775517,0.000028340872,0.00071111193,0.00022115724,0.00009143694,0.00015813691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000722731,0.00015738481,0.049354076,0.000050766106,0.000048007845,0.000034364028,0.0020931116,0.024148954,0.120768145,0.13862012,0.0017823876,0.66293544],"study_design_scores_gemma":[0.00011281692,0.0000044445037,0.0006616439,0.000016940196,0.0000019920406,0.00005549168,0.00010726874,0.9923158,0.004393941,0.0016295956,0.0005974839,0.000102536964],"about_ca_topic_score_codex":0.0000053987137,"about_ca_topic_score_gemma":0.0000037304194,"teacher_disagreement_score":0.9681669,"about_ca_system_score_codex":0.000028013208,"about_ca_system_score_gemma":0.000032242355,"threshold_uncertainty_score":0.20325828},"labels":[],"label_agreement":null},{"id":"W2954582488","doi":"10.1109/cvpr.2019.00448","title":"Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Quantization (signal processing); Computer science; Deep learning; Task (project management); Reduction (mathematics); Residual neural network; Algorithm; Artificial intelligence; Mathematics","score_opus":0.015834412769293302,"score_gpt":0.2701146273165077,"score_spread":0.2542802145472144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954582488","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":[0.0034888238,0.0002717689,0.99152344,0.0014099423,0.00044038187,0.00112375,0.0000023675516,0.00079904584,0.00094047654],"genre_scores_gemma":[0.59988177,0.00019525779,0.397362,0.00089377514,0.00012198937,0.00018983627,0.00009351616,0.0000671753,0.0011946729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969072,0.00014870959,0.0005208303,0.0014374726,0.00039295066,0.00059287867],"domain_scores_gemma":[0.9973948,0.00025968152,0.00044122938,0.00146913,0.00022575473,0.00020940379],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028483363,0.00047601003,0.0005098839,0.00015347316,0.0002054262,0.0005036811,0.001986369,0.000256993,0.00001708662],"category_scores_gemma":[0.00003865539,0.00042521997,0.00010653272,0.0007612545,0.000046593574,0.00041342678,0.0025616956,0.0010439893,0.00014792969],"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":[0.0000124789185,0.000026662077,0.0003876357,0.000023337869,0.000029560519,0.000003186407,0.00027203892,0.98288995,0.00012429958,0.0053499895,0.001496671,0.009384206],"study_design_scores_gemma":[0.00018226722,0.00013901986,0.00008443187,0.00019785775,0.000015896747,0.000011776785,0.0000412635,0.9941583,0.0002773123,0.00042567877,0.0038569195,0.00060927786],"about_ca_topic_score_codex":0.000039610517,"about_ca_topic_score_gemma":0.000028729835,"teacher_disagreement_score":0.5963929,"about_ca_system_score_codex":0.000118014075,"about_ca_system_score_gemma":0.000060600833,"threshold_uncertainty_score":0.99981993},"labels":[],"label_agreement":null},{"id":"W2955013293","doi":"10.1109/ccgrid.2019.00053","title":"A Novel Stochastic Gradient Descent Algorithm Based on Grouping over Heterogeneous Cluster Systems for Distributed Deep Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Stochastic gradient descent; Computer science; MNIST database; Convergence (economics); Algorithm; Artificial intelligence; sync; Artificial neural network; Machine learning; Distributed computing; Computer network","score_opus":0.015333743848002238,"score_gpt":0.2447836575437057,"score_spread":0.22944991369570347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955013293","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":[0.0019011861,0.00004967122,0.99568695,0.00015865616,0.0004233886,0.0014461867,0.000013736508,0.00029291803,0.000027307835],"genre_scores_gemma":[0.8848164,0.0000016657884,0.114011526,0.00051297934,0.00009069604,0.0003588048,0.000053121465,0.000030239333,0.0001245414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831814,0.000035077945,0.0002830948,0.00062198995,0.00027217492,0.00046951242],"domain_scores_gemma":[0.99864364,0.0004241106,0.00014457115,0.0005674977,0.00008587829,0.00013431409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013751592,0.00021392386,0.00021184914,0.00008048717,0.0001904193,0.00014111678,0.00048857386,0.00005881463,0.000005271851],"category_scores_gemma":[0.000024679215,0.00019012067,0.00010265887,0.0003063905,0.000018363997,0.00016665169,0.00014072728,0.0001579088,0.00004581343],"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":[0.000008992284,0.00008054658,0.000022398423,0.00001729301,0.000007678836,8.428807e-7,0.000023076744,0.985379,0.00032279151,0.0030716332,0.000035943853,0.011029828],"study_design_scores_gemma":[0.0009255501,0.00021430264,0.00010608934,0.000048736347,0.0000070355964,0.00001808792,0.000007539657,0.9970796,0.00010461378,0.00008071124,0.0011663899,0.00024134084],"about_ca_topic_score_codex":0.000011181081,"about_ca_topic_score_gemma":0.0000033462527,"teacher_disagreement_score":0.88291526,"about_ca_system_score_codex":0.00019539997,"about_ca_system_score_gemma":0.000017574757,"threshold_uncertainty_score":0.7752896},"labels":[],"label_agreement":null},{"id":"W2955382613","doi":"10.48550/arxiv.1907.01430","title":"Where are the Masks: Instance Segmentation with Image-level Supervision","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of British Columbia","funders":"","keywords":"Segmentation; Image (mathematics); Artificial intelligence; Computer vision; Computer science; Business","score_opus":0.06530653212996881,"score_gpt":0.20377210211328153,"score_spread":0.13846556998331272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955382613","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":[0.055903155,0.0001788777,0.9407194,0.00091521343,0.00020725616,0.0007321194,0.000026022146,0.00020735206,0.0011105563],"genre_scores_gemma":[0.976782,0.00072889065,0.020554304,0.00022923166,0.000057341065,0.0000067611522,0.000019903777,0.00002526291,0.0015962811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982428,0.00010621611,0.00015798138,0.0010570254,0.00014336433,0.00029262505],"domain_scores_gemma":[0.99738896,0.00013526269,0.00034173482,0.00184046,0.00020948958,0.00008407888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010686804,0.0002967607,0.0002246639,0.00008984607,0.00026731912,0.00015028722,0.0021116952,0.00013516785,0.000014548401],"category_scores_gemma":[0.0000060835514,0.00023999128,0.000091324626,0.0007379672,0.00012728298,0.00072022015,0.0014037687,0.0005633835,0.00015889591],"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":[0.000061032726,0.00009856701,0.0071036723,0.00014763454,0.00008741639,0.000116529496,0.00038774268,0.899798,0.00033165023,0.08744369,0.002003521,0.0024205744],"study_design_scores_gemma":[0.0013315304,0.00012000517,0.01132916,0.00061271514,0.00011332168,0.000021019267,0.0008660034,0.938742,0.0008245659,0.04051661,0.0042982814,0.0012248292],"about_ca_topic_score_codex":0.000027698447,"about_ca_topic_score_gemma":0.00011969658,"teacher_disagreement_score":0.9208789,"about_ca_system_score_codex":0.0001973954,"about_ca_system_score_gemma":0.00011098986,"threshold_uncertainty_score":0.978656},"labels":[],"label_agreement":null},{"id":"W2956648669","doi":"10.1109/cvpr.2019.00325","title":"Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":261,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Casa","funders":"","keywords":"Minimum bounding box; Pascal (unit); Computer science; Bounding overwatch; Segmentation; Benchmark (surveying); Artificial intelligence; Pixel; Class (philosophy); Masking (illustration); Pattern recognition (psychology); Machine learning; Image (mathematics)","score_opus":0.031812812013273045,"score_gpt":0.280350522042505,"score_spread":0.24853771002923195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956648669","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":[0.1262288,0.000028228753,0.8701318,0.002223586,0.00012305884,0.0008093882,0.0000010125104,0.00016406855,0.00029007538],"genre_scores_gemma":[0.80441284,0.000055144763,0.19365488,0.00070834695,0.00005477737,0.00008642026,0.000012221331,0.000015813397,0.0009995581],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989054,0.000034520162,0.00023580446,0.00046311907,0.000109753426,0.00025140433],"domain_scores_gemma":[0.9991028,0.00022510377,0.000103718,0.00041936885,0.00008127941,0.00006774147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116573145,0.00013179648,0.00014295756,0.000064099455,0.00014892942,0.00011676742,0.00031271644,0.0000447199,0.000006696562],"category_scores_gemma":[0.0000135893115,0.00012325883,0.000042906828,0.00024989608,0.000024065668,0.00064500514,0.00013105168,0.00006445533,0.000032091266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001039685,0.00017158024,0.017163292,0.0004455301,0.00010541467,0.00002604409,0.0024996558,0.17016187,0.3645958,0.3395073,0.010085824,0.0951337],"study_design_scores_gemma":[0.00068456173,0.000053897715,0.0007372111,0.000035852685,0.0000089119985,0.000018910327,0.000045369747,0.9778722,0.011137409,0.007563959,0.0016295844,0.00021213127],"about_ca_topic_score_codex":0.0000055571654,"about_ca_topic_score_gemma":0.0000068237755,"teacher_disagreement_score":0.80771035,"about_ca_system_score_codex":0.000037782836,"about_ca_system_score_gemma":0.00001789499,"threshold_uncertainty_score":0.5026349},"labels":[],"label_agreement":null},{"id":"W2958433635","doi":"10.1016/j.patcog.2019.07.006","title":"Global-connected network with generalized ReLU activation","year":2019,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"MNIST database; Softmax function; Computer science; Activation function; Benchmark (surveying); Artificial intelligence; Convolutional neural network; Computation; Inference; Layer (electronics); Network architecture; Field (mathematics); Pattern recognition (psychology); Deep learning; Artificial neural network; Machine learning; Algorithm; Mathematics; Computer network","score_opus":0.018699759776739152,"score_gpt":0.24102955480260743,"score_spread":0.22232979502586828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958433635","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":[0.33456135,0.000010083066,0.66285354,0.0005584395,0.00013590134,0.00037669088,0.000004600807,0.00027209098,0.0012273014],"genre_scores_gemma":[0.9545714,0.000015061635,0.043438736,0.0014522842,0.00019089933,0.000119269775,0.00013502254,0.0000145628255,0.00006272863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988754,0.000063633524,0.00017180092,0.00042857797,0.00018971357,0.00027090963],"domain_scores_gemma":[0.9991635,0.00007204967,0.0001527569,0.00043403846,0.00011577382,0.00006188256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000716708,0.00013998373,0.0001274709,0.000031179254,0.000093446084,0.00007384875,0.00027521874,0.000055512,0.000073933465],"category_scores_gemma":[0.0000073362116,0.00012608328,0.000033096283,0.00068688206,0.000013911776,0.00055725203,0.000069222195,0.000087797765,0.00052573194],"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":[0.00006427593,0.000098158715,0.032371525,0.000028770539,0.000049608032,0.0000056748045,0.000081975355,0.011158263,0.001753879,0.0028209856,0.0022238134,0.9493431],"study_design_scores_gemma":[0.008183215,0.0010699924,0.2927918,0.0008126699,0.000093012626,0.00025663868,0.00004884303,0.44926965,0.018163905,0.21164152,0.014651989,0.0030167324],"about_ca_topic_score_codex":0.000022968765,"about_ca_topic_score_gemma":0.000029738736,"teacher_disagreement_score":0.9463263,"about_ca_system_score_codex":0.000072294344,"about_ca_system_score_gemma":0.000018531597,"threshold_uncertainty_score":0.6757396},"labels":[],"label_agreement":null},{"id":"W2961980567","doi":"10.48550/arxiv.1907.06777","title":"Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Orientation (vector space); Computer science; Point cloud; Computer vision; Artificial intelligence; Benchmark (surveying); Pose; Process (computing); Pedestrian; Object (grammar); Point (geometry); Object detection; Tracking (education); Task (project management); Video tracking; Segmentation; Engineering; Mathematics","score_opus":0.05326560229032601,"score_gpt":0.21155585045951475,"score_spread":0.15829024816918874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961980567","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":[0.049840838,0.000012870319,0.9479824,0.000032552696,0.0003335944,0.0014026143,0.000019165027,0.000334052,0.00004194553],"genre_scores_gemma":[0.8946689,0.000039089824,0.1048587,0.00002670198,0.000052709573,0.000043476786,0.000026049956,0.000027985634,0.00025639604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981466,0.0000872183,0.00021366151,0.0011647809,0.00009381733,0.00029392922],"domain_scores_gemma":[0.9978222,0.00041685483,0.00048634034,0.000972586,0.00021245482,0.000089540416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016570435,0.00030006902,0.0002805007,0.00020430307,0.0002830141,0.0001224428,0.0007898351,0.00019489022,0.0000025798577],"category_scores_gemma":[0.00009371513,0.00032581366,0.00011982227,0.00061190367,0.000055829925,0.0007298599,0.00039681958,0.00030185227,0.00003401309],"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":[0.000048695263,0.00004902676,0.0001065133,0.00008666877,0.00003192439,0.0000051865386,0.000080358404,0.9285632,0.00025790837,0.0074213836,0.0000045689003,0.06334458],"study_design_scores_gemma":[0.0004887781,0.00015101495,0.00027228708,0.00008956212,0.000117893615,0.0000046466707,0.000041026193,0.9952147,0.0016804347,0.0015128918,0.00005350788,0.00037327598],"about_ca_topic_score_codex":0.000074579686,"about_ca_topic_score_gemma":0.00011921228,"teacher_disagreement_score":0.84482807,"about_ca_system_score_codex":0.00034500405,"about_ca_system_score_gemma":0.00017889804,"threshold_uncertainty_score":0.9999194},"labels":[],"label_agreement":null},{"id":"W2962752334","doi":"10.15353/vsnl.v3i1.171","title":"Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":369,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Computer science; Object detection; Artificial intelligence; Inference; Speedup; Computer vision; Object (grammar); Frame rate; Viola–Jones object detection framework; Deep learning; Leverage (statistics); Real-time computing; Pattern recognition (psychology); Parallel computing; Face detection","score_opus":0.011080841512763487,"score_gpt":0.29477906507503077,"score_spread":0.28369822356226726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962752334","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":[0.058150027,0.00021489282,0.9395736,0.00061907404,0.00069315865,0.00039089232,0.0000056055324,0.00005327349,0.00029948022],"genre_scores_gemma":[0.9751888,0.000016172646,0.02437007,0.000035661364,0.00026158773,0.000014787765,0.000002038595,0.000014627177,0.00009624627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982656,0.000108315275,0.00073718117,0.0002697294,0.0004027056,0.00021645929],"domain_scores_gemma":[0.9975579,0.0003714092,0.0011730153,0.00031589696,0.00046194394,0.00011986328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073742203,0.00016647724,0.00035983147,0.00025174968,0.00049420586,0.0006482385,0.00058438187,0.000040746745,4.854961e-7],"category_scores_gemma":[0.000082239916,0.00014391784,0.0000971076,0.00015487695,0.00006334554,0.0013043459,0.00011769395,0.00016513745,0.0000075227567],"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":[0.0003543676,0.0002801405,0.003431054,0.0005401568,0.00011197445,0.00011625104,0.0023710532,0.50490725,0.032526746,0.021966595,0.0018325708,0.43156183],"study_design_scores_gemma":[0.0011739458,0.00012866878,0.01127811,0.0006078048,0.000010331217,0.0010357822,0.0002663499,0.98319703,0.00019322829,0.0014778756,0.0004617651,0.00016912314],"about_ca_topic_score_codex":0.000025633173,"about_ca_topic_score_gemma":0.0000037517596,"teacher_disagreement_score":0.9170388,"about_ca_system_score_codex":0.00016329827,"about_ca_system_score_gemma":0.00012307218,"threshold_uncertainty_score":0.6250979},"labels":[],"label_agreement":null},{"id":"W2962767316","doi":"10.1109/isbi.2019.8759329","title":"A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":835,"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":"Dice; Segmentation; Computer science; Artificial intelligence; Pyramid (geometry); Image segmentation; Function (biology); Image (mathematics); Pattern recognition (psychology); Computer vision; Mathematics; Statistics","score_opus":0.018419081275271594,"score_gpt":0.2587298785665166,"score_spread":0.240310797291245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962767316","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":[0.049541563,0.0000036546357,0.9483062,0.0005353527,0.000177554,0.0009891292,0.0000022651748,0.00018402493,0.00026025478],"genre_scores_gemma":[0.65848124,0.000003260121,0.33932695,0.00036665806,0.00006148253,0.00020411095,0.000043983226,0.00001318951,0.0014990984],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991269,0.000008628038,0.0001423425,0.00039504596,0.00014198292,0.00018510393],"domain_scores_gemma":[0.99936855,0.00006159022,0.000100325684,0.00032761737,0.000095492745,0.00004639703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000749193,0.00010659025,0.000085838336,0.000047316586,0.00010380599,0.000058061076,0.00019845815,0.00004118036,0.00000936615],"category_scores_gemma":[0.000002764357,0.000085869164,0.000038696944,0.0002663339,0.000015944199,0.00072126376,0.00005737778,0.00005513366,0.000058359212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021544391,0.00016017877,0.0010813028,0.00003289464,0.000025439027,2.5850264e-7,0.00005451548,0.004016322,0.8298764,0.08529149,0.0005752142,0.0786705],"study_design_scores_gemma":[0.003913596,0.001305555,0.011811491,0.000027220565,0.000030061183,0.000025384907,0.00008369571,0.9506594,0.023422562,0.0043996186,0.003881505,0.00043993222],"about_ca_topic_score_codex":0.000008866871,"about_ca_topic_score_gemma":0.000021266083,"teacher_disagreement_score":0.94664305,"about_ca_system_score_codex":0.000056511897,"about_ca_system_score_gemma":0.000020739955,"threshold_uncertainty_score":0.3501643},"labels":[],"label_agreement":null},{"id":"W2962818306","doi":"10.1109/isbi.2018.8363823","title":"Segmentation-by-detection: A cascade network for volumetric medical image segmentation","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":35,"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":"Segmentation; Artificial intelligence; Computer science; Image segmentation; Computer vision; Cascade; Noise (video); Object detection; Pattern recognition (psychology); Scale-space segmentation; Volume (thermodynamics); Data set; Image (mathematics); Engineering","score_opus":0.013584296977719318,"score_gpt":0.2963684699110353,"score_spread":0.28278417293331604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962818306","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":[0.00143624,0.00007329737,0.99444234,0.001814878,0.00048200667,0.0006423815,0.000004603979,0.0003432661,0.0007609848],"genre_scores_gemma":[0.13305473,0.00006809893,0.86018205,0.0029975686,0.0015244022,0.0007245918,0.00003938344,0.000027286138,0.001381919],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845356,0.00004171683,0.00029430515,0.00046034684,0.00039745437,0.00035260193],"domain_scores_gemma":[0.9988603,0.00030560925,0.000118954435,0.00036868313,0.00017395739,0.00017250373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025212104,0.00013578289,0.00011905666,0.000079329155,0.0004302609,0.00010750443,0.00055221375,0.00007865316,0.00015225123],"category_scores_gemma":[0.000079367725,0.00012900434,0.000056085933,0.001432511,0.00009690389,0.00060957886,0.000121478915,0.000097730015,0.00016166296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028951954,0.0001335796,0.00022681209,0.000018938365,0.00004972459,0.0000026472783,0.0002388656,0.00026570293,0.027748948,0.017115219,0.28981104,0.66435957],"study_design_scores_gemma":[0.0017566952,0.0006118682,0.00062511314,0.00001708087,0.000027659155,0.00006790969,0.00006683668,0.7536418,0.16821142,0.02292904,0.051412318,0.0006322603],"about_ca_topic_score_codex":0.000018709015,"about_ca_topic_score_gemma":0.000068473884,"teacher_disagreement_score":0.7533761,"about_ca_system_score_codex":0.00008587161,"about_ca_system_score_gemma":0.00004515886,"threshold_uncertainty_score":0.52606446},"labels":[],"label_agreement":null},{"id":"W2962845546","doi":"10.1109/crv.2018.00023","title":"Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":174,"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":"Object detection; Computer science; Convolutional neural network; Subnetwork; Artificial intelligence; Deep learning; Object (grammar); Artificial neural network; Computer vision; Stack (abstract data type); Embedded system; Pattern recognition (psychology); Computer network","score_opus":0.02606197856165662,"score_gpt":0.2702069869112208,"score_spread":0.24414500834956418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962845546","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":[0.035037417,0.00002595384,0.9589982,0.00024854369,0.00079242716,0.0008449191,0.0000038021046,0.0009905293,0.0030581802],"genre_scores_gemma":[0.87135255,0.000005304883,0.12538879,0.0004085602,0.0017675718,0.00028682957,0.00001532072,0.000034318153,0.000740752],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772674,0.000093054776,0.00042441412,0.0007893143,0.00028288775,0.0006835623],"domain_scores_gemma":[0.9981698,0.00040452843,0.00023699626,0.000670491,0.00035498207,0.00016324583],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026714802,0.00027113268,0.00023172607,0.00010703045,0.0007930425,0.00013783415,0.000562642,0.00014053295,0.00005838839],"category_scores_gemma":[0.00007655183,0.00026939975,0.00014060065,0.0009976616,0.00015324906,0.0006384923,0.00018073709,0.00016200873,0.00021171058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030632564,0.00021906645,0.000081481434,0.000020080264,0.00006757703,0.0000031323057,0.00034782983,0.020151721,0.6998016,0.010340187,0.0037572384,0.26490375],"study_design_scores_gemma":[0.00043648126,0.0006383946,0.0007066887,0.00000835877,0.000018003015,0.000073472125,0.000012853176,0.9243246,0.060971566,0.0087159155,0.0037296792,0.0003639981],"about_ca_topic_score_codex":0.000024594277,"about_ca_topic_score_gemma":0.00036162144,"teacher_disagreement_score":0.90417284,"about_ca_system_score_codex":0.00017473257,"about_ca_system_score_gemma":0.000039552888,"threshold_uncertainty_score":0.9999758},"labels":[],"label_agreement":null},{"id":"W2962930554","doi":"10.1016/j.artmed.2019.06.008","title":"Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Foothills Medical Centre; University of Calgary; Alberta Health Services","funders":"Canadian Institutes of Health Research; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Sørensen–Dice coefficient; Ground truth; Gold standard (test); Annotation; Pattern recognition (psychology); Segmentation; Deep learning; Machine learning; Image segmentation; Mathematics; Statistics","score_opus":0.05585655674446512,"score_gpt":0.35146120173111794,"score_spread":0.29560464498665284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962930554","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":[0.060306177,0.00038129967,0.930658,0.00691068,0.0008078803,0.00078300707,0.0000027630083,0.000068746616,0.000081439524],"genre_scores_gemma":[0.97463894,0.00001565468,0.023382211,0.0014844376,0.00037334542,0.000051889507,0.000010029021,0.000018369983,0.000025153033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759716,0.000080283666,0.0007650824,0.000626789,0.00032475003,0.0006059141],"domain_scores_gemma":[0.99815965,0.0010001818,0.0001709407,0.00044473325,0.00012740897,0.0000970631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008546062,0.00020843637,0.00034669903,0.0002830604,0.000109220644,0.000045795812,0.0006661119,0.00007037698,0.00005103498],"category_scores_gemma":[0.0002663482,0.00020380304,0.000053780113,0.0012104723,0.00019122618,0.000617886,0.00015599297,0.00036762055,0.00001250377],"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":[0.00007929901,0.00004159587,0.005837317,0.000015924268,0.000005506301,0.000018639454,0.0005191031,0.7372557,0.006377773,0.12413266,0.0002045853,0.12551191],"study_design_scores_gemma":[0.00014894163,0.000070510585,0.00033854585,0.00013171017,0.0000036000504,0.000013521088,0.00022653636,0.94819516,0.0006823407,0.049466964,0.000524639,0.00019755113],"about_ca_topic_score_codex":0.000120513876,"about_ca_topic_score_gemma":0.00016327907,"teacher_disagreement_score":0.91433275,"about_ca_system_score_codex":0.00022942858,"about_ca_system_score_gemma":0.000065770866,"threshold_uncertainty_score":0.83108467},"labels":[],"label_agreement":null},{"id":"W2962971773","doi":"10.1109/cvpr.2017.668","title":"Deep Pyramidal Residual Networks","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":687,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Residual; Convolutional neural network; Upsampling; Computer science; Benchmark (surveying); Artificial intelligence; Feature (linguistics); Deep learning; Dimension (graph theory); Pattern recognition (psychology); Generalization; Residual neural network; Pooling; Artificial neural network; Network architecture; Image (mathematics); Algorithm; Mathematics; Cartography","score_opus":0.030102128685991675,"score_gpt":0.2971226287962158,"score_spread":0.26702050011022416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962971773","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":[0.00014283358,0.00046061413,0.97406685,0.0030066872,0.0010727949,0.00040243845,0.000001457768,0.0007155617,0.020130748],"genre_scores_gemma":[0.45342112,0.0004890475,0.5363629,0.0011759637,0.002126426,0.00036779494,0.000053792304,0.000060058293,0.0059428965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979111,0.000043090156,0.00030058163,0.0010116311,0.00028104743,0.00045254003],"domain_scores_gemma":[0.995428,0.00011649242,0.00033916932,0.00385378,0.000103440565,0.00015910227],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015716013,0.00030982273,0.00029779845,0.000064302425,0.00039077492,0.00060338416,0.0046866196,0.0003211721,0.000022070015],"category_scores_gemma":[0.000034685443,0.000292595,0.00011672825,0.00009501901,0.00012588779,0.00028977596,0.00609278,0.00092181296,0.00012059938],"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":[0.0000036981685,0.00004482769,0.00013160058,0.00001583199,0.00003505131,0.000028446875,0.00006573759,0.54904246,0.000009586151,0.28969657,0.014811569,0.14611463],"study_design_scores_gemma":[0.00009082182,0.0000104149485,0.0011966641,0.00002426427,0.000008040281,0.000010136413,0.0000013601616,0.8765046,0.000059676036,0.11435585,0.0073512946,0.00038688804],"about_ca_topic_score_codex":0.000028925559,"about_ca_topic_score_gemma":0.00007005796,"teacher_disagreement_score":0.45327827,"about_ca_system_score_codex":0.000058698013,"about_ca_system_score_gemma":0.00008673555,"threshold_uncertainty_score":0.9999526},"labels":[],"label_agreement":null},{"id":"W2963188574","doi":"10.1049/el.2019.1719","title":"GenSynth: a generative synthesis approach to learning generative machines for generate efficient neural networks","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo; Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Artificial neural network; Generative grammar; Machine learning; Generator (circuit theory); Computer engineering","score_opus":0.011153224958634791,"score_gpt":0.23433161331312574,"score_spread":0.22317838835449094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963188574","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":[0.10847542,0.00042478094,0.8844579,0.0046084444,0.00026982033,0.0013131042,0.000003747041,0.0002382514,0.00020856458],"genre_scores_gemma":[0.69356316,0.000024612402,0.29444733,0.009503672,0.00063708227,0.001435748,0.00003318542,0.000082416096,0.00027275653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970258,0.00020169186,0.00033955558,0.001104687,0.00028128157,0.0010469678],"domain_scores_gemma":[0.998448,0.00043270463,0.00017818112,0.0006840692,0.00008210867,0.00017493905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029306498,0.000391015,0.00036760033,0.00014667377,0.00046800825,0.0002189371,0.0009661155,0.00008098351,0.00000382612],"category_scores_gemma":[0.00005726597,0.0003691682,0.00016498746,0.000770663,0.0000382526,0.00020049505,0.00023698683,0.00045739394,0.000033383232],"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":[0.000019101355,0.000040473755,0.000024147914,0.00000479405,0.000039631996,6.609951e-7,0.00021938127,0.9136346,0.061283655,0.015681876,0.001575313,0.0074763387],"study_design_scores_gemma":[0.00022143894,0.000114784896,0.000026168327,0.0000050835783,0.00001586553,0.000012713371,0.0000070983488,0.9810382,0.013035403,0.00014799494,0.004937807,0.00043742577],"about_ca_topic_score_codex":0.000003889765,"about_ca_topic_score_gemma":0.0000038231365,"teacher_disagreement_score":0.5900105,"about_ca_system_score_codex":0.00026246824,"about_ca_system_score_gemma":0.00005061896,"threshold_uncertainty_score":0.999876},"labels":[],"label_agreement":null},{"id":"W2963200642","doi":"","title":"Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks","year":2016,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"MNIST database; Computer science; Artificial neural network; Deep learning; Convolutional neural network; Artificial intelligence; Energy consumption","score_opus":0.05047194812276975,"score_gpt":0.3403865215796997,"score_spread":0.28991457345692995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963200642","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":[0.09134344,0.000023285182,0.8986691,0.006798029,0.00077313173,0.00040942055,0.000010353427,0.00028349148,0.0016897205],"genre_scores_gemma":[0.9962163,0.000046620073,0.002865635,0.00018638649,0.00021984243,0.00015902078,0.000061786945,0.000024110872,0.00022029142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973571,0.00022615823,0.00065824087,0.00072571595,0.0006061238,0.00042667252],"domain_scores_gemma":[0.9975248,0.0005663675,0.0005405812,0.0005834406,0.00063670025,0.00014809165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002072884,0.0002601242,0.00023945377,0.00025787315,0.00025473008,0.00015977619,0.0011738201,0.000081615835,0.00039590552],"category_scores_gemma":[0.00019859795,0.00021737025,0.00014231408,0.0006380718,0.00014721681,0.00052008685,0.00032391842,0.00032842948,0.000026779737],"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":[0.000032449814,0.00007259606,0.0048054364,0.000002081341,0.00004664224,0.000005883775,0.00030522616,0.7098384,0.0011357953,0.097551174,0.00008025337,0.1861241],"study_design_scores_gemma":[0.00072007347,0.0001461811,0.027275817,0.000023270082,0.000014476243,0.000012847884,0.00018841239,0.96932304,0.00062257826,0.0013143867,0.00012544286,0.00023349561],"about_ca_topic_score_codex":0.00006965066,"about_ca_topic_score_gemma":0.000051518833,"teacher_disagreement_score":0.90487283,"about_ca_system_score_codex":0.00011903569,"about_ca_system_score_gemma":0.00004523897,"threshold_uncertainty_score":0.8864102},"labels":[],"label_agreement":null},{"id":"W2963285940","doi":"","title":"Graph HyperNetworks for Neural Architecture Search.","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Inference; Architecture; Artificial neural network; Network topology; Graph; Network architecture; Artificial intelligence; Machine learning; Theoretical computer science","score_opus":0.05905441561582941,"score_gpt":0.3606030764095502,"score_spread":0.3015486607937208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963285940","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":[0.01116927,0.000009296609,0.9562113,0.013093712,0.000589182,0.00037316373,0.000009444575,0.00031729703,0.018227285],"genre_scores_gemma":[0.96643335,0.000015298996,0.029357385,0.0004957281,0.0005815353,0.00019053176,0.000056746703,0.000016894446,0.0028525419],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984921,0.00008194859,0.00022737899,0.0005552695,0.00035385636,0.00028940136],"domain_scores_gemma":[0.9983958,0.00041860738,0.00011173447,0.0004244012,0.00054570334,0.00010377518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013355835,0.00014783656,0.00011082742,0.00019391674,0.00042832948,0.00024798175,0.0010777495,0.000053945318,0.000116089606],"category_scores_gemma":[0.00017247003,0.00014542663,0.00009445792,0.00038158867,0.00013715275,0.00031249705,0.00018382892,0.0003584044,0.00010857697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004636589,0.00006735951,0.001137678,0.000002688743,0.000045373683,0.0000032218682,0.0010480939,0.07746403,0.0017101995,0.8357261,0.0018083354,0.080940574],"study_design_scores_gemma":[0.00040146327,0.00023474559,0.0024795595,0.000022748995,0.0000057481225,0.000021870399,0.00010309602,0.91988826,0.0007478315,0.06268867,0.013182084,0.00022392238],"about_ca_topic_score_codex":0.000012798145,"about_ca_topic_score_gemma":0.000016919403,"teacher_disagreement_score":0.9552641,"about_ca_system_score_codex":0.000036817382,"about_ca_system_score_gemma":0.000048544684,"threshold_uncertainty_score":0.5930326},"labels":[],"label_agreement":null},{"id":"W2963324979","doi":"10.1109/icdsp.2018.8631567","title":"SdcNet: A Computation-Efficient CNN for Object Recognition","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"FLOPS; Computation; Computer science; Convolution (computer science); Convolutional neural network; Word error rate; Cognitive neuroscience of visual object recognition; Feature (linguistics); Object (grammar); Task (project management); Artificial intelligence; Pattern recognition (psychology); Volume (thermodynamics); Artificial neural network; Algorithm; Parallel computing","score_opus":0.04071669672122831,"score_gpt":0.3065425189119911,"score_spread":0.2658258221907628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963324979","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":[0.0047680694,0.000010036007,0.98781836,0.0009257118,0.00021547405,0.00045964567,0.0000032313942,0.00030405403,0.0054954127],"genre_scores_gemma":[0.53801024,0.000001243931,0.46080574,0.00068756606,0.00018077824,0.000119129785,0.0000105474355,0.0000065698932,0.0001781555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923205,0.000014364895,0.0001474466,0.00030989185,0.00010554296,0.00019070465],"domain_scores_gemma":[0.99927264,0.00016809485,0.000059772752,0.0002392616,0.00020671674,0.000053490992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000091296104,0.000077454024,0.00006938449,0.000051611496,0.00020847803,0.00005397553,0.00028012288,0.000025910329,0.000014799088],"category_scores_gemma":[0.000024159957,0.00007041466,0.000037621387,0.0003804621,0.000048000566,0.00013378826,0.00007070517,0.000034977264,0.00035119802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023601273,0.00017374505,0.000020972806,0.000010745879,0.000014912911,7.413096e-7,0.00045468676,0.008440907,0.0020635559,0.08722828,0.015492815,0.886075],"study_design_scores_gemma":[0.00027281806,0.00019905332,0.0002419391,0.000006443575,0.0000033460926,0.000006511848,0.0000106800835,0.9455771,0.0059994594,0.040471803,0.0070591755,0.00015170961],"about_ca_topic_score_codex":0.000003116848,"about_ca_topic_score_gemma":0.000008623353,"teacher_disagreement_score":0.9371362,"about_ca_system_score_codex":0.000025307907,"about_ca_system_score_gemma":0.00002329152,"threshold_uncertainty_score":0.4514057},"labels":[],"label_agreement":null},{"id":"W2963328663","doi":"10.1109/acssc.2017.8335696","title":"Minimizing area and energy of deep learning hardware design using collective low precision and structured compression","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Canadian Institute for Advanced Research","keywords":"Computer science; Memory footprint; MNIST database; Deep learning; Artificial neural network; Regularization (linguistics); Block (permutation group theory); Artificial intelligence; Pruning; Floating point; Footprint; Compression ratio; Deep neural networks; Computer engineering; Overhead (engineering); Algorithm; Mathematics","score_opus":0.040799506443188915,"score_gpt":0.2816845119807381,"score_spread":0.24088500553754916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963328663","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":[0.043076042,0.00018450464,0.9562737,0.000037540747,0.00003906022,0.00009708208,4.3628197e-7,0.000036573907,0.00025506632],"genre_scores_gemma":[0.6937799,0.000038655016,0.30604818,0.000012788927,0.000010299584,0.0000044473277,3.7990472e-7,0.0000051073557,0.000100294645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992232,0.000063462176,0.00013955738,0.00032216005,0.000119359385,0.00013230591],"domain_scores_gemma":[0.99901193,0.00026375827,0.00022346605,0.00035727533,0.00007948895,0.00006405654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007175782,0.000103055616,0.00015611859,0.00005196216,0.0007317789,0.00013542018,0.00031926506,0.000047161357,0.00000282706],"category_scores_gemma":[0.000075565586,0.00008694866,0.000015366812,0.0000879715,0.000087272245,0.00043898498,0.00045972035,0.00007766833,8.869611e-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":[0.00008725877,0.000033720262,0.0033731123,0.000025391464,0.000028035549,0.0000068056615,0.0013538861,0.10076158,0.1835681,0.0062177205,0.00008412946,0.70446026],"study_design_scores_gemma":[0.00027322397,0.000044709028,0.0055919457,0.00005900236,0.000005040317,0.000014757778,0.000029482695,0.9488087,0.038427528,0.00656927,0.00006635563,0.00010995724],"about_ca_topic_score_codex":0.000016490967,"about_ca_topic_score_gemma":0.000009775498,"teacher_disagreement_score":0.84804714,"about_ca_system_score_codex":0.000020899277,"about_ca_system_score_gemma":0.000017636146,"threshold_uncertainty_score":0.5628326},"labels":[],"label_agreement":null},{"id":"W2963400571","doi":"10.1109/iros.2018.8594049","title":"Joint 3D Proposal Generation and Object Detection from View Aggregation","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1656,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Object detection; Minimum bounding box; Artificial intelligence; Benchmark (surveying); Point cloud; Memory footprint; Computer vision; Feature (linguistics); Orientation (vector space); Feature extraction; Lidar; Object (grammar); Code (set theory); Pattern recognition (psychology); Image (mathematics)","score_opus":0.026203299135159543,"score_gpt":0.2564876608302475,"score_spread":0.23028436169508795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963400571","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":[0.060761143,0.00008058433,0.9374675,0.0006041484,0.0001869697,0.00022733529,5.695449e-7,0.0001967746,0.00047495516],"genre_scores_gemma":[0.75262624,0.00003602925,0.24653086,0.00027178705,0.0004112245,0.00003439501,0.0000043480177,0.000004850551,0.000080285216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992954,0.000036443766,0.00013777247,0.0003178308,0.00010726186,0.00010529245],"domain_scores_gemma":[0.9995122,0.000020834463,0.000063373875,0.00028353225,0.00007705128,0.000043020787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007201787,0.00007596187,0.0000658935,0.000034838642,0.00020010551,0.00009318196,0.00011514457,0.000034347457,0.000016715156],"category_scores_gemma":[0.000014725001,0.00006542272,0.000013045892,0.00023016006,0.000043386146,0.0004955815,0.00008178861,0.00005010557,0.000058728547],"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":[0.0000016481639,0.000012537329,0.000031090098,0.000001625231,0.000003872501,3.3585775e-7,0.00013070897,0.000058732487,0.14668953,0.011254025,0.00020097455,0.8416149],"study_design_scores_gemma":[0.00013060228,0.000095250376,0.0014218782,0.0000070648935,0.0000043006103,0.000008954067,0.0000031424854,0.681164,0.30042106,0.013487447,0.0031305144,0.00012574736],"about_ca_topic_score_codex":0.000035122626,"about_ca_topic_score_gemma":0.00030559112,"teacher_disagreement_score":0.8414892,"about_ca_system_score_codex":0.000027980226,"about_ca_system_score_gemma":0.000018206609,"threshold_uncertainty_score":0.2667861},"labels":[],"label_agreement":null},{"id":"W2963519357","doi":"10.48550/arxiv.1907.11110","title":"Filter Bank Regularization of Convolutional Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Regularization (linguistics); Convolutional neural network; Generality; Artificial intelligence; Computer science; Robustness (evolution); Pattern recognition (psychology); Kernel (algebra); Reproducing kernel Hilbert space; Mathematics; Algorithm; Hilbert space","score_opus":0.049053531764029455,"score_gpt":0.18749793520502162,"score_spread":0.13844440344099215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963519357","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":[0.017613843,0.00007631219,0.98018587,0.00014626008,0.00056917244,0.0003880872,0.000015771495,0.00015228635,0.00085240015],"genre_scores_gemma":[0.9937149,0.00007024593,0.004222876,0.000117795214,0.000102192185,0.0000018657797,0.00008351747,0.000015998443,0.0016705714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984058,0.000099896875,0.00024296394,0.00088056904,0.00009440211,0.0002763415],"domain_scores_gemma":[0.9977769,0.00014380427,0.00040678168,0.0013580497,0.00022368028,0.00009077098],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009759428,0.00023770805,0.0002838757,0.00014586278,0.00009282145,0.000035955112,0.0015330762,0.00025140788,0.00002661506],"category_scores_gemma":[0.000013677076,0.0002853363,0.00017072499,0.00064018514,0.0001293885,0.0003626813,0.0016236987,0.00045364723,0.000027231563],"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":[0.000008517735,0.000025248974,0.0010131696,0.000015970001,0.000019609284,0.0000066939147,0.000012499104,0.74659336,0.000022555932,0.25170138,0.00031864832,0.0002623726],"study_design_scores_gemma":[0.0002223485,0.000023426746,0.001965978,0.000030832703,0.000025569383,0.000003162149,0.0000029352732,0.9554316,0.000058094553,0.04175632,0.00023315888,0.00024657339],"about_ca_topic_score_codex":0.000011027864,"about_ca_topic_score_gemma":0.0000039194492,"teacher_disagreement_score":0.9761011,"about_ca_system_score_codex":0.00011234408,"about_ca_system_score_gemma":0.00008743743,"threshold_uncertainty_score":0.9999599},"labels":[],"label_agreement":null},{"id":"W2963653539","doi":"10.1109/isbi.2018.8363859","title":"End-to-end detection-segmentation network with ROI convolution","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Segmentation; Artificial intelligence; Computer science; Scale-space segmentation; Pattern recognition (psychology); Segmentation-based object categorization; Image segmentation; Convolutional neural network; Convolution (computer science); Computer vision; End-to-end principle; Object detection; Deep learning; Task (project management); Artificial neural network","score_opus":0.013061925628101241,"score_gpt":0.2537392585079027,"score_spread":0.24067733287980145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963653539","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":[0.008654712,0.000012833906,0.9862417,0.0007220831,0.00022347129,0.00033789527,4.898561e-7,0.00033787554,0.003468939],"genre_scores_gemma":[0.7454407,0.0000029766938,0.25275946,0.00085527066,0.00035617477,0.000084982225,0.0000023446873,0.000007832594,0.00049019884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899906,0.000028077424,0.00014353744,0.00036996964,0.00019112806,0.00026822984],"domain_scores_gemma":[0.9992174,0.000062995874,0.000066816305,0.00042585575,0.00012272826,0.00010421164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089344285,0.000105264335,0.000076794786,0.000049349946,0.00031303955,0.000068897374,0.00031622697,0.000029178158,0.000061000166],"category_scores_gemma":[0.000005683375,0.000088565044,0.000017667446,0.0009274308,0.00006400563,0.00044781697,0.00009709123,0.000066555134,0.00037376396],"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":[0.00008958003,0.00009998034,0.0020124787,0.000006523563,0.000049412516,0.000004320191,0.0005851815,0.03650605,0.035990383,0.23660976,0.010363805,0.6776825],"study_design_scores_gemma":[0.0017756347,0.0025193633,0.04489996,0.00005906587,0.000044974724,0.00015775347,0.0001200404,0.56473994,0.22855699,0.038729437,0.11688743,0.0015094317],"about_ca_topic_score_codex":0.000023692082,"about_ca_topic_score_gemma":0.00037953403,"teacher_disagreement_score":0.73678607,"about_ca_system_score_codex":0.00006690628,"about_ca_system_score_gemma":0.00002266839,"threshold_uncertainty_score":0.4804104},"labels":[],"label_agreement":null},{"id":"W2963684275","doi":"","title":"The Reversible Residual Network: Backpropagation Without Storing Activations","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":228,"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":"Residual; Backpropagation; Computer science; Bottleneck; Convolutional neural network; Layer (electronics); Artificial neural network; Artificial intelligence; Scheme (mathematics); Embedded system; Algorithm; Materials science; Nanotechnology","score_opus":0.08967421426297045,"score_gpt":0.22037119846003747,"score_spread":0.13069698419706702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963684275","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":[0.08722863,0.000020718002,0.9050396,0.0014452954,0.00034215773,0.00025963946,0.0000010627258,0.00019351259,0.005469357],"genre_scores_gemma":[0.9900402,0.00009215085,0.0048074913,0.000050107737,0.00018368418,0.0000019205786,0.0000013513727,0.000008446883,0.0048146904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904287,0.000054283613,0.00009958918,0.00042045926,0.0000722784,0.00031054765],"domain_scores_gemma":[0.99786985,0.00017146555,0.00023889226,0.0015321138,0.00010447309,0.000083212646],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00020659923,0.00010913569,0.00008570575,0.000031111846,0.0033830241,0.00023956019,0.0017318539,0.000044882658,0.0000033825602],"category_scores_gemma":[0.00006526912,0.00010081795,0.00004435674,0.0003192325,0.00012022357,0.0013757413,0.00052088546,0.00017776185,0.00006300204],"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":[0.000014475235,0.00001684677,0.013755514,0.0000021612348,0.000019950541,0.000010652798,0.000047902813,0.108153045,0.0002038149,0.8721002,0.002295676,0.0033797575],"study_design_scores_gemma":[0.0011857754,0.00009207101,0.10830244,0.00006575615,0.00005590769,0.000012039111,0.00012556609,0.61882883,0.0016998209,0.21648216,0.0523739,0.0007757238],"about_ca_topic_score_codex":0.000022081515,"about_ca_topic_score_gemma":0.00008621138,"teacher_disagreement_score":0.9028115,"about_ca_system_score_codex":0.00010183992,"about_ca_system_score_gemma":0.00005217008,"threshold_uncertainty_score":0.99791443},"labels":[],"label_agreement":null},{"id":"W2963694111","doi":"10.1109/ismvl.2018.00039","title":"Efficient Hardware Realization of Convolutional Neural Networks Using Intra-Kernel Regular Pruning","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Convolutional neural network; Pruning; Realization (probability); Kernel (algebra); Artificial intelligence; Computer hardware; Mathematics","score_opus":0.023858632948502014,"score_gpt":0.27173546790414577,"score_spread":0.24787683495564375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963694111","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":[0.036065776,0.000060436676,0.9627146,0.00014516622,0.00022628641,0.00019379062,0.0000013737921,0.00015279472,0.00043976825],"genre_scores_gemma":[0.90108985,0.0000025369457,0.09843111,0.0001772171,0.00020473052,0.0000066195757,0.00000719641,0.000009878247,0.00007084898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988304,0.00004878932,0.0002912839,0.00034243974,0.00023246964,0.00025464187],"domain_scores_gemma":[0.99891937,0.00006339788,0.00017588433,0.00047805536,0.0002914853,0.00007179721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013538226,0.00011423228,0.00012833891,0.00006721272,0.00021416842,0.000033163473,0.00045827,0.000054024506,0.000018823],"category_scores_gemma":[0.000025913683,0.00010948615,0.000041030784,0.00074268866,0.00017468736,0.00015705614,0.0002221299,0.00008293156,0.0000046290593],"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":[0.0000041806916,0.000023019327,0.00026170607,0.000003096898,0.000005023363,6.2380235e-7,0.00007124333,0.7789872,0.002009887,0.21571615,0.00023109875,0.002686767],"study_design_scores_gemma":[0.00013184115,0.00003370479,0.00091796345,0.000016884518,0.0000050147846,0.000016374004,0.000009592798,0.99520963,0.0014450798,0.0018535764,0.00024627906,0.00011404483],"about_ca_topic_score_codex":0.000015527796,"about_ca_topic_score_gemma":0.000005210675,"teacher_disagreement_score":0.8650241,"about_ca_system_score_codex":0.000056161007,"about_ca_system_score_gemma":0.000034729088,"threshold_uncertainty_score":0.44647154},"labels":[],"label_agreement":null},{"id":"W2963730812","doi":"10.1109/tmi.2018.2878669","title":"HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation","year":2019,"lang":"","type":"article","venue":"Espace ÉTS (ETS)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":582,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Segmentation; Abstraction; Artificial intelligence; Convolutional neural network; Representation (politics); Modal; Modality (human–computer interaction); Pattern recognition (psychology); Modalities; Deep learning; Path (computing); Pyramid (geometry); Layer (electronics); Image segmentation; Feature learning; Ranking (information retrieval); Metric (unit); Mathematics","score_opus":0.02677478251896863,"score_gpt":0.2958204707536173,"score_spread":0.26904568823464864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963730812","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":[0.24824981,0.000538513,0.73806757,0.005398712,0.001742446,0.0049305093,0.00009952833,0.00044265526,0.0005302387],"genre_scores_gemma":[0.6462668,0.00013142858,0.33980998,0.0010241204,0.00044796665,0.0005520065,0.000070149035,0.0001435573,0.011553975],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949075,0.00025273362,0.00083401334,0.0019823974,0.00067412807,0.0013492448],"domain_scores_gemma":[0.99540854,0.0009632618,0.00066958944,0.0018621928,0.00064968603,0.00044675125],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041590972,0.00077038823,0.00073487486,0.00025857554,0.0006564821,0.0005341417,0.0014327723,0.00031175104,0.00024136735],"category_scores_gemma":[0.00023359683,0.00084576,0.00033415505,0.0012789118,0.00024670962,0.0013096537,0.0005078131,0.0005436986,0.0031604832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092775264,0.0016642183,0.0023662862,0.00048465372,0.0003427393,0.00005593039,0.010365639,0.014233129,0.8196916,0.024532555,0.013520768,0.11181475],"study_design_scores_gemma":[0.006214077,0.0007224444,0.0049071317,0.00014934353,0.00013817745,0.00009871914,0.0005908728,0.92699486,0.031626333,0.00086637697,0.02618467,0.0015070082],"about_ca_topic_score_codex":0.000026802438,"about_ca_topic_score_gemma":0.00007706276,"teacher_disagreement_score":0.9127617,"about_ca_system_score_codex":0.00029932227,"about_ca_system_score_gemma":0.00026659336,"threshold_uncertainty_score":0.9993993},"labels":[],"label_agreement":null},{"id":"W2963746765","doi":"","title":"Training Neural Networks with Stochastic Hessian-Free Optimization","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Overfitting; Hessian matrix; Autoencoder; Stochastic gradient descent; Computer science; Artificial intelligence; Stochastic optimization; Pattern recognition (psychology); Conjugate gradient method; Mathematical optimization; Dropout (neural networks); Artificial neural network; Machine learning; Algorithm; Mathematics; Applied mathematics","score_opus":0.020890318504978602,"score_gpt":0.23410143582583293,"score_spread":0.21321111732085432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963746765","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":[0.00038910142,0.000024096467,0.99445194,0.0019610014,0.00007781781,0.0003504985,1.9192008e-7,0.00041897682,0.0023263574],"genre_scores_gemma":[0.5749781,0.0000012707377,0.42400438,0.0005914544,0.00006787192,0.00012306157,0.0000027054548,0.000011237895,0.00021988212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902815,0.000019290774,0.00015314338,0.0003280105,0.0001544895,0.000316925],"domain_scores_gemma":[0.99894845,0.000099767334,0.00007384783,0.0006902508,0.000069945425,0.00011770416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003802412,0.00013145266,0.00010889794,0.00004350171,0.00015793313,0.0001577771,0.00080290437,0.000035800622,0.00006241774],"category_scores_gemma":[0.000012147295,0.00009906633,0.000021391314,0.00052148424,0.000045973953,0.0009557585,0.00016649616,0.00012892904,0.000021169928],"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":[9.931541e-7,0.000007752296,0.0000065098293,5.404741e-7,0.0000027313793,6.861442e-7,0.000059492137,0.9576677,0.0000051659003,0.011861171,0.00038829586,0.029998934],"study_design_scores_gemma":[0.0001883967,0.00004439833,0.00015215973,0.0000053482054,0.0000025719157,0.000022877653,0.000021704494,0.9975239,0.0000052445644,0.0018554106,0.000033859473,0.00014412832],"about_ca_topic_score_codex":0.000008724013,"about_ca_topic_score_gemma":0.000008381193,"teacher_disagreement_score":0.574589,"about_ca_system_score_codex":0.000018323995,"about_ca_system_score_gemma":0.00001540294,"threshold_uncertainty_score":0.4039808},"labels":[],"label_agreement":null},{"id":"W2963789586","doi":"","title":"Training deep neural networks via direct loss minimization","year":2016,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Minification; Machine learning; Deep neural networks; Entropy (arrow of time); Context (archaeology); Cross entropy; Ranking (information retrieval); Deep learning; Pattern recognition (psychology)","score_opus":0.037073720324860934,"score_gpt":0.29698504882090826,"score_spread":0.2599113284960473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963789586","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":[0.0019957197,0.000031786865,0.9793006,0.006293365,0.0004990451,0.000118886244,0.0000025855722,0.0003631872,0.011394809],"genre_scores_gemma":[0.9897085,0.00006377894,0.008593721,0.00047948377,0.00025598166,0.00004166,0.000023107727,0.000020818687,0.00081294146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983779,0.00012775218,0.00028941626,0.00053502416,0.000356534,0.0003133786],"domain_scores_gemma":[0.9988525,0.00034486447,0.00021975065,0.00030115276,0.00017554294,0.00010622686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015511792,0.0002041561,0.00016325981,0.00013900481,0.0001988178,0.00014506659,0.0009883615,0.00006314666,0.00020983971],"category_scores_gemma":[0.000154109,0.00015754497,0.00006933644,0.00025413153,0.00006227908,0.00058179634,0.00020176837,0.00031104186,0.000076382814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026585956,0.000029468849,0.0024565477,0.0000011272467,0.000022760853,0.000015356198,0.0001926615,0.15599851,0.0010426557,0.11272587,0.000022708653,0.72746575],"study_design_scores_gemma":[0.00031994111,0.00007013627,0.0010133048,0.000029476618,0.0000029279136,0.000025503747,0.00000739254,0.9906861,0.00011664674,0.0040461277,0.0034726476,0.00020979573],"about_ca_topic_score_codex":0.0000086010705,"about_ca_topic_score_gemma":0.000017403047,"teacher_disagreement_score":0.9877128,"about_ca_system_score_codex":0.00007709866,"about_ca_system_score_gemma":0.0000183462,"threshold_uncertainty_score":0.64244974},"labels":[],"label_agreement":null},{"id":"W2963809933","doi":"10.1109/tpami.2017.2706685","title":"3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":430,"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":"Office of Naval Research; Toyota Motor Corporation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Nvidia","keywords":"Artificial intelligence; Computer vision; Computer science; Object detection; Object (grammar); Object-class detection; Class (philosophy); Pattern recognition (psychology); Face detection; Facial recognition system","score_opus":0.04354686040000782,"score_gpt":0.3256726781947614,"score_spread":0.2821258177947536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963809933","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":[0.006852094,0.000043764394,0.9918632,0.00029869544,0.0002840455,0.0004581242,0.00005012832,0.00011520864,0.000034740926],"genre_scores_gemma":[0.9837689,0.00015947582,0.015547968,0.00020319156,0.000051846393,0.000121592624,0.000002842271,0.000019721032,0.00012447605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982108,0.00006923981,0.0004243372,0.00074883155,0.00021336602,0.0003334661],"domain_scores_gemma":[0.99801767,0.00021272602,0.0003362601,0.0011718598,0.00013902174,0.00012249286],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0002514746,0.000273202,0.00035681998,0.00035934566,0.0013020313,0.0005142318,0.0007664545,0.0000769621,0.000016895365],"category_scores_gemma":[0.000014085421,0.0002446081,0.00029428385,0.0005269281,0.000117260526,0.0007858884,0.000016163289,0.00025397906,0.000012012037],"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":[0.000020921247,0.0000802054,0.00013469087,0.000018628665,0.00025836244,0.0000034914804,0.00008780944,0.049559165,0.006554653,0.00003501014,0.0000018860834,0.9432452],"study_design_scores_gemma":[0.0000922099,0.0000847439,0.00039228552,0.000016928123,0.00030093672,0.000013856484,0.0000110620085,0.7375475,0.2607038,0.00052068307,0.0000665233,0.0002494508],"about_ca_topic_score_codex":0.00047338652,"about_ca_topic_score_gemma":0.0017194388,"teacher_disagreement_score":0.9769168,"about_ca_system_score_codex":0.00006137387,"about_ca_system_score_gemma":0.0000288525,"threshold_uncertainty_score":0.99999815},"labels":[],"label_agreement":null},{"id":"W2963896595","doi":"10.1109/cvpr.2018.00908","title":"SBNet: Sparse Blocks Network for Fast Inference","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":202,"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; FLOPS; Computation; Convolution (computer science); Speedup; Convolutional neural network; Inference; Artificial intelligence; Leverage (statistics); Object detection; Pattern recognition (psychology); Algorithm; Parallel computing; Artificial neural network","score_opus":0.03360285784287599,"score_gpt":0.29917951340829146,"score_spread":0.2655766555654155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963896595","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":[0.0009751062,0.000023625547,0.9888032,0.001117457,0.00026947496,0.00032418975,0.0000017058532,0.00030474973,0.00818049],"genre_scores_gemma":[0.4100507,0.000007889953,0.58492553,0.0015585575,0.00080910797,0.00012758221,0.0000026562511,0.0000093229355,0.002508654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989845,0.000011010651,0.00016193643,0.00037179005,0.000102636885,0.00036810327],"domain_scores_gemma":[0.9988578,0.00020588143,0.00006254905,0.00065108674,0.0001282262,0.000094473064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008786805,0.00010964967,0.00010173846,0.000023515317,0.00023764335,0.00006727533,0.0008058992,0.00004017019,0.000046961428],"category_scores_gemma":[0.000024513454,0.00009643231,0.000038075974,0.00044199754,0.00008247213,0.00024368854,0.00023036028,0.0000623942,0.00016826185],"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":[0.0000079425,0.000041320032,0.00055173907,0.0000029970945,0.0000100335155,7.5835123e-7,0.000119876604,0.0041566724,0.0005068344,0.77493805,0.077490844,0.14217292],"study_design_scores_gemma":[0.00030813122,0.0002205292,0.00096736336,0.000013039277,0.000005367437,0.0000074645855,0.0000056726335,0.5151667,0.003041945,0.15722866,0.3226975,0.00033761765],"about_ca_topic_score_codex":0.000004955916,"about_ca_topic_score_gemma":0.000062715684,"teacher_disagreement_score":0.6177094,"about_ca_system_score_codex":0.000014707812,"about_ca_system_score_gemma":0.000028386197,"threshold_uncertainty_score":0.39323956},"labels":[],"label_agreement":null},{"id":"W2963939004","doi":"","title":"Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Variance reduction; Computer science; Multiplicative function; Control variates; Gaussian; Stochastic gradient descent; Regularization (linguistics); Importance sampling; Convolutional neural network; Artificial neural network; Reduction (mathematics); Algorithm; Variance (accounting); Bayesian probability; Artificial intelligence; Machine learning; Monte Carlo method; Mathematics; Markov chain Monte Carlo; Statistics","score_opus":0.04482411746879406,"score_gpt":0.1925983170088435,"score_spread":0.14777419954004944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963939004","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":[0.34314656,0.000009194706,0.64817184,0.00061682763,0.000300497,0.00021465945,0.0000040089462,0.00027588385,0.007260508],"genre_scores_gemma":[0.99192274,0.000015877313,0.0040369583,0.00039549527,0.00016643858,0.0000023301109,0.0000029418866,0.000012555248,0.003444652],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985633,0.00005629825,0.0001399625,0.00077085255,0.00011912545,0.0003504883],"domain_scores_gemma":[0.99840266,0.00018740757,0.0001049185,0.0009871589,0.00014067779,0.00017719461],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000089719855,0.00018411523,0.00013285759,0.00015416581,0.00044287997,0.00005127405,0.0010943289,0.000072665134,0.000054648135],"category_scores_gemma":[0.000023365905,0.00018944482,0.00008909326,0.001052539,0.00017031157,0.00024992632,0.00028045243,0.00016380669,0.0010255396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024982783,0.00025351456,0.00065360934,0.0000036198653,0.000025469522,0.000051247214,0.0004127034,0.06896191,0.0007762123,0.92487514,0.0018036378,0.002157937],"study_design_scores_gemma":[0.00062835467,0.00024563124,0.0042847996,0.000023253808,0.000026579994,0.00001267707,0.000080187056,0.95882815,0.003662233,0.02061254,0.011130835,0.00046473343],"about_ca_topic_score_codex":0.000009143363,"about_ca_topic_score_gemma":0.000021443153,"teacher_disagreement_score":0.9042626,"about_ca_system_score_codex":0.00014060902,"about_ca_system_score_gemma":0.000051559793,"threshold_uncertainty_score":0.9997523},"labels":[],"label_agreement":null},{"id":"W2964161024","doi":"10.1109/iccv.2019.00145","title":"Similarity-Preserving Knowledge Distillation","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Distillation; Pairwise comparison; Artificial neural network; Computer science; Similarity (geometry); Artificial intelligence; Representation (politics); Machine learning; Space (punctuation); Contrast (vision); Image (mathematics); Chromatography; Chemistry","score_opus":0.04013347021668566,"score_gpt":0.3134829577451662,"score_spread":0.27334948752848054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964161024","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":[0.0006633189,0.00030679643,0.9280241,0.001500234,0.0008046238,0.0005198783,0.0000049185064,0.00057636906,0.06759974],"genre_scores_gemma":[0.76584184,0.00009257041,0.22647867,0.00023969352,0.00034134855,0.000116922834,0.000052427113,0.000029691108,0.006806863],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984341,0.00005610804,0.00026968343,0.0007998354,0.00018365408,0.00025658766],"domain_scores_gemma":[0.9972116,0.00022598903,0.0001636344,0.0021833018,0.0001333206,0.00008218928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012802791,0.00022568616,0.00022738421,0.0000877713,0.00010259133,0.00019602696,0.0022135607,0.00019419237,0.000036902482],"category_scores_gemma":[0.000042743202,0.00021896204,0.0000991649,0.00028498788,0.000022697692,0.0002759356,0.00641147,0.0005182035,0.00042612295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003898582,0.00015525696,0.0018404241,0.0003136734,0.000051805775,0.0000037102052,0.00049804966,0.27876154,0.00028513902,0.5898827,0.04127909,0.08692473],"study_design_scores_gemma":[0.00006782829,0.0000069221906,0.0022924084,0.000044526594,0.0000056863432,0.0000017363192,0.000002124324,0.8607087,0.00019989049,0.102004245,0.034361523,0.00030437837],"about_ca_topic_score_codex":0.0000112181515,"about_ca_topic_score_gemma":0.000023737812,"teacher_disagreement_score":0.7651785,"about_ca_system_score_codex":0.000088026354,"about_ca_system_score_gemma":0.000094075076,"threshold_uncertainty_score":0.89290124},"labels":[],"label_agreement":null},{"id":"W2964211168","doi":"10.1109/cvpr.2016.79","title":"Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":214,"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":"Markov random field; Computer science; ENCODE; Artificial intelligence; Convolutional neural network; Conditional random field; Pattern recognition (psychology); Image segmentation; Segmentation; Heuristics; Markov chain; Inference; Pixel; Computer vision; Machine learning","score_opus":0.03564884137849786,"score_gpt":0.2797257065967211,"score_spread":0.24407686521822322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964211168","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":[0.0040244907,0.000049872786,0.9902762,0.0015427704,0.00030168946,0.0016871167,0.00002219238,0.0007158295,0.0013798468],"genre_scores_gemma":[0.25696254,0.000022809241,0.7401639,0.00037765814,0.00013798811,0.0012487834,0.00004140396,0.000037056037,0.0010078354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796325,0.0000363184,0.00036746397,0.0009927513,0.00023414085,0.00040607483],"domain_scores_gemma":[0.9977515,0.0004037023,0.0003845601,0.0010407385,0.00029969454,0.00011982469],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010565951,0.00033059285,0.00029642158,0.00010920233,0.00021648397,0.00017672367,0.001103497,0.00015259006,0.000012553892],"category_scores_gemma":[0.000029859442,0.00025006116,0.00007800494,0.00023430232,0.000060822906,0.00038125046,0.00062015315,0.00021661595,0.000024728604],"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":[0.00007442972,0.00018116376,0.0008948995,0.00018099265,0.00025194715,0.000014315478,0.0011623802,0.028708741,0.008042134,0.57514113,0.0027653608,0.3825825],"study_design_scores_gemma":[0.0034333374,0.0003757674,0.0059037455,0.00060716347,0.00010424067,0.00007559373,0.000088331646,0.61367005,0.03310577,0.33084652,0.009063947,0.0027255313],"about_ca_topic_score_codex":0.0000048023758,"about_ca_topic_score_gemma":0.00019468693,"teacher_disagreement_score":0.5849613,"about_ca_system_score_codex":0.00024309034,"about_ca_system_score_gemma":0.00020415965,"threshold_uncertainty_score":0.9999952},"labels":[],"label_agreement":null},{"id":"W2965174861","doi":"10.24963/ijcai.2019/525","title":"COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Computer science; Redundancy (engineering); Normalization (sociology); Convolutional neural network; Pruning; Filter (signal processing); Artificial neural network; Algorithm; Computational complexity theory; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.023198823925903316,"score_gpt":0.2510254103924932,"score_spread":0.2278265864665899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965174861","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":[0.004209765,0.000020057845,0.99080515,0.0006473072,0.00018464327,0.00068967306,0.0000028225186,0.0004938839,0.0029467265],"genre_scores_gemma":[0.49608532,0.000001121984,0.5009042,0.0006298512,0.000016027598,0.0000474288,0.0000169925,0.000014187492,0.0022849008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838674,0.00007744533,0.00033488267,0.0005547563,0.00032654262,0.00031961506],"domain_scores_gemma":[0.99808306,0.00040785677,0.00018938628,0.0010715483,0.00012429619,0.00012384009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015958163,0.00019733346,0.0002522967,0.000097686556,0.00019764913,0.00007610214,0.00086006505,0.0000964741,0.000116102274],"category_scores_gemma":[0.00002128972,0.00017429401,0.00008456554,0.0004512649,0.000035557572,0.00052700023,0.00024367387,0.00020925932,0.00037262417],"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":[0.000036721318,0.000044934157,0.00019510147,0.000006116203,0.0000060997145,7.782843e-7,0.000057207402,0.94705325,0.019426098,0.022873364,0.00053899124,0.0097613055],"study_design_scores_gemma":[0.0018129661,0.000018678025,0.0003394528,0.0000229358,0.000005944122,0.0000027084614,0.0000028683464,0.98668206,0.0028825044,0.00767711,0.00032013355,0.00023264615],"about_ca_topic_score_codex":0.000006423237,"about_ca_topic_score_gemma":0.0000041497638,"teacher_disagreement_score":0.49187553,"about_ca_system_score_codex":0.00006397469,"about_ca_system_score_gemma":0.00006454234,"threshold_uncertainty_score":0.71075034},"labels":[],"label_agreement":null},{"id":"W2965182628","doi":"10.1109/cvpr.2019.00902","title":"UPSNet: A Unified Panoptic Segmentation Network","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":422,"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":"Segmentation; Computer science; Artificial intelligence; Inference; Convolution (computer science); Representation (politics); Task (project management); Computer vision; Class (philosophy); Pixel; Panopticon; Image segmentation; Code (set theory); Pattern recognition (psychology); Artificial neural network; Programming language","score_opus":0.026281350477724487,"score_gpt":0.2801040390594792,"score_spread":0.2538226885817547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965182628","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":[0.0010334385,0.00045742103,0.98860097,0.00074630434,0.0013721009,0.00100082,0.0000022689562,0.00048857986,0.006298119],"genre_scores_gemma":[0.20251305,0.00048484636,0.7900978,0.0012308849,0.00083439,0.00045644213,0.0001349239,0.000049921913,0.0041977707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816376,0.00006718429,0.0003186099,0.00082768546,0.00026275587,0.0003599876],"domain_scores_gemma":[0.9977758,0.00012280725,0.00024604725,0.0016835515,0.00008378336,0.000087999564],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013404043,0.00026146023,0.00023835091,0.00006285414,0.000096392396,0.00018482907,0.0014436909,0.00014598013,0.000020238418],"category_scores_gemma":[0.000007798254,0.00025560713,0.000093171264,0.00038635812,0.000025016458,0.0001528839,0.0019353875,0.00046871603,0.00041838505],"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":[0.0000039745164,0.000033301945,0.00023513736,0.000047045498,0.000026269207,0.0000015759899,0.00008025038,0.79973793,0.00015217626,0.13603255,0.012816964,0.05083283],"study_design_scores_gemma":[0.00037684204,0.00005962483,0.00202039,0.00012661342,0.00003121598,0.000008736715,0.000009289623,0.7536798,0.0004880211,0.21560247,0.026651362,0.0009456317],"about_ca_topic_score_codex":0.0000062205404,"about_ca_topic_score_gemma":0.000007797677,"teacher_disagreement_score":0.20147961,"about_ca_system_score_codex":0.00008338453,"about_ca_system_score_gemma":0.00012767226,"threshold_uncertainty_score":0.9999896},"labels":[],"label_agreement":null},{"id":"W2965588423","doi":"10.1109/aicas.2019.8771540","title":"Improved Hybrid Memory Cube for Weight-Sharing Deep Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Convolutional neural network; Bottleneck; Artificial neural network; Deep learning; Memory bandwidth; Memory architecture; Computer architecture; Bandwidth (computing); Parallel computing; Artificial intelligence; Embedded system; Computer network","score_opus":0.01272971498588828,"score_gpt":0.24279712800683922,"score_spread":0.23006741302095093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965588423","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":[0.009287505,0.00014690739,0.9865598,0.00083717715,0.000717227,0.0008008639,0.0000023617904,0.00036094314,0.0012871841],"genre_scores_gemma":[0.8954938,0.000008341731,0.099914774,0.0014517128,0.00037115908,0.00022270204,0.000022907916,0.000022802767,0.0024918038],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984938,0.000013444604,0.0002564666,0.0006334929,0.00013932104,0.0004634944],"domain_scores_gemma":[0.99870336,0.00023109822,0.00010160498,0.0007390664,0.00010488159,0.00011996021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011404718,0.00016964256,0.00016926615,0.00004726586,0.00017424373,0.000084940126,0.0010234739,0.000043879598,0.000058441146],"category_scores_gemma":[0.000011127794,0.00015642575,0.000104859915,0.00022759836,0.000034428955,0.000588857,0.00040033177,0.0001581943,0.000058075202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005973175,0.00013413576,0.0015539331,0.000035186,0.000061885716,0.0000042014626,0.000069148875,0.31538966,0.00618524,0.56690276,0.0056255115,0.10397862],"study_design_scores_gemma":[0.0004639688,0.00004570195,0.0002651557,0.0000029564715,0.0000040165905,0.0000190461,0.0000034598315,0.98849905,0.0009352144,0.00705095,0.0025010502,0.00020941022],"about_ca_topic_score_codex":0.0000050296076,"about_ca_topic_score_gemma":0.000006228189,"teacher_disagreement_score":0.8866451,"about_ca_system_score_codex":0.000053742566,"about_ca_system_score_gemma":0.000020813852,"threshold_uncertainty_score":0.6378857},"labels":[],"label_agreement":null},{"id":"W2965704410","doi":"10.1080/24699322.2019.1649068","title":"A visible human body slice segmentation method framework based on OneCut and adjacent image geometric features","year":2019,"lang":"en","type":"article","venue":"Computer Assisted Surgery","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer vision; Artificial intelligence; Region of interest; Computer science; Segmentation; Image (mathematics); Set (abstract data type); Image segmentation; Scale (ratio); Geography","score_opus":0.019710984896268765,"score_gpt":0.3041286005855688,"score_spread":0.2844176156893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965704410","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":[0.02469243,0.00012077378,0.97266936,0.00080812443,0.000500343,0.00048175335,0.0000047574026,0.00036128258,0.00036114542],"genre_scores_gemma":[0.3412947,0.000015835072,0.6558775,0.0024117504,0.00016389342,0.0000700796,0.00003877302,0.000031091527,0.00009641772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975383,0.00027564343,0.0003887693,0.0009076766,0.00045394406,0.00043565608],"domain_scores_gemma":[0.9954895,0.00288674,0.00025670583,0.0010683802,0.00011686724,0.00018181892],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004714653,0.00029768673,0.000410652,0.0006710183,0.00026016321,0.0003464833,0.000519827,0.0001317119,0.00002538437],"category_scores_gemma":[0.0000442768,0.0002900194,0.00015000587,0.002013137,0.00003685321,0.00045159183,0.0002857571,0.00038507686,0.00007794385],"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":[0.00011177754,0.0014051287,0.023432013,0.0003525714,0.00020274374,0.00013217492,0.00035042342,0.023757728,0.025739813,0.024015183,0.048722286,0.85177815],"study_design_scores_gemma":[0.0009023897,0.00030388977,0.42941144,0.0003892315,0.000040799863,0.00007206709,0.000015472313,0.55097216,0.009136367,0.0029236546,0.00464275,0.0011897818],"about_ca_topic_score_codex":0.000008803396,"about_ca_topic_score_gemma":8.511689e-7,"teacher_disagreement_score":0.8505884,"about_ca_system_score_codex":0.00010383511,"about_ca_system_score_gemma":0.000050016337,"threshold_uncertainty_score":0.9999552},"labels":[],"label_agreement":null},{"id":"W2966367600","doi":"10.1109/pria.2019.8785988","title":"A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Object detection; Intelligent transportation system; Process (computing); Field (mathematics); Computer vision; Residual; Video tracking; Pattern recognition (psychology); Object (grammar); Algorithm","score_opus":0.02116575266329006,"score_gpt":0.22186749522919566,"score_spread":0.2007017425659056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966367600","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":[0.0025879298,0.0000036045988,0.9849618,0.0004200078,0.000121139,0.00031573852,5.26978e-7,0.0003085836,0.011280672],"genre_scores_gemma":[0.48864666,4.120003e-7,0.50873977,0.0014227615,0.000048969512,0.00005545541,0.0000018934342,0.000009712963,0.0010743699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902767,0.000026313688,0.0001194471,0.0004267252,0.00019359788,0.00020624904],"domain_scores_gemma":[0.9991142,0.000062456435,0.000043019132,0.00067117135,0.000047157228,0.00006199356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074249096,0.00010498175,0.00008582958,0.00006238925,0.000097252574,0.00008796546,0.00041198102,0.000043597756,0.000029459256],"category_scores_gemma":[0.0000041432622,0.000089678375,0.000041685045,0.00044626454,0.000016999908,0.00027562832,0.000081042854,0.00012646549,0.00047667915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008218382,0.00019076317,0.00010456007,0.000009773936,0.00000538521,0.0000012513823,0.000044105993,0.3984539,0.0030355167,0.00866294,0.00027896452,0.5892046],"study_design_scores_gemma":[0.0002426547,0.00007432144,0.0003715271,0.0000032272267,0.0000011180229,0.0000023967561,0.000006115056,0.9912562,0.0057796133,0.0003724686,0.0017723257,0.000118038915],"about_ca_topic_score_codex":0.0000042190413,"about_ca_topic_score_gemma":0.0000013132038,"teacher_disagreement_score":0.5928023,"about_ca_system_score_codex":0.000038568636,"about_ca_system_score_gemma":0.0000156749,"threshold_uncertainty_score":0.6126905},"labels":[],"label_agreement":null},{"id":"W2966932193","doi":"10.1109/icra.2019.8794341","title":"IceVisionSet: lossless video dataset collected on Russian winter roads with traffic sign annotations","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Computer science; Traffic sign; Robustness (evolution); License; Annotation; Artificial intelligence; Python (programming language); Computer vision; Sign (mathematics)","score_opus":0.012161013311657878,"score_gpt":0.25810233917430686,"score_spread":0.245941325862649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966932193","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":[0.10029681,0.000029027575,0.87419003,0.0104567055,0.00026373085,0.001904697,0.00045176025,0.00087103894,0.01153618],"genre_scores_gemma":[0.95308506,0.000005108431,0.042063553,0.0022007294,0.000040577437,0.00009465363,0.00042901124,0.000021975095,0.0020593558],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850035,0.0000596394,0.00022901877,0.00062248774,0.0002806395,0.0003078385],"domain_scores_gemma":[0.99816835,0.0003086167,0.00009433512,0.0012391113,0.000054232296,0.00013534212],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006881895,0.0001919887,0.00017292373,0.00013239031,0.00020560274,0.00014681956,0.0008864949,0.000047469905,0.0001868826],"category_scores_gemma":[0.000010495,0.00014234705,0.000032846183,0.0012593181,0.000051780116,0.00051568984,0.00012164349,0.00017898146,0.0011694769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028652343,0.0010558557,0.00060195534,0.000031033782,0.00012848327,0.000057370926,0.00095153257,0.28368253,0.002128044,0.24673143,0.40709266,0.05725259],"study_design_scores_gemma":[0.0041365563,0.0020419464,0.024439301,0.00021876008,0.000045497905,0.00013382417,0.00015758298,0.6626915,0.0028610516,0.0026292496,0.2987445,0.0019002935],"about_ca_topic_score_codex":0.0000044743115,"about_ca_topic_score_gemma":0.00006012421,"teacher_disagreement_score":0.8527882,"about_ca_system_score_codex":0.000046385583,"about_ca_system_score_gemma":0.000071908544,"threshold_uncertainty_score":0.9996082},"labels":[],"label_agreement":null},{"id":"W2967057628","doi":"10.1049/el.2019.1186","title":"Teacher−student framework‐based knowledge transfer with incremental block‐wise retraining","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Advanced Neural Network Applications","field":"Computer 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":"National Research Council of Science and Technology","keywords":"Retraining; Computer science; Block (permutation group theory); Artificial intelligence; Transfer of learning; Residual; Machine learning; Artificial neural network; Residual neural network; Algorithm; Mathematics","score_opus":0.009401468651340955,"score_gpt":0.2545506300387174,"score_spread":0.24514916138737647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967057628","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":[0.41716534,0.00026901002,0.5754668,0.0055217063,0.00008778458,0.0004446549,7.6536037e-7,0.0002569378,0.00078700925],"genre_scores_gemma":[0.97105205,0.0000111467025,0.023940697,0.0046891575,0.00010140863,0.00008022164,0.000005608733,0.000035163386,0.00008453162],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808705,0.00006827876,0.00021155283,0.00061349577,0.00033536094,0.00068428967],"domain_scores_gemma":[0.9988918,0.00015385803,0.000049680686,0.00076768035,0.000031304335,0.00010569517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001797201,0.00024200663,0.00019527189,0.00009026893,0.0001428567,0.000089500914,0.0009668802,0.00007250233,0.000027460112],"category_scores_gemma":[0.0000050793,0.00022339805,0.00007203152,0.0006148358,0.00005076541,0.00026092477,0.000079626305,0.00069350406,0.00011334484],"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":[0.00033313612,0.0019458805,0.021340372,0.00012571183,0.00057233014,0.00009877894,0.012452238,0.049101382,0.6403322,0.2065027,0.01317292,0.054022316],"study_design_scores_gemma":[0.013023728,0.0046091033,0.013708906,0.0008845064,0.00025374675,0.00025347754,0.00039394572,0.59293586,0.21264392,0.0047536404,0.14978229,0.0067568915],"about_ca_topic_score_codex":0.0000018694245,"about_ca_topic_score_gemma":0.000014883392,"teacher_disagreement_score":0.5538867,"about_ca_system_score_codex":0.00028148724,"about_ca_system_score_gemma":0.000107088475,"threshold_uncertainty_score":0.9109908},"labels":[],"label_agreement":null},{"id":"W2967090870","doi":"10.1109/itec.2019.8790493","title":"Leveraging Thermal Imaging for Autonomous Driving","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Pedestrian detection; Pedestrian; Overcast; Artificial intelligence; Computer vision; Sensor fusion; Mode (computer interface); Object detection; Real-time computing; Human–computer interaction; Engineering; Pattern recognition (psychology); Transport engineering","score_opus":0.01303052152783493,"score_gpt":0.2531508559712156,"score_spread":0.24012033444338068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967090870","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":[0.03816798,0.00002327435,0.95128435,0.001746761,0.00017745644,0.00030225885,1.5024078e-7,0.00034157286,0.00795621],"genre_scores_gemma":[0.78928673,6.1328984e-7,0.20866752,0.00067638984,0.000049685408,0.000039186816,7.0486556e-7,0.000008391427,0.0012707883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992741,0.000007512128,0.0001091312,0.00029755762,0.00007040163,0.00024131531],"domain_scores_gemma":[0.9993232,0.00013915348,0.00004186076,0.00042540568,0.00003006983,0.00004031496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077563585,0.00007690226,0.00007374337,0.000032850643,0.00009897773,0.000079612866,0.0005167014,0.000011185484,0.000023177703],"category_scores_gemma":[0.000005469798,0.0000706263,0.00004127454,0.00013385335,0.000009358951,0.00045203578,0.00016989763,0.000053815987,0.000117940566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024906885,0.00003949863,0.016101781,0.000013832264,0.000012401545,0.0000021991768,0.0006333619,0.025676833,0.05647196,0.41668478,0.0014620849,0.4828988],"study_design_scores_gemma":[0.00021090556,0.000009451044,0.0033211412,0.000006287164,0.000001456583,0.000007326566,0.00001762681,0.96829605,0.0043789144,0.007214018,0.016368851,0.00016795594],"about_ca_topic_score_codex":0.0000021760745,"about_ca_topic_score_gemma":5.490215e-7,"teacher_disagreement_score":0.9426192,"about_ca_system_score_codex":0.00002948946,"about_ca_system_score_gemma":0.00001954687,"threshold_uncertainty_score":0.2880057},"labels":[],"label_agreement":null},{"id":"W2967693052","doi":"10.48550/arxiv.1908.05770","title":"Discretely-constrained deep network for weakly supervised segmentation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Regularization (linguistics); Segmentation; Prior probability; Computer science; Artificial intelligence; Convolutional neural network; Benchmark (surveying); Mathematical optimization; Pattern recognition (psychology); Algorithm; Mathematics; Bayesian probability","score_opus":0.05774311155039706,"score_gpt":0.21034444965224763,"score_spread":0.15260133810185056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967693052","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":[0.021749211,0.00006500819,0.97434175,0.00027932742,0.00067946076,0.0015880715,0.000039581035,0.00035088323,0.00090667856],"genre_scores_gemma":[0.90591806,0.00010352836,0.09226818,0.00024315096,0.00023284742,0.000022408025,0.00018384402,0.000032184915,0.0009958075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775237,0.0000872798,0.00025927502,0.0013007971,0.000088125176,0.00051215396],"domain_scores_gemma":[0.997663,0.00033532258,0.00030465337,0.0013774249,0.0001579383,0.00016165612],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015330879,0.0003539609,0.00035056385,0.000111497444,0.00024709248,0.000118318734,0.0017491722,0.00024218025,0.000018500352],"category_scores_gemma":[0.000017752422,0.0004152441,0.00026589114,0.00061178906,0.00009632124,0.0004634864,0.0010844286,0.00036840417,0.000076891425],"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":[0.000026241383,0.000027770491,0.00053110044,0.000044237502,0.000051059484,0.000008866401,0.00006355307,0.79335624,0.000102345555,0.20294665,0.00043671337,0.002405211],"study_design_scores_gemma":[0.00055952853,0.00005135186,0.00038911044,0.00004147053,0.000058536556,0.0000020890436,0.000034582794,0.8833405,0.000121925965,0.11421005,0.0007503238,0.0004405858],"about_ca_topic_score_codex":0.000013179691,"about_ca_topic_score_gemma":0.00003188221,"teacher_disagreement_score":0.88416886,"about_ca_system_score_codex":0.00019453264,"about_ca_system_score_gemma":0.00013459701,"threshold_uncertainty_score":0.99982995},"labels":[],"label_agreement":null},{"id":"W2968249981","doi":"10.7717/peerj-cs.210","title":"Pay attention and you won’t lose it: a deep learning approach to sequence imputation","year":2019,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Waterloo","funders":"","keywords":"Bottleneck; Computer science; Inference; Artificial intelligence; Machine learning; Imputation (statistics); Deep learning; Sequence (biology); Data mining; Missing data","score_opus":0.020976892539503283,"score_gpt":0.2766219643984715,"score_spread":0.2556450718589682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968249981","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":[0.09211152,0.000025248573,0.9045819,0.0017188254,0.00032216206,0.00046923116,4.9353537e-7,0.00023860204,0.00053203444],"genre_scores_gemma":[0.6043111,0.0000062222725,0.3947909,0.0005910893,0.00006288758,0.000030065095,0.0000024578262,0.0000073479628,0.00019791225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734086,0.000057181816,0.00025738808,0.00119676,0.00062901695,0.0005187918],"domain_scores_gemma":[0.998613,0.00008894137,0.00013194083,0.0006762919,0.00022792093,0.0002618899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052309263,0.00019104999,0.00017430434,0.00023694265,0.00042492527,0.0005190249,0.0014203476,0.000040133997,0.0000021021472],"category_scores_gemma":[0.00003338245,0.00016261447,0.000037784346,0.0019764632,0.00017433701,0.0017248096,0.00080502033,0.00023862024,0.00018183861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051815596,0.0000828774,0.004672309,0.00002892852,0.0000052933483,0.0000035500623,0.0021703476,0.26877174,0.016768815,0.03467415,0.00013376052,0.67268306],"study_design_scores_gemma":[0.00016003643,0.00010801579,0.017491655,0.000017762777,0.0000023634302,0.000059214504,0.0000207576,0.9787702,0.00024368452,0.0016339042,0.0012420139,0.0002504237],"about_ca_topic_score_codex":0.000010235111,"about_ca_topic_score_gemma":0.0000014551948,"teacher_disagreement_score":0.7099984,"about_ca_system_score_codex":0.00010569909,"about_ca_system_score_gemma":0.000070893875,"threshold_uncertainty_score":0.6631226},"labels":[],"label_agreement":null},{"id":"W2970395278","doi":"","title":"Fast AutoAugment","year":2019,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Generalization; Code (set theory); Matching (statistics); Image (mathematics); Artificial intelligence; Source code; Pattern recognition (psychology); Machine learning; Set (abstract data type)","score_opus":0.011823379063630374,"score_gpt":0.24414204947072182,"score_spread":0.23231867040709145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970395278","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":[0.016522836,0.00019650378,0.9679417,0.0008696473,0.0010830894,0.00078509795,0.0000027021415,0.00088790973,0.011710513],"genre_scores_gemma":[0.9936424,0.0000027188444,0.004922453,0.00061755755,0.000061113875,0.00008859298,0.000012568373,0.0000061040632,0.0006464952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988397,0.000021777774,0.0004119302,0.00016693289,0.00032507643,0.00023458716],"domain_scores_gemma":[0.99906564,0.00002534386,0.00029197038,0.00040150728,0.00014577055,0.00006975153],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001170087,0.00012476536,0.0001292392,0.00009879017,0.00014962637,0.00055330136,0.00056697015,0.00004541957,0.000005649905],"category_scores_gemma":[0.0000075508447,0.00010865804,0.000030281146,0.0004910835,0.000014761988,0.005532491,0.00011047858,0.00011913509,0.00083028176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000078931125,0.000033056,0.0006640397,0.00045684844,0.000009377759,0.000001128467,0.001941817,0.31961846,0.0012132314,0.11432792,0.0033964452,0.55832976],"study_design_scores_gemma":[0.00019460601,0.000027229786,0.00029272182,0.000041758114,0.0000013189444,0.000036273897,0.00009364417,0.9596623,0.00020401993,0.00016216621,0.03913351,0.00015044678],"about_ca_topic_score_codex":0.0000048939714,"about_ca_topic_score_gemma":1.881696e-7,"teacher_disagreement_score":0.97711957,"about_ca_system_score_codex":0.00006293745,"about_ca_system_score_gemma":0.00004002872,"threshold_uncertainty_score":0.99994767},"labels":[],"label_agreement":null},{"id":"W2970610469","doi":"10.1109/bigdatacongress.2019.00034","title":"DLBench: An Experimental Evaluation of Deep Learning Frameworks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Canadian Institute for Advanced Research","keywords":"Deep learning; Computer science; Benchmarking; Popularity; Artificial intelligence; Machine learning; Data science","score_opus":0.03080481787692442,"score_gpt":0.33411985024692736,"score_spread":0.30331503237000296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970610469","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":[0.42149162,0.00009146007,0.5717845,0.00006474282,0.000083209205,0.00021080808,4.3628003e-8,0.0000837108,0.006189897],"genre_scores_gemma":[0.92263496,0.0000013673177,0.07713279,0.00009059355,0.000021885355,0.000025286146,0.0000023994294,0.0000046158493,0.00008609063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911183,0.00007338036,0.0001275384,0.00023958563,0.00033269654,0.00011495621],"domain_scores_gemma":[0.9993093,0.000058004898,0.0000727537,0.00041763854,0.00010026493,0.000042053103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002313052,0.00006049103,0.00007215801,0.000034021126,0.000044684046,0.000022855565,0.00036514126,0.000060158174,0.00027702926],"category_scores_gemma":[0.000015092388,0.000056791512,0.00002249951,0.00023012362,0.000015375865,0.000492378,0.00010223328,0.0001627356,0.00008252611],"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":[0.0000047821886,0.00019742106,0.0027265514,0.0000035675293,0.000008049752,2.3066833e-7,0.0015380954,0.34933034,0.0753083,0.2717487,0.000035236473,0.29909876],"study_design_scores_gemma":[0.00015066228,0.00008823575,0.0012967411,0.0000031797201,0.0000022081529,0.000001489446,0.00012892521,0.96722454,0.025959799,0.0049150526,0.00015342589,0.00007573024],"about_ca_topic_score_codex":0.000002956368,"about_ca_topic_score_gemma":0.0000018062157,"teacher_disagreement_score":0.61789423,"about_ca_system_score_codex":0.000030242574,"about_ca_system_score_gemma":0.000014089677,"threshold_uncertainty_score":0.30332762},"labels":[],"label_agreement":null},{"id":"W2970803838","doi":"","title":"Lookahead Optimizer: k steps forward, 1 step back","year":2019,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Toronto","funders":"","keywords":"Treebank; Computer science; Stochastic gradient descent; Hyperparameter; Artificial neural network; Machine translation; Computation; Artificial intelligence; Gradient descent; Algorithm; Deep neural networks; Stability (learning theory); Mathematical optimization; Machine learning; Mathematics","score_opus":0.013592823231687565,"score_gpt":0.24653417746470588,"score_spread":0.23294135423301832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970803838","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":[0.0056097186,0.00028407123,0.975105,0.0007283797,0.001075949,0.0012055001,0.0000066074226,0.0006379221,0.015346889],"genre_scores_gemma":[0.9554285,0.000014939131,0.040077507,0.0012667549,0.00015552131,0.00017343198,0.000060364157,0.000023478706,0.0027995035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807495,0.000049822956,0.00071236625,0.0002892051,0.00047087407,0.00040276046],"domain_scores_gemma":[0.9983163,0.000071313734,0.00051585917,0.0006758397,0.00028982168,0.00013084813],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022016368,0.00022657787,0.00026280992,0.00015697583,0.00021158783,0.0008607017,0.0009109073,0.00009758034,0.000017485603],"category_scores_gemma":[0.000017415345,0.00020136316,0.00006575987,0.00072211714,0.000031925072,0.008142939,0.00018098763,0.00021568128,0.0020072013],"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":[0.000050780738,0.0000790811,0.0011895862,0.0015085416,0.00003368526,0.0000026783591,0.0023148945,0.52543354,0.00073846686,0.05159757,0.021075126,0.39597604],"study_design_scores_gemma":[0.0004528568,0.00004971702,0.00008723524,0.000082704624,0.0000033571303,0.00005825325,0.00010240252,0.9085711,0.00014130247,0.00010265005,0.090097636,0.00025078055],"about_ca_topic_score_codex":0.000010651171,"about_ca_topic_score_gemma":6.553132e-7,"teacher_disagreement_score":0.9498188,"about_ca_system_score_codex":0.00008731932,"about_ca_system_score_gemma":0.00007151863,"threshold_uncertainty_score":0.9987699},"labels":[],"label_agreement":null},{"id":"W2971946864","doi":"10.1109/tc.2019.2936192","title":"New Flexible Multiple-Precision Multiply-Accumulate Unit for Deep Neural Network Training and Inference","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Floating-point unit; Computer science; Floating point; IEEE floating point; Binary number; Unit (ring theory); Artificial neural network; 16-bit; Single-precision floating-point format; Algorithm; Computer hardware; Overhead (engineering); Exponent; Arithmetic; Control unit; Parallel computing; Mathematics; Artificial intelligence","score_opus":0.053461033632081566,"score_gpt":0.3028072657203022,"score_spread":0.24934623208822063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971946864","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":[0.013204516,0.000047259287,0.9832313,0.00044062556,0.0016078772,0.00095811725,0.0000062849153,0.00046784736,0.000036169546],"genre_scores_gemma":[0.6293538,0.0000186615,0.36990714,0.00041345097,0.000090876434,0.000049203485,0.0000028621535,0.000022715285,0.00014128913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810904,0.00005686476,0.00034767558,0.00074310176,0.0002130585,0.0005302517],"domain_scores_gemma":[0.9970868,0.0017705682,0.00012117552,0.0006867619,0.0000744654,0.00026018784],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013121478,0.00029396408,0.00030060392,0.00014561146,0.00036606492,0.00017996073,0.0007314142,0.00009937916,0.000010450975],"category_scores_gemma":[0.0000067757364,0.00030201426,0.00012340427,0.0006328118,0.00004238851,0.0006853032,0.000017483928,0.0002988908,0.00003842933],"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":[0.000027264541,0.00001829259,0.00003823456,0.0000048030593,0.000012736022,6.278415e-7,0.00022635405,0.6157026,0.00026968005,0.00046883733,0.000053705222,0.38317683],"study_design_scores_gemma":[0.0013049415,0.00023687273,0.0003343974,0.000052072075,0.0000113707565,0.000013790316,0.000016178152,0.99310404,0.0009420931,0.0014547904,0.002201666,0.0003277777],"about_ca_topic_score_codex":0.000019987763,"about_ca_topic_score_gemma":0.00003335709,"teacher_disagreement_score":0.6161493,"about_ca_system_score_codex":0.00004304483,"about_ca_system_score_gemma":0.000051311876,"threshold_uncertainty_score":0.9999432},"labels":[],"label_agreement":null},{"id":"W2972118985","doi":"10.48550/arxiv.1909.03402","title":"Squeeze-and-Attention Networks for Semantic Segmentation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China","keywords":"Pascal (unit); Segmentation; Computer science; Artificial intelligence; Pixel; Convolution (computer science); Context (archaeology); Channel (broadcasting); Pattern recognition (psychology); Machine learning; Artificial neural network; Geography; Telecommunications","score_opus":0.053275137759655145,"score_gpt":0.21177274530320275,"score_spread":0.1584976075435476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972118985","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":[0.05170262,0.00006433616,0.94617885,0.00018153118,0.00050124095,0.0010158115,0.00000716816,0.00019784138,0.00015062495],"genre_scores_gemma":[0.9872789,0.0002877734,0.010920072,0.00012248354,0.0001042329,0.000011448805,0.00006440409,0.0000183784,0.0011923306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985064,0.000053356587,0.00016562575,0.00095614896,0.000052420008,0.00026602647],"domain_scores_gemma":[0.9985999,0.00015783557,0.0002591609,0.0007876956,0.0001095742,0.00008581759],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011895276,0.00021959173,0.00021492275,0.000120085446,0.00016502831,0.00009487944,0.00072535366,0.00018733458,0.000002915219],"category_scores_gemma":[0.0000081837325,0.00026545877,0.00012634718,0.00031579024,0.00004879981,0.00036846564,0.00089378376,0.0002756351,0.000024897012],"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":[0.000013264824,0.000027058102,0.001157028,0.00006214188,0.000032654014,0.0000049080213,0.000027440354,0.85958076,0.00008910432,0.13645388,0.00028613544,0.0022656508],"study_design_scores_gemma":[0.00037868888,0.000033056862,0.0012604928,0.000044056647,0.0000549531,0.0000023425594,0.000013079614,0.95344573,0.00003888938,0.044190288,0.0002726989,0.00026573136],"about_ca_topic_score_codex":0.000011999624,"about_ca_topic_score_gemma":0.000010844594,"teacher_disagreement_score":0.93557626,"about_ca_system_score_codex":0.00011671429,"about_ca_system_score_gemma":0.000040569084,"threshold_uncertainty_score":0.99997973},"labels":[],"label_agreement":null},{"id":"W2972528742","doi":"10.1007/978-3-030-30484-3_21","title":"Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Convolutional neural network; Pruning; Artificial intelligence; Benchmark (surveying); Modularity (biology); Bridge (graph theory); Deep learning; Network structure; Pattern recognition (psychology); Machine learning","score_opus":0.01566753841014424,"score_gpt":0.249089511902544,"score_spread":0.23342197349239976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972528742","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":[0.0020332665,0.00090049265,0.9936636,0.00017863078,0.001971914,0.00041408185,0.0000044832823,0.00012894181,0.00070458447],"genre_scores_gemma":[0.58050877,0.000044923796,0.41734752,0.0006296738,0.0009720079,0.0000075311614,0.0000063783696,0.000041321087,0.0004418595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957488,0.00006724872,0.0007560421,0.0014977453,0.0011110108,0.0008191132],"domain_scores_gemma":[0.99669826,0.000837914,0.0007375347,0.0012057711,0.0003289266,0.00019160815],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004357424,0.00057693414,0.0006455159,0.0004257622,0.00027286858,0.00023231294,0.003693217,0.00027602637,0.000046255012],"category_scores_gemma":[0.00007435801,0.0005473406,0.0002022622,0.00055441074,0.0009321895,0.0005640351,0.0019296372,0.0014819944,0.000035814053],"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":[0.000013401279,0.000008892975,0.00049962034,0.000016011847,0.000009684136,0.000030591396,0.00011270945,0.75804496,0.00031332858,0.041546654,0.000009164604,0.19939496],"study_design_scores_gemma":[0.00024190202,0.00017318127,0.0012853176,0.00019238403,0.000008891961,0.0002266927,1.5370533e-7,0.8097869,0.00049068,0.18674615,0.00036637654,0.00048135014],"about_ca_topic_score_codex":0.000016418187,"about_ca_topic_score_gemma":0.000042544405,"teacher_disagreement_score":0.57847553,"about_ca_system_score_codex":0.00023124476,"about_ca_system_score_gemma":0.00034347887,"threshold_uncertainty_score":0.9996978},"labels":[],"label_agreement":null},{"id":"W2972781556","doi":"10.1109/icarm.2019.8833973","title":"Semantic Segmentation via Structured Refined Prediction and Dual Global Priors","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Prior probability; Convolution (computer science); Context (archaeology); Residual; Transformation (genetics); Convolutional neural network; Boundary (topology); Scale (ratio); Set (abstract data type); Computer vision; Pattern recognition (psychology); Algorithm; Artificial neural network; Mathematics","score_opus":0.00670501018979337,"score_gpt":0.24236063568399135,"score_spread":0.235655625494198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972781556","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":[0.36356413,0.000016207721,0.6348282,0.00050034735,0.00016885274,0.00026862236,0.000002535685,0.00019148627,0.00045958697],"genre_scores_gemma":[0.92259645,0.0000068110244,0.07692457,0.00022884122,0.00003200395,0.000011233917,0.000008667497,0.0000034217098,0.00018798675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992927,0.000017404464,0.00012853852,0.0002930688,0.00014037793,0.00012791122],"domain_scores_gemma":[0.9995747,0.000021353026,0.000053686552,0.00027194747,0.000028216185,0.000050086048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003796003,0.000079537465,0.00006997471,0.000020546713,0.00006262867,0.00004735386,0.00013128255,0.000033304288,0.000015822503],"category_scores_gemma":[0.00000313726,0.00007014138,0.00001373827,0.00027240568,0.000016221718,0.00041518715,0.00010239783,0.000034144367,0.000041053147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049379298,0.000097260425,0.0988918,0.00006780942,0.000065968874,0.0000071323607,0.0006155105,0.010223757,0.15960765,0.30179587,0.0026127761,0.42596507],"study_design_scores_gemma":[0.0012773966,0.00023347678,0.33544937,0.00001395,0.000017873137,0.000108789805,0.00006220139,0.6074577,0.00853639,0.045185477,0.0012900336,0.0003673522],"about_ca_topic_score_codex":0.000007267749,"about_ca_topic_score_gemma":0.000021703921,"teacher_disagreement_score":0.59723395,"about_ca_system_score_codex":0.000041969593,"about_ca_system_score_gemma":0.00001116241,"threshold_uncertainty_score":0.28602824},"labels":[],"label_agreement":null},{"id":"W2973070166","doi":"10.48550/arxiv.1909.04163","title":"MLOD: A multi-view 3D object detection based on robust feature fusion method","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Header; Minimum bounding box; Computer science; Artificial intelligence; Feature (linguistics); Object detection; Computer vision; Detector; Benchmark (surveying); Point cloud; Object (grammar); Feature extraction; Feature detection (computer vision); Pattern recognition (psychology); Image (mathematics); Image processing; Geography","score_opus":0.07753230667606383,"score_gpt":0.2282689523913911,"score_spread":0.1507366457153273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973070166","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":[0.0023849795,0.000064356325,0.99431956,0.00022354965,0.00070217106,0.0009847287,0.0000142795225,0.0005078324,0.0007985398],"genre_scores_gemma":[0.710948,0.00019334481,0.2860124,0.00049781706,0.000110909226,0.00001023379,0.000031791384,0.000043862958,0.0021516264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970788,0.00035870736,0.00019030865,0.0018087795,0.00015078457,0.00041267023],"domain_scores_gemma":[0.9966841,0.0003428635,0.00037624486,0.0022611467,0.00016993252,0.00016571976],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028015167,0.00046126283,0.00041820595,0.00035176647,0.00028369794,0.00009641813,0.0017712706,0.00047157618,0.000017183704],"category_scores_gemma":[0.000040570427,0.0005011387,0.00027488734,0.0012420686,0.00005212473,0.00029850547,0.0012789691,0.0012195857,0.00020345928],"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":[0.000032235293,0.00009471808,0.000069651294,0.00005840774,0.000018624341,0.00003877898,0.000027898106,0.98376596,0.000264297,0.0026597765,0.00011687398,0.012852755],"study_design_scores_gemma":[0.00057576236,0.00008088491,0.00049869245,0.00015530258,0.000052770607,0.0000045793417,0.0000071748827,0.99255687,0.00074505253,0.0017309485,0.0030899625,0.0005019789],"about_ca_topic_score_codex":0.0000424036,"about_ca_topic_score_gemma":0.00007418545,"teacher_disagreement_score":0.708563,"about_ca_system_score_codex":0.00040127235,"about_ca_system_score_gemma":0.0001516667,"threshold_uncertainty_score":0.999744},"labels":[],"label_agreement":null},{"id":"W2973163720","doi":"10.48550/arxiv.1907.10134","title":"BPPSA: Scaling Back-propagation by Parallel Scan Algorithm","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Speedup; Scalability; Computer science; Recurrent neural network; Algorithm; Context (archaeology); Massively parallel; Parallel computing; Sequence (biology); Scaling; Dependency (UML); Artificial neural network; Computation; Backpropagation; Scale (ratio); Parallel algorithm; Artificial intelligence; Mathematics","score_opus":0.049622968663025076,"score_gpt":0.19655332597304578,"score_spread":0.14693035731002071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973163720","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":[0.008040557,0.0001189349,0.98821425,0.0002527412,0.00048479263,0.0006284233,0.00003052949,0.000325269,0.0019044753],"genre_scores_gemma":[0.91030216,0.00044226556,0.08287107,0.00026251897,0.00015767643,0.0000059490494,0.00014393563,0.000043938526,0.0057705073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975931,0.00010718141,0.00025094647,0.0014748551,0.0001323646,0.000441571],"domain_scores_gemma":[0.99748784,0.00011450866,0.00036568556,0.0016831594,0.00016138009,0.00018740066],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013987678,0.00036202458,0.0003316138,0.0001561037,0.00022507313,0.00013685768,0.0021891221,0.00028202048,0.000033203414],"category_scores_gemma":[0.00001026026,0.00043445377,0.00017227512,0.00074314215,0.00010952592,0.00055638846,0.0018967668,0.0006431841,0.00096808263],"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":[0.000012336694,0.000087676606,0.00026850382,0.00004555149,0.00005453796,0.000031327003,0.00006928322,0.92079014,0.00012571967,0.056256086,0.0058866213,0.016372202],"study_design_scores_gemma":[0.00033480598,0.00001702109,0.000143635,0.000053161257,0.00002612949,0.0000037997688,0.000013706477,0.9610727,0.00021372564,0.034168735,0.0034557516,0.000496863],"about_ca_topic_score_codex":0.000058062175,"about_ca_topic_score_gemma":0.0000064788433,"teacher_disagreement_score":0.9053432,"about_ca_system_score_codex":0.00029337688,"about_ca_system_score_gemma":0.00013304001,"threshold_uncertainty_score":0.9998107},"labels":[],"label_agreement":null},{"id":"W2973514284","doi":"10.1007/978-3-030-30048-7_24","title":"Training Binarized Neural Networks Using MIP and CP","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto; Vector Institute","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Natural Sciences and Engineering Research Council of Canada; Microsoft Research","keywords":"MNIST database; Computer science; Artificial neural network; Artificial intelligence; Constraint (computer-aided design); Set (abstract data type); Integer programming; Range (aeronautics); Machine learning; Class (philosophy); Deep neural networks; Gradient descent; Mathematical optimization; Algorithm; Mathematics","score_opus":0.0415761618633576,"score_gpt":0.27606272597426507,"score_spread":0.23448656411090746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973514284","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":[0.00038626615,0.0007786232,0.9957204,0.00047637144,0.0012955888,0.0005429191,0.0000019473748,0.0001818314,0.0006160567],"genre_scores_gemma":[0.31230447,0.0000699038,0.684731,0.0019950909,0.0006310897,0.000008720143,0.000004333409,0.000059337963,0.0001960562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99639535,0.00003477662,0.00047623448,0.0017312123,0.0005647938,0.0007976046],"domain_scores_gemma":[0.9973468,0.0006758522,0.0003298412,0.0013225453,0.00012541078,0.0001995068],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042937565,0.00054281356,0.0005835727,0.00048625786,0.0003618977,0.0004811015,0.0023056015,0.00029373958,0.000005625537],"category_scores_gemma":[0.00003527462,0.00051896385,0.00009840207,0.0007504923,0.000676083,0.00073115644,0.0015813117,0.000979173,0.000007538468],"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":[0.0000031419036,0.000005344924,0.000022622633,0.000009468289,0.0000044091444,0.000023023553,0.0002465964,0.50130564,0.00018571303,0.010680258,0.000002614795,0.48751113],"study_design_scores_gemma":[0.0002210688,0.00007135157,0.00004769374,0.00014440379,0.0000074669197,0.00016140647,1.4377885e-7,0.9549321,0.000050753388,0.043316662,0.0005153928,0.00053155853],"about_ca_topic_score_codex":0.0000071153845,"about_ca_topic_score_gemma":0.000015088337,"teacher_disagreement_score":0.48697957,"about_ca_system_score_codex":0.00017344877,"about_ca_system_score_gemma":0.00026645072,"threshold_uncertainty_score":0.9997262},"labels":[],"label_agreement":null},{"id":"W2973613048","doi":"10.1109/tc.2019.2941875","title":"Fast and Efficient Convolutional Accelerator for Edge Computing","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":56,"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":"Computer science; Convolutional neural network; Bandwidth (computing); Throughput; Edge device; Memory bandwidth; Computer engineering; Dataflow; Parallel computing; Efficient energy use; Computer hardware; Artificial intelligence; Wireless; Cloud computing","score_opus":0.016998031474346705,"score_gpt":0.2521783771278744,"score_spread":0.2351803456535277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973613048","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":[0.037816565,0.000021034135,0.9593658,0.0005142062,0.0013249379,0.0006353052,0.000015171055,0.00024104462,0.000065980974],"genre_scores_gemma":[0.8675968,0.000003660926,0.13165994,0.0005515096,0.00006316445,0.000038732454,0.0000020134642,0.000013597374,0.00007058033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877083,0.000026037447,0.00021246023,0.00052897225,0.0001678501,0.0002938681],"domain_scores_gemma":[0.99893343,0.00042454377,0.0000720225,0.00037496432,0.00007852589,0.00011653657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008588249,0.00016993906,0.0001675412,0.00011521198,0.00030874033,0.000072672075,0.00040817334,0.000051022194,0.0000055855903],"category_scores_gemma":[8.8090974e-7,0.00017630296,0.000084531144,0.00030739917,0.00005514387,0.00016622899,0.0000088268025,0.00015852989,0.000059128244],"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":[0.000016105769,0.00012051184,0.000016310352,0.000017227385,0.000025091214,6.833618e-7,0.00020565,0.8987205,0.0011568227,0.008105385,0.00030387522,0.09131182],"study_design_scores_gemma":[0.0006657093,0.00011300662,0.00025436943,0.000026829734,0.0000059506906,0.000017245362,0.000011659787,0.99501526,0.0024734163,0.0001657592,0.0010476039,0.00020316133],"about_ca_topic_score_codex":0.0000017926927,"about_ca_topic_score_gemma":0.0000010857855,"teacher_disagreement_score":0.8297802,"about_ca_system_score_codex":0.00006501337,"about_ca_system_score_gemma":0.000035741574,"threshold_uncertainty_score":0.7189426},"labels":[],"label_agreement":null},{"id":"W2977026452","doi":"10.48550/arxiv.1909.12673","title":"A Constructive Prediction of the Generalization Error Across Scales","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Generalization; Dependency (UML); Constructive; Computer science; Construct (python library); Scaling; Scale (ratio); Artificial neural network; Generalization error; Artificial intelligence; Range (aeronautics); Algorithm; Machine learning; Mathematics; Process (computing)","score_opus":0.06250519542778074,"score_gpt":0.21260704713507275,"score_spread":0.150101851707292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977026452","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":[0.3343404,0.000019641155,0.66414416,0.00006823239,0.00060184905,0.000382942,0.000075907185,0.00008529314,0.0002815723],"genre_scores_gemma":[0.9970557,0.00005224372,0.0023338269,0.000043914235,0.000057311812,0.0000022059592,0.000015012737,0.000009875078,0.0004299275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879223,0.000100132296,0.00017520902,0.0006555877,0.0000874282,0.00018939041],"domain_scores_gemma":[0.9980251,0.00006331437,0.00040263115,0.0012630144,0.00020007636,0.000045834273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007917008,0.00016859561,0.00018675368,0.000052151667,0.00016409853,0.00003067253,0.0014148369,0.00016711125,0.000002982269],"category_scores_gemma":[0.000016233704,0.00015667743,0.0001405287,0.000647361,0.0002359746,0.0002735508,0.0018109885,0.0002884035,0.000011996303],"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":[0.00000635452,0.0000247719,0.0117026195,0.000025853908,0.000027758653,0.0000015336825,0.00017057708,0.82583934,0.00020309146,0.16155721,0.00012587213,0.00031503258],"study_design_scores_gemma":[0.00027487596,0.000017418291,0.019997166,0.00006458324,0.000028140288,0.0000046323353,0.000058491525,0.9229091,0.0009774464,0.055204496,0.0002830559,0.00018058653],"about_ca_topic_score_codex":0.00002276078,"about_ca_topic_score_gemma":0.000026831229,"teacher_disagreement_score":0.66271526,"about_ca_system_score_codex":0.00012574634,"about_ca_system_score_gemma":0.00008892235,"threshold_uncertainty_score":0.638912},"labels":[],"label_agreement":null},{"id":"W2978754339","doi":"10.51130/graphicon-2020-2-2-2","title":"MIOpen: An Open Source Library For Deep Learning Primitives","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Deep learning; Implementation; Artificial intelligence; Artificial neural network; Bandwidth (computing); Lexicon; Computer architecture; Computer engineering; Software engineering; Telecommunications","score_opus":0.05133595986222986,"score_gpt":0.31486829753392215,"score_spread":0.26353233767169226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978754339","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":[0.08424775,0.00075520645,0.7936265,0.102262124,0.0042620073,0.007809903,0.0006901465,0.0013510219,0.004995374],"genre_scores_gemma":[0.9334064,0.0024841623,0.05899947,0.0035007158,0.00064874743,0.00033872016,0.00027457566,0.00009937586,0.0002478],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99686027,0.00006116569,0.00071173575,0.0014811499,0.00057349895,0.00031220805],"domain_scores_gemma":[0.9974623,0.00019788154,0.00106171,0.00045637653,0.00056842796,0.0002533188],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00033062705,0.00052139495,0.0005788318,0.00028263283,0.00042461234,0.0016002129,0.0062307925,0.00021970393,0.00001599226],"category_scores_gemma":[0.00006017988,0.0004210437,0.00025423084,0.00055520015,0.00023343327,0.0009791829,0.00940137,0.0011980429,0.000001276185],"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":[0.00012789483,0.0002408654,0.0013810947,0.00014034314,0.00014251705,8.804786e-7,0.00037769458,0.0005336913,0.0004322844,0.94518214,0.0019239266,0.049516637],"study_design_scores_gemma":[0.00057913666,0.0006322232,0.003005942,0.0004643439,0.000031696523,0.0000100819025,0.0000318973,0.7941829,0.00041893666,0.17711046,0.02305577,0.00047661603],"about_ca_topic_score_codex":0.000013257599,"about_ca_topic_score_gemma":0.000006403667,"teacher_disagreement_score":0.8491587,"about_ca_system_score_codex":0.000013924404,"about_ca_system_score_gemma":0.00008040353,"threshold_uncertainty_score":0.99982417},"labels":[],"label_agreement":null},{"id":"W2979374073","doi":"10.1109/embc.2019.8857835","title":"Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"","keywords":"Artificial intelligence; Segmentation; Voxel; Computer science; Atlas (anatomy); Computer vision; Image segmentation; Region growing; Probabilistic logic; Dice; Pattern recognition (psychology); Scale-space segmentation; Contrast (vision); Mathematics; Statistics","score_opus":0.027803602673602525,"score_gpt":0.26539116862820644,"score_spread":0.23758756595460392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979374073","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":[0.34065482,0.000048819144,0.6583919,0.000115805575,0.00003274297,0.00034369525,1.6571511e-7,0.000041632182,0.00037043798],"genre_scores_gemma":[0.7228434,0.000012571179,0.27691686,0.0000951928,0.000014799148,0.00001414996,5.657303e-7,0.0000044112858,0.000097998825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992833,0.000036305148,0.00012741417,0.0003154887,0.000093237715,0.00014424021],"domain_scores_gemma":[0.9995786,0.00011800251,0.00005610798,0.00018936384,0.000026312935,0.000031591833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006529652,0.000082036895,0.000085694504,0.000058701968,0.000052613173,0.00004401777,0.00014524545,0.000013454237,0.000004065569],"category_scores_gemma":[0.0000076119572,0.00007605404,0.000013316087,0.00025860113,0.000031004834,0.0009940533,0.000121718724,0.00007114259,0.00001619616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011236483,0.0002592731,0.024392014,0.00014848227,0.000030008203,0.00020012559,0.0022182413,0.12672555,0.12308697,0.28199926,0.00030853876,0.44051918],"study_design_scores_gemma":[0.000333846,0.000052672556,0.0050734077,0.0000417128,0.000004607313,0.00008927431,0.000066673914,0.9840344,0.0035057114,0.0065993974,0.000036157126,0.000162106],"about_ca_topic_score_codex":0.000052124527,"about_ca_topic_score_gemma":0.000012871082,"teacher_disagreement_score":0.85730886,"about_ca_system_score_codex":0.00007173845,"about_ca_system_score_gemma":0.000015318607,"threshold_uncertainty_score":0.3101394},"labels":[],"label_agreement":null},{"id":"W2979660316","doi":"10.1007/978-3-030-32692-0_73","title":"Tree-LSTM: Using LSTM to Encode Memory in Anatomical Tree Prediction from 3D Images","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Computer science; ENCODE; Tree (set theory); Artificial intelligence; Pattern recognition (psychology); Tree structure; Natural language processing; Binary tree; Algorithm; Mathematics","score_opus":0.019275893937165643,"score_gpt":0.26263458805075873,"score_spread":0.2433586941135931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979660316","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":[0.002016915,0.00028383633,0.99241924,0.00075338496,0.0016670444,0.00086713734,0.000032968368,0.0002164732,0.0017429844],"genre_scores_gemma":[0.11471348,0.00003990549,0.88210136,0.0019381376,0.0008725531,0.000026196365,0.000017833616,0.00006130226,0.00022925668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950699,0.00005967965,0.0007294085,0.002379567,0.0009567495,0.0008046808],"domain_scores_gemma":[0.996626,0.0007333394,0.0002911757,0.0019398347,0.00015311963,0.00025653729],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043311671,0.00061370956,0.0006649167,0.0008488342,0.00019541336,0.00033747713,0.003553328,0.0003707022,0.000018331428],"category_scores_gemma":[0.00007479995,0.00061046093,0.00012189905,0.0012921114,0.0004116289,0.000941932,0.0017556772,0.0008554373,0.00007849596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014020018,0.00004291618,0.00032288485,0.000012366724,0.000008130763,0.000054109903,0.00048804245,0.30770507,0.004412811,0.0017793798,0.00007041656,0.6850898],"study_design_scores_gemma":[0.0003273792,0.00009592057,0.0011781218,0.00031944632,0.000009168407,0.000030928124,3.450742e-7,0.94804037,0.0033717807,0.045409538,0.0005734782,0.0006435133],"about_ca_topic_score_codex":0.00010116859,"about_ca_topic_score_gemma":0.0004946098,"teacher_disagreement_score":0.68444633,"about_ca_system_score_codex":0.00064529787,"about_ca_system_score_gemma":0.0004983957,"threshold_uncertainty_score":0.9996347},"labels":[],"label_agreement":null},{"id":"W2979796099","doi":"10.1109/cvpr.2019.00543","title":"DeepFlux for Skeletons in the Wild","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":60,"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; Vector Institute; University of Toronto","funders":"","keywords":"Skeletonization; Artificial intelligence; Computer science; Computer vision; Pixel; Object detection; Context (archaeology); Benchmark (surveying); Pattern recognition (psychology); Image segmentation; Segmentation; Clutter; Edge detection; Point (geometry); Object (grammar); Representation (politics); Image (mathematics); Image processing; Mathematics; Cartography; Geography","score_opus":0.01574845461490725,"score_gpt":0.27281519326462406,"score_spread":0.2570667386497168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979796099","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":[0.010971474,0.0000165012,0.97037196,0.008160419,0.00005966598,0.0005383914,4.017118e-7,0.000059521582,0.009821693],"genre_scores_gemma":[0.8702277,0.000003550611,0.124702595,0.0034659102,0.000031505933,0.00017447586,0.0000011031541,0.0000037146037,0.001389449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999555,0.000011834617,0.00007401631,0.00015918537,0.0000638761,0.0001360663],"domain_scores_gemma":[0.9992229,0.00027054062,0.000017342074,0.0004627745,0.000012361068,0.000014111535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000088898894,0.000041436862,0.000043279175,0.000019602605,0.000038867103,0.00002792266,0.00072120945,0.00001412003,0.000008261572],"category_scores_gemma":[0.0000068775435,0.000026478525,0.000023435758,0.0002831234,0.0000086877135,0.00015337486,0.000058252226,0.000049832517,0.00012835194],"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":[6.2232726e-7,0.000019409894,0.0005383928,0.0000015572311,8.006323e-7,1.8186549e-7,0.00011592269,0.0007538069,0.0002481867,0.97414804,0.0042690793,0.01990398],"study_design_scores_gemma":[0.00063281826,0.00011343105,0.008395363,0.000007720251,0.0000023758205,0.000012667545,0.00009875211,0.30103394,0.0010779154,0.23860195,0.449754,0.00026908223],"about_ca_topic_score_codex":0.0000035226833,"about_ca_topic_score_gemma":0.000025820274,"teacher_disagreement_score":0.8592562,"about_ca_system_score_codex":0.000008363304,"about_ca_system_score_gemma":0.000009368889,"threshold_uncertainty_score":0.16497473},"labels":[],"label_agreement":null},{"id":"W2979808779","doi":"10.1016/j.media.2019.101587","title":"‘Squeeze & excite’ guided few-shot segmentation of volumetric images","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":166,"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":"Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst; Nvidia","keywords":"Artificial intelligence; Segmentation; Computer vision; Shot (pellet); Computer science; Image segmentation; Pattern recognition (psychology); Chemistry","score_opus":0.02131291264568962,"score_gpt":0.32293430772163023,"score_spread":0.3016213950759406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979808779","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":[0.055939782,0.00013385942,0.94071263,0.0013959787,0.00007197394,0.00017950841,0.0000048621646,0.000102734884,0.0014586492],"genre_scores_gemma":[0.7573558,0.00015683919,0.23882477,0.0008209337,0.00009420799,0.00004516517,0.000085208805,0.000015827247,0.0026012678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975795,0.00011163835,0.0005333631,0.0005181447,0.0009789874,0.00027836388],"domain_scores_gemma":[0.99798566,0.00032359755,0.00025743016,0.0010021123,0.00022568386,0.0002055018],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044429037,0.0001501142,0.000395899,0.0005887245,0.00006443942,0.00006270446,0.001057788,0.000076489676,0.0012666469],"category_scores_gemma":[0.00034147233,0.00013404131,0.00026206105,0.006355573,0.0001117646,0.00053116964,0.0002913912,0.0001739631,0.00042266838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040878967,0.0015598263,0.1664033,0.00025111734,0.0036650384,0.00010540655,0.0012573393,0.012910619,0.2499251,0.0075898278,0.08040439,0.47588715],"study_design_scores_gemma":[0.0025300728,0.00018891608,0.09153708,0.000068297166,0.0016984969,0.000038586983,0.000102838974,0.8280067,0.052123584,0.0060344324,0.016274879,0.0013961325],"about_ca_topic_score_codex":0.000080913014,"about_ca_topic_score_gemma":0.0000312041,"teacher_disagreement_score":0.8150961,"about_ca_system_score_codex":0.000049340997,"about_ca_system_score_gemma":0.00005150311,"threshold_uncertainty_score":0.9996463},"labels":[],"label_agreement":null},{"id":"W2980025636","doi":"10.1007/978-3-030-32520-6_22","title":"Negative Log Likelihood Ratio Loss for Deep Neural Network Classification","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":76,"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; Huawei Technologies (Canada)","funders":"","keywords":"Discriminative model; Cross entropy; Pattern recognition (psychology); Artificial intelligence; Artificial neural network; Generative grammar; Entropy (arrow of time); Mathematics; Contextual image classification; Computer science; Deep neural networks; Image (mathematics)","score_opus":0.027834525460559322,"score_gpt":0.28439524405292416,"score_spread":0.25656071859236484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980025636","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":[0.000035593814,0.016689118,0.9648261,0.00013073017,0.0020256073,0.0020190873,0.000009112463,0.00014530418,0.014119314],"genre_scores_gemma":[0.8475347,0.010654063,0.107360855,0.0008442058,0.0050359876,0.0005932169,0.00019433518,0.0003286392,0.027454006],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970201,0.000053815293,0.0009869187,0.0011048729,0.00027180367,0.0005624623],"domain_scores_gemma":[0.9968544,0.0011939929,0.000909107,0.00072257634,0.00021118562,0.00010869618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034394392,0.0004845418,0.0006560556,0.00014120512,0.00027095768,0.00019376102,0.0007978331,0.00023356828,0.0000022660379],"category_scores_gemma":[0.00002971504,0.0004809551,0.00012725244,0.00016955659,0.0001047483,0.0005293329,0.00032831897,0.00044866052,0.00002901934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009723903,0.000009486699,0.00010327369,0.00013027087,0.000017678922,0.0000027145468,0.00014640384,0.18896328,0.000003027156,0.66381985,0.00006210722,0.1467322],"study_design_scores_gemma":[0.00019796946,0.00010935684,0.000035697944,0.00052167877,0.000014441469,0.000027797818,0.000060273742,0.8618449,0.00001289316,0.07811071,0.058556866,0.0005074226],"about_ca_topic_score_codex":0.0000060800608,"about_ca_topic_score_gemma":0.000041568626,"teacher_disagreement_score":0.85746527,"about_ca_system_score_codex":0.00016529081,"about_ca_system_score_gemma":0.000050391605,"threshold_uncertainty_score":0.9997642},"labels":[],"label_agreement":null},{"id":"W2980583983","doi":"10.48550/arxiv.1910.06548","title":"Training CNNs faster with Dynamic Input and Kernel Downsampling","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Upsampling; Computer science; Hyperparameter; Pooling; Residual; Convolution (computer science); Kernel (algebra); Reduction (mathematics); Algorithm; Backpropagation; Filter (signal processing); Generalization; Feature (linguistics); Key (lock); Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Mathematics; Artificial neural network; Computer vision","score_opus":0.07569608199522461,"score_gpt":0.2062172710992033,"score_spread":0.1305211891039787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980583983","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":[0.22152612,0.000041374573,0.77652913,0.00011476037,0.00012538186,0.00032806688,0.000007854471,0.00020374113,0.0011235906],"genre_scores_gemma":[0.98088145,0.00011623789,0.017686961,0.00014537218,0.000032650758,0.0000025178815,0.000012324974,0.000025490579,0.0010969855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980585,0.000045937366,0.00015313715,0.001286446,0.000080349666,0.00037560484],"domain_scores_gemma":[0.9981955,0.0001485436,0.00022859707,0.001197897,0.00007996499,0.00014944059],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009219773,0.0003172349,0.00031008277,0.0001684964,0.00015979819,0.00012557121,0.0011587824,0.00017246867,0.000005154627],"category_scores_gemma":[0.000006613962,0.00032823402,0.000070420385,0.00045249815,0.00012534887,0.00041726322,0.0015932866,0.000606096,0.000042831605],"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":[0.00002165437,0.000029176499,0.0017961588,0.000059004855,0.000065878274,0.000081254075,0.00070066855,0.9308987,0.00008211785,0.060402375,0.00001660249,0.0058463756],"study_design_scores_gemma":[0.0004176789,0.000049702998,0.0019959495,0.0001339556,0.00004261412,0.000022901324,0.00011328608,0.9708018,0.000016426518,0.02534068,0.0005381326,0.0005269056],"about_ca_topic_score_codex":0.000014847518,"about_ca_topic_score_gemma":0.000039631796,"teacher_disagreement_score":0.75935537,"about_ca_system_score_codex":0.00011782534,"about_ca_system_score_gemma":0.00011475529,"threshold_uncertainty_score":0.999917},"labels":[],"label_agreement":null},{"id":"W2981446015","doi":"10.1109/tci.2019.2948755","title":"Fast Retinomorphic Event-Driven Representations for Video Gameplay and Action Recognition","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège Boréal","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Event (particle physics); Action (physics); Action recognition; Speech recognition; Computer graphics (images)","score_opus":0.03073243884139834,"score_gpt":0.30403732885497853,"score_spread":0.27330489001358016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981446015","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":[0.022994807,0.000017680082,0.9731978,0.0020723937,0.000506349,0.0007754344,0.000049547292,0.00024392984,0.00014202947],"genre_scores_gemma":[0.8563975,0.000011361544,0.14245887,0.0006162833,0.00005446799,0.00025294995,0.000039653183,0.00002138,0.0001475301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856037,0.00005994192,0.00030723948,0.000599752,0.0002406677,0.00023204839],"domain_scores_gemma":[0.99854845,0.00069130096,0.00015519929,0.00029551718,0.0002166197,0.000092926246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114771,0.00017107338,0.00014592541,0.00022293991,0.0003958922,0.00014608858,0.00021367313,0.000032694978,0.000028838722],"category_scores_gemma":[0.000007568144,0.00019765571,0.00009145354,0.00041377812,0.000055154076,0.0009859164,0.0000052704017,0.00019712852,0.00011739671],"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":[0.000035469384,0.00010805086,0.00013258902,0.000025167874,0.000029184908,0.0000016324,0.00017770154,0.8573554,0.0029180765,0.0024426256,0.00032190495,0.13645218],"study_design_scores_gemma":[0.0007175513,0.000055546858,0.0008311279,0.00004200962,0.000019547964,0.00008029249,0.000056579895,0.97332007,0.0025499817,0.021630174,0.00046429707,0.0002328223],"about_ca_topic_score_codex":0.000007612785,"about_ca_topic_score_gemma":0.0000063644757,"teacher_disagreement_score":0.8334027,"about_ca_system_score_codex":0.0000817492,"about_ca_system_score_gemma":0.000052362233,"threshold_uncertainty_score":0.8060166},"labels":[],"label_agreement":null},{"id":"W2982103617","doi":"10.1109/iccv.2019.00077","title":"InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":201,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Boosting (machine learning); Segmentation; Artificial intelligence; Pascal (unit); Inference; Pattern recognition (psychology); Pipeline (software); Data mining","score_opus":0.025581440778618975,"score_gpt":0.2733302783357753,"score_spread":0.2477488375571563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982103617","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":[0.101928204,0.000034021676,0.8865378,0.00086391624,0.00024617868,0.00083994836,0.000001050628,0.00048807473,0.009060817],"genre_scores_gemma":[0.58307505,0.0000023222424,0.4156389,0.0005979497,0.00004068891,0.0000614022,0.0000049763116,0.0000100778325,0.00056861615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983672,0.000050799223,0.0003817123,0.00059967896,0.000267282,0.00033331427],"domain_scores_gemma":[0.998658,0.0001797685,0.00019067283,0.0007542507,0.0001350032,0.000082279534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023728637,0.0001583724,0.00015092384,0.000045737295,0.00017736293,0.00008987807,0.0006380265,0.000043302574,0.00004157073],"category_scores_gemma":[0.000041380506,0.0001522673,0.000039224364,0.00055119465,0.000033969296,0.001030692,0.00031562508,0.00014536371,0.00034615857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002337312,0.0002606323,0.06822289,0.00023006607,0.00003148771,0.0000074554964,0.0013422011,0.04784358,0.16934112,0.35849512,0.0028937834,0.35130832],"study_design_scores_gemma":[0.001423367,0.00016069741,0.010143269,0.00012078411,0.000009978755,0.000042724052,0.00009402245,0.82312185,0.069797456,0.08148869,0.012528375,0.0010688086],"about_ca_topic_score_codex":0.000013279543,"about_ca_topic_score_gemma":0.000015371828,"teacher_disagreement_score":0.7752783,"about_ca_system_score_codex":0.00012982669,"about_ca_system_score_gemma":0.000044823188,"threshold_uncertainty_score":0.62092805},"labels":[],"label_agreement":null},{"id":"W2984254165","doi":"10.1016/j.compmedimag.2019.101660","title":"Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation","year":2020,"lang":"","type":"article","venue":"Espace ÉTS (ETS)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Segmentation; Convolutional neural network; Pattern recognition (psychology); Partial volume; White matter; Ranking (information retrieval); Deep learning; Image segmentation; Machine learning; Magnetic resonance imaging; Medicine; Radiology","score_opus":0.020013966209563524,"score_gpt":0.28576171086079755,"score_spread":0.265747744651234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984254165","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":[0.010082907,0.0009470378,0.8707875,0.11556408,0.000236127,0.002004562,0.00006279673,0.00017570326,0.00013927647],"genre_scores_gemma":[0.6733725,0.00062663975,0.31389,0.009746761,0.00064630294,0.00078475266,0.00009800349,0.00008363162,0.0007514135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997114,0.00015737706,0.0005159971,0.0012337635,0.00035318063,0.0006256767],"domain_scores_gemma":[0.9968284,0.0014425106,0.00041445438,0.00053645053,0.0002944129,0.00048379911],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002097705,0.00042712013,0.00038543492,0.00012291803,0.0007722145,0.00036716895,0.0005620081,0.0001383689,0.000027559436],"category_scores_gemma":[0.0004229187,0.00047748128,0.00011414296,0.001241669,0.00023248821,0.0010551278,0.00036873217,0.0002916411,0.0000919229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006252817,0.00047383137,0.0024132342,0.00050403306,0.00035553853,0.00005083719,0.100212745,0.20151252,0.07621511,0.1880636,0.05812405,0.37144923],"study_design_scores_gemma":[0.0014854436,0.0005534333,0.0031282143,0.000065022876,0.000081217106,0.000018661707,0.0011246515,0.948087,0.006626786,0.004316364,0.033803314,0.0007099078],"about_ca_topic_score_codex":0.0000119088545,"about_ca_topic_score_gemma":0.00010456181,"teacher_disagreement_score":0.74657446,"about_ca_system_score_codex":0.00008627548,"about_ca_system_score_gemma":0.00013937784,"threshold_uncertainty_score":0.99976766},"labels":[],"label_agreement":null},{"id":"W2986445973","doi":"10.1049/iet-ipr.2019.0399","title":"DRAN: Deep recurrent adversarial network for automated pancreassegmentation","year":2019,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Adversarial system; Computer science; Artificial intelligence","score_opus":0.012062420275932384,"score_gpt":0.29318716812551904,"score_spread":0.28112474784958663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986445973","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":[0.0037187305,0.00029215548,0.9929982,0.00038401978,0.00051627815,0.00073314394,0.000002830292,0.0009607239,0.0003939229],"genre_scores_gemma":[0.25315785,0.000010322466,0.7459114,0.00028456483,0.00033543055,0.00017391461,0.000036284335,0.000021524786,0.00006872014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986974,0.00003085054,0.00025308324,0.00045528988,0.00018539977,0.00037798242],"domain_scores_gemma":[0.9991319,0.000116778676,0.0002047582,0.0003199176,0.00015598228,0.00007062989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016435377,0.00014995622,0.00015979866,0.000038982493,0.00024342042,0.00024585903,0.00046162674,0.00004848342,0.00000691058],"category_scores_gemma":[0.000025359248,0.00015054723,0.00005510136,0.000509943,0.000026893542,0.0012209114,0.00010140362,0.0000995495,0.00006835112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013536144,0.0001364584,0.00046356747,0.00025261348,0.000028641394,0.0000037856203,0.0008247736,0.033114113,0.019968215,0.0053843637,0.006345762,0.93334234],"study_design_scores_gemma":[0.00076644396,0.00005337423,0.00036119204,0.000059054775,0.000011467452,0.0000071681734,0.00001608797,0.98846555,0.0011266132,0.0066768257,0.0022514632,0.00020474815],"about_ca_topic_score_codex":0.0000018472336,"about_ca_topic_score_gemma":0.0000023087543,"teacher_disagreement_score":0.9553515,"about_ca_system_score_codex":0.00008609839,"about_ca_system_score_gemma":0.00008134692,"threshold_uncertainty_score":0.61391383},"labels":[],"label_agreement":null},{"id":"W2987837502","doi":"10.48550/arxiv.1911.02897","title":"Efficacy of Pixel-Level OOD Detection for Semantic Segmentation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Task (project management); Segmentation; Ranking (information retrieval); Pixel; Metric (unit); Image (mathematics); Pattern recognition (psychology); Image segmentation; Computer vision; Engineering","score_opus":0.09812074925881781,"score_gpt":0.22490034947418996,"score_spread":0.12677960021537216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987837502","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":[0.1878369,0.000017059663,0.81060994,0.000038024365,0.0003393545,0.00091965735,0.000017292388,0.00011433106,0.000107425265],"genre_scores_gemma":[0.98308575,0.000056346784,0.016212393,0.000028289309,0.000052014067,0.0000067677006,0.00002582446,0.000016290542,0.0005163371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986717,0.000049291153,0.00020889574,0.0007860992,0.00007111236,0.00021286242],"domain_scores_gemma":[0.9981373,0.0002608046,0.000403836,0.00096621056,0.00017007595,0.000061797706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103145794,0.00019827798,0.00024255604,0.00017659669,0.00010301221,0.000029435478,0.0009467867,0.00015704165,0.0000029857265],"category_scores_gemma":[0.000024370853,0.00024227679,0.00017640482,0.00048238508,0.000047328544,0.00027190562,0.00063637743,0.00021645617,0.000029321209],"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":[0.0000628991,0.00012127973,0.0002216738,0.00016823364,0.00006817958,0.0000024840074,0.00011549789,0.9440189,0.006455339,0.04087522,0.000048468304,0.007841809],"study_design_scores_gemma":[0.0012444883,0.0001185304,0.002897415,0.00008594595,0.000104643696,0.0000025150289,0.000027644293,0.9395128,0.014877498,0.040485065,0.00024499232,0.00039849855],"about_ca_topic_score_codex":0.00001548349,"about_ca_topic_score_gemma":0.000021796928,"teacher_disagreement_score":0.79524887,"about_ca_system_score_codex":0.00013965051,"about_ca_system_score_gemma":0.000074713316,"threshold_uncertainty_score":0.987976},"labels":[],"label_agreement":null},{"id":"W2988916019","doi":"10.1007/s11263-019-01247-4","title":"Deep Learning for Generic Object Detection: A Survey","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2776,"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":"Oulun Yliopisto; National Natural Science Foundation of China","keywords":"Computer science; Object detection; Artificial intelligence; Deep learning; Field (mathematics); Representation (politics); Object (grammar); Context (archaeology); Feature (linguistics); Feature learning; Machine learning; Learning object; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Geography; Mathematics","score_opus":0.018233670315557282,"score_gpt":0.3036416907018827,"score_spread":0.2854080203863254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988916019","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":[0.058620933,0.000092467955,0.93815994,0.00035669323,0.0025650673,0.00012857563,5.341892e-7,0.000034111014,0.000041693154],"genre_scores_gemma":[0.86706007,0.00003530955,0.13195318,0.00023563698,0.00063256506,0.000003715869,0.0000035194325,0.0000100557645,0.00006596272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873215,0.000092808754,0.00039462562,0.00020747635,0.00043293322,0.00014001403],"domain_scores_gemma":[0.99798954,0.0004715857,0.00043488832,0.00017909385,0.00085936254,0.000065553555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004354201,0.00010455319,0.00015987006,0.00019770887,0.00006408443,0.00016472489,0.0011122222,0.000040079205,0.000009185543],"category_scores_gemma":[0.00003798741,0.000092842674,0.00013407119,0.00022987639,0.00001266192,0.0006500165,0.0002091607,0.00019288038,0.000035027395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012235614,0.000075283686,0.001673338,0.000004458307,0.00007454799,0.000012655259,0.00010549547,0.2240762,0.004460838,0.001294059,0.00037983124,0.76772094],"study_design_scores_gemma":[0.00076241704,0.0006193373,0.028475242,0.000034842742,0.000003499825,0.0002986819,0.0000023050063,0.949137,0.0014255261,0.0018398471,0.017268492,0.00013284624],"about_ca_topic_score_codex":0.0000024702945,"about_ca_topic_score_gemma":0.0000046619775,"teacher_disagreement_score":0.80843914,"about_ca_system_score_codex":0.00008426865,"about_ca_system_score_gemma":0.0000326393,"threshold_uncertainty_score":0.37860143},"labels":[],"label_agreement":null},{"id":"W2989035356","doi":"10.1109/iccv.2019.00403","title":"DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Differentiable function; Convolutional neural network; Matching (statistics); Artificial intelligence; Object (grammar); Segmentation; Deep learning; Artificial neural network; Gradient descent; Algorithm; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.01284999507981074,"score_gpt":0.2610965531051605,"score_spread":0.24824655802534976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989035356","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":[0.022436589,0.000052657368,0.9732101,0.0004372296,0.0003746779,0.0008616017,0.000001848051,0.00029807107,0.002327203],"genre_scores_gemma":[0.40047053,0.00001031208,0.59309274,0.0013369613,0.00023153651,0.00024419892,0.000021386559,0.000021334114,0.0045709875],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988163,0.000024526296,0.00021026611,0.0004268885,0.0001615029,0.00036051826],"domain_scores_gemma":[0.99897975,0.0002756412,0.000105784624,0.0005330293,0.00004178094,0.00006402275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117741314,0.00013340563,0.00014397282,0.000034972734,0.00017164265,0.00014802336,0.00052881014,0.000034088112,0.00006099772],"category_scores_gemma":[0.000005051413,0.0001196176,0.00006529682,0.0003290754,0.000009948819,0.0006468483,0.00016527702,0.000082806066,0.00019409403],"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":[0.000028390754,0.000106819374,0.0040942063,0.000071479604,0.000058447466,0.0000010549938,0.0004727293,0.23454887,0.023353882,0.66815895,0.026378416,0.04272677],"study_design_scores_gemma":[0.0012067622,0.00017970263,0.0023292354,0.000047828893,0.000019169254,0.000010288923,0.00007464002,0.6449868,0.010949352,0.30660915,0.032936912,0.00065012294],"about_ca_topic_score_codex":0.000011108434,"about_ca_topic_score_gemma":0.000017093062,"teacher_disagreement_score":0.41043797,"about_ca_system_score_codex":0.000042509604,"about_ca_system_score_gemma":0.000019369654,"threshold_uncertainty_score":0.4877864},"labels":[],"label_agreement":null},{"id":"W2989626086","doi":"10.1109/isbi45749.2020.9098651","title":"Adversarial Normalization for Multi Domain Image Segmentation","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Nvidia","keywords":"Adversarial system; Normalization (sociology); Segmentation; Artificial intelligence; Computer science; Spatial normalization; Dice; Image segmentation; Pattern recognition (psychology); Image (mathematics); Computer vision; Mathematics; Voxel; Statistics","score_opus":0.04900962135016573,"score_gpt":0.32880660525711924,"score_spread":0.2797969839069535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989626086","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":[0.000097078424,0.000016335147,0.99384665,0.0027382446,0.000639974,0.0017050216,0.00003461604,0.00047835332,0.0004437404],"genre_scores_gemma":[0.006307664,0.000020144966,0.99141663,0.0007143012,0.0003309404,0.000658952,0.00038235393,0.000020699332,0.00014829285],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860114,0.000038804592,0.00030501786,0.00068085757,0.00017689334,0.00019727918],"domain_scores_gemma":[0.99903005,0.00007233968,0.00023268108,0.00044844052,0.00012869643,0.000087786015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089009074,0.00019864614,0.00017646898,0.000055503635,0.00013166573,0.00016192615,0.00074962276,0.00012021029,0.000010968772],"category_scores_gemma":[0.000029267614,0.00020651083,0.00010648589,0.00021345087,0.000025237889,0.00039558733,0.0008972491,0.0001760579,0.00004343668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012440459,0.00035274788,0.00010327718,0.0005865679,0.00016394294,0.000010669151,0.0034445124,0.072946966,0.061618783,0.74472386,0.03911566,0.0768086],"study_design_scores_gemma":[0.00071990525,0.000029943832,0.000077239274,0.000012598971,0.000014374224,0.000001343599,0.000024268502,0.93353254,0.006171498,0.055152312,0.003956593,0.00030741325],"about_ca_topic_score_codex":0.000009006075,"about_ca_topic_score_gemma":0.000009178848,"teacher_disagreement_score":0.8605856,"about_ca_system_score_codex":0.00008767478,"about_ca_system_score_gemma":0.000077745215,"threshold_uncertainty_score":0.8421267},"labels":[],"label_agreement":null},{"id":"W2989976667","doi":"10.1016/j.neuroimage.2020.117026","title":"AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation","year":2020,"lang":"en","type":"preprint","venue":"NeuroImage","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"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 Center for Research Resources; National Institute of Neurological Disorders and Stroke; National Institute on Drug Abuse; National Institute of Mental Health; University of California, San Diego; Canadian Institutes of Health Research; Pfizer; University of California, Los Angeles; Medical Research Council; National Institute of Biomedical Imaging and Bioengineering; Johnson and Johnson; Engineering and Physical Sciences Research Council; AstraZeneca; Genentech; U.S. Food and Drug Administration; Alzheimer's Drug Discovery Foundation; National Institutes of Health; National Institute on Aging; Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Eisai; Stavros Niarchos Foundation; Child Mind Institute; Northern California Institute for Research and Education; Centre National de la Recherche Scientifique; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Science and Industry Endowment Fund; Schering-Plough; Bayer Schering; Ministerio de Economía y Competitividad; Medpace; Abbott Laboratories; Commonwealth Scientific and Industrial Research Organisation; National Health and Medical Research Council; Leon Levy Foundation; F. Hoffmann-La Roche; Cincinnati Children's Hospital Medical Center; Agence Nationale de la Recherche; Eunice Kennedy Shriver National Institute of Child Health and Human Development; GlaxoSmithKline; Bristol-Myers Squibb; National Institute of Child Health and Human Development; Eli Lilly and Company; Alzheimer's Association; Nvidia; Innogenetics; Elan; Novartis; Synarc; Institut National de la Santé et de la Recherche Médicale; Dana Foundation; Roche","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Segmentation; Convolutional neural network; Pattern recognition (psychology); Machine learning; Consistency (knowledge bases); Voting; Deep learning","score_opus":0.03929641416530071,"score_gpt":0.3191138129232852,"score_spread":0.2798173987579845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989976667","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":[0.0016561033,0.00007468486,0.98161757,0.013691635,0.00037939555,0.0014702062,0.00022365755,0.0002852318,0.0006015097],"genre_scores_gemma":[0.12547612,0.000048216916,0.8673689,0.004619138,0.00037861412,0.0008364305,0.00033561973,0.00009814511,0.00083883625],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772495,0.00009995129,0.00046653353,0.0010241049,0.00031048048,0.00037396088],"domain_scores_gemma":[0.9976778,0.00041731997,0.0004729892,0.0011720513,0.00014083975,0.00011899984],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017393564,0.0002904012,0.0003809729,0.0000964103,0.00011998633,0.00011503147,0.0012874502,0.00012902096,0.0000051678057],"category_scores_gemma":[0.00011755389,0.0003271746,0.00018635404,0.00036290643,0.000035644483,0.0002731027,0.0014132843,0.00042422506,0.000040038605],"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":[0.00005579755,0.0004468707,0.00013399708,0.0008107647,0.00007392297,0.000040586896,0.0013654862,0.013894557,0.75764567,0.035935543,0.13409886,0.05549792],"study_design_scores_gemma":[0.0014174221,0.00035595123,0.001320594,0.00010359125,0.000060213773,0.000013417507,0.000026736168,0.73071736,0.059353396,0.05053769,0.1552504,0.00084320066],"about_ca_topic_score_codex":0.000004192402,"about_ca_topic_score_gemma":0.000008221368,"teacher_disagreement_score":0.7168228,"about_ca_system_score_codex":0.000041020656,"about_ca_system_score_gemma":0.00010522379,"threshold_uncertainty_score":0.99991804},"labels":[],"label_agreement":null},{"id":"W2989996808","doi":"10.1109/cvpr42600.2020.00466","title":"PointPainting: Sequential Fusion for 3D Object Detection","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Point cloud; Lidar; Computer science; Artificial intelligence; Benchmark (surveying); Segmentation; Computer vision; Fusion; Point (geometry); Object detection; Remote sensing; Geography; Mathematics","score_opus":0.043866805177327016,"score_gpt":0.2990854589238096,"score_spread":0.2552186537464826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989996808","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":[0.00047457873,0.000032441214,0.99203664,0.0029685483,0.0009846225,0.0011885513,0.0000063072903,0.0010122488,0.0012960759],"genre_scores_gemma":[0.27116045,0.000023029135,0.726518,0.00075364305,0.00070687354,0.00048276366,0.000032993717,0.000029412038,0.00029285726],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981494,0.000048066322,0.00032714356,0.0009805341,0.00020810474,0.0002867544],"domain_scores_gemma":[0.99855155,0.00014632728,0.0002390027,0.000824443,0.00012876684,0.000109885324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015916894,0.00024557058,0.00023500859,0.00008079779,0.00021205509,0.00018161748,0.0010308973,0.00018714982,0.000012685268],"category_scores_gemma":[0.00007680652,0.00024459398,0.00019174599,0.00027948248,0.00002536337,0.00016675433,0.002048091,0.00042901514,0.000050043152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000506106,0.00009183934,0.000012473198,0.00029123103,0.00007400776,0.000007705871,0.00041546577,0.020235457,0.06369906,0.0759791,0.0042314646,0.8349116],"study_design_scores_gemma":[0.00018694147,0.000065736924,0.000039131788,0.000024105026,0.00001614502,0.000007705509,0.0000047487356,0.86204654,0.024766548,0.093567505,0.018933035,0.00034184524],"about_ca_topic_score_codex":0.000019891611,"about_ca_topic_score_gemma":0.000026982561,"teacher_disagreement_score":0.8418111,"about_ca_system_score_codex":0.000097656004,"about_ca_system_score_gemma":0.00008374825,"threshold_uncertainty_score":0.9974252},"labels":[],"label_agreement":null},{"id":"W2990189480","doi":"10.1109/mlsp49062.2020.9231761","title":"Regularizing Neural Networks by Stochastically Training Layer Ensembles","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Regularization (linguistics); Artificial neural network; Computer science; Stochastic neural network; Network topology; Ensemble learning; Dropout (neural networks); Artificial intelligence; Algorithm; Machine learning; Mathematics; Time delay neural network","score_opus":0.05980751951435766,"score_gpt":0.2871169137032405,"score_spread":0.22730939418888285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990189480","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":[0.00018450803,0.00038206816,0.98230165,0.01290055,0.00052817963,0.00052711926,0.000008068982,0.0011508354,0.0020169942],"genre_scores_gemma":[0.6948794,0.000030348132,0.30054778,0.0035022995,0.00050818734,0.0001674763,0.00005877093,0.000053773398,0.00025195783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693817,0.00008991075,0.0005241314,0.0014137602,0.0003842428,0.000649784],"domain_scores_gemma":[0.9976982,0.0003531408,0.00025217654,0.0012579438,0.0000911637,0.00034736897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014608094,0.00046408505,0.00049241947,0.000059200804,0.00021607215,0.00039397032,0.0023055205,0.00030993234,0.000016723812],"category_scores_gemma":[0.00006389756,0.0004591057,0.0001800095,0.00040989977,0.00008366755,0.0002499862,0.0031405229,0.0013452899,0.000023482871],"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":[0.000013187384,0.000050599305,0.000013947251,0.00003818232,0.000077376935,0.000041189098,0.0008007544,0.6855913,0.0037085414,0.14064872,0.03237389,0.1366423],"study_design_scores_gemma":[0.000115260205,0.00003142952,0.000025170191,0.000032845295,0.000015133257,0.000015457139,0.00001952665,0.9694483,0.00017332053,0.027480844,0.002166744,0.0004759555],"about_ca_topic_score_codex":0.000008455149,"about_ca_topic_score_gemma":0.0000040066916,"teacher_disagreement_score":0.69469494,"about_ca_system_score_codex":0.00005749059,"about_ca_system_score_gemma":0.00007678199,"threshold_uncertainty_score":0.9997861},"labels":[],"label_agreement":null},{"id":"W2991359064","doi":"10.1016/j.isprsjprs.2019.11.005","title":"Vehicle global 6-DoF pose estimation under traffic surveillance camera","year":2019,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Waterloo","funders":"National Natural Science Foundation of China","keywords":"Pose; Artificial intelligence; Computer science; Computer vision; Landmark; Point cloud; Architecture; Articulated body pose estimation; Deep learning; 3D pose estimation; Geography","score_opus":0.009817019884568371,"score_gpt":0.2617623653634358,"score_spread":0.25194534547886743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991359064","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":[0.4612084,0.00029864558,0.53751886,0.0004720699,0.0003095356,0.000083225474,6.2337136e-7,0.000027894626,0.00008073991],"genre_scores_gemma":[0.84107685,0.00007523914,0.1584359,0.00030527695,0.0000838033,1.3200674e-8,6.2298426e-7,0.0000073284295,0.000014975045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987949,0.000086468026,0.0003655783,0.00022970884,0.00026665468,0.00025669762],"domain_scores_gemma":[0.9989097,0.00016283987,0.00034821112,0.0002779095,0.00015814192,0.00014315863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030731203,0.00014148407,0.0002441467,0.000085836306,0.000120480225,0.0001235522,0.00024226778,0.000066719935,0.0000019453728],"category_scores_gemma":[0.00003374867,0.00012310738,0.0000884208,0.000695673,0.00005862627,0.00035518283,0.00006938308,0.00024218984,0.000008697693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002160966,0.00001835937,0.00012511788,0.000011536972,0.000020825702,0.000023809642,0.00007076587,0.03251649,0.006257326,0.00022867588,0.00004810209,0.96065736],"study_design_scores_gemma":[0.00060909084,0.00013467831,0.0030254058,0.000080414145,0.000010470725,0.001242618,0.00006213514,0.9874419,0.001681672,0.004881926,0.00063650287,0.00019321011],"about_ca_topic_score_codex":0.00003823123,"about_ca_topic_score_gemma":0.000024650868,"teacher_disagreement_score":0.9604642,"about_ca_system_score_codex":0.00006735652,"about_ca_system_score_gemma":0.000046049394,"threshold_uncertainty_score":0.5020174},"labels":[],"label_agreement":null},{"id":"W2991471181","doi":"10.1109/iccv.2019.00533","title":"Gated-SCNN: Gated Shape CNNs for Semantic Segmentation","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":737,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Benchmark (surveying); Noise (video); Computer vision; Representation (politics); Focus (optics); Pattern recognition (psychology); Boundary (topology); Architecture; Image segmentation; Image (mathematics); Mathematics","score_opus":0.020494093302038198,"score_gpt":0.2824852809530363,"score_spread":0.2619911876509981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991471181","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":[0.045663133,0.000016892827,0.9494368,0.0014645396,0.0001875928,0.0008910551,0.0000021035391,0.00039375026,0.0019441737],"genre_scores_gemma":[0.7864669,0.000010167433,0.20903793,0.001060709,0.000047525846,0.00006734557,0.000024139006,0.000014484342,0.0032707797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990935,0.000014201261,0.00017313827,0.00036010574,0.0001260235,0.00023304505],"domain_scores_gemma":[0.9992352,0.00012536172,0.00007144559,0.0004335286,0.0000763189,0.000058118043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006836867,0.000101786805,0.0001012246,0.000047558457,0.00008700415,0.00006266852,0.00043311718,0.000035046385,0.00009556585],"category_scores_gemma":[0.000006985197,0.000091764545,0.000043119137,0.0003810472,0.000013344168,0.00041097007,0.000093581664,0.000053231222,0.000535928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034033284,0.00023451753,0.0023843,0.00010416748,0.00006459486,0.0000034739255,0.00055923144,0.017190516,0.19650853,0.54423815,0.02135104,0.21732748],"study_design_scores_gemma":[0.00052137877,0.00008745215,0.0006463597,0.0000100970165,0.000005927378,0.000006742953,0.000023301636,0.9488212,0.030288395,0.00752596,0.011854137,0.00020905017],"about_ca_topic_score_codex":0.000003618987,"about_ca_topic_score_gemma":0.0000049041155,"teacher_disagreement_score":0.9316307,"about_ca_system_score_codex":0.000030197847,"about_ca_system_score_gemma":0.000018742328,"threshold_uncertainty_score":0.6888449},"labels":[],"label_agreement":null},{"id":"W2993553029","doi":"10.48550/arxiv.1812.07032","title":"Boundary loss for highly unbalanced segmentation","year":2018,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Softmax function; Segmentation; Boundary (topology); Cross entropy; Computer science; Computation; Graph; Mathematics; Algorithm; Mathematical optimization; Artificial intelligence; Pattern recognition (psychology); Deep learning; Mathematical analysis; Theoretical computer science","score_opus":0.014931136589150436,"score_gpt":0.28890130575922857,"score_spread":0.2739701691700781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993553029","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":[0.0236877,0.00006649925,0.9674138,0.0066633034,0.00054601644,0.00053057866,0.000008358654,0.00031049515,0.0007732932],"genre_scores_gemma":[0.7035294,0.000021659082,0.29110378,0.001497755,0.0006316633,0.00025894045,0.000019349463,0.000025012394,0.0029124217],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988668,0.000025846362,0.00015969956,0.00044205788,0.00018086756,0.00032472407],"domain_scores_gemma":[0.99892896,0.00013217748,0.00011815026,0.00059197226,0.00013626205,0.00009247408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011099664,0.00013660247,0.00012160994,0.00005785532,0.0003418461,0.00012776232,0.0005785692,0.00004847405,0.000012790173],"category_scores_gemma":[0.000028651151,0.00013498504,0.000048234324,0.00045921895,0.0001213326,0.00053719763,0.00013410927,0.00007468149,0.0002543387],"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":[0.00020863916,0.00030501204,0.0018079387,0.00007034881,0.000084594125,0.000014833111,0.004069232,0.0015127773,0.165033,0.4756873,0.12036711,0.23083924],"study_design_scores_gemma":[0.0023074406,0.0006337148,0.0078002894,0.000052029554,0.000025594896,0.000037877766,0.000091644506,0.10231912,0.1387778,0.068856046,0.6781436,0.00095478795],"about_ca_topic_score_codex":0.0000031499314,"about_ca_topic_score_gemma":0.000025377358,"teacher_disagreement_score":0.67984176,"about_ca_system_score_codex":0.00007218305,"about_ca_system_score_gemma":0.00005327414,"threshold_uncertainty_score":0.550453},"labels":[],"label_agreement":null},{"id":"W2995426144","doi":"10.48550/arxiv.2001.08248","title":"How Much Position Information Do Convolutional Neural Networks Encode?","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":94,"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":"ENCODE; Convolutional neural network; Computer science; Artificial intelligence; Set (abstract data type); Position (finance); Contrast (vision); Artificial neural network; Filter (signal processing); Deep learning; Pattern recognition (psychology); Computer vision","score_opus":0.03836477917555642,"score_gpt":0.16528072201272226,"score_spread":0.12691594283716584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995426144","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":[0.025488995,0.000022135915,0.9695593,0.0038139035,0.00010815861,0.00017922203,0.0000051299894,0.0002737468,0.0005493827],"genre_scores_gemma":[0.99577504,0.000023995095,0.002630975,0.0013629005,0.00010371971,0.0000013542214,0.000028542196,0.0000049196865,0.00006856468],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991815,0.00004560491,0.0001131282,0.00033944886,0.0000797735,0.00024051403],"domain_scores_gemma":[0.9992006,0.00006270386,0.00012575412,0.00030954057,0.00012167037,0.000179762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000040849747,0.00013006174,0.000102417085,0.000060039027,0.00022348277,0.00015011865,0.0006500005,0.000066781846,0.000009172232],"category_scores_gemma":[0.000016993205,0.00015131051,0.00006703759,0.00097427145,0.00006329854,0.0029782653,0.0002300886,0.00018926224,0.000060545426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015764412,0.000012578932,0.00043447802,0.0000036871604,0.000007859667,0.00001329219,0.00007939317,0.4571152,0.00007486109,0.53926694,0.0009316801,0.0020442333],"study_design_scores_gemma":[0.0002985362,0.00004765667,0.0005828999,0.0000029744465,0.000008085745,0.000007829,0.000041844425,0.9882258,0.000064474734,0.008035554,0.0025207072,0.00016361513],"about_ca_topic_score_codex":0.00000271573,"about_ca_topic_score_gemma":0.0000015458074,"teacher_disagreement_score":0.970286,"about_ca_system_score_codex":0.00007568895,"about_ca_system_score_gemma":0.00002513279,"threshold_uncertainty_score":0.6170264},"labels":[],"label_agreement":null},{"id":"W2996273387","doi":"","title":"FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Convolutional neural network; Computer science; Convolution (computer science); Artificial intelligence; Pattern recognition (psychology); Quantization (signal processing); Deep learning; Algorithm; Image compression; Filter (signal processing); Artificial neural network; Computer vision; Image processing; Image (mathematics)","score_opus":0.04455970726502237,"score_gpt":0.1810260813521052,"score_spread":0.13646637408708284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996273387","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":[0.028190713,0.0001619296,0.97034436,0.0004769005,0.00007621473,0.00013163821,0.000008638105,0.00012672564,0.00048286308],"genre_scores_gemma":[0.99722993,0.000041242103,0.0017903819,0.0007128991,0.000053818487,6.2826956e-7,0.000020802176,0.000007948998,0.00014232259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903655,0.00006193932,0.00014440628,0.00046301156,0.00006990005,0.0002242197],"domain_scores_gemma":[0.9991437,0.00013027292,0.00012839014,0.00035846795,0.00006720051,0.0001719304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000032531792,0.00012774851,0.00015069332,0.00003167251,0.00011763358,0.000014488251,0.00085697975,0.000059990845,0.000034301385],"category_scores_gemma":[0.000007934955,0.0001403245,0.00007419752,0.0006645969,0.00011429223,0.00043505253,0.00039676033,0.00017276437,0.000017944898],"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":[0.000040131745,0.0000535696,0.002954068,0.0000076216133,0.00001627289,0.000025009676,0.000048444494,0.92475367,0.0012498472,0.061114363,0.008012496,0.0017245291],"study_design_scores_gemma":[0.00032708028,0.000047869165,0.00071521825,0.000005542828,0.000008051399,0.000002054371,0.000008461796,0.9950983,0.0002691854,0.0015200438,0.001857874,0.00014034515],"about_ca_topic_score_codex":0.000005685274,"about_ca_topic_score_gemma":0.0000022830063,"teacher_disagreement_score":0.96903926,"about_ca_system_score_codex":0.000027106296,"about_ca_system_score_gemma":0.000012229263,"threshold_uncertainty_score":0.5722267},"labels":[],"label_agreement":null},{"id":"W2996690709","doi":"10.1109/aicas48895.2020.9073854","title":"Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Overhead (engineering); Reliability (semiconductor); Computer science; Artificial neural network; Energy (signal processing); Stochastic gradient descent; Inference; Energy consumption; Deep neural networks; Noise (video); Exponential function; Layer (electronics); Algorithm; Artificial intelligence; Statistics; Engineering; Mathematics","score_opus":0.02502243469470757,"score_gpt":0.25909677315195284,"score_spread":0.23407433845724526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996690709","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":[0.00049779133,0.00013425243,0.97592044,0.0196113,0.0007148489,0.0006024218,0.000005560354,0.00100105,0.001512313],"genre_scores_gemma":[0.79068786,0.000044591594,0.19628848,0.011089367,0.0008267289,0.00055560423,0.000092601535,0.000060004924,0.0003547651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697465,0.000104749815,0.0005330972,0.0014626612,0.00037526243,0.0005495838],"domain_scores_gemma":[0.9973834,0.00015553083,0.00022616462,0.0016009844,0.00012356516,0.0005103301],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010207577,0.00047146995,0.00040934628,0.00012983233,0.00014749562,0.00031433138,0.002430586,0.000263021,0.000039472536],"category_scores_gemma":[0.000038207996,0.00048100052,0.0001930444,0.00082874676,0.000036530175,0.00027372842,0.0027220463,0.0006460879,0.00007487663],"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":[0.000008492885,0.000032443444,0.0000090040485,0.000012271239,0.000012386302,0.000013294824,0.00015200116,0.80673957,0.00018139512,0.040306274,0.0033394485,0.14919344],"study_design_scores_gemma":[0.0001315814,0.00003614629,0.00027070922,0.000016766036,0.000009988689,0.000006990547,0.000006421266,0.970474,0.00024325527,0.025500793,0.002804683,0.0004986838],"about_ca_topic_score_codex":0.000062207095,"about_ca_topic_score_gemma":0.00012271601,"teacher_disagreement_score":0.79019004,"about_ca_system_score_codex":0.00012367051,"about_ca_system_score_gemma":0.000059642953,"threshold_uncertainty_score":0.99976414},"labels":[],"label_agreement":null},{"id":"W2997288154","doi":"10.1109/robio49542.2019.8961780","title":"Deep Learning Training with Simulated Approximate Multipliers","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Killam Trusts; Dalhousie University","keywords":"Computer science; Convolutional neural network; Approximation error; Artificial neural network; Lagrange multiplier; Speedup; Mathematical optimization; Training (meteorology); Power (physics); Algorithm; Artificial intelligence; Mathematics; Parallel computing","score_opus":0.015863069783738953,"score_gpt":0.24050810260634797,"score_spread":0.22464503282260903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997288154","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":[0.05397997,0.000012540984,0.936061,0.00016855988,0.000035567216,0.000255534,4.856809e-8,0.0005942082,0.008892575],"genre_scores_gemma":[0.82176095,0.0000022721085,0.17706941,0.0001659039,0.000011604905,0.000008718725,0.00000179111,0.000011673738,0.00096767634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906933,0.000020497213,0.0001148979,0.00036896626,0.00014310319,0.00028322765],"domain_scores_gemma":[0.9993195,0.00012565845,0.00006286214,0.00037987114,0.000037171394,0.00007495986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006776279,0.0001102538,0.00011429137,0.000044153752,0.0001113657,0.000057964717,0.00041221592,0.00003061013,0.000029221615],"category_scores_gemma":[0.000008257484,0.00008591192,0.000022291364,0.00045549817,0.000023940822,0.00035760674,0.00010144783,0.00017409591,0.000174194],"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":[0.000005149445,0.000009913978,0.0008971504,0.0000030963022,0.000007723338,0.0000022013787,0.0006960844,0.9329831,0.0011940894,0.020289015,0.000004846495,0.0439076],"study_design_scores_gemma":[0.00033685993,0.000052784595,0.00032872366,0.0000063439,0.0000016075941,0.000008217941,0.00012779984,0.9965107,0.00033066882,0.0006230778,0.0015250846,0.00014811936],"about_ca_topic_score_codex":0.000002199878,"about_ca_topic_score_gemma":0.0000023122916,"teacher_disagreement_score":0.76778096,"about_ca_system_score_codex":0.00001910848,"about_ca_system_score_gemma":0.000013150231,"threshold_uncertainty_score":0.35033864},"labels":[],"label_agreement":null},{"id":"W2998360197","doi":"10.1007/s11263-020-01373-4","title":"A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":92,"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":"Segmentation; Image segmentation; Pixel; Segmentation-based object categorization; Pattern recognition (psychology); Image (mathematics); Set (abstract data type); Scale-space segmentation","score_opus":0.01786697280962874,"score_gpt":0.309480288127839,"score_spread":0.2916133153182103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998360197","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":[0.3689821,0.000034519573,0.6274615,0.0032251368,0.00019613659,0.00007439091,0.0000032664693,0.00001135099,0.000011594455],"genre_scores_gemma":[0.88320845,0.00005819844,0.11587144,0.0006658096,0.0001781344,0.0000018234118,0.000008965993,0.000005889879,0.0000012832046],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981494,0.0000998152,0.00075440103,0.00022252984,0.0006553116,0.00011855844],"domain_scores_gemma":[0.9983986,0.00023847795,0.0005530258,0.00017735123,0.00053330726,0.00009925315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008714318,0.00013481849,0.0003742756,0.0005444588,0.000023383824,0.0000813674,0.0010125053,0.000031512445,0.000014692809],"category_scores_gemma":[0.000012598448,0.000112256705,0.00023411633,0.00089136284,0.000035430458,0.0006216677,0.00030523277,0.00016658883,0.0000046569517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050663,0.0011423768,0.010984917,0.000059355854,0.0024448517,0.00046371383,0.005554332,0.36257145,0.39996657,0.00830888,0.001596781,0.20640016],"study_design_scores_gemma":[0.0009768469,0.00027317266,0.07634374,0.00006494723,0.0000673149,0.000024601082,0.000027910992,0.916357,0.004540972,0.00094617845,0.00026109422,0.0001162333],"about_ca_topic_score_codex":0.0000038022608,"about_ca_topic_score_gemma":0.000002694108,"teacher_disagreement_score":0.55378556,"about_ca_system_score_codex":0.00007631214,"about_ca_system_score_gemma":0.000024991854,"threshold_uncertainty_score":0.45776954},"labels":[],"label_agreement":null},{"id":"W2998860339","doi":"10.3390/app10020485","title":"3D Dense Separated Convolution Module for Volumetric Medical Image Analysis","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Overfitting; Redundancy (engineering); Kernel (algebra); Convolution (computer science); Pattern recognition (psychology); Deep learning; Segmentation; Computer vision; Algorithm; Artificial neural network; Mathematics","score_opus":0.03169635086599727,"score_gpt":0.30010592346943005,"score_spread":0.2684095726034328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998860339","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":[0.013900461,0.00007516197,0.98009986,0.0039727236,0.0000566258,0.00036808007,0.000004907825,0.0002573311,0.0012648796],"genre_scores_gemma":[0.7970023,0.000010846214,0.20117706,0.0015602446,0.000079809404,0.00012771922,0.0000075906337,0.0000046472273,0.000029753779],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979409,0.000028104674,0.00027458943,0.000756349,0.00062453863,0.0003755294],"domain_scores_gemma":[0.9988823,0.0003140894,0.0001322465,0.00030592803,0.00009130001,0.0002741271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004392002,0.00013216634,0.00022672208,0.00020535038,0.00046085293,0.00014695126,0.0014410161,0.000062223124,0.000036199748],"category_scores_gemma":[0.00016353381,0.00011688698,0.000088893015,0.008799898,0.00033766538,0.0003544776,0.00022443976,0.00010708316,0.000110155386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067939516,0.0003140996,0.0028294907,0.000048516587,0.00037729947,0.000016661426,0.0011483826,0.13039392,0.031172046,0.67331207,0.02675693,0.13356262],"study_design_scores_gemma":[0.00019865364,0.000054027198,0.0006981257,9.811765e-7,0.000039141072,0.0000015467233,0.000014555113,0.99129164,0.0017002544,0.0033064627,0.0025334451,0.00016119055],"about_ca_topic_score_codex":0.0000061499304,"about_ca_topic_score_gemma":0.000008827565,"teacher_disagreement_score":0.8608977,"about_ca_system_score_codex":0.000026972739,"about_ca_system_score_gemma":0.000118597614,"threshold_uncertainty_score":0.47665125},"labels":[],"label_agreement":null},{"id":"W2998905237","doi":"10.2523/iptc-20111-ms","title":"Smart Autopilot Drone System for Surface Surveillance and Anomaly Detection via Customizable Deep Neural Network","year":2020,"lang":"en","type":"article","venue":"International Petroleum Technology Conference","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Drone; Autopilot; Computer science; Anomaly detection; Global Positioning System; Artificial intelligence; Deep learning; Real-time computing; Quadcopter; Cloud computing; Inertial measurement unit; Remote sensing; Computer vision; Engineering; Aerospace engineering; Geography; Telecommunications","score_opus":0.014103551469427574,"score_gpt":0.23069521221274328,"score_spread":0.2165916607433157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998905237","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":[0.091530666,0.00015266586,0.8933292,0.012866401,0.00051136484,0.00029061252,0.000009672872,0.0009800441,0.0003293819],"genre_scores_gemma":[0.96484506,0.000019322306,0.034458805,0.0002812196,0.00015458658,0.0001337091,0.00000936282,0.000017516344,0.00008043457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832875,0.00003784989,0.00034564306,0.00071345846,0.0001919765,0.00038233396],"domain_scores_gemma":[0.99885327,0.00016581487,0.00023140387,0.00038175844,0.00026204326,0.000105698935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013386483,0.00021710276,0.0002707376,0.00009291298,0.00021865695,0.000115866,0.001172223,0.00015155258,0.0000054809593],"category_scores_gemma":[0.000060623897,0.00023297928,0.000049280305,0.0004595692,0.00013931555,0.0003633453,0.0004190087,0.00027741853,0.00002601201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000332505,0.00011968665,0.045538034,0.000126463,0.00025439693,0.0000447181,0.00014315317,0.16864298,0.061412808,0.5801578,0.0004676394,0.14275977],"study_design_scores_gemma":[0.0005435291,0.00016029157,0.0020316818,0.000011489489,0.0000057727543,0.00007625756,0.00001642652,0.98480123,0.0028880974,0.004085267,0.0051536327,0.0002263027],"about_ca_topic_score_codex":0.000018512133,"about_ca_topic_score_gemma":0.000092815746,"teacher_disagreement_score":0.8733144,"about_ca_system_score_codex":0.00009744524,"about_ca_system_score_gemma":0.000030447796,"threshold_uncertainty_score":0.9500619},"labels":[],"label_agreement":null},{"id":"W3000109616","doi":"10.1016/j.media.2020.101638","title":"Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":55,"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":"National Key Research and Development Program of China Stem Cell and Translational Research; National Key Research and Development Program of China","keywords":"Segmentation; Artificial intelligence; Deep learning; Computer science; Hausdorff distance; Inference; Atlas (anatomy); Pattern recognition (psychology); Medicine; Anatomy","score_opus":0.011147254427377887,"score_gpt":0.27307106594755104,"score_spread":0.26192381152017313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000109616","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":[0.0020367906,0.00014332235,0.99182945,0.0053023244,0.00005797418,0.00028483718,0.0000034507882,0.00017287958,0.00016897607],"genre_scores_gemma":[0.6493055,0.000090139634,0.34219354,0.0074597048,0.0006699389,0.00014286791,0.00009896837,0.000021352767,0.000017983399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980758,0.000075728596,0.00030083922,0.00055594766,0.00064631685,0.00034536226],"domain_scores_gemma":[0.9986791,0.00034297263,0.00011195589,0.000418342,0.000082447455,0.00036520595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023424588,0.00014165026,0.00027792665,0.00013458473,0.00020618844,0.000080161226,0.0006718647,0.00005726215,0.00008112158],"category_scores_gemma":[0.00015140117,0.00012688432,0.00040196264,0.0033251285,0.00006225974,0.0001160147,0.00013550301,0.00015355405,0.00007753993],"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":[0.000028537788,0.00015543439,0.0023806745,0.000013061277,0.0007371195,0.00003010091,0.00031901777,0.8755038,0.00034002206,0.0021963373,0.0076757623,0.11062018],"study_design_scores_gemma":[0.000267212,0.00006683234,0.0008878756,0.00000394681,0.00026066406,7.96576e-7,0.000011525777,0.9940306,0.0005707055,0.0004800322,0.0032866222,0.00013319775],"about_ca_topic_score_codex":0.0000031517263,"about_ca_topic_score_gemma":0.0000037754487,"teacher_disagreement_score":0.6496359,"about_ca_system_score_codex":0.000030203408,"about_ca_system_score_gemma":0.000019970046,"threshold_uncertainty_score":0.5174192},"labels":[],"label_agreement":null},{"id":"W3000160544","doi":"10.1109/tvlsi.2019.2961602","title":"Stride 2 1-D, 2-D, and 3-D Winograd for Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":75,"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":"Korea Evaluation Institute of Industrial Technology","keywords":"STRIDE; Convolutional neural network; Kernel (algebra); Computer science; Speedup; Digital signal processing; Convolution (computer science); Algorithm; Computational complexity theory; Parallel computing; Field-programmable gate array; Mathematics; Artificial neural network; Artificial intelligence; Embedded system; Computer hardware; Discrete mathematics","score_opus":0.0222694878945663,"score_gpt":0.2494657186137322,"score_spread":0.2271962307191659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000160544","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":[0.003346482,0.0002538788,0.9909786,0.0023054215,0.0013231101,0.0011458448,0.00016910693,0.0004201838,0.000057380923],"genre_scores_gemma":[0.99169606,0.00005157355,0.006147469,0.000898082,0.00035998583,0.0005743826,0.00003588934,0.000029697105,0.00020683599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786806,0.00012001091,0.0005424081,0.0007138326,0.00032038928,0.00043532593],"domain_scores_gemma":[0.9985716,0.00033748974,0.0001764893,0.00041797248,0.00021105316,0.0002854054],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001857038,0.0002989581,0.00032022997,0.0001148052,0.0005823812,0.0002474945,0.00044764395,0.00015521231,0.00000917463],"category_scores_gemma":[0.000011687115,0.00028647785,0.00017030323,0.00063802674,0.00008253085,0.00083796744,0.00000662996,0.00039129806,0.000021973105],"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":[0.00021455105,0.00031156882,0.00007904003,0.00008095978,0.000104500774,0.0000057233297,0.00088365684,0.92726386,0.0029589292,0.02326371,0.0056743375,0.03915919],"study_design_scores_gemma":[0.00077764434,0.00021035098,0.00004989386,0.000034283286,0.000024164692,0.00003072263,0.00016585308,0.99169654,0.0013688622,0.00015189173,0.005210837,0.0002789491],"about_ca_topic_score_codex":0.000014428497,"about_ca_topic_score_gemma":0.000051366318,"teacher_disagreement_score":0.9883496,"about_ca_system_score_codex":0.00008898417,"about_ca_system_score_gemma":0.000043997763,"threshold_uncertainty_score":0.99995875},"labels":[],"label_agreement":null},{"id":"W3003342783","doi":"10.4271/2020-01-0136","title":"LiDAR and Camera-Based Convolutional Neural Network Detection for Autonomous Driving","year":2020,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; National Research Council Canada","funders":"","keywords":"Convolutional neural network; Computer science; Lidar; Artificial intelligence; Computer vision; Remote sensing; Geology","score_opus":0.015762404657773572,"score_gpt":0.24787861307052747,"score_spread":0.2321162084127539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003342783","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":[0.6057831,0.0036212127,0.07452604,0.24751592,0.0025399525,0.016447403,0.0002963543,0.037068147,0.012201865],"genre_scores_gemma":[0.9282902,0.000051267234,0.06211351,0.008175676,0.00043528245,0.00078880804,0.000025497027,0.00007652685,0.000043220654],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.995917,0.00012556773,0.0008364548,0.0015845645,0.000557618,0.0009787488],"domain_scores_gemma":[0.99690014,0.0010917996,0.00028796413,0.0010165974,0.00013363785,0.0005698445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032503772,0.00062103546,0.0006689762,0.00009372372,0.0008599436,0.00019471496,0.0013158264,0.00044827806,0.00003281366],"category_scores_gemma":[0.00049514504,0.0005979384,0.000325309,0.0010772957,0.0007197107,0.00070747035,0.0005861218,0.0009557901,0.000028395594],"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":[0.00017832758,0.00011347271,0.00016883347,0.00003396854,0.000022849965,0.000013060315,0.000017526383,0.0035465583,0.92733103,0.042019106,0.001565549,0.024989704],"study_design_scores_gemma":[0.0011468632,0.0020870378,0.9034971,0.00008408682,0.000060711256,0.00008950139,0.000014251246,0.00053549674,0.00035388616,0.009124585,0.08199555,0.0010109508],"about_ca_topic_score_codex":0.000006965212,"about_ca_topic_score_gemma":0.006917442,"teacher_disagreement_score":0.92697716,"about_ca_system_score_codex":0.00021065488,"about_ca_system_score_gemma":0.0001178799,"threshold_uncertainty_score":0.9996472},"labels":[],"label_agreement":null},{"id":"W3003410437","doi":"10.1109/globalsip45357.2019.8969121","title":"Ising Dropout with Node Grouping for Training and Compression of Deep Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"MNIST database; Dropout (neural networks); Computer science; Ising model; Artificial neural network; Inference; Artificial intelligence; Multilayer perceptron; Perceptron; Deep learning; Graph; Machine learning; Algorithm; Pattern recognition (psychology); Theoretical computer science","score_opus":0.02046318601140085,"score_gpt":0.26153901968964294,"score_spread":0.2410758336782421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003410437","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":[0.0778559,0.000079982085,0.9210643,0.0001904215,0.000053796586,0.0002944653,2.420581e-7,0.000074114,0.00038674424],"genre_scores_gemma":[0.76680815,0.0000025345207,0.23295188,0.0001727122,0.00002372954,0.000011439572,0.0000013328129,0.0000074993877,0.000020722377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928916,0.00001301504,0.00014473434,0.00026991792,0.000086153166,0.0001970089],"domain_scores_gemma":[0.99932975,0.00022246728,0.00009570488,0.00026540578,0.000039005226,0.000047656544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000064589316,0.000088961184,0.0001465279,0.00003073538,0.00009096848,0.000036298043,0.00023965107,0.000027213526,0.0000023841806],"category_scores_gemma":[0.0000035534424,0.00006823431,0.000021239402,0.00017066616,0.000035341724,0.00032575964,0.000112744434,0.00007158967,4.792323e-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":[0.000039112216,0.000032794487,0.0052605565,0.00004533486,0.000016718157,0.0000010845756,0.0009049715,0.5083222,0.0070968294,0.0679923,0.000025114032,0.410263],"study_design_scores_gemma":[0.00033505526,0.00006991184,0.0006165919,0.000026628086,0.000003160302,0.000010598514,0.00005678655,0.99740195,0.00036836808,0.0009393529,0.00007543122,0.0000961666],"about_ca_topic_score_codex":0.0000028614147,"about_ca_topic_score_gemma":0.0000049252276,"teacher_disagreement_score":0.68895227,"about_ca_system_score_codex":0.0000073858405,"about_ca_system_score_gemma":0.0000043911314,"threshold_uncertainty_score":0.27825144},"labels":[],"label_agreement":null},{"id":"W3003420419","doi":"10.48550/arxiv.2002.00157","title":"Shared Mobile-Cloud Inference for Collaborative Intelligence","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Inference; Computer science; Cloud computing; Mobile device; Energy consumption; Curse of dimensionality; Latency (audio); Bandwidth (computing); Distributed computing; Artificial intelligence; Real-time computing; Data mining; Computer network; Telecommunications; Engineering","score_opus":0.1272527442954257,"score_gpt":0.24805655527153653,"score_spread":0.12080381097611081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003420419","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":[0.0029752566,0.00009394598,0.9931013,0.00041056683,0.00046389928,0.0016402161,0.00014706826,0.0004531883,0.00071458076],"genre_scores_gemma":[0.95844024,0.0002504857,0.040355403,0.00026055455,0.00015777728,0.00006668053,0.000058238333,0.000024668489,0.00038593257],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975409,0.00008198941,0.000274188,0.0016442756,0.00009044321,0.00036819666],"domain_scores_gemma":[0.9970038,0.00054522423,0.00036954522,0.0013505485,0.00048104906,0.0002498306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009481723,0.00037556322,0.0003889702,0.00011920464,0.00022885794,0.00015763684,0.003040968,0.00022639472,0.000016724043],"category_scores_gemma":[0.000109949506,0.00045301198,0.0001711735,0.0015051317,0.00014401172,0.0004577653,0.0025505954,0.0005407476,0.000120648016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028175338,0.000046488633,0.00007296706,0.000050295832,0.000040446594,0.000029630122,0.00031575357,0.41864845,0.00005787566,0.57810456,0.00076013396,0.0018452294],"study_design_scores_gemma":[0.00015522796,0.00010869017,0.000041529674,0.000052778465,0.000031519852,8.9278706e-7,0.00009822766,0.6643806,0.00061000703,0.32966667,0.004376749,0.00047708722],"about_ca_topic_score_codex":0.000010291625,"about_ca_topic_score_gemma":0.000018784693,"teacher_disagreement_score":0.955465,"about_ca_system_score_codex":0.00019252,"about_ca_system_score_gemma":0.0003552329,"threshold_uncertainty_score":0.99979216},"labels":[],"label_agreement":null},{"id":"W3003482062","doi":"10.3390/app10030884","title":"DE-CapsNet: A Diverse Enhanced Capsule Network with Disperse Dynamic Routing","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"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":"Convolutional neural network; Benchmark (surveying); Computer science; Sigmoid function; MNIST database; Routing (electronic design automation); Artificial intelligence; Network architecture; Pattern recognition (psychology); Field (mathematics); Data mining; Deep learning; Artificial neural network; Computer network; Mathematics; Cartography","score_opus":0.018094262127937136,"score_gpt":0.24664874606500503,"score_spread":0.2285544839370679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003482062","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":[0.06985878,0.000049135753,0.9185534,0.0020130153,0.0000577805,0.0003399779,0.00000135747,0.0003522455,0.008774279],"genre_scores_gemma":[0.86130553,0.000010337729,0.13645984,0.0020214794,0.00009831312,0.000064383574,0.0000013151772,0.000008951077,0.000029831252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979986,0.00002688518,0.00018828794,0.0007714765,0.00037111776,0.00064365775],"domain_scores_gemma":[0.9991536,0.0000956415,0.00014202055,0.00034767413,0.000028277875,0.00023279665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021933319,0.00018616914,0.00017740195,0.000029985546,0.0007262271,0.00021243698,0.0014594328,0.000038626866,0.000016521119],"category_scores_gemma":[0.00001581697,0.00015015954,0.000035699817,0.0018421056,0.00036124705,0.00040229762,0.00036134067,0.00017563767,0.00010022305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029860848,0.000065739194,0.0004091545,0.00001481566,0.000027248183,0.000019898764,0.006910662,0.3765468,0.077561356,0.46749467,0.0010986245,0.06982118],"study_design_scores_gemma":[0.0007047668,0.00029491083,0.001425836,0.000035798843,0.000021912143,0.000017584001,0.0013915998,0.97154766,0.010296427,0.011762019,0.0016532651,0.0008482086],"about_ca_topic_score_codex":0.0000073847787,"about_ca_topic_score_gemma":0.00002055385,"teacher_disagreement_score":0.79144675,"about_ca_system_score_codex":0.00004441223,"about_ca_system_score_gemma":0.00010962155,"threshold_uncertainty_score":0.6123329},"labels":[],"label_agreement":null},{"id":"W3003567739","doi":"10.1109/tmi.2021.3065727","title":"Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Réseau en Bio-Imagerie du Quebec; Nvidia","keywords":"Discriminator; Computer science; Segmentation; Artificial intelligence; Context (archaeology); Encoder; Identity (music); Task (project management); Image segmentation; Pattern recognition (psychology); Computer vision; Scheme (mathematics); Image (mathematics); Market segmentation; Machine learning","score_opus":0.02718755279694996,"score_gpt":0.33385051356773565,"score_spread":0.3066629607707857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003567739","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":[0.0013507201,0.00014722516,0.99186265,0.0035141942,0.0017122357,0.0009589094,0.0000483514,0.0003317526,0.00007396689],"genre_scores_gemma":[0.4970901,0.00046995183,0.4997944,0.00090061733,0.00046505406,0.0008910557,0.00027562358,0.000069613896,0.000043586915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948615,0.000264756,0.00086481683,0.0011938579,0.0023655812,0.0004494861],"domain_scores_gemma":[0.99719036,0.00040378352,0.0003511759,0.0012172165,0.0003024605,0.00053500896],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009815344,0.0003770744,0.0005393197,0.00028301677,0.0002760953,0.00024491947,0.00275065,0.00040510824,0.00019678289],"category_scores_gemma":[0.0001203992,0.00039635124,0.0003170347,0.0006171173,0.00029979742,0.0011182498,0.00009563695,0.0014210868,0.000003947542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009811512,0.003085934,0.000028571925,0.00084927963,0.00037167507,0.000075802855,0.002266731,0.109218664,0.0060307393,0.0025370088,0.0009066837,0.8745308],"study_design_scores_gemma":[0.0012787028,0.000043785058,0.000038884973,0.00026502257,0.00010938912,0.000053648284,0.00020557677,0.9801333,0.015140625,0.0021739511,0.00012842528,0.000428705],"about_ca_topic_score_codex":0.00010207022,"about_ca_topic_score_gemma":0.000024419947,"teacher_disagreement_score":0.8741021,"about_ca_system_score_codex":0.00014295227,"about_ca_system_score_gemma":0.0007324061,"threshold_uncertainty_score":0.99984884},"labels":[],"label_agreement":null},{"id":"W3003615215","doi":"10.1109/globalsip45357.2019.8969558","title":"Deep Learning Methods for Image Segmentation Containing Translucent Overlapped Objects","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Convolutional neural network; Image segmentation; Pattern recognition (psychology); Deep learning; Computer vision; Image (mathematics); Scale-space segmentation","score_opus":0.019689548759774113,"score_gpt":0.3495496862145617,"score_spread":0.32986013745478754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003615215","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":[0.0023363465,0.00006320982,0.9932633,0.00030598347,0.00014607243,0.0007872632,2.2522622e-7,0.00027732915,0.002820243],"genre_scores_gemma":[0.17567141,0.000011351237,0.8233605,0.00031666947,0.000025753112,0.0001266434,0.000007016016,0.000011635961,0.00046899068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990005,0.00008442335,0.00018691513,0.00036968561,0.000098788114,0.00025967177],"domain_scores_gemma":[0.99901253,0.0005025919,0.000089320725,0.0002710327,0.000064452855,0.000060096343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027186642,0.000110260175,0.0001362194,0.000046300887,0.00013068451,0.00008016534,0.00031590837,0.000032991495,0.00003263934],"category_scores_gemma":[0.000026112713,0.00010498338,0.00006451681,0.0002488381,0.000012608104,0.0005370294,0.00005710343,0.00010536667,0.000051303894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025028776,0.000031012263,0.00018160418,0.000023367627,0.000018020599,6.1265325e-7,0.0013182363,0.014025027,0.24575455,0.07329297,0.000033926368,0.66529566],"study_design_scores_gemma":[0.0007560316,0.00017542897,0.00024611675,0.0000073720203,0.0000066616535,0.000004987167,0.00025746078,0.9369233,0.05189058,0.0055842744,0.0039495784,0.00019820301],"about_ca_topic_score_codex":0.0000027917295,"about_ca_topic_score_gemma":0.0000031686725,"teacher_disagreement_score":0.9228983,"about_ca_system_score_codex":0.00004640194,"about_ca_system_score_gemma":0.000016862079,"threshold_uncertainty_score":0.4281098},"labels":[],"label_agreement":null},{"id":"W3004414032","doi":"10.1109/iros40897.2019.8968242","title":"Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Orientation (vector space); Computer vision; Artificial intelligence; Point cloud; Pose; Benchmark (surveying); Process (computing); Object detection; Pedestrian; Object (grammar); Point (geometry); View synthesis; Tracking (education); Task (project management); Video tracking; Segmentation; Engineering; Mathematics","score_opus":0.014945333135183056,"score_gpt":0.261779489040155,"score_spread":0.24683415590497196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004414032","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":[0.02386592,0.000006526908,0.97447056,0.00011019178,0.0001368217,0.0010263282,0.0000018249046,0.00029302354,0.00008878287],"genre_scores_gemma":[0.56715494,0.0000023747095,0.4322862,0.000051004048,0.000021327782,0.0002970798,0.0000031317977,0.000009774412,0.00017416255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990487,0.000026594826,0.00018040655,0.00041129984,0.00014276372,0.00019020871],"domain_scores_gemma":[0.99899215,0.00034483252,0.00014204784,0.00038155314,0.00009388856,0.000045522313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012483318,0.00012101542,0.00011686022,0.00006792945,0.00016249031,0.00009081298,0.00023838699,0.000040681003,0.0000069222374],"category_scores_gemma":[0.000071089926,0.00010039955,0.000033971624,0.000389235,0.000015342506,0.0008568741,0.00004054621,0.000060103404,0.00005939697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020441368,0.000036338148,0.000090192196,0.000022810695,0.0000074785025,2.138868e-7,0.00009619809,0.032599,0.010737586,0.0060042436,0.0000064528735,0.9503791],"study_design_scores_gemma":[0.00037170565,0.00023861171,0.00041014,0.000015830852,0.0000130414455,0.000007864535,0.000033885175,0.9702703,0.028167173,0.00017990846,0.00013700912,0.00015457254],"about_ca_topic_score_codex":0.00002619608,"about_ca_topic_score_gemma":0.00007280192,"teacher_disagreement_score":0.95022446,"about_ca_system_score_codex":0.00007397067,"about_ca_system_score_gemma":0.000040428586,"threshold_uncertainty_score":0.40941748},"labels":[],"label_agreement":null},{"id":"W3004550220","doi":"10.1109/icip40778.2020.9190858","title":"Input Dropout for Spatially Aligned Modalities","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Modalities; Computer science; Dropout (neural networks); Artificial intelligence; Robustness (evolution); RGB color model; Computer vision; Machine learning","score_opus":0.046981742066624324,"score_gpt":0.3007218508778042,"score_spread":0.25374010881117987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004550220","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":[0.00016173701,0.000062208666,0.9723937,0.020867767,0.0004799771,0.0011292419,0.000028951517,0.00084203016,0.004034401],"genre_scores_gemma":[0.19372661,0.00003925853,0.79927444,0.0032602702,0.00052818225,0.001013375,0.000077371515,0.000036723657,0.002043767],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99817276,0.000031797805,0.00035751058,0.0009053534,0.00022944644,0.00030314556],"domain_scores_gemma":[0.99813014,0.00021356659,0.00019777764,0.0011819979,0.00013670733,0.00013983365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007470917,0.00027871423,0.0003268246,0.000056533023,0.00012355641,0.0002146297,0.0020208892,0.00015792223,0.000014080645],"category_scores_gemma":[0.000047879563,0.00027045506,0.00017282288,0.00016661808,0.00004888744,0.00016603993,0.0023740497,0.0001794242,0.00005613272],"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":[0.000007974568,0.000025669528,0.000010222956,0.000093022965,0.000033905057,0.0000023819648,0.0003394171,0.012172336,0.000363792,0.96643436,0.008469725,0.012047199],"study_design_scores_gemma":[0.00014335029,0.00003328819,0.000010477566,0.000017281738,0.0000094391535,0.0000014854709,0.0000061985934,0.4118637,0.0025315317,0.5655519,0.019519338,0.00031199557],"about_ca_topic_score_codex":0.000034742625,"about_ca_topic_score_gemma":0.000024131405,"teacher_disagreement_score":0.40088245,"about_ca_system_score_codex":0.000057232446,"about_ca_system_score_gemma":0.0001839623,"threshold_uncertainty_score":0.9999748},"labels":[],"label_agreement":null},{"id":"W3005428380","doi":"","title":"Selective Segmentation Networks Using Top-Down Attention","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Segmentation; Top-down and bottom-up design; Task (project management); Baseline (sea); Artificial intelligence; Benchmark (surveying); Selection (genetic algorithm); Hierarchy; Machine learning; Convolutional neural network; Feed forward; Feature selection; Object (grammar); Pattern recognition (psychology)","score_opus":0.08128177256051665,"score_gpt":0.22127998588555153,"score_spread":0.13999821332503487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005428380","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":[0.07157861,0.000035146117,0.9266064,0.00017428178,0.0003667049,0.000508826,0.0000052149726,0.00034966285,0.0003751407],"genre_scores_gemma":[0.97859913,0.00009721141,0.020711858,0.00018015504,0.00018158712,0.0000032802018,0.00004267717,0.000020349073,0.00016374973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980826,0.00012578466,0.0002011989,0.0011918426,0.0000875211,0.0003110479],"domain_scores_gemma":[0.9985381,0.00007427756,0.0003486495,0.0007331256,0.00015367781,0.0001522158],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007927663,0.00027558464,0.00024276928,0.00012646132,0.00025547255,0.000108088825,0.001050205,0.00020148458,0.000007188418],"category_scores_gemma":[0.0000111770805,0.00035402534,0.00015769574,0.0011513632,0.00005944479,0.0005245756,0.0013749127,0.0006063311,0.000036937356],"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":[0.000011601894,0.000028584785,0.0010917354,0.000015975946,0.00004376605,0.00003050317,0.000064935426,0.95431,0.0006548695,0.04261479,0.00015322935,0.0009800034],"study_design_scores_gemma":[0.00021490762,0.000024861945,0.0012202244,0.000034995355,0.000052752177,0.0000034910636,0.000024343124,0.96115845,0.00023180452,0.036640868,0.00006780556,0.00032546473],"about_ca_topic_score_codex":0.000038504128,"about_ca_topic_score_gemma":0.000011517095,"teacher_disagreement_score":0.9070205,"about_ca_system_score_codex":0.0003970636,"about_ca_system_score_gemma":0.000097388045,"threshold_uncertainty_score":0.99989116},"labels":[],"label_agreement":null},{"id":"W3005621308","doi":"10.3390/s20040993","title":"Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Aeronautical Science Foundation of China; National Natural Science Foundation of China","keywords":"Conditional random field; Computer science; Artificial intelligence; Segmentation; Computer vision; Aerial image; Field (mathematics); Terrain; Image segmentation; Object (grammar); Scale (ratio); Image (mathematics); Pattern recognition (psychology); Geography; Cartography; Mathematics","score_opus":0.01797600338495906,"score_gpt":0.2700800010348486,"score_spread":0.2521039976498895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005621308","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":[0.059668086,0.000018121065,0.9275799,0.011600308,0.00009698883,0.0005844735,0.000016705366,0.0002259827,0.00020942643],"genre_scores_gemma":[0.8981007,0.000011929776,0.0981083,0.0031669633,0.00021125673,0.00008390422,0.000112628,0.000012878406,0.00019147416],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921143,0.00003065417,0.00016599048,0.00029485472,0.00013637722,0.0001607163],"domain_scores_gemma":[0.9992808,0.00031603244,0.000077671124,0.00017223897,0.00006901284,0.00008422362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045409135,0.000093633054,0.00010707505,0.000026833344,0.0001449708,0.00004291141,0.00022116331,0.000034263358,0.000028420714],"category_scores_gemma":[0.00007757705,0.000091765534,0.00005871856,0.0002571918,0.000021956004,0.00019354832,0.000056490848,0.00007145846,0.00007650557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006516773,0.00023756702,0.00054133177,0.00017840219,0.000104775085,0.000044496617,0.004221879,0.052625,0.6277284,0.07279845,0.21078525,0.030082792],"study_design_scores_gemma":[0.0023250117,0.00016802961,0.00034307136,0.000011122599,0.000016007129,0.0000074125,0.00006186597,0.88820803,0.09544911,0.006413435,0.0067478465,0.0002490615],"about_ca_topic_score_codex":0.000001761984,"about_ca_topic_score_gemma":0.0000013659707,"teacher_disagreement_score":0.83843255,"about_ca_system_score_codex":0.000015030804,"about_ca_system_score_gemma":0.000014490384,"threshold_uncertainty_score":0.374209},"labels":[],"label_agreement":null},{"id":"W3005746719","doi":"10.1109/newcas49341.2020.9159818","title":"An Energy-Efficient Accelerator Architecture with Serial Accumulation Dataflow for Deep CNNs","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dram; Computer science; Dataflow; Convolutional neural network; Computation; CAS latency; Latency (audio); Energy consumption; Convolution (computer science); Parallel computing; Embedded system; Computer hardware; Efficient energy use; Dataflow architecture; Architecture; Energy (signal processing); Artificial neural network; Artificial intelligence; Algorithm; Semiconductor memory","score_opus":0.0536550752939182,"score_gpt":0.3169074449826198,"score_spread":0.2632523696887016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005746719","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":[0.0025821188,0.000022081344,0.9929767,0.00207939,0.0003479316,0.001074442,0.000101696794,0.00067697186,0.00013862932],"genre_scores_gemma":[0.37765118,0.000004769656,0.6193009,0.00087008177,0.0008057262,0.0005904852,0.00070583663,0.000041736894,0.000029311574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975355,0.000058869387,0.00031923503,0.0014020351,0.00032125917,0.00036310128],"domain_scores_gemma":[0.99747646,0.0001228626,0.00024402597,0.0017501494,0.00015637839,0.00025013808],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007651914,0.0003819009,0.0003149128,0.00008671913,0.00021732564,0.0004128988,0.002125463,0.00018400048,0.000014732215],"category_scores_gemma":[0.000015193362,0.00031115732,0.000085848405,0.00037460256,0.000040698887,0.00022200945,0.0010684141,0.0003562746,0.000007805748],"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":[0.00005544104,0.00006745728,0.0000047644558,0.000019642179,0.000019451218,0.0000018115371,0.0001468197,0.9317745,0.0012394981,0.039090995,0.00030186283,0.027277786],"study_design_scores_gemma":[0.00034734333,0.00013114263,0.000061053106,0.000015642941,0.000018943958,0.0000050057756,0.0000061623673,0.970713,0.003053163,0.017702334,0.007521255,0.00042495516],"about_ca_topic_score_codex":0.000015294638,"about_ca_topic_score_gemma":0.000121246674,"teacher_disagreement_score":0.37506905,"about_ca_system_score_codex":0.000065837674,"about_ca_system_score_gemma":0.000169724,"threshold_uncertainty_score":0.9999341},"labels":[],"label_agreement":null},{"id":"W3005815523","doi":"10.1109/icassp40776.2020.9053011","title":"Back-And-Forth Prediction for Deep Tensor Compression","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Tensor (intrinsic definition); Computer science; Feature (linguistics); Artificial intelligence; Artificial neural network; Deep learning; Compression (physics); Algorithm; Pattern recognition (psychology); Computer engineering; Mathematics","score_opus":0.04423447246880794,"score_gpt":0.28929771865754156,"score_spread":0.24506324618873362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005815523","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":[0.00027321148,0.00013125174,0.9916603,0.0042375326,0.00035289128,0.0012100338,0.000030408204,0.00037187155,0.00173253],"genre_scores_gemma":[0.061313868,0.00021613899,0.93517613,0.0011748994,0.00041448424,0.0006527361,0.00013361542,0.000030257715,0.00088787475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985442,0.000021935184,0.00027257318,0.00081045186,0.00014827834,0.0002025339],"domain_scores_gemma":[0.9988161,0.00013439902,0.00015670995,0.0006653082,0.00008458719,0.00014295157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049729184,0.00020468223,0.00022811814,0.000042640644,0.00014213653,0.000105922496,0.0007227739,0.00014608786,0.00001660354],"category_scores_gemma":[0.000016958442,0.00017793484,0.00008303729,0.00012635691,0.000034662433,0.00016818792,0.0015884014,0.00028692122,0.000040968363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010547391,0.0002507416,0.001823627,0.0009850945,0.00017578162,0.0000052441096,0.00094191555,0.094686255,0.0039487965,0.426809,0.0881232,0.38214487],"study_design_scores_gemma":[0.00020945758,0.00004629184,0.00091731135,0.00003604023,0.000012387105,0.0000050184694,0.0000040775303,0.9191676,0.00048225795,0.047741354,0.031179348,0.00019886279],"about_ca_topic_score_codex":0.000003496402,"about_ca_topic_score_gemma":0.000003962162,"teacher_disagreement_score":0.82448137,"about_ca_system_score_codex":0.000027753527,"about_ca_system_score_gemma":0.000028087454,"threshold_uncertainty_score":0.72559726},"labels":[],"label_agreement":null},{"id":"W3007581096","doi":"10.1145/3373087.3375380","title":"HPIPE: Heterogeneous Layer-Pipelined and Sparse-Aware CNN Inference for FPGAs","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Stratix; Computer science; Field-programmable gate array; Digital signal processing; Compiler; Convolutional neural network; Computer architecture; Throughput; Parallel computing; Embedded system; Computer hardware; Artificial intelligence","score_opus":0.062153404091027875,"score_gpt":0.30091379027967274,"score_spread":0.23876038618864487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007581096","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":[0.004841366,0.00008558958,0.98532915,0.008699041,0.00004939584,0.00039970307,0.000006187468,0.00033172927,0.00025784964],"genre_scores_gemma":[0.8564021,0.00003781837,0.13913162,0.004047779,0.0001037001,0.0000917762,0.00000461711,0.000010891368,0.00016972651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898815,0.000014795382,0.00018170475,0.00047009182,0.000106664345,0.00023856564],"domain_scores_gemma":[0.99913394,0.00020285347,0.000057435685,0.00033908596,0.00007584077,0.00019086343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003715282,0.00013206669,0.00014046041,0.000019821378,0.0001181203,0.000079803605,0.0005053627,0.00004078484,0.000011973418],"category_scores_gemma":[0.000052019248,0.00011925073,0.000038023205,0.0002493304,0.000032835193,0.00022395421,0.00026976268,0.00007436721,0.000044641463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009460425,0.00018397636,0.0015288054,0.0001863297,0.0000722723,0.000044264783,0.0016091319,0.05959989,0.019588301,0.33221897,0.026523542,0.5583499],"study_design_scores_gemma":[0.0003789833,0.00012782741,0.000096744196,0.000006378717,0.0000061606534,0.000012746918,0.000008996925,0.9460117,0.013320838,0.008977156,0.0307988,0.0002536682],"about_ca_topic_score_codex":0.0000032980297,"about_ca_topic_score_gemma":0.000011163129,"teacher_disagreement_score":0.8864118,"about_ca_system_score_codex":0.000009708614,"about_ca_system_score_gemma":0.000024197832,"threshold_uncertainty_score":0.4862904},"labels":[],"label_agreement":null},{"id":"W3008067816","doi":"10.1109/tcsii.2020.2977015","title":"A Novel Architecture for Early Detection of Negative Output Features in Deep Neural Network Accelerators","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Computer science; Speedup; Computation; Activation function; Benchmark (surveying); Deep learning; Feature (linguistics); Object detection; Dataflow; Pattern recognition (psychology); Contextual image classification; Artificial neural network; Artificial intelligence; Reduction (mathematics); Network architecture; Parallel computing; Image (mathematics); Algorithm; Mathematics","score_opus":0.026444822261214078,"score_gpt":0.24360957775465863,"score_spread":0.21716475549344455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008067816","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":[0.021699792,0.00019631341,0.97472614,0.0004140418,0.00080532645,0.0017748737,0.00006586446,0.00027107223,0.00004657164],"genre_scores_gemma":[0.99413824,0.000007148124,0.004250552,0.00032456,0.00029372243,0.0008859778,0.0000020565249,0.000047143185,0.00005060098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99753755,0.00012675195,0.00062721816,0.0008134073,0.00037256736,0.00052251806],"domain_scores_gemma":[0.9982307,0.00048898475,0.0003156965,0.00059041544,0.000165818,0.00020840767],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011643143,0.0003464708,0.0004952358,0.00017077195,0.00040015744,0.000112126014,0.00084685674,0.00018069749,0.0000012401064],"category_scores_gemma":[0.000020153528,0.00035471228,0.00020185047,0.0013061798,0.00007833648,0.0005156849,0.000012019708,0.00055124355,0.0000027408566],"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":[0.000065290646,0.00011060078,0.0000073598903,0.00007411279,0.000041812564,0.0000021629633,0.0037985812,0.89691025,0.055597875,0.00045626808,0.000055584478,0.0428801],"study_design_scores_gemma":[0.00701942,0.0024844091,0.0034860615,0.00059342803,0.00013910417,0.00017245013,0.00055573636,0.76334333,0.21338497,0.0014325732,0.0051574227,0.0022311118],"about_ca_topic_score_codex":0.00016614568,"about_ca_topic_score_gemma":0.00015557911,"teacher_disagreement_score":0.97243845,"about_ca_system_score_codex":0.00008188697,"about_ca_system_score_gemma":0.000048767284,"threshold_uncertainty_score":0.9998905},"labels":[],"label_agreement":null},{"id":"W3008301614","doi":"10.1109/itsc45102.2020.9294260","title":"Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Convolutional neural network; Pattern recognition (psychology); Deep learning; Machine learning; Image segmentation; Bayesian probability; Pixel; Image (mathematics); Artificial neural network","score_opus":0.047967982290293365,"score_gpt":0.28042712036569495,"score_spread":0.23245913807540158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008301614","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":[0.00748184,0.000015176523,0.98885924,0.0028553272,0.00002075631,0.00037686055,8.384363e-7,0.00008893801,0.00030100672],"genre_scores_gemma":[0.755634,0.0000013174754,0.24359125,0.0005850724,0.00008266883,0.00008109672,0.000007463656,0.000006299388,0.000010828094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931324,0.000036898393,0.00015035938,0.00021743706,0.0001195067,0.00016258974],"domain_scores_gemma":[0.99920225,0.00040946563,0.00014801789,0.00009993807,0.000097008226,0.000043326803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066299835,0.00008102894,0.00009119282,0.00001118006,0.00026520857,0.000048324502,0.0002941638,0.000016870978,0.0000068425015],"category_scores_gemma":[0.000031965465,0.000055283366,0.000031827916,0.00029693358,0.000057743808,0.00044883703,0.00007063225,0.00014326168,6.740256e-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":[0.000034483528,0.000008328675,0.00011273899,0.0000052359305,0.000018021534,4.8077067e-7,0.0027249167,0.8596199,0.005387052,0.03046132,0.00005473101,0.10157279],"study_design_scores_gemma":[0.00027289806,0.00011254667,0.00034784919,0.0000027131987,0.0000053275107,0.0000042252195,0.0007604111,0.99618155,0.0018830923,0.00029812488,0.000062091145,0.00006915325],"about_ca_topic_score_codex":0.0000014759445,"about_ca_topic_score_gemma":0.0000040002305,"teacher_disagreement_score":0.7481522,"about_ca_system_score_codex":0.0000138121395,"about_ca_system_score_gemma":0.0000201045,"threshold_uncertainty_score":0.22543903},"labels":[],"label_agreement":null},{"id":"W3009496543","doi":"10.1016/j.media.2020.101685","title":"An integrated deep learning framework for joint segmentation of blood pool and myocardium","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Segmentation; Pyramid (geometry); Pooling; Computer science; Artificial intelligence; Deep learning; Pattern recognition (psychology); Feature (linguistics); Task (project management); Image segmentation; Residual; Feature learning; Feature extraction; Computer vision; Engineering; Mathematics","score_opus":0.015040855844964224,"score_gpt":0.2940954518938342,"score_spread":0.27905459604886995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009496543","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":[0.009676892,0.00010684035,0.9866785,0.0032990125,0.000011596974,0.00012696734,0.0000023249636,0.00008632897,0.000011526231],"genre_scores_gemma":[0.5383782,0.000049682458,0.4607187,0.0007342092,0.000050707513,0.0000290687,0.000030361598,0.000005560919,0.00000353042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988286,0.000087348,0.00028019608,0.00035305033,0.00030484385,0.00014594663],"domain_scores_gemma":[0.9990613,0.00020971899,0.00013312079,0.00022986077,0.00011531161,0.00025069728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002027514,0.00009241737,0.00026759392,0.00008476027,0.00008470917,0.000049383863,0.00031554076,0.000063276544,0.0000454767],"category_scores_gemma":[0.0005923388,0.00008065555,0.00011285227,0.0014278298,0.0000793967,0.0002439008,0.000088417124,0.00020659377,0.0000027510546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039921495,0.00035725397,0.004225766,0.00010167247,0.0023922487,0.000040492094,0.003945833,0.021355284,0.05565566,0.011919606,0.0002534901,0.8997128],"study_design_scores_gemma":[0.00021144436,0.00012760582,0.00058229594,0.0000076909055,0.00054311677,0.0000015965683,0.00014351883,0.9872495,0.009203447,0.0016728807,0.00015995881,0.00009695397],"about_ca_topic_score_codex":0.0000079169795,"about_ca_topic_score_gemma":0.0000041274843,"teacher_disagreement_score":0.9658942,"about_ca_system_score_codex":0.0000066662355,"about_ca_system_score_gemma":0.00001945764,"threshold_uncertainty_score":0.3289038},"labels":[],"label_agreement":null},{"id":"W3009579997","doi":"10.14569/ijacsa.2020.0110279","title":"An Investigation of a Convolution Neural Network Architecture for Detecting Distracted Pedestrians","year":2020,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sheridan College","funders":"","keywords":"Computer science; SAFER; Pedestrian; Convolutional neural network; Architecture; Artificial intelligence; Deep learning; Distracted driving; Machine learning; Human–computer interaction; Computer security; Poison control; Transport engineering","score_opus":0.019752454113086102,"score_gpt":0.2885755945388652,"score_spread":0.2688231404257791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009579997","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":[0.08549529,0.00005705397,0.9096624,0.004212852,0.00018173733,0.0003363411,0.00000825575,0.000040957562,0.0000050809213],"genre_scores_gemma":[0.65169734,0.000013464007,0.34722453,0.00049257686,0.0005339446,0.000029293295,0.0000036211543,0.0000048367096,3.8485558e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984633,0.00002693241,0.00050153735,0.00033769596,0.0004793861,0.00019118276],"domain_scores_gemma":[0.9972923,0.00021118997,0.0006758204,0.00022465193,0.0013701177,0.00022594705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031280174,0.00011632688,0.00016937449,0.00012577248,0.00022118738,0.00014189497,0.0015020982,0.00003074295,4.2851934e-7],"category_scores_gemma":[0.000059803875,0.0001094902,0.000057885143,0.00085653184,0.0002589123,0.0013191402,0.00014268594,0.00016454973,3.707781e-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":[0.000048471455,0.000041730607,0.00043111274,0.000011192796,0.000019864334,0.0000015186728,0.000588156,0.3367873,0.087776996,0.028484326,0.000048013462,0.5457613],"study_design_scores_gemma":[0.0010463209,0.0007339034,0.006688244,0.000052030275,0.00002086975,0.00016097647,0.00006669082,0.9295548,0.014037253,0.04439334,0.0029686475,0.00027692885],"about_ca_topic_score_codex":0.0000012206335,"about_ca_topic_score_gemma":0.0000015872796,"teacher_disagreement_score":0.5927675,"about_ca_system_score_codex":0.00004819894,"about_ca_system_score_gemma":0.00017981463,"threshold_uncertainty_score":0.44648808},"labels":[],"label_agreement":null},{"id":"W3010200124","doi":"10.1109/wacv45572.2020.9093431","title":"ADNet: Adaptively Dense Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Fuse (electrical); Benchmark (surveying); Reuse; Convolutional neural network; Layer (electronics); Feature (linguistics); Artificial intelligence; Residual; Visualization; Machine learning; Algorithm","score_opus":0.03481171050867766,"score_gpt":0.24778150703906876,"score_spread":0.2129697965303911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010200124","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":[0.001086325,0.00014491018,0.9840154,0.012392672,0.00009842884,0.00015550684,0.0000015391129,0.0004331653,0.0016720706],"genre_scores_gemma":[0.9143636,0.000010055345,0.07550556,0.009618398,0.00026259114,0.000026929356,0.0000041024946,0.0000088522,0.0001998671],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989774,0.000031636733,0.00016543339,0.0003949283,0.00016633894,0.00026426188],"domain_scores_gemma":[0.9992933,0.000112998125,0.000055332526,0.00026207927,0.00006144781,0.0002148274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034167177,0.00011737877,0.00010495007,0.000016667605,0.00012686156,0.000049265396,0.00066270074,0.00003750506,0.000054728156],"category_scores_gemma":[0.000018827766,0.000108917855,0.000050972718,0.00048259913,0.000053613472,0.000355419,0.00027530323,0.00016292876,0.0001309747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018512432,0.00003328507,0.0004562621,0.000002267885,0.000014651779,0.000019196032,0.00012613673,0.26621208,0.0004652514,0.6763333,0.028738692,0.027580349],"study_design_scores_gemma":[0.0001501709,0.000045134682,0.0015610073,9.938725e-7,0.0000019537035,0.000013051024,0.00000499713,0.98551965,0.00007176874,0.0016421627,0.010855445,0.00013364379],"about_ca_topic_score_codex":0.0000025999511,"about_ca_topic_score_gemma":0.0000026716027,"teacher_disagreement_score":0.9132773,"about_ca_system_score_codex":0.00001865346,"about_ca_system_score_gemma":0.00002196257,"threshold_uncertainty_score":0.4441541},"labels":[],"label_agreement":null},{"id":"W3011003272","doi":"10.29007/cd8h","title":"Zero-skipping in CapsNet. Is it worth it?","year":2020,"lang":"en","type":"article","venue":"EPiC series in computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Computer science; Zero (linguistics); Artificial neural network; Multiplication (music); Artificial intelligence; Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.04750061519743814,"score_gpt":0.29756828310977507,"score_spread":0.2500676679123369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011003272","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":[0.09925201,0.00025171885,0.85082245,0.044377442,0.00037620513,0.00036249828,0.0000010593199,0.00027855227,0.0042780484],"genre_scores_gemma":[0.88993603,0.000026975298,0.100527525,0.009267651,0.00015196853,0.000010272644,0.0000015973609,0.000012758392,0.000065234424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998295,0.000062179686,0.00044825257,0.0005918886,0.00018051668,0.00042213532],"domain_scores_gemma":[0.99907464,0.00025450118,0.00012127481,0.0004299665,0.00002717305,0.00009244026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017695043,0.00016508688,0.00023185839,0.000079641606,0.00012176309,0.00008217152,0.0008931375,0.000052020543,0.000019695599],"category_scores_gemma":[0.000102900856,0.00018819865,0.00003875162,0.001417143,0.00005232696,0.00044760868,0.00069568347,0.00034275567,0.00004751521],"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":[0.00004332042,0.00015806241,0.08455097,0.00017395981,0.00003237773,0.00036784323,0.06239902,0.3906187,0.0013146774,0.10428134,0.02107772,0.33498198],"study_design_scores_gemma":[0.0006079558,0.00008320646,0.010124359,0.0002210589,0.0000025256297,0.00003293253,0.00056995905,0.936155,0.0011918078,0.010993124,0.03943029,0.0005877644],"about_ca_topic_score_codex":0.000029554249,"about_ca_topic_score_gemma":0.00007680472,"teacher_disagreement_score":0.790684,"about_ca_system_score_codex":0.0000695633,"about_ca_system_score_gemma":0.00004192802,"threshold_uncertainty_score":0.7674518},"labels":[],"label_agreement":null},{"id":"W3012468284","doi":"10.1109/access.2020.2981411","title":"A Layer-Partitioning Approach for Faster Execution of Neural Network-Based Embedded Applications in Edge Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ontario Institute of Technology","keywords":"Computer science; Artificial neural network; Internet of Things; Enhanced Data Rates for GSM Evolution; Layer (electronics); Edge computing; Latency (audio); Embedded system; Edge device; Distributed computing; Computer network; Cloud computing; Operating system; Artificial intelligence; Telecommunications","score_opus":0.06386654883837793,"score_gpt":0.318396623293407,"score_spread":0.25453007445502907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012468284","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":[0.0070429756,0.00008246787,0.99051267,0.00064200093,0.00007690259,0.0012494355,0.0000053220547,0.00015424253,0.0002339712],"genre_scores_gemma":[0.927517,0.0000041459953,0.069447264,0.001216396,0.00035400686,0.0013848953,0.00005157143,0.000018603485,0.0000061048854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984653,0.00005577873,0.00042323678,0.0005275365,0.00015269751,0.00037543237],"domain_scores_gemma":[0.9988455,0.00022077157,0.00023926262,0.00047198296,0.0001073928,0.00011506876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012402252,0.00016813594,0.0002454182,0.00006217412,0.0001464442,0.00010889298,0.0011491358,0.000079121084,0.0000023777739],"category_scores_gemma":[0.000014267074,0.00017331025,0.00008891266,0.0013989896,0.000055568507,0.0006769952,0.00013870286,0.00017846763,0.0000017309354],"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":[0.000020001982,0.00006103704,0.0019596072,0.000030213514,0.0000047562557,3.0990083e-7,0.00007287913,0.9887477,0.00035173906,0.0046440624,0.0010847336,0.003022947],"study_design_scores_gemma":[0.0004565909,0.00003487641,0.0012273383,0.000011925238,0.000008720345,6.8933707e-7,0.000008591448,0.99415517,0.0021152971,0.0012415757,0.0005697872,0.0001694612],"about_ca_topic_score_codex":0.000006005133,"about_ca_topic_score_gemma":0.000007991183,"teacher_disagreement_score":0.9210654,"about_ca_system_score_codex":0.00003011855,"about_ca_system_score_gemma":0.000036542755,"threshold_uncertainty_score":0.7067387},"labels":[],"label_agreement":null},{"id":"W3014967422","doi":"10.48550/arxiv.2004.00123","title":"EOLO: Embedded Object Segmentation only Look Once","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Task (project management); Embedding; Object (grammar); Pattern recognition (psychology); Scale-space segmentation; Computer vision; Machine learning; Image segmentation","score_opus":0.07316238969611112,"score_gpt":0.21929491677259608,"score_spread":0.14613252707648494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014967422","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":[0.047723126,0.000046653295,0.9477001,0.0005137384,0.00034125798,0.00054667343,0.000013634154,0.00061096624,0.0025038726],"genre_scores_gemma":[0.96214664,0.0001639544,0.03584787,0.000523008,0.00010301149,0.0000042658817,0.000044939396,0.000023697841,0.0011425938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775743,0.0001191758,0.0002240467,0.00143898,0.0001190108,0.00034135208],"domain_scores_gemma":[0.997917,0.00013820337,0.00033293603,0.0012862246,0.00011643454,0.00020920769],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008148128,0.00032626448,0.0002958989,0.0001293276,0.00020112837,0.0001061457,0.002048265,0.00018664943,0.000022983288],"category_scores_gemma":[0.00002366647,0.00040822424,0.00017135938,0.00091576966,0.00009879921,0.00051043706,0.0021678933,0.0006218764,0.0003229423],"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":[0.000033315227,0.000097044496,0.0009210717,0.000069126,0.00009170432,0.00029120978,0.0005965981,0.6646116,0.0010143514,0.3260687,0.0015243113,0.0046809753],"study_design_scores_gemma":[0.00051265623,0.000068385794,0.00084459165,0.00006283292,0.00006797591,0.000011020468,0.00010090575,0.8539054,0.0014356967,0.14117026,0.0011102015,0.0007101114],"about_ca_topic_score_codex":0.000026582056,"about_ca_topic_score_gemma":0.00002898442,"teacher_disagreement_score":0.9144235,"about_ca_system_score_codex":0.0002678778,"about_ca_system_score_gemma":0.0002702944,"threshold_uncertainty_score":0.999837},"labels":[],"label_agreement":null},{"id":"W3015256491","doi":"10.1109/access.2020.2987281","title":"Deep Learning in Next-Frame Prediction: A Benchmark Review","year":2020,"lang":"en","type":"review","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Frame (networking); Architecture; Categorization; Deep learning; Sequence (biology); Machine learning; Representation (politics); Network architecture; Function (biology); Data mining","score_opus":0.07988615315963458,"score_gpt":0.37519718084535075,"score_spread":0.29531102768571615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015256491","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":[2.4060155e-8,0.8408828,0.15609622,0.00047611407,0.00039801648,0.0011606398,0.0000025678746,0.00030314884,0.0006805025],"genre_scores_gemma":[0.0000018463489,0.9939641,0.0036314258,0.0008614934,0.00042478368,0.00096161355,0.0000377899,0.000039776805,0.00007721742],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99709815,0.00027900757,0.00088704756,0.0010103505,0.0003322218,0.0003932217],"domain_scores_gemma":[0.9978349,0.00037107494,0.00056841073,0.0010010012,0.00005753045,0.0001670868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022189599,0.00042666323,0.0014188664,0.00015250429,0.00012633589,0.00030284168,0.0038166055,0.00018925525,0.000037103804],"category_scores_gemma":[0.00012316398,0.0003873032,0.0002937414,0.0031598664,0.000037503756,0.0012836873,0.0007288964,0.0012577008,0.00022707369],"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":[2.1698428e-7,0.000012629265,0.0000028554769,0.008233149,0.00001268245,0.000027836411,0.000010437387,0.0002065315,4.2276824e-8,0.0003991901,0.0018754281,0.989219],"study_design_scores_gemma":[0.000047185913,0.000018001974,0.0000036095755,0.022813408,0.00007873299,0.000056095596,3.266131e-7,0.0046038018,1.4734833e-7,0.00043465602,0.9716442,0.00029981043],"about_ca_topic_score_codex":0.00000413521,"about_ca_topic_score_gemma":0.000005863853,"teacher_disagreement_score":0.9889192,"about_ca_system_score_codex":0.00015685358,"about_ca_system_score_gemma":0.00017218133,"threshold_uncertainty_score":0.9998579},"labels":[],"label_agreement":null},{"id":"W3015359712","doi":"10.1109/icassp40776.2020.9054513","title":"A Probabilistic Scheme for Representation Learning with Radial Transform Images","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Representation (politics); Probabilistic logic; Pattern recognition (psychology); Artificial neural network; Deep learning; Scheme (mathematics); Image segmentation; Mathematics","score_opus":0.03172854819434925,"score_gpt":0.28209159161997654,"score_spread":0.2503630434256273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015359712","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":[0.0014231423,0.000010083441,0.98378336,0.012298214,0.000011671931,0.00062663516,7.553072e-7,0.00029963648,0.0015464729],"genre_scores_gemma":[0.45875755,0.0000042732236,0.5401335,0.00047703172,0.00008860279,0.00026785283,0.0000065733925,0.000009750318,0.00025489085],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934477,0.00001096705,0.000104873325,0.00030256945,0.000096170836,0.00014064784],"domain_scores_gemma":[0.9995809,0.00011775615,0.000037100224,0.0001404636,0.000050375173,0.0000733997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000032640295,0.00006930894,0.00007707836,0.000013668376,0.00010921379,0.000054407366,0.00024056406,0.000014902591,0.000005701919],"category_scores_gemma":[0.000046066314,0.00005544598,0.00002662757,0.0003265639,0.000026952795,0.00036054515,0.00002474151,0.00007460713,0.000009870491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029442093,0.00012171206,0.0008339251,0.00014035661,0.0000539017,0.000009434804,0.00343608,0.13535714,0.028682139,0.4859803,0.006120068,0.3389705],"study_design_scores_gemma":[0.00083170045,0.00038866597,0.0002334237,0.0000063501084,0.000008168694,0.000008052501,0.000051488052,0.9630378,0.013007158,0.010099029,0.012119277,0.00020887972],"about_ca_topic_score_codex":0.0000019555057,"about_ca_topic_score_gemma":0.000002608705,"teacher_disagreement_score":0.82768065,"about_ca_system_score_codex":0.000011597182,"about_ca_system_score_gemma":0.000023114471,"threshold_uncertainty_score":0.22610214},"labels":[],"label_agreement":null},{"id":"W3015858876","doi":"10.1109/icassp40776.2020.9053986","title":"Deep Geometric Knowledge Distillation with Graphs","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Distillation; Inference; Deep learning; Artificial intelligence; Representation (politics); Machine learning; Graph; Dimension (graph theory); Knowledge engineering; Knowledge representation and reasoning; Knowledge graph; Theoretical computer science; Mathematics","score_opus":0.03003185521250493,"score_gpt":0.2763663626853463,"score_spread":0.24633450747284139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015858876","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":[0.0003922515,0.00041442708,0.98318845,0.0013602515,0.00019480834,0.00048575446,0.0000020867417,0.0007028569,0.013259114],"genre_scores_gemma":[0.70832783,0.00008051198,0.29089564,0.00014808003,0.00010672318,0.00013954932,0.00003234016,0.000020873798,0.00024842893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840623,0.000032427226,0.00023179421,0.0008723604,0.00022529608,0.0002318774],"domain_scores_gemma":[0.99835855,0.00014998495,0.00017699006,0.0010218188,0.00013499906,0.00015763991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005093688,0.0002511973,0.0002378933,0.00025567674,0.00010614977,0.00014713897,0.0012484296,0.00010916957,0.000013951492],"category_scores_gemma":[0.000024201017,0.0002035797,0.00007545907,0.00237868,0.00004490007,0.0001712934,0.0015187209,0.00042124168,0.00016317361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010635831,0.00012368015,0.001164991,0.00012550091,0.00007666295,0.000013455413,0.0003349335,0.0760235,0.000043342894,0.6651487,0.0033877806,0.25354686],"study_design_scores_gemma":[0.00021099205,0.00007731486,0.0088100005,0.00003562624,0.000025892099,0.000011854979,0.000005546158,0.81272537,0.00023674004,0.16698551,0.010229508,0.0006456506],"about_ca_topic_score_codex":0.0000060873144,"about_ca_topic_score_gemma":0.000026058797,"teacher_disagreement_score":0.73670185,"about_ca_system_score_codex":0.0000582611,"about_ca_system_score_gemma":0.00007169324,"threshold_uncertainty_score":0.8301739},"labels":[],"label_agreement":null},{"id":"W3016732970","doi":"10.48550/arxiv.2004.08708","title":"Adaptive Attention Span in Computer Vision","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"FLOPS; Computer science; Kernel (algebra); Convolution (computer science); Artificial intelligence; Code (set theory); Transformer; Algorithm; Computer engineering; Computer vision; Parallel computing; Theoretical computer science; Machine learning; Mathematics; Programming language; Voltage; Artificial neural network; Engineering","score_opus":0.0817765409891001,"score_gpt":0.2090942608997732,"score_spread":0.1273177199106731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016732970","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":[0.05497481,0.000017211036,0.94304806,0.0005570083,0.0002848092,0.0003940694,0.0000055392147,0.0002626298,0.0004558797],"genre_scores_gemma":[0.97374994,0.00006632937,0.025725788,0.00018769117,0.000108821834,0.0000020828136,0.000019021007,0.000014717074,0.00012558802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980582,0.00011640296,0.00019662648,0.0012746812,0.00008224142,0.00027184084],"domain_scores_gemma":[0.99859387,0.00008250997,0.00021080923,0.0008974079,0.000074984506,0.00014044165],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000080409685,0.0002516538,0.0002662522,0.00018590904,0.00008880881,0.00006596661,0.0014442141,0.00017745144,0.0000062092977],"category_scores_gemma":[0.0000050049052,0.00031011607,0.00013618174,0.00090198545,0.00006512801,0.00041682893,0.0026407938,0.0006327065,0.00017191307],"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":[0.000027218624,0.00008355378,0.0007480844,0.000019339332,0.000019027146,0.00026640095,0.00008006872,0.82740915,0.00006926447,0.1647402,0.0003495029,0.0061882166],"study_design_scores_gemma":[0.00026151875,0.00006752729,0.00803214,0.0000684856,0.00001060127,0.0000024228775,0.000007204199,0.9048195,0.00001548516,0.086223826,0.00021418385,0.000277109],"about_ca_topic_score_codex":0.000035740653,"about_ca_topic_score_gemma":0.00003759319,"teacher_disagreement_score":0.91877514,"about_ca_system_score_codex":0.00021319106,"about_ca_system_score_gemma":0.00006126964,"threshold_uncertainty_score":0.9999351},"labels":[],"label_agreement":null},{"id":"W3018600685","doi":"10.48550/arxiv.2004.12058","title":"NullSpaceNet: Nullspace Convoluional Neural Network with Differentiable Loss Function","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Differentiable function; Separable space; Computer science; Reduction (mathematics); Inference; Function (biology); Interpretation (philosophy); Feature (linguistics); Algorithm; Mathematics; Artificial intelligence; Point (geometry); Artificial neural network; Pattern recognition (psychology); Mathematical analysis; Geometry","score_opus":0.05029672789779551,"score_gpt":0.17721722030486717,"score_spread":0.12692049240707165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018600685","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":[0.06052842,0.00011924239,0.9356735,0.0012520561,0.0004903925,0.00056186714,0.00001980118,0.00062801846,0.00072671036],"genre_scores_gemma":[0.99013764,0.000114717586,0.0067898487,0.0004912527,0.00040464455,0.000007783381,0.00009303222,0.000044530836,0.001916551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99713314,0.00013019684,0.00020991887,0.0017242478,0.0001806074,0.00062191085],"domain_scores_gemma":[0.99755615,0.00014382902,0.000373958,0.0014003847,0.00017221058,0.00035344536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000070219125,0.00050605857,0.00044190546,0.000096396965,0.00034265456,0.00016208216,0.0016996246,0.00024691597,0.00005283463],"category_scores_gemma":[0.000007538425,0.0005315901,0.00017837324,0.0011475772,0.00018713476,0.00045027642,0.0019727421,0.00096773956,0.00013434459],"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":[0.00011049686,0.000053070187,0.0039082416,0.00003125234,0.000088386245,0.00012402938,0.000028390083,0.8239305,0.000024819652,0.1670209,0.004524836,0.00015508613],"study_design_scores_gemma":[0.0007112323,0.00016589709,0.005282346,0.00007557601,0.00013092485,0.000016847856,0.000016784334,0.9023576,0.00003775568,0.084412515,0.006046229,0.0007463119],"about_ca_topic_score_codex":0.000036337726,"about_ca_topic_score_gemma":0.00006605552,"teacher_disagreement_score":0.92960924,"about_ca_system_score_codex":0.00017817352,"about_ca_system_score_gemma":0.00016191123,"threshold_uncertainty_score":0.99971354},"labels":[],"label_agreement":null},{"id":"W3021232010","doi":"10.1109/mass.2019.00013","title":"Challenges of Designing Computer Vision-Based Pedestrian Detector for Supporting Autonomous Driving","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pedestrian detection; Computer science; Pedestrian; Convolutional neural network; Field (mathematics); Artificial intelligence; Focus (optics); Deep learning; Object detection; Feature (linguistics); Open research; Machine learning; Human–computer interaction; Computer vision; Pattern recognition (psychology); Engineering; Transport engineering","score_opus":0.029057899858318952,"score_gpt":0.29661915458106364,"score_spread":0.26756125472274467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021232010","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":[0.023534527,0.000045711582,0.97415054,0.00083513145,0.00014591982,0.00060336693,6.5361115e-7,0.00023542238,0.00044873502],"genre_scores_gemma":[0.57396823,0.0000019465062,0.42584595,0.000076009586,0.00003974364,0.00002999206,8.371867e-7,0.000009425707,0.00002788386],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873084,0.00003056528,0.00034153913,0.00044984365,0.00013949382,0.00030774082],"domain_scores_gemma":[0.99809635,0.00093278073,0.00024241763,0.00056282105,0.00009117944,0.00007447831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026941398,0.00013067476,0.00020709893,0.000084650346,0.000085075335,0.000042059884,0.000610782,0.00004801115,0.000011493197],"category_scores_gemma":[0.000027435513,0.00012103928,0.00008838207,0.00018035213,0.00002045935,0.00027181447,0.00013695013,0.000072025956,0.000018789695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013896533,0.000118060874,0.001344117,0.00015514043,0.00002162578,0.0000024549463,0.00027861173,0.05839148,0.03939502,0.0592124,0.00016795013,0.8408992],"study_design_scores_gemma":[0.00039865501,0.0003007388,0.0014501676,0.00004266238,0.000003658096,0.000003175464,0.000014721589,0.96902376,0.026187513,0.0018087898,0.0005846288,0.00018151167],"about_ca_topic_score_codex":0.0000025194836,"about_ca_topic_score_gemma":0.000009513064,"teacher_disagreement_score":0.9106323,"about_ca_system_score_codex":0.000029494142,"about_ca_system_score_gemma":0.000067956564,"threshold_uncertainty_score":0.49358386},"labels":[],"label_agreement":null},{"id":"W3023750926","doi":"","title":"Compact Neural Representation Using Attentive Network Pruning.","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Redundancy (engineering); Pruning; Artificial intelligence; Benchmark (surveying); Artificial neural network; Reduction (mathematics); Machine learning; Mathematics","score_opus":0.18050379499768257,"score_gpt":0.25045953028650414,"score_spread":0.06995573528882157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023750926","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":[0.10854572,0.00006567628,0.88859266,0.00047700765,0.0005078582,0.0006674443,0.000008259524,0.00046963393,0.00066571595],"genre_scores_gemma":[0.9880678,0.00005652369,0.011081045,0.00025135124,0.0003071116,0.0000012589092,0.000035768106,0.00002812468,0.00017101916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747294,0.00021604428,0.00026683818,0.0014676282,0.00012209441,0.00045445486],"domain_scores_gemma":[0.99762726,0.00019072575,0.00049907435,0.0012954143,0.0001502374,0.00023726474],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009121491,0.00034827527,0.0003940011,0.000107203705,0.00033232517,0.00015289438,0.0017691077,0.00017112705,0.000011204375],"category_scores_gemma":[0.00002672663,0.00043464816,0.00022634391,0.0014053552,0.00011979792,0.00060641865,0.0020840769,0.00073380827,0.000049052975],"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":[0.000017215716,0.00002303584,0.0028156813,0.000021968777,0.000044455577,0.00012369362,0.00010987211,0.9641731,0.000098442564,0.031872924,0.00044810414,0.00025151868],"study_design_scores_gemma":[0.0002184338,0.0000227681,0.0021617727,0.00011232026,0.00005861701,0.000007557708,0.000024388866,0.9557457,0.00006551057,0.04110075,0.00009643779,0.00038577092],"about_ca_topic_score_codex":0.00007756859,"about_ca_topic_score_gemma":0.000015495123,"teacher_disagreement_score":0.8795221,"about_ca_system_score_codex":0.00021052064,"about_ca_system_score_gemma":0.00012141533,"threshold_uncertainty_score":0.9998105},"labels":[],"label_agreement":null},{"id":"W3027324862","doi":"10.48550/arxiv.2005.11549","title":"Self-supervised Robust Object Detectors from Partially Labelled Datasets","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Computer science; Detector; Object (grammar); Pattern recognition (psychology)","score_opus":0.08109277217692266,"score_gpt":0.19878175336004872,"score_spread":0.11768898118312605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027324862","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":[0.056286164,0.00009080382,0.93991196,0.0004290787,0.0004563551,0.00071100396,0.0004291129,0.0012874248,0.00039807238],"genre_scores_gemma":[0.92942244,0.00028387812,0.06906713,0.000378355,0.00020564102,0.0000066921657,0.0005252826,0.00004043454,0.00007013147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682224,0.00020661183,0.00030047545,0.002047193,0.00014941132,0.00047407474],"domain_scores_gemma":[0.99645215,0.00028776005,0.0003036025,0.0024805171,0.00009442325,0.00038152194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000094491086,0.00047607286,0.00047043545,0.00012518422,0.00026883357,0.0001584817,0.0037921194,0.0002846848,0.00005146185],"category_scores_gemma":[0.00003984916,0.00057064556,0.00021077403,0.0010453652,0.00007941713,0.0004874407,0.004481052,0.00083099824,0.0003722856],"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":[0.00008418841,0.00027998348,0.001260142,0.00009599575,0.00042361228,0.0009504284,0.00050975,0.94397885,0.000719218,0.045740545,0.0038762214,0.002081076],"study_design_scores_gemma":[0.00060942804,0.000038651466,0.00035872986,0.000034529454,0.0001232083,0.0000017078186,0.00001594066,0.96791524,0.0006610466,0.025651652,0.003936599,0.0006532479],"about_ca_topic_score_codex":0.00013347711,"about_ca_topic_score_gemma":0.00016878736,"teacher_disagreement_score":0.8731363,"about_ca_system_score_codex":0.00019497878,"about_ca_system_score_gemma":0.00024134948,"threshold_uncertainty_score":0.9996745},"labels":[],"label_agreement":null},{"id":"W3027597167","doi":"","title":"Daydream: Accurately Estimating the Efficacy of Performance Optimizations for DNN Training","year":2020,"lang":"en","type":"article","venue":"USENIX Annual Technical Conference","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Profiling (computer programming); Artificial neural network; Machine learning; Software; Artificial intelligence; Graph; Parallel computing; Computer engineering; Theoretical computer science; Programming language","score_opus":0.13563331079340182,"score_gpt":0.33832420100376936,"score_spread":0.20269089021036754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027597167","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":[0.004669628,0.00002149988,0.98759335,0.006147426,0.000055971093,0.00061974506,0.00002856107,0.00035688948,0.0005069318],"genre_scores_gemma":[0.64213675,0.0000065674903,0.35733452,0.00033071788,0.00006922004,0.00009310098,0.000006010052,0.000009192996,0.000013899093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985968,0.000035867582,0.0004255599,0.00042930225,0.00021147681,0.00030097962],"domain_scores_gemma":[0.9977794,0.001069811,0.0002192998,0.0005353065,0.0002717982,0.00012435761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016477218,0.00016335161,0.00023140614,0.000029089619,0.00028122324,0.000071925024,0.0015951134,0.00006976046,0.0000070220076],"category_scores_gemma":[0.00080886274,0.00012577245,0.00007910027,0.0006929539,0.00017986627,0.0004848946,0.00035197852,0.00025410551,0.0000079293195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006467305,0.00013961944,0.00008837552,0.00007484999,0.000032443193,0.0000021017358,0.005360084,0.18995172,0.006124929,0.44923577,0.0017950373,0.34713042],"study_design_scores_gemma":[0.00035044883,0.00020894395,0.00045141965,0.000039420538,0.00001566814,0.000009317775,0.000074062424,0.98999536,0.0025651034,0.0019507543,0.0041335225,0.0002059634],"about_ca_topic_score_codex":0.00000122225,"about_ca_topic_score_gemma":0.0000014647144,"teacher_disagreement_score":0.80004364,"about_ca_system_score_codex":0.000014953478,"about_ca_system_score_gemma":0.00013641106,"threshold_uncertainty_score":0.5128852},"labels":[],"label_agreement":null},{"id":"W3027934717","doi":"10.1109/isbi45749.2020.9098325","title":"An Efficient Hybrid Model for Kidney Tumor Segmentation in CT Images","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Context (archaeology); Image segmentation; Scale-space segmentation; Voxel; Pattern recognition (psychology); Computer vision; Deep learning; Inference","score_opus":0.02638130105200402,"score_gpt":0.292202305483108,"score_spread":0.26582100443110396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027934717","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":[0.018772006,0.0000059153617,0.97568834,0.0047559114,0.00002399678,0.00044294112,0.000011007137,0.00017291518,0.00012699721],"genre_scores_gemma":[0.62010896,0.000001080394,0.37549153,0.0042157904,0.000021389935,0.00011362835,0.000010033069,0.0000055973996,0.000031953714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924153,0.000012944555,0.00014612108,0.00034136136,0.00009596811,0.00016207848],"domain_scores_gemma":[0.99951375,0.000035649537,0.00004029382,0.00022832131,0.000027494858,0.00015448537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052297346,0.000075599106,0.00007426642,0.00002878353,0.000055209803,0.00004779141,0.00039540447,0.0000039995743,0.0000033120912],"category_scores_gemma":[0.00002074139,0.00007153396,0.000023261084,0.00022405316,0.00001285303,0.00027596462,0.00006487422,0.000044579047,0.000014334626],"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":[0.0000055060905,0.000057056117,0.000041339423,0.000007493787,0.0000010219372,0.0000028356064,0.00019436463,0.9599998,0.016358253,0.013712333,0.0029241145,0.006695879],"study_design_scores_gemma":[0.00024229674,0.000033080658,0.000037567817,0.0000020023742,0.0000010788262,0.000002312904,0.000011405214,0.9627024,0.034581058,0.0021757213,0.0001248737,0.00008619903],"about_ca_topic_score_codex":0.0000031086759,"about_ca_topic_score_gemma":0.0000015083641,"teacher_disagreement_score":0.60133696,"about_ca_system_score_codex":0.000025971878,"about_ca_system_score_gemma":0.000036353304,"threshold_uncertainty_score":0.291707},"labels":[],"label_agreement":null},{"id":"W3028020181","doi":"10.1155/2020/7286187","title":"Learning Feature Fusion in Deep Learning-Based Object Detector","year":2020,"lang":"en","type":"article","venue":"Journal of Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Pascal (unit); Object detection; Deep learning; Convolutional neural network; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Detector; Feature learning; Machine learning; Computer vision","score_opus":0.008148556892061912,"score_gpt":0.2164947666093686,"score_spread":0.20834620971730666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028020181","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":[0.0807844,0.0003510938,0.9173697,0.0012482223,0.00009938013,0.00004872018,4.6096893e-8,0.00007956826,0.000018842515],"genre_scores_gemma":[0.9342583,0.00002351103,0.065425195,0.000101533085,0.00016810693,0.0000019053523,2.2091837e-7,0.000012258885,0.000008999386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992998,0.000027215528,0.00020604733,0.00012310209,0.00017587714,0.00016793329],"domain_scores_gemma":[0.99946475,0.00014142548,0.000147931,0.00008266251,0.000051237752,0.000111966365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012436628,0.00009513679,0.00015205165,0.0001151707,0.000041085317,0.00003815795,0.00035408617,0.00004266313,0.0000032809469],"category_scores_gemma":[0.00026416843,0.00008970026,0.00006444209,0.0006087324,0.0000044061085,0.0002469366,0.00006000352,0.0008175858,0.000004375172],"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":[0.0000056235313,0.0000050106014,0.00046491786,0.000009138063,0.0000027034068,0.000034424676,0.00017050994,0.96934193,0.021529693,0.000039656374,0.000014479942,0.008381927],"study_design_scores_gemma":[0.00028276132,0.0001684877,0.0023819555,0.000045691773,0.0000025490135,0.000025041958,0.000011537294,0.98706347,0.0036617534,0.000011993431,0.0062471544,0.000097591525],"about_ca_topic_score_codex":3.8999147e-7,"about_ca_topic_score_gemma":8.066804e-7,"teacher_disagreement_score":0.85347384,"about_ca_system_score_codex":0.000049589373,"about_ca_system_score_gemma":0.000022084523,"threshold_uncertainty_score":0.36578703},"labels":[],"label_agreement":null},{"id":"W3031208025","doi":"","title":"Identifying Unknown Instances for Autonomous Driving","year":2019,"lang":"en","type":"article","venue":"Conference on Robot Learning","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Semantics (computer science); Focus (optics); Embedding; Task (project management); Set (abstract data type); Deep learning; Segmentation; Machine learning; Scale (ratio); Geography","score_opus":0.046846799663679925,"score_gpt":0.3055198328957578,"score_spread":0.25867303323207785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031208025","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":[0.02071696,0.00005246802,0.9699999,0.0007524192,0.0003785611,0.0004165596,2.66054e-7,0.00040696765,0.0072759027],"genre_scores_gemma":[0.92386496,0.000024991892,0.07244763,0.00015400306,0.00006400729,0.00009567478,0.000004134411,0.000017214288,0.0033274074],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858195,0.000048412912,0.00022146238,0.0005843987,0.00017676935,0.00038703883],"domain_scores_gemma":[0.9988808,0.00027946808,0.00018396167,0.00046931216,0.000105231,0.00008122814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017593478,0.00016650837,0.00019599378,0.000091432674,0.00031514696,0.00026612382,0.0008160354,0.000055831795,0.000036287285],"category_scores_gemma":[0.00006557807,0.00016840956,0.00006628212,0.0003023015,0.00003142117,0.0005540934,0.00018677943,0.00031554804,0.00027787668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051643096,0.000024219082,0.0018956452,0.00002300993,0.000010155839,0.000001648073,0.00045252423,0.1261725,0.00955121,0.7209819,0.000052198488,0.14082982],"study_design_scores_gemma":[0.00051250844,0.00022009145,0.0032654814,0.0002088081,0.0000062076774,0.0000060978937,0.00013736189,0.9449207,0.0025109213,0.020132622,0.027570216,0.0005089676],"about_ca_topic_score_codex":0.0000016863409,"about_ca_topic_score_gemma":0.0000064690153,"teacher_disagreement_score":0.903148,"about_ca_system_score_codex":0.000057100384,"about_ca_system_score_gemma":0.000070267655,"threshold_uncertainty_score":0.6867542},"labels":[],"label_agreement":null},{"id":"W3032959347","doi":"10.1109/access.2020.2999596","title":"Weakly Supervised Object Detection Using Complementary Learning and Instance Clustering","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Information Technology Research Centre; National Research Foundation","keywords":"Computer science; Cluster analysis; Artificial intelligence; Discriminative model; Pattern recognition (psychology); Pascal (unit); Object detection; Centroid; Object (grammar); Class (philosophy); Machine learning","score_opus":0.07110817050395161,"score_gpt":0.32783290143852073,"score_spread":0.2567247309345691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032959347","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":[0.31830937,0.000051124825,0.6807936,0.0003844463,0.00009719113,0.00013098678,6.7384644e-7,0.00015434381,0.000078283105],"genre_scores_gemma":[0.975231,0.000030608855,0.023808911,0.0007727171,0.00012807442,0.00001278928,0.0000010427548,0.000011238255,0.0000036155589],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991252,0.000042699623,0.00015957699,0.00035802508,0.00012382271,0.00019068587],"domain_scores_gemma":[0.99959815,0.000058964277,0.00007413226,0.00015098363,0.000029672745,0.000088105066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050802195,0.000104383274,0.00010924791,0.000036098958,0.00029222958,0.00019494114,0.0004822336,0.000021637275,0.000004863179],"category_scores_gemma":[0.00001000692,0.00011409128,0.000019915802,0.00047748393,0.000025593441,0.0010901261,0.00034567286,0.00016499832,0.000004094247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051870527,0.00003371657,0.013817036,0.00012054096,0.000036810754,0.000028065888,0.001852685,0.19689053,0.4574356,0.00048486245,0.00008175054,0.32916653],"study_design_scores_gemma":[0.00022239338,0.00003417147,0.0012231317,0.000013962657,0.000004495333,0.000011521689,0.000031402113,0.97814614,0.018344864,0.00026818452,0.0015451934,0.00015455675],"about_ca_topic_score_codex":0.000033627337,"about_ca_topic_score_gemma":0.00005733987,"teacher_disagreement_score":0.7812556,"about_ca_system_score_codex":0.00003370931,"about_ca_system_score_gemma":0.000012029495,"threshold_uncertainty_score":0.46525073},"labels":[],"label_agreement":null},{"id":"W3033009913","doi":"10.1109/tii.2020.2999901","title":"A Tensor-Based Multiattributes Visual Feature Recognition Method for Industrial Intelligence","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":114,"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; St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Cognitive neuroscience of visual object recognition; Artificial intelligence; Feature extraction; Visualization; Industrial production; Feature (linguistics); Computer vision; Pattern recognition (psychology); Human–computer interaction","score_opus":0.15564704649526723,"score_gpt":0.33797063495306273,"score_spread":0.1823235884577955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033009913","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":[0.00040050177,0.000003779658,0.9903064,0.006128149,0.0008250507,0.001557538,0.00027774982,0.0004518441,0.000049032653],"genre_scores_gemma":[0.12904595,0.00001585446,0.8624274,0.0065699513,0.0009071829,0.00081742724,0.0001102727,0.000050997896,0.000054934924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979779,0.00010154358,0.00076908455,0.0003335481,0.00037615365,0.00044176157],"domain_scores_gemma":[0.99748856,0.0012084215,0.00036744622,0.0003775442,0.00025271715,0.00030534161],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003058873,0.0003232713,0.00034273978,0.00018800962,0.000418585,0.00020742814,0.00071579235,0.00048401687,0.00001829051],"category_scores_gemma":[0.00016110743,0.0003168463,0.000220654,0.001353763,0.00005987743,0.000907923,0.0000074499785,0.0010598085,0.00009522104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027267713,0.00016245904,0.0000024965104,0.000023247963,0.000051088664,0.0000010416753,0.00053767103,0.23866875,0.00022178915,0.00023627706,0.0046322565,0.75519025],"study_design_scores_gemma":[0.001789896,0.00070741225,6.241697e-7,0.000054485477,0.00005305409,0.0000056179047,0.0001759652,0.8954209,0.088922665,0.00034742462,0.0121332435,0.0003886867],"about_ca_topic_score_codex":0.000005955125,"about_ca_topic_score_gemma":0.000002444845,"teacher_disagreement_score":0.7548016,"about_ca_system_score_codex":0.00011225885,"about_ca_system_score_gemma":0.00023058688,"threshold_uncertainty_score":0.99992836},"labels":[],"label_agreement":null},{"id":"W3033511611","doi":"10.1016/j.media.2020.101721","title":"MB-FSGAN: Joint segmentation and quantification of kidney tumor on CT by the multi-branch feature sharing generative adversarial network","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":56,"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":"Primary Research and Development Plan of Zhejiang Province; Taishan Scholar Project of Shandong Province; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology)","score_opus":0.026175718185868488,"score_gpt":0.28758356056552725,"score_spread":0.26140784237965875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033511611","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":[0.0063666673,0.00013520253,0.9568748,0.03625342,0.00006134393,0.00021903335,0.000013061783,0.000050241546,0.00002626286],"genre_scores_gemma":[0.8951125,0.00009934212,0.089945786,0.013984913,0.0004663491,0.000086589265,0.00016614035,0.000017304681,0.0001210689],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836165,0.00012668905,0.0003095193,0.00052496436,0.00049186667,0.00018529256],"domain_scores_gemma":[0.9988337,0.00014586236,0.00022532618,0.00040402208,0.00007548617,0.00031558215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029349595,0.00013909976,0.00026861773,0.00004879204,0.00020546562,0.000079083264,0.00052471453,0.000033960026,0.000039667815],"category_scores_gemma":[0.00035689893,0.00009977781,0.00011424482,0.0013865142,0.00013139175,0.00023083975,0.00015370721,0.00025986062,0.000011840864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016970816,0.0007367944,0.0077089937,0.00014081279,0.0030780714,0.000098299395,0.0059746257,0.11822854,0.3296234,0.011841065,0.36393702,0.15846266],"study_design_scores_gemma":[0.0003983593,0.00003617537,0.0006052169,0.000012393309,0.00019766972,0.0000025700658,0.00003576044,0.9733418,0.023962712,0.00019470515,0.0010896972,0.00012294634],"about_ca_topic_score_codex":0.000023009467,"about_ca_topic_score_gemma":0.000012076114,"teacher_disagreement_score":0.88874584,"about_ca_system_score_codex":0.000023427852,"about_ca_system_score_gemma":0.000041749456,"threshold_uncertainty_score":0.4068821},"labels":[],"label_agreement":null},{"id":"W3033960544","doi":"10.1109/tnnls.2021.3069970","title":"EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fujitsu","keywords":"Pruning; Artificial neural network; Dropout (neural networks); Population; Computer science; Backpropagation; Probabilistic logic; Artificial intelligence; Algorithm; Machine learning","score_opus":0.009987216775506999,"score_gpt":0.22601923938033636,"score_spread":0.21603202260482934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033960544","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":[0.012724216,0.00262417,0.98295426,0.0003186288,0.0008723076,0.0001965535,0.0000017222416,0.00024696917,0.000061170256],"genre_scores_gemma":[0.997126,0.00021452295,0.001880696,0.00024540004,0.00018298051,0.00006635196,0.0000060487437,0.0000414079,0.00023660074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748635,0.00047592874,0.0005281568,0.00073658745,0.00027334556,0.00049962575],"domain_scores_gemma":[0.9981511,0.0006824609,0.0002777112,0.00051510526,0.00014573814,0.00022783189],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002179092,0.00033338196,0.00045298674,0.00012846314,0.0007240406,0.00023541448,0.0003036111,0.00017187874,0.0000060309153],"category_scores_gemma":[0.000008833927,0.0003265497,0.00012272185,0.0008170086,0.00015038342,0.00033279613,0.0000152548855,0.00084654713,6.5393795e-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":[0.000017601202,0.000042048126,0.00018908533,0.000025695655,0.000024596211,0.000020974092,0.00006453234,0.9266605,0.00017868972,0.00082425366,0.000013134421,0.07193888],"study_design_scores_gemma":[0.00045440867,0.00017682897,0.00010970749,0.000076085,0.00003299564,0.00013813526,0.00007849806,0.9979042,0.00017926442,0.000020792473,0.0005252029,0.00030388666],"about_ca_topic_score_codex":0.000054304874,"about_ca_topic_score_gemma":0.0000369375,"teacher_disagreement_score":0.98440176,"about_ca_system_score_codex":0.0000305871,"about_ca_system_score_gemma":0.000020629317,"threshold_uncertainty_score":0.99991864},"labels":[],"label_agreement":null},{"id":"W3034512672","doi":"10.1109/cvpr42600.2020.00915","title":"PolyTransform: Deep Polygon Transformer for Instance Segmentation","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":185,"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":"Segmentation; Polygon (computer graphics); Computer science; Artificial intelligence; Exploit; Image segmentation; Scale-space segmentation; Computer vision; Segmentation-based object categorization; Pattern recognition (psychology)","score_opus":0.026919560373345617,"score_gpt":0.27103456269707066,"score_spread":0.24411500232372504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034512672","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":[0.0010356336,0.00007333893,0.9790952,0.016398737,0.00005639423,0.00062262214,0.000004241042,0.0002874591,0.0024263693],"genre_scores_gemma":[0.57525754,0.000045412093,0.4175997,0.006686072,0.000083199884,0.00019360616,0.000010074091,0.000012341542,0.00011206957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991657,0.0000071231457,0.00017867556,0.00030350275,0.00012738426,0.00021758511],"domain_scores_gemma":[0.9995909,0.000049313425,0.000033462256,0.0001717792,0.000036551264,0.00011799692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003301823,0.00010185384,0.0000958583,0.000021492335,0.00011835037,0.000042472297,0.00040125826,0.000029318428,0.00001539273],"category_scores_gemma":[0.0000050801973,0.00009340253,0.000057693884,0.00038917112,0.000018342284,0.00066483376,0.000011167579,0.000057875037,0.000032266074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004078839,0.000039541046,0.000045563982,0.000031707004,0.000014498486,7.701635e-7,0.0016294592,0.0021540301,0.082956985,0.39884678,0.0009820482,0.51325786],"study_design_scores_gemma":[0.0017168404,0.00035884924,0.00025750182,0.000007929231,0.000016151955,0.0000068489544,0.00015803843,0.5835675,0.34058785,0.020337163,0.052426293,0.0005590316],"about_ca_topic_score_codex":0.0000018797803,"about_ca_topic_score_gemma":0.000013309389,"teacher_disagreement_score":0.58141345,"about_ca_system_score_codex":0.000020689347,"about_ca_system_score_gemma":0.000018758148,"threshold_uncertainty_score":0.38088447},"labels":[],"label_agreement":null},{"id":"W3034681889","doi":"10.1109/cvpr42600.2020.01308","title":"Squeeze-and-Attention Networks for Semantic Segmentation","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Waterloo","funders":"","keywords":"Pascal (unit); Segmentation; Computer science; Artificial intelligence; Pixel; Convolution (computer science); Context (archaeology); Pattern recognition (psychology); Grid; Image segmentation; Machine learning; Artificial neural network; Mathematics","score_opus":0.03345037281667305,"score_gpt":0.2995651833011245,"score_spread":0.26611481048445146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034681889","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":[0.00054598757,0.00012909423,0.99025023,0.006723727,0.00043970734,0.0013055542,0.000004209898,0.0004046867,0.00019682664],"genre_scores_gemma":[0.39048272,0.000247319,0.6058213,0.0014316338,0.00045790378,0.00097212556,0.00017100762,0.000029290219,0.0003867011],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863815,0.000029592164,0.00027096985,0.0007301314,0.00012878406,0.00020236918],"domain_scores_gemma":[0.9990528,0.00012473737,0.00018794941,0.00046991048,0.00007150132,0.00009306095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008804196,0.00019306429,0.00019670675,0.00004636309,0.0001272962,0.00018648239,0.0005231829,0.00012880762,0.000003257018],"category_scores_gemma":[0.000013048382,0.0001929601,0.00009018027,0.0001654062,0.00002437829,0.00019228685,0.0009520191,0.00024667394,0.000010811754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027360808,0.00009515326,0.00041867775,0.00045289588,0.00013007982,0.000004568021,0.00029068583,0.1816923,0.003581755,0.5276168,0.016331382,0.26935834],"study_design_scores_gemma":[0.00017599735,0.000027665264,0.00052639656,0.000025238576,0.00002221447,0.000002839176,0.0000054611014,0.9238491,0.00019688578,0.07416657,0.00079370453,0.0002079436],"about_ca_topic_score_codex":0.000005847464,"about_ca_topic_score_gemma":0.0000063791526,"teacher_disagreement_score":0.7421568,"about_ca_system_score_codex":0.000038974093,"about_ca_system_score_gemma":0.000025317611,"threshold_uncertainty_score":0.78686845},"labels":[],"label_agreement":null},{"id":"W3034932471","doi":"","title":"Angular Visual Hardness","year":2020,"lang":"en","type":"article","venue":"CaltechAUTHORS (California Institute of Technology)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Convolutional neural network; Classifier (UML); Computer science; Artificial intelligence; Ambiguity; Domain adaptation; Generalization; Pattern recognition (psychology); Embedding; Machine learning; Feature extraction; Mathematics","score_opus":0.020531893639123836,"score_gpt":0.2674751752489007,"score_spread":0.24694328160977685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034932471","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":[0.034622006,0.0002527207,0.94080615,0.021611515,0.00035366917,0.0004075621,0.000027482165,0.0016395665,0.0002793343],"genre_scores_gemma":[0.75745153,0.000045380373,0.24137805,0.00085758977,0.000098807905,0.000102977494,0.000012008004,0.000025704083,0.0000279329],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981224,0.000015459616,0.0004836334,0.0006854311,0.00027171822,0.00042136206],"domain_scores_gemma":[0.9985924,0.000011659568,0.00023555884,0.00080294383,0.00014356188,0.00021391874],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011118188,0.00025206487,0.0003860692,0.00028330344,0.00017510883,0.000027631782,0.0019361934,0.00029827183,0.000011216851],"category_scores_gemma":[0.0002377357,0.00024818635,0.00011375639,0.002723142,0.0004581112,0.00039283698,0.0008277664,0.00045762598,0.00024300689],"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":[0.000017675346,0.00016967059,0.0005001344,0.0000942869,0.000051792416,0.00008925394,0.00009850972,0.0015483226,0.065947495,0.72669274,0.0034115096,0.2013786],"study_design_scores_gemma":[0.0006499041,0.0003086265,0.00009790462,0.00006240377,0.000028726916,0.000054174077,0.00003829933,0.09145504,0.20299959,0.044322312,0.6593254,0.00065758656],"about_ca_topic_score_codex":0.0000022791721,"about_ca_topic_score_gemma":0.0000011694982,"teacher_disagreement_score":0.7228295,"about_ca_system_score_codex":0.000041831154,"about_ca_system_score_gemma":0.00008510116,"threshold_uncertainty_score":0.999997},"labels":[],"label_agreement":null},{"id":"W3035600434","doi":"10.1109/cvpr42600.2020.01273","title":"Semi-Supervised Semantic Image Segmentation With Self-Correcting Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":104,"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":"Pascal (unit); Computer science; Segmentation; Artificial intelligence; Minimum bounding box; Pattern recognition (psychology); Set (abstract data type); Image segmentation; Convolutional neural network; Bounding overwatch; Annotation; Machine learning; Image (mathematics); Computer vision","score_opus":0.013387582169190792,"score_gpt":0.23697615462652513,"score_spread":0.22358857245733435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035600434","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":[0.009359522,0.000024004437,0.9842693,0.0034114278,0.000059725953,0.00033253885,2.43177e-7,0.0010109394,0.001532264],"genre_scores_gemma":[0.5949883,0.00001006448,0.4024259,0.0023580496,0.00011589909,0.000038662947,0.000004088251,0.000014082724,0.000045015586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989182,0.000034586817,0.00017448024,0.0004405983,0.00017272617,0.0002594336],"domain_scores_gemma":[0.9992972,0.00010763478,0.00007736642,0.00031507752,0.00006193414,0.00014078134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005412845,0.00014050161,0.00012070158,0.000022595823,0.00016939986,0.000128449,0.000457867,0.000029043593,0.000020080497],"category_scores_gemma":[0.000008543869,0.00011679071,0.0000292735,0.0008309752,0.000018119412,0.00066864677,0.00015366086,0.0001532209,0.00006957573],"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":[0.00009696542,0.00039631885,0.011848999,0.0001662462,0.00021808557,0.00015533487,0.010978496,0.5155172,0.0462937,0.025478715,0.018920314,0.36992967],"study_design_scores_gemma":[0.0002850685,0.00007124625,0.00021729643,0.000007759461,0.000008761677,0.000021889307,0.00009214056,0.9936747,0.005055224,0.00013271793,0.00025385097,0.00017933195],"about_ca_topic_score_codex":0.0000047864673,"about_ca_topic_score_gemma":0.000005334291,"teacher_disagreement_score":0.58562875,"about_ca_system_score_codex":0.00002710429,"about_ca_system_score_gemma":0.000020677935,"threshold_uncertainty_score":0.4762587},"labels":[],"label_agreement":null},{"id":"W3035650891","doi":"10.1109/cvprw50498.2020.00352","title":"Retracted: FoNet: A Memory-efficient Fourier-based Orthogonal Network for Object Recognition","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; MNIST database; Convolutional neural network; Convolution (computer science); Bottleneck; Inference; Feature (linguistics); Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Kernel (algebra); Domain (mathematical analysis); Artificial intelligence; Object (grammar); Memory management; Parallel computing; Deep learning; Artificial neural network; Embedded system; Computer hardware; Semiconductor memory","score_opus":0.0458202295891593,"score_gpt":0.2656536569134543,"score_spread":0.21983342732429498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035650891","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":[0.0040574567,0.000043894037,0.9842205,0.008607936,0.00020310683,0.0008910848,0.000018412182,0.00057375187,0.0013838499],"genre_scores_gemma":[0.29705334,0.000003660094,0.69325906,0.008416713,0.0007486262,0.0003401006,0.00007291404,0.000025576825,0.000080009406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834,0.00004709127,0.00029942117,0.0006115142,0.00027871598,0.00042325756],"domain_scores_gemma":[0.99866575,0.0004124634,0.0001403079,0.0004107214,0.00015068037,0.00022008465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019643987,0.00017450767,0.00017680154,0.000035370824,0.0002528921,0.00008022194,0.00053621794,0.0000904991,0.000051778843],"category_scores_gemma":[0.000101786325,0.00016611905,0.00012407379,0.0009074012,0.000034961457,0.00016638516,0.00009585768,0.00021302141,0.00009964337],"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":[0.00018221162,0.00024712132,0.00008674011,0.00005269588,0.000040256444,0.000013822615,0.00022343604,0.47214016,0.0016400904,0.029459227,0.055912852,0.4400014],"study_design_scores_gemma":[0.0005526866,0.00018926265,0.00022158877,0.00001242969,0.000011330062,0.0000040846985,0.000006482879,0.97732794,0.002558981,0.005566126,0.01328763,0.0002614686],"about_ca_topic_score_codex":0.0000017155339,"about_ca_topic_score_gemma":0.000007534888,"teacher_disagreement_score":0.50518775,"about_ca_system_score_codex":0.000033487693,"about_ca_system_score_gemma":0.00009090694,"threshold_uncertainty_score":0.6774138},"labels":[],"label_agreement":null},{"id":"W3035695586","doi":"10.1109/cvpr42600.2020.00162","title":"ReSprop: Reuse Sparsified Backpropagation","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Speedup; Computer science; Computation; Backpropagation; Convolutional neural network; Convolution (computer science); Reuse; Parallel computing; Reduction (mathematics); Sparse matrix; Computational science; Artificial neural network; Artificial intelligence; Algorithm; Computer engineering; Mathematics","score_opus":0.05164835959100823,"score_gpt":0.2591902171441513,"score_spread":0.20754185755314308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035695586","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":[0.002384868,0.000016036609,0.94381326,0.04456492,0.000041395295,0.00016415812,3.2826185e-7,0.00046589342,0.008549125],"genre_scores_gemma":[0.622417,0.000010380372,0.37139577,0.005589705,0.000113904425,0.000023381806,0.000002181206,0.0000071126697,0.00044056965],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993033,0.00002008207,0.00012309843,0.00029785134,0.0001259919,0.0001297091],"domain_scores_gemma":[0.9991398,0.00004127964,0.0000410555,0.0006330646,0.0000389938,0.00010583926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003882076,0.00006357742,0.00006107445,0.00001557252,0.00006822495,0.00004734144,0.0009254233,0.000021393203,0.000028055758],"category_scores_gemma":[0.00007157348,0.000055988,0.000021353653,0.0005012142,0.000016153725,0.00034591483,0.00032386006,0.000075354044,0.00059126236],"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":[0.000016977736,0.0000375709,0.00015421919,0.000009195179,0.0000071577256,0.000010696557,0.0006020461,0.0024717625,0.045326583,0.80354226,0.0765575,0.07126404],"study_design_scores_gemma":[0.00057276135,0.0001864481,0.0016442084,0.000007787112,0.000005302165,0.000010133238,0.000024221328,0.605667,0.08604302,0.06864778,0.23670785,0.00048351788],"about_ca_topic_score_codex":0.0000013023182,"about_ca_topic_score_gemma":8.5294744e-7,"teacher_disagreement_score":0.73489445,"about_ca_system_score_codex":0.000011227449,"about_ca_system_score_gemma":0.000015480355,"threshold_uncertainty_score":0.75996786},"labels":[],"label_agreement":null},{"id":"W3035732661","doi":"10.24963/ijcai.2020/363","title":"Feature Statistics Guided Efficient Filter Pruning","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Convolutional neural network; Feature selection; Leverage (statistics); Pruning; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); FLOPS; Filter (signal processing); Data mining; Machine learning; Computer vision","score_opus":0.0354430583203236,"score_gpt":0.2801391617468346,"score_spread":0.24469610342651102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035732661","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":[0.00034546605,0.000021336047,0.9747386,0.021800201,0.000057095614,0.00013510659,0.000004845972,0.00028574388,0.0026116236],"genre_scores_gemma":[0.078689046,0.0000030805702,0.913636,0.0066559156,0.00008280666,0.000013717124,0.000005619939,0.000007820566,0.00090598274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993021,0.000014864848,0.000089523775,0.00027678453,0.0001461538,0.00017058608],"domain_scores_gemma":[0.99941534,0.000072973446,0.000040651765,0.00030150704,0.000049999177,0.0001195317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000027363822,0.00008064293,0.0000738886,0.0000137231955,0.00008637778,0.000058551417,0.00051806343,0.000022828743,0.000027207427],"category_scores_gemma":[0.00003489513,0.00006866896,0.000018052819,0.00036365518,0.000017160446,0.00007915267,0.00023791261,0.00010857674,0.00017032854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000183747,0.00001980622,0.00006188613,0.000007935231,0.000005958916,0.000014300674,0.0004015126,0.0617705,0.0028099804,0.48699713,0.42893976,0.018969389],"study_design_scores_gemma":[0.00010723318,0.000022445065,0.00022127485,0.0000025712948,0.0000019435606,0.0000054157936,0.0000059945633,0.9069624,0.0018799722,0.0012398978,0.0894322,0.00011862767],"about_ca_topic_score_codex":5.7338065e-7,"about_ca_topic_score_gemma":4.93554e-7,"teacher_disagreement_score":0.8451919,"about_ca_system_score_codex":0.000012726148,"about_ca_system_score_gemma":0.000016837497,"threshold_uncertainty_score":0.28002387},"labels":[],"label_agreement":null},{"id":"W3039314686","doi":"10.48550/arxiv.2007.01277","title":"Automatic Horizontal Fusion for GPU Kernels","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Fusion; CUDA; Parallel computing; Compiler; Kernel (algebra); Thread (computing); Computational science; Operating system; Mathematics","score_opus":0.08560191649728066,"score_gpt":0.21390590664085637,"score_spread":0.1283039901435757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039314686","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":[0.05478524,0.000035023633,0.9417377,0.0006602183,0.00043658775,0.00091366394,0.000028261667,0.0007356227,0.00066767965],"genre_scores_gemma":[0.9647791,0.00006949828,0.034309804,0.0001980082,0.00016725228,0.00001248499,0.00003664284,0.000026770596,0.00040047723],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802864,0.000054219585,0.00021483323,0.0012669611,0.00008556489,0.00034979833],"domain_scores_gemma":[0.99802846,0.00019664821,0.00027770706,0.0011726875,0.00010586599,0.00021863842],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008320722,0.00030655146,0.00032721707,0.00010980207,0.0002229451,0.00009009758,0.0021407285,0.00020438153,0.000016684578],"category_scores_gemma":[0.000033071286,0.00036338012,0.0002483676,0.0005811113,0.00006726547,0.00031929376,0.002355976,0.00041331784,0.00012490794],"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":[0.000031253254,0.00012803206,0.00018813545,0.00020643522,0.00009019819,0.00013749507,0.00025555096,0.113484755,0.0007215356,0.86374587,0.0032233915,0.017787362],"study_design_scores_gemma":[0.0002899051,0.000062729654,0.00019060509,0.00004004798,0.00003632825,0.0000030588078,0.00001247068,0.7958243,0.00023307449,0.20097464,0.002005436,0.00032742912],"about_ca_topic_score_codex":0.000012334851,"about_ca_topic_score_gemma":0.000009433265,"teacher_disagreement_score":0.9099938,"about_ca_system_score_codex":0.0001879112,"about_ca_system_score_gemma":0.00012893102,"threshold_uncertainty_score":0.9998818},"labels":[],"label_agreement":null},{"id":"W3039747048","doi":"10.1145/3386569.3392380","title":"TilinGNN","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Tile; Robustness (evolution); Graph; Convolutional neural network; Dual graph; Tessellation (computer graphics)","score_opus":0.04223254686823975,"score_gpt":0.2721270476595343,"score_spread":0.2298945007912946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039747048","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":[0.0008782029,0.00003090449,0.9723291,0.025779499,0.000099894525,0.00012478411,0.0000042468405,0.00046449708,0.00028886902],"genre_scores_gemma":[0.8829329,0.00012415872,0.108089246,0.008717006,0.00004511587,0.000042874155,0.0000014399407,0.000012804558,0.00003447528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916613,0.000020377578,0.00013736478,0.00033533302,0.0001692027,0.0001716068],"domain_scores_gemma":[0.99888283,0.00014568544,0.00003469717,0.0007512117,0.000039737624,0.0001458252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003377894,0.00010449385,0.00008639846,0.00006205138,0.00021563937,0.000036262656,0.0010006841,0.00004740275,0.000015377895],"category_scores_gemma":[0.00001726309,0.00010597823,0.00008286353,0.0011580614,0.0000425865,0.0002446591,0.000013208293,0.00027789074,0.00013152971],"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":[0.00004241387,0.00043362746,0.00019609665,0.000033387805,0.00009651539,0.000027354059,0.0014863684,0.041632544,0.004487913,0.37598336,0.0032194445,0.572361],"study_design_scores_gemma":[0.0014509539,0.0008279054,0.0015948024,0.00003757844,0.00006842989,0.00004727488,0.00006574029,0.40420985,0.04111993,0.28086886,0.26829535,0.0014133119],"about_ca_topic_score_codex":0.0000012411273,"about_ca_topic_score_gemma":0.0000034360728,"teacher_disagreement_score":0.8820547,"about_ca_system_score_codex":0.000008588809,"about_ca_system_score_gemma":0.000018898992,"threshold_uncertainty_score":0.43216667},"labels":[],"label_agreement":null},{"id":"W3040298941","doi":"10.1016/j.compbiomed.2020.103884","title":"Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric MRI","year":2020,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":48,"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":"Nvidia","keywords":"Segmentation; Artificial intelligence; Hausdorff distance; Computer science; Pixel; Pattern recognition (psychology); Discriminator; Cross entropy; Sørensen–Dice coefficient; Image segmentation; Boundary (topology); Computer vision; Mathematics","score_opus":0.02199990370611662,"score_gpt":0.2950822731724251,"score_spread":0.2730823694663085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040298941","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":[0.020126542,0.0010954345,0.9601162,0.01767952,0.00047730465,0.00048304515,0.000001043072,0.000010902128,0.000009994958],"genre_scores_gemma":[0.98104763,0.0007111695,0.014517783,0.0031775434,0.0005073977,0.000027931328,0.0000055029764,0.0000028939426,0.000002123672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993796,0.000079787686,0.00018781338,0.00019587942,0.00003925984,0.000117689706],"domain_scores_gemma":[0.99912065,0.00061691797,0.000077632925,0.00013783103,0.000019781373,0.000027157876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014818455,0.00006620539,0.00012704654,0.000029195313,0.00006921966,0.0000026871967,0.00035255036,0.000042176493,0.0000010536133],"category_scores_gemma":[0.00003689606,0.00003766303,0.00001780184,0.0003732387,0.000117191965,0.000044559398,0.00014028183,0.0001131726,1.3700974e-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":[0.00033107266,0.00015627638,0.25006735,0.000095889045,0.00007119268,0.0000066542084,0.018884147,0.42005414,0.0063205445,0.09872805,0.012885833,0.19239885],"study_design_scores_gemma":[0.0015258754,0.00015733989,0.021967728,0.000011781819,0.00001054806,0.0000023126192,0.00005136092,0.9724643,0.00017011359,0.0031273502,0.00045525585,0.000055999422],"about_ca_topic_score_codex":0.000004821747,"about_ca_topic_score_gemma":0.000010055411,"teacher_disagreement_score":0.9609211,"about_ca_system_score_codex":0.000012829914,"about_ca_system_score_gemma":0.000015506324,"threshold_uncertainty_score":0.15358537},"labels":[],"label_agreement":null},{"id":"W3040977527","doi":"10.1007/s00521-020-05150-9","title":"Adaptive feature fusion with attention mechanism for multi-scale target detection","year":2020,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pascal (unit); Computer science; Artificial intelligence; Feature (linguistics); Fusion mechanism; Pattern recognition (psychology); Subpixel rendering; Detector; Fusion; Object detection; Computer vision; Scale (ratio); Feature extraction; Pixel","score_opus":0.02783330093977238,"score_gpt":0.2674035864053285,"score_spread":0.23957028546555614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040977527","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":[0.0064164903,0.000044559016,0.9878691,0.004171818,0.000030980398,0.0010268334,0.00000906415,0.0004019486,0.00002922981],"genre_scores_gemma":[0.6343169,0.000005463992,0.36461493,0.0006359812,0.00015443734,0.00021601023,0.000012399365,0.00001256822,0.000031275176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989564,0.000024607943,0.0001424308,0.0005573287,0.00011247127,0.00020678887],"domain_scores_gemma":[0.9992908,0.00007810248,0.00012975471,0.00023245717,0.0001374719,0.00013136571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004421876,0.00015053079,0.00013031551,0.000031586136,0.00061642716,0.000075729,0.00026886104,0.0000556506,3.30808e-7],"category_scores_gemma":[0.0000065718987,0.00013366189,0.000042694235,0.0004535431,0.000035698256,0.00018125918,0.00013038942,0.00017716386,0.000005088141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011546488,0.00025482054,0.00039394136,0.00011436744,0.000047781552,0.0000021972276,0.0009581924,0.021604769,0.20540875,0.1837821,0.00068753015,0.5866301],"study_design_scores_gemma":[0.00040989195,0.00017299513,0.0006084502,0.00000906134,0.0000114422,0.0000141230375,0.000044236403,0.9898289,0.0046102586,0.0025105395,0.0016106657,0.00016948023],"about_ca_topic_score_codex":0.000002405986,"about_ca_topic_score_gemma":0.0000062804766,"teacher_disagreement_score":0.9682241,"about_ca_system_score_codex":0.000016571805,"about_ca_system_score_gemma":0.000009755386,"threshold_uncertainty_score":0.54505736},"labels":[],"label_agreement":null},{"id":"W3041504937","doi":"10.1007/978-3-030-69535-4_45","title":"To Filter Prune, or to Layer Prune, That Is the Question","year":2021,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pruning; Filter (signal processing); Computer science; FLOPS; Reduction (mathematics); Algorithm; Latency (audio); Kernel adaptive filter; Artificial intelligence; Mathematics; Filter design; Parallel computing; Computer vision; Telecommunications","score_opus":0.03906733305325706,"score_gpt":0.3239460670752379,"score_spread":0.28487873402198083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041504937","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":[0.010124188,0.00013893144,0.9437304,0.042184982,0.002147261,0.0014178402,0.0000058207193,0.00022850593,0.000022047796],"genre_scores_gemma":[0.42490956,0.000016319826,0.5515723,0.022886576,0.00033637977,0.00024132305,0.0000028203222,0.000018095185,0.00001658426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950156,0.00016886188,0.00045327365,0.0024121595,0.0010742676,0.0008758329],"domain_scores_gemma":[0.99501026,0.00072755857,0.00019687046,0.0033985442,0.0003538436,0.00031290445],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0008369973,0.00050534034,0.0004245021,0.00040760773,0.00044061133,0.0015501243,0.006668514,0.00020773603,0.000019929934],"category_scores_gemma":[0.00026988363,0.00034722907,0.00011930094,0.0043034935,0.00022428576,0.0005752023,0.008950748,0.0010225902,0.0000686193],"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":[0.000008866184,0.000057987378,0.00028047085,0.00002576879,0.000007980447,0.000031668525,0.003675204,0.61396116,0.0010650468,0.0005281211,0.00057364354,0.37978408],"study_design_scores_gemma":[0.0001856611,0.00017303159,0.008211495,0.0006812344,0.000013164196,0.00009490687,0.0000029169785,0.9040127,0.05689712,0.024633681,0.003924872,0.0011692517],"about_ca_topic_score_codex":0.000077849865,"about_ca_topic_score_gemma":0.00040737144,"teacher_disagreement_score":0.41478536,"about_ca_system_score_codex":0.000304145,"about_ca_system_score_gemma":0.0006036176,"threshold_uncertainty_score":0.99989796},"labels":[],"label_agreement":null},{"id":"W3041519490","doi":"10.1007/978-3-030-51935-3_31","title":"Object Detector Combination for Increasing Accuracy and Detecting More Overlapping Objects","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Pascal (unit); Bounding overwatch; Detector; Artificial intelligence; Convolutional neural network; Object detection; Computer vision; Classifier (UML); Pattern recognition (psychology); Object (grammar); Telecommunications; Programming language","score_opus":0.022191001605907167,"score_gpt":0.27214529766987827,"score_spread":0.2499542960639711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041519490","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":[0.0012379003,0.00038926094,0.9952857,0.00087394734,0.00052015117,0.0011400882,0.0000052884748,0.00030507758,0.00024257803],"genre_scores_gemma":[0.5571041,0.000028200744,0.44135675,0.001102785,0.00031213282,0.000042251228,0.0000043453806,0.00003813461,0.000011306094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965897,0.000038551658,0.00050085026,0.0017104476,0.0005726171,0.00058781815],"domain_scores_gemma":[0.99523157,0.0030185014,0.0004673918,0.0008421253,0.00023710227,0.00020330121],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058341096,0.0005077947,0.0005006928,0.00046667008,0.0006198974,0.00057832315,0.0017174201,0.00022824861,0.0000012461619],"category_scores_gemma":[0.00090009515,0.000523765,0.00010529313,0.00077571016,0.00037147602,0.0009297908,0.001262375,0.0006961099,0.000004318459],"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":[0.000017950564,0.000014011094,0.000046584835,0.0001068392,0.0000126755,0.00001869869,0.0011337693,0.013071672,0.0053438344,0.011639994,0.0000034779982,0.9685905],"study_design_scores_gemma":[0.00042836583,0.00017926928,0.00027933257,0.00036196687,0.000012476571,0.00010052963,7.3007897e-7,0.8444714,0.0050907093,0.14809401,0.0003058362,0.00067538966],"about_ca_topic_score_codex":0.00001211113,"about_ca_topic_score_gemma":0.00003456938,"teacher_disagreement_score":0.9679151,"about_ca_system_score_codex":0.00029056863,"about_ca_system_score_gemma":0.00027643316,"threshold_uncertainty_score":0.9997214},"labels":[],"label_agreement":null},{"id":"W3041769859","doi":"10.21742/ijmue.2020.15.1.04","title":"EOLO: Deep Machine Learning Algorithm for Embedded Object Segmentation that Only Looks Once","year":2020,"lang":"en","type":"article","venue":"International Journal of Multimedia and Ubiquitous Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Task (project management); Embedding; Object (grammar); Function (biology); Pattern recognition (psychology); Machine learning; Computer vision; Algorithm","score_opus":0.014481302704433315,"score_gpt":0.25854472027362324,"score_spread":0.24406341756918992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041769859","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":[0.0052330187,0.00037310598,0.9922593,0.001415921,0.0005121865,0.0001244855,0.0000053436656,0.000062429695,0.000014186809],"genre_scores_gemma":[0.3928866,0.0001767367,0.6061261,0.00035486693,0.0004047896,0.000009664017,0.000008959213,0.000014814206,0.00001740834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991167,0.000018136216,0.00026690436,0.00016377753,0.00029581587,0.00013863345],"domain_scores_gemma":[0.99910253,0.00032271232,0.00021561119,0.000059316997,0.00017473073,0.00012512405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013747609,0.000119538,0.00014724069,0.000093571456,0.00004243251,0.00009017665,0.0004293794,0.000034270015,0.0000028494562],"category_scores_gemma":[0.000116026706,0.000114584545,0.00006844266,0.000084383726,0.000012722176,0.00047026857,0.00009867731,0.000237999,0.0000015200937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002941069,0.000029803221,0.00027447226,0.000014439178,0.00011994374,0.000047340727,0.002674052,0.1407703,0.020302631,0.0005792659,0.000055646215,0.8351027],"study_design_scores_gemma":[0.0007383121,0.00010903286,0.00018582153,0.00002840788,0.0000097566535,0.00015822312,0.000038202154,0.98930717,0.0076556923,0.00012528071,0.0015327261,0.00011136191],"about_ca_topic_score_codex":0.000002091019,"about_ca_topic_score_gemma":0.0000011430437,"teacher_disagreement_score":0.8485369,"about_ca_system_score_codex":0.00004445264,"about_ca_system_score_gemma":0.000033161363,"threshold_uncertainty_score":0.4672622},"labels":[],"label_agreement":null},{"id":"W3042143729","doi":"10.3934/ipi.2020041","title":"Nonlocal regularized CNN for image segmentation","year":2020,"lang":"en","type":"article","venue":"Inverse Problems and Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Softmax function; Computer science; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Feature (linguistics); Segmentation; Pooling; Convolution (computer science); Image segmentation; Image (mathematics); Computer vision; Artificial neural network","score_opus":0.026114355377776406,"score_gpt":0.2547765550000879,"score_spread":0.2286621996223115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042143729","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":[0.00079698075,0.00005954239,0.98471904,0.013621281,0.000042189982,0.0004061016,0.0000022811726,0.0001375401,0.00021506759],"genre_scores_gemma":[0.07212665,0.00003332542,0.9230172,0.004510592,0.00009705067,0.0001379498,0.000010220436,0.000013665122,0.000053326123],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932146,0.000011632478,0.00013243797,0.00030007405,0.000073376184,0.00016099047],"domain_scores_gemma":[0.99963343,0.0000359813,0.000060277056,0.00013405268,0.00004404933,0.000092231014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006140399,0.00008240224,0.00008245864,0.000021407866,0.00013459701,0.00011399948,0.0001758519,0.000012178688,0.0000025633797],"category_scores_gemma":[0.00001512481,0.00008104875,0.000027378004,0.00016749722,0.000045783654,0.0006863609,0.000121333025,0.000060264425,0.000012251191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022439466,0.00004152035,0.00036219,0.00015321148,0.000021156135,0.000009277681,0.0029070068,0.0016405171,0.6143061,0.040421646,0.011239305,0.32887566],"study_design_scores_gemma":[0.0005642745,0.000019909934,0.000022835622,0.0000094151665,0.0000052932814,0.0000064837704,0.00003710094,0.9720115,0.006655465,0.008687613,0.01186646,0.00011361645],"about_ca_topic_score_codex":0.0000048981083,"about_ca_topic_score_gemma":9.280524e-7,"teacher_disagreement_score":0.970371,"about_ca_system_score_codex":0.000012863843,"about_ca_system_score_gemma":0.0000122526535,"threshold_uncertainty_score":0.3305072},"labels":[],"label_agreement":null},{"id":"W3042302007","doi":"10.1016/j.neunet.2020.07.011","title":"Discretely-constrained deep network for weakly supervised segmentation","year":2020,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Hôpital Notre-Dame","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Prior probability; Regularization (linguistics); Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Image segmentation; Mathematical optimization; Mathematics; Algorithm; Bayesian probability","score_opus":0.024217975833678494,"score_gpt":0.26057579263345626,"score_spread":0.23635781679977777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042302007","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":[0.0024410617,0.0003320036,0.98464257,0.010115222,0.00046203955,0.0011500771,0.000007337345,0.00060404075,0.0002456694],"genre_scores_gemma":[0.7712536,0.000043483506,0.21715651,0.009187329,0.0017948461,0.00037259771,0.00010671285,0.000044865006,0.000040033166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779886,0.000079678226,0.000432495,0.00075418036,0.00022349482,0.00071126845],"domain_scores_gemma":[0.998516,0.00040506767,0.00016897747,0.00048307647,0.0000877859,0.00033909874],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010706116,0.00029527204,0.00029162937,0.00002574288,0.00036935866,0.0001761999,0.0009836678,0.00011214722,0.000023109173],"category_scores_gemma":[0.00003807591,0.0002838674,0.00017353463,0.00087729894,0.000077589895,0.00063287816,0.0002239877,0.00028111791,0.000017102995],"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":[0.000062074716,0.000021461663,0.00032897864,0.0000139269705,0.000024536223,0.0000064365304,0.0001677199,0.8155573,0.0008209625,0.022589419,0.007908404,0.1524988],"study_design_scores_gemma":[0.0005918274,0.00017627302,0.0003035971,0.0000069507505,0.000016667942,0.000007932275,0.000016450067,0.99237347,0.0001592912,0.00286476,0.0031750894,0.0003077085],"about_ca_topic_score_codex":0.0000017399371,"about_ca_topic_score_gemma":0.000008797771,"teacher_disagreement_score":0.76881254,"about_ca_system_score_codex":0.00003277813,"about_ca_system_score_gemma":0.000019767192,"threshold_uncertainty_score":0.9999614},"labels":[],"label_agreement":null},{"id":"W3043619075","doi":"10.1109/isca45697.2020.00092","title":"Echo: Compiler-based GPU Memory Footprint Reduction for LSTM RNN Training","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Toronto","funders":"","keywords":"Computer science; Memory footprint; Recurrent neural network; Bottleneck; Compiler; Parallel computing; Deep learning; Artificial intelligence; Artificial neural network; Embedded system; Programming language","score_opus":0.11845452153850833,"score_gpt":0.322659852216518,"score_spread":0.20420533067800967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043619075","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":[0.0005578814,0.00005069284,0.9789234,0.012370347,0.0013107052,0.0017786255,0.00001734074,0.0012947108,0.0036963108],"genre_scores_gemma":[0.35616344,0.00001041194,0.64120865,0.00063879654,0.00063832867,0.0010030932,0.000077071425,0.00004086899,0.00021931979],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722886,0.000065230386,0.0005348075,0.0014264046,0.00030610283,0.0004385767],"domain_scores_gemma":[0.9975775,0.0002453871,0.00035703744,0.0014266502,0.00016887226,0.00022457266],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021547131,0.00038933792,0.00045205146,0.00012369835,0.00023629573,0.00019104172,0.0018055273,0.00022942634,0.000013142901],"category_scores_gemma":[0.00005200084,0.00040772563,0.00028556449,0.0003672713,0.000077925106,0.00011974085,0.0010598294,0.0007003425,0.000030457628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006403694,0.00019699105,0.0000051504667,0.000333341,0.00010345274,0.000007470189,0.0016233787,0.3145252,0.015711708,0.19490737,0.008254612,0.46426728],"study_design_scores_gemma":[0.00062880764,0.00012450681,0.00007108415,0.00010118039,0.00003384319,0.000013280105,0.00011364341,0.81375295,0.021079877,0.14146273,0.021786388,0.0008317305],"about_ca_topic_score_codex":0.000009451347,"about_ca_topic_score_gemma":0.0000047457297,"teacher_disagreement_score":0.49922773,"about_ca_system_score_codex":0.00015890504,"about_ca_system_score_gemma":0.00030104702,"threshold_uncertainty_score":0.99983746},"labels":[],"label_agreement":null},{"id":"W3043656308","doi":"10.3390/iot2020012","title":"ThriftyNets: Convolutional Neural Networks with Tiny Parameter Budget","year":2021,"lang":"en","type":"preprint","venue":"IoT","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Convolutional neural network; Normalization (sociology); Computer science; Computation; Upsampling; Complement (music); Factorization; Algorithm; Artificial intelligence; Image (mathematics)","score_opus":0.018746903615882413,"score_gpt":0.25744619072098024,"score_spread":0.23869928710509783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043656308","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":[0.01433769,0.0012490465,0.97990966,0.0021609382,0.0008518537,0.00046714745,0.0000110144965,0.00038602398,0.00062662666],"genre_scores_gemma":[0.7860474,0.0000621296,0.21085565,0.001764357,0.0005523121,0.00029928752,0.00012947802,0.00004026519,0.00024910708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975464,0.00010144435,0.00034094046,0.0011162733,0.00039299147,0.00050200085],"domain_scores_gemma":[0.9974791,0.0003274833,0.00026389334,0.0015880325,0.00017646642,0.00016505366],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000099779216,0.000376607,0.00037245505,0.00006192353,0.00016634514,0.00032006303,0.0014395415,0.00024411424,0.000037708684],"category_scores_gemma":[0.000023134091,0.00033624456,0.00013508851,0.00036368094,0.00014719536,0.0001612449,0.0017381671,0.0010748383,0.00002547693],"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":[0.000013593194,0.000088477325,0.00080619176,0.000022895589,0.00007382853,0.00007910854,0.00010074197,0.9681058,0.000026921758,0.012212632,0.0031095613,0.015360244],"study_design_scores_gemma":[0.0002308513,0.000048021222,0.0026650778,0.000075222735,0.000020463567,0.00009586308,0.000006230978,0.9876479,0.000059429218,0.0035451842,0.005097864,0.0005078964],"about_ca_topic_score_codex":0.000015695467,"about_ca_topic_score_gemma":0.000024056875,"teacher_disagreement_score":0.77170974,"about_ca_system_score_codex":0.00007617491,"about_ca_system_score_gemma":0.00013499265,"threshold_uncertainty_score":0.999909},"labels":[],"label_agreement":null},{"id":"W3043802286","doi":"10.1109/isca45697.2020.00075","title":"JPEG-ACT: Accelerating Deep Learning via Transform-based Lossy Compression","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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 British Columbia","funders":"","keywords":"JPEG; Computer science; Lossy compression; Convolutional neural network; Data compression; Lossless JPEG; Reduction (mathematics); Image compression; Deep learning; Quantization (signal processing); Compression ratio; Artificial intelligence; JPEG 2000; Computer engineering; Computer hardware; Computer vision; Image processing; Image (mathematics)","score_opus":0.03450381076356811,"score_gpt":0.2696955713069403,"score_spread":0.23519176054337215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043802286","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":[0.0024792473,0.000041805273,0.9835531,0.007481726,0.000039678795,0.0002157671,1.924688e-7,0.0006694371,0.0055190357],"genre_scores_gemma":[0.8062651,0.0000045138404,0.19113807,0.0024178207,0.00007509374,0.000025852949,0.00000490397,0.000011851537,0.00005676648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988622,0.00004200274,0.00022097361,0.0004044053,0.00020616408,0.000264232],"domain_scores_gemma":[0.9993268,0.00013665536,0.00007264324,0.00024653683,0.00004317976,0.00017417443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005276786,0.00013918949,0.00013673952,0.000026027992,0.00031757183,0.00010813275,0.000639673,0.000043588825,0.00008093896],"category_scores_gemma":[0.000017228305,0.00012334454,0.000056406672,0.00048678988,0.000025391108,0.00047826587,0.00010569931,0.000282413,0.00010628161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015178137,0.000052290907,0.0008779578,0.000027988974,0.000007258097,0.000010790851,0.00085922994,0.2657474,0.0472412,0.010726982,0.00032808745,0.67410564],"study_design_scores_gemma":[0.00024469092,0.000061573024,0.0002623589,0.00000781503,0.00000204053,0.00000224997,0.000011443334,0.96722907,0.022647925,0.0005089035,0.008861811,0.0001601439],"about_ca_topic_score_codex":0.000004531374,"about_ca_topic_score_gemma":0.0000048122884,"teacher_disagreement_score":0.80378586,"about_ca_system_score_codex":0.000018108512,"about_ca_system_score_gemma":0.000018658102,"threshold_uncertainty_score":0.50298446},"labels":[],"label_agreement":null},{"id":"W3043884287","doi":"10.48550/arxiv.2007.10451","title":"HPIPE: Heterogeneous Layer-Pipelined and Sparse-Aware CNN Inference for FPGAs","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Stratix; Field-programmable gate array; Compiler; Convolutional neural network; Parallel computing; Throughput; Computer architecture; Layer (electronics); Lookup table; Embedded system; Computer hardware; Artificial intelligence; Operating system","score_opus":0.13613019143316576,"score_gpt":0.23551889514670127,"score_spread":0.0993887037135355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043884287","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":[0.03554778,0.00009133003,0.9619783,0.00073361956,0.00022997831,0.00078773324,0.00005354527,0.0004102932,0.00016742495],"genre_scores_gemma":[0.98399764,0.00031777957,0.014699244,0.00039223183,0.00012702307,0.0000129535365,0.000036380497,0.000027537553,0.0003892114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771595,0.000062242514,0.00023150006,0.00152149,0.00007934496,0.00038946385],"domain_scores_gemma":[0.997814,0.0003016047,0.00026642779,0.0011435039,0.00018703086,0.0002874388],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007570032,0.00036864553,0.00037663738,0.00011113602,0.00021979732,0.00011971484,0.0016381681,0.00022923431,0.0000066308467],"category_scores_gemma":[0.000053671745,0.00044123194,0.00015763871,0.00046652177,0.0001108831,0.00026030856,0.0023662862,0.0004344512,0.00004289693],"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":[0.00007167458,0.00009005049,0.0007286621,0.0002137045,0.00010382777,0.00021987995,0.0002271961,0.7938918,0.00029399458,0.19647452,0.00097245787,0.006712253],"study_design_scores_gemma":[0.00039868575,0.00006143747,0.00012047578,0.00004714097,0.000050404644,0.0000072702846,0.000010456624,0.8806662,0.000594424,0.11467665,0.002897872,0.00046897327],"about_ca_topic_score_codex":0.00002112023,"about_ca_topic_score_gemma":0.000037859274,"teacher_disagreement_score":0.94844985,"about_ca_system_score_codex":0.000094980554,"about_ca_system_score_gemma":0.00012868478,"threshold_uncertainty_score":0.99980396},"labels":[],"label_agreement":null},{"id":"W3044932337","doi":"10.1109/ispass48437.2020.00017","title":"SeqPoint: Identifying Representative Iterations of Sequence-Based Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Advanced Micro Devices","keywords":"Computer science; Profiling (computer programming); Computation; Artificial neural network; Exploit; Convolutional neural network; Deep neural networks; Sequence (biology); Artificial intelligence; Recurrent neural network; Machine learning; Algorithm; Programming language","score_opus":0.11170555102544796,"score_gpt":0.3580130596449512,"score_spread":0.24630750861950326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044932337","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":[0.0007241533,0.00014373881,0.99011004,0.0062823067,0.000498902,0.000796794,0.00002622952,0.00048124147,0.000936587],"genre_scores_gemma":[0.7369865,0.000026849288,0.26127577,0.0010597415,0.0001816209,0.00022484889,0.00011511294,0.000024887295,0.000104645245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972072,0.00018121944,0.0007293586,0.0011512773,0.00039501427,0.0003359121],"domain_scores_gemma":[0.9969164,0.00040667833,0.0006023452,0.0016221408,0.00027674306,0.00017567659],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013573769,0.0003443408,0.00045041632,0.0001404923,0.00017192677,0.00025831486,0.0019808374,0.00017024403,0.000032277672],"category_scores_gemma":[0.00007172933,0.0003476843,0.00025005473,0.00088362716,0.00014570974,0.00043267835,0.002101748,0.0008434848,0.000014550701],"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":[0.0000063727093,0.000035613637,0.0001251798,0.000055796925,0.000033042623,0.000017710807,0.0003457539,0.9459888,0.0009277923,0.04720839,0.0016388866,0.0036167076],"study_design_scores_gemma":[0.00015245382,0.000025809648,0.00019273764,0.000061433675,0.000018638983,0.00000473035,0.00001669073,0.96762943,0.0028547193,0.028652996,0.000100418554,0.0002899355],"about_ca_topic_score_codex":0.000051612664,"about_ca_topic_score_gemma":0.000019230712,"teacher_disagreement_score":0.7362624,"about_ca_system_score_codex":0.00007851992,"about_ca_system_score_gemma":0.00015008659,"threshold_uncertainty_score":0.99989754},"labels":[],"label_agreement":null},{"id":"W3045424063","doi":"10.22581/muet1982.2003.13","title":"A Multi-blocked Image Classifier for Deep Learning","year":2020,"lang":"en","type":"article","venue":"Mehran University Research Journal of Engineering and Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"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":"Sungkyunkwan University","keywords":"MNIST database; Overfitting; Computer science; Convolutional neural network; Dropout (neural networks); Artificial intelligence; Deep learning; Machine learning; Classifier (UML); Artificial neural network; Contextual image classification; Data mining; Deep neural networks; Pattern recognition (psychology); Image (mathematics)","score_opus":0.037230653298130315,"score_gpt":0.2837392438525288,"score_spread":0.2465085905543985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045424063","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":[0.028679477,0.00020879645,0.96419847,0.0066930605,0.00001895764,0.00009083686,4.8418286e-7,0.000089785884,0.000020106912],"genre_scores_gemma":[0.68881875,0.00016423326,0.31091294,0.000009362467,0.000030779735,6.680256e-7,1.8880088e-7,0.0000060410416,0.000057017354],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993991,0.00002552476,0.000093724724,0.00014289185,0.00011662585,0.00022212138],"domain_scores_gemma":[0.99926686,0.00017360575,0.000049010854,0.00010128596,0.00029043524,0.00011877105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021835566,0.000057571855,0.00012411237,0.00036585148,0.00015928941,0.000023249577,0.00056320755,0.00006493222,9.557582e-7],"category_scores_gemma":[0.00033527223,0.00005957855,0.000031809395,0.00088028534,0.00008902816,0.00019620369,0.00022057275,0.00060685055,0.0000015984916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017813344,0.00014514284,0.0012749012,0.00017543414,0.00020043735,0.00061302114,0.0014421141,0.025207186,0.6936363,0.13373849,0.0008119906,0.14257684],"study_design_scores_gemma":[0.001355227,0.0005244462,0.00018614448,0.000027964637,0.000008208008,0.000094061266,0.0004154119,0.911347,0.0056171515,0.00061619823,0.07967534,0.0001328927],"about_ca_topic_score_codex":4.4051183e-7,"about_ca_topic_score_gemma":5.207936e-7,"teacher_disagreement_score":0.88613975,"about_ca_system_score_codex":0.0000334554,"about_ca_system_score_gemma":0.000024584731,"threshold_uncertainty_score":0.26364973},"labels":[],"label_agreement":null},{"id":"W3045897451","doi":"10.3390/s20154220","title":"Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":469,"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":"Perception; Computer science; Sensor fusion; Set (abstract data type); Anticipation (artificial intelligence); Human–computer interaction; Range (aeronautics); Artificial intelligence; Deep learning; Real-time computing; Engineering; Simulation","score_opus":0.035556953549532186,"score_gpt":0.3170844727122842,"score_spread":0.281527519162752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045897451","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":[4.3248951e-7,0.7366028,0.26134065,0.00040090605,0.00006528118,0.0012986166,0.0000027075466,0.00023412393,0.000054510667],"genre_scores_gemma":[0.0000021895567,0.9746305,0.024177229,0.00042187746,0.00019349901,0.00021453002,0.00006666081,0.000044446475,0.00024906642],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998173,0.00018512434,0.00047936433,0.0007539972,0.00015635864,0.00025214744],"domain_scores_gemma":[0.9987403,0.0002617516,0.00035254503,0.0004228911,0.000085117,0.00013740655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001397555,0.0003159583,0.0008827788,0.000063828615,0.00028765175,0.000070697926,0.00035940713,0.00014282917,0.000009341513],"category_scores_gemma":[0.00012040688,0.0002833935,0.00021481409,0.0006294,0.000041319272,0.00012486323,0.00021327706,0.0003035211,0.00010268006],"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":[5.1619946e-7,0.00000511543,2.2679035e-7,0.0075435084,0.0000089466275,0.0000034635054,0.000052172203,0.0005041846,5.4680953e-7,0.0005006457,0.00019951326,0.99118114],"study_design_scores_gemma":[0.00005524546,0.000039386086,7.040562e-7,0.0044546644,0.00011873162,0.00006177757,0.0000051819197,0.17138624,1.5273626e-7,0.00007494589,0.82356364,0.00023934842],"about_ca_topic_score_codex":0.0000010319498,"about_ca_topic_score_gemma":8.6223764e-7,"teacher_disagreement_score":0.9909418,"about_ca_system_score_codex":0.00009337368,"about_ca_system_score_gemma":0.00005125339,"threshold_uncertainty_score":0.9999618},"labels":[],"label_agreement":null},{"id":"W3046648263","doi":"","title":"Daydream: Accurately Estimating the Efficacy of Optimizations for {DNN} Training","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Profiling (computer programming); Software deployment; Artificial neural network; Software; Artificial intelligence; Machine learning; Graph; Dependency graph; Parallel computing; Computer engineering; Theoretical computer science; Software engineering; Programming language","score_opus":0.2144990876850486,"score_gpt":0.24658236047301102,"score_spread":0.032083272787962414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046648263","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":[0.017667605,0.00001107317,0.980214,0.0012573737,0.00005067415,0.00029549864,0.0000057514853,0.00012474575,0.0003732948],"genre_scores_gemma":[0.876399,0.000004276763,0.12323082,0.00024181674,0.000051433082,0.0000019804397,0.0000033294266,0.0000072835114,0.000060106686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999291,0.000029730536,0.00013362124,0.0003355306,0.000038110004,0.00017199431],"domain_scores_gemma":[0.99867123,0.00065734965,0.00014601677,0.00036146937,0.0000815555,0.000082397884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066870714,0.0000909663,0.00011437269,0.000028914788,0.00025458026,0.000027007363,0.00092248427,0.000027681082,0.0000042248034],"category_scores_gemma":[0.00016423009,0.00008357597,0.00007129103,0.00088637165,0.00007342091,0.00034445128,0.00017367427,0.00008635124,0.000007189986],"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":[0.0000068881586,0.000012763873,0.000031691296,0.0000041915087,0.000009358018,0.0000016996141,0.0005386999,0.8104711,0.00014700713,0.18569678,0.00010780664,0.0029719828],"study_design_scores_gemma":[0.00034651443,0.000038108963,0.00008609407,0.000006697869,0.000017382887,0.0000012628246,0.00008917803,0.9930541,0.0003440746,0.005510808,0.00041555188,0.0000902402],"about_ca_topic_score_codex":0.0000018732659,"about_ca_topic_score_gemma":0.0000012353248,"teacher_disagreement_score":0.8587313,"about_ca_system_score_codex":0.000017993136,"about_ca_system_score_gemma":0.000052129213,"threshold_uncertainty_score":0.34081292},"labels":[],"label_agreement":null},{"id":"W3047417463","doi":"10.1109/iscc50000.2020.9219660","title":"An AI-based Visual Attention Model for Vehicle Make and Model Recognition","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Discriminative model; Convolutional neural network; Benchmark (surveying); Modular design; Artificial intelligence; Intelligent transportation system; Machine learning; Pattern recognition (psychology); Engineering; Transport engineering","score_opus":0.06108289711059157,"score_gpt":0.3177819601327514,"score_spread":0.25669906302215983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047417463","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":[0.043851513,0.000004897611,0.94986683,0.0056671943,0.000009982245,0.00030308877,0.000008394927,0.00023757074,0.000050520903],"genre_scores_gemma":[0.6477834,0.000001705011,0.34754092,0.0045095016,0.00002424875,0.000075783246,0.000021870364,0.000007012613,0.000035570058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992919,0.000011311815,0.00011934499,0.00035253927,0.00008718044,0.00013770263],"domain_scores_gemma":[0.99959254,0.000029737426,0.00003675751,0.00015343698,0.0000706749,0.000116864714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004598288,0.00007759417,0.00006935849,0.000021368576,0.00011247922,0.000064134685,0.00016991497,0.000036303045,0.0000012176853],"category_scores_gemma":[0.000006781557,0.00007793165,0.000026649917,0.0001441853,0.000016984917,0.00043566056,0.000039582785,0.000057363242,0.0000074877603],"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":[0.000033221633,0.0000871528,0.000046393514,0.000017324413,0.0000030969663,2.984653e-7,0.00008938151,0.777365,0.044698667,0.01616539,0.0003985572,0.1610955],"study_design_scores_gemma":[0.00033082915,0.000087648805,0.00003303476,0.0000023382124,0.0000041656795,3.7083532e-7,0.0000033567496,0.9731015,0.0024102293,0.023899468,0.000021797403,0.00010529286],"about_ca_topic_score_codex":7.584088e-7,"about_ca_topic_score_gemma":0.000004982772,"teacher_disagreement_score":0.60393184,"about_ca_system_score_codex":0.00001021038,"about_ca_system_score_gemma":0.000022247155,"threshold_uncertainty_score":0.31779605},"labels":[],"label_agreement":null},{"id":"W3047759693","doi":"10.1109/newcas49341.2020.9159814","title":"Heterogeneous Distributed SRAM Configuration for Energy-Efficient Deep CNN Accelerators","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Dram; Static random-access memory; Computer science; Convolutional neural network; Efficient energy use; Universal memory; Chip; Computer hardware; Process (computing); Embedded system; Energy (signal processing); Computation; Parallel computing; Memory management; Semiconductor memory; Artificial intelligence; Interleaved memory; Engineering; Electrical engineering; Algorithm; Operating system","score_opus":0.0277544517513867,"score_gpt":0.2561044593429329,"score_spread":0.22835000759154622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047759693","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":[0.0019982394,0.00004576265,0.99259335,0.004228111,0.00009496733,0.00032845797,0.000012375306,0.00042130347,0.00027744222],"genre_scores_gemma":[0.9588701,0.000004272906,0.03780612,0.0028970696,0.00011596546,0.0001933953,0.00006739854,0.00001100744,0.000034640965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989143,0.000019852305,0.00022565301,0.00043962535,0.00014645477,0.00025409585],"domain_scores_gemma":[0.9992033,0.00010625247,0.00008225652,0.00032358017,0.00010763957,0.00017694206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003130651,0.00013224536,0.00012224613,0.000020473182,0.00017801722,0.00011592236,0.00054829894,0.000043985205,0.000021894559],"category_scores_gemma":[0.000029112867,0.00012110464,0.000066468856,0.00043226022,0.00002358574,0.00014336713,0.0001111262,0.000050415467,0.000032624037],"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":[0.000017434744,0.000080558224,0.000012769171,0.000008451531,0.000018018936,0.0000039300444,0.00014679841,0.5583978,0.0065966453,0.37599,0.0034192374,0.055308323],"study_design_scores_gemma":[0.00019663328,0.000071427436,0.000017805089,0.0000011251386,0.0000036537645,0.0000031129362,0.0000061259598,0.922385,0.045009214,0.0013588589,0.030800266,0.00014677696],"about_ca_topic_score_codex":0.0000018710431,"about_ca_topic_score_gemma":0.000003816762,"teacher_disagreement_score":0.95687187,"about_ca_system_score_codex":0.000033171626,"about_ca_system_score_gemma":0.000023457374,"threshold_uncertainty_score":0.49385038},"labels":[],"label_agreement":null},{"id":"W3047947467","doi":"10.1109/newcas49341.2020.9159769","title":"Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Convolutional neural network; Pruning; Field-programmable gate array; Computer engineering; Speedup; Quantization (signal processing); Pipeline (software); Inference; Computer architecture; Multiplexer; Computational complexity theory; Artificial intelligence; Computer hardware; Embedded system; Parallel computing; Algorithm; Multiplexing","score_opus":0.0736787948726227,"score_gpt":0.3577589631810635,"score_spread":0.2840801683084408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047947467","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":[0.0010341156,0.00010932774,0.9919152,0.0034877313,0.0005271306,0.0021708184,0.000038538143,0.0004199038,0.0002971927],"genre_scores_gemma":[0.51578844,0.000011981843,0.4827228,0.00045912454,0.0001394846,0.0006558388,0.00016798213,0.000028851668,0.000025479361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748445,0.00006758698,0.00083733845,0.0009047892,0.00027880934,0.00042702],"domain_scores_gemma":[0.99739045,0.0004360307,0.0005959924,0.0011083437,0.00031679537,0.00015241253],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001763863,0.0003173704,0.00047928878,0.000103245446,0.00021125984,0.00011761857,0.0017537299,0.00012376498,0.000027847633],"category_scores_gemma":[0.00007899329,0.00031430865,0.00028901978,0.00051837054,0.000053886823,0.00009876709,0.0020590385,0.0003505296,0.0000033665644],"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":[0.000007864467,0.00003199284,0.000040394112,0.00005475803,0.000035520377,8.799809e-7,0.00019940692,0.912691,0.00024351734,0.07567063,0.0011773364,0.009846722],"study_design_scores_gemma":[0.00045007383,0.00004149156,0.00016141265,0.000016659003,0.000035129142,0.0000027453414,0.000035405672,0.9913055,0.00058659265,0.0064515397,0.0006275851,0.00028592476],"about_ca_topic_score_codex":0.000019271694,"about_ca_topic_score_gemma":0.00002244577,"teacher_disagreement_score":0.51475435,"about_ca_system_score_codex":0.00006065807,"about_ca_system_score_gemma":0.00006565044,"threshold_uncertainty_score":0.9999309},"labels":[],"label_agreement":null},{"id":"W3049037379","doi":"10.1109/cjece.2019.2925780","title":"A Study of Dimensionality Reduction Impact on an Approach to People Detection in Gigapixel Images","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de Goiás; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Dimensionality reduction; Reduction (mathematics); Computer science; Artificial intelligence; Business; Computer vision; Risk analysis (engineering); Mathematics","score_opus":0.014184633827141736,"score_gpt":0.22905117779044698,"score_spread":0.21486654396330523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049037379","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":[0.5642052,0.000025778292,0.43555048,0.000102272184,0.000030803385,0.000076408425,2.7772023e-7,0.0000063664006,0.0000023699376],"genre_scores_gemma":[0.9878875,0.0000013486064,0.011955417,0.00004354282,0.00010360827,0.0000031557734,1.9008226e-7,0.0000048943193,3.304646e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993446,0.00002988538,0.00020920788,0.00016072948,0.00009988374,0.0001557148],"domain_scores_gemma":[0.9993173,0.00003714221,0.00005538434,0.00009589096,0.000052071846,0.00044219993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008247991,0.000083459825,0.00016407845,0.00021192197,0.000036189173,0.00003166427,0.0001992395,0.000020921254,2.1171846e-7],"category_scores_gemma":[0.000017603837,0.00007284307,0.000027470296,0.0008322269,0.000004276194,0.00018898757,0.000017792705,0.00019524395,2.1010759e-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":[0.000023409286,0.00013873122,0.00096397987,0.0000065204913,0.000017050104,0.000015775553,0.0014231387,0.9525062,0.003677422,0.00083638023,0.000037243008,0.040354185],"study_design_scores_gemma":[0.00031038624,0.0019429082,0.14176433,0.000010969515,0.0000046266705,0.00013528261,0.000020528036,0.8545739,0.00097545714,0.00012183739,0.000019534255,0.00012021295],"about_ca_topic_score_codex":0.00022182631,"about_ca_topic_score_gemma":0.0000733009,"teacher_disagreement_score":0.42368227,"about_ca_system_score_codex":0.00007193566,"about_ca_system_score_gemma":0.00005829252,"threshold_uncertainty_score":0.2970454},"labels":[],"label_agreement":null},{"id":"W3049320518","doi":"10.1109/jbhi.2020.3016306","title":"Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; Natural Science Foundation of Hunan Province; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Concatenation (mathematics); Convolutional neural network; Segmentation; Image segmentation; Receptive field; Feature (linguistics); Pattern recognition (psychology); Context (archaeology); Subnet; Field (mathematics); Encoder; Scale-space segmentation; Computer vision; Feature extraction; Context model; Mathematics","score_opus":0.07036109749813316,"score_gpt":0.37827224301248535,"score_spread":0.30791114551435217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049320518","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":[0.002125405,0.00016041531,0.920317,0.077022366,0.00018471178,0.00016732572,0.000004924606,0.0000112865555,0.0000065465942],"genre_scores_gemma":[0.13434447,0.0020964378,0.8446406,0.018552935,0.00034375733,0.0000075327794,0.0000034915868,0.000006222307,0.000004527206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812603,0.00002098166,0.0010816193,0.00005615389,0.0005502247,0.00016497856],"domain_scores_gemma":[0.9982989,0.00023546063,0.00078776793,0.00007517275,0.00012557523,0.0004771215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067172496,0.00007402959,0.00024919052,0.000072208866,0.00007579323,0.000020106303,0.000392756,0.000066965826,0.0000046710848],"category_scores_gemma":[0.0001455737,0.00005461296,0.000050115792,0.00022356435,0.00008178578,0.00033760146,0.000061006103,0.00020891512,0.0000021504354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045985136,0.0001944272,0.00017928756,0.0012412813,0.000048085574,0.0000042251536,0.008215406,0.00013722434,0.000850079,0.0015796713,0.040060427,0.9474439],"study_design_scores_gemma":[0.004271384,0.003911681,0.0009307676,0.0005501643,0.0000243429,0.00018532948,0.0011300162,0.9560512,0.0052726837,0.001588114,0.025840325,0.00024400583],"about_ca_topic_score_codex":0.0000025363788,"about_ca_topic_score_gemma":7.8880254e-7,"teacher_disagreement_score":0.95591396,"about_ca_system_score_codex":0.000017535143,"about_ca_system_score_gemma":0.00022744092,"threshold_uncertainty_score":0.22270519},"labels":[],"label_agreement":null},{"id":"W30703046","doi":"10.1109/tnnls.2018.2890787","title":"道路空間のオープン化による民間力の活用 (特集 道路分野における民間力の活用)","year":2012,"lang":"en","type":"article","venue":"road engineering & management review","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geology","score_opus":0.013036129860645418,"score_gpt":0.25015450061074185,"score_spread":0.23711837075009642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W30703046","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":[0.000065928674,0.10690669,0.8814967,0.0014210807,0.0004556581,0.00082199404,6.441243e-7,0.0008330547,0.007998244],"genre_scores_gemma":[0.13160732,0.26145345,0.592493,0.007341915,0.0009159115,0.0017444062,0.000029851506,0.00014875972,0.0042653796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985148,0.000021288488,0.00031742902,0.00032939028,0.0002592738,0.0005578273],"domain_scores_gemma":[0.9986393,0.000029158939,0.00008035479,0.0010509754,0.000021779168,0.00017842666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031903575,0.00023269161,0.00026072105,0.00008574369,0.0000681878,0.00004423489,0.0009361957,0.000027476928,0.00003504165],"category_scores_gemma":[0.000015798862,0.00022282146,0.000107640844,0.0008365802,0.000009253174,0.00066194026,0.00044791884,0.00013890718,0.00066927326],"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":[3.9854544e-7,0.000080283826,0.00006984706,0.00234105,0.000067220724,0.000008932974,0.000028106093,0.0041781003,0.00006158887,0.36662614,0.018695302,0.60784304],"study_design_scores_gemma":[0.00009130867,0.000008370326,0.0024042432,0.0008015514,0.000045118974,0.0000142319195,8.733941e-7,0.011540849,0.000034020188,0.00015876177,0.9845718,0.00032883638],"about_ca_topic_score_codex":0.0000011611924,"about_ca_topic_score_gemma":1.0246282e-7,"teacher_disagreement_score":0.9658765,"about_ca_system_score_codex":0.00006445336,"about_ca_system_score_gemma":0.000003356963,"threshold_uncertainty_score":0.90863955},"labels":[],"label_agreement":null},{"id":"W3080089874","doi":"10.1109/aipr47015.2019.9174593","title":"A Comparison of Deep Learning Object Detection Models for Satellite Imagery","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Object detection; Deep learning; Satellite; Task (project management); Window (computing); Sliding window protocol; Detector; Measure (data warehouse); Object (grammar)","score_opus":0.029176560439514086,"score_gpt":0.3049232051338081,"score_spread":0.275746644694294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080089874","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":[0.021541163,0.00014312346,0.9751797,0.000048408307,0.00007628412,0.0003512144,1.8633345e-7,0.00015353854,0.0025064063],"genre_scores_gemma":[0.84831095,0.0000126481855,0.15129097,0.000031756696,0.000015968146,0.00003764135,0.0000012538998,0.0000066785765,0.00029211768],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927217,0.000021196776,0.00019946389,0.00024779545,0.000101343176,0.00015802597],"domain_scores_gemma":[0.9992493,0.00022933197,0.0001180083,0.00029638686,0.00007670627,0.000030260077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009192106,0.00007301069,0.00014630711,0.000053272273,0.00006371262,0.000024207333,0.000264858,0.000030980696,0.0000043597925],"category_scores_gemma":[0.000010619351,0.000068562746,0.000057615314,0.00026733207,0.000013785574,0.0004056326,0.0000777595,0.00008732482,0.000028139008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014330108,0.000040601873,0.0012717447,0.000024897834,0.000008008856,7.264511e-8,0.000345665,0.36619395,0.05447684,0.03132949,0.000010870947,0.54628354],"study_design_scores_gemma":[0.000118352196,0.00007973802,0.00035281756,0.0000036923254,0.000002308615,0.0000012178541,0.00003077785,0.9326285,0.056145687,0.00972087,0.0008380371,0.00007797706],"about_ca_topic_score_codex":0.0000036365716,"about_ca_topic_score_gemma":0.00000881448,"teacher_disagreement_score":0.8267698,"about_ca_system_score_codex":0.000019016099,"about_ca_system_score_gemma":0.000006904989,"threshold_uncertainty_score":0.27959076},"labels":[],"label_agreement":null},{"id":"W3080224235","doi":"10.1109/tnnls.2020.3016688","title":"Parameter-Efficient Deep Neural Networks With Bilinear Projections","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Australian Research Council; Canadian Institute for Advanced Research; Nvidia","keywords":"Redundancy (engineering); Bilinear interpolation; Computer science; Projection (relational algebra); Deep neural networks; Benchmark (surveying); Deep learning; Algorithm; Artificial neural network; Artificial intelligence; Computer vision","score_opus":0.018400066954203598,"score_gpt":0.23247424994823065,"score_spread":0.21407418299402706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080224235","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":[0.022202494,0.00044100292,0.9737113,0.0011135071,0.0007252818,0.00091425533,0.0000021947417,0.00084952964,0.00004042976],"genre_scores_gemma":[0.996012,0.00006155305,0.0025703427,0.0005404782,0.00038589578,0.00026518866,0.000004053721,0.00005778252,0.00010271822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971144,0.00038624697,0.0004911051,0.0010011746,0.0003422759,0.00066477107],"domain_scores_gemma":[0.9981494,0.0006000632,0.00023737067,0.00049671356,0.000109294146,0.00040716806],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017781867,0.00042904823,0.00042139812,0.00011659966,0.0011616257,0.00041719273,0.00048381466,0.00015409027,0.0000052576347],"category_scores_gemma":[0.000011210311,0.0003523315,0.00012840371,0.0014661266,0.00014165405,0.0003293979,0.000012864168,0.0015619675,0.000007974357],"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":[0.00005614328,0.00006137118,0.000066582914,0.000016150883,0.000032819797,0.000016076447,0.00021064068,0.97181034,0.000022579965,0.00014301571,0.000031123047,0.027533138],"study_design_scores_gemma":[0.0004620684,0.00075754046,0.0000639505,0.000032102238,0.000037336937,0.0001606169,0.000118213764,0.9971423,0.000015762289,0.0000020431655,0.0008071303,0.00040092506],"about_ca_topic_score_codex":0.00003843463,"about_ca_topic_score_gemma":0.000014892934,"teacher_disagreement_score":0.9738095,"about_ca_system_score_codex":0.000043643187,"about_ca_system_score_gemma":0.000016532254,"threshold_uncertainty_score":0.9998929},"labels":[],"label_agreement":null},{"id":"W3080980548","doi":"10.1109/tnnls.2020.3015992","title":"Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review","year":2020,"lang":"en","type":"review","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":574,"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; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Lidar; Computer science; Deep learning; Artificial intelligence; Segmentation; Object detection; Discriminative model; Field (mathematics); Milestone; Feature (linguistics); Key (lock); Point (geometry); Task (project management); Machine learning; Data science; Remote sensing; Systems engineering; Engineering; Geography; Cartography","score_opus":0.028058343954906265,"score_gpt":0.2917329016432967,"score_spread":0.26367455768839043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080980548","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":[3.8898003e-7,0.502178,0.49528894,0.00015458545,0.00051327096,0.0015729413,0.000001145356,0.00027452418,0.000016211628],"genre_scores_gemma":[0.0036597808,0.99234116,0.0010772059,0.00017128479,0.0003700637,0.0018957327,0.000017985898,0.00012595353,0.00034085862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952351,0.0010698322,0.0013171775,0.0013674353,0.0002704553,0.0007399857],"domain_scores_gemma":[0.9966058,0.0016414291,0.00078702625,0.00059322425,0.00007381345,0.00029867052],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00065049843,0.0007741958,0.0022474055,0.0002370985,0.0006951257,0.00030701273,0.0008497222,0.00036697008,0.0000041870685],"category_scores_gemma":[0.000044589306,0.0007026326,0.0005972623,0.0013920392,0.0000610949,0.0003154076,0.000020326466,0.0032251365,0.00001721358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002471731,0.00001889743,4.970006e-7,0.0039151954,0.00003229896,0.000012687868,0.00003471209,0.4041271,8.614303e-8,0.000097416254,0.00003561265,0.591723],"study_design_scores_gemma":[0.00013047637,0.00017726298,4.4096075e-7,0.01087346,0.00012800879,0.00013449884,0.0000075507032,0.5238148,4.7981104e-8,0.0000041468465,0.46434712,0.00038218522],"about_ca_topic_score_codex":0.000014678254,"about_ca_topic_score_gemma":0.000011902047,"teacher_disagreement_score":0.59134084,"about_ca_system_score_codex":0.0001924037,"about_ca_system_score_gemma":0.00008141878,"threshold_uncertainty_score":0.9995425},"labels":[],"label_agreement":null},{"id":"W3081366416","doi":"10.3390/electronics9091356","title":"FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Pooling; Detector; Object detection; Benchmark (surveying); Object (grammar); Computer vision; Ground truth; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Feature extraction","score_opus":0.013171799616301617,"score_gpt":0.24657868107291128,"score_spread":0.23340688145660968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081366416","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":[0.0063902703,0.001681365,0.98727787,0.0030518437,0.00016801148,0.00084671733,0.0000056245412,0.0004018379,0.0001764537],"genre_scores_gemma":[0.49977323,0.0010300071,0.49385014,0.0038224554,0.0012851227,0.00003111805,0.000029152285,0.000080020196,0.00009874851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982031,0.000083532614,0.00025933486,0.0006040799,0.00019185338,0.0006580908],"domain_scores_gemma":[0.9988818,0.00024591104,0.00014212681,0.000421937,0.00017301898,0.00013517588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014274825,0.00021405533,0.00022802269,0.000031667936,0.0003968144,0.00012597497,0.00042934387,0.00008712773,0.0000017966494],"category_scores_gemma":[0.000078431345,0.00023247185,0.00008814969,0.0015814259,0.000025151749,0.00032537614,0.00011139333,0.0002989293,0.000022224176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020329762,0.000017536238,0.0000017937741,0.000021139229,0.000059934435,0.000006602315,0.00075957994,0.053855073,0.09843537,0.009780877,0.0024615086,0.83439726],"study_design_scores_gemma":[0.00047571672,0.00054736255,0.000008841844,0.000009237732,0.000014880003,0.0000147665105,0.00001201017,0.8744178,0.021895625,0.024175964,0.07814487,0.0002829155],"about_ca_topic_score_codex":0.0000018601878,"about_ca_topic_score_gemma":0.00013598219,"teacher_disagreement_score":0.8341144,"about_ca_system_score_codex":0.00016873005,"about_ca_system_score_gemma":0.00020196526,"threshold_uncertainty_score":0.9479927},"labels":[],"label_agreement":null},{"id":"W3083086448","doi":"10.1101/2020.09.03.282210","title":"Integrating GPU support for FreeSurfer with OpenACC","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Japan Society for the Promotion of Science; Alzheimer's Disease Neuroimaging Initiative","keywords":"Speedup; Computer science; Overhead (engineering); Parallel computing; Graphics processing unit; General-purpose computing on graphics processing units; Programming paradigm; Software; Graphics; Operating system","score_opus":0.023542076475150945,"score_gpt":0.2477754235408866,"score_spread":0.22423334706573567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083086448","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":[0.008112096,0.00014890352,0.98541594,0.0022489817,0.00059834734,0.0021039266,0.00014744513,0.0011817764,0.000042569147],"genre_scores_gemma":[0.54020786,0.000029200297,0.45716962,0.00087835157,0.0003965802,0.0011951323,6.6987786e-7,0.00011405391,0.000008521669],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964049,0.0000715993,0.00059170503,0.0018136422,0.00041780793,0.00070033176],"domain_scores_gemma":[0.9959279,0.00019673882,0.000564312,0.0023036033,0.0006098239,0.00039762567],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034616614,0.00065590226,0.00064207235,0.00014379818,0.0002955341,0.000544431,0.0026891427,0.00030323517,0.000012515558],"category_scores_gemma":[0.0001642403,0.0006139888,0.00016280745,0.0008972872,0.0001044404,0.0005056288,0.0015391125,0.00090024155,0.000069763795],"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":[0.00016426557,0.00046498651,0.003316301,0.0011513829,0.0006278889,0.0002420197,0.00011858776,0.004381294,0.67825764,0.29691306,0.0142020825,0.00016050288],"study_design_scores_gemma":[0.0038657845,0.0012466173,0.023141528,0.0015567681,0.00043150425,3.30257e-7,0.0000120791265,0.17259331,0.57344955,0.0007734201,0.2153897,0.007539387],"about_ca_topic_score_codex":0.0000123503205,"about_ca_topic_score_gemma":0.0000043568834,"teacher_disagreement_score":0.5320958,"about_ca_system_score_codex":0.00019462575,"about_ca_system_score_gemma":0.0007416833,"threshold_uncertainty_score":0.99963117},"labels":[],"label_agreement":null},{"id":"W3085407149","doi":"10.1109/tnnls.2022.3199703","title":"A Progressive Subnetwork Searching Framework for Dynamic Inference","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Subnet; Subnetwork; Computer engineering; Artificial neural network; Inference; Deep learning; Latency (audio); Artificial intelligence; Real-time computing; Parallel computing; Computer network","score_opus":0.017645540565349817,"score_gpt":0.29177089775161785,"score_spread":0.274125357186268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085407149","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":[0.010668545,0.0009819957,0.9850532,0.00046847167,0.0012870033,0.0010779572,0.00000811991,0.0004412927,0.000013420351],"genre_scores_gemma":[0.9878917,0.000055776967,0.009643431,0.000163175,0.00015521355,0.0017504379,0.000006392218,0.000041695035,0.0002922259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975092,0.0004965644,0.00035585358,0.0007115024,0.00033069443,0.00059617753],"domain_scores_gemma":[0.9972114,0.0018722037,0.00023447625,0.00044951818,0.00006611344,0.00016629428],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000435996,0.00026433813,0.00029254553,0.00013533383,0.0027197734,0.00029788192,0.0006098643,0.0000843539,0.000008525515],"category_scores_gemma":[0.000017067523,0.0002657917,0.0001167829,0.0008156449,0.00007191998,0.00027870428,0.000024258952,0.0017681527,0.0000020517846],"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":[0.0000360096,0.000043327487,0.00003002707,0.000017917138,0.000021920006,0.000008414166,0.00022088033,0.9400617,0.000016018867,0.0052187224,0.00002826387,0.054296825],"study_design_scores_gemma":[0.0002472986,0.00045298715,0.00004533024,0.000056983008,0.000014136554,0.000100184996,0.00014117587,0.9961962,0.000004438838,0.00082936237,0.0016301433,0.00028177607],"about_ca_topic_score_codex":0.0000132831165,"about_ca_topic_score_gemma":0.00000276221,"teacher_disagreement_score":0.9772231,"about_ca_system_score_codex":0.00009219429,"about_ca_system_score_gemma":0.000028048347,"threshold_uncertainty_score":0.99997944},"labels":[],"label_agreement":null},{"id":"W3087194612","doi":"10.48550/arxiv.2009.08576","title":"Pruning Neural Networks at Initialization: Why are We Missing the Mark?","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Toronto","funders":"","keywords":"Initialization; Pruning; Computer science; Artificial neural network; Fraction (chemistry); Shuffling; Heuristics; Machine learning; Weighting; Artificial intelligence; Baseline (sea); Layer (electronics); GRASP","score_opus":0.12038429085239426,"score_gpt":0.21645732250102823,"score_spread":0.09607303164863397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087194612","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":[0.0028614083,0.00031300975,0.9845075,0.00994396,0.00057334686,0.00046532723,0.000006072564,0.00047426016,0.0008550887],"genre_scores_gemma":[0.99413824,0.00045730497,0.0024313186,0.0021227503,0.00037757136,0.000004291997,0.000028058903,0.00003502949,0.00040542206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775803,0.00024819377,0.0002487818,0.0012090428,0.00012290273,0.00041302532],"domain_scores_gemma":[0.9974119,0.00028640355,0.00050831656,0.0014548014,0.0001310644,0.0002075064],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011321761,0.00036173992,0.00030034964,0.00008614337,0.00076889724,0.00023194352,0.0025989427,0.00021307026,0.000022633314],"category_scores_gemma":[0.000029816805,0.00035555757,0.00018263835,0.0012250377,0.00017216324,0.00040371474,0.004523378,0.00084642274,0.000025694322],"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":[0.000014499171,0.00001452987,0.00068686175,0.000024196745,0.000028788854,0.00015186306,0.00016532581,0.95249146,0.000007998796,0.041889265,0.0032420189,0.0012831857],"study_design_scores_gemma":[0.00016312524,0.000012713129,0.00032117846,0.000080565354,0.000043881595,0.000012695393,0.000037929967,0.9582433,0.000015628895,0.033007797,0.00770448,0.0003567061],"about_ca_topic_score_codex":0.0000111839445,"about_ca_topic_score_gemma":0.000026515272,"teacher_disagreement_score":0.99127686,"about_ca_system_score_codex":0.0002184713,"about_ca_system_score_gemma":0.000060526156,"threshold_uncertainty_score":0.9998896},"labels":[],"label_agreement":null},{"id":"W3087886149","doi":"10.1109/access.2020.3040256","title":"Pruning Convolutional Filters Using Batch Bridgeout","year":2020,"lang":"en","type":"preprint","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Regularization (linguistics); Inference; Convolutional neural network; Pruning; Residual neural network; Artificial intelligence; Machine learning; Generalization; Overfitting; Artificial neural network; FLOPS; Pattern recognition (psychology); Mathematics; Parallel computing","score_opus":0.144974455153314,"score_gpt":0.3717675072465475,"score_spread":0.2267930520932335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087886149","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":[0.017794896,0.000118903605,0.97656596,0.0025404403,0.0016629917,0.00044425388,0.00003105997,0.0004171081,0.00042436479],"genre_scores_gemma":[0.87736875,0.000039109313,0.11981028,0.001640187,0.0008800441,0.000110384826,0.000040317955,0.000037597,0.00007331795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780774,0.0000633037,0.00036670585,0.0010022889,0.00038596554,0.00037398993],"domain_scores_gemma":[0.9981939,0.00014560962,0.00034558488,0.001004247,0.00013769088,0.00017294203],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009162498,0.00029467433,0.00030699556,0.00009358393,0.00022410325,0.0005394456,0.0037399165,0.0001540711,0.000013602335],"category_scores_gemma":[0.000029997802,0.00033328382,0.00012427763,0.0004901073,0.000080861006,0.00072428485,0.0032291536,0.0007261909,0.000049667626],"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":[0.000012709251,0.00007013772,0.001406071,0.00018029618,0.00011339797,0.000075792195,0.00037412107,0.9441866,0.0055552586,0.014726933,0.01696661,0.016332021],"study_design_scores_gemma":[0.00015862985,0.000008561213,0.001479913,0.00011850535,0.000020760104,0.000023758597,0.000002568872,0.9604442,0.0026842768,0.031017011,0.0035203998,0.0005214005],"about_ca_topic_score_codex":0.00007540497,"about_ca_topic_score_gemma":0.000005697788,"teacher_disagreement_score":0.85957384,"about_ca_system_score_codex":0.00015800376,"about_ca_system_score_gemma":0.00027065456,"threshold_uncertainty_score":0.9999119},"labels":[],"label_agreement":null},{"id":"W3087924192","doi":"10.1007/978-3-030-58577-8_6","title":"Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":27,"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":"","keywords":"Computer science; Backpropagation; Convolutional neural network; Inference; Convolution (computer science); Artificial intelligence; Block (permutation group theory); Baseline (sea); Code (set theory); Artificial neural network; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.018592354694867173,"score_gpt":0.2496386267304804,"score_spread":0.23104627203561323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087924192","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":[0.00009589321,0.0003830738,0.99315524,0.0038586098,0.0013456544,0.00063522474,0.000003456368,0.00015085939,0.0003720145],"genre_scores_gemma":[0.58967984,0.00006734025,0.4065288,0.0028443814,0.00063900015,0.00007433074,0.000032266555,0.000045426168,0.00008861878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958826,0.0000630112,0.00074252643,0.0018059177,0.00081783463,0.0006881356],"domain_scores_gemma":[0.997943,0.00049853715,0.00031626775,0.0009244917,0.00014221719,0.00017549873],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004513094,0.0004980882,0.0004993731,0.0007247615,0.00017429712,0.00023100163,0.0026724432,0.00030442566,0.000014544611],"category_scores_gemma":[0.000048756003,0.0005224942,0.00011337144,0.001680016,0.00038302713,0.00084296556,0.001068548,0.0011950934,0.00003096162],"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":[0.000008941992,0.000030199051,0.000165167,0.000013988896,0.0000034440195,0.00011479902,0.00012093688,0.59232193,0.00015655847,0.2826572,0.00002025814,0.12438656],"study_design_scores_gemma":[0.00023864572,0.000050457144,0.001276339,0.00010151683,0.0000018663527,0.000026982236,8.5289464e-8,0.64539444,0.000019591002,0.35241127,0.0001478304,0.00033094524],"about_ca_topic_score_codex":0.000012150001,"about_ca_topic_score_gemma":0.00023454516,"teacher_disagreement_score":0.58958393,"about_ca_system_score_codex":0.00046455,"about_ca_system_score_gemma":0.00021439312,"threshold_uncertainty_score":0.99972266},"labels":[],"label_agreement":null},{"id":"W3089018380","doi":"10.1109/access.2020.3025193","title":"Image Object Extraction Based on Semantic Detection and Improved K-Means Algorithm","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Chongqing Municipal Education Commission; National Natural Science Foundation of China; Chongqing Municipal Education Commission Foundation","keywords":"Computer science; Initialization; Artificial intelligence; Segmentation; Algorithm; Image segmentation; Randomness; Object (grammar); Object detection; Computer vision; Pattern recognition (psychology); Mathematics","score_opus":0.02257230037657178,"score_gpt":0.2980667599614856,"score_spread":0.2754944595849138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089018380","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":[0.0068612094,0.000011778412,0.99035686,0.0017822534,0.00023112395,0.00028686042,0.0000024446242,0.00029872434,0.00016875773],"genre_scores_gemma":[0.9291349,0.000013163165,0.06897883,0.0015725845,0.0002118729,0.00006256917,0.0000015052736,0.00001424804,0.0000103453185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990531,0.000036262776,0.00014291705,0.00045009877,0.00014080013,0.00017681446],"domain_scores_gemma":[0.99932003,0.00011972908,0.000093513845,0.00031222653,0.000047733538,0.00010674803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000057901314,0.00012316863,0.0001033907,0.00005437842,0.00016400771,0.00023209436,0.0004364657,0.000042281652,0.000003897209],"category_scores_gemma":[0.000023499786,0.000120950936,0.000032209507,0.0005101443,0.000028231234,0.0010070419,0.000067104374,0.00017445136,0.000023812909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002757838,0.000068127476,0.000056176716,0.000032376625,0.0000086694945,0.00001651446,0.00010737969,0.0057839327,0.26506898,0.00007711508,0.00024884794,0.7285043],"study_design_scores_gemma":[0.00022943714,0.00008572892,0.0010406103,0.0000057831344,0.0000054321126,0.0000055598452,0.0000027125109,0.9304093,0.06720805,0.00041752393,0.0004630383,0.00012678307],"about_ca_topic_score_codex":0.000018318882,"about_ca_topic_score_gemma":0.000012391995,"teacher_disagreement_score":0.9246254,"about_ca_system_score_codex":0.00002793696,"about_ca_system_score_gemma":0.000017179698,"threshold_uncertainty_score":0.49322358},"labels":[],"label_agreement":null},{"id":"W3089741206","doi":"10.1016/j.media.2021.102191","title":"Realistic image normalization for multi-Domain segmentation","year":2021,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fonds Québécois de la Recherche sur la Nature et les Technologies; Nvidia","keywords":"Normalization (sociology); Computer science; Artificial intelligence; Segmentation; Dice; Pattern recognition (psychology); Adversarial system; Image segmentation; Spatial normalization; Machine learning; Mathematics","score_opus":0.028599127600292113,"score_gpt":0.35044219239771185,"score_spread":0.32184306479741975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089741206","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":[0.00073326373,0.00016399314,0.9955503,0.0024522631,0.00018527948,0.0005445641,0.00005369824,0.00021494583,0.00010170594],"genre_scores_gemma":[0.013935868,0.00024812637,0.9804571,0.00090528873,0.0002229446,0.0007267872,0.0032635902,0.00002825987,0.00021201355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968143,0.00020846979,0.0006965936,0.0010826545,0.00083135004,0.00036663833],"domain_scores_gemma":[0.9974113,0.00027879231,0.00041275824,0.0011040171,0.00050059066,0.00029255406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005878073,0.00029620828,0.0005803066,0.00033637,0.00020785442,0.00041076538,0.0011874426,0.00025458614,0.00017037637],"category_scores_gemma":[0.0006236382,0.0002984464,0.00051283115,0.0018689013,0.00012440984,0.00043390138,0.0010936789,0.0004055213,0.000017570177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012425528,0.005767866,0.004679924,0.0037323823,0.019428441,0.0019946941,0.010409837,0.15626438,0.037153132,0.04131208,0.04607143,0.6730616],"study_design_scores_gemma":[0.000344303,0.000010586827,0.00044857178,0.000037860234,0.0007303746,0.0000041789376,0.000053463158,0.9946304,0.0009963481,0.0021416713,0.00028954883,0.00031268824],"about_ca_topic_score_codex":0.00009120664,"about_ca_topic_score_gemma":0.00028342698,"teacher_disagreement_score":0.83836603,"about_ca_system_score_codex":0.00014560856,"about_ca_system_score_gemma":0.0002021177,"threshold_uncertainty_score":0.9999468},"labels":[],"label_agreement":null},{"id":"W3090302053","doi":"10.1109/tcsi.2021.3066967","title":"CARLA: A Convolution Accelerator With a Reconfigurable and Low-Energy Architecture","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Dram; Convolution (computer science); Architecture; Latency (audio); Computation; Efficient energy use; Computer architecture; Computer engineering; Residual neural network; Embedded system; Parallel computing; Computer hardware; Artificial intelligence; Algorithm; Artificial neural network; Telecommunications","score_opus":0.01462263658816678,"score_gpt":0.2101305978701999,"score_spread":0.1955079612820331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090302053","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":[0.017273389,0.0024232583,0.97733915,0.00046831352,0.0007242434,0.00067620154,0.00004242673,0.00023136285,0.0008216611],"genre_scores_gemma":[0.99676526,0.0008665839,0.0007280437,0.00019683817,0.00008778083,0.00052312476,0.000012399197,0.00004289921,0.0007770994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975083,0.00019151815,0.00037472136,0.001209641,0.00034155234,0.00037429208],"domain_scores_gemma":[0.9982007,0.00011713271,0.0002177976,0.00100182,0.00015132352,0.00031120575],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013231859,0.0004406477,0.000547966,0.0001761255,0.00044271548,0.00055654946,0.00037620324,0.00032649096,0.0000057636043],"category_scores_gemma":[0.0000031135098,0.00038737696,0.00010277016,0.00035805663,0.00014504332,0.00020394073,0.00001053492,0.00062971964,0.0000011226064],"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":[0.00005030395,0.0003297193,0.00001937743,0.0015402414,0.00096398924,0.00018327807,0.003202383,0.47298208,0.05618467,0.011983765,0.00023530144,0.4523249],"study_design_scores_gemma":[0.013226075,0.0031172151,0.001503719,0.020185575,0.0019105906,0.012472601,0.0062086657,0.75357676,0.08787498,0.005665007,0.07862061,0.015638199],"about_ca_topic_score_codex":0.00016952046,"about_ca_topic_score_gemma":0.0001882415,"teacher_disagreement_score":0.97949183,"about_ca_system_score_codex":0.00010885848,"about_ca_system_score_gemma":0.00024773335,"threshold_uncertainty_score":0.99985784},"labels":[],"label_agreement":null},{"id":"W3091093964","doi":"10.1109/icip40778.2020.9190782","title":"Proposal-Based Instance Segmentation With Point Supervision","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pascal (unit); Segmentation; Computer science; Pixel; Embedding; Artificial intelligence; Object (grammar); Point (geometry); Pattern recognition (psychology); Image segmentation; Computer vision; Mathematics","score_opus":0.017855462470950666,"score_gpt":0.24674915889249383,"score_spread":0.22889369642154317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091093964","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":[0.004862068,0.000012728433,0.96549684,0.02796068,0.000015147365,0.00030500905,5.638342e-7,0.00034173063,0.0010052614],"genre_scores_gemma":[0.50011504,0.0000020610773,0.49582964,0.003972445,0.000018522422,0.00003023234,0.0000033894135,0.000005240954,0.000023426395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992767,0.00001814095,0.00010531266,0.00030051888,0.00017646307,0.0001228683],"domain_scores_gemma":[0.9995367,0.000029688354,0.000038801718,0.00025672757,0.000048938433,0.00008916173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000028094319,0.00007975053,0.00006412355,0.000016657505,0.000076318574,0.000048167876,0.00032841298,0.000014288598,0.000017676091],"category_scores_gemma":[0.0000051759184,0.000058427624,0.000014446691,0.0005128106,0.000021381067,0.00046248653,0.00005779408,0.00006175383,0.000065798384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015213611,0.00026524157,0.0033768401,0.000080628015,0.000019939494,0.000037712154,0.0017108835,0.096985705,0.1051117,0.56820434,0.0072998814,0.21675502],"study_design_scores_gemma":[0.00083554955,0.0003924526,0.000750404,0.000016981676,0.000003654921,0.0000046435384,0.000049688766,0.87824637,0.11264844,0.0031825688,0.0035909347,0.00027828527],"about_ca_topic_score_codex":0.0000018464474,"about_ca_topic_score_gemma":0.000004909292,"teacher_disagreement_score":0.78126067,"about_ca_system_score_codex":0.00002023759,"about_ca_system_score_gemma":0.000048444606,"threshold_uncertainty_score":0.23826094},"labels":[],"label_agreement":null},{"id":"W3091448116","doi":"10.1109/camad50429.2020.9209289","title":"Evaluating Traffic Signs Detection using Faster R-CNN for Autonomous driving","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Convolutional neural network; Object detection; Key (lock); Deep learning; Artificial intelligence; Traffic sign; Computer vision; Real-time computing; Sign (mathematics); Representation (politics); Machine learning; Pattern recognition (psychology); Computer security","score_opus":0.12211918818115426,"score_gpt":0.3533325540906413,"score_spread":0.23121336590948705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091448116","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":[0.118390076,0.000010182005,0.8796469,0.0009408313,0.000089452806,0.000419186,3.4190782e-7,0.00039914102,0.00010389728],"genre_scores_gemma":[0.6481073,3.0957966e-7,0.3511939,0.00049011887,0.00012453205,0.00004212404,4.9166584e-7,0.000009829207,0.000031402713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998998,0.000028206508,0.00020430029,0.00040822104,0.00012680331,0.0002344718],"domain_scores_gemma":[0.99935853,0.0001723664,0.00008741105,0.00022882318,0.00005937851,0.0000934601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011166041,0.00010516763,0.00010392178,0.000030377729,0.00024231209,0.00009570391,0.00034665517,0.000033573666,0.0000066441967],"category_scores_gemma":[0.000056126246,0.00010396272,0.00005757814,0.00036014663,0.000013878763,0.00041637316,0.000120058634,0.000082347186,0.000020830781],"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":[0.0000027500782,0.000010229281,0.000009942719,0.0000073647643,0.000005069625,3.7414037e-7,0.0003492125,0.48200577,0.15131336,0.0009317851,0.000022159575,0.36534196],"study_design_scores_gemma":[0.00016219393,0.00012885939,0.000023728962,0.0000042148986,0.000006455978,0.0000042622623,0.000020146586,0.98144424,0.017079221,0.00041934563,0.00057540106,0.0001319032],"about_ca_topic_score_codex":9.660874e-7,"about_ca_topic_score_gemma":0.0000053889826,"teacher_disagreement_score":0.5297172,"about_ca_system_score_codex":0.00005191497,"about_ca_system_score_gemma":0.000032388572,"threshold_uncertainty_score":0.42394766},"labels":[],"label_agreement":null},{"id":"W3092446983","doi":"10.1609/aaai.v36i6.20611","title":"Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":37,"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":"Initialization; Computer science; Pruning; Generalization; Artificial intelligence; Artificial neural network; Inference; Machine learning; Flow (mathematics); Sparse matrix; Lottery; Early stopping; Work flow; Mathematics","score_opus":0.06280376046387758,"score_gpt":0.26998419598391143,"score_spread":0.20718043552003385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092446983","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":[0.85574645,0.00015370402,0.11111159,0.029572284,0.0009180958,0.0013223874,0.000008899878,0.00018246076,0.0009841148],"genre_scores_gemma":[0.99681,0.000036807003,0.0023921293,0.00048275685,0.000049855014,0.00012859571,6.299486e-7,0.000012151979,0.00008703284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983103,0.000034442877,0.00032229166,0.0005643009,0.00037853635,0.00039010355],"domain_scores_gemma":[0.9990908,0.00011548366,0.000263304,0.00031294455,0.00013089018,0.0000865749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035881778,0.00019853101,0.00021988059,0.00013088333,0.0003077725,0.00016176594,0.001604697,0.000038558734,0.000018937557],"category_scores_gemma":[0.00008737955,0.000170951,0.000060683422,0.0010062122,0.00020494222,0.0003666359,0.001062804,0.0005055804,0.0000041146986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008441964,0.00025320536,0.0015179346,0.000021529448,0.00000835221,0.000004273066,0.0019063606,0.06309442,0.0060808742,0.71593434,0.00047804817,0.21061628],"study_design_scores_gemma":[0.000030569172,0.00013880433,0.00068652804,0.000029366745,0.0000042713436,0.000017319338,0.00028884,0.9319219,0.008229821,0.058332246,0.00013357455,0.00018674733],"about_ca_topic_score_codex":0.0000100668285,"about_ca_topic_score_gemma":0.000017926583,"teacher_disagreement_score":0.86882746,"about_ca_system_score_codex":0.00006658185,"about_ca_system_score_gemma":0.000027267952,"threshold_uncertainty_score":0.6971179},"labels":[],"label_agreement":null},{"id":"W3092942046","doi":"10.1007/978-3-030-64559-5_33","title":"Semantic Segmentation with Peripheral Vision","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Pascal (unit); Segmentation; Encoder; Artificial intelligence; Convolutional neural network; Benchmark (surveying); Deep learning; Image segmentation; Machine vision; Computer vision; Machine learning; Pattern recognition (psychology)","score_opus":0.013320507794946439,"score_gpt":0.25853472666804217,"score_spread":0.24521421887309572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092942046","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":[0.000049998154,0.00013905573,0.99463457,0.0027131934,0.0003723245,0.0005300965,0.000001935703,0.00024042565,0.001318417],"genre_scores_gemma":[0.13869585,0.000033562355,0.8579311,0.0026995696,0.0003662048,0.000023153225,0.000009724302,0.000041940137,0.00019889514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969022,0.000020427256,0.00034001935,0.0014887803,0.000825689,0.00042285095],"domain_scores_gemma":[0.9982479,0.00019089604,0.00024464718,0.0010015212,0.00014088937,0.00017410383],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016415362,0.0004215299,0.0003492103,0.00026421464,0.00026511797,0.00039427908,0.0020830669,0.00013437179,0.000012628242],"category_scores_gemma":[0.000013568911,0.00035266715,0.00006315371,0.0007911697,0.0004419907,0.00077398226,0.00076924317,0.000589872,0.00005101356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011978179,0.000023075858,0.00003945731,0.000032542226,0.000009131887,0.000095045114,0.0005434451,0.17650555,0.0010045763,0.030738136,0.000038803213,0.7909582],"study_design_scores_gemma":[0.0003178184,0.00041798936,0.00011663442,0.00024697953,0.000008936184,0.00009753038,1.5945984e-7,0.8877329,0.0017643684,0.10724754,0.0013702771,0.0006788588],"about_ca_topic_score_codex":0.00000360577,"about_ca_topic_score_gemma":0.000036227357,"teacher_disagreement_score":0.7902794,"about_ca_system_score_codex":0.00024742846,"about_ca_system_score_gemma":0.00023898175,"threshold_uncertainty_score":0.99989253},"labels":[],"label_agreement":null},{"id":"W3096344818","doi":"10.1109/cvpr46437.2021.01544","title":"Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Vector quantization; Computer science; Quantization (signal processing); Algorithm; Uncompressed video; Image compression; Artificial neural network; Data compression ratio; Convolutional neural network; Theoretical computer science; Artificial intelligence; Image processing; Image (mathematics); Object (grammar)","score_opus":0.022029106226633188,"score_gpt":0.27972445362488657,"score_spread":0.2576953473982534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096344818","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":[0.09988536,0.0023395915,0.8955266,0.0006397354,0.0005602914,0.00041084475,0.0000039465813,0.00018979229,0.00044386755],"genre_scores_gemma":[0.91976917,0.00020310591,0.07954799,0.00015530776,0.00008891484,0.00005042417,0.000031878342,0.000017579521,0.00013560732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790525,0.00009742565,0.0004813964,0.00091196696,0.0002974508,0.00030649977],"domain_scores_gemma":[0.99775314,0.00025688185,0.0003159114,0.0013673626,0.0001724179,0.0001342686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012026573,0.00029679138,0.00045672955,0.00008266063,0.000118357675,0.00014311406,0.001045827,0.0001964141,0.000015643009],"category_scores_gemma":[0.000020924896,0.00026201227,0.00012365574,0.00038224537,0.000110657216,0.00010275685,0.0044027935,0.00058624067,0.000001271733],"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":[0.000004754837,0.000084820414,0.00023702359,0.00006309085,0.000014891796,0.000009788432,0.00009406653,0.9661004,0.0006137277,0.0113705015,0.0005984259,0.020808537],"study_design_scores_gemma":[0.00016283325,0.000019331963,0.002517065,0.000106908316,0.000011355306,0.000018447992,0.000010577914,0.9955479,0.00070484646,0.0004931627,0.000155862,0.00025169927],"about_ca_topic_score_codex":0.000029975985,"about_ca_topic_score_gemma":0.000017791028,"teacher_disagreement_score":0.8198838,"about_ca_system_score_codex":0.000020096799,"about_ca_system_score_gemma":0.000044798642,"threshold_uncertainty_score":0.9999832},"labels":[],"label_agreement":null},{"id":"W3096393735","doi":"10.48550/arxiv.2011.00652","title":"Multi-View Adaptive Fusion Network for 3D Object Detection","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pointwise; Computer science; Benchmark (surveying); Fuse (electrical); Artificial intelligence; Lidar; Fusion; Weighting; Computer vision; Object detection; Object (grammar); Feature (linguistics); Key (lock); Point (geometry); Segmentation; Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.11184438615954485,"score_gpt":0.22001244301155923,"score_spread":0.10816805685201437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096393735","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":[0.0015857983,0.00016622664,0.99511534,0.00014187745,0.00065044797,0.0015960458,0.000020468913,0.00054875476,0.00017503728],"genre_scores_gemma":[0.74896896,0.0003821134,0.24964757,0.00024268999,0.00033410257,0.000038750444,0.00002518948,0.000032133037,0.00032847156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976691,0.00011786211,0.0002280313,0.0014855072,0.000076702534,0.00042279562],"domain_scores_gemma":[0.9979948,0.00023152401,0.00035732484,0.0010390083,0.00018501577,0.0001923694],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013438042,0.00035274596,0.00036550534,0.00009320244,0.00038609447,0.000066763234,0.001453518,0.00025723578,0.0000050618914],"category_scores_gemma":[0.000032403495,0.00042020396,0.00025872848,0.0009667305,0.000067863475,0.00031093962,0.0018371857,0.0005766808,0.000068240064],"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":[0.000081093895,0.00006407035,0.000057908055,0.000066097746,0.0000692581,0.000036666537,0.00008755541,0.9304445,0.00025202378,0.040925175,0.0003935602,0.027522095],"study_design_scores_gemma":[0.0004001629,0.00009715348,0.00026008295,0.0000696073,0.00006100534,0.000002510519,0.0000106691905,0.9508981,0.00014967416,0.041912735,0.005728783,0.0004095173],"about_ca_topic_score_codex":0.000030171394,"about_ca_topic_score_gemma":0.0001195561,"teacher_disagreement_score":0.7473832,"about_ca_system_score_codex":0.00023406943,"about_ca_system_score_gemma":0.00010667682,"threshold_uncertainty_score":0.999825},"labels":[],"label_agreement":null},{"id":"W3097711533","doi":"10.1016/j.patcog.2020.107737","title":"Deep feature augmentation for occluded image classification","year":2020,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":74,"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":"Artificial intelligence; Computer science; Pattern recognition (psychology); Convolutional neural network; Feature (linguistics); Image (mathematics); Set (abstract data type); Deep learning; Feature extraction; Contextual image classification; Feature vector; Computer vision","score_opus":0.06730579002303476,"score_gpt":0.3001896617219861,"score_spread":0.2328838716989513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097711533","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":[0.003333488,0.000018375898,0.9819051,0.013401315,0.000111010064,0.0006485594,0.000021525937,0.00025278385,0.00030785764],"genre_scores_gemma":[0.7340843,0.000023326127,0.25978366,0.0043409006,0.0003869782,0.0006221149,0.0007080576,0.00002316254,0.000027493483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991179,0.0000342234,0.00016036782,0.00039429427,0.00012922421,0.00016398232],"domain_scores_gemma":[0.9993587,0.00009213018,0.00013784076,0.00019326147,0.00013074896,0.00008728679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005468259,0.000107988046,0.00008787597,0.000034025077,0.00012949758,0.00009582693,0.00026946192,0.000051547795,0.000020413918],"category_scores_gemma":[0.000038603266,0.00011501886,0.00005368412,0.00027178886,0.00001598142,0.00060054095,0.000043683944,0.00009314468,0.00024969326],"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":[0.00001732616,0.000038750793,0.00014916393,0.000039759943,0.000009874262,0.0000017171847,0.0004113759,0.000051055933,0.035411596,0.00033035688,0.0038236782,0.95971537],"study_design_scores_gemma":[0.0010233687,0.00014707129,0.005270925,0.000021169186,0.000026157151,0.000014517797,0.000085872896,0.947752,0.029651454,0.012715547,0.0029275413,0.00036436119],"about_ca_topic_score_codex":9.413529e-7,"about_ca_topic_score_gemma":0.000002488249,"teacher_disagreement_score":0.959351,"about_ca_system_score_codex":0.00003612609,"about_ca_system_score_gemma":0.000009766566,"threshold_uncertainty_score":0.4690333},"labels":[],"label_agreement":null},{"id":"W3101113336","doi":"10.1109/wacv48630.2021.00155","title":"Deep Template-based Object Instance Detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Scan|Design Fonden v. Inger og Jens Bruun; Nvidia","keywords":"Artificial intelligence; Computer science; Object (grammar); Viewpoints; Object detection; Computer vision; Focus (optics); Context (archaeology); Minimum bounding box; Class (philosophy); Cognitive neuroscience of visual object recognition; Matching (statistics); Viola–Jones object detection framework; Process (computing); Pattern recognition (psychology); Image (mathematics); Face detection; Mathematics; Facial recognition system","score_opus":0.02146998178595112,"score_gpt":0.26471822990162336,"score_spread":0.24324824811567225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101113336","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":[0.0022968638,0.00028080176,0.9920716,0.00052967994,0.0011427407,0.00039431668,0.000001092168,0.00078327814,0.0024995955],"genre_scores_gemma":[0.6814323,0.000039178856,0.31730208,0.0006196053,0.0001799556,0.00024579573,0.000015910118,0.000018341194,0.00014679942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980831,0.000070245296,0.00030309716,0.0009794307,0.00027518813,0.00028891483],"domain_scores_gemma":[0.9977563,0.00013011233,0.00019425983,0.0016618635,0.00016061714,0.00009684796],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009681666,0.00026169914,0.0002441645,0.00010294433,0.00016627365,0.00028304037,0.0011629584,0.00020837839,0.000026117084],"category_scores_gemma":[0.000027910157,0.00026976032,0.00013473198,0.00060693367,0.000035582674,0.00024980097,0.0009903525,0.0005954277,0.000044772238],"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":[0.00001099778,0.00015477206,0.00011927727,0.00012923083,0.000045125995,0.00004426738,0.00017568382,0.7261527,0.0046125655,0.010143409,0.0004852271,0.25792673],"study_design_scores_gemma":[0.00017369821,0.000022327129,0.00043987916,0.000060138213,0.000008460125,0.000010434294,0.00001045083,0.9500689,0.036751032,0.0082881665,0.0036870234,0.00047949425],"about_ca_topic_score_codex":0.000029594337,"about_ca_topic_score_gemma":0.00044803845,"teacher_disagreement_score":0.67913544,"about_ca_system_score_codex":0.00015270316,"about_ca_system_score_gemma":0.00017151464,"threshold_uncertainty_score":0.99997544},"labels":[],"label_agreement":null},{"id":"W3101401226","doi":"10.48550/arxiv.2011.07205","title":"Bi-Dimensional Feature Alignment for Cross-Domain Object Detection","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Domain (mathematical analysis); Computer science; Benchmark (surveying); Artificial intelligence; Dimension (graph theory); Object detection; Feature (linguistics); Divergence (linguistics); Object (grammar); Pattern recognition (psychology); Channel (broadcasting); Representation (politics); Computer vision; Exploit; Feature extraction; Mathematics; Geography; Telecommunications","score_opus":0.0574827531333036,"score_gpt":0.2213925375591742,"score_spread":0.16390978442587062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101401226","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":[0.037321292,0.00004869477,0.9596346,0.00083948806,0.00058529887,0.0008958906,0.00004544818,0.0003956608,0.00023364487],"genre_scores_gemma":[0.9696364,0.000028008288,0.028704936,0.0003925406,0.00021533814,0.000018210123,0.00004287041,0.000027079703,0.00093463727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978231,0.00006817991,0.00017252052,0.0014569333,0.00011762941,0.00036163573],"domain_scores_gemma":[0.9981963,0.000172318,0.0002645021,0.001011286,0.0001527627,0.00020283023],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011750937,0.0003277216,0.0002787256,0.00011996346,0.00035577154,0.000117825795,0.0013363669,0.00028084917,0.0000073659908],"category_scores_gemma":[0.00002356669,0.0003818644,0.00027051326,0.00061020534,0.00009282052,0.0002811627,0.0015914536,0.0005193215,0.000052058895],"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":[0.000104106875,0.00007635198,0.00014041777,0.00006718729,0.00009479579,0.00007902144,0.00009426839,0.86077845,0.002135109,0.13339038,0.0016395376,0.0014003569],"study_design_scores_gemma":[0.00069059606,0.00010512791,0.0005765218,0.000037539707,0.000043515334,0.000009792812,0.000010395778,0.6538068,0.0037567483,0.33080187,0.009596359,0.00056471274],"about_ca_topic_score_codex":0.000009455832,"about_ca_topic_score_gemma":0.000018346745,"teacher_disagreement_score":0.9323151,"about_ca_system_score_codex":0.00031350437,"about_ca_system_score_gemma":0.00011887228,"threshold_uncertainty_score":0.9998633},"labels":[],"label_agreement":null},{"id":"W3101629288","doi":"","title":"Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperparameter; Computer science; Mathematical optimization; Bilevel optimization; Artificial neural network; Jacobian matrix and determinant; Perceptron; Artificial intelligence; Machine learning; Optimization problem; Algorithm; Mathematics; Applied mathematics","score_opus":0.08668726734114977,"score_gpt":0.2088544038859706,"score_spread":0.12216713654482082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101629288","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":[0.20371057,0.000014790791,0.7951384,0.0004077145,0.00011582471,0.000392213,0.000010974291,0.00020183367,0.000007681121],"genre_scores_gemma":[0.92736006,0.000005040551,0.07193038,0.0005718827,0.000082142054,0.0000017785635,0.000009139787,0.000018239245,0.000021370292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862885,0.00010495296,0.0001650332,0.0006912253,0.00006254747,0.00034740198],"domain_scores_gemma":[0.9988334,0.00022967695,0.00014604912,0.00045519488,0.00013024463,0.00020542387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010067846,0.0001792707,0.00016112358,0.00007946215,0.00033597395,0.00006121483,0.00078035967,0.00008228756,0.000008820076],"category_scores_gemma":[0.000056090263,0.00020370276,0.000099011864,0.0013383047,0.00006497346,0.0003235988,0.00025069682,0.0001405993,0.0000031341851],"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":[0.00019475928,0.00001553119,0.00005826222,0.000003750629,0.000008929097,0.000015247843,0.00007846341,0.982288,0.0004202519,0.016305272,0.000057941248,0.0005536088],"study_design_scores_gemma":[0.000539276,0.0000763665,0.000114995586,0.0000044594335,0.000022543405,0.0000050855315,0.000019415584,0.99810475,0.00016233821,0.0004997705,0.00023055436,0.00022045452],"about_ca_topic_score_codex":0.0000044984367,"about_ca_topic_score_gemma":0.0000028109866,"teacher_disagreement_score":0.72364944,"about_ca_system_score_codex":0.00013450219,"about_ca_system_score_gemma":0.00004894483,"threshold_uncertainty_score":0.8306757},"labels":[],"label_agreement":null},{"id":"W3102093079","doi":"10.48550/arxiv.2001.01969","title":"Sparse Weight Activation Training","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Memory footprint; Computer science; FLOPS; Speedup; Computation; Training (meteorology); Convergence (economics); Convolutional neural network; Reduction (mathematics); Residual neural network; Artificial neural network; Artificial intelligence; Computer engineering; Parallel computing; Machine learning; Algorithm; Mathematics","score_opus":0.1596876820713097,"score_gpt":0.2096994079966755,"score_spread":0.05001172592536579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102093079","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":[0.02526024,0.000011927441,0.9691547,0.0012383092,0.00025879682,0.00029388556,0.0000052349,0.0005308441,0.0032460405],"genre_scores_gemma":[0.9841161,0.00007324515,0.0147899715,0.000363756,0.000180308,0.0000027446176,0.000021937216,0.00001871074,0.00043322932],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982932,0.000067869834,0.00015945999,0.0011185515,0.000079696445,0.0002812184],"domain_scores_gemma":[0.9983666,0.00011579823,0.00024964818,0.001010035,0.000076934804,0.0001809817],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000064630476,0.00024965144,0.00023968866,0.0001182874,0.00016440236,0.00007186194,0.0016640512,0.00017260494,0.000016412749],"category_scores_gemma":[0.000025235078,0.00030907395,0.00013048762,0.00080988236,0.00006146662,0.00047277065,0.0015086364,0.00061403005,0.00012992986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001297963,0.00003663528,0.00013702537,0.000023400034,0.00004241151,0.00008556605,0.00045997085,0.3041902,0.00045940862,0.688311,0.00082832045,0.0054131215],"study_design_scores_gemma":[0.00023285147,0.000021209153,0.00046536166,0.000046866386,0.000024401847,0.0000026065536,0.000034129513,0.743031,0.0006783887,0.2489523,0.0061005084,0.00041034067],"about_ca_topic_score_codex":0.000010346549,"about_ca_topic_score_gemma":0.0000043257046,"teacher_disagreement_score":0.95885587,"about_ca_system_score_codex":0.00014801206,"about_ca_system_score_gemma":0.00013585089,"threshold_uncertainty_score":0.99993616},"labels":[],"label_agreement":null},{"id":"W3103682594","doi":"10.48550/arxiv.2011.04006","title":"Long Range Arena: A Benchmark for Efficient Transformers","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":195,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Benchmark (surveying); Transformer; Range (aeronautics); Computer science; Engineering; Electrical engineering; Geography; Cartography; Aerospace engineering; Voltage","score_opus":0.0710414840204032,"score_gpt":0.2081560915259502,"score_spread":0.137114607505547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103682594","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":[0.023228472,0.00008663487,0.9727205,0.0008930168,0.00037426242,0.0012426256,0.000050869774,0.0003595006,0.0010441057],"genre_scores_gemma":[0.9899383,0.00011069961,0.009239902,0.00024425262,0.00011236307,0.000019602074,0.000044189695,0.000025741596,0.0002649472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778646,0.00004779462,0.00022245369,0.0013966821,0.000097436045,0.0004491783],"domain_scores_gemma":[0.99834806,0.00020469067,0.00020218085,0.0008631338,0.00012362037,0.00025830683],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000106682644,0.00034118848,0.00035131638,0.0001422146,0.00023651702,0.000074173266,0.0018168645,0.00019488424,0.000011161656],"category_scores_gemma":[0.000019918982,0.00039995043,0.00034846336,0.0007903604,0.000099391524,0.0001774747,0.00077904237,0.00046306162,0.00004325327],"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":[0.00006926525,0.00010489835,0.00026775198,0.00016036004,0.000085686916,0.00009812413,0.00031433636,0.81570137,0.000054355256,0.17876749,0.00082208886,0.003554282],"study_design_scores_gemma":[0.0006942709,0.00006127933,0.0004968713,0.000057599806,0.000079165424,0.000003225539,0.00002523643,0.959959,0.00014906489,0.03554657,0.0024040798,0.00052366895],"about_ca_topic_score_codex":0.000011790963,"about_ca_topic_score_gemma":0.000021086213,"teacher_disagreement_score":0.96670985,"about_ca_system_score_codex":0.00019300736,"about_ca_system_score_gemma":0.00013857603,"threshold_uncertainty_score":0.99984527},"labels":[],"label_agreement":null},{"id":"W3105249545","doi":"","title":"Winning the Lottery with Continuous Sparsification","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Pruning; Ticket; Heuristic; Regularization (linguistics); Machine learning; Artificial intelligence; Lottery; Artificial neural network; Heuristics; Deep neural networks; Mathematics","score_opus":0.07439868089584833,"score_gpt":0.16904430460711153,"score_spread":0.0946456237112632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105249545","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":[0.10042922,0.000014000721,0.8950629,0.0031288033,0.000026711954,0.00014996996,5.951175e-7,0.00019468674,0.0009931078],"genre_scores_gemma":[0.995721,0.000012248611,0.0026600587,0.0013519566,0.000047163994,9.295194e-7,0.0000013285234,0.0000065538693,0.00019875063],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930435,0.000035892204,0.000067125206,0.0003872956,0.000045881257,0.00015945476],"domain_scores_gemma":[0.9992407,0.000083838946,0.00008291296,0.00045957073,0.00005012453,0.00008287649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004205376,0.00008744788,0.00007658172,0.000020537533,0.00019133698,0.000043065203,0.00082127494,0.000021610687,0.000005882145],"category_scores_gemma":[0.000008772305,0.00006917442,0.000027604949,0.0008666374,0.00007962342,0.00038558728,0.00014367877,0.0001235537,0.00009774376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004428563,0.000038678845,0.007983551,0.000006474314,0.000030856696,0.00009932226,0.0006603151,0.23454769,0.0012475739,0.7497856,0.0014266252,0.004129022],"study_design_scores_gemma":[0.00057443604,0.00014326227,0.0057263593,0.000013820109,0.000030591236,0.000016038624,0.00021358185,0.96651244,0.0011765996,0.006861535,0.018393882,0.00033744637],"about_ca_topic_score_codex":0.000004396316,"about_ca_topic_score_gemma":0.0000032766318,"teacher_disagreement_score":0.8952918,"about_ca_system_score_codex":0.000022602331,"about_ca_system_score_gemma":0.000020219815,"threshold_uncertainty_score":0.2820851},"labels":[],"label_agreement":null},{"id":"W3105950517","doi":"","title":"Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hessian matrix; Stochastic gradient descent; Eigenvalues and eigenvectors; Computer science; Context (archaeology); Artificial intelligence; Mathematical optimization; Gradient descent; Ridge; Machine learning; Artificial neural network; Algorithm; Mathematics; Applied mathematics; Geology","score_opus":0.09298876824310644,"score_gpt":0.19143797550671823,"score_spread":0.0984492072636118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105950517","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":[0.3326411,0.000037285525,0.6650412,0.0011191327,0.00015737498,0.00017992106,0.000014997579,0.00013782924,0.0006711473],"genre_scores_gemma":[0.9987254,0.000014655248,0.0007855697,0.00027541467,0.000022495056,5.3138507e-7,0.0000020896,0.000006762915,0.00016705305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911195,0.000057382564,0.00011368133,0.00039561896,0.00007299319,0.00024836662],"domain_scores_gemma":[0.9991728,0.0000860447,0.000117984906,0.0004749844,0.00002520576,0.00012295897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052776828,0.00010993926,0.00011890097,0.00003459716,0.00042839043,0.000018445286,0.0011574882,0.00003963786,0.000010413544],"category_scores_gemma":[0.000026850475,0.00010409379,0.0001609758,0.0013218756,0.00008254565,0.00043594962,0.00058903947,0.00014668818,0.00003690179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017203636,0.00015192575,0.041650675,0.00002524134,0.00014792585,0.00007483533,0.0015237607,0.19658946,0.016164228,0.72897655,0.012882619,0.0017955957],"study_design_scores_gemma":[0.001427817,0.00011625384,0.00972052,0.00006328787,0.00016505182,0.000005082516,0.00062895165,0.9417075,0.013303143,0.017576018,0.014400457,0.00088593975],"about_ca_topic_score_codex":0.000022377562,"about_ca_topic_score_gemma":0.0000106731495,"teacher_disagreement_score":0.745118,"about_ca_system_score_codex":0.00005648614,"about_ca_system_score_gemma":0.00003530571,"threshold_uncertainty_score":0.42448217},"labels":[],"label_agreement":null},{"id":"W3106518779","doi":"","title":"Hybrid Models for Learning to Branch","year":2020,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Toronto; Polytechnique Montréal","funders":"","keywords":"Computer science; Solver; Branch predictor; Parallel computing; Perceptron; Integer programming; Key (lock); Architecture; Theoretical computer science; Computer engineering; Artificial intelligence; Artificial neural network; Algorithm; Programming language","score_opus":0.020526632752566092,"score_gpt":0.24845684532745763,"score_spread":0.22793021257489154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106518779","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":[0.0051665134,0.00022746749,0.956503,0.03481843,0.000052673844,0.0012812825,0.0000149178695,0.0016983193,0.00023739309],"genre_scores_gemma":[0.5284426,0.000026158741,0.45923686,0.010849397,0.00014359989,0.001064388,0.000008174935,0.00003941161,0.00018938683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789613,0.000055842986,0.0003564117,0.0007357413,0.00025960582,0.00069626496],"domain_scores_gemma":[0.9982425,0.00018858431,0.00013546961,0.00073329214,0.00012655227,0.0005736106],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021562184,0.00026498563,0.00028450598,0.00014335719,0.00033924566,0.00020289233,0.0013674725,0.000081820515,0.0000041042],"category_scores_gemma":[0.00018159358,0.0002905525,0.00014295775,0.0007465654,0.000026672988,0.0006913812,0.00049611734,0.0003403959,0.00003968929],"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":[0.000044752825,0.00005391769,0.00022140049,0.000020995236,0.000015961466,0.000009399731,0.00047772535,0.645389,0.012861351,0.22317225,0.0085514635,0.10918176],"study_design_scores_gemma":[0.0002388253,0.00017651802,0.00016381261,0.000011178422,0.000006206016,0.000021486308,0.000009043351,0.92477536,0.01447447,0.039018326,0.020784445,0.00032034752],"about_ca_topic_score_codex":0.00012342325,"about_ca_topic_score_gemma":0.000040747338,"teacher_disagreement_score":0.5232761,"about_ca_system_score_codex":0.0001430639,"about_ca_system_score_gemma":0.000095284835,"threshold_uncertainty_score":0.99995464},"labels":[],"label_agreement":null},{"id":"W3106965408","doi":"10.1007/978-3-030-58601-0_18","title":"ScribbleBox: Interactive Annotation Framework for Video Object Segmentation","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Annotation; Segmentation; Artificial intelligence; Computer vision; Object (grammar); Trajectory; Track (disk drive); Computer graphics (images)","score_opus":0.023568015670990615,"score_gpt":0.2988393565910849,"score_spread":0.2752713409200943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106965408","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":[0.0000119340575,0.0001511793,0.99376625,0.0026693665,0.001325585,0.0013266479,0.00001689766,0.00026150947,0.00047064948],"genre_scores_gemma":[0.03291728,0.000020299354,0.961469,0.0046919202,0.0006463979,0.00013455619,0.000026619882,0.000043293978,0.000050629507],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965706,0.000030708048,0.00052889774,0.0017285451,0.0006476556,0.000493612],"domain_scores_gemma":[0.996361,0.0016724195,0.00050166965,0.00095644663,0.00034072367,0.00016771581],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029315153,0.0004777692,0.00042971215,0.00043122872,0.00034245392,0.00045503327,0.002186701,0.00021031585,0.000007959526],"category_scores_gemma":[0.00026676833,0.00048331163,0.00014194631,0.0008601943,0.0003096671,0.0011091062,0.00069946167,0.0007922414,0.000047573034],"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":[0.000023518143,0.00002126458,0.000007164795,0.000038073053,0.000015213212,0.000013651417,0.0010270522,0.05623858,0.0006905635,0.19553064,0.00006914264,0.74632514],"study_design_scores_gemma":[0.00015742988,0.00015295608,0.000014063997,0.00018665828,0.000007876753,0.00001333889,4.0020467e-7,0.4231433,0.004028215,0.5709177,0.0010006219,0.00037743014],"about_ca_topic_score_codex":0.000003574885,"about_ca_topic_score_gemma":0.000015777277,"teacher_disagreement_score":0.7459477,"about_ca_system_score_codex":0.00041545174,"about_ca_system_score_gemma":0.0003112847,"threshold_uncertainty_score":0.9997619},"labels":[],"label_agreement":null},{"id":"W3107995456","doi":"10.48550/arxiv.2011.14578","title":"Where Should We Begin? A Low-Level Exploration of Weight Initialization Impact on Quantized Behaviour of Deep Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Quantization (signal processing); Convolutional neural network; Computer science; Inference; Deep neural networks; Artificial intelligence; Deep learning; Artificial neural network; Algorithm","score_opus":0.16949766856198376,"score_gpt":0.26183985694759576,"score_spread":0.092342188385612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107995456","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":[0.0674206,0.00010023139,0.93083847,0.00040378963,0.00029848865,0.0006268024,0.00004194271,0.00017102186,0.00009862606],"genre_scores_gemma":[0.9962956,0.0006199588,0.0027558296,0.00006180644,0.00010171678,0.000005720661,0.00010167138,0.000033409822,0.00002430332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787706,0.0002116643,0.00046185614,0.00097063935,0.00017886763,0.00029993156],"domain_scores_gemma":[0.99728316,0.00018872411,0.00089543656,0.0011606003,0.00029920036,0.00017290666],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001094647,0.00040316524,0.00056017743,0.00026066575,0.00010492235,0.000044492255,0.0014389771,0.00031348853,0.00001890467],"category_scores_gemma":[0.000023834897,0.00042824203,0.00032745113,0.0012951519,0.00012063355,0.00079584203,0.0009205907,0.0005952015,0.0000072899784],"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":[0.00012952085,0.00014174206,0.0012781753,0.0000714535,0.000050748775,0.000030205258,0.00023945399,0.9431937,0.00013525163,0.053170826,0.00009163438,0.0014673268],"study_design_scores_gemma":[0.0005486856,0.00017023097,0.0012148122,0.00014084774,0.0000851868,0.0000015139597,0.000035254518,0.97770286,0.00056462083,0.019186363,0.000009097543,0.00034053586],"about_ca_topic_score_codex":0.000051583374,"about_ca_topic_score_gemma":0.000055458473,"teacher_disagreement_score":0.92887497,"about_ca_system_score_codex":0.00014233268,"about_ca_system_score_gemma":0.00009011134,"threshold_uncertainty_score":0.99981695},"labels":[],"label_agreement":null},{"id":"W3108828338","doi":"","title":"Many-shot from low-shot: learning to annotate using mixed supervision for object detection","year":2020,"lang":"en","type":"book-chapter","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Pascal (unit); Shot (pellet); Annotation; Artificial intelligence; Object detection; Single shot; One shot; Object (grammar); Training set; Set (abstract data type); Supervised learning; Classifier (UML); Pattern recognition (psychology); Labeled data; Machine learning; Computer vision; Programming language; Artificial neural network","score_opus":0.05895967697017019,"score_gpt":0.2762568271738079,"score_spread":0.2172971502036377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108828338","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":[0.011364752,0.000045487126,0.9485449,0.0010224816,0.00016147959,0.0018881884,0.0006431336,0.00028351232,0.036046058],"genre_scores_gemma":[0.28347254,0.004401815,0.5759289,0.00026946573,0.0007573124,0.0000051458633,0.0017879035,0.00042215717,0.13295475],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99585897,0.0002809108,0.00028736558,0.0016455885,0.0010284943,0.0008986984],"domain_scores_gemma":[0.9961727,0.0009950724,0.00036572965,0.0010626145,0.0007057433,0.0006981327],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00042893045,0.0005134105,0.000752621,0.0013309936,0.001980785,0.000096952834,0.0032713832,0.00032018637,0.00007746502],"category_scores_gemma":[0.00009166988,0.0007260677,0.00049984746,0.00088403764,0.00049720745,0.000988414,0.003650966,0.0015892474,0.000039298007],"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":[0.010517905,0.0005837472,0.0004619659,0.0012765442,0.0019861644,0.0021302314,0.0175558,0.04302295,0.12297729,0.3912818,0.009358242,0.39884737],"study_design_scores_gemma":[0.0019224074,0.0014082832,0.00046160183,0.00053033687,0.00014526557,0.000012877096,0.0022301807,0.20896673,0.00087937736,0.031438727,0.7507899,0.0012143505],"about_ca_topic_score_codex":0.00035745357,"about_ca_topic_score_gemma":0.0009234525,"teacher_disagreement_score":0.7414316,"about_ca_system_score_codex":0.00062037056,"about_ca_system_score_gemma":0.00037289647,"threshold_uncertainty_score":0.99951905},"labels":[],"label_agreement":null},{"id":"W3109039129","doi":"10.18280/isi.250517","title":"Object Detection Using Stacked YOLOv3","year":2020,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"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":"Bounding overwatch; Computer science; Object detection; Artificial intelligence; Minimum bounding box; Convolutional neural network; Intersection (aeronautics); Hyperparameter; Task (project management); Pattern recognition (psychology); Object (grammar); Deep learning; Margin (machine learning); Computer vision; Convolution (computer science); Artificial neural network; Machine learning; Image (mathematics)","score_opus":0.02941936145044345,"score_gpt":0.2517203979680687,"score_spread":0.22230103651762528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109039129","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":[0.10043444,0.000028200937,0.89728427,0.0001689126,0.00014608067,0.00027744865,0.0000033492622,0.00048507555,0.0011722193],"genre_scores_gemma":[0.9519596,0.00000856931,0.047093656,0.0007958515,0.000088314555,0.00003172515,0.000010576624,0.000007508208,0.000004162585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894196,0.000040113013,0.00039473255,0.0001675944,0.0002107478,0.00024487695],"domain_scores_gemma":[0.99915534,0.000043955028,0.00024877128,0.0002846958,0.0001521574,0.00011510313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010805785,0.00013687633,0.00013225495,0.00009649016,0.00030868093,0.00024392798,0.0004089119,0.00006347044,0.0000066311577],"category_scores_gemma":[0.00012074636,0.00014139204,0.000049985647,0.0009602426,0.000054207303,0.005055506,0.00014948688,0.00013159627,0.00015946558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006251137,0.000031469717,0.000605952,0.00028641766,0.00005198994,0.0000055811724,0.01892899,0.08392985,0.02731175,0.031113818,0.00037298328,0.8372987],"study_design_scores_gemma":[0.0002859,0.00009906721,0.0011821064,0.000025686031,0.000008011982,0.000039586906,0.00021481105,0.9664479,0.018849429,0.0064193537,0.0061462317,0.00028194522],"about_ca_topic_score_codex":0.000014515747,"about_ca_topic_score_gemma":0.000002696465,"teacher_disagreement_score":0.882518,"about_ca_system_score_codex":0.00017640593,"about_ca_system_score_gemma":0.00004963989,"threshold_uncertainty_score":0.57658005},"labels":[],"label_agreement":null},{"id":"W3109576276","doi":"10.1145/3416012.3424628","title":"Challenges and Potential Solutions for Designing A Practical Pedestrian Detection Framework for Supporting Autonomous Driving","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Obstacle; Computer science; Focus (optics); Pedestrian; Risk analysis (engineering); Collision avoidance; Order (exchange); Transport engineering; Collision; Computer security; Engineering","score_opus":0.10123419537388625,"score_gpt":0.34143121556081063,"score_spread":0.24019702018692438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109576276","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":[0.0003966682,0.00011131827,0.97367185,0.024572358,0.00008834613,0.00074694696,0.0000010000564,0.00033056427,0.00008094469],"genre_scores_gemma":[0.49267063,0.00002354996,0.506773,0.0001556921,0.00017063171,0.0001921435,7.8673827e-7,0.000008449434,0.0000050984645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879515,0.000027170598,0.00022585216,0.00047541535,0.00008258336,0.00039380955],"domain_scores_gemma":[0.9985635,0.0008986183,0.00013855187,0.000177518,0.00006609731,0.00015569494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023231738,0.00010954133,0.00012686556,0.000030294943,0.00051961903,0.00011049661,0.00017558801,0.00007783503,0.0000015172692],"category_scores_gemma":[0.00074226613,0.000113500624,0.00005825082,0.0001269743,0.000027082504,0.00048509586,0.0001625462,0.00013947544,0.0000021979436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020189886,0.00003625527,0.0000057622515,0.00004632651,0.000020705394,0.0000031019624,0.0007726357,0.0017233097,0.007860605,0.5051502,0.00007955513,0.48428136],"study_design_scores_gemma":[0.00025891696,0.0002396124,0.00011596565,0.000010901071,0.000021557307,0.00005702442,0.00021048721,0.8942275,0.00289806,0.10044383,0.0013308446,0.0001853074],"about_ca_topic_score_codex":7.1012505e-7,"about_ca_topic_score_gemma":0.0000061555475,"teacher_disagreement_score":0.89250416,"about_ca_system_score_codex":0.000023208962,"about_ca_system_score_gemma":0.000047553982,"threshold_uncertainty_score":0.4628421},"labels":[],"label_agreement":null},{"id":"W3109915642","doi":"10.1109/tits.2021.3096854","title":"A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving","year":2021,"lang":"en","type":"review","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":308,"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":"Probabilistic logic; Computer science; Object detection; Object (grammar); Artificial intelligence; Code (set theory); Range (aeronautics); Machine learning; Deep learning; Key (lock); Data mining; Pattern recognition (psychology); Engineering; Computer security","score_opus":0.10208717591419388,"score_gpt":0.35798905189954056,"score_spread":0.2559018759853467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109915642","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":[0.00001234335,0.5124349,0.482837,0.000009954665,0.00041452027,0.0041063945,0.000026317834,0.00013697994,0.000021610811],"genre_scores_gemma":[0.036347877,0.9581052,0.00024681375,0.000040952877,0.00003103071,0.005063596,0.000027383338,0.000042224976,0.00009491771],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956711,0.0006458852,0.0016795062,0.0012881965,0.00037925478,0.00033608134],"domain_scores_gemma":[0.9975873,0.0006876747,0.0005406385,0.00088479585,0.00015597782,0.00014356319],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039539533,0.00064797833,0.0020217858,0.0005249062,0.00022211234,0.00013822067,0.0004898416,0.00019235922,0.0000074819764],"category_scores_gemma":[0.000006281887,0.00060184224,0.00032727706,0.0019044465,0.00004837708,0.00027242608,0.0000014958953,0.0008440474,0.00005289113],"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":[0.000007351827,0.0014622097,0.0000020684297,0.017524913,0.00034858615,0.000050566952,0.001976353,0.04224102,0.0000017976229,0.0011570344,0.000026732358,0.93520135],"study_design_scores_gemma":[0.0014507599,0.003122611,0.000102853184,0.24388993,0.0057678474,0.00035714783,0.0020105834,0.016632736,0.00023300084,0.000117647774,0.7212177,0.0050971964],"about_ca_topic_score_codex":0.000048579474,"about_ca_topic_score_gemma":0.0006263742,"teacher_disagreement_score":0.9301042,"about_ca_system_score_codex":0.0004281918,"about_ca_system_score_gemma":0.00017001567,"threshold_uncertainty_score":0.99964327},"labels":[],"label_agreement":null},{"id":"W3110006947","doi":"10.1155/2020/8870211","title":"A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Korea Institute for Advancement of Technology; Ministry of Trade, Industry and Energy","keywords":"Occupancy; Computer science; Process (computing); Toll; Binary number; Artificial intelligence; Face (sociological concept); Computer vision; Real-time computing; Engineering; Mathematics","score_opus":0.04340077653075523,"score_gpt":0.3335237510633671,"score_spread":0.2901229745326119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110006947","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":[0.021789435,0.00007619608,0.9680063,0.009564407,0.0001706114,0.00032634902,0.000006054967,0.000054279204,0.000006331634],"genre_scores_gemma":[0.45257315,0.000008033765,0.5454505,0.0018247619,0.00011251695,0.000019587873,0.0000011017357,0.000009788774,5.57373e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987204,0.000054503802,0.0005042135,0.00022977467,0.00029267903,0.0001984485],"domain_scores_gemma":[0.99725604,0.001585227,0.0005872951,0.00022849264,0.00023629097,0.000106660744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032040037,0.00013178005,0.0001987577,0.000052612402,0.00020353096,0.000039391343,0.0005143621,0.000047417798,0.0000029957992],"category_scores_gemma":[0.00032085137,0.00009671002,0.00015682218,0.00059017865,0.00001476994,0.00041910334,0.0000044127496,0.00036451634,0.0000018899615],"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":[0.0002739825,0.00004362774,0.00017679417,0.000028505687,0.000018940955,0.000008414605,0.0013584872,0.8607407,0.020515982,0.04024539,0.00003163235,0.07655751],"study_design_scores_gemma":[0.0024786745,0.0015020223,0.00869867,0.00039434907,0.000106298765,0.000008924876,0.0005832293,0.79072726,0.07956714,0.110984094,0.0044335895,0.0005157674],"about_ca_topic_score_codex":3.2239458e-7,"about_ca_topic_score_gemma":0.000001463746,"teacher_disagreement_score":0.43078372,"about_ca_system_score_codex":0.000028964152,"about_ca_system_score_gemma":0.000057803074,"threshold_uncertainty_score":0.39437202},"labels":[],"label_agreement":null},{"id":"W3111027253","doi":"10.1109/smc42975.2020.9283427","title":"Instance Segmentation of Personal Protective Equipment using a Multi-stage Transfer Learning Process","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Personal protective equipment; Computer science; Segmentation; Visibility; Process (computing); Transfer of learning; Artificial intelligence; Hazardous waste; Human–computer interaction; Engineering","score_opus":0.09104418257880388,"score_gpt":0.3355138451696684,"score_spread":0.24446966259086456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111027253","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":[0.1643212,0.000017990342,0.83467793,0.00031774302,0.00000988331,0.00047663503,0.0000012945067,0.00009602464,0.00008131002],"genre_scores_gemma":[0.8722809,0.0000021766605,0.12740901,0.00018605177,0.000013625716,0.00006296997,9.79259e-7,0.0000066290772,0.00003766437],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916893,0.000032301403,0.00016587904,0.00029824697,0.0001946131,0.00013999738],"domain_scores_gemma":[0.99970496,0.000022951826,0.000055871904,0.0000783405,0.000078016834,0.000059847658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047008467,0.00008918597,0.0001003988,0.000024446925,0.00010198977,0.000020232355,0.00022252275,0.000021156871,0.000012687859],"category_scores_gemma":[0.000011899735,0.00008451409,0.000030026347,0.0004675154,0.00003062089,0.00043003514,0.000045013447,0.00012929949,0.0000033102676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055661407,0.00015427654,0.0010828446,0.00018591288,0.000031642223,0.0000048770107,0.03284875,0.34230268,0.5892854,0.008709172,0.0000024932297,0.025336312],"study_design_scores_gemma":[0.00035013686,0.000078083955,0.00015553454,0.000014745226,0.000002846712,0.0000012245329,0.0005819508,0.8954751,0.10310981,0.000080931015,0.000047202593,0.00010242081],"about_ca_topic_score_codex":0.0000050040567,"about_ca_topic_score_gemma":0.000003455342,"teacher_disagreement_score":0.7079597,"about_ca_system_score_codex":0.000040824085,"about_ca_system_score_gemma":0.00004543356,"threshold_uncertainty_score":0.34463847},"labels":[],"label_agreement":null},{"id":"W3111046435","doi":"10.1007/978-3-030-78191-0_40","title":"Teach Me to Segment with Mixed Supervision: Confident Students Become Masters","year":2021,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Pixel; Computer science; Kullback–Leibler divergence; Entropy (arrow of time); Artificial intelligence; Divergence (linguistics); Market segmentation; Minification; Pattern recognition (psychology); Machine learning","score_opus":0.018917922523192326,"score_gpt":0.29204038906765867,"score_spread":0.27312246654446637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111046435","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":[0.17471363,0.00016010462,0.8184468,0.0037787976,0.001657595,0.0010415071,0.0000029905684,0.0001923304,0.000006217998],"genre_scores_gemma":[0.5315014,0.000012984715,0.46621326,0.0019798432,0.00013726304,0.00012767862,0.0000068220356,0.000017833645,0.0000028775937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932806,0.00013727877,0.0006272513,0.0029104562,0.002058754,0.0009856799],"domain_scores_gemma":[0.99555594,0.00032262248,0.00023220314,0.0030605926,0.00037743128,0.0004512227],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0008175995,0.00062673277,0.0006465874,0.0005523329,0.00033910395,0.0018319847,0.008089147,0.00018107933,0.00001000366],"category_scores_gemma":[0.000049760994,0.00054020935,0.000108990665,0.002918908,0.00029992042,0.00057439366,0.012191328,0.0010803578,0.000025507818],"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":[0.000008534842,0.00018657005,0.0053291544,0.00003925075,0.000020132,0.000114822404,0.0029188497,0.8096345,0.00069849344,0.00014260378,0.000023310125,0.1808838],"study_design_scores_gemma":[0.0011928421,0.0006498663,0.0422215,0.0015015007,0.000029632283,0.00024079258,0.000019589314,0.924979,0.018943291,0.007467997,0.00032167544,0.0024323673],"about_ca_topic_score_codex":0.000059380884,"about_ca_topic_score_gemma":0.00025264407,"teacher_disagreement_score":0.3567878,"about_ca_system_score_codex":0.00056886516,"about_ca_system_score_gemma":0.00052722346,"threshold_uncertainty_score":0.99970496},"labels":[],"label_agreement":null},{"id":"W3111248667","doi":"10.1109/tits.2020.3041278","title":"SA-YOLOv3: An Efficient and Accurate Object Detector Using Self-Attention Mechanism for Autonomous Driving","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":68,"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":"Beijing Municipal Natural Science Foundation; China Postdoctoral Science Foundation; Beihang University; National Natural Science Foundation of China","keywords":"Detector; Object detection; Inference; Object (grammar); Computer science; Convolution (computer science); Artificial intelligence; Computer vision; Notation; Function (biology); Deep learning; Pattern recognition (psychology); Artificial neural network; Mathematics; Arithmetic","score_opus":0.0413205380829545,"score_gpt":0.2817225926846917,"score_spread":0.2404020546017372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111248667","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":[0.1740332,0.000029928691,0.8231647,0.00014914961,0.00066590606,0.0013337953,0.000049268496,0.00057027384,0.000003791574],"genre_scores_gemma":[0.9618042,0.000035223133,0.037513707,0.00013552814,0.00007912355,0.00036712788,0.000012419068,0.0000400872,0.000012579156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978314,0.000083813764,0.0006734581,0.00077848753,0.00029704065,0.00033579182],"domain_scores_gemma":[0.9987529,0.00015674814,0.00024526758,0.00035635216,0.00021663329,0.00027212818],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016487691,0.00028914103,0.00028702986,0.0001616059,0.0004597956,0.00018365779,0.00034962167,0.00011633487,0.000004200724],"category_scores_gemma":[0.0000033204583,0.00030445776,0.00014018365,0.0005321737,0.000028047521,0.0004968477,9.737789e-7,0.00018893997,0.000014084539],"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":[0.00003599837,0.0001600203,0.00001812316,0.000118790034,0.000067347006,0.0000038115425,0.0026481666,0.95131564,0.030480936,0.010453476,0.0000029156129,0.004694791],"study_design_scores_gemma":[0.00032874415,0.00027418864,0.000090432884,0.000051306182,0.0000743659,0.000010993933,0.0002979819,0.9565967,0.04168371,0.0001495939,0.00012423482,0.0003177442],"about_ca_topic_score_codex":0.00003249913,"about_ca_topic_score_gemma":0.0000453308,"teacher_disagreement_score":0.78777105,"about_ca_system_score_codex":0.00012025766,"about_ca_system_score_gemma":0.000061133105,"threshold_uncertainty_score":0.99994075},"labels":[],"label_agreement":null},{"id":"W3111591377","doi":"10.1109/smc42975.2020.9283092","title":"Power and Performance Analysis of Deep Neural Networks for Energy-aware Heterogeneous Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Convolution (computer science); Artificial neural network; CUDA; Symmetric multiprocessor system; Computer architecture; Power (physics); Supercomputer; Efficient energy use; Convolutional neural network; Embedded system; Green computing; Electrical efficiency; Energy (signal processing); Computer engineering; Artificial intelligence; Parallel computing; Operating system; Cloud computing; Engineering; Electrical engineering","score_opus":0.013930581333947053,"score_gpt":0.23267336643273062,"score_spread":0.21874278509878356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111591377","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":[0.022051517,0.00041492845,0.97685635,0.00031398938,0.00005570574,0.00014688421,0.000003276992,0.00009135252,0.00006597293],"genre_scores_gemma":[0.9937827,0.000033035147,0.0054553524,0.0006048998,0.000036535494,0.000053918513,0.0000071481036,0.0000075728954,0.00001885765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991219,0.000017109911,0.00023309846,0.0003357272,0.000102316844,0.0001898513],"domain_scores_gemma":[0.9992891,0.00010626617,0.00010611063,0.00030938233,0.00007555469,0.00011360663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034289595,0.00010824861,0.000237409,0.00005857263,0.00007848728,0.00004238027,0.00038105,0.00003682837,0.0000041146386],"category_scores_gemma":[0.000003265979,0.000093961135,0.000083964536,0.0008938704,0.000029171477,0.0001814153,0.00014673795,0.000041230644,3.784913e-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":[0.000010442188,0.000009160557,0.0010096292,0.000011369382,0.00010883731,7.660274e-7,0.000063093765,0.976442,0.00004925837,0.0058730002,0.000058924234,0.016363563],"study_design_scores_gemma":[0.000090586516,0.00011106414,0.00031826374,0.0000013112574,0.00004997058,0.000003686354,0.0000069527964,0.9987728,0.00015090812,0.000021228352,0.00037179983,0.000101408834],"about_ca_topic_score_codex":0.0000039609904,"about_ca_topic_score_gemma":0.0000061626433,"teacher_disagreement_score":0.9717312,"about_ca_system_score_codex":0.000007984157,"about_ca_system_score_gemma":0.0000035091268,"threshold_uncertainty_score":0.3831624},"labels":[],"label_agreement":null},{"id":"W3113078706","doi":"10.1007/s11263-022-01706-5","title":"Robots Understanding Contextual Information in Human-Centered Environments Using Weakly Supervised Mask Data Distillation","year":2022,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Vector Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL; Nvidia","keywords":"Computer science; Artificial intelligence; Segmentation; Convolutional neural network; Pattern recognition (psychology); Robot; Heuristic; Categorization; Object (grammar); Image segmentation; Machine learning; Computer vision","score_opus":0.09627518311913438,"score_gpt":0.33828362499254555,"score_spread":0.24200844187341117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113078706","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":[0.06110801,0.000028057426,0.93687767,0.0007863577,0.0009994445,0.00013607749,0.000018728799,0.000013433409,0.000032239623],"genre_scores_gemma":[0.9607858,0.000013928572,0.03863344,0.0002845198,0.00017348785,0.0000019299225,0.00009507473,0.000007301043,0.0000045161883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786454,0.00012777459,0.00073642953,0.00020912584,0.0009072948,0.00015481883],"domain_scores_gemma":[0.99878544,0.00010230095,0.00061179756,0.0003714532,0.000067766974,0.00006125043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043126394,0.000121087614,0.00015991525,0.00041570351,0.00018464068,0.00019797703,0.0021136021,0.000026467576,0.000023170545],"category_scores_gemma":[0.000012051276,0.0001268403,0.000054657325,0.00023579366,0.000027442704,0.0033956345,0.0016178624,0.000271494,0.0000035597502],"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":[0.000111174064,0.00022781732,0.0011662189,0.0000038204644,0.00005592332,0.00006186309,0.0007752984,0.94474393,0.0059948927,0.012026668,0.0006764272,0.034155942],"study_design_scores_gemma":[0.0012122215,0.00014680462,0.002847933,0.000050634695,0.000004922885,0.00020423965,0.00008077459,0.9881522,0.000080314414,0.003631694,0.003456987,0.0001312837],"about_ca_topic_score_codex":0.000008074806,"about_ca_topic_score_gemma":0.0000023376633,"teacher_disagreement_score":0.8996778,"about_ca_system_score_codex":0.0007655721,"about_ca_system_score_gemma":0.0000458698,"threshold_uncertainty_score":0.51723975},"labels":[],"label_agreement":null},{"id":"W3113702544","doi":"10.1109/isncc49221.2020.9297332","title":"Vulnerable Road Users Detection based on Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Context (archaeology); Convolutional neural network; Object detection; Computer security; Work (physics); Transport engineering; Artificial intelligence; Pattern recognition (psychology); Engineering","score_opus":0.0218459331223152,"score_gpt":0.2413753621508496,"score_spread":0.2195294290285344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113702544","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":[0.0017588994,0.000015826165,0.9884808,0.007375183,0.00018065477,0.00017906855,7.7582337e-7,0.00047997694,0.001528806],"genre_scores_gemma":[0.9724087,0.0000014964164,0.016523117,0.010726357,0.00019494708,0.000043950848,0.000004352105,0.000009003169,0.00008804506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893665,0.00006412507,0.00015072199,0.00040729984,0.00019700568,0.00024420084],"domain_scores_gemma":[0.99929184,0.00013793053,0.00005419608,0.00031171177,0.00004459186,0.0001597542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006664804,0.000119492805,0.00009317607,0.000031133986,0.00026034558,0.000062744315,0.00043298007,0.000045270976,0.000042552056],"category_scores_gemma":[0.000029671395,0.000111938505,0.00005281875,0.00059501233,0.000030535397,0.00029698337,0.0000745242,0.00026908473,0.000073314215],"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":[0.000012245207,0.00001583037,0.00006923027,0.0000010329484,0.000002043987,0.000001692746,0.000008422902,0.9648384,0.0003634758,0.006714623,0.0011249632,0.026848067],"study_design_scores_gemma":[0.00022911566,0.00010064173,0.00091886445,0.0000015095278,0.0000018043496,0.0000021285332,0.00000230412,0.99469584,0.0008677442,0.00027509683,0.0027781897,0.00012675126],"about_ca_topic_score_codex":0.000007820526,"about_ca_topic_score_gemma":0.0000062964946,"teacher_disagreement_score":0.9719577,"about_ca_system_score_codex":0.000039179933,"about_ca_system_score_gemma":0.000017555796,"threshold_uncertainty_score":0.45647198},"labels":[],"label_agreement":null},{"id":"W3114868419","doi":"10.1016/j.asoc.2020.107051","title":"A tucker decomposition based knowledge distillation for intelligent edge applications","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":28,"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":"National Key Research and Development Program of China; Basic Research Programs of Sichuan Province; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Distillation; Computer science; Enhanced Data Rates for GSM Evolution; Decomposition; Ideal (ethics); Variance (accounting); Artificial intelligence; Tucker decomposition; Machine learning; Mathematics; Chemistry; Chromatography; Tensor decomposition","score_opus":0.03213700420455585,"score_gpt":0.30766156665623096,"score_spread":0.2755245624516751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114868419","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":[0.0005976595,0.00009070157,0.9941563,0.0016249572,0.000076189186,0.001591092,0.00000612911,0.0007527,0.0011042814],"genre_scores_gemma":[0.66893214,0.0000014794837,0.32941255,0.00093893224,0.0002790107,0.00035147084,0.000057436922,0.000021923555,0.000005042462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835515,0.000024989835,0.00040791568,0.0007151033,0.00014746432,0.00034936072],"domain_scores_gemma":[0.9983896,0.0006352511,0.00020094954,0.00044669735,0.00013258203,0.00019489444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001444709,0.00021544173,0.0002109743,0.00006370388,0.0004845987,0.000116248826,0.0006994642,0.00006908314,0.00000330566],"category_scores_gemma":[0.00002326149,0.00023860806,0.000097278986,0.0007969326,0.000045079327,0.00016025454,0.00024454776,0.0001577521,0.00013010115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023039218,0.00013902201,0.00007344264,0.000089157285,0.000019418692,3.787993e-7,0.00074888027,0.092543975,0.006359769,0.38540232,0.0011108454,0.5134897],"study_design_scores_gemma":[0.00033662375,0.000034494948,0.00012926132,0.000011470242,0.0000125540755,0.0000014116703,0.000018203511,0.9425738,0.003532497,0.008839277,0.044235755,0.00027464746],"about_ca_topic_score_codex":5.3769264e-7,"about_ca_topic_score_gemma":9.768864e-7,"teacher_disagreement_score":0.8500298,"about_ca_system_score_codex":0.00007958038,"about_ca_system_score_gemma":0.00005971877,"threshold_uncertainty_score":0.9730154},"labels":[],"label_agreement":null},{"id":"W3117804044","doi":"10.1007/978-3-030-01270-0_39","title":"Deep Continuous Fusion for Multi-sensor 3D Object Detection","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":841,"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; Fuse (electrical); Benchmark (surveying); Artificial intelligence; Computer vision; Lidar; Object detection; Detector; Exploit; Object (grammar); ENCODE; Feature (linguistics); Image sensor; State (computer science); Sensor fusion; Pattern recognition (psychology); Algorithm; Remote sensing","score_opus":0.02506021675575439,"score_gpt":0.27528089048730403,"score_spread":0.25022067373154966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117804044","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":[0.00007706627,0.00023210287,0.99549377,0.00020484111,0.0019788567,0.0013161205,0.000005363841,0.0003510759,0.00034078042],"genre_scores_gemma":[0.016904607,0.000048429858,0.98037803,0.00094562396,0.0010040922,0.00008842195,0.000006611996,0.000055083743,0.0005691009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611986,0.0000290364,0.0005451449,0.0019374789,0.00060246594,0.0007660323],"domain_scores_gemma":[0.9967343,0.0006223251,0.00041897607,0.0015551948,0.00049275847,0.00017641074],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048939796,0.0005519692,0.00050920324,0.0005590127,0.0005817561,0.00034242138,0.002575487,0.00036133608,0.000014892623],"category_scores_gemma":[0.0001395679,0.00051857595,0.00016628152,0.0006664967,0.00065964827,0.00051503023,0.0009784854,0.0005561203,0.00008064784],"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":[0.0000131085635,0.000030959363,0.0000073735987,0.000022382177,0.0000072203848,0.000010867127,0.00034407378,0.020228945,0.0019138914,0.0017054058,0.000015234279,0.97570056],"study_design_scores_gemma":[0.00039346598,0.00029176218,0.000041909,0.00011282176,0.000009623157,0.00006686332,1.5404368e-7,0.94488174,0.0062232055,0.042904772,0.0044659493,0.0006077243],"about_ca_topic_score_codex":0.00000824189,"about_ca_topic_score_gemma":0.0002718933,"teacher_disagreement_score":0.9750928,"about_ca_system_score_codex":0.00030901586,"about_ca_system_score_gemma":0.00016860943,"threshold_uncertainty_score":0.9997266},"labels":[],"label_agreement":null},{"id":"W3118325553","doi":"10.48550/arxiv.2301.12006","title":"Improved knowledge distillation by utilizing backward pass knowledge in neural networks","year":2023,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Waterloo","funders":"","keywords":"Distillation; Divergence (linguistics); Artificial neural network; Computer science; Matching (statistics); Set (abstract data type); Domain knowledge; Artificial intelligence; Training set; Machine learning; Natural language processing; Mathematics; Statistics; Chromatography","score_opus":0.05746430418862464,"score_gpt":0.21941729363009518,"score_spread":0.16195298944147055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118325553","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":[0.19738378,0.00020937297,0.79844815,0.00017025681,0.00039332738,0.00036184076,0.0000050338836,0.0007410568,0.0022871876],"genre_scores_gemma":[0.9981694,0.0001109597,0.00027684667,0.00003190314,0.00007279784,0.0000039014603,0.000022630878,0.000019192243,0.0012923342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837166,0.000113013855,0.00020261972,0.0007679189,0.00003413554,0.0005106259],"domain_scores_gemma":[0.99875903,0.00029019822,0.00010220504,0.00062268245,0.00007476027,0.00015111527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016567028,0.00020376199,0.0001885084,0.00020537144,0.0002077425,0.000054014767,0.00086760963,0.00010488286,0.0000057332186],"category_scores_gemma":[0.000019828216,0.00023952863,0.000080319405,0.0036612235,0.000076719734,0.00064703036,0.0005296282,0.0002629356,0.00013469408],"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":[0.00004738233,0.00025202986,0.011531947,0.000035004323,0.000031360505,0.00007469094,0.0005371016,0.74727106,0.0013635076,0.17888477,0.007030334,0.0529408],"study_design_scores_gemma":[0.00040523402,0.000026556698,0.005346619,0.0000117444315,0.0000061551523,0.0000015629345,0.00005264712,0.9891805,0.000038134633,0.002008388,0.0026760376,0.0002464251],"about_ca_topic_score_codex":0.000016210879,"about_ca_topic_score_gemma":0.00013964646,"teacher_disagreement_score":0.80078566,"about_ca_system_score_codex":0.00016192946,"about_ca_system_score_gemma":0.000030802123,"threshold_uncertainty_score":0.9767694},"labels":[],"label_agreement":null},{"id":"W3119197883","doi":"10.1109/iv47402.2020.9304798","title":"A Monocular Forward Leading Vehicle Distance Estimation using Mobile Devices","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Monocular; Estimator; Mobile device; Real-time computing; Feature (linguistics); Artificial intelligence; Computer vision; Detector; Vehicle tracking system; Kalman filter; Telecommunications","score_opus":0.03410368428960744,"score_gpt":0.2977776285235259,"score_spread":0.2636739442339185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119197883","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":[0.023690127,0.00012965215,0.97416186,0.0010521233,0.000026799138,0.00022011512,6.399263e-7,0.0002993229,0.00041938596],"genre_scores_gemma":[0.6104275,0.0000028657637,0.3888348,0.0006458341,0.000027262276,0.000024969351,9.547741e-7,0.0000056045046,0.000030186106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999209,0.000015172675,0.00015257308,0.00031610476,0.0001322892,0.00017484387],"domain_scores_gemma":[0.99948996,0.00004909184,0.00006864894,0.0002623087,0.000032451095,0.00009753167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037579834,0.00008811663,0.00009921567,0.000017613362,0.00014572674,0.00009144545,0.000423948,0.00002178988,0.000006867076],"category_scores_gemma":[0.000013861769,0.00008519618,0.000033807282,0.00051867607,0.00001909475,0.0007802302,0.00013794456,0.00006698289,0.000077474535],"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":[0.0000045808333,0.000027630424,0.0009002209,0.00003145434,0.000011120225,0.000006345364,0.000588768,0.75102407,0.01721066,0.06277879,0.00034052663,0.16707583],"study_design_scores_gemma":[0.00006925843,0.000019405607,0.0000590443,0.0000066346765,0.0000031202428,0.00000190998,0.000013190101,0.987143,0.0057598706,0.0018802993,0.0049390434,0.00010523249],"about_ca_topic_score_codex":0.000005010677,"about_ca_topic_score_gemma":0.0000035235719,"teacher_disagreement_score":0.5867374,"about_ca_system_score_codex":0.000033803473,"about_ca_system_score_gemma":0.000015289183,"threshold_uncertainty_score":0.34741992},"labels":[],"label_agreement":null},{"id":"W3119368576","doi":"10.1109/iv47402.2020.9304558","title":"Understanding Strengths and Weaknesses of Complementary Sensor Modalities in Early Fusion for Object Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Lidar; Computer vision; Artificial intelligence; Computer science; RGB color model; Object detection; Robustness (evolution); Sensor fusion; Modalities; Detector; Segmentation; Remote sensing; Geography","score_opus":0.0841526091470618,"score_gpt":0.29625795965112034,"score_spread":0.21210535050405854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119368576","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":[0.1589922,0.000016622917,0.8394371,0.0011481526,0.000020965967,0.00023239125,0.00000528083,0.000048218655,0.0000990527],"genre_scores_gemma":[0.9559623,0.000020707714,0.043844856,0.00012290468,0.000018917026,0.00001758261,0.0000017064101,0.000003821937,0.0000071985496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994794,0.000016682134,0.00014691036,0.00018351914,0.00007117332,0.000102317405],"domain_scores_gemma":[0.9994934,0.00031598823,0.000047411027,0.00009270331,0.000017493785,0.000032962005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000040373772,0.000058700647,0.00008980486,0.000040554878,0.0000654485,0.000019873132,0.00010931566,0.00001496873,0.0000026324753],"category_scores_gemma":[0.00001791406,0.00005616027,0.000015682286,0.00022940699,0.000024749068,0.00022363424,0.00008975391,0.000036610203,3.2908173e-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":[0.00023086298,0.00013212561,0.011017157,0.00039678434,0.000047798898,0.000004157908,0.010722094,0.013024903,0.20785984,0.67758846,0.0003710495,0.07860473],"study_design_scores_gemma":[0.0014952171,0.0005369988,0.004913529,0.000039360297,0.000010598322,0.000005702145,0.0038262017,0.8317816,0.08434254,0.07197529,0.0007421597,0.00033077312],"about_ca_topic_score_codex":0.00005107067,"about_ca_topic_score_gemma":0.00015491314,"teacher_disagreement_score":0.8187567,"about_ca_system_score_codex":0.000026975655,"about_ca_system_score_gemma":0.0000073359874,"threshold_uncertainty_score":0.22901492},"labels":[],"label_agreement":null},{"id":"W3119446159","doi":"10.48550/arxiv.2101.00793","title":"A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"","keywords":"MNIST database; Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Object detection; Transfer of learning; Inference; Scalability; Machine learning; Segmentation; Object (grammar); Contextual image classification; Artificial neural network; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Computer vision; Image (mathematics); Database","score_opus":0.10544282586290533,"score_gpt":0.2386242129336166,"score_spread":0.13318138707071125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119446159","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":[0.24643883,0.000109412504,0.7527319,0.000062879095,0.0001223462,0.0003030492,0.0000062142317,0.00014237416,0.00008295219],"genre_scores_gemma":[0.8678849,0.00028733636,0.1314445,0.00007249363,0.0000569781,0.0000044412363,0.000012202996,0.000019898378,0.00021727031],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981413,0.00008325168,0.00016874069,0.0011722671,0.000064755106,0.00036967985],"domain_scores_gemma":[0.998433,0.00040700287,0.000102885424,0.00074591965,0.00015703376,0.00015414259],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000104070445,0.00027142165,0.00030967264,0.00011349649,0.000334713,0.00020855121,0.000853873,0.00028071005,0.000009747403],"category_scores_gemma":[0.00005671738,0.0003418287,0.00012149196,0.0006157815,0.000103856015,0.0004110867,0.0010817654,0.0007440179,0.0000037926095],"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":[0.000008895053,0.000028780401,0.0011086233,0.00006451062,0.00003059816,0.00002235893,0.00021349487,0.76960623,0.00021206621,0.22662449,0.0000040015834,0.0020759685],"study_design_scores_gemma":[0.00020932946,0.000030147174,0.00025651883,0.00019375606,0.00004926649,0.000004996944,0.00007767315,0.90867186,0.0002866035,0.08949203,0.0003458571,0.00038196184],"about_ca_topic_score_codex":0.000030316472,"about_ca_topic_score_gemma":0.00001943485,"teacher_disagreement_score":0.6214461,"about_ca_system_score_codex":0.00010144543,"about_ca_system_score_gemma":0.00015136995,"threshold_uncertainty_score":0.9999034},"labels":[],"label_agreement":null},{"id":"W3120453208","doi":"10.1109/ssci47803.2020.9308364","title":"Vision-based Vehicle Detection and Distance Estimation","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Truck; Object detection; Artificial intelligence; Software deployment; Deep learning; Computer vision; Measure (data warehouse); Distance measurement; Real-time computing; Pattern recognition (psychology); Automotive engineering; Data mining; Engineering","score_opus":0.012327969910710335,"score_gpt":0.25460078769422145,"score_spread":0.24227281778351112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120453208","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":[0.0070298263,0.000021596246,0.98537654,0.0070360075,0.000018216653,0.00009545521,3.0894518e-7,0.00024532966,0.00017670759],"genre_scores_gemma":[0.8383554,0.0000012899355,0.16041832,0.001187336,0.000014188974,0.000011861211,4.8411283e-7,0.000002596625,0.000008535673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999567,0.000010377863,0.000073374715,0.00020232647,0.00007567028,0.00007130748],"domain_scores_gemma":[0.9996826,0.000061913444,0.000027830985,0.00014318831,0.00001815507,0.00006631109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000021537837,0.00004572192,0.000040216146,0.000010579643,0.00011538236,0.000069502974,0.00013043954,0.0000140303355,0.000003279245],"category_scores_gemma":[0.000018525669,0.000042073596,0.0000095410915,0.00027438125,0.000023415818,0.00028453663,0.000041126776,0.00004199769,0.000018860528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001124733,0.00002476231,0.00021230215,0.0000142688705,0.0000019329068,0.0000016628798,0.00011235766,0.07286425,0.02547429,0.07600278,0.00022633022,0.8250538],"study_design_scores_gemma":[0.00009423232,0.00003757597,0.0011321655,0.0000017952076,7.151007e-7,4.799444e-7,0.0000013381285,0.98456025,0.009956224,0.002876025,0.001287895,0.000051318733],"about_ca_topic_score_codex":0.0000015318195,"about_ca_topic_score_gemma":0.000004203796,"teacher_disagreement_score":0.911696,"about_ca_system_score_codex":0.000009439742,"about_ca_system_score_gemma":0.0000076194337,"threshold_uncertainty_score":0.17157114},"labels":[],"label_agreement":null},{"id":"W3120626278","doi":"10.1109/jiot.2021.3051611","title":"A Novel Adaptive Gradient Compression Scheme: Reducing the Communication Overhead for Distributed Deep Learning in the Internet of Things","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Carleton University","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Overhead (engineering); Deep learning; Data compression; Artificial intelligence; Node (physics); Compression ratio; Edge computing; Artificial neural network; Transmission (telecommunications); Distributed computing; Machine learning; Computer network; Computer engineering; Enhanced Data Rates for GSM Evolution; Telecommunications","score_opus":0.03464347156434745,"score_gpt":0.2928884088350212,"score_spread":0.25824493727067377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120626278","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":[0.08291837,0.0005333923,0.91358596,0.002413001,0.0001942333,0.00023083204,0.0000018370781,0.000021470278,0.00010088839],"genre_scores_gemma":[0.88861465,0.000076188284,0.110846885,0.00031840158,0.000037723723,0.000025540006,0.000010112729,0.00001058999,0.00005992462],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821997,0.00025767452,0.000666222,0.000245827,0.00037018722,0.00024012996],"domain_scores_gemma":[0.997073,0.0009943477,0.00095196965,0.0005475765,0.00038683138,0.00004628106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010928431,0.00014982007,0.0002457135,0.000079241945,0.0001717325,0.00014222585,0.0021028696,0.00006150416,0.0000044125863],"category_scores_gemma":[0.00028676807,0.000099446166,0.00015633611,0.00042722787,0.00013619199,0.0007474578,0.00040789595,0.0010049288,8.170376e-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":[0.0006824567,0.0018482122,0.001541485,0.00018202988,0.00049892755,0.00003549285,0.2369886,0.082330085,0.34406415,0.1409479,0.008047949,0.18283275],"study_design_scores_gemma":[0.0007291533,0.00017266283,0.00063702546,0.00086766534,0.000020643629,0.00039475958,0.0013983776,0.9453687,0.03958269,0.009006134,0.0016722771,0.00014989093],"about_ca_topic_score_codex":0.00012938003,"about_ca_topic_score_gemma":0.000014293369,"teacher_disagreement_score":0.86303866,"about_ca_system_score_codex":0.00010462907,"about_ca_system_score_gemma":0.000049115828,"threshold_uncertainty_score":0.43659714},"labels":[],"label_agreement":null},{"id":"W3123590971","doi":"","title":"Response Modeling of Hyper-Parameters for Deep Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Bottleneck; Convolutional neural network; Artificial intelligence; Convolution (computer science); Machine learning; Deep learning; Artificial neural network; Selection (genetic algorithm)","score_opus":0.037461337878651524,"score_gpt":0.27787584092427087,"score_spread":0.24041450304561934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123590971","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":[0.035076294,0.0001993894,0.9625074,0.0017321561,0.00014116873,0.00016184282,0.0000021403885,0.000083253886,0.00009634584],"genre_scores_gemma":[0.6172967,0.000005646944,0.38200712,0.00051352894,0.000020775107,0.00004091772,0.0000049219466,0.000005593024,0.00010482798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999032,0.00006210478,0.0002361908,0.0003179389,0.00012697765,0.00022476522],"domain_scores_gemma":[0.9985756,0.00065543473,0.000058751037,0.00043253246,0.00021171359,0.00006592862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001609123,0.00008790887,0.0001297111,0.00003479193,0.00009898884,0.000026276199,0.00036854445,0.00004122082,0.0000059229133],"category_scores_gemma":[0.000091225666,0.000087590124,0.00008838009,0.0003606583,0.000037018664,0.00021591784,0.00015509177,0.00007425724,0.0000020215139],"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":[0.00004790842,0.00002020075,0.000021209094,0.0000017832449,0.0000054456095,9.343002e-7,0.000018729028,0.95175886,0.0011218666,0.041613277,0.00011017243,0.0052796425],"study_design_scores_gemma":[0.00022197785,0.000030382253,0.000050930485,0.000003105333,0.0000037995585,0.000018723495,0.000012481083,0.9915675,0.0009910475,0.0066890647,0.00031627805,0.000094743715],"about_ca_topic_score_codex":0.000001838098,"about_ca_topic_score_gemma":0.000004844558,"teacher_disagreement_score":0.5822204,"about_ca_system_score_codex":0.000024801442,"about_ca_system_score_gemma":0.00004791432,"threshold_uncertainty_score":0.35718217},"labels":[],"label_agreement":null},{"id":"W3124175555","doi":"10.15353/jcvis.v6i1.3538","title":"Where Should We Begin? A Low-Level Exploration of Weight Initialization Impact on Quantized Behaviour of Deep Neural Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Waterloo","funders":"","keywords":"Initialization; Quantization (signal processing); Convolutional neural network; Computer science; Inference; Artificial intelligence; Deep neural networks; Artificial neural network; Deep learning; Algorithm","score_opus":0.04322401672907961,"score_gpt":0.3389066658185412,"score_spread":0.29568264908946157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124175555","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":[0.04343393,0.0020971303,0.9524664,0.0013396466,0.0004745024,0.00014443637,0.000006704774,0.000016715641,0.000020483647],"genre_scores_gemma":[0.98773944,0.00019537039,0.011808345,0.000075362186,0.00014324122,0.0000033045292,0.000015400577,0.000013065519,0.000006456048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794877,0.0002263687,0.00091311894,0.00018387729,0.0005886158,0.0001392377],"domain_scores_gemma":[0.99712133,0.00040024295,0.0011162054,0.00018862427,0.0010722574,0.0001013087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032792456,0.00016251675,0.00039350524,0.00022689647,0.00009534469,0.00012042178,0.00024300712,0.000045485627,0.000004641108],"category_scores_gemma":[0.00003972973,0.00012896632,0.00014121534,0.0005377472,0.000050886676,0.0011778516,0.00007024233,0.0001839741,6.19091e-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":[0.000055375913,0.00014843738,0.0017100904,0.000054095504,0.00002831084,0.000015329118,0.00032882087,0.9713766,0.0007786673,0.007722313,0.000352994,0.017428953],"study_design_scores_gemma":[0.0007868396,0.0001598457,0.007458428,0.00041005138,0.00001956568,0.0002257395,0.00012384323,0.98755395,0.00023955196,0.0028679215,0.000041110336,0.00011314774],"about_ca_topic_score_codex":0.0000047975655,"about_ca_topic_score_gemma":0.0000011851964,"teacher_disagreement_score":0.94430554,"about_ca_system_score_codex":0.000044748573,"about_ca_system_score_gemma":0.000082850376,"threshold_uncertainty_score":0.5259094},"labels":[],"label_agreement":null},{"id":"W3124751981","doi":"10.1109/globecom42002.2020.9322113","title":"A Novel Internet-of-Vehicles Assisted Collaborative Low-visible Pedestrian Detection Approach","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Pedestrian detection; Computer science; Pedestrian; Object detection; Convolutional neural network; Feature extraction; The Internet; Artificial intelligence; Frame (networking); Computer vision; Feature (linguistics); Pattern recognition (psychology); Computer network; Transport engineering; World Wide Web; Engineering","score_opus":0.03823349516543204,"score_gpt":0.2652672373066136,"score_spread":0.22703374214118158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124751981","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":[0.008866623,0.000032313455,0.98600334,0.0009920639,0.00004458302,0.00031678722,0.0000023208893,0.0002494976,0.0034924871],"genre_scores_gemma":[0.73743683,0.0000041307626,0.26208273,0.00027855142,0.00003804915,0.000040298182,0.0000024853089,0.000007136357,0.00010976304],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898744,0.00003326602,0.0002456014,0.00040307824,0.00017122716,0.00015939523],"domain_scores_gemma":[0.99922204,0.00008509096,0.00014361486,0.00029452262,0.00014042617,0.00011431365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005707412,0.00011880352,0.00015800327,0.000047250403,0.000059658087,0.000062956075,0.000569773,0.00004908456,0.000005978691],"category_scores_gemma":[0.00004413299,0.00010771787,0.000044039567,0.0016193879,0.000049394344,0.00036634714,0.0002061754,0.00012110668,0.00002221912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009790177,0.0005941096,0.00030333002,0.00011132496,0.00008574548,0.0000035672538,0.0026720325,0.00659427,0.6096156,0.103068486,0.0016257241,0.2752279],"study_design_scores_gemma":[0.0005603724,0.00017466002,0.001207533,0.000010110377,0.0000071402283,0.000009047396,0.00012212495,0.82633454,0.16865435,0.0005256622,0.0021829715,0.00021152152],"about_ca_topic_score_codex":0.000018785882,"about_ca_topic_score_gemma":0.000010771854,"teacher_disagreement_score":0.81974024,"about_ca_system_score_codex":0.000026777954,"about_ca_system_score_gemma":0.00004995705,"threshold_uncertainty_score":0.43926072},"labels":[],"label_agreement":null},{"id":"W3124818821","doi":"10.48550/arxiv.2101.07528","title":"The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels\\n Methods","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Kernel (algebra); Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Feature extraction; Representation (politics); Mathematics","score_opus":0.06143092665756005,"score_gpt":0.24233201598581644,"score_spread":0.18090108932825638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124818821","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":[0.12335207,0.000540317,0.87505114,0.000049533777,0.00023670352,0.00032519075,0.0000035963615,0.00005939814,0.00038202794],"genre_scores_gemma":[0.97213763,0.00037190795,0.027270101,0.000015203238,0.000020175165,0.000009594607,0.00001115322,0.000011090937,0.00015314334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762505,0.00093705097,0.0002168758,0.00083663204,0.00008685384,0.00029753242],"domain_scores_gemma":[0.99598414,0.0023228573,0.00024691672,0.0011503354,0.00021946059,0.00007627018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084202096,0.00021311096,0.00032700342,0.00009554258,0.00016084542,0.000054716613,0.0016696844,0.00016921575,0.0000047473163],"category_scores_gemma":[0.00010692651,0.00020830591,0.00016421403,0.0010405305,0.00021634744,0.00024195439,0.0020230697,0.0004928379,0.000004887108],"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":[0.000029457056,0.00008085269,0.0048111374,0.0000743804,0.00005162651,0.000042146392,0.000072694886,0.577511,0.00034818047,0.41411808,0.0000053146937,0.0028551004],"study_design_scores_gemma":[0.00039705588,0.000021040707,0.03528581,0.00017321376,0.00002853977,0.0000052726336,0.00010424533,0.7101534,0.0027023542,0.2505699,0.00023633898,0.00032283514],"about_ca_topic_score_codex":0.00012686098,"about_ca_topic_score_gemma":0.000110332476,"teacher_disagreement_score":0.8487856,"about_ca_system_score_codex":0.0002495964,"about_ca_system_score_gemma":0.00024996704,"threshold_uncertainty_score":0.8494469},"labels":[],"label_agreement":null},{"id":"W3125886068","doi":"10.1109/cvpr46437.2021.00465","title":"Deep Multi-Task Learning for Joint Localization, Perception, and Prediction","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Perception; Task (project management); Artificial intelligence; Reuse; Motion (physics); Autonomy; Architecture; Joint (building); Computation; Scale (ratio); Machine learning; Feature (linguistics); Motion planning; Robot; Algorithm; Engineering; Psychology","score_opus":0.0309097520200706,"score_gpt":0.2755455068126552,"score_spread":0.24463575479258462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125886068","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":[0.0006494903,0.00024338579,0.99686724,0.00064128643,0.0002973861,0.0007416838,0.0000025378658,0.00048206068,0.000074925236],"genre_scores_gemma":[0.214503,0.00055579294,0.782607,0.00040696992,0.00020512858,0.0006281598,0.000294455,0.0000302902,0.0007691622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837786,0.00006197442,0.0003428028,0.0008558548,0.00015463187,0.0002068524],"domain_scores_gemma":[0.99876946,0.00006944708,0.00017203085,0.00053338613,0.000359421,0.000096255084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014644519,0.00020240292,0.00020971683,0.00008247178,0.00033321668,0.00027667938,0.00030675443,0.00017951838,0.000014821259],"category_scores_gemma":[0.000090242975,0.0002092576,0.000076326425,0.00020713187,0.00004519059,0.00028105718,0.00087989355,0.00033029346,0.0000052603577],"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":[0.0000022054844,0.000064083455,0.0010402483,0.00012311686,0.00002723383,9.312164e-7,0.0013434872,0.93257046,0.00089504407,0.0037564284,0.0006497992,0.059526958],"study_design_scores_gemma":[0.00019375065,0.00002229587,0.0032872064,0.000040047275,0.000014063774,0.000010057249,0.000117166564,0.99064463,0.00009735133,0.002352911,0.003027013,0.00019349509],"about_ca_topic_score_codex":0.000012285347,"about_ca_topic_score_gemma":0.000030898045,"teacher_disagreement_score":0.2142602,"about_ca_system_score_codex":0.0000790341,"about_ca_system_score_gemma":0.00005677275,"threshold_uncertainty_score":0.85332775},"labels":[],"label_agreement":null},{"id":"W3126027909","doi":"10.48550/arxiv.2101.06608","title":"Network Automatic Pruning: Start NAP and Take a Nap","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Pruning; Nap; Memory footprint; FLOPS; Machine learning; Artificial intelligence; Parallel computing","score_opus":0.07025394555470299,"score_gpt":0.19450679493803383,"score_spread":0.12425284938333084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126027909","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":[0.24867162,0.00029056022,0.74849075,0.00032565458,0.00034381344,0.00045049304,0.000004009449,0.0005045509,0.0009185391],"genre_scores_gemma":[0.96473956,0.00023258061,0.033594035,0.00031263285,0.00012640958,0.0000057345896,0.000020005526,0.00002497508,0.0009440714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768746,0.00014568902,0.00023562799,0.001344707,0.000105169696,0.0004813197],"domain_scores_gemma":[0.997535,0.00020990317,0.00028913852,0.0016018004,0.0001310994,0.00023304248],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016000305,0.00034642458,0.00038532543,0.00011028356,0.00031082844,0.00022564663,0.0014142978,0.00022307121,0.000023451383],"category_scores_gemma":[0.000025921418,0.00042341268,0.00011942414,0.0009935768,0.00013735212,0.00043739643,0.0032379183,0.0006498994,0.000027321392],"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":[0.000009303376,0.000094648014,0.0020020935,0.0001429474,0.00010929402,0.00051320734,0.00045020308,0.7817447,0.000037747206,0.20869754,0.0016375156,0.0045607802],"study_design_scores_gemma":[0.00026174594,0.000031633073,0.0024086407,0.00016482691,0.00005837911,0.000020212648,0.00006145378,0.92802,0.000018916373,0.06625129,0.0021786904,0.00052421493],"about_ca_topic_score_codex":0.00002184088,"about_ca_topic_score_gemma":0.000045311288,"teacher_disagreement_score":0.7160679,"about_ca_system_score_codex":0.00013531577,"about_ca_system_score_gemma":0.0001849373,"threshold_uncertainty_score":0.9998218},"labels":[],"label_agreement":null},{"id":"W3126271000","doi":"10.1007/s11263-021-01430-6","title":"DeepFlux for Skeleton Detection in the Wild","year":2021,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Vector Institute; Centre for Social Innovation; University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Skeleton (computer programming); Pattern recognition (psychology); Topological skeleton; Representation (politics); Benchmark (surveying); Context (archaeology); Pixel; Computer vision; Clutter; Object detection; Convolutional neural network; Field (mathematics); Segmentation; Mathematics; Active shape model; Geography; Cartography","score_opus":0.013789640078803938,"score_gpt":0.3143395868782434,"score_spread":0.30054994679943947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126271000","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":[0.026227226,0.00009460483,0.9646929,0.0075721745,0.0012635033,0.00008506227,7.004866e-7,0.000011058461,0.000052782078],"genre_scores_gemma":[0.85724574,0.000042621003,0.14054263,0.0014020911,0.00074010494,0.000006253233,0.0000018874822,0.000004743664,0.000013920076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892217,0.00007231376,0.00035150652,0.00014698543,0.00040287283,0.00010414921],"domain_scores_gemma":[0.99864507,0.00037358116,0.00022562996,0.00019159084,0.0005347381,0.00002938232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003203841,0.00007131484,0.00009720867,0.000121632555,0.000050640578,0.00016282852,0.0010826414,0.00003016529,0.0000022174404],"category_scores_gemma":[0.000034674427,0.000053041495,0.00010671335,0.00025246807,0.000013788939,0.00052208995,0.00013006435,0.00016166794,0.0000039371025],"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":[0.000034924793,0.00015661826,0.000075665375,0.000002829148,0.000023770546,0.00009715203,0.00026784302,0.010182728,0.004170317,0.016983218,0.0016534787,0.96635145],"study_design_scores_gemma":[0.0023214822,0.0006850676,0.014373043,0.00018801718,0.0000127268795,0.0029952778,0.000040625222,0.68852663,0.021579841,0.095349066,0.17364983,0.0002783944],"about_ca_topic_score_codex":9.665554e-7,"about_ca_topic_score_gemma":0.000008917089,"teacher_disagreement_score":0.96607304,"about_ca_system_score_codex":0.0000632068,"about_ca_system_score_gemma":0.00004454113,"threshold_uncertainty_score":0.21629694},"labels":[],"label_agreement":null},{"id":"W3127078660","doi":"10.1109/icassp39728.2021.9414645","title":"Pruning of Convolutional Neural Networks using ising Energy Model","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Pruning; Computer science; Redundancy (engineering); Ising model; Deep neural networks; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Algorithm; Statistical physics","score_opus":0.04968997476252107,"score_gpt":0.28724453919901755,"score_spread":0.23755456443649647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127078660","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":[0.0071948543,0.0007869908,0.9906678,0.00018635853,0.00037576503,0.00011518046,0.0000027739904,0.00016766002,0.0005026234],"genre_scores_gemma":[0.58276033,0.00003554541,0.41675165,0.00022497273,0.00010278726,0.000013729186,0.000022411232,0.0000147658975,0.000073818745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998027,0.000067963556,0.00049322663,0.0007528235,0.00031176122,0.0003471703],"domain_scores_gemma":[0.9981162,0.00012248091,0.00038214837,0.0010136332,0.0002689352,0.000096603006],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010304236,0.00026245802,0.0003622341,0.00009988159,0.00015067348,0.00012805106,0.0010850099,0.00020415589,0.000007958842],"category_scores_gemma":[0.000013078478,0.00028529452,0.00016750653,0.00040780776,0.00008594492,0.0003346073,0.0030248906,0.00045697132,2.0901113e-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":[0.0000011400314,0.00001964889,0.000023545757,0.000010402459,0.00001546147,0.0000026234916,0.000033923374,0.91574496,0.0007168474,0.079819806,0.000038642276,0.0035730258],"study_design_scores_gemma":[0.0000713284,0.0000042165084,0.000023257597,0.000070048714,0.0000124229355,0.000019142886,0.0000071945387,0.98861957,0.00048424464,0.0104175005,0.0000187482,0.00025232584],"about_ca_topic_score_codex":0.00007175784,"about_ca_topic_score_gemma":0.000014957521,"teacher_disagreement_score":0.57556546,"about_ca_system_score_codex":0.00010418447,"about_ca_system_score_gemma":0.00024088295,"threshold_uncertainty_score":0.99995995},"labels":[],"label_agreement":null},{"id":"W3127178140","doi":"10.1109/aiccsa50499.2020.9316525","title":"Quick-CapsNet (QCN): A Fast Alternative to Capsule Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Computer science; Convolutional neural network; Robustness (evolution); Bottleneck; Artificial intelligence; Inference; Affine transformation; Pattern recognition (psychology); Deep learning; Embedded system; Mathematics","score_opus":0.03345229799583384,"score_gpt":0.2931242798028724,"score_spread":0.2596719818070386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127178140","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":[0.00037892855,0.00013121699,0.9757248,0.0129013145,0.0010371417,0.001167472,0.000020879972,0.0008219707,0.007816275],"genre_scores_gemma":[0.4720935,0.00013753344,0.5108744,0.0122460835,0.0017083959,0.0009892969,0.000079560516,0.000082051454,0.0017892292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967886,0.000082779254,0.00047422026,0.0016584761,0.00041074533,0.0005851552],"domain_scores_gemma":[0.9971532,0.00010368678,0.00025168544,0.0018132507,0.00015616001,0.000521982],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000107761516,0.00047318265,0.0004783695,0.0001104955,0.00014512378,0.00030271907,0.0036376035,0.00020023712,0.00003961053],"category_scores_gemma":[0.00003585029,0.00046378258,0.00016347568,0.00073537667,0.00005026427,0.00021140148,0.006969298,0.0009922804,0.0005629544],"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":[0.000008748256,0.000073531926,0.000020589276,0.000025244797,0.00008149582,0.00004324113,0.0010664281,0.745154,0.0002476685,0.15287487,0.056384105,0.044020023],"study_design_scores_gemma":[0.00016749532,0.00007284989,0.00021700982,0.000056549583,0.000012772711,0.000009027279,0.000018734117,0.9376525,0.0007777038,0.04344207,0.016858907,0.0007143504],"about_ca_topic_score_codex":0.00012191515,"about_ca_topic_score_gemma":0.000043066164,"teacher_disagreement_score":0.47171456,"about_ca_system_score_codex":0.0001448814,"about_ca_system_score_gemma":0.00011163678,"threshold_uncertainty_score":0.99978137},"labels":[],"label_agreement":null},{"id":"W3128690705","doi":"10.1016/j.eswa.2022.116555","title":"Counting and locating high-density objects using convolutional neural network","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.017363725926031737,"score_gpt":0.24615818182477409,"score_spread":0.22879445589874234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128690705","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":[0.022639045,0.0016126819,0.9733691,0.0004695526,0.00020263139,0.0011772002,0.000008656142,0.0003579573,0.00016312457],"genre_scores_gemma":[0.9204734,0.0000075108264,0.07633444,0.0003828564,0.00043285187,0.0022601818,0.000020078518,0.000024230572,0.000064432235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812007,0.00011157639,0.0003237122,0.00064223015,0.00039929623,0.00040313526],"domain_scores_gemma":[0.9985497,0.00021414763,0.0002886272,0.00069241534,0.00012972296,0.00012539233],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00024381277,0.00019044816,0.00021568399,0.00006273089,0.0022647849,0.00014031617,0.0005555601,0.00003227308,0.000004419874],"category_scores_gemma":[0.0000063165708,0.00019155814,0.000025846673,0.00099137,0.00009867396,0.0003191578,0.0005110728,0.000245476,0.000005712103],"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":[0.00001040089,0.00007448578,0.0019982485,0.000022948494,0.000034859626,0.0000061676956,0.0005749255,0.6881476,0.002514227,0.3027309,0.001193504,0.0026917132],"study_design_scores_gemma":[0.00034972397,0.000047879574,0.001121496,0.000021856553,0.00001032003,0.00055001216,0.00043524036,0.98128676,0.00006319044,0.0014568635,0.01425315,0.00040351503],"about_ca_topic_score_codex":0.0003499175,"about_ca_topic_score_gemma":0.000017961169,"teacher_disagreement_score":0.89783436,"about_ca_system_score_codex":0.00018509079,"about_ca_system_score_gemma":0.0000959817,"threshold_uncertainty_score":0.9990341},"labels":[],"label_agreement":null},{"id":"W3128763197","doi":"10.1109/a-sscc48613.2020.9336148","title":"CompAcc: Efficient Hardware Realization for Processing Compressed Neural Networks Using Accumulator Arrays","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Taiwan Semiconductor Manufacturing Company","keywords":"Computer science; Convolutional neural network; Artificial neural network; Accumulator (cryptography); Realization (probability); Computation; Field-programmable gate array; Computer hardware; Chip; Parallel computing; Computer engineering; Algorithm; Artificial intelligence","score_opus":0.07814666081046574,"score_gpt":0.3188172859913296,"score_spread":0.24067062518086385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128763197","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":[0.0036905853,0.0001154738,0.9932399,0.0013881583,0.00011898413,0.00077965175,0.000003944958,0.0005791693,0.000084127816],"genre_scores_gemma":[0.8250472,0.0000019312738,0.17289484,0.0017459817,0.00022908922,0.000028886623,0.000022163327,0.000022279195,0.00000764786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985488,0.000033761982,0.0003117361,0.00054780405,0.00019643709,0.0003614462],"domain_scores_gemma":[0.9989949,0.00011086402,0.0001797002,0.0003445418,0.00017868799,0.00019133139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006287813,0.00018566237,0.00020993802,0.00003584585,0.00039550706,0.00022169096,0.0006780318,0.00005462269,0.0000046022706],"category_scores_gemma":[0.000029565439,0.00017509828,0.00006889651,0.00075341965,0.00003574238,0.000374313,0.00019305784,0.00011478744,0.0000026050106],"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":[0.000010651082,0.000021503889,0.00006770569,0.000020500516,0.0000033935241,6.330567e-7,0.00014346,0.98549736,0.0012721105,0.006374394,0.0005596225,0.006028655],"study_design_scores_gemma":[0.0003416475,0.000030215293,0.00008519422,0.000014358111,0.000008999335,0.0000036334334,0.000016187967,0.99688274,0.00098613,0.00020028395,0.0012253206,0.00020528534],"about_ca_topic_score_codex":0.000003153125,"about_ca_topic_score_gemma":0.0000013485201,"teacher_disagreement_score":0.8213566,"about_ca_system_score_codex":0.000040815776,"about_ca_system_score_gemma":0.000032217806,"threshold_uncertainty_score":0.71403},"labels":[],"label_agreement":null},{"id":"W3128767807","doi":"10.21203/rs.3.rs-409625/v1","title":"Semantic Segmentation With Labeling Uncertainty and Class Imbalance","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Universidade Federal de Mato Grosso do Sul; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Segmentation; Pixel; Artificial intelligence; Computer science; Robustness (evolution); Convolutional neural network; Pattern recognition (psychology); Class (philosophy); Range (aeronautics); Machine learning; Computer vision","score_opus":0.0490922352616434,"score_gpt":0.37653549917735707,"score_spread":0.32744326391571366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128767807","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":[0.26932207,0.0030218281,0.71874034,0.0058076284,0.00016327233,0.0020153606,0.000021207845,0.0003516942,0.0005565937],"genre_scores_gemma":[0.9305558,0.0011214701,0.06739667,0.000079526704,0.00011536937,0.0004405575,0.00008961225,0.000025381203,0.00017558728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971347,0.00029961366,0.00021020486,0.000982457,0.0008618812,0.00051118666],"domain_scores_gemma":[0.9975182,0.00042253005,0.00010047808,0.0010823298,0.0007155153,0.00016096231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005421055,0.00019884993,0.00023532574,0.00015451499,0.0003405853,0.0005550802,0.0007597921,0.00012508381,0.0000061007927],"category_scores_gemma":[0.00007875396,0.00017680999,0.000032048923,0.0009309449,0.00014779622,0.00026395053,0.0019788702,0.0011237978,0.000010803623],"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":[0.00014933721,0.0006864672,0.02913192,0.006118687,0.0003829032,0.00082490355,0.0066697127,0.6197928,0.030724712,0.07627887,0.0032172012,0.22602247],"study_design_scores_gemma":[0.00097095413,0.00032054516,0.010662192,0.0027006383,0.000022605249,0.000085178675,0.0012186256,0.9593564,0.0043068803,0.017352609,0.0019878333,0.0010154928],"about_ca_topic_score_codex":0.00008077833,"about_ca_topic_score_gemma":0.00017456034,"teacher_disagreement_score":0.6612338,"about_ca_system_score_codex":0.00018574782,"about_ca_system_score_gemma":0.00033517604,"threshold_uncertainty_score":0.7210102},"labels":[],"label_agreement":null},{"id":"W3128842017","doi":"10.1145/3441110.3441148","title":"Convolutional Fully-Connected Capsule Network (CFC-CapsNet)","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Convolutional neural network; Computer science; Robustness (evolution); MNIST database; Artificial intelligence; Pattern recognition (psychology); Affine transformation; Convolutional code; Deep learning; Decoding methods; Algorithm; Mathematics","score_opus":0.014360357241940895,"score_gpt":0.23881271127019554,"score_spread":0.22445235402825464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128842017","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":[0.0036469277,0.0005079398,0.9722574,0.003956619,0.00038075104,0.00013772365,0.0000027855706,0.0005123886,0.01859749],"genre_scores_gemma":[0.512594,0.00006622419,0.47366506,0.0048052603,0.0007133579,0.0000929267,0.000057245135,0.00002124959,0.007984706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856234,0.00005891408,0.0002296709,0.0005148474,0.00022126,0.00041299517],"domain_scores_gemma":[0.9987201,0.0001518111,0.00006297917,0.00072585075,0.00019797448,0.00014129253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007998996,0.00013419644,0.00014437905,0.00002306958,0.00023646033,0.00008240049,0.00056059303,0.000058194764,0.00037029738],"category_scores_gemma":[0.00004124031,0.00013331539,0.00006681944,0.0010104154,0.000050568728,0.00030514188,0.00035781233,0.00015296103,0.00043430628],"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":[0.000001280442,0.00004542901,0.00014550686,0.00000221314,0.0000151108425,0.00002204937,0.000020166355,0.010824289,0.0033784641,0.9331294,0.047466822,0.0049492945],"study_design_scores_gemma":[0.0010249574,0.0000638998,0.014107989,0.000025914687,0.000017155924,0.000315744,0.000026449115,0.40445572,0.016303219,0.25576887,0.3069823,0.000907784],"about_ca_topic_score_codex":0.000005481825,"about_ca_topic_score_gemma":0.000034794255,"teacher_disagreement_score":0.67736053,"about_ca_system_score_codex":0.000044902554,"about_ca_system_score_gemma":0.00013281674,"threshold_uncertainty_score":0.55822736},"labels":[],"label_agreement":null},{"id":"W3128974447","doi":"10.21203/rs.3.rs-93060/v1","title":"Enhance Image Classification Performance Via Unsupervised Pre-trained Transformers Language Models","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Tsinghua University; Canadian Institute for Advanced Research","keywords":"Computer science; Transformer; Artificial intelligence; Pattern recognition (psychology); Contextual image classification; Unsupervised learning; Machine learning; Natural language processing; Image (mathematics); Engineering; Electrical engineering","score_opus":0.03019822765891393,"score_gpt":0.28980251298436965,"score_spread":0.2596042853254557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128974447","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":[0.0136833135,0.00013254564,0.9692929,0.0053879465,0.00019852631,0.0013057687,0.000020680352,0.0011813546,0.008796983],"genre_scores_gemma":[0.7667905,0.000326898,0.23101796,0.0006525142,0.00013167124,0.00041163707,0.00011940326,0.00004101249,0.0005083675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969932,0.00006638152,0.00058935536,0.0013914598,0.00046482967,0.00049481814],"domain_scores_gemma":[0.99787533,0.00008236093,0.0002423857,0.0014236465,0.00013848081,0.00023778602],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013172261,0.00047730046,0.0004146943,0.000109514476,0.00017986841,0.00018781253,0.0022675502,0.00024489782,0.00003548129],"category_scores_gemma":[0.000010454022,0.000466693,0.0001870948,0.0006029102,0.00010228487,0.0011276094,0.0006117177,0.0008164678,0.00012236908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008971013,0.00028354925,0.000023179482,0.00085031765,0.00012414086,0.00001646659,0.009655506,0.058948316,0.337994,0.046861798,0.0016415445,0.54351145],"study_design_scores_gemma":[0.00018923129,0.000037954935,0.0004215276,0.000045551027,0.000016730724,0.00000443469,0.00003883835,0.9603131,0.027984885,0.010187394,0.0002450368,0.00051536335],"about_ca_topic_score_codex":0.000022833765,"about_ca_topic_score_gemma":0.0000146226885,"teacher_disagreement_score":0.90136474,"about_ca_system_score_codex":0.00010150631,"about_ca_system_score_gemma":0.00016364752,"threshold_uncertainty_score":0.99977845},"labels":[],"label_agreement":null},{"id":"W3130423852","doi":"10.1016/j.array.2021.100057","title":"Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues","year":2021,"lang":"en","type":"article","venue":"Array","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":559,"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":"Deep learning; Self driving; Artificial intelligence; Computer science; Object detection; Perception; Implementation; Open research; Scalability; Machine learning; Human–computer interaction; Engineering; Transport engineering; Psychology; Pattern recognition (psychology); Software engineering","score_opus":0.03883518648875101,"score_gpt":0.3038080404009055,"score_spread":0.26497285391215447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130423852","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":[0.34207293,0.0065946802,0.6490981,0.0011173249,0.000109497414,0.0005765489,4.5525226e-7,0.00015922889,0.0002712323],"genre_scores_gemma":[0.92621654,0.004379383,0.06920981,0.000033903096,0.00003851558,0.000069079855,0.0000035444186,0.000008372955,0.000040865572],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991782,0.00013570156,0.00010672385,0.00037597152,0.000057189838,0.0001462291],"domain_scores_gemma":[0.99948674,0.00020076125,0.00004258522,0.00017834279,0.000052858464,0.00003868879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000319565,0.00007497244,0.000113319715,0.000029780565,0.00014959634,0.00011573082,0.0001671452,0.000037201436,0.0000013460055],"category_scores_gemma":[0.00007927116,0.00008125526,0.00001037436,0.0001436443,0.000013418629,0.00045871842,0.00018260899,0.000096257936,0.0000016294698],"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":[0.000005160778,0.00003528634,0.008049421,0.000023610484,0.000008869724,0.0000022519755,0.004457066,0.000685566,0.03368117,0.0006232175,0.0000051161296,0.9524233],"study_design_scores_gemma":[0.00052760134,0.00011322631,0.83648443,0.000048835755,0.0000058436212,0.000039643204,0.00044634027,0.15033562,0.0039313417,0.0031232499,0.004695422,0.0002484338],"about_ca_topic_score_codex":0.000080052065,"about_ca_topic_score_gemma":0.0043167165,"teacher_disagreement_score":0.95217484,"about_ca_system_score_codex":0.000032095155,"about_ca_system_score_gemma":0.0000115186285,"threshold_uncertainty_score":0.33134934},"labels":[],"label_agreement":null},{"id":"W3130574226","doi":"10.1109/iv48863.2021.9575445","title":"Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-Task Baseline","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Computer vision; Baseline (sea); Encoder; Task (project management); Class (philosophy); Motion (physics); Monocular; Trajectory; Object (grammar); Object detection","score_opus":0.03706056192273975,"score_gpt":0.30626889921312056,"score_spread":0.2692083372903808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130574226","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":[0.0031861763,0.00067413674,0.9921442,0.001059232,0.00039813315,0.0016210676,0.00059323566,0.0002963349,0.000027453963],"genre_scores_gemma":[0.060677987,0.00051571603,0.9298438,0.0007505406,0.00011352561,0.00072008907,0.0071482756,0.000034519413,0.0001955897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726355,0.00010149314,0.0005649174,0.0014821583,0.00023661058,0.000351292],"domain_scores_gemma":[0.9975533,0.00019805864,0.0003822971,0.0015039351,0.00020594205,0.00015647295],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033141355,0.000383593,0.0003781163,0.00011360341,0.00024213974,0.00037830704,0.00083604833,0.00019855938,0.000007151913],"category_scores_gemma":[0.00008671257,0.00041359922,0.000084443855,0.00031145453,0.00006281313,0.0007838633,0.001334589,0.00036111425,0.00000504018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019056275,0.0005177722,0.0005727264,0.00043699163,0.00013387697,0.000024174466,0.00064054737,0.08477677,0.009969364,0.011927104,0.0051983246,0.8857833],"study_design_scores_gemma":[0.00064958876,0.000024567187,0.0005138338,0.000079327976,0.000028525317,0.000009815935,0.000041495525,0.9839538,0.0030875755,0.0019608254,0.009176379,0.00047430795],"about_ca_topic_score_codex":0.000030175972,"about_ca_topic_score_gemma":0.00014951463,"teacher_disagreement_score":0.899177,"about_ca_system_score_codex":0.00017908246,"about_ca_system_score_gemma":0.00012043344,"threshold_uncertainty_score":0.99983156},"labels":[],"label_agreement":null},{"id":"W3131349494","doi":"10.1007/s11760-021-01857-x","title":"A real-time small target detection network","year":2021,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Preprocessor; Pattern recognition (psychology); Pyramid (geometry); Convolutional neural network; Feature (linguistics); Context (archaeology); Field (mathematics); Feature extraction; Matching (statistics); Computer vision; Mathematics","score_opus":0.013437430210473852,"score_gpt":0.2524156758068042,"score_spread":0.23897824559633032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131349494","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":[0.0043013613,0.0007792397,0.9924808,0.0004179389,0.00003525177,0.00008235454,5.501103e-7,0.00022609383,0.0016763688],"genre_scores_gemma":[0.36086008,0.00011187312,0.6374052,0.00052082114,0.0004098024,0.000040660103,0.0000058136284,0.000021453821,0.0006242508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989497,0.000057136167,0.00017269717,0.00042716152,0.00010637875,0.00028689828],"domain_scores_gemma":[0.99937075,0.000092758644,0.000089159286,0.00021321946,0.00014209692,0.00009203662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015810208,0.00012065852,0.00013058534,0.00002730898,0.00038768165,0.00032281282,0.00018867108,0.000042830827,0.000015718684],"category_scores_gemma":[0.000022250731,0.00012064596,0.000031571042,0.00058568356,0.000045635312,0.0007054289,0.00018105943,0.000131304,0.000024462182],"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":[0.0000066977486,0.000025553287,0.000046749734,0.000035207166,0.0000064926994,0.00005038792,0.0001610302,0.00079002394,0.47948423,0.0004752149,0.00029392258,0.5186245],"study_design_scores_gemma":[0.0003828615,0.000068801244,0.0006254853,0.0001508585,0.000023466015,0.00026515295,0.000035127672,0.56439286,0.36060223,0.067662306,0.0052586603,0.0005321806],"about_ca_topic_score_codex":0.000003732566,"about_ca_topic_score_gemma":0.0000041178623,"teacher_disagreement_score":0.56360286,"about_ca_system_score_codex":0.000018347275,"about_ca_system_score_gemma":0.00006900772,"threshold_uncertainty_score":0.49197993},"labels":[],"label_agreement":null},{"id":"W3131910208","doi":"10.3390/jimaging8030064","title":"Rethinking Weight Decay for Efficient Neural Network Pruning","year":2022,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":27,"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":"Grand Équipement National De Calcul Intensif; Association Nationale de la Recherche et de la Technologie; Compute Canada","keywords":"Pruning; Computer science; Scalability; Generalization; Artificial neural network; Smoothing; Artificial intelligence; Deep neural networks; Machine learning; Face (sociological concept); Field (mathematics); Mathematics","score_opus":0.017546296960527643,"score_gpt":0.26931445941099935,"score_spread":0.25176816245047173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131910208","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":[0.015825951,0.0011071204,0.97583264,0.0056409165,0.0011766821,0.00016335321,6.394895e-7,0.000052871193,0.00019984294],"genre_scores_gemma":[0.77487624,0.0000066179446,0.22354606,0.0009609912,0.0005445816,0.000018926086,5.586187e-7,0.000015179487,0.000030830346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998611,0.00008122342,0.00041593314,0.00018766093,0.00036978326,0.0003344537],"domain_scores_gemma":[0.9986517,0.00034937364,0.00055269117,0.00025983888,0.000103847306,0.00008258536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084461353,0.000100716905,0.0001638174,0.00010826651,0.0007482654,0.00009689264,0.0009744885,0.000008965636,0.0000062680797],"category_scores_gemma":[0.000030366977,0.000095113224,0.00013067578,0.00053832034,0.00002301472,0.00032542433,0.00040151956,0.0004211122,7.1591967e-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":[0.000014122159,0.000026885582,0.000462645,0.0000034155516,0.00001021918,0.00003414091,0.00059729465,0.9427998,0.0010632394,0.012279244,0.0035063783,0.03920261],"study_design_scores_gemma":[0.00037276506,0.000053696607,0.00017393338,0.000023001656,0.000012090011,0.0006204821,0.000037776463,0.95227826,0.00039476526,0.016783198,0.029119575,0.00013047832],"about_ca_topic_score_codex":4.984692e-7,"about_ca_topic_score_gemma":2.1190348e-7,"teacher_disagreement_score":0.7590503,"about_ca_system_score_codex":0.00012758095,"about_ca_system_score_gemma":0.000062084786,"threshold_uncertainty_score":0.5755128},"labels":[],"label_agreement":null},{"id":"W3134042610","doi":"10.1007/978-3-030-68799-1_5","title":"Use of Frequency Domain for Complexity Reduction of Convolutional Neural Networks","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Fast Fourier transform; Convolutional neural network; Computer science; Split-radix FFT algorithm; Reduction (mathematics); Computation; Convolution (computer science); Prime-factor FFT algorithm; Computational complexity theory; Quantization (signal processing); Frequency domain; Algorithm; Pruning; Rader's FFT algorithm; Redundancy (engineering); Twiddle factor; Multiplication (music); Parallel computing; Artificial neural network; Fourier transform; Mathematics; Artificial intelligence; Fractional Fourier transform; Fourier analysis","score_opus":0.055151961088720114,"score_gpt":0.2787569622757698,"score_spread":0.22360500118704968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134042610","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":[0.0003584969,0.00044658867,0.99674386,0.000530863,0.0011054024,0.00063886633,0.00003263917,0.000057355322,0.00008593486],"genre_scores_gemma":[0.14169642,0.000032353935,0.8576452,0.00018480985,0.0003233639,0.00002376861,0.000035030058,0.000020828318,0.00003822103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706733,0.000042419866,0.0007497149,0.0011128319,0.00058764365,0.00044007797],"domain_scores_gemma":[0.9965855,0.00075804256,0.00066571665,0.0012007765,0.00068584085,0.000104155784],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035800584,0.000346915,0.00057561224,0.00033660483,0.00018622131,0.00009133686,0.001641796,0.00022662201,0.000008594568],"category_scores_gemma":[0.00007139321,0.00035330313,0.00019752621,0.0007607549,0.0014944524,0.0006061993,0.0006790277,0.0004579627,6.065845e-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":[0.000010335161,0.000042036114,0.000035980505,0.000042659674,0.0000136797535,0.000004274835,0.000101877566,0.38593325,0.0014604821,0.5358217,0.000023728287,0.07651],"study_design_scores_gemma":[0.00013440741,0.00009083528,0.00013770362,0.00011722466,0.0000062481963,0.00004628762,1.00394196e-7,0.5881864,0.00069231854,0.41022706,0.00012365729,0.00023774931],"about_ca_topic_score_codex":0.000018504637,"about_ca_topic_score_gemma":0.000036404297,"teacher_disagreement_score":0.20225313,"about_ca_system_score_codex":0.0001762218,"about_ca_system_score_gemma":0.0003061007,"threshold_uncertainty_score":0.9998919},"labels":[],"label_agreement":null},{"id":"W3134055916","doi":"10.1109/tcsi.2021.3060945","title":"A Real-Time Architecture for Pruning the Effectual Computations in Deep Neural Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pruning; Benchmark (surveying); Computation; Computer science; Inference; Artificial neural network; Dataflow; Speedup; Filter (signal processing); Algorithm; Artificial intelligence; Theoretical computer science; Parallel computing; Computer engineering","score_opus":0.012207463790503803,"score_gpt":0.23232414301164037,"score_spread":0.22011667922113656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134055916","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":[0.0079762135,0.00029194631,0.9891917,0.00094702264,0.00037110518,0.0008716279,0.000008304254,0.00012977404,0.00021227408],"genre_scores_gemma":[0.9972315,0.00004479159,0.0018140614,0.00018971857,0.00007783729,0.00040500876,0.0000061468704,0.000023358825,0.00020756833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984314,0.000248593,0.00029877215,0.00049955206,0.00018237797,0.0003393197],"domain_scores_gemma":[0.99824893,0.0009760172,0.00008380395,0.000501203,0.00006929941,0.00012071663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022542106,0.00019339361,0.00024694193,0.00008856666,0.0005530389,0.00017698728,0.00030450433,0.000091152266,0.0000023939344],"category_scores_gemma":[0.000011897391,0.0001550832,0.000106068714,0.0006535883,0.000080123136,0.00012682416,0.0000046229407,0.00026913008,0.0000020761122],"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":[0.0000034954192,0.000031917683,0.0000029638363,0.00002382081,0.00002209987,0.000007475715,0.00052161916,0.903243,0.004058897,0.0022652848,0.000018814399,0.08980059],"study_design_scores_gemma":[0.00046603527,0.00006726255,0.00013770524,0.000049584734,0.000021154732,0.00016996627,0.0001294356,0.99768436,0.00023175146,0.00023215858,0.0006200823,0.0001905325],"about_ca_topic_score_codex":0.0000151500135,"about_ca_topic_score_gemma":0.00007906154,"teacher_disagreement_score":0.9892553,"about_ca_system_score_codex":0.000050697585,"about_ca_system_score_gemma":0.0000403275,"threshold_uncertainty_score":0.63241094},"labels":[],"label_agreement":null},{"id":"W3135002377","doi":"10.1109/icassp39728.2021.9414434","title":"A Framework for Pruning Deep Neural Networks Using Energy-Based Models","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fujitsu","keywords":"Pruning; Computer science; Artificial neural network; Deep neural networks; Function (biology); Artificial intelligence; Simple (philosophy); Energy (signal processing); Machine learning; Population; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.06789464731993908,"score_gpt":0.3146569498918673,"score_spread":0.24676230257192824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135002377","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":[0.00032191083,0.00086902565,0.9959625,0.00070902886,0.0008573399,0.0005775627,0.0000031702662,0.00057954784,0.00011989102],"genre_scores_gemma":[0.35535514,0.000012017527,0.6422908,0.001578777,0.00031185796,0.0003466005,0.000043526725,0.000042891235,0.000018408824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99713343,0.00008649313,0.00048537183,0.0013495411,0.00028301787,0.0006621422],"domain_scores_gemma":[0.99664956,0.0006274864,0.00035823387,0.0019081836,0.00026728865,0.00018923341],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013501818,0.00045157655,0.00046748208,0.000116892195,0.0003288577,0.00055687514,0.0017961178,0.00048390072,0.0000074313202],"category_scores_gemma":[0.00003552299,0.00047590464,0.0003238617,0.00059962616,0.000051524778,0.00040065698,0.0019073873,0.00078529475,3.4753006e-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":[0.0000034182149,0.000024271012,0.000003256022,0.000021761458,0.00001324672,0.000004032304,0.0000347054,0.7776985,0.000015613945,0.20785317,0.000014719809,0.014313259],"study_design_scores_gemma":[0.00010339594,0.000011961889,0.0000015050678,0.00008155143,0.000021242427,0.000004957338,0.000007704904,0.8036728,0.00014424739,0.1954862,0.000059428352,0.00040503024],"about_ca_topic_score_codex":0.000031267504,"about_ca_topic_score_gemma":0.000027844699,"teacher_disagreement_score":0.35503322,"about_ca_system_score_codex":0.0001357008,"about_ca_system_score_gemma":0.000167948,"threshold_uncertainty_score":0.9997693},"labels":[],"label_agreement":null},{"id":"W3135437426","doi":"10.5220/0010434600002932","title":"PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain","year":2021,"lang":"en","type":"article","venue":"Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Intertek (Canada); University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Lidar; Radar; Computer vision; Occupancy grid mapping; Radar imaging; Deep learning; Automotive industry; Advanced driver assistance systems; Segmentation; Process (computing); Real-time computing; Remote sensing; Mobile robot; Engineering; Telecommunications; Geography; Robot; Aerospace engineering","score_opus":0.050540073102392814,"score_gpt":0.30462431116695465,"score_spread":0.2540842380645618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135437426","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":[0.92124736,0.00019472653,0.06802588,0.006811319,0.00028634403,0.00077705097,0.000016576394,0.000104792765,0.0025359453],"genre_scores_gemma":[0.9921579,0.00011543308,0.007394434,0.00009729364,0.000015109638,0.00004926461,0.0000051420498,0.000009233611,0.00015621637],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866265,0.00001690249,0.00042488298,0.00044944682,0.0002501989,0.00019589798],"domain_scores_gemma":[0.99893963,0.000022746677,0.00028546236,0.00017747974,0.00053505955,0.000039604332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022105603,0.00015840688,0.00022768864,0.00025256796,0.00012364486,0.000104065,0.0014265835,0.00013244885,0.000010590126],"category_scores_gemma":[0.000025918556,0.00013668524,0.00003617096,0.0009836853,0.00013351794,0.0005529727,0.00026087696,0.000298678,0.0000024817568],"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":[0.000027100245,0.00011195373,0.021376831,0.000024324401,0.000043643005,0.0000043100067,0.0005479488,0.0003694414,0.109275654,0.8669059,0.0000091723105,0.0013036709],"study_design_scores_gemma":[0.00140691,0.00024198151,0.013419224,0.0010787421,0.000032881482,0.00019808147,0.006273453,0.19498688,0.6691384,0.11124076,0.0013225732,0.0006601393],"about_ca_topic_score_codex":0.00005912383,"about_ca_topic_score_gemma":0.000025795422,"teacher_disagreement_score":0.7556652,"about_ca_system_score_codex":0.00010821667,"about_ca_system_score_gemma":0.00007048336,"threshold_uncertainty_score":0.5573862},"labels":[],"label_agreement":null},{"id":"W3135688577","doi":"10.1145/3434398","title":"Object Detection Using Deep Learning Methods in Traffic Scenarios","year":2021,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Object detection; Deep learning; Field (mathematics); Artificial intelligence; Task (project management); Open research; Boom; Object (grammar); Feature extraction; Machine learning; Data science; Pattern recognition (psychology); Systems engineering; World Wide Web","score_opus":0.10716219592539708,"score_gpt":0.4194512386503125,"score_spread":0.3122890427249154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135688577","genre_codex":"methods","genre_gemma":"review","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":[0.000062064944,0.4791269,0.5198791,0.0000038799203,0.00037292854,0.00030100296,3.0704376e-7,0.00024307179,0.000010731944],"genre_scores_gemma":[0.00041408752,0.5149497,0.48430896,0.00001459156,0.00018718031,0.000022529377,0.000020450896,0.00006686137,0.000015662234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98741883,0.009155633,0.0010663525,0.0013591014,0.0002893999,0.00071066286],"domain_scores_gemma":[0.99230653,0.005095262,0.00076338265,0.0015936873,0.0001270643,0.00011406473],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005333301,0.0005678822,0.0016789166,0.00046440068,0.00065491814,0.00021422422,0.0021338973,0.00034301792,0.0000034094128],"category_scores_gemma":[0.0015030302,0.00059405493,0.0004204214,0.0040406724,0.00004728805,0.00024370858,0.002395335,0.0016316668,0.000017884979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.0595791e-7,0.000020857731,0.000008513577,0.00041205186,0.000023551958,0.000018600516,0.0001123528,0.0948462,0.0000038758308,0.000023816034,7.0722797e-7,0.9045294],"study_design_scores_gemma":[0.00015629186,0.000035399768,0.00008559472,0.0049219537,0.00009048191,0.0003013147,0.000013373147,0.85069937,0.0000100246125,0.000101634476,0.14260852,0.000976044],"about_ca_topic_score_codex":0.000042588446,"about_ca_topic_score_gemma":0.000083058934,"teacher_disagreement_score":0.9035533,"about_ca_system_score_codex":0.00042794796,"about_ca_system_score_gemma":0.00024053102,"threshold_uncertainty_score":0.9996511},"labels":[],"label_agreement":null},{"id":"W3136191271","doi":"10.48550/arxiv.2103.08852","title":"Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Lidar; Segmentation; Benchmark (surveying); Encoder; Artificial intelligence; Point cloud; Convolutional neural network; Computer vision; Remote sensing; Geography","score_opus":0.0935573175115638,"score_gpt":0.22670742369042826,"score_spread":0.13315010617886447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136191271","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":[0.38846716,0.00025490567,0.6101523,0.000105218416,0.0003223817,0.00033501157,0.0000114047125,0.00024222887,0.00010939227],"genre_scores_gemma":[0.96462923,0.00050030847,0.033858903,0.00017081796,0.000098635166,0.0000045924976,0.00006569233,0.000028953924,0.0006428934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974283,0.0002069284,0.00031435536,0.0014461345,0.00013499703,0.00046927365],"domain_scores_gemma":[0.9972787,0.0001645271,0.00036160083,0.0016616139,0.00031752343,0.00021604967],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013703939,0.00037106412,0.00034609193,0.00026689388,0.0004407267,0.00027681468,0.0013371082,0.00024223875,0.00002583803],"category_scores_gemma":[0.000028336499,0.00049088424,0.00025082653,0.0014534587,0.00013901074,0.00079086394,0.0020694628,0.0006596255,0.000074291114],"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":[0.000015890113,0.00022339,0.003293787,0.00014273758,0.00018736025,0.00059737003,0.0007428545,0.9084172,0.0061061154,0.078798406,0.00013258186,0.0013422816],"study_design_scores_gemma":[0.0003901568,0.000022390726,0.0014429963,0.00018369388,0.00016274647,0.000036089852,0.00015454709,0.97610736,0.0013217774,0.019285556,0.00026126945,0.0006314344],"about_ca_topic_score_codex":0.00008879494,"about_ca_topic_score_gemma":0.000045498597,"teacher_disagreement_score":0.5762934,"about_ca_system_score_codex":0.0005270976,"about_ca_system_score_gemma":0.00035096865,"threshold_uncertainty_score":0.9997543},"labels":[],"label_agreement":null},{"id":"W3136263466","doi":"10.1109/cvprw53098.2021.00317","title":"Video Class Agnostic Segmentation Benchmark for Autonomous Driving","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Computer science; Benchmark (surveying); Artificial intelligence; Class (philosophy); Computer vision; Set (abstract data type); Task (project management); Baseline (sea); Motion (physics); Scale-space segmentation; Robotics; Image segmentation; Machine learning; Robot","score_opus":0.021417335071350494,"score_gpt":0.29010251940717774,"score_spread":0.2686851843358272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136263466","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":[0.0039084773,0.00015984871,0.98974776,0.002196403,0.0009063652,0.00117839,0.0000063573852,0.00041980966,0.0014765877],"genre_scores_gemma":[0.2654545,0.00007233951,0.73123825,0.0007508484,0.00021835676,0.0013191637,0.00020777798,0.000024930767,0.00071387365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800897,0.0000457831,0.00039898083,0.0010034832,0.00020973681,0.0003330414],"domain_scores_gemma":[0.9975847,0.00074400246,0.00024260874,0.0011431025,0.00017816544,0.00010741937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013598398,0.0002609335,0.0002688208,0.00008452023,0.00018876197,0.00039225776,0.0010909031,0.00016218751,0.00003309845],"category_scores_gemma":[0.000104843726,0.00027924337,0.0001596483,0.00025711991,0.000028298036,0.00033768013,0.001586565,0.0003075191,0.000016222742],"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":[0.000006191686,0.00030251607,0.00071286195,0.0003869588,0.00018082716,0.000026924241,0.0013502697,0.4672181,0.0093147885,0.16352877,0.018453145,0.33851865],"study_design_scores_gemma":[0.0003349049,0.00005171943,0.0016102757,0.00016631071,0.000048756017,0.000016286816,0.0000540803,0.92917806,0.009961529,0.050378118,0.0074084243,0.0007915285],"about_ca_topic_score_codex":0.00001159741,"about_ca_topic_score_gemma":0.000058945916,"teacher_disagreement_score":0.46195996,"about_ca_system_score_codex":0.00020081499,"about_ca_system_score_gemma":0.00020685914,"threshold_uncertainty_score":0.99996597},"labels":[],"label_agreement":null},{"id":"W3136424010","doi":"10.1016/j.media.2021.102035","title":"Loss odyssey in medical image segmentation","year":2021,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":618,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Dice; Artificial intelligence; Function (biology); Hausdorff distance; Loss function; Boundary (topology); Benchmark (surveying); Image segmentation; Mathematics; Geography; Statistics; Cartography","score_opus":0.02602045313479882,"score_gpt":0.3855040646034736,"score_spread":0.35948361146867475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136424010","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":[7.2026273e-7,0.50752246,0.49072316,0.0011356191,0.00007393076,0.0002013767,0.0000067578203,0.00008444151,0.000251488],"genre_scores_gemma":[0.0000045351285,0.9583774,0.039614294,0.0006668722,0.0002562257,0.00029711454,0.0005281306,0.00003271284,0.00022269663],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99340415,0.00074706675,0.0014688679,0.0013636056,0.002411006,0.0006052745],"domain_scores_gemma":[0.9961274,0.0010327204,0.0004495098,0.001519711,0.0001550136,0.000715608],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001236884,0.0004948057,0.002434032,0.00080658303,0.00011177465,0.00021148463,0.002791936,0.000531085,0.002177654],"category_scores_gemma":[0.0012121235,0.00041435362,0.0011206097,0.009672324,0.00028246932,0.00050882954,0.0010628687,0.0012700427,0.00036905622],"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":[2.674819e-7,0.00012800419,0.000007652737,0.0004714188,0.00042798033,0.0017602367,0.000028051387,0.0000044353246,4.9381197e-7,0.0005421234,0.0013563806,0.99527293],"study_design_scores_gemma":[0.00041909318,0.000020778609,0.000017042305,0.003700453,0.0032689702,0.00024769458,0.000018613393,0.04441783,0.000008077558,0.0004435167,0.94644445,0.0009934835],"about_ca_topic_score_codex":0.000044626897,"about_ca_topic_score_gemma":0.0001825909,"teacher_disagreement_score":0.99427944,"about_ca_system_score_codex":0.00023156764,"about_ca_system_score_gemma":0.00080250116,"threshold_uncertainty_score":0.99983084},"labels":[],"label_agreement":null},{"id":"W3137003709","doi":"10.1145/3442536.3442539","title":"Ship Detection in Satellite Optical Imagery","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Cloud computing; Class (philosophy); Limiting; Personalization; Satellite; Artificial intelligence; Satellite imagery; Deep learning; Remote sensing; Machine learning; Data mining; Engineering; Geology; World Wide Web","score_opus":0.03027830964009991,"score_gpt":0.2624747519057412,"score_spread":0.2321964422656413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137003709","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":[0.009016472,0.00003459659,0.9782031,0.0069246963,0.000033559154,0.00009396356,7.1323576e-8,0.00021787632,0.0054756366],"genre_scores_gemma":[0.90292925,0.00001628667,0.09516399,0.0017824089,0.000052049672,0.000014949359,2.8134303e-7,0.0000036053912,0.000037161364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994108,0.000014969639,0.000114981725,0.00024178055,0.00007922775,0.00013820964],"domain_scores_gemma":[0.9996327,0.00007681689,0.000015977788,0.00018125225,0.000012218897,0.000081008184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004067667,0.00005278808,0.00005825776,0.000023331313,0.000025777132,0.00003581126,0.00027218612,0.000022240114,0.0000070469778],"category_scores_gemma":[0.00002628436,0.000049485527,0.000018949133,0.0005466554,0.00001649843,0.0002775526,0.000108282846,0.00010695882,0.0002038749],"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":[0.000008317468,0.000029564097,0.00048739504,0.0000055776327,0.0000017486594,0.0000189616,0.00018237608,0.0014579495,0.040189072,0.12277377,0.00011814789,0.8347271],"study_design_scores_gemma":[0.0004503554,0.0001244917,0.027829705,0.000007643292,0.000002705894,0.000023229446,0.000028134007,0.7348439,0.18380186,0.031252496,0.02118512,0.00045031868],"about_ca_topic_score_codex":0.0000015506889,"about_ca_topic_score_gemma":0.000009241024,"teacher_disagreement_score":0.8939128,"about_ca_system_score_codex":0.000015751706,"about_ca_system_score_gemma":0.0000072130133,"threshold_uncertainty_score":0.26204672},"labels":[],"label_agreement":null},{"id":"W3138676993","doi":"10.1109/iccvw54120.2021.00337","title":"SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Discriminative model; Point cloud; Artificial intelligence; Convolutional neural network; Object detection; Context (archaeology); Convolution (computer science); Pattern recognition (psychology); Code (set theory); Pairwise comparison; Machine learning; Set (abstract data type); Computer vision; Artificial neural network","score_opus":0.01730548626761732,"score_gpt":0.2594052995174738,"score_spread":0.2420998132498565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138676993","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":[0.0062323585,0.00014813068,0.9884023,0.0008464435,0.001069273,0.0007322117,0.0000050899034,0.0016656955,0.00089851493],"genre_scores_gemma":[0.84065646,0.000045555502,0.15746988,0.00083213876,0.00018009344,0.00039035248,0.000067072,0.000030555188,0.0003278898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973412,0.0001678657,0.0004430291,0.0012841962,0.00039586856,0.0003678249],"domain_scores_gemma":[0.9973624,0.0001701565,0.00031204798,0.0017076978,0.00031454233,0.00013315468],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018364388,0.00036914868,0.00034347436,0.00016384987,0.00025423578,0.0003847934,0.00107564,0.00031176137,0.000058115038],"category_scores_gemma":[0.000025008289,0.00038382225,0.00024391853,0.0005894131,0.00003138991,0.0003713589,0.0011575682,0.00071589136,0.00009424895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027896049,0.0008372715,0.0006950054,0.0005095928,0.0002911235,0.000098976256,0.00052605703,0.07337806,0.01423643,0.007332374,0.0019717924,0.9000954],"study_design_scores_gemma":[0.00041162616,0.00005238206,0.0025765987,0.00012334235,0.000046605786,0.000024772498,0.000049102415,0.975054,0.0154185295,0.002041248,0.0034748698,0.0007268737],"about_ca_topic_score_codex":0.0000713693,"about_ca_topic_score_gemma":0.00035501062,"teacher_disagreement_score":0.901676,"about_ca_system_score_codex":0.00026381898,"about_ca_system_score_gemma":0.00022623816,"threshold_uncertainty_score":0.99986136},"labels":[],"label_agreement":null},{"id":"W3139279841","doi":"10.1016/b978-0-32-395674-1.00014-7","title":"Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization","year":2021,"lang":"en","type":"preprint","venue":"Elsevier eBooks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Segmentation; Computer science; Artificial intelligence; Mutual information; Feature learning; Pattern recognition (psychology); Scale-space segmentation; Segmentation-based object categorization; Cluster analysis; Image segmentation; Encoder; Regularization (linguistics)","score_opus":0.008836309312299308,"score_gpt":0.24163230989097842,"score_spread":0.2327960005786791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139279841","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":[0.007195194,0.00013078189,0.9738767,0.0002772551,0.00014842952,0.00083010003,0.000015046042,0.00024633773,0.01728012],"genre_scores_gemma":[0.13712598,0.00006206958,0.859056,0.0011053263,0.0001732121,0.00045820486,0.00065207714,0.000032502365,0.0013346141],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983185,0.0000922273,0.00042150056,0.0005337516,0.00037327353,0.00026073933],"domain_scores_gemma":[0.99856347,0.000045488294,0.00033134848,0.00067681336,0.0002672078,0.00011565921],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014695284,0.0002911938,0.00025255588,0.000062102794,0.00021081764,0.0005966551,0.0004330168,0.00015944421,0.0000026869625],"category_scores_gemma":[0.00002180087,0.0002853024,0.00004725057,0.00019486829,0.00011394869,0.0010313051,0.0009956734,0.00032372744,0.000008370608],"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":[0.000007106568,0.000007771314,0.000077026605,0.000091897644,0.000019108822,0.000005289557,0.00076560857,0.0024699557,0.00020049915,0.0006490302,0.000012960357,0.99569374],"study_design_scores_gemma":[0.0015370011,0.00013167915,0.0022766336,0.0008551063,0.00012931469,0.0002335472,0.00058654073,0.9682766,0.0044345404,0.011602321,0.00858024,0.0013564468],"about_ca_topic_score_codex":0.000001747245,"about_ca_topic_score_gemma":0.000023638666,"teacher_disagreement_score":0.9943373,"about_ca_system_score_codex":0.0001918201,"about_ca_system_score_gemma":0.0001919649,"threshold_uncertainty_score":0.9999599},"labels":[],"label_agreement":null},{"id":"W3140904135","doi":"10.1016/j.asoc.2021.107361","title":"A deep residual computation model for heterogeneous data learning in smart Internet of Things","year":2021,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China","keywords":"Deep learning; Computer science; Residual; Artificial intelligence; Big data; Computation; Machine learning; Data modeling; Data mining; Algorithm","score_opus":0.04410528554013326,"score_gpt":0.29584098540630316,"score_spread":0.2517356998661699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140904135","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":[0.020971125,0.000093204944,0.97806793,0.00017259967,0.0000540304,0.00029162734,0.0000012888568,0.00017696379,0.00017120798],"genre_scores_gemma":[0.5725227,0.0000019551223,0.42715412,0.00019565833,0.000020330495,0.000010811844,0.000066304565,0.000012720899,0.000015380245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982465,0.00004349813,0.00047778458,0.0007208486,0.00019564186,0.00031571745],"domain_scores_gemma":[0.9983304,0.0006582944,0.0002542875,0.00060244754,0.00010400386,0.000050522878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003720052,0.00015073291,0.00025379084,0.00007898748,0.000116180956,0.00007046333,0.00097424036,0.00006218254,5.917304e-7],"category_scores_gemma":[0.00008575718,0.00017933926,0.000035849505,0.0004394552,0.000035934823,0.00024687982,0.0013684123,0.0002362144,0.0000033373794],"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":[0.000007746675,0.000039354396,0.00010377465,0.000026578402,0.000010049592,0.0000032903338,0.001350908,0.9029871,0.0011528315,0.0108372085,0.000048177386,0.08343297],"study_design_scores_gemma":[0.00037582868,0.000013205416,0.000048095997,0.000029949391,0.0000056571007,0.000013962747,0.000039199505,0.98752505,0.0015931105,0.010118278,0.00007815465,0.00015952052],"about_ca_topic_score_codex":0.0000077240475,"about_ca_topic_score_gemma":0.000027749007,"teacher_disagreement_score":0.5515516,"about_ca_system_score_codex":0.00004470144,"about_ca_system_score_gemma":0.00007691047,"threshold_uncertainty_score":0.7313243},"labels":[],"label_agreement":null},{"id":"W3149170955","doi":"10.1016/j.media.2021.102055","title":"Meta grayscale adaptive network for 3D integrated renal structures segmentation","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":48,"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":"Grayscale; Artificial intelligence; Segmentation; Computer science; Computer vision; Pattern recognition (psychology); Deep learning; Image segmentation; Image (mathematics)","score_opus":0.029290088657429034,"score_gpt":0.303212484430983,"score_spread":0.27392239577355393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149170955","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":[0.00030780496,0.0005227697,0.9964131,0.0022661686,0.00006942377,0.00015357189,0.00001911757,0.00011143889,0.00013657301],"genre_scores_gemma":[0.014838826,0.00008324734,0.98248076,0.0014664439,0.00021126679,0.00018476247,0.00029643564,0.000010961833,0.00042727712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807537,0.0001571627,0.00035695414,0.00057149556,0.00052032375,0.00031872417],"domain_scores_gemma":[0.99825996,0.00053050654,0.00013826114,0.0005454659,0.0003112689,0.0002145501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025248522,0.00016541003,0.00042430527,0.00009673828,0.00020519749,0.00010403495,0.0005601727,0.00007890176,0.0004989086],"category_scores_gemma":[0.00021794195,0.00012967657,0.00040866758,0.0031394265,0.000097915596,0.00030609587,0.0001788733,0.0001909394,0.000010495562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010751248,0.0005471013,0.0007455839,0.00004932573,0.046248607,0.0004689106,0.00090155005,0.11123452,0.008510512,0.096535705,0.093757905,0.64089274],"study_design_scores_gemma":[0.00034633258,0.00003888838,0.00038311016,0.000006757778,0.0049548703,0.000011759429,0.00004333904,0.9552628,0.004914842,0.026697913,0.007088882,0.0002505122],"about_ca_topic_score_codex":0.000023958017,"about_ca_topic_score_gemma":0.00036245718,"teacher_disagreement_score":0.8440283,"about_ca_system_score_codex":0.000047134974,"about_ca_system_score_gemma":0.00013678579,"threshold_uncertainty_score":0.5462699},"labels":[],"label_agreement":null},{"id":"W3150202690","doi":"10.48550/arxiv.2103.16617","title":"HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Context (archaeology); Modality (human–computer interaction); Inference; Contrast (vision); Dice; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.03022948599163722,"score_gpt":0.22253617863167147,"score_spread":0.19230669264003425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150202690","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":[0.06499869,0.000094306924,0.93130594,0.00026575098,0.0009927263,0.0015888632,0.00008243382,0.00032943662,0.00034187685],"genre_scores_gemma":[0.93604785,0.00009653532,0.062022522,0.00009860132,0.0006546768,0.000038646398,0.0004608233,0.000040737676,0.0005396079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970836,0.00019648635,0.00038497613,0.0016572736,0.00008998086,0.0005876541],"domain_scores_gemma":[0.9973838,0.00039373754,0.00047824119,0.0009858946,0.0005332337,0.00022509448],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000200708,0.00045715817,0.00049816276,0.00013765256,0.0004178471,0.00023105336,0.0011679896,0.00029376778,0.000011249461],"category_scores_gemma":[0.00008033566,0.0005485383,0.00031579405,0.00069260603,0.00013730233,0.0006349977,0.0011468199,0.0005832938,0.000014552786],"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":[0.00062257337,0.00038353534,0.00053350587,0.00018969538,0.0003111736,0.00003390085,0.00075767783,0.8675055,0.043905728,0.06496725,0.0006878368,0.02010163],"study_design_scores_gemma":[0.0018334566,0.00013602292,0.0024685448,0.00012065465,0.00015989985,0.0000046838886,0.000073656316,0.9637861,0.004168405,0.026130032,0.00032027086,0.0007982315],"about_ca_topic_score_codex":0.000035145673,"about_ca_topic_score_gemma":0.00018489835,"teacher_disagreement_score":0.87104917,"about_ca_system_score_codex":0.0003378398,"about_ca_system_score_gemma":0.00036419675,"threshold_uncertainty_score":0.9996966},"labels":[],"label_agreement":null},{"id":"W3152607317","doi":"10.18653/v1/2021.eacl-main.212","title":"Annealing Knowledge Distillation","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Distillation; Artificial neural network; Benchmark (surveying); Simulated annealing; Inference; Artificial intelligence; Annealing (glass); Machine learning; Deep learning; Exploit; Materials science","score_opus":0.025310007159642947,"score_gpt":0.2946477081653764,"score_spread":0.26933770100573345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152607317","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":[0.0019235902,0.00018882181,0.96142346,0.0010878792,0.000096214986,0.000031265223,2.2701468e-7,0.00018672962,0.035061844],"genre_scores_gemma":[0.79519624,0.000015554702,0.20203583,0.00017011559,0.000073983916,0.000007992047,0.000003638546,0.0000033469407,0.0024933012],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959165,0.000013613494,0.000074065196,0.00018251107,0.000046943514,0.00009122966],"domain_scores_gemma":[0.99951774,0.0000616257,0.000016996522,0.00030066885,0.00006714524,0.000035827183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002642264,0.00003771373,0.00003873727,0.000012527992,0.00007549205,0.000039432063,0.00016302464,0.000013439216,0.000018875922],"category_scores_gemma":[0.000015149737,0.00003598074,0.000017633482,0.00039594353,0.000007562499,0.00017856409,0.000122012636,0.000033602097,0.00013056381],"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":[1.8349078e-7,0.000017218146,0.00022626777,0.0000021338712,0.0000019071216,0.0000033798397,0.00007619933,0.0016124024,0.0022592898,0.8670021,0.0016453831,0.12715349],"study_design_scores_gemma":[0.00015264048,0.0000094377665,0.006014573,0.000008550737,0.0000025256725,0.000034708886,0.000016689202,0.7309002,0.0274514,0.06309384,0.17209423,0.00022120339],"about_ca_topic_score_codex":4.953807e-7,"about_ca_topic_score_gemma":0.000007310867,"teacher_disagreement_score":0.8039083,"about_ca_system_score_codex":0.000013048255,"about_ca_system_score_gemma":0.00002161931,"threshold_uncertainty_score":0.16781773},"labels":[],"label_agreement":null},{"id":"W3153216549","doi":"10.3390/s21082824","title":"Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Drone; Artificial intelligence; Computer science; Visibility; Constant false alarm rate; ALARM; False alarm; Machine learning; Position (finance); Point (geometry); False positive rate; Deep learning; Computer vision; Geography; Engineering; Mathematics","score_opus":0.017918550219570718,"score_gpt":0.2437758308296785,"score_spread":0.22585728061010776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153216549","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":[0.3800996,0.002165128,0.60704863,0.0075489976,0.00047678617,0.00026338856,0.00000892859,0.0011189806,0.0012695835],"genre_scores_gemma":[0.93696034,0.0008346163,0.06138747,0.00011895419,0.0002194537,0.000013584535,0.0000056166004,0.00002085814,0.0004391164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988317,0.00007794876,0.00020063027,0.00053611817,0.00014843719,0.00020516277],"domain_scores_gemma":[0.9991101,0.000248243,0.00008300247,0.00039805772,0.000063853644,0.000096729294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081059545,0.000113045855,0.00013301527,0.00003600868,0.00024036951,0.00007256201,0.00016062711,0.000056837096,0.0000037650736],"category_scores_gemma":[0.00008461219,0.00011752041,0.000031442763,0.00036999822,0.000043993034,0.0001468737,0.00016233261,0.00022102022,0.000048014386],"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":[0.000041617248,0.000057992132,0.000080325735,0.0000069063117,0.000034876586,0.00016546478,0.002828222,0.010198898,0.0038753622,0.0013881603,0.00004830407,0.9812739],"study_design_scores_gemma":[0.002159958,0.0001821156,0.006095729,0.000041134856,0.000022756105,0.00015567156,0.00030953865,0.88560736,0.021708824,0.013367004,0.0697299,0.00062001613],"about_ca_topic_score_codex":0.00003529137,"about_ca_topic_score_gemma":0.00022237824,"teacher_disagreement_score":0.9806539,"about_ca_system_score_codex":0.000019073348,"about_ca_system_score_gemma":0.000009910796,"threshold_uncertainty_score":0.47923434},"labels":[],"label_agreement":null},{"id":"W3157630927","doi":"10.32920/22734374.v1","title":"Motion Vector Extrapolation for Video Object Detection","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object detection; Latency (audio); Motion vector; Optical flow; Motion estimation; Low latency (capital markets); Benchmark (surveying); Pattern recognition (psychology)","score_opus":0.055852395272416526,"score_gpt":0.3089934045694444,"score_spread":0.2531410092970279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157630927","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":[0.0007933312,0.000032008727,0.9934002,0.0009782596,0.0015666803,0.0013555235,0.00001135551,0.0016716695,0.00019097289],"genre_scores_gemma":[0.7541921,0.000030918134,0.24179919,0.00017592094,0.0006428367,0.001968887,0.00008178607,0.000052175896,0.0010561789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839056,0.00003805569,0.00030758462,0.0008170612,0.0001954654,0.00025126498],"domain_scores_gemma":[0.99841195,0.00026850056,0.00022695381,0.000885751,0.00014487396,0.00006196212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020258651,0.00020568514,0.00017450462,0.00017168556,0.00018939639,0.00015531945,0.0006397882,0.0001677408,0.00000412352],"category_scores_gemma":[0.0000779462,0.00021349809,0.00014866098,0.0004135236,0.0000169084,0.00029668034,0.00049297663,0.00026775262,0.00008722466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032929103,0.00011362313,0.00008818755,0.00028757224,0.00008644626,0.0000025098707,0.00031573029,0.17234412,0.027541442,0.12488657,0.004082993,0.6702179],"study_design_scores_gemma":[0.00013442595,0.000036440146,0.0022913623,0.000023210858,0.000012261273,0.0000029253893,0.0000033155457,0.8165655,0.012416162,0.16588317,0.0023574538,0.000273742],"about_ca_topic_score_codex":0.00003231781,"about_ca_topic_score_gemma":0.0001187779,"teacher_disagreement_score":0.7533988,"about_ca_system_score_codex":0.00014689365,"about_ca_system_score_gemma":0.00004387633,"threshold_uncertainty_score":0.87061995},"labels":[],"label_agreement":null},{"id":"W3157770533","doi":"10.48550/arxiv.2104.12898","title":"SGNet: A Super-class Guided Network for Image Classification and Object Detection","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Nuclear Safety and Security Commission; National Aeronautics and Space Administration","keywords":"Inference; Class (philosophy); Computer science; Artificial intelligence; Object (grammar); Pattern recognition (psychology); Image (mathematics); Object detection; Exploit; Machine learning; Data mining","score_opus":0.08308838273271177,"score_gpt":0.21977342804764166,"score_spread":0.1366850453149299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157770533","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":[0.086405545,0.00012426384,0.91132385,0.000271509,0.00037528912,0.0007584441,0.000010682956,0.0002880344,0.00044237982],"genre_scores_gemma":[0.9610545,0.00035507916,0.03769965,0.0001626034,0.00021217039,0.000026696303,0.000054879638,0.000026828451,0.00040758477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978082,0.00011659453,0.00024107212,0.0013812918,0.00007241662,0.00038038963],"domain_scores_gemma":[0.9978508,0.00023455784,0.00026244626,0.0012208462,0.00028846928,0.00014289824],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019340903,0.00029173572,0.0002938667,0.00011815858,0.00035977663,0.00021688071,0.0008526598,0.00026044168,0.0000048456886],"category_scores_gemma":[0.00004643081,0.00036592054,0.00016186344,0.0007641291,0.00010872024,0.00051890477,0.0010096336,0.00038262317,0.000008917374],"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":[0.00010723295,0.00023180846,0.0011725539,0.00035061061,0.00026500752,0.00014138842,0.00052797125,0.6045751,0.014141585,0.35205767,0.0040092943,0.022419788],"study_design_scores_gemma":[0.0003811765,0.000042118354,0.0016515502,0.000048097547,0.00006417438,0.000015696356,0.000059055666,0.96041983,0.00074056373,0.033846702,0.0023157015,0.00041535747],"about_ca_topic_score_codex":0.000036223413,"about_ca_topic_score_gemma":0.00012762724,"teacher_disagreement_score":0.874649,"about_ca_system_score_codex":0.00020803892,"about_ca_system_score_gemma":0.00012000929,"threshold_uncertainty_score":0.9998793},"labels":[],"label_agreement":null},{"id":"W3158216587","doi":"10.3390/s21092984","title":"Quantization and Deployment of Deep Neural Networks on Microcontrollers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Centre National de la Recherche Scientifique; Agence Nationale de la Recherche","keywords":"Computer science; Microcontroller; Quantization (signal processing); Artificial neural network; Deep learning; Software deployment; MNIST database; Inference; Embedded system; Artificial intelligence; Computer engineering; Computer hardware; Algorithm","score_opus":0.011372284754760318,"score_gpt":0.24267625202229698,"score_spread":0.23130396726753666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158216587","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":[0.37569547,0.00037523554,0.62167704,0.0015919651,0.00013737867,0.0001816769,7.744064e-7,0.0000821154,0.0002583239],"genre_scores_gemma":[0.98767513,0.00006539341,0.011759012,0.00040536845,0.00003238557,0.0000065139116,0.0000024817152,0.000007161379,0.00004655612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933374,0.000045851073,0.00014586125,0.0002437203,0.00009337045,0.00013743629],"domain_scores_gemma":[0.999424,0.00012003155,0.0000718198,0.00028183314,0.000054931093,0.000047412028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037293008,0.00007594252,0.000108871805,0.00003005769,0.00006176095,0.000022234113,0.00012417082,0.000028373142,0.0000022722702],"category_scores_gemma":[0.000015927455,0.00007351914,0.000029213745,0.00028324028,0.000029784987,0.00006523559,0.00006764443,0.00006640852,0.0000023639327],"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":[0.000012614854,0.000043799784,0.0006273873,0.000005123505,0.000013511455,0.000012192602,0.00012690503,0.89343214,0.0042332704,0.029733056,0.00007159983,0.07168839],"study_design_scores_gemma":[0.00025870942,0.000029766265,0.0016910182,0.0000049015857,0.000004137769,0.000012514427,0.000017459703,0.99168736,0.005331961,0.0005549546,0.0003319241,0.000075322154],"about_ca_topic_score_codex":0.000001772369,"about_ca_topic_score_gemma":0.00001037853,"teacher_disagreement_score":0.61197966,"about_ca_system_score_codex":0.000011631611,"about_ca_system_score_gemma":0.000005300279,"threshold_uncertainty_score":0.29980236},"labels":[],"label_agreement":null},{"id":"W3158750411","doi":"10.1145/3447866","title":"Vision-based Autonomous Vehicle Recognition","year":2021,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Machine learning; Deep learning; Task (project management); Identification (biology); Intelligent transportation system","score_opus":0.08687058763802843,"score_gpt":0.36112370279001715,"score_spread":0.2742531151519887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158750411","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":[0.0000024765775,0.55669594,0.44187358,0.0000927902,0.00041649674,0.0003418246,0.000012160453,0.00045636902,0.00010838113],"genre_scores_gemma":[0.00007977667,0.81296957,0.18549548,0.00025196,0.00038620597,0.00006879414,0.0005766849,0.00008776063,0.000083800005],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99509436,0.002006637,0.00081168493,0.0012349387,0.0003265037,0.0005258724],"domain_scores_gemma":[0.99220705,0.004212346,0.00061809184,0.0025867969,0.00022194274,0.0001537829],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016959156,0.00048336427,0.0012346846,0.00018663534,0.00035050095,0.00030316444,0.0027195725,0.0002561254,0.000017191178],"category_scores_gemma":[0.0005439309,0.00047794485,0.00044853368,0.0017835192,0.00005440743,0.00019318401,0.0013606342,0.00063283753,0.00046453468],"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":[7.5295986e-8,0.0000497752,0.0000022129234,0.00046660745,0.000020187012,0.000019852629,0.0000071407035,0.00030007018,2.4309134e-7,0.00015588378,0.0005955924,0.99838233],"study_design_scores_gemma":[0.00016895433,0.000053038686,0.00005145862,0.0062567056,0.00008154359,0.000046416913,8.831101e-7,0.044715796,0.0000060892003,0.000969323,0.94676644,0.0008833679],"about_ca_topic_score_codex":0.000014291057,"about_ca_topic_score_gemma":0.000005749282,"teacher_disagreement_score":0.997499,"about_ca_system_score_codex":0.00018321142,"about_ca_system_score_gemma":0.00048477438,"threshold_uncertainty_score":0.99976724},"labels":[],"label_agreement":null},{"id":"W3158909043","doi":"10.48550/arxiv.1908.06077","title":"NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Stochastic gradient descent; Quantization (signal processing); Heuristic; Deep neural networks; Gradient descent; Communication complexity; Algorithm; Theoretical computer science; Artificial neural network; Artificial intelligence","score_opus":0.10127558862817974,"score_gpt":0.22619910955554684,"score_spread":0.1249235209273671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158909043","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":[0.012532659,0.00013521282,0.9819862,0.00039840772,0.00035114118,0.001202749,0.00005258185,0.00048128146,0.0028597426],"genre_scores_gemma":[0.9527342,0.00042697068,0.044642743,0.00016231391,0.00005303129,0.000007094396,0.0006815429,0.000035288842,0.001256817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715453,0.00015656176,0.00037388195,0.0016941868,0.00017955794,0.00044130092],"domain_scores_gemma":[0.9907593,0.00020760315,0.0006117245,0.007959929,0.00030019632,0.00016125446],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0003164695,0.00039717022,0.0003778735,0.00024262354,0.00036256714,0.00016997158,0.008086503,0.0002769778,0.000018432347],"category_scores_gemma":[0.000029822004,0.00046627485,0.00011787242,0.0010861495,0.00015641616,0.00077095785,0.008837634,0.0007649466,0.0005314876],"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":[0.000010175358,0.00011925709,0.0002279645,0.000033442568,0.000032502874,0.000008213121,0.00006710517,0.78745794,0.000024732542,0.21066307,0.00044334182,0.00091222575],"study_design_scores_gemma":[0.0003340509,0.000021834017,0.00044709307,0.00006103071,0.000044529057,0.0000051540233,0.000023329427,0.97196954,0.00003211827,0.023075828,0.0034992686,0.00048619517],"about_ca_topic_score_codex":0.000113702234,"about_ca_topic_score_gemma":0.00007616921,"teacher_disagreement_score":0.9402015,"about_ca_system_score_codex":0.00026925362,"about_ca_system_score_gemma":0.00024875233,"threshold_uncertainty_score":0.99977887},"labels":[],"label_agreement":null},{"id":"W3159160396","doi":"10.1109/tgrs.2021.3076050","title":"Oriented Bounding Boxes for Small and Freely Rotated Objects","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":82,"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","keywords":"Computer science; Minimum bounding box; Pixel; Object detection; Bounding overwatch; Artificial intelligence; Computer vision; Computation; Scalability; Pattern recognition (psychology); Algorithm; Image (mathematics)","score_opus":0.021932365735138907,"score_gpt":0.2550540790408896,"score_spread":0.2331217133057507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159160396","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":[0.05887649,0.00007757202,0.93934816,0.0008749734,0.00041981848,0.00020831283,0.000004110832,0.00012780768,0.00006274836],"genre_scores_gemma":[0.37984902,0.00015720242,0.619183,0.0003880132,0.000028617967,8.1486866e-7,8.9803535e-7,0.000009672884,0.00038273464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987595,0.000033687025,0.00016757892,0.0005843092,0.00013438513,0.00032054604],"domain_scores_gemma":[0.9991667,0.00022509415,0.000057171215,0.00030322437,0.00011990995,0.00012784437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001290247,0.00013690132,0.00013712939,0.00010400149,0.00094685995,0.00018683706,0.000122023914,0.00005112786,4.9875155e-7],"category_scores_gemma":[0.000018140052,0.00013192969,0.000041059648,0.0007826459,0.00015602142,0.00030577596,0.000007113221,0.00014368496,0.0000015461424],"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":[0.000008579577,0.000022360842,9.1431224e-7,0.000015723572,0.000007803597,0.000016707976,0.00056157087,0.00081939925,0.043110307,0.00043574182,0.000007880675,0.954993],"study_design_scores_gemma":[0.00039421252,0.00008997861,0.00012327314,0.00009881785,0.00001777928,0.00031661935,0.00017530152,0.89464736,0.09885189,0.0037966755,0.0012255863,0.00026250677],"about_ca_topic_score_codex":0.000043399505,"about_ca_topic_score_gemma":0.00020446412,"teacher_disagreement_score":0.9547305,"about_ca_system_score_codex":0.000027477263,"about_ca_system_score_gemma":0.000068594585,"threshold_uncertainty_score":0.7282577},"labels":[],"label_agreement":null},{"id":"W3159953536","doi":"10.48550/arxiv.2104.11854","title":"Oriented Bounding Boxes for Small and Freely Rotated Objects","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Minimum bounding box; Computer science; Bounding overwatch; Pixel; Artificial intelligence; Object detection; Computer vision; Scalability; Computation; Pattern recognition (psychology); Algorithm; Image (mathematics)","score_opus":0.0801222119730783,"score_gpt":0.20134047827046347,"score_spread":0.12121826629738518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159953536","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":[0.2826058,0.00011199981,0.715955,0.00009565479,0.00028307602,0.00047874378,0.000011485333,0.00023053626,0.00022772264],"genre_scores_gemma":[0.95643675,0.00019908733,0.04240154,0.00010756657,0.00006368979,0.000009988887,0.000047125763,0.00002130608,0.00071294815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981078,0.0000628267,0.00017069849,0.001243,0.000054107397,0.00036156838],"domain_scores_gemma":[0.9982107,0.00023956527,0.00021702894,0.00094615016,0.00021907275,0.00016746123],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010873867,0.00026872384,0.0002966702,0.00014916528,0.00030262856,0.00018270557,0.0009085936,0.00017679192,0.000003446933],"category_scores_gemma":[0.00005689086,0.00033211,0.00012130285,0.0006943382,0.00009507503,0.000304331,0.0017545094,0.00034482367,0.0000044014782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088626184,0.00025742402,0.00459464,0.0003856905,0.0003153634,0.00047279446,0.0014528403,0.28590426,0.0019691358,0.6992227,0.00050069013,0.0048358585],"study_design_scores_gemma":[0.00095552515,0.00008050755,0.0014193822,0.0001923849,0.000104904175,0.000017186787,0.00021407765,0.9178751,0.0014965527,0.07463676,0.0021699292,0.0008376772],"about_ca_topic_score_codex":0.000043271495,"about_ca_topic_score_gemma":0.00016085793,"teacher_disagreement_score":0.6738309,"about_ca_system_score_codex":0.00012783852,"about_ca_system_score_gemma":0.00013919658,"threshold_uncertainty_score":0.9999131},"labels":[],"label_agreement":null},{"id":"W3160038630","doi":"10.1016/j.compbiomed.2021.104449","title":"Hybrid dilation and attention residual U-Net for medical image segmentation","year":2021,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Dilation (metric space); Artificial intelligence; Residual; Segmentation; Computer science; Image segmentation; Computer vision; Pattern recognition (psychology); Algorithm; Mathematics; Geometry","score_opus":0.016743992512957055,"score_gpt":0.3360908776720004,"score_spread":0.31934688515904336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160038630","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":[0.08340444,0.0005813277,0.90257484,0.0129286945,0.00027990722,0.00015588258,0.0000016167273,0.00003033867,0.000042970052],"genre_scores_gemma":[0.6629655,0.0012733014,0.33164108,0.0032300306,0.00048812947,0.00007354057,0.00027426734,0.000007622615,0.000046572535],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99928045,0.000061636645,0.0001798674,0.00029785954,0.000060906255,0.00011927695],"domain_scores_gemma":[0.99936754,0.00035813055,0.000050241477,0.00012207383,0.00004098013,0.00006106428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023598682,0.000067420246,0.00012529068,0.000054269665,0.00007596658,0.000008255552,0.00009995584,0.00004730176,0.0000033326014],"category_scores_gemma":[0.00008903948,0.00005800766,0.0000081904245,0.000106372245,0.00016020717,0.000108993445,0.00011040587,0.00007783634,5.3820355e-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":[0.000041108186,0.00007440666,0.011954084,0.00007011967,0.000026304082,0.000046680776,0.00047260112,0.00003094338,0.03183257,0.24417217,0.006445742,0.70483327],"study_design_scores_gemma":[0.0088498825,0.0008915332,0.17169793,0.00040622833,0.000041164407,0.00074311526,0.00013317453,0.3752834,0.0049898373,0.4265136,0.009898739,0.00055139285],"about_ca_topic_score_codex":0.0000024402061,"about_ca_topic_score_gemma":0.00000738545,"teacher_disagreement_score":0.70428187,"about_ca_system_score_codex":0.000012591388,"about_ca_system_score_gemma":0.000018829289,"threshold_uncertainty_score":0.23654838},"labels":[],"label_agreement":null},{"id":"W3160205797","doi":"10.18280/ts.380228","title":"A Region-Based Efficient Network for Accurate Object Detection","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"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":"Hubei Provincial Department of Education","keywords":"Pascal (unit); Object detection; Computer science; Artificial intelligence; Viola–Jones object detection framework; Object (grammar); Object-class detection; Computer vision; Pattern recognition (psychology); Set (abstract data type); Precision and recall; Cognitive neuroscience of visual object recognition; Face detection","score_opus":0.027219834620603716,"score_gpt":0.26209974662120306,"score_spread":0.23487991200059935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160205797","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":[0.0070576207,0.00010120985,0.9904116,0.0013138512,0.00024059559,0.0005358497,0.0000036131466,0.00022620083,0.0001094657],"genre_scores_gemma":[0.93356484,0.0000042694974,0.064550094,0.0010268338,0.0003392318,0.0004205171,0.000015182021,0.0000142346635,0.00006479444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986133,0.000063296175,0.0002611734,0.00048118242,0.00020914397,0.00037192443],"domain_scores_gemma":[0.99892914,0.0003179192,0.000121883764,0.00038759946,0.00015317799,0.000090280926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016575975,0.00014749552,0.00013700924,0.00003978057,0.00032162917,0.00009904367,0.0003443585,0.00004124665,0.000016343927],"category_scores_gemma":[0.000019231842,0.00014800727,0.0001159928,0.00065737107,0.000026552138,0.000110462555,0.000064359556,0.000089937384,0.000014375416],"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":[0.000038208225,0.00014134048,0.000038429982,0.00001745524,0.000019643754,0.000016165459,0.000066608125,0.91878057,0.0067457673,0.014191024,0.0017845965,0.058160186],"study_design_scores_gemma":[0.00071687816,0.00011169808,0.00050668174,0.000022148002,0.000013823628,0.000014858263,0.0000073380493,0.9589059,0.022958886,0.0027166833,0.013822135,0.00020301667],"about_ca_topic_score_codex":0.0000015167511,"about_ca_topic_score_gemma":0.000014489272,"teacher_disagreement_score":0.92650723,"about_ca_system_score_codex":0.00007660143,"about_ca_system_score_gemma":0.00009366608,"threshold_uncertainty_score":0.6035561},"labels":[],"label_agreement":null},{"id":"W3160916081","doi":"","title":"LeanResNet: A Low-cost yet Effective Convolutional Residual Networks","year":2019,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Convolutional neural network; Convolution (computer science); Residual; Computer science; Computational complexity theory; Range (aeronautics); Convolutional code; Pattern recognition (psychology); Artificial intelligence; Filter (signal processing); Algorithm; Artificial neural network; Computer vision; Decoding methods","score_opus":0.01699334081363223,"score_gpt":0.29154294039788387,"score_spread":0.2745495995842516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160916081","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":[0.051003855,0.00009553709,0.8936955,0.0057757245,0.0016776762,0.0011644183,0.000024313244,0.00060131116,0.045961667],"genre_scores_gemma":[0.9928026,0.000036626283,0.0030156735,0.0007222898,0.00022016687,0.00012056811,0.00011094065,0.000020194711,0.0029509217],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807763,0.00016309871,0.00026829247,0.0006418278,0.0005123448,0.00033680178],"domain_scores_gemma":[0.9985676,0.00050763,0.0002077781,0.00037489572,0.00023823522,0.00010390493],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002474448,0.00023025672,0.0001926231,0.0001576304,0.00017424618,0.000187711,0.0011388472,0.00008351314,0.00047037555],"category_scores_gemma":[0.00012225955,0.00022600626,0.000075515694,0.00027068655,0.00006072696,0.00043813375,0.0003473257,0.0008463543,0.0009082626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008326635,0.000072053714,0.012263533,0.0000045354163,0.00004675102,0.000011914341,0.000114405826,0.28151825,0.00085113157,0.666926,0.00044203218,0.03766612],"study_design_scores_gemma":[0.00063111645,0.00012232833,0.012794985,0.000074086456,0.0000030601523,0.000019405898,0.000014381816,0.971175,0.00016441685,0.0061887684,0.008544647,0.00026780088],"about_ca_topic_score_codex":0.000027147727,"about_ca_topic_score_gemma":0.000015765107,"teacher_disagreement_score":0.94179875,"about_ca_system_score_codex":0.0001314686,"about_ca_system_score_gemma":0.00005775587,"threshold_uncertainty_score":0.99986964},"labels":[],"label_agreement":null},{"id":"W3161118201","doi":"10.1109/icpr48806.2021.9412859","title":"Attention Based Pruning for Shift Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Convolution (computer science); Inference; Pruning; Block (permutation group theory); CLs upper limits; Computation; Artificial intelligence; Function (biology); Convolutional neural network; Key (lock); Algorithm; Computer engineering; Theoretical computer science; Artificial neural network; Mathematics","score_opus":0.030130049993781008,"score_gpt":0.2885999547064105,"score_spread":0.2584699047126295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161118201","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":[0.0005297496,0.00017477707,0.99428225,0.0025041688,0.000691719,0.00080492144,0.0000023925427,0.0004678128,0.00054222875],"genre_scores_gemma":[0.35280746,0.00001475428,0.64508045,0.0007718803,0.00022770463,0.0006529228,0.00015726277,0.000020452282,0.00026707308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983776,0.00004333088,0.00028252558,0.0008316221,0.00015829189,0.00030660874],"domain_scores_gemma":[0.9981716,0.00023739898,0.0001907534,0.0011897584,0.00012748053,0.000083007435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016344815,0.00021028784,0.00022162774,0.00006007142,0.00016215433,0.00032543007,0.001039533,0.0001890265,0.000012814395],"category_scores_gemma":[0.000021413605,0.00021820387,0.00019557512,0.000270009,0.000021308137,0.0001897766,0.0010664419,0.0003616566,0.0000044612743],"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":[0.0000031583786,0.00005602626,0.00014169885,0.00006295192,0.000021828451,0.0000031179895,0.000030675103,0.902756,0.00008963728,0.06293324,0.0014082579,0.03249346],"study_design_scores_gemma":[0.00013864426,0.00001156408,0.00084606034,0.00007100652,0.000010925501,9.074986e-7,0.0000033193767,0.98850405,0.00012606177,0.008217713,0.00182452,0.00024522288],"about_ca_topic_score_codex":0.0000058977585,"about_ca_topic_score_gemma":0.000022237526,"teacher_disagreement_score":0.35227773,"about_ca_system_score_codex":0.000058105943,"about_ca_system_score_gemma":0.00010610313,"threshold_uncertainty_score":0.88980955},"labels":[],"label_agreement":null},{"id":"W3161126843","doi":"10.1109/icpr48806.2021.9412173","title":"Attention-based Selection Strategy for Weakly Supervised Object Localization","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Object (grammar); Selection (genetic algorithm); Pattern recognition (psychology); Task (project management); Position (finance); Object detection; Image (mathematics); Feature (linguistics); Computer vision; Machine learning","score_opus":0.028197952051826708,"score_gpt":0.2839790692748551,"score_spread":0.25578111722302843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161126843","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":[0.0010340769,0.000027815966,0.99636066,0.0010851768,0.00009608543,0.00028088494,0.0000019898353,0.00030445517,0.0008088294],"genre_scores_gemma":[0.8014117,0.0000062788895,0.19604862,0.00093579333,0.00008389978,0.00018164233,0.00007869814,0.00001327108,0.0012401312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999112,0.000036114594,0.00017010458,0.00037070134,0.00012563684,0.00018544197],"domain_scores_gemma":[0.99920493,0.00011711829,0.000045383113,0.0002821483,0.0002978001,0.000052615404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006461598,0.000090307476,0.0000838995,0.000043341286,0.00020694078,0.000106509666,0.00020217647,0.000043777774,0.000040442013],"category_scores_gemma":[0.000024465688,0.000092023896,0.000062005725,0.00080841087,0.000013144901,0.00030395822,0.000029297093,0.000047698562,0.000023296117],"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":[0.000012179706,0.00021049556,0.0012858305,0.000040005165,0.000019513402,0.0000023124483,0.000026307547,0.5716401,0.04048604,0.32273066,0.005091922,0.058454674],"study_design_scores_gemma":[0.00028242983,0.00004447635,0.000686823,0.0000050966346,0.000004968414,0.0000040176337,0.0000098876,0.96034366,0.028447855,0.006944017,0.0031088497,0.00011789401],"about_ca_topic_score_codex":0.000005736243,"about_ca_topic_score_gemma":0.00007085367,"teacher_disagreement_score":0.8003776,"about_ca_system_score_codex":0.00004442099,"about_ca_system_score_gemma":0.00013885106,"threshold_uncertainty_score":0.37526256},"labels":[],"label_agreement":null},{"id":"W3162914052","doi":"10.24963/ijcai.2021/307","title":"Generative Adversarial Neural Architecture Search","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Discriminator; Reinforcement learning; Artificial intelligence; Machine learning; Generator (circuit theory); Space (punctuation); Architecture; Stability (learning theory); Adversarial system; Generative grammar; Convergence (economics)","score_opus":0.031574696492496904,"score_gpt":0.29581859635863195,"score_spread":0.26424389986613506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162914052","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":[0.0050445264,0.00020823113,0.9830187,0.0070282747,0.0009683375,0.00048279742,0.00000778273,0.00038687003,0.0028545072],"genre_scores_gemma":[0.29334047,0.000048706657,0.7025474,0.001554238,0.0009750238,0.00015998757,0.000094266565,0.000027495696,0.001252427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976458,0.00017405534,0.00025860223,0.001126646,0.00039996236,0.00039496808],"domain_scores_gemma":[0.99788165,0.00014178401,0.00008818523,0.0015614114,0.00017072605,0.00015625289],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009382113,0.00030177174,0.00029212487,0.00009178666,0.00016790483,0.00038186862,0.0018344146,0.00020237688,0.00006744524],"category_scores_gemma":[0.000021122207,0.00027388422,0.0001752485,0.0004001234,0.000054021562,0.00017350455,0.0047861743,0.0012544219,0.000028647652],"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":[0.000008335976,0.00007965775,0.000028486436,0.00003309886,0.00006981275,0.00008589602,0.0014182145,0.8321521,0.0023351938,0.07194795,0.0022205417,0.08962066],"study_design_scores_gemma":[0.00034251204,0.000045967587,0.0003024463,0.00003439252,0.000015978381,0.000060457536,0.000051648276,0.9594508,0.010382814,0.024873706,0.0036363495,0.0008029697],"about_ca_topic_score_codex":0.00003558881,"about_ca_topic_score_gemma":0.000050396397,"teacher_disagreement_score":0.28829592,"about_ca_system_score_codex":0.00007544238,"about_ca_system_score_gemma":0.00025047848,"threshold_uncertainty_score":0.99997133},"labels":[],"label_agreement":null},{"id":"W3164282918","doi":"10.1109/tits.2020.2993926","title":"Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":259,"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 Key Research and Development Program of China","keywords":"Computer science; Object detection; Pedestrian detection; Benchmark (surveying); Artificial intelligence; Task (project management); Feature (linguistics); Feature selection; Pedestrian; Real-time computing; Pattern recognition (psychology); Engineering","score_opus":0.03511149309185774,"score_gpt":0.2680781131232352,"score_spread":0.23296662003137747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164282918","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":[0.022167115,0.00019231172,0.9748548,0.0001892909,0.0013834395,0.0008500316,0.000042781317,0.0003099668,0.0000102226595],"genre_scores_gemma":[0.9921541,0.000045051558,0.006812701,0.00012367744,0.0000848804,0.0005882103,0.00004853419,0.00003369982,0.0001091386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997921,0.00018048912,0.00060482253,0.000677441,0.00025038767,0.0003658912],"domain_scores_gemma":[0.99815506,0.0007104728,0.00017240387,0.00048287484,0.0002993201,0.00017985486],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029730913,0.00024602917,0.0002746745,0.00011471747,0.00047710622,0.00018611382,0.00024692013,0.0001227921,0.000007801582],"category_scores_gemma":[0.000008184722,0.00027286512,0.00014319266,0.00082586816,0.00004264717,0.00034144928,6.4679773e-7,0.00021026067,0.000009894181],"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":[0.000040525643,0.000099260425,0.0003453921,0.000045201214,0.000031331343,0.000004973122,0.00016836416,0.9654283,0.0008881912,0.00050740526,0.000013628885,0.032427434],"study_design_scores_gemma":[0.00046788598,0.00015041679,0.004508219,0.000035974954,0.000037176695,0.000012572073,0.000058407182,0.9741893,0.01898526,0.00015564679,0.0011002608,0.00029891796],"about_ca_topic_score_codex":0.00009672052,"about_ca_topic_score_gemma":0.0031390076,"teacher_disagreement_score":0.969987,"about_ca_system_score_codex":0.000087338194,"about_ca_system_score_gemma":0.00007565094,"threshold_uncertainty_score":0.99997234},"labels":[],"label_agreement":null},{"id":"W3164437715","doi":"10.48550/arxiv.2105.12822","title":"Issues in Object Detection in Videos using Common Single-Image CNNs","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Frame (networking); Object detection; Object (grammar); Segmentation; Ground truth; Artificial neural network; Image segmentation; Pattern recognition (psychology)","score_opus":0.08719554785917802,"score_gpt":0.23161983500548053,"score_spread":0.1444242871463025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164437715","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":[0.5379563,0.000071623035,0.4611417,0.000057895493,0.0001618954,0.00023963215,0.0000015968707,0.00011178232,0.00025757132],"genre_scores_gemma":[0.98800254,0.00013748402,0.011629601,0.000054706477,0.00004813766,0.0000025480538,0.000007838854,0.000019298159,0.00009782719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793464,0.00022929575,0.0002730245,0.001119337,0.00008269337,0.00036101116],"domain_scores_gemma":[0.99836326,0.00013717148,0.0002176222,0.0011200702,0.00008167669,0.000080197955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015515681,0.00027048998,0.00035966732,0.00043755418,0.000103721635,0.00013805443,0.00107453,0.00022906541,0.000006764113],"category_scores_gemma":[0.00003132004,0.00036954167,0.00011206395,0.0017795877,0.0000769383,0.00071805785,0.0016916597,0.00075849774,0.000012294395],"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":[0.00001917224,0.00024111834,0.004474956,0.000058677302,0.000016999986,0.0008349871,0.00052793673,0.97295433,0.012564049,0.004200164,0.0000063495213,0.0041012787],"study_design_scores_gemma":[0.00035046288,0.000028262726,0.0034941165,0.0002231394,0.000015663434,0.000012053681,0.00014980952,0.9682815,0.008955939,0.017927017,0.00009185751,0.0004701695],"about_ca_topic_score_codex":0.00097033725,"about_ca_topic_score_gemma":0.0046299174,"teacher_disagreement_score":0.45004624,"about_ca_system_score_codex":0.00066992233,"about_ca_system_score_gemma":0.00008978746,"threshold_uncertainty_score":0.99987566},"labels":[],"label_agreement":null},{"id":"W3164538928","doi":"10.3390/a14060159","title":"Circle-U-Net: An Efficient Architecture for Semantic Segmentation","year":2021,"lang":"en","type":"article","venue":"Algorithms","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Petroleum Technology Research Centre; National Natural Science Foundation of China - State Grid Corporation Joint Fund for Smart Grid; National Natural Science Foundation of China","keywords":"Net (polyhedron); Computer science; Intersection (aeronautics); Bottleneck; Segmentation; Context (archaeology); Architecture; Residual; Artificial intelligence; Pixel; Algorithm; Data mining; Mathematics; Cartography; Geometry; Embedded system; Geography","score_opus":0.01652831685320631,"score_gpt":0.28439586679994167,"score_spread":0.26786754994673534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164538928","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":[0.008728349,0.00012439257,0.9885265,0.0017940359,0.00024469252,0.0003272504,0.000007784941,0.00016996772,0.000077006785],"genre_scores_gemma":[0.12803183,0.000011335838,0.8701988,0.0010592629,0.00022998564,0.00018817803,0.00006475948,0.000019013129,0.0001968292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989523,0.000035491787,0.00015006431,0.00045179605,0.00016934008,0.00024097374],"domain_scores_gemma":[0.99909663,0.00011179409,0.000057324974,0.0005420527,0.00010076418,0.00009142937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079100246,0.000097190736,0.00010310798,0.000038152917,0.00017406837,0.00007919481,0.00035218752,0.000035614667,0.0000046002774],"category_scores_gemma":[0.000016467264,0.000103994724,0.0000500372,0.00040323997,0.000023245406,0.0001323409,0.000094856085,0.000083340405,0.000020492496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029023086,0.00015636372,0.00001695705,0.000017302316,0.000015016453,0.000020010475,0.00059427804,0.089662135,0.011462746,0.008905202,0.0003299231,0.88881713],"study_design_scores_gemma":[0.0004915759,0.0000936772,0.00090668455,0.00001165621,0.00001239851,0.000107489475,0.00005152118,0.9483939,0.026875393,0.017440794,0.0053609163,0.0002539995],"about_ca_topic_score_codex":0.0000024800445,"about_ca_topic_score_gemma":0.000007101633,"teacher_disagreement_score":0.88856316,"about_ca_system_score_codex":0.000028600532,"about_ca_system_score_gemma":0.000041358155,"threshold_uncertainty_score":0.4240782},"labels":[],"label_agreement":null},{"id":"W3164721408","doi":"10.1155/2021/9500304","title":"Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":60,"is_retracted":true,"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":"Taif University","keywords":"Robustness (evolution); Fuse (electrical); Computer science; Convolutional neural network; Artificial intelligence; Object detection; Computational intelligence; Artificial neural network; Algorithm; Machine learning; Backpropagation; Face detection; Field (mathematics); Pattern recognition (psychology); Facial recognition system","score_opus":0.022601397701450764,"score_gpt":0.29858915478299086,"score_spread":0.2759877570815401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164721408","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":[0.010751211,0.0010095686,0.97568154,0.011133121,0.0008981664,0.0004273225,0.000005534036,0.00009148397,0.0000020563136],"genre_scores_gemma":[0.4124731,0.000035625733,0.586689,0.00033586595,0.00038082924,0.00005501603,9.773252e-7,0.000021250602,0.000008292716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842596,0.000025067546,0.00056764507,0.00027918097,0.00029612854,0.00040601165],"domain_scores_gemma":[0.9981412,0.00020145996,0.000224532,0.00032155475,0.0007740611,0.00033715693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033065391,0.00015002726,0.00025792155,0.00019773166,0.00015202911,0.00005615842,0.0003405799,0.000070885406,5.7508674e-7],"category_scores_gemma":[0.00013353424,0.00015067699,0.00013076188,0.0007446359,0.000004879433,0.00013993142,0.0000889724,0.00034846878,0.0000031692484],"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":[0.000017610124,0.000059818827,0.000016413016,0.0002599606,0.000022555634,0.00003949113,0.0006910152,0.7889276,0.01468887,0.0053617433,0.00007953875,0.18983537],"study_design_scores_gemma":[0.00079778046,0.00064055197,0.00075443916,0.00035511266,0.000013142255,0.0007653936,0.00011435571,0.943198,0.034129247,0.000689206,0.018160839,0.00038191455],"about_ca_topic_score_codex":0.000012449629,"about_ca_topic_score_gemma":0.000034349738,"teacher_disagreement_score":0.4017219,"about_ca_system_score_codex":0.00031438807,"about_ca_system_score_gemma":0.00020121607,"threshold_uncertainty_score":0.61444294},"labels":[],"label_agreement":null},{"id":"W3165329374","doi":"10.1007/978-3-030-87199-4_56","title":"Orthogonal Ensemble Networks for Biomedical Image Segmentation","year":2021,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Orthogonality; Segmentation; Artificial intelligence; Ensemble learning; Benchmark (surveying); Context (archaeology); Machine learning; Pattern recognition (psychology); Pairwise comparison; Ensemble forecasting; Mathematics","score_opus":0.01809883677509189,"score_gpt":0.29905016874005025,"score_spread":0.28095133196495836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165329374","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":[0.0033464737,0.00030080669,0.9905291,0.0020747026,0.0026038634,0.00091766316,0.0000065455642,0.00021342783,0.000007415889],"genre_scores_gemma":[0.32405275,0.00003005498,0.6741552,0.000992857,0.00050588953,0.00019076663,0.000056960293,0.0000149412,6.2665384e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958711,0.00008733166,0.0005462992,0.0019690858,0.0007160474,0.0008100894],"domain_scores_gemma":[0.99688697,0.00082009996,0.00028567715,0.0014311561,0.00035735162,0.00021876709],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007257502,0.00039327366,0.00041291272,0.00034864535,0.00034013792,0.0009149148,0.0030666094,0.00027382447,0.0000038798926],"category_scores_gemma":[0.00014317282,0.0003869383,0.0001549295,0.002206858,0.00049900747,0.0006395208,0.0034604797,0.0008332835,0.0000034982995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033186277,0.00006791863,0.00005425168,0.000030432275,0.0000055458454,0.000017154161,0.0002483043,0.49308005,0.0025682377,0.00069487636,0.00002884871,0.50320107],"study_design_scores_gemma":[0.00024350572,0.00005789465,0.00027363384,0.000110635854,0.000005754489,0.00003515888,5.248552e-7,0.9626201,0.0047148974,0.031490598,0.00005863969,0.0003886812],"about_ca_topic_score_codex":0.000008792673,"about_ca_topic_score_gemma":0.000040273848,"teacher_disagreement_score":0.5028124,"about_ca_system_score_codex":0.0002697901,"about_ca_system_score_gemma":0.00067748927,"threshold_uncertainty_score":0.99985826},"labels":[],"label_agreement":null},{"id":"W3165895193","doi":"10.1016/j.patcog.2022.108893","title":"Learning deep morphological networks with neural architecture search","year":2022,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":33,"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":"Pooling; Computer science; Artificial intelligence; Salient; Segmentation; Deep learning; Representation (politics); Pattern recognition (psychology); Linear map; Feature (linguistics); Artificial neural network; Mathematical morphology; Deep neural networks; Image processing; Image (mathematics); Mathematics","score_opus":0.025132125549383988,"score_gpt":0.24624129086523228,"score_spread":0.2211091653158483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165895193","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":[0.18919004,0.000033078253,0.8091799,0.0008516153,0.000074592834,0.00022346764,0.0000029032824,0.0002560595,0.00018834615],"genre_scores_gemma":[0.98620456,0.000008348344,0.012177809,0.0010492587,0.00012036195,0.00028398106,0.0001021078,0.000016714588,0.000036880996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985019,0.000263437,0.00013806843,0.0004561978,0.0002968893,0.00034350433],"domain_scores_gemma":[0.9993933,0.00015069003,0.00007407199,0.00025654145,0.00004416867,0.00008123586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015111301,0.00012984806,0.00010815483,0.00007277985,0.0005470227,0.00006697143,0.00047026947,0.00002942973,0.00017084251],"category_scores_gemma":[0.000006968635,0.00011469811,0.000040998042,0.00052728836,0.00003228117,0.00014826961,0.0003996469,0.0008409831,0.000038204536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012435768,0.0000319403,0.0014303518,0.0000019769936,0.0000044523363,0.00005791418,0.00012703224,0.38984373,0.00011186976,0.000016652848,0.00002592308,0.60833573],"study_design_scores_gemma":[0.00036955392,0.00056402956,0.0040845126,0.0000064634787,0.0000071925692,0.000756108,0.00007832238,0.9918987,0.00013828669,0.0011272813,0.0006911257,0.0002783935],"about_ca_topic_score_codex":0.000008050819,"about_ca_topic_score_gemma":0.000007643485,"teacher_disagreement_score":0.7970145,"about_ca_system_score_codex":0.000047725847,"about_ca_system_score_gemma":0.000008994073,"threshold_uncertainty_score":0.4677253},"labels":[],"label_agreement":null},{"id":"W3166166866","doi":"10.1109/icip42928.2021.9506372","title":"CALTEC: Content-Adaptive Linear Tensor Completion For Collaborative Intelligence","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Imperfect; Cloud computing; Tensor (intrinsic definition); Enhanced Data Rates for GSM Evolution; Feature (linguistics); Artificial intelligence; Network packet; Channel (broadcasting); Content (measure theory); Data mining; Pattern recognition (psychology); Machine learning; Mathematics; Computer network","score_opus":0.15053531611482657,"score_gpt":0.3340665752551191,"score_spread":0.18353125914029256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166166866","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":[0.00036234502,0.00036620983,0.9929188,0.00247152,0.00058901496,0.0021543382,0.00009894959,0.0003593723,0.0006794443],"genre_scores_gemma":[0.11638983,0.00018085251,0.8798978,0.0009131193,0.00020449545,0.0013560859,0.00015712145,0.000029038794,0.000871709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751955,0.0001007521,0.000514824,0.0011987358,0.00029351795,0.00037260042],"domain_scores_gemma":[0.9960403,0.00052053994,0.00035681247,0.0011628863,0.0017722662,0.00014720904],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016554708,0.0003555352,0.0004924812,0.00008916899,0.00022112577,0.00020053243,0.0013213967,0.00020785707,0.000020002346],"category_scores_gemma":[0.00013388961,0.00033949057,0.00017356523,0.0005984842,0.00012237208,0.00026427215,0.0014830037,0.00047753405,0.000031623258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077313634,0.00031178683,0.00004417346,0.00011792033,0.00018654838,0.000018456249,0.001210862,0.09515219,0.0017570229,0.8277725,0.002383853,0.07096737],"study_design_scores_gemma":[0.00020199078,0.00015700527,0.00012702147,0.00012425917,0.000026631295,0.000008936186,0.0006244031,0.95417076,0.012263334,0.025979893,0.00567021,0.0006455708],"about_ca_topic_score_codex":0.00003927685,"about_ca_topic_score_gemma":0.00006787192,"teacher_disagreement_score":0.85901856,"about_ca_system_score_codex":0.00016308414,"about_ca_system_score_gemma":0.00029251224,"threshold_uncertainty_score":0.9999057},"labels":[],"label_agreement":null},{"id":"W3166245539","doi":"","title":"Detection and Segmentation of Elements in Traffic: A Survey","year":2021,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Computer science; Geography; Transport engineering; Artificial intelligence; Engineering","score_opus":0.02752350237943528,"score_gpt":0.26210571964275065,"score_spread":0.23458221726331535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166245539","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":[0.50058496,0.0010857403,0.49486065,0.0019328082,0.00016636329,0.0005355963,0.000029492587,0.00005500451,0.0007493773],"genre_scores_gemma":[0.8532738,0.0017702545,0.14327967,0.000042888765,0.000010700139,0.00012722937,0.0002725598,0.00002836352,0.0011945136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98870665,0.008293088,0.0010289929,0.0011301959,0.0004461396,0.0003949474],"domain_scores_gemma":[0.99304044,0.0022287576,0.0008976282,0.0016852556,0.001982838,0.00016509753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0058262562,0.000348931,0.0004654706,0.00022496238,0.0003540539,0.0002741049,0.0009958204,0.0002529217,0.000037439528],"category_scores_gemma":[0.00096758216,0.0004401804,0.00011478871,0.0013406951,0.00028209333,0.00047861482,0.0019124481,0.00058307545,0.000006365703],"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":[0.000031915,0.0016055808,0.021430794,0.00036361508,0.000102804996,0.0000096042995,0.027391115,0.007520741,0.03440846,0.046967424,0.00004043328,0.8601275],"study_design_scores_gemma":[0.0015171433,0.0000021934088,0.30124757,0.0025335737,0.000052121613,0.000035715217,0.0003735913,0.55670756,0.13249527,0.0024991753,0.0016984529,0.00083761336],"about_ca_topic_score_codex":0.0023704716,"about_ca_topic_score_gemma":0.03457308,"teacher_disagreement_score":0.8592899,"about_ca_system_score_codex":0.00019236658,"about_ca_system_score_gemma":0.0002407195,"threshold_uncertainty_score":0.999805},"labels":[],"label_agreement":null},{"id":"W3167788848","doi":"10.1109/cvpr46437.2021.01001","title":"DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":231,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Generator (circuit theory); Artificial intelligence; Code (set theory); Face (sociological concept); Pixel; Object (grammar); Pattern recognition (psychology); Factory (object-oriented programming); Computer vision; Power (physics); Set (abstract data type)","score_opus":0.05134722377062128,"score_gpt":0.3075878291716737,"score_spread":0.2562406054010524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167788848","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":[0.074295245,0.00010558166,0.92067415,0.0011280035,0.00008961348,0.00026540796,0.00021283975,0.00039423822,0.0028349352],"genre_scores_gemma":[0.78660685,0.00000594238,0.20951515,0.00088288466,0.000081867875,0.00002593702,0.0018958295,0.000017503526,0.0009680408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853706,0.00002146833,0.00016834727,0.000745959,0.00026074404,0.00026643183],"domain_scores_gemma":[0.99676985,0.00006800242,0.0000572065,0.0029337928,0.00006252459,0.00010865202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008305536,0.00012832659,0.00012715785,0.000028634044,0.00019642193,0.000096323485,0.001727602,0.000026467062,0.00007228712],"category_scores_gemma":[0.000009952984,0.000102015285,0.000014466699,0.0004939124,0.000054241023,0.00033729538,0.0014834692,0.0001187907,0.00009638789],"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":[0.000037734713,0.0020295272,0.005387651,0.00008189338,0.000224122,0.00090959715,0.0004462169,0.016750691,0.076085046,0.6320718,0.22868936,0.03728637],"study_design_scores_gemma":[0.0027693142,0.00033454338,0.019049581,0.00006861505,0.00007343585,0.00041382844,0.00012406798,0.40515617,0.08417379,0.0014345422,0.48456222,0.0018399042],"about_ca_topic_score_codex":0.0000064673122,"about_ca_topic_score_gemma":0.00007665313,"teacher_disagreement_score":0.71231157,"about_ca_system_score_codex":0.00002075408,"about_ca_system_score_gemma":0.00007796423,"threshold_uncertainty_score":0.41600624},"labels":[],"label_agreement":null},{"id":"W3168534128","doi":"10.1016/j.cmpb.2021.106230","title":"MHSU-Net: A more versatile neural network for medical image segmentation","year":2021,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Carleton University","funders":"Natural Science Foundation of Jiangxi Province; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Pooling; Block (permutation group theory); Context (archaeology); Segmentation; Convolutional neural network; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Image segmentation; Net (polyhedron); Convolution (computer science); Feature extraction; Data mining; Artificial neural network; Mathematics","score_opus":0.04390738990284225,"score_gpt":0.3931053984382085,"score_spread":0.34919800853536626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168534128","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":[0.0028779868,0.0012865535,0.9862366,0.0082131745,0.0006749541,0.00056220614,0.0000016522431,0.00012471105,0.000022211949],"genre_scores_gemma":[0.0024542278,0.00012881437,0.9944001,0.0020010874,0.00069344183,0.00020497097,0.00007984925,0.000013890184,0.00002366081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981893,0.00018828301,0.0003927778,0.00058197515,0.00025186958,0.000395828],"domain_scores_gemma":[0.9986384,0.0005572829,0.00009936897,0.00040024382,0.00010261064,0.00020211778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009175756,0.00017542302,0.00029861717,0.000072360956,0.00011798552,0.000090964044,0.00039943034,0.00009278097,0.000009006643],"category_scores_gemma":[0.000046852998,0.00015118565,0.000053511892,0.0010361084,0.00018281915,0.00020273909,0.00035564028,0.00018524581,9.902329e-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":[0.000008363676,0.00006254517,0.0005020611,0.000036957692,0.000012460353,0.00003243737,0.00035374428,0.00009918922,0.00044287049,0.0024171292,0.0009897294,0.9950425],"study_design_scores_gemma":[0.0016409095,0.00032472593,0.0023499716,0.000119457865,0.000015444852,0.00014363234,0.00005383018,0.9463237,0.00042873406,0.013876488,0.034474086,0.00024900652],"about_ca_topic_score_codex":0.000006269468,"about_ca_topic_score_gemma":0.0000070384303,"teacher_disagreement_score":0.99479353,"about_ca_system_score_codex":0.00002716494,"about_ca_system_score_gemma":0.0000493733,"threshold_uncertainty_score":0.61651725},"labels":[],"label_agreement":null},{"id":"W3169997198","doi":"10.1145/3447818.3460378","title":"Accelerating DNNs inference with predictive layer fusion","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Air Force Research Laboratory; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Government of Canada","keywords":"Inference; Computer science; Layer (electronics); Fusion; Artificial intelligence; Materials science","score_opus":0.037973381175804934,"score_gpt":0.2874358111529913,"score_spread":0.2494624299771864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169997198","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":[0.0124844,0.00002292178,0.9679984,0.0011032065,0.00003671871,0.0000939826,6.7869684e-7,0.00022501184,0.018034702],"genre_scores_gemma":[0.7434904,0.00001170438,0.25516364,0.0005320235,0.00003601426,0.00003056487,0.0000022559263,0.000004322349,0.000729077],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922985,0.000022040227,0.00010005591,0.00033004748,0.00015492883,0.00016307195],"domain_scores_gemma":[0.9992153,0.00013481351,0.000041254567,0.00040251398,0.00014545018,0.000060660383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031307063,0.00007957589,0.0000726332,0.000017900624,0.0001512326,0.000090383226,0.00030443375,0.000023052853,0.00004810338],"category_scores_gemma":[0.0000238452,0.000060858474,0.000014655755,0.0005191099,0.000020108622,0.00046847854,0.00029330564,0.00010841088,0.000034950266],"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":[0.000008495065,0.00013305759,0.00597638,0.00000788978,0.000020167754,0.0000583867,0.00080023275,0.01893799,0.032561496,0.84619945,0.0011793842,0.094117075],"study_design_scores_gemma":[0.000599102,0.000215056,0.015692301,0.00006362084,0.000009809507,0.00009304684,0.00019498401,0.80654186,0.14852922,0.019281214,0.008247699,0.00053207274],"about_ca_topic_score_codex":0.000003555983,"about_ca_topic_score_gemma":0.000035553334,"teacher_disagreement_score":0.82691824,"about_ca_system_score_codex":0.000018191357,"about_ca_system_score_gemma":0.00007174885,"threshold_uncertainty_score":0.24817365},"labels":[],"label_agreement":null},{"id":"W3170809304","doi":"10.48550/arxiv.2106.06401","title":"Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous\\n Distributed Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Asynchronous communication; Computer science; Greedy algorithm; Asynchronous learning; Artificial intelligence; Theoretical computer science; Distributed computing; Algorithm; Computer network; Mathematics; Synchronous learning; Cooperative learning; Mathematics education; Teaching method","score_opus":0.03883672623548102,"score_gpt":0.2018811241617418,"score_spread":0.1630443979262608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170809304","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":[0.27370644,0.00019446161,0.7253173,0.000050756993,0.00009813361,0.00036036893,0.000012800315,0.00017890267,0.00008082674],"genre_scores_gemma":[0.98145896,0.000444065,0.017690085,0.000015153032,0.00005008013,0.000007779328,0.00013180291,0.000026125086,0.00017596045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798685,0.00011976354,0.0002762555,0.0011496225,0.00007932391,0.00038817487],"domain_scores_gemma":[0.9978627,0.00046963818,0.00048645842,0.000737015,0.00028689715,0.00015726991],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000183519,0.00030545983,0.00046917173,0.00014168792,0.00033935244,0.00009678461,0.0009461122,0.000230166,0.000009977988],"category_scores_gemma":[0.0001322256,0.00038866917,0.0001855395,0.0006072635,0.00014872973,0.0003056406,0.0018555567,0.0007185536,0.0000036037636],"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":[0.000017639952,0.000061098006,0.0050239754,0.00014764607,0.000088080844,0.000040011022,0.00017748124,0.94711775,0.00031834358,0.044125408,0.000016368022,0.0028662179],"study_design_scores_gemma":[0.0005101042,0.000115089046,0.0019184395,0.00012001448,0.00008303649,0.000009519253,0.00017361576,0.98875874,0.0003309738,0.007137248,0.00044076864,0.0004024361],"about_ca_topic_score_codex":0.000043772216,"about_ca_topic_score_gemma":0.000039235667,"teacher_disagreement_score":0.7077525,"about_ca_system_score_codex":0.00017732622,"about_ca_system_score_gemma":0.0001898873,"threshold_uncertainty_score":0.99985653},"labels":[],"label_agreement":null},{"id":"W3170952646","doi":"10.1109/tcsvt.2021.3087002","title":"CATFPN: Adaptive Feature Pyramid With Scale-Wise Concatenation and Self-Attention","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; Key Laboratory of System Control and Information Processing; State Key Laboratory of Integrated Services Networks; National Natural Science Foundation of China","keywords":"Pyramid (geometry); Feature (linguistics); Concatenation (mathematics); Computer science; Artificial intelligence; Pattern recognition (psychology); Block (permutation group theory); Object detection; Backbone network; Feature extraction; Benchmark (surveying); Context (archaeology); Computer vision; Mathematics","score_opus":0.013627417906207341,"score_gpt":0.2313012032247169,"score_spread":0.21767378531850956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170952646","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":[0.030320136,0.0007262137,0.96523917,0.0021775106,0.00024507227,0.0007327566,0.000027405149,0.00048360418,0.000048136117],"genre_scores_gemma":[0.9935828,0.00020790119,0.005293759,0.00007920763,0.000021556802,0.00053736294,0.000003594869,0.000018281315,0.00025553876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998797,0.000036630045,0.00019318012,0.00059509283,0.00013354617,0.0002445747],"domain_scores_gemma":[0.9990497,0.000103303646,0.00010115585,0.00040324594,0.00026857544,0.000073967065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009374152,0.00017444593,0.00023675695,0.00021062032,0.00038791748,0.00009164762,0.00017583522,0.00018914061,4.9789554e-7],"category_scores_gemma":[0.000003389664,0.00015563375,0.00003640587,0.00066938123,0.00011474201,0.00029877815,0.000004442126,0.00022798825,0.0000027387416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093895986,0.0012446981,0.00064506754,0.00061819545,0.0006893659,0.000104492436,0.0012506181,0.015000918,0.073546395,0.3039253,0.0006541362,0.6022269],"study_design_scores_gemma":[0.010765902,0.0046114554,0.0032794466,0.0010650229,0.000693712,0.007999116,0.0031362884,0.7889128,0.12007074,0.02120892,0.03513677,0.0031198617],"about_ca_topic_score_codex":0.000008543089,"about_ca_topic_score_gemma":0.00009097209,"teacher_disagreement_score":0.9632627,"about_ca_system_score_codex":0.000064717104,"about_ca_system_score_gemma":0.000053282572,"threshold_uncertainty_score":0.634656},"labels":[],"label_agreement":null},{"id":"W3171439159","doi":"10.1109/access.2021.3086321","title":"Substituting Convolutions for Neural Network Compression","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Staatssekretariat für Bildung, Forschung und Innovation; Engineering and Physical Sciences Research Council; European Commission","keywords":"Computer science; Pointwise; Leverage (statistics); Hyperparameter; Convolutional neural network; Artificial neural network; Network architecture; Data compression ratio; Simple (philosophy); Pareto principle; Computer engineering; Artificial intelligence; Machine learning; Mathematical optimization; Image compression; Mathematics; Computer network","score_opus":0.06417957861793859,"score_gpt":0.3479631749883805,"score_spread":0.28378359637044187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171439159","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":[0.018718228,0.0003826448,0.9772847,0.0013231925,0.0012073211,0.00026833455,0.0000048635784,0.00024955452,0.0005611513],"genre_scores_gemma":[0.9259151,0.000022391585,0.072047815,0.0010422125,0.0005891734,0.00015779691,0.000015199102,0.000011866122,0.00019843447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988708,0.000037868354,0.00020561015,0.0004042206,0.00012459767,0.00035694192],"domain_scores_gemma":[0.9987366,0.00033553742,0.0000994227,0.0005730808,0.00016936856,0.000085978645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007501237,0.00010848589,0.00013246697,0.000022860504,0.0005139609,0.00022126078,0.0009778364,0.000042603217,0.0000066275825],"category_scores_gemma":[0.000032810123,0.00010944071,0.00006685287,0.00065111695,0.00004205866,0.00079575303,0.00029647537,0.000117721305,0.00001194424],"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":[0.0000087643775,0.00008530793,0.002316329,0.000025806397,0.000019482051,0.000021083895,0.000063327956,0.74841154,0.006595126,0.1798035,0.029977659,0.032672055],"study_design_scores_gemma":[0.00047864855,0.00001618351,0.0034027568,0.000044408167,0.0000132193545,0.00004062237,0.0000048537413,0.9007111,0.01524281,0.03919825,0.04053021,0.0003169068],"about_ca_topic_score_codex":0.000003974604,"about_ca_topic_score_gemma":0.000019341944,"teacher_disagreement_score":0.9071969,"about_ca_system_score_codex":0.000026709691,"about_ca_system_score_gemma":0.000055387638,"threshold_uncertainty_score":0.44628626},"labels":[],"label_agreement":null},{"id":"W3174164794","doi":"10.1109/cvpr46437.2021.01027","title":"QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":238,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Leverage (statistics); Pairwise comparison; Object detection; Extractor; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Data mining","score_opus":0.013228717949087093,"score_gpt":0.26055283239431437,"score_spread":0.24732411444522728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174164794","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":[0.019317856,0.0000068554805,0.9755837,0.0010479504,0.00011629868,0.00021988641,0.0000012268259,0.00045528196,0.003250971],"genre_scores_gemma":[0.93338645,0.0000024220021,0.064844236,0.0014017458,0.000040949424,0.000093498114,0.000030373505,0.000007886238,0.0001924554],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989974,0.00006109553,0.00026293812,0.00026620584,0.00021601946,0.00019634022],"domain_scores_gemma":[0.9988125,0.0001656141,0.00014486234,0.0004972529,0.0003095309,0.00007026423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008784787,0.00013701951,0.000113750575,0.00010083309,0.0002446425,0.00023136815,0.0002267781,0.000044565048,0.00004211475],"category_scores_gemma":[0.000052445164,0.000119977565,0.000048018017,0.0005991349,0.000032008506,0.0027498382,0.00007442132,0.00017824324,0.00013374184],"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":[0.0002073171,0.00049415487,0.0018639796,0.00011277482,0.00009772032,0.000069163005,0.0013074264,0.039756708,0.16070183,0.05585022,0.00624084,0.7332979],"study_design_scores_gemma":[0.0018064867,0.000414022,0.0063319467,0.000065784305,0.0000227431,0.00012342323,0.00039836077,0.30506918,0.64847577,0.002544474,0.034076374,0.00067141536],"about_ca_topic_score_codex":0.00003338492,"about_ca_topic_score_gemma":0.00036027053,"teacher_disagreement_score":0.9140686,"about_ca_system_score_codex":0.000107107386,"about_ca_system_score_gemma":0.000077278346,"threshold_uncertainty_score":0.48925433},"labels":[],"label_agreement":null},{"id":"W3174700868","doi":"10.1609/aaai.v35i9.16978","title":"Linearly Replaceable Filters for Deep Network Channel Pruning","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Electronics and Telecommunications Research Institute","keywords":"FLOPS; Computer science; Pruning; Convolutional neural network; Deep learning; Filter (signal processing); Algorithm; Artificial intelligence; Compensation (psychology); Channel (broadcasting); Computer engineering; Pattern recognition (psychology); Parallel computing; Computer vision; Telecommunications","score_opus":0.08086057145799233,"score_gpt":0.3017327517650332,"score_spread":0.2208721803070409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174700868","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":[0.008194929,0.00010054442,0.97571874,0.008698101,0.00060231984,0.00081285305,0.0000053922267,0.00018512673,0.005681976],"genre_scores_gemma":[0.8994915,0.000064395055,0.0981005,0.00080238713,0.0002904938,0.00019491116,0.0000021224685,0.00002487612,0.0010288341],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980339,0.000014437479,0.0004990406,0.0006509791,0.00031086107,0.00049078034],"domain_scores_gemma":[0.99782956,0.0002475919,0.00038453867,0.0004760882,0.0009634201,0.00009879961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003597766,0.00020958247,0.00025492822,0.000048986276,0.0003976085,0.00024707912,0.0016576175,0.00008248169,0.000020136888],"category_scores_gemma":[0.00046256892,0.00017837746,0.00014098249,0.0010622651,0.00013856651,0.0004337675,0.00047168505,0.00025840188,0.000029203266],"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":[0.000037846272,0.00007212619,0.00002582078,0.000029593231,0.000014441189,4.71783e-7,0.0005619352,0.0066347546,0.012732546,0.94872814,0.00080133526,0.030360973],"study_design_scores_gemma":[0.00002460764,0.000085760454,0.000016429049,0.000120884346,0.000008971165,0.000006294314,0.00015936226,0.4083788,0.2786322,0.31143993,0.0009504916,0.0001762656],"about_ca_topic_score_codex":0.000004565074,"about_ca_topic_score_gemma":0.000011217622,"teacher_disagreement_score":0.89129657,"about_ca_system_score_codex":0.000047756777,"about_ca_system_score_gemma":0.00010234533,"threshold_uncertainty_score":0.72740215},"labels":[],"label_agreement":null},{"id":"W3174882114","doi":"10.1109/icaibd51990.2021.9458968","title":"Applying Deep Learning to Autonomous Vehicles: A Survey","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Deep learning; Automotive industry; Motion planning; Computer science; Sensor fusion; Artificial intelligence; Fault (geology); Control (management); Engineering; Systems engineering; Control engineering; Robot","score_opus":0.02933757563918616,"score_gpt":0.28013970435599606,"score_spread":0.2508021287168099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174882114","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":[0.010127774,0.00006942998,0.9854925,0.000683556,0.000068755886,0.00019021746,3.25773e-7,0.00038282198,0.002984614],"genre_scores_gemma":[0.69671863,0.000009804887,0.29864553,0.001714539,0.000041951753,0.00022170175,0.0000057222405,0.000011817003,0.0026303418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896866,0.00008909909,0.00014501148,0.00041524778,0.00013025214,0.00025174665],"domain_scores_gemma":[0.99901456,0.00026693539,0.00003245174,0.0004586308,0.000101895144,0.00012553534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017150676,0.00008609147,0.00010119635,0.00003582618,0.0001831951,0.00011738049,0.00045655272,0.00002630046,0.00002379569],"category_scores_gemma":[0.0001053429,0.000088927045,0.000028163086,0.0009256105,0.000009700723,0.00018936943,0.00042144864,0.00013810152,0.00036212482],"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":[0.0000024900164,0.00005454502,0.0053816084,0.0000048434486,0.000011262078,0.000032456348,0.00051990966,0.07781442,0.0074161696,0.045845024,0.00080734235,0.8621099],"study_design_scores_gemma":[0.00036100604,0.00007712179,0.060847193,0.000018841312,0.0000047968856,0.00008172134,0.00018045759,0.7021738,0.017295016,0.0054397946,0.2127401,0.0007801283],"about_ca_topic_score_codex":0.00002194786,"about_ca_topic_score_gemma":0.00013596077,"teacher_disagreement_score":0.8613298,"about_ca_system_score_codex":0.00003767565,"about_ca_system_score_gemma":0.000039966482,"threshold_uncertainty_score":0.4654503},"labels":[],"label_agreement":null},{"id":"W3174904875","doi":"10.1155/2021/1060182","title":"ShipYOLO: An Enhanced Model for Ship Detection","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":52,"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":"Fundamental Research Funds for the Central Universities","keywords":"Marine engineering; Computer science; Aeronautics; Environmental science; Operations research; Transport engineering; Engineering","score_opus":0.02443507060500659,"score_gpt":0.2924806647850641,"score_spread":0.2680455941800575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174904875","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":[0.17762147,0.000093749135,0.8215443,0.0002788813,0.00026062405,0.0001401174,0.000004598225,0.000042890846,0.000013367723],"genre_scores_gemma":[0.65207696,0.000054935645,0.34759888,0.00010903981,0.000085812,0.000021433681,0.000011041703,0.000010305469,0.00003159948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987943,0.000026268912,0.00050721556,0.0002528782,0.00023316176,0.00018615498],"domain_scores_gemma":[0.9984371,0.00009947998,0.00042984023,0.0002728694,0.00064510433,0.000115625575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014768585,0.00011768106,0.00018896574,0.000081238555,0.00011901031,0.000036665147,0.00030534773,0.000057606292,0.0000018990171],"category_scores_gemma":[0.000036784106,0.00012002063,0.00012864447,0.00038671942,0.00001691192,0.0018523593,0.000003447232,0.00016203438,9.5324765e-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":[0.00006599841,0.00006570203,0.0000080606305,0.00001209228,0.000009118394,0.0000052743007,0.00074383075,0.6624619,0.25108063,0.006656239,0.000004321863,0.078886844],"study_design_scores_gemma":[0.0019043545,0.0004541224,0.006520574,0.00005204523,0.000054942353,0.00004396175,0.00022208126,0.34285596,0.5140447,0.13289677,0.00061197806,0.00033852388],"about_ca_topic_score_codex":2.298529e-7,"about_ca_topic_score_gemma":0.000080188955,"teacher_disagreement_score":0.47445548,"about_ca_system_score_codex":0.0000522739,"about_ca_system_score_gemma":0.00010623686,"threshold_uncertainty_score":0.48942992},"labels":[],"label_agreement":null},{"id":"W3176068477","doi":"10.1109/cvprw53098.2021.00277","title":"Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quantization (signal processing); Convolutional neural network; Computer science; Artificial intelligence; Algorithm","score_opus":0.01823582138482233,"score_gpt":0.2988817446018015,"score_spread":0.2806459232169792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176068477","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":[0.08829943,0.00091621117,0.9090017,0.0008156498,0.00017858842,0.0004056158,0.000010601905,0.00006126609,0.00031091282],"genre_scores_gemma":[0.98691523,0.00021187474,0.012298825,0.0002212951,0.00004938766,0.000089511326,0.00014133782,0.000011938745,0.000060588423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809307,0.00033014166,0.0004563625,0.00044441412,0.00041593183,0.000260102],"domain_scores_gemma":[0.9968482,0.001780523,0.00029434153,0.00077698246,0.00025462426,0.000045316647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024612123,0.00020174947,0.00025792877,0.000027664604,0.00033175995,0.000051576706,0.0007602693,0.00008844837,0.00001129844],"category_scores_gemma":[0.00008817728,0.00012025879,0.00013408429,0.00084545085,0.00027165955,0.00028863843,0.00029787584,0.00022888201,0.0000060444804],"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":[0.000030457406,0.0001048058,0.0027609302,0.000012764076,0.00004662741,0.0000013657819,0.00044435286,0.59781253,0.0010651989,0.39551315,0.00025004224,0.0019577574],"study_design_scores_gemma":[0.00044116782,0.00012431425,0.003003057,0.000043703356,0.00002169273,0.000007322323,0.0000808541,0.97673833,0.011187596,0.007630852,0.00058218604,0.00013890275],"about_ca_topic_score_codex":0.00005666233,"about_ca_topic_score_gemma":0.00024381894,"teacher_disagreement_score":0.89861584,"about_ca_system_score_codex":0.00008317264,"about_ca_system_score_gemma":0.00006867251,"threshold_uncertainty_score":0.49040112},"labels":[],"label_agreement":null},{"id":"W3176239531","doi":"10.48550/arxiv.2103.04813","title":"Boosting Semi-supervised Image Segmentation with Global and Local Mutual\\n Information Regularization","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Feature learning; Pattern recognition (psychology); Mutual information; Scale-space segmentation; Cluster analysis; Segmentation-based object categorization; Encoder; Image segmentation; Boosting (machine learning); Regularization (linguistics)","score_opus":0.020234564661472013,"score_gpt":0.17496034951506445,"score_spread":0.15472578485359245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176239531","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":[0.079167604,0.000010223418,0.9192283,0.000164088,0.000024564615,0.00011832544,0.0000035590826,0.00012274215,0.0011605767],"genre_scores_gemma":[0.94957507,0.000024449682,0.050001416,0.00022058883,0.000012375639,8.941239e-7,0.000040385265,0.0000034868128,0.00012133214],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936604,0.00003998495,0.00010105006,0.00028220232,0.00006484788,0.00014588254],"domain_scores_gemma":[0.99936396,0.000036677244,0.00008420488,0.00027727662,0.00016252426,0.00007538362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004475287,0.00009455559,0.00007421857,0.000034737863,0.00018355453,0.000093323935,0.00017646495,0.000038198654,0.0000049804694],"category_scores_gemma":[0.000012697165,0.000104740095,0.000016956235,0.0009757705,0.00006627916,0.0023393643,0.00015440957,0.00006251237,0.000020379643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006360561,0.00007364295,0.0086385105,0.00005355908,0.00004318367,0.000146925,0.0006217811,0.25169054,0.0030288668,0.6652345,0.00022268061,0.07018221],"study_design_scores_gemma":[0.0006274461,0.00003362715,0.0023741107,0.000016669905,0.000016281827,0.00003804153,0.00028602078,0.9853025,0.002182423,0.008741425,0.00022172074,0.00015974564],"about_ca_topic_score_codex":0.0000062803842,"about_ca_topic_score_gemma":0.000015851843,"teacher_disagreement_score":0.87040746,"about_ca_system_score_codex":0.00010501768,"about_ca_system_score_gemma":0.0000540801,"threshold_uncertainty_score":0.4271177},"labels":[],"label_agreement":null},{"id":"W3177177219","doi":"10.1109/tai.2021.3081057","title":"Robust Vehicle Detection in High-Resolution Aerial Images With Imbalanced Data","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Context (archaeology); Class (philosophy); Computer vision; Detector; Intersection (aeronautics); Feature (linguistics); Aerial image; Flexibility (engineering); Image (mathematics); Mathematics; Geography","score_opus":0.06558423746214823,"score_gpt":0.28720486041413296,"score_spread":0.22162062295198473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177177219","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":[0.018675605,0.000025689642,0.9792618,0.0008702302,0.00063800707,0.00024260543,0.000029111177,0.00021282761,0.00004410948],"genre_scores_gemma":[0.9377446,0.000055481774,0.061848838,0.000111412635,0.00009518631,0.000073963136,0.000009399547,0.000016241815,0.000044854318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980684,0.00010440973,0.00037151363,0.00083012186,0.0002782576,0.00034729383],"domain_scores_gemma":[0.9982607,0.00016218881,0.0000901366,0.0012700593,0.00012932847,0.00008758976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015253799,0.00018211335,0.00017482703,0.00013106698,0.00034312427,0.00018980827,0.0007763444,0.00007954798,0.000032207157],"category_scores_gemma":[0.000014411082,0.00018562145,0.00003588948,0.0015077644,0.00012717264,0.0008837142,0.00001590014,0.00035985972,0.00009296813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008754445,0.00026956626,0.0000021040516,0.000004527814,0.0000090589765,0.000025813319,0.00007752448,0.65742844,0.056411393,0.0030170865,0.000010451933,0.2826565],"study_design_scores_gemma":[0.00006186085,0.00007399377,0.00006129458,0.00002076105,0.0000071500913,0.000018013745,0.000043758057,0.41711164,0.5793094,0.003056283,0.00006615241,0.00016967313],"about_ca_topic_score_codex":0.00013200601,"about_ca_topic_score_gemma":0.0030932245,"teacher_disagreement_score":0.919069,"about_ca_system_score_codex":0.00010926725,"about_ca_system_score_gemma":0.00011195072,"threshold_uncertainty_score":0.7569423},"labels":[],"label_agreement":null},{"id":"W3177451412","doi":"10.1109/ipdps49936.2021.00068","title":"EAGLE: Expedited Device Placement with Automatic Grouping for Large Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Huawei Technologies; Louisiana Board of Regents","keywords":"Computer science; Reinforcement learning; Dimension (graph theory); State (computer science); Artificial neural network; Artificial intelligence; Deep learning; Performance improvement; Algorithm; Engineering","score_opus":0.0358507021015743,"score_gpt":0.2877214875099629,"score_spread":0.2518707854083886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177451412","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":[0.0031595537,0.000072378505,0.99337924,0.0012778542,0.000062782114,0.0003834775,0.0000024901844,0.0003670181,0.001295194],"genre_scores_gemma":[0.27592203,0.000006394587,0.72107655,0.0016405872,0.000059703747,0.0003849971,0.000014442342,0.000012920821,0.00088233675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989712,0.000018293613,0.00017154645,0.00037620362,0.00017159493,0.00029113473],"domain_scores_gemma":[0.9990323,0.00018376303,0.00006238762,0.0005214738,0.00012663043,0.00007345507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079251884,0.000107696986,0.00011649138,0.000029330611,0.0001691647,0.00007623174,0.0003388572,0.000024539255,0.000021736585],"category_scores_gemma":[0.000010998907,0.00008980595,0.000029278493,0.00047928919,0.00000784805,0.0005006102,0.0001751042,0.000054048745,0.000015754094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000077879895,0.00024002758,0.000040423365,0.00006886877,0.000045047353,0.000015008981,0.00067480205,0.056611452,0.00096359005,0.91847605,0.005532573,0.017324358],"study_design_scores_gemma":[0.00046584854,0.000034037723,0.00002643813,0.000021428348,0.0000058465353,0.0000137264215,0.00008151538,0.98618627,0.0021334642,0.0064654076,0.004424113,0.00014191205],"about_ca_topic_score_codex":0.0000010721961,"about_ca_topic_score_gemma":0.00002021602,"teacher_disagreement_score":0.9295748,"about_ca_system_score_codex":0.00003718311,"about_ca_system_score_gemma":0.000054296994,"threshold_uncertainty_score":0.36621803},"labels":[],"label_agreement":null},{"id":"W3177672515","doi":"10.1109/tcad.2020.3031240","title":"Enhancing the Utilization of Processing Elements in Spatial Deep Neural Network Accelerators","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Dataflow; Speedup; Computation; Pruning; Bandwidth (computing); Overhead (engineering); Artificial neural network; Memory bandwidth; Efficient energy use; Computer engineering; Computer architecture; Distributed computing; Embedded system; Parallel computing; Artificial intelligence; Computer network; Algorithm; Engineering","score_opus":0.06024876581410656,"score_gpt":0.26575548405747257,"score_spread":0.205506718243366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177672515","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":[0.011088074,0.00026342276,0.98727053,0.00011237026,0.00036867033,0.00079655444,0.0000047663793,0.00008522043,0.000010370118],"genre_scores_gemma":[0.9927108,0.000040542185,0.006936181,0.00014559328,0.000078196215,0.00006723597,0.0000020742063,0.000016814442,0.0000025130737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978213,0.0003091116,0.00089031656,0.00042595813,0.00028302774,0.00027033992],"domain_scores_gemma":[0.9987272,0.00029143607,0.00039369173,0.00028743327,0.00020912001,0.000091087735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032466426,0.0002216885,0.000372736,0.000117253425,0.00018667891,0.00010694922,0.000523827,0.00008283655,0.000002416398],"category_scores_gemma":[0.0000072017674,0.00017078921,0.000054911863,0.0013217498,0.00006508447,0.0003351184,0.0000064730693,0.00027893722,0.0000011492306],"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":[0.000016303662,0.00005467232,0.000043524487,0.000061495324,0.000016819176,0.000002039655,0.00069143815,0.7760521,0.0066947103,0.00031682197,0.000013137747,0.21603692],"study_design_scores_gemma":[0.00035778084,0.0002737986,0.00010887943,0.00017927188,0.000013037745,0.000009647166,0.00010102716,0.9847296,0.013981747,0.000080938575,0.000020593832,0.00014368107],"about_ca_topic_score_codex":0.00007396914,"about_ca_topic_score_gemma":0.00003155753,"teacher_disagreement_score":0.98162276,"about_ca_system_score_codex":0.000044167693,"about_ca_system_score_gemma":0.00007943707,"threshold_uncertainty_score":0.6964582},"labels":[],"label_agreement":null},{"id":"W3178812510","doi":"10.1109/icicsp55539.2022.10050624","title":"Transclaw U-Net: Claw U-Net With Transformers for Medical Image Segmentation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":97,"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":"Claw; Net (polyhedron); Image segmentation; Artificial intelligence; Computer science; Segmentation; Computer vision; Mathematics; Engineering; Geometry","score_opus":0.016721598034785387,"score_gpt":0.2993248054391024,"score_spread":0.282603207404317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178812510","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":[0.00046117188,0.00006790432,0.9793614,0.009983559,0.00029440413,0.002247397,0.00007530029,0.00047270197,0.0070361756],"genre_scores_gemma":[0.05886443,0.0003484371,0.927507,0.0036112675,0.00026179137,0.0066006915,0.0008190379,0.00010418361,0.0018831589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707097,0.00007048985,0.00047367,0.0010550283,0.00087358465,0.00045627676],"domain_scores_gemma":[0.9986116,0.00025445953,0.0001644665,0.0006338607,0.00008433639,0.00025126745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031472932,0.00035842348,0.00034562786,0.00010545863,0.000304689,0.00013610953,0.0015266939,0.0001933595,0.00065688125],"category_scores_gemma":[0.0000078477415,0.00031108825,0.0001844445,0.00038111146,0.0001440897,0.00037545062,0.00027469383,0.000756518,0.000010497588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042628762,0.0008592926,0.00006880618,0.00094090856,0.00053012924,0.00007731813,0.0031090986,0.032735817,0.003712029,0.44367126,0.034065153,0.47980392],"study_design_scores_gemma":[0.004868128,0.0012005473,0.00018865953,0.00017855893,0.0002380255,0.00013475743,0.00043120538,0.6480642,0.013118234,0.0900581,0.23858567,0.0029339148],"about_ca_topic_score_codex":0.0000310102,"about_ca_topic_score_gemma":0.00015295517,"teacher_disagreement_score":0.6153284,"about_ca_system_score_codex":0.00014941159,"about_ca_system_score_gemma":0.00034017145,"threshold_uncertainty_score":0.99993414},"labels":[],"label_agreement":null},{"id":"W3178868030","doi":"10.1109/crv52889.2021.00025","title":"SGNet: A Super-class Guided Network for Image Classification and Object Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"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; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Inference; Class (philosophy); Computer science; Artificial intelligence; Object (grammar); Pattern recognition (psychology); Object detection; Image (mathematics); Machine learning; Contextual image classification; Exploit; Data mining","score_opus":0.03242305784321598,"score_gpt":0.2881669660721662,"score_spread":0.2557439082289502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178868030","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":[0.0042323703,0.00014401859,0.98991466,0.0028767043,0.00015743567,0.0003416421,0.0000018427482,0.00024006887,0.0020912609],"genre_scores_gemma":[0.44004562,0.00009079354,0.5569261,0.0011793592,0.00027969334,0.00031987493,0.000016254766,0.000017350878,0.0011249759],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989482,0.000039446033,0.00019916931,0.0004689948,0.00010031953,0.00024385029],"domain_scores_gemma":[0.9989988,0.00020281985,0.000059077476,0.00049348286,0.0001742047,0.00007160618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012598652,0.00010544973,0.0001096263,0.000027386035,0.00024946168,0.00013432854,0.00021207963,0.000052096366,0.000008087581],"category_scores_gemma":[0.00005382919,0.00010409354,0.00004367059,0.0005185847,0.00003452526,0.0004015167,0.00011601191,0.00007120963,0.000014173901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015548865,0.00008615662,0.00024151907,0.00003551304,0.000030567408,0.0000070058827,0.00017719317,0.0015906428,0.32250044,0.39078075,0.026166115,0.25836855],"study_design_scores_gemma":[0.0004993926,0.000067396926,0.0039200503,0.000010021535,0.0000135824175,0.00010619927,0.00004340868,0.8354698,0.046394236,0.038792185,0.074385375,0.0002983862],"about_ca_topic_score_codex":0.000004742017,"about_ca_topic_score_gemma":0.000071295915,"teacher_disagreement_score":0.8338791,"about_ca_system_score_codex":0.000037151403,"about_ca_system_score_gemma":0.00003771087,"threshold_uncertainty_score":0.42448112},"labels":[],"label_agreement":null},{"id":"W3181126643","doi":"10.1109/crv52889.2021.00030","title":"Multi-Resolution and Multi-Domain Analysis of Off-Road Datasets for Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Artificial intelligence; Convolutional neural network; Domain adaptation; Adaptation (eye); Pattern recognition (psychology); Class (philosophy); Local binary patterns; Domain (mathematical analysis); Flexibility (engineering); Computer vision; Image (mathematics); Histogram","score_opus":0.030919383806495584,"score_gpt":0.3104968772085717,"score_spread":0.27957749340207616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181126643","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":[0.015718373,0.00022094644,0.9833364,0.00035147485,0.000030932537,0.00017528757,0.00008421332,0.00006542606,0.000016929462],"genre_scores_gemma":[0.16069008,0.00003459932,0.838801,0.000114213144,0.000009429235,0.00003836215,0.00017175607,0.0000047968992,0.0001357648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990539,0.00003290805,0.00023186357,0.0004182961,0.000087138484,0.00017591316],"domain_scores_gemma":[0.9990762,0.00016134296,0.00009987012,0.00051937747,0.0000770762,0.000066142115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012247641,0.00008882198,0.00019213812,0.000107647546,0.00011512407,0.000036234193,0.0002619526,0.000033438577,0.0000062421655],"category_scores_gemma":[0.00005394591,0.00008748306,0.00006965524,0.00087508326,0.00003958279,0.00025804617,0.0002885903,0.000043957367,0.000001802746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011913503,0.0008839991,0.008324528,0.000057918453,0.0009988878,0.000017860875,0.00076587975,0.05607649,0.07240362,0.05418507,0.0019173224,0.8043565],"study_design_scores_gemma":[0.000296339,0.000009884652,0.020673985,0.0000037404247,0.000067535904,0.0000027683332,0.000019101415,0.97546345,0.0011405568,0.000115729075,0.0021099292,0.00009698741],"about_ca_topic_score_codex":0.000017424813,"about_ca_topic_score_gemma":0.00048773587,"teacher_disagreement_score":0.919387,"about_ca_system_score_codex":0.000024617346,"about_ca_system_score_gemma":0.000030755746,"threshold_uncertainty_score":0.35674554},"labels":[],"label_agreement":null},{"id":"W3181190968","doi":"10.1109/cvpr46437.2021.01236","title":"(AF)<sup>2</sup>-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":245,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Benchmark (surveying); Feature (linguistics); Feature extraction; Pattern recognition (psychology); Machine learning","score_opus":0.015663363865626733,"score_gpt":0.24963780381273243,"score_spread":0.2339744399471057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181190968","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":[0.005875118,0.00042732147,0.9883449,0.0033583168,0.00015728654,0.0011108943,0.000007900964,0.0003268158,0.0003914186],"genre_scores_gemma":[0.13116176,0.00015498031,0.8585852,0.001266237,0.0007377013,0.0004787687,0.00019270094,0.000053869313,0.0073687895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802595,0.000094779294,0.00018458812,0.0008768122,0.0003218567,0.00049601146],"domain_scores_gemma":[0.99856675,0.00020151799,0.0001708022,0.00046734785,0.00047107894,0.00012253271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011316341,0.00028755408,0.00023636525,0.00006225682,0.00053226104,0.0001567861,0.00033481442,0.00012792631,0.000016163864],"category_scores_gemma":[0.000014485687,0.0002420897,0.00009498583,0.001529712,0.00004120615,0.0007026093,0.00016403146,0.0003293681,0.000024399846],"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":[0.0004054186,0.000364729,0.0029742555,0.00008524145,0.00024890076,0.000029969315,0.0013425365,0.6278151,0.010625206,0.083381705,0.13722578,0.13550115],"study_design_scores_gemma":[0.0014517095,0.00040688348,0.0028996607,0.0001559115,0.00009768151,0.00016152652,0.00035815666,0.95762575,0.009989458,0.009068716,0.017109461,0.0006750664],"about_ca_topic_score_codex":0.000005428488,"about_ca_topic_score_gemma":0.00015129635,"teacher_disagreement_score":0.32981065,"about_ca_system_score_codex":0.0001263259,"about_ca_system_score_gemma":0.00011025023,"threshold_uncertainty_score":0.98721313},"labels":[],"label_agreement":null},{"id":"W3181228931","doi":"","title":"Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training.","year":2021,"lang":"en","type":"article","venue":"USENIX Annual Technical Conference","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Training (meteorology); Artificial neural network; Artificial intelligence; Machine learning","score_opus":0.036475680659231506,"score_gpt":0.27778259075908046,"score_spread":0.24130691009984895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181228931","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":[0.011936917,0.000087872875,0.9829063,0.003079794,0.00021822168,0.0005617175,0.000064523214,0.00073236384,0.0004123163],"genre_scores_gemma":[0.6521976,0.0000044456815,0.34620082,0.0010425681,0.00015916319,0.00024433265,0.00006647531,0.00001768724,0.000066922716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748904,0.000076390854,0.00049507595,0.0008447254,0.00041748898,0.0006772561],"domain_scores_gemma":[0.9972696,0.00090461015,0.0001722823,0.00071443856,0.0007055996,0.00023346928],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022023762,0.0002834638,0.000345753,0.000057718466,0.00038283505,0.00016513625,0.0011139793,0.00014804304,0.00002545032],"category_scores_gemma":[0.00019427823,0.00028911332,0.00015489859,0.0008326123,0.00020454383,0.0005464409,0.00032088938,0.00036847792,0.000024672461],"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":[0.00011302621,0.000370555,0.0024472403,0.00008412501,0.00004332988,0.00004080776,0.00043251063,0.54416823,0.0008482307,0.3142519,0.0071177403,0.13008231],"study_design_scores_gemma":[0.0006634011,0.00023174063,0.00961011,0.000040334144,0.000014566674,0.000047719175,0.000016970878,0.95903087,0.00026500307,0.0201667,0.009532932,0.00037965254],"about_ca_topic_score_codex":9.57748e-7,"about_ca_topic_score_gemma":0.000027441281,"teacher_disagreement_score":0.6402607,"about_ca_system_score_codex":0.000052679134,"about_ca_system_score_gemma":0.00035729457,"threshold_uncertainty_score":0.99995613},"labels":[],"label_agreement":null},{"id":"W3181445415","doi":"10.1016/j.comcom.2021.06.009","title":"A novel visibility semantic feature-aided pedestrian detection scheme for autonomous vehicles","year":2021,"lang":"en","type":"article","venue":"Computer Communications","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Computer science; Visibility; Pedestrian; Pedestrian detection; Detector; Scheme (mathematics); Artificial intelligence; Feature (linguistics); Object detection; Intelligent transportation system; Computer vision; Real-time computing; Machine learning; Pattern recognition (psychology); Telecommunications; Transport engineering","score_opus":0.05320120745418959,"score_gpt":0.3082202435480576,"score_spread":0.255019036093868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181445415","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":[0.0031836235,0.00052521314,0.9836568,0.011115482,0.00023447808,0.00059921073,0.000014122471,0.00051472476,0.00015630209],"genre_scores_gemma":[0.33791512,0.000051606043,0.6610317,0.00048647856,0.000101648,0.00023664987,0.000047809506,0.000015863083,0.00011309059],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984328,0.00012080799,0.00035950253,0.00061567064,0.00014721531,0.00032399088],"domain_scores_gemma":[0.9940395,0.0007621816,0.00017829602,0.0044805463,0.00041210683,0.00012741741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021865629,0.00019769368,0.00023536079,0.0000827604,0.0008005107,0.0002568887,0.0023647393,0.000099588586,0.000001693625],"category_scores_gemma":[0.00008137087,0.00022281043,0.00016349467,0.0008757098,0.00011434571,0.00046461067,0.0015324228,0.00031663658,0.000025274587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021427979,0.0018860028,0.00058844464,0.0001370186,0.00018680302,0.0000052997852,0.0010152938,0.0040948903,0.12638278,0.22908962,0.0035274448,0.633065],"study_design_scores_gemma":[0.0006387562,0.000051813873,0.004708098,0.000028368386,0.000018448458,0.00007736292,0.000012674091,0.93287337,0.005734153,0.011703063,0.043846194,0.0003076755],"about_ca_topic_score_codex":0.000012837441,"about_ca_topic_score_gemma":0.00018196004,"teacher_disagreement_score":0.9287785,"about_ca_system_score_codex":0.000119957665,"about_ca_system_score_gemma":0.00016349593,"threshold_uncertainty_score":0.90859455},"labels":[],"label_agreement":null},{"id":"W3181837204","doi":"10.1109/crv52889.2021.00013","title":"Sequential Fusion via Bounding Box and Motion PointPainting for 3D Objection Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lidar; Artificial intelligence; Computer science; Computer vision; Minimum bounding box; Bounding overwatch; Point cloud; Object detection; Benchmark (surveying); Segmentation; Detector; Pedestrian detection; Point (geometry); Focus (optics); Image fusion; Image (mathematics); Remote sensing; Mathematics; Engineering; Geography; Pedestrian","score_opus":0.016913816223799727,"score_gpt":0.2657703040903048,"score_spread":0.24885648786650505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181837204","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":[0.022594566,0.000032397013,0.97592634,0.00041427554,0.00032640444,0.00020975972,3.3476465e-7,0.00022991157,0.00026599615],"genre_scores_gemma":[0.6728063,0.000012518908,0.32670578,0.00011457191,0.00015260409,0.000038436207,0.0000045803877,0.000006801723,0.00015836331],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991826,0.000035008583,0.00015224775,0.00036428214,0.00009616084,0.00016971666],"domain_scores_gemma":[0.9995099,0.00008134759,0.0000710974,0.0001900548,0.0001038463,0.000043812215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001644533,0.00007889391,0.00007182366,0.000053842054,0.00043525835,0.0001343329,0.00007919558,0.000045544803,0.0000068079107],"category_scores_gemma":[0.00003523912,0.00008350795,0.000034496763,0.00033557898,0.000014958001,0.00053000706,0.00012286207,0.00007153277,0.000004439437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037554262,0.000015886908,0.000049493112,0.0000126607,0.000003924042,9.644815e-7,0.000068911766,0.0006260354,0.34916463,0.008204193,0.000019637962,0.6418299],"study_design_scores_gemma":[0.00023563976,0.000038949453,0.00048501568,0.000010104713,0.0000072862417,0.000095059855,0.00002716227,0.7611573,0.2182186,0.017282736,0.002310746,0.00013141938],"about_ca_topic_score_codex":0.000011613626,"about_ca_topic_score_gemma":0.0000681196,"teacher_disagreement_score":0.76053125,"about_ca_system_score_codex":0.000064241576,"about_ca_system_score_gemma":0.000012982686,"threshold_uncertainty_score":0.34053552},"labels":[],"label_agreement":null},{"id":"W3183476427","doi":"10.1007/978-3-030-80129-8_2","title":"Automatic Detection and Segmentation of Liver Tumors in Computed Tomography Images: Methods and Limitations","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; Carleton University","funders":"","keywords":"Segmentation; False positive paradox; Voxel; Dice; Artificial intelligence; Computer science; Pattern recognition (psychology); Sørensen–Dice coefficient; Image segmentation; Region growing; Scale-space segmentation; Computer vision; Mathematics; Statistics","score_opus":0.024749636739235196,"score_gpt":0.27542340126807147,"score_spread":0.2506737645288363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183476427","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":[0.0009103388,0.022971123,0.9749354,0.000047140868,0.00016135401,0.0006349489,0.000002862387,0.00004724164,0.00028958407],"genre_scores_gemma":[0.87535113,0.0026569974,0.12158258,0.00007240152,0.000109340144,0.00008547295,0.00002161804,0.000030961433,0.0000895227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985042,0.00017390882,0.00051751244,0.0005058154,0.00012607455,0.00017245708],"domain_scores_gemma":[0.99772024,0.0015566901,0.00031592135,0.00027714553,0.00007201473,0.000058006477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029808984,0.00025013485,0.00047616335,0.00031084125,0.0000779622,0.000108811015,0.0001270638,0.00023195663,8.948577e-7],"category_scores_gemma":[0.00003770457,0.00024405916,0.000041349842,0.0003533289,0.00010190329,0.00015500467,0.00012735385,0.00035227035,1.0750742e-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":[0.0000056919243,0.000019034414,0.00033998012,0.00039728516,0.000047408616,0.00002192389,0.00067788677,0.111561194,0.0005996857,0.010865861,0.000007410799,0.87545663],"study_design_scores_gemma":[0.00023300729,0.00005889303,0.0026605148,0.0007460318,0.000022894626,0.00007429002,0.000011062315,0.99006206,0.00009991361,0.005664003,0.00012464942,0.00024266356],"about_ca_topic_score_codex":0.000041402775,"about_ca_topic_score_gemma":0.00032651587,"teacher_disagreement_score":0.8785009,"about_ca_system_score_codex":0.000035416524,"about_ca_system_score_gemma":0.00001603001,"threshold_uncertainty_score":0.9952444},"labels":[],"label_agreement":null},{"id":"W3185489052","doi":"10.18280/ts.380319","title":"An Object Detection Framework Based on Deep Features and High-Quality Object Locations","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"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":"Pascal (unit); Softmax function; Artificial intelligence; Computer science; Object detection; Classifier (UML); Normalization (sociology); Viola–Jones object detection framework; Object (grammar); Pattern recognition (psychology); Artificial neural network; Pixel; Cognitive neuroscience of visual object recognition; Computer vision; Data mining","score_opus":0.016636816309286998,"score_gpt":0.2868740743987126,"score_spread":0.2702372580894256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185489052","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":[0.038809154,0.00007927054,0.95907694,0.0012568451,0.000120453944,0.0002507471,0.000006953663,0.00023133396,0.00016828642],"genre_scores_gemma":[0.9096504,0.000010013533,0.0885063,0.001515982,0.00015682075,0.00010927545,0.000022681526,0.000011974986,0.000016581575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983626,0.00021332338,0.00024374078,0.00059810077,0.00033080234,0.0002514554],"domain_scores_gemma":[0.99861604,0.00041724605,0.0000985921,0.00062446523,0.00010667291,0.00013696401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002153287,0.00017362073,0.00015187312,0.00007689548,0.00037470355,0.0001774019,0.00032029054,0.000081043065,0.00005200531],"category_scores_gemma":[0.00004144217,0.0001734933,0.0000499019,0.0005714738,0.00005234606,0.0003092604,0.000054054344,0.00024571258,0.0000121080075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101514815,0.001157445,0.0009692715,0.00005653084,0.00006124454,0.000033495915,0.0008663373,0.18585733,0.08515655,0.27816078,0.0002647437,0.44731474],"study_design_scores_gemma":[0.001388577,0.00081684324,0.25482172,0.00009049403,0.000051721807,0.000044131353,0.00013980416,0.49608347,0.17144163,0.07287837,0.0012885178,0.0009547345],"about_ca_topic_score_codex":0.000016606395,"about_ca_topic_score_gemma":0.00011165641,"teacher_disagreement_score":0.8708412,"about_ca_system_score_codex":0.000068677255,"about_ca_system_score_gemma":0.000059208345,"threshold_uncertainty_score":0.7074852},"labels":[],"label_agreement":null},{"id":"W3188344990","doi":"10.1155/2021/5513552","title":"The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates","keywords":"Traffic sign recognition; Sign (mathematics); Traffic sign; Computer science; Cognitive neuroscience of visual object recognition; Variable (mathematics); Warning signs; Warning system; Artificial intelligence; Object (grammar); Engineering; Transport engineering; Telecommunications","score_opus":0.019004452206404066,"score_gpt":0.2727217742057294,"score_spread":0.25371732199932534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188344990","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":[0.6995425,0.00018451842,0.2977292,0.0021024724,0.00013154003,0.00016715017,0.0000071293116,0.000029675997,0.00010577705],"genre_scores_gemma":[0.93444026,0.0006426889,0.06476884,0.000061918225,0.000032318385,0.000020946063,0.0000054080556,0.000007595194,0.000020000445],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990026,0.000057209774,0.00047618544,0.00015298136,0.00019552791,0.00011552712],"domain_scores_gemma":[0.99829775,0.00044169067,0.000540544,0.00025156236,0.00041784078,0.000050607934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020166957,0.0000891509,0.00014400111,0.000066620494,0.00023338702,0.000027701086,0.00019018911,0.00003320455,0.0000037833213],"category_scores_gemma":[0.000049426468,0.00007077855,0.000061855746,0.0005475241,0.000042986463,0.0006319157,0.0000041675717,0.00016114739,0.0000017883334],"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":[0.000031524916,0.00016031264,0.00018127229,0.000026140327,0.000033956632,0.0000024793678,0.00093757955,0.08568463,0.01683309,0.012630087,0.00003149743,0.8834474],"study_design_scores_gemma":[0.0016001245,0.0007340924,0.90335727,0.0002645216,0.0001172892,0.00012113176,0.00025635908,0.020525482,0.02453306,0.046623144,0.00153716,0.00033036113],"about_ca_topic_score_codex":2.1590563e-7,"about_ca_topic_score_gemma":0.000016628184,"teacher_disagreement_score":0.903176,"about_ca_system_score_codex":0.000036492882,"about_ca_system_score_gemma":0.000087568755,"threshold_uncertainty_score":0.28862655},"labels":[],"label_agreement":null},{"id":"W3189789601","doi":"10.1145/3460770","title":"Design Guidelines on Deep Learning–based Pedestrian Detection Methods for Supporting Autonomous Vehicles","year":2021,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Pedestrian; Pedestrian detection; Task (project management); Deep learning; Artificial intelligence; Perspective (graphical); Machine learning; Transport engineering; Systems engineering","score_opus":0.22368500969575225,"score_gpt":0.4700697877486195,"score_spread":0.24638477805286726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189789601","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":[6.8149274e-7,0.38009417,0.6178642,0.000088373585,0.00045999288,0.0009398052,0.0000017720777,0.0005432573,0.000007743917],"genre_scores_gemma":[0.000032188433,0.17476726,0.82415366,0.00011962748,0.00044371013,0.00021165266,0.000099925666,0.00010846428,0.00006353981],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98949087,0.006231001,0.0016340455,0.0015682474,0.0002735647,0.000802287],"domain_scores_gemma":[0.97828,0.017491182,0.001686725,0.001812381,0.00056326564,0.00016644812],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.010514293,0.00069941976,0.0016879463,0.0003021097,0.0008304277,0.000368043,0.0023837825,0.00035692487,0.0000029705325],"category_scores_gemma":[0.008488078,0.00068002305,0.0006337336,0.0014758073,0.000045971577,0.00014645595,0.00074102706,0.0008335126,0.000023363104],"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":[9.208355e-7,0.00002674024,0.0000010045836,0.00058815506,0.00004121137,0.0000047747353,0.000016093963,0.045867573,0.000005061361,0.00009169148,0.000053332027,0.95330346],"study_design_scores_gemma":[0.00016987903,0.00014924635,0.00000441995,0.0015085787,0.00010516427,0.000029174616,0.0000027344718,0.5821186,0.00012854298,0.00053913303,0.41463834,0.0006062304],"about_ca_topic_score_codex":0.000015046282,"about_ca_topic_score_gemma":0.000005698883,"teacher_disagreement_score":0.9526972,"about_ca_system_score_codex":0.0002462195,"about_ca_system_score_gemma":0.00047289368,"threshold_uncertainty_score":0.99986386},"labels":[],"label_agreement":null},{"id":"W3192829953","doi":"10.1109/tip.2021.3102509","title":"MGSeg: Multiple Granularity-Based Real-Time Semantic Segmentation Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bank of Canada","funders":"Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Granularity; Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Benchmark (surveying); Feature (linguistics); Semantics (computer science); Feature extraction","score_opus":0.015319373668716656,"score_gpt":0.2683782895688636,"score_spread":0.25305891590014695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192829953","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":[0.0022319462,0.00007602795,0.9952929,0.0010086398,0.00020555893,0.00026891296,0.0000066611283,0.00061051256,0.00029883333],"genre_scores_gemma":[0.4671149,0.000031507,0.53182983,0.0005066558,0.000073520896,0.0001238498,0.000013341574,0.000031222087,0.0002751797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981647,0.000107754735,0.00033425173,0.0006480027,0.0003224067,0.00042284976],"domain_scores_gemma":[0.9987044,0.00022285162,0.00013897456,0.00057848025,0.00023223455,0.0001230948],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014440974,0.00022639456,0.0002133074,0.00009520378,0.0008359834,0.00032828483,0.00037251733,0.0000746466,0.000033670844],"category_scores_gemma":[0.000008119818,0.00025154598,0.00011078816,0.0013469857,0.000074366755,0.0009439878,0.000005013029,0.00029003317,0.00011772482],"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":[0.000033254724,0.0004884361,0.000037151993,0.0001327981,0.000026582034,0.00007069737,0.00029642196,0.4010236,0.33827487,0.00011930968,0.00033712407,0.25915977],"study_design_scores_gemma":[0.0005688696,0.000030017647,0.00007473007,0.0001006202,0.000031793392,0.000028849043,0.000016710846,0.74753314,0.24981225,0.0013471927,0.00016239441,0.0002934512],"about_ca_topic_score_codex":0.000008740397,"about_ca_topic_score_gemma":0.000022905413,"teacher_disagreement_score":0.46488294,"about_ca_system_score_codex":0.0000925257,"about_ca_system_score_gemma":0.00018692382,"threshold_uncertainty_score":0.9999937},"labels":[],"label_agreement":null},{"id":"W3193712709","doi":"10.20944/preprints202108.0282.v1","title":"Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"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":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Government of Canada; Canadian Bureau for International Education; University of Regina","keywords":"Kurtosis; Contamination; Computer science; Support vector machine; Subspace topology; Artificial intelligence; Decision tree; k-nearest neighbors algorithm; Entropy (arrow of time); Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.12025390982553302,"score_gpt":0.36074763401607873,"score_spread":0.2404937241905457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193712709","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":[0.5784753,0.000018514713,0.41981515,0.00025012737,0.00044811403,0.0005231811,0.000007116736,0.00018669532,0.00027579474],"genre_scores_gemma":[0.96239126,0.000017875036,0.037054565,0.00021998401,0.00009677788,0.00007607231,0.000081020706,0.000040331437,0.000022089363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99639803,0.00030134292,0.0007924591,0.0015829067,0.00058195664,0.0003432818],"domain_scores_gemma":[0.99510866,0.00028437836,0.0008444668,0.0031453434,0.000458873,0.00015824956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039243777,0.0004125381,0.0005335773,0.00028993833,0.00015096742,0.000073487805,0.0016772945,0.000358466,0.000048780104],"category_scores_gemma":[0.000093898365,0.00044981396,0.00022055733,0.0007771286,0.00013123962,0.0002708804,0.002446207,0.00082819734,0.00007185032],"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":[0.00005848737,0.00087058346,0.1548772,0.0002482667,0.00008274169,0.000031694333,0.00046752385,0.74592566,0.07850343,0.010013492,0.000019428597,0.008901507],"study_design_scores_gemma":[0.00021999251,0.00002840401,0.35244197,0.00028937217,0.000015599624,0.0000027861174,0.0000068689787,0.62892586,0.017226145,0.0004616979,0.000113320195,0.0002680216],"about_ca_topic_score_codex":0.000055622397,"about_ca_topic_score_gemma":0.0000061211363,"teacher_disagreement_score":0.383916,"about_ca_system_score_codex":0.00028343836,"about_ca_system_score_gemma":0.00029279184,"threshold_uncertainty_score":0.9997954},"labels":[],"label_agreement":null},{"id":"W3193773166","doi":"10.1109/iccv48922.2021.01577","title":"GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Ground truth; Graph; Pattern recognition (psychology); Theoretical computer science","score_opus":0.03919903238366152,"score_gpt":0.30924836863046856,"score_spread":0.27004933624680705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193773166","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":[0.009415596,0.000064660875,0.97520924,0.008873823,0.0037222018,0.00039200456,0.000017855831,0.00025681325,0.0020478205],"genre_scores_gemma":[0.75579494,0.00018469615,0.23628958,0.004856337,0.0012688932,0.00013026495,0.00025645655,0.000047563382,0.0011712955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611217,0.00025175148,0.0007132774,0.0013140682,0.0010413739,0.00056738977],"domain_scores_gemma":[0.9970589,0.00037514867,0.0003686816,0.0011236299,0.0008250345,0.00024856295],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026266882,0.00044551503,0.0003829669,0.00024908982,0.00032415264,0.0006993166,0.0015758941,0.0001398526,0.00058972853],"category_scores_gemma":[0.00002499679,0.0004576807,0.0002320119,0.0010545792,0.00008380884,0.00068221294,0.00042427226,0.0004423662,0.00067315553],"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":[0.00011912655,0.001083236,0.00060850347,0.00004604703,0.00023685275,0.0004910503,0.00023893577,0.38347986,0.025536845,0.33508396,0.04111946,0.21195611],"study_design_scores_gemma":[0.000906041,0.00025462732,0.001238639,0.00028590576,0.000018160034,0.000053012318,0.000014022252,0.9681168,0.0064013344,0.016814776,0.005342788,0.0005538463],"about_ca_topic_score_codex":0.000010297684,"about_ca_topic_score_gemma":0.00003461215,"teacher_disagreement_score":0.7463793,"about_ca_system_score_codex":0.00019866692,"about_ca_system_score_gemma":0.00025524988,"threshold_uncertainty_score":0.9997875},"labels":[],"label_agreement":null},{"id":"W3194225869","doi":"10.1109/isaiam53259.2021.00038","title":"A Comprehensive Review of Deep Reinforcement Learning for Object Detection","year":2021,"lang":"en","type":"review","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Object detection; Object (grammar); Context (archaeology); Perception; Deep learning; Cognitive neuroscience of visual object recognition; Class (philosophy); Machine learning; Computer vision; Pattern recognition (psychology); Psychology","score_opus":0.06095609730121904,"score_gpt":0.35554928759033544,"score_spread":0.2945931902891164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194225869","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":[7.618527e-10,0.5062877,0.49238333,0.000013685625,0.000062907,0.0009883738,2.3371808e-7,0.00005254967,0.0002112438],"genre_scores_gemma":[5.257003e-7,0.963743,0.034711003,0.00019601721,0.0000564491,0.00094218994,0.000052975665,0.00002043372,0.00027744324],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998201,0.00012944287,0.00075778225,0.00049811916,0.00019693747,0.00021670404],"domain_scores_gemma":[0.99762994,0.0005878201,0.0006887415,0.0007091138,0.00033028433,0.000054083634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013475034,0.000264948,0.0013179666,0.00008502632,0.00010778658,0.000023718429,0.000603148,0.00009145972,0.000021301066],"category_scores_gemma":[0.00009870991,0.00022128706,0.0005733472,0.0008870506,0.000021065649,0.000121789286,0.00030617815,0.00024347073,0.000015980979],"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":[1.5492508e-7,0.0000049811097,3.4670464e-9,0.06555757,0.000032216412,4.5451804e-7,0.0000038194703,0.00042974882,7.3680707e-7,0.002115248,0.00010143698,0.93175364],"study_design_scores_gemma":[0.000045541794,0.00005782069,2.009338e-8,0.036466483,0.00013934873,0.000027917926,0.0000017424517,0.0074130427,0.00001657085,0.00005700168,0.95558125,0.00019327294],"about_ca_topic_score_codex":0.0000015475749,"about_ca_topic_score_gemma":0.000001116029,"teacher_disagreement_score":0.9554798,"about_ca_system_score_codex":0.000087549735,"about_ca_system_score_gemma":0.00010095384,"threshold_uncertainty_score":0.90238243},"labels":[],"label_agreement":null},{"id":"W3195180572","doi":"10.1109/wacv56688.2023.00529","title":"Serf: Towards better training of deep neural networks using log-Softplus ERror activation Function","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Activation function; Artificial intelligence; Regularization (linguistics); Artificial neural network; Monotonic function; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.06425871713562263,"score_gpt":0.32289148878931506,"score_spread":0.2586327716536924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195180572","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":[0.03669823,0.000015678183,0.9592672,0.0018127614,0.0005321604,0.0009362691,0.000014737736,0.00043188024,0.00029109858],"genre_scores_gemma":[0.9472864,0.000015661415,0.05124762,0.00060750276,0.00038315513,0.00025343357,0.0000773035,0.00004655508,0.00008234041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996687,0.0001448524,0.0009983737,0.001013528,0.00060339906,0.00055286277],"domain_scores_gemma":[0.9967461,0.00032714056,0.00072190125,0.0014742827,0.0005527532,0.00017780176],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034747837,0.00042037477,0.00055498764,0.00060077244,0.00027338107,0.0001376415,0.001553114,0.00020623869,0.00004955837],"category_scores_gemma":[0.000013878448,0.000423073,0.00023202239,0.002444052,0.00019626344,0.0007573174,0.00048001675,0.00044663507,0.000078629535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074237716,0.00019133442,0.000077257304,0.00005172559,0.00007004789,0.0000024080719,0.0005551804,0.26451138,0.013444009,0.013033931,0.0013229742,0.7066655],"study_design_scores_gemma":[0.00044493,0.00031702346,0.002683637,0.000118131466,0.000025857496,0.000009674624,0.0000710559,0.9874451,0.0023845802,0.005319919,0.0008213304,0.00035872363],"about_ca_topic_score_codex":0.00001715325,"about_ca_topic_score_gemma":0.0000077159,"teacher_disagreement_score":0.9105882,"about_ca_system_score_codex":0.000090934074,"about_ca_system_score_gemma":0.00008275498,"threshold_uncertainty_score":0.9998221},"labels":[],"label_agreement":null},{"id":"W3196643773","doi":"10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00017","title":"CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Data deduplication; Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Hash function; Image (mathematics); Computation; Data mining; Machine learning; Database; Algorithm","score_opus":0.031268293015714585,"score_gpt":0.2960097585946011,"score_spread":0.2647414655788865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196643773","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":[0.0027373065,0.000035705874,0.9788331,0.0070920936,0.00016447643,0.0008144391,0.000006378449,0.00036362442,0.009952877],"genre_scores_gemma":[0.8403209,0.0000030562624,0.15190183,0.0064510475,0.00010501248,0.0008575382,0.00005096356,0.000015826945,0.00029382028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847615,0.000104275285,0.00020022507,0.0006275289,0.00026667796,0.00032514517],"domain_scores_gemma":[0.9983176,0.00059495476,0.00008659253,0.0006594066,0.00020534247,0.00013611956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010912182,0.0001636768,0.0001433733,0.000062902276,0.00023175265,0.00010146071,0.0004291838,0.00005162529,0.00008882041],"category_scores_gemma":[0.00008515415,0.00016352122,0.00007815691,0.0006835039,0.00006862463,0.00041496634,0.00011285603,0.00019889203,0.00013681833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028557488,0.00033963696,0.00026914486,0.000010593421,0.000020498694,0.000042087388,0.00027069802,0.080673836,0.03579543,0.38828096,0.0068620676,0.4874065],"study_design_scores_gemma":[0.00044650605,0.000054112606,0.008142249,0.000010037795,0.0000044795534,0.000028893237,0.000023968869,0.9645663,0.01259154,0.0048659258,0.009051626,0.00021437307],"about_ca_topic_score_codex":0.0000030175365,"about_ca_topic_score_gemma":0.0000072036883,"teacher_disagreement_score":0.8838925,"about_ca_system_score_codex":0.00010912525,"about_ca_system_score_gemma":0.0001296738,"threshold_uncertainty_score":0.6668202},"labels":[],"label_agreement":null},{"id":"W3196790965","doi":"10.1155/2021/9440212","title":"Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":30,"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":"Feature (linguistics); Feature extraction; Computer science; Field (mathematics); Identification (biology); Artificial intelligence; Pattern recognition (psychology); Process (computing); Data mining; Mathematics","score_opus":0.008627895890527546,"score_gpt":0.25339883137834224,"score_spread":0.2447709354878147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196790965","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":[0.004239532,0.00007367863,0.99385864,0.0010064981,0.0004586352,0.0002577713,0.000024042996,0.000052994616,0.00002820123],"genre_scores_gemma":[0.23504966,0.000022527121,0.7643311,0.0003285871,0.00014792976,0.00003646025,0.000034919834,0.00001696965,0.000031852578],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987007,0.000035278732,0.0005147192,0.00027891746,0.00025463226,0.00021577872],"domain_scores_gemma":[0.9983233,0.00023322729,0.00045142643,0.00026789546,0.00061958184,0.000104570456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017313627,0.00014635587,0.00021529393,0.000117559095,0.00012690663,0.000047057572,0.0002618249,0.00006188327,0.000008239092],"category_scores_gemma":[0.000053911783,0.00014710778,0.00018633548,0.00046004576,0.00002240572,0.0009130314,0.0000033388042,0.00021954512,0.000002285293],"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":[0.00026135184,0.00029595112,0.000034224275,0.000050220217,0.00003166554,0.00007254764,0.00022015157,0.22104242,0.2747542,0.0016614906,0.000069158064,0.5015066],"study_design_scores_gemma":[0.0033398832,0.0011089096,0.013075839,0.00010041612,0.00006281114,0.00004918572,0.00009276467,0.46279052,0.47966015,0.031890206,0.0073759276,0.00045339388],"about_ca_topic_score_codex":5.179494e-7,"about_ca_topic_score_gemma":0.000011191407,"teacher_disagreement_score":0.5010532,"about_ca_system_score_codex":0.00007570586,"about_ca_system_score_gemma":0.00010542626,"threshold_uncertainty_score":0.59988815},"labels":[],"label_agreement":null},{"id":"W3197903162","doi":"10.3390/computers10090112","title":"Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision","year":2021,"lang":"en","type":"article","venue":"Computers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":61,"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":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Government of Canada; Canadian Bureau for International Education; University of Regina","keywords":"Kurtosis; Computer science; Contamination; Artificial intelligence; k-nearest neighbors algorithm; Support vector machine; Decision tree; Insulator (electricity); Subspace topology; Pattern recognition (psychology); Reliability engineering; Statistics; Mathematics; Engineering; Electrical engineering","score_opus":0.028567042236733252,"score_gpt":0.2863325378922142,"score_spread":0.25776549565548096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197903162","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":[0.14615518,0.000021529178,0.8523891,0.00053184794,0.00048082124,0.00016066311,0.000002910962,0.00014035299,0.00011759979],"genre_scores_gemma":[0.7886911,0.0000025824645,0.2106125,0.00059640245,0.000056265704,0.000004656173,0.00001740941,0.000012758657,0.000006320027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847096,0.00012210922,0.00034549556,0.000541945,0.00029145132,0.00022802458],"domain_scores_gemma":[0.99824125,0.00031549213,0.00021651322,0.00089306076,0.00022882069,0.00010487021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009931771,0.00017111441,0.00022197234,0.00014576342,0.00013004823,0.00007444688,0.0005811237,0.00007250867,0.000004623662],"category_scores_gemma":[0.000015816478,0.00017770301,0.00008595715,0.0010532151,0.000076764285,0.00025881082,0.0002147896,0.00014628647,0.00001306421],"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":[0.000031934225,0.0005832324,0.0028126573,0.000053233303,0.000033495424,0.00006924382,0.0001940929,0.7302754,0.036914773,0.094877765,0.0010118844,0.13314228],"study_design_scores_gemma":[0.00040165964,0.000107767395,0.034138154,0.00008484106,0.000005225864,0.000009389008,0.0000038115802,0.9596231,0.004326807,0.0004013639,0.0007323966,0.00016546786],"about_ca_topic_score_codex":0.000003894103,"about_ca_topic_score_gemma":0.0000014143259,"teacher_disagreement_score":0.6425359,"about_ca_system_score_codex":0.000096276584,"about_ca_system_score_gemma":0.000101772705,"threshold_uncertainty_score":0.7246518},"labels":[],"label_agreement":null},{"id":"W3197957534","doi":"10.1609/aaai.v36i3.20144","title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1017,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Segmentation; Encoder; Computer science; Transformer; Channel (broadcasting); Artificial intelligence; Ambiguity; Pattern recognition (psychology); Computer network; Programming language; Engineering","score_opus":0.05825692916390028,"score_gpt":0.2837522452940704,"score_spread":0.2254953161301701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197957534","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":[0.48658273,0.00022819043,0.37261853,0.11804677,0.0008134125,0.003913747,0.00008513049,0.00040376722,0.017307743],"genre_scores_gemma":[0.99777055,0.000032257063,0.0012048441,0.0005056754,0.000047763373,0.00035463486,8.724839e-7,0.000015251026,0.00006815416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99810207,0.000042832602,0.000387737,0.0005915884,0.00052925054,0.00034649836],"domain_scores_gemma":[0.9987762,0.0002456782,0.00025294645,0.00036627508,0.00030517287,0.00005373667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036743935,0.00021040622,0.00021362217,0.000115563285,0.0007176123,0.00012372115,0.0022271958,0.0000417626,0.000073755175],"category_scores_gemma":[0.00006566154,0.00014004503,0.00008549949,0.0016067768,0.00031255418,0.00035825488,0.00019774812,0.0007665737,0.000010300622],"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":[0.00009657871,0.00013252029,0.00007500543,0.0000037142856,0.000015470425,8.384702e-7,0.016394274,0.0045012706,0.005810731,0.9685891,0.000033965334,0.0043465192],"study_design_scores_gemma":[0.00006547766,0.000267892,0.0003184964,0.00007187133,0.000016561555,0.000013481897,0.0072395485,0.06994282,0.07082673,0.8507678,0.00021126078,0.0002580562],"about_ca_topic_score_codex":0.00037540033,"about_ca_topic_score_gemma":0.00041364468,"teacher_disagreement_score":0.51118785,"about_ca_system_score_codex":0.00014520579,"about_ca_system_score_gemma":0.00010756332,"threshold_uncertainty_score":0.57108706},"labels":[],"label_agreement":null},{"id":"W3198139586","doi":"10.1016/j.displa.2021.102080","title":"A brief survey on RGB-D semantic segmentation using deep learning","year":2021,"lang":"en","type":"article","venue":"Displays","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":64,"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":"Chinese Academy of Sciences; Special Project for Research and Development in Key areas of Guangdong Province; National Natural Science Foundation of China","keywords":"RGB color model; Artificial intelligence; Segmentation; Computer science; Image segmentation; Computer vision; Scale-space segmentation; Segmentation-based object categorization; Pattern recognition (psychology)","score_opus":0.03463717292674742,"score_gpt":0.3031554227184883,"score_spread":0.2685182497917409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198139586","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":[0.24459039,0.000059494894,0.7546076,0.000115805415,0.00010755792,0.00007945133,0.0000018266968,0.000079157966,0.00035869394],"genre_scores_gemma":[0.9736606,0.000019532685,0.025729528,0.00034288707,0.000041996325,0.000010313477,0.00005206058,0.000011795188,0.000131296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990046,0.00013358094,0.00013878102,0.0003515909,0.00017203134,0.00019941705],"domain_scores_gemma":[0.99926764,0.00021545388,0.00007392885,0.00033301229,0.000053421198,0.00005656786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010212619,0.00009448559,0.00009541494,0.00003419084,0.00022142247,0.00009585521,0.0002234047,0.000028590766,0.000010284707],"category_scores_gemma":[0.00007026037,0.000101334124,0.000033831544,0.0005746828,0.000016301145,0.00023009024,0.00012209467,0.0001377349,0.000050255763],"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":[0.000026936616,0.00035643118,0.065899834,0.000044485867,0.00006217274,0.00018103802,0.0019780584,0.55823874,0.19176634,0.057467707,0.0004295785,0.123548664],"study_design_scores_gemma":[0.0002996362,0.000038143116,0.1391441,0.00003387394,0.000008549502,0.00003427629,0.00004448183,0.84048426,0.01812338,0.0010712576,0.00045327295,0.00026474075],"about_ca_topic_score_codex":0.000029589808,"about_ca_topic_score_gemma":0.00006316443,"teacher_disagreement_score":0.7290702,"about_ca_system_score_codex":0.000035248646,"about_ca_system_score_gemma":0.000025753632,"threshold_uncertainty_score":0.41322854},"labels":[],"label_agreement":null},{"id":"W3199886509","doi":"10.36227/techrxiv.16546281.v1","title":"Comparison of Kidney Segmentation Under Attention U-Net Architectures","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Context (archaeology); Computer science; Artificial intelligence; Focus (optics); Architecture; Deep learning; Noise (video); Net (polyhedron); Machine learning; Image segmentation; Data science; Image (mathematics); Geography","score_opus":0.04313599020403548,"score_gpt":0.3512392427054531,"score_spread":0.3081032525014176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199886509","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":[0.03498361,0.00013905084,0.96081036,0.00218754,0.00036596172,0.00037535455,0.0000064281744,0.00017008111,0.00096160977],"genre_scores_gemma":[0.69164586,0.000018006182,0.307284,0.0005483211,0.00005205774,0.00006873072,0.00017785408,0.00001037782,0.00019477577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841285,0.000088875735,0.00043591086,0.000578321,0.00031508805,0.00016898323],"domain_scores_gemma":[0.9983992,0.00008416712,0.00036417128,0.0008953323,0.00012434785,0.00013278497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007597941,0.0001877643,0.00029843388,0.00010146769,0.000064620835,0.00010161439,0.0007490388,0.0001213257,0.00003685104],"category_scores_gemma":[0.000027591921,0.00018345514,0.00012134241,0.0002815859,0.000046035027,0.00008548956,0.0011306397,0.00034683745,0.000008291343],"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":[0.000009519783,0.0005980658,0.0059148874,0.00035871344,0.00016500053,0.0000026981488,0.0012321518,0.7877772,0.06363131,0.059606552,0.010023671,0.07068022],"study_design_scores_gemma":[0.0006568653,0.00010610253,0.031534214,0.0004040989,0.000093630726,0.000015462312,0.0004062568,0.70835567,0.15460525,0.10170386,0.0010140942,0.0011045117],"about_ca_topic_score_codex":0.000027756625,"about_ca_topic_score_gemma":0.000024217547,"teacher_disagreement_score":0.6566623,"about_ca_system_score_codex":0.0000530705,"about_ca_system_score_gemma":0.00014928232,"threshold_uncertainty_score":0.7481083},"labels":[],"label_agreement":null},{"id":"W3200582272","doi":"10.1007/978-3-030-87193-2_25","title":"Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Computer science; Context (archaeology); Consistency (knowledge bases); Artificial intelligence; Overhead (engineering); Image segmentation; Ground truth; Variance (accounting); Market segmentation; Pattern recognition (psychology); Data mining","score_opus":0.018253255734269894,"score_gpt":0.26415022849453845,"score_spread":0.24589697276026856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200582272","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":[0.00017336565,0.0010087822,0.9957249,0.0012923018,0.0005120924,0.00062717265,0.0000042999072,0.00014003678,0.00051703333],"genre_scores_gemma":[0.0861107,0.00018018768,0.91089076,0.0013924172,0.00031836316,0.00006531978,0.00002422316,0.00004151243,0.0009765304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972993,0.000027362168,0.0004002101,0.0014305122,0.00040232795,0.00044024596],"domain_scores_gemma":[0.9977996,0.0007906096,0.00027357478,0.00075114204,0.0002521397,0.00013298732],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031630805,0.00036107146,0.000388307,0.0002567552,0.0004895004,0.0004082765,0.0010447856,0.00016098176,0.000007950852],"category_scores_gemma":[0.00011658944,0.0003539835,0.000081405306,0.00043558134,0.00048173443,0.00050743093,0.0008192964,0.0004906149,0.0000063656857],"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":[0.000002719721,0.0000095485275,0.000039788967,0.000028810799,0.000007464755,0.00001181158,0.00050740637,0.0083114905,0.0005195354,0.070350885,0.00000972965,0.9202008],"study_design_scores_gemma":[0.00058430765,0.00029099328,0.00010387751,0.0004314207,0.000023210852,0.000151478,0.0000037028087,0.69906247,0.0050237626,0.28782734,0.0055129607,0.0009844615],"about_ca_topic_score_codex":0.0000052715545,"about_ca_topic_score_gemma":0.000084663145,"teacher_disagreement_score":0.91921633,"about_ca_system_score_codex":0.00015464236,"about_ca_system_score_gemma":0.0002888053,"threshold_uncertainty_score":0.9998912},"labels":[],"label_agreement":null},{"id":"W3201213425","doi":"10.36227/techrxiv.16553580.v1","title":"LiCaNet: Further Enhancement of Joint Perception and Motion Prediction based on Multi-Modal Fusion","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Perception; Motion (physics); Fusion; Sensor fusion; Pipeline (software); Displacement (psychology); Image fusion; Modal; Reliability (semiconductor); Lidar; Image (mathematics); Geography; Remote sensing; Psychology","score_opus":0.030041126265375563,"score_gpt":0.26828718433640275,"score_spread":0.2382460580710272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201213425","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":[0.10572206,0.000033535987,0.89198786,0.00096495677,0.00026229635,0.00063522055,0.000008877047,0.00013597202,0.00024920228],"genre_scores_gemma":[0.808899,0.00013959467,0.19032156,0.00020420906,0.00006772232,0.00014625085,0.00009653311,0.000013019234,0.000112107635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815434,0.00009642669,0.00039743097,0.0008233991,0.00035728794,0.00017112745],"domain_scores_gemma":[0.9985742,0.000039791674,0.00026365358,0.0008895242,0.00015341793,0.000079429774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017167772,0.00022985929,0.00024474174,0.00013542286,0.0000796366,0.000066117274,0.0002706289,0.00018008403,0.00009393918],"category_scores_gemma":[0.000014532712,0.00021712275,0.0000860122,0.00018509886,0.00004768806,0.00014768512,0.0005776132,0.000309218,0.0000074049617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047850346,0.0014679605,0.0009775583,0.00034656085,0.000044331427,0.0000042726256,0.0019433108,0.22927538,0.33519933,0.0017203776,0.0002840982,0.42868897],"study_design_scores_gemma":[0.0003169651,0.00012351379,0.02730839,0.00015638971,0.000012000963,0.0000019407928,0.000043418688,0.95854867,0.012946608,0.00030257474,0.000060540697,0.0001790036],"about_ca_topic_score_codex":0.000047575635,"about_ca_topic_score_gemma":0.000019776175,"teacher_disagreement_score":0.72927326,"about_ca_system_score_codex":0.00012941755,"about_ca_system_score_gemma":0.000053117667,"threshold_uncertainty_score":0.8854009},"labels":[],"label_agreement":null},{"id":"W3201298227","doi":"10.1155/2021/3839543","title":"Pipelined Training with Stale Weights in Deep Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"Applied Computational Intelligence and Soft Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Backpropagation; Convolutional neural network; Inference; Parallel computing; FLOPS; Residual neural network; Artificial neural network; Computer engineering; Artificial intelligence","score_opus":0.026059096375998746,"score_gpt":0.26016038454053536,"score_spread":0.23410128816453663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201298227","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":[0.032267693,0.00036641958,0.9660874,0.0004406991,0.000087973334,0.00020013981,0.0000010915038,0.00015512046,0.0003934474],"genre_scores_gemma":[0.77638656,0.000011019045,0.22271088,0.00070549705,0.00010828273,0.000018088946,0.000036050387,0.000013050061,0.0000105703775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979208,0.000053860058,0.00048408392,0.000750897,0.0003302078,0.00046014733],"domain_scores_gemma":[0.9982571,0.0010087356,0.00015533577,0.00024606075,0.00019742979,0.00013534325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019486053,0.00024179563,0.00027365316,0.00011229225,0.0003471026,0.00016671527,0.0003997457,0.00007058144,0.00000796635],"category_scores_gemma":[0.000019155214,0.00024141856,0.000040721166,0.0009862982,0.00014711307,0.00026201262,0.00028814632,0.00035894444,0.0000074430022],"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":[0.000007691205,0.00003318273,0.00032232932,0.0000043922787,0.000008890922,0.00002275389,0.00060121575,0.6993878,0.0000109229295,0.19520512,0.0000039121637,0.10439182],"study_design_scores_gemma":[0.00021456393,0.0000262079,0.002239201,0.00002698881,0.0000040772734,0.0001397596,0.00024692557,0.93908155,0.00006824088,0.057591714,0.00008827455,0.00027246677],"about_ca_topic_score_codex":0.000004832512,"about_ca_topic_score_gemma":0.000023732024,"teacher_disagreement_score":0.74411887,"about_ca_system_score_codex":0.000051457755,"about_ca_system_score_gemma":0.00012239233,"threshold_uncertainty_score":0.9844763},"labels":[],"label_agreement":null},{"id":"W3201843693","doi":"10.1007/978-3-030-87196-3_55","title":"CPNet: Cycle Prototype Network for Weakly-Supervised 3D Renal Compartments Segmentation on CT Images","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Compartment (ship); Pattern recognition (psychology); Feature (linguistics); Deep learning; Consistency (knowledge bases)","score_opus":0.022409565163791174,"score_gpt":0.28236557673314644,"score_spread":0.25995601156935527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201843693","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":[0.000086348125,0.00030042938,0.99380213,0.0008639723,0.001015931,0.0025838085,0.000021778618,0.00019728874,0.0011282857],"genre_scores_gemma":[0.014883315,0.000053890024,0.9804934,0.0025373185,0.0009987799,0.0004478568,0.000093703056,0.00006113078,0.0004305864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99589187,0.000050798455,0.0005847943,0.0018469698,0.0008255056,0.0008000362],"domain_scores_gemma":[0.99709666,0.0006728124,0.00035713403,0.0014257143,0.00026657773,0.00018113245],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042458496,0.0005724722,0.0005360458,0.0002713713,0.00053651654,0.0004600303,0.002199366,0.00013189828,0.00002236033],"category_scores_gemma":[0.000036376496,0.00054966996,0.00015604246,0.00075562525,0.00033894205,0.00052816275,0.00071507774,0.00056878576,0.000039390226],"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":[0.000024611905,0.000057793597,0.000026797074,0.000032932086,0.00001838244,0.000032293337,0.00009223868,0.49357408,0.0005478447,0.012623444,0.00023979,0.4927298],"study_design_scores_gemma":[0.0006977452,0.00055637513,0.00013220923,0.0005402462,0.000018445575,0.000048030797,1.7810832e-7,0.8496444,0.0070561995,0.13340183,0.0069824476,0.00092187023],"about_ca_topic_score_codex":0.000004035695,"about_ca_topic_score_gemma":0.00002687289,"teacher_disagreement_score":0.49180794,"about_ca_system_score_codex":0.0003193948,"about_ca_system_score_gemma":0.00029782232,"threshold_uncertainty_score":0.9996955},"labels":[],"label_agreement":null},{"id":"W3201845662","doi":"10.1109/iccv48922.2021.00654","title":"Robust Trust Region for Weakly Supervised Segmentation","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Prior probability; Artificial intelligence; Computer science; Pixel; Segmentation; Generalization; Pattern recognition (psychology); Deep neural networks; Artificial neural network; Image segmentation; Gradient descent; Machine learning; Mathematics; Bayesian probability","score_opus":0.08571104079294203,"score_gpt":0.3223119249986248,"score_spread":0.2366008842056828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201845662","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":[0.0050755595,0.000031753396,0.9740915,0.014900895,0.0028864285,0.000523973,0.000028921708,0.00018627572,0.002274744],"genre_scores_gemma":[0.41560367,0.00038506262,0.56991756,0.0049230414,0.0020463981,0.00044924786,0.00055828464,0.000072633695,0.006044112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702734,0.00012258677,0.0005851743,0.0011864961,0.0006812582,0.00039712217],"domain_scores_gemma":[0.99729174,0.0002903065,0.00026166116,0.00086574355,0.0011051706,0.0001853732],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001810079,0.00034562944,0.0003048897,0.0002117693,0.000290736,0.0006196924,0.0013969891,0.00013022052,0.0002545063],"category_scores_gemma":[0.000036512643,0.00035477077,0.00021250069,0.00052706746,0.000052414187,0.00088617625,0.00035505943,0.00027497896,0.00020670016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014681081,0.0006744096,0.00013379527,0.00003577754,0.00012996377,0.00013042928,0.00038136088,0.028660363,0.029832408,0.42451805,0.046902187,0.46845445],"study_design_scores_gemma":[0.0010751572,0.000278933,0.00040876892,0.00014243,0.000011456648,0.00007596325,0.000042669246,0.9591097,0.012815072,0.01001269,0.015583165,0.00044398537],"about_ca_topic_score_codex":0.0000060465613,"about_ca_topic_score_gemma":0.000013353381,"teacher_disagreement_score":0.93044937,"about_ca_system_score_codex":0.00023356813,"about_ca_system_score_gemma":0.00018811692,"threshold_uncertainty_score":0.99989045},"labels":[],"label_agreement":null},{"id":"W3203627589","doi":"10.1609/aaai.v36i1.19958","title":"Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"","keywords":"Convolutional neural network; Computer science; Kronecker product; Kronecker delta; Decomposition; Pruning; Convolution (computer science); Layer (electronics); Tensor product; Algorithm; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Mathematics","score_opus":0.07785216731124393,"score_gpt":0.3236004177940165,"score_spread":0.24574825048277255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203627589","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":[0.5412674,0.0005674646,0.394179,0.04342689,0.00417735,0.0038908492,0.00005584952,0.00068501494,0.0117502],"genre_scores_gemma":[0.9734841,0.000028352028,0.02522638,0.0006650661,0.00019983691,0.00021006224,0.0000054776756,0.00001513357,0.0001655754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755573,0.00006243123,0.0005675325,0.0006840409,0.0006992656,0.00043100334],"domain_scores_gemma":[0.9985146,0.000098078424,0.0005119484,0.00042369452,0.00038222555,0.000069486625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003293275,0.0002340927,0.00025032068,0.000057368794,0.0010407926,0.000121025645,0.00218572,0.00003679386,0.0001589763],"category_scores_gemma":[0.000054637203,0.00019519954,0.0001277935,0.0010594544,0.00023876116,0.0005342943,0.0011642469,0.00047695538,0.000034207886],"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":[0.00008464917,0.00016240902,0.00010974244,0.000007898884,0.000010206193,3.9535192e-7,0.00034271355,0.050103668,0.041626316,0.8890861,0.0016991468,0.016766708],"study_design_scores_gemma":[0.000059939663,0.0001741127,0.00031119835,0.000047982638,0.000012961043,0.000027112894,0.00008905866,0.4708155,0.12198306,0.40374488,0.0024254413,0.00030876038],"about_ca_topic_score_codex":0.000017208233,"about_ca_topic_score_gemma":0.0000025045713,"teacher_disagreement_score":0.48534128,"about_ca_system_score_codex":0.00011446685,"about_ca_system_score_gemma":0.00007338085,"threshold_uncertainty_score":0.800504},"labels":[],"label_agreement":null},{"id":"W3203889165","doi":"10.1145/3472456.3472523","title":"Accelerated Device Placement Optimization with Contrastive Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Louisiana Board of Regents; National Science Foundation","keywords":"Computer science; Speedup; Mars Exploration Program; Artificial neural network; Artificial intelligence; Graph; Deep learning; Computational complexity theory; Machine learning; Computer engineering; Theoretical computer science; Algorithm; Parallel computing","score_opus":0.02417643615179895,"score_gpt":0.266016235102697,"score_spread":0.24183979895089805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203889165","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":[0.0012898507,0.000029888803,0.9912259,0.0008905868,0.000021735332,0.00011416543,1.8663265e-7,0.00021805738,0.006209612],"genre_scores_gemma":[0.46415475,0.000021193775,0.5333563,0.0006740884,0.000021669875,0.00004268254,0.000015521571,0.000007733538,0.0017060799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934566,0.0000358066,0.00009329818,0.0002675935,0.00011641332,0.00014122506],"domain_scores_gemma":[0.9994323,0.000082910985,0.0000484383,0.00018056265,0.00020754045,0.00004822755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000033564895,0.00007216715,0.00006893894,0.000017566008,0.00013661964,0.00008779412,0.00015910504,0.000017326476,0.000072676536],"category_scores_gemma":[0.000016020822,0.00006028505,0.000009515705,0.00059529196,0.000012811153,0.00030764987,0.000087308734,0.00009004696,0.000021367816],"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":[0.0000034965467,0.000018637822,0.00013115352,9.760204e-7,0.0000074408404,0.0000069754797,0.00004258847,0.9845176,0.0005020316,0.012100194,0.000077898505,0.0025910237],"study_design_scores_gemma":[0.00032279707,0.000044199067,0.00023981293,0.000008372605,0.000003886844,0.000018230436,0.00007067951,0.9850988,0.011674349,0.000076572185,0.0023253188,0.000117005555],"about_ca_topic_score_codex":0.0000015560536,"about_ca_topic_score_gemma":0.000006915047,"teacher_disagreement_score":0.4628649,"about_ca_system_score_codex":0.000028667979,"about_ca_system_score_gemma":0.000055464407,"threshold_uncertainty_score":0.24583529},"labels":[],"label_agreement":null},{"id":"W3204937691","doi":"","title":"Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Feature extraction; Computer vision; Object detection; Kernel (algebra); Feature (linguistics); Pyramid (geometry); Convolution (computer science); Pattern recognition (psychology); Rotation (mathematics); Convolutional neural network; Aerial image; Orientation (vector space); Image (mathematics); Artificial neural network; Mathematics","score_opus":0.027998768372507914,"score_gpt":0.283809961415422,"score_spread":0.25581119304291405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204937691","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":[0.00011785193,0.00014499982,0.9956541,0.001191229,0.00015558382,0.0005157874,0.0015642975,0.00022907824,0.00042705552],"genre_scores_gemma":[0.059517782,0.000006804281,0.92842793,0.0007106501,0.00072197855,0.00006794885,0.010330683,0.000040819203,0.0001753705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873364,0.000051145336,0.00019118015,0.0004991597,0.00014850251,0.00037634853],"domain_scores_gemma":[0.99867177,0.00049197325,0.000094264935,0.0004597173,0.00021270424,0.00006958453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007573301,0.00013473067,0.00014907938,0.00004657477,0.00016530146,0.0001842868,0.00022914784,0.00010236926,0.00017007683],"category_scores_gemma":[0.0008806586,0.00015424333,0.00007782954,0.0008628321,0.0000068003555,0.00045535236,0.00020138959,0.00024695712,0.00010164954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024564142,0.000025678519,8.914788e-7,0.00010542228,0.000011791627,0.00019634496,0.00011561956,0.021784324,0.026261332,0.00065259,0.04193672,0.9088847],"study_design_scores_gemma":[0.00031111613,0.000028661338,0.0007552565,0.00046125855,0.0000042292813,0.0001166983,0.000010939053,0.94574714,0.030105712,0.009375856,0.012819757,0.00026338358],"about_ca_topic_score_codex":0.0000018495417,"about_ca_topic_score_gemma":0.00004482446,"teacher_disagreement_score":0.92396283,"about_ca_system_score_codex":0.000096623524,"about_ca_system_score_gemma":0.00007790107,"threshold_uncertainty_score":0.628986},"labels":[],"label_agreement":null},{"id":"W3205652345","doi":"10.1109/cvpr52688.2022.01213","title":"Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Huawei Technologies (Canada); University of Alberta","funders":"","keywords":"FLOPS; Computer science; Regularization (linguistics); Hyperparameter; Artificial intelligence; Reduction (mathematics); Artificial neural network; Machine learning; Pruning; Computational complexity theory; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.025838379657593485,"score_gpt":0.24675575112125525,"score_spread":0.22091737146366178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205652345","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":[0.18858004,0.000048084614,0.80711436,0.0013032395,0.00045301448,0.00092594797,0.00010224977,0.00064859784,0.00082447956],"genre_scores_gemma":[0.9459079,0.00015729043,0.04989434,0.0027013887,0.00015730778,0.0006136293,0.00029217283,0.00005815455,0.00021782401],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679065,0.00040733418,0.00043533478,0.001221674,0.00068900187,0.00045601078],"domain_scores_gemma":[0.99850386,0.00014206479,0.00026043062,0.00072397303,0.00015507815,0.00021459286],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030032874,0.0004277279,0.00037046988,0.00020637864,0.00086588296,0.00034014194,0.0007522278,0.00010114744,0.0002769354],"category_scores_gemma":[0.000002682448,0.00038838157,0.000092596194,0.00067492865,0.00007166686,0.00059094216,0.00045265307,0.00070633076,0.00005804977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089861525,0.00052232115,0.000509276,0.000058321853,0.00006312399,0.000035677454,0.001960601,0.0029679558,0.0006689427,0.0003032823,0.0011705161,0.9916501],"study_design_scores_gemma":[0.0012265433,0.0012022915,0.0008475909,0.00010857208,0.000024543659,0.00021700398,0.00020204282,0.99272716,0.00008931632,0.0011223988,0.0017162452,0.00051630655],"about_ca_topic_score_codex":0.000012971114,"about_ca_topic_score_gemma":0.000008757042,"teacher_disagreement_score":0.9911338,"about_ca_system_score_codex":0.00011797616,"about_ca_system_score_gemma":0.000072759336,"threshold_uncertainty_score":0.9998568},"labels":[],"label_agreement":null},{"id":"W3206480303","doi":"10.1109/icra48506.2021.9561171","title":"Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Lidar; Segmentation; Artificial intelligence; Encoder; Computer vision; Point cloud; Convolutional neural network; Remote sensing","score_opus":0.03718968977370427,"score_gpt":0.2994782723740695,"score_spread":0.2622885826003652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206480303","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":[0.08786286,0.00050783134,0.9089079,0.0015072197,0.00018636571,0.00018955713,0.0000029148373,0.00028050286,0.000554837],"genre_scores_gemma":[0.61348146,0.00009843874,0.3839284,0.0009590607,0.00008036976,0.000029949197,0.000014478513,0.000013470351,0.0013943757],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866825,0.00007451714,0.00026677136,0.00048030104,0.00020390181,0.0003062839],"domain_scores_gemma":[0.9987947,0.00013476262,0.00008336587,0.0006824314,0.00019106967,0.00011368343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000082159895,0.000135594,0.00012915477,0.0000684192,0.00031422384,0.00017865599,0.00034276012,0.000045617577,0.000054949367],"category_scores_gemma":[0.000029797295,0.00014238173,0.00007025771,0.0010648528,0.000045107245,0.00064749044,0.00026145502,0.00012964595,0.00016723362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007319731,0.0003844012,0.0036889976,0.000059896684,0.00009488091,0.00017401145,0.0012590932,0.015844153,0.58220637,0.34750336,0.0022567476,0.04652076],"study_design_scores_gemma":[0.00068682915,0.000042379215,0.0046567144,0.00006630314,0.000054444,0.00038587046,0.00017076351,0.8361399,0.121673316,0.027523022,0.007953123,0.00064731523],"about_ca_topic_score_codex":0.000013717262,"about_ca_topic_score_gemma":0.000022128614,"teacher_disagreement_score":0.82029575,"about_ca_system_score_codex":0.0001082334,"about_ca_system_score_gemma":0.00012909863,"threshold_uncertainty_score":0.5806159},"labels":[],"label_agreement":null},{"id":"W3206990726","doi":"10.1145/3487025","title":"Adaptive Computation Reuse for Energy-Efficient Training of Deep Neural Networks","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Embedded Computing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Computation; Speedup; Operand; Artificial neural network; Inference; Efficient energy use; Computer engineering; Reuse; Deep neural networks; Energy (signal processing); Artificial intelligence; Parallel computing; Algorithm; Computer hardware","score_opus":0.03808900066656054,"score_gpt":0.28163727943535805,"score_spread":0.2435482787687975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206990726","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":[0.0026394469,0.00030100753,0.9946804,0.0002906269,0.001213576,0.00043925637,0.000009860439,0.00033974933,0.000086085354],"genre_scores_gemma":[0.7968104,0.000005013492,0.20279987,0.000114664734,0.00012474673,0.00008023239,0.000013501185,0.000027856666,0.000023688634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975401,0.00021895305,0.00071127404,0.0007329375,0.0003313682,0.000465367],"domain_scores_gemma":[0.9961809,0.0014928563,0.00039049203,0.0013603828,0.00044717646,0.00012819066],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027468268,0.0002650893,0.00042443286,0.00015738417,0.00046575046,0.000106748674,0.0011019501,0.00011606274,0.0000013134306],"category_scores_gemma":[0.00006526378,0.00028898628,0.00021359863,0.0011217338,0.000060119415,0.00015882525,0.000060469985,0.00024043261,0.0000016230761],"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":[0.00001593129,0.00010584664,8.310787e-7,0.000016738679,0.000044851688,0.000003852488,0.0010923832,0.87595725,0.0002015071,0.008472585,0.000023951563,0.11406425],"study_design_scores_gemma":[0.000577939,0.00014568133,0.000013632402,0.00007747516,0.000027849066,0.00006867237,0.0006779703,0.9959611,0.00085196586,0.0012633745,0.00007935929,0.00025493023],"about_ca_topic_score_codex":0.000012245612,"about_ca_topic_score_gemma":0.0000073066126,"teacher_disagreement_score":0.794171,"about_ca_system_score_codex":0.00010499778,"about_ca_system_score_gemma":0.000061085535,"threshold_uncertainty_score":0.99995625},"labels":[],"label_agreement":null},{"id":"W3207820301","doi":"10.1109/fpl53798.2021.00060","title":"Leveraging Fine-grained Structured Sparsity for CNN Inference on Systolic Array Architectures","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Systolic array; Convolutional neural network; Field-programmable gate array; Computer science; Inference; Leverage (statistics); Parallel computing; Speedup; Benchmark (surveying); Pattern recognition (psychology); Artificial intelligence; Computer engineering; Very-large-scale integration; Computer hardware; Embedded system","score_opus":0.033967923211109585,"score_gpt":0.2865431310557664,"score_spread":0.2525752078446568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207820301","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":[0.035671543,0.00003495201,0.9593023,0.0030499587,0.00016709801,0.000293598,0.0000052721807,0.00027492337,0.0012003239],"genre_scores_gemma":[0.78778684,0.000001939158,0.21046196,0.0010960327,0.000091465874,0.00005140068,0.0000095697615,0.000008225403,0.00049256114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987387,0.00004029615,0.00017600889,0.00055009965,0.00017700838,0.00031788775],"domain_scores_gemma":[0.9985066,0.0004522306,0.00007309591,0.00076158333,0.00010793733,0.00009851943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005970609,0.00016399656,0.00017522908,0.00006140245,0.000241017,0.00009528427,0.0006250897,0.000042632957,0.000018972398],"category_scores_gemma":[0.00013985157,0.00014382758,0.000077927456,0.0004393942,0.00003137524,0.00008342539,0.00014834978,0.00015553895,0.000014819491],"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":[0.000036423084,0.0001296464,0.00082099106,0.000076997974,0.0000611684,0.000025507881,0.0011412993,0.10880249,0.07871037,0.67087495,0.003205646,0.13611448],"study_design_scores_gemma":[0.0014018943,0.00017953981,0.014526038,0.00009150348,0.000022627235,0.00009033574,0.000044965844,0.11330062,0.34929943,0.49548233,0.024485266,0.0010754486],"about_ca_topic_score_codex":0.000005523028,"about_ca_topic_score_gemma":0.000057945603,"teacher_disagreement_score":0.7521153,"about_ca_system_score_codex":0.00003724914,"about_ca_system_score_gemma":0.000072667426,"threshold_uncertainty_score":0.58651185},"labels":[],"label_agreement":null},{"id":"W3208248311","doi":"10.3390/jimaging7110227","title":"A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms","year":2021,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Wind power; Object detection; Set (abstract data type); Artificial intelligence; Collision; Data set; Data mining; Pattern recognition (psychology)","score_opus":0.02628347770258195,"score_gpt":0.28905108503114374,"score_spread":0.2627676073285618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208248311","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":[0.020226669,0.00026520676,0.9758073,0.0033150928,0.00021820265,0.00009197402,0.0000014408965,0.000039920895,0.000034190893],"genre_scores_gemma":[0.7771427,0.0000055595438,0.22204255,0.00060725264,0.00013940802,0.0000041042435,7.2582145e-7,0.000010630015,0.000047052356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906814,0.000027741127,0.0003378258,0.00017642012,0.00019878485,0.00019108181],"domain_scores_gemma":[0.9988425,0.00011225982,0.00033616569,0.00024067657,0.0003851151,0.00008328049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002076455,0.00009387674,0.00014819673,0.00011084927,0.00014542317,0.00013004485,0.00031573232,0.000021321508,9.841941e-7],"category_scores_gemma":[0.00006318057,0.0000871917,0.00012354212,0.00037242565,0.000023319748,0.0006583207,0.000052988893,0.0001780266,0.0000015673818],"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":[0.000068513,0.00017104912,0.0023388606,0.00003217315,0.000039608756,0.00010118929,0.0002658662,0.6584909,0.16902068,0.0009366687,0.0008247328,0.16770975],"study_design_scores_gemma":[0.00055946177,0.000027317465,0.00016980428,0.000027001917,0.000011600238,0.00021663563,0.000023420713,0.9689074,0.021123786,0.0074562957,0.0013828339,0.0000943981],"about_ca_topic_score_codex":8.471864e-7,"about_ca_topic_score_gemma":0.0000075178496,"teacher_disagreement_score":0.75691605,"about_ca_system_score_codex":0.00008877279,"about_ca_system_score_gemma":0.00019122334,"threshold_uncertainty_score":0.35555744},"labels":[],"label_agreement":null},{"id":"W3208470653","doi":"10.1109/iccvw54120.2021.00091","title":"Knowledge Distillation for Low-Power Object Detection: A Simple Technique and Its Extensions for Training Compact Models Using Unlabeled Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Distillation; Computer science; Simple (philosophy); Artificial intelligence; Object (grammar); Training set; Training (meteorology); Machine learning; Object detection; Power (physics); Pattern recognition (psychology); Chromatography; Chemistry","score_opus":0.19034744264367703,"score_gpt":0.377449432259311,"score_spread":0.18710198961563396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208470653","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":[0.005151974,0.00021909119,0.9927674,0.00021344646,0.0000775843,0.0011504547,0.000063781496,0.00021325967,0.0001430034],"genre_scores_gemma":[0.7448592,0.000008462862,0.2548136,0.000052609314,0.000049804526,0.00010393104,0.000045747263,0.000015396678,0.00005124433],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998755,0.000032538956,0.00023954349,0.0006299958,0.00007806663,0.00026490286],"domain_scores_gemma":[0.99826133,0.0005556604,0.00008608606,0.00071328215,0.00029289018,0.000090771195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000195795,0.00014135729,0.00019301305,0.00005840475,0.0004500405,0.00009238307,0.00034132288,0.000060770053,0.0000028974862],"category_scores_gemma":[0.00013510718,0.00013825118,0.000042775384,0.0004910897,0.000021774367,0.0007988315,0.00027817264,0.00007617771,5.8549557e-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":[0.000061076644,0.00025908183,0.000010400898,0.00015592301,0.00008529955,0.000004908561,0.0012083864,0.08301364,0.6394676,0.18262006,0.0011199088,0.09199371],"study_design_scores_gemma":[0.0002913289,0.000033491666,0.000018911087,0.000023087541,0.000013931973,0.000044957884,0.00003792558,0.9453591,0.025803043,0.026671736,0.0015341195,0.00016834617],"about_ca_topic_score_codex":0.000001997711,"about_ca_topic_score_gemma":0.000048163278,"teacher_disagreement_score":0.86234546,"about_ca_system_score_codex":0.000043383443,"about_ca_system_score_gemma":0.00011377798,"threshold_uncertainty_score":0.56377196},"labels":[],"label_agreement":null},{"id":"W3208814753","doi":"10.1109/itsc48978.2021.9564842","title":"Pattern-Aware Data Augmentation for LiDAR 3D Object Detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Point cloud; Computer science; Lidar; Artificial intelligence; Object detection; Ground truth; Object (grammar); Computer vision; Detector; Point (geometry); Pattern recognition (psychology); Remote sensing; Mathematics; Geography","score_opus":0.06687734152946967,"score_gpt":0.3356732612044193,"score_spread":0.26879591967494965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208814753","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":[0.0010162448,0.000088922156,0.9955854,0.00070949557,0.00095806946,0.0010112125,0.000083022016,0.00041122537,0.00013645166],"genre_scores_gemma":[0.5666628,0.00011365396,0.42882586,0.0007358237,0.0004632169,0.00087640516,0.0019727498,0.000036280853,0.00031323443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978987,0.000051949795,0.00031659732,0.0012682795,0.00022693897,0.00023752482],"domain_scores_gemma":[0.9965683,0.00016640077,0.00022468073,0.0028148154,0.00015927205,0.00006649233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015507714,0.00022244043,0.00020454836,0.00007304101,0.00017351014,0.00036290172,0.0019990574,0.00014850563,0.000020769301],"category_scores_gemma":[0.000030665444,0.00023209582,0.000073512,0.00023987706,0.000017107077,0.00060318253,0.0035856983,0.0002855289,0.000017119899],"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":[0.0000041325025,0.000058515998,0.00004667332,0.00012221797,0.00004446735,0.0000034111001,0.00014798087,0.006429917,0.0012679372,0.00027428987,0.0011554919,0.99044496],"study_design_scores_gemma":[0.0002508985,0.000035894158,0.0004584603,0.000059452548,0.00003177026,0.000011155521,0.000044664208,0.979391,0.0121829305,0.002926552,0.004193191,0.0004140276],"about_ca_topic_score_codex":0.00006521662,"about_ca_topic_score_gemma":0.00037875347,"teacher_disagreement_score":0.99003094,"about_ca_system_score_codex":0.000105415944,"about_ca_system_score_gemma":0.000110520334,"threshold_uncertainty_score":0.9464593},"labels":[],"label_agreement":null},{"id":"W3209839293","doi":"10.48550/arxiv.2110.13220","title":"Demystifying and Generalizing BinaryConnect","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quantization (signal processing); Generalization; Computer science; De facto; Artificial neural network; Convergence (economics); Algorithm; Artificial intelligence; Mathematics; Theoretical computer science","score_opus":0.10771480772941842,"score_gpt":0.20660039366899716,"score_spread":0.09888558593957873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209839293","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":[0.32381326,0.00036313292,0.674639,0.00025486684,0.00017950386,0.0001397377,0.0000019264207,0.00020702847,0.00040157768],"genre_scores_gemma":[0.9715443,0.00066193304,0.027202005,0.00018644147,0.00006746527,0.0000019012842,0.000010571572,0.000015493033,0.00030983542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982777,0.00009148469,0.00013904466,0.0011562589,0.000052149862,0.00028332527],"domain_scores_gemma":[0.9984825,0.00012845948,0.00015358512,0.0010060654,0.00008278372,0.0001465969],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009333819,0.00023377503,0.00023933104,0.00012612606,0.00023996696,0.00016669296,0.0009638337,0.00015717378,0.00000755621],"category_scores_gemma":[0.000019629619,0.00029258936,0.00009417317,0.00057104987,0.00007028202,0.00039540077,0.0030650045,0.00042394668,0.000011279076],"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":[0.0000052276237,0.00004477075,0.0019298779,0.00008077842,0.00006705517,0.00059882295,0.00024394126,0.51703703,0.00092756114,0.47477806,0.00013759253,0.0041493154],"study_design_scores_gemma":[0.00022904045,0.000017428702,0.0010184513,0.000103280174,0.000042198273,0.00003475041,0.00006238772,0.9341356,0.00043702696,0.06268594,0.00070653466,0.00052732974],"about_ca_topic_score_codex":0.000037285416,"about_ca_topic_score_gemma":0.000021840804,"teacher_disagreement_score":0.64773107,"about_ca_system_score_codex":0.00009589284,"about_ca_system_score_gemma":0.000080802696,"threshold_uncertainty_score":0.9999526},"labels":[],"label_agreement":null},{"id":"W3209952223","doi":"10.3390/ai2040034","title":"A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models","year":2021,"lang":"en","type":"article","venue":"AI","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":35,"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; University of Victoria","funders":"","keywords":"Computer science; Object detection; Convolutional neural network; Artificial intelligence; Object (grammar); Focus (optics); Wildlife; Range (aeronautics); Computer vision; Animal species; Class (philosophy); Machine learning; Pattern recognition (psychology); Ecology; Engineering","score_opus":0.019337182629627192,"score_gpt":0.2562978702347038,"score_spread":0.2369606876050766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209952223","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":[0.5102483,0.00010870977,0.4890106,0.00019907817,0.0000103688135,0.000054424156,2.712318e-7,0.0000143675625,0.0003538903],"genre_scores_gemma":[0.9672438,0.00009180349,0.03251007,0.00005964247,0.000009152309,0.000019222181,5.8817005e-7,0.0000029718453,0.000062740735],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947417,0.000013056608,0.00012475363,0.00018858798,0.00009648877,0.00010291871],"domain_scores_gemma":[0.9996659,0.00002471916,0.000050544575,0.00018950478,0.00004956423,0.000019725612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000035241752,0.000056857443,0.00010016628,0.000029309269,0.00004092632,0.000020495749,0.000104775514,0.000013897506,0.0000021228045],"category_scores_gemma":[0.0000020658654,0.00005193385,0.000007351749,0.00029328416,0.0000350228,0.0003270649,0.00008855776,0.000071308845,0.0000014371223],"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":[0.00015319172,0.00049214193,0.018370733,0.000116561336,0.000030244722,0.00002382767,0.003102998,0.06843046,0.57464564,0.023370445,0.00016153685,0.3111022],"study_design_scores_gemma":[0.00020155453,0.00018536524,0.014457472,0.000021484882,0.0000024788062,0.00001200902,0.000024348225,0.6437053,0.34032613,0.0008368942,0.00015109942,0.00007587376],"about_ca_topic_score_codex":0.000007695603,"about_ca_topic_score_gemma":0.000048895396,"teacher_disagreement_score":0.5752748,"about_ca_system_score_codex":0.000018852177,"about_ca_system_score_gemma":0.000018932704,"threshold_uncertainty_score":0.21178009},"labels":[],"label_agreement":null},{"id":"W3210283651","doi":"10.1109/jiot.2021.3058192","title":"Integrated Air-Ground Vehicles for UAV Emergency Landing Based on Graph Convolution Network","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Domain (mathematical analysis); Heuristic; Convolution (computer science); Graph; Terrain; Emergency management; Real-time computing; Collision avoidance; Simulation; Artificial intelligence; Collision; Computer security; Artificial neural network","score_opus":0.0272052211438782,"score_gpt":0.2783655265841718,"score_spread":0.2511603054402936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210283651","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":[0.035350777,0.00025710216,0.96067613,0.0014265832,0.0018646833,0.00012954121,0.0000027384665,0.00006990043,0.00022253174],"genre_scores_gemma":[0.89416987,0.00007963737,0.10400512,0.000932174,0.00037353148,0.000019027684,0.000007888379,0.000019399251,0.0003933414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984899,0.00008990111,0.0005138845,0.00030274419,0.0002767741,0.00032675432],"domain_scores_gemma":[0.9985273,0.00022756394,0.00044121477,0.00030157814,0.0003914205,0.00011093764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039995046,0.00016399428,0.00022292865,0.00011280965,0.00017500223,0.000083209066,0.0007000154,0.00007382913,0.00002678824],"category_scores_gemma":[0.00008644957,0.00014861784,0.00020787993,0.00051479525,0.000039779472,0.00051330036,0.00006134311,0.00039467175,0.0000066215466],"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":[0.0006660303,0.0010081974,0.0064733727,0.00018783826,0.00051305734,0.00017521446,0.0024283482,0.35853693,0.10814042,0.12000298,0.30544946,0.09641815],"study_design_scores_gemma":[0.00089556456,0.0003523005,0.0007255045,0.0003724846,0.000032589778,0.00017334986,0.00003861548,0.89204055,0.026684787,0.06418625,0.014165593,0.0003323943],"about_ca_topic_score_codex":0.000011760742,"about_ca_topic_score_gemma":0.000006870389,"teacher_disagreement_score":0.8588191,"about_ca_system_score_codex":0.00009336068,"about_ca_system_score_gemma":0.00007447071,"threshold_uncertainty_score":0.60604596},"labels":[],"label_agreement":null},{"id":"W3211288915","doi":"10.3390/s21217018","title":"Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"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; Toronto Metropolitan University; St. Michael's Hospital","funders":"Ryerson University","keywords":"Generalizability theory; Computer science; Medical imaging; Artificial intelligence; Segmentation; CMA-ES; Machine learning; Set (abstract data type); Deep learning; Adaptation (eye); Selection (genetic algorithm); Variety (cybernetics); Data mining; Evolution strategy; Evolutionary algorithm; Mathematics","score_opus":0.03043058301822709,"score_gpt":0.33652721590187035,"score_spread":0.30609663288364325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211288915","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":[0.047334686,0.00019680306,0.9459333,0.0056345672,0.0003272444,0.00028015446,0.0000029438331,0.00013874787,0.00015157323],"genre_scores_gemma":[0.7504121,0.0007203375,0.24368516,0.0023068388,0.0008001056,0.00019233061,0.0013291776,0.000051438125,0.00050248916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765635,0.0001767568,0.00040364685,0.0006875902,0.0007020615,0.0003736234],"domain_scores_gemma":[0.99826974,0.00069118704,0.00011485764,0.0006266638,0.00007865566,0.00021889723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006161485,0.00013659589,0.00017469822,0.00006576041,0.00016373734,0.000072764415,0.00092568743,0.00014907123,0.000105783816],"category_scores_gemma":[0.0006859896,0.00014571942,0.00003790288,0.00064718974,0.000062661165,0.00042216724,0.0005264109,0.00037888138,0.000019930569],"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":[0.000030114757,0.00013493183,0.00090837764,0.000019122963,0.000026388123,0.00016637251,0.00051058404,0.4995311,0.00010826825,0.010699742,0.0013278691,0.48653713],"study_design_scores_gemma":[0.00078151986,0.0000266898,0.0009412243,0.000033388227,0.000006733568,0.000056477915,0.00014152286,0.99263394,0.0002176543,0.00044501078,0.004567026,0.00014879892],"about_ca_topic_score_codex":0.000013565067,"about_ca_topic_score_gemma":0.00025014547,"teacher_disagreement_score":0.70307744,"about_ca_system_score_codex":0.000073976626,"about_ca_system_score_gemma":0.00014372864,"threshold_uncertainty_score":0.59422654},"labels":[],"label_agreement":null},{"id":"W3212132242","doi":"10.1109/embc48229.2022.9871361","title":"Interactive Segmentation Using U-Net with Weight Map and Dynamic User Interactions","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Function (biology); Image segmentation; Artificial neural network; Net (polyhedron); Pattern recognition (psychology); Mathematics","score_opus":0.02442783485139987,"score_gpt":0.3093675847972429,"score_spread":0.28493974994584303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212132242","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":[0.3672405,0.00011135825,0.62506706,0.0052047772,0.0018259076,0.00033882834,0.000070446884,0.00006304394,0.00007808541],"genre_scores_gemma":[0.96224016,0.00010386083,0.03651844,0.00036949097,0.00009571303,0.00011341518,0.00006678882,0.000014749015,0.0004773729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988345,0.0000806194,0.00031040152,0.000353846,0.00022464759,0.00019598713],"domain_scores_gemma":[0.99902576,0.0002469836,0.00023092267,0.00028565497,0.00016472573,0.000045929028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000217662,0.00017027448,0.000204438,0.00016069718,0.00014636724,0.000015600506,0.00078397663,0.00003904176,0.00009148054],"category_scores_gemma":[0.00003838421,0.0001337521,0.00005504503,0.00052881683,0.0001719783,0.00032617993,0.00040653805,0.00056458806,0.0000012366154],"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":[0.00026038627,0.0004859048,0.02182614,0.000119026474,0.0010232648,0.000007887885,0.029237425,0.52963036,0.34744054,0.04822046,0.017168483,0.0045800894],"study_design_scores_gemma":[0.0013135463,0.0002251819,0.004106557,0.00016992578,0.000039349365,0.00013850014,0.0020540657,0.9654994,0.00068459153,0.002029916,0.023346694,0.00039227857],"about_ca_topic_score_codex":0.00010343738,"about_ca_topic_score_gemma":0.00009120207,"teacher_disagreement_score":0.5949997,"about_ca_system_score_codex":0.00021704136,"about_ca_system_score_gemma":0.000054209,"threshold_uncertainty_score":0.54542524},"labels":[],"label_agreement":null},{"id":"W3214067976","doi":"10.1016/j.neucom.2021.10.114","title":"OASIS: One-pass aligned atlas set for medical image segmentation","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"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":"Major Science and Technology Project of Hainan Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Segmentation; Image warping; Atlas (anatomy); Computer vision; Pattern recognition (psychology); Image segmentation; Deep learning; Image registration; Convolutional neural network; Scale-space segmentation; Image (mathematics)","score_opus":0.026678490075744035,"score_gpt":0.311344583029763,"score_spread":0.28466609295401896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214067976","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":[0.019363848,0.000039536866,0.97307354,0.0061429343,0.0002795689,0.0003637501,0.0000031691134,0.00034635293,0.00038729902],"genre_scores_gemma":[0.27814168,0.000019750156,0.71676356,0.00416531,0.0005717674,0.00014238383,0.000045606568,0.0000355292,0.000114393435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981383,0.00009479245,0.0003478688,0.00063498784,0.00041390327,0.0003701615],"domain_scores_gemma":[0.99846876,0.0006064148,0.00014129472,0.00046446887,0.00016316338,0.00015589243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020162825,0.00014545991,0.0001713247,0.000043339405,0.00030348593,0.00015742789,0.00064505794,0.000061067476,0.000013398794],"category_scores_gemma":[0.00017853118,0.00016322016,0.000086134394,0.0005342206,0.000030840518,0.00031707002,0.00043780654,0.0001226053,0.00003913282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001954314,0.00038404265,0.00042255476,0.00016003981,0.00006914915,0.00020141777,0.0010201448,0.0060676476,0.26671112,0.072203256,0.012565504,0.6401756],"study_design_scores_gemma":[0.0013485817,0.00010117612,0.0014323596,0.000083329796,0.00002278323,0.00018184946,0.00006245747,0.83677125,0.13389021,0.010670884,0.014952025,0.00048311276],"about_ca_topic_score_codex":0.0000016147716,"about_ca_topic_score_gemma":0.0000041891476,"teacher_disagreement_score":0.83070356,"about_ca_system_score_codex":0.000034094843,"about_ca_system_score_gemma":0.000102425736,"threshold_uncertainty_score":0.6655925},"labels":[],"label_agreement":null},{"id":"W3214592045","doi":"10.18280/ria.350510","title":"Deep Learning-Based X-Ray Baggage Hazardous Object Detection – An FPGA Implementation","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"","keywords":"Convolutional neural network; Field-programmable gate array; Computer science; Object detection; Deep learning; Process (computing); Artificial intelligence; Implementation; Artificial neural network; Function (biology); Object (grammar); Real-time computing; Resource (disambiguation); Task (project management); Embedded system; Computer vision; Computer engineering; Pattern recognition (psychology); Systems engineering; Engineering; Software engineering; Operating system","score_opus":0.03143180504127736,"score_gpt":0.30555335015818325,"score_spread":0.2741215451169059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214592045","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":[0.014550978,0.00022883869,0.98340917,0.0005360356,0.00027644262,0.0002726416,0.0000012207104,0.00037273802,0.00035193015],"genre_scores_gemma":[0.97399026,0.00004971367,0.025045665,0.00027376766,0.00011433343,0.00010575787,0.000033671484,0.000023694083,0.00036311455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997978,0.00020975794,0.00042679335,0.00074799865,0.00022753338,0.0004099651],"domain_scores_gemma":[0.9983575,0.00020852535,0.00017433047,0.0008857296,0.00022943344,0.0001445314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028815484,0.00018938156,0.00017167324,0.00010458857,0.00050189084,0.0002003832,0.0005526202,0.00007137654,0.00018200977],"category_scores_gemma":[0.000084462845,0.00021572456,0.00010006146,0.0011734575,0.00005098905,0.000552384,0.00012303036,0.0002967572,0.00034880755],"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":[0.0000036491651,0.00007167504,0.00006041491,0.000008898255,0.0000042895126,0.000017340406,0.00035622914,0.552058,0.021670707,0.0011589817,0.000013432008,0.4245764],"study_design_scores_gemma":[0.0000365204,0.00010455227,0.00013243746,0.000008225397,0.0000056812178,0.000017670032,0.0003078547,0.61679596,0.3765107,0.0014697342,0.004443297,0.00016739168],"about_ca_topic_score_codex":0.000018276321,"about_ca_topic_score_gemma":0.00031700864,"teacher_disagreement_score":0.9594393,"about_ca_system_score_codex":0.00010314784,"about_ca_system_score_gemma":0.000075382224,"threshold_uncertainty_score":0.87969923},"labels":[],"label_agreement":null},{"id":"W3214734349","doi":"10.1038/s41598-021-01681-w","title":"Effect of data leakage in brain MRI classification using 2D convolutional neural networks","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":123,"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 Institute of Mental Health; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Nvidia; National Center for Research Resources; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; European Commission; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Michael J. Fox Foundation for Parkinson's Research; Foundation for the National Institutes of Health","keywords":"Convolutional neural network; Neuroimaging; Computer science; Alzheimer's Disease Neuroimaging Initiative; Magnetic resonance imaging; Artificial intelligence; Pattern recognition (psychology); Generalization; Dementia; Machine learning; Disease; Medicine; Neuroscience; Pathology; Psychology; Mathematics; Radiology","score_opus":0.051471089204939764,"score_gpt":0.331244131534037,"score_spread":0.2797730423290972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214734349","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":[0.1944509,0.00036520063,0.80135983,0.00068124925,0.0025860576,0.000344857,0.000003978804,0.00007416101,0.0001337795],"genre_scores_gemma":[0.9760257,0.0000034317131,0.023391824,0.00005012486,0.00007054124,0.000015720194,0.00022510145,0.00000948882,0.00020808206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717194,0.00024468976,0.0006016256,0.0012229185,0.00044950016,0.00030934718],"domain_scores_gemma":[0.99621594,0.0003531944,0.00040026385,0.0027945924,0.00015499938,0.000081013066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020523712,0.0001301174,0.00021027282,0.00012665658,0.00020791824,0.0001757295,0.00077689934,0.00006158841,0.00001153916],"category_scores_gemma":[0.00024977684,0.00012865652,0.00005148698,0.0019266999,0.0002214104,0.0009995679,0.00073980005,0.00017667016,0.0000029180103],"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":[0.00002735876,0.00035442354,0.031498082,0.000118613345,0.000034785397,0.0012579302,0.0002830136,0.5434102,0.2648872,0.015931172,0.02007571,0.122121535],"study_design_scores_gemma":[0.00011350678,0.00001380597,0.0021079427,0.000024264717,0.0000058663454,0.00028388726,0.00000503647,0.9871981,0.0052651344,0.0026567103,0.0022079092,0.00011779951],"about_ca_topic_score_codex":0.000009013766,"about_ca_topic_score_gemma":0.0000311768,"teacher_disagreement_score":0.7815748,"about_ca_system_score_codex":0.00006910764,"about_ca_system_score_gemma":0.0001662718,"threshold_uncertainty_score":0.52464604},"labels":[],"label_agreement":null},{"id":"W3214953945","doi":"10.1109/cvprw56347.2022.00310","title":"MAPLE: Microprocessor A Priori for Latency Estimation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Latency (audio); Microprocessor; Artificial neural network; Deep learning; Field-programmable gate array; A priori and a posteriori; Efficient energy use; Energy consumption; Hardware architecture; Maple; Computer hardware; Computer engineering; Artificial intelligence; Embedded system; Software; Operating system","score_opus":0.04150276098953664,"score_gpt":0.29499153669012596,"score_spread":0.2534887757005893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214953945","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":[0.028356943,0.000083501254,0.96492827,0.0036932295,0.0012666106,0.0010756424,0.00011612237,0.00033035723,0.00014932983],"genre_scores_gemma":[0.892459,0.00021809876,0.100185774,0.0046582576,0.0003372083,0.0012178143,0.00042364837,0.00005584002,0.00044440394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723536,0.00018498243,0.0005313364,0.0010904806,0.00048472165,0.00047310806],"domain_scores_gemma":[0.99825567,0.00038600882,0.00034051042,0.00058581895,0.00023345897,0.00019853404],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003480458,0.00037171447,0.00034098787,0.0002525356,0.00092272967,0.00037904695,0.00081287546,0.00009940943,0.00023297759],"category_scores_gemma":[0.000014955205,0.00037736743,0.000120599594,0.0006933871,0.000054816308,0.0004967637,0.00044631597,0.0005145912,0.00008953417],"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":[0.000058426707,0.00020190752,0.000040643867,0.000037246897,0.000017749646,0.00001047414,0.00044751377,0.0014361533,0.00084408716,0.0010921948,0.007681173,0.9881324],"study_design_scores_gemma":[0.0013443249,0.00084101903,0.0006614749,0.00013184319,0.000022330732,0.00009626712,0.00005553321,0.9661367,0.000697332,0.021746805,0.0075828205,0.00068357546],"about_ca_topic_score_codex":0.0000059398117,"about_ca_topic_score_gemma":0.000009756828,"teacher_disagreement_score":0.9874489,"about_ca_system_score_codex":0.00008567145,"about_ca_system_score_gemma":0.000069138914,"threshold_uncertainty_score":0.9998678},"labels":[],"label_agreement":null},{"id":"W3216738415","doi":"10.1145/3479243.3487300","title":"Flexi-Compression: A Flexible Model Compression Method for Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Leverage (statistics); Convolutional neural network; Computation; Data compression; Image compression; Normalization (sociology); Data compression ratio; Artificial intelligence; Artificial neural network; Compression (physics); Quantization (signal processing); Algorithm; Image processing; Image (mathematics)","score_opus":0.04434523240509253,"score_gpt":0.3512171176312873,"score_spread":0.3068718852261948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216738415","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":[0.0001537306,0.00015648406,0.9897906,0.0028085476,0.00023634237,0.00034368082,0.0000030667363,0.0007143275,0.0057931873],"genre_scores_gemma":[0.023105444,0.00001126803,0.9674828,0.0011734841,0.00012430156,0.00022870464,0.00001071068,0.000020337227,0.007842929],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983319,0.000061001294,0.0002974771,0.0007217728,0.00020304964,0.00038481946],"domain_scores_gemma":[0.9981359,0.00042239326,0.00010421571,0.0010078935,0.00017262335,0.00015694054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015179464,0.00018009062,0.00023335425,0.00005380229,0.00037404182,0.00013474695,0.0008178144,0.000075905285,0.000029311994],"category_scores_gemma":[0.000033885546,0.00016071298,0.000117287986,0.00044087824,0.000021249283,0.0005017342,0.0006975819,0.00014805686,0.000023218337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056137833,0.00011035272,0.00001272578,0.000016571372,0.000011624005,0.000004532675,0.00013152757,0.38933906,0.054740958,0.4139701,0.009216216,0.13244073],"study_design_scores_gemma":[0.00024606028,0.000013648848,0.000018929542,0.000024534978,0.0000048359357,0.000019595174,0.000007669605,0.82246745,0.07815446,0.07250824,0.026356293,0.00017830911],"about_ca_topic_score_codex":0.0000024958706,"about_ca_topic_score_gemma":0.0000045862143,"teacher_disagreement_score":0.4331284,"about_ca_system_score_codex":0.000047879388,"about_ca_system_score_gemma":0.00012682744,"threshold_uncertainty_score":0.6553685},"labels":[],"label_agreement":null},{"id":"W3216943664","doi":"10.48550/arxiv.2111.14953","title":"Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Dice; Visualization; Pipeline (software); Similarity (geometry); Sørensen–Dice coefficient; Image segmentation; Mathematics; Image (mathematics)","score_opus":0.06276065334857993,"score_gpt":0.22388278144944174,"score_spread":0.1611221281008618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216943664","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":[0.16160548,0.00006633266,0.83639723,0.0006617733,0.00021179032,0.00070913066,0.000035056437,0.00014243671,0.00017077824],"genre_scores_gemma":[0.9380562,0.000045249413,0.06093859,0.00016035017,0.00004870667,0.000019355944,0.00016109085,0.000020794309,0.0005496917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983053,0.000115949384,0.00028327218,0.00093492755,0.00009832484,0.00026217892],"domain_scores_gemma":[0.99766356,0.00043169153,0.00033688554,0.0010830852,0.0003654194,0.0001193593],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012970688,0.0002457084,0.00032903676,0.00021097218,0.00017280788,0.00007121741,0.0012113589,0.00016892486,0.000019602958],"category_scores_gemma":[0.000070049995,0.00030560777,0.00026275578,0.0009890514,0.00009797471,0.0004037646,0.00082540733,0.00022550758,0.0000060858433],"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":[0.000053615397,0.00026183182,0.00044361947,0.00019186974,0.00012357556,0.000049282164,0.0006621005,0.6991457,0.01839685,0.27694103,0.0012489082,0.0024816585],"study_design_scores_gemma":[0.0008722213,0.000056699006,0.00042108478,0.00008347938,0.00008037679,0.0000051322895,0.00024746725,0.9478445,0.009565992,0.040108122,0.000314689,0.00040022773],"about_ca_topic_score_codex":0.000028749953,"about_ca_topic_score_gemma":0.000026408847,"teacher_disagreement_score":0.7764507,"about_ca_system_score_codex":0.00015990196,"about_ca_system_score_gemma":0.00021403623,"threshold_uncertainty_score":0.9999396},"labels":[],"label_agreement":null},{"id":"W3217057835","doi":"10.18280/isi.260508","title":"Hybrid Approach for Detection of Objects from Images Using Fisher Vector and PSO Based CNN","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Kernel (algebra); Object detection; Pattern recognition (psychology); Fisher kernel; Support vector machine; Cognitive neuroscience of visual object recognition; Object (grammar); Computer vision; Feature extraction; Feature (linguistics); Feature vector; Kernel method; Mathematics","score_opus":0.018905733020598395,"score_gpt":0.23526271882965938,"score_spread":0.216356985809061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217057835","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":[0.12725414,0.0000877243,0.87191004,0.000020031232,0.00009290303,0.0002759605,0.000030822484,0.00008214617,0.00024620004],"genre_scores_gemma":[0.7144774,0.0000051086354,0.2852855,0.00007597196,0.000031026393,0.000054045988,0.000061271545,0.000005355634,0.0000042910565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991717,0.000033684813,0.00033142837,0.0001707292,0.00013507677,0.00015742959],"domain_scores_gemma":[0.99901384,0.00013692117,0.00024970077,0.0002929659,0.00026179606,0.00004478634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009476822,0.000110982415,0.0001524396,0.00008473289,0.00018217249,0.00015183941,0.00015838476,0.00004431658,0.0000021139708],"category_scores_gemma":[0.0001225818,0.00011707976,0.000045746892,0.0003006125,0.00005836857,0.0021589668,0.000074771066,0.000060387592,0.0000013691398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011686826,0.00016197597,0.0010308705,0.0012997504,0.00011962816,0.0000035604246,0.0054022917,0.0648021,0.3453648,0.00591602,0.00032610993,0.575456],"study_design_scores_gemma":[0.00030925864,0.000028888251,0.0014546043,0.000034472763,0.000011909074,0.000013185403,0.00007240954,0.68686783,0.30543274,0.005454232,0.00019233362,0.00012814692],"about_ca_topic_score_codex":0.00002686759,"about_ca_topic_score_gemma":0.0000025535667,"teacher_disagreement_score":0.6220657,"about_ca_system_score_codex":0.000085414234,"about_ca_system_score_gemma":0.000066163186,"threshold_uncertainty_score":0.4774374},"labels":[],"label_agreement":null},{"id":"W3217515045","doi":"10.21203/rs.3.rs-1107274/v1","title":"End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Joint (building); Computer science; Intelligent transportation system; Tracking (education); End-to-end principle; Video tracking; Object detection; Object (grammar); Computer vision; Artificial intelligence; Tracking system; Real-time computing; Transport engineering; Engineering; Kalman filter; Pattern recognition (psychology); Psychology","score_opus":0.14420239238186427,"score_gpt":0.40244697617986236,"score_spread":0.2582445837979981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217515045","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":[0.031384993,0.0013745393,0.9626492,0.0004327776,0.00042460437,0.0034773836,0.00006121171,0.00017806627,0.000017208084],"genre_scores_gemma":[0.95283884,0.00042673777,0.043529853,0.00002047266,0.0002147765,0.0027376388,0.00010167868,0.000038989176,0.00009101234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966544,0.00028495377,0.00049355184,0.0012189979,0.0007568241,0.0005913084],"domain_scores_gemma":[0.9972261,0.0005298023,0.0001408257,0.00090736593,0.000930478,0.00026542018],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011846297,0.0002528151,0.00034603875,0.00044078403,0.00042965807,0.0006883733,0.0006079315,0.00022070753,0.0000045349025],"category_scores_gemma":[0.00021529413,0.00026364683,0.00014234219,0.00076892774,0.00006412555,0.00027698514,0.00037152247,0.0009248044,0.000008004296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007764957,0.00037092812,0.00034310034,0.0045174565,0.00014458346,0.00006522625,0.009448892,0.19472107,0.04332489,0.009491258,0.00015680623,0.7373381],"study_design_scores_gemma":[0.0007147653,0.0005507077,0.019610668,0.00240168,0.0000372034,0.000033346056,0.0023071875,0.89026535,0.07193833,0.0031429804,0.007893803,0.0011040003],"about_ca_topic_score_codex":0.00024177578,"about_ca_topic_score_gemma":0.00073567464,"teacher_disagreement_score":0.92145383,"about_ca_system_score_codex":0.00032241686,"about_ca_system_score_gemma":0.00019490748,"threshold_uncertainty_score":0.9999816},"labels":[],"label_agreement":null},{"id":"W4200029631","doi":"10.3390/app12010089","title":"MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Jiangsu Academy of Agricultural Sciences","keywords":"Field-programmable gate array; Computer science; Verilog; Embedded system; Machine learning; Software deployment; Artificial intelligence; Operating system","score_opus":0.040623994196480294,"score_gpt":0.28741742099197587,"score_spread":0.24679342679549557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200029631","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":[0.011625234,0.00033700423,0.98016626,0.0028371755,0.0002843755,0.00062149944,0.0000034879763,0.00022247947,0.0039025147],"genre_scores_gemma":[0.9483793,0.00008601784,0.049250975,0.0013529464,0.00013280477,0.00042935042,0.0000058329774,0.000009867076,0.00035292926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984881,0.000009823597,0.0001971219,0.00056640175,0.00032266692,0.00041586367],"domain_scores_gemma":[0.998574,0.000740721,0.00010852961,0.00043650295,0.00006196268,0.00007828877],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00043277518,0.0001376996,0.00012605816,0.00004198033,0.0015706919,0.00034457564,0.0015081777,0.000036796002,0.000019231662],"category_scores_gemma":[0.0000580205,0.000088056004,0.000056677138,0.0013951564,0.00023670886,0.0003869303,0.00035946278,0.00019476043,0.000047691945],"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":[0.000005522611,0.000045941088,0.00037706783,0.000008346793,0.000012529792,0.00000246193,0.0004292938,0.034910202,0.007960094,0.68693596,0.0010134917,0.2682991],"study_design_scores_gemma":[0.0007765392,0.00008242269,0.001212321,0.0000197965,0.000018691926,0.000040110757,0.0006348265,0.5181871,0.16877647,0.119534045,0.18995345,0.00076421734],"about_ca_topic_score_codex":0.0000030239012,"about_ca_topic_score_gemma":0.000024389059,"teacher_disagreement_score":0.93675405,"about_ca_system_score_codex":0.00002307828,"about_ca_system_score_gemma":0.000110588255,"threshold_uncertainty_score":0.99972916},"labels":[],"label_agreement":null},{"id":"W4200061105","doi":"10.1016/j.patrec.2021.12.004","title":"A discriminative channel diversification network for image classification","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"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":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Agricultural Research Service; National Institutes of Health; U.S. Department of Agriculture","keywords":"Discriminative model; Computer science; Margin (machine learning); Computation; Convolutional neural network; Channel (broadcasting); Block (permutation group theory); Plug-in; Artificial intelligence; Context (archaeology); Pattern recognition (psychology); Machine learning; Algorithm; Computer network; Mathematics","score_opus":0.06345785352901455,"score_gpt":0.2815284641893757,"score_spread":0.21807061066036115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200061105","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":[0.0066799857,0.000026358908,0.9658163,0.026170092,0.00034873563,0.00050261756,0.000062064006,0.00019454969,0.0001992617],"genre_scores_gemma":[0.7238216,0.00008486748,0.25644115,0.015538246,0.0008513027,0.0015170355,0.0016022037,0.000045142075,0.000098455916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985646,0.000092971095,0.0002502285,0.0005971506,0.00017309685,0.0003219745],"domain_scores_gemma":[0.99874854,0.00025006666,0.00020080814,0.00044570246,0.00027555262,0.00007932808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013168885,0.0001548592,0.00013375706,0.000058840415,0.0002997427,0.00013414666,0.0003468175,0.000046936613,0.000019784547],"category_scores_gemma":[0.000050289553,0.0001737021,0.000100424295,0.0004448315,0.000050768565,0.0007020466,0.00010180301,0.000115354866,0.0001579395],"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":[0.000023251681,0.00025936178,0.00041340565,0.0000956652,0.000082285864,0.000026014088,0.0012711148,0.0006742658,0.07644214,0.0035599594,0.04525973,0.8718928],"study_design_scores_gemma":[0.0047516795,0.00024141533,0.068585396,0.00042533933,0.00027825127,0.00019238744,0.0010679566,0.66524756,0.09980872,0.13180982,0.024417693,0.003173794],"about_ca_topic_score_codex":0.000003075026,"about_ca_topic_score_gemma":0.0000072460966,"teacher_disagreement_score":0.86871904,"about_ca_system_score_codex":0.00008050186,"about_ca_system_score_gemma":0.000020418325,"threshold_uncertainty_score":0.7083366},"labels":[],"label_agreement":null},{"id":"W4200094847","doi":"10.1109/vtc2021-fall52928.2021.9625271","title":"End-to-End Multi-View Fusion for Enhanced Perception and Motion Prediction","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Perception; Motion (physics); Artificial intelligence; Computer vision; Fusion; Sensor fusion; End-to-end principle","score_opus":0.022531578955769488,"score_gpt":0.2700151100072144,"score_spread":0.2474835310514449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200094847","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":[0.16811097,0.00037123405,0.82267743,0.0067489375,0.00046748726,0.0010177303,0.000032097723,0.0004911606,0.00008294463],"genre_scores_gemma":[0.8400657,0.0010892373,0.15689522,0.00040426318,0.0001301453,0.0008506079,0.00010945256,0.000034358472,0.00042097713],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967605,0.00013031493,0.0005639876,0.0015596924,0.0003457264,0.00063978933],"domain_scores_gemma":[0.9974471,0.00011256752,0.00022471783,0.0012888036,0.000732755,0.00019407207],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029581445,0.00040509537,0.00048247355,0.00036164833,0.0004935026,0.0001589497,0.0007887772,0.0005212948,0.00006441808],"category_scores_gemma":[0.00019193637,0.00043523897,0.00012870715,0.0015689057,0.00018865937,0.00055611687,0.0005029048,0.00052039686,0.00010111889],"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":[0.000009667521,0.00015117027,0.00028501346,0.00004956049,0.000035816673,0.000018827775,0.00021025224,0.0006459269,0.5238746,0.021559626,0.00015476941,0.45300478],"study_design_scores_gemma":[0.003630135,0.00075467577,0.011391765,0.0006184285,0.00021847934,0.00040509412,0.00092186854,0.51028997,0.39575207,0.027298408,0.046774253,0.0019448331],"about_ca_topic_score_codex":0.000013736504,"about_ca_topic_score_gemma":0.00024287602,"teacher_disagreement_score":0.67195475,"about_ca_system_score_codex":0.00015711838,"about_ca_system_score_gemma":0.00017834952,"threshold_uncertainty_score":0.9998099},"labels":[],"label_agreement":null},{"id":"W4200200310","doi":"10.1109/iros51168.2021.9635875","title":"PLUMENet: Efficient 3D Object Detection from Stereo Images","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":38,"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":"Artificial intelligence; Computer science; Point cloud; Computer vision; Lidar; Benchmark (surveying); Metric (unit); Object detection; Object (grammar); Feature (linguistics); Representation (politics); Exploit; Inference; Detector; Key (lock); Pattern recognition (psychology); Remote sensing; Engineering; Geography","score_opus":0.04345949719033659,"score_gpt":0.2905566820600707,"score_spread":0.2470971848697341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200200310","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":[0.027126892,0.00058311864,0.961106,0.0008337137,0.0039343755,0.0004028662,0.00007233308,0.000121957375,0.0058187577],"genre_scores_gemma":[0.99289423,0.00063041964,0.0027485539,0.00021710104,0.00048897514,0.00014430338,0.000051905343,0.000022091983,0.0028024039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997222,0.00016298959,0.00060316024,0.0010055011,0.0006678833,0.00033846337],"domain_scores_gemma":[0.9981247,0.00021638822,0.00028246208,0.0006874923,0.0005044857,0.00018442859],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019399532,0.00032075288,0.00033235602,0.00015616312,0.00023734565,0.00076276873,0.00074939936,0.000116884155,0.00023773302],"category_scores_gemma":[0.000049801634,0.00030078265,0.00010553697,0.00031844978,0.000078386955,0.00026389735,0.00027202597,0.00031069756,0.0002560285],"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":[0.00011208259,0.0007959015,0.00081736606,0.00007842668,0.0005068165,0.00027092508,0.0016465895,0.34636402,0.17045318,0.22600558,0.0016932443,0.2512559],"study_design_scores_gemma":[0.00038809626,0.00011734986,0.0008634134,0.00030317507,0.00002100239,0.00007936715,0.00034319586,0.92832065,0.060467716,0.0022654627,0.0062901513,0.0005403981],"about_ca_topic_score_codex":0.00021959486,"about_ca_topic_score_gemma":0.000091426904,"teacher_disagreement_score":0.9657674,"about_ca_system_score_codex":0.00017969673,"about_ca_system_score_gemma":0.00009832333,"threshold_uncertainty_score":0.99994445},"labels":[],"label_agreement":null},{"id":"W4200489299","doi":"10.1145/3482854","title":"<i>AIgean</i> : An Open Framework for Deploying Machine Learning on Heterogeneous Clusters","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Engineering and Physical Sciences Research Council; Xilinx; CMC Microsystems","keywords":"Computer science; Field-programmable gate array; Implementation; Cluster analysis; Artificial neural network; Computer architecture; Embedded system; Distributed computing; Artificial intelligence; Software engineering","score_opus":0.03746381765758826,"score_gpt":0.29433442982457286,"score_spread":0.2568706121669846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200489299","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":[0.008005903,0.00062905875,0.9854946,0.003731925,0.00039758344,0.00079118507,0.000016375792,0.0006663197,0.00026702468],"genre_scores_gemma":[0.93004894,0.00024404954,0.06756196,0.00056138594,0.000026923979,0.00080509786,0.00001019154,0.000035730453,0.000705712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983317,0.00011184154,0.0003145984,0.00077534874,0.00010184215,0.00036463744],"domain_scores_gemma":[0.9978481,0.0005500186,0.00013152459,0.0012784195,0.000090368994,0.00010155008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002121091,0.00021009856,0.0003141611,0.0002380025,0.0009252005,0.00024493528,0.0012711851,0.00028864876,0.000011769162],"category_scores_gemma":[0.00007618362,0.00021213402,0.00004952469,0.00074994226,0.00006439454,0.0003320399,0.000034095825,0.00060096214,0.000017365897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121859004,0.0004551946,0.00016403987,0.00010700664,0.00020575446,0.00006122591,0.00029085265,0.2020013,0.005715882,0.23969655,0.00007266096,0.5511077],"study_design_scores_gemma":[0.0027264557,0.003289156,0.000030937576,0.0007813132,0.00010924291,0.0022158069,0.0010719977,0.5265655,0.12912998,0.25713894,0.07518763,0.0017530698],"about_ca_topic_score_codex":0.00001531297,"about_ca_topic_score_gemma":0.00004293521,"teacher_disagreement_score":0.922043,"about_ca_system_score_codex":0.000046721936,"about_ca_system_score_gemma":0.00003961993,"threshold_uncertainty_score":0.8650574},"labels":[],"label_agreement":null},{"id":"W4200559323","doi":"10.1109/mlhpc54614.2021.00006","title":"Semantic-Aware Lossless Data Compression for Deep Learning Recommendation Model (DLRM)","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Advanced Micro Devices","keywords":"Computer science; Deep learning; Bottleneck; Artificial neural network; Lossless compression; Deep neural networks; Inference; Artificial intelligence; Volume (thermodynamics); Data compression; Parallel computing; Machine learning; Embedded system","score_opus":0.09610207281267812,"score_gpt":0.3477033958897529,"score_spread":0.2516013230770748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200559323","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":[0.0004157792,0.000042162785,0.9947358,0.0035998963,0.00012832017,0.00020915012,0.000009977053,0.00024906523,0.00060984236],"genre_scores_gemma":[0.29332078,0.00008092872,0.70324385,0.00070080266,0.000086890395,0.000059550643,0.0008284447,0.000018909941,0.0016598258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881226,0.000043394662,0.00020165303,0.00060763245,0.00011614671,0.00021894388],"domain_scores_gemma":[0.998474,0.00022281506,0.0000908643,0.0009950959,0.00015021431,0.000067020934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012918386,0.000105354695,0.00012117082,0.000028858192,0.000314095,0.00011638317,0.0009051396,0.000043196567,0.000025614543],"category_scores_gemma":[0.000045764493,0.00010185839,0.000028732784,0.0002776517,0.000014162446,0.0007807651,0.0010832234,0.00013212323,0.000020242343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007824158,0.00012156608,0.000117362884,0.000038333612,0.000015481099,0.0000027684212,0.00018368963,0.32911557,0.004756869,0.053403195,0.010637744,0.6015996],"study_design_scores_gemma":[0.00019505905,0.000008760904,0.000028518984,0.000012645536,0.0000051023417,0.0000067401334,0.000030846502,0.9742547,0.0027660676,0.006521506,0.016039118,0.00013087886],"about_ca_topic_score_codex":0.0000026159332,"about_ca_topic_score_gemma":0.00003164124,"teacher_disagreement_score":0.64513916,"about_ca_system_score_codex":0.000022642576,"about_ca_system_score_gemma":0.000035443285,"threshold_uncertainty_score":0.41536647},"labels":[],"label_agreement":null},{"id":"W4200617393","doi":"10.1007/s11831-021-09670-y","title":"Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review","year":2021,"lang":"en","type":"review","venue":"Archives of Computational Methods in Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"National Research Foundation of Korea; Ministry of Education","keywords":"Computer science; Lidar; Artificial intelligence; Deep learning; Scalability; Segmentation; Object detection; Sensor fusion; Open research; Consolidation (business); Feature extraction; Machine learning; Computer vision; Real-time computing; Remote sensing; Database","score_opus":0.17866445168288886,"score_gpt":0.4234500129943904,"score_spread":0.24478556131150153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200617393","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":[1.3479104e-7,0.48691973,0.51235884,0.000013246086,0.00006505432,0.0005657819,0.000006930313,0.000053136577,0.000017116654],"genre_scores_gemma":[0.0000032223031,0.44454175,0.55492026,0.0000074985614,0.00004673976,0.00013171116,0.00031562196,0.000030758816,0.0000024115595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969938,0.0005800553,0.0010966529,0.00083811616,0.00019076689,0.0003006375],"domain_scores_gemma":[0.9908409,0.0074906303,0.0006592758,0.00088648585,0.000051704694,0.000070988855],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008982895,0.00036777285,0.0014591782,0.00040577893,0.00008323362,0.00005174473,0.0015430049,0.00008570914,9.4240363e-7],"category_scores_gemma":[0.0013115696,0.00039931812,0.00026196815,0.00095120026,0.000049414586,0.00032629463,0.00095087336,0.0004524759,4.3502783e-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":[2.2197702e-7,0.00001435378,4.7217657e-7,0.0073111877,0.00004073463,9.605235e-7,0.00003216352,0.38848633,0.0000022583893,0.0045138127,6.084711e-7,0.5995969],"study_design_scores_gemma":[0.00008704434,0.000013694435,0.0000046122063,0.019418787,0.000114336,0.000043888314,0.0000022434147,0.89693487,0.000003805259,0.0016891233,0.08138308,0.00030453264],"about_ca_topic_score_codex":9.659116e-7,"about_ca_topic_score_gemma":3.8154238e-7,"teacher_disagreement_score":0.5992924,"about_ca_system_score_codex":0.00006578127,"about_ca_system_score_gemma":0.00031534318,"threshold_uncertainty_score":0.99984586},"labels":[],"label_agreement":null},{"id":"W4205395965","doi":"10.1109/tits.2021.3131530","title":"A Novel Smart Lightweight Visual Attention Model for Fine-Grained Vehicle Recognition","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Discriminative model; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Cognitive neuroscience of visual object recognition; Construct (python library); Machine learning","score_opus":0.05268000235141015,"score_gpt":0.28822337149160954,"score_spread":0.23554336914019938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205395965","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":[0.007704097,0.00006186901,0.98874325,0.000520644,0.0011566395,0.0010797324,0.00027857977,0.00040300234,0.00005219788],"genre_scores_gemma":[0.95053494,0.00005383396,0.0458969,0.00017320461,0.00008840374,0.001455016,0.00027611433,0.000045969995,0.0014755926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976081,0.000051624007,0.00080164016,0.0007717115,0.00040756929,0.00035931362],"domain_scores_gemma":[0.9983464,0.00021931535,0.00021204521,0.00046863357,0.00059300347,0.0001606414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017992241,0.00028424885,0.000289493,0.00020411074,0.00038403706,0.00014077027,0.00029352697,0.0001651874,0.000015984355],"category_scores_gemma":[0.0000047977724,0.00030860596,0.00029923412,0.0008208205,0.00003379386,0.00061193196,5.807621e-7,0.00022681936,0.00008364679],"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":[0.00013359841,0.0012914526,0.000018163839,0.00019495696,0.00015981442,0.00000898491,0.0012262312,0.8305484,0.110734776,0.009908086,0.0002874102,0.04548811],"study_design_scores_gemma":[0.0006805812,0.00010892867,0.000064965454,0.0001104004,0.000064350075,0.000014723802,0.000102489634,0.90501225,0.09183442,0.00069844065,0.0009520035,0.00035644183],"about_ca_topic_score_codex":0.000019800455,"about_ca_topic_score_gemma":0.00024739114,"teacher_disagreement_score":0.94284636,"about_ca_system_score_codex":0.0001174863,"about_ca_system_score_gemma":0.000109859626,"threshold_uncertainty_score":0.9999366},"labels":[],"label_agreement":null},{"id":"W4205576441","doi":"10.1109/smc52423.2021.9659265","title":"Collision Detection: An Improved Deep Learning Approach Using SENet and ResNext","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Collision; Computer science; Deep learning; Artificial intelligence; Machine learning; Field (mathematics); Overhead (engineering); Baseline (sea); Population; Computer security; Mathematics; Medicine","score_opus":0.06065582340973732,"score_gpt":0.3013403357631358,"score_spread":0.24068451235339847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205576441","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":[0.15831424,0.0004796844,0.83404195,0.00020522121,0.0008817433,0.00032402927,0.00000972716,0.00009064763,0.0056527513],"genre_scores_gemma":[0.98832214,0.000483236,0.009192184,0.00009534532,0.00032327947,0.000042660333,0.000022615293,0.000019526678,0.0014990068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800533,0.00021803315,0.00037022794,0.0007740635,0.00038923882,0.00024308711],"domain_scores_gemma":[0.9986328,0.000116078765,0.00021855549,0.00040470943,0.00046862356,0.00015926179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024911546,0.00022343619,0.00023604366,0.00010826834,0.00028280535,0.00070063694,0.0004005766,0.00012061505,0.000013741508],"category_scores_gemma":[0.000039482973,0.00023183216,0.00003538758,0.00026151075,0.000076026154,0.000384907,0.00020677349,0.000304111,0.000009300796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014530454,0.00051457435,0.00096105615,0.00020550302,0.00027293767,0.00014630776,0.0023384853,0.10287677,0.2928307,0.36658326,0.00009525865,0.23302984],"study_design_scores_gemma":[0.0003251634,0.00009316812,0.00029687243,0.00006131295,0.000012256943,0.0002008287,0.00043731826,0.9895094,0.0041068643,0.0009971953,0.0036966864,0.00026292342],"about_ca_topic_score_codex":0.000089264046,"about_ca_topic_score_gemma":0.00010558306,"teacher_disagreement_score":0.8866326,"about_ca_system_score_codex":0.000078318684,"about_ca_system_score_gemma":0.00006022949,"threshold_uncertainty_score":0.94538414},"labels":[],"label_agreement":null},{"id":"W4205584113","doi":"10.1109/sbcci53441.2021.9529995","title":"34th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design [Front matter]","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Instituto Superior Técnico; Universidade Federal do Rio Grande do Norte; Universidade Federal de Minas Gerais; Universidade Federal de Santa Catarina; Universidade Federal do Pampa; Central European Institute of Technology; Universidade Estadual de Campinas; Université Paris-Saclay; Universidade Federal de Itajubá; Universidade de Lisboa; Centre National de la Recherche Scientifique; University of Patras; Universidade Federal do Rio Grande do Sul; Universidade Federal de Pelotas; Institut national de recherche en informatique et en automatique (INRIA); Universidade Tecnológica Federal do Paraná; Northern Illinois University; University of Toronto; Universidade de São Paulo","keywords":"Computer science; Front (military); Electronic circuit; Electrical engineering; Engineering; Mechanical engineering","score_opus":0.03450522066471512,"score_gpt":0.2556973106969008,"score_spread":0.2211920900321857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205584113","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":[0.004027129,0.00023860954,0.98764664,0.0025057462,0.000547611,0.00038906385,0.0000037659136,0.00028551178,0.004355928],"genre_scores_gemma":[0.8156445,0.00028171518,0.14771909,0.0055158297,0.00031289892,0.00033138224,0.000020065323,0.00007195886,0.030102585],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984334,0.00013607435,0.00025850517,0.0006662826,0.00018346775,0.00032227486],"domain_scores_gemma":[0.99819154,0.00030054915,0.00008089064,0.0011539309,0.00013164028,0.0001414323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012516663,0.00019736911,0.00020760718,0.000062770916,0.0001551759,0.0002730205,0.00071625377,0.00006699565,0.000034633704],"category_scores_gemma":[0.000020517511,0.00016967664,0.000033353095,0.0003990019,0.00004042223,0.0002691037,0.00022015377,0.00017014935,0.00057677884],"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":[0.000020633079,0.00050388783,0.0011086214,0.00016633928,0.00013337434,0.00026856395,0.0011099059,0.0338717,0.62044036,0.09490502,0.21673591,0.03073569],"study_design_scores_gemma":[0.001958411,0.0005757966,0.0031115606,0.0005665634,0.000060618368,0.0014698324,0.00035352612,0.48701993,0.3834788,0.012827593,0.106034964,0.0025423928],"about_ca_topic_score_codex":0.000018642588,"about_ca_topic_score_gemma":0.000005038663,"teacher_disagreement_score":0.83992755,"about_ca_system_score_codex":0.00007864746,"about_ca_system_score_gemma":0.0000689687,"threshold_uncertainty_score":0.7413518},"labels":[],"label_agreement":null},{"id":"W4205911079","doi":"10.1109/tiv.2021.3133849","title":"Traffic Object Detection and Recognition Based on the Attentional Visual Field of Drivers","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object detection; Advanced driver assistance systems; Gaze; Focus (optics); Cognitive neuroscience of visual object recognition; Support vector machine; Field (mathematics); Object (grammar); Deep learning; Pattern recognition (psychology)","score_opus":0.02559201957530174,"score_gpt":0.2689052677131892,"score_spread":0.24331324813788743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205911079","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":[0.21275608,0.000012339493,0.7857493,0.001042508,0.00017821482,0.00013896293,0.000006587972,0.000060776518,0.000055198252],"genre_scores_gemma":[0.99659264,0.00008307142,0.0026551068,0.00052652735,0.00002200567,0.000058578225,0.0000019048399,0.0000075846965,0.000052554293],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990649,0.00009544435,0.00019785982,0.00029772508,0.0002178856,0.00012615303],"domain_scores_gemma":[0.99876565,0.0007743118,0.00006748771,0.0002435996,0.00010434338,0.00004463717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009677653,0.00010924394,0.00009260133,0.00009901784,0.000240904,0.00003868897,0.00015109328,0.000056270677,0.000060853916],"category_scores_gemma":[0.000010674249,0.00009360836,0.00009885682,0.00041856908,0.000059765825,0.00012460981,0.0000024357369,0.00020977997,0.000026335158],"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":[0.00007033969,0.00039047966,0.000008803023,0.000014814451,0.000034085217,0.0000036030083,0.00014945256,0.1281316,0.026395082,0.00048169115,0.000046659763,0.8442734],"study_design_scores_gemma":[0.0001247644,0.00023656402,0.00013054894,0.000041618307,0.000015231525,0.00000880523,0.000073405274,0.29689386,0.70136106,0.0008834553,0.00013011937,0.00010058785],"about_ca_topic_score_codex":0.000004384634,"about_ca_topic_score_gemma":0.00004145771,"teacher_disagreement_score":0.8441728,"about_ca_system_score_codex":0.000033887027,"about_ca_system_score_gemma":0.000032491113,"threshold_uncertainty_score":0.38172382},"labels":[],"label_agreement":null},{"id":"W4205987915","doi":"10.3390/app12031051","title":"Semantic Segmentation Based on Depth Background Blur","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Pascal (unit); Artificial intelligence; Convolutional neural network; Inference; Pattern recognition (psychology); Fuzzy inference system; Focus (optics); Image segmentation; Fuzzy logic; Adaptive neuro fuzzy inference system; Fuzzy control system","score_opus":0.03967018903237247,"score_gpt":0.2926895184385241,"score_spread":0.2530193294061516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205987915","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":[0.046952177,0.000023964523,0.9147667,0.0027562135,0.000310714,0.0005717466,0.0000024600508,0.00032864968,0.03428736],"genre_scores_gemma":[0.93670356,0.0000012647392,0.060275927,0.002566853,0.00003324764,0.0003122291,0.0000041392614,0.0000055207397,0.00009726283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827456,0.000049021157,0.00015848254,0.00056878297,0.0006608861,0.00028823546],"domain_scores_gemma":[0.99918836,0.00022586725,0.000104348364,0.00040625728,0.000013279617,0.00006189167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039926427,0.00011129525,0.000089697525,0.0001292998,0.0012882539,0.00013465068,0.0012627114,0.000012811197,0.00006590705],"category_scores_gemma":[0.000003907821,0.00010485151,0.000030366915,0.0016040617,0.00013451074,0.00021570791,0.0002662075,0.00014096718,0.00009928861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009021012,0.00015378011,0.00030035834,0.0000045036777,0.0000033172246,0.000004227718,0.00022156599,0.4297111,0.011956804,0.517989,0.0018945918,0.03775176],"study_design_scores_gemma":[0.0005601749,0.00035560332,0.0021422976,0.000004572422,0.0000067984056,0.000010552021,0.0005722813,0.9378505,0.010423923,0.038931914,0.008663244,0.0004781477],"about_ca_topic_score_codex":0.0000044159287,"about_ca_topic_score_gemma":0.0000055585297,"teacher_disagreement_score":0.8897514,"about_ca_system_score_codex":0.000075269076,"about_ca_system_score_gemma":0.00007775479,"threshold_uncertainty_score":0.9908338},"labels":[],"label_agreement":null},{"id":"W4206461356","doi":"10.18280/ts.380615","title":"Three-Dimensional Image Reconstruction for Virtual Talent Training Scene","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"State Grid Shandong Electric Power Company; Science and Technology Project of State Grid","keywords":"Training (meteorology); Artificial intelligence; Computer science; Computer vision; Virtual reality; Image (mathematics); Segmentation; Virtual training; Perception; Limit (mathematics); Mathematics; Geography","score_opus":0.03433681235688518,"score_gpt":0.2627920794161145,"score_spread":0.2284552670592293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206461356","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":[0.036885355,0.000054538883,0.9603157,0.001778546,0.00033192948,0.00032365293,0.000018442262,0.00015328197,0.00013854167],"genre_scores_gemma":[0.38725945,0.0000051481397,0.61144406,0.0005899211,0.00036709994,0.00019834499,0.000041521947,0.000017183795,0.00007728843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985894,0.000026035152,0.00030516164,0.0005048951,0.00025853195,0.00031600727],"domain_scores_gemma":[0.99917567,0.00017902807,0.00010559147,0.0002745198,0.00015853473,0.00010668772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001683564,0.00015027629,0.00015570236,0.000049472255,0.00026063173,0.00008311347,0.00029083824,0.000040318773,0.00014398717],"category_scores_gemma":[0.000017605682,0.0001564211,0.00010436248,0.00027560475,0.00006273505,0.00043556964,0.00010703816,0.00010359897,0.00002298177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031423762,0.00016767193,0.000057448753,0.000012539625,0.000048348633,0.000020619424,0.000236446,0.0060534035,0.13868277,0.089252725,0.0023085494,0.76312804],"study_design_scores_gemma":[0.0020538473,0.0003113345,0.0016772905,0.00007238174,0.000031605257,0.00032841382,0.00009964482,0.9002063,0.049334787,0.03847917,0.0068413476,0.0005638263],"about_ca_topic_score_codex":0.0000012509845,"about_ca_topic_score_gemma":0.000014896711,"teacher_disagreement_score":0.89415294,"about_ca_system_score_codex":0.00005994547,"about_ca_system_score_gemma":0.00010949738,"threshold_uncertainty_score":0.63786674},"labels":[],"label_agreement":null},{"id":"W4206608377","doi":"10.1109/access.2022.3144407","title":"Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, and Challenges","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Benchmark (surveying); Perception; Object detection; Segmentation; Artificial intelligence; Task (project management); Component (thermodynamics); Process (computing); Field (mathematics); Deep learning; Lidar; Machine learning; Computer vision; Human–computer interaction; Systems engineering; Engineering","score_opus":0.11468929278662654,"score_gpt":0.36207099992701136,"score_spread":0.24738170714038482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206608377","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":[0.351642,0.00055594667,0.6464057,0.00068455486,0.00014030281,0.00013241392,6.018409e-7,0.00019066365,0.00024780573],"genre_scores_gemma":[0.97772133,0.00063085376,0.021286169,0.00014905081,0.00008364439,0.00008484724,0.00000215078,0.00001488349,0.000027089285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988721,0.00010523419,0.00015577266,0.0004415338,0.00020823123,0.00021712449],"domain_scores_gemma":[0.999431,0.000052225085,0.00009807507,0.00031971777,0.000037129375,0.000061844046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016426682,0.00011188029,0.00011279124,0.00008336023,0.00069962826,0.00017002784,0.0007819113,0.000026082762,0.0000139416325],"category_scores_gemma":[0.0000035839068,0.00012492518,0.000027231412,0.00024391858,0.00002617557,0.0008519709,0.0006343735,0.0002851648,0.000002465844],"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":[0.0000017732644,0.00003457059,0.0005757246,0.000008620134,0.0000055865403,0.000003561499,0.00043700487,0.8480157,0.0019941,0.0035272348,0.000010832959,0.14538525],"study_design_scores_gemma":[0.00011622926,0.000034360026,0.0019284188,0.0000027942672,0.0000052773507,0.00002691699,0.00012527163,0.99155647,0.000033990877,0.004726403,0.0012975074,0.00014638215],"about_ca_topic_score_codex":0.000024096451,"about_ca_topic_score_gemma":0.00000729785,"teacher_disagreement_score":0.62607926,"about_ca_system_score_codex":0.00012867193,"about_ca_system_score_gemma":0.000033149056,"threshold_uncertainty_score":0.53810453},"labels":[],"label_agreement":null},{"id":"W4207078582","doi":"10.1007/s11265-021-01731-6","title":"Convolutional Fully-Connected Capsule Network (CFC-CapsNet): A Novel and Fast Capsule Network","year":2022,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Capsule; Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Computer vision; Biology; Botany","score_opus":0.016864744375231267,"score_gpt":0.23390020479214968,"score_spread":0.2170354604169184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207078582","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":[0.018993877,0.01338036,0.96492714,0.00079210673,0.0010743844,0.00039909768,0.000014077692,0.000119716744,0.0002992377],"genre_scores_gemma":[0.97603005,0.000023571372,0.021203734,0.0003396322,0.0020603742,0.00006126147,0.000005587684,0.000032951066,0.00024282734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967504,0.00020675275,0.0010400614,0.00045107256,0.0009133436,0.0006383601],"domain_scores_gemma":[0.99735314,0.00017133256,0.0014514324,0.0002806333,0.0004678171,0.00027567527],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0011579423,0.00027909636,0.00052784587,0.00013779271,0.0013011406,0.00035048806,0.0010283501,0.00007610332,0.000020869256],"category_scores_gemma":[0.000025101373,0.00026294374,0.000113864065,0.0013334559,0.000119386124,0.0008200003,0.00043542267,0.00076392246,0.000004315837],"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":[0.000045922934,0.0001487018,0.00027396606,0.00008690919,0.00008267778,0.000057550318,0.00039981696,0.95710653,0.009781701,0.012930805,0.0117398035,0.007345629],"study_design_scores_gemma":[0.0017675225,0.00054175634,0.0012300904,0.00041798825,0.00006746894,0.0052916356,0.00034589664,0.9571659,0.00010771891,0.005612784,0.026803646,0.0006476161],"about_ca_topic_score_codex":0.0000146127395,"about_ca_topic_score_gemma":0.0000022511076,"teacher_disagreement_score":0.9570362,"about_ca_system_score_codex":0.0002264411,"about_ca_system_score_gemma":0.00042355715,"threshold_uncertainty_score":0.99999905},"labels":[],"label_agreement":null},{"id":"W4210383040","doi":"10.1109/icmla52953.2021.00280","title":"LE-CapsNet: A Light and Enhanced Capsule Network","year":2021,"lang":"en","type":"article","venue":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Artificial intelligence; Computer science; Affine transformation; Pattern recognition (psychology); Inference; Deep learning; Computer vision; Mathematics","score_opus":0.021778058679081943,"score_gpt":0.29134603120881336,"score_spread":0.2695679725297314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210383040","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":[0.004765017,0.0008201483,0.9145347,0.019989958,0.00027099755,0.00040068175,0.000017617836,0.00023958349,0.058961283],"genre_scores_gemma":[0.9729921,0.0018965193,0.019180302,0.0006370578,0.0005173965,0.00041064463,0.000076049364,0.000023598404,0.004266323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99824065,0.00009803786,0.00030053515,0.00079784956,0.00027625513,0.00028668318],"domain_scores_gemma":[0.998842,0.00013074848,0.000182159,0.00041929437,0.00026786438,0.00015790988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014851648,0.00022397144,0.00021347404,0.000085016836,0.00051672617,0.00032210973,0.00048465363,0.000080567545,0.00006847297],"category_scores_gemma":[0.00004078827,0.00023878262,0.000050710143,0.00046550526,0.000067551555,0.00030083014,0.00025013418,0.00048596336,0.00006846209],"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":[0.000007253411,0.00008459624,0.00019702766,0.000007961193,0.000040732368,0.000005911844,0.000107574975,0.0033768928,0.025262162,0.8897176,0.00043156903,0.08076075],"study_design_scores_gemma":[0.0013901974,0.00015139715,0.0012742887,0.00015938775,0.000033325123,0.00013565255,0.00016939935,0.4121285,0.019374475,0.07478922,0.4893486,0.0010455834],"about_ca_topic_score_codex":0.000022448397,"about_ca_topic_score_gemma":0.000056045374,"teacher_disagreement_score":0.9682271,"about_ca_system_score_codex":0.000028190225,"about_ca_system_score_gemma":0.00012881965,"threshold_uncertainty_score":0.9737273},"labels":[],"label_agreement":null},{"id":"W4210647952","doi":"10.1145/3506695","title":"An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; University of Alberta; Concordia University","funders":"","keywords":"Hyperparameter; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Inference; Hyperparameter optimization; Pruning; Deep learning; Support vector machine","score_opus":0.09199875935483277,"score_gpt":0.3210991654779176,"score_spread":0.22910040612308485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210647952","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":[0.48150855,0.000029252766,0.5182699,0.000030176267,0.000026967686,0.00011622214,9.924167e-7,0.000017848457,1.1440984e-7],"genre_scores_gemma":[0.81890506,0.000012214236,0.18099077,0.00002236297,0.0000036429767,0.000057255333,2.2256948e-7,0.0000072437733,0.0000012443344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917996,0.000268885,0.00017839392,0.00017143258,0.000095615105,0.00010570137],"domain_scores_gemma":[0.998641,0.00074404944,0.00007805795,0.00047926573,0.000033391378,0.000024248313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000303055,0.00009238294,0.00016494417,0.00006758331,0.00017595089,0.0000053469817,0.00036596303,0.00002694887,0.0000012807106],"category_scores_gemma":[0.000081059785,0.000057052588,0.00003903421,0.00031257252,0.000053514803,0.000088381654,0.000041692743,0.00025010962,7.638315e-9],"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":[0.000025509942,0.000081895945,0.00057876646,0.000005526494,0.000018518354,5.4497814e-8,0.0013680224,0.9850713,0.0006353149,0.00001698926,2.0915361e-7,0.012197881],"study_design_scores_gemma":[0.000129801,0.0006124711,0.0020879398,0.000005055383,0.000014770987,0.000011270202,0.00010461237,0.99625343,0.0007022023,0.000025788262,3.9234212e-7,0.000052240026],"about_ca_topic_score_codex":0.000009879298,"about_ca_topic_score_gemma":8.223881e-7,"teacher_disagreement_score":0.3373965,"about_ca_system_score_codex":0.000014263399,"about_ca_system_score_gemma":0.000008894861,"threshold_uncertainty_score":0.23265369},"labels":[],"label_agreement":null},{"id":"W4210717153","doi":"10.1016/j.adhoc.2022.102784","title":"Performance evaluation of CNN-based pedestrian detectors for autonomous vehicles","year":2022,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":39,"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":"Convolutional neural network; Detector; Computer science; Pedestrian detection; Pedestrian; Deep learning; Artificial intelligence; Focus (optics); Artificial neural network; Intelligent transportation system; Machine learning; Simulation; Transport engineering; Telecommunications; Engineering","score_opus":0.033613827773707354,"score_gpt":0.27912153995386085,"score_spread":0.2455077121801535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210717153","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":[0.3992242,0.0014388192,0.59704703,0.00041703793,0.0004053791,0.0011355054,0.000007475519,0.00018037729,0.00014418193],"genre_scores_gemma":[0.97974324,0.00003308834,0.018907929,0.00017598041,0.000079720485,0.0009833687,0.000023178993,0.00001672146,0.000036750796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846774,0.00012158074,0.00028766424,0.0003632349,0.00046125564,0.0002984974],"domain_scores_gemma":[0.9986177,0.0003086709,0.00024178877,0.0005917043,0.00017757618,0.0000625459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008412328,0.00012532636,0.00015249482,0.00008587657,0.00043520445,0.000026151603,0.0007853597,0.000038983148,0.000021901302],"category_scores_gemma":[0.000028540037,0.00013774374,0.00008073243,0.0007250996,0.000041851523,0.0001878735,0.00017999508,0.00017268468,0.000002317926],"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":[0.000015289192,0.000026219077,0.00021816161,0.0000038245594,0.00000459937,1.3166937e-7,0.000032432985,0.67389494,0.00009172444,0.00033516798,0.00020291994,0.3251746],"study_design_scores_gemma":[0.0006388731,0.00024161038,0.0022648773,0.000006136572,0.000023340783,0.000001911642,0.000009674634,0.9885601,0.00064107106,0.0008056219,0.0066626696,0.00014410607],"about_ca_topic_score_codex":0.0000018199617,"about_ca_topic_score_gemma":0.000007772074,"teacher_disagreement_score":0.5805191,"about_ca_system_score_codex":0.00016591432,"about_ca_system_score_gemma":0.0001834114,"threshold_uncertainty_score":0.56170267},"labels":[],"label_agreement":null},{"id":"W4210735328","doi":"10.1109/icmla52953.2021.00231","title":"PrunedCaps: A Case For Primary Capsules Discrimination","year":2021,"lang":"en","type":"article","venue":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"MNIST database; Computer science; Convolutional neural network; Artificial intelligence; Robustness (evolution); Affine transformation; Pruning; Contextual image classification; Deep learning; Pattern recognition (psychology); Architecture; Routing (electronic design automation); Machine learning; Image (mathematics); Mathematics; Embedded system","score_opus":0.04282396396391006,"score_gpt":0.33164496418708367,"score_spread":0.2888210002231736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210735328","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":[0.0036779374,0.00027679885,0.9735479,0.010139161,0.00021637419,0.00058781734,0.00009020167,0.00017844065,0.01128541],"genre_scores_gemma":[0.9089428,0.0010909815,0.078636974,0.00075898616,0.00058911456,0.0019875688,0.00059760787,0.00003796117,0.007358003],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834317,0.000077499906,0.0003249984,0.000743256,0.00027188612,0.0002391724],"domain_scores_gemma":[0.99851614,0.00028013502,0.0002060291,0.00040279288,0.0004744587,0.00012044535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017439571,0.00021465654,0.00019184119,0.00013191343,0.00050507253,0.0003178235,0.00044613893,0.00007394531,0.000048599213],"category_scores_gemma":[0.000080228674,0.00022066686,0.000078736884,0.00034767762,0.00007173681,0.00039947085,0.00015867253,0.00033669028,0.00003505465],"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":[0.00001411475,0.00017005307,0.0002024828,0.00003244199,0.00004156391,0.000042180087,0.00015922297,0.0015798038,0.007535022,0.7509488,0.0003865214,0.23888783],"study_design_scores_gemma":[0.0012854565,0.00016640166,0.000507243,0.000112085894,0.000053291464,0.0010219803,0.00035003683,0.6553445,0.0049168956,0.05049183,0.28495064,0.0007996344],"about_ca_topic_score_codex":0.000020957756,"about_ca_topic_score_gemma":0.00005630441,"teacher_disagreement_score":0.90526485,"about_ca_system_score_codex":0.00007702404,"about_ca_system_score_gemma":0.00011029059,"threshold_uncertainty_score":0.89985335},"labels":[],"label_agreement":null},{"id":"W4210768747","doi":"10.1145/3472393","title":"RD-IOD: Two-Level Residual-Distillation-Based Triple-Network for Incremental Object Detection","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Beijing Municipal Science and Technology Commission; National Natural Science Foundation of China","keywords":"Computer science; Residual; Artificial intelligence; Object (grammar); Convolutional neural network; Forgetting; Distillation; Machine learning; Feature (linguistics); Learning object; Object detection; Pattern recognition (psychology); Algorithm","score_opus":0.04402762313761858,"score_gpt":0.3098828924033286,"score_spread":0.26585526926571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210768747","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":[0.0006263848,0.00027458314,0.9906759,0.0046423934,0.00018286509,0.0025694887,0.00029300738,0.00059677294,0.00013858396],"genre_scores_gemma":[0.5326507,0.00005345247,0.46285322,0.00040690348,0.000101921985,0.0036520213,0.00019853785,0.00003112849,0.000052122956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739677,0.0002974586,0.00067068054,0.00079744816,0.0003620156,0.00047565662],"domain_scores_gemma":[0.9926399,0.003161219,0.0003723455,0.0034389375,0.00020913208,0.00017848567],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006188553,0.0003178209,0.00028441017,0.0002939596,0.006202996,0.00014988003,0.0025224737,0.00007168432,0.000017850234],"category_scores_gemma":[0.000051742118,0.00038182767,0.00015376088,0.0016253762,0.00021571621,0.00022881801,0.00030695225,0.00064610987,0.000015124738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045864108,0.0006882583,0.000108807384,0.000017520146,0.00007728667,3.6606116e-7,0.00036014942,0.2431931,0.0027650618,0.009851765,0.0003151497,0.74257666],"study_design_scores_gemma":[0.0016035982,0.00018184549,0.0006123142,0.000015702006,0.000053443706,0.000017735496,0.0001449224,0.93949354,0.0011429837,0.0066248244,0.049635157,0.00047394415],"about_ca_topic_score_codex":0.00006945733,"about_ca_topic_score_gemma":0.00017004731,"teacher_disagreement_score":0.74210274,"about_ca_system_score_codex":0.0002592304,"about_ca_system_score_gemma":0.00013798401,"threshold_uncertainty_score":0.9998634},"labels":[],"label_agreement":null},{"id":"W4210794514","doi":"10.1109/cvprw53098.2021.00004","title":"Table of Contents","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California; Hochschule Niederrhein; University at Buffalo; Istituto Italiano di Tecnologia; Southwest University; Universidad Autónoma de San Luis Potosí; Centre National de la Recherche Scientifique; Universidad Politécnica de Madrid; Instituto de Telecomunicações; Universidad de Antioquia; Industrial Technology Research Institute; Indian Institute of Technology Indore; Shanghai Educational Development Foundation; Open University of Israel; Eidgenössische Technische Hochschule Zürich; Tencent; Khalifa University of Science, Technology and Research; University of Hong Kong; Hong Kong University of Science and Technology; West Virginia University; ZTE Corporation; Huazhong University of Science and Technology; Universidade da Beira Interior","keywords":"Table (database); Computer science; Database","score_opus":0.03089603177110812,"score_gpt":0.27107793300983757,"score_spread":0.24018190123872946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210794514","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":[0.0020193702,0.00007517504,0.9764392,0.00088771805,0.000045522327,0.000027550535,4.1809488e-7,0.000045082914,0.020460004],"genre_scores_gemma":[0.7683349,0.000015089708,0.22573985,0.00046151102,0.000009828514,0.0000058086066,8.7554065e-7,0.0000018050448,0.0054303273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997088,0.0000058330315,0.00006575149,0.000101624864,0.000055944474,0.00006204891],"domain_scores_gemma":[0.9995809,0.00002903861,0.000020334339,0.00027964724,0.00006973845,0.000020350564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000012606934,0.00002191105,0.000042437066,0.000006656304,0.000017164868,0.00000751001,0.00019278206,0.000007581187,0.000031592906],"category_scores_gemma":[0.000009627966,0.000019795034,0.000011872922,0.00027220277,0.000008866533,0.00011087666,0.000110430614,0.000018611152,0.000023268547],"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":[3.401133e-7,0.000040007428,0.0004604281,0.000002375584,0.0000037321156,0.0000037314292,0.000019057043,0.00030584223,0.04400057,0.9361855,0.0040517817,0.014926672],"study_design_scores_gemma":[0.0003149672,0.000021749102,0.003193504,0.000009263595,0.0000024543378,0.000024418789,0.000021980684,0.045340605,0.8058462,0.050966676,0.09411597,0.00014220014],"about_ca_topic_score_codex":0.000002225138,"about_ca_topic_score_gemma":0.0000032673822,"teacher_disagreement_score":0.8852188,"about_ca_system_score_codex":0.0000023181228,"about_ca_system_score_gemma":0.000011905254,"threshold_uncertainty_score":0.0807218},"labels":[],"label_agreement":null},{"id":"W4211085526","doi":"10.1145/3490422.3502364","title":"FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":85,"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 Science Foundation","keywords":"Computer science; Field-programmable gate array; Quantization (signal processing); Speedup; Computation; Parallel computing; Stratix; Computer engineering; Edge device; Throughput; Artificial neural network; Hardware acceleration; Algorithm; Embedded system; Artificial intelligence","score_opus":0.01767659962897853,"score_gpt":0.2546906161643103,"score_spread":0.2370140165353318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211085526","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":[0.046392996,0.0000961814,0.951686,0.00054295245,0.00029755707,0.00045858257,0.0000018092422,0.00019023352,0.0003336545],"genre_scores_gemma":[0.95512724,0.000008634239,0.044314757,0.0002744982,0.000049785143,0.00012448187,0.0000318232,0.000015562286,0.00005322419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982629,0.00013089151,0.00036654764,0.00048516094,0.0004847173,0.0002697984],"domain_scores_gemma":[0.9987,0.00019718788,0.00023839594,0.0006585221,0.00013144744,0.00007440649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002089729,0.00015807722,0.0001702396,0.00011601538,0.00041432056,0.00006677786,0.0007826991,0.000034686287,0.000082722705],"category_scores_gemma":[0.00001648731,0.00013694809,0.000045591085,0.0014608575,0.00004269057,0.00030297262,0.0004558803,0.00021572385,0.0000042145093],"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":[0.000022579641,0.000086085274,0.00009765045,0.000002208558,0.0000041776098,0.0000015855768,0.000099024954,0.91488117,0.00058895233,0.012010868,0.000302534,0.07190316],"study_design_scores_gemma":[0.00028992904,0.0002208946,0.0009427491,0.0000031302395,0.000005858129,0.000021516185,0.000050197516,0.9938529,0.0037365097,0.00038854027,0.0003250387,0.00016271169],"about_ca_topic_score_codex":0.000009903165,"about_ca_topic_score_gemma":0.000026563126,"teacher_disagreement_score":0.90873426,"about_ca_system_score_codex":0.000066855675,"about_ca_system_score_gemma":0.00002086849,"threshold_uncertainty_score":0.5584581},"labels":[],"label_agreement":null},{"id":"W4213083240","doi":"10.36227/techrxiv.13049129.v1","title":"A scale-aware YOLO model for pedestrian detection","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pedestrian detection; Computer science; Leverage (statistics); Pedestrian; Convolutional neural network; Artificial intelligence; Object detection; Scale (ratio); Deep learning; Pattern recognition (psychology); Machine learning; Computer vision; Cartography; Geography","score_opus":0.06985393552889282,"score_gpt":0.3077132519185155,"score_spread":0.23785931638962268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213083240","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":[0.00023632761,0.00002052772,0.9908547,0.0062257536,0.00027132255,0.0011663375,0.000023058048,0.00085587543,0.0003461438],"genre_scores_gemma":[0.51968277,0.000026248303,0.47755954,0.0006827719,0.00023476509,0.0010507539,0.00002511217,0.00002554636,0.0007124792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983777,0.000018657607,0.00026661766,0.00092229235,0.00016041718,0.00025432132],"domain_scores_gemma":[0.9985835,0.000085603104,0.00015950893,0.0009219278,0.00010682675,0.00014262936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006235906,0.00022823986,0.00023211158,0.000058726153,0.00015497093,0.00013824651,0.0012279977,0.00019044479,0.0000021801404],"category_scores_gemma":[0.000020840751,0.00019878964,0.00016287615,0.00022685196,0.000023741208,0.00016555312,0.0012619251,0.00037573243,0.000028648055],"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":[0.00003126349,0.000073058734,0.000013381317,0.00021629434,0.000035904523,0.0000019052385,0.00042991253,0.55846876,0.0016167808,0.014748493,0.0037000647,0.42066416],"study_design_scores_gemma":[0.00011492119,0.00002138273,0.000010790806,0.0000098120645,0.00000924189,0.0000024103683,0.0000030638623,0.8464058,0.0017952899,0.15033363,0.0010871434,0.00020650984],"about_ca_topic_score_codex":0.000010430866,"about_ca_topic_score_gemma":0.00009495246,"teacher_disagreement_score":0.51944643,"about_ca_system_score_codex":0.00007511688,"about_ca_system_score_gemma":0.0001132069,"threshold_uncertainty_score":0.81064063},"labels":[],"label_agreement":null},{"id":"W4213139101","doi":"10.1007/978-981-16-7389-4_26","title":"A Feature Extraction and Heatmap Generation Approach Based on 3D Object Models and CNNS","year":2022,"lang":"en","type":"book-chapter","venue":"Algorithms for intelligent systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Graphics; Bridging (networking); Artificial intelligence; Object (grammar); Feature (linguistics); Variable (mathematics); Feature extraction; Computer graphics; Computer vision; Pattern recognition (psychology); Computer graphics (images)","score_opus":0.07168462817828679,"score_gpt":0.28898799168834016,"score_spread":0.21730336351005336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213139101","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":[0.0000023893954,0.0026660522,0.98274666,0.00037397686,0.0008390292,0.0024588846,0.000109497654,0.00017756077,0.010625969],"genre_scores_gemma":[0.010966708,0.003983706,0.7098238,0.0013488976,0.0040569487,0.007450313,0.0024167986,0.00050104945,0.2594518],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774706,0.000044128745,0.00037567996,0.0011117124,0.00043950335,0.00028189764],"domain_scores_gemma":[0.99843967,0.00021689566,0.00033097502,0.00072930043,0.00014633324,0.00013680721],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028594022,0.00044694758,0.0004284894,0.00023249647,0.00045655985,0.00022629378,0.00035099813,0.00027681305,0.000004489995],"category_scores_gemma":[0.00000863444,0.00043435482,0.00011672221,0.00009261024,0.00004879329,0.0003405026,0.00012701018,0.0004521624,0.0000045248757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030695708,0.00008328955,6.005619e-7,0.0002386704,0.00010561992,0.0000081530525,0.00024265931,0.3371334,0.000080278514,0.54224783,0.0053612255,0.11446757],"study_design_scores_gemma":[0.0001388036,0.00020059863,2.3129023e-7,0.000050852384,0.00003373053,0.00007178028,0.000014225721,0.82198405,0.000048033067,0.0027831907,0.17433509,0.00033940736],"about_ca_topic_score_codex":0.000018055556,"about_ca_topic_score_gemma":0.0000033621718,"teacher_disagreement_score":0.53946465,"about_ca_system_score_codex":0.00022896938,"about_ca_system_score_gemma":0.000057810357,"threshold_uncertainty_score":0.9998108},"labels":[],"label_agreement":null},{"id":"W4213267140","doi":"10.3390/electronics11040563","title":"Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Thales (Canada); Université du Québec à Chicoutimi","funders":"","keywords":"Identification (biology); Truck; Computer science; Traffic congestion; Transport engineering; Object detection; Intelligent transportation system; Real-time computing; Artificial intelligence; Engineering; Automotive engineering; Pattern recognition (psychology)","score_opus":0.008267467910630086,"score_gpt":0.24557790062225524,"score_spread":0.23731043271162516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213267140","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":[0.052712057,0.000787395,0.9449058,0.0008837458,0.00006747283,0.00019975174,9.4328783e-7,0.0002790264,0.00016376538],"genre_scores_gemma":[0.9954888,0.00009847582,0.00345456,0.00046990762,0.000035692745,0.00018019335,0.000014316909,0.000015253009,0.00024279379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990221,0.00011301444,0.000105644096,0.00031749174,0.0001712412,0.00027050264],"domain_scores_gemma":[0.9995394,0.00007375084,0.000064921995,0.00024584756,0.000029199451,0.000046904235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011610724,0.00009675251,0.000074925,0.00007692011,0.0008399393,0.000044967375,0.00021619402,0.000027595368,0.00007732035],"category_scores_gemma":[0.000008228052,0.00011115884,0.000034657176,0.00047579047,0.000031872627,0.00015769748,0.00010112233,0.00039431785,0.000018019477],"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":[0.000019709456,0.0001353842,0.00023314367,0.000004956036,0.000021553862,0.0000024880073,0.00031488904,0.76397467,0.061845362,0.023403546,0.00013441905,0.14990988],"study_design_scores_gemma":[0.0003400803,0.00036721595,0.0038698278,0.0000015456595,0.000010913708,0.000043763794,0.000058549253,0.9506784,0.0013685693,0.013308697,0.029746333,0.00020610179],"about_ca_topic_score_codex":0.0000025868999,"about_ca_topic_score_gemma":0.000054320913,"teacher_disagreement_score":0.94277674,"about_ca_system_score_codex":0.00027565713,"about_ca_system_score_gemma":0.00005453267,"threshold_uncertainty_score":0.6460219},"labels":[],"label_agreement":null},{"id":"W4213292153","doi":"10.36227/techrxiv.13049129","title":"A scale-aware YOLO model for pedestrian detection","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pedestrian detection; Leverage (statistics); Computer science; Pedestrian; Convolutional neural network; Artificial intelligence; Object detection; Scale (ratio); Deep learning; Pattern recognition (psychology); Machine learning; Computer vision; Cartography; Geography","score_opus":0.06985393552889282,"score_gpt":0.3077132519185155,"score_spread":0.23785931638962268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213292153","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":[0.00023632761,0.00002052772,0.9908547,0.0062257536,0.00027132255,0.0011663375,0.000023058048,0.00085587543,0.0003461438],"genre_scores_gemma":[0.51968277,0.000026248303,0.47755954,0.0006827719,0.00023476509,0.0010507539,0.00002511217,0.00002554636,0.0007124792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983777,0.000018657607,0.00026661766,0.00092229235,0.00016041718,0.00025432132],"domain_scores_gemma":[0.9985835,0.000085603104,0.00015950893,0.0009219278,0.00010682675,0.00014262936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006235906,0.00022823986,0.00023211158,0.000058726153,0.00015497093,0.00013824651,0.0012279977,0.00019044479,0.0000021801404],"category_scores_gemma":[0.000020840751,0.00019878964,0.00016287615,0.00022685196,0.000023741208,0.00016555312,0.0012619251,0.00037573243,0.000028648055],"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":[0.00003126349,0.000073058734,0.000013381317,0.00021629434,0.000035904523,0.0000019052385,0.00042991253,0.55846876,0.0016167808,0.014748493,0.0037000647,0.42066416],"study_design_scores_gemma":[0.00011492119,0.00002138273,0.000010790806,0.0000098120645,0.00000924189,0.0000024103683,0.0000030638623,0.8464058,0.0017952899,0.15033363,0.0010871434,0.00020650984],"about_ca_topic_score_codex":0.000010430866,"about_ca_topic_score_gemma":0.00009495246,"teacher_disagreement_score":0.51944643,"about_ca_system_score_codex":0.00007511688,"about_ca_system_score_gemma":0.0001132069,"threshold_uncertainty_score":0.81064063},"labels":[],"label_agreement":null},{"id":"W4213334978","doi":"10.36227/techrxiv.13049129.v2","title":"A scale-aware YOLO model for pedestrian detection","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pedestrian detection; Computer science; Leverage (statistics); Pedestrian; Convolutional neural network; Artificial intelligence; Object detection; Scale (ratio); Deep learning; Pattern recognition (psychology); Machine learning; Computer vision; Cartography; Geography","score_opus":0.06985393552889282,"score_gpt":0.3077132519185155,"score_spread":0.23785931638962268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213334978","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":[0.00023632761,0.00002052772,0.9908547,0.0062257536,0.00027132255,0.0011663375,0.000023058048,0.00085587543,0.0003461438],"genre_scores_gemma":[0.51968277,0.000026248303,0.47755954,0.0006827719,0.00023476509,0.0010507539,0.00002511217,0.00002554636,0.0007124792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983777,0.000018657607,0.00026661766,0.00092229235,0.00016041718,0.00025432132],"domain_scores_gemma":[0.9985835,0.000085603104,0.00015950893,0.0009219278,0.00010682675,0.00014262936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006235906,0.00022823986,0.00023211158,0.000058726153,0.00015497093,0.00013824651,0.0012279977,0.00019044479,0.0000021801404],"category_scores_gemma":[0.000020840751,0.00019878964,0.00016287615,0.00022685196,0.000023741208,0.00016555312,0.0012619251,0.00037573243,0.000028648055],"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":[0.00003126349,0.000073058734,0.000013381317,0.00021629434,0.000035904523,0.0000019052385,0.00042991253,0.55846876,0.0016167808,0.014748493,0.0037000647,0.42066416],"study_design_scores_gemma":[0.00011492119,0.00002138273,0.000010790806,0.0000098120645,0.00000924189,0.0000024103683,0.0000030638623,0.8464058,0.0017952899,0.15033363,0.0010871434,0.00020650984],"about_ca_topic_score_codex":0.000010430866,"about_ca_topic_score_gemma":0.00009495246,"teacher_disagreement_score":0.51944643,"about_ca_system_score_codex":0.00007511688,"about_ca_system_score_gemma":0.0001132069,"threshold_uncertainty_score":0.81064063},"labels":[],"label_agreement":null},{"id":"W4214700196","doi":"10.1155/2022/3825532","title":"Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":110,"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 Union University; National Natural Science Foundation of China","keywords":"Traffic sign; Computer science; Robustness (evolution); Artificial intelligence; Block (permutation group theory); Pyramid (geometry); Focus (optics); Traffic sign recognition; Sign (mathematics); Feature extraction; Computer vision; Data mining; Pattern recognition (psychology)","score_opus":0.011729391465823786,"score_gpt":0.243861178121797,"score_spread":0.23213178665597323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214700196","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":[0.23588033,0.00013017228,0.76266766,0.00038234392,0.00049441564,0.0003642979,0.000008191667,0.000068853085,0.0000037682792],"genre_scores_gemma":[0.8816993,0.00002165918,0.11787953,0.00011269256,0.00010574064,0.00012624788,0.000010824411,0.000017897664,0.000026055035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986566,0.000038613463,0.00058819726,0.00024111422,0.00026208194,0.00021341842],"domain_scores_gemma":[0.9986811,0.00014041817,0.00069026387,0.00020806683,0.00019381588,0.00008632258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023818374,0.00013219887,0.00019980977,0.00015570143,0.0003372658,0.000024208486,0.00043847397,0.000030885705,0.0000046568684],"category_scores_gemma":[0.000011325456,0.000139611,0.00016511531,0.0004710329,0.00002074086,0.0008502715,0.000007831547,0.00025970436,9.779781e-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":[0.00012506146,0.00007304587,0.0000075517455,0.000007899027,0.000012604405,0.0000052491705,0.00044618337,0.61331093,0.08014461,0.0004684133,0.000012233394,0.30538625],"study_design_scores_gemma":[0.012604242,0.008122321,0.04402818,0.000060941016,0.00031508435,0.00044411406,0.001386494,0.6580105,0.1418871,0.058947135,0.07246715,0.0017267225],"about_ca_topic_score_codex":0.000001052854,"about_ca_topic_score_gemma":0.000017735707,"teacher_disagreement_score":0.645819,"about_ca_system_score_codex":0.0001465219,"about_ca_system_score_gemma":0.00007170998,"threshold_uncertainty_score":0.56931716},"labels":[],"label_agreement":null},{"id":"W4214874980","doi":"10.1109/access.2022.3156578","title":"GasBotty: Multi-Metric Extraction in the Wild","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Metric (unit); Computer science; Context (archaeology); Meaning (existential); Benchmark (surveying); Matching (statistics); Artificial intelligence; Operationalization; Machine learning; Natural language processing; Data mining; Mathematics; Psychology; Statistics","score_opus":0.0626052468910665,"score_gpt":0.360125344177517,"score_spread":0.2975200972864505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214874980","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":[0.03664116,0.0001473444,0.95914894,0.0024106128,0.0005715521,0.00036745463,0.0000019016929,0.000119357326,0.00059167173],"genre_scores_gemma":[0.9926636,0.000016671946,0.0052369535,0.001558227,0.000066111796,0.00034499154,0.0000014821737,0.0000059542385,0.00010598918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990371,0.00011490821,0.00014376923,0.0002675931,0.00025878637,0.0001778843],"domain_scores_gemma":[0.9991025,0.00023901844,0.00008402682,0.0005355657,0.000016111622,0.000022779614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026306883,0.00006823677,0.000062798164,0.00015119063,0.0002941721,0.00012507048,0.0021347858,0.000013448057,0.000013648813],"category_scores_gemma":[0.000016999044,0.00005661298,0.00002751289,0.0025445917,0.000015178876,0.0007445147,0.00026927554,0.00028640326,0.000018488923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024026462,0.0013431225,0.01822765,0.000015185259,0.000016676475,0.00015776374,0.00187943,0.3016009,0.0034512265,0.030262196,0.045226593,0.5977952],"study_design_scores_gemma":[0.0013096567,0.00013691757,0.15781458,0.00000880053,0.000012265213,0.00024311573,0.00021352188,0.6327937,0.0023845078,0.016721018,0.18766153,0.0007003842],"about_ca_topic_score_codex":0.000043534194,"about_ca_topic_score_gemma":0.00003071233,"teacher_disagreement_score":0.95602244,"about_ca_system_score_codex":0.0000628982,"about_ca_system_score_gemma":0.0000175571,"threshold_uncertainty_score":0.39669985},"labels":[],"label_agreement":null},{"id":"W4214936898","doi":"10.1109/tits.2022.3154537","title":"CL3D: Camera-LiDAR 3D Object Detection With Point Feature Enhancement and Point-Guided Fusion","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Waterloo","funders":"Beijing Municipal Natural Science Foundation; China Postdoctoral Science Foundation; Beihang University; National Natural Science Foundation of China","keywords":"Lidar; Artificial intelligence; Computer vision; Computer science; Object detection; Feature (linguistics); Point cloud; Point (geometry); RGB color model; Pattern recognition (psychology); Remote sensing; Geography; Mathematics","score_opus":0.01685132038772213,"score_gpt":0.24752630189577124,"score_spread":0.2306749815080491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214936898","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":[0.04073968,0.00017316343,0.9557375,0.00042051094,0.0010053997,0.0014064949,0.000061542036,0.00034577213,0.00010993702],"genre_scores_gemma":[0.99131817,0.00017045531,0.005970989,0.0002300634,0.0000448263,0.0014665846,0.000029476345,0.000040774936,0.0007286405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99730957,0.00016915638,0.0005910118,0.00083433004,0.000732428,0.00036350897],"domain_scores_gemma":[0.9987195,0.00012295049,0.00021155244,0.00061739405,0.00016033989,0.00016827261],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028187188,0.00034383524,0.00030657498,0.00032797476,0.0009850324,0.000114759,0.00038042568,0.00007985817,0.00006810395],"category_scores_gemma":[0.0000011145545,0.00032724804,0.00010819698,0.0010161868,0.000058641977,0.0005008599,0.0000024678434,0.0005391436,0.00002682061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003901766,0.0006515119,0.000066883826,0.00015138969,0.0002010769,0.000050232087,0.006200174,0.86225396,0.045603942,0.0020898257,0.00036319564,0.0819776],"study_design_scores_gemma":[0.0034600073,0.004543359,0.0008882245,0.00038484845,0.00036569076,0.0008162202,0.005072838,0.26845288,0.65638244,0.0005209522,0.05641503,0.0026975241],"about_ca_topic_score_codex":0.00015300165,"about_ca_topic_score_gemma":0.0002720536,"teacher_disagreement_score":0.9505785,"about_ca_system_score_codex":0.0002921808,"about_ca_system_score_gemma":0.00006490761,"threshold_uncertainty_score":0.999918},"labels":[],"label_agreement":null},{"id":"W4220654614","doi":"10.1117/12.2613039","title":"Stroke lesion localization in 3D MRI datasets with deep reinforcement learning","year":2022,"lang":"en","type":"article","venue":"Medical Imaging 2022: Computer-Aided Diagnosis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Reinforcement learning; Fluid-attenuated inversion recovery; Deep learning; Test set; Stroke (engine); Context (archaeology); Machine learning; Pattern recognition (psychology); Magnetic resonance imaging; Radiology; Medicine","score_opus":0.011296487176863898,"score_gpt":0.2633712060021093,"score_spread":0.2520747188252454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220654614","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":[0.0017386292,0.0003382695,0.990829,0.0059022284,0.0003337959,0.00044778385,0.000006986738,0.00032839362,0.000074949545],"genre_scores_gemma":[0.8821088,0.00058085186,0.104178995,0.009916774,0.00033602412,0.0018231232,0.00091299205,0.0000761924,0.00006626043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613893,0.00035308735,0.0005537168,0.00090409163,0.0014641674,0.00058599625],"domain_scores_gemma":[0.9980262,0.0006423137,0.00022755994,0.00075998897,0.000054847882,0.0002891195],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00064600445,0.00027213755,0.00030177282,0.00031222936,0.0006331674,0.00014155454,0.0014965483,0.000040417446,0.00027635336],"category_scores_gemma":[0.00010092719,0.00027028142,0.000054352568,0.0014662528,0.00010582114,0.00061408035,0.0022124178,0.0008455285,0.000025375344],"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":[0.000010678998,0.00018825349,0.016267247,0.000012028646,0.000011329518,0.0002420133,0.0002135318,0.75160366,0.000009004537,0.0012223428,0.008748069,0.22147185],"study_design_scores_gemma":[0.0008547855,0.00013796377,0.00076349353,0.00007345757,0.000009176024,0.0000647136,0.0000426979,0.94597864,0.00009530467,0.0001823514,0.051500507,0.00029688748],"about_ca_topic_score_codex":0.00009936252,"about_ca_topic_score_gemma":0.00004157147,"teacher_disagreement_score":0.88664997,"about_ca_system_score_codex":0.00030845156,"about_ca_system_score_gemma":0.000116857336,"threshold_uncertainty_score":0.99997497},"labels":[],"label_agreement":null},{"id":"W4220710197","doi":"10.31219/osf.io/vaucf","title":"3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"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":"York University; Khalifa University of Science, Technology and Research; Abu Dhabi National Oil Company; New York University Abu Dhabi","keywords":"Computer science; Segmentation; Efficient energy use; Computation; Random forest; Probabilistic logic; Energy consumption; Artificial intelligence; Decision tree; Deep learning; Image segmentation; Tree (set theory); Machine learning; Computer vision; Algorithm; Engineering","score_opus":0.023562082133295068,"score_gpt":0.3030855940401427,"score_spread":0.2795235119068476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220710197","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":[0.053069614,0.0009865348,0.94029397,0.0007564937,0.0007724936,0.0031323945,0.000006817654,0.0005916178,0.0003900727],"genre_scores_gemma":[0.9228773,0.00043237352,0.07285084,0.0003614413,0.00028167054,0.001994711,0.00027130128,0.00009458116,0.0008357747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99627644,0.00022468634,0.0007747379,0.0014013327,0.0006591112,0.00066368113],"domain_scores_gemma":[0.99504566,0.0027466319,0.00082481554,0.0011202605,0.0001427693,0.00011983306],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006680456,0.000497132,0.0004679718,0.00018440347,0.0023186929,0.00045826787,0.002049185,0.0001465656,0.000111152425],"category_scores_gemma":[0.00025203667,0.000403419,0.00032146796,0.0007875691,0.00008327964,0.0003625661,0.004415125,0.0008351587,0.000023036677],"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":[0.000063038984,0.000044755154,0.00023929914,0.000059782607,0.00003287202,0.0000049227137,0.0005948507,0.93891007,0.0017226668,0.0008497301,0.000068196656,0.05740983],"study_design_scores_gemma":[0.001061582,0.000118243865,0.000117066316,0.00006424835,0.000060311777,0.000039332514,0.00013034683,0.99096394,0.0014170732,0.0019568044,0.003594452,0.00047659187],"about_ca_topic_score_codex":0.000023948345,"about_ca_topic_score_gemma":0.00009224651,"teacher_disagreement_score":0.8698077,"about_ca_system_score_codex":0.00040394138,"about_ca_system_score_gemma":0.000071590584,"threshold_uncertainty_score":0.99984175},"labels":[],"label_agreement":null},{"id":"W4220827132","doi":"10.1109/tits.2022.3158253","title":"Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":334,"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":"King Saud University","keywords":"Cloud computing; Computer science; Object detection; Enhanced Data Rates for GSM Evolution; Convolutional neural network; Reliability (semiconductor); Real-time computing; Edge computing; Artificial intelligence; Feature extraction; Deep learning; Feature (linguistics); Energy consumption; Pruning; Intelligent transportation system; Embedded system; Engineering; Power (physics); Pattern recognition (psychology)","score_opus":0.01914620768282576,"score_gpt":0.24513281422841016,"score_spread":0.2259866065455844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220827132","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":[0.046893332,0.000045455814,0.9466019,0.00018057015,0.0029919895,0.0020115164,0.00012785957,0.00092237757,0.00022495794],"genre_scores_gemma":[0.9955717,0.000036972393,0.0009344876,0.00011319456,0.00009839811,0.0026779391,0.00006627281,0.00006828509,0.0004327616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955394,0.000664888,0.0013424772,0.0010973146,0.00083615776,0.000519737],"domain_scores_gemma":[0.9977842,0.00051073014,0.0003688822,0.0009473535,0.0001989135,0.0001899329],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008421845,0.00046958792,0.00048251558,0.0008536827,0.00087788544,0.00015954358,0.0007567862,0.00015503359,0.00007075694],"category_scores_gemma":[0.000004656302,0.0005231208,0.00024486112,0.0020160507,0.00005748845,0.0004083557,0.0000024939677,0.0006757311,0.00025884568],"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":[0.00028930724,0.0004346939,0.000017027936,0.00006462488,0.000026720028,0.000021886117,0.00097501546,0.9766583,0.010545607,0.0014414707,0.000051646333,0.009473735],"study_design_scores_gemma":[0.0006317988,0.00087384105,0.0002837202,0.00015318445,0.0000389981,0.000026349395,0.0007651263,0.90229356,0.09171245,0.00002857469,0.0025792066,0.00061317696],"about_ca_topic_score_codex":0.0003412301,"about_ca_topic_score_gemma":0.00023556224,"teacher_disagreement_score":0.9486784,"about_ca_system_score_codex":0.0016142393,"about_ca_system_score_gemma":0.00019949913,"threshold_uncertainty_score":0.99972206},"labels":[],"label_agreement":null},{"id":"W4220829461","doi":"10.1109/ojits.2022.3160888","title":"LiCaNet: Further Enhancement of Joint Perception and Motion Prediction Based on Multi-Modal Fusion","year":2022,"lang":"en","type":"article","venue":"IEEE Open Journal of Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modal; Fusion; Joint (building); Artificial intelligence; Computer science; Perception; Motion (physics); Computer vision; Psychology; Engineering; Materials science; Philosophy; Structural engineering; Neuroscience","score_opus":0.04733512426786524,"score_gpt":0.2878660744115949,"score_spread":0.24053095014372966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220829461","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":[0.18381627,0.00006130415,0.81451374,0.00025314055,0.0006349852,0.0006539322,0.000024424566,0.000012608001,0.000029591502],"genre_scores_gemma":[0.98964316,0.00006899137,0.0099763,0.00007532924,0.00005728532,0.0000854235,0.000020898207,0.000012214335,0.000060384606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789596,0.00018170034,0.00094892574,0.00025330362,0.0005959198,0.00012421221],"domain_scores_gemma":[0.99847424,0.00005124517,0.00088196003,0.00024918615,0.00025489982,0.00008848685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007476502,0.00013123793,0.00025467997,0.00021542783,0.00016797829,0.00006168885,0.0004736209,0.00003398432,0.000056955363],"category_scores_gemma":[0.0000051191005,0.00012055875,0.00008097213,0.0003217117,0.00002711225,0.00043409635,0.000017272181,0.00020343013,0.0000029547166],"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":[0.00022545311,0.0006240001,0.0015972554,0.00006118571,0.00003765271,0.000009514032,0.0037014533,0.8538516,0.11265805,0.0009518582,0.00019748817,0.026084483],"study_design_scores_gemma":[0.0022408685,0.0029552851,0.031735417,0.00041728065,0.00006864111,0.00008244363,0.0027760826,0.91371346,0.04289707,0.00020454232,0.0025248125,0.00038408602],"about_ca_topic_score_codex":0.000053760756,"about_ca_topic_score_gemma":0.000010063213,"teacher_disagreement_score":0.8058269,"about_ca_system_score_codex":0.00015132238,"about_ca_system_score_gemma":0.000057287853,"threshold_uncertainty_score":0.4916243},"labels":[],"label_agreement":null},{"id":"W4220960024","doi":"10.18280/ts.390128","title":"Optimal Squeeze Net with Deep Neural Network-Based Arial Image Classification Model in Unmanned Aerial Vehicles","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":49,"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; Hyperparameter; Aerial image; Computer science; Classifier (UML); Artificial neural network; Pattern recognition (psychology); Benchmark (surveying); Extractor; Feature (linguistics); Feature extraction; Contextual image classification; Deep learning; Computer vision; Aerial imagery; Image (mathematics); Engineering","score_opus":0.021293480669036427,"score_gpt":0.24146260905804995,"score_spread":0.22016912838901354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220960024","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":[0.2100849,0.00001864414,0.7869623,0.0017954055,0.00013731637,0.0006872994,0.000013499245,0.00019505598,0.00010559965],"genre_scores_gemma":[0.87731564,0.0000011817642,0.120902985,0.0006590285,0.00031580005,0.00068719144,0.00007898546,0.000025324753,0.000013861689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975493,0.00019711214,0.00044166925,0.00067412946,0.00056873,0.0005690846],"domain_scores_gemma":[0.9990344,0.00011926554,0.00021266322,0.00045959643,0.000056108573,0.00011797429],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032605798,0.0002511172,0.00022293057,0.000118600416,0.0004445957,0.00013457886,0.0009585921,0.00004141271,0.00007814064],"category_scores_gemma":[0.0000041402477,0.00025294427,0.00006752879,0.0008017908,0.00009022608,0.0004973513,0.00024410669,0.00035137776,0.0000065861523],"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":[0.00039868391,0.00019757597,0.00018138265,0.000003609106,0.000007711271,0.000015303545,0.000144067,0.9770682,0.004904492,0.012983071,0.0004972616,0.003598652],"study_design_scores_gemma":[0.0021520713,0.00032423035,0.0017131666,0.0000045366855,0.000011293643,0.0000075144,0.000022440163,0.99331194,0.00024608415,0.00149845,0.00041685865,0.0002913828],"about_ca_topic_score_codex":0.000012291757,"about_ca_topic_score_gemma":0.00003468711,"teacher_disagreement_score":0.6672307,"about_ca_system_score_codex":0.00019781238,"about_ca_system_score_gemma":0.000120648474,"threshold_uncertainty_score":0.99999225},"labels":[],"label_agreement":null},{"id":"W4221026608","doi":"10.1007/s11227-022-04399-2","title":"Heterogeneous gradient computing optimization for scalable deep neural networks","year":2022,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Agencia Estatal de Investigación; Universidad de Extremadura; Horizon 2020; European Commission; European Regional Development Fund; Banco Bilbao Vizcaya Argentaria; Consejería de Educación y Empleo, Junta de Extremadura; Fundación BBVA; Ministerio de Ciencia e Innovación; Canadian Institute for Advanced Research; Junta de Extremadura; Ministerio de Ciencia, Innovación y Universidades","keywords":"Computer science; Scalability; Replica; Distributed computing; Workload; Artificial neural network; Computation; Partition (number theory); Symmetric multiprocessor system; Data parallelism; Supercomputer; Parallel computing; Process (computing); Artificial intelligence; Parallelism (grammar); Algorithm; Database","score_opus":0.01791124502814537,"score_gpt":0.24772969038386516,"score_spread":0.2298184453557198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221026608","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":[0.07055359,0.00068490155,0.9268458,0.0009039346,0.0006739849,0.0002683303,7.688355e-7,0.000056462493,0.000012236184],"genre_scores_gemma":[0.8473774,0.0000121612175,0.15168409,0.00053780264,0.00035968033,0.0000046408354,0.0000023288005,0.00001806415,0.000003833912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998271,0.00025003607,0.00057635945,0.000186378,0.00032074863,0.00039551055],"domain_scores_gemma":[0.99826497,0.00071494206,0.00042467017,0.00033208504,0.00017650228,0.00008682017],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012451488,0.00014263566,0.00021478168,0.000093276234,0.0014050315,0.00008667831,0.0014415216,0.000021596778,0.000007899917],"category_scores_gemma":[0.000032772623,0.00011388681,0.00014009023,0.00058139965,0.000039439357,0.00028441934,0.000664837,0.00040790817,4.0374886e-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":[0.000014317782,0.000031992957,0.000057894627,0.0000029976736,0.000014965649,0.000004341016,0.00040966013,0.9661212,0.000076178454,0.0006262488,0.00010576809,0.032534458],"study_design_scores_gemma":[0.00031926567,0.00019914779,0.00003594477,0.000007230402,0.000018959287,0.0010404125,0.0001222555,0.9972423,0.00006265056,0.00053907646,0.00029383306,0.00011895057],"about_ca_topic_score_codex":0.0000032784674,"about_ca_topic_score_gemma":8.3851097e-7,"teacher_disagreement_score":0.7768238,"about_ca_system_score_codex":0.00012657049,"about_ca_system_score_gemma":0.000022124446,"threshold_uncertainty_score":0.999895},"labels":[],"label_agreement":null},{"id":"W4221039672","doi":"10.1016/j.compmedimag.2022.102054","title":"Two-stage segmentation network with feature aggregation and multi-level attention mechanism for multi-modality heart images","year":2022,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"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":"Natural Science Foundation of Anhui Province","keywords":"Segmentation; Computer science; Feature (linguistics); Artificial intelligence; Encoder; Modality (human–computer interaction); Pattern recognition (psychology); Image segmentation; Decoding methods; Fuse (electrical); Computer vision; Representation (politics); Algorithm","score_opus":0.03477640809921554,"score_gpt":0.31463725622931193,"score_spread":0.2798608481300964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221039672","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":[0.008323071,0.0003197819,0.97924054,0.010987682,0.0002675917,0.0006298515,0.00002950188,0.00020053286,0.0000014609892],"genre_scores_gemma":[0.12746222,0.00012910551,0.8687532,0.0030505825,0.0001222015,0.00029513365,0.000100633646,0.000021094862,0.000065835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832225,0.00015458668,0.00022243074,0.00059280585,0.0003953382,0.0003125772],"domain_scores_gemma":[0.99905735,0.00018752246,0.00015648823,0.00028197453,0.000114507624,0.00020214333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005072124,0.00019095637,0.00021590738,0.00008442321,0.0008781422,0.00014204583,0.000284737,0.0000402243,0.0000016707775],"category_scores_gemma":[0.000029017967,0.0001772098,0.000049863836,0.0004337573,0.00015642829,0.00034167102,0.00039894573,0.00033864137,2.2343038e-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":[0.00028739497,0.0008462361,0.011455011,0.0003874129,0.0001965467,0.00009122307,0.0011119269,0.0045829844,0.014607359,0.37774205,0.005224664,0.5834672],"study_design_scores_gemma":[0.0030373384,0.000066406596,0.0046754335,0.000045609468,0.00001878983,0.0001139701,0.000044621906,0.9847408,0.00015293901,0.0061587403,0.0007137545,0.00023160745],"about_ca_topic_score_codex":0.000014019402,"about_ca_topic_score_gemma":0.00001103746,"teacher_disagreement_score":0.9801578,"about_ca_system_score_codex":0.000027513846,"about_ca_system_score_gemma":0.00004403271,"threshold_uncertainty_score":0.72264063},"labels":[],"label_agreement":null},{"id":"W4221102300","doi":"10.32657/10356/155131","title":"Addressing challenges in real-world image classification : long-tailed distribution and knowledge distillation","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Scratch; Distillation; Image (mathematics); Computer science; Value (mathematics); Residual neural network; Baseline (sea); Artificial intelligence; Mathematics education; Test (biology); Machine learning; Pattern recognition (psychology); Mathematics; Deep learning; Programming language; Geology","score_opus":0.11341717380512534,"score_gpt":0.3714291996802639,"score_spread":0.25801202587513855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221102300","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":[0.10254647,0.022850063,0.66551095,0.0052506044,0.0021521777,0.0037325195,0.00008609062,0.001808232,0.19606286],"genre_scores_gemma":[0.96633804,0.011000287,0.009566167,0.000012403395,0.00022007772,0.00037013023,0.0062941904,0.000050351056,0.0061483476],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978091,0.00014582064,0.0005168224,0.0009983964,0.00022785527,0.0003019812],"domain_scores_gemma":[0.99835354,0.0002388565,0.00034751833,0.00069531205,0.0002659304,0.000098818324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020726587,0.00031349916,0.0003446032,0.00022242064,0.00019034557,0.00022524041,0.00037619102,0.00021456648,0.000013541415],"category_scores_gemma":[0.00006943149,0.00033051867,0.00005937495,0.0009848455,0.000041154715,0.0006728961,0.000111617126,0.00035885812,0.000019126128],"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":[0.000028511375,0.00022865945,0.0018867558,0.00048595795,0.000022915081,0.000016249634,0.0013022764,0.000049802617,0.0023516123,0.26312348,0.00054084376,0.72996294],"study_design_scores_gemma":[0.0004268207,0.000025254307,0.9164762,0.0006487869,0.000032247317,0.0000067692745,0.0004897296,0.07058599,0.001327073,0.006885696,0.0023564245,0.00073902047],"about_ca_topic_score_codex":0.000027224349,"about_ca_topic_score_gemma":0.011487364,"teacher_disagreement_score":0.9145894,"about_ca_system_score_codex":0.0002520033,"about_ca_system_score_gemma":0.00013717318,"threshold_uncertainty_score":0.9999147},"labels":[],"label_agreement":null},{"id":"W4221149139","doi":"10.1609/aaai.v36i6.20542","title":"FisheyeHDK: Hyperbolic Deformable Kernel Learning for Ultra-Wide Field-of-View Image Recognition","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada)","funders":"","keywords":"Artificial intelligence; Kernel (algebra); Convolution (computer science); Computer vision; Computer science; Euclidean geometry; Convolutional neural network; Distortion (music); Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Mathematics; Artificial neural network; Object (grammar); Geometry","score_opus":0.07102955172633385,"score_gpt":0.3064330249673988,"score_spread":0.23540347324106498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221149139","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":[0.25936538,0.00013383717,0.705568,0.013852857,0.0008512003,0.0029065467,0.000035609668,0.00035865552,0.016927894],"genre_scores_gemma":[0.9878162,0.000083472834,0.011109681,0.0004142209,0.000040279887,0.00031646772,0.0000027297347,0.00001414585,0.00020276535],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99838233,0.000022890612,0.0005267778,0.000407741,0.00034582097,0.00031444104],"domain_scores_gemma":[0.99830323,0.00038746346,0.0005207429,0.0002467945,0.00048575905,0.000056026493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043461597,0.00016529966,0.0002392199,0.00009450029,0.00048006355,0.00008159066,0.0015660898,0.000041318268,0.000073166244],"category_scores_gemma":[0.0005740987,0.00014504323,0.00014203513,0.00075677084,0.000106381965,0.00045071272,0.0003447854,0.00038059035,0.000019150448],"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":[0.0001617378,0.00027783395,0.00027325717,0.00016008611,0.00002455913,3.2731677e-7,0.001572307,0.0029811668,0.18077138,0.38269183,0.00085255806,0.43023297],"study_design_scores_gemma":[0.000037527163,0.0003567135,0.000032413598,0.00008059364,0.000013476745,0.0000065733443,0.0003910107,0.072034135,0.70504856,0.22016832,0.0016390308,0.00019163633],"about_ca_topic_score_codex":0.000019528341,"about_ca_topic_score_gemma":0.0000027667577,"teacher_disagreement_score":0.72845083,"about_ca_system_score_codex":0.000046208763,"about_ca_system_score_gemma":0.00006489901,"threshold_uncertainty_score":0.59146917},"labels":[],"label_agreement":null},{"id":"W4221166146","doi":"10.1016/j.jvcir.2023.103800","title":"TransCAM: Transformer attention-based CAM refinement for Weakly supervised semantic segmentation","year":2023,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":85,"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; University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Transformer; Segmentation; Convolutional neural network; Discriminative model; Pattern recognition (psychology); Pixel; Pascal (unit); Voltage","score_opus":0.04025519472113439,"score_gpt":0.3715744347563467,"score_spread":0.33131924003521235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221166146","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":[0.086713605,0.00011905586,0.9028779,0.009402143,0.0001066911,0.0005736342,0.000005455351,0.00007660734,0.00012488602],"genre_scores_gemma":[0.8770842,0.0010707445,0.12113113,0.00025566536,0.00009046634,0.00011661999,0.00012543301,0.00001879743,0.000106946085],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985999,0.00016662068,0.0005921722,0.00018156944,0.0003007215,0.00015904556],"domain_scores_gemma":[0.9983785,0.00042970513,0.0003629237,0.00035104068,0.00039778562,0.00008003117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057413726,0.000115276176,0.00017763709,0.00025857412,0.00028229202,0.00014074749,0.00039752203,0.000039226194,0.0000057444267],"category_scores_gemma":[0.00005767221,0.00010946221,0.0001257403,0.00067429786,0.000058352907,0.0010330934,0.000027766902,0.00012774905,0.0000056968665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020208678,0.0004648291,0.0011616467,0.00017102582,0.00010297027,0.0000026442824,0.0017767311,0.0026985814,0.77821046,0.0045703496,0.0054817777,0.20515689],"study_design_scores_gemma":[0.0055520963,0.0008460499,0.029041274,0.00019588077,0.00015269408,0.000045357458,0.001719754,0.8727619,0.080647536,0.005289739,0.0032803582,0.00046736136],"about_ca_topic_score_codex":0.0000075537737,"about_ca_topic_score_gemma":0.000007372734,"teacher_disagreement_score":0.8700633,"about_ca_system_score_codex":0.00004498115,"about_ca_system_score_gemma":0.000049531212,"threshold_uncertainty_score":0.44637397},"labels":[],"label_agreement":null},{"id":"W4224124906","doi":"10.3390/sym14040810","title":"Perceptual Hash of Neural Networks","year":2022,"lang":"en","type":"article","venue":"Symmetry","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Hash function; Computer science; Artificial neural network; Theoretical computer science; Artificial intelligence; Graph; Encoder; Deep learning","score_opus":0.015312599329586585,"score_gpt":0.2509887297029218,"score_spread":0.2356761303733352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224124906","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":[0.064802386,0.00045040468,0.9282035,0.0012340349,0.0006984222,0.00024269622,0.0000075870303,0.00025171542,0.004109253],"genre_scores_gemma":[0.98710376,0.0000037843429,0.0119647235,0.0005799124,0.00007547042,0.000043807908,0.0000047632793,0.000008777435,0.00021502575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906,0.0000653183,0.0001738356,0.00026076505,0.00022922328,0.00021086274],"domain_scores_gemma":[0.99919367,0.00010368911,0.00008715902,0.00053677085,0.000024998266,0.00005369475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107872984,0.00007962103,0.00011223077,0.00006589992,0.00021326012,0.000014535701,0.000917429,0.00001833578,0.000070143666],"category_scores_gemma":[0.000009759334,0.00008452757,0.000059660015,0.0008727026,0.000047138223,0.00014365219,0.00076462387,0.00023381562,0.0000069918046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013154936,0.00020779498,0.003006005,0.000008649849,0.000022026592,0.000016352593,0.00050132687,0.27035362,0.0013134883,0.48675877,0.013908638,0.2238902],"study_design_scores_gemma":[0.00022476022,0.00013037836,0.0042031817,0.0000017202593,0.000004940849,0.00004514563,0.000119247765,0.97827166,0.00019257572,0.0073727584,0.009237652,0.00019594982],"about_ca_topic_score_codex":0.00000541424,"about_ca_topic_score_gemma":9.810404e-7,"teacher_disagreement_score":0.92230135,"about_ca_system_score_codex":0.000033227436,"about_ca_system_score_gemma":0.0000143921025,"threshold_uncertainty_score":0.34469345},"labels":[],"label_agreement":null},{"id":"W4224214784","doi":"10.1145/3511598","title":"An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":40,"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":"Retraining; Computer science; Artificial intelligence; Machine learning; Selection (genetic algorithm); Test data; Artificial neural network; Software deployment; Metric (unit); Data mining; Data set; Test set; Process (computing); Model selection; Deep learning; Distribution (mathematics); Set (abstract data type); Empirical research; Statistics; Mathematics; Engineering","score_opus":0.12392267985852783,"score_gpt":0.38876204613595167,"score_spread":0.2648393662774238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224214784","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":[0.015580451,0.000023436647,0.9829394,0.0002634234,0.0002625925,0.00043429033,0.000059189977,0.0004368734,3.0028633e-7],"genre_scores_gemma":[0.49606165,0.0000111286,0.5029699,0.00009045655,0.000041883904,0.00066987413,0.00010854934,0.000017261595,0.000029277495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850214,0.00026202615,0.00019123302,0.0006499662,0.00014348661,0.00025118203],"domain_scores_gemma":[0.99562395,0.00339508,0.00005180684,0.0008043034,0.000040674622,0.00008421118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006482058,0.00015169017,0.00017748166,0.00009816579,0.0007210692,0.00002907281,0.00070988486,0.000042429725,0.000009700846],"category_scores_gemma":[0.0005155624,0.00016603584,0.0000301459,0.00044031203,0.000014655976,0.00017518016,0.00006345774,0.0004545787,0.0000015203746],"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":[0.00003667355,0.0006674354,0.0012724008,0.000010560537,0.000041884698,0.0000016351673,0.0005973113,0.8083981,0.00072161516,0.00016337294,0.000045829398,0.18804318],"study_design_scores_gemma":[0.0009443067,0.0054059518,0.0057420395,0.0000074110385,0.000071913884,0.00007012738,0.0004330742,0.96110296,0.001751128,0.00053703587,0.023451958,0.00048208633],"about_ca_topic_score_codex":0.0000052305168,"about_ca_topic_score_gemma":0.0000058329297,"teacher_disagreement_score":0.4804812,"about_ca_system_score_codex":0.00009180562,"about_ca_system_score_gemma":0.000019233908,"threshold_uncertainty_score":0.6770745},"labels":[],"label_agreement":null},{"id":"W4224273618","doi":"10.1016/j.treng.2022.100115","title":"3D object detection for autonomous driving: Methods, models, sensors, data, and challenges","year":2022,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Object detection; Categorization; Artificial intelligence; Object (grammar); Focus (optics); Computer vision; Data mining; Pattern recognition (psychology)","score_opus":0.05284242472348057,"score_gpt":0.2838832401896235,"score_spread":0.23104081546614294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224273618","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":[0.003478526,0.0009806125,0.9943726,0.0003069711,0.00015341272,0.00029344184,0.000027584034,0.00037546764,0.0000113788965],"genre_scores_gemma":[0.49852508,0.00020929993,0.5009311,0.000014206733,0.000029014342,0.0002129663,0.000050532482,0.000016450154,0.00001134247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991389,0.000021017533,0.00017555858,0.00039742218,0.00010324724,0.00016386407],"domain_scores_gemma":[0.99930364,0.00016418146,0.00005479986,0.00040815843,0.000021142714,0.00004807773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024640744,0.00010496102,0.00010726158,0.000081737526,0.0001804555,0.000019424873,0.00034258366,0.000021745738,0.0000015064531],"category_scores_gemma":[0.000008063073,0.00012935679,0.000021780004,0.00019021612,0.0000064279525,0.00047949102,0.000033459386,0.00011515867,2.5579837e-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":[0.0000020014436,0.000009064768,0.0000025736038,0.000026048536,0.000008611065,8.5234245e-7,0.00053562137,0.88406044,0.0023114039,0.012635777,0.0000035120722,0.1004041],"study_design_scores_gemma":[0.00013927548,0.000028565593,0.00056581205,0.000002675463,0.000010628623,0.0000066526313,0.000038705803,0.98105055,0.00080138684,0.0012083787,0.016009562,0.00013782625],"about_ca_topic_score_codex":0.000003606176,"about_ca_topic_score_gemma":0.000016738079,"teacher_disagreement_score":0.49504656,"about_ca_system_score_codex":0.000032476295,"about_ca_system_score_gemma":0.000013051972,"threshold_uncertainty_score":0.52750164},"labels":[],"label_agreement":null},{"id":"W4224862961","doi":"10.1038/s41598-022-10429-z","title":"Clinical target segmentation using a novel deep neural network: double attention Res-U-Net","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Jaccard index; Segmentation; Computer science; Artificial intelligence; Sørensen–Dice coefficient; Pattern recognition (psychology); Dropout (neural networks); Dice; Noise (video); Image segmentation; Artificial neural network; Encoder; Encoding (memory); Image (mathematics); Machine learning; Mathematics","score_opus":0.06503603948376563,"score_gpt":0.3438051097556136,"score_spread":0.278769070271848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224862961","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":[0.20487714,0.0001387222,0.77954036,0.00036522592,0.013814065,0.0007174825,0.000002108193,0.0002715146,0.00027336937],"genre_scores_gemma":[0.7142438,0.0000017996693,0.28385437,0.00026462073,0.00035879272,0.00017117348,0.00014408185,0.000024550855,0.0009367712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99605376,0.00015131671,0.000969884,0.001381136,0.00088953006,0.0005543709],"domain_scores_gemma":[0.9973617,0.00007402304,0.0007721675,0.0014806105,0.00014310218,0.00016835167],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0027194577,0.00017235638,0.00021420604,0.00013524137,0.0019202371,0.000416822,0.00065600144,0.000043173142,0.00008839857],"category_scores_gemma":[0.000027905797,0.00018446479,0.00016340712,0.0019309489,0.00016205129,0.00081679353,0.0009842672,0.0003330181,0.000011483708],"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":[0.000021965796,0.0002575355,0.01543114,0.0000068351574,0.000019857902,0.00014031475,0.00014681723,0.9458497,0.016658999,0.0033441551,0.008623809,0.00949887],"study_design_scores_gemma":[0.00046206467,0.000059118644,0.0026082576,0.000006650985,0.000018351719,0.0007770159,0.000044661178,0.93750906,0.00029625793,0.018794939,0.039093483,0.00033015024],"about_ca_topic_score_codex":0.000014742858,"about_ca_topic_score_gemma":0.000009329045,"teacher_disagreement_score":0.5093667,"about_ca_system_score_codex":0.00016114027,"about_ca_system_score_gemma":0.00011834359,"threshold_uncertainty_score":0.9993791},"labels":[],"label_agreement":null},{"id":"W4225094779","doi":"10.1109/mlke55170.2022.00026","title":"Analysis on the Selection of the Appropriate Batch Size in CNN Neural Network","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Queen's University","funders":"","keywords":"MNIST database; Computer science; Convolutional neural network; Artificial neural network; Selection (genetic algorithm); Artificial intelligence; Batch processing; Range (aeronautics); Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.013337657065969396,"score_gpt":0.23776667765634804,"score_spread":0.22442902059037864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225094779","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":[0.6991997,0.000083946645,0.27047595,0.024183685,0.0005776324,0.0012217128,0.0000026875616,0.00022125122,0.004033437],"genre_scores_gemma":[0.9967451,0.0000036806239,0.0018988276,0.00083796465,0.000039722327,0.0001235048,4.367681e-7,0.000004038467,0.0003467175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889576,0.00024140411,0.00018282284,0.0002476299,0.00025568635,0.00017666422],"domain_scores_gemma":[0.9988052,0.0004616371,0.00011503518,0.00057934265,0.000022668155,0.000016119673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029662502,0.00007160071,0.00010458398,0.00003956428,0.0003297869,0.000019692545,0.0009866816,0.00001107242,0.00007885636],"category_scores_gemma":[0.000023094406,0.000040982883,0.00009472593,0.0054937415,0.000026072483,0.000068160254,0.00043869967,0.00027665938,0.0000026396845],"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":[0.0000044367284,0.000031499694,0.008164773,4.8509844e-7,0.000015715013,1.1670554e-7,0.00006571559,0.94539964,0.00011493359,0.03938605,0.00055948703,0.0062571475],"study_design_scores_gemma":[0.0000701247,0.000027689848,0.06073771,9.816713e-7,0.000017162634,0.0000012390294,0.000017179254,0.92621964,0.00022478765,0.011074442,0.0015397498,0.00006930024],"about_ca_topic_score_codex":0.00003360735,"about_ca_topic_score_gemma":0.00010894613,"teacher_disagreement_score":0.2975454,"about_ca_system_score_codex":0.000046952966,"about_ca_system_score_gemma":0.000018433882,"threshold_uncertainty_score":0.26395607},"labels":[],"label_agreement":null},{"id":"W4225135483","doi":"10.1007/s10489-022-03549-6","title":"MS-SSD: multi-scale single shot detector for ship detection in remote sensing images","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Detector; Object detection; Robustness (evolution); Single shot; Artificial intelligence; Task (project management); Scale (ratio); Context (archaeology); One shot; Computer vision; Variance (accounting); Real-time computing; Pattern recognition (psychology); Telecommunications; Systems engineering","score_opus":0.055520805082109694,"score_gpt":0.29475650474804965,"score_spread":0.23923569966593997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225135483","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":[0.006635157,0.000086837994,0.9911199,0.00019937943,0.00024129551,0.0008756243,0.000006305795,0.00026720695,0.00056829967],"genre_scores_gemma":[0.6949017,0.000008192369,0.304618,0.0002532025,0.000041523734,0.00008088879,0.000003447866,0.000021682774,0.00007138919],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99811536,0.00005138718,0.00038157846,0.00074509135,0.00024105058,0.00046555302],"domain_scores_gemma":[0.998691,0.0003790123,0.00015034652,0.000660047,0.000052333537,0.00006726719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031871465,0.00019743071,0.0001899271,0.00017271568,0.00043784158,0.00007357053,0.00081675866,0.000049790764,0.000015302343],"category_scores_gemma":[0.000041319916,0.00023144054,0.000069287795,0.0011216673,0.000065535896,0.00018226003,0.00044053604,0.0003372425,0.00002656704],"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":[0.00002634836,0.00004865152,0.000004005875,0.000008366521,0.0000029938362,0.000002607769,0.00031306938,0.016386854,0.30893773,0.00083903084,0.000027554728,0.6734028],"study_design_scores_gemma":[0.00012070549,0.000076201504,0.00006690045,0.0000060491357,0.0000035567657,0.000021345126,0.000118186195,0.41903993,0.5641762,0.012463733,0.0036484855,0.00025871187],"about_ca_topic_score_codex":0.00005225876,"about_ca_topic_score_gemma":0.00017290482,"teacher_disagreement_score":0.6882665,"about_ca_system_score_codex":0.00025371078,"about_ca_system_score_gemma":0.000027186896,"threshold_uncertainty_score":0.94378716},"labels":[],"label_agreement":null},{"id":"W4225316881","doi":"10.36227/techrxiv.19412546","title":"3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; Khalifa University of Science, Technology and Research; Abu Dhabi National Oil Company; New York University Abu Dhabi","keywords":"Computer science; Segmentation; Efficient energy use; Random forest; Computation; Probabilistic logic; Energy consumption; Artificial intelligence; Deep learning; Decision tree; Image segmentation; Tree (set theory); Machine learning; Computer vision; Algorithm; Engineering","score_opus":0.023562082133295068,"score_gpt":0.3030855940401427,"score_spread":0.2795235119068476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225316881","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":[0.053069614,0.0009865348,0.94029397,0.0007564937,0.0007724936,0.0031323945,0.000006817654,0.0005916178,0.0003900727],"genre_scores_gemma":[0.9228773,0.00043237352,0.07285084,0.0003614413,0.00028167054,0.001994711,0.00027130128,0.00009458116,0.0008357747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99627644,0.00022468634,0.0007747379,0.0014013327,0.0006591112,0.00066368113],"domain_scores_gemma":[0.99504566,0.0027466319,0.00082481554,0.0011202605,0.0001427693,0.00011983306],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006680456,0.000497132,0.0004679718,0.00018440347,0.0023186929,0.00045826787,0.002049185,0.0001465656,0.000111152425],"category_scores_gemma":[0.00025203667,0.000403419,0.00032146796,0.0007875691,0.00008327964,0.0003625661,0.004415125,0.0008351587,0.000023036677],"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":[0.000063038984,0.000044755154,0.00023929914,0.000059782607,0.00003287202,0.0000049227137,0.0005948507,0.93891007,0.0017226668,0.0008497301,0.000068196656,0.05740983],"study_design_scores_gemma":[0.001061582,0.000118243865,0.000117066316,0.00006424835,0.000060311777,0.000039332514,0.00013034683,0.99096394,0.0014170732,0.0019568044,0.003594452,0.00047659187],"about_ca_topic_score_codex":0.000023948345,"about_ca_topic_score_gemma":0.00009224651,"teacher_disagreement_score":0.8698077,"about_ca_system_score_codex":0.00040394138,"about_ca_system_score_gemma":0.000071590584,"threshold_uncertainty_score":0.99984175},"labels":[],"label_agreement":null},{"id":"W4225329800","doi":"10.36227/techrxiv.19412546.v1","title":"3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"York University; Khalifa University of Science, Technology and Research; Abu Dhabi National Oil Company; New York University Abu Dhabi","keywords":"Computer science; Segmentation; Efficient energy use; Random forest; Computation; Probabilistic logic; Energy consumption; Artificial intelligence; Decision tree; Deep learning; Image segmentation; Machine learning; Tree (set theory); Algorithm; Engineering","score_opus":0.023562082133295068,"score_gpt":0.3030855940401427,"score_spread":0.2795235119068476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225329800","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":[0.053069614,0.0009865348,0.94029397,0.0007564937,0.0007724936,0.0031323945,0.000006817654,0.0005916178,0.0003900727],"genre_scores_gemma":[0.9228773,0.00043237352,0.07285084,0.0003614413,0.00028167054,0.001994711,0.00027130128,0.00009458116,0.0008357747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99627644,0.00022468634,0.0007747379,0.0014013327,0.0006591112,0.00066368113],"domain_scores_gemma":[0.99504566,0.0027466319,0.00082481554,0.0011202605,0.0001427693,0.00011983306],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006680456,0.000497132,0.0004679718,0.00018440347,0.0023186929,0.00045826787,0.002049185,0.0001465656,0.000111152425],"category_scores_gemma":[0.00025203667,0.000403419,0.00032146796,0.0007875691,0.00008327964,0.0003625661,0.004415125,0.0008351587,0.000023036677],"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":[0.000063038984,0.000044755154,0.00023929914,0.000059782607,0.00003287202,0.0000049227137,0.0005948507,0.93891007,0.0017226668,0.0008497301,0.000068196656,0.05740983],"study_design_scores_gemma":[0.001061582,0.000118243865,0.000117066316,0.00006424835,0.000060311777,0.000039332514,0.00013034683,0.99096394,0.0014170732,0.0019568044,0.003594452,0.00047659187],"about_ca_topic_score_codex":0.000023948345,"about_ca_topic_score_gemma":0.00009224651,"teacher_disagreement_score":0.8698077,"about_ca_system_score_codex":0.00040394138,"about_ca_system_score_gemma":0.000071590584,"threshold_uncertainty_score":0.99984175},"labels":[],"label_agreement":null},{"id":"W4225368580","doi":"10.24963/ijcai.2022/122","title":"MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Southeast University","keywords":"Computer science; Overfitting; Convolutional neural network; Representation (politics); Artificial intelligence; Pattern recognition (psychology); Segmentation; Code (set theory); Convolution (computer science); Noise (video); Embedding; Image (mathematics); Artificial neural network","score_opus":0.06027301354964597,"score_gpt":0.3071543382944801,"score_spread":0.2468813247448341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225368580","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":[0.011706395,0.000024338691,0.95549995,0.02664086,0.0017537752,0.0010413904,0.000023883758,0.00018597314,0.0031234191],"genre_scores_gemma":[0.95856553,0.00006865746,0.039263655,0.0011540106,0.0002705952,0.00047049957,0.000010274956,0.00002317705,0.00017359843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996898,0.000030755386,0.0007808053,0.00064240926,0.0012894031,0.0003586371],"domain_scores_gemma":[0.9979214,0.0002665384,0.000694094,0.00030495485,0.0007054167,0.00010761176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077481614,0.00023921387,0.00024214869,0.00016117214,0.00078908354,0.00022720126,0.0031389408,0.00007879839,0.00028247302],"category_scores_gemma":[0.00042879264,0.00020904116,0.00017375602,0.0005852371,0.00021740001,0.00052333047,0.0012038782,0.0006022161,0.000017584418],"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":[0.0000784144,0.00017323524,0.00006757413,0.00001711049,0.000030635656,0.0000010548412,0.00091584295,0.008095788,0.0070340154,0.95130396,0.0006193277,0.031663045],"study_design_scores_gemma":[0.000078450685,0.00018104776,0.00008185965,0.00009130237,0.000010791187,0.000021449172,0.0003719465,0.7018959,0.036126282,0.25991338,0.0010037055,0.00022389411],"about_ca_topic_score_codex":0.000021022277,"about_ca_topic_score_gemma":0.00001983986,"teacher_disagreement_score":0.9468591,"about_ca_system_score_codex":0.00024380012,"about_ca_system_score_gemma":0.000109961955,"threshold_uncertainty_score":0.8524451},"labels":[],"label_agreement":null},{"id":"W4225819607","doi":"","title":"Cat Recognition Based on Deep Learning","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Deep learning; Psychology","score_opus":0.01979876752286917,"score_gpt":0.23767133253423392,"score_spread":0.21787256501136476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225819607","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":[0.011604784,0.00030032627,0.94781953,0.009593466,0.00024675686,0.0004350092,0.000009340017,0.0006752391,0.029315565],"genre_scores_gemma":[0.51536393,0.00036634092,0.48037657,0.0004929852,0.00004002645,0.0002668195,0.0008615662,0.000055143795,0.002176638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99265695,0.004578043,0.00048579212,0.0013294632,0.0005304204,0.00041930695],"domain_scores_gemma":[0.9920425,0.0022283266,0.00055089575,0.003080804,0.0018838235,0.00021367641],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023031824,0.00038012787,0.00034513458,0.00021040779,0.00054991385,0.0007679163,0.0020794072,0.00028182627,0.00010024161],"category_scores_gemma":[0.0012620676,0.00044277558,0.00023373247,0.0007219113,0.00012155483,0.00021438964,0.001699057,0.0010118042,0.00011845957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000123425525,0.0010760591,0.00051163736,0.00017927373,0.00006226187,0.000029945983,0.004991383,0.036148135,0.0028583708,0.050134704,0.0006131446,0.9033827],"study_design_scores_gemma":[0.00039190732,7.7543007e-7,0.001043196,0.0014438482,0.000025216536,0.000010152816,0.000045861874,0.9518325,0.029105624,0.008294369,0.00714006,0.0006665406],"about_ca_topic_score_codex":0.00015354624,"about_ca_topic_score_gemma":0.00045239786,"teacher_disagreement_score":0.9156843,"about_ca_system_score_codex":0.00019968269,"about_ca_system_score_gemma":0.00025488378,"threshold_uncertainty_score":0.9998024},"labels":[],"label_agreement":null},{"id":"W4225981417","doi":"10.4018/978-1-7998-9201-4.ch008","title":"Machine Intelligence as a Foundation of Self-Driving Automotive (SDA) Systems","year":2022,"lang":"en","type":"book-chapter","venue":"Advances in logistics, operations, and management science book series","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Computer science; Automotive industry; Architecture; Artificial intelligence; Object detection; Resource (disambiguation); Artificial neural network; Object (grammar); Convolutional neural network; Scope (computer science); Machine learning; Engineering; Pattern recognition (psychology); Computer network","score_opus":0.015261542480029224,"score_gpt":0.2780338179082225,"score_spread":0.2627722754281933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225981417","genre_codex":"methods","genre_gemma":"review","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":[0.0000020335597,0.006940936,0.7671871,0.00039016485,0.00071768276,0.0012506373,0.000014456613,0.00017741194,0.22331958],"genre_scores_gemma":[0.03465341,0.43894663,0.35762993,0.0008942519,0.0001871122,0.0010073932,0.00012368089,0.00008866595,0.16646892],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99732673,0.00003651936,0.00068441516,0.0009646663,0.0006651998,0.00032244422],"domain_scores_gemma":[0.99844784,0.00013363243,0.00033306892,0.0007747909,0.0002274924,0.00008316471],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005311895,0.0003218317,0.0003618198,0.0004965403,0.00075042614,0.00027535725,0.0012790777,0.00005426974,0.000046093035],"category_scores_gemma":[0.00007804961,0.0003341276,0.00003845467,0.00038633763,0.0010886993,0.004539802,0.0011830535,0.00025239313,0.000015160953],"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":[0.0000033796116,0.000022696113,0.000013536234,0.00009043846,0.000011970775,0.000010497131,0.00019539903,0.106087655,0.0000038653316,0.8841184,0.000013177567,0.009428986],"study_design_scores_gemma":[0.00018221344,0.00039791892,0.000059148384,0.0003394697,0.00006438778,0.00004444195,0.00036066235,0.13485576,0.00008755654,0.20583642,0.65692556,0.00084646954],"about_ca_topic_score_codex":0.000022416856,"about_ca_topic_score_gemma":0.00010958783,"teacher_disagreement_score":0.67828196,"about_ca_system_score_codex":0.00027362132,"about_ca_system_score_gemma":0.00012406924,"threshold_uncertainty_score":0.99991107},"labels":[],"label_agreement":null},{"id":"W4226093231","doi":"10.1109/tvt.2022.3165172","title":"Intelligence Networking for Autonomous Driving in Beyond 5G Networks With Multi-Access Edge Computing","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Upload; Enhanced Data Rates for GSM Evolution; Edge computing; Artificial neural network; Curse of dimensionality; Artificial intelligence; Scheme (mathematics); Distributed computing; Real-time computing","score_opus":0.0211958398522331,"score_gpt":0.2790304076852188,"score_spread":0.2578345678329857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226093231","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":[0.01002372,0.00017745023,0.9862967,0.0010281326,0.0006826929,0.00096767966,0.0000031859022,0.0007991845,0.000021265478],"genre_scores_gemma":[0.9088951,0.000023858342,0.08973767,0.0002880283,0.000051843763,0.00093484385,0.000002982554,0.000037879923,0.000027835667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978413,0.00007243233,0.0004134487,0.00083978963,0.000184122,0.00064891],"domain_scores_gemma":[0.9986171,0.00026395076,0.00018844858,0.00080031954,0.000065483626,0.00006468272],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025316761,0.00026131407,0.00029929745,0.00057521084,0.00095478544,0.00007968545,0.001708666,0.00014140364,0.000005250998],"category_scores_gemma":[0.000003669808,0.00028423982,0.000086494096,0.0028401683,0.00013956957,0.0002742927,0.000049167083,0.0010207745,0.0000026315613],"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":[0.000009716599,0.00014470806,0.0001412334,0.0000040887226,0.000016266386,0.0000185261,0.000060631486,0.8472123,0.00015749551,0.0020690118,0.000010997639,0.15015502],"study_design_scores_gemma":[0.0003822115,0.00021930858,0.000060360373,0.000030200092,0.000012300338,0.000105756735,0.000067100766,0.991786,0.003477863,0.0015575753,0.0019741782,0.00032711984],"about_ca_topic_score_codex":0.000010792244,"about_ca_topic_score_gemma":0.00018521627,"teacher_disagreement_score":0.89887136,"about_ca_system_score_codex":0.0002722901,"about_ca_system_score_gemma":0.00006291887,"threshold_uncertainty_score":0.99996096},"labels":[],"label_agreement":null},{"id":"W4226154576","doi":"10.1109/tim.2022.3162615","title":"Attention-Guided Multitask Convolutional Neural Network for Power Line Parts Detection","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Changsha Science and Technology Project; National Key Research and Development Program of China; State Key Laboratory of Robotics; National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Robustness (evolution); Feature extraction; Electric power transmission; Pattern recognition (psychology); Aerial image; Object detection; Computer vision; Machine learning; Image (mathematics); Engineering","score_opus":0.05328217204729416,"score_gpt":0.27888504872559494,"score_spread":0.22560287667830078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226154576","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":[0.019702187,0.00004106554,0.9764216,0.0011971312,0.0015013311,0.0009151468,0.000029409186,0.00015546096,0.000036699137],"genre_scores_gemma":[0.98458374,0.00001614444,0.012885227,0.0007633465,0.00005231907,0.0015638879,0.000012944399,0.000013566127,0.00010881633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838597,0.00009282573,0.0003228449,0.00041040694,0.000535418,0.0002525525],"domain_scores_gemma":[0.99933183,0.000055278702,0.00013123291,0.00022862358,0.00015496196,0.00009807961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033331272,0.00015189372,0.00011565058,0.000100675425,0.0012855465,0.000057628706,0.0001695911,0.000028171731,0.000038902424],"category_scores_gemma":[0.000003611352,0.0001701092,0.000089810084,0.0003393726,0.000036455276,0.00029754906,0.000005851758,0.00016961635,0.0000048776014],"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":[0.00015354757,0.0004317984,0.000067471665,0.00001746796,0.00009660591,9.98361e-7,0.00022609024,0.8089436,0.023172885,0.0056949635,0.00097128924,0.16022325],"study_design_scores_gemma":[0.0075467187,0.0022571979,0.003781201,0.000037664155,0.0001368976,0.00013706171,0.0004004475,0.9163367,0.037225477,0.005601211,0.025524678,0.0010147203],"about_ca_topic_score_codex":0.000008125473,"about_ca_topic_score_gemma":0.00004149023,"teacher_disagreement_score":0.96488154,"about_ca_system_score_codex":0.00029205516,"about_ca_system_score_gemma":0.000043054806,"threshold_uncertainty_score":0.9887514},"labels":[],"label_agreement":null},{"id":"W4226172412","doi":"10.22215/etd/2021-14814","title":"Flexi-Compression: A Flexible Model Compression Method for Autonomous Driving","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Leverage (statistics); Convolutional neural network; Computation; Normalization (sociology); Artificial intelligence; Data compression ratio; Image compression; Data compression; Computer engineering; Algorithm; Image processing; Image (mathematics)","score_opus":0.035349032588381006,"score_gpt":0.36326596838360875,"score_spread":0.32791693579522774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226172412","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":[0.00012047283,0.0005585482,0.9818829,0.00042324545,0.0010526486,0.0010314618,0.000010855834,0.00089960784,0.014020204],"genre_scores_gemma":[0.0018897874,0.000050218623,0.919293,0.00038618664,0.0002758613,0.0010240799,0.0007243783,0.00007539917,0.0762811],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693775,0.000083632476,0.00062329794,0.0013921643,0.00041134228,0.0005517956],"domain_scores_gemma":[0.99700826,0.00051290327,0.00043580262,0.0014769433,0.00035911085,0.00020696069],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019561406,0.0004941197,0.0006152689,0.00018110676,0.0005450187,0.00027414726,0.0016958693,0.000376669,0.000044107524],"category_scores_gemma":[0.000044388154,0.00046313478,0.0003036844,0.00055106945,0.000015918256,0.00052422035,0.00036322305,0.00045793966,0.000020956542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004622944,0.00025633362,0.0000031912246,0.0002974988,0.00007860369,0.000008000029,0.00094976154,0.30569845,0.035750758,0.28286725,0.025537793,0.34850612],"study_design_scores_gemma":[0.00027565603,0.000025620442,0.000016962807,0.00024437293,0.00002849006,0.0000077723,0.000057901394,0.90809125,0.03607825,0.03664722,0.01801799,0.00050851755],"about_ca_topic_score_codex":0.000010552679,"about_ca_topic_score_gemma":0.000050141076,"teacher_disagreement_score":0.6023928,"about_ca_system_score_codex":0.0001048876,"about_ca_system_score_gemma":0.0003416701,"threshold_uncertainty_score":0.999782},"labels":[],"label_agreement":null},{"id":"W4226196214","doi":"10.14778/3503585.3503597","title":"COMET","year":2021,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Lossy compression; Computer science; Speedup; Overhead (engineering); Convolutional neural network; Compression ratio; Bandwidth (computing); Process (computing); Computer engineering; Artificial neural network; Compression (physics); Bounded function; Data compression; Algorithm; Parallel computing; Artificial intelligence; Telecommunications","score_opus":0.0152569447225827,"score_gpt":0.24091374650963057,"score_spread":0.22565680178704786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226196214","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":[0.47190452,0.0027802293,0.17560425,0.13743098,0.0034428397,0.0029521321,0.000020235753,0.0012463516,0.20461845],"genre_scores_gemma":[0.9385456,0.00004757461,0.05972239,0.00061027665,0.00005749932,0.000051917355,2.908566e-7,0.000006250638,0.0009582337],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926805,0.0000030018755,0.00014898919,0.00021274322,0.00021874357,0.00014845155],"domain_scores_gemma":[0.99943423,0.000030855594,0.000113514725,0.00022163421,0.0001614546,0.000038308848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000724316,0.00007008555,0.00009179431,0.000020310377,0.00008794058,0.00003873004,0.00084603997,0.00001647306,0.000007668394],"category_scores_gemma":[0.000038849852,0.00005039876,0.000060795748,0.00059479085,0.000034025496,0.0001806629,0.0006610377,0.000078154895,0.000011010392],"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":[0.000001988579,0.00011137374,0.0016733636,0.000026209576,0.00001977483,6.793973e-7,0.00022095174,0.00009895884,0.16498858,0.81213385,0.008713459,0.012010807],"study_design_scores_gemma":[0.00023997674,0.000021904943,0.0031805988,0.000032900807,0.000009205817,0.00003697409,0.000045848526,0.0018894122,0.88763875,0.07006523,0.03671422,0.0001249891],"about_ca_topic_score_codex":0.0000013062825,"about_ca_topic_score_gemma":4.682423e-7,"teacher_disagreement_score":0.74206865,"about_ca_system_score_codex":0.00002987925,"about_ca_system_score_gemma":0.000018928256,"threshold_uncertainty_score":0.20552018},"labels":[],"label_agreement":null},{"id":"W4226344270","doi":"10.1109/cvpr52688.2022.00118","title":"RBGNet: Ray-based Grouping for 3D Object Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Toronto","funders":"","keywords":"Computer science; Point cloud; Object detection; Artificial intelligence; Object (grammar); Feature (linguistics); Computer vision; Point (geometry); Process (computing); RGB color model; Pattern recognition (psychology); Sample (material); Cluster (spacecraft); Detector; Code (set theory); Mathematics","score_opus":0.0476342173372355,"score_gpt":0.2843524919410307,"score_spread":0.2367182746037952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226344270","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":[0.025306018,0.00002770522,0.9706099,0.0012692283,0.001299237,0.00091907225,0.00008767712,0.0003429143,0.00013826236],"genre_scores_gemma":[0.9494521,0.00004881073,0.043269385,0.0056879786,0.0003352016,0.00091347046,0.00018370895,0.00003940768,0.00006991756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974235,0.0002701706,0.00043361037,0.0010089402,0.000447614,0.00041615873],"domain_scores_gemma":[0.99844354,0.00040774752,0.000277707,0.0005271224,0.00017712668,0.00016674126],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003626668,0.0003252015,0.00031347823,0.00030301965,0.0009954295,0.00031361615,0.00057971064,0.00007742882,0.00021870299],"category_scores_gemma":[0.000009542167,0.0003403059,0.0001336007,0.00051096786,0.000050875373,0.00039052792,0.0002761081,0.0004313641,0.00006401443],"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":[0.000065470005,0.00014439625,0.000026789183,0.000031028783,0.000012969943,0.000008022163,0.00012830565,0.0021336116,0.0030879427,0.00021047435,0.0010890379,0.99306196],"study_design_scores_gemma":[0.0013549043,0.0014108841,0.0004406002,0.00007248209,0.000014592321,0.000035393437,0.000026957598,0.9818029,0.0022738967,0.0036813188,0.008380494,0.00050561817],"about_ca_topic_score_codex":0.000012272669,"about_ca_topic_score_gemma":0.000020442681,"teacher_disagreement_score":0.99255633,"about_ca_system_score_codex":0.00009367654,"about_ca_system_score_gemma":0.000055833396,"threshold_uncertainty_score":0.9999049},"labels":[],"label_agreement":null},{"id":"W4226453938","doi":"10.1109/lsp.2022.3164328","title":"A Low-Complexity Modified ThiNet Algorithm for Pruning Convolutional Neural Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pruning; Convolutional neural network; Computer science; Algorithm; Reduction (mathematics); Layer (electronics); Computational complexity theory; Norm (philosophy); Time complexity; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.036402694225251206,"score_gpt":0.26769570597848535,"score_spread":0.23129301175323413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226453938","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":[0.0045229727,0.00013301331,0.9897553,0.004278887,0.0003703493,0.00050330284,0.000020895754,0.00038819137,0.000027140171],"genre_scores_gemma":[0.8227643,5.1101046e-7,0.16795908,0.008172404,0.00045932404,0.0005343006,0.00004086579,0.00002976493,0.00003947296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786156,0.00009470517,0.00033676202,0.0006693218,0.00044848458,0.0005891562],"domain_scores_gemma":[0.9990471,0.00018861296,0.0002527892,0.00031030734,0.00008707196,0.000114107206],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00028692448,0.00023345288,0.00020931909,0.00009965121,0.0015046194,0.00018600484,0.0011448788,0.000036172314,0.000011106039],"category_scores_gemma":[0.0000051314137,0.00025694593,0.00010458225,0.0006701214,0.00016107698,0.0005855094,0.00025719276,0.00047843324,0.0000018747058],"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":[0.000014600232,0.000052662217,0.0000065158624,0.000016347463,0.0000073057768,0.00000794067,0.0001281966,0.85309696,0.0046562687,0.0014257247,0.0024060663,0.13818142],"study_design_scores_gemma":[0.00044921186,0.000053577194,0.000051408362,0.000009464889,0.000008645298,0.00004607898,0.000010700458,0.9941445,0.0003024421,0.004072916,0.0005511861,0.00029987405],"about_ca_topic_score_codex":0.000006147031,"about_ca_topic_score_gemma":7.774498e-7,"teacher_disagreement_score":0.8217962,"about_ca_system_score_codex":0.00015743866,"about_ca_system_score_gemma":0.00007332747,"threshold_uncertainty_score":0.99998826},"labels":[],"label_agreement":null},{"id":"W4226524069","doi":"10.1155/2022/2259113","title":"Improved Real-Time Traffic Obstacle Detection and Classification Method Applied in Intelligent and Connected Vehicles in Mixed Traffic Environment","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"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":"Hubei Technological Innovation Special Fund; National Key Research and Development Program of China","keywords":"Obstacle; Computer science; Pedestrian; Task (project management); Real-time computing; Intelligent transportation system; Simulation; Artificial intelligence; Transport engineering; Engineering; Geography","score_opus":0.012844684796280243,"score_gpt":0.24614328805011992,"score_spread":0.2332986032538397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226524069","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":[0.8435106,0.00013682488,0.1556679,0.00022607071,0.000057248897,0.00036834006,0.0000030621957,0.000025805863,0.0000041094368],"genre_scores_gemma":[0.9380622,0.00041385798,0.06138137,0.000019394382,0.000012555182,0.00008398785,0.0000099549625,0.00001232833,0.0000043692053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852675,0.00011364561,0.00065419404,0.00031679226,0.00022027998,0.00016833183],"domain_scores_gemma":[0.9990956,0.0002099059,0.00044557225,0.00014807089,0.000030340832,0.000070495495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000412089,0.00013312302,0.00023272895,0.00027283703,0.00010387299,0.000020015108,0.00016804451,0.00004119198,0.0000035355695],"category_scores_gemma":[0.000009161736,0.0001449024,0.000033914588,0.00048909354,0.000030814066,0.00039642554,0.0000114965005,0.0002964107,4.0674564e-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":[0.00009535607,0.00008005917,0.000041599225,0.0000069305847,0.0000043740065,0.000004474851,0.0012487603,0.45834932,0.3014756,0.00039695887,3.3632062e-7,0.23829623],"study_design_scores_gemma":[0.0035232385,0.0005743914,0.42267916,0.000029892122,0.000034540866,0.00006317407,0.0021589363,0.5513989,0.015871037,0.0028382011,0.00042371755,0.0004048248],"about_ca_topic_score_codex":0.0000023568625,"about_ca_topic_score_gemma":0.000072259645,"teacher_disagreement_score":0.42263755,"about_ca_system_score_codex":0.00018833758,"about_ca_system_score_gemma":0.000027496544,"threshold_uncertainty_score":0.5908948},"labels":[],"label_agreement":null},{"id":"W4230481654","doi":"10.1109/aspdac.2018.8297303","title":"Quad-multiplier packing based on customized floating point for convolutional neural networks on FPGA","year":2018,"lang":"en","type":"article","venue":"2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Waseda University; Canadian Institute for Advanced Research","keywords":"Field-programmable gate array; Floating point; Computer science; Convolutional neural network; Adder; Multiplier (economics); Throughput; Computation; Artificial neural network; Deep learning; Reduction (mathematics); Parallel computing; Computer hardware; Inference; Computer engineering; Digital signal processing; Artificial intelligence; Algorithm; Latency (audio)","score_opus":0.046419436793458724,"score_gpt":0.27270817405306574,"score_spread":0.22628873725960702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230481654","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":[0.002648756,0.000013858669,0.9915525,0.0012601481,0.0005976444,0.0014549515,0.000020343354,0.0006050955,0.001846731],"genre_scores_gemma":[0.89456666,0.00000323462,0.10393157,0.00057235896,0.00036589918,0.0002644385,0.000050572016,0.000034970286,0.0002102948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968566,0.00033694584,0.00063609343,0.0009985799,0.00048561647,0.0006861979],"domain_scores_gemma":[0.9968166,0.0012232894,0.0005050774,0.00077927025,0.0004259585,0.0002498348],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007987877,0.00044554053,0.00042365215,0.00023443291,0.00094383373,0.0004125922,0.00057895295,0.00021375077,0.00007797659],"category_scores_gemma":[0.0002618969,0.00041139586,0.00012792548,0.00047190758,0.00027435474,0.00054943486,0.000091592636,0.00030211735,0.00013590057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002094041,0.00070334494,0.0014613747,0.00014644752,0.00018504617,0.000017323431,0.010569616,0.30604407,0.006420207,0.27516705,0.018232226,0.37895924],"study_design_scores_gemma":[0.0018063078,0.00041760394,0.0011734705,0.00010634886,0.000016846441,0.000005773397,0.00027794435,0.99139875,0.00090995844,0.002755681,0.00066732644,0.00046398005],"about_ca_topic_score_codex":0.000003268913,"about_ca_topic_score_gemma":0.0000021744704,"teacher_disagreement_score":0.8919179,"about_ca_system_score_codex":0.000112810965,"about_ca_system_score_gemma":0.00015317393,"threshold_uncertainty_score":0.99983376},"labels":[],"label_agreement":null},{"id":"W4231224646","doi":"10.1109/bigcomp.2018.00054","title":"Optimization Method of Residual Networks of Residual Networks for Image Classification","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Canadian Institute for Advanced Research","keywords":"Residual; Computer science; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Algorithm","score_opus":0.033371475353631976,"score_gpt":0.3305453230939199,"score_spread":0.2971738477402879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231224646","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":[0.000111727466,0.00005104511,0.9966441,0.00078279147,0.0001656473,0.0006139752,0.000005234093,0.00013147999,0.0014940152],"genre_scores_gemma":[0.08864348,0.000042719636,0.9104228,0.00013583468,0.00039887647,0.000086157415,0.00003293222,0.00001952949,0.00021769255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828935,0.000120670506,0.000596567,0.00048194965,0.00021193898,0.00029953566],"domain_scores_gemma":[0.997325,0.0006128739,0.0004819593,0.0008722802,0.00063663156,0.00007125649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005712636,0.00015480616,0.00025371742,0.00010211835,0.0001472346,0.000034904497,0.00078229496,0.00012949148,0.000019271283],"category_scores_gemma":[0.00008998257,0.00014664643,0.00006783408,0.0008414256,0.00018552806,0.0004782311,0.00018998189,0.00010552551,0.0000020643547],"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":[0.000055037603,0.00007256953,0.000045997534,0.000013286987,0.000021142285,1.6416935e-7,0.000087411485,0.7856853,0.0034116583,0.17086783,0.006910194,0.032829404],"study_design_scores_gemma":[0.00030462295,0.00017495225,0.0005647284,0.000014316568,0.000017233011,0.0000034236436,0.000018866402,0.98571134,0.009194309,0.0034258894,0.0004287445,0.00014158184],"about_ca_topic_score_codex":0.000008753835,"about_ca_topic_score_gemma":0.000012771278,"teacher_disagreement_score":0.20002602,"about_ca_system_score_codex":0.000027554373,"about_ca_system_score_gemma":0.000046645615,"threshold_uncertainty_score":0.5980068},"labels":[],"label_agreement":null},{"id":"W4246088338","doi":"10.36227/techrxiv.16546281","title":"Comparison of Kidney Segmentation Under Attention U-Net Architectures","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Context (archaeology); Artificial intelligence; Focus (optics); Architecture; Deep learning; Noise (video); Machine learning; Image segmentation; Net (polyhedron); Data science; Image (mathematics); Geography","score_opus":0.04313599020403548,"score_gpt":0.3512392427054531,"score_spread":0.3081032525014176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246088338","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":[0.03498361,0.00013905084,0.96081036,0.00218754,0.00036596172,0.00037535455,0.0000064281744,0.00017008111,0.00096160977],"genre_scores_gemma":[0.69164586,0.000018006182,0.307284,0.0005483211,0.00005205774,0.00006873072,0.00017785408,0.00001037782,0.00019477577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841285,0.000088875735,0.00043591086,0.000578321,0.00031508805,0.00016898323],"domain_scores_gemma":[0.9983992,0.00008416712,0.00036417128,0.0008953323,0.00012434785,0.00013278497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007597941,0.0001877643,0.00029843388,0.00010146769,0.000064620835,0.00010161439,0.0007490388,0.0001213257,0.00003685104],"category_scores_gemma":[0.000027591921,0.00018345514,0.00012134241,0.0002815859,0.000046035027,0.00008548956,0.0011306397,0.00034683745,0.000008291343],"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":[0.000009519783,0.0005980658,0.0059148874,0.00035871344,0.00016500053,0.0000026981488,0.0012321518,0.7877772,0.06363131,0.059606552,0.010023671,0.07068022],"study_design_scores_gemma":[0.0006568653,0.00010610253,0.031534214,0.0004040989,0.000093630726,0.000015462312,0.0004062568,0.70835567,0.15460525,0.10170386,0.0010140942,0.0011045117],"about_ca_topic_score_codex":0.000027756625,"about_ca_topic_score_gemma":0.000024217547,"teacher_disagreement_score":0.6566623,"about_ca_system_score_codex":0.0000530705,"about_ca_system_score_gemma":0.00014928232,"threshold_uncertainty_score":0.7481083},"labels":[],"label_agreement":null},{"id":"W4247067131","doi":"10.1109/cvprw.2009.5206577","title":"Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Restricted Boltzmann machine; Pattern recognition (psychology); Artificial intelligence; Discriminative model; Computer science; Feature extraction; Boltzmann machine; Feature learning; Classifier (UML); Convolutional neural network; Invariant (physics); Feature (linguistics); Cognitive neuroscience of visual object recognition; Deep learning; Mathematics","score_opus":0.036909895567094717,"score_gpt":0.29189500764385595,"score_spread":0.2549851120767612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247067131","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":[0.03771712,0.000050903753,0.95844567,0.0027805802,0.0002620968,0.00041395068,0.00005187689,0.00013522847,0.00014257658],"genre_scores_gemma":[0.94991314,0.00009287649,0.048506085,0.0010120354,0.00020545273,0.000025483345,0.00016515184,0.000009714493,0.00007006447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986329,0.00009902993,0.00028937677,0.00051354466,0.0002220324,0.00024311015],"domain_scores_gemma":[0.99893874,0.00023222758,0.00023237357,0.00024782075,0.00023897062,0.00010985024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011561721,0.00021393456,0.00023969458,0.00015406882,0.00019635235,0.00011988244,0.00029787884,0.00011542285,0.000012577221],"category_scores_gemma":[0.0000142502295,0.0001907942,0.00006924074,0.00023978506,0.000040409723,0.0003065351,0.000045341305,0.0002604705,0.000015863408],"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":[0.000052270574,0.00013361814,0.00008899242,0.000020300438,0.000009894193,0.0000045581596,0.00009976291,0.00040610426,0.002336621,0.0058004768,0.0022018843,0.9888455],"study_design_scores_gemma":[0.0012567242,0.0018436459,0.031255636,0.00030478663,0.000014596137,0.000024080997,0.00000415945,0.925451,0.0011920409,0.036955733,0.0013018949,0.00039572813],"about_ca_topic_score_codex":0.0000041991025,"about_ca_topic_score_gemma":0.000003948332,"teacher_disagreement_score":0.9884498,"about_ca_system_score_codex":0.000017858867,"about_ca_system_score_gemma":0.000034304463,"threshold_uncertainty_score":0.77803624},"labels":[],"label_agreement":null},{"id":"W4247198796","doi":"10.1109/micro.2016.7783722","title":"Stripes: Bit-serial deep neural network computing","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":329,"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; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Speedup; Energy (signal processing); Artificial neural network; Deep neural networks; Efficient energy use; Set (abstract data type); Variance (accounting); Representation (politics); Parallel computing; Computer engineering; Algorithm; Real-time computing; Artificial intelligence; Statistics","score_opus":0.017434682666048973,"score_gpt":0.2561888464617708,"score_spread":0.23875416379572184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247198796","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":[0.010965552,0.000049102408,0.9832885,0.0024946337,0.0006661327,0.00017789062,4.815097e-7,0.00061018777,0.0017475191],"genre_scores_gemma":[0.8250045,0.000009020854,0.17284462,0.00083958055,0.0009559174,0.00001113516,8.2377977e-7,0.000011836001,0.0003225812],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985949,0.000047321904,0.00024989678,0.00044972135,0.0001725865,0.00048559648],"domain_scores_gemma":[0.9987424,0.0003435364,0.000095860436,0.00063896953,0.000051177798,0.00012802852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113600894,0.00014430197,0.00013750944,0.000031207102,0.00021622962,0.00007984576,0.00095168204,0.000045137953,0.000040059054],"category_scores_gemma":[0.00002349252,0.00009452725,0.00005673706,0.0005100532,0.00005249996,0.00046509152,0.0004189857,0.000078981415,0.00017332083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007551901,0.000027045468,0.00091160095,0.0000019256156,0.000009484516,0.0000063921125,0.000040155846,0.026459906,0.0017795654,0.34112993,0.004235475,0.625391],"study_design_scores_gemma":[0.0013406929,0.00015476983,0.0055855894,0.00003538956,0.000008436125,0.000059776376,0.000011474896,0.89087844,0.0023302208,0.070336364,0.028457936,0.00080093654],"about_ca_topic_score_codex":0.0000035765047,"about_ca_topic_score_gemma":0.000015510737,"teacher_disagreement_score":0.8644185,"about_ca_system_score_codex":0.000032273045,"about_ca_system_score_gemma":0.000015152893,"threshold_uncertainty_score":0.38547093},"labels":[],"label_agreement":null},{"id":"W4247230206","doi":"10.36227/techrxiv.16546281.v2","title":"Comparison of Kidney Segmentation Under Attention U-Net Architectures","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Context (archaeology); Artificial intelligence; Focus (optics); Architecture; Noise (video); Deep learning; Image segmentation; Machine learning; Net (polyhedron); Data science; Image (mathematics); Geography","score_opus":0.04313599020403548,"score_gpt":0.3512392427054531,"score_spread":0.3081032525014176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247230206","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":[0.03498361,0.00013905084,0.96081036,0.00218754,0.00036596172,0.00037535455,0.0000064281744,0.00017008111,0.00096160977],"genre_scores_gemma":[0.69164586,0.000018006182,0.307284,0.0005483211,0.00005205774,0.00006873072,0.00017785408,0.00001037782,0.00019477577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841285,0.000088875735,0.00043591086,0.000578321,0.00031508805,0.00016898323],"domain_scores_gemma":[0.9983992,0.00008416712,0.00036417128,0.0008953323,0.00012434785,0.00013278497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007597941,0.0001877643,0.00029843388,0.00010146769,0.000064620835,0.00010161439,0.0007490388,0.0001213257,0.00003685104],"category_scores_gemma":[0.000027591921,0.00018345514,0.00012134241,0.0002815859,0.000046035027,0.00008548956,0.0011306397,0.00034683745,0.000008291343],"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":[0.000009519783,0.0005980658,0.0059148874,0.00035871344,0.00016500053,0.0000026981488,0.0012321518,0.7877772,0.06363131,0.059606552,0.010023671,0.07068022],"study_design_scores_gemma":[0.0006568653,0.00010610253,0.031534214,0.0004040989,0.000093630726,0.000015462312,0.0004062568,0.70835567,0.15460525,0.10170386,0.0010140942,0.0011045117],"about_ca_topic_score_codex":0.000027756625,"about_ca_topic_score_gemma":0.000024217547,"teacher_disagreement_score":0.6566623,"about_ca_system_score_codex":0.0000530705,"about_ca_system_score_gemma":0.00014928232,"threshold_uncertainty_score":0.7481083},"labels":[],"label_agreement":null},{"id":"W4252671957","doi":"10.1109/cvpr42600.2020.00004","title":"Table of Contents","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"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":"Shenyang Institute of Automation; Institute of Automation, Chinese Academy of Sciences; Leibniz-Gemeinschaft; University of California, San Diego; University of North Carolina at Chapel Hill; University of Colorado Boulder; University of Illinois at Urbana-Champaign; Samsung; Australian Centre for Robotic Vision; Indraprastha Institute of Information Technology, Delhi; Indian Institute of Technology Ropar; National Technical University of Athens; Ant Financial Services Group; Beijing National Research Center For Information Science And Technology; Macau University of Science and Technology; Hefei University of Technology; University of Chinese Academy of Sciences; State Key Laboratory of Industrial Control Technology; State Key Laboratory of Robotics; Hefei University; University of Science and Technology of China; Futurewei Technologies; Nanjing University; Peking University; Skolkovo Institute of Science and Technology; Korea Advanced Institute of Science and Technology; Wuhan University; University of Toronto; University of Electronic Science and Technology of China; Singapore University of Technology and Design; Nanjing University of Science and Technology; Los Alamos National Laboratory; National and Kapodistrian University of Athens; Nankai University; Ministry of Education, India; Shanghai Jiao Tong University; Chinese University of Hong Kong; National Laboratory of Pattern Recognition; Indiana University-Purdue University Indianapolis; Zhejiang University; Siemens; Università degli Studi di Trento; University of Tokyo; University of Rochester; Zhengzhou University; Tianjin University; Tel Aviv University; RIKEN; Gottfried Wilhelm Leibniz Universität Hannover; Texas State University; University of Hong Kong; National Research University Higher School of Economics; Harbin Institute of Technology; Hong Kong University of Science and Technology; Universität Heidelberg; Huazhong University of Science and Technology; Peng Cheng Laboratory; Dalian University of Technology; Beijing University of Posts and Telecommunications; Northwestern Polytechnical University; Beijing Institute of Technology; East China Normal University; Sun Yat-sen University; Indian Institute of Technology Jodhpur; Karl-Franzens-Universität Graz; Technische Universität München; Tsinghua University; Chinese Academy of Sciences; University of Twente; University of California, Santa Barbara; Universiteit van Amsterdam; University of Adelaide; Shandong University; Nanyang Technological University; University of Oxford; King Abdullah University of Science and Technology; University of Pennsylvania; Xiamen University; Tencent; Institute of Science and Technology Austria; Shenzhen Research Institute, City University of Hong Kong; Massachusetts Institute of Technology; South China University of Technology; University of Surrey; New York University Abu Dhabi; Multimedia University; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; Università di Bologna; Baidu; Drexel University; University of Oregon; Eberhard Karls Universität Tübingen; Taser International; York University; Rensselaer Polytechnic Institute; University of Central Florida; Northwestern University; Linköpings Universitet; University of Miami; Universität Mannheim; Tsinghua Shenzhen International Graduate School; University of Technology Sydney; Università Degli Studi di Modena e Reggio Emila; Carnegie Mellon University; Technische Universität Darmstadt; SenseTime Group; Michigan State University; Microsoft Research Asia; City University of Hong Kong; Harvard University; Albert-Ludwigs-Universität Freiburg; Ministry of Education of the People's Republic of China; State Key Laboratory for Management and Control of Complex Systems; Birmingham City University; Shenzhen Research Institute of Big Data; Universitat Autònoma de Barcelona; Microsoft Research; Stony Brook University; Waseda University; Alibaba DAMO Academy; Princeton University; Memorial Sloan-Kettering Cancer Center; Nvidia; École Polytechnique Fédérale de Lausanne; National University of Singapore; Qualcomm; České Vysoké Učení Technické v Praze; National Institute of Informatics; University of Sydney; College of Engineering, Michigan State University; Huaqiao University; Purdue University","keywords":"Computer science; Table (database); Information retrieval; Database","score_opus":0.04680659424210543,"score_gpt":0.26508074745786164,"score_spread":0.21827415321575622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252671957","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":[0.00101476,0.00002038379,0.9855022,0.0048128893,0.00001598664,0.00005283169,4.2013764e-7,0.00007846842,0.008502063],"genre_scores_gemma":[0.89756286,0.0000037166158,0.10045419,0.0017246921,0.000012593832,0.0000039848064,2.587503e-7,0.0000014825702,0.00023620618],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997255,0.0000035170387,0.00006549554,0.00009625253,0.00005322886,0.000055958684],"domain_scores_gemma":[0.99975264,0.000018803099,0.000023791403,0.00014016984,0.000023922694,0.000040673116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000008079704,0.000023440702,0.000043249278,0.000004534183,0.000013233516,0.000005262423,0.00031138575,0.0000063518687,0.000016272805],"category_scores_gemma":[0.00000880659,0.000019995003,0.000010124908,0.00023471777,0.000009444645,0.000119458695,0.00009593773,0.000020368105,0.000044593402],"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":[0.0000024835958,0.000027304171,0.000751703,0.000006257324,0.0000054070474,0.000001283042,0.00014264553,0.0012370285,0.044756662,0.91528374,0.017520886,0.020264594],"study_design_scores_gemma":[0.0005789797,0.0001517094,0.0020710097,0.0000065272775,0.000003475674,0.000003618487,0.000027610113,0.57302296,0.24069616,0.019110177,0.16408835,0.00023943416],"about_ca_topic_score_codex":0.0000019516492,"about_ca_topic_score_gemma":3.5901866e-7,"teacher_disagreement_score":0.8965481,"about_ca_system_score_codex":0.0000012240736,"about_ca_system_score_gemma":0.000004041227,"threshold_uncertainty_score":0.08153726},"labels":[],"label_agreement":null},{"id":"W4280492519","doi":"10.1109/tnnls.2022.3171553","title":"3D-DFM: Anchor-Free Multimodal 3-D Object Detection With Dynamic Fusion Module for Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":40,"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":"Beijing Municipal Natural Science Foundation; Engineering and Physical Sciences Research Council; Beijing Municipal Science and Technology Commission; China Postdoctoral Science Foundation; Beihang University; National Natural Science Foundation of China","keywords":"Computer science; Object detection; Design for manufacturability; Artificial intelligence; Computer vision; Bridging (networking); Pipeline (software); Pattern recognition (psychology); Engineering","score_opus":0.007561438456001374,"score_gpt":0.2206683192090688,"score_spread":0.2131068807530674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280492519","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":[0.07155408,0.00019861147,0.9254952,0.00014961265,0.0010673472,0.0009867304,0.000007483356,0.0005245562,0.000016397425],"genre_scores_gemma":[0.9933939,0.000030306432,0.00472635,0.00006367913,0.00012056357,0.0011072659,0.0000061518062,0.000051688814,0.00050011475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978182,0.00030144502,0.00033582115,0.0007644287,0.0002953852,0.00048476466],"domain_scores_gemma":[0.9985823,0.00042441237,0.00022627528,0.00057106296,0.000065367094,0.00013055922],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00031457556,0.00028571687,0.00028804844,0.00017193948,0.0027179644,0.00019840417,0.00049273827,0.00007795736,0.000004608904],"category_scores_gemma":[0.000005817764,0.00027208394,0.000095144365,0.00064983877,0.000051262807,0.00037129066,0.000021960632,0.000998422,0.0000011939973],"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":[0.00005704787,0.000045533954,0.000025948772,0.000013680698,0.000019017436,0.000003949196,0.00010768436,0.9057849,0.0006333156,0.00003628628,0.0000137839315,0.09325882],"study_design_scores_gemma":[0.0007877715,0.00088322366,0.00022635236,0.00002760441,0.000024653513,0.00017749643,0.00009173589,0.9961672,0.00006643331,0.000020618567,0.0012087435,0.00031817044],"about_ca_topic_score_codex":0.000068187626,"about_ca_topic_score_gemma":0.00010167344,"teacher_disagreement_score":0.9218398,"about_ca_system_score_codex":0.00017257572,"about_ca_system_score_gemma":0.000022530847,"threshold_uncertainty_score":0.9999731},"labels":[],"label_agreement":null},{"id":"W4281263827","doi":"10.1007/s00521-022-07333-y","title":"Transformers only look once with nonlinear combination for real-time object detection","year":2022,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computational Science and Engineering; Nonlinear system; Computer science; Transformer; Artificial intelligence; Pattern recognition (psychology); Algorithm; Machine learning; Physics; Voltage","score_opus":0.009806789125557385,"score_gpt":0.2553637932675937,"score_spread":0.2455570041420363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281263827","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":[0.06758274,0.000029539118,0.92903346,0.0012306147,0.000037599824,0.0012408965,0.000017015827,0.0004169427,0.00041120822],"genre_scores_gemma":[0.960148,0.000012742456,0.038586456,0.0002362925,0.0000948328,0.00068279164,0.00004846112,0.000021581382,0.00016884689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987851,0.000046317073,0.00021641975,0.00050554035,0.00019224978,0.00025436908],"domain_scores_gemma":[0.99913234,0.0002760875,0.00014043905,0.0002942289,0.00008060446,0.0000763152],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00015115459,0.00014477821,0.00013918112,0.00008158937,0.0014929655,0.000073648785,0.00035506563,0.000024757424,0.000001992254],"category_scores_gemma":[0.000004193251,0.0001466129,0.00004464551,0.0006915261,0.00005696545,0.0001894878,0.00011512358,0.00019120115,0.0000035619992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051640327,0.0002544217,0.00016213588,0.000037769474,0.000024045166,0.0000011576899,0.0004119428,0.049547195,0.020785358,0.035297595,0.00017525756,0.8932515],"study_design_scores_gemma":[0.00064634316,0.0003961476,0.00060601236,0.000005280005,0.000017601691,0.00009722756,0.00006790273,0.98071903,0.0015759976,0.0021757507,0.013436273,0.00025643318],"about_ca_topic_score_codex":0.0000135961445,"about_ca_topic_score_gemma":0.000008335002,"teacher_disagreement_score":0.93117183,"about_ca_system_score_codex":0.000067462584,"about_ca_system_score_gemma":0.00004539841,"threshold_uncertainty_score":0.99980694},"labels":[],"label_agreement":null},{"id":"W4281654129","doi":"10.5194/isprs-archives-xliii-b1-2022-257-2022","title":"AUTOMOTIVE RADAR BASED LEAN DETECTION OF VEHICLES","year":2022,"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 Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Radar; Object detection; Automotive industry; Intersection (aeronautics); Computer vision; Minimum bounding box; Pixel; Convolutional neural network; Advanced driver assistance systems; Kernel (algebra); Real-time computing; Pattern recognition (psychology); Engineering; Image (mathematics); Telecommunications; Mathematics","score_opus":0.01605219941642698,"score_gpt":0.24922378412075838,"score_spread":0.2331715847043314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281654129","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":[0.00792886,0.000032224718,0.98168516,0.005061487,0.0014642581,0.0007016484,0.000100164856,0.0000709519,0.0029552216],"genre_scores_gemma":[0.98878866,0.000054972053,0.009699322,0.0012850006,0.000081641025,0.0000010256492,0.00002457616,0.000010420296,0.0000543847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951153,0.0003787075,0.001413406,0.00045883033,0.0021615103,0.00047227883],"domain_scores_gemma":[0.99537855,0.0013429683,0.0021340968,0.0006589016,0.0003525358,0.000132942],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0014316798,0.000370281,0.00040059045,0.0009915573,0.0020371173,0.00043499577,0.0028179393,0.000060017646,0.0000059424533],"category_scores_gemma":[0.00051236164,0.00025554857,0.00037828353,0.0016844894,0.0025390189,0.00060327095,0.0017555776,0.0005278992,0.0000016146645],"study_design_candidate":"simulation_or_modeling","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":[0.000102931255,0.000027758113,0.00008965957,0.000023833805,0.000057052723,2.3495689e-7,0.0021498227,0.020056613,0.0062985,0.000036259218,0.000027467548,0.9711299],"study_design_scores_gemma":[0.0006419134,0.00020234837,0.002019423,0.00012208043,0.000029688628,0.00010088087,0.0011339425,0.9542944,0.026312916,0.011154595,0.0037259439,0.0002618936],"about_ca_topic_score_codex":0.49109083,"about_ca_topic_score_gemma":0.065591775,"teacher_disagreement_score":0.9808598,"about_ca_system_score_codex":0.000072920215,"about_ca_system_score_gemma":0.00028392352,"threshold_uncertainty_score":0.9999897},"labels":[],"label_agreement":null},{"id":"W4281679836","doi":"10.48550/arxiv.2206.02424","title":"Slim-neck by GSConv: A lightweight-design for real-time detector architectures","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":257,"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":"Computer science; Convolution (computer science); Detector; Code (set theory); State (computer science); Task (project management); Object detection; Computer engineering; Separable space; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Real-time computing; Simulation; Computer vision; Embedded system; Algorithm; Pattern recognition (psychology); Programming language; Telecommunications; Engineering; Artificial neural network","score_opus":0.05051061162297856,"score_gpt":0.20407883562194737,"score_spread":0.1535682239989688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281679836","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":[0.039260376,0.000089530746,0.9565142,0.00035472718,0.00031053458,0.0016467033,0.00012900542,0.0007700485,0.0009249112],"genre_scores_gemma":[0.88150644,0.00032094066,0.108018115,0.0003184775,0.00025142924,0.00013986611,0.00013090883,0.00011527416,0.009198549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969415,0.00021250108,0.00028292558,0.0018123729,0.00014291207,0.0006077585],"domain_scores_gemma":[0.9966277,0.0006813426,0.00039860772,0.0019397592,0.000103472565,0.00024915638],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019235825,0.00048032633,0.00045683727,0.0002552719,0.00049582514,0.00011076504,0.0034014313,0.00022529971,0.00010558387],"category_scores_gemma":[0.0000342115,0.00055561715,0.00032116263,0.00079451886,0.00010349933,0.00017112309,0.0025655364,0.00067026407,0.00006893018],"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":[0.0001943107,0.00022363325,0.00007803739,0.00010006194,0.00020402219,0.00011531002,0.0003178191,0.90225655,0.0065892045,0.06651682,0.021358298,0.0020459373],"study_design_scores_gemma":[0.0008419895,0.00029785256,0.0000785503,0.00004212205,0.00010962599,0.000009324968,0.000012982528,0.7796748,0.0050920234,0.19307673,0.019585751,0.0011782247],"about_ca_topic_score_codex":0.000034202574,"about_ca_topic_score_gemma":0.000005280019,"teacher_disagreement_score":0.8484961,"about_ca_system_score_codex":0.0003234665,"about_ca_system_score_gemma":0.0001968771,"threshold_uncertainty_score":0.9996895},"labels":[],"label_agreement":null},{"id":"W4281691647","doi":"10.1016/j.comcom.2022.05.035","title":"A federated calibration scheme for convolutional neural networks: Models, applications and challenges","year":2022,"lang":"en","type":"article","venue":"Computer Communications","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"École de Technologie Supérieure","funders":"King Saud University","keywords":"Computer science; Convolutional neural network; Deep learning; Artificial intelligence; Network architecture; Artificial neural network; Path (computing); Cluster analysis; Pattern recognition (psychology); Machine learning; Computer network","score_opus":0.09128292449671148,"score_gpt":0.2811332553182015,"score_spread":0.18985033082149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281691647","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":[0.000039698436,0.008722919,0.9706806,0.018709285,0.000096191354,0.0011636106,0.000032607797,0.0004058143,0.00014925083],"genre_scores_gemma":[0.29272756,0.0015709195,0.6970081,0.0012248164,0.00015334321,0.0068933214,0.00034879352,0.000028156957,0.00004502422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998517,0.0001908535,0.0003370787,0.00050356134,0.0001729539,0.0002785501],"domain_scores_gemma":[0.9975777,0.0005500991,0.0001698128,0.0014313745,0.00015847634,0.00011251249],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00020800183,0.00017301111,0.00017299535,0.00010462565,0.0023383661,0.0001553118,0.0017529237,0.000042902037,0.0000031580616],"category_scores_gemma":[0.0000048731654,0.00020659219,0.00006534276,0.0005079608,0.00013551736,0.0006099723,0.0023916592,0.0002920532,0.0000013129896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045548218,0.00014525394,0.000010675113,0.000008812039,0.000020617881,1.6042921e-7,0.00013708045,0.12990426,0.0000227152,0.7738278,0.0010956497,0.094822444],"study_design_scores_gemma":[0.00029236687,0.000053341028,0.00007091388,0.0000025962083,0.000006668759,0.000029122773,0.000029350587,0.8927843,0.0000018180767,0.028377092,0.07816482,0.00018762061],"about_ca_topic_score_codex":0.0000043519053,"about_ca_topic_score_gemma":0.000012240786,"teacher_disagreement_score":0.76288,"about_ca_system_score_codex":0.000081391096,"about_ca_system_score_gemma":0.000056998175,"threshold_uncertainty_score":0.99896044},"labels":[],"label_agreement":null},{"id":"W4281731612","doi":"10.1155/2022/4377953","title":"Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":17,"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":"Department of Education of Liaoning Province; National Natural Science Foundation of China","keywords":"Object detection; Computer science; Artificial intelligence; Convolutional neural network; Cluster analysis; Train; Object (grammar); Computer vision; Artificial neural network; Frame (networking); Viola–Jones object detection framework; Minimum bounding box; Pattern recognition (psychology); Image (mathematics); Face detection","score_opus":0.012208083116220245,"score_gpt":0.24807825479711743,"score_spread":0.2358701716808972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281731612","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":[0.0888008,0.000089782974,0.90410477,0.0051319255,0.00078914396,0.0009639192,0.000011817064,0.00008028433,0.000027575152],"genre_scores_gemma":[0.96376073,0.00002580145,0.03494489,0.0006764219,0.0002748517,0.00025491227,0.000008975079,0.00002223564,0.00003115189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855024,0.00009583141,0.00049795577,0.00022204634,0.00036888776,0.00026503432],"domain_scores_gemma":[0.9976363,0.0011384448,0.0006932114,0.00034134008,0.00013988654,0.000050821076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006908693,0.00014852971,0.00017170927,0.000069281596,0.0009842484,0.00004387456,0.0006814361,0.000026302252,0.000011541089],"category_scores_gemma":[0.000030327694,0.000098317425,0.00020807883,0.0006877691,0.000035046607,0.0003790955,0.000008713367,0.00041983146,6.459232e-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":[0.00024045132,0.000027193724,0.000012995747,0.0000029270961,0.000013197147,0.0000026259029,0.0003248034,0.9394131,0.024877865,0.0010340535,0.000089523965,0.033961296],"study_design_scores_gemma":[0.0012475171,0.0009515135,0.012162256,0.00001860189,0.000061265884,0.00002060215,0.00025865683,0.97326267,0.0026948676,0.005037473,0.0040848334,0.00019976214],"about_ca_topic_score_codex":0.0000028940217,"about_ca_topic_score_gemma":0.00001689902,"teacher_disagreement_score":0.87495995,"about_ca_system_score_codex":0.0000986709,"about_ca_system_score_gemma":0.00006464203,"threshold_uncertainty_score":0.7570142},"labels":[],"label_agreement":null},{"id":"W4283378874","doi":"10.36227/techrxiv.19501954.v1","title":"Multi-Class Object Detection Using Adaptive Non-Maximum Suppression in Dense Images","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Minimum bounding box; Object (grammar); Constraint (computer-aided design); Object detection; Bounding overwatch; Computer science; Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Algorithm; Image (mathematics); Computer vision; Mathematics","score_opus":0.038606942941742356,"score_gpt":0.3076568789688517,"score_spread":0.26904993602710936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283378874","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":[0.041624837,0.00010335433,0.95565534,0.00013644209,0.00081375096,0.000944161,0.000011582634,0.00030814813,0.000402408],"genre_scores_gemma":[0.67268,0.00004183921,0.32658616,0.00008845534,0.000065155065,0.00031164466,0.000008785179,0.000028351467,0.00018956051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974103,0.00020082424,0.00042194378,0.0012052159,0.0003485598,0.00041313306],"domain_scores_gemma":[0.99808455,0.00021162441,0.00029571203,0.0012277303,0.00008822652,0.00009214587],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022834659,0.00034583325,0.00032939346,0.0003269998,0.00028082967,0.00012445322,0.0012642547,0.0001862789,0.00003717061],"category_scores_gemma":[0.00003801755,0.00035286538,0.00012637778,0.0007074824,0.000051151364,0.00039167728,0.004501458,0.001181558,0.000015897274],"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":[0.000038443737,0.0002274396,0.00020453088,0.000036658224,0.000021590939,0.000070904425,0.00052948884,0.89539844,0.06774031,0.00016961154,0.0001363249,0.035426278],"study_design_scores_gemma":[0.00029197842,0.000036286718,0.001505421,0.000057794536,0.000008178026,0.000018074703,0.00007022747,0.97056997,0.022824232,0.003996562,0.0002217964,0.00039946195],"about_ca_topic_score_codex":0.0003696828,"about_ca_topic_score_gemma":0.00022881484,"teacher_disagreement_score":0.63105524,"about_ca_system_score_codex":0.0005318259,"about_ca_system_score_gemma":0.0001393577,"threshold_uncertainty_score":0.99989235},"labels":[],"label_agreement":null},{"id":"W4283383339","doi":"10.1016/j.jag.2022.102875","title":"Mask R-CNN based automated identification and extraction of oil well sites","year":2022,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":72,"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; University of Waterloo","funders":"Natural Resources Canada; China Scholarship Council; Central University of Finance and Economics","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Scale (ratio); Segmentation; Pattern recognition (psychology); Feature extraction; Pixel; Identification (biology); Image resolution; Remote sensing; Land cover; Geography; Cartography; Land use; Engineering","score_opus":0.015776124911149197,"score_gpt":0.25703711895224474,"score_spread":0.24126099404109555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283383339","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":[0.6410101,0.00007239822,0.35371062,0.0033944095,0.0004653157,0.00018202713,0.000015577614,0.00013577395,0.0010137331],"genre_scores_gemma":[0.973088,0.00007939641,0.026141178,0.00048689346,0.000044982713,0.000016947073,0.000069115085,0.0000050214962,0.00006846002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854034,0.00002687207,0.00068354124,0.0001151101,0.00054984645,0.00008427126],"domain_scores_gemma":[0.9982481,0.00011182001,0.0010970072,0.00013159693,0.00036333446,0.000048133952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045208007,0.00008863209,0.00011754226,0.00029370195,0.0001501684,0.000102757745,0.00031283795,0.000030067933,0.000028675271],"category_scores_gemma":[0.00002636251,0.00009273113,0.000035473975,0.00030796864,0.00003091267,0.0013358818,0.00009556974,0.00014874616,0.000004550066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045620932,0.0002903496,0.0032334097,0.00010281305,0.00012864501,0.0000053989897,0.0028463255,0.37300915,0.11105028,0.12997699,0.0016430146,0.3772574],"study_design_scores_gemma":[0.000987443,0.00007658178,0.048729707,0.000015245078,0.000012969899,0.000067883186,0.00022534627,0.92384607,0.008441301,0.0028584627,0.014603856,0.00013513656],"about_ca_topic_score_codex":0.0000032214823,"about_ca_topic_score_gemma":0.0000018711668,"teacher_disagreement_score":0.5508369,"about_ca_system_score_codex":0.0000419801,"about_ca_system_score_gemma":0.0000514197,"threshold_uncertainty_score":0.37814656},"labels":[],"label_agreement":null},{"id":"W4283388753","doi":"10.36227/techrxiv.19501954","title":"Multi-Class Object Detection Using Adaptive Non-Maximum Suppression in Dense Images","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Minimum bounding box; Object (grammar); Constraint (computer-aided design); Object detection; Bounding overwatch; Computer science; Artificial intelligence; Class (philosophy); Image (mathematics); Computer vision; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.038606942941742356,"score_gpt":0.3076568789688517,"score_spread":0.26904993602710936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283388753","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":[0.041624837,0.00010335433,0.95565534,0.00013644209,0.00081375096,0.000944161,0.000011582634,0.00030814813,0.000402408],"genre_scores_gemma":[0.67268,0.00004183921,0.32658616,0.00008845534,0.000065155065,0.00031164466,0.000008785179,0.000028351467,0.00018956051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974103,0.00020082424,0.00042194378,0.0012052159,0.0003485598,0.00041313306],"domain_scores_gemma":[0.99808455,0.00021162441,0.00029571203,0.0012277303,0.00008822652,0.00009214587],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022834659,0.00034583325,0.00032939346,0.0003269998,0.00028082967,0.00012445322,0.0012642547,0.0001862789,0.00003717061],"category_scores_gemma":[0.00003801755,0.00035286538,0.00012637778,0.0007074824,0.000051151364,0.00039167728,0.004501458,0.001181558,0.000015897274],"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":[0.000038443737,0.0002274396,0.00020453088,0.000036658224,0.000021590939,0.000070904425,0.00052948884,0.89539844,0.06774031,0.00016961154,0.0001363249,0.035426278],"study_design_scores_gemma":[0.00029197842,0.000036286718,0.001505421,0.000057794536,0.000008178026,0.000018074703,0.00007022747,0.97056997,0.022824232,0.003996562,0.0002217964,0.00039946195],"about_ca_topic_score_codex":0.0003696828,"about_ca_topic_score_gemma":0.00022881484,"teacher_disagreement_score":0.63105524,"about_ca_system_score_codex":0.0005318259,"about_ca_system_score_gemma":0.0001393577,"threshold_uncertainty_score":0.99989235},"labels":[],"label_agreement":null},{"id":"W4283740537","doi":"10.1038/s41598-022-15272-w","title":"A lightweight YOLOv3 algorithm used for safety helmet detection","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Key Technology Research and Development Program of Shandong; Qilu University of Technology; Natural Science Foundation of Shandong Province","keywords":"Computer science; Backbone network; Algorithm; Residual; Object detection; Network model; Artificial intelligence; Feature (linguistics); Point (geometry); Data mining; Pattern recognition (psychology); Computer network; Mathematics","score_opus":0.014531910315471352,"score_gpt":0.25572493121977136,"score_spread":0.2411930209043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283740537","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":[0.00329572,0.00007958022,0.9859184,0.0006811309,0.008596623,0.00084365165,0.000008888597,0.0003098342,0.00026614778],"genre_scores_gemma":[0.6105515,0.0000040905566,0.3688992,0.0003095949,0.0003358801,0.0023594804,0.00013581134,0.000044534474,0.017359953],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974382,0.00006210307,0.00044767722,0.0011070063,0.0005686815,0.000376348],"domain_scores_gemma":[0.9978921,0.00010009902,0.0003343189,0.0014357485,0.00012620058,0.00011157893],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012082933,0.00013035847,0.00015028066,0.00018399712,0.001987895,0.00024420393,0.00057853054,0.00002827274,0.000051232415],"category_scores_gemma":[0.000041657757,0.0001317176,0.00012047843,0.0015948049,0.000080155405,0.0004064971,0.00047683422,0.00014723481,0.000018048944],"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":[0.000021081722,0.00041955887,0.00013516999,0.0000225468,0.0000469305,0.00031524178,0.0012827681,0.012887721,0.11755348,0.012756285,0.05159555,0.8029637],"study_design_scores_gemma":[0.00014058051,0.0000640614,0.00005138455,0.0000022241852,0.0000068478507,0.00043303572,0.000019336847,0.09439977,0.04711071,0.06559509,0.79196835,0.0002086224],"about_ca_topic_score_codex":0.0000031032519,"about_ca_topic_score_gemma":0.000007810191,"teacher_disagreement_score":0.80275506,"about_ca_system_score_codex":0.0001615981,"about_ca_system_score_gemma":0.000086166816,"threshold_uncertainty_score":0.9993114},"labels":[],"label_agreement":null},{"id":"W4283746388","doi":"10.1109/codit55151.2022.9804060","title":"Real-Time Pedestrian Detection Using Enhanced Representations from Light-Weight YOLO Network","year":2022,"lang":"en","type":"article","venue":"2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pedestrian detection; Computer science; Pedestrian; Minimum bounding box; Artificial intelligence; Bounding overwatch; Object detection; Task (project management); Computer vision; Pattern recognition (psychology); Image (mathematics); Engineering","score_opus":0.021606006575376615,"score_gpt":0.27955961750977026,"score_spread":0.2579536109343937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283746388","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":[0.03791516,0.000055037402,0.9444402,0.0059962417,0.0015801055,0.0007462545,0.00015805422,0.0012579635,0.007850987],"genre_scores_gemma":[0.97971725,0.00068652077,0.018313536,0.0005620017,0.00011003648,0.00037437966,0.00011679483,0.000012325258,0.000107144064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750984,0.000099072255,0.0007632319,0.00051800534,0.00079131534,0.00031855784],"domain_scores_gemma":[0.99774957,0.00045110576,0.0006243104,0.00076469,0.00033640192,0.00007390374],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032816385,0.00025559383,0.00027777508,0.0004761306,0.0009325648,0.00040102957,0.0014467114,0.00013078691,0.00040597073],"category_scores_gemma":[0.000339773,0.0002530229,0.00008990844,0.0008491661,0.00008146553,0.0025253447,0.00085804163,0.0005036228,0.00011527178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046953812,0.000111347355,0.00013182667,0.000004179173,0.00011370559,0.0000081635,0.00030964462,0.035399888,0.028101144,0.33324575,0.004318369,0.5977864],"study_design_scores_gemma":[0.0015373483,0.00022390003,0.0007058389,0.000026506765,0.000015564037,0.00001695508,0.00044262098,0.86870176,0.003875559,0.09318495,0.030889012,0.00037999943],"about_ca_topic_score_codex":0.000036071073,"about_ca_topic_score_gemma":0.000013347736,"teacher_disagreement_score":0.9418021,"about_ca_system_score_codex":0.00027803253,"about_ca_system_score_gemma":0.0000969876,"threshold_uncertainty_score":0.9999922},"labels":[],"label_agreement":null},{"id":"W4285021064","doi":"10.3390/jimaging8070193","title":"Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs","year":2022,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":30,"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; U.S. Department of Agriculture","keywords":"False positive paradox; Computer science; Intersection (aeronautics); Bounding overwatch; Minimum bounding box; Ground truth; Artificial intelligence; Object detection; Detector; Pattern recognition (psychology); Object (grammar); Sorting; Boundary (topology); False positives and false negatives; Algorithm; Mathematics; Image (mathematics)","score_opus":0.007190987209588884,"score_gpt":0.2542217019932521,"score_spread":0.24703071478366323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285021064","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":[0.04703995,0.0007890344,0.95022887,0.0013685082,0.0003176766,0.00018488232,0.0000071596605,0.000043115982,0.000020804395],"genre_scores_gemma":[0.9618707,0.000029963754,0.037677728,0.00030820628,0.000035046873,0.000037340393,0.0000013614675,0.000011313899,0.000028341185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990868,0.000049508326,0.000283693,0.00016471434,0.000234054,0.00018121359],"domain_scores_gemma":[0.9992093,0.00014925424,0.00036089885,0.00013845944,0.00007375792,0.00006834263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029733038,0.00008294809,0.00012856066,0.00009678644,0.00038968082,0.00007397322,0.00030663033,0.000009344344,0.0000016598036],"category_scores_gemma":[0.000028557615,0.00008325997,0.000036986698,0.00024067196,0.000021429549,0.00034222347,0.00016136227,0.00024237762,1.834321e-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":[0.000077734396,0.00022920655,0.0010110231,0.000021398728,0.0000647552,0.000038742273,0.0007946622,0.014209123,0.25208592,0.000352319,0.0046390104,0.7264761],"study_design_scores_gemma":[0.001354155,0.00033190564,0.00047807224,0.000016079024,0.000021168476,0.0007106707,0.0001259643,0.98557603,0.002549092,0.004004022,0.004693312,0.00013950144],"about_ca_topic_score_codex":0.000001767895,"about_ca_topic_score_gemma":9.983057e-7,"teacher_disagreement_score":0.97136694,"about_ca_system_score_codex":0.00018287354,"about_ca_system_score_gemma":0.00002840826,"threshold_uncertainty_score":0.3395243},"labels":[],"label_agreement":null},{"id":"W4285129920","doi":"10.54364/aaiml.2022.1128","title":"Lesion Segmentation in Paediatric Epilepsy Utilizing Deep Learning Approaches","year":2022,"lang":"en","type":"article","venue":"Advances in Artificial Intelligence and Machine Learning","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"SickKids Foundation; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Epilepsy Research Program of the Ontario Brain Institute; Government of Ontario; Nvidia","keywords":"Cortical dysplasia; Epilepsy; Lesion; Fluid-attenuated inversion recovery; Sørensen–Dice coefficient; Artificial intelligence; Pattern recognition (psychology); Computer science; Segmentation; Sequence (biology); Medicine; Radiology; Magnetic resonance imaging; Image segmentation; Pathology","score_opus":0.07133549752918397,"score_gpt":0.31355981828395696,"score_spread":0.242224320754773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285129920","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":[0.043824747,0.00751043,0.94690526,0.0003866726,0.00018430344,0.00035250024,6.025569e-7,0.00015264604,0.00068281253],"genre_scores_gemma":[0.9773515,0.0029647215,0.019329652,0.00007360544,0.00004890058,0.00017112699,0.000015389849,0.000015083416,0.000030049057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798816,0.0003529993,0.00046051954,0.0005822812,0.00025098716,0.00036506116],"domain_scores_gemma":[0.9991795,0.0003852471,0.00019353146,0.00017209131,0.000015452035,0.00005419277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067255856,0.00016833752,0.00018866723,0.00034094532,0.00057632604,0.00006719968,0.00040097983,0.000033264416,0.000018223502],"category_scores_gemma":[0.0001218634,0.00018732631,0.000034435772,0.0014881261,0.000055155626,0.0009182243,0.0005007371,0.00097419624,0.000008298432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009613245,0.000030488425,0.009653405,0.000005845191,6.1668186e-7,0.0000060097705,0.000882314,0.4785976,0.00015514363,0.020265082,1.1839446e-7,0.49039376],"study_design_scores_gemma":[0.000057320005,0.00013332034,0.0005635092,0.00001188348,0.000002456502,0.000013136317,0.0028824918,0.95857215,0.0008977607,0.034069784,0.0025668547,0.00022930511],"about_ca_topic_score_codex":0.000043292013,"about_ca_topic_score_gemma":0.00023321391,"teacher_disagreement_score":0.9335267,"about_ca_system_score_codex":0.00010141245,"about_ca_system_score_gemma":0.000015479085,"threshold_uncertainty_score":0.76389456},"labels":[],"label_agreement":null},{"id":"W4285241469","doi":"10.1109/tai.2022.3177394","title":"Nonoverlapping Feature Projection Convolutional Neural Network With Differentiable Loss Function","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Feature (linguistics); Differentiable function; Convolution (computer science); Computer science; Pattern recognition (psychology); Pixel; Artificial intelligence; Feature vector; Convolutional neural network; Projection (relational algebra); Separable space; Algorithm; Function (biology); Mathematics; Artificial neural network; Mathematical analysis","score_opus":0.030979467884886477,"score_gpt":0.25520140654106543,"score_spread":0.22422193865617895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285241469","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":[0.0070403703,0.00003558531,0.9891388,0.0012194526,0.0014066389,0.0005631671,0.000016603235,0.00041949959,0.00015987735],"genre_scores_gemma":[0.9882467,0.00001035795,0.009792364,0.000476919,0.00019772032,0.000567382,0.000009539401,0.000024708923,0.0006743294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997867,0.00012548493,0.000307105,0.00069936126,0.00052549836,0.00047558747],"domain_scores_gemma":[0.99893975,0.0001539551,0.00015561712,0.00053305377,0.00011406087,0.000103533785],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00016077321,0.00024509602,0.00017773271,0.00015970618,0.0018618854,0.00012975423,0.00056216755,0.000058314996,0.0001650803],"category_scores_gemma":[0.0000021351025,0.00024375055,0.00010326577,0.0017946248,0.0001112528,0.0005264571,0.000014783191,0.00077234197,0.00006142051],"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":[0.00018796013,0.00021858115,0.000014026051,0.000004140336,0.00002650135,0.0000050462586,0.00008890695,0.9271175,0.0007130342,0.01991887,0.0002934334,0.051411945],"study_design_scores_gemma":[0.0000862395,0.00068075437,0.00013365193,0.000016404729,0.00003421264,0.00011921785,0.00013206594,0.97272515,0.007255034,0.014386885,0.003999208,0.00043117136],"about_ca_topic_score_codex":0.000024792762,"about_ca_topic_score_gemma":0.00009237651,"teacher_disagreement_score":0.9812063,"about_ca_system_score_codex":0.00022930533,"about_ca_system_score_gemma":0.0000871446,"threshold_uncertainty_score":0.9994376},"labels":[],"label_agreement":null},{"id":"W4285328108","doi":"10.1109/icpads53394.2021.00021","title":"Towards Network-accelerated ML-based Distributed Computer Vision Systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Cloud computing; Server; Latency (audio); Software; Low latency (capital markets); Artificial intelligence; Bandwidth (computing); Real-time computing; Embedded system; Computer vision; Operating system; Computer network","score_opus":0.025634375419096925,"score_gpt":0.27951202445620027,"score_spread":0.25387764903710336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285328108","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":[0.001229776,0.00019810136,0.9940343,0.0019077115,0.00069099717,0.00024005598,0.00000938822,0.0007241768,0.0009655193],"genre_scores_gemma":[0.673131,0.000017519795,0.32416734,0.0015897722,0.0004545393,0.00006943073,0.0002217006,0.000022014234,0.00032666515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820846,0.00011685802,0.0003364593,0.00062128995,0.000285333,0.0004315669],"domain_scores_gemma":[0.9983508,0.00013230556,0.00010047056,0.0009410291,0.0003107687,0.00016461422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012221649,0.00019104843,0.0002327337,0.000031252326,0.00022032183,0.00037252644,0.00067378074,0.00008016793,0.00003880408],"category_scores_gemma":[0.000009462901,0.00017153875,0.00007217808,0.0014850016,0.000033381264,0.00034066397,0.00035580224,0.00015726611,0.00010475336],"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":[0.0000071295512,0.00013380752,0.00015034305,0.000016776306,0.0000199175,0.00009603556,0.000010493694,0.81892926,0.0010001028,0.09719012,0.05156624,0.030879749],"study_design_scores_gemma":[0.00030028902,0.00005142809,0.001770628,0.000028765786,0.0000040683562,0.000029565535,0.000001911459,0.9552478,0.0014005728,0.00079176197,0.040147062,0.00022611006],"about_ca_topic_score_codex":0.000011391721,"about_ca_topic_score_gemma":0.00000740868,"teacher_disagreement_score":0.6719012,"about_ca_system_score_codex":0.00006992557,"about_ca_system_score_gemma":0.000108549364,"threshold_uncertainty_score":0.6995147},"labels":[],"label_agreement":null},{"id":"W4285328266","doi":"10.1109/hpcc-dss-smartcity-dependsys53884.2021.00206","title":"Deep Learning-Based Semantic Segmentation in Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Metric (unit); Artificial intelligence; Intersection (aeronautics); Task (project management); Machine learning; Perception; Image segmentation; Pattern recognition (psychology); Engineering","score_opus":0.011847513117982189,"score_gpt":0.26115505612606277,"score_spread":0.24930754300808058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285328266","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":[0.019878007,0.00004135654,0.97752887,0.0012951996,0.000051346535,0.00008545997,2.4609777e-8,0.00019376582,0.0009259789],"genre_scores_gemma":[0.83544844,0.0000056606455,0.16370869,0.00038266505,0.000013605322,0.000028038981,0.0000043455234,0.0000051961024,0.0004033558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992622,0.00005210181,0.00014211424,0.00027977195,0.000099810335,0.00016399608],"domain_scores_gemma":[0.9995066,0.00012600697,0.000041991483,0.00025264258,0.000034900917,0.0000378437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000063012216,0.00006538269,0.00007131316,0.00005112918,0.000074275034,0.00006062686,0.00020281565,0.000021926577,0.000035212728],"category_scores_gemma":[0.00002718803,0.000069803034,0.000022527798,0.0005885546,0.000010800302,0.00022645525,0.00008803206,0.00010996838,0.000061921324],"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":[6.654333e-7,0.00007623195,0.013315037,0.0000073251144,0.0000029132052,0.000042247822,0.00020003978,0.87309027,0.0085056685,0.016784977,0.000041184372,0.08793342],"study_design_scores_gemma":[0.00016324225,0.000010621569,0.009452273,0.000007014768,0.0000010727632,0.000005910684,0.000024703413,0.97598505,0.0123219285,0.001437081,0.0005002292,0.000090865244],"about_ca_topic_score_codex":0.000005370759,"about_ca_topic_score_gemma":0.00018758254,"teacher_disagreement_score":0.8155704,"about_ca_system_score_codex":0.000057527363,"about_ca_system_score_gemma":0.00004239756,"threshold_uncertainty_score":0.28464854},"labels":[],"label_agreement":null},{"id":"W4285407586","doi":"10.1109/cwit55308.2022.9817671","title":"Vector Quantization of Deep Convolutional Neural Networks With Learned Codebook","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Codebook; Linde–Buzo–Gray algorithm; Vector quantization; Convolutional neural network; Quantization (signal processing); Computer science; Learning vector quantization; Artificial intelligence; Pattern recognition (psychology); Deep learning; Artificial neural network; Algorithm","score_opus":0.017908900160295768,"score_gpt":0.24573964268202808,"score_spread":0.2278307425217323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285407586","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":[0.003417841,0.00008222102,0.99450177,0.00085092115,0.00008657064,0.00020482471,0.0000015245357,0.00014440795,0.00070991233],"genre_scores_gemma":[0.9662374,0.0000031672246,0.03293241,0.00038951103,0.000029080795,0.00008463259,0.000016276059,0.000008397924,0.000299089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908423,0.00006348287,0.00016500663,0.0002595053,0.0002588539,0.00016894442],"domain_scores_gemma":[0.9993354,0.00011007236,0.00011711244,0.00032266442,0.000070313356,0.000044405162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007560559,0.00008057325,0.000100070676,0.00004386562,0.00023886752,0.00001582434,0.0004698044,0.000014522427,0.000105971456],"category_scores_gemma":[0.000005151161,0.00007252378,0.000027483058,0.00055670633,0.000057742207,0.00018531916,0.00024079996,0.00013275149,0.0000030595709],"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":[0.000015021061,0.000030265035,0.000505804,0.0000011577235,0.000005197491,0.000001127929,0.0000324779,0.7519808,0.00023103184,0.24341694,0.0001895736,0.003590656],"study_design_scores_gemma":[0.00021808028,0.00012278785,0.0016639913,8.376804e-7,0.0000030488127,0.000021620555,0.000014561393,0.99544704,0.00012839174,0.0011792573,0.0011069968,0.00009336767],"about_ca_topic_score_codex":0.000007110178,"about_ca_topic_score_gemma":0.000014429323,"teacher_disagreement_score":0.9628196,"about_ca_system_score_codex":0.000038503884,"about_ca_system_score_gemma":0.000027252694,"threshold_uncertainty_score":0.29574338},"labels":[],"label_agreement":null},{"id":"W4285411971","doi":"10.1109/jsen.2022.3188985","title":"Improved 3D Object Detector Under Snowfall Weather Condition Based on LiDAR Point Cloud","year":2022,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":27,"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":"Computer science; Lidar; Block (permutation group theory); Point cloud; Context (archaeology); Object detection; Snow; Artificial intelligence; Feature (linguistics); Feature extraction; Detector; Focus (optics); Remote sensing; Data mining; Pattern recognition (psychology); Meteorology; Geography; Mathematics; Telecommunications","score_opus":0.015005660984265218,"score_gpt":0.25559173798271306,"score_spread":0.24058607699844783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285411971","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":[0.16119361,0.00004213822,0.83174634,0.0034881781,0.002125062,0.00037882396,0.000020063662,0.00024369243,0.0007620919],"genre_scores_gemma":[0.97741354,0.000007864363,0.018567815,0.002870811,0.0006193262,0.000040661926,0.0000028450838,0.000035529138,0.00044162132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980663,0.0002909389,0.000334224,0.00039925452,0.00049368676,0.0004156079],"domain_scores_gemma":[0.9986273,0.00024323122,0.0002896457,0.00055864017,0.000092839764,0.00018835728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035595556,0.00020368262,0.00017929883,0.00018483883,0.0008429597,0.0001442433,0.0006977211,0.00004514246,0.00027439327],"category_scores_gemma":[0.000023231732,0.0001932713,0.00014797026,0.00048697038,0.00004565525,0.00023814062,0.00008911521,0.0008294922,0.000064162436],"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":[0.00015484626,0.00035616494,0.000040040264,0.0000066929147,0.000055204604,0.0001755591,0.0004101151,0.8591204,0.11737885,0.002254332,0.009173238,0.010874496],"study_design_scores_gemma":[0.0022026678,0.0011677818,0.0005761238,0.000028357903,0.000030448997,0.0012090004,0.00012579857,0.92635787,0.03357606,0.011890352,0.022089647,0.0007458863],"about_ca_topic_score_codex":0.0000039436577,"about_ca_topic_score_gemma":0.0000036270544,"teacher_disagreement_score":0.8162199,"about_ca_system_score_codex":0.0003308645,"about_ca_system_score_gemma":0.00011326319,"threshold_uncertainty_score":0.78813756},"labels":[],"label_agreement":null},{"id":"W4285604884","doi":"10.24963/ijcai.2022/389","title":"One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Western Canada Research Grid; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Segmentation; Classifier (UML); Artificial intelligence; Hyperparameter; Pattern recognition (psychology); Pixel; Image segmentation; Machine learning","score_opus":0.090667810114998,"score_gpt":0.3025308155715213,"score_spread":0.21186300545652328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285604884","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":[0.12958995,0.00001730045,0.81050545,0.049817447,0.0018233759,0.0019891604,0.00008509158,0.00031787768,0.005854378],"genre_scores_gemma":[0.9716387,0.00002807277,0.0258781,0.0012168366,0.00014648006,0.00044835467,0.000005503791,0.000026002082,0.00061198033],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99660635,0.00002552993,0.0008549038,0.0008216781,0.0012909092,0.000400643],"domain_scores_gemma":[0.99793506,0.00010182395,0.0005617261,0.00043619535,0.0008170667,0.00014811366],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000501606,0.00028742693,0.00029724577,0.00032333544,0.0005894069,0.00025146303,0.0032762287,0.000055333174,0.00012826141],"category_scores_gemma":[0.00020466342,0.0002732318,0.00017853807,0.00095000723,0.000108110115,0.0006035674,0.0016195336,0.0005158938,0.000116026575],"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":[0.000114904135,0.00038645745,0.000051097082,0.000022087392,0.00003713773,3.7431113e-7,0.0017695931,0.05661092,0.23814102,0.67554784,0.00034517323,0.0269734],"study_design_scores_gemma":[0.00005659283,0.00018563197,0.00008850235,0.000058487472,0.0000127341955,0.00000601797,0.00050214067,0.6279014,0.210611,0.16014177,0.0001754122,0.0002603649],"about_ca_topic_score_codex":0.000040186093,"about_ca_topic_score_gemma":0.000013726324,"teacher_disagreement_score":0.8420487,"about_ca_system_score_codex":0.00034872344,"about_ca_system_score_gemma":0.00012013441,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W4286640356","doi":"10.1016/j.neuroimage.2022.119505","title":"DAFT: A universal module to interweave tabular data and 3D images in CNNs","year":2022,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; National Institutes of Health; Bayerisches Staatsministerium für Wissenschaft und Kunst; Takeda Pharmaceutical Company; IXICO; Eisai; Northern California Institute for Research and Education; Bundesministerium für Forschung und Technologie; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Johnson and Johnson; Medpace; University of Southern California; Janssen Research and Development; Nvidia; Leibniz-Rechenzentrum; F. Hoffmann-La Roche; Merck; Alzheimer's Drug Discovery Foundation; National Institute on Aging; Alzheimer's Association","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images)","score_opus":0.026753367401787766,"score_gpt":0.2738923052506021,"score_spread":0.24713893784881433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286640356","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":[0.16517739,0.00015352658,0.82061917,0.0106791975,0.00030200332,0.00077154307,0.00021261393,0.00033151926,0.0017530327],"genre_scores_gemma":[0.9372385,0.000020758003,0.06039131,0.0018174751,0.0000355954,0.000050324667,0.000021629487,0.000019731293,0.0004046689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868166,0.000079661106,0.00013427601,0.0006930304,0.00017714997,0.0002342281],"domain_scores_gemma":[0.99845237,0.00008089289,0.000040377356,0.0013266932,0.000013628265,0.00008603983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012368953,0.00010476946,0.00010824503,0.00012669661,0.00015615897,0.000068367925,0.0017359925,0.000010760103,0.000018321369],"category_scores_gemma":[0.000033500517,0.0001219467,0.000011541133,0.0006803353,0.000038868344,0.00055459887,0.005083057,0.00025283374,0.000018008543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018760614,0.0012791966,0.010886622,0.0000678405,0.000038939146,0.0042195045,0.0062619927,0.0701208,0.26192242,0.05509206,0.12872785,0.46119517],"study_design_scores_gemma":[0.0009290239,0.00032524054,0.016076207,0.000012074437,0.000009484833,0.00015038069,0.0001775812,0.73532134,0.0019182966,0.0033835084,0.24109277,0.00060410035],"about_ca_topic_score_codex":0.000021934988,"about_ca_topic_score_gemma":0.000021727012,"teacher_disagreement_score":0.7720611,"about_ca_system_score_codex":0.000038807124,"about_ca_system_score_gemma":0.000021445094,"threshold_uncertainty_score":0.63356674},"labels":[],"label_agreement":null},{"id":"W4286828226","doi":"10.1007/s12652-022-04341-7","title":"CIOD: an intelligent class-incremental object detection system with nearest mean of exemplars","year":2022,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Science and Technology Program of Hunan Province; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Object detection; Viola–Jones object detection framework; Classifier (UML); Pattern recognition (psychology); Object (grammar); Computer vision; Object-class detection; Video tracking; Class (philosophy); Machine learning; Face detection","score_opus":0.03173486457740647,"score_gpt":0.27111645679026536,"score_spread":0.23938159221285887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286828226","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":[0.4250156,0.0001321611,0.57437265,0.00003394267,0.00021207854,0.00015759806,0.0000010128851,0.000036259757,0.000038710587],"genre_scores_gemma":[0.97848105,0.000026680957,0.0212971,0.00006585875,0.00010185723,0.000005057606,0.0000012704353,0.000014773368,0.000006377684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997818,0.00017509473,0.00080527604,0.00031992572,0.00061836466,0.00026332468],"domain_scores_gemma":[0.99813604,0.00014080615,0.0009974007,0.00033410074,0.00026047605,0.00013115938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079062086,0.00017630045,0.00033077167,0.00027184043,0.0007548547,0.00011422218,0.00084558566,0.000025898957,0.000008907665],"category_scores_gemma":[0.000010321878,0.00015662098,0.00008789537,0.00053811667,0.00008153315,0.00043603164,0.00042495335,0.000435989,0.0000012392345],"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":[0.0006511414,0.00092321256,0.0015862088,0.00020321296,0.00028382728,0.00022671533,0.02029754,0.45718896,0.051713746,0.11779973,0.00004847773,0.34907722],"study_design_scores_gemma":[0.0009003282,0.007834913,0.0008544516,0.000426446,0.00010479171,0.0040766597,0.018050987,0.7896267,0.17337795,0.0027345929,0.0012646992,0.00074745],"about_ca_topic_score_codex":0.00003513876,"about_ca_topic_score_gemma":0.00003826594,"teacher_disagreement_score":0.5534654,"about_ca_system_score_codex":0.0002839033,"about_ca_system_score_gemma":0.00006416563,"threshold_uncertainty_score":0.6386818},"labels":[],"label_agreement":null},{"id":"W4287121607","doi":"10.48550/arxiv.2106.05531","title":"CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative\\n Intelligence","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Tensor (intrinsic definition); Imperfect; Enhanced Data Rates for GSM Evolution; Feature (linguistics); Cloud computing; Artificial intelligence; Big data; Network packet; Data mining; Pattern recognition (psychology); Machine learning; Mathematics; Computer network","score_opus":0.2507620983232365,"score_gpt":0.24389284381342535,"score_spread":0.006869254509811129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287121607","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":[0.023017963,0.00034277875,0.97079456,0.0005387567,0.00107294,0.0032543868,0.00028313673,0.00027434304,0.0004211595],"genre_scores_gemma":[0.92095083,0.0014918125,0.07422716,0.0003514016,0.00027134546,0.000048173973,0.0001587943,0.0000662628,0.00243422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937795,0.0004792294,0.0008528288,0.003590482,0.00025047522,0.001047486],"domain_scores_gemma":[0.99102277,0.0011391466,0.0011537296,0.0023952844,0.0037290268,0.0005600572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040114258,0.000944416,0.0011254882,0.00031208366,0.0010659568,0.00028332334,0.002967426,0.0005519536,0.00008268661],"category_scores_gemma":[0.00022091538,0.0011938105,0.00059645885,0.0032260297,0.00079280185,0.0010480094,0.00253899,0.001145621,0.00014909568],"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":[0.00030862872,0.00035904336,0.00022285533,0.000094147574,0.00021745829,0.00014029724,0.0005258242,0.6369052,0.0004898232,0.3557778,0.00011038684,0.004848514],"study_design_scores_gemma":[0.0007555586,0.00039604882,0.0003604897,0.0002952738,0.0002123283,0.000015351652,0.001954939,0.97207755,0.002083133,0.017513683,0.0031389764,0.0011966638],"about_ca_topic_score_codex":0.00011162286,"about_ca_topic_score_gemma":0.00015352137,"teacher_disagreement_score":0.8979329,"about_ca_system_score_codex":0.0007771431,"about_ca_system_score_gemma":0.0007923425,"threshold_uncertainty_score":0.99905115},"labels":[],"label_agreement":null},{"id":"W4287394362","doi":"10.57702/81s5oo9s","title":"Framework for Fast Scalable BNN Inference Using GoogleNet and Transfer Learning","year":2024,"lang":"en","type":"preprint","venue":"TIB Data Manager","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"MNIST database; Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Object detection; Transfer of learning; Inference; Scalability; Machine learning; Object (grammar); Segmentation; Contextual image classification; Pattern recognition (psychology); Artificial neural network; Cognitive neuroscience of visual object recognition; Image (mathematics); Computer vision; Database","score_opus":0.1086547582003038,"score_gpt":0.3628196647897565,"score_spread":0.25416490658945273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287394362","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":[0.0018933619,0.0010852487,0.993972,0.0010066346,0.000405847,0.00071041693,0.00022415601,0.0003824477,0.00031989702],"genre_scores_gemma":[0.123028815,0.0005696475,0.8737036,0.0003245635,0.00039811523,0.00015109174,0.0005509589,0.0000769258,0.0011962695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975303,0.000045995435,0.00029837375,0.001523551,0.00020444916,0.00039732683],"domain_scores_gemma":[0.996933,0.0003775329,0.0000726787,0.0024667636,0.000046574085,0.000103443985],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.00027647862,0.00031091127,0.00031025158,0.00012836311,0.00021052816,0.00072013156,0.0025283904,0.00022538638,0.000012192969],"category_scores_gemma":[0.000079952995,0.00031507216,0.00004983982,0.0003633117,0.000082893624,0.0005195405,0.008660697,0.0011069989,0.000042975113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013890002,0.00005491237,0.000118783,0.0013609594,0.00016653392,0.000027000257,0.0004976137,0.09830583,0.00043109257,0.5972258,0.0037333851,0.29806423],"study_design_scores_gemma":[0.00006460541,0.000012829724,0.00005686587,0.00034432163,0.00006436584,0.0000049148334,0.000009783802,0.7563099,0.00004608069,0.20932117,0.03341462,0.00035050587],"about_ca_topic_score_codex":0.000029562081,"about_ca_topic_score_gemma":0.000009983641,"teacher_disagreement_score":0.6580041,"about_ca_system_score_codex":0.000039097704,"about_ca_system_score_gemma":0.000066066954,"threshold_uncertainty_score":0.99993014},"labels":[],"label_agreement":null},{"id":"W4287688428","doi":"10.48550/arxiv.2008.07083","title":"Edge Network-Assisted Real-Time Object Detection Framework for\\n Autonomous Driving","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Real-time computing; Latency (audio); Cloud computing; Frame (networking); Duration (music); Low latency (capital markets); Edge device; Artificial intelligence; Channel (broadcasting); Transmission (telecommunications); Computer vision; Object (grammar); Computer network; Telecommunications","score_opus":0.059643909088938796,"score_gpt":0.21673638092497918,"score_spread":0.1570924718360404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287688428","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":[0.012710816,0.000033085347,0.9826453,0.0003047457,0.0007739883,0.00096639356,0.000010322783,0.0013875681,0.0011677985],"genre_scores_gemma":[0.8835255,0.00015359899,0.114902735,0.00014581675,0.00059961627,0.000020632884,0.00002859349,0.000053713487,0.00056979165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996974,0.00014550677,0.0003296242,0.0018186255,0.000099715144,0.0006325542],"domain_scores_gemma":[0.9966816,0.00065721455,0.0005314439,0.0016700585,0.0001706568,0.00028905933],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001782875,0.00045750636,0.0004977634,0.0001458316,0.0005099409,0.00017277108,0.0021186129,0.0004975792,0.000013220992],"category_scores_gemma":[0.000089072004,0.0005893069,0.000357845,0.0013514729,0.0000937349,0.00037041184,0.0021360184,0.00091944105,0.00012021897],"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":[0.000042457137,0.00006494373,0.00025358557,0.00006225387,0.0001275453,0.00007285093,0.00011969565,0.69911027,0.00072895107,0.28666002,0.0007298827,0.012027578],"study_design_scores_gemma":[0.00019503766,0.000064827465,0.0012619754,0.000089047826,0.00007300683,0.0000053404838,0.0000061536116,0.6977691,0.00022959018,0.29852572,0.0012697191,0.000510474],"about_ca_topic_score_codex":0.000030296005,"about_ca_topic_score_gemma":0.000041891944,"teacher_disagreement_score":0.8708147,"about_ca_system_score_codex":0.00045623424,"about_ca_system_score_gemma":0.00019669482,"threshold_uncertainty_score":0.99965584},"labels":[],"label_agreement":null},{"id":"W4287751941","doi":"10.48550/arxiv.2007.13483","title":"Post-Workshop Report on Science meets Engineering in Deep Learning,\\n NeurIPS 2019, Vancouver","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Deep learning; Data science; Computer science; Event (particle physics); Robustness (evolution); Architecture; Artificial intelligence; Engineering ethics; Engineering; History","score_opus":0.03858935646820156,"score_gpt":0.2008012819524995,"score_spread":0.16221192548429794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287751941","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":[0.17859472,0.00005168235,0.81614727,0.000808521,0.0011935574,0.0006486947,0.0000031013612,0.00080542977,0.0017469927],"genre_scores_gemma":[0.99366385,0.00009814456,0.0049846694,0.00020411366,0.00009076774,0.0000038739363,0.00000863326,0.000029978244,0.000915986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967161,0.000061418556,0.00031526206,0.0020948958,0.00023429714,0.000578045],"domain_scores_gemma":[0.9973698,0.0002200723,0.00034669988,0.0015641337,0.0001986314,0.00030062348],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002946459,0.00038325126,0.0003489896,0.0005115878,0.00022096533,0.0001232145,0.0028883899,0.00018722056,0.000007864306],"category_scores_gemma":[0.0003169072,0.00047192254,0.0001374772,0.0024206683,0.00016036513,0.0007944874,0.0030318545,0.0015231594,0.000079484176],"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":[0.00001241461,0.000041448213,0.0004553216,0.000015220945,0.0000085022975,0.001382041,0.00010397144,0.95754224,0.00025595108,0.039282046,0.00019175348,0.00070909684],"study_design_scores_gemma":[0.0002824942,0.000065305954,0.0026629537,0.000086357366,0.0000142287945,0.000020894846,0.000020190097,0.9869316,0.00029750355,0.0058833226,0.0031630187,0.00057208724],"about_ca_topic_score_codex":0.000038652426,"about_ca_topic_score_gemma":0.00010240072,"teacher_disagreement_score":0.8150691,"about_ca_system_score_codex":0.00041530552,"about_ca_system_score_gemma":0.00023176645,"threshold_uncertainty_score":0.99977326},"labels":[],"label_agreement":null},{"id":"W4287867706","doi":"10.48550/arxiv.2002.07711","title":"An Energy-Efficient Accelerator Architecture with Serial Accumulation\\n Dataflow for Deep CNNs","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Dram; Computer science; Dataflow; Convolutional neural network; Computation; Convolution (computer science); Energy consumption; Parallel computing; CAS latency; Computer hardware; Latency (audio); Dataflow architecture; Embedded system; Efficient energy use; Hardware acceleration; Architecture; Energy (signal processing); Artificial neural network; Artificial intelligence; Field-programmable gate array; Algorithm; Semiconductor memory","score_opus":0.11362898848860202,"score_gpt":0.23470722906439453,"score_spread":0.12107824057579251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287867706","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":[0.055038035,0.000029911896,0.9411312,0.0005370482,0.0006140791,0.0017065206,0.00037166648,0.00043814335,0.00013343246],"genre_scores_gemma":[0.93903816,0.00007251465,0.058582656,0.0005044769,0.0009335968,0.0000310671,0.0005762902,0.00010094456,0.00016029924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939102,0.00027260694,0.0005526299,0.0039898027,0.00027209404,0.0010026537],"domain_scores_gemma":[0.99391454,0.0003897349,0.0008127796,0.003392174,0.0005636194,0.0009271316],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018108015,0.0009923107,0.00077699235,0.00029568994,0.0010745141,0.0005190716,0.0047959895,0.0004715342,0.0000620752],"category_scores_gemma":[0.000036917245,0.0010880393,0.0003240176,0.002184989,0.0003548888,0.0008049929,0.0021007664,0.00092967425,0.000045443616],"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":[0.0005805779,0.00027581962,0.000082715975,0.00004858927,0.000113175665,0.00006403419,0.0003350356,0.85875016,0.0004960552,0.13516404,0.00006788793,0.0040219314],"study_design_scores_gemma":[0.0016022222,0.00046209834,0.00017898814,0.00006314529,0.00022249029,0.000012532795,0.00007377615,0.9624119,0.00070414715,0.026988082,0.0060876613,0.001193],"about_ca_topic_score_codex":0.000044057175,"about_ca_topic_score_gemma":0.00023950197,"teacher_disagreement_score":0.8840001,"about_ca_system_score_codex":0.00034919716,"about_ca_system_score_gemma":0.0005907166,"threshold_uncertainty_score":0.99915695},"labels":[],"label_agreement":null},{"id":"W4288033329","doi":"10.21203/rs.3.rs-1641094/v2","title":"Learning to Detect Boundary Information for Brain Image Segmentation","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Segmentation; Artificial intelligence; Computer science; Boundary (topology); Image segmentation; Pattern recognition (psychology); Computer vision; Scale-space segmentation; Mathematics","score_opus":0.04681458175345153,"score_gpt":0.4124881130710341,"score_spread":0.36567353131758257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288033329","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":[0.0054456475,0.00005847141,0.9830612,0.0065080244,0.00019674268,0.003662811,0.000066754554,0.0003274709,0.0006728997],"genre_scores_gemma":[0.115662426,0.00015798892,0.85483474,0.0012572594,0.0005345315,0.023561573,0.0019048417,0.00009341326,0.0019932028],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970691,0.00042522917,0.00034297456,0.00055887934,0.0010300047,0.00057378673],"domain_scores_gemma":[0.9972996,0.0009959049,0.00014691659,0.0008464394,0.000538877,0.00017225363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018544248,0.00017523026,0.00017189812,0.0005773991,0.001132462,0.0008182748,0.001438856,0.00009419648,0.000051036324],"category_scores_gemma":[0.0006839168,0.00019989871,0.00010557017,0.0010605057,0.00004934158,0.00094399776,0.0037222577,0.001379375,0.00013059651],"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":[0.00008875895,0.00004294835,0.000081414844,0.00087910116,0.000031756612,0.000006095155,0.0064101964,0.09447324,0.0054571815,0.008439819,0.041141387,0.8429481],"study_design_scores_gemma":[0.0006901861,0.0011907,0.0014489369,0.0001944432,0.000006530776,0.0000061536443,0.0014909867,0.22767594,0.004454657,0.07218375,0.6898624,0.0007953242],"about_ca_topic_score_codex":0.000027303284,"about_ca_topic_score_gemma":0.000010394794,"teacher_disagreement_score":0.8421528,"about_ca_system_score_codex":0.0006373328,"about_ca_system_score_gemma":0.0003713121,"threshold_uncertainty_score":0.8710097},"labels":[],"label_agreement":null},{"id":"W4288318880","doi":"10.48550/arxiv.1906.08746","title":"Progressive Gradient Pruning for Classification, Detection and\\n DomainAdaptation","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pruning; Computer science; Filter (signal processing); Artificial intelligence; Convolutional neural network; Computational complexity theory; Pattern recognition (psychology); Machine learning; Algorithm; Computer vision","score_opus":0.09234867096554043,"score_gpt":0.21583436280927426,"score_spread":0.12348569184373383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288318880","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":[0.13633609,0.00017288399,0.8588514,0.00025370813,0.0007082464,0.0032501677,0.000018416273,0.00019679208,0.00021234665],"genre_scores_gemma":[0.98608816,0.00037925903,0.012289307,0.0000565045,0.00016197645,0.000070977825,0.000040420564,0.000044735756,0.000868678],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99617386,0.00019258715,0.0004664009,0.0024050747,0.00014135045,0.000620712],"domain_scores_gemma":[0.99593264,0.00047753836,0.0011409338,0.0014683646,0.0007109892,0.00026954364],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033447886,0.00053244515,0.00044343254,0.00033177223,0.0009470256,0.00022997013,0.0011424818,0.0003842427,0.0000059228078],"category_scores_gemma":[0.00007755015,0.00067188434,0.0002154576,0.0011486636,0.00030863227,0.0009425124,0.00089759205,0.0005822022,0.000041697193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019832393,0.00016658421,0.00066148053,0.0002644415,0.0001278507,0.00002309205,0.0007789869,0.45663762,0.0018017717,0.50716215,0.00004158897,0.032136112],"study_design_scores_gemma":[0.00085919444,0.00022417927,0.004378962,0.00013737289,0.0001508569,0.000019939049,0.00029189896,0.9239682,0.0004433424,0.0671057,0.001791559,0.0006287757],"about_ca_topic_score_codex":0.000021386571,"about_ca_topic_score_gemma":0.00003266806,"teacher_disagreement_score":0.84975207,"about_ca_system_score_codex":0.00057493517,"about_ca_system_score_gemma":0.00022278177,"threshold_uncertainty_score":0.99957323},"labels":[],"label_agreement":null},{"id":"W4288781673","doi":"10.1007/978-3-031-12748-9_5","title":"DL-CapsNet: A Deep and Light Capsule Network","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Affine transformation; Automatic summarization; Deep learning; Pattern recognition (psychology); Inference; Process (computing); Mathematics","score_opus":0.012040986194202028,"score_gpt":0.23367137156207302,"score_spread":0.221630385367871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288781673","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":[0.000024081446,0.002566109,0.9882709,0.0022841021,0.00116691,0.00053418864,0.000003179334,0.00023624428,0.0049143126],"genre_scores_gemma":[0.03979102,0.00060075853,0.95063955,0.0061848303,0.00162523,0.000125303,0.000014094731,0.000103324775,0.00091588526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959003,0.000041178875,0.000474619,0.0019167997,0.0008259398,0.0008411795],"domain_scores_gemma":[0.9973134,0.00035319463,0.0003005513,0.0016894628,0.00010379768,0.00023960572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050603406,0.0005306589,0.00049515377,0.0003805159,0.00072183873,0.00036463502,0.0032072347,0.00019457193,0.00006544545],"category_scores_gemma":[0.000030402702,0.00052371435,0.00009459164,0.0010679588,0.0004971206,0.0006218108,0.0032748955,0.0010701494,0.000030184892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031292973,0.000019749326,0.00002137956,0.0000165194,0.000010515971,0.000089553236,0.00045287996,0.1981685,0.000104968974,0.14918803,0.00018263486,0.65174216],"study_design_scores_gemma":[0.00021663505,0.00015511001,0.00009126415,0.00009005241,0.000009825043,0.00019786193,1.07942206e-7,0.5122147,0.00018518021,0.41855568,0.06743776,0.0008458237],"about_ca_topic_score_codex":0.000009079508,"about_ca_topic_score_gemma":0.000069747686,"teacher_disagreement_score":0.6508963,"about_ca_system_score_codex":0.00025966731,"about_ca_system_score_gemma":0.0002222375,"threshold_uncertainty_score":0.99972147},"labels":[],"label_agreement":null},{"id":"W4289518813","doi":"10.1007/s11276-022-03076-9","title":"Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things","year":2022,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Høgskulen på Vestlandet","keywords":"Computer science; Frame (networking); Dependability; Real-time computing; Variety (cybernetics); Frame rate; Object (grammar); The Internet; Artificial intelligence; Simulation; Telecommunications; World Wide Web","score_opus":0.014914005538164132,"score_gpt":0.24472764074958112,"score_spread":0.22981363521141698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289518813","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":[0.33247182,0.00011881107,0.666852,0.00012915596,0.000067480956,0.0002586319,7.398884e-7,0.00009223747,0.000009140985],"genre_scores_gemma":[0.9708945,0.00003334276,0.02869231,0.000116820054,0.00001496054,0.00018300394,0.0000054127077,0.000019238068,0.000040402883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986802,0.00010996872,0.0002927445,0.0004831856,0.00018083185,0.00025312058],"domain_scores_gemma":[0.9991491,0.00012194094,0.00024363077,0.00039213113,0.00004124202,0.000051967716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002029167,0.00014322817,0.00018888945,0.00012695033,0.0001597931,0.000041356932,0.00046408715,0.00005764139,0.0000027106587],"category_scores_gemma":[0.0000072168714,0.0001464774,0.000026837784,0.0008671418,0.00007717704,0.0003788982,0.00032700514,0.0004821396,0.0000011249786],"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":[0.00014970188,0.0005503515,0.0149640795,0.00004977562,0.000046487523,0.000020146668,0.0034887702,0.64438206,0.06611843,0.0076072407,0.000020214618,0.26260272],"study_design_scores_gemma":[0.00066059065,0.00007677798,0.05287529,0.000031227664,0.0000037136197,0.000015590964,0.00010257446,0.9456633,0.00025528116,0.000104122155,0.00006045318,0.00015110517],"about_ca_topic_score_codex":0.00008412269,"about_ca_topic_score_gemma":0.00030459755,"teacher_disagreement_score":0.6384227,"about_ca_system_score_codex":0.00015012667,"about_ca_system_score_gemma":0.000018263438,"threshold_uncertainty_score":0.59731746},"labels":[],"label_agreement":null},{"id":"W4289643891","doi":"10.1109/tcbb.2022.3195705","title":"RLSegNet: An Medical Image Segmentation Network Based on Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Reinforcement learning; Segmentation; Pattern recognition (psychology); Feature (linguistics); Image segmentation; Feature extraction; Scale-space segmentation; Process (computing); Computer vision","score_opus":0.015649314280470197,"score_gpt":0.2855532805708636,"score_spread":0.2699039662903934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289643891","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":[0.0017149982,0.000009979188,0.99516696,0.0020202894,0.00032737385,0.0002986916,0.000016104199,0.0001891206,0.00025650064],"genre_scores_gemma":[0.6003343,0.000023654256,0.3942077,0.0048288973,0.00007101112,0.0001813135,0.00029001647,0.000011070929,0.000052076906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998439,0.00017085942,0.00042095323,0.0002776025,0.00042039147,0.00027117724],"domain_scores_gemma":[0.9986884,0.00061926397,0.00017246531,0.0003086506,0.00006370438,0.0001474966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003799791,0.00017586476,0.00015022422,0.00016306236,0.0012971774,0.00005431425,0.0005129049,0.00007598098,0.00014337237],"category_scores_gemma":[0.000016607624,0.00016977228,0.000053881467,0.0004626623,0.00011053386,0.00033235972,0.000032494863,0.0005125069,0.000027550075],"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":[0.000032606116,0.00008273892,0.00002752149,0.0000063754333,0.000012863911,0.0000015885801,0.00015275118,0.91991544,0.000018015124,0.0040480737,0.00012172242,0.07558029],"study_design_scores_gemma":[0.0005800336,0.00089299225,0.00012785032,0.000008953644,0.000007914958,0.000028036675,0.00006593645,0.9900758,0.00007504311,0.00663457,0.001317616,0.00018525099],"about_ca_topic_score_codex":0.0000030138988,"about_ca_topic_score_gemma":0.0000026162556,"teacher_disagreement_score":0.60095924,"about_ca_system_score_codex":0.00008138484,"about_ca_system_score_gemma":0.00011351624,"threshold_uncertainty_score":0.99769706},"labels":[],"label_agreement":null},{"id":"W4289753134","doi":"10.48550/arxiv.1807.06072","title":"Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth\\n Generation","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Minimum bounding box; Annotation; Artificial intelligence; Task (project management); Object (grammar); Bounding overwatch; Segmentation; Object detection; Ground truth; Scheme (mathematics); Orientation (vector space); Point cloud; Generalization; Computer vision; Point (geometry); Machine learning; Pattern recognition (psychology); Image (mathematics)","score_opus":0.10873771142639366,"score_gpt":0.21845105413690108,"score_spread":0.10971334271050742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289753134","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":[0.108132,0.00002123578,0.8893944,0.000044366145,0.0007995215,0.00084353297,0.000013701542,0.0004086483,0.00034257813],"genre_scores_gemma":[0.96542627,0.00004975837,0.033201057,0.00007255811,0.00055232906,0.000025443489,0.000050748742,0.000031398566,0.00059046474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977268,0.00008975846,0.00024516814,0.0014447988,0.000098150864,0.0003953543],"domain_scores_gemma":[0.9979603,0.00015091395,0.0003236565,0.001182983,0.0002575953,0.00012452374],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019189103,0.0003370056,0.0002679898,0.00022415849,0.00048820564,0.00018035204,0.0011837913,0.00025071207,0.000004445695],"category_scores_gemma":[0.000022930175,0.00041546387,0.00018785166,0.0005046066,0.00008719618,0.00088919385,0.00079980894,0.00033007236,0.000015529395],"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":[0.00002504811,0.000036406855,0.0000083144905,0.0000284864,0.000038367507,0.0000050186586,0.0002319933,0.9609854,0.00081358425,0.031563368,0.000067400666,0.006196591],"study_design_scores_gemma":[0.0003388105,0.000059254202,0.00007875115,0.000022383976,0.000042949203,0.0000043262676,0.000013435154,0.8674693,0.0011931325,0.13023104,0.00018362266,0.00036298603],"about_ca_topic_score_codex":0.00003931464,"about_ca_topic_score_gemma":0.00006278232,"teacher_disagreement_score":0.85729426,"about_ca_system_score_codex":0.00036649042,"about_ca_system_score_gemma":0.00011005067,"threshold_uncertainty_score":0.9998297},"labels":[],"label_agreement":null},{"id":"W4289828123","doi":"10.1109/ipdpsw55747.2022.00026","title":"Optimization of Compiler-Generated OpenCL CNN Kernels and Runtime for FPGAs","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Compiler; Stratix; Field-programmable gate array; Parallel computing; CAS latency; Latency (audio); Computer architecture; Embedded system; Computer hardware; Operating system","score_opus":0.02072030816206441,"score_gpt":0.2766303517242872,"score_spread":0.2559100435622228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289828123","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":[0.0075632064,0.00067404,0.9853604,0.0047945194,0.00037801213,0.0005194952,0.00033670387,0.00012208577,0.0002515444],"genre_scores_gemma":[0.922321,0.0002148767,0.074539736,0.00040272973,0.00013141286,0.0006086807,0.0010986887,0.00002810126,0.00065475056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981104,0.00006774874,0.0005017863,0.00063455617,0.00040502567,0.00028049288],"domain_scores_gemma":[0.99876016,0.00018149428,0.00039083094,0.0002611331,0.00029562326,0.00011073292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026240697,0.00022634288,0.00026782413,0.0001245803,0.00064695923,0.000230735,0.00079441397,0.000062702056,0.000042528998],"category_scores_gemma":[0.000030928975,0.00024101838,0.000060061513,0.0006257268,0.000114434115,0.0005591012,0.0004883078,0.00022154475,0.0000010792372],"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":[0.000101331636,0.00014518853,0.0003157798,0.000037082016,0.000046839985,0.0000030632586,0.00020310379,0.98086333,0.0034734572,0.0076581324,0.0025984677,0.004554222],"study_design_scores_gemma":[0.0009609061,0.00007541527,0.00025171868,0.000038503433,0.000024502839,0.000042866002,0.00007725584,0.98990566,0.00056679855,0.003941919,0.0038289984,0.00028544542],"about_ca_topic_score_codex":0.00000803719,"about_ca_topic_score_gemma":0.000002112867,"teacher_disagreement_score":0.9147578,"about_ca_system_score_codex":0.000087538145,"about_ca_system_score_gemma":0.000095823205,"threshold_uncertainty_score":0.9828444},"labels":[],"label_agreement":null},{"id":"W4289861074","doi":"10.3390/s22155817","title":"SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":185,"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; Pooling; Feature (linguistics); Object detection; Artificial intelligence; FLOPS; Pyramid (geometry); Mode (computer interface); Algorithm; Pattern recognition (psychology); Focus (optics); Computer vision; Mathematics","score_opus":0.010425799147298265,"score_gpt":0.22884592122204844,"score_spread":0.21842012207475017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289861074","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":[0.07015635,0.00005803505,0.9220285,0.0039457697,0.0009248835,0.00090662256,0.000033972927,0.0009106261,0.0010352721],"genre_scores_gemma":[0.8038405,0.000012472471,0.19144017,0.001893232,0.00027819397,0.00032606005,0.000030940195,0.00005098753,0.0021274623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835753,0.00015423982,0.00016653257,0.0006595758,0.00030918917,0.00035295732],"domain_scores_gemma":[0.99874794,0.00014592259,0.00012858135,0.0008206858,0.000052661435,0.00010421573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012338608,0.00020327615,0.00015509833,0.00016609956,0.0006655478,0.00005189356,0.0006017025,0.00006817956,0.00001668119],"category_scores_gemma":[0.000018079461,0.00019567282,0.00010392497,0.0009481262,0.000024264808,0.00009486763,0.0002662203,0.0005032215,0.000030512092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009343482,0.00027864173,0.000016056454,0.000010782311,0.00001755336,0.00005789856,0.0004475447,0.36339596,0.06788793,0.0012839009,0.001151312,0.565359],"study_design_scores_gemma":[0.00032885643,0.00024882364,0.00012285226,0.0000041627895,0.0000057191414,0.00002401396,0.0000174071,0.9540146,0.023972265,0.0009276185,0.020109244,0.00022445798],"about_ca_topic_score_codex":0.000024796855,"about_ca_topic_score_gemma":0.000029684983,"teacher_disagreement_score":0.7336841,"about_ca_system_score_codex":0.0001904067,"about_ca_system_score_gemma":0.00004298999,"threshold_uncertainty_score":0.7979306},"labels":[],"label_agreement":null},{"id":"W4291141759","doi":"10.1145/3552513","title":"SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Networks","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Embedded Computing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsoft (Canada)","funders":"Agencia Estatal de Investigación; Institució Catalana de Recerca i Estudis Avançats","keywords":"Computer science; Recurrent neural network; Application-specific integrated circuit; Computation; Field-programmable gate array; Scheduling (production processes); Efficient energy use; Hardware acceleration; Parallel computing; Computer engineering; Artificial intelligence; Embedded system; Artificial neural network; Algorithm","score_opus":0.03637421211420172,"score_gpt":0.2820211464391624,"score_spread":0.24564693432496065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291141759","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":[0.0051622973,0.000303573,0.9887831,0.0003556943,0.0034942345,0.00094936957,0.00004515822,0.0008487529,0.000057798574],"genre_scores_gemma":[0.9694047,0.000006043271,0.028574212,0.00041601763,0.0003365581,0.0010364394,0.000038155078,0.000055768727,0.00013212168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664193,0.00033452353,0.00065314263,0.0011020568,0.00052830466,0.00074003625],"domain_scores_gemma":[0.9968258,0.0005655886,0.0003138733,0.001890956,0.00015694113,0.00024684102],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00045439103,0.00035509584,0.00036827524,0.00022578392,0.00216111,0.00022528548,0.0024771693,0.000074239324,0.000012777559],"category_scores_gemma":[0.000012077372,0.0003870628,0.0001962974,0.0012768091,0.00003569378,0.00027559162,0.00012787328,0.000518989,0.000003762022],"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":[0.000032027685,0.00028758353,0.0000017634515,0.000009552901,0.00002440773,0.0000027067013,0.00028124638,0.9192144,0.00005827752,0.010493896,0.00029897757,0.06929516],"study_design_scores_gemma":[0.00053813023,0.00051487196,0.000009759217,0.000018750152,0.00001829415,0.000067529894,0.0002443394,0.9913431,0.000080763384,0.00025067918,0.00651408,0.0003997159],"about_ca_topic_score_codex":0.0000315719,"about_ca_topic_score_gemma":0.00000715601,"teacher_disagreement_score":0.9642424,"about_ca_system_score_codex":0.00029796228,"about_ca_system_score_gemma":0.00006640633,"threshold_uncertainty_score":0.99985814},"labels":[],"label_agreement":null},{"id":"W4291470723","doi":"10.1007/978-3-031-14391-5_4","title":"Active Federated YOLOR Model for Enhancing Autonomous Vehicles Safety","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Gossip; Convolutional neural network; Artificial intelligence; Object detection; Object (grammar); Representation (politics); Deep learning; Distributed computing; Machine learning; Real-time computing; Pattern recognition (psychology)","score_opus":0.022878768798895156,"score_gpt":0.26512032461424667,"score_spread":0.2422415558153515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291470723","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":[0.000033844637,0.00015590867,0.99488974,0.0014748261,0.00070194533,0.0011231991,0.000040130348,0.00033866483,0.0012417171],"genre_scores_gemma":[0.09795065,0.000056796696,0.89789003,0.0024859859,0.00035436728,0.00023172227,0.000034814453,0.00007735851,0.00091825734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99598795,0.000028671018,0.00059496437,0.0019418487,0.0006622108,0.00078434905],"domain_scores_gemma":[0.99705845,0.0009737765,0.00040124138,0.0011484239,0.0002504466,0.00016766675],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004931569,0.0005384051,0.0005364586,0.00044712867,0.0011610044,0.00033549298,0.003034721,0.00021435958,0.000017212378],"category_scores_gemma":[0.000071871815,0.00055023725,0.00016072036,0.0006703329,0.00037702036,0.00081627263,0.0017886952,0.00090202474,0.000011954288],"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":[0.000014886504,0.000016383809,4.994361e-7,0.000010738881,0.0000073270385,0.0000070344017,0.00048408244,0.5988409,0.00039579233,0.045044668,0.000013635262,0.35516408],"study_design_scores_gemma":[0.00020782616,0.00009256087,0.000005592402,0.000047138205,0.0000062025724,0.00002176947,1.8136228e-7,0.82698745,0.002115308,0.16712055,0.002901622,0.0004938119],"about_ca_topic_score_codex":0.000008604867,"about_ca_topic_score_gemma":0.00016801796,"teacher_disagreement_score":0.35467026,"about_ca_system_score_codex":0.0008965742,"about_ca_system_score_gemma":0.00085805013,"threshold_uncertainty_score":0.9996949},"labels":[],"label_agreement":null},{"id":"W4291963738","doi":"10.1007/s13369-022-07169-7","title":"Fully Convolutional Neural Network for Improved Brain Segmentation","year":2022,"lang":"en","type":"article","venue":"Arabian Journal for Science and Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Pixel; Cluster analysis; Neuroimaging; Scale-space segmentation; Image segmentation; Psychology; Neuroscience","score_opus":0.01489790604833234,"score_gpt":0.25654020742462497,"score_spread":0.2416423013762926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291963738","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":[0.021281581,0.00014132961,0.9718236,0.0054125977,0.00093616947,0.00034075405,0.000007750393,0.000050038285,0.0000062025533],"genre_scores_gemma":[0.5711402,0.000012270853,0.42632344,0.0014750272,0.00051330513,0.0003792204,0.0000062962827,0.000016154458,0.00013406396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990084,0.000008037259,0.00016937336,0.00023709357,0.0001982763,0.00037881223],"domain_scores_gemma":[0.99938655,0.000163741,0.00007885441,0.00011841973,0.000119065466,0.00013337539],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008327851,0.00008078368,0.00007863351,0.000093413575,0.0015443498,0.00018037853,0.00051482016,0.000009711599,0.0000017627704],"category_scores_gemma":[0.000083567036,0.000083411534,0.000040848023,0.00058158144,0.000049523383,0.0007785952,0.00013111181,0.00013635657,2.1983018e-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":[0.000013120234,0.000011171274,0.00004742112,0.0000071369946,0.0000066912235,9.259659e-7,0.00017226627,0.85884774,0.044755407,0.050209966,0.0033446923,0.04258349],"study_design_scores_gemma":[0.00030731186,0.00016059591,0.00046818593,0.000003037601,0.0000026734149,0.00017759958,0.000026167601,0.9754905,0.0002050484,0.005683654,0.017362459,0.00011274191],"about_ca_topic_score_codex":4.0082932e-7,"about_ca_topic_score_gemma":4.4558726e-7,"teacher_disagreement_score":0.5498586,"about_ca_system_score_codex":0.00011186013,"about_ca_system_score_gemma":0.00009432412,"threshold_uncertainty_score":0.9997555},"labels":[],"label_agreement":null},{"id":"W4292070219","doi":"10.1109/newcas52662.2022.9842211","title":"ASIP Accelerator for LUT-based Neural Networks Inference","year":2022,"lang":"en","type":"article","venue":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Very long instruction word; Lookup table; Throughput; Computer architecture; Field-programmable gate array; Inference; Instruction set; Flexibility (engineering); Artificial neural network; Set (abstract data type); Table (database); Parallel computing; Embedded system; Computer engineering; Artificial intelligence; Programming language; Operating system","score_opus":0.07651010844688673,"score_gpt":0.3209964015245712,"score_spread":0.2444862930776845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292070219","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":[0.012200215,0.00019918219,0.9723735,0.009654781,0.002771045,0.0014539529,0.00012541073,0.00061823014,0.000603694],"genre_scores_gemma":[0.9739521,0.000022266213,0.01495341,0.0063772467,0.0005701538,0.002723714,0.00017358629,0.00007789485,0.0011495845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99491876,0.00030997416,0.0008955449,0.0016668104,0.0009792947,0.0012296127],"domain_scores_gemma":[0.9957694,0.0011270532,0.0005755139,0.0016066914,0.0004611569,0.00046023162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004321006,0.000679385,0.0006220223,0.0003509195,0.0012161542,0.00032713835,0.004092854,0.00015264771,0.0005274079],"category_scores_gemma":[0.00010455785,0.0007313444,0.00041388714,0.0014123014,0.00024707924,0.00091181224,0.001115298,0.0011722846,0.000052261985],"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":[0.0006155607,0.00087791524,0.0011050494,0.0000649228,0.00017023394,0.00017661948,0.0007105585,0.40382257,0.002949563,0.4006801,0.08603659,0.10279032],"study_design_scores_gemma":[0.0011445269,0.00053872046,0.00026741414,0.000027976395,0.00002532606,0.000109026834,0.00010623371,0.9356334,0.0004762203,0.008824027,0.052025437,0.0008216506],"about_ca_topic_score_codex":0.000058173224,"about_ca_topic_score_gemma":0.00013278879,"teacher_disagreement_score":0.96175194,"about_ca_system_score_codex":0.00035397676,"about_ca_system_score_gemma":0.00075706793,"threshold_uncertainty_score":0.99951375},"labels":[],"label_agreement":null},{"id":"W4292603899","doi":"10.3390/rs14164107","title":"IoT Enabled Deep Learning Based Framework for Multiple Object Detection in Remote Sensing Images","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"King Saud University","keywords":"Computer science; Artificial intelligence; Deep learning; Segmentation; Object detection; Drone; Machine learning; Convolutional neural network; Real-time computing","score_opus":0.016716270860371803,"score_gpt":0.26597707829392386,"score_spread":0.24926080743355206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292603899","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":[0.033012882,0.00011611565,0.96487105,0.00054943294,0.00034751027,0.00058763375,9.0819947e-7,0.00044507635,0.000069399735],"genre_scores_gemma":[0.49860674,0.000003739451,0.5010239,0.00023589874,0.00007281034,1.0038327e-7,0.0000033016383,0.000028816208,0.00002473832],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766576,0.00033027472,0.00036717096,0.0007401799,0.0003059688,0.0005906256],"domain_scores_gemma":[0.99745417,0.0015217591,0.00024690386,0.0006087872,0.0000978243,0.00007053579],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005583947,0.00022775623,0.00026732826,0.0002760644,0.0010755892,0.00011639211,0.0002882402,0.0000849408,0.0000018771594],"category_scores_gemma":[0.0006875293,0.0002786528,0.000119416305,0.0013773661,0.000035386976,0.00015120125,0.00027643723,0.0008155779,0.000005113303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002324555,0.0000044281583,0.0000017493464,0.00000938225,0.0000035360251,0.000019709998,0.00016827093,0.31175485,0.035498295,0.000023437002,0.000002469195,0.6524906],"study_design_scores_gemma":[0.00044205418,0.000060884006,0.00004926354,0.00005746827,0.000007280275,0.00007587837,0.00010021098,0.94766754,0.037758287,0.011412899,0.0020839672,0.0002842882],"about_ca_topic_score_codex":0.00013784903,"about_ca_topic_score_gemma":0.00009213712,"teacher_disagreement_score":0.6522063,"about_ca_system_score_codex":0.00038511606,"about_ca_system_score_gemma":0.000052503492,"threshold_uncertainty_score":0.99996656},"labels":[],"label_agreement":null},{"id":"W4292729083","doi":"10.1186/s12859-022-04882-w","title":"Learning to detect boundary information for brain image segmentation","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Segmentation; Computer science; Boundary (topology); Artificial intelligence; Image segmentation; Pattern recognition (psychology); Sørensen–Dice coefficient; Computer vision; Context (archaeology); Scale-space segmentation; Mathematics; Biology","score_opus":0.015227836235929859,"score_gpt":0.2715470974248548,"score_spread":0.2563192611889249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292729083","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":[0.0014555304,0.0000052702762,0.9957124,0.0008840747,0.00016251346,0.00093908876,0.000020422458,0.00027204776,0.0005486582],"genre_scores_gemma":[0.0033512039,0.0000016786571,0.9932149,0.0024480713,0.000029061548,0.0006944419,0.00011093905,0.00000788162,0.00014179891],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989393,0.00003507764,0.00039220235,0.00010537656,0.00028070377,0.00024737348],"domain_scores_gemma":[0.9990944,0.00022671619,0.00021753785,0.00030712222,0.00007606555,0.000078114186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003692134,0.00010719987,0.00009379906,0.00016432621,0.00079630857,0.0002358507,0.00053592294,0.000020365505,0.000011180926],"category_scores_gemma":[0.00010757584,0.000117686235,0.000052900472,0.0006213326,0.000016334872,0.0021349979,0.0004136829,0.00014858274,0.00010398563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000471825,0.000023838327,0.000082218925,0.00016727697,0.000012642083,3.2102113e-7,0.011852304,0.24215129,0.0012790029,0.0133314505,0.03156627,0.6994862],"study_design_scores_gemma":[0.0003156307,0.00022022166,0.000099342105,0.0000029803857,0.0000028566371,0.000009358081,0.00080698065,0.8563496,0.00060407765,0.0020170915,0.1394102,0.00016165593],"about_ca_topic_score_codex":0.0000014021757,"about_ca_topic_score_gemma":0.0000023513214,"teacher_disagreement_score":0.69932455,"about_ca_system_score_codex":0.00014751905,"about_ca_system_score_gemma":0.000085749874,"threshold_uncertainty_score":0.61246425},"labels":[],"label_agreement":null},{"id":"W4292829073","doi":"10.1109/cvprw56347.2022.00410","title":"MAPLE-Edge: A Runtime Latency Predictor for Edge Devices","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Latency (audio); Edge device; Enhanced Data Rates for GSM Evolution; Maple; Artificial neural network; Artificial intelligence; Computer architecture; Parallel computing; Operating system; Cloud computing","score_opus":0.04519587777785523,"score_gpt":0.28865977287192046,"score_spread":0.24346389509406524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292829073","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":[0.049812775,0.00014952215,0.9409229,0.003928106,0.002477384,0.0014613791,0.00028125674,0.00051504106,0.00045161537],"genre_scores_gemma":[0.96962965,0.00023324661,0.020797681,0.0059609185,0.00070137455,0.0013383557,0.00045438227,0.00006661555,0.00081780815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965949,0.0002761402,0.0006191457,0.0013169243,0.0005870619,0.0006058114],"domain_scores_gemma":[0.9977556,0.00057429785,0.00036189504,0.0007653479,0.00024943333,0.00029345002],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039805644,0.00046193463,0.00044082868,0.00029049892,0.0010272723,0.0004383515,0.0011619505,0.00012438747,0.00074777723],"category_scores_gemma":[0.000012991479,0.00045841018,0.00017565109,0.00073638815,0.00007816821,0.00055750564,0.00074426364,0.0006345771,0.00015594467],"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":[0.00008519834,0.00033723,0.0002939921,0.000057088604,0.000043229564,0.000021144086,0.0005388898,0.00041177997,0.00034099867,0.0012213961,0.022895265,0.9737538],"study_design_scores_gemma":[0.0025822318,0.0017431988,0.0047988887,0.0003365964,0.000055941375,0.00013196676,0.00011451875,0.92623913,0.00038155733,0.016944617,0.045327306,0.0013440212],"about_ca_topic_score_codex":0.0000069339644,"about_ca_topic_score_gemma":0.000018713203,"teacher_disagreement_score":0.9724098,"about_ca_system_score_codex":0.00008194643,"about_ca_system_score_gemma":0.00007463253,"threshold_uncertainty_score":0.99978673},"labels":[],"label_agreement":null},{"id":"W4293069434","doi":"10.5194/isprs-archives-xliii-b3-2022-41-2022","title":"IMPROVING CNN-BASED BUILDING SEMANTIC SEGMENTATION USING OBJECT BOUNDARIES","year":2022,"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 Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Computer science; Segmentation; Leverage (statistics); Artificial intelligence; Convolutional neural network; Encoder; Deep learning; Ground truth; Pattern recognition (psychology); Machine learning; Data mining","score_opus":0.01861091933652285,"score_gpt":0.26750864037636024,"score_spread":0.2488977210398374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293069434","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":[0.012654229,0.00003397497,0.9787302,0.00401626,0.0022009423,0.00080947403,0.00007259365,0.00008347668,0.0013988247],"genre_scores_gemma":[0.9702949,0.00003187659,0.027963959,0.0015193546,0.00010881327,0.0000011875258,0.00002896866,0.000012923303,0.000038032904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947755,0.00043533478,0.0014288693,0.000529723,0.0022471463,0.0005834786],"domain_scores_gemma":[0.99558514,0.0011881988,0.002161301,0.0006510895,0.00027293983,0.00014134588],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.0015845178,0.00042351344,0.0003956545,0.0010256603,0.004085913,0.0014799272,0.0027450502,0.000059605132,0.0000069136936],"category_scores_gemma":[0.0005042764,0.00029900784,0.00035440834,0.0016532192,0.0027515055,0.0008454518,0.002062681,0.0005516475,0.0000014060664],"study_design_candidate":"simulation_or_modeling","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":[0.00007428731,0.000019439576,0.0001368173,0.00003133768,0.00004972729,3.678311e-7,0.002409578,0.047718786,0.00866708,0.000039669874,0.000020661502,0.94083226],"study_design_scores_gemma":[0.0006577445,0.00013440406,0.0006886018,0.00014578526,0.000038006998,0.00015656365,0.001351491,0.973315,0.011954327,0.008715827,0.0025112124,0.0003310388],"about_ca_topic_score_codex":0.6720306,"about_ca_topic_score_gemma":0.07080215,"teacher_disagreement_score":0.95764065,"about_ca_system_score_codex":0.00013320059,"about_ca_system_score_gemma":0.00053953,"threshold_uncertainty_score":0.99996245},"labels":[],"label_agreement":null},{"id":"W4293095155","doi":"10.1109/vtc2022-spring54318.2022.9860676","title":"Object Detection for Connected and Autonomous Vehicles using CNN with Attention Mechanism","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Convolutional neural network; False positive paradox; Computer vision; Deep learning; Inference; Pattern recognition (psychology)","score_opus":0.01769779640582466,"score_gpt":0.2412152179047322,"score_spread":0.22351742149890752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293095155","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":[0.48567685,0.0001607636,0.5117969,0.0005588045,0.00025772673,0.00080486306,0.0000104626215,0.0007214679,0.000012186867],"genre_scores_gemma":[0.9546324,0.000032315016,0.043644633,0.00016044926,0.000048375332,0.0013582052,0.0000072496796,0.00005106156,0.000065326465],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971667,0.00011227213,0.00042900158,0.0012321298,0.0003990962,0.0006608154],"domain_scores_gemma":[0.9981802,0.00010827608,0.00038650498,0.0009886824,0.00023037555,0.0001059354],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00037112704,0.00038223807,0.0004235045,0.00058952236,0.0015010516,0.00012960478,0.0010245002,0.0002284888,0.00001666453],"category_scores_gemma":[0.000040308776,0.00040932506,0.0000971166,0.0016080512,0.00022105605,0.00035250332,0.0007544544,0.0008057683,0.0000063699063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000695955,0.00015869072,0.00090015563,0.000048542337,0.0001559741,0.0000621719,0.00014182512,0.0064579365,0.6698599,0.2919558,0.000016254384,0.030173171],"study_design_scores_gemma":[0.0019919858,0.001119096,0.0019275268,0.000040801526,0.0001367686,0.0004640526,0.0003988927,0.8425435,0.081222676,0.06600909,0.0031385864,0.0010070278],"about_ca_topic_score_codex":0.00007835775,"about_ca_topic_score_gemma":0.00011602055,"teacher_disagreement_score":0.83608556,"about_ca_system_score_codex":0.00029963444,"about_ca_system_score_gemma":0.00018325404,"threshold_uncertainty_score":0.99983585},"labels":[],"label_agreement":null},{"id":"W4293811845","doi":"10.1109/tgrs.2022.3203163","title":"CasA: A Cascade Attention Network for 3-D Object Detection From LiDAR Point Clouds","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":145,"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 Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Cascade; Point cloud; Computer science; Lidar; Object detection; Artificial intelligence; Detector; Convolutional neural network; Object (grammar); Computer vision; Pattern recognition (psychology); Remote sensing; Engineering","score_opus":0.016048021238888835,"score_gpt":0.2467887987605746,"score_spread":0.23074077752168576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293811845","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":[0.0875726,0.000050949366,0.9097344,0.0007090371,0.001289827,0.00041547243,0.000017400096,0.0001891504,0.000021142867],"genre_scores_gemma":[0.7594684,0.00003861811,0.23978093,0.00043032077,0.00012441677,0.0000051924535,0.000002167936,0.000013630849,0.00013632658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982789,0.00009734237,0.0002412407,0.0006896687,0.00028985352,0.00040297466],"domain_scores_gemma":[0.99906665,0.00023680522,0.00011879662,0.00041977628,0.000053459193,0.0001045168],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00029837128,0.00016669417,0.00015669265,0.000118915996,0.002532891,0.000116162024,0.00026435716,0.00005043313,0.0000026777575],"category_scores_gemma":[0.0000054514135,0.00017256202,0.00010661638,0.0009813083,0.00009383784,0.0003482585,0.000014239608,0.00032687504,0.000004113373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027291951,0.00002213436,3.7044032e-7,0.00000316741,0.000008822659,0.0000068149875,0.0002482093,0.036647424,0.022024922,0.00004883849,0.000022802544,0.9409392],"study_design_scores_gemma":[0.00030018485,0.00019324085,0.0002063988,0.000020071313,0.000022299408,0.00020955832,0.00011454727,0.9824598,0.008683927,0.0060284836,0.0015280072,0.00023352547],"about_ca_topic_score_codex":0.00045879636,"about_ca_topic_score_gemma":0.00023598236,"teacher_disagreement_score":0.94581234,"about_ca_system_score_codex":0.00013295228,"about_ca_system_score_gemma":0.000049359238,"threshold_uncertainty_score":0.99876565},"labels":[],"label_agreement":null},{"id":"W4293868171","doi":"10.1109/crv55824.2022.00034","title":"TemporalNet: Real-time 2D-3D Video Object Detection","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Object detection; Frame (networking); Block (permutation group theory); Convolution (computer science); Computer vision; Feature (linguistics); Network architecture; Frame rate; Feature extraction; Pattern recognition (psychology); Artificial neural network","score_opus":0.012672929556298415,"score_gpt":0.24645273220282607,"score_spread":0.23377980264652765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293868171","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":[0.015882691,0.000036326866,0.94810975,0.0015301191,0.00030577448,0.0004715689,0.0000046145046,0.0014393966,0.032219756],"genre_scores_gemma":[0.9061803,0.000016227574,0.08628884,0.00074795145,0.00010968478,0.00037152003,0.000008874751,0.000020460955,0.0062561464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988704,0.00007569383,0.00017268427,0.00040718034,0.00025073945,0.00022332564],"domain_scores_gemma":[0.9990855,0.00009518238,0.00008034872,0.00064742623,0.00002651701,0.00006503981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015613623,0.00009969612,0.00009577381,0.000077610246,0.0004918754,0.000044891,0.00066702,0.000018756271,0.00026996838],"category_scores_gemma":[0.00000828225,0.00010103432,0.000044503715,0.00087247713,0.00001930745,0.0003123703,0.0005214428,0.0001518426,0.00025533824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039687962,0.00031549437,0.00046253848,0.000010863598,0.000040277744,0.00004471564,0.0006624794,0.05349716,0.27636307,0.06848386,0.04020195,0.5598779],"study_design_scores_gemma":[0.00051995384,0.0004197755,0.0016323797,0.0000032282232,0.00001007645,0.00017184469,0.00005155572,0.66623694,0.029691156,0.037921768,0.2626399,0.0007013934],"about_ca_topic_score_codex":0.00006904978,"about_ca_topic_score_gemma":0.000016557535,"teacher_disagreement_score":0.8902976,"about_ca_system_score_codex":0.00010265735,"about_ca_system_score_gemma":0.000029649354,"threshold_uncertainty_score":0.412006},"labels":[],"label_agreement":null},{"id":"W4293868179","doi":"10.1109/crv55824.2022.00033","title":"Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Artificial intelligence; Computer science; Computer vision; Robot; Monocular; Mobile robot navigation; Segmentation; Mobile robot; Image segmentation; Frame rate; Robot control","score_opus":0.008782726224248595,"score_gpt":0.23739123713422183,"score_spread":0.22860851090997325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293868179","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":[0.013864301,0.000025776837,0.98036313,0.0021579093,0.000046190034,0.0005026581,0.0000016183737,0.00055314705,0.0024852448],"genre_scores_gemma":[0.6399008,0.0000052037362,0.35920805,0.00038500933,0.000014288536,0.00022750325,0.000018279197,0.000012068894,0.00022877112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988522,0.000050010705,0.00015022182,0.00038133978,0.00035180003,0.00021446112],"domain_scores_gemma":[0.99942946,0.000038953003,0.00004372776,0.00037563016,0.000035867935,0.000076382654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000111857946,0.000113531096,0.000093237606,0.000051023868,0.0004134819,0.00004596595,0.00052679615,0.000018130591,0.000056409295],"category_scores_gemma":[0.0000010228277,0.00009741933,0.00003980074,0.0008545047,0.000018662462,0.00052603247,0.00010351795,0.00015975471,0.000011274667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008963067,0.0005861923,0.00030141548,0.000036559373,0.000080490645,0.000041613035,0.003634911,0.27005485,0.03297396,0.10751095,0.0012161449,0.58347327],"study_design_scores_gemma":[0.00059758127,0.0003645072,0.00026724255,0.0000042526553,0.000007991943,0.00004072798,0.00007880547,0.98567414,0.0026716813,0.002099124,0.007970234,0.00022369865],"about_ca_topic_score_codex":0.000008309831,"about_ca_topic_score_gemma":0.000002751514,"teacher_disagreement_score":0.7156193,"about_ca_system_score_codex":0.0000814356,"about_ca_system_score_gemma":0.000038270788,"threshold_uncertainty_score":0.3972645},"labels":[],"label_agreement":null},{"id":"W4294168482","doi":"10.1007/s10489-022-04062-6","title":"A low-complexity residual deep neural network for image edge detection","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Residual; Convolutional neural network; Computational complexity theory; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Edge detection; Artificial neural network; Pattern recognition (psychology); Image (mathematics); Network architecture; Deep learning; Image processing; Machine learning; Algorithm","score_opus":0.03492349588547442,"score_gpt":0.28111217096435565,"score_spread":0.24618867507888123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294168482","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":[0.0033103402,0.00008170086,0.99325013,0.00046856274,0.00046956717,0.0010565766,0.00001061762,0.0004274632,0.00092503347],"genre_scores_gemma":[0.81001455,0.000008245829,0.18668951,0.00088218064,0.00036437224,0.001929762,0.000019080238,0.000027980639,0.00006430325],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978406,0.00005909394,0.00038576082,0.0007850902,0.00031711828,0.000612326],"domain_scores_gemma":[0.9983995,0.00035692597,0.0001885413,0.00086672197,0.00007432684,0.0001139937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033226222,0.00022104298,0.00019864217,0.00007043567,0.0011680035,0.00009943498,0.0015732785,0.00004074782,0.000053940355],"category_scores_gemma":[0.000024603707,0.0002503168,0.000088641624,0.0010719692,0.00013776953,0.00025523585,0.0008220439,0.0003787029,0.0000622926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008596941,0.0001136605,0.000006615229,0.000018619363,0.000011738572,0.0000048670404,0.000328357,0.1744178,0.0105349785,0.4006915,0.0015875223,0.41219836],"study_design_scores_gemma":[0.00016733666,0.00020170104,0.00014671791,0.0000028172794,0.0000117290465,0.00004910124,0.00011551735,0.57937676,0.05887348,0.3503316,0.010208792,0.0005144277],"about_ca_topic_score_codex":0.000008653012,"about_ca_topic_score_gemma":0.0000358119,"teacher_disagreement_score":0.8067042,"about_ca_system_score_codex":0.00013557072,"about_ca_system_score_gemma":0.000029812587,"threshold_uncertainty_score":0.99999493},"labels":[],"label_agreement":null},{"id":"W4294234266","doi":"10.1049/ipr2.12562","title":"Attention transfer from human to neural networks for road object detection in winter","year":2022,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object detection; Object (grammar); Artificial intelligence; Artificial neural network; Transfer (computing); Computer vision; Pattern recognition (psychology)","score_opus":0.017876984929245106,"score_gpt":0.28620364804583415,"score_spread":0.26832666311658904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294234266","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":[0.19443843,0.00007478499,0.8042723,0.00048255574,0.00014328407,0.00041296642,0.0000040917485,0.00013980981,0.000031777538],"genre_scores_gemma":[0.9759013,6.270922e-7,0.02270488,0.0005078321,0.00013739595,0.0006669282,0.000016267446,0.00002046849,0.000044272434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987559,0.00005335985,0.00025443055,0.0004886079,0.00015590775,0.00029182655],"domain_scores_gemma":[0.9995733,0.0000367698,0.000048695554,0.00024078396,0.000048560454,0.000051902887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015248897,0.0001249708,0.00012502857,0.00010616956,0.0005079277,0.00017023251,0.0004619526,0.000025951924,0.000008303857],"category_scores_gemma":[0.000008003707,0.00014107655,0.000059616727,0.00066473434,0.000017695453,0.0006944591,0.00015236512,0.00023194909,0.0000021864882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004728195,0.00008430162,0.000252823,0.000014916615,0.0000046983896,0.000005751878,0.0008534004,0.0613076,0.17531945,0.000060588158,0.000097230004,0.761952],"study_design_scores_gemma":[0.00044982988,0.00010608122,0.0047594067,0.000017906379,0.0000066533157,0.000007978885,0.000073439216,0.9871444,0.00466634,0.002184543,0.00035854854,0.00022484433],"about_ca_topic_score_codex":0.000026195916,"about_ca_topic_score_gemma":0.000095887786,"teacher_disagreement_score":0.9258368,"about_ca_system_score_codex":0.00010702624,"about_ca_system_score_gemma":0.000012571938,"threshold_uncertainty_score":0.5752935},"labels":[],"label_agreement":null},{"id":"W4295408928","doi":"10.3390/su141811413","title":"Computer-Vision-Based Statue Detection with Gaussian Smoothing Filter and EfficientDet","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Smoothing; Object detection; Filter (signal processing); Object (grammar); Enhanced Data Rates for GSM Evolution; Gaussian; Pattern recognition (psychology)","score_opus":0.006304635929988091,"score_gpt":0.2454849375365495,"score_spread":0.23918030160656142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295408928","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":[0.21380821,0.00001836929,0.7833556,0.0020232326,0.00007001957,0.0004930824,0.00000469234,0.00019632789,0.000030476886],"genre_scores_gemma":[0.9745178,3.0236896e-7,0.024859942,0.0003753234,0.000024471965,0.00017168313,0.0000049444966,0.000010403274,0.00003513178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998403,0.00019215455,0.00017377677,0.0006161559,0.00029097634,0.00032392587],"domain_scores_gemma":[0.9986796,0.0002367726,0.00009160361,0.0007156219,0.00017213717,0.00010431219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003594202,0.00013745982,0.00012591672,0.000091670554,0.00080320425,0.0001032526,0.0003997272,0.000021953801,0.000012085963],"category_scores_gemma":[0.000034377143,0.00012460863,0.000030920906,0.0007331179,0.00012250501,0.00025363546,0.00047285485,0.00027947553,0.0000011418894],"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":[0.00019070094,0.0005080469,0.004919356,0.00015103532,0.0000135711825,0.00007668588,0.0013794736,0.5935888,0.00034116855,0.017610643,0.00033288432,0.3808876],"study_design_scores_gemma":[0.0006438404,0.00092196645,0.022743562,0.000005671024,0.000006513088,0.000035618985,0.0001922494,0.95004165,0.0005947921,0.014839534,0.0096444385,0.00033017943],"about_ca_topic_score_codex":0.00004302318,"about_ca_topic_score_gemma":0.000022696058,"teacher_disagreement_score":0.7607096,"about_ca_system_score_codex":0.000531045,"about_ca_system_score_gemma":0.00017771024,"threshold_uncertainty_score":0.61776793},"labels":[],"label_agreement":null},{"id":"W4295915865","doi":"10.1007/978-3-031-16452-1_26","title":"Leveraging Labeling Representations in Uncertainty-Based Semi-supervised Segmentation","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Representation (politics); Inference; Consistency (knowledge bases); Pattern recognition (psychology); Pixel; Image segmentation; Scale-space segmentation; Labeled data; Machine learning","score_opus":0.029018815216041022,"score_gpt":0.2846402789262108,"score_spread":0.2556214637101698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295915865","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":[0.00034074482,0.00021072368,0.9945505,0.002171295,0.00067661674,0.00075217074,0.000006830943,0.00021322619,0.0010778585],"genre_scores_gemma":[0.13222921,0.000061812934,0.86331344,0.003648112,0.00023632363,0.00019243946,0.00007535097,0.00005907152,0.00018422487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995902,0.00007993765,0.0006556487,0.0017466253,0.0009909957,0.00062478567],"domain_scores_gemma":[0.99707747,0.0009315762,0.0003036104,0.0014162967,0.00014468943,0.00012635058],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006646795,0.00043164205,0.00038998845,0.0010754333,0.00049987994,0.0003008654,0.0025685572,0.00013284452,0.000068589594],"category_scores_gemma":[0.00006924638,0.00047853577,0.00010104004,0.0018212,0.00027670767,0.0007542886,0.0010528045,0.0010155538,0.000016252658],"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":[0.0000032122623,0.00002120842,0.00011910142,0.00001222938,0.0000027019084,0.000032467262,0.0005501636,0.8251259,0.00049412856,0.0059629367,0.0000071828626,0.16766882],"study_design_scores_gemma":[0.00040989346,0.000045908964,0.000076635384,0.00012731501,0.0000046396976,0.000014738022,0.0000012059351,0.9334088,0.0007219911,0.06397567,0.0007375351,0.00047569687],"about_ca_topic_score_codex":0.00007133514,"about_ca_topic_score_gemma":0.00013063184,"teacher_disagreement_score":0.16719311,"about_ca_system_score_codex":0.0008903122,"about_ca_system_score_gemma":0.0005529279,"threshold_uncertainty_score":0.99976665},"labels":[],"label_agreement":null},{"id":"W4295990984","doi":"10.1080/07038992.2022.2118109","title":"Ship Detection in SAR Images via Cross-Attention Mechanism","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Synthetic aperture radar; Feature (linguistics); Backbone network; Object detection; Noise (video); Pattern recognition (psychology); Convolutional neural network; Benchmark (surveying); Weighting; Computer vision; Object (grammar); Image (mathematics); Geography; Telecommunications","score_opus":0.016253817069234722,"score_gpt":0.25079144566257644,"score_spread":0.2345376285933417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295990984","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":[0.15347804,0.00008488502,0.84487844,0.00083589385,0.0005613891,0.00006946663,9.112207e-7,0.000012563887,0.00007840315],"genre_scores_gemma":[0.86155146,0.0000033722456,0.13809183,0.00021854475,0.00008425964,3.2571737e-8,4.6340406e-7,0.0000092937,0.00004072868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990115,0.000099506025,0.00028424757,0.00016200986,0.00017760239,0.00026513718],"domain_scores_gemma":[0.9992055,0.00003753602,0.00022855376,0.00020811705,0.00010353173,0.00021675321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039684525,0.00007983717,0.00011290012,0.00039414127,0.000419318,0.00009828692,0.00031325265,0.000026392987,0.0000058267697],"category_scores_gemma":[0.00003612763,0.00009402889,0.00006124092,0.00064852415,0.000028953436,0.0003533196,0.000043909844,0.00040054196,0.00000344415],"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":[0.000003764929,0.0000025293793,0.000051520958,0.0000026116493,0.0000048966676,0.00040095433,0.00020050294,0.0054827514,0.024622066,0.0004242167,0.000039079157,0.9687651],"study_design_scores_gemma":[0.000854627,0.00024136207,0.0075852694,0.00007472611,0.00001532606,0.008432987,0.00020268842,0.78523195,0.035050105,0.14821246,0.013602329,0.00049615686],"about_ca_topic_score_codex":0.0015822691,"about_ca_topic_score_gemma":0.0076499693,"teacher_disagreement_score":0.96826893,"about_ca_system_score_codex":0.00049696944,"about_ca_system_score_gemma":0.00022279249,"threshold_uncertainty_score":0.42688626},"labels":[],"label_agreement":null},{"id":"W4296500117","doi":"10.1007/978-3-031-16210-7_9","title":"Reinforcement of BERT with Dependency-Parsing Based Attention Mask","year":2022,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Montréal","funders":"","keywords":"Padding; Computer science; Dependency grammar; Parsing; Reinforcement learning; Dependency (UML); Filter (signal processing); Artificial intelligence; Speech recognition; Computer vision; Computer security","score_opus":0.03140664009761266,"score_gpt":0.27540618129268707,"score_spread":0.24399954119507442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296500117","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":[0.00005412933,0.00011605083,0.8952379,0.0008788289,0.00009296972,0.0005164676,0.0000072523367,0.00006729742,0.10302909],"genre_scores_gemma":[0.24179953,0.0014588053,0.7539968,0.0011991055,0.000024241352,0.0001843798,0.00019008448,0.000020600677,0.001126458],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812675,0.000026738566,0.0006935287,0.00028817204,0.00066000805,0.00020482889],"domain_scores_gemma":[0.99651116,0.0001852686,0.0005957247,0.0023418616,0.00028997034,0.000075988864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006080728,0.000192745,0.00022325653,0.00080352696,0.00054497033,0.00016720618,0.00296398,0.000056745128,0.000020284455],"category_scores_gemma":[0.0000114576305,0.00019065027,0.000040933002,0.0007058393,0.0006148141,0.0050571724,0.00197701,0.0003747282,0.000008898706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048146944,0.000017554064,0.00006232512,0.000026351203,0.0000046812047,2.920703e-7,0.00037395753,0.022501683,0.0000109675175,0.8725582,0.000040614184,0.104398556],"study_design_scores_gemma":[0.00040806684,0.00014719553,0.0007353151,0.00019957218,0.00000949618,0.00002279561,0.000014963704,0.90833294,0.000033313452,0.0065170447,0.08324411,0.00033517054],"about_ca_topic_score_codex":0.000010012234,"about_ca_topic_score_gemma":0.000008165533,"teacher_disagreement_score":0.8858313,"about_ca_system_score_codex":0.0001993642,"about_ca_system_score_gemma":0.0003193997,"threshold_uncertainty_score":0.77744925},"labels":[],"label_agreement":null},{"id":"W4296960568","doi":"10.1007/978-3-031-16760-7_1","title":"Heatmap Regression for Lesion Detection Using Pointwise Annotations","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Segmentation; Computer science; Pointwise; Artificial intelligence; Probabilistic logic; Pattern recognition (psychology); Regression; Point (geometry); Lesion; Computer vision; Mathematics; Statistics; Medicine; Pathology","score_opus":0.04466713705296635,"score_gpt":0.30923647288546136,"score_spread":0.264569335832495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296960568","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":[0.00027135116,0.00027795066,0.9957626,0.00083857967,0.0014131056,0.0009456549,0.000009332521,0.00021122383,0.0002702199],"genre_scores_gemma":[0.10110365,0.00008117902,0.8970333,0.00086389406,0.00050890533,0.000118738346,0.000019273335,0.00005978069,0.00021131047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968877,0.00003594911,0.0004623664,0.0015041534,0.00058727385,0.0005225487],"domain_scores_gemma":[0.9974563,0.00058951875,0.0003713418,0.0012426525,0.00020641866,0.00013377873],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042232106,0.0004140711,0.0003515984,0.0007010567,0.0011131425,0.00022206786,0.0018638823,0.00017854197,0.000012427534],"category_scores_gemma":[0.000059064605,0.0003972469,0.00014872458,0.00092993275,0.00023058167,0.00071062625,0.0010952326,0.00060503196,0.0000056133986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010531131,0.000024331079,0.000004646266,0.000016749644,0.00000404639,0.000008273881,0.00020794173,0.28881198,0.0029451356,0.024628216,0.0000065545782,0.6833316],"study_design_scores_gemma":[0.00020728819,0.00016032062,0.000022285638,0.00012581932,0.000008483181,0.00006936627,1.3695426e-7,0.8124149,0.0028766738,0.17604607,0.0076492704,0.00041939298],"about_ca_topic_score_codex":0.00001122475,"about_ca_topic_score_gemma":0.00005289822,"teacher_disagreement_score":0.6829122,"about_ca_system_score_codex":0.00061471504,"about_ca_system_score_gemma":0.0003049311,"threshold_uncertainty_score":0.99984795},"labels":[],"label_agreement":null},{"id":"W4297097444","doi":"10.1109/hcs55958.2022.9895624","title":"HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Adaptation (eye); Computer science; Object detection; Training (meteorology); Object (grammar); Embedded system; Multimedia; Computer hardware; Artificial intelligence; Pattern recognition (psychology); Psychology","score_opus":0.04531056898658536,"score_gpt":0.26739948427845195,"score_spread":0.22208891529186658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297097444","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":[0.025901921,0.000022893224,0.9711585,0.00030696526,0.00013613176,0.0012420471,0.000018960023,0.00035342324,0.0008591377],"genre_scores_gemma":[0.9240362,0.0000035408818,0.0711519,0.00058713095,0.00007304258,0.0035382018,0.000037444846,0.000021656559,0.0005508993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855065,0.000060549155,0.0002378054,0.00056258304,0.00030724643,0.00028113608],"domain_scores_gemma":[0.9990752,0.00033303205,0.00016816445,0.00034126666,0.000015642394,0.00006674737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019721522,0.0001570473,0.00012961279,0.00014593634,0.00070005644,0.00005794486,0.0004364129,0.0000234521,0.0000583582],"category_scores_gemma":[0.000009877711,0.00016147623,0.00006890745,0.00056584156,0.000023791501,0.00035380074,0.00009876142,0.00015598512,0.000011710309],"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":[0.000052699,0.00014608547,0.000042792766,0.000012670756,0.0000081480985,0.0000013183138,0.00070913945,0.73309314,0.0034211138,0.0018391309,0.000097808406,0.26057592],"study_design_scores_gemma":[0.00045257344,0.000470524,0.00045550044,0.0000059095623,0.000009140586,0.000004271069,0.00061861833,0.9762639,0.01128075,0.0006782659,0.009534547,0.00022602982],"about_ca_topic_score_codex":0.00002065925,"about_ca_topic_score_gemma":0.00038392845,"teacher_disagreement_score":0.9000066,"about_ca_system_score_codex":0.00027542398,"about_ca_system_score_gemma":0.000058260244,"threshold_uncertainty_score":0.658481},"labels":[],"label_agreement":null},{"id":"W4297798708","doi":"10.1109/prml56267.2022.9882247","title":"Wasserstein-Based Feature Map Knowledge Transfer to Improve the Performance of Small Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Deep learning; Artificial neural network; Convolutional neural network; Benchmark (surveying); Discriminator; Convolution (computer science); Generalization; Pattern recognition (psychology); Transfer of learning; Metric (unit); Machine learning","score_opus":0.011341615997167312,"score_gpt":0.2268503730230231,"score_spread":0.21550875702585578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297798708","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":[0.0786384,0.0002657799,0.91198003,0.0073155444,0.00047086304,0.0007486515,0.0000035562607,0.0001634756,0.0004136878],"genre_scores_gemma":[0.98457056,0.0000025179045,0.012300369,0.0019437844,0.00008290758,0.00035232175,0.0000043533246,0.00001600865,0.00072719954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987599,0.00008737571,0.00020358466,0.00040627364,0.00019066868,0.00035218074],"domain_scores_gemma":[0.99872094,0.0002005958,0.000030596395,0.00088093925,0.00007249707,0.00009442449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018482369,0.00016243868,0.00015362797,0.00005712627,0.00042318014,0.000034847395,0.0016790272,0.000033797573,0.000029564684],"category_scores_gemma":[0.000003422178,0.000118233045,0.00009541489,0.0009760752,0.000043361713,0.000115424424,0.00036215317,0.00038579735,0.00000886432],"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":[0.000029774306,0.00007428324,0.00015537725,0.000013112745,0.000008212596,0.0000011383283,0.0002846596,0.9483122,0.0020194075,0.007720568,0.0010048039,0.040376455],"study_design_scores_gemma":[0.00021496363,0.00025535608,0.00038466178,0.0000022974189,0.000006693424,0.0000036196989,0.000030412652,0.9874458,0.0035252091,0.000044912016,0.007933685,0.00015243782],"about_ca_topic_score_codex":0.00000448039,"about_ca_topic_score_gemma":0.00004666086,"teacher_disagreement_score":0.9059321,"about_ca_system_score_codex":0.000054256805,"about_ca_system_score_gemma":0.000033763903,"threshold_uncertainty_score":0.48214036},"labels":[],"label_agreement":null},{"id":"W4298009715","doi":"10.18280/ts.390416","title":"A Semantic Segmentation Method for Road Environment Images Based on Hybrid Convolutional Auto-Encoder","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; North China University of Water Resources and Electric Power","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pooling; Segmentation; Pattern recognition (psychology); Deep learning; Encoder; Feature (linguistics); Convolution (computer science); Computer vision; Artificial neural network","score_opus":0.01935431643260377,"score_gpt":0.2723734306935158,"score_spread":0.253019114260912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298009715","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":[0.0012810337,0.000029198112,0.994055,0.0031972935,0.0001025346,0.0010279401,0.00009782089,0.00012446813,0.00008471465],"genre_scores_gemma":[0.5272142,0.0000025247796,0.46801153,0.0021306234,0.00008493578,0.0022301904,0.00013945696,0.000017299337,0.00016919493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981716,0.00015919609,0.00028552493,0.00053219806,0.00056090765,0.00029055815],"domain_scores_gemma":[0.9991711,0.00028108983,0.00014781005,0.00030052543,0.000023747125,0.000075728865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043052822,0.00016711325,0.00013764793,0.00009847539,0.0005388008,0.000047820045,0.00047225444,0.000012670145,0.0004576868],"category_scores_gemma":[0.000004916554,0.00017703544,0.00010051204,0.00015759388,0.00003198637,0.00018930688,0.00013888444,0.00012386797,0.000022939867],"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":[0.00006990536,0.00050290575,0.000057841444,0.00001477728,0.00003030205,0.0000069311845,0.00012953793,0.8696378,0.029633122,0.00962522,0.005734835,0.08455682],"study_design_scores_gemma":[0.0010299474,0.000348733,0.0008545982,0.0000040189693,0.000017804212,0.000008967427,0.00002224425,0.97747344,0.008453956,0.0035856008,0.008000602,0.00020007556],"about_ca_topic_score_codex":0.000004008122,"about_ca_topic_score_gemma":3.227041e-7,"teacher_disagreement_score":0.5260435,"about_ca_system_score_codex":0.00026376988,"about_ca_system_score_gemma":0.00005448658,"threshold_uncertainty_score":0.7219296},"labels":[],"label_agreement":null},{"id":"W4298018940","doi":"10.48550/arxiv.1806.07987","title":"A Hierarchical Deep Architecture and Mini-Batch Selection Method For\\n Joint Traffic Sign and Light Detection","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Benchmark (surveying); Traffic sign; Software deployment; Deep learning; Memory footprint; Artificial intelligence; Traffic sign recognition; Convolutional neural network; Host (biology); Graphics processing unit; Real-time computing; Sign (mathematics)","score_opus":0.039998841520913,"score_gpt":0.21256641400897155,"score_spread":0.17256757248805854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298018940","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":[0.10709791,0.00006526948,0.89127,0.00040984925,0.00015745376,0.00067765336,0.000004278718,0.00025251898,0.00006510794],"genre_scores_gemma":[0.7851427,0.00009001112,0.2143181,0.00008343835,0.00017369004,0.000014189649,0.0000049136925,0.000021780348,0.00015118353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783444,0.00018956892,0.00019327788,0.0013779477,0.000071101524,0.00033366712],"domain_scores_gemma":[0.9986507,0.00029102943,0.00020402383,0.0005237506,0.00013155915,0.00019889585],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023619442,0.00031089768,0.00030913154,0.0002693174,0.00036999315,0.00010773243,0.0004613579,0.0003111433,0.000002319421],"category_scores_gemma":[0.000039323422,0.0003404769,0.000110222165,0.0005706629,0.00012856099,0.00018755354,0.0006880563,0.00059915555,0.0000031932393],"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":[0.00030939272,0.00018817531,0.0001338417,0.0003271376,0.00023076565,0.000037841055,0.0016795307,0.539739,0.006673652,0.051546145,0.0002687553,0.39886576],"study_design_scores_gemma":[0.00037024365,0.00019697037,0.00035127773,0.000030521074,0.00006590544,0.000049297796,0.000015066973,0.8690652,0.0015204726,0.12689285,0.0010900254,0.00035211444],"about_ca_topic_score_codex":0.000010644828,"about_ca_topic_score_gemma":0.00014179888,"teacher_disagreement_score":0.6780448,"about_ca_system_score_codex":0.00010558299,"about_ca_system_score_gemma":0.000055919758,"threshold_uncertainty_score":0.99990475},"labels":[],"label_agreement":null},{"id":"W4298733999","doi":"10.48550/arxiv.1605.09332","title":"Parametric Exponential Linear Unit for Deep Convolutional Neural\\n Networks","year":2016,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"MNIST database; Parametric statistics; Convolutional neural network; Computer science; Artificial intelligence; Set (abstract data type); Deep learning; Pattern recognition (psychology); Flexibility (engineering); Artificial neural network; Deep neural networks; Realization (probability); Residual neural network; Exponential function; Mathematics","score_opus":0.09866047435014201,"score_gpt":0.22235068055208493,"score_spread":0.12369020620194292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298733999","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":[0.020792758,0.0003730241,0.9727555,0.00044859087,0.0025902637,0.0022058366,0.00011878484,0.0003900612,0.00032516423],"genre_scores_gemma":[0.98526865,0.000922846,0.009285991,0.00027664666,0.0014021798,0.000039090977,0.00013063314,0.00008973625,0.0025842297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934989,0.0003724359,0.0008464175,0.0034478258,0.0002505015,0.0015839235],"domain_scores_gemma":[0.99226284,0.002084761,0.0011718286,0.0027326683,0.0009904052,0.00075751985],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042042843,0.0010236689,0.0008936648,0.0007034831,0.0011937695,0.00018991162,0.004371507,0.0008364646,0.00015394879],"category_scores_gemma":[0.00015853468,0.0011446201,0.0008779358,0.0032689583,0.0007885611,0.0010765056,0.0033338678,0.0011894498,0.00022826609],"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":[0.00019632801,0.00018594206,0.0006830181,0.00003701624,0.00013836581,0.00005108302,0.000020690912,0.7693454,0.000025433734,0.22453095,0.00023335288,0.0045523955],"study_design_scores_gemma":[0.0018009554,0.0001828337,0.0006499646,0.000083633626,0.00022722865,0.000020384281,0.00001660259,0.9254965,0.000051383264,0.06573695,0.0045677456,0.0011657935],"about_ca_topic_score_codex":0.000021302772,"about_ca_topic_score_gemma":0.000021799387,"teacher_disagreement_score":0.96447587,"about_ca_system_score_codex":0.00048018582,"about_ca_system_score_gemma":0.000314688,"threshold_uncertainty_score":0.9991004},"labels":[],"label_agreement":null},{"id":"W4299509637","doi":"10.48550/arxiv.1802.06488","title":"Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network\\n for Real-time Embedded Object Detection","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Object detection; Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Object (grammar); Stack (abstract data type); Artificial neural network; Feature (linguistics); Computer vision; Pattern recognition (psychology)","score_opus":0.07309862874550152,"score_gpt":0.21574546445952464,"score_spread":0.14264683571402312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299509637","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":[0.21219164,0.00006237902,0.780659,0.00007006296,0.0023904953,0.0025550823,0.000058060155,0.00080154755,0.0012117143],"genre_scores_gemma":[0.98351157,0.00022114592,0.011342227,0.00014941303,0.0025035033,0.00006680007,0.00013253819,0.00013604015,0.0019367517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9919222,0.0006320831,0.0010763487,0.004105517,0.0003650009,0.0018988792],"domain_scores_gemma":[0.9925286,0.0011215643,0.0017614755,0.0025494748,0.0013789284,0.0006599624],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00076073536,0.0012731835,0.0010496919,0.0005410785,0.002300664,0.00036798592,0.0025586414,0.0010488647,0.00015146252],"category_scores_gemma":[0.00017032477,0.0016824102,0.00092847814,0.0030306445,0.0008541029,0.0013765609,0.0020088418,0.0012437638,0.00046541676],"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":[0.0010519384,0.00036769322,0.00015443814,0.00010990881,0.00027171487,0.000047313424,0.00026654935,0.9551541,0.02374972,0.0048083067,0.0003209628,0.013697357],"study_design_scores_gemma":[0.0013618845,0.00096015405,0.0008045987,0.000119878765,0.00038139516,0.00009066283,0.00005853961,0.9604972,0.005386629,0.02744954,0.001376519,0.001512988],"about_ca_topic_score_codex":0.000117033625,"about_ca_topic_score_gemma":0.0005913077,"teacher_disagreement_score":0.7713199,"about_ca_system_score_codex":0.0015738693,"about_ca_system_score_gemma":0.00033084044,"threshold_uncertainty_score":0.9989982},"labels":[],"label_agreement":null},{"id":"W4303645613","doi":"10.1007/s11265-022-01814-y","title":"PipeBERT: High-throughput BERT Inference for ARM Big.LITTLE Multi-core Processors","year":2022,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Huawei Technologies","keywords":"Computer science; Inference; Pipeline (software); Edge device; Throughput; Enhanced Data Rates for GSM Evolution; Parallel computing; Cluster analysis; Artificial intelligence; Computer engineering; Operating system","score_opus":0.061445300657523155,"score_gpt":0.3143183680113405,"score_spread":0.2528730673538173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303645613","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":[0.009107321,0.0023764698,0.9863476,0.0007184567,0.0007674071,0.0005167197,0.000011394758,0.00009289338,0.00006172356],"genre_scores_gemma":[0.95646584,0.000018067167,0.04205057,0.00019639013,0.00056530925,0.00021844833,0.0000034759582,0.00003260066,0.0004492887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973607,0.00008062944,0.00095108507,0.0004130401,0.0007699462,0.00042460393],"domain_scores_gemma":[0.9969903,0.00032744443,0.001507413,0.00030417932,0.00070369756,0.0001669653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007712728,0.00023832878,0.00045018172,0.0002167254,0.00079958525,0.00027881513,0.0012235068,0.00005865583,0.000006170504],"category_scores_gemma":[0.00007757212,0.00020730669,0.00012969186,0.0009769999,0.00006646833,0.00089626596,0.0002624289,0.0004939067,0.0000040164355],"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":[0.00031216812,0.0019702727,0.0012936952,0.0014576435,0.00018800638,0.00015476067,0.0057230406,0.68430626,0.020177007,0.025815956,0.0061739828,0.25242722],"study_design_scores_gemma":[0.0030310468,0.0015479869,0.00032728113,0.00066645193,0.00006859641,0.0016062668,0.00083141215,0.9158578,0.0025031338,0.01634174,0.056275822,0.0009424799],"about_ca_topic_score_codex":0.000015695843,"about_ca_topic_score_gemma":0.000002456713,"teacher_disagreement_score":0.94735855,"about_ca_system_score_codex":0.00023210017,"about_ca_system_score_gemma":0.0005138032,"threshold_uncertainty_score":0.84537214},"labels":[],"label_agreement":null},{"id":"W4307021380","doi":"10.1016/j.compeleceng.2022.108412","title":"3D Object detector: A multiscale region proposal network based on autonomous driving","year":2022,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Japan Society for the Promotion of Science; National Natural Science Foundation of China","keywords":"Computer science; Object (grammar); Detector; Computer vision; Artificial intelligence; Telecommunications","score_opus":0.007013453790741973,"score_gpt":0.20398481655618086,"score_spread":0.1969713627654389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307021380","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":[0.0017034089,0.000113254326,0.99533105,0.00050075335,0.00059419905,0.00044066535,5.19297e-7,0.0012337128,0.000082423205],"genre_scores_gemma":[0.73214614,0.0000024450999,0.2668778,0.00042014782,0.00026445615,0.00021937792,0.000004280542,0.000039215272,0.00002615793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977576,0.000089186855,0.00029828865,0.0007070703,0.0003889515,0.000758875],"domain_scores_gemma":[0.99847084,0.0005270735,0.00009965542,0.0006905449,0.000027540646,0.0001843333],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016146482,0.00027619433,0.0002670087,0.00020562431,0.00046367606,0.000094807,0.0011277441,0.000050538267,0.0000037706873],"category_scores_gemma":[0.000034206936,0.00030891842,0.000119362085,0.0017987433,0.000016941844,0.00016337287,0.00046112586,0.00070469844,0.000009952522],"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":[0.000006690063,0.00005571752,0.00006182445,0.0000040194327,0.000007498568,0.00004572575,0.000024794464,0.94860405,0.0003675778,0.005516256,0.0006768144,0.04462902],"study_design_scores_gemma":[0.0003051104,0.0003066198,0.00047927257,0.000016054812,0.0000050555263,0.00006292481,3.2432095e-7,0.9876226,0.000226184,0.00019445705,0.010441266,0.00034012095],"about_ca_topic_score_codex":0.0000024173053,"about_ca_topic_score_gemma":6.7064883e-7,"teacher_disagreement_score":0.7304427,"about_ca_system_score_codex":0.00048145783,"about_ca_system_score_gemma":0.00009600958,"threshold_uncertainty_score":0.9999363},"labels":[],"label_agreement":null},{"id":"W4308085167","doi":"10.1109/cases55004.2022.00024","title":"Work-in-Progress: SuperNAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Pascal (unit); Segmentation; FLOPS; Pareto principle; Training set; Pareto optimal; Machine learning; Artificial intelligence; Architecture; Set (abstract data type); Multi-objective optimization; Data mining; Pattern recognition (psychology); Parallel computing; Mathematical optimization; Mathematics","score_opus":0.02534179134285195,"score_gpt":0.29862216573010003,"score_spread":0.27328037438724806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308085167","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":[0.05770527,0.00014383341,0.93824095,0.002049156,0.0001322479,0.0014023754,0.000014203291,0.00020580403,0.000106187006],"genre_scores_gemma":[0.7223693,0.0000068716704,0.2749535,0.00035630562,0.000046542536,0.0014931972,0.000031642274,0.000020858275,0.0007217455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983231,0.00011520808,0.00022240297,0.0005970369,0.00031981454,0.0004224837],"domain_scores_gemma":[0.9991631,0.00022552712,0.0000416432,0.00044193811,0.000054843134,0.00007298475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021616487,0.00015296211,0.00014797691,0.00017020153,0.00032761524,0.000073233794,0.00082746113,0.00002655732,0.000043805914],"category_scores_gemma":[0.000007842176,0.0001495519,0.000066372646,0.001114222,0.000052933887,0.00024399272,0.0005111977,0.00028610651,0.000013375235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026738158,0.0012522633,0.043442946,0.00007864241,0.000084845706,0.000048240818,0.019876363,0.35964882,0.00939775,0.055750765,0.0020890075,0.50806296],"study_design_scores_gemma":[0.0068267044,0.0010136473,0.04712258,0.0000555755,0.00002825934,0.00013888715,0.005404412,0.8914319,0.025361458,0.010277047,0.010575302,0.0017641728],"about_ca_topic_score_codex":0.000027168711,"about_ca_topic_score_gemma":0.00006960344,"teacher_disagreement_score":0.66466403,"about_ca_system_score_codex":0.00016817306,"about_ca_system_score_gemma":0.000047994585,"threshold_uncertainty_score":0.60985494},"labels":[],"label_agreement":null},{"id":"W4308695562","doi":"10.1117/12.2641879","title":"Research advanced in object detection of autonomous driving based on deep learning","year":2022,"lang":"en","type":"article","venue":"2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Object detection; Computer science; Artificial intelligence; Object (grammar); Deep learning; Field (mathematics); Object-class detection; Computer vision; Viola–Jones object detection framework; Machine learning; Cognitive neuroscience of visual object recognition; Feature extraction; Pattern recognition (psychology); Face detection","score_opus":0.040726323133292615,"score_gpt":0.3229548734589401,"score_spread":0.2822285503256475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308695562","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":[0.59322435,0.000024729025,0.4024824,0.00058602047,0.00074543623,0.0003925151,0.0000035234732,0.00020775625,0.0023332639],"genre_scores_gemma":[0.99491996,0.00006446289,0.004586637,0.00010176406,0.00009635043,0.000111265195,0.00002454852,0.000019091274,0.00007593338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967773,0.00033364835,0.0007909016,0.00070980855,0.00097477523,0.00041355356],"domain_scores_gemma":[0.9981539,0.00056837045,0.0003852366,0.00039190252,0.00042182545,0.000078756995],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013440629,0.00022804097,0.000262481,0.0010617685,0.0010443517,0.00017422557,0.0009971919,0.0000651665,0.000114562456],"category_scores_gemma":[0.00015085403,0.00025558582,0.000050279217,0.0015503716,0.00015141463,0.00046840016,0.0003804295,0.00094958826,0.000026539326],"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":[0.000046185796,0.00012040131,0.00035848693,0.000010226824,0.0000057216985,0.0000019783029,0.0004447547,0.5631068,0.0024699273,0.14049065,0.0000030939818,0.29294178],"study_design_scores_gemma":[0.00012324912,0.00060386513,0.007493862,0.000095574,0.0000020612163,0.0000052747037,0.0004622759,0.9676094,0.014506724,0.008616175,0.00025006649,0.00023144127],"about_ca_topic_score_codex":0.000054449596,"about_ca_topic_score_gemma":0.00007675835,"teacher_disagreement_score":0.40450263,"about_ca_system_score_codex":0.00035318325,"about_ca_system_score_gemma":0.00014526054,"threshold_uncertainty_score":0.9999896},"labels":[],"label_agreement":null},{"id":"W4308718568","doi":"10.36227/techrxiv.21506124.v1","title":"Learnable Fusion Mechanisms for Object Detection in Autonomous Vehicles","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Leverage (statistics); Pooling; Computer science; Artificial intelligence; Concatenation (mathematics); Deep learning; Fusion; Convolutional neural network; Modal; Sensor fusion; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.023627804295047454,"score_gpt":0.2752158605925179,"score_spread":0.25158805629747044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308718568","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":[0.004332268,0.000067276065,0.99163157,0.00065026945,0.00060226704,0.0012435354,0.000005136834,0.00048844656,0.0009792069],"genre_scores_gemma":[0.55807066,0.000068684756,0.4357254,0.0003403963,0.000089752255,0.0030558382,0.000027893737,0.00003626751,0.0025851247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835074,0.00006118153,0.0002920574,0.00080467825,0.00019022968,0.000301112],"domain_scores_gemma":[0.99878806,0.00015284019,0.00016698547,0.00080082193,0.000042087322,0.00004919409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029102253,0.00018990661,0.00021154598,0.00019052924,0.00024844788,0.00009809901,0.0010435965,0.0001310554,0.00003976937],"category_scores_gemma":[0.000022711904,0.00020309493,0.00010285979,0.00038417077,0.000011618717,0.00016493899,0.0021968938,0.0005244211,0.000012502662],"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":[0.000025197325,0.00014231796,0.000013211172,0.00008089683,0.000013666483,0.0000070064684,0.00026319368,0.4135098,0.025380578,0.18478051,0.00033814894,0.3754455],"study_design_scores_gemma":[0.00019427107,0.00008513238,0.0001397195,0.000010857921,0.0000040345203,0.0000047855306,0.000026499243,0.60865456,0.021429272,0.36057103,0.0086085545,0.00027127683],"about_ca_topic_score_codex":0.00012098005,"about_ca_topic_score_gemma":0.00019490212,"teacher_disagreement_score":0.55590624,"about_ca_system_score_codex":0.00031861474,"about_ca_system_score_gemma":0.00010210388,"threshold_uncertainty_score":0.8281971},"labels":[],"label_agreement":null},{"id":"W4308872194","doi":"10.3390/s22228694","title":"Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"MNIST database; Computer science; Field-programmable gate array; Convolutional neural network; Artificial intelligence; Artificial neural network; Deep learning; Computation; Computer engineering; Coprocessor; Convolution (computer science); Machine learning; Embedded system; Computer hardware; Algorithm","score_opus":0.047036598868040014,"score_gpt":0.30004488978155136,"score_spread":0.25300829091351135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308872194","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":[0.33784893,0.000043104606,0.6598688,0.0012006783,0.00036458994,0.00045255473,0.0000087633825,0.00019223706,0.000020369029],"genre_scores_gemma":[0.9750238,9.628723e-7,0.023196021,0.0014329953,0.00009703197,0.0001612657,0.000022799702,0.000027601336,0.00003753167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840474,0.00013093256,0.00029049336,0.000531653,0.00025979072,0.00038241385],"domain_scores_gemma":[0.99863577,0.00032892497,0.00023393275,0.00064375607,0.000077464836,0.00008012471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018979242,0.00016566183,0.00016646211,0.00009068402,0.0006262734,0.000079891026,0.0006862979,0.00003935681,0.000019257115],"category_scores_gemma":[0.000017760045,0.0001764353,0.00010622924,0.0006164889,0.000040722767,0.00011355423,0.00016745753,0.00020513109,0.0000048971715],"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":[0.000018132607,0.00004876284,0.0003693698,0.000006392454,0.0000065216373,0.0000028796003,0.000063183186,0.98270756,0.0012838033,0.0013229499,0.00018409136,0.013986383],"study_design_scores_gemma":[0.00038394995,0.00005175299,0.0010554908,0.000004695109,0.00000820058,0.0000076044394,0.000043615106,0.9946195,0.00035293656,0.0002265368,0.0030390916,0.00020666675],"about_ca_topic_score_codex":0.000010210968,"about_ca_topic_score_gemma":0.000009687101,"teacher_disagreement_score":0.63717484,"about_ca_system_score_codex":0.000098560595,"about_ca_system_score_gemma":0.00003409991,"threshold_uncertainty_score":0.71948224},"labels":[],"label_agreement":null},{"id":"W4309229072","doi":"10.1109/codes-isss55005.2022.00014","title":"Work-in-Progress: Utilizing latency and accuracy predictors for efficient hardware-aware NAS","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Huawei Technologies","keywords":"Latency (audio); Computer science; Speedup; Architecture; Process (computing); Instruction set; Computer engineering; Real-time computing; Distributed computing; Parallel computing; Operating system","score_opus":0.026186434915760164,"score_gpt":0.2854621782955933,"score_spread":0.2592757433798331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309229072","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":[0.20778061,0.0012535066,0.7824142,0.004704964,0.00054063933,0.0020406842,0.000020760204,0.00072710856,0.0005175332],"genre_scores_gemma":[0.9522944,0.000016900965,0.04642755,0.0002389785,0.000029570314,0.00078337727,0.0000068445006,0.000013210874,0.00018913858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868774,0.00003516259,0.0002254323,0.00050539564,0.00021024513,0.0003360299],"domain_scores_gemma":[0.9990691,0.00030723723,0.00008146173,0.00042536302,0.00003509748,0.000081768674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017173204,0.00012325686,0.00012706264,0.00009400206,0.00037743253,0.00006597074,0.0006357879,0.000021606922,0.000021497257],"category_scores_gemma":[0.000029273926,0.000118058044,0.000040907595,0.00078243105,0.00004218687,0.00018090663,0.000734417,0.00015457744,0.0000031093612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015451713,0.0008047254,0.056348737,0.00009953112,0.00005075644,0.00005126672,0.0042457995,0.13260391,0.00031583852,0.36124668,0.011603418,0.43247482],"study_design_scores_gemma":[0.0012963731,0.000233343,0.021429183,0.00003963427,0.0000115081675,0.000031697837,0.00043874042,0.89913654,0.000485942,0.0064540543,0.0698289,0.0006140923],"about_ca_topic_score_codex":0.0000032785758,"about_ca_topic_score_gemma":0.0000032981686,"teacher_disagreement_score":0.7665326,"about_ca_system_score_codex":0.00006475492,"about_ca_system_score_gemma":0.000032197895,"threshold_uncertainty_score":0.48142675},"labels":[],"label_agreement":null},{"id":"W4309855231","doi":"10.3390/electronics11233883","title":"A Study on the Design Procedure of Re-Configurable Convolutional Neural Network Engine for FPGA-Based Applications","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Sungkyunkwan University","keywords":"Computer science; MNIST database; Verilog; Field-programmable gate array; Convolutional neural network; Compiler; Computer architecture; Artificial neural network; Computer engineering; Graphics; Deep learning; Computer hardware; Artificial intelligence; Embedded system; Programming language; Operating system","score_opus":0.03148656262033533,"score_gpt":0.26959787142626657,"score_spread":0.23811130880593123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309855231","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":[0.0021590737,0.0007361498,0.9889915,0.0041195736,0.000050651593,0.0037195585,0.000012683754,0.00012969621,0.0000810994],"genre_scores_gemma":[0.97391385,0.0000066556186,0.015306436,0.0009728489,0.00010026134,0.009515492,0.000020289674,0.000020678839,0.00014351556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985582,0.00013387519,0.00023697886,0.0003713881,0.0002939961,0.00040554532],"domain_scores_gemma":[0.9981222,0.00092214165,0.00017237054,0.0006525194,0.00009213321,0.000038594186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041335783,0.00013423942,0.00014051779,0.00004069199,0.0007381026,0.000022827031,0.0010190746,0.000020053214,0.000014036579],"category_scores_gemma":[0.000027406655,0.00011401382,0.000064854146,0.00093468034,0.000034274068,0.000061137995,0.00009284312,0.000305986,0.000002855039],"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":[0.000035276615,0.00025788898,0.000020004378,0.0000031657735,0.000017156995,2.5189635e-7,0.00006278632,0.85858977,0.00023430042,0.13343064,0.0055556255,0.0017931444],"study_design_scores_gemma":[0.00074133463,0.0017138751,0.00016174033,0.0000034927562,0.000025770283,0.0000070082483,0.000071380324,0.8888392,0.0025171628,0.037321042,0.0683603,0.00023773145],"about_ca_topic_score_codex":4.980656e-7,"about_ca_topic_score_gemma":0.000004270147,"teacher_disagreement_score":0.9736851,"about_ca_system_score_codex":0.00013436958,"about_ca_system_score_gemma":0.00030744326,"threshold_uncertainty_score":0.5676963},"labels":[],"label_agreement":null},{"id":"W4311119077","doi":"10.1145/3570305","title":"YaConv: Convolution with Low Cache Footprint","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Architecture and Code Optimization","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Cache; Speedup; Memory footprint; Parallel computing; Convolution (computer science); CPU cache; Memory hierarchy; Reduction (mathematics); Cache algorithms; Algorithm; Computer engineering; Computational science; Operating system; Artificial intelligence","score_opus":0.008153065125548538,"score_gpt":0.2143165338795878,"score_spread":0.20616346875403926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311119077","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":[0.0016959056,0.000043073338,0.99320585,0.004223312,0.00006933871,0.00034363978,0.000015106117,0.00022147322,0.00018230952],"genre_scores_gemma":[0.6355888,0.00004429944,0.363258,0.0005784552,0.000016699983,0.0003050484,0.00001678327,0.000016768518,0.00017510925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989745,0.00007806705,0.00013982088,0.000406871,0.00021763882,0.00018312337],"domain_scores_gemma":[0.9991312,0.000097938784,0.00007093562,0.0005887817,0.00003773643,0.00007340991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006848051,0.0001401479,0.00010351512,0.00013084126,0.00074323517,0.0000486157,0.00042128115,0.000029268234,0.000041545976],"category_scores_gemma":[0.0000046629634,0.00012882623,0.0000324936,0.0005838963,0.000051564573,0.00010970733,0.00003690513,0.00032473225,0.0000017565624],"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":[0.000033368495,0.000048431622,0.0000055697274,0.0000033991018,0.000011037161,0.0000014826609,0.00026204486,0.8975719,0.00015211981,0.0012777491,0.0000070500173,0.10062586],"study_design_scores_gemma":[0.0010823295,0.00067124766,0.00022437992,0.000017211372,0.00003930766,0.0002237329,0.00012258823,0.97824323,0.0021649115,0.009640361,0.0071038525,0.0004668335],"about_ca_topic_score_codex":0.0000071327695,"about_ca_topic_score_gemma":0.00003118024,"teacher_disagreement_score":0.63389295,"about_ca_system_score_codex":0.000057158042,"about_ca_system_score_gemma":0.00003598354,"threshold_uncertainty_score":0.57164395},"labels":[],"label_agreement":null},{"id":"W4311163792","doi":"10.18280/ts.390525","title":"Traffic Lights Detection and Recognition with New Benchmark Datasets Using Deep Learning and TensorFlow Object Detection API","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Deep learning; Artificial intelligence; Computer science; Transfer of learning; Object detection; Machine learning; Variable (mathematics); Cognitive neuroscience of visual object recognition; Object (grammar); Computer vision; Pattern recognition (psychology); Geography; Mathematics","score_opus":0.01742262742400995,"score_gpt":0.23139805567890703,"score_spread":0.21397542825489707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311163792","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":[0.5045515,0.00011837523,0.49482638,0.000048356385,0.000042124368,0.0002861382,0.000006549829,0.00011023174,0.000010334735],"genre_scores_gemma":[0.9850995,0.000026502716,0.014580181,0.00006554963,0.00009195236,0.00006656455,0.00004661179,0.000013280228,0.000009886887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877405,0.000102476,0.00018144073,0.00047295197,0.00025296697,0.00021611688],"domain_scores_gemma":[0.99953336,0.000080747224,0.00012909436,0.00012841808,0.000024974292,0.00010338371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016651879,0.00014937832,0.00011415915,0.00012632766,0.0008944549,0.00011003729,0.0001265929,0.000025590185,0.000036956666],"category_scores_gemma":[0.0000053911685,0.00014733976,0.00001872758,0.00043943434,0.000032698932,0.0004921557,0.00011485141,0.00023807648,0.000001945062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088965455,0.000053195814,0.000077447265,0.000013710875,0.000025073741,0.000009745447,0.00056869263,0.06490282,0.051054973,0.000025823701,0.000015232614,0.88316435],"study_design_scores_gemma":[0.0011470167,0.00096867693,0.0018988949,0.00002168374,0.000059059483,0.0004882153,0.00016579288,0.98153865,0.009219668,0.00077532063,0.003310971,0.0004060447],"about_ca_topic_score_codex":0.000021646629,"about_ca_topic_score_gemma":0.00011015444,"teacher_disagreement_score":0.9166358,"about_ca_system_score_codex":0.00008131712,"about_ca_system_score_gemma":0.000019332252,"threshold_uncertainty_score":0.68795145},"labels":[],"label_agreement":null},{"id":"W4311163811","doi":"10.18280/ts.390529","title":"HayCAM: A Novel Visual Explanation for Deep Convolutional Neural Networks","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Visualization; Pattern recognition (psychology); Dimension (graph theory); Dimensionality reduction; Minimum bounding box; Reduction (mathematics); Bounding overwatch; Deep learning; Object (grammar); Activation function; Computer vision; Artificial neural network; Image (mathematics); Mathematics","score_opus":0.024066421944721948,"score_gpt":0.26648646365654954,"score_spread":0.2424200417118276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311163811","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":[0.0055392366,0.000080845915,0.9915821,0.0015301687,0.00029962225,0.00070031063,0.000026230431,0.00017963245,0.000061843784],"genre_scores_gemma":[0.96692157,0.0000017582576,0.029708028,0.0014270509,0.00032140678,0.0013822211,0.0001608291,0.000015236514,0.00006190801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984549,0.000045449615,0.00030148536,0.000434886,0.0004029781,0.00036028144],"domain_scores_gemma":[0.9992762,0.00022379919,0.00014504149,0.00018874329,0.000075346106,0.00009085931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023978596,0.00014982127,0.00012672739,0.00008536218,0.0006834022,0.00006431339,0.0006136988,0.000025082945,0.00014153845],"category_scores_gemma":[0.000006613806,0.0001666535,0.00008994619,0.0003997082,0.00003580183,0.00033835063,0.00024412845,0.00016226574,0.000003253624],"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":[0.000060468847,0.0003185008,0.00015120726,0.0000047293715,0.000025592026,0.0000024724197,0.00015578196,0.8211719,0.002173935,0.14833361,0.0028982323,0.02470358],"study_design_scores_gemma":[0.00091503945,0.0002206099,0.00081643526,0.000001318252,0.000008462914,0.000023765875,0.00002872878,0.9856414,0.000074287236,0.001624458,0.010460945,0.00018455044],"about_ca_topic_score_codex":0.0000029567946,"about_ca_topic_score_gemma":0.0000052575847,"teacher_disagreement_score":0.96187407,"about_ca_system_score_codex":0.00014459471,"about_ca_system_score_gemma":0.000036383703,"threshold_uncertainty_score":0.6795932},"labels":[],"label_agreement":null},{"id":"W4311164560","doi":"10.18280/ts.390521","title":"Driver Identification System Using Finger Vein and YOLO Object Detection","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Adaptive histogram equalization; Artificial intelligence; Computer science; Computer vision; Identification (biology); Biometrics; Histogram of oriented gradients; Pattern recognition (psychology); Histogram equalization; Histogram; Image (mathematics)","score_opus":0.019476068747203923,"score_gpt":0.23821333529241634,"score_spread":0.21873726654521242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311164560","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":[0.26934043,0.000056916084,0.7298385,0.00012905519,0.00015546627,0.00027337624,0.0000035463672,0.00015388278,0.000048862265],"genre_scores_gemma":[0.99442136,0.00000207894,0.0052290745,0.00007873413,0.000071410206,0.00014217882,0.000004227278,0.000008589962,0.000042366355],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989013,0.00009646035,0.00021970198,0.0003494822,0.00026706277,0.00016595745],"domain_scores_gemma":[0.999516,0.000046668516,0.00013249702,0.0002275366,0.000032235097,0.000045045326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002474036,0.000093085175,0.00008429542,0.00009035131,0.0006067505,0.000085684216,0.00027944564,0.000016631062,0.00002345913],"category_scores_gemma":[0.0000025938823,0.00010342425,0.00003008472,0.00037829496,0.000023543731,0.0003164354,0.0001974428,0.00010849289,0.0000075256544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032447915,0.00017275121,0.00050489954,0.00006129157,0.00004942259,0.000018898358,0.0017291728,0.1186225,0.7345604,0.03159951,0.00034667013,0.11230209],"study_design_scores_gemma":[0.0003314628,0.000090693364,0.0045495816,0.000009152016,0.000018913324,0.00008068096,0.0001598025,0.97775775,0.013402729,0.0006195398,0.002776445,0.00020324466],"about_ca_topic_score_codex":0.000010873781,"about_ca_topic_score_gemma":0.0000030365832,"teacher_disagreement_score":0.85913527,"about_ca_system_score_codex":0.00019268095,"about_ca_system_score_gemma":0.00001697956,"threshold_uncertainty_score":0.46666956},"labels":[],"label_agreement":null},{"id":"W4311622297","doi":"10.36227/techrxiv.21624585","title":"SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Drone; Computer science; Focus (optics); Scalability; Feature (linguistics); Convolution (computer science); Backbone network; Popularity; Deep learning; Object detection; Artificial intelligence; Architecture; Distributed computing; Real-time computing; Pattern recognition (psychology); Computer network; Artificial neural network; Database; Geography","score_opus":0.011416553845304156,"score_gpt":0.25482485549889583,"score_spread":0.24340830165359167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311622297","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":[0.0068918164,0.0028143032,0.9848567,0.0010832605,0.0015027578,0.0011797368,0.0000075196745,0.0006759164,0.0009879353],"genre_scores_gemma":[0.6684085,0.0010749813,0.32392582,0.00047381746,0.0010425029,0.0014152194,0.000047945374,0.00008579503,0.0035253735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759066,0.00012942909,0.00028111233,0.0012145713,0.00036140578,0.00042283896],"domain_scores_gemma":[0.99790764,0.00021957586,0.00024286048,0.0014086465,0.0000626007,0.00015866804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024632306,0.00033558678,0.0002923726,0.00009506468,0.00046323438,0.00017464175,0.0010337527,0.0001737217,0.000038583195],"category_scores_gemma":[0.000022787866,0.0003326285,0.00008996739,0.0005983051,0.000052942476,0.00009161282,0.0048075253,0.001259699,0.000016121283],"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":[0.000012163897,0.000053599717,0.000039311657,0.00003099713,0.000015225554,0.000003646495,0.00021248002,0.84153235,0.00018610436,0.0058925394,0.0015575822,0.15046401],"study_design_scores_gemma":[0.0002491839,0.000049175214,0.0022926182,0.000043206746,0.000021950409,0.000023488814,0.000012494206,0.91088116,0.00034984053,0.009742137,0.07574791,0.0005868475],"about_ca_topic_score_codex":0.000019583484,"about_ca_topic_score_gemma":0.00005815152,"teacher_disagreement_score":0.6615167,"about_ca_system_score_codex":0.00019284776,"about_ca_system_score_gemma":0.00005620944,"threshold_uncertainty_score":0.99991256},"labels":[],"label_agreement":null},{"id":"W4311622298","doi":"10.36227/techrxiv.21624585.v1","title":"SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Drone; Computer science; Focus (optics); Scalability; Feature (linguistics); Backbone network; Convolution (computer science); Popularity; Deep learning; Object detection; Artificial intelligence; Distributed computing; Real-time computing; Pattern recognition (psychology); Computer network; Artificial neural network; Database","score_opus":0.011416553845304156,"score_gpt":0.25482485549889583,"score_spread":0.24340830165359167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311622298","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":[0.0068918164,0.0028143032,0.9848567,0.0010832605,0.0015027578,0.0011797368,0.0000075196745,0.0006759164,0.0009879353],"genre_scores_gemma":[0.6684085,0.0010749813,0.32392582,0.00047381746,0.0010425029,0.0014152194,0.000047945374,0.00008579503,0.0035253735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759066,0.00012942909,0.00028111233,0.0012145713,0.00036140578,0.00042283896],"domain_scores_gemma":[0.99790764,0.00021957586,0.00024286048,0.0014086465,0.0000626007,0.00015866804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024632306,0.00033558678,0.0002923726,0.00009506468,0.00046323438,0.00017464175,0.0010337527,0.0001737217,0.000038583195],"category_scores_gemma":[0.000022787866,0.0003326285,0.00008996739,0.0005983051,0.000052942476,0.00009161282,0.0048075253,0.001259699,0.000016121283],"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":[0.000012163897,0.000053599717,0.000039311657,0.00003099713,0.000015225554,0.000003646495,0.00021248002,0.84153235,0.00018610436,0.0058925394,0.0015575822,0.15046401],"study_design_scores_gemma":[0.0002491839,0.000049175214,0.0022926182,0.000043206746,0.000021950409,0.000023488814,0.000012494206,0.91088116,0.00034984053,0.009742137,0.07574791,0.0005868475],"about_ca_topic_score_codex":0.000019583484,"about_ca_topic_score_gemma":0.00005815152,"teacher_disagreement_score":0.6615167,"about_ca_system_score_codex":0.00019284776,"about_ca_system_score_gemma":0.00005620944,"threshold_uncertainty_score":0.99991256},"labels":[],"label_agreement":null},{"id":"W4311681113","doi":"10.22215/etd/2022-15289","title":"Optimization of Convolutional Neural Networks for Constrained Devices through Binarization","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Convolutional neural network; Quantization (signal processing); Artificial intelligence; Convolution (computer science); Artificial neural network; Binary number; Feature extraction; Deep learning; Pattern recognition (psychology); Constrained optimization problem; Feature (linguistics); Algorithm; Optimization problem; Mathematics","score_opus":0.019061456021489547,"score_gpt":0.29253021184637107,"score_spread":0.2734687558248815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311681113","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":[0.00021856178,0.00044532467,0.99547094,0.00021160138,0.00083271577,0.0010743152,0.000035280267,0.00020012543,0.0015111624],"genre_scores_gemma":[0.34975785,0.00018833074,0.630854,0.00058374734,0.00028351258,0.0015478394,0.013745141,0.0000846949,0.0029548875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998275,0.000053424967,0.00055613666,0.00055318093,0.00030884557,0.00025342448],"domain_scores_gemma":[0.99823624,0.00026301714,0.0006786693,0.0004103438,0.000368863,0.00004285341],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010547817,0.00024683858,0.00030399364,0.000110770176,0.0003267053,0.000051199924,0.0007603314,0.00016152924,0.00014058259],"category_scores_gemma":[0.00003877159,0.000264962,0.00013686997,0.00077067176,0.00004872609,0.0005491613,0.000083824176,0.00019121946,7.4591486e-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":[0.000023952154,0.00003910489,0.000012440881,0.000044240212,0.000020486294,2.2847642e-7,0.00012653941,0.7957351,0.000038023867,0.20091927,0.00030354748,0.0027370322],"study_design_scores_gemma":[0.0003225998,0.00012028971,0.00006935308,0.000018336263,0.00003116416,0.0000035606927,0.000113728,0.9937486,0.00009903886,0.0037067167,0.001516656,0.00024993176],"about_ca_topic_score_codex":0.000012749802,"about_ca_topic_score_gemma":0.00003268166,"teacher_disagreement_score":0.3646169,"about_ca_system_score_codex":0.000066309694,"about_ca_system_score_gemma":0.00013632627,"threshold_uncertainty_score":0.9999803},"labels":[],"label_agreement":null},{"id":"W4311839757","doi":"10.1109/icfpt56656.2022.9974441","title":"HPIPE NX: Boosting CNN Inference Acceleration Performance with AI-Optimized FPGAs","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Stratix; Parallel computing; Convolutional neural network; Computer architecture; Adder; Compiler; Application-specific integrated circuit; Gate array; Embedded system; Computer hardware; Computer engineering; Artificial intelligence; Latency (audio)","score_opus":0.023378908411029794,"score_gpt":0.2631445875434609,"score_spread":0.23976567913243108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311839757","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":[0.041972335,0.000019465304,0.9492199,0.0021802997,0.00006942778,0.00031405236,9.124937e-7,0.00042200641,0.0058016325],"genre_scores_gemma":[0.7899843,0.000011235752,0.20735326,0.0013916838,0.000035748497,0.00025855316,0.000007297656,0.00000938914,0.00094850635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873143,0.0000453949,0.00020163805,0.00041435254,0.00032857462,0.00027860244],"domain_scores_gemma":[0.99907994,0.00012812055,0.0001137472,0.00052929594,0.00008248811,0.000066405526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013962662,0.00013265018,0.00011964584,0.00006625526,0.00077987765,0.00012639195,0.00079530437,0.000016754158,0.00015413534],"category_scores_gemma":[0.000011387209,0.00011556626,0.000022281678,0.0008058464,0.000031498454,0.0009864806,0.00052558153,0.00027588385,0.000038658258],"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":[0.000032844342,0.00006827404,0.0014108007,0.00000688745,0.000008597841,0.0000050801204,0.00032585644,0.87512165,0.0012562001,0.04450731,0.0010723663,0.07618411],"study_design_scores_gemma":[0.000422314,0.0001677215,0.0012204183,0.0000055487017,0.0000037514528,0.000028387405,0.000025706498,0.9875199,0.0023157902,0.0008803004,0.0071754144,0.00023472832],"about_ca_topic_score_codex":0.000008436034,"about_ca_topic_score_gemma":0.000005130618,"teacher_disagreement_score":0.748012,"about_ca_system_score_codex":0.00007318639,"about_ca_system_score_gemma":0.000079666796,"threshold_uncertainty_score":0.5998267},"labels":[],"label_agreement":null},{"id":"W4312038605","doi":"10.1145/3528535.3565248","title":"CGX","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"European Research Council","keywords":"Computer science; Server; Software; Node (physics); Cloud computing; Parallel computing; Distributed computing; Computer engineering; Operating system","score_opus":0.032855251208678285,"score_gpt":0.3005221157120461,"score_spread":0.26766686450336785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312038605","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":[0.00016138972,0.00011124187,0.9388716,0.003163827,0.0005369266,0.0002223519,0.000002879503,0.0006217067,0.05630808],"genre_scores_gemma":[0.07154274,0.00012095077,0.90734535,0.0027963412,0.00022408755,0.0008846091,0.000035732293,0.000025081015,0.01702509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999007,0.000026238997,0.00013039558,0.00050456176,0.00018245175,0.00014938126],"domain_scores_gemma":[0.9984204,0.00005517456,0.00007490273,0.0013808535,0.000019633166,0.00004904633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059741422,0.00010936336,0.00010367179,0.000045684636,0.000109307504,0.000068178924,0.0020625591,0.000043346678,0.00037068516],"category_scores_gemma":[0.0000053470617,0.00010927058,0.00006153519,0.00021029681,0.00001324057,0.00007412751,0.0068733213,0.00046184665,0.00010063045],"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":[4.0183778e-7,0.00002971584,0.00003686019,0.000008226717,0.000008059315,0.0000069696375,0.000061919854,0.037346557,0.000032197346,0.8994168,0.023664687,0.03938763],"study_design_scores_gemma":[0.00005474768,0.000013382362,0.00032134244,0.000004121349,0.000003294113,0.000007014323,0.0000053699614,0.118967675,0.00018279043,0.40499878,0.47509837,0.00034312656],"about_ca_topic_score_codex":0.000007393575,"about_ca_topic_score_gemma":0.000002042502,"teacher_disagreement_score":0.494418,"about_ca_system_score_codex":0.000054962587,"about_ca_system_score_gemma":0.000053569558,"threshold_uncertainty_score":0.8567103},"labels":[],"label_agreement":null},{"id":"W4312258650","doi":"10.1109/igarss46834.2022.9883974","title":"Dpnet: end-to-end Aerial Image Segmentation Via Deformable Point Network","year":2022,"lang":"en","type":"article","venue":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Segmentation; Computer vision; Discriminative model; Pyramid (geometry); Feature (linguistics); Clutter; Pattern recognition (psychology); Image segmentation; Robustness (evolution); Mathematics","score_opus":0.010211346352765388,"score_gpt":0.2574292105289875,"score_spread":0.2472178641762221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312258650","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":[0.049032457,0.00007040378,0.9308481,0.0099000605,0.007417345,0.00059411,0.000045677552,0.00022822648,0.0018636208],"genre_scores_gemma":[0.4393879,0.000324858,0.53966063,0.011130404,0.002476272,0.000035802488,0.000195064,0.000097271324,0.006691816],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965621,0.00014825826,0.0004896146,0.0009928568,0.0011575036,0.0006496678],"domain_scores_gemma":[0.9986521,0.00014205943,0.00027722924,0.0005569309,0.0001457422,0.00022595342],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007367122,0.0002748014,0.00022399257,0.00027135864,0.001417394,0.00036778944,0.0011899825,0.000045011024,0.000098694734],"category_scores_gemma":[0.000022785385,0.0002925187,0.0000970701,0.001337662,0.00017069784,0.0009717041,0.0012188378,0.00041583885,0.000040233645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107945045,0.000098197146,0.000040026636,0.000011637183,0.000057361518,0.00014488958,0.0017690436,0.16186343,0.5785266,0.0037194202,0.008158053,0.24550335],"study_design_scores_gemma":[0.00044573427,0.00017114851,0.00018602549,0.00002115893,0.000013860824,0.00067991903,0.00013631956,0.9538893,0.00913328,0.008144656,0.02669781,0.00048077886],"about_ca_topic_score_codex":0.0002942567,"about_ca_topic_score_gemma":0.00005228428,"teacher_disagreement_score":0.79202586,"about_ca_system_score_codex":0.00036674057,"about_ca_system_score_gemma":0.00011258076,"threshold_uncertainty_score":0.9999527},"labels":[],"label_agreement":null},{"id":"W4312304153","doi":"10.1109/jiot.2022.3231369","title":"F-PVNet: Frustum-Level 3-D Object Detection on Point–Voxel Feature Representation for Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Science and Technology Program of Suzhou; China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Point cloud; Artificial intelligence; Robustness (evolution); Frustum; Object detection; Feature (linguistics); Feature extraction; Voxel; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.02940677403342595,"score_gpt":0.29122045845920347,"score_spread":0.2618136844257775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312304153","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":[0.06136423,0.00003981185,0.9343656,0.0017978513,0.0017046143,0.0003210665,0.000004243641,0.00009857998,0.00030402755],"genre_scores_gemma":[0.946423,0.000007992025,0.051192485,0.00060753815,0.00027520276,0.00008379637,0.0000025530217,0.000022993212,0.0013844391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841696,0.00010442797,0.0003989127,0.00037676693,0.0004293959,0.0002735136],"domain_scores_gemma":[0.99846494,0.000273419,0.0006561072,0.00036786444,0.00014829807,0.0000893691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000473512,0.00015985215,0.0001999042,0.00022953351,0.0004065144,0.00014713523,0.0010650171,0.000049466602,0.000021147664],"category_scores_gemma":[0.000101341604,0.00016007527,0.00019337799,0.00034647508,0.000029247509,0.00077518955,0.00021294916,0.0007425544,0.0000051014326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005690899,0.0005952231,0.00062385696,0.00005138838,0.0003104717,0.00008147006,0.013304069,0.14505702,0.2177502,0.012502816,0.06557338,0.543581],"study_design_scores_gemma":[0.001661178,0.0018376136,0.002011514,0.00012707105,0.0000466976,0.0022968082,0.00037027034,0.5966259,0.33619934,0.042862944,0.015341052,0.0006195621],"about_ca_topic_score_codex":0.00002055067,"about_ca_topic_score_gemma":0.000006466283,"teacher_disagreement_score":0.88505876,"about_ca_system_score_codex":0.00030398453,"about_ca_system_score_gemma":0.000052757267,"threshold_uncertainty_score":0.652768},"labels":[],"label_agreement":null},{"id":"W4312380174","doi":"10.1109/ijcnn55064.2022.9891933","title":"Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); École de Technologie Supérieure","funders":"","keywords":"Pascal (unit); Ground truth; Artificial intelligence; Computer science; Minimum bounding box; Annotation; Machine learning; Bounding overwatch; Object detection; Object (grammar); Source code; Pattern recognition (psychology); Natural language processing; Image (mathematics)","score_opus":0.03577843570185908,"score_gpt":0.267846178377684,"score_spread":0.23206774267582492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312380174","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":[0.033105344,0.00011715462,0.94734627,0.007289389,0.004892692,0.002728532,0.00011962283,0.0010513494,0.003349668],"genre_scores_gemma":[0.9906312,0.00006620623,0.0022099796,0.0022659893,0.0005451733,0.0033508732,0.00020193953,0.00005653674,0.0006720794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956869,0.0003094215,0.00074611756,0.0013013297,0.0012354051,0.00072083075],"domain_scores_gemma":[0.99775577,0.00044426243,0.0004377777,0.00088375754,0.00024761556,0.00023079437],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037180824,0.000491171,0.00038213728,0.00029151439,0.0009833322,0.00050446624,0.0023024525,0.000102365026,0.00083906646],"category_scores_gemma":[0.0000723728,0.0005230863,0.00024885402,0.00097425526,0.000099482175,0.0008735532,0.0012842668,0.0012848807,0.00005078653],"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":[0.0003387333,0.00066758523,0.0003183019,0.00001656148,0.00018577088,0.00007420912,0.0003557192,0.607087,0.040683888,0.17383523,0.014826794,0.16161017],"study_design_scores_gemma":[0.0006473675,0.00030232294,0.00054817693,0.000025358968,0.000009860842,0.00013384219,0.00008632674,0.9824172,0.0009255579,0.0075091245,0.0068331873,0.0005616611],"about_ca_topic_score_codex":0.0000694222,"about_ca_topic_score_gemma":0.000030662355,"teacher_disagreement_score":0.95752585,"about_ca_system_score_codex":0.0004804984,"about_ca_system_score_gemma":0.00006826274,"threshold_uncertainty_score":0.99972206},"labels":[],"label_agreement":null},{"id":"W4312426226","doi":"10.1109/tits.2022.3210490","title":"A Smart IoT Enabled End-to-End 3D Object Detection System for Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Royal Military College of Canada","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning","keywords":"Point cloud; Artificial intelligence; Computer science; Object detection; Computer vision; Minimum bounding box; End-to-end principle; Deep learning; Object (grammar); Set (abstract data type); Cloud computing; Real-time computing; Image (mathematics); Pattern recognition (psychology)","score_opus":0.02388843154836638,"score_gpt":0.2533485336958623,"score_spread":0.2294601021474959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312426226","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":[0.012119278,0.00007513951,0.9799905,0.00017784731,0.0033429507,0.00289383,0.00033966027,0.0009820845,0.00007875581],"genre_scores_gemma":[0.984316,0.000009603838,0.00899693,0.00012865852,0.000093629336,0.0057370197,0.000034073775,0.00006170088,0.0006224033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967172,0.00018626131,0.0009752693,0.0009379232,0.0006686566,0.0005146636],"domain_scores_gemma":[0.998123,0.00037777252,0.0002852927,0.0007656194,0.00021687371,0.00023147755],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048465768,0.00035662972,0.0003985372,0.0005383324,0.0012887078,0.00014350828,0.0007453019,0.000094542236,0.000036428413],"category_scores_gemma":[0.0000026853277,0.00040067965,0.00027777415,0.0014129861,0.000033590106,0.00027045122,0.00000219377,0.000366797,0.0000797496],"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":[0.0001285141,0.00018234982,0.0000062611375,0.000120083016,0.00007988819,0.0000074701784,0.00152575,0.93447775,0.011256562,0.0025809393,0.000049754686,0.049584683],"study_design_scores_gemma":[0.0014251336,0.001575795,0.00018644691,0.00016109372,0.00021029823,0.00014367365,0.002891137,0.6020948,0.32415545,0.00024523743,0.06554218,0.0013687527],"about_ca_topic_score_codex":0.00025564837,"about_ca_topic_score_gemma":0.00028019364,"teacher_disagreement_score":0.9721967,"about_ca_system_score_codex":0.0007385568,"about_ca_system_score_gemma":0.00012566028,"threshold_uncertainty_score":0.9998445},"labels":[],"label_agreement":null},{"id":"W4312709677","doi":"10.1109/cvpr52688.2022.01005","title":"Exploiting Explainable Metrics for Augmented SGD","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Computer science; Stochastic gradient descent; Generalization; Exploit; Artificial intelligence; Machine learning; Overhead (engineering); Deep learning; Learning to rank; Artificial neural network; Measure (data warehouse); Rank (graph theory); Layer (electronics); Deep neural networks; Network architecture; Data mining; Ranking (information retrieval); Mathematics","score_opus":0.07788462505945132,"score_gpt":0.3020840148417718,"score_spread":0.22419938978232046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312709677","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":[0.028539399,0.000045759494,0.9665362,0.0020761949,0.0010368332,0.00088400266,0.0001137072,0.00031440542,0.0004534674],"genre_scores_gemma":[0.94285035,0.00019251907,0.047233716,0.006981296,0.00038450278,0.0015074436,0.0003411709,0.000054576172,0.00045445043],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730563,0.00022464454,0.0004767408,0.0009838148,0.0005207269,0.0004884206],"domain_scores_gemma":[0.9982078,0.000526542,0.0002984195,0.00053025445,0.00023547989,0.00020148222],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004316493,0.00031598267,0.00033682532,0.00039082498,0.0010603302,0.00036145383,0.0007419902,0.00006183607,0.00029335884],"category_scores_gemma":[0.000019474064,0.0003294998,0.00011862029,0.0008286324,0.000042049334,0.00055031985,0.0005833844,0.00040768602,0.00006702144],"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":[0.000041186442,0.00022750576,0.00006208444,0.000037561207,0.000021328218,0.000018679077,0.00027821286,0.0005979648,0.00096702384,0.0024144473,0.009816179,0.9855178],"study_design_scores_gemma":[0.0017810981,0.0015174695,0.00021581679,0.000100620746,0.00001977992,0.000081546656,0.00021334784,0.9536358,0.0021881335,0.014293135,0.025221402,0.0007318366],"about_ca_topic_score_codex":0.000008870848,"about_ca_topic_score_gemma":0.0000043081163,"teacher_disagreement_score":0.984786,"about_ca_system_score_codex":0.00009844797,"about_ca_system_score_gemma":0.00004777456,"threshold_uncertainty_score":0.9999157},"labels":[],"label_agreement":null},{"id":"W4312723735","doi":"10.1109/cvpr52688.2022.01354","title":"TubeFormer-DeepLab: Video Mask Transformer","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Segmentation; Transformer; Task (project management); Artificial intelligence; Image segmentation; Computer vision; Natural language processing; Pattern recognition (psychology)","score_opus":0.037876617348983455,"score_gpt":0.27413317807829907,"score_spread":0.2362565607293156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312723735","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":[0.07226875,0.000050308503,0.91748595,0.005715551,0.0014018123,0.0007331025,0.00013487751,0.00041559397,0.0017940763],"genre_scores_gemma":[0.9809313,0.00028711825,0.008234154,0.00923211,0.00028172863,0.0004318372,0.00018048007,0.000044552726,0.00037671713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687076,0.00026392462,0.0005464228,0.0010986386,0.0006883189,0.00053196494],"domain_scores_gemma":[0.99850327,0.00021068098,0.00020973268,0.0006648071,0.00013531317,0.0002761781],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000312025,0.00040607413,0.00038706194,0.0003026627,0.0008492902,0.00033119597,0.00090291724,0.00008647839,0.001106327],"category_scores_gemma":[0.0000041760054,0.0003921844,0.00014455595,0.0006469087,0.00008306202,0.00069978414,0.00031150633,0.0006989651,0.00038580294],"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":[0.000041669122,0.00021059849,0.0000824052,0.00002162844,0.000022149925,0.000031089625,0.0004492615,0.00026034968,0.0015234541,0.0013994939,0.0055971257,0.9903608],"study_design_scores_gemma":[0.004267851,0.0042152302,0.0027819104,0.00026794273,0.00005895414,0.0006723507,0.00025251924,0.85415184,0.0059830504,0.03436048,0.090539806,0.0024480885],"about_ca_topic_score_codex":0.000016395968,"about_ca_topic_score_gemma":0.00001718959,"teacher_disagreement_score":0.9879127,"about_ca_system_score_codex":0.0000818034,"about_ca_system_score_gemma":0.000060367078,"threshold_uncertainty_score":0.999853},"labels":[],"label_agreement":null},{"id":"W4312772843","doi":"10.1007/978-3-031-19745-1_13","title":"Recognition of Graphological Wartegg Hand-Drawings","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object (grammar); Task (project management); Cognitive neuroscience of visual object recognition; Margin (machine learning); Pattern recognition (psychology); Object detection; Machine learning","score_opus":0.029955437661247572,"score_gpt":0.2590526981907191,"score_spread":0.2290972605294715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312772843","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":[0.0002724884,0.000276559,0.9951059,0.00077441795,0.00071781146,0.00039377992,0.00001142293,0.00013607979,0.0023115203],"genre_scores_gemma":[0.25931713,0.00020733319,0.73776317,0.002046299,0.00032898632,0.00007205631,0.000030586198,0.00004500431,0.00018946211],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99680436,0.000038361253,0.00052733906,0.0013438364,0.00082001556,0.0004660942],"domain_scores_gemma":[0.9975819,0.00055701996,0.00039663003,0.0011624971,0.0001850051,0.00011697251],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004894122,0.00035732644,0.00044421703,0.0006207319,0.00037716975,0.00013972787,0.0028792878,0.00018564418,0.00009831572],"category_scores_gemma":[0.000060658287,0.00033656295,0.00014956415,0.0010966425,0.00095524,0.000508778,0.0015787409,0.00084082433,0.000023897283],"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":[0.0000073208525,0.0000483119,0.000021178957,0.000019207366,0.0000070295264,0.000047668113,0.0003236001,0.01924643,0.0010193543,0.041124724,0.000041234125,0.93809396],"study_design_scores_gemma":[0.00026715128,0.00035196528,0.00013716744,0.00013430891,0.000010044958,0.00013799248,1.3275171e-7,0.10094959,0.0051579354,0.8856223,0.0065529807,0.0006784077],"about_ca_topic_score_codex":0.000007912201,"about_ca_topic_score_gemma":0.000011430413,"teacher_disagreement_score":0.93741554,"about_ca_system_score_codex":0.0001613015,"about_ca_system_score_gemma":0.00019265393,"threshold_uncertainty_score":0.9999086},"labels":[],"label_agreement":null},{"id":"W4312783726","doi":"10.1109/icpr56361.2022.9956061","title":"CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Curriculum; Selection (genetic algorithm); Computer science; Distillation; Enhanced Data Rates for GSM Evolution; Bridge (graph theory); Image (mathematics); Machine learning; Artificial intelligence; Chemistry; Chromatography; Pedagogy","score_opus":0.09278766435745964,"score_gpt":0.3462020560925823,"score_spread":0.2534143917351227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312783726","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":[0.012348174,0.000030822728,0.97404885,0.005260547,0.0018115096,0.0009398377,0.00038089766,0.0003805845,0.0047987443],"genre_scores_gemma":[0.9850646,0.000025027637,0.0076662055,0.001887039,0.0003871333,0.0028832904,0.0015327528,0.00003404837,0.0005198952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975442,0.00018049276,0.0005041448,0.000826836,0.00061677356,0.0003275165],"domain_scores_gemma":[0.99831575,0.00020519731,0.00037811988,0.00034627196,0.0006414389,0.000113240974],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029582498,0.00026923514,0.00020056973,0.00034092876,0.00060596364,0.00018296816,0.0009718994,0.000060109243,0.0018888883],"category_scores_gemma":[0.00006085209,0.0003003208,0.0001566546,0.00055670395,0.000037067133,0.0004317493,0.00024087065,0.00032139165,0.00016097953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013698504,0.0013873873,0.003551308,0.000039181316,0.00011897788,0.0000070484116,0.0004541237,0.007545488,0.004848292,0.04269518,0.046432287,0.89278376],"study_design_scores_gemma":[0.0010666755,0.0003111736,0.00061766926,0.000039027193,0.000010986715,0.000022589355,0.0000707038,0.9436092,0.0031409424,0.013898908,0.036752164,0.00045998997],"about_ca_topic_score_codex":0.000045317454,"about_ca_topic_score_gemma":0.000056996196,"teacher_disagreement_score":0.97271645,"about_ca_system_score_codex":0.0004516372,"about_ca_system_score_gemma":0.0001438922,"threshold_uncertainty_score":0.99994487},"labels":[],"label_agreement":null},{"id":"W4312842781","doi":"10.1109/iai55780.2022.9976690","title":"Intelligent Interpretation of High-resolution Remote Sensing Images based on Deep Learning","year":2022,"lang":"en","type":"article","venue":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Object (grammar); Feature extraction; Remote sensing; Feature (linguistics); Computer vision; Image resolution; Deep learning; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Geography","score_opus":0.10181077570315866,"score_gpt":0.3241590426755018,"score_spread":0.2223482669723431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312842781","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":[0.005160343,0.000016026523,0.9840304,0.004148311,0.002615276,0.0005407215,0.00004059166,0.0001971858,0.0032511],"genre_scores_gemma":[0.989007,0.000024596451,0.009824662,0.0004385484,0.00029750683,0.000033824737,0.00011327837,0.000028672439,0.00023190593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957739,0.0005277005,0.0010280095,0.00091213116,0.0013310263,0.00042723643],"domain_scores_gemma":[0.99743396,0.00060929084,0.00075052277,0.0006258976,0.0004651961,0.00011510549],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008190765,0.0003389216,0.0003494611,0.0006243814,0.00057342125,0.00020086553,0.001582274,0.0001234158,0.00086992135],"category_scores_gemma":[0.0006128391,0.00037658354,0.000176009,0.0011041383,0.00016808842,0.0003540421,0.0005426884,0.0013349297,0.000108785294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029329478,0.00009011343,0.000004428135,0.0000019577394,0.000016861271,0.000007773503,0.00017918454,0.38446566,0.0019734728,0.14791359,0.00007843383,0.4649752],"study_design_scores_gemma":[0.00011140565,0.00066234724,0.000009689474,0.000067663845,0.000010940817,0.0000069683365,0.00044511282,0.9059168,0.037303615,0.053883992,0.001277554,0.00030390473],"about_ca_topic_score_codex":0.00022065031,"about_ca_topic_score_gemma":0.000026331663,"teacher_disagreement_score":0.98384666,"about_ca_system_score_codex":0.00053784304,"about_ca_system_score_gemma":0.0002080685,"threshold_uncertainty_score":0.99986863},"labels":[],"label_agreement":null},{"id":"W4312868735","doi":"10.1109/igarss46834.2022.9883734","title":"A Graph Attention Network for Object Detection from Raw LiDAR Data","year":2022,"lang":"en","type":"article","venue":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Upsampling; Computer science; Lidar; Artificial intelligence; Graph; Object detection; Pattern recognition (psychology); Computer vision; Feature extraction; Raw data; Object (grammar); Theoretical computer science; Image (mathematics); Remote sensing","score_opus":0.024098379508969214,"score_gpt":0.27847100460038055,"score_spread":0.25437262509141134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312868735","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":[0.04169545,0.00018951572,0.9469945,0.0037284028,0.006406501,0.0004932023,0.00014206959,0.00018945418,0.00016093372],"genre_scores_gemma":[0.75337154,0.0005611916,0.23768538,0.0030119065,0.0022365176,0.000027728438,0.0007855339,0.00007017846,0.002250012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969504,0.00013624062,0.000394722,0.0012906584,0.0007820409,0.00044593876],"domain_scores_gemma":[0.998145,0.0003151759,0.00030053873,0.0010013048,0.00012596318,0.00011205045],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007047174,0.00022015108,0.00019601872,0.0002060281,0.0015294554,0.00029741644,0.001817605,0.000052327847,0.000011009194],"category_scores_gemma":[0.000046773286,0.00023901447,0.00009766482,0.001047621,0.00013931359,0.0008165934,0.0013888908,0.0003612992,0.0000056362933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016098436,0.0001293946,0.00014340997,0.000015581467,0.00014380067,0.000058779577,0.0006183168,0.05064227,0.39866862,0.0026137684,0.00544488,0.5413602],"study_design_scores_gemma":[0.00036909757,0.00010306731,0.000505001,0.00002105316,0.000021983476,0.00014151432,0.000071439375,0.9564511,0.0016122374,0.016819963,0.02358074,0.000302775],"about_ca_topic_score_codex":0.00041738353,"about_ca_topic_score_gemma":0.0001654241,"teacher_disagreement_score":0.90580887,"about_ca_system_score_codex":0.00015296448,"about_ca_system_score_gemma":0.00007833694,"threshold_uncertainty_score":0.9997704},"labels":[],"label_agreement":null},{"id":"W4312868944","doi":"10.1109/iscas48785.2022.9937567","title":"Efficient Fine-Tuning of BERT Models on the Edge","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Adaptability; Software deployment; Edge device; Fine-tuning; Computation; Inference; Distributed computing; Resource (disambiguation); Metric (unit); Resource allocation; Artificial intelligence; Computer engineering; Operating system; Computer network","score_opus":0.03886087501171164,"score_gpt":0.2612023358593475,"score_spread":0.22234146084763584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312868944","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":[0.6035157,0.00047286222,0.30236223,0.019799357,0.009337189,0.0020256562,0.00026538843,0.0002709892,0.061950635],"genre_scores_gemma":[0.998158,0.000015651538,0.00002765874,0.00040458562,0.00020783026,0.00031392643,0.000007191776,0.0000148198305,0.00085036183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805516,0.00015263703,0.00037327156,0.0004436127,0.00078429864,0.00019104008],"domain_scores_gemma":[0.99863386,0.00045982664,0.00025397213,0.0004884743,0.00010389429,0.000059956226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047187068,0.00015338387,0.00017593596,0.000106003004,0.00043972486,0.000091964306,0.0010909374,0.00002674847,0.00002524522],"category_scores_gemma":[0.000015374217,0.00012007227,0.00007620933,0.00031264522,0.000040728308,0.000083222374,0.0002820421,0.00024521456,0.000012154967],"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":[0.00000480786,0.00008517834,0.00004130053,0.0000052539535,0.000023046441,0.0000049393584,0.00045396603,0.5960106,0.004821332,0.39587167,0.0016697913,0.001008107],"study_design_scores_gemma":[0.00021470105,0.000121234596,0.00009279907,0.000034488276,0.000004725711,0.0000672565,0.00009971636,0.9901181,0.00043463343,0.00092515844,0.007740679,0.00014652437],"about_ca_topic_score_codex":0.00002894934,"about_ca_topic_score_gemma":0.0000022666143,"teacher_disagreement_score":0.3949465,"about_ca_system_score_codex":0.0001377975,"about_ca_system_score_gemma":0.000024120334,"threshold_uncertainty_score":0.4896405},"labels":[],"label_agreement":null},{"id":"W4312990730","doi":"10.14778/3554821.3554858","title":"POEM","year":2022,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Poetry; Image (mathematics); Convolutional neural network; Inference; Modular design; Bedroom; Deep learning; Pattern recognition (psychology); Machine learning; Art; Geography","score_opus":0.012971825591411225,"score_gpt":0.22596645889845804,"score_spread":0.21299463330704682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312990730","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":[0.6240905,0.00149683,0.06292016,0.114053525,0.0035016348,0.0062897764,0.000041929416,0.0017339077,0.18587172],"genre_scores_gemma":[0.986137,0.000007770871,0.012153956,0.00052129506,0.00002865602,0.0002450434,1.8929632e-7,0.000006085034,0.0009000435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991697,0.000004042843,0.0001409162,0.0002029101,0.000326633,0.00015577985],"domain_scores_gemma":[0.99956447,0.000021252608,0.00014811286,0.0001893544,0.000046374877,0.000030430787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012451805,0.00006671644,0.00007329701,0.000032142598,0.00028380615,0.000020856576,0.0015475412,0.000007002151,0.000017620405],"category_scores_gemma":[0.000011375244,0.000050597086,0.00005613212,0.00053542847,0.000026527263,0.00014531171,0.0015696728,0.00012323803,0.000004760373],"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":[0.0000060454636,0.00012863877,0.0009461279,0.000012454342,0.000014132651,2.554929e-7,0.000555896,0.0011586484,0.057644315,0.90927905,0.017226523,0.013027898],"study_design_scores_gemma":[0.0008953139,0.0002904406,0.0041278475,0.00002312884,0.000025277377,0.000115915565,0.000419084,0.018265372,0.3320203,0.28672063,0.3566057,0.0004909401],"about_ca_topic_score_codex":0.000003549028,"about_ca_topic_score_gemma":1.4490983e-7,"teacher_disagreement_score":0.6225584,"about_ca_system_score_codex":0.00007228728,"about_ca_system_score_gemma":0.000012752453,"threshold_uncertainty_score":0.28757423},"labels":[],"label_agreement":null},{"id":"W4313006142","doi":"10.1109/ijcnn55064.2022.9892861","title":"Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free Evaluation","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de Recherche du Québec-Société et Culture; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Firefly algorithm; Computer science; Metric (unit); Artificial neural network; Artificial intelligence; Machine learning; Algorithm; Baseline (sea); Architecture; Data mining; Engineering","score_opus":0.04243884968605028,"score_gpt":0.2803751729514579,"score_spread":0.23793632326540765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313006142","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":[0.009449659,0.00010579747,0.96933347,0.014193948,0.0011174231,0.0037080639,0.000109595116,0.0005018363,0.0014801944],"genre_scores_gemma":[0.96504277,0.0000107723645,0.02480634,0.0027041575,0.00056101097,0.0060778256,0.00027612253,0.000073877214,0.00044710044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945565,0.00049101876,0.0005948307,0.0013048365,0.002245193,0.00080764276],"domain_scores_gemma":[0.99764663,0.00031147373,0.00041884795,0.0010003677,0.00038219985,0.00024045513],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00074356387,0.00047367907,0.00035671567,0.0002976848,0.00087801914,0.00045824356,0.0027287456,0.00007721366,0.0005513581],"category_scores_gemma":[0.0000711055,0.00044531884,0.0001555735,0.00093826174,0.00010590314,0.0006681411,0.0014976664,0.001685772,0.0000078838475],"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":[0.00008380366,0.00011008806,0.000028664539,0.000004055443,0.000045120578,0.000044069093,0.0005194782,0.50062263,0.0012128156,0.006750789,0.0021588895,0.4884196],"study_design_scores_gemma":[0.0013717448,0.00047587155,0.00014635046,0.00003720985,0.0000140617085,0.00023173694,0.00019988173,0.9931493,0.00012906895,0.002556728,0.0012080581,0.00048002866],"about_ca_topic_score_codex":0.00006849787,"about_ca_topic_score_gemma":0.000033275144,"teacher_disagreement_score":0.9555931,"about_ca_system_score_codex":0.00043642338,"about_ca_system_score_gemma":0.0002055341,"threshold_uncertainty_score":0.99979985},"labels":[],"label_agreement":null},{"id":"W4313016841","doi":"10.1007/978-3-031-20713-6_14","title":"Sensor Fusion Operators for Multimodal 2D Object Detection","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Concatenation (mathematics); Leverage (statistics); Pooling; Convolutional neural network; Sensor fusion; Artificial intelligence; Object detection; Fusion; Data mining; Pattern recognition (psychology); Computer vision","score_opus":0.01649318996735038,"score_gpt":0.2610685270858204,"score_spread":0.24457533711847002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313016841","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":[0.00019101019,0.00015935466,0.99497354,0.0005761282,0.0020346271,0.0012582728,0.000015616619,0.00030403456,0.0004874146],"genre_scores_gemma":[0.15150405,0.000067609166,0.8452247,0.0016139788,0.00083378435,0.0002481462,0.000017678743,0.00007822931,0.00041186076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996187,0.000038343635,0.00048014015,0.0018624166,0.0007740123,0.00065814017],"domain_scores_gemma":[0.9972244,0.00072066335,0.00028291202,0.0013953208,0.00021794083,0.00015879209],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005206357,0.0004950833,0.00042086153,0.00062021933,0.0009202651,0.0002897989,0.002615474,0.00021859736,0.000029963701],"category_scores_gemma":[0.00008489666,0.0004861188,0.00017164492,0.00088412495,0.00031666819,0.000640782,0.001544142,0.0008135767,0.000020361196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011200326,0.000023125167,0.0000057909206,0.0000169192,0.0000056642575,0.000014284127,0.00024361104,0.19767134,0.0024878548,0.008375778,0.000012091243,0.79113233],"study_design_scores_gemma":[0.0003510466,0.00028939778,0.00003407116,0.000054265794,0.000008362881,0.000074263655,2.2747022e-7,0.92498094,0.0075983433,0.049425166,0.016479457,0.0007044801],"about_ca_topic_score_codex":0.00001638702,"about_ca_topic_score_gemma":0.00007542656,"teacher_disagreement_score":0.79042786,"about_ca_system_score_codex":0.0005400056,"about_ca_system_score_gemma":0.00028757894,"threshold_uncertainty_score":0.999759},"labels":[],"label_agreement":null},{"id":"W4313018251","doi":"10.1109/cvpr52688.2022.01668","title":"A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Lidar; Computer science; Object detection; Panopticon; Segmentation; Computer vision; Artificial intelligence; Object (grammar); Image segmentation; Remote sensing; Geography","score_opus":0.04952368373759086,"score_gpt":0.29843731774443555,"score_spread":0.2489136340068447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313018251","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":[0.048330646,0.000052863204,0.9486059,0.0006658236,0.00070734555,0.0011575979,0.00019372262,0.00026055225,0.00002555233],"genre_scores_gemma":[0.7279787,0.00004856852,0.26711783,0.0032466345,0.00015822196,0.0011388405,0.00025550497,0.00003357294,0.000022127197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974461,0.00029176704,0.00041645882,0.0010278462,0.0004718552,0.00034598267],"domain_scores_gemma":[0.99812394,0.00061550934,0.0003636483,0.0005469312,0.00020082833,0.00014911778],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002172045,0.0003442686,0.0003302723,0.0001843238,0.0007383413,0.00031582222,0.0004792095,0.00008459975,0.00022848119],"category_scores_gemma":[0.000013212906,0.00033082283,0.00008914299,0.00050355075,0.000057066707,0.00040913944,0.00016644686,0.00043214523,0.00005332472],"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":[0.00016033632,0.00026206413,0.00015219879,0.00004185841,0.000031147356,0.000010422941,0.00035758558,0.002670792,0.0033880628,0.00012290802,0.0001940461,0.9926086],"study_design_scores_gemma":[0.0022757978,0.00211368,0.0015401145,0.00030753348,0.00003877502,0.000018130453,0.000076976474,0.9805018,0.0073394002,0.003886419,0.0012768748,0.00062450685],"about_ca_topic_score_codex":0.000029359071,"about_ca_topic_score_gemma":0.0000498919,"teacher_disagreement_score":0.99198407,"about_ca_system_score_codex":0.00011867953,"about_ca_system_score_gemma":0.00007238058,"threshold_uncertainty_score":0.9999144},"labels":[],"label_agreement":null},{"id":"W4313342734","doi":"10.1109/bigmm55396.2022.00009","title":"Improving Interactive Segmentation using a Novel Weighted Loss Function with an Adaptive Click Size and Two-Stream Fusion","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Artificial intelligence; Generalization; Feature (linguistics); Image segmentation; Function (biology); Pattern recognition (psychology); Scale-space segmentation; Artificial neural network; Mathematics","score_opus":0.01879038178968504,"score_gpt":0.2673144078001218,"score_spread":0.24852402601043674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313342734","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":[0.32802853,0.0000064389087,0.671358,0.000067501656,0.00005638097,0.00027902494,0.0000035580756,0.00010936166,0.00009123849],"genre_scores_gemma":[0.6949282,0.0000010668866,0.30468675,0.00021701776,0.00003161385,0.00006756485,0.000007549635,0.000009528607,0.00005067728],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989132,0.00005960468,0.00014849518,0.00049306016,0.00021170254,0.00017392146],"domain_scores_gemma":[0.9992767,0.00013901484,0.00017421115,0.000265241,0.00007325818,0.00007158645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008354839,0.00013080367,0.00009694757,0.00006577083,0.0005234038,0.00007653073,0.00018847172,0.000015299707,0.000029907646],"category_scores_gemma":[0.0000042714732,0.00011604357,0.000016079228,0.00051353313,0.00003537066,0.0012713793,0.00035606063,0.00017222585,0.0000011783889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015484397,0.0007932722,0.0012203938,0.000012803425,0.00008173473,0.000019625768,0.002560174,0.06707923,0.5961033,0.057743438,0.00001959843,0.27281797],"study_design_scores_gemma":[0.0009338438,0.000913897,0.00090141484,0.000006425655,0.000018385377,0.000100040386,0.0007551877,0.99139255,0.0031863947,0.0015060767,0.00007845543,0.00020734506],"about_ca_topic_score_codex":0.00012894723,"about_ca_topic_score_gemma":0.000049688864,"teacher_disagreement_score":0.9243133,"about_ca_system_score_codex":0.0001665805,"about_ca_system_score_gemma":0.000038138176,"threshold_uncertainty_score":0.47321194},"labels":[],"label_agreement":null},{"id":"W4313414396","doi":"10.3390/f14010053","title":"An SSD-MobileNet Acceleration Strategy for FPGAs Based on Network Compression and Subgraph Fusion","year":2022,"lang":"en","type":"article","venue":"Forests","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Field-programmable gate array; Pruning; Hardware acceleration; Artificial neural network; Scheduling (production processes); Acceleration; Algorithm; Quantization (signal processing); Artificial intelligence; Real-time computing; Computer engineering; Machine learning; Embedded system; Mathematics","score_opus":0.028838018665582672,"score_gpt":0.3002388521970409,"score_spread":0.2714008335314582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313414396","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":[0.19053951,0.000086645545,0.8073845,0.0004204144,0.00022482211,0.0009734069,0.000018290635,0.00020957392,0.00014282073],"genre_scores_gemma":[0.98119354,0.000006619274,0.017467597,0.0004457127,0.0001335277,0.00058445585,0.00012290549,0.00001434912,0.00003129047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988485,0.000090671696,0.00015920745,0.00043770115,0.0002136114,0.00025033284],"domain_scores_gemma":[0.99910915,0.00016235502,0.00009863932,0.0004983293,0.000037968144,0.000093542156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017302539,0.00012269162,0.00010489087,0.00006293176,0.00074642466,0.00008940613,0.00044265218,0.000032590862,0.000014280095],"category_scores_gemma":[0.0000044902226,0.00011871716,0.000032871714,0.00035370313,0.00002305377,0.00027611008,0.0001411306,0.00012782292,0.0000016152648],"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":[0.00004960717,0.00008400923,0.0017506039,0.000005346227,0.0000014627436,0.0000013651049,0.000029669389,0.92806673,0.0005886627,0.025257425,0.0034459636,0.04071917],"study_design_scores_gemma":[0.00043349806,0.00063386,0.033434987,0.000009290308,0.0000030090773,0.00000300544,0.0000047004373,0.94036067,0.0003188306,0.018983211,0.0056699268,0.00014500432],"about_ca_topic_score_codex":0.0000057910797,"about_ca_topic_score_gemma":0.0000803109,"teacher_disagreement_score":0.790654,"about_ca_system_score_codex":0.000030290668,"about_ca_system_score_gemma":0.000033998887,"threshold_uncertainty_score":0.57409704},"labels":[],"label_agreement":null},{"id":"W4313527288","doi":"10.1109/bibm55620.2022.9995336","title":"Depth Encoding for Neonatal Patient Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Encoding (memory); Computer science; Segmentation; Computer vision; Artificial intelligence; Image segmentation","score_opus":0.05224953634822624,"score_gpt":0.30956783490850326,"score_spread":0.257318298560277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313527288","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":[0.016848948,0.00006446446,0.95888764,0.013502439,0.0023419445,0.001019358,0.0002619653,0.00014555069,0.0069277207],"genre_scores_gemma":[0.9414593,0.00019153801,0.053190447,0.0037977227,0.00016671035,0.00051211374,0.00036668638,0.00001215801,0.00030333834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862707,0.000017550721,0.00038496076,0.00023728078,0.0005395438,0.00019361146],"domain_scores_gemma":[0.99923205,0.0001101632,0.00024678814,0.0002081901,0.000114622926,0.00008818453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017835051,0.00014129948,0.00012783577,0.00027819836,0.0003407246,0.000094473304,0.00055683096,0.000023724404,0.00008222851],"category_scores_gemma":[0.000018785242,0.000126199,0.000041906813,0.00032971991,0.000055235192,0.00034729452,0.00025799024,0.00016025871,0.000007856757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009116039,0.00013869471,0.00011790877,0.000041683354,0.000072367926,0.00000855265,0.0032532949,0.001083394,0.008491054,0.29327208,0.013802753,0.67962706],"study_design_scores_gemma":[0.0009875668,0.0008599694,0.000091926086,0.000027969005,0.000009114614,0.00006570255,0.0017097127,0.9358776,0.0026110136,0.0070495107,0.05044648,0.0002634419],"about_ca_topic_score_codex":0.0000058983023,"about_ca_topic_score_gemma":0.00000412429,"teacher_disagreement_score":0.9347942,"about_ca_system_score_codex":0.000108455606,"about_ca_system_score_gemma":0.00004826306,"threshold_uncertainty_score":0.5146246},"labels":[],"label_agreement":null},{"id":"W4313546603","doi":"10.1109/jstars.2022.3229460","title":"LRAD-Net: An Improved Lightweight Network for Building Extraction From Remote Sensing Images","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Feature extraction; Segmentation; Artificial intelligence; Decoding methods; Pattern recognition (psychology); Block (permutation group theory); Pyramid (geometry); Aerial image; Pooling; Image segmentation; Data mining; Remote sensing; Computer vision; Image (mathematics); Algorithm","score_opus":0.025269525581983483,"score_gpt":0.26528241044037204,"score_spread":0.24001288485838856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313546603","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":[0.17410685,0.00021303678,0.82351243,0.0011708082,0.0005720076,0.00032583237,0.000005631647,0.000057979636,0.000035398065],"genre_scores_gemma":[0.16532548,0.00010974404,0.83335114,0.0003165182,0.00082635885,1.9995274e-7,0.000011006943,0.000024204866,0.000035335263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981072,0.00010927796,0.0006997192,0.0003989774,0.0003055074,0.00037934742],"domain_scores_gemma":[0.99819034,0.00036279965,0.0006340429,0.00035020217,0.0003658136,0.00009678892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005088647,0.0002000169,0.00032301105,0.00019969727,0.0008187053,0.000162839,0.00028091122,0.000087047745,0.0000013034404],"category_scores_gemma":[0.00005262842,0.00020998514,0.0000626865,0.0011987202,0.000036613383,0.00039829954,0.000083424246,0.00068686594,1.3700361e-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":[0.00004793713,0.000019507124,0.000012016531,0.00000778289,0.000029507044,0.000014451143,0.00026345404,0.052004088,0.22810982,0.0006781014,0.0001208145,0.71869254],"study_design_scores_gemma":[0.0006324146,0.000100643025,0.0018925647,0.00004864443,0.000027522949,0.00015701386,0.000049698316,0.9371476,0.010326116,0.038643703,0.010720186,0.0002538734],"about_ca_topic_score_codex":0.00008788222,"about_ca_topic_score_gemma":0.00012118849,"teacher_disagreement_score":0.8851435,"about_ca_system_score_codex":0.00012770384,"about_ca_system_score_gemma":0.00014102538,"threshold_uncertainty_score":0.8562945},"labels":[],"label_agreement":null},{"id":"W4313590974","doi":"10.1109/tcsii.2022.3233923","title":"Booth Encoding-Based Energy Efficient Multipliers for Deep Learning Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Multiplier (economics); Quantization (signal processing); Computer science; Encoding (memory); Convolutional neural network; Inference; Computation; Algorithm; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.023728094359847324,"score_gpt":0.2504844160274998,"score_spread":0.2267563216676525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313590974","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":[0.0017800543,0.0003127894,0.9910861,0.00013116957,0.0029692685,0.0013382068,0.00005597028,0.0020469325,0.00027948423],"genre_scores_gemma":[0.9934639,0.00003272325,0.0007623384,0.00009964241,0.00021248324,0.0034412793,0.000017232856,0.000092399125,0.0018780348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962888,0.00022559507,0.0007366107,0.001166933,0.00067285827,0.0009092482],"domain_scores_gemma":[0.996919,0.0010650693,0.0003333726,0.0011382848,0.00023484355,0.0003094589],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00039038144,0.00044007247,0.0005068962,0.0005056662,0.0016787649,0.00030920174,0.0011217057,0.00020428066,0.000002992239],"category_scores_gemma":[0.000021193557,0.00046890858,0.00026886768,0.001494547,0.0000909623,0.00030304788,0.000012090573,0.00036102117,0.00006349002],"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":[0.000009685813,0.00011130188,0.0000018828301,0.00010136567,0.000038824233,0.000007261557,0.00053636386,0.9734355,0.011388098,0.004711335,0.00033183975,0.009326538],"study_design_scores_gemma":[0.00087399874,0.00016185478,0.000008155065,0.0002101784,0.000025686786,0.000020413065,0.00019921645,0.95308554,0.007089036,0.000026772806,0.037804507,0.0004946212],"about_ca_topic_score_codex":0.00013683162,"about_ca_topic_score_gemma":0.000013427687,"teacher_disagreement_score":0.99168384,"about_ca_system_score_codex":0.0002576749,"about_ca_system_score_gemma":0.00011003737,"threshold_uncertainty_score":0.99977624},"labels":[],"label_agreement":null},{"id":"W4313643958","doi":"10.1007/s00138-022-01369-9","title":"Pixel-wise confidence estimation for segmentation in Bayesian Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Uncertainty quantification; Pixel; Overfitting; Computer science; Weighting; Artificial intelligence; Convolutional neural network; Bayesian probability; Confidence interval; Thresholding; Robust confidence intervals; Sensitivity analysis; Entropy (arrow of time); Measurement uncertainty; Uncertainty analysis; Pattern recognition (psychology); Machine learning; Artificial neural network; Statistics; Mathematics; Image (mathematics)","score_opus":0.013933763416643123,"score_gpt":0.31623213423493046,"score_spread":0.30229837081828737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313643958","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":[0.001635385,0.000120672295,0.99302447,0.0036438245,0.00005053778,0.0011600086,0.000018338445,0.00028418735,0.00006257383],"genre_scores_gemma":[0.9336705,0.000100677404,0.063208625,0.00044767163,0.000070971946,0.0020808687,0.00028545354,0.0000142380395,0.00012097346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886703,0.00003255286,0.00029135248,0.00043905215,0.00013970974,0.00023028615],"domain_scores_gemma":[0.999083,0.00034027564,0.00010309394,0.00032576802,0.0000555056,0.00009237429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020501202,0.0001315056,0.0001228843,0.00017109618,0.00031844468,0.000090793365,0.00029564128,0.000049892104,0.0000052277933],"category_scores_gemma":[0.000021193891,0.00012875312,0.000034775672,0.0010817735,0.000059128837,0.00035691008,0.00012299613,0.000111259855,0.000018181057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010735231,0.000071710856,0.0006714811,0.000018739553,0.0000041009102,5.607475e-7,0.00008569775,0.23915249,0.0015455477,0.39465368,0.0009546948,0.36283055],"study_design_scores_gemma":[0.00036453776,0.000029512705,0.0058635743,0.0000079519305,0.0000034242019,0.000005179817,0.000010954211,0.95651954,0.000048848888,0.034094527,0.0029266072,0.00012532467],"about_ca_topic_score_codex":0.000010601526,"about_ca_topic_score_gemma":0.000026831849,"teacher_disagreement_score":0.93203515,"about_ca_system_score_codex":0.00003356852,"about_ca_system_score_gemma":0.000016362284,"threshold_uncertainty_score":0.52504},"labels":[],"label_agreement":null},{"id":"W4315629604","doi":"10.1109/globecom48099.2022.10001152","title":"LidNet: Boosting Perception and Motion Prediction from a Sequence of LIDAR Point Clouds for Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pooling; Computer science; Encoder; Convolution (computer science); Boosting (machine learning); Artificial intelligence; Computer vision; Lidar; Point cloud; Residual; Algorithm; Artificial neural network; Remote sensing; Geography","score_opus":0.04719044268613398,"score_gpt":0.2982042658041004,"score_spread":0.25101382311796644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629604","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":[0.10629144,0.0005810512,0.8845624,0.0035089604,0.00050801213,0.0012494242,0.0020170154,0.00040946298,0.00087226904],"genre_scores_gemma":[0.89783347,0.00033354564,0.1002467,0.00021052844,0.000047745827,0.0008007536,0.0004711129,0.000014897112,0.000041240073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974374,0.0004088346,0.0006753993,0.00071882067,0.00039617,0.0003633472],"domain_scores_gemma":[0.9963634,0.00040923848,0.00053067884,0.0023007928,0.00025921667,0.00013666012],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005326539,0.00025203693,0.00030983018,0.000095352,0.0014168868,0.00013563137,0.002640691,0.00007384308,0.00007600171],"category_scores_gemma":[0.000117745076,0.00031843386,0.00010799689,0.0009807396,0.0002187909,0.0006629868,0.0022370731,0.00044141925,0.0000061561855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001064846,0.0010269826,0.01955812,0.000092715716,0.00020067493,0.000005088315,0.0048987195,0.017748635,0.093510225,0.3554239,0.0039691934,0.5034593],"study_design_scores_gemma":[0.0006670171,0.00029672275,0.017968096,0.00005229581,0.00006055924,0.00006145453,0.0010095445,0.9144212,0.00023455708,0.0559439,0.00883492,0.00044971687],"about_ca_topic_score_codex":0.00048627445,"about_ca_topic_score_gemma":0.000218913,"teacher_disagreement_score":0.8966726,"about_ca_system_score_codex":0.00062625174,"about_ca_system_score_gemma":0.00023499315,"threshold_uncertainty_score":0.99992675},"labels":[],"label_agreement":null},{"id":"W4315926799","doi":"10.1109/cicn56167.2022.10008312","title":"Deep Learning-based Ship Detection on FPGAs","year":2022,"lang":"en","type":"article","venue":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Field-programmable gate array; Computer science; Latency (audio); Software deployment; Object detection; Deep learning; Inference; Deep neural networks; Efficient energy use; Energy consumption; Power consumption; Embedded system; Artificial neural network; Artificial intelligence; Real-time computing; Power (physics); Computer engineering; Pattern recognition (psychology); Engineering; Telecommunications","score_opus":0.05236299706022658,"score_gpt":0.3093302467206102,"score_spread":0.25696724966038365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315926799","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":[0.0010475125,0.00020993775,0.98697186,0.0066912575,0.00048628633,0.00036899443,0.000008395297,0.0002779218,0.0039378405],"genre_scores_gemma":[0.98728645,0.0003220882,0.008389502,0.0027243162,0.000103650396,0.000522723,0.00027268697,0.000026022455,0.00035257675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698627,0.0005200497,0.00054566347,0.00073056953,0.00089696347,0.00032050145],"domain_scores_gemma":[0.9972388,0.0010317528,0.00041563762,0.0007603229,0.00041118733,0.00014228847],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005392474,0.00030060735,0.00021396716,0.0003350587,0.001400167,0.0003095195,0.0022010447,0.00007681245,0.0004141948],"category_scores_gemma":[0.0000733158,0.0003399673,0.00010410825,0.00079244084,0.00015538996,0.00037869858,0.0007452451,0.0012190918,0.000074453],"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":[0.000051993527,0.00011046109,0.0000369161,0.0000013626606,0.000016404942,0.0000017860394,0.00007926599,0.59288883,0.0000064108585,0.3244587,0.00008994853,0.08225793],"study_design_scores_gemma":[0.00018851308,0.00037122253,0.0004604207,0.000024480507,0.0000059734366,0.000019603993,0.00016025928,0.92532474,0.00009051048,0.06667045,0.0063824123,0.0003014136],"about_ca_topic_score_codex":0.000017650438,"about_ca_topic_score_gemma":0.00002523595,"teacher_disagreement_score":0.9862389,"about_ca_system_score_codex":0.00027497453,"about_ca_system_score_gemma":0.0000900955,"threshold_uncertainty_score":0.9999052},"labels":[],"label_agreement":null},{"id":"W4316368921","doi":"10.18280/ts.390609","title":"A Pose Estimation Algorithm for Multimodal Data Fusion","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Jimei University","keywords":"Pose; Artificial intelligence; Computer science; Computer vision; Benchmark (surveying); 3D pose estimation; Iterative closest point; Point cloud; Sensor fusion; Object (grammar); Fusion; Pattern recognition (psychology)","score_opus":0.039291979454107416,"score_gpt":0.2971197486319397,"score_spread":0.2578277691778323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316368921","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":[0.0010645738,0.000034255027,0.9963251,0.0012481182,0.00016899401,0.00074565515,0.00020943537,0.00016725881,0.000036646597],"genre_scores_gemma":[0.1552715,0.0000027072897,0.8427934,0.0005237054,0.00014900108,0.0005705322,0.0006096249,0.000012058338,0.0000674841],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987457,0.00004373965,0.00021245268,0.0004674025,0.00031434334,0.00021639111],"domain_scores_gemma":[0.9990562,0.00012867844,0.00009929755,0.0006212951,0.00003460945,0.00005991151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002847644,0.000102826394,0.00009052506,0.000056566565,0.0005104607,0.00005267897,0.0013172443,0.000013827509,0.00010278995],"category_scores_gemma":[0.0000070268106,0.000108594184,0.00003320019,0.00027885384,0.000016928856,0.00047316155,0.00090169895,0.000094325944,0.000010106782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009640066,0.00017479698,0.000006759304,0.000003869406,0.000008642955,0.0000026214304,0.00014519658,0.03142284,0.0016439382,0.006707451,0.0062702345,0.953604],"study_design_scores_gemma":[0.000561575,0.00012882576,0.0001379467,0.000002052095,0.0000071074664,0.000010229975,0.000013972778,0.97089666,0.00025397298,0.003672786,0.024192315,0.00012255672],"about_ca_topic_score_codex":0.0000063969273,"about_ca_topic_score_gemma":0.000001530717,"teacher_disagreement_score":0.95348144,"about_ca_system_score_codex":0.00006616253,"about_ca_system_score_gemma":0.000037040416,"threshold_uncertainty_score":0.44283423},"labels":[],"label_agreement":null},{"id":"W4316876965","doi":"10.1109/tai.2023.3237787","title":"Accelerating Point-Voxel Representation of 3-D Object Detection for Automatic Driving","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Voxel; Computer science; Artificial intelligence; Feature (linguistics); Representation (politics); Computer vision; Benchmark (surveying); Matching (statistics); Object (grammar); Point (geometry); Pattern recognition (psychology); Set (abstract data type); Mathematics","score_opus":0.09089608000098295,"score_gpt":0.34780675142971024,"score_spread":0.25691067142872726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316876965","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":[0.031273417,0.0000033169433,0.9665286,0.00032629448,0.00062247546,0.0006100647,0.0000055455203,0.0005773482,0.000052923253],"genre_scores_gemma":[0.95478606,0.00001740992,0.044675894,0.000041141197,0.00005458217,0.0003421166,0.0000018524499,0.000018011853,0.000062928935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984505,0.000058582245,0.00053845026,0.0004395629,0.0002346996,0.00027819126],"domain_scores_gemma":[0.99827296,0.0008329569,0.0001879696,0.0004992385,0.0001456598,0.00006124523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024148171,0.00014182893,0.00017529693,0.00029590243,0.0004154555,0.00008158385,0.0004162576,0.00006194367,0.000013547745],"category_scores_gemma":[0.00005382218,0.00015509693,0.00013779543,0.001984466,0.000059116912,0.0005325235,0.000005252009,0.00015621264,0.00009884033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008085091,0.00005470145,0.0000013734219,0.00001478964,0.000010409469,6.177027e-7,0.00039123188,0.2092973,0.08483514,0.003467581,0.0000122823885,0.7019065],"study_design_scores_gemma":[0.000015032683,0.00006278631,0.000020253854,0.000018167537,0.000005853339,0.00000217494,0.00009904931,0.5169252,0.46898067,0.013785164,0.000008472826,0.0000771442],"about_ca_topic_score_codex":0.000018147506,"about_ca_topic_score_gemma":0.00011808824,"teacher_disagreement_score":0.92351264,"about_ca_system_score_codex":0.000056612265,"about_ca_system_score_gemma":0.00003232802,"threshold_uncertainty_score":0.6324669},"labels":[],"label_agreement":null},{"id":"W4317639934","doi":"10.1007/978-3-031-23911-3","title":"Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation","year":2022,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"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 Tobacco Research Unit; University of Toronto","funders":"","keywords":"Computer science; Resource (disambiguation); Segmentation; Artificial intelligence; Image segmentation; Computer vision","score_opus":0.009952557160992583,"score_gpt":0.2417389707928539,"score_spread":0.2317864136318613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317639934","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":[0.00040139374,0.000325265,0.9963635,0.00093387475,0.00048147622,0.0006149744,0.000008839989,0.00020130891,0.0006693391],"genre_scores_gemma":[0.021849291,0.000092991475,0.96957403,0.0057517295,0.0008987333,0.00016605921,0.0000751969,0.000088466746,0.0015034752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962092,0.00008908448,0.00043880707,0.0016964282,0.00093955785,0.0006269475],"domain_scores_gemma":[0.9976037,0.0006162964,0.00026372838,0.0012218507,0.00009477344,0.00019964999],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047102882,0.00045024,0.0003943085,0.00051195204,0.0006316398,0.00044230753,0.0029331432,0.0001475189,0.000037846345],"category_scores_gemma":[0.00004033972,0.00045693226,0.00006652025,0.0017199684,0.00054933375,0.00063609245,0.0024329515,0.0008848443,0.000016018068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005490445,0.000033608532,0.00004028968,0.000035638455,0.000005344997,0.00003676207,0.0010836701,0.117019966,0.0012858704,0.00088874606,0.0003087082,0.8792559],"study_design_scores_gemma":[0.00041695396,0.00013992538,0.00009482685,0.00011009185,0.000008968237,0.00015027692,0.0000014980056,0.9663575,0.002177744,0.026632562,0.0032423916,0.00066722813],"about_ca_topic_score_codex":0.000004614427,"about_ca_topic_score_gemma":0.000017215538,"teacher_disagreement_score":0.8785887,"about_ca_system_score_codex":0.0005676906,"about_ca_system_score_gemma":0.0005606442,"threshold_uncertainty_score":0.9997882},"labels":[],"label_agreement":null},{"id":"W4317717687","doi":"10.1002/cpe.7635","title":"Performance comparison of Dask and Apache Spark on HPC systems for neuroimaging","year":2023,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Computer science; SPARK (programming language); Big data; Python (programming language); File system; Neuroimaging; Pipeline (software); Database; Operating system; Programming language","score_opus":0.07232339667677312,"score_gpt":0.3845238603157263,"score_spread":0.31220046363895315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317717687","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":[0.632308,0.001286616,0.3645159,0.0008802982,0.00027810963,0.00036664633,0.000003104274,0.0001058134,0.00025549042],"genre_scores_gemma":[0.995157,0.00081265223,0.0037625278,0.000125797,0.00002719225,0.000091063965,0.0000046014397,0.0000049435603,0.000014236728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990755,0.000047332713,0.00023931758,0.00035166854,0.0001329184,0.0001533131],"domain_scores_gemma":[0.9984833,0.0009999652,0.00020825493,0.00014647027,0.000098067874,0.00006396743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001702957,0.00010415822,0.00015172495,0.00006676205,0.00027243537,0.000104782586,0.00013886899,0.000024782774,1.8863778e-7],"category_scores_gemma":[0.0001294705,0.00010085702,0.000011075779,0.00034032614,0.00011180007,0.0009238669,0.00009907918,0.000091067675,0.0000021566495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085966094,0.00012607109,0.006854047,0.0003287408,0.000018005085,0.0000032207552,0.02648223,0.020700349,0.0016057193,0.07950528,0.0009336874,0.8633567],"study_design_scores_gemma":[0.00027188365,0.00019828568,0.0025251545,0.00004825156,0.000007671129,0.000021004484,0.0017058492,0.9888098,0.00031570857,0.0003778998,0.0055919,0.00012658497],"about_ca_topic_score_codex":0.0000040706027,"about_ca_topic_score_gemma":1.423595e-7,"teacher_disagreement_score":0.9681094,"about_ca_system_score_codex":0.000005058054,"about_ca_system_score_gemma":0.000015635907,"threshold_uncertainty_score":0.411283},"labels":[],"label_agreement":null},{"id":"W4317885027","doi":"10.32604/csse.2023.034230","title":"3D Object Detection with Attention: Shell-Based Modeling","year":2023,"lang":"en","type":"article","venue":"Computer Systems Science and Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Nanjing University; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Artificial intelligence; Minimum bounding box; Object detection; Computer vision; Weighting; Feature (linguistics); Object (grammar); Bounding overwatch; Feature extraction; Point (geometry); Lidar; Pattern recognition (psychology); Image (mathematics); Remote sensing; Mathematics","score_opus":0.01514413620866092,"score_gpt":0.21214225390187363,"score_spread":0.1969981176932127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317885027","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":[0.066159576,0.000051099556,0.93210244,0.00006258578,0.00072329806,0.00016812525,2.2473542e-7,0.0007059222,0.000026739437],"genre_scores_gemma":[0.9589859,0.000004962913,0.04071518,0.000026847736,0.00019323203,0.000051527313,5.9363293e-7,0.000009254675,0.000012478411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987033,0.0000084027415,0.00014846359,0.00045466612,0.00035416093,0.00033100255],"domain_scores_gemma":[0.99932,0.00004321295,0.000034408895,0.00036768444,0.00012628353,0.0001084419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036389614,0.00012089397,0.000110413115,0.0002830593,0.00030685216,0.00032939782,0.00046321127,0.000024475861,8.549209e-8],"category_scores_gemma":[0.0000067911024,0.000105664694,0.000015490214,0.0022898992,0.000044796303,0.00070262013,0.00015641964,0.000088018074,0.000015868805],"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":[4.461715e-7,0.0000022698061,0.000029468916,0.000021970536,0.000001700514,0.00000414307,0.000037137124,0.98741794,0.0051396647,0.0007886351,0.000010410968,0.006546193],"study_design_scores_gemma":[0.00009918674,0.000038697723,0.00045048655,0.0000644032,0.0000017198936,0.000038577364,0.000005785919,0.9986072,0.00023757949,0.000009864462,0.00030409728,0.00014237939],"about_ca_topic_score_codex":0.000007855034,"about_ca_topic_score_gemma":0.0000011413819,"teacher_disagreement_score":0.8928263,"about_ca_system_score_codex":0.000057151163,"about_ca_system_score_gemma":0.000045830784,"threshold_uncertainty_score":0.43088812},"labels":[],"label_agreement":null},{"id":"W4317906222","doi":"10.32604/csse.2023.034672","title":"Constructing an AI Compiler for ARM Cortex-M Devices","year":2023,"lang":"en","type":"article","venue":"Computer Systems Science and Engineering","topic":"Advanced Neural Network Applications","field":"Computer 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 Science and Technology, Taiwan","keywords":"Compiler; Computer science; MNIST database; Computation; Code (set theory); Deep learning; Artificial neural network; Source code; Computer engineering; Parallel computing; Software; Embedded system; Artificial intelligence; Computer architecture; Programming language","score_opus":0.0240396076290203,"score_gpt":0.26465481481947645,"score_spread":0.24061520719045615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317906222","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":[0.046361815,0.00005717782,0.95051455,0.00021452458,0.0018094253,0.00031286496,0.0000025687534,0.000683966,0.000043094326],"genre_scores_gemma":[0.9093986,0.0000050875947,0.0898856,0.00014804256,0.00044689135,0.000083915795,0.0000034061543,0.000012360135,0.000016068178],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985692,0.000008410075,0.00021391192,0.0005234148,0.000253588,0.00043147325],"domain_scores_gemma":[0.9990458,0.00014980687,0.0000589666,0.00039315142,0.00017716474,0.0001751278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004951642,0.000133,0.00016262887,0.00022241415,0.00034266806,0.0005133467,0.00081804325,0.000029348023,1.6425952e-7],"category_scores_gemma":[0.000017036582,0.00012876706,0.000020255206,0.0013607588,0.000098254415,0.00121476,0.0002953778,0.00008288529,0.0000086989585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019388624,0.00002961496,0.0015003859,0.00025823622,0.000017786653,0.000012738277,0.00078389025,0.27977878,0.009373897,0.59255505,0.0016906227,0.11399704],"study_design_scores_gemma":[0.0000992371,0.00004440179,0.0018498463,0.00004254639,0.0000016561495,0.00005642699,0.00003954244,0.9926226,0.00013547667,0.0001271074,0.0048149084,0.00016623372],"about_ca_topic_score_codex":0.0000048110123,"about_ca_topic_score_gemma":0.0000017187324,"teacher_disagreement_score":0.8630368,"about_ca_system_score_codex":0.00004715032,"about_ca_system_score_gemma":0.000048577785,"threshold_uncertainty_score":0.52509683},"labels":[],"label_agreement":null},{"id":"W4318328349","doi":"10.1145/3559009.3569678","title":"Improving Convolution via Cache Hierarchy Tiling and Reduced Packing","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Kernel (algebra); Parallel computing; Memory hierarchy; Cache; CPU cache; Convolution (computer science); Cache-oblivious algorithm; Cache algorithms; Algorithm; Mathematics; Artificial intelligence; Discrete mathematics","score_opus":0.017640199676236486,"score_gpt":0.24864129290146303,"score_spread":0.23100109322522655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318328349","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":[0.064655885,0.00013512412,0.93323016,0.00092339487,0.000114664435,0.00016282023,5.9631554e-7,0.00023297197,0.00054437056],"genre_scores_gemma":[0.9269645,0.000004338555,0.07238112,0.00031572024,0.00003745498,0.00006196521,0.0000022024408,0.000006408508,0.00022628918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991806,0.000045460576,0.00012343437,0.00031988282,0.000139758,0.00019087429],"domain_scores_gemma":[0.9994887,0.000082428276,0.00006157362,0.0002920347,0.000020747955,0.00005449253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015774528,0.00006934951,0.00006953548,0.000052448846,0.00052945275,0.00004492736,0.00030628932,0.0000129661485,0.000014534867],"category_scores_gemma":[0.000012106305,0.00007246746,0.00001775188,0.00035278703,0.000025578165,0.0002794873,0.0006232518,0.00012714021,0.0000036489832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060831,0.000036627585,0.00028527202,0.00000941957,0.000007829724,0.000007113309,0.0006364804,0.00661271,0.32830608,0.11038475,0.00027404955,0.5534336],"study_design_scores_gemma":[0.00016721591,0.000042237534,0.00058102154,0.000001776317,0.0000030009871,0.000060308612,0.000060330738,0.97983634,0.0056389673,0.010934722,0.0025149363,0.00015912065],"about_ca_topic_score_codex":0.000028459754,"about_ca_topic_score_gemma":0.0000034111652,"teacher_disagreement_score":0.9732236,"about_ca_system_score_codex":0.00006936973,"about_ca_system_score_gemma":0.000023166582,"threshold_uncertainty_score":0.4072176},"labels":[],"label_agreement":null},{"id":"W4318701505","doi":"10.1007/s00521-023-08199-4","title":"TRCA-Net: stronger U structured network for human image segmentation","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Computer science; Segmentation; Artificial intelligence; Feature (linguistics); Image segmentation; Scale-space segmentation; Pattern recognition (psychology); Segmentation-based object categorization; Net (polyhedron); Computer vision; Mathematics","score_opus":0.022656464889601654,"score_gpt":0.3186901678808902,"score_spread":0.29603370299128856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318701505","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":[0.04958951,0.000056714136,0.9470008,0.001157437,0.00008866158,0.0010890741,0.000016792745,0.00084929826,0.00015169613],"genre_scores_gemma":[0.85413593,0.0000112825455,0.14396434,0.00031822076,0.0006721807,0.00051616033,0.00017157051,0.000026301554,0.00018401744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986663,0.000031001015,0.0002657624,0.0005269274,0.00012798862,0.0003820284],"domain_scores_gemma":[0.99899393,0.00025652212,0.00014060269,0.00043493044,0.000075290336,0.00009875005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001292915,0.00015970218,0.00014605344,0.000067080975,0.0010119226,0.00016479909,0.00043803477,0.000046154364,0.0000018367662],"category_scores_gemma":[0.000007024043,0.00016235345,0.000053604897,0.0008438418,0.000065859014,0.0001966436,0.00020250265,0.00013028963,0.00001657174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065431113,0.000047366793,0.0007087778,0.00006019217,0.000030701005,0.0000013406292,0.00035311712,0.07923338,0.045278836,0.35737818,0.012885816,0.50401574],"study_design_scores_gemma":[0.0007303172,0.00009163911,0.013233725,0.000015157377,0.000024919764,0.00001588993,0.00006804039,0.8554191,0.0017866346,0.108972095,0.019161938,0.0004805212],"about_ca_topic_score_codex":0.0000046236873,"about_ca_topic_score_gemma":0.0000065059453,"teacher_disagreement_score":0.8045464,"about_ca_system_score_codex":0.000024891306,"about_ca_system_score_gemma":0.000014227205,"threshold_uncertainty_score":0.77829933},"labels":[],"label_agreement":null},{"id":"W4318719584","doi":"10.48550/arxiv.2301.13197","title":"Unlocking Slot Attention by Changing Optimal Transport Costs","year":2023,"lang":"en","type":"preprint","venue":"UvA-DARE (University of Amsterdam)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Alliance de recherche numérique du Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canadian Institute for Advanced Research","keywords":"Computer science; Object (grammar); Entropy (arrow of time); Set (abstract data type); Perspective (graphical); Mathematical optimization; Theoretical computer science; Artificial intelligence; Mathematics; Physics","score_opus":0.023131059665618913,"score_gpt":0.2325179958907582,"score_spread":0.2093869362251393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318719584","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":[0.10478421,0.00008042414,0.8908213,0.0019102977,0.0004946774,0.0005088696,0.00014506043,0.0006072781,0.00064788695],"genre_scores_gemma":[0.9358169,0.00019227323,0.058786973,0.000092254006,0.00010409976,0.000005093706,0.0004816564,0.00005099095,0.004469754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786204,0.000057482644,0.00023460666,0.0009427131,0.00043133166,0.00047179995],"domain_scores_gemma":[0.99820954,0.00007695847,0.0004160944,0.0009916074,0.00016293633,0.00014283987],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019657513,0.0002997033,0.00040934916,0.00038727466,0.00038451838,0.000044326498,0.0019326973,0.00023338934,0.000021766456],"category_scores_gemma":[0.0000050580293,0.00042494063,0.0002631872,0.00078971894,0.000119213306,0.0004832321,0.0017704038,0.00050311466,0.000075168085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004116576,0.0014763868,0.01450922,0.0032762804,0.0018795324,0.0016908785,0.03686449,0.23734331,0.034969546,0.067172356,0.053010892,0.54739547],"study_design_scores_gemma":[0.0050458256,0.0005799076,0.06853157,0.0048655826,0.0007238591,0.000093385475,0.008406853,0.83803076,0.002582418,0.0078096557,0.057191588,0.0061386074],"about_ca_topic_score_codex":0.00016840153,"about_ca_topic_score_gemma":0.00008250788,"teacher_disagreement_score":0.83203435,"about_ca_system_score_codex":0.00023274115,"about_ca_system_score_gemma":0.00007466859,"threshold_uncertainty_score":0.99982023},"labels":[],"label_agreement":null},{"id":"W4319068839","doi":"10.1109/tcss.2023.3237923","title":"Attention U-Net Based on Bi-ConvLSTM and Its Optimization for Smart Healthcare","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Benchmark (surveying); Field (mathematics); Segmentation; Machine learning; Convolutional neural network; Convolution (computer science); Medical imaging; Pattern recognition (psychology); Artificial neural network","score_opus":0.03929290253433671,"score_gpt":0.3013295694089528,"score_spread":0.2620366668746161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319068839","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":[0.0017178277,0.000017475093,0.99041164,0.005299013,0.0008717131,0.0010459558,0.00011827429,0.00048127022,0.0000368303],"genre_scores_gemma":[0.98933786,0.000008063644,0.009115562,0.00039217013,0.00016158128,0.0006916707,0.00009578173,0.000025559739,0.00017172951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984918,0.00011355403,0.0003165192,0.00045473053,0.0003695812,0.00025377466],"domain_scores_gemma":[0.9988852,0.0005115472,0.00013840245,0.00013696731,0.00023490598,0.00009296153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002041539,0.00016381795,0.0001793109,0.00023080537,0.0009465912,0.000116781965,0.0001945808,0.00010020858,0.0000027536514],"category_scores_gemma":[0.000005334304,0.00018243342,0.00009084562,0.0008829589,0.000027587963,0.00023577528,0.0000024160659,0.00012908841,0.00004187861],"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":[0.000018423641,0.000048871385,0.000004634188,0.000046295274,0.000012107269,5.699709e-7,0.00007255909,0.9801397,0.000027607317,0.014667964,0.0003438951,0.004617375],"study_design_scores_gemma":[0.0005366193,0.00011690825,0.0003701774,0.000034501747,0.000008302745,0.0000023258801,0.00002315975,0.99701494,0.000035795154,0.0013176437,0.00037323387,0.00016640157],"about_ca_topic_score_codex":0.000018080076,"about_ca_topic_score_gemma":0.000006960258,"teacher_disagreement_score":0.98762006,"about_ca_system_score_codex":0.00012523126,"about_ca_system_score_gemma":0.00007019367,"threshold_uncertainty_score":0.7439419},"labels":[],"label_agreement":null},{"id":"W4319299723","doi":"10.1109/wacv56688.2023.00073","title":"Dense Voxel Fusion for 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":74,"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":"Voxel; Artificial intelligence; Computer science; Point cloud; Computer vision; Lidar; Object detection; Benchmark (surveying); Pixel; Point (geometry); Feature (linguistics); Sensor fusion; Detector; Ground truth; Pattern recognition (psychology); Image fusion; Fusion; Image (mathematics); Mathematics; Remote sensing","score_opus":0.03079439693062873,"score_gpt":0.3126943239145511,"score_spread":0.28189992698392236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299723","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":[0.005288564,0.00001967191,0.98866534,0.0020135755,0.0006099348,0.0019440501,0.000048026224,0.0007991433,0.00061167765],"genre_scores_gemma":[0.82042414,0.00013239135,0.1743771,0.0006206255,0.00053342263,0.0023655451,0.000095799194,0.000067631605,0.0013833541],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969044,0.00009480611,0.0007774807,0.00117449,0.00050560426,0.0005432657],"domain_scores_gemma":[0.99636674,0.00058997376,0.00042317001,0.0018131916,0.0006040129,0.00020288947],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037769924,0.0003908287,0.00043430255,0.00060474995,0.00042274638,0.0001932793,0.0017630544,0.00017379229,0.0000306393],"category_scores_gemma":[0.000022244729,0.00038550823,0.0002489757,0.0020075901,0.0001366468,0.00044608588,0.00049119827,0.00029462876,0.00084360765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000874645,0.00031655226,0.000017378183,0.00007502054,0.00004710824,0.0000033129402,0.00026687759,0.004266613,0.041715674,0.036650248,0.01609763,0.90045613],"study_design_scores_gemma":[0.0008571327,0.0007587147,0.0010365699,0.00015270038,0.000024277399,0.000018732822,0.000030282883,0.9079484,0.01982737,0.022480719,0.04629433,0.000570815],"about_ca_topic_score_codex":0.000009606685,"about_ca_topic_score_gemma":0.000023076944,"teacher_disagreement_score":0.90368176,"about_ca_system_score_codex":0.000085805084,"about_ca_system_score_gemma":0.0000921506,"threshold_uncertainty_score":0.9999344},"labels":[],"label_agreement":null},{"id":"W4319299845","doi":"10.1109/wacv56688.2023.00523","title":"Hyperspherical Quantization: Toward Smaller and More Accurate Models","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Quantization (signal processing); Vector quantization; Computer science; Algorithm; Linde–Buzo–Gray algorithm; Computation; Estimator; Ternary operation; Learning vector quantization; Mathematics","score_opus":0.06798127665387065,"score_gpt":0.32911624943362666,"score_spread":0.261134972779756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299845","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":[0.0048214355,0.00004531805,0.9825393,0.009854884,0.00021719735,0.0008547084,0.000024043708,0.0005679173,0.0010752102],"genre_scores_gemma":[0.8941026,0.00037358212,0.10293214,0.0009771342,0.00022343975,0.0005514705,0.00005734671,0.000045347202,0.0007369519],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972556,0.00008434367,0.0006584158,0.0010732716,0.00049333845,0.00043502374],"domain_scores_gemma":[0.99735266,0.00028850074,0.00029429494,0.0013894059,0.0004365589,0.00023859223],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020148185,0.00036057353,0.00042678142,0.00028725312,0.00023348466,0.00023241123,0.001587614,0.00014297242,0.000037232312],"category_scores_gemma":[0.000009009584,0.00034283314,0.00012649175,0.0017989449,0.00023472242,0.0006534028,0.00068088324,0.0002933764,0.0003986574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063304506,0.00043154947,0.00006384387,0.00009590009,0.00009135968,0.000013500821,0.0014332447,0.06426204,0.0039443183,0.5484059,0.029647907,0.35154715],"study_design_scores_gemma":[0.0004356635,0.00019520526,0.0009149899,0.00008614916,0.000011644019,0.000015156552,0.00007453486,0.9653934,0.0006351781,0.024629341,0.007223859,0.00038485587],"about_ca_topic_score_codex":0.000007903159,"about_ca_topic_score_gemma":0.0000028782936,"teacher_disagreement_score":0.9011314,"about_ca_system_score_codex":0.000039091316,"about_ca_system_score_gemma":0.00008105893,"threshold_uncertainty_score":0.99990237},"labels":[],"label_agreement":null},{"id":"W4319299982","doi":"10.1109/wacv56688.2023.00124","title":"Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Perception; Feature (linguistics); Artificial intelligence; Pedestrian detection; Pedestrian; Lidar; Fusion; Feature selection; Computer vision; Sensor fusion; Point cloud; Adaptation (eye); Object detection; Pattern recognition (psychology); Engineering; Remote sensing; Geography; Transport engineering","score_opus":0.04618363410657625,"score_gpt":0.33074855624856675,"score_spread":0.2845649221419905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299982","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":[0.0088867685,0.000018596287,0.9835021,0.0038594087,0.0004262215,0.0022941732,0.000109849476,0.0004872424,0.00041567898],"genre_scores_gemma":[0.7195597,0.00008833703,0.27690634,0.00070700474,0.0005350141,0.0010452794,0.00015613336,0.000061092076,0.0009410756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969935,0.00013237626,0.000647844,0.0012126202,0.0004938199,0.00051984465],"domain_scores_gemma":[0.9965968,0.00040274314,0.00041914784,0.0014446564,0.00093239703,0.00020426358],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033423337,0.00044194763,0.0004995684,0.00045472666,0.0005013955,0.00020675392,0.0015814359,0.00021024399,0.000042145784],"category_scores_gemma":[0.000014726189,0.00041809768,0.0002492852,0.0018527389,0.0001849829,0.00058214867,0.00053789746,0.0003826012,0.0003759252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036728752,0.0009335497,0.00003816229,0.0001376646,0.00017245799,0.00000962606,0.0026192078,0.04722905,0.15912083,0.23766501,0.07551319,0.47619396],"study_design_scores_gemma":[0.0007324182,0.0008199826,0.0007319605,0.00024068655,0.000025312938,0.000015304411,0.00018595182,0.9718815,0.0041334135,0.012136998,0.008574423,0.0005220218],"about_ca_topic_score_codex":0.000009461237,"about_ca_topic_score_gemma":0.0000084171415,"teacher_disagreement_score":0.92465246,"about_ca_system_score_codex":0.00014718126,"about_ca_system_score_gemma":0.00015302196,"threshold_uncertainty_score":0.9998271},"labels":[],"label_agreement":null},{"id":"W4319300623","doi":"10.1109/wacv56688.2023.00426","title":"Multivariate Probabilistic Monocular 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Monocular; Artificial intelligence; Robustness (evolution); Probabilistic logic; Computer science; Covariance; Probability distribution; Multivariate statistics; Covariance matrix; Joint probability distribution; Computer vision; Posterior probability; Object detection; Pattern recognition (psychology); Mathematics; Machine learning; Algorithm; Bayesian probability; Statistics","score_opus":0.02941803805642603,"score_gpt":0.30584552395998427,"score_spread":0.27642748590355826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300623","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":[0.005284802,0.000017207434,0.98936254,0.0013471663,0.00046683333,0.0016547937,0.000027069882,0.00094839314,0.00089122006],"genre_scores_gemma":[0.90338916,0.00006050989,0.09370531,0.00029867233,0.00029131141,0.0014255756,0.00004907545,0.000052673015,0.000727728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996618,0.00016052999,0.0008289624,0.0012710079,0.0005743264,0.00054717244],"domain_scores_gemma":[0.9962836,0.0004197216,0.00042934087,0.002149816,0.000499654,0.0002178809],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036549743,0.00042408265,0.0004626195,0.00056508655,0.00033425388,0.00021648529,0.0020039843,0.00016725573,0.000041064926],"category_scores_gemma":[0.0000271928,0.0004135489,0.00020952609,0.00248929,0.0001746071,0.00048785034,0.0005804259,0.00040026908,0.0015787906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062328465,0.0005767343,0.00003199279,0.00010345646,0.000095449184,0.00001184823,0.00047172586,0.030128494,0.04282137,0.05492454,0.0045211576,0.8662509],"study_design_scores_gemma":[0.0005460573,0.00039939693,0.0017252588,0.00013249727,0.000020382147,0.000014606933,0.000017275752,0.95432174,0.00839608,0.023394084,0.010537337,0.0004952703],"about_ca_topic_score_codex":0.00002425415,"about_ca_topic_score_gemma":0.00001725971,"teacher_disagreement_score":0.92419326,"about_ca_system_score_codex":0.00010355854,"about_ca_system_score_gemma":0.00009565136,"threshold_uncertainty_score":0.9998316},"labels":[],"label_agreement":null},{"id":"W4319300710","doi":"10.1109/wacv56688.2023.00207","title":"BoxMask: Revisiting Bounding Box Supervision for Video Object Detection","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"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":"CODE","keywords":"Computer science; Discriminative model; Minimum bounding box; Artificial intelligence; Bounding overwatch; Representation (politics); Object detection; Computer vision; Confusion; Pixel; Class (philosophy); Detector; Object (grammar); Feature (linguistics); Feature extraction; EPIC; Pattern recognition (psychology); Image (mathematics)","score_opus":0.03525540149132339,"score_gpt":0.32025917711750057,"score_spread":0.2850037756261772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300710","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":[0.0075616064,0.000039965435,0.9859693,0.0022406492,0.0006010135,0.00197089,0.000039791987,0.00087103853,0.0007057361],"genre_scores_gemma":[0.89266944,0.00013227931,0.10379847,0.0005361616,0.0008008278,0.0013821536,0.0000839907,0.000073235504,0.00052345963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99622804,0.00012877032,0.0010158841,0.0013639414,0.0005924443,0.0006709265],"domain_scores_gemma":[0.99592185,0.00087087165,0.00052676606,0.0018450648,0.0006126017,0.00022283562],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006962529,0.00044609426,0.0005429639,0.0007040956,0.0007113949,0.00038360225,0.0019657465,0.00015814236,0.000028358205],"category_scores_gemma":[0.000039820075,0.0004510904,0.0003184884,0.002368796,0.0001287193,0.00074334815,0.00045715904,0.0003660417,0.00053604203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008054278,0.00020679792,0.000042864016,0.00019887023,0.00006606883,0.00000505522,0.0004729808,0.0047202413,0.06038863,0.07865315,0.00812568,0.8470391],"study_design_scores_gemma":[0.00097307476,0.0007161286,0.00075251615,0.0005641321,0.000033787717,0.000027700207,0.00008290476,0.8690359,0.035994004,0.025017211,0.06600085,0.00080181134],"about_ca_topic_score_codex":0.0000131912675,"about_ca_topic_score_gemma":0.00001582176,"teacher_disagreement_score":0.8851078,"about_ca_system_score_codex":0.00012516936,"about_ca_system_score_gemma":0.00011502118,"threshold_uncertainty_score":0.99979407},"labels":[],"label_agreement":null},{"id":"W4319300975","doi":"10.1109/wacv56688.2023.00614","title":"HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":477,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Encoder; Segmentation; Artificial intelligence; Transformer; Convolutional neural network; Image segmentation; Pattern recognition (psychology); Scale-space segmentation; Computer vision; Voltage","score_opus":0.04367816882844694,"score_gpt":0.37248254271214737,"score_spread":0.32880437388370043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300975","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":[0.0046838103,0.000009815155,0.98645014,0.0054911184,0.00033835636,0.0020999394,0.000111819856,0.00044989702,0.00036508072],"genre_scores_gemma":[0.26413852,0.00016684982,0.731347,0.0007899248,0.000400765,0.0021290323,0.00035727318,0.000076550605,0.00059407076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99645835,0.00011185143,0.0009973837,0.0010719186,0.00080113503,0.00055933266],"domain_scores_gemma":[0.99729896,0.000589075,0.00033191964,0.00097493513,0.00045408524,0.00035104438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044350344,0.00035447485,0.00043340714,0.0005461372,0.0004239651,0.00017861069,0.0015171017,0.00018534299,0.00008287039],"category_scores_gemma":[0.000024501389,0.000350309,0.0002811537,0.0017280153,0.00030688968,0.0006862064,0.00027338986,0.00036500182,0.0002670192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016464334,0.0014463605,0.00016069895,0.00023264447,0.0001789688,0.000011383088,0.0015748573,0.0100977635,0.1095349,0.054305,0.019822624,0.80247015],"study_design_scores_gemma":[0.0011640957,0.00024277161,0.0006318408,0.00011883161,0.000024347833,0.000017144173,0.00009752841,0.9798535,0.008594164,0.005955681,0.0029298863,0.00037018376],"about_ca_topic_score_codex":0.000015729747,"about_ca_topic_score_gemma":0.000026370562,"teacher_disagreement_score":0.96975577,"about_ca_system_score_codex":0.000091775335,"about_ca_system_score_gemma":0.00020771057,"threshold_uncertainty_score":0.9998949},"labels":[],"label_agreement":null},{"id":"W4319865456","doi":"10.1109/tim.2023.3241078","title":"An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":36,"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 Association of Optometrists; Swine Innovation Porc","keywords":"Robot; Petri dish; Artificial intelligence; GRASP; Computer science; Computer vision; Container (type theory); Feature extraction; Object detection; Engineering; Pattern recognition (psychology); Mechanical engineering","score_opus":0.053512424688621364,"score_gpt":0.29792725143781945,"score_spread":0.2444148267491981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319865456","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":[0.017687112,0.00001389229,0.97924805,0.0006505382,0.0010396369,0.00077635783,0.00000795496,0.0005280653,0.00004838162],"genre_scores_gemma":[0.9703031,0.00013378217,0.028381255,0.00030200506,0.00011033977,0.0006651729,0.000009616529,0.000020147601,0.00007457712],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99846065,0.00007240656,0.00029696999,0.0004787465,0.00037595254,0.00031530322],"domain_scores_gemma":[0.999243,0.00006914041,0.00009987285,0.00032741483,0.00012254388,0.00013798825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040504464,0.00016769143,0.00013109618,0.0001553245,0.0007278301,0.00012532235,0.00019614938,0.0000609227,0.0000055894243],"category_scores_gemma":[0.000003897948,0.00017261747,0.00006892171,0.0007102125,0.00002682492,0.0005834026,0.0000014669604,0.0001212626,0.000023051829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007350799,0.00014347528,0.000030364912,0.000026768172,0.000056966262,8.2812795e-7,0.00054664654,0.28740478,0.05868061,0.0013719959,0.00034424412,0.6513198],"study_design_scores_gemma":[0.00308119,0.0009522188,0.0032604274,0.00012567207,0.00010669733,0.00002365489,0.00032261328,0.3946563,0.5799817,0.009965544,0.006622995,0.0009009847],"about_ca_topic_score_codex":0.000008727183,"about_ca_topic_score_gemma":0.00019688875,"teacher_disagreement_score":0.952616,"about_ca_system_score_codex":0.00013899984,"about_ca_system_score_gemma":0.000032625176,"threshold_uncertainty_score":0.70391357},"labels":[],"label_agreement":null},{"id":"W4320002812","doi":"10.1109/jproc.2023.3238524","title":"Object Detection in 20 Years: A Survey","year":2023,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2868,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Object (grammar); Artificial intelligence; Computer vision","score_opus":0.02860654961029068,"score_gpt":0.267200256518802,"score_spread":0.23859370690851134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320002812","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":[0.9969153,0.000008832938,0.0019303237,0.00028590337,0.00019686825,0.0001852707,0.000001398632,0.00014198838,0.00033411884],"genre_scores_gemma":[0.9988086,0.000010845986,0.0008746472,0.00003800079,0.00003014004,0.000032040094,1.5651841e-7,0.000005826008,0.00019974477],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994166,0.0000059423696,0.00012083623,0.00017346014,0.0001403904,0.00014279706],"domain_scores_gemma":[0.99965376,0.00006905979,0.00007788633,0.000121747544,0.00005943408,0.000018144698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030063072,0.000050725816,0.00006949318,0.00007915361,0.0000372054,0.000023463224,0.00067450944,0.00002549299,4.0190318e-7],"category_scores_gemma":[0.00008072522,0.000042024945,0.000027290938,0.0019186029,0.000024377327,0.00022024993,0.00015187234,0.0000963623,0.000025344998],"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":[0.00004615534,0.00013048775,0.073353305,0.00007890945,0.000029146535,0.000001952106,0.0024457623,0.0033927753,0.7873461,0.006591725,0.015059025,0.11152466],"study_design_scores_gemma":[0.00021423813,0.000033494896,0.7136896,0.000035412308,0.000002514858,0.0000061411492,0.000023500106,0.035046123,0.2306113,0.019296048,0.00088823703,0.000153393],"about_ca_topic_score_codex":0.000033585155,"about_ca_topic_score_gemma":0.00007761928,"teacher_disagreement_score":0.6403363,"about_ca_system_score_codex":0.000024378922,"about_ca_system_score_gemma":0.000009696472,"threshold_uncertainty_score":0.17137276},"labels":[],"label_agreement":null},{"id":"W4320853969","doi":"10.48550/arxiv.2302.05996","title":"Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs; CMC Microsystems","keywords":"Byte; Computer science; Parallel computing; Inference; Floating point; Multi-core processor; Algorithm; Particle physics; Physics; Artificial intelligence; Computer hardware","score_opus":0.16813443554008223,"score_gpt":0.2606120660657393,"score_spread":0.09247763052565705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320853969","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":[0.099494144,0.000028745366,0.89719707,0.00034028632,0.0005651258,0.0011138496,0.00005442907,0.0010793449,0.00012703001],"genre_scores_gemma":[0.9865454,0.00017388244,0.011189049,0.00008947246,0.0001648359,0.000052586,0.00008123463,0.000057031946,0.0016464958],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969051,0.00011713005,0.00033785438,0.0019140046,0.00012321679,0.00060270587],"domain_scores_gemma":[0.9962544,0.0006264445,0.00044882015,0.001983303,0.00041450327,0.0002725645],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002732616,0.00045466347,0.00047763833,0.00029481685,0.00030841416,0.00018777489,0.003305113,0.00031481675,0.000008541967],"category_scores_gemma":[0.00017660021,0.00051532703,0.00023906116,0.0012049865,0.00014382508,0.0007659707,0.0018265917,0.00066146493,0.0002275439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016296576,0.00028132048,0.0015732882,0.00029421007,0.00013240281,0.00009137969,0.0004237193,0.46547797,0.0008289392,0.527255,0.0016427365,0.0018360596],"study_design_scores_gemma":[0.00056503183,0.00011373105,0.00093190727,0.00010338167,0.000057597765,0.000001921273,0.000039716215,0.84915817,0.0009011375,0.14619045,0.0012443846,0.0006925675],"about_ca_topic_score_codex":0.000054642405,"about_ca_topic_score_gemma":0.00017561913,"teacher_disagreement_score":0.8870513,"about_ca_system_score_codex":0.00021076755,"about_ca_system_score_gemma":0.00033604895,"threshold_uncertainty_score":0.9997298},"labels":[],"label_agreement":null},{"id":"W4323310471","doi":"10.5121/csit.2023.130402","title":"LEON: Light Weight Edge Detection Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Convolutional neural network; Scratch; Artificial intelligence; Edge device; Edge detection; Feature extraction; Feature (linguistics); Artificial neural network; Separable space; Pattern recognition (psychology); Architecture; Image (mathematics); Image processing; Mathematics; Cloud computing","score_opus":0.015855967477809686,"score_gpt":0.24912144409138803,"score_spread":0.23326547661357835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323310471","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":[0.00061698444,0.00012687805,0.9827415,0.003238778,0.0008829269,0.00016238456,2.3251981e-7,0.0018291351,0.010401203],"genre_scores_gemma":[0.89801157,0.00052080816,0.08310687,0.0015257071,0.003352814,0.00038865977,0.0000112968,0.0000529108,0.013029352],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990311,0.000023900006,0.00014092136,0.00033962924,0.00013894134,0.00032549477],"domain_scores_gemma":[0.99920416,0.00008572113,0.000041813004,0.00055510795,0.00003443263,0.00007877222],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000104369705,0.00009428046,0.000080431884,0.000059403028,0.00023733865,0.000055215896,0.0005305743,0.000036107685,0.000016016384],"category_scores_gemma":[0.000007833192,0.00008334147,0.0000422691,0.002042046,0.000014389854,0.00033267366,0.00024467148,0.000115151146,0.0017914096],"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":[0.0000025775807,0.000018461753,0.00016284359,0.0000048041106,0.0000075685743,0.0000073700826,0.000061896644,0.005881288,0.0040235934,0.24805121,0.0916385,0.65013987],"study_design_scores_gemma":[0.000107475775,0.000023299805,0.0018672553,0.0000059977924,0.000002508014,0.000009720009,0.000002129446,0.1468657,0.0070679947,0.06685569,0.77699596,0.00019630327],"about_ca_topic_score_codex":0.000001182441,"about_ca_topic_score_gemma":0.00001904681,"teacher_disagreement_score":0.8996346,"about_ca_system_score_codex":0.000022855504,"about_ca_system_score_gemma":0.000011989671,"threshold_uncertainty_score":0.9989858},"labels":[],"label_agreement":null},{"id":"W4327521834","doi":"10.1109/tcsii.2023.3257728","title":"A Multi-Scale Channel Attention Network for Prostate Segmentation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nanyang Technological University","keywords":"Segmentation; Prostate cancer; Prostate biopsy; Prostate; Computer science; Artificial intelligence; Magnetic resonance imaging; Medicine; Pattern recognition (psychology); Radiology; Cancer; Internal medicine","score_opus":0.03580512116646565,"score_gpt":0.27827856612222635,"score_spread":0.2424734449557607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327521834","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":[0.0041847406,0.00009123581,0.98850393,0.0003508645,0.0024584222,0.0028877764,0.00015339737,0.0013324358,0.000037200756],"genre_scores_gemma":[0.97419864,0.0000925193,0.01329853,0.0001819346,0.00036311793,0.006882199,0.0000666277,0.00008304417,0.0048333933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973404,0.00012109448,0.00057072105,0.0008754658,0.00040002368,0.0006923164],"domain_scores_gemma":[0.9983344,0.00023345675,0.00025281936,0.0008119953,0.0001924914,0.00017481153],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003103276,0.0003051504,0.00031564027,0.0002133827,0.0011928462,0.00019160048,0.00061361503,0.00012945292,0.0000023157168],"category_scores_gemma":[0.0000040636623,0.00033021215,0.00018704719,0.0013419357,0.000051091312,0.00078396674,0.000009904388,0.0002114514,0.00011569644],"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":[0.000018155006,0.00023191559,0.000009768484,0.0001354327,0.00006437448,0.000004388846,0.0017338212,0.8967603,0.0705585,0.000491234,0.0041020275,0.025890112],"study_design_scores_gemma":[0.0021508324,0.00031130828,0.00045210888,0.00033035348,0.000055496188,0.00004233702,0.00030718505,0.97509384,0.010310599,0.00056794967,0.009604165,0.0007738304],"about_ca_topic_score_codex":0.000045777553,"about_ca_topic_score_gemma":0.000033245462,"teacher_disagreement_score":0.9752054,"about_ca_system_score_codex":0.00012587296,"about_ca_system_score_gemma":0.000050858536,"threshold_uncertainty_score":0.999915},"labels":[],"label_agreement":null},{"id":"W4327559110","doi":"10.1016/j.cviu.2023.103682","title":"Grow-push-prune: Aligning deep discriminants for effective structural network compression","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; MNIST database; Pruning; Artificial intelligence; Deep learning; Machine learning; Construct (python library); Residual; Set (abstract data type); Algorithm","score_opus":0.034815677722036704,"score_gpt":0.32051942513960374,"score_spread":0.28570374741756704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327559110","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":[0.0058121295,0.000120314995,0.99134076,0.00067514775,0.00073774275,0.00073407794,0.0000030833846,0.00048080276,0.00009593109],"genre_scores_gemma":[0.77569,0.00004830636,0.22351296,0.00027491216,0.0003383942,0.000053581636,0.000023947929,0.000027730099,0.000030132289],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982308,0.00008239076,0.00026469427,0.0006725949,0.00020820073,0.0005413013],"domain_scores_gemma":[0.99854743,0.00073723384,0.00013181038,0.00037453044,0.00005231202,0.0001566925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002413564,0.00024780523,0.00026874998,0.00014249339,0.0010382026,0.00039621137,0.00046549877,0.00006443222,0.0000020762923],"category_scores_gemma":[0.000016492671,0.00020741764,0.00008564018,0.00068451813,0.00010415423,0.00087362685,0.00076337275,0.00014774952,0.000010770208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015445137,0.00005248228,0.0009033966,0.0002602783,0.000092526316,0.00007730508,0.0032023762,0.016708817,0.013323336,0.39451003,0.042476397,0.5282386],"study_design_scores_gemma":[0.0006762313,0.00016238676,0.0025897147,0.00014315311,0.00000976625,0.000021834134,0.00008952726,0.89677733,0.0003938456,0.09827454,0.0005890648,0.00027261328],"about_ca_topic_score_codex":0.000001351235,"about_ca_topic_score_gemma":0.0000021016929,"teacher_disagreement_score":0.8800685,"about_ca_system_score_codex":0.00010417989,"about_ca_system_score_gemma":0.000009335549,"threshold_uncertainty_score":0.8458246},"labels":[],"label_agreement":null},{"id":"W4327700031","doi":"10.3390/s23063205","title":"EDPNet: An Encoding–Decoding Network with Pyramidal Representation for Semantic Image Segmentation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Decoding methods; Encoding (memory); Representation (politics); Computer science; Segmentation; Artificial intelligence; Image (mathematics); Computer vision; Pattern recognition (psychology); Natural language processing; Algorithm","score_opus":0.034309431348626206,"score_gpt":0.3186547016821164,"score_spread":0.2843452703334902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327700031","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":[0.3607176,0.0000062551385,0.6366998,0.0008314525,0.0001876796,0.0006523995,0.0000033429765,0.00065339834,0.0002480983],"genre_scores_gemma":[0.7248482,0.000021410153,0.2738435,0.0001780129,0.00040206118,0.00018977832,0.0000841701,0.000034946745,0.00039788173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986292,0.000050290935,0.00020943761,0.00050991244,0.0002105614,0.00039061139],"domain_scores_gemma":[0.9989614,0.00023886995,0.00013467664,0.0004877435,0.000089108034,0.00008821556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001897029,0.00013710112,0.00012959374,0.00009349944,0.00042297065,0.00019272573,0.0003237516,0.00003691492,0.000003137437],"category_scores_gemma":[0.000026398691,0.00012963347,0.000040428597,0.0011443193,0.00005934032,0.00063975994,0.00007112741,0.000081476755,0.00005341711],"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":[0.00006140015,0.000053846292,0.0028087818,0.000041906682,0.000039804196,0.000031540454,0.0022373463,0.87181497,0.03667608,0.033388775,0.00361824,0.04922731],"study_design_scores_gemma":[0.0004171357,0.00011492734,0.0036460652,0.000020400143,0.000013323192,0.000016341186,0.00023664767,0.97524905,0.00837083,0.0111879185,0.00048435,0.00024301177],"about_ca_topic_score_codex":0.000008906131,"about_ca_topic_score_gemma":0.00003217258,"teacher_disagreement_score":0.36413065,"about_ca_system_score_codex":0.000039456874,"about_ca_system_score_gemma":0.000023045091,"threshold_uncertainty_score":0.5286299},"labels":[],"label_agreement":null},{"id":"W4327899673","doi":"10.1002/mp.16374","title":"Prostate cancer segmentation from MRI by a multistream fusion encoder","year":2023,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Innovation and Technology Commission; Research Grants Council, University Grants Committee; City University of Hong Kong","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Encoder; Feature (linguistics); Modality (human–computer interaction); Convolution (computer science); Weighting; Image segmentation; Computer vision; Artificial neural network; Medicine; Radiology","score_opus":0.014887372710692862,"score_gpt":0.2992948942534812,"score_spread":0.28440752154278837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327899673","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":[0.034359973,0.00012286475,0.9584455,0.0059191375,0.00029766382,0.00025761957,0.00004049891,0.00045028393,0.00010649099],"genre_scores_gemma":[0.9678452,0.0025194974,0.021007482,0.0040174583,0.0011314083,0.00093879196,0.0006591083,0.00005435237,0.0018266945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874085,0.000025553985,0.00014958292,0.00034677517,0.0005156871,0.00022156812],"domain_scores_gemma":[0.9993191,0.00014340834,0.00006383897,0.00029339417,0.000032729487,0.00014757218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058638685,0.00009975473,0.00009848071,0.000013191797,0.000114940696,0.000030505742,0.00041470805,0.0000451657,0.000040143776],"category_scores_gemma":[0.000018675379,0.00008857251,0.000028770797,0.0005919449,0.000053779757,0.00022672389,0.00021308321,0.00014751572,0.00023213727],"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":[0.000004209988,0.00009316877,0.0009894294,0.0000059039626,0.000011930193,0.000010712968,0.0008221556,0.001151644,0.0072059915,0.00051871885,0.06739907,0.9217871],"study_design_scores_gemma":[0.001762845,0.000061014227,0.0029817228,0.00012726021,0.00001743006,0.0000015384481,0.000046004847,0.80787563,0.081408456,0.07586208,0.029301075,0.0005549611],"about_ca_topic_score_codex":0.00011702768,"about_ca_topic_score_gemma":0.000023048664,"teacher_disagreement_score":0.937438,"about_ca_system_score_codex":0.000041368123,"about_ca_system_score_gemma":0.00004847684,"threshold_uncertainty_score":0.3611882},"labels":[],"label_agreement":null},{"id":"W4328049061","doi":"10.1007/s42979-023-01748-7","title":"CPPE-5: Medical Personal Protective Equipment Dataset","year":2023,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Google","keywords":"Computer science; Bounding overwatch; Pascal (unit); Categorization; Minimum bounding box; Artificial intelligence; Context (archaeology); Set (abstract data type); Machine learning; Image (mathematics)","score_opus":0.032283049241319986,"score_gpt":0.3160355754937501,"score_spread":0.28375252625243014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328049061","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":[0.0030900864,0.000014797396,0.9884415,0.006614992,0.0006745744,0.00037233188,0.00003148697,0.00058709,0.00017308928],"genre_scores_gemma":[0.6608524,0.00003524684,0.33105946,0.0062821577,0.0010211465,0.00043146443,0.00012844514,0.000030322171,0.00015939228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99644786,0.000048294496,0.00022836622,0.0010339133,0.0015372228,0.00070434785],"domain_scores_gemma":[0.9983789,0.00016052081,0.00007973564,0.00088186376,0.000108696295,0.00039031205],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001025968,0.00016521747,0.00014258258,0.0002521071,0.00063156715,0.00026129792,0.0036693108,0.000044587337,0.00002365055],"category_scores_gemma":[0.000065876964,0.00014587997,0.000039751558,0.0040557827,0.0005619914,0.0010623351,0.0029903194,0.00025124944,0.00082780863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006508449,0.00015738858,0.00015920226,0.000019395604,0.00001525905,0.00021171592,0.0015511719,0.0028575924,0.0019750614,0.10188145,0.06331665,0.8278486],"study_design_scores_gemma":[0.00017742287,0.00008622485,0.0021027708,0.000022480604,0.000001161547,0.00006957099,0.0000064373294,0.9724126,0.00081989705,0.007973238,0.016098114,0.00023007087],"about_ca_topic_score_codex":0.0000078187095,"about_ca_topic_score_gemma":0.00000484096,"teacher_disagreement_score":0.969555,"about_ca_system_score_codex":0.000102670914,"about_ca_system_score_gemma":0.00040684434,"threshold_uncertainty_score":0.9999502},"labels":[],"label_agreement":null},{"id":"W4352981296","doi":"10.1109/iscmi56532.2022.10068455","title":"Enhanced RecycleNet for Efficient Waste Classification","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Block (permutation group theory); Computational complexity theory; Architecture; Artificial intelligence; Contextual image classification; Image (mathematics); Deep learning; Machine learning; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.03203772157466062,"score_gpt":0.2837311162751512,"score_spread":0.25169339470049057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4352981296","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":[0.010269374,0.00002023622,0.9830541,0.0027794198,0.00020707522,0.00048673217,0.0000033794504,0.00022211106,0.002957579],"genre_scores_gemma":[0.8927069,0.000002638323,0.10430969,0.000490693,0.000044032528,0.0011781249,0.0000098649025,0.0000073948227,0.0012507127],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991381,0.000028612003,0.00014638329,0.0003443448,0.0001626159,0.00017993919],"domain_scores_gemma":[0.9992188,0.00011999116,0.00007358807,0.0005063389,0.00003893122,0.000042311895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012806975,0.00006477257,0.00006306783,0.00004367441,0.00038242197,0.000025594893,0.0006342363,0.000011026445,0.000026795511],"category_scores_gemma":[0.000010441369,0.000065379885,0.000038477294,0.00041912578,0.000012898575,0.00006838899,0.0002463384,0.00006837625,0.000016869715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010062719,0.00009814432,0.0000025066693,0.000003305633,0.0000039481947,1.9006616e-7,0.00018116276,0.18657757,0.06889225,0.6368474,0.0045613362,0.102822155],"study_design_scores_gemma":[0.00020914905,0.00006541598,0.00008712147,7.682661e-7,0.0000018070778,0.0000024371595,0.000089618115,0.9364277,0.023254713,0.008139892,0.031599976,0.0001213827],"about_ca_topic_score_codex":7.6325375e-7,"about_ca_topic_score_gemma":7.6314416e-7,"teacher_disagreement_score":0.88243747,"about_ca_system_score_codex":0.00007300032,"about_ca_system_score_gemma":0.000026582604,"threshold_uncertainty_score":0.29413193},"labels":[],"label_agreement":null},{"id":"W4353031949","doi":"10.29007/bbg7","title":"3D Objects Detection and Recognition from Color and LiDAR Data for Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"EPiC series in computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Lidar; Computer vision; Computer science; Detector; Point cloud; Artificial intelligence; RGB color model; Object detection; Object (grammar); Field of view; Remote sensing; Geography; Pattern recognition (psychology)","score_opus":0.04800009858700535,"score_gpt":0.2942359108278875,"score_spread":0.24623581224088217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353031949","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":[0.58449954,0.00006573948,0.41439486,0.00026116462,0.00024080338,0.0002419858,0.000008930302,0.00025462735,0.000032359538],"genre_scores_gemma":[0.8185481,0.000035354176,0.1811279,0.0000701724,0.00013873572,0.000022568052,0.000037261987,0.000008740061,0.000011157815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990451,0.000034546516,0.0001810052,0.00048422543,0.000058958693,0.00019617047],"domain_scores_gemma":[0.99867195,0.0008168686,0.000081553015,0.0003715568,0.00002245759,0.00003563264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024411482,0.00008772568,0.00011636942,0.00007198119,0.00024695354,0.00010374769,0.00028902738,0.00004051104,6.662836e-7],"category_scores_gemma":[0.00018788489,0.000100047444,0.000007729023,0.00039073807,0.000041354528,0.0005263636,0.00076240307,0.0000907045,0.0000035337246],"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":[0.000008253293,0.000008902391,0.0014725752,0.000021407628,0.000007624997,0.0000052095857,0.0010703178,0.0009235257,0.002231481,0.00034669373,0.000038702696,0.9938653],"study_design_scores_gemma":[0.00024154561,0.00005064214,0.018560817,0.000050338454,0.000004074163,0.000014384213,0.0001222772,0.9603505,0.0011092407,0.018333457,0.0010177457,0.00014498651],"about_ca_topic_score_codex":0.000025626621,"about_ca_topic_score_gemma":0.00017189958,"teacher_disagreement_score":0.9937203,"about_ca_system_score_codex":0.000025526757,"about_ca_system_score_gemma":0.0000144523165,"threshold_uncertainty_score":0.40798163},"labels":[],"label_agreement":null},{"id":"W4353100298","doi":"10.18280/ts.400129","title":"Fusion of Ensembled UNET and Ensembled FPN for Semantic Segmentation","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Fusion; Artificial intelligence; Computer science; Natural language processing; Pattern recognition (psychology); Linguistics; Philosophy","score_opus":0.02610803958523121,"score_gpt":0.27684161636948224,"score_spread":0.250733576784251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353100298","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":[0.35171756,0.000026835374,0.64658535,0.0007553076,0.000058786478,0.0006543996,0.0000070502556,0.00014230383,0.00005243738],"genre_scores_gemma":[0.9649831,0.00004921547,0.03446784,0.00013309708,0.000052312753,0.00016849277,0.0000329684,0.000010383328,0.00010261499],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990212,0.000030381494,0.000264117,0.00028805423,0.00019113814,0.00020513605],"domain_scores_gemma":[0.99934965,0.00023273732,0.00011986052,0.00018375293,0.00005912233,0.00005487685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020417038,0.00010547492,0.00013762362,0.00010335172,0.00012537818,0.00002863266,0.00020935094,0.000028415914,0.000011844916],"category_scores_gemma":[0.000007746972,0.00010113737,0.00004010461,0.0004456754,0.000025851026,0.0001832422,0.00009377988,0.00003703634,0.0000110098],"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":[0.00006114775,0.00010884805,0.00047784034,0.0001279882,0.000037430465,0.000004130454,0.0012180071,0.008948776,0.85565585,0.030908078,0.0031740277,0.0992779],"study_design_scores_gemma":[0.0029114538,0.00051204104,0.009141777,0.00006923317,0.0000464813,0.000010478681,0.00014050478,0.80367416,0.15919156,0.021208314,0.0027145287,0.00037948287],"about_ca_topic_score_codex":0.0000034450327,"about_ca_topic_score_gemma":0.0000049725704,"teacher_disagreement_score":0.79472536,"about_ca_system_score_codex":0.000016706084,"about_ca_system_score_gemma":0.000016101078,"threshold_uncertainty_score":0.41242623},"labels":[],"label_agreement":null},{"id":"W4353100327","doi":"10.18280/ts.400120","title":"Depth Invariant 3D-CU-Net Model with Completely Connected Dense Skip Networks for MRI Kidney Tumor Segmentation","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Invariant (physics); Net (polyhedron); Segmentation; Artificial intelligence; Computer science; Mathematics; Algorithm; Pattern recognition (psychology); Geometry","score_opus":0.03559485502011728,"score_gpt":0.26643360377552133,"score_spread":0.23083874875540406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353100327","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":[0.014055513,0.000012380361,0.98186165,0.0017060132,0.00008405682,0.0015308199,0.0000543138,0.00059657893,0.000098647084],"genre_scores_gemma":[0.68340576,0.000013515275,0.31145856,0.003229629,0.00019163555,0.0011126837,0.0004150689,0.00004692873,0.00012626767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791694,0.00006579526,0.00040987218,0.0006520252,0.00037050567,0.0005848692],"domain_scores_gemma":[0.9986506,0.00031073552,0.00021244574,0.00041193748,0.0001501532,0.0002640885],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026578936,0.00027221296,0.00024283539,0.00012732067,0.00036515412,0.00015067274,0.00063042354,0.00004492295,0.00002327573],"category_scores_gemma":[0.00001616698,0.00024641218,0.000064599975,0.00089102914,0.00006679601,0.00043057994,0.00013720496,0.00014177272,0.000033263965],"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":[0.000110119385,0.00009743258,0.00017698477,0.000021304362,0.000061038416,0.000019344201,0.00033054376,0.95101136,0.004498363,0.023495853,0.016091252,0.004086432],"study_design_scores_gemma":[0.0015897729,0.00024828294,0.0010457851,0.000030116473,0.000032706506,0.000019213354,0.000028323804,0.9923309,0.000690376,0.0029356265,0.00072802056,0.00032088874],"about_ca_topic_score_codex":0.00000961826,"about_ca_topic_score_gemma":0.000045513785,"teacher_disagreement_score":0.6704031,"about_ca_system_score_codex":0.000085241525,"about_ca_system_score_gemma":0.000117627504,"threshold_uncertainty_score":0.9999988},"labels":[],"label_agreement":null},{"id":"W4362419701","doi":"10.31237/osf.io/gz5rw","title":"Extended Abstract: CNN-based Patch Benchmarking","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Benchmarking; ENCODE; Task (project management); Variety (cybernetics); Image (mathematics); Object (grammar); Matching (statistics); Computer vision; Subroutine; Pattern recognition (psychology); Similarity (geometry); Range (aeronautics); Function (biology); Mathematics","score_opus":0.05589014290483632,"score_gpt":0.31022635105945745,"score_spread":0.2543362081546211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362419701","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":[0.0010433383,0.00004920243,0.9858047,0.0039356756,0.0011770957,0.000552099,0.000009523942,0.0020002313,0.0054281624],"genre_scores_gemma":[0.5021091,0.00006286058,0.4933047,0.0011007085,0.00053299527,0.00056970067,0.00010907773,0.00006552926,0.0021453456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748003,0.000026920923,0.00043451498,0.0011849381,0.0004173304,0.00045625423],"domain_scores_gemma":[0.9970268,0.00039377404,0.0002681108,0.0020560003,0.00010725697,0.00014804132],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023016665,0.00032759501,0.0002845616,0.00015814655,0.00016522748,0.00024202812,0.0022107884,0.00022972457,0.000088233726],"category_scores_gemma":[0.000027863105,0.00032219343,0.00017093109,0.0004895112,0.000045317312,0.00014816137,0.0018952093,0.00074771256,0.0004920056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000977691,0.0003258304,0.0007745934,0.0002943401,0.000087052234,0.00018083371,0.00024159769,0.31503835,0.00081471755,0.12263226,0.04238369,0.517217],"study_design_scores_gemma":[0.00018598275,0.000022745275,0.01602989,0.0001366015,0.000011786351,0.0000026849818,0.0000051835964,0.81355834,0.0015197345,0.16048908,0.007286059,0.0007518933],"about_ca_topic_score_codex":0.00009873104,"about_ca_topic_score_gemma":0.0000601215,"teacher_disagreement_score":0.51646507,"about_ca_system_score_codex":0.00009521916,"about_ca_system_score_gemma":0.00018295528,"threshold_uncertainty_score":0.999923},"labels":[],"label_agreement":null},{"id":"W4362601298","doi":"10.1109/tmi.2023.3264433","title":"MISSU: 3D Medical Image Segmentation via Self-Distilling TransUNet","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","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 British Columbia","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Computer science; Encoder; Artificial intelligence; Segmentation; Image segmentation; Transformer; Inference; Image resolution; Pattern recognition (psychology); Computer vision","score_opus":0.012338912383116447,"score_gpt":0.29272860682093843,"score_spread":0.28038969443782197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362601298","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":[0.0009751522,0.000042904165,0.98095644,0.014434059,0.0008737013,0.0002848079,0.0000066374982,0.002123284,0.00030301345],"genre_scores_gemma":[0.80346066,0.00074342877,0.18964837,0.004924808,0.00042514983,0.00039818158,0.00003090774,0.000094116614,0.00027437348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964846,0.00012636247,0.00050281006,0.000685529,0.0016103531,0.0005903832],"domain_scores_gemma":[0.99810463,0.000569711,0.000078358906,0.0005366509,0.0000756342,0.00063503376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058705,0.0002517055,0.00023204171,0.00026082815,0.00046596813,0.00009913889,0.0010005094,0.00012043971,0.0004150677],"category_scores_gemma":[0.00003503542,0.00024790058,0.00012691852,0.0015287054,0.00016884602,0.00062508555,0.000009036104,0.0007483692,0.00065417745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010836438,0.00029109232,0.000012446624,0.00004469411,0.000045411663,0.0003257274,0.00079512625,0.0064336574,0.0048667514,0.00032293555,0.0018043182,0.985047],"study_design_scores_gemma":[0.0006404514,0.000021976108,0.000041873027,0.000075273805,0.000023531491,0.0001299514,0.00004156807,0.9877206,0.0073858975,0.0009000288,0.0027415543,0.0002772792],"about_ca_topic_score_codex":0.000018521458,"about_ca_topic_score_gemma":0.00001534817,"teacher_disagreement_score":0.9847697,"about_ca_system_score_codex":0.000094093986,"about_ca_system_score_gemma":0.00014980661,"threshold_uncertainty_score":0.9999973},"labels":[],"label_agreement":null},{"id":"W4362606282","doi":"10.1007/s00138-023-01390-6","title":"Interpretable visual transmission lines inspections using pseudo-prototypical part network","year":2023,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electric power transmission; Artificial intelligence; Computer science; Reliability (semiconductor); Identification (biology); Transmission (telecommunications); Similarity (geometry); Focus (optics); Position (finance); Computer vision; Power transmission; Pattern recognition (psychology); Image (mathematics); Power (physics); Engineering; Electrical engineering; Telecommunications; Physics; Optics","score_opus":0.01926296997282099,"score_gpt":0.33044107451534716,"score_spread":0.31117810454252615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362606282","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":[0.0048522386,0.00019459415,0.99048376,0.002144796,0.00008465972,0.00086483103,0.0000074591053,0.00086527783,0.0005023776],"genre_scores_gemma":[0.7956575,0.0011750852,0.19717537,0.0010793203,0.0011517961,0.002252953,0.00013387203,0.00007800598,0.001296085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985501,0.000046649206,0.00032773326,0.00056370377,0.00017890111,0.00033289005],"domain_scores_gemma":[0.99903387,0.00014744524,0.00008447149,0.00048244625,0.000056266184,0.0001954777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016788606,0.00018549792,0.0001805003,0.00013313255,0.00087449176,0.00013288348,0.00039913465,0.00007511544,0.00002649305],"category_scores_gemma":[0.000009142932,0.00016012418,0.00006731928,0.0019999254,0.000082203966,0.000300272,0.00028427428,0.00022359575,0.000086780856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028293007,0.00036586684,0.0012651262,0.000039885854,0.00003053414,0.000003988446,0.00017855396,0.055774663,0.012752761,0.21408318,0.0071506444,0.7083265],"study_design_scores_gemma":[0.00015859757,0.00004058956,0.0007520598,0.000026043228,0.000008775082,0.00001644053,0.000008216116,0.80168045,0.00015248341,0.013933114,0.18305798,0.00016525338],"about_ca_topic_score_codex":0.000011068365,"about_ca_topic_score_gemma":0.00000560208,"teacher_disagreement_score":0.7933084,"about_ca_system_score_codex":0.000023129618,"about_ca_system_score_gemma":0.00002670163,"threshold_uncertainty_score":0.6725972},"labels":[],"label_agreement":null},{"id":"W4362705188","doi":"10.1016/j.microrel.2023.114974","title":"Evaluation of SEU impact on convolutional neural networks based on BRAM and CRAM in FPGAs","year":2023,"lang":"en","type":"article","venue":"Microelectronics Reliability","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; National Key Research and Development Program of China; TRIUMF","keywords":"Computer science; Convolutional neural network; Redundancy (engineering); Triple modular redundancy; Field-programmable gate array; Modular design; Error detection and correction; Algorithm; Parallel computing; Embedded system; Artificial intelligence","score_opus":0.019635262379650634,"score_gpt":0.31322943299036715,"score_spread":0.2935941706107165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362705188","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":[0.9489078,0.00014944212,0.048660196,0.001251254,0.00007948922,0.0007097351,0.000009213749,0.00012889001,0.00010401187],"genre_scores_gemma":[0.99882644,0.000024186613,0.00082702754,0.00017376908,0.00002472669,0.000079175734,0.000027131668,0.000010995094,0.0000065598842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977927,0.00033950678,0.00031663894,0.0005735051,0.0005254425,0.00045217772],"domain_scores_gemma":[0.99828017,0.00060834555,0.00011752039,0.0006981728,0.000224547,0.000071247756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019282851,0.00017390658,0.00020060004,0.00015045037,0.00008867869,0.000028879096,0.0003614795,0.00008844892,0.0000083171535],"category_scores_gemma":[0.00019821776,0.00015883263,0.000076271484,0.0011991796,0.000092838935,0.000138677,0.00009467611,0.00036993274,0.000007848373],"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":[0.00003886707,0.00011168656,0.0016268928,0.000004817176,0.0000039908196,4.1572946e-7,0.0000176495,0.9756805,0.00083746493,0.00190795,0.00019970456,0.019570118],"study_design_scores_gemma":[0.000537314,0.0003193591,0.053782236,0.000011161691,0.000007793433,0.0000016387914,0.0000011151188,0.9323454,0.00075829704,0.012063427,0.000055475215,0.000116799994],"about_ca_topic_score_codex":0.000024423744,"about_ca_topic_score_gemma":0.00003218674,"teacher_disagreement_score":0.052155342,"about_ca_system_score_codex":0.0006161147,"about_ca_system_score_gemma":0.00025592122,"threshold_uncertainty_score":0.6477006},"labels":[],"label_agreement":null},{"id":"W4362731281","doi":"10.20944/preprints202304.0124.v1","title":"DC-YOLOv8: Small Size Object Detection Algorithm Based on Camera Sensor","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":134,"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":"Pascal (unit); Computer science; Artificial intelligence; Object detection; Feature (linguistics); Pattern recognition (psychology); Computer vision","score_opus":0.1083111905199494,"score_gpt":0.3302626466974352,"score_spread":0.2219514561774858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362731281","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":[0.095010564,0.000024809793,0.8939377,0.0023114413,0.0022357255,0.0018166207,0.00004070682,0.003017969,0.0016044361],"genre_scores_gemma":[0.88263077,0.00009851516,0.11087664,0.0010669618,0.0006548167,0.001495253,0.00003943782,0.00015403147,0.0029836025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99503934,0.0003213448,0.0006925038,0.0026043793,0.0006095776,0.0007328612],"domain_scores_gemma":[0.99367976,0.0010980123,0.000538834,0.0041779066,0.00023705862,0.00026841814],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00064437283,0.00064970437,0.00056234753,0.00029105655,0.00032650647,0.0001031922,0.0024149928,0.00047612868,0.000079203535],"category_scores_gemma":[0.0005284734,0.00071263994,0.0003621978,0.000879784,0.00010858039,0.00015158954,0.0032917955,0.001801491,0.004556616],"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":[0.00012957776,0.0009037293,0.019557789,0.00036498226,0.00032676102,0.00029896587,0.001000761,0.6900913,0.04417358,0.0012667598,0.00027998185,0.2416058],"study_design_scores_gemma":[0.0006877425,0.000098871096,0.10760965,0.00025607497,0.00006238777,0.000023906261,0.000021833544,0.7312622,0.13950153,0.013544705,0.0054402347,0.0014908366],"about_ca_topic_score_codex":0.00017653531,"about_ca_topic_score_gemma":0.00005194349,"teacher_disagreement_score":0.7876202,"about_ca_system_score_codex":0.00039154408,"about_ca_system_score_gemma":0.0002051792,"threshold_uncertainty_score":0.99953246},"labels":[],"label_agreement":null},{"id":"W4366352791","doi":"10.1109/tpami.2023.3268446","title":"Dual Vision Transformer","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Transformer; Computational complexity theory; Pixel; Computation; Semantics (computer science); Feature extraction; Exploit; Theoretical computer science; Algorithm","score_opus":0.022594822720621646,"score_gpt":0.2986789647305413,"score_spread":0.27608414200991965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366352791","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":[0.0027935817,0.000032336404,0.9948973,0.0015897529,0.0001246291,0.00013264678,0.00002460553,0.00030359082,0.00010154972],"genre_scores_gemma":[0.9969036,0.0006741685,0.0015209184,0.0003976572,0.000017860762,0.0000493144,0.0000076053443,0.000011132691,0.00041777015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985946,0.000043033167,0.00029883924,0.00053832185,0.0002557131,0.00026951527],"domain_scores_gemma":[0.9991094,0.00016341652,0.000054953154,0.00050165196,0.00004137507,0.00012923013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015151022,0.00018199245,0.00021900077,0.0005475625,0.00028120735,0.0000919706,0.000346594,0.00005036662,0.00008071751],"category_scores_gemma":[0.0000013985248,0.0001581092,0.00020233866,0.0030904957,0.000057754518,0.00029425998,0.0000038286616,0.00021908333,0.00022195745],"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":[0.0000037713316,0.000060041923,0.000059656693,0.0000047302324,0.00012584247,0.0000077351715,0.00017083113,0.067657076,0.0008459766,0.0003031008,0.000032360193,0.93072885],"study_design_scores_gemma":[0.00011184259,0.00016212802,0.0020354274,0.000015524925,0.00025804836,0.000016430713,0.00003462403,0.850171,0.14378582,0.0020412141,0.00096776854,0.00040015395],"about_ca_topic_score_codex":0.00011380323,"about_ca_topic_score_gemma":0.0004345737,"teacher_disagreement_score":0.99411,"about_ca_system_score_codex":0.000018235973,"about_ca_system_score_gemma":0.0000089673,"threshold_uncertainty_score":0.64475054},"labels":[],"label_agreement":null},{"id":"W4366819366","doi":"10.1371/journal.pone.0284449","title":"Do you get what you see? Insights of using mAP to select architectures of pretrained neural networks for automated aerial animal detection","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs","keywords":"Artificial intelligence; Computer science; Metric (unit); Context (archaeology); F1 score; Pattern recognition (psychology); Deep learning; Focus (optics); Object detection; Convolutional neural network; Artificial neural network; Aerial image; Task (project management); Aerial imagery; Machine learning; Computer vision; Cartography; Image (mathematics); Geography","score_opus":0.04199708950813311,"score_gpt":0.27358052316314224,"score_spread":0.23158343365500914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366819366","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":[0.8013386,0.00007582439,0.19682258,0.000207455,0.0001407526,0.0008534104,0.000007445789,0.0005506973,0.000003223894],"genre_scores_gemma":[0.94938767,0.000009066187,0.05017024,0.000042491953,0.00024091032,0.00010314975,0.000010705768,0.000025068584,0.000010716674],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857175,0.00006401694,0.0003224983,0.0004165579,0.0002828698,0.00034230083],"domain_scores_gemma":[0.9988842,0.00018894955,0.00024423306,0.0004211201,0.00017209549,0.000089403926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011133663,0.00016310147,0.00029473443,0.00020819664,0.00012738143,0.00006954825,0.00044654534,0.00008245485,0.0000016634862],"category_scores_gemma":[0.00007841307,0.00016146607,0.00006892278,0.0013673941,0.00004554593,0.0002781956,0.00018705946,0.000121510144,0.0000021780893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019712395,0.00016014974,0.00007330385,0.00008735864,0.00007337021,0.00000147285,0.00067553157,0.3314467,0.6595413,0.00023987149,0.000037494065,0.007466297],"study_design_scores_gemma":[0.0002799854,0.000316015,0.0007884527,0.00007835368,0.00002767211,0.0000023446805,0.000011787497,0.85632104,0.14137743,0.0006495037,0.000012068943,0.00013532795],"about_ca_topic_score_codex":0.0000063695834,"about_ca_topic_score_gemma":0.000017611646,"teacher_disagreement_score":0.5248743,"about_ca_system_score_codex":0.000037092683,"about_ca_system_score_gemma":0.000025460347,"threshold_uncertainty_score":0.6584396},"labels":[],"label_agreement":null},{"id":"W4366831358","doi":"10.1016/j.media.2023.102826","title":"Calibrating segmentation networks with margin-based label smoothing","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure","funders":"H2020 Marie Skłodowska-Curie Actions; Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Softmax function; Discriminative model; Computer science; Margin (machine learning); Segmentation; Artificial intelligence; Machine learning; Logit; Artificial neural network; Mathematical optimization; Pattern recognition (psychology); Mathematics","score_opus":0.013038805535436885,"score_gpt":0.28536634430265007,"score_spread":0.27232753876721316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366831358","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":[0.0057939244,0.000025227471,0.9880047,0.0053118127,0.00003042571,0.00010797793,0.000001238848,0.00052949414,0.00019523934],"genre_scores_gemma":[0.5337835,0.000033365544,0.46099284,0.0042782477,0.00019821707,0.00016678633,0.0001924596,0.000031183245,0.00032338646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981435,0.00009493187,0.0002676683,0.00045059496,0.00069756765,0.00034572004],"domain_scores_gemma":[0.9986781,0.00041341133,0.00011902106,0.0005043957,0.00006845237,0.00021663764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040425485,0.00013361636,0.00021683521,0.00026160103,0.00023458649,0.00015075954,0.00061547506,0.00005916994,0.00009430244],"category_scores_gemma":[0.000096226904,0.00010766898,0.000082596736,0.0067726835,0.00008562837,0.00039730468,0.00014707382,0.0002280925,0.000053372343],"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":[0.000013902697,0.00015164862,0.010896955,0.000026839753,0.00064967206,0.00047457614,0.00032930958,0.7381834,0.0014477277,0.0024621554,0.004717118,0.24064672],"study_design_scores_gemma":[0.0002559761,0.000017180471,0.0010929721,0.000012973488,0.00011511443,0.0000015526282,0.00002095949,0.99769896,0.0003478779,0.0001407217,0.00016436247,0.00013136864],"about_ca_topic_score_codex":0.000029157625,"about_ca_topic_score_gemma":0.000066825305,"teacher_disagreement_score":0.52798957,"about_ca_system_score_codex":0.000033034175,"about_ca_system_score_gemma":0.000059387396,"threshold_uncertainty_score":0.43906134},"labels":[],"label_agreement":null},{"id":"W4366989306","doi":"10.21203/rs.3.rs-2807694/v1","title":"GBS-YOLOv5: Improved Algorithm for UAV Intelligent Transportation Based on YOLOv5","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Bottleneck; Computer science; Drone; Feature (linguistics); Pyramid (geometry); Data mining; Feature extraction; Task (project management); Real-time computing; Convolution (computer science); Artificial intelligence; Engineering; Artificial neural network; Embedded system","score_opus":0.12273310302428342,"score_gpt":0.4227470433066812,"score_spread":0.3000139402823978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366989306","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":[0.00032194838,0.000090594076,0.9887594,0.004747254,0.0005607347,0.004281623,0.00046970963,0.00067130895,0.00009742114],"genre_scores_gemma":[0.14448693,0.0006837464,0.82594454,0.000588264,0.0014485597,0.019226823,0.0034388648,0.00033706258,0.0038451736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953817,0.00024675237,0.0005616668,0.0016455434,0.0011606397,0.0010036698],"domain_scores_gemma":[0.99480915,0.0017890774,0.00019162298,0.0020166335,0.0008966957,0.00029681428],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013724518,0.00037842884,0.00038831204,0.00064179354,0.000421069,0.00026898808,0.002263814,0.00036900194,0.000016867825],"category_scores_gemma":[0.0002059697,0.00037444098,0.0003139933,0.0012581248,0.00013416496,0.00015948979,0.00042568022,0.0015488206,0.00019262568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012611716,0.00052212493,0.00006953738,0.00092117453,0.00007378305,0.00004701032,0.0006573201,0.12047842,0.00059212185,0.017950844,0.009881288,0.84868026],"study_design_scores_gemma":[0.00032160612,0.00049013406,0.00071442995,0.00028427003,0.000007763578,3.36923e-7,0.00004650419,0.95368373,0.0026766546,0.032912243,0.008497813,0.00036451253],"about_ca_topic_score_codex":0.00008563682,"about_ca_topic_score_gemma":0.00008161164,"teacher_disagreement_score":0.8483157,"about_ca_system_score_codex":0.00039332767,"about_ca_system_score_gemma":0.00047974652,"threshold_uncertainty_score":0.9998708},"labels":[],"label_agreement":null},{"id":"W4367307676","doi":"10.36227/techrxiv.22689664.v1","title":"Adaptive Exponential Weight Discretization for Compact and energy-efficient CNN Inference","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Bank Note Company (Canada)","funders":"","keywords":"Convolutional neural network; Discretization; Quantization (signal processing); Computer science; Speedup; Algorithm; Inference; Computation; FLOPS; Pruning; Artificial intelligence; Mathematics; Parallel computing","score_opus":0.05108126965155778,"score_gpt":0.3022181511714865,"score_spread":0.2511368815199287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367307676","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":[0.00062682584,0.000083798324,0.9955281,0.0015461963,0.00056080095,0.0004652833,0.000040856106,0.0005190104,0.0006291093],"genre_scores_gemma":[0.9033321,0.00016847087,0.09426009,0.00013234856,0.00023140987,0.0004115401,0.00015472446,0.00003290102,0.0012764102],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998204,0.00004627287,0.0003106613,0.00090252503,0.00023825528,0.00029829968],"domain_scores_gemma":[0.9983843,0.00040722778,0.00021901625,0.0007225188,0.00014208884,0.00012486806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010614644,0.0002691002,0.0002631743,0.00013049667,0.00020032875,0.00018403174,0.00077984115,0.00013671129,0.0000057322127],"category_scores_gemma":[0.000032886266,0.0002423162,0.00008442008,0.00029011883,0.00007920705,0.00013089555,0.0010176024,0.00019151204,0.0000120235245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001681444,0.000052379113,0.000038996026,0.000023416464,0.000027941167,0.0000012576737,0.00017391994,0.22111921,0.00018641067,0.76140285,0.0016303421,0.015326491],"study_design_scores_gemma":[0.00015178253,0.00004860176,0.00068355486,0.000047767247,0.000012642302,0.0000011120759,0.000009385018,0.93277055,0.0006945175,0.06355531,0.0017333098,0.00029147134],"about_ca_topic_score_codex":0.00005847346,"about_ca_topic_score_gemma":0.000038760307,"teacher_disagreement_score":0.90270525,"about_ca_system_score_codex":0.00006076725,"about_ca_system_score_gemma":0.00007683802,"threshold_uncertainty_score":0.9881367},"labels":[],"label_agreement":null},{"id":"W4368404611","doi":"10.1002/cjce.24939","title":"Deep learning‐based hybrid reconstruction algorithm for fibre instance segmentation from <scp>3D</scp> x‐ray tomographic images","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"McMaster University; University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Algorithm; Sample (material); Computer vision; Image segmentation; Outlier; Pattern recognition (psychology)","score_opus":0.00791408704297116,"score_gpt":0.20424554361137312,"score_spread":0.19633145656840195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4368404611","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":[0.079233736,0.00021027212,0.91957724,0.0004809546,0.00028276426,0.00012183126,0.000009623177,0.00007588638,0.0000076986635],"genre_scores_gemma":[0.73429424,0.000014070865,0.26518974,0.00008727975,0.00032259032,0.000029529847,0.000018814608,0.00002438235,0.000019377605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916136,0.000015903066,0.00024757275,0.00015022181,0.00014134891,0.00028361098],"domain_scores_gemma":[0.9988719,0.00046007815,0.00015384809,0.00016768416,0.0001141538,0.00023232604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016516543,0.00011251914,0.00013268818,0.000174145,0.0001424872,0.000094664916,0.00046137438,0.000038872968,0.0000019246088],"category_scores_gemma":[0.00016123583,0.00010208651,0.00008547326,0.00058492186,0.000044841167,0.00029057582,0.000016701015,0.0003036228,0.0000032726202],"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":[0.0000014097592,0.0000037031584,0.00008511326,0.000011414502,0.000040841707,0.00001998699,0.00019055122,0.71235955,0.06159962,0.00017618312,0.00056485564,0.22494678],"study_design_scores_gemma":[0.00023239484,0.000019861103,0.00014523396,0.000044159846,0.000011624283,0.000042956137,0.00001174522,0.90977365,0.087237954,0.0012097119,0.001209741,0.00006096042],"about_ca_topic_score_codex":0.00005454457,"about_ca_topic_score_gemma":0.000028729766,"teacher_disagreement_score":0.65506047,"about_ca_system_score_codex":0.00012793376,"about_ca_system_score_gemma":0.000115254465,"threshold_uncertainty_score":0.4162967},"labels":[],"label_agreement":null},{"id":"W4372267664","doi":"10.1109/icassp49357.2023.10097215","title":"Pseudo-Inverted Bottleneck Convolution for Darts Search Space","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Bottleneck; Computer science; Block (permutation group theory); Memory footprint; Architecture; Footprint; Convolutional neural network; Convolution (computer science); Layer (electronics); Network architecture; Code (set theory); Algorithm; Computer engineering; Artificial intelligence; Artificial neural network; Computer network; Embedded system; Mathematics; Set (abstract data type)","score_opus":0.04723404990737102,"score_gpt":0.31069919164030974,"score_spread":0.2634651417329387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372267664","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":[0.005644311,0.000012162854,0.9822325,0.008735212,0.00016617758,0.0004630131,0.0000035245134,0.00095832674,0.001784782],"genre_scores_gemma":[0.67346317,0.000054015185,0.30976897,0.0010178835,0.00019916987,0.00039047733,0.000042249743,0.000029026827,0.015035015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899936,0.000020665915,0.00011965234,0.00035666328,0.00016623987,0.00033744925],"domain_scores_gemma":[0.9990915,0.00021231991,0.000026907788,0.00049360516,0.00008867636,0.000086957785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015253943,0.000083748506,0.00008537986,0.00008149639,0.00019132513,0.000049998478,0.00047624347,0.000037407048,0.000011324127],"category_scores_gemma":[0.000025923015,0.00007907535,0.00004309716,0.00097167486,0.00003487779,0.00028671537,0.00019493798,0.00006413412,0.00054056046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005313687,0.000024223904,0.00014031313,0.000012722827,0.000008190992,0.000002149997,0.000109188164,0.00401397,0.01132739,0.87604755,0.087394856,0.020914104],"study_design_scores_gemma":[0.00023140019,0.00003815559,0.0012080149,0.0000041471153,0.0000018791694,0.0000036933095,0.00001605798,0.9414534,0.0073914635,0.029221002,0.020306228,0.00012453937],"about_ca_topic_score_codex":0.0000088717625,"about_ca_topic_score_gemma":0.000010910283,"teacher_disagreement_score":0.93743944,"about_ca_system_score_codex":0.000033540786,"about_ca_system_score_gemma":0.000036188812,"threshold_uncertainty_score":0.6947991},"labels":[],"label_agreement":null},{"id":"W4375869437","doi":"10.1109/icassp49357.2023.10094582","title":"Performing Neural Architecture Search Without Gradients","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial neural network; Architecture; Kernel (algebra); Ranking (information retrieval); Process (computing); Artificial intelligence; Code (set theory); Beam search; Margin (machine learning); Machine learning; Gaussian process; Search algorithm; Gaussian; Algorithm; Mathematics","score_opus":0.03050067906354909,"score_gpt":0.298351156787732,"score_spread":0.2678504777241829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375869437","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":[0.3392451,0.00001320254,0.65126395,0.0038868752,0.0001865928,0.00026892227,9.360195e-7,0.0013450506,0.0037893725],"genre_scores_gemma":[0.9586571,0.0000072230155,0.038225643,0.00035985874,0.000077445795,0.000033910783,0.000003904068,0.0000108867125,0.0026240526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989259,0.000023538847,0.000109029745,0.00033513087,0.00022793567,0.000378458],"domain_scores_gemma":[0.999276,0.00007607733,0.000019430025,0.00049548957,0.000029121857,0.00010387175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009927924,0.00009442435,0.00008001283,0.00011400319,0.00021697854,0.00006123326,0.00074266875,0.00002370587,0.000010357102],"category_scores_gemma":[0.000010116238,0.00007672596,0.000037001235,0.0011524342,0.000026346588,0.0002353728,0.0003912651,0.00017858503,0.0003805197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008542935,0.000048030844,0.013697434,0.000021644883,0.000016910986,0.000036818732,0.0016385975,0.08029152,0.0060094646,0.1572986,0.0039316453,0.7370008],"study_design_scores_gemma":[0.00017625009,0.00004063222,0.010454578,0.000005809402,0.0000015540764,0.000039353545,0.00002573437,0.97242427,0.0019801662,0.008978096,0.0056789652,0.00019458453],"about_ca_topic_score_codex":0.000005592492,"about_ca_topic_score_gemma":0.0000071098725,"teacher_disagreement_score":0.89213276,"about_ca_system_score_codex":0.000016542637,"about_ca_system_score_gemma":0.000013446684,"threshold_uncertainty_score":0.48909378},"labels":[],"label_agreement":null},{"id":"W4376881345","doi":"10.31399/asm.cp.itsc2023p0022","title":"Visual Navigation for Autonomous Mobile Material Deposition Systems using Remote Sensing","year":2023,"lang":"en","type":"article","venue":"Thermal spray","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Mobile robot; Context (archaeology); Scalability; Sensor fusion; Kinematics; Mobile device; Real-time computing; Robot","score_opus":0.025312721267104498,"score_gpt":0.30687971326573826,"score_spread":0.2815669919986338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376881345","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":[0.48055246,0.0000088113,0.51824224,0.000028536853,0.0003879861,0.00039418048,0.0000042382585,0.0003553614,0.000026166914],"genre_scores_gemma":[0.93347037,0.0000013374739,0.065920115,0.000033738543,0.00044582057,0.00001470477,0.00004463762,0.000021849686,0.00004743016],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990851,0.000049475926,0.00020007537,0.00027557986,0.00012353959,0.00026620153],"domain_scores_gemma":[0.9994687,0.00007127868,0.00011558993,0.00024333174,0.000053511696,0.00004760491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015920187,0.000105494175,0.00011230101,0.000049952923,0.00025855712,0.00013402643,0.00019012971,0.000053648473,0.0000011475412],"category_scores_gemma":[0.0000041813237,0.00010726361,0.000037761736,0.0003480494,0.000020763548,0.00031568564,0.000089786954,0.00005270202,0.000047037167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000141693245,0.00000672558,0.0000019073302,0.000021511618,0.000006312132,0.0000066925973,0.000121483994,0.12005091,0.80693656,0.00087994675,0.00002480236,0.07192898],"study_design_scores_gemma":[0.0001247713,0.00004826473,0.00004607878,0.000033433476,0.0000063494326,0.000033226515,0.000013125727,0.92617124,0.07258955,0.0004167417,0.00039248168,0.00012471547],"about_ca_topic_score_codex":0.00004617406,"about_ca_topic_score_gemma":0.0000011749145,"teacher_disagreement_score":0.80612034,"about_ca_system_score_codex":0.00008155116,"about_ca_system_score_gemma":0.00002198171,"threshold_uncertainty_score":0.4374083},"labels":[],"label_agreement":null},{"id":"W4377236361","doi":"10.1145/3581807.3581815","title":"Coordinate Attention-enabled Ship Object Detection with Electro-optical Image","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Object detection; Object (grammar); Computer vision; Coordinate system; Artificial intelligence; Set (abstract data type); Range (aeronautics); Image (mathematics); Shore; Attention network; Real-time computing; Simulation; Pattern recognition (psychology); Engineering; Aerospace engineering","score_opus":0.008073177542105027,"score_gpt":0.22929211734880883,"score_spread":0.22121893980670382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377236361","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":[0.037613925,0.000012840864,0.954081,0.0012510222,0.000070569986,0.00028526888,6.92919e-7,0.00046647765,0.0062182075],"genre_scores_gemma":[0.9441234,0.0000020647608,0.053474877,0.0003377092,0.00003519782,0.00031557077,0.0000036003835,0.0000131980705,0.0016943805],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873835,0.00006368871,0.00014451426,0.00043607983,0.0002818045,0.0003355935],"domain_scores_gemma":[0.99924374,0.0000884332,0.000062102554,0.0004707698,0.00005965034,0.000075316486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015176061,0.000115863615,0.00010358665,0.00007819725,0.00056949316,0.000086175,0.0005310972,0.00001688626,0.00007917994],"category_scores_gemma":[0.000009365675,0.00010163896,0.000042578915,0.0010425439,0.000038247974,0.0004109034,0.00027843093,0.0002753471,0.000056756347],"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":[0.00015893031,0.00041245774,0.00070948334,0.000015833211,0.00006887097,0.00008304751,0.00012939693,0.00958046,0.56791604,0.34261853,0.0030356077,0.075271346],"study_design_scores_gemma":[0.0030280391,0.0028080277,0.016739286,0.000010893521,0.00006649354,0.0011910406,0.00021968028,0.6291329,0.24346587,0.06934754,0.03227358,0.0017166145],"about_ca_topic_score_codex":0.000009549441,"about_ca_topic_score_gemma":0.000017261606,"teacher_disagreement_score":0.90650946,"about_ca_system_score_codex":0.00011947961,"about_ca_system_score_gemma":0.000035921705,"threshold_uncertainty_score":0.43801385},"labels":[],"label_agreement":null},{"id":"W4377241880","doi":"10.3390/electronics12102323","title":"DC-YOLOv8: Small-Size Object Detection Algorithm Based on Camera Sensor","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":505,"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; Artificial intelligence; Object detection; Upsampling; Object (grammar); Precision and recall; Feature (linguistics); Context (archaeology); Point (geometry); Computer vision; Pattern recognition (psychology); Algorithm; Image (mathematics); Mathematics","score_opus":0.01266948918731378,"score_gpt":0.24808744516944786,"score_spread":0.23541795598213408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377241880","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":[0.0138291,0.00019475052,0.9814213,0.0019741708,0.00028725693,0.0003809778,0.0000036821582,0.0013951529,0.0005136059],"genre_scores_gemma":[0.75715494,0.0007527819,0.232477,0.0043721264,0.00074737996,0.0005713945,0.00003797056,0.00014076167,0.0037456402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983102,0.00007533743,0.0001840053,0.0005407199,0.0002498946,0.0006398343],"domain_scores_gemma":[0.99855375,0.00047496567,0.000086620814,0.0007348924,0.000059298796,0.000090469344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018314394,0.00018284662,0.00014418991,0.00011944592,0.00027053183,0.00006794908,0.000536108,0.00008137942,0.000007726397],"category_scores_gemma":[0.00007190628,0.00018692703,0.000080900805,0.0016966832,0.000027255737,0.00012415388,0.00008568248,0.00038645318,0.00043368968],"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":[0.000015491927,0.00007408165,0.000016354772,0.0000066170182,0.000017713364,0.000023936644,0.00007174033,0.040181533,0.020351736,0.0037987875,0.0009981039,0.9344439],"study_design_scores_gemma":[0.000314202,0.00031461276,0.00039483985,0.0000067609417,0.0000065404565,0.000013174529,0.0000047696194,0.89233166,0.05274258,0.0041284645,0.04947808,0.00026429098],"about_ca_topic_score_codex":0.000003740849,"about_ca_topic_score_gemma":0.000037605343,"teacher_disagreement_score":0.9341796,"about_ca_system_score_codex":0.00020883461,"about_ca_system_score_gemma":0.00011330141,"threshold_uncertainty_score":0.76226634},"labels":[],"label_agreement":null},{"id":"W4377832635","doi":"10.18280/ts.400220","title":"Master-Slave Convolutional Deep Architecture for Vehicle Identification and Type Classification","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agence Thématique de Recherche en Science et Technologie","keywords":"Convolutional neural network; Computer science; Architecture; Deep learning; Artificial intelligence; Intelligent transportation system; Reduction (mathematics); Identification (biology); Real-time computing; Similarity (geometry); Artificial neural network; Pattern recognition (psychology); Engineering; Image (mathematics); Transport engineering","score_opus":0.04714815104217402,"score_gpt":0.2769717606557643,"score_spread":0.22982360961359027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377832635","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":[0.045570575,0.000048764035,0.9494239,0.0039803507,0.00011289568,0.00055224804,0.000012323294,0.00023899283,0.000059963975],"genre_scores_gemma":[0.9825546,0.000017590903,0.016460087,0.00021063656,0.00013922612,0.00026362034,0.00010950559,0.000011256831,0.00023347643],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989839,0.000029189774,0.00022443646,0.00036787253,0.00018617616,0.000208432],"domain_scores_gemma":[0.9993467,0.00016443232,0.000098061886,0.00022582128,0.00009952288,0.00006549196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018619483,0.000100563964,0.00008148555,0.000091871436,0.00021425867,0.00008201981,0.00027831565,0.00003799595,0.000012480349],"category_scores_gemma":[0.000014936947,0.00010055166,0.000031112908,0.00047075687,0.000051735748,0.00021142486,0.00006750224,0.000065816086,0.00006145934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008251299,0.0001332443,0.0013143698,0.00006459751,0.000051780644,0.0000023385444,0.0010727355,0.013928617,0.31525147,0.37731987,0.006321572,0.28445688],"study_design_scores_gemma":[0.0005582259,0.000100207835,0.07249649,0.0000091335405,0.000012658282,0.00000643948,0.000043231605,0.8705437,0.002511355,0.04056318,0.012961881,0.00019347468],"about_ca_topic_score_codex":7.426789e-7,"about_ca_topic_score_gemma":0.0000041418366,"teacher_disagreement_score":0.936984,"about_ca_system_score_codex":0.000033109733,"about_ca_system_score_gemma":0.000021323764,"threshold_uncertainty_score":0.4100378},"labels":[],"label_agreement":null},{"id":"W4378191129","doi":"10.1109/syscon53073.2023.10131084","title":"Small-Object Detection for UAV-Based Images","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Object detection; Background subtraction; Artificial intelligence; Computer science; Computer vision; Object (grammar); Tracking (education); Viola–Jones object detection framework; Video tracking; Deep learning; Object-class detection; Feature extraction; Pattern recognition (psychology); Pixel; Face detection","score_opus":0.04068879297365272,"score_gpt":0.2844854875642075,"score_spread":0.24379669459055478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378191129","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":[0.0020721592,0.000006714457,0.99393654,0.0016994607,0.00013516452,0.00030846195,0.0000017256607,0.0011430169,0.00069673033],"genre_scores_gemma":[0.69532394,0.000006540712,0.30060253,0.00073616725,0.00012127994,0.0005820024,0.0000076030033,0.000016317668,0.0026036543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937284,0.000011232784,0.00009346187,0.00026030812,0.00006763349,0.00019449461],"domain_scores_gemma":[0.9992901,0.00025154892,0.00003100138,0.0003415907,0.000046154466,0.000039600585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000092066635,0.000066269,0.000057505687,0.000076206605,0.00014907349,0.000047341695,0.0003297782,0.000023414126,0.0000033923793],"category_scores_gemma":[0.000029754443,0.000060281054,0.000046496578,0.0007313746,0.000014643605,0.00013095533,0.00006224305,0.000040164916,0.00013651949],"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":[0.000014208995,0.00004766676,0.0000848205,0.000027756396,0.000010940857,0.0000036240751,0.00004655587,0.029789783,0.11051257,0.03765356,0.011204245,0.8106043],"study_design_scores_gemma":[0.00024544686,0.00006549345,0.0010617037,0.0000029451342,0.0000026280854,0.0000017837164,0.0000050756194,0.6933303,0.27113166,0.01599511,0.018016998,0.00014082924],"about_ca_topic_score_codex":0.000004441983,"about_ca_topic_score_gemma":0.000026902851,"teacher_disagreement_score":0.8104634,"about_ca_system_score_codex":0.000017748507,"about_ca_system_score_gemma":0.00001669988,"threshold_uncertainty_score":0.245819},"labels":[],"label_agreement":null},{"id":"W4378801057","doi":"10.1145/3583781.3590302","title":"High-Throughput Edge Inference for BERT Models via Neural Architecture Search and Pipeline","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Pipeline (software); Throughput; Inference; Enhanced Data Rates for GSM Evolution; Architecture; Artificial neural network; Computer architecture; Parallel computing; Distributed computing; Artificial intelligence; Operating system; Wireless","score_opus":0.04471021459728105,"score_gpt":0.3134345893846869,"score_spread":0.26872437478740585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378801057","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":[0.0100807445,0.000038040118,0.98067755,0.007916061,0.00010418892,0.00048278653,0.0000086885,0.0005389146,0.00015301691],"genre_scores_gemma":[0.75245297,0.000042810938,0.24516791,0.0007535393,0.00013577515,0.00014789008,0.00002037825,0.000016173903,0.001262564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869204,0.000025074023,0.00018197471,0.0005245807,0.00017560445,0.00040073585],"domain_scores_gemma":[0.9988055,0.00043020287,0.00002714313,0.0005265184,0.00008988491,0.00012075346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011949334,0.00014640835,0.00014807573,0.00009056454,0.00019777326,0.00007886707,0.0005919823,0.000049388083,0.0000038358385],"category_scores_gemma":[0.000021430733,0.0001225084,0.000037095495,0.00072215754,0.000055613436,0.00037601506,0.0005283365,0.00016051054,0.000028568058],"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":[0.000011888618,0.0000277562,0.000035287783,0.00002526241,0.000007020948,0.0000036240897,0.00036354476,0.3968828,0.00211553,0.3059927,0.0041672853,0.2903673],"study_design_scores_gemma":[0.00019533507,0.00004089289,0.00016981828,0.000003515938,0.0000020274376,0.000007265963,0.000003893894,0.85433173,0.0010251035,0.14292103,0.0011699587,0.00012942044],"about_ca_topic_score_codex":0.00003561779,"about_ca_topic_score_gemma":0.000044913086,"teacher_disagreement_score":0.7423722,"about_ca_system_score_codex":0.000013595683,"about_ca_system_score_gemma":0.000024133016,"threshold_uncertainty_score":0.49957478},"labels":[],"label_agreement":null},{"id":"W4378803348","doi":"10.1088/1361-6560/acda78","title":"Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis","year":2023,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature (linguistics); Artificial intelligence; Modality (human–computer interaction); Pattern recognition (psychology); Feature vector; Feature learning; Matching (statistics); Deep learning; Pairwise comparison; Modalities; Parametric statistics; Image (mathematics); Mathematics","score_opus":0.09728787595367502,"score_gpt":0.3662861870463183,"score_spread":0.26899831109264327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378803348","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":[0.008489838,0.00006914695,0.9855631,0.0052806614,0.00012674,0.0002341088,0.000003735692,0.000106977335,0.0001256998],"genre_scores_gemma":[0.9591758,0.00011943,0.039368942,0.0007049894,0.00032973624,0.00023212912,0.000032376207,0.000009444651,0.000027126845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992712,0.00006959918,0.0001018208,0.00030903047,0.000054973367,0.0001933695],"domain_scores_gemma":[0.99596816,0.0037788684,0.00006119734,0.00011553819,0.00004004476,0.00003620257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022151406,0.00009626707,0.00017959719,0.00014764846,0.00011822248,0.000007184448,0.00014377233,0.000046677324,0.0000011998949],"category_scores_gemma":[0.00035438515,0.000073654424,0.000024925646,0.00086322956,0.00009241503,0.000056680772,0.000042508094,0.00019490003,0.000004619863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012696629,0.00015649073,0.0056968783,0.00006902646,0.000041838353,0.000011687889,0.00070340466,0.11473768,0.02363137,0.06018866,0.0028184163,0.7918176],"study_design_scores_gemma":[0.0005159997,0.00017189089,0.0035948907,0.000043826425,0.000007119322,0.0000014308777,0.000057394907,0.95136493,0.0006682685,0.041954905,0.0015079067,0.000111453504],"about_ca_topic_score_codex":0.000008880081,"about_ca_topic_score_gemma":0.000003812052,"teacher_disagreement_score":0.950686,"about_ca_system_score_codex":0.000019632493,"about_ca_system_score_gemma":0.000010958451,"threshold_uncertainty_score":0.30035403},"labels":[],"label_agreement":null},{"id":"W4379116572","doi":"10.1109/lra.2023.3282381","title":"Use Your Imagination: A Detector-Independent Approach for LiDAR Quality Booster","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Point cloud; Lidar; Computer science; Inference; Artificial intelligence; RGB color model; Point (geometry); Block (permutation group theory); Detector; Process (computing); Computer vision; Remote sensing; Mathematics; Geography; Telecommunications","score_opus":0.06530455764265676,"score_gpt":0.31174152298987323,"score_spread":0.24643696534721649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379116572","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":[0.036108594,0.000009956007,0.9527117,0.01004819,0.00019599935,0.00043420523,0.000007924695,0.00046909612,0.000014352963],"genre_scores_gemma":[0.6462201,0.000011668574,0.35106152,0.002223265,0.00013574964,0.00017071255,0.000034579287,0.000018822486,0.00012357725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988728,0.00004433355,0.0002533105,0.00037165036,0.00022248404,0.00023541652],"domain_scores_gemma":[0.99918723,0.00018457879,0.0001329227,0.0003531471,0.000072447074,0.000069674126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002247531,0.0001259302,0.00012640143,0.000114126444,0.00020997666,0.00028821215,0.000261619,0.000040556017,5.3170737e-7],"category_scores_gemma":[0.000027508158,0.00012514315,0.00005141141,0.0004027547,0.00003596372,0.00071152684,0.000072656156,0.000080794984,0.000014324917],"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":[0.000012234992,0.00010690987,0.0012817294,0.00016908463,0.00006026098,0.000007114373,0.0011304917,0.7897727,0.07721618,0.06824747,0.009332055,0.052663762],"study_design_scores_gemma":[0.00029612993,0.000014943895,0.013679338,0.000006808252,0.000007636221,0.000006937879,0.000012142304,0.9821417,0.0014487751,0.0013694242,0.0008113409,0.00020483666],"about_ca_topic_score_codex":0.0000032737644,"about_ca_topic_score_gemma":9.4128643e-7,"teacher_disagreement_score":0.61011153,"about_ca_system_score_codex":0.000039103743,"about_ca_system_score_gemma":0.000012560078,"threshold_uncertainty_score":0.510319},"labels":[],"label_agreement":null},{"id":"W4379160650","doi":"10.21203/rs.3.rs-2986682/v1","title":"Counting Objects in Images using DeepLearning: Methods and Current Challenges","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Simon Fraser University","keywords":"Computer science; Benchmark (surveying); Annotation; Artificial intelligence; Context (archaeology); Bottleneck; Object (grammar); Object detection; Machine learning; Relevance (law); Deep learning; Task (project management); Focus (optics); Pattern recognition (psychology)","score_opus":0.3368250251843426,"score_gpt":0.543061354615628,"score_spread":0.2062363294312854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379160650","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":[0.017814819,0.055804767,0.91920924,0.0032991292,0.00061661727,0.002030758,0.000012281181,0.0006928513,0.000519552],"genre_scores_gemma":[0.29812437,0.038094603,0.66231674,0.000015120611,0.00049456046,0.0007080953,0.00001878847,0.00011611577,0.00011157837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955509,0.0012773797,0.00034332857,0.0012651669,0.00074356835,0.0008197111],"domain_scores_gemma":[0.996172,0.0021288723,0.00014278396,0.0010648486,0.00033360426,0.00015785683],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004056173,0.00024330989,0.00034499366,0.00073490187,0.00031065225,0.00034338087,0.0012003168,0.00016733166,0.0000021020583],"category_scores_gemma":[0.0010037193,0.00025327972,0.000060480335,0.0011019589,0.0001534664,0.00026064797,0.006536772,0.002466911,0.000019118346],"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":[0.0000083190125,0.00010915206,0.0022242565,0.0023075552,0.000025201132,0.00007631785,0.0038349486,0.027298871,0.0014308413,0.021165976,0.0001849379,0.94133365],"study_design_scores_gemma":[0.00032374437,0.00007283583,0.025431314,0.0028899196,0.000007419964,0.000017457001,0.0007367725,0.7831755,0.0010732699,0.18015781,0.005347313,0.0007666238],"about_ca_topic_score_codex":0.00008576497,"about_ca_topic_score_gemma":0.0000721411,"teacher_disagreement_score":0.940567,"about_ca_system_score_codex":0.00024040905,"about_ca_system_score_gemma":0.0002464337,"threshold_uncertainty_score":0.99999195},"labels":[],"label_agreement":null},{"id":"W4379522931","doi":"10.21428/594757db.acb1ea67","title":"Optimizing DNNs Model Partitioning for Enhanced Performance on Edge Devices","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Workload; Parallelism (grammar); Enhanced Data Rates for GSM Evolution; Data parallelism; Edge device; Reduction (mathematics); Task (project management); Parallel computing; Deep neural networks; Distributed computing; Artificial neural network; Computer engineering; Artificial intelligence; Cloud computing","score_opus":0.05333885072025477,"score_gpt":0.3041997679785108,"score_spread":0.250860917258256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379522931","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":[0.03126149,0.0000089355435,0.963719,0.00087043404,0.00008343774,0.00026153558,0.0000015548853,0.0007084666,0.0030851173],"genre_scores_gemma":[0.7355444,0.000026548872,0.26229522,0.00058047066,0.00005195782,0.00034724522,0.000006568937,0.000009562207,0.0011380438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990913,0.0000063601783,0.00014741834,0.00033645536,0.000115953866,0.00030245612],"domain_scores_gemma":[0.9993335,0.00016846844,0.0000531157,0.0003375693,0.000051314604,0.000056014524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000096248805,0.000096652206,0.00008698315,0.00006753194,0.00032841973,0.000065462256,0.00043860122,0.000027032098,0.0000030421986],"category_scores_gemma":[0.000012575323,0.00008963126,0.000038641967,0.00051663315,0.0000163805,0.0004923871,0.00011448924,0.00006525145,0.00016217312],"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":[0.0000030880356,0.0000105730505,0.000010975426,0.000008893755,0.0000026303,1.400584e-7,0.00016097765,0.8922906,0.0032154508,0.08703329,0.00076126267,0.016502105],"study_design_scores_gemma":[0.000114265575,0.000037173984,0.00010908317,0.000016988459,0.0000015063131,3.5902795e-7,0.000009233159,0.9556277,0.039055455,0.0038022555,0.0011105164,0.000115489056],"about_ca_topic_score_codex":3.0744047e-7,"about_ca_topic_score_gemma":0.0000036900901,"teacher_disagreement_score":0.7042829,"about_ca_system_score_codex":0.000024157212,"about_ca_system_score_gemma":0.000020631182,"threshold_uncertainty_score":0.3655057},"labels":[],"label_agreement":null},{"id":"W4379527722","doi":"10.11834/jig.211099","title":"NAS-FPNLite object detection method fused with cross stage connection and inverted residual","year":2023,"lang":"en","type":"article","venue":"Journal of Image and Graphics","topic":"Advanced Neural Network Applications","field":"Computer 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":"","keywords":"Pyramid (geometry); Residual; Computer science; Object detection; Feature (linguistics); Artificial intelligence; Convolutional neural network; Computation; Convolution (computer science); Pattern recognition (psychology); Context (archaeology); Feature extraction; Detector; Artificial neural network; Algorithm; Mathematics; Telecommunications; Geometry","score_opus":0.020563680936405218,"score_gpt":0.31117113431668625,"score_spread":0.29060745338028104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379527722","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":[0.3756437,0.00010645917,0.6234529,0.0005711202,0.000067466885,0.00007882625,0.0000022325316,0.00005481186,0.000022474549],"genre_scores_gemma":[0.91174,0.0009413025,0.08665699,0.00037581817,0.00014232063,0.0000072151756,0.0000016862255,0.000017356637,0.00011730331],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991409,0.000088180546,0.00023479683,0.00018122466,0.00020475367,0.00015013381],"domain_scores_gemma":[0.9990154,0.0002528953,0.00025150148,0.00016182843,0.00022646628,0.00009190854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053544465,0.00009690865,0.00014580379,0.0002645497,0.00023707641,0.00017799341,0.00014070005,0.000047498466,6.94644e-7],"category_scores_gemma":[0.00006267077,0.00007537731,0.000032169086,0.0010873461,0.00009229651,0.00083207345,0.00006371579,0.00026283087,8.0283013e-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":[0.0009858804,0.00026995662,0.028015379,0.00035887322,0.0005832083,0.0008257765,0.006014388,0.0042261775,0.55697423,0.049449664,0.0020884178,0.35020804],"study_design_scores_gemma":[0.0071856286,0.0032052463,0.46127978,0.00022382432,0.00016515782,0.003438261,0.00079048675,0.2946522,0.12059799,0.098595455,0.008732607,0.0011333902],"about_ca_topic_score_codex":0.000008935486,"about_ca_topic_score_gemma":0.000045134388,"teacher_disagreement_score":0.5367959,"about_ca_system_score_codex":0.000010565468,"about_ca_system_score_gemma":0.000027169339,"threshold_uncertainty_score":0.30737975},"labels":[],"label_agreement":null},{"id":"W4379528655","doi":"10.1109/tcyb.2023.3278110","title":"Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Pyramid (geometry); Computer science; Artificial intelligence; Block (permutation group theory); Channel (broadcasting); Feature (linguistics); Data mining; Deep learning; Representation (politics); Scale (ratio); Feature extraction; Convolution (computer science); Machine learning; Artificial neural network; Pattern recognition (psychology)","score_opus":0.03607447482670018,"score_gpt":0.2989788397853624,"score_spread":0.2629043649586622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379528655","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":[0.0075430423,0.000009170539,0.99129736,0.00013730786,0.00020739056,0.0003373502,0.000020685544,0.0004055741,0.000042125226],"genre_scores_gemma":[0.5721955,0.0001157686,0.42749822,0.000079815596,0.000017601484,0.0000432493,0.000013438398,0.000023354094,0.000013107792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822116,0.000049873423,0.00032058722,0.0006904253,0.00035109933,0.00036683807],"domain_scores_gemma":[0.9982575,0.00019468139,0.000066399385,0.0013005456,0.00007855789,0.000102284546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018369421,0.00018204702,0.00015712985,0.00029113,0.00022326253,0.00010175951,0.0010907934,0.00008233913,0.000014563327],"category_scores_gemma":[0.000005091792,0.00017915091,0.000029193201,0.0015082485,0.000021843358,0.0006398079,0.000023928527,0.0002796781,0.000055108423],"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":[0.000014743149,0.00011464103,0.0000013941115,0.000002248534,0.0000056296153,0.0000076173224,0.000569734,0.9650075,0.000019577728,0.0020722202,0.000004226362,0.032180477],"study_design_scores_gemma":[0.00020799365,0.000092310154,0.0000040429445,0.000059158385,0.0000112471025,0.00000929586,0.00015724287,0.9907742,0.00026359173,0.008183701,0.000032830838,0.00020435022],"about_ca_topic_score_codex":0.0000064865076,"about_ca_topic_score_gemma":0.00081542565,"teacher_disagreement_score":0.5646524,"about_ca_system_score_codex":0.00007952679,"about_ca_system_score_gemma":0.00003872611,"threshold_uncertainty_score":0.7305562},"labels":[],"label_agreement":null},{"id":"W4379660134","doi":"10.26634/jmat.12.1.19398","title":"Performance analysis of convolutional neural networks for image classification with appropriate optimizers","year":2023,"lang":"en","type":"article","venue":"i-manager’s Journal on Mathematics","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"","keywords":"Softmax function; Convolutional neural network; Stochastic gradient descent; Computer science; Artificial intelligence; Machine learning; Activation function; Artificial neural network; Gradient descent; Pattern recognition (psychology); Selection (genetic algorithm); Contextual image classification; Function (biology); Image (mathematics)","score_opus":0.03655396320317812,"score_gpt":0.28611528992833934,"score_spread":0.24956132672516124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379660134","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":[0.044925034,0.000016364907,0.9533387,0.0008067262,0.00010866353,0.00036246577,0.000007134563,0.00012772717,0.00030721838],"genre_scores_gemma":[0.6145529,0.0001522196,0.38465634,0.00012190481,0.00009839818,0.00008795066,0.00002803394,0.00002866978,0.00027355528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983032,0.0000400777,0.00056669576,0.0002932627,0.00044778836,0.00034901104],"domain_scores_gemma":[0.9978091,0.00048915565,0.0007201145,0.0005744572,0.0002946052,0.00011256524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005321443,0.00018989395,0.0003760742,0.00051980454,0.00029370858,0.00011940341,0.0007844583,0.00005582501,0.0000066499315],"category_scores_gemma":[0.000048708407,0.00014859742,0.0001966946,0.0025880616,0.00010183939,0.0004731362,0.00008547579,0.0002496128,0.000012482904],"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":[0.000029262128,0.000083104904,0.00012005802,0.000043161344,0.00029579093,0.0000055654054,0.00014757102,0.9568513,0.0001754964,0.035773613,0.0005540421,0.005921006],"study_design_scores_gemma":[0.00034708017,0.0001249541,0.002412252,0.00003575395,0.00023133686,0.000024077555,0.000059078087,0.99301827,0.000052168893,0.0034064509,0.00012513707,0.00016343307],"about_ca_topic_score_codex":2.4014466e-7,"about_ca_topic_score_gemma":7.73275e-7,"teacher_disagreement_score":0.5696279,"about_ca_system_score_codex":0.000065948254,"about_ca_system_score_gemma":0.000029444831,"threshold_uncertainty_score":0.6059627},"labels":[],"label_agreement":null},{"id":"W4379660281","doi":"10.1007/978-3-031-34048-2_55","title":"Mixup-Privacy: A Simple yet Effective Approach for Privacy-Preserving Segmentation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; École de Technologie Supérieure","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Image segmentation; Computer vision; Overhead (engineering); Scale-space segmentation; Segmentation-based object categorization; Pattern recognition (psychology)","score_opus":0.03011319231438692,"score_gpt":0.2927729024916699,"score_spread":0.26265971017728296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379660281","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":[0.00006451275,0.00010989638,0.9944941,0.00063287833,0.00075888366,0.0027846203,0.000020821546,0.00049864093,0.00063567533],"genre_scores_gemma":[0.020355187,0.00002824758,0.9772307,0.0008057228,0.0004866254,0.00055057387,0.000076327706,0.000086787,0.00037977414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99559915,0.000047365673,0.0005384317,0.0021602488,0.0008211242,0.00083367183],"domain_scores_gemma":[0.99510914,0.0019261491,0.00040396888,0.0021038644,0.00027791367,0.00017897905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007331209,0.00057761424,0.00054061593,0.000653664,0.00050798146,0.0004701243,0.0051734457,0.00027034257,0.0000040144596],"category_scores_gemma":[0.00029376693,0.00056364055,0.00017928696,0.00123879,0.0003623056,0.0008310089,0.0035095,0.0006376835,0.00003003782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013154526,0.000041316744,0.00003551817,0.0001222855,0.00002467078,0.000011408424,0.001025612,0.23739165,0.0007747406,0.023372702,0.00034008655,0.73684686],"study_design_scores_gemma":[0.00026520964,0.00010495693,0.00008338115,0.00008692471,0.000008936374,0.000012301578,2.3600562e-7,0.6659386,0.0015681925,0.3306269,0.0008537623,0.00045058024],"about_ca_topic_score_codex":0.000010959062,"about_ca_topic_score_gemma":0.000016100797,"teacher_disagreement_score":0.73639625,"about_ca_system_score_codex":0.00040099284,"about_ca_system_score_gemma":0.00022288994,"threshold_uncertainty_score":0.99968153},"labels":[],"label_agreement":null},{"id":"W4379964854","doi":"10.1109/picc57976.2023.10142914","title":"Research on Enhancing Accuracy in Vehicle Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Convolutional neural network; Field (mathematics); Artificial intelligence; Support vector machine; Detector; Variety (cybernetics); Object detection; Machine learning; Real-time computing; Pattern recognition (psychology); Telecommunications","score_opus":0.0971992657476115,"score_gpt":0.4069404132767043,"score_spread":0.3097411475290928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379964854","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":[0.36971936,0.000006914589,0.62260216,0.002500089,0.00009170403,0.00024470137,1.2958975e-7,0.0005941885,0.004240759],"genre_scores_gemma":[0.99556315,0.000014745558,0.0037648336,0.00010560601,0.000040997526,0.00008143394,3.5503083e-7,0.000004919881,0.00042395183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905837,0.000063761574,0.000106813,0.00027548536,0.00022064819,0.00027491563],"domain_scores_gemma":[0.9987,0.0008223801,0.000014905767,0.00038716488,0.00003744997,0.00003810213],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045797767,0.000042404376,0.000043330554,0.00026524728,0.00017840626,0.000065474334,0.0003906038,0.000025326131,0.000006892088],"category_scores_gemma":[0.000085133484,0.000039859056,0.000012363756,0.0026855078,0.000019791052,0.00026707802,0.00020022539,0.00021589546,0.0012051227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009333003,0.000056611454,0.00012573347,0.000006598087,0.0000018330757,0.000020732938,0.0003290673,0.044975188,0.13252246,0.21755151,0.0010663932,0.60333455],"study_design_scores_gemma":[0.0001796874,0.000082598934,0.008075449,0.00001566553,1.8913722e-7,0.0000019252386,0.0000911819,0.6899152,0.24946767,0.04748917,0.0045522484,0.00012903537],"about_ca_topic_score_codex":0.000028261815,"about_ca_topic_score_gemma":0.00016813268,"teacher_disagreement_score":0.64494,"about_ca_system_score_codex":0.000060399456,"about_ca_system_score_gemma":0.000019256086,"threshold_uncertainty_score":0.9995726},"labels":[],"label_agreement":null},{"id":"W4380558639","doi":"10.48550/arxiv.2306.06968","title":"Can Forward Gradient Match Backpropagation?","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Sorbonne Université; Grand Équipement National De Calcul Intensif; Agence Nationale de la Recherche; Compute Canada","keywords":"Backpropagation; Artificial neural network; Computer science; Computation; Isotropy; Gradient descent; Noise (video); Gradient method; Artificial intelligence; Algorithm; Physics; Image (mathematics); Optics","score_opus":0.0851859728657592,"score_gpt":0.20830901537105653,"score_spread":0.12312304250529733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380558639","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":[0.04709387,0.000017583972,0.9476396,0.0019381304,0.00074583717,0.0005917543,0.00003188184,0.0010420489,0.0008993271],"genre_scores_gemma":[0.9873567,0.00018468016,0.0067092385,0.00015363748,0.00010706502,0.000010047229,0.000049524766,0.000034955356,0.0053941384],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978396,0.00007250727,0.00021827569,0.0013250503,0.00011531743,0.00042919331],"domain_scores_gemma":[0.9974578,0.000121828256,0.00025354142,0.0018033647,0.00015096526,0.00021251744],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013017494,0.00029971809,0.00026876407,0.00022147184,0.00023771937,0.00010142765,0.0022278863,0.00018129052,0.000011214843],"category_scores_gemma":[0.000018514585,0.0003573867,0.00017964879,0.0011625233,0.00009172087,0.00023571157,0.0025822117,0.0005162496,0.00045124747],"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":[0.000005041832,0.000044451368,0.00084280316,0.000038714807,0.00004586405,0.00013786784,0.00013601172,0.4820262,0.000024553327,0.5134572,0.0023704793,0.0008708632],"study_design_scores_gemma":[0.00020678145,0.000025398811,0.0016736675,0.00004790423,0.000034277145,0.0000044427698,0.000031405947,0.48989913,0.00013600575,0.505846,0.0016274411,0.0004675453],"about_ca_topic_score_codex":0.000106103835,"about_ca_topic_score_gemma":0.00018422921,"teacher_disagreement_score":0.9409303,"about_ca_system_score_codex":0.00029766574,"about_ca_system_score_gemma":0.00013356507,"threshold_uncertainty_score":0.9998878},"labels":[],"label_agreement":null},{"id":"W4380576476","doi":"10.1038/s41598-023-36781-2","title":"Improved GBS-YOLOv5 algorithm based on YOLOv5 applied to UAV intelligent traffic","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Dalhousie University","funders":"","keywords":"Computer science; Drone; Bottleneck; Data mining; Task (project management); Pyramid (geometry); Real-time computing; Feature (linguistics); Feature extraction; Residual; Artificial intelligence; Algorithm; Embedded system; Engineering","score_opus":0.020498757264265897,"score_gpt":0.27031340972342005,"score_spread":0.24981465245915416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380576476","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":[0.020407265,0.000016223128,0.9631994,0.0028080232,0.0083758375,0.0017112463,0.0000052717487,0.0018688106,0.001607919],"genre_scores_gemma":[0.81573766,0.000003743712,0.17288868,0.0011953047,0.00027487765,0.00093537365,0.00013218186,0.00007076598,0.008761397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957739,0.000039406903,0.0006616027,0.001965874,0.0007909471,0.00076828204],"domain_scores_gemma":[0.9962302,0.00013025875,0.00027140105,0.002823024,0.00015470565,0.00039040108],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012551893,0.0002821942,0.00026298477,0.0005535946,0.0005707005,0.000492771,0.001014145,0.000084625615,0.000030279523],"category_scores_gemma":[0.00009671043,0.00026350352,0.00012956571,0.0046259547,0.00014967257,0.00019372352,0.0003955077,0.00021884331,0.0009007076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009702897,0.00021037372,0.000011769717,0.000013790181,0.000011780307,0.00031180875,0.00047084095,0.2667463,0.026522199,0.0017299982,0.07475525,0.6292062],"study_design_scores_gemma":[0.00012073195,0.000087586755,0.00012875804,0.000021147973,0.000006594265,0.000039032355,0.00003187258,0.8249584,0.03897602,0.006227506,0.12895723,0.00044516328],"about_ca_topic_score_codex":0.0000028555214,"about_ca_topic_score_gemma":0.0000067795936,"teacher_disagreement_score":0.7953304,"about_ca_system_score_codex":0.00012468049,"about_ca_system_score_gemma":0.00016846013,"threshold_uncertainty_score":0.9999817},"labels":[],"label_agreement":null},{"id":"W4381135269","doi":"10.32920/22734374","title":"Motion Vector Extrapolation for Video Object Detection","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Computer vision; Latency (audio); Motion vector; Optical flow; Low latency (capital markets); Motion estimation; Pattern recognition (psychology)","score_opus":0.055852395272416526,"score_gpt":0.3089934045694444,"score_spread":0.2531410092970279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381135269","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":[0.0007933312,0.000032008727,0.9934002,0.0009782596,0.0015666803,0.0013555235,0.00001135551,0.0016716695,0.00019097289],"genre_scores_gemma":[0.7541921,0.000030918134,0.24179919,0.00017592094,0.0006428367,0.001968887,0.00008178607,0.000052175896,0.0010561789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839056,0.00003805569,0.00030758462,0.0008170612,0.0001954654,0.00025126498],"domain_scores_gemma":[0.99841195,0.00026850056,0.00022695381,0.000885751,0.00014487396,0.00006196212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020258651,0.00020568514,0.00017450462,0.00017168556,0.00018939639,0.00015531945,0.0006397882,0.0001677408,0.00000412352],"category_scores_gemma":[0.0000779462,0.00021349809,0.00014866098,0.0004135236,0.0000169084,0.00029668034,0.00049297663,0.00026775262,0.00008722466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032929103,0.00011362313,0.00008818755,0.00028757224,0.00008644626,0.0000025098707,0.00031573029,0.17234412,0.027541442,0.12488657,0.004082993,0.6702179],"study_design_scores_gemma":[0.00013442595,0.000036440146,0.0022913623,0.000023210858,0.000012261273,0.0000029253893,0.0000033155457,0.8165655,0.012416162,0.16588317,0.0023574538,0.000273742],"about_ca_topic_score_codex":0.00003231781,"about_ca_topic_score_gemma":0.0001187779,"teacher_disagreement_score":0.7533988,"about_ca_system_score_codex":0.00014689365,"about_ca_system_score_gemma":0.00004387633,"threshold_uncertainty_score":0.87061995},"labels":[],"label_agreement":null},{"id":"W4381429452","doi":"10.1049/gtd2.12886","title":"Optimized hybrid YOLOu‐Quasi‐ProtoPNet for insulators classification","year":2023,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":52,"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; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Grid; Scratch; Residual neural network; Artificial intelligence; Task (project management); Identification (biology); Pattern recognition (psychology); Contextual image classification; Power grid; Machine learning; Power (physics); Deep learning; Engineering; Image (mathematics); Mathematics; Systems engineering","score_opus":0.05087753080644816,"score_gpt":0.30059995672756074,"score_spread":0.24972242592111257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381429452","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":[0.01055722,0.000052752257,0.9807739,0.005551857,0.00032998814,0.001643551,0.00016239981,0.0009003789,0.000027910188],"genre_scores_gemma":[0.9035896,0.00022325774,0.08487381,0.00022546176,0.0003602234,0.0022021544,0.008137442,0.000031795775,0.00035622122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979668,0.00010166844,0.0005086421,0.0006942568,0.00034388353,0.00038474533],"domain_scores_gemma":[0.9986978,0.000109649205,0.0001863637,0.0005799244,0.00024732944,0.00017892006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039971585,0.00022227807,0.00019868356,0.000104992716,0.0006130989,0.00016007638,0.00046765973,0.00011506401,0.000016314814],"category_scores_gemma":[0.00005652271,0.00021772752,0.00013995425,0.0010535418,0.000041933596,0.00061287597,0.000038666538,0.0001297675,0.000086928536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001131555,0.00038080695,0.000045953617,0.00006004185,0.00003397212,0.0000036517113,0.00021744394,0.084951684,0.28676262,0.20508999,0.07149058,0.3508501],"study_design_scores_gemma":[0.00078072917,0.00008412921,0.0005959443,0.000011224343,0.000011818527,0.000004700291,0.0000075119033,0.8512122,0.05036941,0.0067239576,0.0899621,0.00023625664],"about_ca_topic_score_codex":0.00000214666,"about_ca_topic_score_gemma":3.517266e-7,"teacher_disagreement_score":0.89590013,"about_ca_system_score_codex":0.00012446671,"about_ca_system_score_gemma":0.000088181856,"threshold_uncertainty_score":0.8878671},"labels":[],"label_agreement":null},{"id":"W4381561077","doi":"10.1145/3580305.3599485","title":"Rapid Image Labeling via Neuro-Symbolic Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Purdue University","keywords":"Computer science; Domain (mathematical analysis); Artificial intelligence; Image (mathematics); Labeled data; Key (lock); Code (set theory); Machine learning; Pattern recognition (psychology); Programming language; Set (abstract data type)","score_opus":0.021753759757688656,"score_gpt":0.2692581314790212,"score_spread":0.24750437172133258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381561077","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":[0.0069131213,0.000039828774,0.9805037,0.0035361343,0.00015296236,0.00013534274,2.1232687e-7,0.0023521788,0.0063665397],"genre_scores_gemma":[0.6689737,0.00034884192,0.31966582,0.0029541359,0.00033709474,0.00013302546,0.00001299237,0.000061361774,0.0075130737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898654,0.00003915452,0.00014576281,0.00035750362,0.00015096353,0.00032010002],"domain_scores_gemma":[0.9991775,0.00023470879,0.000044522065,0.00042455495,0.00003997084,0.0000787575],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012357318,0.000096540985,0.000089933965,0.00009335299,0.00023338622,0.00008370326,0.00060231506,0.000024137324,0.000032022792],"category_scores_gemma":[0.000052404488,0.00009234568,0.00003812409,0.0013843111,0.00002431153,0.0003709144,0.00037131435,0.0001898318,0.0018605813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026286118,0.000038610808,0.00042510385,0.000012995824,0.000013081823,0.00005653567,0.00051000854,0.022994826,0.20979063,0.093731426,0.013366038,0.6590581],"study_design_scores_gemma":[0.00015178713,0.000032825137,0.0011990913,0.0000044313983,0.0000023404386,0.000017504652,0.000016503049,0.9114373,0.0071016457,0.010431714,0.06938345,0.00022141008],"about_ca_topic_score_codex":0.000003475639,"about_ca_topic_score_gemma":0.0000011644596,"teacher_disagreement_score":0.88844246,"about_ca_system_score_codex":0.0000103682505,"about_ca_system_score_gemma":0.000010535439,"threshold_uncertainty_score":0.99891657},"labels":[],"label_agreement":null},{"id":"W4381612937","doi":"10.36227/techrxiv.23528403.v1","title":"Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a Safety Framework","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Lidar; Computer science; Baseline (sea); Advanced driver assistance systems; Active safety; Contrast (vision); Artificial intelligence; Simulation; Automotive engineering; Engineering; Geography; Remote sensing","score_opus":0.03075110088374135,"score_gpt":0.304501327495733,"score_spread":0.27375022661199166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381612937","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":[0.14082946,0.00024086928,0.8476869,0.009393051,0.00017613039,0.0007425861,0.0000114624145,0.0008370064,0.000082567],"genre_scores_gemma":[0.6672861,0.0001872821,0.33197522,0.00029708951,0.000040538373,0.0001547045,0.000010324702,0.000022932049,0.000025815441],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99768215,0.000046937657,0.00047871642,0.0012099843,0.00013793026,0.00044427402],"domain_scores_gemma":[0.9982937,0.00031056875,0.00017332297,0.0010126915,0.00008258302,0.00012711281],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019085575,0.00032779694,0.0005092028,0.00034050536,0.0001710236,0.00020317652,0.0010687576,0.00040323887,0.0000019356528],"category_scores_gemma":[0.000050157778,0.00034035515,0.000024188808,0.0008199471,0.00019265554,0.0002162264,0.0031544524,0.001089896,0.000013570511],"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":[0.000029656616,0.00011837487,0.07748403,0.00025837353,0.00006763048,0.0001452815,0.001961507,0.0077973455,0.0005276004,0.7235693,0.00011124434,0.1879297],"study_design_scores_gemma":[0.00055786746,0.00006960516,0.18081263,0.0006500094,0.00001203627,0.000049855233,0.00019711278,0.2870451,0.00052771583,0.5277261,0.0012844668,0.0010675214],"about_ca_topic_score_codex":0.000044280627,"about_ca_topic_score_gemma":0.00032099633,"teacher_disagreement_score":0.5264566,"about_ca_system_score_codex":0.00012247259,"about_ca_system_score_gemma":0.0001641074,"threshold_uncertainty_score":0.9999049},"labels":[],"label_agreement":null},{"id":"W4381613505","doi":"10.36227/techrxiv.23528403","title":"Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a Safety Framework","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Lidar; Baseline (sea); Computer science; Advanced driver assistance systems; Active safety; Contrast (vision); Artificial intelligence; Simulation; Automotive engineering; Engineering; Geography; Remote sensing","score_opus":0.03075110088374135,"score_gpt":0.304501327495733,"score_spread":0.27375022661199166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381613505","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":[0.14082946,0.00024086928,0.8476869,0.009393051,0.00017613039,0.0007425861,0.0000114624145,0.0008370064,0.000082567],"genre_scores_gemma":[0.6672861,0.0001872821,0.33197522,0.00029708951,0.000040538373,0.0001547045,0.000010324702,0.000022932049,0.000025815441],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99768215,0.000046937657,0.00047871642,0.0012099843,0.00013793026,0.00044427402],"domain_scores_gemma":[0.9982937,0.00031056875,0.00017332297,0.0010126915,0.00008258302,0.00012711281],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019085575,0.00032779694,0.0005092028,0.00034050536,0.0001710236,0.00020317652,0.0010687576,0.00040323887,0.0000019356528],"category_scores_gemma":[0.000050157778,0.00034035515,0.000024188808,0.0008199471,0.00019265554,0.0002162264,0.0031544524,0.001089896,0.000013570511],"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":[0.000029656616,0.00011837487,0.07748403,0.00025837353,0.00006763048,0.0001452815,0.001961507,0.0077973455,0.0005276004,0.7235693,0.00011124434,0.1879297],"study_design_scores_gemma":[0.00055786746,0.00006960516,0.18081263,0.0006500094,0.00001203627,0.000049855233,0.00019711278,0.2870451,0.00052771583,0.5277261,0.0012844668,0.0010675214],"about_ca_topic_score_codex":0.000044280627,"about_ca_topic_score_gemma":0.00032099633,"teacher_disagreement_score":0.5264566,"about_ca_system_score_codex":0.00012247259,"about_ca_system_score_gemma":0.0001641074,"threshold_uncertainty_score":0.9999049},"labels":[],"label_agreement":null},{"id":"W4381615915","doi":"10.1007/s11042-023-15845-5","title":"An improved dense-to-sparse cross-modal fusion network for 3D object detection in RGB-D images","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Computer science; RGB color model; Artificial intelligence; Computer vision; Context (archaeology); Object detection; Process (computing); Modal; Fuse (electrical); Sensor fusion; Object (grammar); Pattern recognition (psychology)","score_opus":0.024651544927002306,"score_gpt":0.312014989238469,"score_spread":0.28736344431146665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381615915","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":[0.034991372,0.00004904159,0.9608902,0.00046001532,0.00011189351,0.0028700796,0.000075513366,0.00050042407,0.000051509884],"genre_scores_gemma":[0.5637414,0.00010632608,0.41974935,0.00031957444,0.00062624685,0.015110748,0.00015020552,0.00003923396,0.00015690688],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983466,0.00003067265,0.0003173456,0.00071552565,0.000121128745,0.00046868727],"domain_scores_gemma":[0.99848074,0.0004490253,0.00009529315,0.00067212654,0.0001023867,0.00020041032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026578963,0.00018059408,0.00017821227,0.00012976187,0.00048370459,0.0002402552,0.00047941148,0.00009405508,0.0000029386815],"category_scores_gemma":[0.00005207447,0.00018507757,0.000044751894,0.0013826316,0.000064170876,0.000512257,0.0001811227,0.00014383705,0.00006181649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018703484,0.000066608496,0.0005891988,0.000011061387,0.0000040450363,7.330253e-7,0.0001489717,0.01563294,0.05219071,0.0008574483,0.00025394175,0.9302256],"study_design_scores_gemma":[0.0005772548,0.00009263491,0.05269698,0.000010029981,0.00000639641,0.0000041936864,0.000019915253,0.9196366,0.0044438248,0.0045553986,0.017659934,0.0002968347],"about_ca_topic_score_codex":0.000025072717,"about_ca_topic_score_gemma":0.00014266418,"teacher_disagreement_score":0.9299288,"about_ca_system_score_codex":0.00004358945,"about_ca_system_score_gemma":0.000030343468,"threshold_uncertainty_score":0.75472444},"labels":[],"label_agreement":null},{"id":"W4381733084","doi":"10.1109/tits.2023.3285035","title":"CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather With a High-Quality Real Snow Dataset","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":81,"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":"Shenzhen Science and Technology Innovation Program; University Research Committee, University of Hong Kong; Lingnan University; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Snow; Artificial intelligence; Computer science; Object detection; Feature (linguistics); Pattern recognition (psychology); Geography; Meteorology","score_opus":0.04151138772248466,"score_gpt":0.32495354317301556,"score_spread":0.2834421554505309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381733084","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":[0.1532785,0.000008171083,0.8424718,0.00009495408,0.00081979187,0.0015824378,0.001236148,0.0004895509,0.000018672225],"genre_scores_gemma":[0.9948267,0.00013880544,0.0020666495,0.000042848995,0.000056583744,0.0018500461,0.0006334168,0.000043070177,0.00034191803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972256,0.00013386531,0.00086652685,0.0008550005,0.00047101488,0.00044799005],"domain_scores_gemma":[0.9982205,0.00045953318,0.00025665676,0.0007597689,0.00017278109,0.00013076555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000517377,0.00030938862,0.0003400712,0.00042613476,0.00035857366,0.00008339,0.0004585753,0.00016392094,0.000013111947],"category_scores_gemma":[0.000004707023,0.00028828924,0.00012694624,0.0017373544,0.000075125914,0.0006514455,0.0000011455189,0.00027294524,0.000117347576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054992107,0.00028829736,0.00026967208,0.00015104913,0.000064818814,0.000018540963,0.0015519476,0.9698261,0.0070849196,0.0025968368,0.00011382938,0.017484052],"study_design_scores_gemma":[0.009733869,0.0029743048,0.056606267,0.001218277,0.00028954525,0.000070385846,0.0037665437,0.36045513,0.53747994,0.0024892853,0.02100568,0.0039107897],"about_ca_topic_score_codex":0.0010093177,"about_ca_topic_score_gemma":0.0042756535,"teacher_disagreement_score":0.84154814,"about_ca_system_score_codex":0.00018640445,"about_ca_system_score_gemma":0.000063642125,"threshold_uncertainty_score":0.9999569},"labels":[],"label_agreement":null},{"id":"W4382536871","doi":"10.1101/2023.06.28.546903","title":"Efficient Visual Perception of Human-Robot Walking Environments using Semi-Supervised Learning","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Toronto Rehabilitation Institute; University of Waterloo; University of Toronto","funders":"AGE-WELL","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Deep learning; Machine learning; Software deployment; Inference; Robot; Supervised learning; Artificial neural network","score_opus":0.03208939583616948,"score_gpt":0.2736413938008779,"score_spread":0.2415519979647084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382536871","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":[0.5824874,0.00008229174,0.4161619,0.00003371586,0.00036658355,0.00046637305,0.000008924067,0.00039145743,0.0000013444387],"genre_scores_gemma":[0.9550537,0.0000627713,0.044328544,0.000038183367,0.00025336148,0.00012335744,5.8223645e-7,0.00013184751,0.000007662601],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961663,0.0002330685,0.00078235817,0.0014643063,0.0006945023,0.0006595042],"domain_scores_gemma":[0.9973224,0.000117969415,0.0007424872,0.001458272,0.00014851564,0.00021038257],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005865974,0.0005485319,0.0005750593,0.0003823507,0.0004696198,0.00016713563,0.0013259257,0.00037632347,0.000013706033],"category_scores_gemma":[0.00007902204,0.0006552728,0.00019916373,0.0009165151,0.0001413399,0.00017028919,0.0021018567,0.00096932705,0.00008157197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025602692,0.00008954696,0.0013179812,0.000086593864,0.000033042856,0.000009128501,0.000033849163,0.22089635,0.7772942,0.00022162797,0.0000046359933,0.000010450852],"study_design_scores_gemma":[0.0004185869,0.00007605857,0.06832243,0.00056902657,0.000079774865,3.3704136e-8,0.000012531713,0.8043857,0.12500206,0.000010889103,0.00012453664,0.0009983494],"about_ca_topic_score_codex":0.000027669825,"about_ca_topic_score_gemma":4.2100322e-7,"teacher_disagreement_score":0.6522922,"about_ca_system_score_codex":0.000495482,"about_ca_system_score_gemma":0.0001462302,"threshold_uncertainty_score":0.99958986},"labels":[],"label_agreement":null},{"id":"W4382681649","doi":"10.1007/s42979-023-01921-y","title":"EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models","year":2023,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University; Huawei Technologies (Canada)","funders":"","keywords":"Deep learning; Computer science; Leverage (statistics); Pruning; Artificial intelligence; Computer engineering; Inference; Edge device; Quantization (signal processing); Multiplication (music); Architecture; Computer architecture; Theoretical computer science; Machine learning; Algorithm; Mathematics","score_opus":0.058981360355936104,"score_gpt":0.32978200421716436,"score_spread":0.27080064386122826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382681649","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":[0.045398436,0.000013015048,0.9531942,0.00021188622,0.0003725449,0.00040017648,0.000002173938,0.0002989742,0.000108578875],"genre_scores_gemma":[0.7249153,0.000008181068,0.27483258,0.00009022708,0.00007070603,0.000058818503,0.000004509265,0.000006455719,0.000013170772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980047,0.000049597198,0.00028815228,0.0007518384,0.00048294276,0.00042278654],"domain_scores_gemma":[0.9983864,0.00031011537,0.00016154659,0.0006164549,0.0003813999,0.00014409074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006855408,0.00013737194,0.00015697574,0.00027723273,0.00048821062,0.00020542201,0.0020061613,0.00002827927,0.0000011177841],"category_scores_gemma":[0.00006159586,0.00013025793,0.000042180098,0.0020947102,0.0002534741,0.0013852249,0.0006942537,0.00011445657,0.000019314673],"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":[0.0000015176793,0.000022994758,0.000018257844,0.000003886923,0.0000013480117,5.435388e-7,0.001002413,0.8055414,0.0020122856,0.105786,0.000008184614,0.08560116],"study_design_scores_gemma":[0.00015750651,0.00018190304,0.00036679071,0.000012889622,0.0000014586141,0.0000023699686,0.000014415666,0.9730314,0.005029956,0.020968076,0.00008698448,0.00014626377],"about_ca_topic_score_codex":0.0000068489085,"about_ca_topic_score_gemma":0.0000036970387,"teacher_disagreement_score":0.6795169,"about_ca_system_score_codex":0.000059962716,"about_ca_system_score_gemma":0.00011094692,"threshold_uncertainty_score":0.53117645},"labels":[],"label_agreement":null},{"id":"W4382775073","doi":"10.3390/jimaging9070132","title":"Motion Vector Extrapolation for Video Object Detection","year":2023,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Optical flow; Object detection; Motion vector; Extrapolation; Latency (audio); Motion estimation; Motion detection; Low latency (capital markets); Detector; Real-time computing; Motion (physics); Pattern recognition (psychology); Telecommunications","score_opus":0.02030773970651257,"score_gpt":0.2913831905822255,"score_spread":0.27107545087571294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382775073","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":[0.013596744,0.000066097244,0.9840436,0.0015613112,0.00049333967,0.0001097968,4.865817e-7,0.00009087663,0.00003775758],"genre_scores_gemma":[0.95875627,0.000012850223,0.04076139,0.000107018255,0.0003231522,0.000009474416,6.064491e-7,0.000008024273,0.000021189011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993426,0.000021890215,0.00024143286,0.00010912784,0.00014807012,0.00013683252],"domain_scores_gemma":[0.9992307,0.00015896134,0.000273301,0.00013190399,0.00016378079,0.000041345622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032146813,0.00005629923,0.000079078876,0.00019570587,0.0001286419,0.00006636998,0.0002148697,0.000011210499,9.743596e-7],"category_scores_gemma":[0.00009548957,0.000052877112,0.00007554693,0.00056789204,0.000009716782,0.0009080879,0.000028917308,0.000093708484,0.000009137601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016368973,0.00001963572,0.00054798136,0.000013249123,0.000011747775,0.000008272323,0.00022235356,0.012007775,0.22280692,0.002471312,0.001237602,0.7606368],"study_design_scores_gemma":[0.00044759773,0.000059442336,0.014155195,0.0000324616,0.00001207491,0.00018001895,0.000032205236,0.9133523,0.040694684,0.024830023,0.0060833804,0.000120604884],"about_ca_topic_score_codex":8.53897e-7,"about_ca_topic_score_gemma":0.0000011814863,"teacher_disagreement_score":0.94515955,"about_ca_system_score_codex":0.000061765255,"about_ca_system_score_gemma":0.000019996667,"threshold_uncertainty_score":0.2156266},"labels":[],"label_agreement":null},{"id":"W4382874089","doi":"10.1007/s10489-023-04630-4","title":"3D object detection algorithm based on multi-sensor segmental fusion of frustum association for autonomous driving","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Frustum; Computer science; Artificial intelligence; Point cloud; Computer vision; Lidar; Feature (linguistics); Radar; Object detection; Convolutional neural network; Algorithm; Pattern recognition (psychology); Remote sensing; Mathematics; Geology","score_opus":0.02347218845548944,"score_gpt":0.2810216369447971,"score_spread":0.25754944848930766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382874089","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":[0.0042082183,0.000004477602,0.99380195,0.00010819303,0.00029792436,0.00086395326,0.0000096976,0.0004451761,0.0002604181],"genre_scores_gemma":[0.72971934,0.000014213459,0.26947394,0.00012298048,0.00006949111,0.00038051675,0.000017528855,0.000020242403,0.00018176124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984145,0.000028655646,0.0003591322,0.0005298005,0.00031678268,0.00035109452],"domain_scores_gemma":[0.99837184,0.0007154897,0.00029852992,0.00044979044,0.00010082036,0.000063514686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003493617,0.00017225799,0.00018318441,0.00018250613,0.000243702,0.000040185358,0.0004739211,0.00010161284,0.000005144811],"category_scores_gemma":[0.00008190561,0.0001818863,0.000082650266,0.0010065682,0.000028368455,0.00012715058,0.00012940499,0.00015481391,0.000118852775],"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":[0.000015017955,0.00013537958,0.00012169263,0.000018122515,0.000013744883,0.000001008758,0.00022706496,0.06745158,0.08935814,0.0027124041,0.00010000752,0.83984584],"study_design_scores_gemma":[0.00011247969,0.00007869956,0.00055031857,0.000011907238,0.0000051913526,4.869361e-7,0.00003738727,0.6937018,0.30413726,0.00086935784,0.00036448537,0.00013061693],"about_ca_topic_score_codex":0.000009369729,"about_ca_topic_score_gemma":0.000011790565,"teacher_disagreement_score":0.83971524,"about_ca_system_score_codex":0.00027782397,"about_ca_system_score_gemma":0.00003683375,"threshold_uncertainty_score":0.7417108},"labels":[],"label_agreement":null},{"id":"W4382934439","doi":"10.1016/j.patcog.2023.109801","title":"Construction of a feature enhancement network for small object detection","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Computer science; Pyramid (geometry); Feature (linguistics); Granularity; Object detection; Block (permutation group theory); Artificial intelligence; Feature extraction; Pattern recognition (psychology); Convolution (computer science); Object (grammar); Context (archaeology); Embedding; Data mining; Computer vision; Artificial neural network; Mathematics","score_opus":0.03551432279182822,"score_gpt":0.2635734508925961,"score_spread":0.22805912810076787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382934439","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":[0.068160795,0.000020151421,0.9300924,0.00035986962,0.0005031792,0.0005404375,0.000017064498,0.00022665723,0.00007944775],"genre_scores_gemma":[0.8190545,0.00013651527,0.17869776,0.0002587488,0.0005251483,0.00097323174,0.00023560411,0.000022778513,0.00009572836],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917144,0.000033850483,0.00018292415,0.00028969845,0.00010384087,0.0002182307],"domain_scores_gemma":[0.9993083,0.0001445502,0.00017194549,0.00021721741,0.0001239795,0.000034026423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014851429,0.00009591623,0.00011081725,0.000082924846,0.0001243358,0.000023942615,0.00015909881,0.000059393897,0.0000054988177],"category_scores_gemma":[0.000020047777,0.00010139725,0.00006510098,0.0006366909,0.000022281656,0.00015328627,0.00005216594,0.00007856472,0.000058053418],"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":[0.000009304747,0.0000130337,0.00013413836,0.000030647505,0.000010455169,3.6896614e-7,0.000045793044,0.0002703667,0.0072565,0.000092138245,0.00038224255,0.991755],"study_design_scores_gemma":[0.0016758075,0.0006998849,0.008665959,0.00030364026,0.00007435285,0.000059026086,0.00009076134,0.3103583,0.51133716,0.1583377,0.007621733,0.0007756938],"about_ca_topic_score_codex":0.0000051808665,"about_ca_topic_score_gemma":0.000041015628,"teacher_disagreement_score":0.9909793,"about_ca_system_score_codex":0.000031704334,"about_ca_system_score_gemma":0.000011345039,"threshold_uncertainty_score":0.413486},"labels":[],"label_agreement":null},{"id":"W4383163195","doi":"10.1016/j.aei.2023.102069","title":"An efficient 3D object detection method based on Fast Guided Anchor Stereo RCNN","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"McMaster University","funders":"Science and Technology Program of Suzhou; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Minimum bounding box; Robustness (evolution); Object detection; Computer vision; Bounding overwatch; Segmentation; Feature extraction; Pattern recognition (psychology); Feature (linguistics); Position (finance); Convolutional neural network; Pyramid (geometry); Image (mathematics); Mathematics","score_opus":0.012268340610133158,"score_gpt":0.27812306996464947,"score_spread":0.2658547293545163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383163195","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":[0.009860249,0.000004845664,0.98708946,0.000054022436,0.00040932928,0.00037133048,0.0000061432747,0.0018674856,0.00033713516],"genre_scores_gemma":[0.29875794,0.000006197337,0.70073485,0.00025846754,0.000044266482,0.00013391288,0.000017692739,0.000028395385,0.000018257977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984381,0.000021876405,0.00048120978,0.00024259095,0.0003445769,0.00047161538],"domain_scores_gemma":[0.9983743,0.00024148046,0.00014103124,0.0010058525,0.00008217558,0.00015520152],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027105265,0.00025128614,0.00019456485,0.00033892563,0.00015766479,0.00009523262,0.00064001593,0.00006891618,0.0000015485743],"category_scores_gemma":[0.000065056935,0.00025389658,0.000060044244,0.0015759179,0.000012619759,0.00066457957,0.00009795753,0.0002442929,0.000119926844],"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":[0.000002958738,0.000016408609,0.000001933519,0.000029298944,0.000003577914,0.0000013618549,0.00023248054,0.8917579,0.001672811,0.00096937554,0.000016116892,0.10529576],"study_design_scores_gemma":[0.0003275363,0.00011865177,0.0003735426,0.000038694317,0.0000039491024,0.0000069465204,0.000024496034,0.9857023,0.008969001,0.00007013657,0.0040858393,0.0002789179],"about_ca_topic_score_codex":7.756437e-7,"about_ca_topic_score_gemma":7.975257e-7,"teacher_disagreement_score":0.2888977,"about_ca_system_score_codex":0.00012919086,"about_ca_system_score_gemma":0.00002487271,"threshold_uncertainty_score":0.9999913},"labels":[],"label_agreement":null},{"id":"W4383423478","doi":"10.1016/b978-0-323-85280-7.00013-0","title":"Deep learning with connectomes","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Machine learning; Connectome; Artificial neural network; Task (project management); Deep neural networks; Class (philosophy); Functional connectivity; Engineering; Neuroscience; Psychology","score_opus":0.018647923908512724,"score_gpt":0.243044807700569,"score_spread":0.22439688379205627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383423478","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":[0.0000022970182,0.0003027306,0.026490657,0.0001812213,0.00016111485,0.0004416969,0.0000015255454,0.0009797657,0.971439],"genre_scores_gemma":[0.00032766868,0.00012051583,0.012104244,0.00025163704,0.00019828648,0.000104419065,0.000011300182,0.00011318212,0.9867687],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998241,0.000020457606,0.0002759339,0.00074155186,0.00036155077,0.00035952244],"domain_scores_gemma":[0.99834275,0.00025131664,0.00026330707,0.000907377,0.000101248734,0.0001339877],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000103658094,0.00037835003,0.0003812203,0.00015125702,0.00023678012,0.00010177218,0.0009536407,0.0001624605,0.00002356536],"category_scores_gemma":[0.00001160245,0.00032792255,0.00010875823,0.00006115356,0.000118134834,0.00010009374,0.0004072415,0.0006871088,0.0009683847],"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":[0.0000019237698,0.00000127172,0.0000019778995,0.0000120190025,0.000031182517,0.000041596242,0.00008179932,0.000292076,0.000004239047,0.18031365,0.00003003613,0.81918824],"study_design_scores_gemma":[0.00014677382,0.00008045278,0.0000114039,0.0001593456,0.00002531031,0.000038924944,0.0000029379503,0.0022553713,0.00001832118,0.058438353,0.93834597,0.00047681038],"about_ca_topic_score_codex":9.563172e-8,"about_ca_topic_score_gemma":0.000019096307,"teacher_disagreement_score":0.938316,"about_ca_system_score_codex":0.000053791104,"about_ca_system_score_gemma":0.000056359888,"threshold_uncertainty_score":0.99991727},"labels":[],"label_agreement":null},{"id":"W4383560355","doi":"10.54254/2755-2721/5/20230533","title":"A review of the application of CNN-based computer vision in auto-driving","year":2023,"lang":"en","type":"review","venue":"Applied and Computational Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Field (mathematics); Segmentation; Deep learning; Self driving; Computer vision; Machine learning; Engineering","score_opus":0.018005471204747495,"score_gpt":0.28981054722581817,"score_spread":0.2718050760210707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560355","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001176717,0.42464912,0.5746584,0.000034720088,0.00003421966,0.0005566597,0.0000030678643,0.000051985004,0.000010634328],"genre_scores_gemma":[0.00059378584,0.9182108,0.080608785,0.00010384252,0.00004834444,0.00035514208,0.00004221537,0.000034772806,0.000002294223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852794,0.000027187583,0.0006876178,0.0003577957,0.0002554749,0.00014399184],"domain_scores_gemma":[0.99846953,0.00064742053,0.0004036162,0.00038966656,0.00005418037,0.00003555854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023105781,0.00021305314,0.0006872752,0.00019109772,0.000034183817,0.000013809925,0.00063111,0.0000760366,3.908266e-7],"category_scores_gemma":[0.000013631258,0.00017055114,0.00013521765,0.0015558477,0.00003241294,0.000053568798,0.0002929977,0.00020913032,0.0000037942734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.3120214e-7,0.000017887916,6.925549e-7,0.028008612,0.000012750697,2.465481e-7,0.0000070872557,0.25183293,0.0000041916896,0.04812707,0.000026467163,0.67196184],"study_design_scores_gemma":[0.00009778624,0.000010127042,0.000097295626,0.037805803,0.000034500306,0.0000048235875,2.3275504e-7,0.89584625,0.0000020150549,0.0011025227,0.064807616,0.00019104207],"about_ca_topic_score_codex":0.0000017529725,"about_ca_topic_score_gemma":4.714007e-7,"teacher_disagreement_score":0.6717708,"about_ca_system_score_codex":0.000042605803,"about_ca_system_score_gemma":0.00009631824,"threshold_uncertainty_score":0.6954874},"labels":[],"label_agreement":null},{"id":"W4383563363","doi":"10.1016/b978-0-12-805320-1.00006-3","title":"Markov random fields","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Graphical model; Pairwise comparison; Computer science; Prior probability; Hidden variable theory; Hidden Markov model; Artificial intelligence; Embedding; Random variable; Graph; Markov random field; Conditional random field; Theoretical computer science; Markov chain; Regularization (linguistics); Bayesian probability; Machine learning; Segmentation; Mathematics; Image segmentation; Statistics","score_opus":0.020502692894440344,"score_gpt":0.2521948063979777,"score_spread":0.23169211350353736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383563363","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":[4.5141246e-7,0.00025386264,0.022264611,0.00060141034,0.00065787585,0.0005873336,0.0000089858695,0.0006577208,0.9749678],"genre_scores_gemma":[0.00007180597,0.00019508725,0.008770113,0.0008405427,0.0004078844,0.0001289635,0.000011673938,0.00007627367,0.98949766],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99819696,0.000020094467,0.00039426386,0.000706307,0.00034105635,0.00034130205],"domain_scores_gemma":[0.9978436,0.00029776903,0.00021354682,0.0014340859,0.00007293697,0.00013804028],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015875934,0.00036123575,0.00042102,0.00013345147,0.00016187372,0.00008734065,0.001346493,0.0002907474,0.000050487015],"category_scores_gemma":[0.000015185621,0.00034828836,0.0002399507,0.000043126463,0.00008026836,0.000083069266,0.00061855605,0.00056661054,0.0013855994],"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":[0.0000035749079,0.000001567924,1.580546e-7,0.000012021393,0.000022938826,0.000028592565,0.000034370936,0.000011122674,0.0000030564602,0.12208838,0.0017288373,0.8760654],"study_design_scores_gemma":[0.00024161053,0.000018045863,0.000002242266,0.00008515694,0.000016953627,0.0000128098145,3.1124463e-7,0.00047382238,0.000015011138,0.20085222,0.79796934,0.00031249895],"about_ca_topic_score_codex":1.1875335e-7,"about_ca_topic_score_gemma":0.000011566799,"teacher_disagreement_score":0.87575287,"about_ca_system_score_codex":0.000043717944,"about_ca_system_score_gemma":0.00006582437,"threshold_uncertainty_score":0.9998969},"labels":[],"label_agreement":null},{"id":"W4383566674","doi":"10.1016/b978-0-12-805320-1.00014-2","title":"Random field losses for deep networks","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Conditional random field; CRFS; Computer science; Leverage (statistics); Segmentation; Artificial intelligence; Pairwise comparison; Regularization (linguistics); Convolutional neural network; Ground truth; Deep learning; Prior probability; Cluster analysis; Machine learning; Pattern recognition (psychology); Bayesian probability","score_opus":0.022071248264464525,"score_gpt":0.2631527858397047,"score_spread":0.24108153757524017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383566674","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":[1.263115e-7,0.0005679182,0.44799563,0.00036479963,0.0006406414,0.00103164,0.000006593719,0.00041267838,0.54898],"genre_scores_gemma":[0.00007048339,0.0003129029,0.022475747,0.0014183293,0.00095386506,0.00055999926,0.000025505791,0.00010967648,0.97407347],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980298,0.000015477137,0.00046747152,0.00078900956,0.00024163147,0.00045657964],"domain_scores_gemma":[0.9969506,0.0012687143,0.00028474192,0.0012254696,0.00012837234,0.00014206646],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017562456,0.0004145791,0.00051445526,0.00011434946,0.00024934893,0.00011935717,0.0013034268,0.0003373261,0.000018406858],"category_scores_gemma":[0.00004095848,0.0004012232,0.00034015803,0.00004610378,0.00007049099,0.00009353841,0.00042989437,0.00044292936,0.00017716068],"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":[0.000013366605,0.0000023681498,2.1686468e-7,0.000021447808,0.000037556958,0.000008604455,0.000024393088,0.0005385223,0.000001497485,0.12464999,0.001474704,0.87322736],"study_design_scores_gemma":[0.0004227271,0.000058696693,6.599485e-7,0.000112690155,0.000037458325,0.000008657201,5.9493885e-7,0.01519006,0.000023314502,0.17198911,0.81176,0.00039605424],"about_ca_topic_score_codex":1.1976624e-7,"about_ca_topic_score_gemma":0.000023685505,"teacher_disagreement_score":0.8728313,"about_ca_system_score_codex":0.000041340107,"about_ca_system_score_gemma":0.00005251044,"threshold_uncertainty_score":0.99984396},"labels":[],"label_agreement":null},{"id":"W4383824322","doi":"10.2139/ssrn.4505116","title":"Bndcnet: Bilateral Nonlocal Decoupled Convergence Network for Semantic Segmentation","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Convergence (economics); Segmentation; Computer science; Artificial intelligence; Psychology; Economics","score_opus":0.023308391979464854,"score_gpt":0.2962553539342589,"score_spread":0.27294696195479407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383824322","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":[0.008268201,0.0011674744,0.9842299,0.0024900371,0.0024105858,0.0010741751,0.000010000574,0.00033853226,0.000011075345],"genre_scores_gemma":[0.74562114,0.021202596,0.2194953,0.00095432904,0.006260402,0.001360639,0.00031330576,0.00030607142,0.0044861976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9946545,0.00010503858,0.00072654075,0.0008672126,0.00043123338,0.003215493],"domain_scores_gemma":[0.99801576,0.0002640543,0.00066091743,0.0006623045,0.00023122938,0.00016570598],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0014839685,0.0004189824,0.00042915452,0.00016211803,0.000533062,0.0002816731,0.0018034313,0.00024311249,0.0000063129046],"category_scores_gemma":[0.000041533396,0.0004115306,0.0003089316,0.0005419408,0.000056501754,0.00035538553,0.00073860475,0.0028157986,0.00009084582],"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":[0.00013416533,0.00014750975,0.001348419,0.00015935428,0.0008071236,0.000024842651,0.00033667823,0.52768165,0.000967545,0.36682007,0.005991574,0.095581084],"study_design_scores_gemma":[0.00038505072,0.00013636872,0.000167745,0.0000678529,0.000042768606,0.0001400159,0.000029530032,0.4169602,0.00009311697,0.581189,0.00043411404,0.00035427138],"about_ca_topic_score_codex":0.000028554381,"about_ca_topic_score_gemma":0.0006799851,"teacher_disagreement_score":0.7647346,"about_ca_system_score_codex":0.0011215732,"about_ca_system_score_gemma":0.002207954,"threshold_uncertainty_score":0.99983364},"labels":[],"label_agreement":null},{"id":"W4384833556","doi":"10.1145/3597031.3597057","title":"cuSCNN : an Efficient CUDA Implementation of Sparse CNNs","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Speedup; Parallel computing; CUDA; Sparse matrix; Convolutional neural network; Inference; Computation; Memory bandwidth; Kernel (algebra); Latency (audio); Throughput; Computer engineering; Algorithm; Artificial intelligence","score_opus":0.03867691439024008,"score_gpt":0.3471304006101584,"score_spread":0.3084534862199183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384833556","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":[0.3275204,0.0000044127232,0.67023534,0.0007827972,0.000092697686,0.00021072618,0.000003181897,0.0003794417,0.0007710252],"genre_scores_gemma":[0.9615744,0.0000068556874,0.038071293,0.000106772335,0.000025314537,0.000034511697,0.000010464771,0.000005296048,0.00016513893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926066,0.000019505924,0.00016677272,0.00022512843,0.00015794492,0.00016999239],"domain_scores_gemma":[0.99934924,0.000045644243,0.00006070216,0.00044551457,0.000042973956,0.0000559445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010281894,0.00005795169,0.00006463112,0.0000760403,0.00006424708,0.000017922186,0.00039834264,0.000013369975,0.000036666144],"category_scores_gemma":[0.000003250316,0.00005273091,0.000023455217,0.00089426123,0.000018422403,0.00016675201,0.00015269392,0.000032418084,0.00013853771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024382553,0.00011073057,0.0021076023,0.000009999549,0.000009391104,0.000004266786,0.0011655538,0.11120754,0.016990012,0.6484839,0.004319845,0.21558873],"study_design_scores_gemma":[0.00042874186,0.00014270363,0.042337257,0.0000051998054,0.000005824822,0.0000034869597,0.0004899009,0.8842755,0.053482626,0.0126095,0.005968453,0.00025079722],"about_ca_topic_score_codex":0.000029686253,"about_ca_topic_score_gemma":0.000048918904,"teacher_disagreement_score":0.77306795,"about_ca_system_score_codex":0.000014150585,"about_ca_system_score_gemma":0.000020308846,"threshold_uncertainty_score":0.21503042},"labels":[],"label_agreement":null},{"id":"W4384945971","doi":"10.1109/iwcmc58020.2023.10183163","title":"Scalable Multi-Task Learning R-CNN for Object Detection in Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object detection; Scalability; Artificial intelligence; Task (project management); Construct (python library); Object (grammar); Segmentation; Deep learning; Field (mathematics); Machine learning; Video tracking; Computer vision","score_opus":0.023926874487074156,"score_gpt":0.28466329202149093,"score_spread":0.2607364175344168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384945971","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":[0.026848437,0.000010705747,0.97096056,0.0002990801,0.00012954112,0.00035372426,2.0614436e-7,0.0010215169,0.00037622984],"genre_scores_gemma":[0.89716315,0.000010052897,0.09963855,0.000060050097,0.000039330458,0.0002794075,0.0000024336532,0.000013371266,0.0027936783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990273,0.000027572214,0.00017034769,0.0003699063,0.00008052152,0.00032436638],"domain_scores_gemma":[0.99935466,0.00026913747,0.00005110299,0.00024856548,0.00003083182,0.000045691828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021528282,0.00008768548,0.000096736054,0.00015366281,0.000211219,0.00006425267,0.0003144045,0.00004141342,0.0000023931518],"category_scores_gemma":[0.000088838744,0.00008955364,0.000038484453,0.0011456811,0.0000133846825,0.00034960516,0.0001605972,0.00013777877,0.00014738992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003982199,0.000052320393,0.0032784243,0.000015941643,0.0000068023687,0.0000048027223,0.0004405661,0.32959062,0.040796388,0.005918334,0.00027739146,0.6196144],"study_design_scores_gemma":[0.00023093795,0.000031629348,0.006672543,0.000006734927,9.647948e-7,0.0000023929115,0.000025229128,0.9771001,0.009254697,0.0014361056,0.0051242537,0.000114426824],"about_ca_topic_score_codex":0.000023747609,"about_ca_topic_score_gemma":0.00025626746,"teacher_disagreement_score":0.87132204,"about_ca_system_score_codex":0.00007394872,"about_ca_system_score_gemma":0.000021019583,"threshold_uncertainty_score":0.36518916},"labels":[],"label_agreement":null},{"id":"W4385190584","doi":"10.1016/j.neuroimage.2023.120289","title":"ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI","year":2023,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; Heart and Stroke Foundation; University of Toronto; Western University; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Canada Research Chairs; Compute Canada; Temerty Family Foundation; Ontario Brain Institute; University of Ottawa","keywords":"Benchmarking; Robustness (evolution); Segmentation; Computer science; Artificial intelligence; Benchmark (surveying); Deep learning; Pattern recognition (psychology); Machine learning; Data mining; Cartography","score_opus":0.06557973590540603,"score_gpt":0.31272127656027626,"score_spread":0.24714154065487023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385190584","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":[0.1137518,0.000021234038,0.8851703,0.0005359743,0.00011210301,0.0002873091,0.000015439335,0.00006451825,0.000041346695],"genre_scores_gemma":[0.98146033,0.00006827633,0.018217677,0.00004574166,0.000027574495,0.000023778293,0.00013465846,0.000008490747,0.000013466188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895585,0.000095047144,0.00023406596,0.0003639772,0.00018690825,0.0001641586],"domain_scores_gemma":[0.99891764,0.00025488302,0.00012725273,0.0006178441,0.000049319737,0.000033053915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028202817,0.00008077961,0.000111275054,0.00006715633,0.0000994234,0.000033614113,0.00065625913,0.000021632699,0.0000011125773],"category_scores_gemma":[0.00005064325,0.000073057396,0.000012847045,0.0009874136,0.000042091484,0.00049836584,0.0007101202,0.00014028103,0.0000025480624],"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":[0.000006104837,0.000015960108,0.00075357116,0.000013528534,0.0000020772936,0.0000027197427,0.00050246215,0.9459393,0.0073205903,0.00092172617,0.00015414499,0.044367794],"study_design_scores_gemma":[0.00012217333,0.000030840158,0.013515599,0.000014331859,0.0000036177285,0.0000018437818,0.000056464596,0.9849981,0.0006932476,0.00036801022,0.00013397919,0.00006181457],"about_ca_topic_score_codex":0.000010878331,"about_ca_topic_score_gemma":0.000039211915,"teacher_disagreement_score":0.8677085,"about_ca_system_score_codex":0.000014308393,"about_ca_system_score_gemma":0.00001071149,"threshold_uncertainty_score":0.29791942},"labels":[],"label_agreement":null},{"id":"W4385245213","doi":"10.1109/iscas46773.2023.10181694","title":"Training Acceleration of Frequency Domain CNNs Using Activation Compression","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Convolutional neural network; Backpropagation; Overhead (engineering); Training (meteorology); Data compression; Compression (physics); Artificial neural network; Frequency domain; Acceleration; Reduction (mathematics); Time domain; Domain (mathematical analysis); Artificial intelligence; Speech recognition; Computer vision; Operating system","score_opus":0.14084612854184123,"score_gpt":0.34606922911202576,"score_spread":0.20522310057018453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385245213","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":[0.24780193,0.0000026411265,0.75001323,0.00035043547,0.00005419622,0.00012433238,5.2432654e-7,0.00022805811,0.0014246433],"genre_scores_gemma":[0.7240952,0.0000038207286,0.27576268,0.000053175914,0.000036400234,0.000010841629,0.0000066830626,0.000004848432,0.000026367097],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925196,0.000031381755,0.00019748289,0.00020880598,0.00016430928,0.00014604077],"domain_scores_gemma":[0.9994096,0.00010555177,0.0001133023,0.0002864178,0.000052248426,0.000032842254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110298235,0.000067393135,0.00008681911,0.000085769665,0.000138454,0.00002764593,0.00029164902,0.000033751185,0.000010635224],"category_scores_gemma":[0.000015010674,0.00006245395,0.000025273717,0.0010234957,0.000020173431,0.00066086184,0.00009861475,0.00006118694,0.000011163349],"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":[0.0000012010962,0.000013268621,0.00012122685,0.0000048391876,0.0000029751368,6.7503504e-7,0.0007421252,0.01253903,0.8105115,0.14775383,0.00013516095,0.02817412],"study_design_scores_gemma":[0.000253368,0.000031773303,0.005542344,0.000050879094,0.000002493995,0.000005504066,0.00022108195,0.70139945,0.08943869,0.2025636,0.00029622015,0.00019460003],"about_ca_topic_score_codex":0.000010873657,"about_ca_topic_score_gemma":0.0000027648784,"teacher_disagreement_score":0.72107285,"about_ca_system_score_codex":0.000026938036,"about_ca_system_score_gemma":0.000028284141,"threshold_uncertainty_score":0.25467986},"labels":[],"label_agreement":null},{"id":"W4385288900","doi":"10.1145/3592144","title":"∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China; Bundesministerium für Wirtschaft und Klimaschutz","keywords":"Computer science; Optimization problem; Differentiable function; Deep learning; Artificial intelligence; Mathematical optimization; Artificial neural network; Algorithm; Mathematics","score_opus":0.030298278489210847,"score_gpt":0.27654556298665517,"score_spread":0.24624728449744432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385288900","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":[0.00047312255,0.000016491016,0.9959064,0.0017649079,0.0002128699,0.0006690348,0.00005759904,0.000878944,0.000020597201],"genre_scores_gemma":[0.06470654,0.00023685061,0.93328756,0.0003198584,0.00006199975,0.00096506363,0.00007444041,0.000041271607,0.00030642012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857396,0.00002468521,0.00023991399,0.0005065174,0.00023295476,0.00042198363],"domain_scores_gemma":[0.9986636,0.00016045255,0.000055396336,0.0008896376,0.00013693607,0.00009398057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014923724,0.00016758477,0.00014376256,0.0002861349,0.0006789023,0.00008780979,0.0007535428,0.00009838777,0.000007095291],"category_scores_gemma":[0.000012488167,0.00017313428,0.00013416812,0.0018124476,0.00002216899,0.0004490982,0.000024861476,0.00021303158,0.000018773088],"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":[0.0000059414256,0.00012350605,0.000004178649,0.000011700567,0.000016669132,4.7509272e-7,0.0001161643,0.97381157,0.000057997044,0.0059086015,0.00013212713,0.01981105],"study_design_scores_gemma":[0.0003689358,0.00006226166,0.000009028414,0.000011930449,0.000016850432,0.0000023325356,0.000028177335,0.96637017,0.000501851,0.031752124,0.00069674535,0.00017958473],"about_ca_topic_score_codex":0.0000022020185,"about_ca_topic_score_gemma":0.000010336662,"teacher_disagreement_score":0.06423342,"about_ca_system_score_codex":0.000024291725,"about_ca_system_score_gemma":0.00002816469,"threshold_uncertainty_score":0.7060211},"labels":[],"label_agreement":null},{"id":"W4385327582","doi":"10.48550/arxiv.2307.13755","title":"Training-based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; fRI Research","keywords":"Overfitting; Computer science; Pascal (unit); Artificial intelligence; Margin (machine learning); Machine learning; Representation (politics); Object (grammar); Generalization; Pattern recognition (psychology); Artificial neural network; Mathematics","score_opus":0.21370143949245743,"score_gpt":0.2541779916524084,"score_spread":0.04047655215995097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385327582","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":[0.04562645,0.000014072374,0.9521966,0.0004039043,0.00019948339,0.0010110175,0.000030619853,0.0004214966,0.00009636716],"genre_scores_gemma":[0.98173887,0.00007392164,0.01723343,0.0001272351,0.00005597885,0.0000592392,0.00006456333,0.00002618291,0.00062057126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808234,0.000055831555,0.00022199814,0.0012408824,0.00010211926,0.0002968373],"domain_scores_gemma":[0.99838483,0.00020869858,0.0002176311,0.000949726,0.0001192747,0.00011984339],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018002657,0.0002512911,0.0002349127,0.00019680058,0.0002596127,0.00007834688,0.0006428415,0.00014274905,0.0000017948278],"category_scores_gemma":[0.00003381538,0.00030279922,0.00013589053,0.0005190867,0.00005885089,0.0002205917,0.00066277914,0.00023098737,0.0000070251294],"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":[0.00003087756,0.00002980426,0.000088936235,0.0000479945,0.00002792252,0.0000056453646,0.00019057999,0.98368084,0.00034943776,0.009899001,0.00008494066,0.005563988],"study_design_scores_gemma":[0.0005498706,0.000054832795,0.0003061512,0.000038729373,0.000049579427,5.4412305e-7,0.00006369523,0.9345579,0.0005831885,0.063411295,0.0001106627,0.00027353645],"about_ca_topic_score_codex":0.00004251244,"about_ca_topic_score_gemma":0.00022275142,"teacher_disagreement_score":0.9361124,"about_ca_system_score_codex":0.00020448977,"about_ca_system_score_gemma":0.00010382792,"threshold_uncertainty_score":0.9999424},"labels":[],"label_agreement":null},{"id":"W4385341668","doi":"10.1117/12.2692337","title":"XRANet: an extra-wide, residual and attention-based deep convolutional neural network for semantic segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Convolutional neural network; Segmentation; Artificial intelligence; Deep learning; Residual; Sørensen–Dice coefficient; Encoder; Feature extraction; Pattern recognition (psychology); Metric (unit); Pixel; Artificial neural network; Dice; Architecture; Feature (linguistics); Image segmentation; Algorithm; Mathematics; Engineering","score_opus":0.02947848911674843,"score_gpt":0.2900478681880862,"score_spread":0.26056937907133776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385341668","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":[0.07520704,0.000056677032,0.92061114,0.0028583147,0.0001814051,0.00056543807,0.000009270198,0.0004706413,0.000040091843],"genre_scores_gemma":[0.7517917,0.000015059959,0.24523659,0.0015039674,0.00029445166,0.0002982291,0.0004221825,0.000023228497,0.0004146038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987324,0.00005162558,0.00022821808,0.00045269632,0.00019323642,0.00034181273],"domain_scores_gemma":[0.9988842,0.0005499071,0.00007795536,0.00030167509,0.000079029625,0.000107217704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019688072,0.00012796678,0.00011004243,0.00007345594,0.0003746155,0.00009882368,0.00027747088,0.000042922253,0.000012003614],"category_scores_gemma":[0.00001930062,0.00012735029,0.000038206963,0.00056895235,0.00006121693,0.00047663922,0.00006237129,0.0000650062,0.00002471627],"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":[0.000059104907,0.00010316585,0.036944,0.000059889422,0.000033857174,0.000009288047,0.00013858241,0.74165547,0.006620344,0.15336326,0.019115407,0.041897636],"study_design_scores_gemma":[0.00047739095,0.00007898497,0.045574345,0.0000055603655,0.0000084307885,0.0000046151436,0.000017674869,0.9398038,0.00015779524,0.013231773,0.0004834341,0.00015619861],"about_ca_topic_score_codex":0.0000070199335,"about_ca_topic_score_gemma":0.00007398055,"teacher_disagreement_score":0.67658466,"about_ca_system_score_codex":0.0000219924,"about_ca_system_score_gemma":0.000027895003,"threshold_uncertainty_score":0.5193194},"labels":[],"label_agreement":null},{"id":"W4385585286","doi":"10.1145/3587135.3592204","title":"T-RecX","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Classifier (UML); Spotting; Benchmark (surveying); Artificial intelligence; Deep learning; Keyword spotting; Enhanced Data Rates for GSM Evolution; Internet of Things; Edge device; Suite; Machine learning; Embedded system","score_opus":0.030072797202466115,"score_gpt":0.29058017221330157,"score_spread":0.26050737501083543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385585286","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":[0.0016123179,0.000004895075,0.96450853,0.005254291,0.00008958267,0.0000502926,1.3750793e-7,0.001303199,0.027176727],"genre_scores_gemma":[0.7196301,0.000037479964,0.24815905,0.0022740343,0.00011801874,0.00008240989,0.000003331369,0.000009915314,0.029685646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967813,0.0000039093047,0.00004048228,0.00011771089,0.000056151603,0.00010362644],"domain_scores_gemma":[0.9996367,0.000043723332,0.0000077958575,0.0002734493,0.0000093512435,0.000028995708],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002940156,0.000025151268,0.000022974704,0.00002736278,0.00004503426,0.000017641722,0.00033470447,0.000007819851,0.0000132783025],"category_scores_gemma":[0.0000049087917,0.000021490221,0.000010891146,0.00073434523,0.000006572595,0.00012510648,0.0001278265,0.000023410295,0.0025409034],"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":[8.687932e-8,0.0000031659802,0.000058571994,4.156206e-7,7.918534e-7,0.0000026356336,0.000022443832,0.000662636,0.00080267736,0.8621856,0.051001772,0.08525921],"study_design_scores_gemma":[0.000100701356,0.000014821044,0.005504377,0.0000017843229,6.072991e-7,0.0000077013365,0.000007632118,0.3653338,0.0045355754,0.21422827,0.41010144,0.00016329197],"about_ca_topic_score_codex":0.0000010479731,"about_ca_topic_score_gemma":0.0000016259185,"teacher_disagreement_score":0.7180178,"about_ca_system_score_codex":0.000004104552,"about_ca_system_score_gemma":0.000004725792,"threshold_uncertainty_score":0.99823576},"labels":[],"label_agreement":null},{"id":"W4385605073","doi":"10.1016/j.neunet.2023.07.042","title":"Long-range zero-shot generative deep network quantization","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"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":"National Natural Science Foundation of China; Natural Science Foundation for Distinguished Young Scholars of Anhui Province; Natural Science Foundation of Anhui Province; Canadian Allergy, Asthma and Immunology Foundation","keywords":"Computer science; Quantization (signal processing); Deep learning; Algorithm; Inference; Artificial intelligence; Artificial neural network; Pattern recognition (psychology)","score_opus":0.03483911809452436,"score_gpt":0.28494940916600536,"score_spread":0.250110291071481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385605073","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":[0.008468321,0.0006167614,0.984973,0.0019292919,0.00139489,0.0005515933,0.000001830235,0.0015597041,0.00050457235],"genre_scores_gemma":[0.97796017,0.00044163966,0.016290959,0.0026016624,0.0017078032,0.00022477093,0.0001161657,0.00006456265,0.00059224176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972333,0.00017359147,0.00043220582,0.00083427684,0.0003727907,0.00095388095],"domain_scores_gemma":[0.9981491,0.00035720342,0.00020031127,0.0009506502,0.00012628276,0.0002164671],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023956635,0.00033217276,0.00029551735,0.000110682806,0.0005859317,0.00021819696,0.0011291903,0.00016385842,0.000020535537],"category_scores_gemma":[0.000029424516,0.000322477,0.00012893086,0.0034660099,0.000089340254,0.0007197847,0.00048759574,0.00042052232,0.00022851895],"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":[0.0000081166145,0.00001720632,0.0037228074,0.0000034330494,0.000012814081,0.000034507753,0.000058622394,0.9412354,0.000038343424,0.019859334,0.013191184,0.021818183],"study_design_scores_gemma":[0.00023028582,0.00005778273,0.0120572075,0.000012323053,0.000009506594,0.000020124393,0.0000033215767,0.97860885,0.000032286378,0.0064122155,0.0022189042,0.00033718065],"about_ca_topic_score_codex":0.000003940882,"about_ca_topic_score_gemma":0.000086164124,"teacher_disagreement_score":0.9694919,"about_ca_system_score_codex":0.000040431663,"about_ca_system_score_gemma":0.00001839728,"threshold_uncertainty_score":0.99992275},"labels":[],"label_agreement":null},{"id":"W4385624928","doi":"10.1109/newcas57931.2023.10198137","title":"Iterative pruning algorithm for efficient look-up table implementation of binary neural networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Field-programmable gate array; Pruning; Lookup table; Throughput; Artificial neural network; Algorithm; Power consumption; Table (database); Binary number; Layer (electronics); Parallel computing; Power (physics); Computer hardware; Artificial intelligence; Arithmetic; Mathematics; Data mining","score_opus":0.024171743865021942,"score_gpt":0.32187191431790413,"score_spread":0.29770017045288216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385624928","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":[0.014391403,0.000029056759,0.98377657,0.0005512162,0.00026923607,0.00066305354,0.000010610634,0.00025842743,0.00005041128],"genre_scores_gemma":[0.76806635,0.000012929261,0.23052557,0.0002042797,0.00014251415,0.00040019097,0.00007808107,0.000018890683,0.0005511924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989303,0.000025928543,0.00025912886,0.000325964,0.0001436516,0.00031506392],"domain_scores_gemma":[0.99918246,0.00022204744,0.00012714733,0.0002976707,0.000118104974,0.000052566244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001536211,0.000108231776,0.0001264649,0.00009769351,0.00018547935,0.00004509756,0.00035969235,0.000026912989,0.000010567572],"category_scores_gemma":[0.0000051771017,0.00010015264,0.000047257898,0.0011698378,0.000029171466,0.00025379632,0.00022421895,0.000063194624,0.00000640236],"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":[0.000002839511,0.00001823909,0.00009049786,0.0000067152096,0.000011215433,0.0000011333415,0.00033183125,0.6162506,0.0015253399,0.021903198,0.0025848793,0.35727352],"study_design_scores_gemma":[0.00034274167,0.000095527816,0.00079510733,0.000004951969,0.000004077969,0.0000016863441,0.00018840838,0.99477124,0.0025346421,0.00074319885,0.00041409917,0.00010432467],"about_ca_topic_score_codex":0.000010881866,"about_ca_topic_score_gemma":0.0000045228735,"teacher_disagreement_score":0.7536749,"about_ca_system_score_codex":0.000026418204,"about_ca_system_score_gemma":0.000022601298,"threshold_uncertainty_score":0.4084106},"labels":[],"label_agreement":null},{"id":"W4385624933","doi":"10.1109/newcas57931.2023.10198172","title":"Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inference","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Mitacs; CMC Microsystems","keywords":"Computer science; Byte; Parallel computing; Instruction set; Floating point; Quantization (signal processing); Convolutional neural network; Computation; Computer engineering; Computer hardware; Algorithm; Artificial intelligence","score_opus":0.04177418059275271,"score_gpt":0.32218240248657637,"score_spread":0.28040822189382364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385624933","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":[0.044888142,0.000029205747,0.94967365,0.00242378,0.00020736281,0.00088920083,0.0000070586516,0.0013070549,0.0005745598],"genre_scores_gemma":[0.93177587,0.00002648113,0.06521383,0.00032592568,0.00009600077,0.00090134505,0.000011949401,0.000022286607,0.0016263247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843633,0.000021344125,0.0002562186,0.00057144143,0.00024144874,0.00047319932],"domain_scores_gemma":[0.99845356,0.0005407052,0.00010367881,0.0006291184,0.00015294753,0.00011996445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019038444,0.00016179489,0.00018111264,0.0001164799,0.00022643189,0.00010609725,0.00091687846,0.00004872995,0.000010240558],"category_scores_gemma":[0.0001582365,0.0001368995,0.00007298917,0.0016186197,0.00004508043,0.00018640373,0.00029066703,0.00009815217,0.0007448139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007238296,0.00030481734,0.00039847777,0.00014702385,0.000038857746,0.0000122352685,0.00066757906,0.092491,0.039579865,0.7863482,0.034895785,0.04504375],"study_design_scores_gemma":[0.00051573233,0.000065783854,0.0015131952,0.000016579083,0.00000581965,0.0000028551492,0.000011388744,0.95608425,0.016747367,0.007912248,0.016853854,0.00027094464],"about_ca_topic_score_codex":0.0000044929902,"about_ca_topic_score_gemma":0.000014826974,"teacher_disagreement_score":0.8868877,"about_ca_system_score_codex":0.000032648528,"about_ca_system_score_gemma":0.00008141875,"threshold_uncertainty_score":0.9573324},"labels":[],"label_agreement":null},{"id":"W4385696128","doi":"10.1109/lra.2023.3303696","title":"Multi-Modal Streaming 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Computer science; Artificial intelligence; Computer vision; Context (archaeology); Object detection; Perception; Field of view; Pattern recognition (psychology); Remote sensing; Geography","score_opus":0.019717748110599294,"score_gpt":0.26056861909984685,"score_spread":0.24085087098924757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385696128","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":[0.092668355,0.0000070918986,0.9035448,0.0027254785,0.00025607878,0.00012767623,9.908965e-7,0.00065985974,0.000009679329],"genre_scores_gemma":[0.8132926,0.00001769891,0.1859353,0.00059976504,0.00008015234,0.000023749146,0.000004373444,0.000010150316,0.000036173264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992489,0.000025092873,0.00015107983,0.0002545122,0.00013114921,0.00018923203],"domain_scores_gemma":[0.99955416,0.00008409453,0.000075171076,0.00021564073,0.00002212969,0.000048835132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008328979,0.000094512776,0.000079314355,0.000120039054,0.00021744172,0.00010464491,0.00016401426,0.000032214586,5.3482927e-7],"category_scores_gemma":[0.000008837604,0.00009699897,0.00002455315,0.0005440946,0.000026508518,0.00034966366,0.00005230939,0.000082328464,0.000054738808],"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":[6.67386e-7,0.000013221654,0.00010878247,0.000011644992,0.000008305265,0.0000059915046,0.00021259076,0.7173938,0.17868988,0.00081509654,0.0002872172,0.10245277],"study_design_scores_gemma":[0.00014452745,0.0000100018315,0.009545321,0.000008576822,0.0000034763395,0.000007473876,0.000006105969,0.9865518,0.003236798,0.00016815367,0.00020579038,0.00011193397],"about_ca_topic_score_codex":0.0000047965023,"about_ca_topic_score_gemma":0.00000655557,"teacher_disagreement_score":0.72062427,"about_ca_system_score_codex":0.000029283665,"about_ca_system_score_gemma":0.0000067437068,"threshold_uncertainty_score":0.3955503},"labels":[],"label_agreement":null},{"id":"W4385764544","doi":"10.24963/ijcai.2023/101","title":"GeNAS: Neural Architecture Search with Better Generalization","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Architecture; Generalization; Artificial neural network; Artificial intelligence; Network architecture; Segmentation; Measure (data warehouse); Flatness (cosmology); Pattern recognition (psychology); Machine learning; Data mining; Mathematics","score_opus":0.020750760151028697,"score_gpt":0.2667131147926719,"score_spread":0.2459623546416432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385764544","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":[0.06427999,0.000008466655,0.9245795,0.009388351,0.000040482617,0.00017431806,7.779679e-7,0.0007327142,0.0007953784],"genre_scores_gemma":[0.7349062,0.000015199817,0.25704873,0.0044540134,0.00025696185,0.000106097104,0.000035287547,0.000029986617,0.003147537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990831,0.000030435183,0.0000888632,0.00031443444,0.00021544112,0.00026772288],"domain_scores_gemma":[0.99936974,0.000047461683,0.000019564759,0.00045409313,0.000042504038,0.0000666537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052075375,0.00008952694,0.00006381978,0.00009389303,0.00012879987,0.00007129399,0.0004694846,0.000023468663,0.000015175159],"category_scores_gemma":[0.000002680119,0.0000638889,0.000019631589,0.0013332281,0.000023934537,0.0002038974,0.00017514563,0.00010390162,0.00017864454],"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":[0.000009067897,0.000029918323,0.002897927,0.000014295425,0.000019102621,0.000043449232,0.0006567297,0.6410446,0.010282643,0.11970497,0.014670443,0.21062683],"study_design_scores_gemma":[0.0002234026,0.00006520349,0.007482269,0.000004563869,0.0000025199868,0.000037478912,0.000009690069,0.96897525,0.005372424,0.0070531853,0.01053579,0.00023825534],"about_ca_topic_score_codex":0.000005364731,"about_ca_topic_score_gemma":0.0000146713255,"teacher_disagreement_score":0.6706262,"about_ca_system_score_codex":0.000011866424,"about_ca_system_score_gemma":0.0000137498,"threshold_uncertainty_score":0.26053137},"labels":[],"label_agreement":null},{"id":"W4385767742","doi":"10.24963/ijcai.2023/467","title":"Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Computer science; Segmentation; Consistency (knowledge bases); Regularization (linguistics); Artificial intelligence; Modalities; Code (set theory); Image segmentation; Machine learning; Pattern recognition (psychology)","score_opus":0.034350191559364175,"score_gpt":0.3182801567292685,"score_spread":0.2839299651699043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385767742","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":[0.020669319,0.0000053995473,0.9712238,0.006118461,0.00007848672,0.0005006212,0.000006431037,0.00079083606,0.0006066657],"genre_scores_gemma":[0.34658948,0.000038829876,0.649236,0.0021715504,0.00015529514,0.00063948426,0.000069311376,0.000028993174,0.0010710519],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998533,0.000027182406,0.00020848098,0.00044608372,0.00043604086,0.0003491743],"domain_scores_gemma":[0.9987765,0.00051435793,0.000063459236,0.00039860012,0.00009175247,0.00015535703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027511985,0.00012691409,0.00013773466,0.000071802686,0.00020255786,0.00009073893,0.00066855195,0.00004950004,0.00006895915],"category_scores_gemma":[0.000058554666,0.00010339379,0.000028592449,0.0007843651,0.00007711222,0.0005780597,0.000077106335,0.000101897254,0.0001534203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049233888,0.00010485543,0.0003975189,0.00008582267,0.000069999005,0.00008120349,0.0031877009,0.0035975599,0.15846696,0.62202847,0.034503475,0.1774272],"study_design_scores_gemma":[0.0026040634,0.00032893472,0.0014623143,0.00009902965,0.000018699955,0.00007723666,0.00064156356,0.86034817,0.089665495,0.041323528,0.0027187471,0.0007122506],"about_ca_topic_score_codex":0.0000094930265,"about_ca_topic_score_gemma":0.000017821227,"teacher_disagreement_score":0.85675055,"about_ca_system_score_codex":0.000026378522,"about_ca_system_score_gemma":0.00009945569,"threshold_uncertainty_score":0.42162764},"labels":[],"label_agreement":null},{"id":"W4385768183","doi":"10.24963/ijcai.2023/108","title":"IMF: Integrating Matched Features Using Attentive Logit in Knowledge Distillation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Sungkyunkwan University","keywords":"Softmax function; Computer science; Margin (machine learning); Distillation; Inference; Feature (linguistics); Machine learning; Artificial intelligence; Logit; Matching (statistics); Representation (politics); Pattern recognition (psychology); Artificial neural network; Mathematics; Statistics","score_opus":0.04652173233494302,"score_gpt":0.333634815200821,"score_spread":0.287113082865878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385768183","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":[0.12823471,0.000041180043,0.8665741,0.0009596393,0.0001519424,0.000233595,0.0000010289679,0.0004969513,0.003306828],"genre_scores_gemma":[0.93423676,0.000006057246,0.0650442,0.000046886387,0.000055940192,0.000020785908,0.000006157341,0.00000773318,0.00057544786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999192,0.00003556413,0.00016499242,0.00030448276,0.000086802305,0.00021616412],"domain_scores_gemma":[0.9994227,0.00017664986,0.000055721568,0.00026406214,0.000046141944,0.000034711404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011083745,0.0000944053,0.00010231554,0.0001296375,0.00009856415,0.00006472725,0.00031914035,0.00003807154,0.0000045799393],"category_scores_gemma":[0.00004188039,0.00008132266,0.00002913142,0.0015338211,0.000024362029,0.0003204898,0.0002521479,0.00012176495,0.00009669415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000118343105,0.00013849637,0.021949226,0.000041543182,0.000024309535,0.00003621676,0.0064096963,0.07044586,0.057465408,0.6074458,0.004339523,0.23169214],"study_design_scores_gemma":[0.00014158379,0.000010177507,0.057756387,0.00003283571,0.0000019169788,0.000006226052,0.00021300056,0.9201455,0.0018094211,0.01932096,0.00038244834,0.00017956145],"about_ca_topic_score_codex":0.000027541997,"about_ca_topic_score_gemma":0.00016860748,"teacher_disagreement_score":0.8496996,"about_ca_system_score_codex":0.000072556155,"about_ca_system_score_gemma":0.000019857478,"threshold_uncertainty_score":0.33162418},"labels":[],"label_agreement":null},{"id":"W4385800854","doi":"10.1109/cvprw59228.2023.00223","title":"Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; FLOPS; Convolutional neural network; Hyperparameter; Similarity (geometry); Artificial intelligence; Scale (ratio); Class (philosophy); Machine learning; Pattern recognition (psychology); Parallel computing","score_opus":0.0403098429386843,"score_gpt":0.3127134070743566,"score_spread":0.2724035641356723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385800854","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":[0.13084869,0.000010046635,0.86684495,0.0011006196,0.000047115398,0.0008359478,0.000012722916,0.00027578618,0.000024094537],"genre_scores_gemma":[0.8444457,0.0000037158286,0.15432706,0.00008701966,0.000010890607,0.00020801043,0.000032277418,0.000010187064,0.0008751246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746543,0.00059181097,0.00064637733,0.00051372603,0.0004829199,0.00029971707],"domain_scores_gemma":[0.9972546,0.0015167872,0.00020987759,0.0007365616,0.0001735682,0.00010861146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088040944,0.00016429917,0.0003999234,0.0004345563,0.0001698578,0.000045816938,0.00060837314,0.000067375826,0.000023770917],"category_scores_gemma":[0.000084516476,0.0001412992,0.00025945526,0.003303782,0.000108464985,0.00030587864,0.0001033891,0.00012778662,0.000060930648],"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":[0.00040328535,0.00030723063,0.0062237363,0.0009375055,0.0007900588,0.000017079126,0.0026382587,0.6810159,0.3044203,0.0016516498,0.00035997364,0.0012350215],"study_design_scores_gemma":[0.0003622346,0.00003697781,0.063505635,0.000052485302,0.00014057782,0.000011931849,0.00009396718,0.9285049,0.0058151935,0.0013211761,0.0000019248807,0.0001529852],"about_ca_topic_score_codex":0.000024034998,"about_ca_topic_score_gemma":0.00017001703,"teacher_disagreement_score":0.713597,"about_ca_system_score_codex":0.00005110315,"about_ca_system_score_gemma":0.000040940453,"threshold_uncertainty_score":0.57620144},"labels":[],"label_agreement":null},{"id":"W4385801313","doi":"10.1109/cvprw59228.2023.00496","title":"DeCAtt: Efficient Vision Transformers with Decorrelated Attention Heads","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transformer; Overfitting; Artificial intelligence; Regularization (linguistics); Machine learning; Computer engineering; Engineering; Artificial neural network; Electrical engineering","score_opus":0.009890696637562682,"score_gpt":0.26661870379260133,"score_spread":0.25672800715503863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385801313","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":[0.13899642,0.000012357066,0.8561606,0.0020958662,0.00009390991,0.00024650662,5.283885e-7,0.00084531354,0.0015484993],"genre_scores_gemma":[0.97830606,0.000027895387,0.02064801,0.00016910615,0.0000140209195,0.000051210707,0.000013016449,0.000012166554,0.0007584948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989687,0.000016776085,0.00015150894,0.00035660993,0.00022680017,0.0002795909],"domain_scores_gemma":[0.99945045,0.00006751476,0.00003607343,0.00031071054,0.000045399753,0.000089861984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010055082,0.00010361968,0.00008310364,0.00011471487,0.00017851304,0.00004671274,0.00031492297,0.000035518686,0.000012622304],"category_scores_gemma":[0.0000027524097,0.00007759952,0.000038121925,0.0018311372,0.000033933404,0.0002135611,0.00005488319,0.000088941466,0.0005863123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047689144,0.00026010748,0.0016596224,0.000021770924,0.000037179056,0.000034314864,0.00051054655,0.3442528,0.016317157,0.09805847,0.0068147443,0.53198564],"study_design_scores_gemma":[0.00042029686,0.00013792791,0.01497679,0.000020956879,0.000005680513,0.000019001634,0.000026593036,0.97891176,0.0015299037,0.0007374802,0.00301606,0.00019756042],"about_ca_topic_score_codex":0.000004483928,"about_ca_topic_score_gemma":0.000014880226,"teacher_disagreement_score":0.83930963,"about_ca_system_score_codex":0.00003251816,"about_ca_system_score_gemma":0.000019119962,"threshold_uncertainty_score":0.7536054},"labels":[],"label_agreement":null},{"id":"W4385834296","doi":"10.1109/access.2023.3305385","title":"RandMixAugment: A Novel Unified Technique for Region- and Image-Level Data Augmentations","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Korea Agency for Infrastructure Technology Advancement; Ministry of Land, Infrastructure and Transport","keywords":"Computer science; Generalization; Artificial intelligence; Deep learning; Image (mathematics); Masking (illustration); Machine learning; Data modeling; Contextual image classification; Pattern recognition (psychology); Mathematics; Database","score_opus":0.23662832978084494,"score_gpt":0.4054351063097579,"score_spread":0.16880677652891293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385834296","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":[0.00065888057,0.000017695647,0.99362063,0.0038737124,0.0001573532,0.0010887791,0.000114992465,0.00033908375,0.0001288737],"genre_scores_gemma":[0.3364382,0.00029735593,0.65632606,0.0015155502,0.0003080922,0.0035403057,0.00037104535,0.00005965466,0.001143766],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887496,0.000015247129,0.00019471129,0.00054528174,0.00012680795,0.00024300186],"domain_scores_gemma":[0.9982925,0.0003208593,0.0000966059,0.0011406413,0.000074606425,0.0000747717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018308383,0.00011576033,0.00011679269,0.00012363488,0.00027581115,0.0002374205,0.002014225,0.00003951064,0.0000011517275],"category_scores_gemma":[0.000042971144,0.00011408487,0.000021597225,0.00094785244,0.000060861883,0.001593264,0.0007392116,0.0000784289,0.000010524286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010447993,0.00040673668,0.0005240862,0.00027352976,0.0001472399,0.000042848078,0.0006633358,0.0031992525,0.34784824,0.1979454,0.33302087,0.115823984],"study_design_scores_gemma":[0.0061092204,0.00014199174,0.008180789,0.00017001528,0.00009750755,0.00017686881,0.00007023869,0.56482846,0.14969163,0.21241558,0.056517363,0.0016003527],"about_ca_topic_score_codex":0.000013175687,"about_ca_topic_score_gemma":0.000015725114,"teacher_disagreement_score":0.5616292,"about_ca_system_score_codex":0.000019099525,"about_ca_system_score_gemma":0.000039394396,"threshold_uncertainty_score":0.4652246},"labels":[],"label_agreement":null},{"id":"W4386011182","doi":"10.1007/s00138-023-01435-w","title":"RGB-LiDAR fusion for accurate 2D and 3D object detection","year":2023,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pedestrian detection; Computer science; Object detection; Fusion; Sensor fusion; RGB color model; Artificial intelligence; Convolutional neural network; Computer vision; Detector; Lidar; Object (grammar); Pattern recognition (psychology); Data mining; Engineering; Pedestrian; Remote sensing; Geography","score_opus":0.016270706337742614,"score_gpt":0.3120637469570447,"score_spread":0.29579304061930206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386011182","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":[0.010509907,0.00016147694,0.9855316,0.0021088982,0.000043941636,0.000911749,0.000021259624,0.00048658633,0.00022453307],"genre_scores_gemma":[0.9612241,0.0006341439,0.035677128,0.00037289303,0.00012626707,0.0014962999,0.000054922162,0.000022273394,0.00039198907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904674,0.000020252653,0.00018152456,0.00046118334,0.00010199872,0.00018827232],"domain_scores_gemma":[0.99915546,0.00022068403,0.000078008205,0.00039762462,0.000046446,0.000101753976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001565623,0.000126776,0.00011254922,0.00012799092,0.0006402716,0.00011062164,0.00022498674,0.00004798626,0.000002829635],"category_scores_gemma":[0.000017569968,0.00010976298,0.000030398638,0.0007601623,0.000043893364,0.00021824516,0.00024850838,0.00009218725,0.00004032807],"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":[0.0000065234763,0.00003000671,0.00006142162,0.000018466628,0.000004115188,2.6470272e-7,0.000063229425,0.00035510425,0.021934388,0.02828844,0.00042953578,0.9488085],"study_design_scores_gemma":[0.00044455056,0.000097497286,0.0060553756,0.00001016162,0.00001088104,0.0000151064505,0.000017460476,0.7456017,0.0026527268,0.02599939,0.2188689,0.00022622282],"about_ca_topic_score_codex":0.000007885756,"about_ca_topic_score_gemma":0.00001983638,"teacher_disagreement_score":0.9507142,"about_ca_system_score_codex":0.00001208033,"about_ca_system_score_gemma":0.000009074808,"threshold_uncertainty_score":0.4924516},"labels":[],"label_agreement":null},{"id":"W4386053890","doi":"10.37256/aie.4220233058","title":"A Framework for Open World Object Detection","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Evolution","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Tamkeen; York University; New York University Abu Dhabi","keywords":"Pascal (unit); Object detection; Computer science; Viola–Jones object detection framework; Artificial intelligence; Benchmark (surveying); Object (grammar); Cognitive neuroscience of visual object recognition; Object-class detection; Parametric statistics; Machine learning; Computer vision; Method; Pattern recognition (psychology); Object-oriented programming; Mathematics; Face detection","score_opus":0.11080578736215904,"score_gpt":0.37796090781394404,"score_spread":0.267155120451785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386053890","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":[0.001288672,0.000031043637,0.99432844,0.0017305512,0.0008051066,0.00096777466,0.0000037538825,0.00063060655,0.00021403538],"genre_scores_gemma":[0.8712563,0.000008005523,0.12731616,0.00018986697,0.00028856308,0.00067677937,0.0000060269717,0.000015747219,0.0002425801],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985088,0.000047404803,0.00033101474,0.00053862034,0.00017478611,0.0003993438],"domain_scores_gemma":[0.9985811,0.0005041192,0.00012408351,0.0005837139,0.00012731091,0.000079685815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040811676,0.00012984153,0.00012957456,0.00023564,0.00051254634,0.00024675633,0.0012046568,0.00006912285,0.000010649292],"category_scores_gemma":[0.00029159835,0.00013852218,0.000072884744,0.003283672,0.00005448678,0.00079124235,0.00038238114,0.00015837484,0.0007778682],"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":[0.000014289005,0.000021911876,0.00001720631,0.0000028184775,0.0000036746515,5.177007e-7,0.000090154404,0.0061696745,0.0016906566,0.7895153,0.00024333633,0.20223047],"study_design_scores_gemma":[0.000009294636,0.0000449238,0.00019641676,0.000012266673,0.000002852029,0.0000012823633,0.000043641918,0.31787738,0.023580201,0.6560714,0.0020446775,0.00011562794],"about_ca_topic_score_codex":0.00004721605,"about_ca_topic_score_gemma":0.00046163145,"teacher_disagreement_score":0.8699676,"about_ca_system_score_codex":0.00014676723,"about_ca_system_score_gemma":0.000048312042,"threshold_uncertainty_score":0.9998182},"labels":[],"label_agreement":null},{"id":"W4386071619","doi":"10.1109/cvpr52729.2023.00112","title":"Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Multispectral image; Computer science; Segmentation; RGB color model; Artificial intelligence; Benchmark (surveying); Computer vision; Baseline (sea); Image segmentation; Focus (optics); Scale-space segmentation; Pixel; Segmentation-based object categorization; Semantics (computer science); Cartography; Geography","score_opus":0.021472929825525785,"score_gpt":0.2973099770474439,"score_spread":0.2758370472219181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386071619","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":[0.0059198057,0.00005290062,0.9865667,0.006279126,0.00009310986,0.00026870496,0.00009406803,0.00047797977,0.00024761178],"genre_scores_gemma":[0.4290167,0.00032635234,0.563559,0.0033994217,0.00021046834,0.00017733654,0.0019831986,0.00002088928,0.0013066095],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992061,0.000026127562,0.00013151306,0.00032855073,0.00012119316,0.00018648843],"domain_scores_gemma":[0.99928576,0.00019479629,0.000030299292,0.00039692194,0.000015358635,0.00007685269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000137976,0.000079501704,0.00007044844,0.000060149134,0.00012287869,0.0000629464,0.0002792003,0.000016389824,0.000040492352],"category_scores_gemma":[0.000018699202,0.000071793904,0.000012213554,0.00066171575,0.000028336743,0.0003834791,0.00024660744,0.00005477616,0.0002158957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000091093325,0.00009892959,0.001962211,0.000042989497,0.000033551587,0.00009885564,0.00044337776,0.004941578,0.017897183,0.13246338,0.6218637,0.22014509],"study_design_scores_gemma":[0.00039915007,0.000034810422,0.0051359492,0.0000075932276,0.0000061616984,0.000038076483,0.00003508112,0.95639855,0.002972236,0.008281747,0.026464615,0.0002260054],"about_ca_topic_score_codex":0.000011396503,"about_ca_topic_score_gemma":0.000034938443,"teacher_disagreement_score":0.951457,"about_ca_system_score_codex":0.000009751984,"about_ca_system_score_gemma":0.000009284242,"threshold_uncertainty_score":0.29276702},"labels":[],"label_agreement":null},{"id":"W4386076566","doi":"10.1109/cvpr52729.2023.01901","title":"LINe: Out-of-Distribution Detection by Leveraging Important Neurons","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Iran Telecommunication Research Center","keywords":"Computer science; Pruning; Feature (linguistics); Code (set theory); Artificial intelligence; Class (philosophy); Feature engineering; Masking (illustration); Data mining; Machine learning; Deep learning","score_opus":0.027180446412774326,"score_gpt":0.2780179329100598,"score_spread":0.25083748649728543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386076566","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":[0.020554535,0.000019219822,0.97728336,0.0010316278,0.00027545422,0.000119876524,0.000011180948,0.0005628191,0.00014190782],"genre_scores_gemma":[0.99643916,0.000041028627,0.002952833,0.000101381374,0.000037680267,0.000027475813,0.0000461001,0.0000066594316,0.00034768682],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991872,0.000017361639,0.00021715782,0.00025689643,0.00013601038,0.00018542826],"domain_scores_gemma":[0.999397,0.0000744473,0.000089624096,0.0003465688,0.000043394506,0.000049005455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102021324,0.000073445706,0.000074948526,0.00003691636,0.00010509431,0.000023073906,0.0002902879,0.000026275871,0.000004907869],"category_scores_gemma":[0.000027990885,0.00007087514,0.000035618854,0.0008026981,0.000019626925,0.00021776865,0.00013174437,0.00008329242,0.00005914444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000090597005,0.00012303858,0.001044247,0.000023101727,0.00002250857,0.00001356331,0.00029070931,0.00964209,0.56908536,0.044745814,0.042192336,0.3328082],"study_design_scores_gemma":[0.00016716395,0.00006701754,0.0017340623,0.00000490285,0.000004708746,0.0000060593866,0.000015616199,0.76028264,0.19702315,0.007976549,0.03252645,0.00019167353],"about_ca_topic_score_codex":0.000009008644,"about_ca_topic_score_gemma":0.000008814655,"teacher_disagreement_score":0.9758846,"about_ca_system_score_codex":0.00002338718,"about_ca_system_score_gemma":0.000011191142,"threshold_uncertainty_score":0.28902045},"labels":[],"label_agreement":null},{"id":"W4386076662","doi":"10.1109/cvpr52729.2023.01480","title":"Exemplar-FreeSOLO: Enhancing Unsupervised Instance Segmentation with Exemplars","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Carleton University","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Embedding; Discriminative model; Pattern recognition (psychology); Object (grammar); Annotation; Unsupervised learning; Image segmentation; Perspective (graphical); Abstraction; Segmentation-based object categorization; Scale-space segmentation; Machine learning","score_opus":0.017537342281233218,"score_gpt":0.25822057017555755,"score_spread":0.24068322789432434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386076662","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":[0.09075933,0.000021122309,0.90423787,0.0018659247,0.0000878395,0.00031817428,0.0000015684675,0.0011641874,0.0015439945],"genre_scores_gemma":[0.6681605,0.000064378015,0.32889548,0.00090037123,0.000055276512,0.00024318616,0.000023352108,0.000025881482,0.0016315856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868995,0.000027871192,0.0001955402,0.0004591133,0.00027638313,0.00035115957],"domain_scores_gemma":[0.9990323,0.00012832346,0.00006511739,0.0006239298,0.000060750124,0.00008954744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012294558,0.00014088381,0.00011784437,0.000106678046,0.00027308965,0.0000831673,0.00061938487,0.000033445933,0.000017748136],"category_scores_gemma":[0.00001004477,0.00011870256,0.000025269612,0.0017962765,0.00003622277,0.00075846206,0.00016766234,0.00010069729,0.00032650828],"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":[0.00007969764,0.00020828814,0.005833978,0.00009440663,0.0000959999,0.00019727414,0.0047582737,0.06627109,0.27723277,0.3807467,0.019732889,0.24474864],"study_design_scores_gemma":[0.004568739,0.0007227182,0.023013616,0.00022520185,0.00003882737,0.00017744124,0.0012174231,0.45328808,0.43222028,0.053588204,0.0282865,0.0026529802],"about_ca_topic_score_codex":0.00000804562,"about_ca_topic_score_gemma":0.00013276929,"teacher_disagreement_score":0.57740116,"about_ca_system_score_codex":0.000058594887,"about_ca_system_score_gemma":0.00003687257,"threshold_uncertainty_score":0.484055},"labels":[],"label_agreement":null},{"id":"W4386243176","doi":"10.1109/crv60082.2023.00022","title":"Kaskade-CNN and NB-KDE-CNN: CNN Architectures with KDE and Probabilistic Reasoning Layers","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Allison University","funders":"","keywords":"Computer science; Probabilistic logic; Artificial intelligence","score_opus":0.012117305890808954,"score_gpt":0.24136303022384253,"score_spread":0.22924572433303358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386243176","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":[0.57409877,0.0003013948,0.41148567,0.007996357,0.000088026325,0.00095639285,0.0000036512822,0.0014394583,0.0036303017],"genre_scores_gemma":[0.9100802,0.00003640472,0.08818269,0.00038933664,0.000060908595,0.000101904596,0.0000032164876,0.000019469742,0.0011258828],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986546,0.00003719628,0.00015329517,0.0005986735,0.00019506908,0.000361144],"domain_scores_gemma":[0.9989272,0.00034819322,0.000061984356,0.0004688166,0.000034813205,0.00015902436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012621653,0.00017133243,0.00016173339,0.00009403686,0.00024590158,0.00013443126,0.00033857604,0.00004052746,0.000005877856],"category_scores_gemma":[0.000056935583,0.00013266048,0.00001821012,0.0007481745,0.00016732923,0.00015538183,0.00032487948,0.0001726229,0.000026403382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110044566,0.00014908882,0.023886085,0.00028708772,0.00015068818,0.00019406195,0.0066448445,0.13166437,0.0056998557,0.49902558,0.006742833,0.32544547],"study_design_scores_gemma":[0.0018567025,0.0006446081,0.09936097,0.00024719545,0.000059167913,0.00041767626,0.00037748233,0.7128201,0.001973601,0.1678801,0.0127826035,0.0015797537],"about_ca_topic_score_codex":0.00002101844,"about_ca_topic_score_gemma":0.00009924436,"teacher_disagreement_score":0.5811558,"about_ca_system_score_codex":0.000017408045,"about_ca_system_score_gemma":0.00003061029,"threshold_uncertainty_score":0.5409738},"labels":[],"label_agreement":null},{"id":"W4386245243","doi":"10.1109/crv60082.2023.00035","title":"HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Object (grammar); Precision and recall; Code (set theory); Process (computing); Filter (signal processing); Recall; Range (aeronautics); Baseline (sea); Low Confidence; Detector; F1 score; Machine learning; Pattern recognition (psychology); Computer vision","score_opus":0.02601647411069827,"score_gpt":0.24623297558900917,"score_spread":0.2202165014783109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386245243","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":[0.11151388,0.00000926191,0.8841355,0.0002245818,0.000078251105,0.0002484441,5.454175e-7,0.0020854326,0.0017041203],"genre_scores_gemma":[0.8774561,0.000024150431,0.12183837,0.00020899205,0.00004757686,0.0001162778,0.00000177346,0.000014227779,0.00029250194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998911,0.000022284758,0.00013356311,0.00043565553,0.00020680916,0.00029064892],"domain_scores_gemma":[0.99911183,0.00011197218,0.00004200866,0.00059305085,0.000060282546,0.00008084842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008265959,0.00012796577,0.00010446093,0.00009027843,0.00020982101,0.00009368209,0.00053026644,0.00003312689,0.000010990835],"category_scores_gemma":[0.000009550622,0.00010577086,0.000025049285,0.0013006887,0.00002791987,0.00053480966,0.00019000746,0.00011056054,0.00025052624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047295307,0.00013321161,0.0020873179,0.00010436445,0.00008001521,0.00013214696,0.0020563519,0.07344,0.37654564,0.064118914,0.0007253196,0.48052943],"study_design_scores_gemma":[0.00021438731,0.00009302469,0.002827161,0.000020553323,0.000004810941,0.00007442898,0.000022127912,0.963629,0.030257912,0.0019469319,0.00067372416,0.00023592233],"about_ca_topic_score_codex":0.000015877184,"about_ca_topic_score_gemma":0.00004575727,"teacher_disagreement_score":0.890189,"about_ca_system_score_codex":0.000034321805,"about_ca_system_score_gemma":0.000031193147,"threshold_uncertainty_score":0.43132105},"labels":[],"label_agreement":null},{"id":"W4386249652","doi":"10.1109/crv60082.2023.00027","title":"Real-Time Instance Segmentation with Polygons Using an Intersection-Over-Union Loss","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Minimum bounding box; Bounding overwatch; Artificial intelligence; Process (computing); Intersection (aeronautics); Image segmentation; Code (set theory); Inference; Frame (networking); Set (abstract data type); Binary number; Pattern recognition (psychology); Computer vision; Image (mathematics); Mathematics","score_opus":0.023721490758841047,"score_gpt":0.29589307846484797,"score_spread":0.2721715877060069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386249652","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":[0.40769097,0.0000010231395,0.5906289,0.00020812463,0.00006366834,0.00014033455,0.0000014582387,0.000577923,0.00068757],"genre_scores_gemma":[0.85231495,0.000016039276,0.14664426,0.00013130305,0.00006909812,0.000032491436,0.000016968546,0.000018116789,0.0007567785],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907845,0.000044002318,0.00013482971,0.00035863728,0.00017308921,0.0002109656],"domain_scores_gemma":[0.9993149,0.00004716781,0.000075342316,0.00043882686,0.00004736669,0.00007641172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009039175,0.00010378984,0.00007938381,0.00011288466,0.00021103988,0.000077646524,0.00031395003,0.000027087439,0.000018205934],"category_scores_gemma":[0.000002895889,0.00009217493,0.000017785076,0.001368468,0.00003935443,0.0011779313,0.00009778612,0.00006777341,0.00008501044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057342182,0.00028197723,0.0071258773,0.000023149458,0.000055592638,0.000053785767,0.002599072,0.17672357,0.60439354,0.1326437,0.0021778848,0.073864505],"study_design_scores_gemma":[0.00042907285,0.00017495707,0.008125986,0.000030488165,0.000007754109,0.000048759888,0.00023499105,0.9708166,0.015261118,0.0042244587,0.0002979978,0.0003478062],"about_ca_topic_score_codex":0.0001075716,"about_ca_topic_score_gemma":0.000093643954,"teacher_disagreement_score":0.794093,"about_ca_system_score_codex":0.00009981748,"about_ca_system_score_gemma":0.00003267512,"threshold_uncertainty_score":0.37587845},"labels":[],"label_agreement":null},{"id":"W4386253585","doi":"10.1007/s10489-023-04935-4","title":"Dense-scale dynamic network with filter-varying atrous convolution for semantic segmentation","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Pixel; Convolution (computer science); Pattern recognition (psychology); Filter (signal processing); Computer vision; Convolutional neural network; Algorithm; Artificial neural network","score_opus":0.022201793327833973,"score_gpt":0.27622045817937996,"score_spread":0.254018664851546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386253585","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":[0.009173154,0.000051668987,0.98828214,0.0003056847,0.00016924528,0.0010600929,0.0000039081997,0.00065341644,0.00030066134],"genre_scores_gemma":[0.7698605,0.00007230576,0.22867936,0.00027117142,0.00008796109,0.0007594258,0.000057409707,0.000026739433,0.0001851455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983525,0.000017658609,0.00028873375,0.0006006212,0.00022181765,0.00051863655],"domain_scores_gemma":[0.9988124,0.00033306633,0.00015276656,0.0005442219,0.00007689562,0.000080687416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017509118,0.00019097881,0.00016876511,0.00007912203,0.00036343653,0.00008288511,0.0006029689,0.00005627909,0.0000048859874],"category_scores_gemma":[0.000007329034,0.00018274279,0.000045990586,0.0013030192,0.00007568002,0.0002458714,0.00015134053,0.00012388875,0.00024744077],"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":[0.00007404283,0.00004816255,0.00016622712,0.00006876155,0.000038945305,0.0000065718064,0.0007408653,0.67441195,0.018155066,0.09211718,0.0017498578,0.21242236],"study_design_scores_gemma":[0.00017215671,0.000113139344,0.00037116578,0.00003892485,0.000019328136,0.000020006002,0.00008525674,0.92782664,0.01498257,0.05513634,0.0008770554,0.00035741876],"about_ca_topic_score_codex":0.000004704517,"about_ca_topic_score_gemma":0.000031025753,"teacher_disagreement_score":0.76068735,"about_ca_system_score_codex":0.00008510422,"about_ca_system_score_gemma":0.000032777632,"threshold_uncertainty_score":0.74520344},"labels":[],"label_agreement":null},{"id":"W4386345866","doi":"10.21203/rs.3.rs-3299732/v1","title":"Real-time traffic sign detection network based on Swin Transformer","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Transformer; Computation; Artificial intelligence; Traffic sign; Residual; Traffic sign recognition; Real-time computing; Sign (mathematics); Pattern recognition (psychology); Voltage; Engineering; Algorithm","score_opus":0.0705037209025731,"score_gpt":0.37164878487410724,"score_spread":0.3011450639715341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386345866","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":[0.009879649,0.0001089918,0.96972686,0.006541789,0.00083358487,0.005149223,0.000066225286,0.003354984,0.0043387036],"genre_scores_gemma":[0.9685034,0.0006328447,0.023150893,0.00011941121,0.0015488592,0.0031326483,0.00019183013,0.00020247632,0.002517627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942738,0.00077660027,0.00045530038,0.0015341514,0.0016743817,0.0012857784],"domain_scores_gemma":[0.99516875,0.0018603286,0.00011858313,0.0021564187,0.00037110216,0.00032483702],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019299992,0.00038889342,0.0003936725,0.0005692602,0.0006797348,0.00032967937,0.002065286,0.000426381,0.000049401548],"category_scores_gemma":[0.0001240292,0.00038676057,0.0002695365,0.0026464236,0.000134809,0.00018264963,0.00048419656,0.0022652226,0.0015065979],"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":[0.000050842,0.00009195313,0.0000061602022,0.00013951096,0.000016102393,0.000029495981,0.00009496786,0.90950924,0.000988054,0.0009585075,0.0055275657,0.08258762],"study_design_scores_gemma":[0.0002767138,0.00037960682,0.0010447018,0.00045977393,0.0000066293974,0.0000019304455,0.000010448746,0.98129165,0.0011243478,0.011265355,0.0036986019,0.00044022032],"about_ca_topic_score_codex":0.000049431535,"about_ca_topic_score_gemma":0.00007255209,"teacher_disagreement_score":0.95862377,"about_ca_system_score_codex":0.00046358205,"about_ca_system_score_gemma":0.00040300033,"threshold_uncertainty_score":0.99985844},"labels":[],"label_agreement":null},{"id":"W4386352573","doi":"10.1109/isbi53787.2023.10230732","title":"End-To-End Deformable Attention Graph Neural Network for Single-View Liver Mesh Reconstruction","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Graph; Segmentation; Computer vision; Pattern recognition (psychology); Algorithm; Theoretical computer science","score_opus":0.03760597479812968,"score_gpt":0.26342389044384734,"score_spread":0.22581791564571765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386352573","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":[0.015124612,0.00009368559,0.9784439,0.0019566827,0.0010122494,0.00092150626,0.000005524068,0.0010194689,0.0014223529],"genre_scores_gemma":[0.48842132,0.00029127748,0.50401026,0.0019310367,0.00082685746,0.00090966513,0.00009145343,0.00005484653,0.0034632736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984639,0.000035646768,0.00029983834,0.0004961137,0.00017885608,0.0005256132],"domain_scores_gemma":[0.9990044,0.00016414441,0.00010588574,0.0004881068,0.00010417982,0.00013324234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022334624,0.00015876522,0.00016264513,0.00013398338,0.00037966942,0.00011500373,0.00048021376,0.00005452402,0.00002841667],"category_scores_gemma":[0.000016450549,0.00014857699,0.00012265008,0.0019248647,0.000038697926,0.00084038725,0.00020748618,0.00009508775,0.00022965515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013096528,0.000029314684,0.0003416025,0.000027582279,0.00002039715,0.0000034086167,0.000055914676,0.048133705,0.0026229233,0.10186638,0.018484492,0.8284012],"study_design_scores_gemma":[0.00044002384,0.00022238155,0.0030070187,0.00005366546,0.00002537558,0.00015844796,0.000025718722,0.9044681,0.001282807,0.049764942,0.040036492,0.00051502895],"about_ca_topic_score_codex":0.000009296663,"about_ca_topic_score_gemma":0.00005494056,"teacher_disagreement_score":0.8563344,"about_ca_system_score_codex":0.000049648388,"about_ca_system_score_gemma":0.00001531309,"threshold_uncertainty_score":0.60587937},"labels":[],"label_agreement":null},{"id":"W4386373083","doi":"10.3390/computers12090174","title":"Automated Optimization-Based Deep Learning Models for Image Classification Tasks","year":2023,"lang":"en","type":"article","venue":"Computers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"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":"International Development Research Centre","keywords":"Artificial intelligence; Computer science; Deep learning; Machine learning; Transformer; Scheme (mathematics); Contextual image classification; Image (mathematics); Engineering; Mathematics","score_opus":0.04582645541361682,"score_gpt":0.29197379670007845,"score_spread":0.24614734128646162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386373083","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":[0.00013070703,0.000012813781,0.9927945,0.001770419,0.00022802138,0.00043819615,0.000002979107,0.004447339,0.00017501583],"genre_scores_gemma":[0.17085953,0.000010011736,0.8282136,0.00033842746,0.000060990576,0.00023986894,0.00019400097,0.000025736339,0.00005787006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883515,0.000046225592,0.0002188104,0.0004515678,0.00015090748,0.0002973556],"domain_scores_gemma":[0.99878037,0.0004157125,0.00012745771,0.00044860228,0.0001442592,0.00008360011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013746027,0.00013196286,0.00012190416,0.00016292228,0.00031503322,0.00014535425,0.000664056,0.000050968978,0.0000015780366],"category_scores_gemma":[0.000030939806,0.00014480007,0.00006426214,0.0011044481,0.000039743467,0.0004674641,0.000115405,0.00009616993,0.000052010924],"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":[0.0000020245734,0.000012200905,0.0000060601187,0.000008153927,0.000004402891,0.0000011406654,0.000066652356,0.9741889,0.00031932216,0.009563836,0.0024692158,0.013358058],"study_design_scores_gemma":[0.0002854945,0.000027963504,0.00009535091,0.000010464797,0.0000036560293,0.00000128548,0.000007706162,0.99597496,0.000111101836,0.0022492644,0.0010744244,0.00015834456],"about_ca_topic_score_codex":9.570101e-7,"about_ca_topic_score_gemma":3.9188296e-7,"teacher_disagreement_score":0.17072883,"about_ca_system_score_codex":0.000056655885,"about_ca_system_score_gemma":0.00003569507,"threshold_uncertainty_score":0.5904776},"labels":[],"label_agreement":null},{"id":"W4386381498","doi":"10.4236/ojsst.2023.133006","title":"Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a Safety Framework","year":2023,"lang":"en","type":"article","venue":"Open Journal of Safety Science and Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Lidar; Computer science; Situation awareness; Key (lock); Perception; Anticipation (artificial intelligence); Transport engineering; Remote sensing; Computer security; Artificial intelligence; Engineering; Geography","score_opus":0.019992239483431692,"score_gpt":0.30874462410843356,"score_spread":0.28875238462500186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386381498","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":[0.84135014,0.00044248134,0.1016102,0.055749953,0.00012841793,0.00046831655,0.0000034898533,0.00013054872,0.00011647099],"genre_scores_gemma":[0.9134273,0.0006525075,0.08565287,0.0002281189,0.000015311274,0.000011472852,3.2583736e-7,0.0000070757096,0.0000050017557],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998224,0.000027019092,0.0005172377,0.0005318061,0.00022722874,0.00047270392],"domain_scores_gemma":[0.99866337,0.00016284504,0.0002965866,0.00042439584,0.00030784673,0.00014496243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012003288,0.00015938586,0.00040497023,0.0011350191,0.00054753874,0.000231048,0.0021262707,0.00015603706,0.0000012535352],"category_scores_gemma":[0.00024224565,0.00014274924,0.000010801677,0.005686865,0.0013969444,0.001525593,0.0017872005,0.0005823389,0.0000032701257],"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":[0.000046672023,0.000047172725,0.05227616,0.000012973155,0.000011941596,0.0001630701,0.0006387634,0.00012234891,0.010701751,0.44455668,0.000019787401,0.49140266],"study_design_scores_gemma":[0.0026859671,0.0010627784,0.29043907,0.000664261,0.000017430022,0.0027881574,0.0028940798,0.039010946,0.006966927,0.6440103,0.008591386,0.00086871284],"about_ca_topic_score_codex":0.000005729691,"about_ca_topic_score_gemma":0.000031776617,"teacher_disagreement_score":0.49053395,"about_ca_system_score_codex":0.00009406867,"about_ca_system_score_gemma":0.00038632355,"threshold_uncertainty_score":0.5821145},"labels":[],"label_agreement":null},{"id":"W4386590510","doi":"10.1109/icip49359.2023.10223061","title":"YOLO-MAXVOD for Real-Time Video Object Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fractal Systems (Canada)","funders":"","keywords":"Computer science; Leverage (statistics); Inference; Object detection; Latency (audio); Artificial intelligence; Ranging; Architecture; Video tracking; Real-time computing; Object (grammar); Computer vision; Pattern recognition (psychology)","score_opus":0.020406464392873906,"score_gpt":0.2816889068936459,"score_spread":0.261282442500772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386590510","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":[0.0074743857,0.000004285263,0.9846568,0.0012327576,0.00015804697,0.00041433706,0.0000019614379,0.0017571648,0.0043002763],"genre_scores_gemma":[0.70253086,0.00010162181,0.2717884,0.0005758984,0.00042136712,0.0011418194,0.00002141222,0.00004770308,0.023370918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991639,0.00001541731,0.00013213996,0.00033579458,0.00010135176,0.00025139944],"domain_scores_gemma":[0.9991193,0.00028050068,0.000042676438,0.00045266727,0.00004825564,0.00005658862],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012557536,0.000081288264,0.00008383296,0.000082392806,0.00017990914,0.000045672932,0.0003941467,0.000037245776,0.000012108026],"category_scores_gemma":[0.000035422454,0.000075095435,0.000052865773,0.0009208743,0.000015999976,0.00026567906,0.00012587436,0.000044920238,0.0009324938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020047817,0.000045626926,0.00003981834,0.000017846976,0.000022989367,0.000004778204,0.00018485899,0.006799648,0.33149716,0.103053354,0.041779324,0.51653457],"study_design_scores_gemma":[0.00036900147,0.0001560068,0.0018868351,0.0000060091074,0.000006503429,0.00001248299,0.000010515461,0.783301,0.095316455,0.07411795,0.04450625,0.00031100606],"about_ca_topic_score_codex":0.000010615779,"about_ca_topic_score_gemma":0.000017360635,"teacher_disagreement_score":0.77650136,"about_ca_system_score_codex":0.000029886764,"about_ca_system_score_gemma":0.000015251649,"threshold_uncertainty_score":0.9998454},"labels":[],"label_agreement":null},{"id":"W4386591361","doi":"10.1016/j.engappai.2023.107087","title":"Multimodal fusion for sensorimotor control in steering angle prediction","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science, ICT and Future Planning; Korea Creative Content Agency; National Research Foundation of Korea; Ministry of Culture, Sports and Tourism; Gwangju Institute of Science and Technology","keywords":"Computer science; Artificial intelligence; RGB color model; Computer vision; Frame (networking); Encoder; Feature (linguistics); Event (particle physics); Sensor fusion; Fusion mechanism; Fusion","score_opus":0.022121993031271273,"score_gpt":0.2745244826444739,"score_spread":0.2524024896132026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386591361","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":[0.011234162,0.000028959439,0.9867934,0.0002609996,0.00012496264,0.0010556863,0.000032637075,0.00045364382,0.000015566642],"genre_scores_gemma":[0.8867697,0.0000192121,0.1115922,0.000011776574,0.00010769352,0.0014516346,0.000014243233,0.000016535787,0.000016987964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988231,0.0000092188075,0.00042837535,0.00034216794,0.00013873185,0.00025842502],"domain_scores_gemma":[0.998916,0.00037715712,0.000083767096,0.000466671,0.000100019744,0.00005640121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022921941,0.000116629235,0.0001470172,0.0002810522,0.00007981728,0.000024550473,0.0004665864,0.00005861349,0.0000016621642],"category_scores_gemma":[0.00007415635,0.00013645657,0.000054538228,0.0014607233,0.000025698453,0.00019224221,0.00007003448,0.00010484714,0.000033690554],"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":[0.0000062859817,0.000046987065,0.00006142847,0.00002578693,0.0000044826234,3.3490753e-7,0.000116440424,0.7773163,0.0612551,0.07846203,0.000024956335,0.082679875],"study_design_scores_gemma":[0.00004316385,0.000029266883,0.0005839218,0.000016906593,0.0000028045379,9.709098e-7,0.000024957819,0.96280164,0.029794443,0.005284259,0.0013119564,0.00010573214],"about_ca_topic_score_codex":0.000011061688,"about_ca_topic_score_gemma":0.000004891321,"teacher_disagreement_score":0.87553555,"about_ca_system_score_codex":0.00004981061,"about_ca_system_score_gemma":0.000018562298,"threshold_uncertainty_score":0.55645376},"labels":[],"label_agreement":null},{"id":"W4386596874","doi":"10.1109/icip49359.2023.10222897","title":"A Novel Weakly Supervised Segmentation Approach for Rapid Left Ventricle Annotation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Computer science; Annotation; Segmentation; Pixel; Convolutional neural network; Regularization (linguistics); Pattern recognition (psychology); Supervised learning; Classifier (UML); Image segmentation; Computer vision; Artificial neural network","score_opus":0.04558591506153541,"score_gpt":0.28971636299451237,"score_spread":0.24413044793297695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386596874","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":[0.003999444,0.000012789061,0.99285907,0.0010776612,0.000081356004,0.0007185516,0.0000065676795,0.000579043,0.0006655088],"genre_scores_gemma":[0.15980716,0.000015150389,0.8382756,0.00043646942,0.0000801946,0.00036353455,0.00016506146,0.000014654082,0.0008421979],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991029,0.000013141411,0.00016553035,0.00033719593,0.00015516413,0.00022607706],"domain_scores_gemma":[0.99939835,0.00012981937,0.000051495324,0.00029515108,0.00007254497,0.00005261729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013678388,0.00008558759,0.00007744838,0.00009940294,0.00016690933,0.00006825432,0.0003440594,0.000028773049,0.0000074728755],"category_scores_gemma":[0.00001849627,0.0000830301,0.00004542391,0.0008479953,0.000013976477,0.00048550742,0.000078368495,0.00003966952,0.000055215907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027537733,0.00032999692,0.0002405237,0.00006262049,0.000032376272,0.0000010895741,0.0015629629,0.07594724,0.419943,0.14073867,0.0181574,0.34295657],"study_design_scores_gemma":[0.00049045216,0.00004013219,0.0013367317,0.0000015382298,0.0000034822863,0.0000039934057,0.00007787645,0.98099196,0.013162881,0.0022177023,0.0015541902,0.00011904907],"about_ca_topic_score_codex":0.0000028297736,"about_ca_topic_score_gemma":0.00000117671,"teacher_disagreement_score":0.90504473,"about_ca_system_score_codex":0.000031006537,"about_ca_system_score_gemma":0.000019403662,"threshold_uncertainty_score":0.3385869},"labels":[],"label_agreement":null},{"id":"W4386596889","doi":"10.1109/icip49359.2023.10222609","title":"Segmentation of the Left Ventricle for the Cardiac Phases between End-Diastole and End-Systole","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Segmentation; Ventricle; Artificial intelligence; Ground truth; Computer science; Deep learning; End-to-end principle; Cardiac cycle; Systole; Pattern recognition (psychology); Image segmentation; Data set; Computer vision; Diastole; Cardiology; Medicine; Internal medicine","score_opus":0.030920082148784816,"score_gpt":0.2980388553381152,"score_spread":0.26711877318933036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386596889","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":[0.13857257,0.00032334268,0.85337013,0.0056081936,0.00028540785,0.0012600337,0.000064358,0.0001936943,0.00032229428],"genre_scores_gemma":[0.9943424,0.00003337292,0.0049266582,0.000096745,0.000070263006,0.00008483052,0.0000053663703,0.0000054272414,0.00043493704],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994229,0.00002668308,0.00012527381,0.00016185908,0.00013333552,0.00012992762],"domain_scores_gemma":[0.9987528,0.0007848955,0.00006822288,0.00033878448,0.000030758925,0.00002457475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013967478,0.00005784768,0.00008173576,0.00002333476,0.0002163855,0.00002561202,0.0003723031,0.000014874842,0.0000048975808],"category_scores_gemma":[0.000018639059,0.00003405242,0.00005119484,0.00038536437,0.00005267843,0.00013324684,0.00017554207,0.000036052992,0.000005218748],"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":[0.000009745353,0.000062808176,0.034233306,0.000048599704,0.00013248764,5.96919e-7,0.00235119,0.007216153,0.029547809,0.098918654,0.025580598,0.80189806],"study_design_scores_gemma":[0.0014129081,0.00019145146,0.26127583,0.00003663127,0.00014805047,0.000008284079,0.0008544448,0.13131754,0.4970568,0.026699454,0.08050182,0.0004967661],"about_ca_topic_score_codex":0.000015027966,"about_ca_topic_score_gemma":0.00000618471,"teacher_disagreement_score":0.8557698,"about_ca_system_score_codex":0.000011613218,"about_ca_system_score_gemma":0.000016271744,"threshold_uncertainty_score":0.16642842},"labels":[],"label_agreement":null},{"id":"W4386596956","doi":"10.1109/icip49359.2023.10222555","title":"LKBQ: Pushing the Limit of Post-Training Quantization to Extreme 1 bit","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Quantization (signal processing); Initialization; Computer science; Artificial neural network; Binary number; Dither; Algorithm; Artificial intelligence; Speech recognition; Arithmetic; Mathematics; Computer network; Bandwidth (computing)","score_opus":0.11245408041605312,"score_gpt":0.30233018550157953,"score_spread":0.1898761050855264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386596956","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":[0.03968582,0.00001023014,0.941197,0.016407933,0.000117219344,0.00023307375,0.0000011816448,0.0003968754,0.0019506812],"genre_scores_gemma":[0.92628163,0.00001624625,0.07159487,0.0014161955,0.000053959426,0.00003718575,0.000004200249,0.0000099486515,0.000585779],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992069,0.000025592324,0.00017379038,0.0002217381,0.00018051063,0.00019147371],"domain_scores_gemma":[0.99904764,0.00027953665,0.000060138238,0.0004801904,0.00008580628,0.00004668095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019714386,0.00006640864,0.000074837815,0.00008926118,0.00013882614,0.000050761853,0.00070981623,0.000019522837,0.000007687365],"category_scores_gemma":[0.000096645505,0.00004792297,0.000030190995,0.001720595,0.000019291418,0.00026286836,0.0002795576,0.000060928207,0.00014961747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033703716,0.000018827213,0.00030943935,0.0000067569936,0.000009789552,0.0000024300668,0.0044823377,0.051640797,0.090734124,0.6592055,0.0032867854,0.19029985],"study_design_scores_gemma":[0.0002654071,0.00016568038,0.030005924,0.000065918386,0.0000109535395,0.000016372951,0.0014213924,0.8713941,0.024857093,0.029921234,0.04137106,0.0005048859],"about_ca_topic_score_codex":0.000012522922,"about_ca_topic_score_gemma":0.000038063827,"teacher_disagreement_score":0.8865958,"about_ca_system_score_codex":0.000009975604,"about_ca_system_score_gemma":0.000020442962,"threshold_uncertainty_score":0.1954242},"labels":[],"label_agreement":null},{"id":"W4386597007","doi":"10.1109/icip49359.2023.10221982","title":"JPEG Compliant Compression for DNN Vision","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"JPEG; Computer science; Image compression; Artificial intelligence; Quantization (signal processing); Pixel; Lossless JPEG; JPEG 2000; Data compression; Data compression ratio; Artificial neural network; Compression ratio; Computer vision; Deep neural networks; Image processing; Image (mathematics); Engineering","score_opus":0.047979497554909406,"score_gpt":0.34738205693769264,"score_spread":0.29940255938278326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386597007","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":[0.0013113343,0.000014549994,0.991014,0.004445631,0.00017801434,0.00034685602,0.0000025030731,0.0008667731,0.0018203214],"genre_scores_gemma":[0.47231445,0.00004284999,0.5186657,0.0014772062,0.00017900491,0.0003360707,0.000050754574,0.000026203625,0.0069077895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922955,0.000011568577,0.00012850396,0.0002942209,0.00012204533,0.00021408843],"domain_scores_gemma":[0.9991356,0.00026571553,0.000038038048,0.000453098,0.000043029664,0.00006447989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078698264,0.00007305982,0.00008212214,0.000053338732,0.00019893206,0.000047274567,0.0005498735,0.00002495811,0.000010007094],"category_scores_gemma":[0.000011075398,0.00005862715,0.00004016537,0.0005155361,0.00001842983,0.00022260347,0.00028985715,0.00004518016,0.0003952107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008177325,0.000058308007,0.000057209632,0.000016194166,0.000004934882,0.0000031972716,0.000080820566,0.008674835,0.024647998,0.4529514,0.31257123,0.20092571],"study_design_scores_gemma":[0.00019929088,0.00004382015,0.0012807413,0.0000122835145,0.0000010269902,0.0000024682274,0.0000054456073,0.79273176,0.0032136692,0.036620952,0.16578217,0.000106398635],"about_ca_topic_score_codex":0.0000013750218,"about_ca_topic_score_gemma":0.0000023998468,"teacher_disagreement_score":0.7840569,"about_ca_system_score_codex":0.00001401029,"about_ca_system_score_gemma":0.000008552061,"threshold_uncertainty_score":0.5079766},"labels":[],"label_agreement":null},{"id":"W4386598466","doi":"10.1109/icip49359.2023.10222624","title":"Dodging the Double Descent in Deep Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","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":"Grand Équipement National De Calcul Intensif; Canadian Institute for Advanced Research","keywords":"Regularization (linguistics); Phenomenon; Computer science; Gradient descent; Generalization; Perspective (graphical); Deep neural networks; Deep learning; Artificial neural network; Artificial intelligence; Stochastic gradient descent; Descent (aeronautics); Machine learning; Mathematical optimization; Mathematics; Epistemology; Engineering; Philosophy","score_opus":0.02933795449060126,"score_gpt":0.27729264827449845,"score_spread":0.2479546937838972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386598466","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":[0.011939508,0.000070821836,0.9779637,0.007517025,0.00022335193,0.00027386204,5.4185463e-8,0.00051516824,0.0014965425],"genre_scores_gemma":[0.99386644,0.000028149676,0.0047542215,0.00092954404,0.00007898709,0.000084699655,0.0000013792858,0.0000072961966,0.00024927323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991111,0.000025152774,0.0001447652,0.0002553368,0.00011959373,0.00034406377],"domain_scores_gemma":[0.9992519,0.00014977355,0.000030720494,0.0005078285,0.000015405501,0.000044346732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015569078,0.00007983497,0.000066963104,0.00005348015,0.00015096409,0.00008353384,0.00081778195,0.000022860791,0.0000065488566],"category_scores_gemma":[0.000004380289,0.000053955147,0.000028902688,0.0016590572,0.000027352486,0.00023272686,0.00037654082,0.00016267251,0.00007970089],"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":[0.0000024662106,0.0000098593255,0.0011450725,8.8191257e-7,0.0000014436765,0.000008220352,0.00012178897,0.8469787,0.000042523643,0.08217311,0.00092157983,0.068594314],"study_design_scores_gemma":[0.00014566316,0.0000046968166,0.00616635,0.000001916702,5.735951e-7,0.0000045689635,0.000016987071,0.9894456,0.00005765221,0.0028818825,0.0012028597,0.000071217575],"about_ca_topic_score_codex":0.000016525926,"about_ca_topic_score_gemma":0.00012231614,"teacher_disagreement_score":0.9819269,"about_ca_system_score_codex":0.000026381455,"about_ca_system_score_gemma":0.0000053013982,"threshold_uncertainty_score":0.22002271},"labels":[],"label_agreement":null},{"id":"W4386735370","doi":"10.1007/978-3-031-43520-1_8","title":"Genetic Algorithm for CNN Architecture Optimization","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Convolutional neural network; MNIST database; Computer science; Genetic algorithm; Pooling; Architecture; Artificial intelligence; Hyperparameter; Focus (optics); Algorithm; Deep learning; Pattern recognition (psychology); Machine learning","score_opus":0.01760286386522514,"score_gpt":0.2385212704245129,"score_spread":0.22091840655928777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386735370","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":[3.3092186e-7,0.0058160475,0.9901824,0.00023673299,0.001032209,0.0014336534,0.000020251624,0.00023661714,0.0010417447],"genre_scores_gemma":[0.0039810417,0.0049729072,0.95622647,0.00080136524,0.006288109,0.0012269864,0.00032145975,0.0004284509,0.025753234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980467,0.000033015902,0.00048010077,0.0008270738,0.00020533877,0.00040777013],"domain_scores_gemma":[0.9981144,0.00078838173,0.00027156115,0.0006427316,0.000083836385,0.000099081306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015460652,0.00041200855,0.0005034616,0.00018587161,0.0001600322,0.00018544457,0.00052013324,0.0005492331,0.0000021700557],"category_scores_gemma":[0.00002096489,0.00037256526,0.000101428246,0.00020535153,0.00005750201,0.00006113572,0.00017952811,0.00050635997,0.000004090056],"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":[0.0000016987478,0.0000022849483,0.0000021554704,0.000031587584,0.000015299236,0.000007020015,0.000026640362,0.76677686,3.9783606e-7,0.009059522,0.00012831992,0.22394824],"study_design_scores_gemma":[0.00018741723,0.00005226024,0.0000055185383,0.00024420503,0.000016343107,0.000036801994,3.539177e-7,0.9520256,5.180194e-7,0.035254445,0.011813878,0.00036267328],"about_ca_topic_score_codex":0.000008097532,"about_ca_topic_score_gemma":0.000028562268,"teacher_disagreement_score":0.22358558,"about_ca_system_score_codex":0.000056129782,"about_ca_system_score_gemma":0.000029407163,"threshold_uncertainty_score":0.9998726},"labels":[],"label_agreement":null},{"id":"W4386735373","doi":"10.1007/978-3-031-43520-1_16","title":"Learning Sparse Fully Connected Layers in Convolutional Neural Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Convolutional neural network; Computer science; Regularization (linguistics); Deep learning; Smoothing; Artificial intelligence; Simple (philosophy); Pattern recognition (psychology)","score_opus":0.022603502804095375,"score_gpt":0.23749204102790283,"score_spread":0.21488853822380746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386735373","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":[0.00026542562,0.008673746,0.98398066,0.00047172362,0.00196066,0.0011044691,0.0000067722985,0.0004586096,0.0030779191],"genre_scores_gemma":[0.9903372,0.001361118,0.00073765696,0.00033268967,0.0014515596,0.00016060032,0.00014325991,0.00013316497,0.005342743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968104,0.00016281933,0.00082465314,0.0010969668,0.00034656248,0.0007586312],"domain_scores_gemma":[0.9970737,0.0016564446,0.000424092,0.0005868253,0.00009854383,0.00016038737],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043934336,0.00058617466,0.0008122402,0.00032691995,0.00020346047,0.00022136256,0.00066201197,0.0008075296,0.000007255913],"category_scores_gemma":[0.000078376914,0.0005782111,0.00011502911,0.0005401342,0.00014360917,0.00017640095,0.0003680278,0.0020984292,0.00001181365],"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":[0.000014904537,0.0000060432303,0.00059626467,0.000024858215,0.000021687032,0.00009549326,0.00007301547,0.9261825,0.0000019092006,0.06310028,0.00014499703,0.009738035],"study_design_scores_gemma":[0.00039616117,0.000072965886,0.00037605592,0.00041928206,0.000010922291,0.00007441101,0.0000035670955,0.9860589,1.8018754e-7,0.0073280395,0.004705315,0.00055418955],"about_ca_topic_score_codex":0.00006714143,"about_ca_topic_score_gemma":0.0005923284,"teacher_disagreement_score":0.9900718,"about_ca_system_score_codex":0.00016003508,"about_ca_system_score_gemma":0.000047469784,"threshold_uncertainty_score":0.9996669},"labels":[],"label_agreement":null},{"id":"W4386776962","doi":"10.3390/make5030060","title":"Gradient-Based Neural Architecture Search: A Comprehensive Evaluation","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"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":"Rashtriya Uchchatar Shiksha Abhiyan; Canadian Institute for Advanced Research","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Artificial neural network; Architecture; Gradient descent; Machine learning; Resource (disambiguation); Deep learning","score_opus":0.04902472630016129,"score_gpt":0.3505566359418409,"score_spread":0.3015319096416796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386776962","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":[0.3499535,0.0017203944,0.63923347,0.004125346,0.0006640415,0.0007599881,0.000003360662,0.0018253678,0.0017145098],"genre_scores_gemma":[0.9952875,0.00004353515,0.003810665,0.000052929805,0.00013072659,0.00007008076,0.00006437975,0.000016365717,0.00052385166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856776,0.00035738936,0.00015221238,0.0004241031,0.00023379787,0.0002647628],"domain_scores_gemma":[0.99906874,0.00040532704,0.00007395857,0.00021322489,0.00014236169,0.00009640972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035435476,0.00014405733,0.00011821138,0.0002495313,0.00048289695,0.000090972106,0.0001719984,0.000051638875,0.000010986738],"category_scores_gemma":[0.00007946011,0.00013644605,0.000046585676,0.0009567296,0.000036692567,0.00019504478,0.000106000734,0.0005259921,0.00012339492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001732028,0.000048706508,0.001431703,0.00002686234,0.000008989014,0.000005012821,0.0006860619,0.18137652,0.0065550054,0.001365393,0.00022853848,0.8082499],"study_design_scores_gemma":[0.00037501001,0.00008071681,0.011006381,0.000013074615,0.000009185888,0.000024229137,0.00003267356,0.9623845,0.00029728576,0.00079630496,0.024845112,0.0001355047],"about_ca_topic_score_codex":0.000017622531,"about_ca_topic_score_gemma":0.000029055085,"teacher_disagreement_score":0.8081144,"about_ca_system_score_codex":0.000045625166,"about_ca_system_score_gemma":0.000036799367,"threshold_uncertainty_score":0.55641085},"labels":[],"label_agreement":null},{"id":"W4386852495","doi":"10.59380/crj.v1i1.2723","title":"A review of deep learning for self-driving cars: case study","year":2023,"lang":"en","type":"review","venue":"CRJ","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Machine learning; Field (mathematics); Artificial neural network; Process (computing); Deep neural networks; Mathematics","score_opus":0.10787033377526462,"score_gpt":0.41272198269675925,"score_spread":0.30485164892149463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386852495","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":[8.705687e-7,0.9108307,0.085681595,0.000013329329,0.000116175324,0.0029698596,0.000001935194,0.000354536,0.00003101633],"genre_scores_gemma":[0.0000032804408,0.9824275,0.015661938,0.000035924917,0.000108274726,0.0016255657,0.000008914554,0.00004929345,0.00007930484],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99811053,0.00018788561,0.0006971647,0.00058869645,0.00015309834,0.00026261],"domain_scores_gemma":[0.9973645,0.0008966853,0.0006169347,0.0009256898,0.00012335609,0.0000728029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046594927,0.00026558823,0.0012113242,0.000108387205,0.0001633706,0.00003396037,0.0009468778,0.00006945493,0.0000020568996],"category_scores_gemma":[0.00021057022,0.00023071535,0.00034693468,0.0012266749,0.000016029915,0.0001171688,0.0004868144,0.0003024444,0.0000388195],"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":[3.363284e-8,0.000034801506,9.959542e-7,0.05084414,0.000051385527,0.00009369755,0.00010282036,0.000018046616,1.3284594e-8,0.00021356158,0.00022507932,0.9484154],"study_design_scores_gemma":[0.0000557167,0.000073738665,2.088855e-7,0.039087202,0.00039083144,0.00048152223,0.000023397104,0.0016808785,5.0519308e-8,0.00006492848,0.9579334,0.00020812236],"about_ca_topic_score_codex":0.000004441213,"about_ca_topic_score_gemma":0.000010435784,"teacher_disagreement_score":0.9577083,"about_ca_system_score_codex":0.00006877684,"about_ca_system_score_gemma":0.00008677338,"threshold_uncertainty_score":0.9408299},"labels":[],"label_agreement":null},{"id":"W4386864037","doi":"10.1007/s11042-023-16790-z","title":"Segmentation of 3D MRI Using 2D Convolutional Neural Networks in Infants’ Brain","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; White matter; Segmentation; Artificial intelligence; Convolutional neural network; Neuroimaging; Magnetic resonance imaging; Pattern recognition (psychology); Market segmentation; Image segmentation; Medicine; Radiology; Neuroscience; Psychology","score_opus":0.03612563585214185,"score_gpt":0.30286365974216645,"score_spread":0.2667380238900246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386864037","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":[0.04403695,0.00012646797,0.95376325,0.001006557,0.000060272225,0.00076921127,0.000035435412,0.00013740985,0.00006446896],"genre_scores_gemma":[0.78771913,0.00017138523,0.21036163,0.00033783307,0.00020173591,0.00092027016,0.00021091243,0.000019154579,0.000057975278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892646,0.000034519977,0.00032066094,0.00033745248,0.00014616245,0.00023476731],"domain_scores_gemma":[0.99891466,0.0005123823,0.0001305176,0.000316103,0.000053574924,0.00007276005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014862916,0.00011069172,0.00014164446,0.00012992913,0.00013780252,0.000041702853,0.00028593265,0.00005653206,0.000005318402],"category_scores_gemma":[0.000023986127,0.00011723519,0.000026387557,0.0012506562,0.000101941994,0.0003748811,0.00016713749,0.00012157589,0.00001019094],"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":[0.000008242624,0.00010705,0.009700885,0.000022175307,0.00001408219,0.0000020896105,0.00039438717,0.4896543,0.015007984,0.024417974,0.000631946,0.4600389],"study_design_scores_gemma":[0.0003121819,0.000008806338,0.027216727,0.000008157745,0.0000034895704,0.0000040223636,0.000026621756,0.9697799,0.00022021022,0.0013659984,0.0009415568,0.00011230391],"about_ca_topic_score_codex":0.000019960438,"about_ca_topic_score_gemma":0.00001540397,"teacher_disagreement_score":0.74368215,"about_ca_system_score_codex":0.000030357038,"about_ca_system_score_gemma":0.000025270616,"threshold_uncertainty_score":0.47807124},"labels":[],"label_agreement":null},{"id":"W4386897337","doi":"10.36713/epra14418","title":"ADVANCEMENTS IN OBJECT DETECTION AND TRACKING ALGORITHMS: AN OVERVIEW OF RECENT PROGRESS","year":2023,"lang":"en","type":"article","venue":"EPRA International Journal of Research & Development (IJRD)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Video tracking; Convolutional neural network; Deep learning; Robotics; Computer vision; Machine learning; Object (grammar); Robot; Pattern recognition (psychology)","score_opus":0.1998817750059806,"score_gpt":0.4750446525250136,"score_spread":0.275162877519033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386897337","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":[0.7938743,0.0042411555,0.192219,0.0066617136,0.0015397802,0.0010627083,0.00000577568,0.00012050772,0.00027506228],"genre_scores_gemma":[0.876968,0.008850397,0.11369321,0.00007049629,0.00019550673,0.00007969917,0.000007407418,0.000020094049,0.0001151614],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9970692,0.0001863253,0.00063712976,0.0002766979,0.0015230122,0.00030767775],"domain_scores_gemma":[0.99798846,0.00022880906,0.0003103649,0.00018053596,0.0011712288,0.00012057056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023090362,0.00010538771,0.00017247644,0.00094148016,0.00009985349,0.00011332175,0.0010388794,0.000038953636,0.000014857591],"category_scores_gemma":[0.00021833245,0.00009828153,0.00003110378,0.0013974832,0.00008452477,0.0011482938,0.00041479344,0.00037512032,0.000010273056],"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":[0.000046205005,0.00012203209,0.0040640873,0.000016656422,0.000040086456,0.000073051495,0.00074622396,0.00038564467,0.0018323907,0.0007802076,0.000048925758,0.9918445],"study_design_scores_gemma":[0.0034966157,0.0010757617,0.6718003,0.0017345032,0.00000926291,0.00046367358,0.00082930306,0.037697893,0.11524144,0.026214773,0.14070395,0.0007325251],"about_ca_topic_score_codex":0.0000036279662,"about_ca_topic_score_gemma":0.00003154652,"teacher_disagreement_score":0.99111193,"about_ca_system_score_codex":0.0002759818,"about_ca_system_score_gemma":0.0002451329,"threshold_uncertainty_score":0.40078047},"labels":[],"label_agreement":null},{"id":"W4386919598","doi":"10.1109/sas58821.2023.10254046","title":"Simulated Dataset for the Loaded vs. Unloaded UAV Classification Problem Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Pattern recognition (psychology); Machine learning","score_opus":0.08600659142343764,"score_gpt":0.33539314716515345,"score_spread":0.2493865557417158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386919598","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":[0.0014292271,0.000025889774,0.99329484,0.003685969,0.000072598414,0.00082692347,0.000028087774,0.0005808185,0.000055678116],"genre_scores_gemma":[0.85248464,0.000036795063,0.14423934,0.00075931964,0.00013045286,0.00021404013,0.0014765633,0.000035192028,0.000623635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879426,0.000049586488,0.00023758387,0.00042424494,0.0001693135,0.00032499162],"domain_scores_gemma":[0.99832207,0.0007564385,0.00012953172,0.0006600873,0.00007621716,0.000055638968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024925964,0.00012094547,0.00010316104,0.00007098047,0.00059484335,0.00013674024,0.00083637657,0.000050028786,0.000008505453],"category_scores_gemma":[0.00005696266,0.00008900061,0.000039354753,0.0011618529,0.000044123215,0.00038216566,0.0002418816,0.00015751219,0.00015097261],"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":[0.000017028817,0.000022924543,0.000039506376,0.000012522564,0.000021412005,0.0000010374762,0.00017132104,0.87334436,0.053948496,0.022045152,0.0061194543,0.044256795],"study_design_scores_gemma":[0.00020207882,0.00001639873,0.00020322748,0.0000036571,0.000009487049,0.0000024794795,0.000038636226,0.952081,0.004061658,0.002925732,0.040336914,0.00011870623],"about_ca_topic_score_codex":0.000010785124,"about_ca_topic_score_gemma":0.000004206465,"teacher_disagreement_score":0.85105544,"about_ca_system_score_codex":0.000040274957,"about_ca_system_score_gemma":0.000024779014,"threshold_uncertainty_score":0.45751143},"labels":[],"label_agreement":null},{"id":"W4386923660","doi":"10.2139/ssrn.4579060","title":"Enhancing Voxel Transformer with Spatial-Semantic Feature Aggregation and Optimized Rpn for 3d Object Detection","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Voxel; Transformer; Computer science; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Computer vision; Object (grammar); Object based; Engineering; Voltage; Electrical engineering","score_opus":0.01012712021134749,"score_gpt":0.24928215850032015,"score_spread":0.23915503828897267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386923660","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":[0.00526156,0.0013294847,0.9896652,0.0019483634,0.00038456623,0.0011584109,0.000004335807,0.00023361733,0.000014425916],"genre_scores_gemma":[0.791126,0.010363819,0.19574182,0.00010116381,0.000868409,0.00056707603,0.00003481697,0.00013032174,0.0010665819],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969034,0.00007963301,0.00037491752,0.0007145103,0.0003322869,0.0015952459],"domain_scores_gemma":[0.9986116,0.00019269258,0.00047012558,0.0004285322,0.00019407588,0.000102962105],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009415966,0.00036673108,0.00038930835,0.00024348196,0.0005330681,0.00026363527,0.00057053554,0.00024443786,7.114784e-7],"category_scores_gemma":[0.00004586768,0.0003142644,0.00014903853,0.0003635916,0.000045581604,0.0003806049,0.00008376463,0.0028577188,0.0000039517304],"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":[0.00041477184,0.00006838421,0.000047400605,0.0002240634,0.000579349,0.000008344741,0.0007703269,0.0473032,0.007990355,0.012165575,0.00003488545,0.93039334],"study_design_scores_gemma":[0.0034011211,0.0011589555,0.00030847432,0.00066672784,0.00036066902,0.001488937,0.0002312279,0.39075366,0.021306701,0.57849073,0.0005585729,0.0012742237],"about_ca_topic_score_codex":0.000058891266,"about_ca_topic_score_gemma":0.004836692,"teacher_disagreement_score":0.9291191,"about_ca_system_score_codex":0.00068573584,"about_ca_system_score_gemma":0.0012001896,"threshold_uncertainty_score":0.9999309},"labels":[],"label_agreement":null},{"id":"W4387004670","doi":"10.1016/j.media.2023.102980","title":"Spatiotemporal knowledge teacher–student reinforcement learning to detect liver tumors without contrast agents","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reinforcement learning; Computer science; Contrast (vision); Set (abstract data type); Workflow; Pixel; Artificial intelligence","score_opus":0.02162950575016027,"score_gpt":0.33032660725676816,"score_spread":0.3086971015066079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387004670","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":[0.054100133,0.000049048755,0.94245845,0.001318928,0.00005852372,0.00027546476,6.462861e-7,0.0004811856,0.0012576422],"genre_scores_gemma":[0.986189,0.000055478966,0.009097922,0.0004918672,0.00013799898,0.00013837678,0.000030894156,0.000017344535,0.003841132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728566,0.00016823149,0.0004712777,0.00062787445,0.0009417626,0.0005051986],"domain_scores_gemma":[0.99842393,0.00015760116,0.00013398165,0.0006206823,0.00011979386,0.0005440339],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00072049495,0.00020540207,0.00038887456,0.00045257004,0.00026733006,0.0001261912,0.0011413888,0.00006694937,0.00045447642],"category_scores_gemma":[0.0003666228,0.00018394346,0.00021738833,0.004420246,0.00008066737,0.00025345618,0.00077363534,0.0003556196,0.0018797719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044759297,0.00050780596,0.0662777,0.00007203316,0.0028887053,0.0012548831,0.01167245,0.15486236,0.0024189346,0.0012617509,0.038488384,0.72025025],"study_design_scores_gemma":[0.00029004307,0.000068179026,0.031799734,0.000023998073,0.00017241642,0.0000033423944,0.0000748776,0.9595617,0.00051345176,0.00005374299,0.007170155,0.0002683314],"about_ca_topic_score_codex":0.00007765975,"about_ca_topic_score_gemma":0.0001535799,"teacher_disagreement_score":0.9333605,"about_ca_system_score_codex":0.00010924483,"about_ca_system_score_gemma":0.00006505198,"threshold_uncertainty_score":0.9988974},"labels":[],"label_agreement":null},{"id":"W4387048924","doi":"10.1007/978-3-031-43605-5_8","title":"Fast Rotated Bounding Box Annotations for Object Detection","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Annotation; Computer science; Minimum bounding box; Workload; Bounding overwatch; Object (grammar); Publication; Data mining; Object detection; Artificial intelligence; Information retrieval; Pattern recognition (psychology); Image (mathematics); Operating system","score_opus":0.05062299964689058,"score_gpt":0.3157571102447346,"score_spread":0.265134110597844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387048924","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":[0.00001662788,0.00007355549,0.97133183,0.0010603208,0.00042860792,0.0010075902,0.000032460706,0.00037537064,0.025673617],"genre_scores_gemma":[0.046995692,0.003732077,0.9386854,0.0016686264,0.00020341852,0.0012082196,0.00041329494,0.00006937536,0.007023878],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998104,0.000023479433,0.00075422286,0.0004138632,0.00037109206,0.0003333542],"domain_scores_gemma":[0.99628407,0.00060170237,0.00044261734,0.0019797145,0.00057852844,0.000113399416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007992798,0.0002489137,0.00024407721,0.0012725531,0.0012968687,0.0007432359,0.002756425,0.00012743386,0.0000013903159],"category_scores_gemma":[0.00009306148,0.00027464665,0.0000668322,0.0012353897,0.00059515215,0.0066316132,0.0015160106,0.00039376845,0.000083733736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002048416,0.0000084590765,0.000003920216,0.000020502217,0.0000053994563,1.3050595e-7,0.0008621606,0.002279799,0.00003174046,0.6013654,0.00016811882,0.39525232],"study_design_scores_gemma":[0.00028140724,0.000060233582,0.0003514531,0.00011879852,0.000007369157,0.000016753887,0.000022555976,0.85858184,0.00006557579,0.054505404,0.08562641,0.00036220878],"about_ca_topic_score_codex":0.0000057945595,"about_ca_topic_score_gemma":0.00004117318,"teacher_disagreement_score":0.856302,"about_ca_system_score_codex":0.00022105005,"about_ca_system_score_gemma":0.00022449195,"threshold_uncertainty_score":0.99997056},"labels":[],"label_agreement":null},{"id":"W4387092527","doi":"10.1109/tits.2023.3314919","title":"V2VFormer++: Multi-Modal Vehicle-to-Vehicle Cooperative Perception via Global-Local Transformer","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Notation; Lidar; Computer science; Perception; Sensor fusion; Security token; Artificial intelligence; Theoretical computer science; Mathematics; Geography; Computer security","score_opus":0.032692367579991055,"score_gpt":0.29839219867861055,"score_spread":0.2656998310986195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387092527","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":[0.040723033,0.00002484996,0.95410335,0.0006357943,0.0013395839,0.0016488583,0.00022430248,0.0012183087,0.00008194228],"genre_scores_gemma":[0.99382377,0.00012417645,0.0039459607,0.00032863708,0.0000661648,0.0010409562,0.00006822415,0.00004760787,0.00055451243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966172,0.000120659344,0.0009421872,0.00095143076,0.0007089747,0.0006595223],"domain_scores_gemma":[0.9983833,0.0001335656,0.00012649896,0.0006383694,0.00032177405,0.00039653256],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002514596,0.00044045574,0.00039230735,0.00033956312,0.0005738757,0.00014303121,0.000672741,0.00020062644,0.00005021541],"category_scores_gemma":[0.0000019009387,0.00044192155,0.00025558946,0.0029722892,0.0001054969,0.00084826694,9.984497e-7,0.00034616768,0.0021642921],"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":[0.00007321595,0.00025421072,0.000057356785,0.000043488093,0.000067095076,0.000013543867,0.002570543,0.87825614,0.011521556,0.0019293544,0.00016536891,0.10504812],"study_design_scores_gemma":[0.0010645249,0.0005172061,0.0055583017,0.00015008861,0.000069637885,0.000024245819,0.0017365813,0.9526938,0.03312468,0.00021086496,0.0038698276,0.0009802192],"about_ca_topic_score_codex":0.00025996816,"about_ca_topic_score_gemma":0.0005637658,"teacher_disagreement_score":0.95310074,"about_ca_system_score_codex":0.00038082676,"about_ca_system_score_gemma":0.000081865786,"threshold_uncertainty_score":0.99980325},"labels":[],"label_agreement":null},{"id":"W4387129549","doi":"10.1109/access.2023.3320642","title":"Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Frequency domain; Regularization (linguistics); Computer science; Convolutional neural network; Redundancy (engineering); Algorithm; Artificial neural network; Reduction (mathematics); Pattern recognition (psychology); Artificial intelligence; Mathematics; Computer vision","score_opus":0.027096312830563636,"score_gpt":0.295344432431939,"score_spread":0.26824811960137535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387129549","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":[0.035110377,0.000054958964,0.95965683,0.0022707963,0.0010484788,0.00034102128,0.0000031272082,0.0005656934,0.00094873394],"genre_scores_gemma":[0.9936601,0.000032851098,0.005262218,0.0004971099,0.00022789929,0.00014012662,0.00006946272,0.000009420596,0.000100836165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986968,0.00004715803,0.0003894614,0.0002826337,0.000241927,0.0003420414],"domain_scores_gemma":[0.99907523,0.00010234708,0.00015614189,0.0004929683,0.000105317056,0.00006800533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018061062,0.00012222836,0.00011790357,0.0002520043,0.00019492631,0.00026423455,0.0010855718,0.00007290905,0.000008371947],"category_scores_gemma":[0.000041855932,0.00013278094,0.00003379934,0.0029694487,0.00004548748,0.0047906917,0.0002117259,0.00020162802,0.00006433213],"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":[0.0000027869712,0.00001445581,0.003326018,0.000009842563,0.0000037580276,0.000007435339,0.0001682719,0.90098274,0.0001905877,0.06908399,0.0035571738,0.022652956],"study_design_scores_gemma":[0.00019655157,0.000008064824,0.025206093,0.000020359372,0.000001426819,0.000010880358,0.0000045453876,0.9444022,0.00020650522,0.02938682,0.00039900275,0.00015750345],"about_ca_topic_score_codex":0.00002909971,"about_ca_topic_score_gemma":0.0000119071465,"teacher_disagreement_score":0.9585497,"about_ca_system_score_codex":0.00008404448,"about_ca_system_score_gemma":0.00005107317,"threshold_uncertainty_score":0.541465},"labels":[],"label_agreement":null},{"id":"W4387211254","doi":"10.1007/978-3-031-43901-8_22","title":"EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Landmark; Computer science; Smoothing; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Graph; Machine learning; Computer vision; Theoretical computer science","score_opus":0.017633118464651192,"score_gpt":0.25526072146045226,"score_spread":0.23762760299580107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387211254","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":[0.00008316443,0.000097058684,0.9945664,0.00075466424,0.0025881995,0.0010537443,0.00000900005,0.00059623556,0.00025155154],"genre_scores_gemma":[0.74217796,0.00019446533,0.2509117,0.003005468,0.002806563,0.00020924176,0.000043564993,0.00018434557,0.0004666793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959236,0.000044565077,0.00049775135,0.0018485097,0.0007568711,0.00092874217],"domain_scores_gemma":[0.996398,0.0014855136,0.00025213856,0.0014683253,0.00016186517,0.00023420739],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005992215,0.0005408434,0.00050483487,0.00083369634,0.00054015184,0.00040063326,0.0025920984,0.00036670742,0.0000021857359],"category_scores_gemma":[0.000086988104,0.0005081053,0.0003137827,0.0013228916,0.00042557166,0.0003563593,0.00077617966,0.001171123,0.000025589497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013588382,0.000013095154,0.0000149562175,0.000007822507,0.000007801255,0.000017528115,0.000039323506,0.4415217,0.00002287122,0.0056189336,0.000023118024,0.55269927],"study_design_scores_gemma":[0.00026268803,0.00028176178,0.00014934519,0.000079251906,0.000008615382,0.000038222755,3.8531947e-8,0.8311473,0.00033676953,0.16606106,0.0011798528,0.00045509107],"about_ca_topic_score_codex":0.0000048876946,"about_ca_topic_score_gemma":0.000058464044,"teacher_disagreement_score":0.74365467,"about_ca_system_score_codex":0.00022339559,"about_ca_system_score_gemma":0.00008411318,"threshold_uncertainty_score":0.9997371},"labels":[],"label_agreement":null},{"id":"W4387211475","doi":"10.1007/978-3-031-43898-1_55","title":"Trust Your Neighbours: Penalty-Based Constraints for Model Calibration","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Constraint (computer-aided design); Smoothing; Pixel; Mathematical optimization; Flexibility (engineering); Artificial intelligence; Segmentation; Perspective (graphical); Code (set theory); Data mining; Machine learning; Algorithm; Computer vision; Mathematics","score_opus":0.0470612905123171,"score_gpt":0.2920188113869439,"score_spread":0.2449575208746268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387211475","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":[0.000010631686,0.000044750563,0.9943715,0.0025017099,0.00078986224,0.0010540202,0.000047919104,0.0004583972,0.0007212102],"genre_scores_gemma":[0.041412484,0.000015394302,0.9540251,0.0029965222,0.00049197854,0.00013774456,0.0000469624,0.00007922291,0.0007945893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996253,0.000018387054,0.00054970017,0.0016980369,0.00074139686,0.0007394752],"domain_scores_gemma":[0.99702823,0.0007832663,0.0003547113,0.0013709862,0.00025330798,0.00020950663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004971519,0.00050685136,0.00045223022,0.00061314,0.00040469802,0.0004438381,0.0029092191,0.00029578732,0.00000778348],"category_scores_gemma":[0.00010006569,0.0004974115,0.0001717642,0.00077350065,0.0008143533,0.00063831,0.00056092365,0.0005633693,0.000029377741],"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":[0.0000044060566,0.000012644715,0.0000040934256,0.000020095933,0.0000037304485,0.000007730655,0.00008515694,0.7149581,0.00013073345,0.11602733,0.000053856038,0.16869214],"study_design_scores_gemma":[0.00021406672,0.000056128356,0.0000052222094,0.000098946,0.000005242672,0.000007831377,5.72915e-8,0.68353647,0.0005818195,0.31494996,0.00018516261,0.000359117],"about_ca_topic_score_codex":0.000002605051,"about_ca_topic_score_gemma":0.000025543259,"teacher_disagreement_score":0.19892262,"about_ca_system_score_codex":0.00023910368,"about_ca_system_score_gemma":0.0009401006,"threshold_uncertainty_score":0.99974775},"labels":[],"label_agreement":null},{"id":"W4387211538","doi":"10.1007/978-3-031-43901-8_14","title":"M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Modality (human–computer interaction); Leverage (statistics); Annotation; Modalities; Pattern recognition (psychology); Deep learning; Image segmentation; Machine learning","score_opus":0.025157437015896456,"score_gpt":0.26593736050759653,"score_spread":0.24077992349170008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387211538","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":[0.00058586703,0.00006376643,0.99498814,0.0014431095,0.000677133,0.0017380642,0.000035081688,0.00034209213,0.00012671763],"genre_scores_gemma":[0.04795899,0.000010728936,0.95054144,0.0007149815,0.0002577482,0.00020101243,0.0000894344,0.000058564387,0.0001670761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99605775,0.000038524595,0.00056770886,0.0016958448,0.0010301496,0.00061004714],"domain_scores_gemma":[0.99709177,0.00078135036,0.0004038977,0.0010737713,0.0004928759,0.00015634441],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007262071,0.00047859948,0.00039693573,0.00057347346,0.0005085587,0.00032009254,0.001545292,0.0001426195,0.0000036176932],"category_scores_gemma":[0.00005484727,0.00042522643,0.00010003354,0.0011637115,0.0003697766,0.0005436319,0.0004205182,0.0003766672,0.000030767915],"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":[0.000018983135,0.000027394073,0.000013742625,0.0000321729,0.000006674984,0.000012972554,0.0009888536,0.874226,0.0004185324,0.033585824,0.000008906198,0.09065997],"study_design_scores_gemma":[0.00033815304,0.0001782935,0.00014140717,0.00011917205,0.0000059566332,0.000016886584,0.0000017829022,0.7576641,0.001095155,0.23987697,0.00015529564,0.00040683593],"about_ca_topic_score_codex":0.000012180415,"about_ca_topic_score_gemma":0.00007331198,"teacher_disagreement_score":0.20629115,"about_ca_system_score_codex":0.00037062165,"about_ca_system_score_gemma":0.00040912878,"threshold_uncertainty_score":0.99981993},"labels":[],"label_agreement":null},{"id":"W4387211908","doi":"10.1007/978-3-031-43898-1_27","title":"Maximum Entropy on Erroneous Predictions: Improving Model Calibration for Medical Image Segmentation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Image segmentation; Benchmark (surveying); Artificial neural network; Principle of maximum entropy; Pixel; Entropy (arrow of time); Calibration; Scale-space segmentation; Pattern recognition (psychology); Computer vision; Mathematics; Statistics","score_opus":0.019186803281780327,"score_gpt":0.2736180290045879,"score_spread":0.2544312257228076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387211908","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":[0.000017829607,0.00003527776,0.9945734,0.0022954447,0.0011386609,0.0011873969,0.00002340037,0.00052926753,0.00019932583],"genre_scores_gemma":[0.0102830455,0.00006813977,0.9862663,0.0017171805,0.00077237503,0.0002760279,0.00005958692,0.00007768198,0.00047964547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960813,0.000023114213,0.00057096663,0.001520946,0.0012288174,0.0005748703],"domain_scores_gemma":[0.99746853,0.0008131613,0.00031354668,0.0010135428,0.00018225578,0.00020894296],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000528941,0.00041304267,0.00032409173,0.0005384766,0.0004500268,0.00035230222,0.001977395,0.0003066636,0.0000063639536],"category_scores_gemma":[0.0001649776,0.00039881983,0.000116789066,0.00054469216,0.00029857617,0.0007610027,0.0006633911,0.0006262805,0.000029326593],"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":[0.000009016598,0.000023395069,7.979067e-7,0.00002626014,0.0000052341516,0.0000141220435,0.00014538779,0.56989974,0.0006584295,0.041936297,0.00008391241,0.3871974],"study_design_scores_gemma":[0.00020492691,0.00011968161,0.0000017686148,0.000099093144,0.0000057030215,0.00002207112,1.03697836e-7,0.7222491,0.001041599,0.27595437,0.000043494187,0.0002581013],"about_ca_topic_score_codex":0.000005109313,"about_ca_topic_score_gemma":0.00003885962,"teacher_disagreement_score":0.38693932,"about_ca_system_score_codex":0.00039117507,"about_ca_system_score_gemma":0.00054235,"threshold_uncertainty_score":0.99984634},"labels":[],"label_agreement":null},{"id":"W4387250519","doi":"10.1109/asap57973.2023.00024","title":"Efficient 1D Grouped Convolution for PyTorch a Case Study: Fast On-Device Fine-Tuning for SqueezeBERT","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Convolution (computer science); Locality; Edge device; Enhanced Data Rates for GSM Evolution; Inference; Memory management; Parallel computing; Reduction (mathematics); Algorithm; Edge computing; Base (topology); Computational science; Computer engineering; Theoretical computer science; Artificial intelligence; Computer hardware; Mathematics; Semiconductor memory; Operating system","score_opus":0.060235494131934963,"score_gpt":0.34033098981593174,"score_spread":0.2800954956839968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387250519","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":[0.1806393,0.0000063461557,0.81493723,0.0010326094,0.00024023975,0.0023965188,0.000011909755,0.000609545,0.00012627934],"genre_scores_gemma":[0.95663637,7.1362956e-7,0.040006336,0.00030305533,0.00016140107,0.0018135731,0.000016784294,0.000024312278,0.0010374631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844396,0.000034787598,0.0002726059,0.00062596996,0.0001942109,0.0004284365],"domain_scores_gemma":[0.99801624,0.0010334272,0.000087256296,0.00060005346,0.00016262285,0.00010040015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033406354,0.00016731913,0.00017220886,0.00013779163,0.0004934122,0.00007434203,0.0004558005,0.000042882708,0.000002535456],"category_scores_gemma":[0.00007848859,0.00015255538,0.00008343573,0.0009688796,0.00002500196,0.000100701895,0.0001933041,0.00008965256,0.000057999292],"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":[0.00019981091,0.0015900608,0.00032894887,0.000120914294,0.0001203093,0.00034189428,0.005611611,0.49844238,0.0070158113,0.30175555,0.02873044,0.15574229],"study_design_scores_gemma":[0.0010262358,0.00038855843,0.0001429618,0.000009182933,0.000010899299,0.00005112946,0.0003913648,0.9936184,0.00024031858,0.0009127299,0.0030090092,0.00019916148],"about_ca_topic_score_codex":0.00005277871,"about_ca_topic_score_gemma":0.00019316757,"teacher_disagreement_score":0.77599704,"about_ca_system_score_codex":0.00007858611,"about_ca_system_score_gemma":0.000036388697,"threshold_uncertainty_score":0.6221028},"labels":[],"label_agreement":null},{"id":"W4387414813","doi":"10.1007/978-3-031-44336-7_7","title":"Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Rounding; Segmentation; Artificial intelligence; Inference; Pipeline (software); Pattern recognition (psychology); Dice; Convolution (computer science); Artificial neural network; Algorithm; Statistics; Mathematics","score_opus":0.022077396594155196,"score_gpt":0.2923303012268479,"score_spread":0.2702529046326927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387414813","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":[0.00006312948,0.00014496516,0.99646455,0.0014957989,0.00088371296,0.0006397237,0.0000381615,0.00020174895,0.00006823482],"genre_scores_gemma":[0.64804846,0.000026606667,0.34986743,0.001124911,0.0005207944,0.00006814343,0.00009112698,0.000044853397,0.0002076446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958311,0.00004447059,0.0008051388,0.0016789498,0.0008545757,0.00078577985],"domain_scores_gemma":[0.99391764,0.0034421026,0.0005779757,0.0013765784,0.00048666698,0.00019900828],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005194446,0.0005277319,0.0008459236,0.000902764,0.00031501258,0.00017968453,0.0033763528,0.00029355244,0.000010527177],"category_scores_gemma":[0.00021080417,0.0004893415,0.00038360705,0.0029852015,0.00092407723,0.00041972214,0.0009831493,0.0006959162,0.0000048236857],"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":[0.000008121267,0.000006410117,0.00014131685,0.000012676043,0.00004575602,0.000004459391,0.00006384542,0.8695169,0.00001194165,0.052976627,0.00002909265,0.077182844],"study_design_scores_gemma":[0.00016881284,0.00009818845,0.0005718049,0.000038993174,0.00003534762,0.00000776181,6.896274e-8,0.8464445,0.000032369404,0.15207052,0.00012698135,0.00040467843],"about_ca_topic_score_codex":0.000029312489,"about_ca_topic_score_gemma":0.0001105313,"teacher_disagreement_score":0.64798534,"about_ca_system_score_codex":0.00022944192,"about_ca_system_score_gemma":0.00031122216,"threshold_uncertainty_score":0.9997558},"labels":[],"label_agreement":null},{"id":"W4387422461","doi":"10.1007/978-3-031-44917-8_16","title":"Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto; Hospital for Sick Children","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Leverage (statistics); Pipeline (software); Focus (optics); Pattern recognition (psychology); Machine learning; Multi-task learning; Image segmentation; Contextual image classification; Pixel; Region of interest; Deep learning; Task (project management); Medical imaging; Computer vision; Image (mathematics)","score_opus":0.0275006106379515,"score_gpt":0.27299376287097765,"score_spread":0.24549315223302615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387422461","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":[0.00008590768,0.00003974929,0.99368215,0.003400951,0.0005486546,0.001478971,0.000007138053,0.0005396955,0.00021679219],"genre_scores_gemma":[0.04924213,0.000033194337,0.94807893,0.0010804533,0.00031900275,0.00024214006,0.00009523982,0.00009861158,0.00081032416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996402,0.000045150522,0.00053795765,0.0017700958,0.00057141273,0.00067334925],"domain_scores_gemma":[0.99654555,0.0015374817,0.00047449293,0.00093109644,0.00035875908,0.00015261542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069594267,0.000503386,0.0004347518,0.00058034796,0.0006216666,0.00046974726,0.0019519405,0.00021412903,0.000002767099],"category_scores_gemma":[0.0001268216,0.00046941522,0.00009246891,0.0009993126,0.00037048146,0.0007857979,0.00053687557,0.00076591497,0.000028028659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026760837,0.000031306583,0.0000416581,0.000054755576,0.000019036706,0.000008526746,0.0008242181,0.3337189,0.023325576,0.017810017,0.0000459401,0.6240933],"study_design_scores_gemma":[0.00056544837,0.00030457223,0.00015700815,0.00019936719,0.000010674305,0.000015945914,0.0000014857718,0.9698506,0.0020864161,0.025684362,0.0005651389,0.00055900635],"about_ca_topic_score_codex":0.000008340606,"about_ca_topic_score_gemma":0.000078944424,"teacher_disagreement_score":0.6361317,"about_ca_system_score_codex":0.00031012722,"about_ca_system_score_gemma":0.0003189183,"threshold_uncertainty_score":0.99977577},"labels":[],"label_agreement":null},{"id":"W4387432194","doi":"10.48550/arxiv.2310.03212","title":"PDR-CapsNet: an Energy-Efficient Parallel Approach to Dynamic Routing in Capsule Networks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Overfitting; Adaptive routing; Energy consumption; Pooling; Routing (electronic design automation); Artificial intelligence; Efficient energy use; Throughput; Deep learning; FLOPS; Computational complexity theory; Parallel computing; Artificial neural network; Algorithm; Link-state routing protocol; Computer network; Routing protocol","score_opus":0.07348075076730022,"score_gpt":0.21010589593401846,"score_spread":0.13662514516671825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387432194","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":[0.10051795,0.000023791874,0.89704293,0.000074345015,0.00032674987,0.0005232265,0.0000074976856,0.00058983784,0.00089367916],"genre_scores_gemma":[0.98492455,0.00007402623,0.013552528,0.00013989031,0.00007951211,0.000024641684,0.00007576464,0.000051391882,0.0010776699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996363,0.00019293785,0.00035429926,0.0021769812,0.00013227465,0.00078051956],"domain_scores_gemma":[0.99713117,0.00009010924,0.00026487422,0.0020674167,0.00009028556,0.00035615166],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003073863,0.00044306225,0.0004224517,0.00043819618,0.0002284728,0.00013415581,0.0030024294,0.00032857322,0.0000023489824],"category_scores_gemma":[0.00001763742,0.00054716,0.00015117896,0.0022951767,0.00009062463,0.00026183907,0.0032766815,0.0007771309,0.000044541775],"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":[0.000006060828,0.00012973032,0.0002109201,0.000007937747,0.000013061823,0.00005241311,0.00016766031,0.8589637,0.000008933032,0.13984299,0.00005493061,0.00054161216],"study_design_scores_gemma":[0.00023946306,0.000026012298,0.0033840698,0.000052269672,0.000013997362,0.000002332401,0.00007284373,0.9844402,0.0000040057735,0.011165885,0.00004548691,0.0005533974],"about_ca_topic_score_codex":0.00040454688,"about_ca_topic_score_gemma":0.00046556242,"teacher_disagreement_score":0.8844066,"about_ca_system_score_codex":0.00046205113,"about_ca_system_score_gemma":0.00009723469,"threshold_uncertainty_score":0.999698},"labels":[],"label_agreement":null},{"id":"W4387490417","doi":"10.1109/tiv.2023.3323518","title":"MCHFormer: A Multi-Cross Hybrid Former of Point-Image for 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Artificial intelligence; Computer vision; Object detection; Feature extraction; Feature (linguistics); Pattern recognition (psychology)","score_opus":0.03565442813648844,"score_gpt":0.3113261137491017,"score_spread":0.27567168561261324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387490417","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":[0.033996828,0.000019335925,0.963851,0.00018074375,0.00053264294,0.0007779109,0.00007682729,0.00052517303,0.000039552233],"genre_scores_gemma":[0.91711926,0.00017134818,0.08141598,0.00009981241,0.000042643824,0.000576253,0.0000056215863,0.000036148052,0.00053290883],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982926,0.000034343557,0.00048587704,0.0005038331,0.00024914247,0.000434184],"domain_scores_gemma":[0.99854356,0.000412115,0.00015571473,0.0005882895,0.00019816202,0.00010218803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023372399,0.00021992877,0.00022154437,0.00032317798,0.0003889121,0.00007034566,0.00054186635,0.00006945514,0.000014053653],"category_scores_gemma":[0.0000147292585,0.00021688214,0.00025691616,0.00088634563,0.00011988709,0.0006026568,0.0000074857103,0.0002083613,0.00020736741],"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":[0.00016665636,0.0004332755,0.000015396427,0.00009858307,0.00009116995,0.000003614423,0.0005815448,0.07692189,0.10815371,0.00050549716,0.0002249958,0.8128037],"study_design_scores_gemma":[0.00029096127,0.00018594228,0.00007827047,0.00002129251,0.000013547295,0.000010663639,0.00003882893,0.3346822,0.661847,0.001222505,0.0014387381,0.00017005556],"about_ca_topic_score_codex":0.00002067653,"about_ca_topic_score_gemma":0.00006663836,"teacher_disagreement_score":0.88312244,"about_ca_system_score_codex":0.000083404084,"about_ca_system_score_gemma":0.000034398152,"threshold_uncertainty_score":0.8844197},"labels":[],"label_agreement":null},{"id":"W4387491074","doi":"10.1109/tvt.2023.3323313","title":"SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Higher Education Commission, Pakistan","keywords":"Drone; Scalability; Feature (linguistics); Computer science; FLOPS; Artificial intelligence; Feature engineering; Feature extraction; Deep learning; Machine learning; Real-time computing; Data mining; Database","score_opus":0.007779800556877475,"score_gpt":0.23233624061225827,"score_spread":0.2245564400553808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387491074","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":[0.0835514,0.00016253002,0.90973836,0.0034638718,0.00042142445,0.00047713247,0.0000038871726,0.0021576278,0.00002374459],"genre_scores_gemma":[0.9833408,0.00018904216,0.015710624,0.00010278878,0.000043391225,0.00031389942,0.0000014443197,0.000026650783,0.00027138644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841183,0.000047902955,0.00020422827,0.00066803716,0.00019553247,0.00047248742],"domain_scores_gemma":[0.9988203,0.00012527747,0.00007707562,0.00082708,0.00006106041,0.000089229805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000135132,0.00021498236,0.00020015064,0.00044072213,0.0005142983,0.00004026162,0.00046408235,0.00031630512,0.000003459409],"category_scores_gemma":[0.000007783288,0.00021674493,0.00006817698,0.0031935775,0.00014362237,0.00011318249,0.000016267266,0.0005788933,0.00015122184],"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":[0.0000137934985,0.0000731221,0.000006761813,0.000007647663,0.000027002032,0.000020418342,0.000070674534,0.7279698,0.020399455,0.002091503,0.00013461302,0.24918516],"study_design_scores_gemma":[0.0005931843,0.00021582404,0.00032733745,0.000040891835,0.000029387269,0.0001386509,0.00003531214,0.8825883,0.1024065,0.0042923638,0.008935366,0.00039687374],"about_ca_topic_score_codex":0.0000039277866,"about_ca_topic_score_gemma":0.000045716384,"teacher_disagreement_score":0.8997894,"about_ca_system_score_codex":0.00007796831,"about_ca_system_score_gemma":0.00001702546,"threshold_uncertainty_score":0.8838602},"labels":[],"label_agreement":null},{"id":"W4387491256","doi":"10.1109/comst.2023.3323091","title":"Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Analytics; Data science; Cloud computing; Enhanced Data Rates for GSM Evolution; Edge computing; Big data; Variety (cybernetics); Edge device; SPARK (programming language); Digital forensics; Intersection (aeronautics); Computer security; Telecommunications; Artificial intelligence; Data mining","score_opus":0.10228225564618446,"score_gpt":0.3517013479694292,"score_spread":0.24941909232324475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387491256","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":[0.0022720164,0.001535277,0.98623997,0.0016109634,0.0011238033,0.0026401533,0.0002362395,0.0028992149,0.0014423797],"genre_scores_gemma":[0.95754224,0.0070823617,0.028544325,0.00024220171,0.0008720304,0.003986483,0.00050078536,0.000092093695,0.0011374536],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.995221,0.002552156,0.0007468626,0.0006873887,0.00036420458,0.0004283891],"domain_scores_gemma":[0.9876482,0.0060503827,0.0003611139,0.0053083524,0.00046213093,0.00016981894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00690451,0.0002768777,0.000423432,0.00043756465,0.0008461445,0.00038977122,0.0032396573,0.00014819947,0.0000016015491],"category_scores_gemma":[0.00050485577,0.0002861573,0.00006611057,0.0031452603,0.00025007376,0.0004580955,0.00085150433,0.00033473023,0.00024293292],"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":[0.000029817109,0.00093304977,0.010690481,0.00017493468,0.0003405632,0.000008158819,0.0010492273,0.007026545,0.020104198,0.6053974,0.07904603,0.27519962],"study_design_scores_gemma":[0.00085709814,0.00022596352,0.059719924,0.00026463685,0.00006914548,0.000024464494,0.00011955827,0.06550221,0.005762075,0.02206461,0.843396,0.0019943092],"about_ca_topic_score_codex":0.0004138829,"about_ca_topic_score_gemma":0.00019616626,"teacher_disagreement_score":0.9576956,"about_ca_system_score_codex":0.00012433196,"about_ca_system_score_gemma":0.000112564354,"threshold_uncertainty_score":0.99995905},"labels":[],"label_agreement":null},{"id":"W4387582224","doi":"10.1016/j.imavis.2023.104838","title":"DiPS: Discriminative pseudo-label sampling with self-supervised transformers for weakly supervised object localization","year":2023,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); École de Technologie Supérieure; Artificial Intelligence in Medicine (Canada)","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Discriminative model; Artificial intelligence; Overfitting; Computer science; Pattern recognition (psychology); Transformer; Leverage (statistics); Classifier (UML); Novelty; Computer vision; Machine learning; Artificial neural network","score_opus":0.031881302347562444,"score_gpt":0.33360989887608816,"score_spread":0.30172859652852574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387582224","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":[0.047024984,0.00004273609,0.9504698,0.00086873845,0.000082323386,0.0007051694,0.000006604257,0.0006749101,0.00012475242],"genre_scores_gemma":[0.58287364,0.00006434276,0.41655806,0.00027765785,0.00008710216,0.00004429108,0.00004065861,0.000032268814,0.000021945858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983397,0.00005158566,0.00029961552,0.00063697016,0.00023761473,0.00043450735],"domain_scores_gemma":[0.998798,0.00056594826,0.00009200969,0.00024119363,0.0001819021,0.00012091927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029491665,0.00022451274,0.00022748788,0.000153079,0.00074128754,0.00024661087,0.00032374734,0.000052394586,0.0000014683334],"category_scores_gemma":[0.00003294147,0.00018824617,0.00005043182,0.0011013771,0.00006471302,0.00078202895,0.00016004781,0.00013044955,0.000009232601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006670246,0.00016193387,0.00028709302,0.00024089201,0.000052136096,0.00001093582,0.0066051553,0.006789281,0.018759236,0.0051321397,0.00060224393,0.96129227],"study_design_scores_gemma":[0.0010433331,0.00023621955,0.0006118755,0.00011220008,0.000019048784,0.000009811632,0.000362194,0.99399966,0.0016340902,0.0013117121,0.00039095982,0.00026891803],"about_ca_topic_score_codex":0.0000063974285,"about_ca_topic_score_gemma":0.000004714378,"teacher_disagreement_score":0.98721033,"about_ca_system_score_codex":0.000033666976,"about_ca_system_score_gemma":0.00003796444,"threshold_uncertainty_score":0.76764554},"labels":[],"label_agreement":null},{"id":"W4387623862","doi":"10.1109/access.2023.3324600","title":"Efficient Attention-Convolution Feature Extractor in Semantic Segmentation for Autonomous Driving Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Segmentation; Feature (linguistics); Convolution (computer science); Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Feature extraction; Representation (politics); Convolutional neural network; Reliability (semiconductor); Block (permutation group theory); Machine learning; Data mining; Artificial neural network; Mathematics","score_opus":0.032978389884151915,"score_gpt":0.32593006777946126,"score_spread":0.29295167789530935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387623862","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":[0.38949648,0.000040829073,0.6082483,0.00035083355,0.0008351593,0.0007439165,0.000003344806,0.0002642026,0.000016934035],"genre_scores_gemma":[0.9953006,0.0000076187266,0.003660875,0.0000334247,0.0001244348,0.0006192555,0.000023151333,0.000014022479,0.00021664976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882835,0.00004567701,0.00023060985,0.0004149313,0.00017804523,0.0003023853],"domain_scores_gemma":[0.99914837,0.0002523865,0.00013874893,0.00034348277,0.00006773726,0.000049266604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025171993,0.00011542652,0.00013146964,0.00020016018,0.00016090878,0.00023632616,0.0006187142,0.000060099574,6.816038e-7],"category_scores_gemma":[0.000020944102,0.00011790104,0.00004687628,0.0010999257,0.00001608618,0.0004593925,0.00009273022,0.0000995063,0.000042504184],"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":[0.000006985389,0.00008114762,0.006961635,0.00013864067,0.000011936018,0.000008930204,0.00018940248,0.92832255,0.04871196,0.006761988,0.0022629404,0.0065418845],"study_design_scores_gemma":[0.0002982259,0.000013155999,0.068892404,0.00006735685,0.0000047219146,0.0000049483892,0.00002137011,0.92850846,0.0011423983,0.0006132235,0.0002889716,0.00014476765],"about_ca_topic_score_codex":0.00001811961,"about_ca_topic_score_gemma":0.000022200356,"teacher_disagreement_score":0.6058041,"about_ca_system_score_codex":0.00014983231,"about_ca_system_score_gemma":0.000031277206,"threshold_uncertainty_score":0.4807865},"labels":[],"label_agreement":null},{"id":"W4387761026","doi":"10.1109/ipccc59175.2023.10253877","title":"Evaluation of Pruning Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Pruning; Computer science; Memory footprint; Speedup; Inference; Convolutional neural network; Computation; Artificial intelligence; Contextual image classification; Machine learning; Parallel computing; Image (mathematics); Algorithm","score_opus":0.08284009450376618,"score_gpt":0.3678937605204616,"score_spread":0.2850536660166954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387761026","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":[0.009889673,0.000013592637,0.96985,0.0006966092,0.000028095992,0.0002101631,1.5911195e-7,0.00076482916,0.018546898],"genre_scores_gemma":[0.84458023,0.000008370785,0.1550984,0.000043897504,0.000014099313,0.00007008535,9.894335e-7,0.0000025749162,0.00018134233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994548,0.000027203054,0.00007659767,0.00010068214,0.0002768182,0.00006388344],"domain_scores_gemma":[0.9995161,0.000043395492,0.000032288022,0.00024902477,0.00014703299,0.000012116049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055917626,0.000025212315,0.00003249321,0.000054649743,0.000027550193,0.000006726847,0.00023448207,0.000011091732,0.000007163692],"category_scores_gemma":[0.00003501988,0.0000225533,0.000011025027,0.0007152895,0.000009996931,0.00014903255,0.000094526076,0.000021086806,0.000032585252],"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":[1.3509657e-7,0.000006861915,0.00005141336,0.0000015030303,0.0000023196096,1.2894577e-7,0.00008210112,0.0021698086,0.016380986,0.15583862,0.001448395,0.8240177],"study_design_scores_gemma":[0.000043087228,0.000013283379,0.0014324603,0.000005466722,0.000004124608,8.9446166e-7,0.000007854235,0.78861547,0.11312432,0.09433488,0.0023687086,0.000049445352],"about_ca_topic_score_codex":0.00000154189,"about_ca_topic_score_gemma":0.0000013589508,"teacher_disagreement_score":0.8346906,"about_ca_system_score_codex":0.000012710969,"about_ca_system_score_gemma":0.00002142851,"threshold_uncertainty_score":0.09196969},"labels":[],"label_agreement":null},{"id":"W4387776070","doi":"10.21203/rs.3.rs-3420978/v1","title":"FPNC Net: A Hydrogenation Catalyst Image Recognition Algorithm Based on Deep Learning","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Petro-Canada","funders":"","keywords":"Catalysis; Stacking; Feature (linguistics); Computer science; Artificial intelligence; Convolution (computer science); Image (mathematics); Kernel (algebra); Pyramid (geometry); Deep learning; Pattern recognition (psychology); Convolutional neural network; Catalytic hydrogenation; Algorithm; Materials science; Artificial neural network; Chemistry; Mathematics; Organic chemistry","score_opus":0.08274310112729825,"score_gpt":0.3816547802213104,"score_spread":0.2989116790940121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387776070","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":[0.0011894879,0.00011965639,0.99163175,0.0023660555,0.00021385182,0.0017016666,0.00007080565,0.0015323372,0.0011743936],"genre_scores_gemma":[0.6110954,0.0010106273,0.366371,0.00036635745,0.0019865194,0.0078112846,0.009344563,0.0004244056,0.0015897945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99476856,0.0008064318,0.00040235856,0.0014673528,0.0017159459,0.0008393724],"domain_scores_gemma":[0.99596566,0.0011686385,0.00020937315,0.0015247924,0.00087727327,0.00025424565],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016699056,0.00032206834,0.00028877042,0.00096019654,0.0006925168,0.0005214197,0.0015805252,0.00029656687,0.000026964408],"category_scores_gemma":[0.0006304616,0.00034619667,0.00018042065,0.0020867174,0.00012624888,0.00033514993,0.001724821,0.002689115,0.0018976283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023957271,0.00024052407,0.00006854736,0.0003601927,0.00003765429,0.00016394728,0.00036014302,0.16427764,0.0007835017,0.00060992007,0.0031729303,0.82990104],"study_design_scores_gemma":[0.00022392557,0.00014746714,0.00053097896,0.0003104219,0.0000054911734,0.0000031201407,0.00003738815,0.9731818,0.0018312464,0.021494497,0.0018971293,0.00033653463],"about_ca_topic_score_codex":0.000102381826,"about_ca_topic_score_gemma":0.000042109365,"teacher_disagreement_score":0.8295645,"about_ca_system_score_codex":0.0005168076,"about_ca_system_score_gemma":0.00023635493,"threshold_uncertainty_score":0.999899},"labels":[],"label_agreement":null},{"id":"W4387787767","doi":"10.3934/electreng.2023016","title":"Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review","year":2023,"lang":"en","type":"review","venue":"AIMS Electronics and Electrical Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Software deployment; Computer science; Robustness (evolution); Economic shortage; Automation; Field (mathematics); Sensor fusion; Artificial intelligence; Image processing; Data science; Risk analysis (engineering); Systems engineering; Engineering; Image (mathematics); Software engineering; Business","score_opus":0.14299713813916595,"score_gpt":0.30297585091041457,"score_spread":0.15997871277124862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387787767","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":[0.000098732045,0.99634546,0.0025861338,0.000340964,0.000014852799,0.00050983444,9.761856e-7,0.00009558629,0.000007430594],"genre_scores_gemma":[0.000110228364,0.9984676,0.0010850496,0.000036518475,0.000027505252,0.00022981019,0.0000021963972,0.00003495176,0.0000061264745],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99854666,0.000049971393,0.00043661793,0.00042803353,0.00012938853,0.0004093195],"domain_scores_gemma":[0.9990043,0.00047216757,0.00015738046,0.00024982484,0.0000409631,0.000075332326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021177913,0.0002880186,0.0008373921,0.00017587509,0.000080996244,0.000045012133,0.00025889612,0.00006627294,6.517373e-8],"category_scores_gemma":[0.00003636949,0.00021839804,0.000056314926,0.000557624,0.000041878306,0.00022593405,0.00024570135,0.0005313987,3.4706395e-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":[4.0755776e-7,0.0000052568416,2.525563e-8,0.010589568,0.000013411559,0.000006148088,0.00006342355,0.000022090608,0.000013179202,0.008709234,0.0000022852962,0.98057497],"study_design_scores_gemma":[0.0002542859,0.000286883,0.00003952445,0.03783902,0.00026053103,0.00046210174,0.000027818409,0.15395427,0.000017414683,0.0012862382,0.80455226,0.0010196689],"about_ca_topic_score_codex":0.0000019923077,"about_ca_topic_score_gemma":0.0000011096831,"teacher_disagreement_score":0.9795553,"about_ca_system_score_codex":0.000058907237,"about_ca_system_score_gemma":0.000089831614,"threshold_uncertainty_score":0.89060134},"labels":[],"label_agreement":null},{"id":"W4387874254","doi":"10.3390/smartcities6050134","title":"A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems","year":2023,"lang":"en","type":"article","venue":"Smart Cities","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université Laval","funders":"Mitacs","keywords":"Object detection; Computer science; Convolutional neural network; Truck; Pedestrian detection; Component (thermodynamics); Intelligent transportation system; Artificial intelligence; Deep learning; Real-time computing; Computation; Detector; Machine learning; Pattern recognition (psychology); Engineering; Automotive engineering; Pedestrian; Transport engineering","score_opus":0.06480022087357425,"score_gpt":0.32364037175247673,"score_spread":0.2588401508789025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387874254","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":[0.28439298,0.000010179563,0.71479255,0.00004870735,0.00009312913,0.0003889148,0.00001716642,0.00020994867,0.000046423058],"genre_scores_gemma":[0.9852733,0.000019116984,0.01351847,0.00001718662,0.000020629948,0.00036648815,0.000091976704,0.000008533015,0.0006842772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989425,0.000049314443,0.00032736466,0.0002963249,0.00016969834,0.00021480663],"domain_scores_gemma":[0.99914217,0.0002570524,0.00015038364,0.00021656428,0.00016545175,0.00006836347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018735485,0.00010736255,0.00028462728,0.00038058322,0.0002060015,0.000048989583,0.00022125956,0.000039639952,0.000003243236],"category_scores_gemma":[0.000016162283,0.000112804984,0.00009168196,0.0020312597,0.00004904061,0.00019130707,0.00002106974,0.00006619552,0.00002773441],"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":[0.00005136934,0.000056250155,0.0020341887,0.000058176174,0.00041444995,0.0000016549143,0.014622806,0.90798134,0.043537892,0.00779428,0.00018504076,0.02326258],"study_design_scores_gemma":[0.00014009312,0.00016487445,0.006903603,0.000008810782,0.00008757747,2.1581289e-7,0.001461875,0.9834753,0.0069560227,0.00010829137,0.00057681126,0.00011649598],"about_ca_topic_score_codex":0.000075992226,"about_ca_topic_score_gemma":0.00042024362,"teacher_disagreement_score":0.7012741,"about_ca_system_score_codex":0.000057408433,"about_ca_system_score_gemma":0.000021168375,"threshold_uncertainty_score":0.46000537},"labels":[],"label_agreement":null},{"id":"W4387951161","doi":"10.1109/ccece58730.2023.10288934","title":"Scene Parsing Using Fully Convolutional Network for Semantic Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Artificial intelligence; Parsing; Segmentation; Convolutional neural network; Transfer of learning; Robustness (evolution); Inference; Deep learning; Image segmentation; Object detection; Pattern recognition (psychology); Computer vision; Natural language processing; Machine learning","score_opus":0.062373137616553484,"score_gpt":0.3260679198329695,"score_spread":0.263694782216416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951161","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":[0.011510842,0.000031947897,0.9862992,0.0009607666,0.00028933134,0.0003720244,0.00000224008,0.00042442093,0.00010921879],"genre_scores_gemma":[0.18188296,0.000010646404,0.8168452,0.00048611916,0.00031573384,0.00007593978,0.0000343247,0.000012291661,0.0003368131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990751,0.000018867362,0.0001704515,0.00028039157,0.00013955019,0.00031562656],"domain_scores_gemma":[0.99940366,0.0001904248,0.00006564018,0.00022131721,0.00006725905,0.000051724157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015484146,0.000081539794,0.000082486695,0.000051088522,0.0003490024,0.00005955685,0.00024240144,0.000026721673,0.0000057314633],"category_scores_gemma":[0.000011523521,0.000083295105,0.000040895,0.00085452903,0.000025108802,0.00032160443,0.00010154571,0.000040932726,0.000050812527],"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":[0.000008139572,0.000023801791,0.00095173705,0.000019176578,0.00001863984,0.0000023045473,0.00007768441,0.66711676,0.015728384,0.27417395,0.011538117,0.030341316],"study_design_scores_gemma":[0.00017273586,0.000014135788,0.001050613,0.000011322457,0.0000047256012,0.0000068013815,0.0000099069275,0.96031475,0.0008796053,0.036247306,0.0011847949,0.000103329454],"about_ca_topic_score_codex":0.000004213833,"about_ca_topic_score_gemma":0.0000062928043,"teacher_disagreement_score":0.293198,"about_ca_system_score_codex":0.00005442027,"about_ca_system_score_gemma":0.00003828532,"threshold_uncertainty_score":0.3396676},"labels":[],"label_agreement":null},{"id":"W4387951207","doi":"10.1109/ccece58730.2023.10288852","title":"Subtractor-Based CNN Inference Accelerator","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Computer science; Adder; Multiplication (music); Rounding; Sorting; Inference; Subtractor; Reduction (mathematics); Convolution (computer science); Subtraction; Power (physics); Preprocessor; Algorithm; Parallel computing; Computer engineering; Deep learning; Artificial intelligence; Arithmetic; Artificial neural network; Mathematics","score_opus":0.05533576626550679,"score_gpt":0.3215462121114724,"score_spread":0.2662104458459656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951207","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":[0.02155263,0.000006903673,0.9683651,0.0043595526,0.0001439855,0.00015399564,0.0000012969676,0.0017177896,0.0036987958],"genre_scores_gemma":[0.9552678,0.000007337896,0.04223465,0.0014952797,0.00004899335,0.000067211506,0.0000051818292,0.0000075423895,0.00086604035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916536,0.000015353851,0.00012287247,0.00029302508,0.000158898,0.00024452014],"domain_scores_gemma":[0.99899447,0.0002595679,0.000036146528,0.0005723708,0.000049142996,0.000088312234],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006456672,0.00008550863,0.000072422336,0.00007424042,0.00010570689,0.0000871679,0.0007602484,0.000030847244,0.00005450194],"category_scores_gemma":[0.000030682215,0.000075428376,0.000029769159,0.0012929344,0.0000228863,0.00034915202,0.00014949983,0.000088587454,0.0015861376],"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":[0.000004278594,0.00010537578,0.0041080955,0.00001368617,0.000009265799,0.000029242694,0.00012796011,0.013526876,0.02114957,0.8155215,0.042788982,0.10261517],"study_design_scores_gemma":[0.00042076348,0.00006949985,0.03652531,0.000012119322,0.0000030245099,0.0000023099683,0.000012203171,0.77997434,0.040688448,0.03409571,0.10764722,0.0005490326],"about_ca_topic_score_codex":0.0000037986229,"about_ca_topic_score_gemma":0.000004756311,"teacher_disagreement_score":0.9337151,"about_ca_system_score_codex":0.00001689068,"about_ca_system_score_gemma":0.000052513944,"threshold_uncertainty_score":0.9991912},"labels":[],"label_agreement":null},{"id":"W4387951210","doi":"10.1109/ccece58730.2023.10288711","title":"Self-Attention blocks in UNet and FCN for accurate semantic segmentation of difficult object classes in autonomous driving","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Block (permutation group theory); Object (grammar); Artificial intelligence; Encoder; Segmentation; Representation (politics); Field (mathematics); Pedestrian; Computer vision; Cityscape; Object detection; Machine learning; Pattern recognition (psychology)","score_opus":0.017159595559411808,"score_gpt":0.28822999740408195,"score_spread":0.27107040184467013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951210","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":[0.75820804,0.000012582981,0.24054709,0.0004647771,0.000043855074,0.0005102622,0.0000010893324,0.00016146978,0.00005086341],"genre_scores_gemma":[0.95603937,0.0000593794,0.043602526,0.00002855368,0.000010289402,0.0001382945,0.000010592468,0.000005908567,0.00010508749],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917763,0.000026332227,0.00026139047,0.00027408748,0.00008148837,0.00017905193],"domain_scores_gemma":[0.99943686,0.00025193644,0.00009259514,0.00016009195,0.00003480685,0.000023736053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014183341,0.00008056909,0.000118003125,0.00017590745,0.000042621898,0.000034308934,0.00016427205,0.000032259224,8.104814e-7],"category_scores_gemma":[0.000022495142,0.00007685724,0.00001815126,0.0010239161,0.0000137254965,0.00031624592,0.000103027975,0.00005118204,0.0000027834303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000368345,0.00083823816,0.2625411,0.00059644214,0.00007392491,0.000027052634,0.0063008447,0.17119925,0.28085396,0.06919003,0.0014939159,0.20684843],"study_design_scores_gemma":[0.00049402326,0.000042251988,0.1527196,0.000032973443,0.000003860386,0.000002696622,0.00014047584,0.8400454,0.004059888,0.0022897823,0.000056375993,0.00011269563],"about_ca_topic_score_codex":0.000016901851,"about_ca_topic_score_gemma":0.0003793262,"teacher_disagreement_score":0.66884613,"about_ca_system_score_codex":0.000041960902,"about_ca_system_score_gemma":0.000021252063,"threshold_uncertainty_score":0.31341472},"labels":[],"label_agreement":null},{"id":"W4387951242","doi":"10.1109/iiswc59245.2023.00026","title":"Tale of Two Cs: Computation vs. Communication Scaling for Future Transformers on Future Hardware","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Profiling (computer programming); Computation; Transformer; Scaling; Distributed computing; Computer engineering; Algorithm","score_opus":0.022276829471377522,"score_gpt":0.31166738412639644,"score_spread":0.2893905546550189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951242","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":[0.010352564,0.000066581604,0.9769306,0.0106273005,0.00024248252,0.0006378083,0.000017682907,0.00042554786,0.0006994037],"genre_scores_gemma":[0.7803882,0.00022212963,0.21775733,0.0006897247,0.00031082315,0.00019007603,0.00021526487,0.000023350847,0.0002031133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990567,0.000034640772,0.00025080694,0.00027261427,0.00018691213,0.00019831893],"domain_scores_gemma":[0.9990764,0.00021458084,0.0001102271,0.00042362788,0.00012366858,0.00005145696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015561505,0.00011728307,0.00014688377,0.000107047184,0.00023285196,0.00003412917,0.00055661536,0.000054559554,0.0000038124483],"category_scores_gemma":[0.00000570386,0.00010720675,0.00008610828,0.00085458957,0.000035496214,0.00032407197,0.00005859112,0.000114750575,0.000023353283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044619715,0.00008965079,0.00006923311,0.00008069134,0.000027193199,6.020183e-7,0.0019247636,0.114125095,0.0015464056,0.3201458,0.011329165,0.5506168],"study_design_scores_gemma":[0.0012583204,0.00017111357,0.0017677059,0.00006109999,0.0000150318865,0.0000039647934,0.00093337754,0.9232404,0.008874895,0.03238139,0.030952131,0.00034053714],"about_ca_topic_score_codex":0.000004122253,"about_ca_topic_score_gemma":0.00001928285,"teacher_disagreement_score":0.80911535,"about_ca_system_score_codex":0.000030135441,"about_ca_system_score_gemma":0.00002155706,"threshold_uncertainty_score":0.43717647},"labels":[],"label_agreement":null},{"id":"W4387978272","doi":"10.1016/j.media.2023.103015","title":"Do we really need dice? The hidden region-size biases of segmentation losses","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure","funders":"H2020 Marie Skłodowska-Curie Actions; Fonds de recherche du Québec – Nature et technologies; HORIZON EUROPE Marie Sklodowska-Curie Actions; Natural Sciences and Engineering Research Council of Canada","keywords":"Dice; Ground truth; Segmentation; Computer science; Artificial intelligence; Perspective (graphical); Matching (statistics); Upper and lower bounds; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.030278542921383895,"score_gpt":0.3202295275761869,"score_spread":0.289950984654803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387978272","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":[0.020825407,0.0002989491,0.93059194,0.0474908,0.00005865275,0.00018113918,0.000004643392,0.00026131983,0.00028713833],"genre_scores_gemma":[0.9699131,0.0035876685,0.023979815,0.0011736365,0.00019811402,0.00012048054,0.000038167465,0.000016943768,0.0009720399],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811494,0.00013687357,0.00037059063,0.0003494514,0.00080416165,0.00022399773],"domain_scores_gemma":[0.99680406,0.0019253405,0.00019533816,0.000818621,0.00012362821,0.0001329876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037977876,0.00011121173,0.00024099984,0.00020391119,0.00015479591,0.00007250532,0.0010966236,0.000048492286,0.000113647],"category_scores_gemma":[0.00091572833,0.00007466985,0.00019081956,0.0063144835,0.00035392668,0.00028354488,0.00030302134,0.00013624164,0.00007581594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024905727,0.00031803478,0.007869409,0.000059961865,0.0017030021,0.00037434432,0.0028429038,0.003599856,0.0079175355,0.014005537,0.09711391,0.8641706],"study_design_scores_gemma":[0.0014353873,0.00018146554,0.04162998,0.00025060074,0.0023556165,0.000056352674,0.0021065162,0.8764092,0.013282383,0.051254116,0.010063688,0.00097473536],"about_ca_topic_score_codex":0.00005088501,"about_ca_topic_score_gemma":0.000036450645,"teacher_disagreement_score":0.94908774,"about_ca_system_score_codex":0.00002024289,"about_ca_system_score_gemma":0.000052303807,"threshold_uncertainty_score":0.30449483},"labels":[],"label_agreement":null},{"id":"W4388021911","doi":"10.1145/3616392.3623417","title":"Performance Evaluation of CNN-based Object Detectors on Embedded Devices","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object detection; Convolutional neural network; Frame (networking); Server; Cloud computing; Process (computing); Low latency (capital markets); Latency (audio); Mobile device; Video processing; Frame rate; Image processing; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Embedded system; Edge computing; Real-time computing; Computer network; Telecommunications; Operating system","score_opus":0.0544900512425269,"score_gpt":0.32444994027844387,"score_spread":0.26995988903591694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388021911","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":[0.94334424,0.000010023313,0.051817585,0.00022851405,0.00010810353,0.00032621232,7.1670627e-7,0.0003937646,0.003770866],"genre_scores_gemma":[0.9896618,0.00000483664,0.009979484,0.00015895386,0.000021345963,0.00008857022,0.0000038369462,0.0000062595577,0.000074909214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876887,0.000057225934,0.0001709491,0.0002627753,0.0005706166,0.00016955344],"domain_scores_gemma":[0.9989572,0.00021926213,0.00009218824,0.00051856093,0.00017425432,0.00003851511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005118356,0.000083908286,0.00008684405,0.00016011907,0.00008660275,0.00001878338,0.00047605982,0.00002761142,0.000026264306],"category_scores_gemma":[0.00005219324,0.000071332484,0.000034111978,0.0015015872,0.000023073404,0.00020038478,0.000059235233,0.0000569367,0.00020857171],"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":[0.000010655354,0.000052077405,0.0025256204,0.000020920077,0.00000960051,4.9222234e-7,0.00019885038,0.6403673,0.0074441223,0.004052311,0.0005849914,0.34473303],"study_design_scores_gemma":[0.00018600958,0.00006836523,0.021836098,0.000012840677,0.0000049147675,2.165536e-7,0.000011082174,0.9125503,0.064789794,0.00033898462,0.00012376181,0.0000776743],"about_ca_topic_score_codex":0.0000026996456,"about_ca_topic_score_gemma":0.00000992275,"teacher_disagreement_score":0.34465533,"about_ca_system_score_codex":0.000038110713,"about_ca_system_score_gemma":0.00007326841,"threshold_uncertainty_score":0.29088542},"labels":[],"label_agreement":null},{"id":"W4388039396","doi":"10.1007/s00521-023-09073-z","title":"Explored seeds generation for weakly supervised semantic segmentation","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Vector Institute; Western University; Lakehead University","funders":"","keywords":"Computer science; Pascal (unit); Segmentation; Artificial intelligence; Discriminative model; Machine learning; Task (project management); Pattern recognition (psychology); Cluster analysis; Computational Science and Engineering; Training set","score_opus":0.05420708235517225,"score_gpt":0.3110183331884959,"score_spread":0.2568112508333237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388039396","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":[0.1199424,0.000051292885,0.87580574,0.002300864,0.000095698924,0.00097511127,0.000006932637,0.00077083014,0.00005115698],"genre_scores_gemma":[0.94226223,0.000038509213,0.05581581,0.00038642838,0.000403728,0.0007989424,0.00016394889,0.000019127558,0.00011126903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988112,0.000031450843,0.00025314942,0.0005004519,0.00013258845,0.00027118807],"domain_scores_gemma":[0.99910724,0.00023635966,0.000092719405,0.00038772676,0.000089822526,0.000086158965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014318559,0.00013775569,0.00012366756,0.00011140357,0.00069374446,0.00015026734,0.0003231998,0.000041310042,7.8598345e-7],"category_scores_gemma":[0.000012662198,0.00014232901,0.000046834415,0.0009182906,0.00003573743,0.00025884816,0.00014594047,0.000083368475,0.000030540614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049335385,0.0000694285,0.00041495473,0.00006434951,0.000020775082,9.3307744e-7,0.0007647858,0.039489515,0.23948307,0.10810895,0.0043003396,0.607278],"study_design_scores_gemma":[0.00023886112,0.000032204203,0.00094143697,0.000004960301,0.000008692856,0.0000071059226,0.00004928013,0.9880706,0.003555001,0.0043494026,0.0025830904,0.00015933269],"about_ca_topic_score_codex":0.0000029856003,"about_ca_topic_score_gemma":0.000002921604,"teacher_disagreement_score":0.9485811,"about_ca_system_score_codex":0.000019658557,"about_ca_system_score_gemma":0.000014685496,"threshold_uncertainty_score":0.5804009},"labels":[],"label_agreement":null},{"id":"W4388082345","doi":"10.1007/978-3-031-47451-4_18","title":"Deep Learning-Based Vehicle Classification","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Residual; Transfer of learning; Deep learning; Contextual image classification; Residual neural network; Intelligent transportation system; Pattern recognition (psychology); Machine learning; Image (mathematics); Engineering; Algorithm","score_opus":0.0270076766911452,"score_gpt":0.2480713011747034,"score_spread":0.2210636244835582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388082345","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":[0.000007738094,0.003982075,0.98960644,0.00044894306,0.0006015802,0.00052983785,0.0000012881812,0.00036570357,0.0044563957],"genre_scores_gemma":[0.98014444,0.0012820648,0.002045753,0.00040262367,0.0013004703,0.00024233188,0.00011731168,0.00016068404,0.014304306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981791,0.00006689093,0.00043230737,0.00073505315,0.00024720034,0.000339442],"domain_scores_gemma":[0.9980248,0.00086529204,0.00030978036,0.0006354694,0.000070895934,0.00009376665],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023871397,0.00032802683,0.000400127,0.00016043113,0.00017764624,0.00018235836,0.00048009277,0.0005039748,0.0000031049206],"category_scores_gemma":[0.000037774364,0.0003077472,0.000073027055,0.00025041864,0.000068427136,0.00008609924,0.00012328898,0.00088548934,0.00003415139],"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":[0.0000034721027,0.000003973417,0.00010051779,0.000031292482,0.000009089432,0.000010761429,0.000028009741,0.89394945,0.0000065326844,0.05919122,0.00006509235,0.0466006],"study_design_scores_gemma":[0.00014326102,0.000039704362,0.00017669235,0.00021437882,0.000008435627,0.000006576812,8.4942076e-7,0.9713225,0.0000012931737,0.011179477,0.016605156,0.00030170052],"about_ca_topic_score_codex":0.000011910943,"about_ca_topic_score_gemma":0.00009904863,"teacher_disagreement_score":0.9875607,"about_ca_system_score_codex":0.00008146681,"about_ca_system_score_gemma":0.00002622029,"threshold_uncertainty_score":0.9999375},"labels":[],"label_agreement":null},{"id":"W4388134656","doi":"10.1007/s13369-023-08389-1","title":"Autonomous Vehicle Driving in Harsh Weather: Adaptive Fusion Alignment Modeling and Analysis","year":2023,"lang":"en","type":"article","venue":"Arabian Journal for Science and Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Lidar; Convolutional neural network; Computer science; Artificial intelligence; Ranging; Object detection; Computer vision; Radar; Deep learning; Adverse weather; Segmentation; Remote sensing; Real-time computing; Meteorology; Geography","score_opus":0.02027896464231267,"score_gpt":0.25424394043358417,"score_spread":0.2339649757912715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388134656","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":[0.4696528,0.00006050249,0.52977014,0.0003494957,0.000058807847,0.000061605795,3.3727957e-7,0.00003921015,0.0000070999463],"genre_scores_gemma":[0.9640539,0.00012544675,0.03573375,0.000028052575,0.000024762625,0.000014063726,2.1477214e-7,0.000004871687,0.000014914167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990453,0.0000055427736,0.0001669185,0.00027207818,0.00019058044,0.0003195831],"domain_scores_gemma":[0.9995766,0.0000610243,0.00003703303,0.00013431012,0.000050044153,0.00014098095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007030919,0.00008065641,0.000113187765,0.00045888926,0.00034667974,0.00018985613,0.00029920915,0.000017264669,3.0800805e-7],"category_scores_gemma":[0.000041382744,0.000076287266,0.000029743427,0.0020033307,0.00003358767,0.0007809054,0.00014446533,0.00010428051,9.0192435e-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":[8.640973e-7,0.000003596122,0.00062178855,0.0000020320258,0.000007701331,0.0000043105356,0.00056584226,0.96828806,0.0070497775,0.0030508814,0.0000037185202,0.020401401],"study_design_scores_gemma":[0.00010089334,0.00002739687,0.0043679634,0.000021064812,0.00000788249,0.000016047918,0.00006992317,0.99349433,0.00020141498,0.0015286964,0.000071775445,0.00009262271],"about_ca_topic_score_codex":0.0000029085443,"about_ca_topic_score_gemma":0.00000477284,"teacher_disagreement_score":0.49440113,"about_ca_system_score_codex":0.00007096647,"about_ca_system_score_gemma":0.000031294097,"threshold_uncertainty_score":0.31109044},"labels":[],"label_agreement":null},{"id":"W4388191449","doi":"10.1145/3581783.3611738","title":"SemanticRT: A Large-Scale Dataset and Method for Robust Semantic Segmentation in Multispectral Images","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Multispectral image; Computer science; Artificial intelligence; Segmentation; Scale (ratio); Image segmentation; Pattern recognition (psychology); Computer vision; Cartography; Geography","score_opus":0.028506277197495933,"score_gpt":0.33903853554721025,"score_spread":0.3105322583497143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388191449","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":[0.0037044315,0.000022454828,0.99343336,0.0018626185,0.00004273952,0.0005345549,0.00018243119,0.00019154893,0.000025846794],"genre_scores_gemma":[0.011039865,0.000054538014,0.98778725,0.0002814811,0.000027460032,0.00019004624,0.0003887173,0.000010252381,0.0002203755],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989975,0.00003634616,0.00017167989,0.00041169033,0.000090229354,0.00029255636],"domain_scores_gemma":[0.99928564,0.00029268116,0.000042545013,0.00031113348,0.000017692197,0.00005029391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029109028,0.00009498778,0.000115884824,0.00011421952,0.00010578234,0.00006770873,0.00025418584,0.000026301153,0.000004306864],"category_scores_gemma":[0.000019532281,0.0000896537,0.000018988143,0.0006124881,0.000016251652,0.00043229442,0.00019718408,0.000060778497,0.000021004138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080486876,0.0010335812,0.016737113,0.0006506741,0.00012982602,0.00015826461,0.0056907893,0.13872764,0.25999716,0.14338969,0.21348391,0.21992087],"study_design_scores_gemma":[0.00062069995,0.000025185329,0.005022218,0.000007969098,0.000007327217,0.000012060581,0.000093974835,0.9808841,0.00828308,0.0041759196,0.0007349076,0.00013259244],"about_ca_topic_score_codex":0.00002242344,"about_ca_topic_score_gemma":0.00028215756,"teacher_disagreement_score":0.8421564,"about_ca_system_score_codex":0.000012032269,"about_ca_system_score_gemma":0.000008562741,"threshold_uncertainty_score":0.36559716},"labels":[],"label_agreement":null},{"id":"W4388212574","doi":"10.1109/issre59848.2023.00072","title":"Evaluating the Effect of Common Annotation Faults on Object Detection Techniques","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Bounding overwatch; Computer science; Annotation; Artificial intelligence; Object detection; Minimum bounding box; Data mining; Machine learning; Fault detection and isolation; Pattern recognition (psychology); Image (mathematics)","score_opus":0.0393353541990927,"score_gpt":0.38630954691823566,"score_spread":0.346974192719143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388212574","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":[0.63316846,0.0000062972936,0.3629062,0.00056700327,0.000092917464,0.0007402492,6.4497067e-7,0.0009661703,0.0015520892],"genre_scores_gemma":[0.9942163,0.0000042144407,0.00539777,0.00006841127,0.000030194211,0.00018960802,0.0000019086383,0.0000057544266,0.00008582105],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916303,0.00016404301,0.00014884066,0.0001898274,0.00021949042,0.000114776594],"domain_scores_gemma":[0.9984854,0.00094789744,0.000095446696,0.0004200166,0.00003653157,0.0000147095925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005719359,0.000073097,0.000082047496,0.00007412179,0.00016192438,0.000016564734,0.00033420493,0.00002646641,0.000001076976],"category_scores_gemma":[0.00008411431,0.000045637786,0.000035657707,0.0010136999,0.00002360758,0.00013932241,0.00008948989,0.000093050876,0.000037100377],"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":[0.000014269793,0.0000065285444,0.00009375551,0.0000073460697,0.0000033526344,3.0769118e-7,0.0000860362,0.00730488,0.08357744,0.0024785367,0.0000913222,0.90633625],"study_design_scores_gemma":[0.000077897974,0.00088423345,0.003168868,0.000012536342,0.0000030390129,0.0000018118136,0.0000045271095,0.36867478,0.6241,0.002935021,0.00008519238,0.0000520387],"about_ca_topic_score_codex":0.000012757081,"about_ca_topic_score_gemma":0.000012574399,"teacher_disagreement_score":0.9062842,"about_ca_system_score_codex":0.000022547467,"about_ca_system_score_gemma":0.0000050726194,"threshold_uncertainty_score":0.18610549},"labels":[],"label_agreement":null},{"id":"W4388336536","doi":"10.1007/978-3-031-46338-9_14","title":"A Real-Time Deep UAV Detection Framework Based on a YOLOv8 Perception Module","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Drone; Computer science; Artificial intelligence; Deep learning; Inference; Object detection; Popularity; Computer vision; Machine learning; Real-time computing; Pattern recognition (psychology)","score_opus":0.031422292435214605,"score_gpt":0.2928416212418092,"score_spread":0.2614193288065946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388336536","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":[0.00005532688,0.000014629013,0.933009,0.0012054652,0.00024602763,0.00060206925,0.000009426243,0.00048475797,0.064373285],"genre_scores_gemma":[0.08413327,0.0034141864,0.90659374,0.0024310728,0.0001692004,0.00037326055,0.00016466035,0.000057330195,0.002663288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782133,0.00005172623,0.00068402337,0.0005062472,0.000611932,0.00032476784],"domain_scores_gemma":[0.9952087,0.00064349204,0.0004015766,0.0032666905,0.00034024377,0.00013933278],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007925062,0.00029897984,0.00026927888,0.0013104511,0.0008155669,0.0005070344,0.003130022,0.00022935521,0.00001150389],"category_scores_gemma":[0.00008172795,0.00031609737,0.0000714925,0.0012187134,0.0006196798,0.0040091644,0.0014524086,0.00068530213,0.0006893681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066523285,0.000029472321,0.0000065810677,0.000019648993,0.000003941765,5.1095526e-7,0.0008121318,0.023582682,0.000063107924,0.47138542,0.000112006324,0.50397784],"study_design_scores_gemma":[0.00014521525,0.00008401018,0.0011749688,0.00017480977,0.0000041348735,0.0000067292326,0.000006429463,0.95665574,0.000010603436,0.03340915,0.0080220485,0.00030618516],"about_ca_topic_score_codex":0.00000846408,"about_ca_topic_score_gemma":0.000008957077,"teacher_disagreement_score":0.93307304,"about_ca_system_score_codex":0.00032501438,"about_ca_system_score_gemma":0.00015302195,"threshold_uncertainty_score":0.99992913},"labels":[],"label_agreement":null},{"id":"W4388337125","doi":"10.1007/978-3-031-46335-8_6","title":"A Semi-supervised Teacher-Student Model Based on MMAN for Brain Tissue Segmentation","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Exploit; Medical diagnosis; Labeled data; Set (abstract data type); Pattern recognition (psychology); Supervised learning; Machine learning; Data set; Image segmentation; Artificial neural network; Radiology; Medicine","score_opus":0.062446489358525176,"score_gpt":0.35563548471391626,"score_spread":0.2931889953553911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388337125","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":[0.000016320582,0.000028770912,0.9608017,0.004871977,0.00014240442,0.0012987194,0.000032618114,0.00024214093,0.03256531],"genre_scores_gemma":[0.019588184,0.00063910004,0.96186346,0.0077443533,0.000094888084,0.0010912004,0.00049164286,0.0000511233,0.008436078],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979645,0.00003056774,0.0006987541,0.00045777712,0.000551147,0.00029727383],"domain_scores_gemma":[0.9961843,0.0006146986,0.00033842973,0.0024249903,0.0003146652,0.00012293652],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095213676,0.0002789579,0.0002501857,0.0009086656,0.00068433676,0.00048161324,0.0034382914,0.00012743351,0.0000024557812],"category_scores_gemma":[0.00004861264,0.00029330672,0.000057054538,0.00061360205,0.00038815988,0.0033412222,0.0012768395,0.000363431,0.00006262045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042354372,0.00003598587,0.000006215067,0.000028014227,0.0000039181327,1.4955035e-7,0.0011021579,0.12184882,0.00003059648,0.6112216,0.0013524051,0.2643659],"study_design_scores_gemma":[0.00040049106,0.00008137422,0.0000978994,0.000110881134,0.0000044126414,0.0000018183731,0.000010860476,0.95982814,0.00002977267,0.016265668,0.022883346,0.0002853284],"about_ca_topic_score_codex":0.0000021994215,"about_ca_topic_score_gemma":0.000008339252,"teacher_disagreement_score":0.8379793,"about_ca_system_score_codex":0.00024276257,"about_ca_system_score_gemma":0.00024648182,"threshold_uncertainty_score":0.9999519},"labels":[],"label_agreement":null},{"id":"W4388340943","doi":"10.1177/09544070231203059","title":"Vision-based environmental perception for autonomous driving","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Mitacs","keywords":"Artificial intelligence; Computer vision; Computer science; Monocular; Monocular vision; Identification (biology); Parallax; Stereopsis; RGB color model; Feature (linguistics); Deep learning; Pose; Visual odometry; Object detection; Object (grammar); Pattern recognition (psychology); Robot","score_opus":0.0084092623463645,"score_gpt":0.22769865669423583,"score_spread":0.21928939434787134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388340943","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":[0.18672459,0.00003715883,0.8115979,0.00041653303,0.0007287794,0.00034300817,0.0000061142364,0.00013336007,0.000012543587],"genre_scores_gemma":[0.9453963,0.0000218217,0.05440267,0.000013244941,0.00010748417,0.000030442421,0.0000011002621,0.000014970055,0.000011988918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986165,0.000003955375,0.0006199597,0.00017018402,0.00036953544,0.00021984754],"domain_scores_gemma":[0.99904794,0.000158236,0.00043489985,0.00014622597,0.0001237359,0.00008897536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053415453,0.00014768778,0.00026546486,0.000219621,0.00008015049,0.00002098428,0.0007649146,0.000078698264,0.000003647392],"category_scores_gemma":[0.00023535412,0.000122002384,0.0002454473,0.0005617007,0.00004553738,0.00048244398,0.00012652675,0.0002156383,0.0000018220102],"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":[0.000008169106,0.000050883264,0.000019060011,0.00006836378,0.000021205338,4.036312e-7,0.0000608306,0.71055454,0.26943284,0.016902518,0.00022249397,0.0026586982],"study_design_scores_gemma":[0.00040996852,0.00019228332,0.0004135003,0.00022437294,0.000022947415,0.000020193533,0.000033566717,0.9045105,0.09168569,0.00046747425,0.0018977484,0.00012173272],"about_ca_topic_score_codex":2.145393e-7,"about_ca_topic_score_gemma":3.4097194e-8,"teacher_disagreement_score":0.7586717,"about_ca_system_score_codex":0.00011796707,"about_ca_system_score_gemma":0.000052718966,"threshold_uncertainty_score":0.4975113},"labels":[],"label_agreement":null},{"id":"W4388405697","doi":"10.1155/2023/3044155","title":"A New Multinetwork Mean Distillation Loss Function for Open‐World Domain Incremental Object Detection","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Program of Guizhou Province; Petroleum Technology Research Centre; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Pascal (unit); Computer science; Distillation; Object detection; Artificial intelligence; Benchmark (surveying); Detector; Pattern recognition (psychology); Computer vision; Chromatography","score_opus":0.03736207295549617,"score_gpt":0.32318409551757526,"score_spread":0.2858220225620791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388405697","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":[0.0063513163,0.00014267018,0.9847224,0.00075271534,0.007066423,0.00067789876,0.000008362148,0.000094281495,0.00018396894],"genre_scores_gemma":[0.9797234,0.000052847543,0.016842967,0.000094118644,0.0022953192,0.000061912426,0.000024526009,0.000022458127,0.00088242715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785215,0.00007332341,0.00090973097,0.0002888983,0.00064914825,0.0002267285],"domain_scores_gemma":[0.9978077,0.00030274174,0.00088235614,0.00024150805,0.00062793784,0.0001377887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080802006,0.00016683339,0.00023810066,0.00046798395,0.000145296,0.0004268912,0.0015266615,0.000049303886,0.000011049151],"category_scores_gemma":[0.000061691324,0.00015142589,0.0001627519,0.0007672934,0.00001923772,0.0008573989,0.00027560003,0.0001626122,0.00006153211],"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":[0.0018802644,0.0002619367,0.0040459866,0.000058924106,0.0011455057,0.00008882968,0.0015629799,0.40098667,0.017212676,0.1495328,0.04052016,0.38270324],"study_design_scores_gemma":[0.0034979098,0.0009683933,0.0043044416,0.0007496591,0.0000804928,0.00088016764,0.0006620802,0.5843806,0.02159339,0.065369405,0.31662267,0.00089078624],"about_ca_topic_score_codex":0.0000762453,"about_ca_topic_score_gemma":0.00013536795,"teacher_disagreement_score":0.9733721,"about_ca_system_score_codex":0.00038921027,"about_ca_system_score_gemma":0.00007844014,"threshold_uncertainty_score":0.6174969},"labels":[],"label_agreement":null},{"id":"W4388455116","doi":"10.1145/3631610","title":"HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Xidian University; China Association for Science and Technology","keywords":"Computer science; Floating point; Quantization (signal processing); Overhead (engineering); Computer hardware; Binary number; Hardware acceleration; Deep learning; Artificial neural network; Computer engineering; Inference; Edge device; Algorithm; Artificial intelligence; Field-programmable gate array; Arithmetic; Mathematics","score_opus":0.03841834910659853,"score_gpt":0.2736909359617335,"score_spread":0.23527258685513497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388455116","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":[0.13767996,0.0019916454,0.8556654,0.0024067024,0.0004920172,0.0004783914,0.000007662253,0.0011250209,0.00015324139],"genre_scores_gemma":[0.99520963,0.00076871633,0.00364679,0.000024975287,0.000011415393,0.00012895366,0.0000020399782,0.000013669217,0.00019378746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869925,0.000049616352,0.0003386911,0.00048071385,0.00010055671,0.00033118547],"domain_scores_gemma":[0.9986765,0.0003086373,0.00013066798,0.0007667758,0.00005626163,0.000061185194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021387478,0.00016563702,0.0002850331,0.0006277717,0.00045331565,0.00004886191,0.00057721796,0.00019817887,0.0000029210937],"category_scores_gemma":[0.000020878531,0.00015926035,0.000039905415,0.0019433636,0.00017862902,0.00024590382,0.00002623546,0.00034841584,0.000007161391],"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":[0.000030465715,0.00011993745,0.00068627985,0.00021956679,0.00009672726,0.00004713646,0.0003494122,0.694713,0.014803687,0.03597384,0.00020569816,0.25275427],"study_design_scores_gemma":[0.00028777006,0.00018790156,0.00021323015,0.00011237267,0.000014542677,0.00017909778,0.00042370206,0.9938106,0.0029914544,0.0011221787,0.00043786637,0.00021929685],"about_ca_topic_score_codex":0.000011843109,"about_ca_topic_score_gemma":0.000003357324,"teacher_disagreement_score":0.8575297,"about_ca_system_score_codex":0.00001428913,"about_ca_system_score_gemma":0.000012525928,"threshold_uncertainty_score":0.6494448},"labels":[],"label_agreement":null},{"id":"W4388464957","doi":"10.3390/info14110604","title":"POSS-CNN: An Automatically Generated Convolutional Neural Network with Precision and Operation Separable Structure Aiming at Target Recognition and Detection","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Convolutional neural network; Benchmark (surveying); Artificial intelligence; Task (project management); Reinforcement learning; Pattern recognition (psychology); Deep learning; Field (mathematics); Machine learning","score_opus":0.015822272681247848,"score_gpt":0.23828038274097063,"score_spread":0.22245811005972277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388464957","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":[0.65181625,0.0000170118,0.3471456,0.00018328316,0.00009378072,0.00034459686,0.000013412478,0.00033820007,0.000047878468],"genre_scores_gemma":[0.94427073,0.00002194947,0.054646425,0.0002645715,0.00009059769,0.000055535726,0.00062483636,0.000007858969,0.000017466844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899405,0.00005368241,0.00028349657,0.00021916987,0.00023165077,0.00021793408],"domain_scores_gemma":[0.9993477,0.0000713191,0.00014375315,0.00018366582,0.00016193083,0.00009166022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017707536,0.0001333799,0.00010250502,0.000109938366,0.0005751861,0.00027721934,0.0001067891,0.00008354037,0.000009873861],"category_scores_gemma":[0.000023818351,0.000117800424,0.000010210066,0.00057325646,0.000042567,0.0046702004,0.00010274591,0.00011228859,0.000036413],"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":[0.0001042863,0.000011466635,0.00072221895,0.00003677455,0.000015565103,0.0000017087091,0.0011438628,0.7378925,0.009562736,0.0021837754,0.0004666298,0.2478585],"study_design_scores_gemma":[0.000329556,0.00012884106,0.014555326,0.000015381862,0.000005721695,0.00008116219,0.000018389976,0.9753863,0.0020440428,0.0068809125,0.00039855353,0.00015581916],"about_ca_topic_score_codex":0.000006404631,"about_ca_topic_score_gemma":0.000034134115,"teacher_disagreement_score":0.29249915,"about_ca_system_score_codex":0.00006482262,"about_ca_system_score_gemma":0.000022863462,"threshold_uncertainty_score":0.48037618},"labels":[],"label_agreement":null},{"id":"W4388484077","doi":"10.2139/ssrn.4627340","title":"Redundant Co-Training: Semi-Supervised Segmentation of the Left Ventricle Using Informative Redundancy","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Segmentation; Redundancy (engineering); Artificial intelligence; Computer science; Training (meteorology); Ventricle; Pattern recognition (psychology); Machine learning; Medicine; Internal medicine; Geography","score_opus":0.048292345111479305,"score_gpt":0.3166691421960085,"score_spread":0.2683767970845292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388484077","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":[0.10618165,0.0006585374,0.88995117,0.0013309038,0.0007819798,0.0006963353,0.000014851712,0.0001381252,0.0002464191],"genre_scores_gemma":[0.98578215,0.0019285288,0.011328048,0.00010721887,0.00027897285,0.000024167715,0.000016563381,0.00004771425,0.0004866606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966466,0.00018245015,0.00074451126,0.00035650493,0.0005964586,0.0014734878],"domain_scores_gemma":[0.99772245,0.000120500066,0.0011349667,0.0007412185,0.00019909228,0.000081788836],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0011370832,0.0002674505,0.00031838496,0.0001839237,0.00048939383,0.00014440702,0.0019284457,0.00013663295,0.000003922119],"category_scores_gemma":[0.00006795048,0.00021825732,0.00024836272,0.00059803517,0.000105521714,0.00049220334,0.00083548896,0.0030165717,0.000011488335],"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":[0.00007968937,0.00024131771,0.0016020505,0.00021157725,0.0010945452,0.000012627481,0.029334126,0.47344622,0.017104378,0.32304245,0.0005002912,0.15333073],"study_design_scores_gemma":[0.0008099419,0.00014686845,0.0008375432,0.00041239918,0.00008802671,0.00070887955,0.0030525893,0.23032281,0.0068044914,0.75580645,0.00044642008,0.00056359277],"about_ca_topic_score_codex":0.000023225439,"about_ca_topic_score_gemma":0.00004481928,"teacher_disagreement_score":0.87960047,"about_ca_system_score_codex":0.0013967494,"about_ca_system_score_gemma":0.004008145,"threshold_uncertainty_score":0.9992835},"labels":[],"label_agreement":null},{"id":"W4388561618","doi":"10.1109/dasc58513.2023.10311339","title":"Air-to-Air Simulated Drone Dataset for AI-powered problems","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Drone; Computer science; Payload (computing); Artificial intelligence; Machine learning; Object detection; Data mining; Pattern recognition (psychology); Computer security","score_opus":0.03443398454723882,"score_gpt":0.32119835621074144,"score_spread":0.2867643716635026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388561618","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":[0.0011860665,0.000009173831,0.9824916,0.013298032,0.00014656402,0.0012230607,0.00043775124,0.0010188615,0.0001888985],"genre_scores_gemma":[0.75552577,0.000019569763,0.21809533,0.01528471,0.00020456161,0.00081441086,0.004850282,0.000071154514,0.0051342216],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986564,0.000014654015,0.00021904835,0.00054426293,0.00015968927,0.000405927],"domain_scores_gemma":[0.99859506,0.00019312969,0.000046183413,0.000947529,0.000066815475,0.00015128097],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012482444,0.00013277549,0.0001346125,0.000102600185,0.00016103144,0.00003803315,0.00093212514,0.000042386822,0.000013266018],"category_scores_gemma":[0.00003137322,0.000120702876,0.000030000427,0.0014657911,0.000020552401,0.00035305953,0.0004403328,0.000078188576,0.00092086056],"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":[0.0000108823715,0.00007204907,0.00002977488,0.000019843368,0.000018594728,0.0000044628755,0.00012866854,0.33259445,0.00594198,0.037260916,0.61173326,0.012185127],"study_design_scores_gemma":[0.00036317835,0.00010489129,0.0002838271,0.0000083697905,0.000003636168,0.0000023705763,0.000005236873,0.5405256,0.0019008971,0.020095516,0.4364695,0.00023696729],"about_ca_topic_score_codex":0.0000094104635,"about_ca_topic_score_gemma":0.000021821108,"teacher_disagreement_score":0.76439625,"about_ca_system_score_codex":0.000025682042,"about_ca_system_score_gemma":0.000024744168,"threshold_uncertainty_score":0.999857},"labels":[],"label_agreement":null},{"id":"W4388569893","doi":"10.20944/preprints202311.0614.v1","title":"3D Object Detection Using Multiple Frame Proposal Features Fusion","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Calgary","funders":"","keywords":"Merge (version control); Computer science; Artificial intelligence; Frame (networking); Fusion; Point cloud; Object detection; Computer vision; Object (grammar); Feature (linguistics); Pattern recognition (psychology); Sensor fusion; Data mining; Information retrieval","score_opus":0.1254677092596467,"score_gpt":0.3589060847646049,"score_spread":0.2334383755049582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388569893","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":[0.4622444,0.00006632868,0.53198284,0.00046411596,0.0017624297,0.0013979612,0.000010553028,0.0018165654,0.00025482356],"genre_scores_gemma":[0.93313366,0.00008670916,0.06519453,0.00009545057,0.0004490678,0.0003824096,0.000023497865,0.000077285884,0.00055736804],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99614894,0.0002031383,0.00053723436,0.0019647856,0.0005667573,0.00057914486],"domain_scores_gemma":[0.99612445,0.00024062488,0.00048825928,0.0027647796,0.00020703698,0.00017484333],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046891396,0.00048265338,0.000417415,0.00027755756,0.00044380737,0.00011456153,0.0020240673,0.0005036937,0.000024522455],"category_scores_gemma":[0.00035684367,0.0005057247,0.00022510314,0.00072673726,0.000096824755,0.00033729384,0.0076149516,0.0016528011,0.0009804593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099602505,0.00039225226,0.12438569,0.0004155919,0.00021289627,0.00006987021,0.0021904174,0.39069703,0.43258008,0.0024284893,0.00011238747,0.04641568],"study_design_scores_gemma":[0.00043749157,0.000034926798,0.22650161,0.00033039387,0.000058797712,0.00006958539,0.000030516625,0.55305105,0.17694528,0.03959439,0.0016814355,0.0012644944],"about_ca_topic_score_codex":0.00034685558,"about_ca_topic_score_gemma":0.00017328499,"teacher_disagreement_score":0.47088927,"about_ca_system_score_codex":0.00032253817,"about_ca_system_score_gemma":0.00022023769,"threshold_uncertainty_score":0.9997974},"labels":[],"label_agreement":null},{"id":"W4388621756","doi":"10.1007/978-3-031-45382-3_29","title":"Texture-Based Data Augmentation for Small Datasets","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial intelligence; Regularization (linguistics); Generalization; Robustness (evolution); Pattern recognition (psychology); Training set; Machine learning; Data mining; Mathematics","score_opus":0.07709191171857734,"score_gpt":0.31888997629896015,"score_spread":0.24179806458038283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388621756","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":[0.0000016505467,0.00010444109,0.9945112,0.0020314013,0.0012013894,0.0011108594,0.0005169027,0.00036457588,0.00015757499],"genre_scores_gemma":[0.002160125,0.000028429718,0.9919484,0.0028502743,0.0005780198,0.00009084422,0.0019778162,0.00006527699,0.0003007936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959056,0.000021242395,0.0004880425,0.0023388907,0.0006283521,0.0006178704],"domain_scores_gemma":[0.99392676,0.0013783217,0.00033392623,0.004057356,0.00014942927,0.00015418873],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.00074154034,0.0004489333,0.00036948943,0.00052768446,0.00034929946,0.00042499285,0.0078063956,0.0002114606,0.000004649077],"category_scores_gemma":[0.0001378781,0.0004363151,0.00007210742,0.000834259,0.00038046876,0.00070550985,0.0025055741,0.0004757347,0.00006822903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007967611,0.000023657023,0.0000058475916,0.000046701425,0.00000827624,0.000019331506,0.000048561495,0.122139774,0.00011940694,0.02693655,0.00074092246,0.849903],"study_design_scores_gemma":[0.00029071554,0.00007047643,0.000025203059,0.00012935983,0.000010030145,0.000008060925,3.3850107e-8,0.83630633,0.00044420557,0.15156145,0.010713779,0.00044037122],"about_ca_topic_score_codex":0.000010198733,"about_ca_topic_score_gemma":0.0002541876,"teacher_disagreement_score":0.8494626,"about_ca_system_score_codex":0.00018265957,"about_ca_system_score_gemma":0.00046623973,"threshold_uncertainty_score":0.99980885},"labels":[],"label_agreement":null},{"id":"W4388654380","doi":"10.3390/s23229162","title":"3D Object Detection Using Multiple-Frame Proposal Features Fusion","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Calgary","funders":"","keywords":"Merge (version control); Computer science; Artificial intelligence; Frame (networking); Fusion; Computer vision; Object detection; Point cloud; Object (grammar); Feature (linguistics); Sensor fusion; Pattern recognition (psychology); Data mining; Information retrieval","score_opus":0.02157973476433038,"score_gpt":0.2798126025572462,"score_spread":0.2582328677929158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388654380","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":[0.8190099,0.00002232381,0.17838651,0.00047976605,0.00045234617,0.00035410826,0.0000019693812,0.0011224007,0.00017068889],"genre_scores_gemma":[0.9389933,0.000015456348,0.060360454,0.000077655925,0.00016471367,0.000015606334,0.0000032299686,0.000017112658,0.00035249823],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989206,0.000056016226,0.00013418867,0.00038251097,0.00021032234,0.00029634355],"domain_scores_gemma":[0.9992309,0.00014052795,0.000069830094,0.00044347372,0.00005014286,0.00006510242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000093689596,0.00012055538,0.00009695061,0.0001300202,0.00030051058,0.00006153991,0.00029831237,0.00006827409,0.0000019866388],"category_scores_gemma":[0.00007557927,0.000111422065,0.000047145302,0.0011903397,0.000036893427,0.00018768614,0.00019367377,0.00017978602,0.0001497432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026157975,0.00004650312,0.0006765105,0.00002255869,0.000017360731,0.000044161054,0.00095716846,0.33347383,0.41055942,0.0028604364,0.00043509874,0.2508808],"study_design_scores_gemma":[0.0001461784,0.000029635783,0.0043289266,0.000011912322,0.000003881333,0.0000459056,0.00003069517,0.96196604,0.029482884,0.002198159,0.0015831413,0.00017263263],"about_ca_topic_score_codex":0.000034183166,"about_ca_topic_score_gemma":0.00005636034,"teacher_disagreement_score":0.62849224,"about_ca_system_score_codex":0.000052811603,"about_ca_system_score_gemma":0.00002481831,"threshold_uncertainty_score":0.454366},"labels":[],"label_agreement":null},{"id":"W4388688937","doi":"10.1016/j.compeleceng.2023.109023","title":"Optimized voxel transformer for 3D detection with spatial-semantic feature aggregation","year":2023,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Voxel; Computer science; Artificial intelligence; Point cloud; Pattern recognition (psychology); Computer vision; Object detection; Minimum bounding box; Feature extraction; Image (mathematics)","score_opus":0.007328796932998384,"score_gpt":0.2130649804126613,"score_spread":0.20573618347966294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388688937","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":[0.0014663808,0.0000670034,0.9958452,0.00061051967,0.000232413,0.0006148511,9.4180206e-7,0.0011528288,0.000009849122],"genre_scores_gemma":[0.36595464,0.00004280744,0.6332209,0.000115059716,0.00021885161,0.00031368792,0.000018711515,0.0000448373,0.0000704998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987505,0.000011900327,0.00017026674,0.00042243366,0.00019073536,0.00045413748],"domain_scores_gemma":[0.99919635,0.00029697845,0.000052745512,0.0002854856,0.00006568418,0.000102765065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008784691,0.00019520345,0.00020704481,0.00022805823,0.00014484253,0.000081497856,0.00036966227,0.0000768291,3.925704e-7],"category_scores_gemma":[0.000024598214,0.0001800696,0.000075037526,0.0017766781,0.0000115804405,0.00028279758,0.000028788581,0.00021162214,0.000010953459],"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":[0.000021837743,0.000014011036,0.000002912457,0.000021652835,0.00002210197,0.0000045414213,0.00004975198,0.6231565,0.0059575727,0.001112966,0.00020589613,0.36943024],"study_design_scores_gemma":[0.000614416,0.00015296633,0.00026421822,0.000026638749,0.0000125994775,0.000023710456,4.4852666e-7,0.98831594,0.0074936943,0.00021890686,0.0026484474,0.00022802652],"about_ca_topic_score_codex":0.0000030527467,"about_ca_topic_score_gemma":0.0000019304778,"teacher_disagreement_score":0.36920223,"about_ca_system_score_codex":0.00008415018,"about_ca_system_score_gemma":0.00002193606,"threshold_uncertainty_score":0.7343026},"labels":[],"label_agreement":null},{"id":"W4388692010","doi":"10.1109/jsen.2023.3330146","title":"A Novel Keypoint Supplemented R-CNN for UAV Object Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Student Aid","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Object detection; Pyramid (geometry); Feature (linguistics); Pattern recognition (psychology); Backbone network; Computer vision; Set (abstract data type); Object (grammar); Deep learning; Aerial image; Data set; Image (mathematics); Mathematics","score_opus":0.037433633159386845,"score_gpt":0.30145535421703934,"score_spread":0.2640217210576525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388692010","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":[0.043343764,0.000010036931,0.9527143,0.0021167782,0.0010910997,0.00032243255,0.000011255053,0.0003104061,0.00007988283],"genre_scores_gemma":[0.8745085,0.000070334114,0.122616574,0.0003990311,0.0011653196,0.000096759715,0.0000060493144,0.000042127096,0.0010953136],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986359,0.000035199137,0.0003237009,0.00029712197,0.00026208867,0.000446028],"domain_scores_gemma":[0.9989687,0.00021686875,0.0001831096,0.00032189576,0.0001643128,0.00014511093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033620963,0.00013796643,0.0001386094,0.00021633178,0.0004849561,0.00014815823,0.00044806712,0.000046563076,0.000008408323],"category_scores_gemma":[0.00006202857,0.00012895661,0.00013354122,0.0008769142,0.000025944893,0.0003365185,0.000058545298,0.00025349174,0.000109062225],"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":[0.0000727233,0.0001277776,0.00005209011,0.000020007546,0.00009412569,0.000049519414,0.00072333595,0.0932506,0.76386636,0.0030479187,0.03434302,0.104352534],"study_design_scores_gemma":[0.0019734812,0.0003212759,0.0011076727,0.00003373846,0.000023699835,0.0014820687,0.00010123001,0.71768886,0.19606626,0.014046542,0.06668424,0.00047091456],"about_ca_topic_score_codex":0.0000035529974,"about_ca_topic_score_gemma":0.000016430151,"teacher_disagreement_score":0.8311647,"about_ca_system_score_codex":0.00009109419,"about_ca_system_score_gemma":0.000041103754,"threshold_uncertainty_score":0.52586985},"labels":[],"label_agreement":null},{"id":"W4388742679","doi":"10.1145/3626562.3626829","title":"Federated Computing in Electric Vehicles to Predict Coolant Temperature","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie","keywords":"Computer science; Overheating (electricity); Cluster analysis; Coolant; Architecture; Greenhouse gas; Machine learning; Engineering; Electrical engineering","score_opus":0.014372156187566744,"score_gpt":0.26406125142171766,"score_spread":0.24968909523415092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388742679","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":[0.7787219,0.000032050084,0.21443963,0.0039281617,0.00009856989,0.00044864154,0.0000011520715,0.0016090547,0.00072082476],"genre_scores_gemma":[0.9894207,0.000010068755,0.0090156095,0.001027697,0.000052271105,0.000025603822,0.000005292737,0.0000087404915,0.00043398296],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989294,0.000033435586,0.00017812016,0.00036507106,0.0001480116,0.0003459623],"domain_scores_gemma":[0.9994797,0.00014728103,0.00002598044,0.00021720356,0.000042625255,0.00008718495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013423082,0.00009846378,0.000108906526,0.00019040813,0.00015005544,0.00012060791,0.0004148669,0.00004196206,0.0000022305353],"category_scores_gemma":[0.000030735624,0.000087563996,0.000017899614,0.0042764405,0.0000057822476,0.00014960652,0.00022383235,0.00016612255,0.00023010826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027084108,0.00017628985,0.014714994,0.000026562318,0.00002388241,0.00018404513,0.0011198352,0.12257011,0.5193197,0.058499094,0.12034015,0.16299823],"study_design_scores_gemma":[0.000190032,0.000050391503,0.053077642,0.000018933564,7.2064705e-7,0.00001117489,0.000015096727,0.93057406,0.01318246,0.0012125216,0.00148308,0.00018389865],"about_ca_topic_score_codex":0.000010637195,"about_ca_topic_score_gemma":0.000025234835,"teacher_disagreement_score":0.80800396,"about_ca_system_score_codex":0.000038892704,"about_ca_system_score_gemma":0.00003208885,"threshold_uncertainty_score":0.35707563},"labels":[],"label_agreement":null},{"id":"W4388752296","doi":"10.1016/j.energy.2023.129654","title":"Enhancing coal-gangue object detection using GAN-based data augmentation strategy with dual attention mechanism","year":2023,"lang":"en","type":"article","venue":"Energy","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Lakes Environmental (Canada); University of Waterloo","funders":"","keywords":"Computer science; Normalization (sociology); Artificial intelligence; Deep learning; Object detection; Object (grammar); Computer vision; Data mining; Pattern recognition (psychology)","score_opus":0.045452090137408155,"score_gpt":0.29328535550895274,"score_spread":0.2478332653715446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388752296","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":[0.09096366,0.000008959304,0.90819055,0.000081257,0.00017059573,0.0000974038,0.000005381243,0.00041391462,0.000068266025],"genre_scores_gemma":[0.9827626,0.0000064268334,0.016660532,0.00009994812,0.00012411564,0.000037265974,0.0001711534,0.000019747813,0.00011823122],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998736,0.00006521549,0.00018019196,0.00051151594,0.00025870383,0.0002483566],"domain_scores_gemma":[0.99898016,0.000076543976,0.00013189085,0.0007057546,0.000049999766,0.00005562497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015373653,0.00012754792,0.00009532271,0.0001374966,0.00026826587,0.000093869465,0.0003780255,0.000044357726,0.0000045122315],"category_scores_gemma":[0.000008898127,0.00012528994,0.0000200845,0.00095413043,0.000019206615,0.0006535775,0.00009030445,0.00007444331,0.000017515202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012830862,0.000027126589,0.000014117696,0.000008935273,0.000015507536,0.000022108225,0.000038026814,0.32393396,0.6373631,0.0168072,0.000030193216,0.021726953],"study_design_scores_gemma":[0.00022296741,0.00006023151,0.00027068885,0.000017219203,0.000009646307,0.0000132115265,0.00003375616,0.7207833,0.27608266,0.002244211,0.00012591839,0.00013617049],"about_ca_topic_score_codex":0.00019763608,"about_ca_topic_score_gemma":0.00091399736,"teacher_disagreement_score":0.8917989,"about_ca_system_score_codex":0.00007669375,"about_ca_system_score_gemma":0.000069660244,"threshold_uncertainty_score":0.51091754},"labels":[],"label_agreement":null},{"id":"W4388853272","doi":"10.47749/t/unicamp.2021.1166643","title":"Accelerated pooling","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; University of Alberta","keywords":"Computer science; Parallel computing; Pooling; Speedup; Overhead (engineering); Convolution (computer science); Bottleneck; Vectorization (mathematics); Convolutional neural network; Computer engineering; Artificial intelligence; Embedded system; Artificial neural network; Programming language","score_opus":0.031009862814731774,"score_gpt":0.3119823864135028,"score_spread":0.280972523598771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388853272","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":[0.006796482,0.0012093253,0.7782574,0.0006032237,0.0016903665,0.00041393342,0.0000020354778,0.0011503595,0.20987684],"genre_scores_gemma":[0.14915821,0.00071621337,0.47566554,0.0019799767,0.00068289804,0.0003743621,0.0027778908,0.00011518336,0.36852974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900836,0.000015692227,0.00018570575,0.00044916067,0.0001600561,0.00018100951],"domain_scores_gemma":[0.99906135,0.000047730427,0.00009756448,0.00057682896,0.00016014093,0.00005639223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000027326263,0.00014833715,0.00015082929,0.000058752925,0.000106813925,0.00015769935,0.00071308017,0.0001182781,0.000102054924],"category_scores_gemma":[0.000014350719,0.00014742743,0.000056822646,0.00063623086,0.0000038619,0.00021894243,0.000066090746,0.00021857486,0.00012428855],"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":[0.00000743093,0.000120537654,0.000018341472,0.000086032414,0.0000775824,0.0000774507,0.0007429362,0.0016130811,0.01588176,0.5620765,0.01834434,0.400954],"study_design_scores_gemma":[0.001140385,0.00010987388,0.005808975,0.0006112861,0.000112232396,0.000107574706,0.0010766813,0.2512263,0.38969103,0.09390918,0.25137067,0.0048357956],"about_ca_topic_score_codex":0.0000061473384,"about_ca_topic_score_gemma":0.000053218366,"teacher_disagreement_score":0.46816733,"about_ca_system_score_codex":0.000024004868,"about_ca_system_score_gemma":0.00009866492,"threshold_uncertainty_score":0.60119164},"labels":[],"label_agreement":null},{"id":"W4388945936","doi":"10.3390/make5040085","title":"FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Segmentation; Class (philosophy); Function (biology); Computer science; Terrain; Artificial intelligence; Machine learning; Geography; Cartography","score_opus":0.01779713589971847,"score_gpt":0.29313076773951846,"score_spread":0.2753336318398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388945936","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":[0.063859016,0.00046273446,0.93399256,0.000063976666,0.00031817274,0.0005602666,0.0000012531113,0.000477904,0.0002641376],"genre_scores_gemma":[0.989948,0.00003345309,0.008625791,0.000004291548,0.0001496655,0.00033984822,0.000052458836,0.000013768122,0.0008327277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887794,0.0001406367,0.00022812336,0.00042518612,0.000091587775,0.00023654893],"domain_scores_gemma":[0.99941355,0.00020910491,0.00014051246,0.00013172683,0.000056334367,0.00004879639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000483916,0.0001282132,0.00013576142,0.00025145817,0.00034624184,0.00010749234,0.0001044018,0.00007890058,4.2363064e-7],"category_scores_gemma":[0.000086066924,0.00013321298,0.00003280842,0.00080649764,0.000015107736,0.000399728,0.000060119644,0.00030688618,0.000012639446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005018872,0.00016623923,0.007831006,0.00056344044,0.00001661373,0.0000026932419,0.0012108369,0.088569865,0.1808825,0.0015027836,0.00006896033,0.71913487],"study_design_scores_gemma":[0.00038348942,0.00006724244,0.0051319757,0.000009443464,0.000007967667,0.000016229333,0.00012989018,0.991069,0.0012388246,0.00014961646,0.0016587225,0.00013757069],"about_ca_topic_score_codex":0.000115130366,"about_ca_topic_score_gemma":0.00009048518,"teacher_disagreement_score":0.926089,"about_ca_system_score_codex":0.00009285375,"about_ca_system_score_gemma":0.000015640888,"threshold_uncertainty_score":0.5432267},"labels":[],"label_agreement":null},{"id":"W4388947366","doi":"10.1016/j.eswa.2023.122695","title":"Scale-pyramid dynamic atrous convolution for pixel-level labeling","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Pixel; Convolutional neural network; Convolution (computer science); Kernel (algebra); Artificial intelligence; Scale (ratio); Granularity; Upsampling; Exploit; Pattern recognition (psychology); Algorithm; Image (mathematics); Artificial neural network; Mathematics","score_opus":0.030097741912789345,"score_gpt":0.2960657762361329,"score_spread":0.26596803432334354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388947366","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":[0.00065515004,0.0005140056,0.9919505,0.0021484266,0.00019052149,0.0028801346,0.000054551932,0.0014350163,0.00017166056],"genre_scores_gemma":[0.63540715,0.00019184638,0.31890634,0.00037666058,0.0005095687,0.040482767,0.0003033867,0.000104825456,0.0037174728],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805427,0.000033856002,0.00039057867,0.00074356725,0.00028068383,0.00049705897],"domain_scores_gemma":[0.9979507,0.00027439778,0.00020352882,0.0011750793,0.00024196191,0.0001543664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001954522,0.00022425417,0.00024148704,0.00015905598,0.0006758149,0.00013399048,0.0008693639,0.000088601955,9.907518e-7],"category_scores_gemma":[0.000012064537,0.0001825024,0.00006724333,0.0015484124,0.00008555515,0.00033183288,0.00012868525,0.0001153989,0.0002874176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068516456,0.00047299484,0.0004592954,0.00035067866,0.00021763255,0.000005551728,0.00294449,0.057456024,0.09653045,0.5924089,0.029088859,0.21999662],"study_design_scores_gemma":[0.0006979748,0.00008875515,0.00030549854,0.0000711073,0.000013984629,0.000059623457,0.00030140302,0.669698,0.00051808136,0.0038197034,0.32388416,0.00054174353],"about_ca_topic_score_codex":0.00003112555,"about_ca_topic_score_gemma":0.000035761073,"teacher_disagreement_score":0.6730442,"about_ca_system_score_codex":0.0001558204,"about_ca_system_score_gemma":0.00007748674,"threshold_uncertainty_score":0.7442232},"labels":[],"label_agreement":null},{"id":"W4389169791","doi":"10.1109/icjece.2023.3305323","title":"Efficient Edge Computing Device for Traffic Monitoring Using Deep Learning Detectors","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"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 Toronto; McMaster University","funders":"","keywords":"Deep learning; Intersection (aeronautics); Artificial intelligence; Computer science; Enhanced Data Rates for GSM Evolution; Transfer of learning; Frame rate; Detector; Software deployment; Edge computing; Frame (networking); Computer vision; Engineering; Transport engineering; Operating system; Computer network; Telecommunications","score_opus":0.016793653442812813,"score_gpt":0.23267274370791102,"score_spread":0.21587909026509822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389169791","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":[0.43542394,0.00039408854,0.5637612,0.000034621287,0.0002763839,0.00005792486,1.4394831e-7,0.000050969742,7.552102e-7],"genre_scores_gemma":[0.9144678,0.000007680204,0.08499426,0.000016853484,0.0004923235,0.0000019634751,2.8279948e-7,0.000016512347,0.0000023284663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988858,0.000018270575,0.00028949432,0.00019554375,0.000110543166,0.0005003181],"domain_scores_gemma":[0.9988588,0.00040508262,0.00010375373,0.00009484524,0.00010717957,0.00043031524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019712414,0.00013666831,0.00019599232,0.00042332645,0.00026954367,0.00012672097,0.00034985886,0.00004685319,2.1956232e-7],"category_scores_gemma":[0.000060859125,0.00013974977,0.00007470211,0.0011044755,0.000012508084,0.00009053509,0.000041977193,0.00030197453,0.0000010157692],"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":[7.970613e-7,0.0000021528306,0.00025446643,0.000009694837,0.000011033772,0.000020616184,0.00013512751,0.90678,0.00044412274,0.0002923834,0.0000062323497,0.092043385],"study_design_scores_gemma":[0.00016268459,0.000083551604,0.0022544023,0.000050546634,0.000008075931,0.00013431135,0.0000047551293,0.9962075,0.0002482937,0.000027216458,0.0006718905,0.00014681628],"about_ca_topic_score_codex":0.000012433137,"about_ca_topic_score_gemma":0.000008882303,"teacher_disagreement_score":0.47904384,"about_ca_system_score_codex":0.00012705738,"about_ca_system_score_gemma":0.00010280765,"threshold_uncertainty_score":0.56988305},"labels":[],"label_agreement":null},{"id":"W4389299466","doi":"10.1145/3634919","title":"High Throughput FPGA-Based Object Detection via Algorithm-Hardware Co-Design","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; VMware; National Science Foundation","keywords":"Computer science; Field-programmable gate array; Throughput; Computer hardware; Object detection; Embedded system; Object (grammar); Parallel computing; Algorithm; Computer architecture; Artificial intelligence; Pattern recognition (psychology); Operating system","score_opus":0.02606879315728383,"score_gpt":0.2613096891591751,"score_spread":0.23524089600189124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389299466","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":[0.0014828246,0.00019668527,0.9915394,0.002072151,0.00068211224,0.00080146076,0.000021069287,0.0031071366,0.00009716997],"genre_scores_gemma":[0.9695385,0.00020531235,0.02823592,0.00012462797,0.000043872755,0.0010641889,0.000010537376,0.00003518193,0.00074183027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998076,0.00012871074,0.00038294468,0.0007531333,0.00019186489,0.00046730047],"domain_scores_gemma":[0.9977831,0.00056692737,0.00014726387,0.0013220861,0.000095693686,0.000084902735],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032686576,0.000265877,0.00033811954,0.00086352497,0.00077658467,0.000091429465,0.00084084575,0.00037028652,0.000015894197],"category_scores_gemma":[0.0000305612,0.00026258992,0.0000702761,0.00264764,0.00014461574,0.0003172214,0.000009532155,0.0004994867,0.0003160126],"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":[0.000019211768,0.00007820646,0.000010454114,0.000041112457,0.00007289662,0.00002754403,0.00004607347,0.05386362,0.007794631,0.00260195,0.000281675,0.9351626],"study_design_scores_gemma":[0.0013419152,0.000814661,0.00014623036,0.00013358897,0.00004913349,0.00036194024,0.00021464839,0.5566568,0.38868192,0.03922406,0.011515607,0.0008594817],"about_ca_topic_score_codex":0.000048005477,"about_ca_topic_score_gemma":0.000012019706,"teacher_disagreement_score":0.9680557,"about_ca_system_score_codex":0.00008344304,"about_ca_system_score_gemma":0.000048092683,"threshold_uncertainty_score":0.99998266},"labels":[],"label_agreement":null},{"id":"W4389356391","doi":"10.5194/isprs-annals-x-1-w1-2023-657-2023","title":"DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Lidar; Artificial intelligence; Computer science; Object detection; Adverse weather; Radar; Deep learning; Ranging; Computer vision; Class (philosophy); Object (grammar); Remote sensing; Machine learning; Pattern recognition (psychology); Geography; Telecommunications; Meteorology","score_opus":0.11271217378061384,"score_gpt":0.35544887570744366,"score_spread":0.24273670192682983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389356391","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":[0.0164214,0.000031864376,0.98182976,0.0009027486,0.00031823275,0.00030349166,0.000004159394,0.000116315634,0.00007204932],"genre_scores_gemma":[0.97019213,0.000078801466,0.029385291,0.00027403815,0.00004802758,3.153001e-7,0.000008160657,0.0000043471405,0.000008903117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869657,0.00006974215,0.000347039,0.00024401888,0.00035252745,0.00029010818],"domain_scores_gemma":[0.9986276,0.00027325502,0.00037771813,0.0004828596,0.0001832378,0.000055304525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011552541,0.00010997729,0.00014099012,0.00023673,0.0009558543,0.00026409834,0.00075649185,0.000045121706,2.7593728e-7],"category_scores_gemma":[0.0005189436,0.00008379914,0.000058645008,0.0019037628,0.00020314442,0.0008698854,0.00047910202,0.00011213514,0.000002214191],"study_design_candidate":"simulation_or_modeling","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":[0.000004362655,0.0000015497369,0.000011425275,0.000010532255,0.0000043879463,5.2253434e-8,0.00022173145,0.019634606,0.0014989267,0.000013559695,0.00003309934,0.97856575],"study_design_scores_gemma":[0.0000853463,0.00005109913,0.000289399,0.000025981477,0.0000049757873,0.000011524824,0.00017936107,0.97875553,0.014799877,0.002087548,0.0036076866,0.000101660895],"about_ca_topic_score_codex":0.012100037,"about_ca_topic_score_gemma":0.00214714,"teacher_disagreement_score":0.9784641,"about_ca_system_score_codex":0.000008818198,"about_ca_system_score_gemma":0.000040561383,"threshold_uncertainty_score":0.99447846},"labels":[],"label_agreement":null},{"id":"W4389451416","doi":"10.1007/s10489-023-05207-x","title":"Automatic filter pruning algorithm for image classification","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"","keywords":"Pruning; Convolutional neural network; Computer science; Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Filter (signal processing); Similarity (geometry); Algorithm; Image (mathematics); Computer vision","score_opus":0.05195435379711269,"score_gpt":0.3155728016241508,"score_spread":0.26361844782703814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389451416","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":[0.0004894723,0.000012289567,0.9951614,0.00071517495,0.00014181975,0.0007418526,0.000005014404,0.0011098905,0.001623087],"genre_scores_gemma":[0.15229589,0.000036418074,0.84545344,0.0003373046,0.000104933286,0.0013786578,0.00003367065,0.00002547799,0.00033419073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860096,0.00001304109,0.00030238897,0.00051820325,0.00018902154,0.00037636398],"domain_scores_gemma":[0.9985968,0.0004084063,0.00012346485,0.00071524945,0.000078149234,0.000077956225],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022385835,0.00014875938,0.00013368472,0.00011740605,0.00023253406,0.00011076635,0.0009903385,0.000053297023,0.000018175651],"category_scores_gemma":[0.000031060354,0.00014943916,0.000051610677,0.0011962788,0.00006762758,0.00029543348,0.00019967776,0.000114376795,0.0010673595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.8695097e-7,0.0000124995795,0.0000010561624,0.000008657339,0.000004112381,7.658561e-7,0.00015626759,0.00031783571,0.005282588,0.15190445,0.0019124425,0.84039885],"study_design_scores_gemma":[0.000040396826,0.000016552925,0.00018761432,0.000008532622,0.0000034757572,0.000003424256,0.000063441854,0.9050137,0.01951941,0.06969574,0.005279609,0.00016810076],"about_ca_topic_score_codex":9.900858e-7,"about_ca_topic_score_gemma":5.338452e-7,"teacher_disagreement_score":0.90469587,"about_ca_system_score_codex":0.000043167645,"about_ca_system_score_gemma":0.000028398394,"threshold_uncertainty_score":0.99971044},"labels":[],"label_agreement":null},{"id":"W4389491867","doi":"10.1145/3613424.3614248","title":"Grape: Practical and Efficient Graphed Execution for Dynamic Deep Neural Networks on GPUs","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; VMware","keywords":"Computer science; Artificial neural network; Parallel computing; Deep neural networks; Artificial intelligence; Computational science","score_opus":0.019471328994146914,"score_gpt":0.30887528261425223,"score_spread":0.2894039536201053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389491867","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":[0.025581919,0.000033672524,0.96775895,0.0050905664,0.00026990645,0.0005743732,0.0000011320386,0.0005679452,0.000121506404],"genre_scores_gemma":[0.95899427,0.000036656467,0.03997406,0.00061477924,0.00004519706,0.00021269177,0.0000147602705,0.000013539344,0.00009403177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879986,0.000033611945,0.00016968594,0.00047897446,0.00015564328,0.0003622087],"domain_scores_gemma":[0.9988024,0.00059208245,0.00006225249,0.0003870123,0.00004750769,0.00010873607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014665244,0.000131101,0.00011169255,0.000106021515,0.0002520915,0.00008086038,0.00023424934,0.00005569021,0.0000016984105],"category_scores_gemma":[0.000042660224,0.00011358815,0.00005390786,0.00093902514,0.00005627551,0.00015009471,0.00014247764,0.00012981072,0.000016445189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037550297,0.00008165379,0.000043397882,0.000007267424,0.000010473049,0.000007912273,0.000057584475,0.4254572,0.0003900673,0.48265648,0.0013078707,0.08994254],"study_design_scores_gemma":[0.0002495193,0.000109387125,0.0015881401,0.0000027896326,0.0000053971858,0.000016021462,0.000011932393,0.9873048,0.00006236311,0.010093926,0.00042590103,0.00012981227],"about_ca_topic_score_codex":0.0000012204317,"about_ca_topic_score_gemma":0.00001045083,"teacher_disagreement_score":0.9334124,"about_ca_system_score_codex":0.000023628136,"about_ca_system_score_gemma":0.000008669255,"threshold_uncertainty_score":0.46319902},"labels":[],"label_agreement":null},{"id":"W4389510032","doi":"10.1049/cvi2.12260","title":"Deep network with double reuses and convolutional shortcuts","year":2023,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Feature (linguistics); Reuse; Benchmark (surveying); Convolutional code; Artificial intelligence; Pascal (unit); Convolution (computer science); Pattern recognition (psychology); Deep learning; Algorithm; Decoding methods; Artificial neural network; Engineering","score_opus":0.0162655516393827,"score_gpt":0.26936004475072534,"score_spread":0.25309449311134263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389510032","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":[0.02571477,0.00012762421,0.9711569,0.0015730668,0.0002699512,0.00023284045,7.112478e-7,0.00073859433,0.00018555939],"genre_scores_gemma":[0.5631099,0.00012507706,0.43474832,0.0009496689,0.0007139862,0.000054435983,0.000028971854,0.000026664457,0.00024300392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871844,0.000026763171,0.00016471677,0.00050346606,0.0002528371,0.0003338041],"domain_scores_gemma":[0.9990886,0.00016160354,0.0000614828,0.00050300115,0.00007060957,0.00011474461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001211567,0.00014404775,0.00013860493,0.00006769463,0.0002725811,0.00013407739,0.0004737902,0.000045207384,0.000004514246],"category_scores_gemma":[0.0000012455214,0.00011951491,0.000025824478,0.0007747924,0.00007271482,0.00043185166,0.0006025773,0.000119515375,0.00012650462],"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":[0.00012049246,0.00010295311,0.004711855,0.000027411046,0.00005302735,0.00012358767,0.00038551135,0.5745026,0.0002754311,0.13554849,0.12102177,0.16312687],"study_design_scores_gemma":[0.00046982747,0.00020101356,0.02422409,0.00003662348,0.0000039248616,0.00006806713,0.0000016855091,0.94394535,0.000038262944,0.008864273,0.021939341,0.00020756076],"about_ca_topic_score_codex":0.0000018643175,"about_ca_topic_score_gemma":0.000008341304,"teacher_disagreement_score":0.5373951,"about_ca_system_score_codex":0.000019360092,"about_ca_system_score_gemma":0.000021343496,"threshold_uncertainty_score":0.48736766},"labels":[],"label_agreement":null},{"id":"W4389542671","doi":"10.1109/embc40787.2023.10340968","title":"Domain-Adversarial Transformer Network for Multiphase Liver Tumor Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Image segmentation; Feature (linguistics); Feature extraction; Computer vision; Adversarial system; Transformer; Engineering","score_opus":0.025707166717819845,"score_gpt":0.287630665403569,"score_spread":0.26192349868574916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389542671","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":[0.0035808303,0.000014884502,0.99309164,0.0010002422,0.00037184384,0.0008812398,0.0000079472575,0.0005708167,0.00048053227],"genre_scores_gemma":[0.028042283,0.000024463367,0.9685365,0.001176893,0.0005083338,0.00055835815,0.000052617357,0.000019104546,0.0010814319],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895203,0.000018849907,0.00018658415,0.00033842414,0.0001451495,0.00035893585],"domain_scores_gemma":[0.9993103,0.00023889582,0.000049646347,0.00027605263,0.000044019118,0.00008104232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014243126,0.00011159657,0.00009971068,0.00005011686,0.00025168882,0.000048936192,0.0003647205,0.000027995291,0.000021284332],"category_scores_gemma":[0.00000551916,0.000102393176,0.000076518045,0.0007204908,0.000026562586,0.0004274049,0.000039728922,0.000055080556,0.00018803481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012035649,0.000121161676,0.0001323708,0.000032847835,0.000048405345,0.000024632009,0.0013488892,0.1401732,0.01616388,0.5765123,0.09799291,0.16732909],"study_design_scores_gemma":[0.00295929,0.0001643297,0.00046215256,0.000011379441,0.000017028216,0.000012636038,0.00010259111,0.756024,0.008689902,0.06230945,0.16877757,0.00046964062],"about_ca_topic_score_codex":0.0000073342903,"about_ca_topic_score_gemma":0.000026577505,"teacher_disagreement_score":0.6158508,"about_ca_system_score_codex":0.00003299756,"about_ca_system_score_gemma":0.000023097962,"threshold_uncertainty_score":0.41754726},"labels":[],"label_agreement":null},{"id":"W4389569092","doi":"10.21203/rs.3.rs-3692500/v1","title":"Meta Generative Image and Text Data Augmentation Optimization","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Hokkaido University; Canadian Institute for Advanced Research","keywords":"Generative grammar; Image (mathematics); Computer science; Generative model; Artificial intelligence; Natural language processing; Pattern recognition (psychology)","score_opus":0.3406723437218783,"score_gpt":0.4736635514287049,"score_spread":0.1329912077068266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389569092","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":[0.00011732859,0.00089336606,0.9903218,0.0064982655,0.00011966574,0.0012940569,0.00023643763,0.00029235758,0.0002266797],"genre_scores_gemma":[0.009564694,0.004485095,0.97974235,0.00009866234,0.00035830587,0.0013402785,0.002697566,0.00006912951,0.0016438917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966274,0.0004945128,0.00027615888,0.0013659131,0.00083434803,0.00040170533],"domain_scores_gemma":[0.996047,0.00062911445,0.00012315565,0.0025892302,0.00046658434,0.00014487583],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0012157267,0.00020420064,0.00026009025,0.00030929962,0.00040527285,0.00067100796,0.0022180711,0.00012799804,0.000029246625],"category_scores_gemma":[0.0002204239,0.00018897558,0.000047848873,0.0009262981,0.0001552782,0.00083756977,0.010607331,0.000779957,0.000092991875],"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":[0.000024395857,0.00018822281,0.000085671934,0.0006614057,0.0010264561,0.00007203868,0.0016080869,0.8480322,0.0014224271,0.04263084,0.0552586,0.048989654],"study_design_scores_gemma":[0.00011937552,0.000031921645,0.00022845528,0.000046471585,0.000049499096,0.0000024836615,0.000052415737,0.9781743,0.00034327453,0.019688152,0.0010700179,0.00019360686],"about_ca_topic_score_codex":0.00006986018,"about_ca_topic_score_gemma":0.000042300395,"teacher_disagreement_score":0.13014212,"about_ca_system_score_codex":0.00010768403,"about_ca_system_score_gemma":0.00017865382,"threshold_uncertainty_score":0.9973947},"labels":[],"label_agreement":null},{"id":"W4389600472","doi":"10.48550/arxiv.2312.05247","title":"Dynamic LiDAR Re-simulation using Compositional Neural Fields","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Azrieli Foundation","keywords":"Lidar; Computer science; Bounding overwatch; Field (mathematics); Artificial intelligence; Computer vision; High fidelity; Fidelity; Key (lock); Viewpoints; Ranging; Artificial neural network; Object (grammar); Computer graphics (images); Remote sensing; Geology; Engineering; Acoustics","score_opus":0.13260625101734685,"score_gpt":0.25153114733755405,"score_spread":0.1189248963202072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389600472","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":[0.23210776,0.000014624576,0.7663423,0.00026549472,0.00044305634,0.00024420465,0.000016646118,0.0004536742,0.00011226904],"genre_scores_gemma":[0.9866283,0.000026287884,0.012597329,0.00013406943,0.00007214255,0.0000015113715,0.000053473934,0.000023563252,0.00046330428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983268,0.00008528454,0.0002057012,0.0009739485,0.000109352266,0.0002989242],"domain_scores_gemma":[0.998267,0.00024418422,0.00023489914,0.0010096576,0.0001305984,0.00011368936],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000815369,0.00024863478,0.0002186516,0.00021419933,0.0002844037,0.00009459677,0.0012450524,0.00023127443,0.000011779162],"category_scores_gemma":[0.000012341441,0.0003208795,0.000159361,0.0007575053,0.00007365797,0.00039865504,0.001662471,0.0005656358,0.000075201766],"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":[0.000007910914,0.000022668519,0.0002490506,0.000014910971,0.000018957753,0.000069639085,0.000040999043,0.9818556,0.00004758941,0.017432416,0.00003654405,0.00020375528],"study_design_scores_gemma":[0.0001467434,0.00001547682,0.0012823695,0.00003874012,0.000025914627,0.000003253801,0.000008796518,0.9340677,0.0000151308495,0.0640508,0.00006165991,0.00028339963],"about_ca_topic_score_codex":0.000036146306,"about_ca_topic_score_gemma":0.000039213643,"teacher_disagreement_score":0.75452054,"about_ca_system_score_codex":0.0002483783,"about_ca_system_score_gemma":0.000079294456,"threshold_uncertainty_score":0.9999243},"labels":[],"label_agreement":null},{"id":"W4389665500","doi":"10.1109/iros55552.2023.10341610","title":"Pseudo-Stereo++: Cycled Generative Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Monocular; Computer vision; Computer science; Object detection; Benchmark (surveying); Stereopsis; Object (grammar); Detector; Stereo cameras; Feature (linguistics); Pattern recognition (psychology); Geology","score_opus":0.023288657351010923,"score_gpt":0.28154394795109866,"score_spread":0.25825529060008773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389665500","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":[0.16839105,0.000015604137,0.8282924,0.000751986,0.00027399955,0.0009456375,0.0000026113191,0.00082324323,0.000503415],"genre_scores_gemma":[0.7481422,0.000023022209,0.24932216,0.00033790176,0.0001539243,0.00090406695,0.00000983284,0.000035362922,0.0010715269],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787986,0.000086030814,0.0004371102,0.00081973756,0.00019448274,0.0005827703],"domain_scores_gemma":[0.9985941,0.0003236216,0.00013886468,0.0007516659,0.0000804414,0.00011129922],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028702774,0.00025391515,0.0002689318,0.00031507266,0.00029051627,0.00015673252,0.00069888827,0.000103875536,0.000008650309],"category_scores_gemma":[0.00005537612,0.00025079146,0.000112215246,0.0015498055,0.00003664285,0.0006879813,0.00033368453,0.00018832291,0.00014178413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005754148,0.00022214971,0.0015711436,0.000063398824,0.00009674567,0.00007162499,0.00253359,0.12365092,0.18063894,0.027046014,0.0013494483,0.6626985],"study_design_scores_gemma":[0.0005056452,0.00008551302,0.0031551814,0.000014496439,0.0000060000357,0.00001808937,0.000040857347,0.95403534,0.032406047,0.008251763,0.0011642437,0.00031680852],"about_ca_topic_score_codex":0.000028887122,"about_ca_topic_score_gemma":0.00040496126,"teacher_disagreement_score":0.83038443,"about_ca_system_score_codex":0.00017849942,"about_ca_system_score_gemma":0.000052489453,"threshold_uncertainty_score":0.99999446},"labels":[],"label_agreement":null},{"id":"W4389668083","doi":"10.1109/iros55552.2023.10341654","title":"Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Waterloo; University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Machine learning; Deep learning; Robot; Supervised learning; Inference; Software deployment; Artificial neural network","score_opus":0.038572154515704116,"score_gpt":0.31777447773185824,"score_spread":0.2792023232161541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389668083","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":[0.506997,0.0000039130937,0.4925754,0.000028787714,0.000031476855,0.000091065805,1.2541301e-7,0.00012325932,0.00014895889],"genre_scores_gemma":[0.9776971,0.000006828542,0.021828022,0.000035496098,0.000037181355,0.000010923387,0.000005712125,0.00001283745,0.00036592517],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988411,0.00005554959,0.00023254572,0.00034376755,0.00027211424,0.0002549274],"domain_scores_gemma":[0.9995034,0.0000688373,0.00009129054,0.0002695572,0.000014196752,0.000052766263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016397827,0.000106369625,0.00011966465,0.00011651363,0.0002766861,0.00002622949,0.00033209368,0.00003874397,0.000037756447],"category_scores_gemma":[0.000010202058,0.000106486994,0.00005118842,0.0006384103,0.00004130241,0.0001135962,0.00035142395,0.0001207725,0.0001092574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.370502e-7,0.000022243905,0.00043248647,0.0000033021606,0.000002458527,8.543697e-7,0.00028367655,0.46592668,0.5256422,0.00047998803,0.0000063212733,0.0071992544],"study_design_scores_gemma":[0.00013550604,0.00003717804,0.008789023,0.000014667696,0.0000038113212,0.000002355025,0.00017601566,0.9831961,0.0073049944,0.00015959583,0.0000648749,0.000115903895],"about_ca_topic_score_codex":0.00000853995,"about_ca_topic_score_gemma":5.553756e-7,"teacher_disagreement_score":0.5183372,"about_ca_system_score_codex":0.00006579286,"about_ca_system_score_gemma":0.000007741254,"threshold_uncertainty_score":0.43424135},"labels":[],"label_agreement":null},{"id":"W4389937182","doi":"10.1016/j.jvcir.2023.104028","title":"BNDCNet: Bilateral nonlocal decoupled convergence network for semantic segmentation","year":2023,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Convergence (economics); Segmentation; Computer science; Artificial intelligence; Computer vision; Economics","score_opus":0.03614113776747117,"score_gpt":0.3804621385552828,"score_spread":0.34432100078781164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389937182","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":[0.093373775,0.00022676203,0.9032782,0.0024621012,0.0001811336,0.00036836928,0.000001565469,0.000068876965,0.00003924815],"genre_scores_gemma":[0.78558487,0.0022212283,0.21158122,0.00022863553,0.00016401065,0.00005156059,0.000051862276,0.000015548001,0.000101100086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872035,0.00015956665,0.0005356171,0.0001742845,0.00022853322,0.00018162817],"domain_scores_gemma":[0.99811083,0.00057269365,0.0005327539,0.000320447,0.00037946118,0.00008380292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006197116,0.00010306816,0.00017613832,0.00014255488,0.00027976854,0.00015268206,0.00043196313,0.00004039028,0.00000582648],"category_scores_gemma":[0.00011484693,0.00009708507,0.00007454542,0.0007416816,0.000071719776,0.001231122,0.00017038459,0.00013660682,0.000012617161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000616286,0.0006832453,0.0137571795,0.00026895784,0.00035432124,0.000027719923,0.005510377,0.037812013,0.36176774,0.03240205,0.052314375,0.49448574],"study_design_scores_gemma":[0.000998004,0.00022998209,0.012625384,0.000056282825,0.00002720225,0.00006179965,0.000251702,0.9682257,0.006421657,0.0099857,0.0009558195,0.00016072515],"about_ca_topic_score_codex":0.0000061818328,"about_ca_topic_score_gemma":0.000005718231,"teacher_disagreement_score":0.9304137,"about_ca_system_score_codex":0.000034006316,"about_ca_system_score_gemma":0.000036927602,"threshold_uncertainty_score":0.3959014},"labels":[],"label_agreement":null},{"id":"W4390097914","doi":"10.1109/icedcs60513.2023.00060","title":"Research on Image Classification Algorithm Based on Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Residual neural network; Computer science; Convolutional neural network; Set (abstract data type); Artificial neural network; Network structure; Artificial intelligence; Test set; Algorithm; Machine learning; Data mining; Pattern recognition (psychology)","score_opus":0.11503557849069942,"score_gpt":0.3856498712885314,"score_spread":0.270614292797832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390097914","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":[0.00068237865,0.000012633451,0.9734473,0.013148064,0.00038342542,0.00043277856,0.0000063027837,0.00096795,0.010919144],"genre_scores_gemma":[0.6644617,0.000047651258,0.32545117,0.0034996364,0.0016457788,0.00083202875,0.00018366557,0.000059551698,0.0038188496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770594,0.00019972837,0.00019580907,0.0006089561,0.0007283595,0.00056119985],"domain_scores_gemma":[0.9975988,0.0011674658,0.000048661645,0.00086211384,0.00019593608,0.0001269886],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006743608,0.0001237809,0.00009551105,0.00023587269,0.0005053822,0.00011270598,0.0008273352,0.000050716008,0.00003275799],"category_scores_gemma":[0.000040254636,0.000112247995,0.000050322305,0.0030576393,0.00012108905,0.00023649479,0.00016850217,0.00042724586,0.0023388625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013236505,0.00009218906,0.00005637386,0.000003151965,0.000002651396,0.00000803671,0.000009887333,0.1527097,0.0003670597,0.5136348,0.16033834,0.1727646],"study_design_scores_gemma":[0.00016191548,0.000088156434,0.012295786,0.000009296398,6.2183886e-7,0.0000010072113,0.0000063822936,0.95701146,0.00012802171,0.016270015,0.013914452,0.00011289016],"about_ca_topic_score_codex":0.0000027610297,"about_ca_topic_score_gemma":0.0000017055329,"teacher_disagreement_score":0.8043018,"about_ca_system_score_codex":0.00010377222,"about_ca_system_score_gemma":0.00007109273,"threshold_uncertainty_score":0.99843794},"labels":[],"label_agreement":null},{"id":"W4390109971","doi":"10.3390/ecsa-10-16202","title":"Evaluating Compact Convolutional Neural Networks for Object Recognition Using Sensor Data on Resource-Constrained Devices","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Inference; Resource (disambiguation); Edge device; Deep learning; Key (lock); Cognitive neuroscience of visual object recognition; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Computer architecture; Object (grammar); Edge computing; Computer engineering; Machine learning; Computer network; Cloud computing; Operating system","score_opus":0.2728378311770661,"score_gpt":0.40851649379624677,"score_spread":0.13567866261918066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390109971","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":[0.06288909,0.00002396078,0.9327294,0.0017767322,0.0002459477,0.0009117799,0.00011214012,0.0008429033,0.00046802787],"genre_scores_gemma":[0.742292,0.000004043624,0.2544713,0.0013778366,0.0005838155,0.000047771802,0.0010830415,0.000031277978,0.000108923574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980838,0.00012469603,0.00032820803,0.0007144354,0.00029644763,0.00045243444],"domain_scores_gemma":[0.9970697,0.001698803,0.00017410114,0.0008393045,0.00011994563,0.000098117576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006801975,0.00017650696,0.00017443593,0.00012176695,0.00050071493,0.00013472216,0.00087018724,0.000059618655,0.000014833263],"category_scores_gemma":[0.00019416702,0.000166736,0.000058504727,0.00088119606,0.000067714696,0.0005274598,0.0002803134,0.00016229527,0.000036962],"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":[0.00004885316,0.00004011394,0.00016651742,0.000014474313,0.000031866537,0.00000379736,0.000047347738,0.862905,0.0012221673,0.0030508994,0.0044493326,0.12801959],"study_design_scores_gemma":[0.0003994297,0.00008392219,0.0007414322,0.000028321214,0.00001446822,0.00002221866,0.000039570168,0.99608773,0.00009435439,0.0016423524,0.00064224907,0.0002039526],"about_ca_topic_score_codex":0.000009601813,"about_ca_topic_score_gemma":0.00001294014,"teacher_disagreement_score":0.6794029,"about_ca_system_score_codex":0.000055149343,"about_ca_system_score_gemma":0.000052862797,"threshold_uncertainty_score":0.67992973},"labels":[],"label_agreement":null},{"id":"W4390423664","doi":"10.1109/jstars.2023.3348269","title":"A General Multiscale Pyramid Attention Module for Ship Detection in SAR Images","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"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":"Chinese Academy of Surveying and Mapping; Jiangsu Association for Science and Technology; Ministry of Industry and Information Technology of the People's Republic of China; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China; Ministry of Education, Libya; Fundamental Research Funds for the Central Universities; Nanjing University; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; Ministry of Natural Resources","keywords":"Computer science; Pyramid (geometry); Fuse (electrical); Feature (linguistics); Feature extraction; Artificial intelligence; Channel (broadcasting); Scale (ratio); Pixel; Synthetic aperture radar; Computer vision; Pattern recognition (psychology); Image resolution; Representation (politics); Remote sensing; Telecommunications; Engineering; Geology; Geography; Cartography","score_opus":0.03394374629079105,"score_gpt":0.2665574653688898,"score_spread":0.23261371907809875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390423664","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":[0.4944376,0.000019943722,0.50457025,0.0006095292,0.00013583167,0.00017936455,9.758284e-7,0.00003143688,0.000015047595],"genre_scores_gemma":[0.61936986,0.00012435623,0.38012716,0.000064550375,0.00022030866,5.5906287e-7,0.0000033204785,0.000010269916,0.00007960897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998944,0.00003228988,0.00043030723,0.00020205324,0.0001623876,0.00022899982],"domain_scores_gemma":[0.9992628,0.000117622614,0.00020251538,0.00014585463,0.00022498214,0.00004624284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030691596,0.00010177333,0.00017428867,0.00031788694,0.00014311928,0.000064418025,0.00012421765,0.00007693479,1.3686362e-7],"category_scores_gemma":[0.00005857088,0.00010211757,0.000036148,0.0015239096,0.000029062687,0.00021464814,0.000027632412,0.00025575992,6.225348e-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":[0.000021632282,0.000024354282,0.00035172314,0.000025051151,0.000010890994,0.000009800303,0.00017434437,0.04675331,0.29699743,0.0009782405,0.00004936191,0.6546039],"study_design_scores_gemma":[0.00062738283,0.000036405683,0.09245221,0.000047459558,0.00000527578,0.000032468768,0.00001699543,0.8768762,0.017095495,0.012171935,0.00052159425,0.0001165921],"about_ca_topic_score_codex":0.0000201904,"about_ca_topic_score_gemma":0.00025023968,"teacher_disagreement_score":0.8301229,"about_ca_system_score_codex":0.00005419243,"about_ca_system_score_gemma":0.00004275304,"threshold_uncertainty_score":0.41642335},"labels":[],"label_agreement":null},{"id":"W4390442854","doi":"10.1155/2023/5384844","title":"Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centre Scientifique et Technique du Bâtiment; Chongqing Municipal Education Commission; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision; Object detection; False positive paradox; Segmentation; Aerial image; Feature (linguistics); Tracking (education); Scale (ratio); Block (permutation group theory); Detector; Minimum bounding box; Vehicle tracking system; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.018298242105340784,"score_gpt":0.268423504663936,"score_spread":0.25012526255859524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390442854","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":[0.85351646,0.00017647196,0.14551544,0.00031875016,0.00024372956,0.00015625927,0.000002709849,0.00006858152,0.0000016156996],"genre_scores_gemma":[0.97291565,0.0008081216,0.02614253,0.000027714486,0.000071449904,0.0000113806045,0.0000027357266,0.000017196036,0.0000032406667],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986543,0.00004560732,0.0006948433,0.00020389009,0.00021187727,0.00018944955],"domain_scores_gemma":[0.99890256,0.00015918267,0.0005530472,0.00015364998,0.00016564252,0.00006590978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027295365,0.000117906486,0.00026518424,0.00026361484,0.000067505185,0.000026710826,0.0002295382,0.000051186667,8.732164e-7],"category_scores_gemma":[0.000024026498,0.00011610824,0.00006452122,0.0010307551,0.000041946598,0.0011602341,0.000009187859,0.00018962551,0.000002047487],"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":[0.00019123263,0.000079544465,0.0031715909,0.00009218096,0.0000167796,0.000042119435,0.0013028966,0.33219552,0.47794577,0.0008187163,0.000003087638,0.18414058],"study_design_scores_gemma":[0.0018232652,0.00022126769,0.9156923,0.00020173095,0.000024366072,0.000014725239,0.00017062787,0.008570892,0.063773386,0.009219239,0.00009819591,0.00018995175],"about_ca_topic_score_codex":0.000013189667,"about_ca_topic_score_gemma":0.0003661521,"teacher_disagreement_score":0.91252077,"about_ca_system_score_codex":0.000033651184,"about_ca_system_score_gemma":0.00003255274,"threshold_uncertainty_score":0.47347566},"labels":[],"label_agreement":null},{"id":"W4390481005","doi":"10.1109/jsac.2023.3345385","title":"MLOps in the Metaverse: Human-Centric Continuous Integration","year":2024,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Metaverse; Human–computer interaction; Artificial intelligence; Virtual machine; Field (mathematics); Virtual reality; Machine learning; Programming language","score_opus":0.044867566702838375,"score_gpt":0.3355270953609135,"score_spread":0.29065952865807515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390481005","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":[0.10014844,0.007968446,0.7934928,0.073349774,0.0015945807,0.0023649875,0.000017844775,0.0009176281,0.020145496],"genre_scores_gemma":[0.987745,0.0011563681,0.010210167,0.0005611085,0.000094616495,0.0001091261,0.0000116528245,0.000013919781,0.00009808094],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979605,0.0006802575,0.000525263,0.00024497864,0.00030634194,0.00028267512],"domain_scores_gemma":[0.9969342,0.0012384523,0.00014036064,0.0014527561,0.00017569312,0.000058554204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073480303,0.00015651752,0.00017032494,0.00053308124,0.00043048605,0.0004519529,0.003076409,0.0000665306,0.00000959124],"category_scores_gemma":[0.00013781061,0.00011578505,0.000069312715,0.0043010213,0.00007421995,0.0005696727,0.00011590001,0.0015883144,0.00006473171],"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":[0.00001747815,0.0012320669,0.0008791429,0.00001137566,0.00007497347,0.00014623576,0.0060435454,0.0075989463,0.0061450396,0.80767304,0.01613739,0.1540408],"study_design_scores_gemma":[0.0023121182,0.0006309625,0.0411096,0.0014093292,0.000109843044,0.0025635532,0.0011127596,0.46313316,0.0027746975,0.2206279,0.26273438,0.001481687],"about_ca_topic_score_codex":0.000016750373,"about_ca_topic_score_gemma":0.0003116889,"teacher_disagreement_score":0.88759655,"about_ca_system_score_codex":0.0002695317,"about_ca_system_score_gemma":0.00012967938,"threshold_uncertainty_score":0.6900524},"labels":[],"label_agreement":null},{"id":"W4390483110","doi":"10.1109/ssci52147.2023.10371958","title":"Training Artificial Neural Networks by Coordinate Search Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"","keywords":"Artificial neural network; Computer science; Gradient descent; Convergence (economics); Differentiable function; Stochastic gradient descent; Dimension (graph theory); Artificial intelligence; Mathematical optimization; Gradient method; Maxima and minima; Optimization problem; Algorithm; Variable (mathematics); Machine learning; Mathematics","score_opus":0.060387268992035205,"score_gpt":0.31260921120866614,"score_spread":0.2522219422166309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390483110","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":[0.002129279,0.000031095504,0.99173945,0.0033393034,0.00027483937,0.00018651583,0.0000034862787,0.0011862927,0.0011097197],"genre_scores_gemma":[0.91024595,0.00003582865,0.08400421,0.0012710564,0.00047817576,0.00012432726,0.000053549782,0.000039681367,0.0037472064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845004,0.00005050731,0.00020779172,0.00046183364,0.00023435312,0.000595497],"domain_scores_gemma":[0.99913126,0.00020071836,0.000035225592,0.0004405514,0.0000468107,0.00014545346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022773308,0.00013095421,0.00013114484,0.00007976293,0.00028074006,0.00014033903,0.0007731769,0.000052740328,0.000020053687],"category_scores_gemma":[0.000009252532,0.00012490462,0.00005035676,0.0017783712,0.00005828179,0.00034951238,0.00032828475,0.00023669994,0.00019547547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.768791e-7,0.000010114584,0.000007681862,6.6198544e-7,0.000003854447,0.000009982015,0.000112305526,0.048422445,0.00034717712,0.016064823,0.011464337,0.92355573],"study_design_scores_gemma":[0.00006605366,0.000024383131,0.00006555753,0.0000015942759,0.000001222198,0.000008077005,0.00004517748,0.9903293,0.0005557151,0.0036971914,0.0050588194,0.00014685668],"about_ca_topic_score_codex":0.000009260052,"about_ca_topic_score_gemma":0.0000034018965,"teacher_disagreement_score":0.9419069,"about_ca_system_score_codex":0.000021249141,"about_ca_system_score_gemma":0.000016890208,"threshold_uncertainty_score":0.50934625},"labels":[],"label_agreement":null},{"id":"W4390591099","doi":"10.1109/lra.2024.3349812","title":"Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Segmentation; Semantics (computer science); Scheme (mathematics); Computation; Artificial intelligence; Image resolution; Pixel; Image segmentation; Computer vision; Computer engineering; Algorithm","score_opus":0.014213060752968172,"score_gpt":0.2581731518465156,"score_spread":0.24396009109354747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390591099","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":[0.011162857,0.000074602525,0.9689734,0.018223576,0.0006447243,0.00043517686,0.000008100189,0.00045211887,0.000025449974],"genre_scores_gemma":[0.71503603,0.000030182624,0.28320464,0.001334446,0.00018898393,0.00009405199,0.00003512776,0.000015943238,0.00006058899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990595,0.000027186949,0.0002361111,0.0003403506,0.00014150115,0.00019533964],"domain_scores_gemma":[0.9994235,0.00021619492,0.0000711304,0.00020131521,0.000037013444,0.000050829134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001417067,0.000121912,0.00010388763,0.00011859652,0.00018005283,0.0002712243,0.00014466935,0.000037179496,0.0000016181815],"category_scores_gemma":[0.000012388897,0.00012139837,0.000045888315,0.0002965763,0.000053643515,0.0006264102,0.000015029248,0.00007589911,0.00001536884],"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":[0.000003835024,0.000026005502,0.000007280121,0.00018109789,0.000036788835,0.000009270758,0.00043584348,0.4525373,0.408593,0.058761083,0.007874589,0.07153392],"study_design_scores_gemma":[0.00021104851,0.000023022352,0.00020862707,0.000053731434,0.000021441661,0.000024689933,0.000012070021,0.9788913,0.015672999,0.0044693644,0.00024155258,0.00017015323],"about_ca_topic_score_codex":0.0000033064994,"about_ca_topic_score_gemma":0.0000012850345,"teacher_disagreement_score":0.7038732,"about_ca_system_score_codex":0.00004942373,"about_ca_system_score_gemma":0.000021111624,"threshold_uncertainty_score":0.4950482},"labels":[],"label_agreement":null},{"id":"W4390604119","doi":"10.1109/tpami.2024.3350450","title":"Regularized Loss With Hyperparameter Estimation for Weakly Supervised Single Class Segmentation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Hyperparameter; Segmentation; Computer science; Pattern recognition (psychology); Image segmentation; Conditional random field; Ground truth; Pixel; Machine learning; Scale-space segmentation","score_opus":0.023455177214289788,"score_gpt":0.27950683149201555,"score_spread":0.25605165427772575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390604119","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":[0.006474562,0.000069639216,0.9915601,0.0011628364,0.00010838077,0.00037794447,0.000038921113,0.00019479656,0.000012841803],"genre_scores_gemma":[0.90814173,0.00006566041,0.091129735,0.00025137828,0.000015104464,0.00019875869,0.00002333844,0.00001612332,0.00015816523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987033,0.000041972835,0.00029033085,0.00059025874,0.00017928611,0.00019486804],"domain_scores_gemma":[0.9990946,0.00029223936,0.00005846584,0.00040072246,0.00007136037,0.000082595325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011789894,0.00019652069,0.0002182642,0.00035603772,0.0001943795,0.00027056652,0.00024617743,0.00004871559,0.000024623878],"category_scores_gemma":[0.000002225851,0.00015841711,0.00015751686,0.0012556969,0.00006143424,0.00045422767,0.0000032974733,0.00014483194,0.000014568173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022217366,0.00009024397,0.000023918166,0.000029237994,0.000275551,0.0000035649368,0.00016745256,0.18142587,0.0042981612,0.00045206482,0.000006166886,0.81320554],"study_design_scores_gemma":[0.00008517968,0.00016441123,0.00003900818,0.000026899153,0.00034328605,0.000013487446,0.000012108022,0.8514155,0.14635314,0.001304846,0.00007013237,0.0001719947],"about_ca_topic_score_codex":0.0000638443,"about_ca_topic_score_gemma":0.0002839373,"teacher_disagreement_score":0.9016672,"about_ca_system_score_codex":0.000052911888,"about_ca_system_score_gemma":0.000016327975,"threshold_uncertainty_score":0.6460062},"labels":[],"label_agreement":null},{"id":"W4390660866","doi":"10.1016/j.aei.2023.102348","title":"VSL-Net: Voxel structure learning for 3D object detection","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Science and Technology Program of Suzhou; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Voxel; Object detection; Point cloud; Pattern recognition (psychology); Computer vision; Feature extraction; Segmentation; Feature (linguistics); Convolution (computer science); Benchmark (surveying); Artificial neural network; Geography","score_opus":0.004725352463849946,"score_gpt":0.22136998495744842,"score_spread":0.21664463249359847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390660866","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":[0.0027096008,0.0002077575,0.994627,0.000058280326,0.00067044754,0.00031655983,0.00000484778,0.0012871531,0.00011834502],"genre_scores_gemma":[0.3470626,0.00004539083,0.6525208,0.0000745122,0.000092943264,0.00009213494,0.00001256113,0.00002729756,0.0000718278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990547,0.000003839474,0.0003156874,0.0001615822,0.00014430268,0.00031992127],"domain_scores_gemma":[0.99931395,0.00019708813,0.000061732564,0.00030727734,0.00005146079,0.000068484784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006751015,0.00018211098,0.00012942118,0.00014009925,0.00012861883,0.00015376405,0.00032840986,0.00006635314,0.0000013972664],"category_scores_gemma":[0.00005719468,0.00018121694,0.000058802696,0.000564266,0.0000106849575,0.0012974658,0.00008343925,0.00031498057,0.000018858842],"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":[9.629606e-7,0.0000013185621,9.097819e-7,0.00010841869,0.000009128045,4.8148166e-7,0.00038120509,0.7729729,0.0034039258,0.0062260204,0.000022501301,0.21687225],"study_design_scores_gemma":[0.00010475266,0.000045718345,0.000030131156,0.000051539857,0.0000054765096,0.000017116068,0.000013746946,0.8833918,0.0055698273,0.0012483392,0.10933023,0.00019131912],"about_ca_topic_score_codex":3.1122556e-7,"about_ca_topic_score_gemma":0.0000010559838,"teacher_disagreement_score":0.344353,"about_ca_system_score_codex":0.000089782545,"about_ca_system_score_gemma":0.000021017908,"threshold_uncertainty_score":0.7389812},"labels":[],"label_agreement":null},{"id":"W4390872528","doi":"10.1109/iccv51070.2023.01558","title":"A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Accumulator (cryptography); Computer science; Quantization (signal processing); Hydraulic accumulator; Field-programmable gate array; Floating point; Algorithm; Computer engineering; Artificial intelligence; Computer hardware; Engineering","score_opus":0.024778239151044983,"score_gpt":0.27781372464684495,"score_spread":0.25303548549579996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390872528","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":[0.025889639,0.000019975754,0.97025025,0.0012075163,0.00009348372,0.00024229356,0.0000020540774,0.0012374148,0.0010573702],"genre_scores_gemma":[0.9633896,0.0000328269,0.03429631,0.0006656883,0.000057454636,0.000056498888,0.000010835218,0.000017275788,0.0014735046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988887,0.000022361199,0.00015202379,0.00041311086,0.000246254,0.00027755596],"domain_scores_gemma":[0.9990153,0.00011854096,0.000067097346,0.0006671125,0.000070774244,0.00006119189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007881347,0.00012369707,0.00010804028,0.00008192836,0.00018120086,0.00008064193,0.00059697183,0.00003552925,0.000015512727],"category_scores_gemma":[0.000013386962,0.00009968831,0.000026678164,0.0018628738,0.000032392047,0.00050400436,0.00014958548,0.000089916626,0.00054541155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003135835,0.00008768883,0.011454662,0.000034559387,0.00003316931,0.00004132781,0.00047794174,0.20059454,0.005306336,0.7298379,0.024470434,0.027630063],"study_design_scores_gemma":[0.0003483641,0.000052857864,0.01087853,0.000022975695,0.0000032975454,0.00001414637,0.00002406321,0.97076476,0.0037249306,0.00471809,0.009174735,0.00027322923],"about_ca_topic_score_codex":0.000006567715,"about_ca_topic_score_gemma":0.000022829287,"teacher_disagreement_score":0.93749994,"about_ca_system_score_codex":0.000023141834,"about_ca_system_score_gemma":0.000031577856,"threshold_uncertainty_score":0.70103437},"labels":[],"label_agreement":null},{"id":"W4390873268","doi":"10.1109/iccv51070.2023.01655","title":"Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Computer vision; Object (grammar); Pattern recognition (psychology)","score_opus":0.03830298131322912,"score_gpt":0.30330359612677704,"score_spread":0.2650006148135479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390873268","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":[0.04533905,0.000009004575,0.9502927,0.00028668763,0.0005867169,0.00045717056,0.000009090291,0.0021984417,0.000821156],"genre_scores_gemma":[0.8221774,0.000014559287,0.1762915,0.00026418956,0.0003679099,0.0001487654,0.000005456525,0.000025637675,0.00070454314],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984311,0.000047405672,0.00027717193,0.00055383,0.00030185407,0.0003886759],"domain_scores_gemma":[0.998982,0.00009001132,0.0000638315,0.00061244715,0.00010028063,0.00015140754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018511112,0.0001519737,0.00012102569,0.00025451847,0.00035259107,0.00012583958,0.0005209289,0.000058945363,0.000017388395],"category_scores_gemma":[0.00006181117,0.00015089354,0.00005935295,0.002245724,0.000029740266,0.0005800194,0.00037252266,0.00012564434,0.0002901322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014503399,0.000026875503,0.00051326485,0.000018005472,0.000041285253,0.00002401653,0.00024805596,0.293547,0.5332343,0.0013050439,0.0069013643,0.16412629],"study_design_scores_gemma":[0.00021773943,0.00008961251,0.0031982167,0.000013392132,0.000012165011,0.000070826994,0.000011676601,0.81431305,0.17530413,0.0030315674,0.0034995761,0.00023801991],"about_ca_topic_score_codex":0.00005253858,"about_ca_topic_score_gemma":0.00003073527,"teacher_disagreement_score":0.77683836,"about_ca_system_score_codex":0.00011311409,"about_ca_system_score_gemma":0.000045413646,"threshold_uncertainty_score":0.615326},"labels":[],"label_agreement":null},{"id":"W4390905761","doi":"10.1109/icaicta59291.2023.10390158","title":"Enhancing Communication Efficiency in Adam Optimizer for Distributed Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Computer science; Overhead (engineering); Reduction (mathematics); Artificial intelligence; Distributed computing; Communications system; Models of communication; Computer network; Mathematics","score_opus":0.02050733473298952,"score_gpt":0.28922745435325886,"score_spread":0.2687201196202693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390905761","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":[0.0058296756,0.00005774611,0.9915667,0.0013114393,0.00003350916,0.0003285619,9.196485e-7,0.00052967237,0.0003417806],"genre_scores_gemma":[0.7710211,0.0000809847,0.22808293,0.00009911217,0.000014558782,0.00029018548,0.00006241827,0.000009878611,0.0003387936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999127,0.000044243443,0.00020876866,0.00025744506,0.00009614688,0.00026643937],"domain_scores_gemma":[0.99880534,0.0005758476,0.00006294161,0.0004660678,0.000050724604,0.00003905773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028651496,0.00007640117,0.00009512104,0.000088095425,0.00021503953,0.00004858218,0.00066401286,0.00003299599,0.0000045658826],"category_scores_gemma":[0.00012480374,0.00007554199,0.000031342013,0.0013270001,0.000021358865,0.0002580273,0.00026962417,0.00012828242,0.000052549563],"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":[0.0000062602167,0.00005699352,0.0003439656,0.000011596051,0.0000038677003,0.0000013213198,0.0005813373,0.85258526,0.0041173524,0.091213755,0.00044185907,0.05063642],"study_design_scores_gemma":[0.00024572798,0.000020227064,0.0010883843,0.0000111419895,0.0000010961799,0.0000010249739,0.00007699964,0.98842585,0.0024126552,0.004287209,0.0033205973,0.00010908489],"about_ca_topic_score_codex":0.000007754524,"about_ca_topic_score_gemma":0.000059810915,"teacher_disagreement_score":0.76519144,"about_ca_system_score_codex":0.000045765126,"about_ca_system_score_gemma":0.0000136126055,"threshold_uncertainty_score":0.3080513},"labels":[],"label_agreement":null},{"id":"W4390952867","doi":"10.31237/osf.io/b934c","title":"Pedestrian Detection in Aerial Image Based on Convolutional Neural Network with Attention Mechanism and Multi-scale Prediction","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Pedestrian detection; Computer science; Benchmark (surveying); Convolutional neural network; Object detection; Artificial intelligence; Pedestrian; Perspective (graphical); Scale (ratio); Pattern recognition (psychology); Aerial image; Deep learning; Data mining; Computer vision; Machine learning; Image (mathematics); Engineering","score_opus":0.015826222616036313,"score_gpt":0.24790557205766142,"score_spread":0.23207934944162512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390952867","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":[0.049347434,0.00003666224,0.9467709,0.0008258797,0.0012762799,0.0010370862,0.000026062975,0.00052143866,0.00015825476],"genre_scores_gemma":[0.84296095,0.000015945934,0.15576303,0.0001244991,0.0005010398,0.00043589785,0.000069614616,0.000028899947,0.00010012893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978133,0.000115426956,0.00035325994,0.0010705375,0.0003192705,0.00032822342],"domain_scores_gemma":[0.9990541,0.000087975975,0.00015447533,0.0005250763,0.000074180905,0.00010419153],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022220961,0.00031642462,0.0002349157,0.00020297717,0.00016222536,0.00024239943,0.0003112494,0.00022738912,0.000006369368],"category_scores_gemma":[0.000008929329,0.00028707908,0.00006945278,0.0004637834,0.000071500275,0.00021477223,0.0005458729,0.0008053012,0.000015214004],"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":[0.00041411747,0.00038506597,0.0010573909,0.00027347836,0.000050087572,0.000050239152,0.00011177425,0.9290627,0.010153999,0.030352617,0.0003619643,0.027726566],"study_design_scores_gemma":[0.0007467373,0.00015930076,0.0066622808,0.0001290132,0.00002023663,0.000018645098,0.0000047319195,0.9679036,0.0003725104,0.023703262,0.000031133786,0.00024856234],"about_ca_topic_score_codex":0.00006650615,"about_ca_topic_score_gemma":0.0005809188,"teacher_disagreement_score":0.7936135,"about_ca_system_score_codex":0.00019086325,"about_ca_system_score_gemma":0.00009019488,"threshold_uncertainty_score":0.99995816},"labels":[],"label_agreement":null},{"id":"W4390992101","doi":"10.1109/bibm58861.2023.10385820","title":"Morphological Guided Causal Constraint Network for Medical Image Multi-Object Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Segmentation; Computer science; Context (archaeology); Pattern recognition (psychology); Feature (linguistics); Image segmentation; Representation (politics); Constraint (computer-aided design); Decorrelation; Similarity (geometry); Object (grammar); Scale-space segmentation; Dice; Computer vision; Boundary (topology); Medical imaging; Sample (material); Image (mathematics); Mathematics","score_opus":0.054632005635478376,"score_gpt":0.35302471004679997,"score_spread":0.2983927044113216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390992101","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":[0.0033452413,0.0000106659945,0.9893769,0.004896531,0.00027693677,0.0006493128,0.000006985408,0.000974622,0.00046281423],"genre_scores_gemma":[0.056705862,0.000032587144,0.93958557,0.0021670815,0.00028481949,0.0004979334,0.00007507153,0.000015059485,0.00063601776],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838966,0.00005490965,0.00032530815,0.00047237144,0.00030390802,0.00045384394],"domain_scores_gemma":[0.9987335,0.0005714749,0.00007325827,0.00036334142,0.000080780395,0.00017764213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004684022,0.00013475583,0.00015477512,0.0000454376,0.00020896872,0.00006392316,0.00061588123,0.00009161295,0.000102816346],"category_scores_gemma":[0.00018819507,0.000111168345,0.00007295835,0.0007049447,0.00013038763,0.0002080068,0.0002694882,0.00012467934,0.00024108806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002306818,0.00022338184,0.00073274877,0.000026625248,0.000060514973,0.00029055122,0.00023560543,0.020374605,0.022648081,0.32163006,0.5044435,0.12931126],"study_design_scores_gemma":[0.0010645448,0.000087011445,0.0018641335,0.000010380868,0.0000064866654,0.00011834189,0.000034122964,0.97444177,0.0023733615,0.015483873,0.0042455127,0.00027049278],"about_ca_topic_score_codex":0.0000060081397,"about_ca_topic_score_gemma":0.000016373564,"teacher_disagreement_score":0.9540671,"about_ca_system_score_codex":0.00003661189,"about_ca_system_score_gemma":0.000065097345,"threshold_uncertainty_score":0.45333135},"labels":[],"label_agreement":null},{"id":"W4391093474","doi":"10.1109/bigdata59044.2023.10386451","title":"Lightweight CNN based on Spatial Features for a Vehicular Damage Detection System","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Silesian University of Technology","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Regularization (linguistics); Automotive industry; Key (lock); Transfer of learning; Dropout (neural networks); Architecture; Computer vision; Machine learning; Pattern recognition (psychology); Engineering; Computer security","score_opus":0.012555192694658485,"score_gpt":0.24752442446147763,"score_spread":0.23496923176681914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391093474","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":[0.0031697738,0.000004808909,0.9921016,0.0013543874,0.0003052965,0.0005698826,0.0000033499714,0.0012830065,0.0012078992],"genre_scores_gemma":[0.9683499,0.0000010108494,0.029928977,0.0003353194,0.00019966079,0.00041900945,0.000012325154,0.000015079017,0.00073876104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903876,0.000030811818,0.00012829278,0.00038454685,0.00018714934,0.0002304284],"domain_scores_gemma":[0.9990553,0.00022337821,0.00005300263,0.00055054366,0.000053490832,0.0000642291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114028626,0.00011258961,0.00010138337,0.00012001331,0.00024116556,0.00007193136,0.0004030697,0.000056421657,0.00000219226],"category_scores_gemma":[0.000018350773,0.000092452115,0.00007087209,0.00066566485,0.000012314919,0.00012877263,0.00006523445,0.000082027174,0.00013784805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013229143,0.00017922261,0.000095806536,0.0002624015,0.000045501125,0.00006547226,0.00018861658,0.3458154,0.061917055,0.36675468,0.025069905,0.19947368],"study_design_scores_gemma":[0.00024199193,0.000092233895,0.0010089775,0.000015941332,0.0000039015363,0.000002105935,0.0000054294346,0.935124,0.054450266,0.00071299245,0.008223807,0.00011834631],"about_ca_topic_score_codex":0.000010549725,"about_ca_topic_score_gemma":0.000033759366,"teacher_disagreement_score":0.9651801,"about_ca_system_score_codex":0.00005672388,"about_ca_system_score_gemma":0.000016643233,"threshold_uncertainty_score":0.3770088},"labels":[],"label_agreement":null},{"id":"W4391095384","doi":"10.1088/1402-4896/ad2147","title":"A novel small object detection algorithm for UAVs based on YOLOv5","year":2024,"lang":"en","type":"article","venue":"Physica Scripta","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Feature (linguistics); Computer vision; Convolution (computer science); Object (grammar); Object-class detection; Viola–Jones object detection framework; Pattern recognition (psychology); Algorithm; Face detection; Artificial neural network","score_opus":0.03184214656329765,"score_gpt":0.27026887557825,"score_spread":0.23842672901495238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391095384","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":[0.00011690454,0.00006036496,0.995904,0.0014081766,0.0009869868,0.00044070603,0.000020925694,0.0005949537,0.00046697079],"genre_scores_gemma":[0.4830375,0.000005614981,0.51303524,0.0013929792,0.00078891113,0.0007797406,0.000014395935,0.000046148227,0.00089950574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988613,0.000015029026,0.0001317732,0.0005718429,0.00014999766,0.00027003622],"domain_scores_gemma":[0.9991484,0.00013451825,0.000038015358,0.00056076364,0.000051870335,0.000066429566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059368544,0.00015424358,0.00012122674,0.00008683487,0.00015092589,0.00015321159,0.00047941887,0.00003443514,0.0000015016005],"category_scores_gemma":[0.000014007339,0.0001445867,0.00012192016,0.0006474299,0.000027787844,0.00024483787,0.00007010543,0.00014888942,0.00008403492],"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":[0.000005635939,0.00012707768,2.3245083e-7,0.000018854702,0.000012632887,0.0000017400082,0.000064455475,0.0013031497,0.04957534,0.018578915,0.0028615717,0.9274504],"study_design_scores_gemma":[0.00016925996,0.00014087364,0.000035320234,0.000025509025,0.000009166003,0.0000027265507,0.000002498564,0.9150196,0.020705434,0.006844821,0.056891553,0.0001532824],"about_ca_topic_score_codex":0.000009868415,"about_ca_topic_score_gemma":0.0000065575728,"teacher_disagreement_score":0.9272971,"about_ca_system_score_codex":0.00006837319,"about_ca_system_score_gemma":0.000042267122,"threshold_uncertainty_score":0.5896074},"labels":[],"label_agreement":null},{"id":"W4391170797","doi":"10.20944/preprints202401.1672.v1","title":"Pedestrian Detection in Aerial Image Based on Convolutional Neural Network with Attention Mechanism and Multi-scale Prediction","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Convolutional neural network; Aerial image; Computer science; Artificial intelligence; Pedestrian detection; Pedestrian; Scale (ratio); Mechanism (biology); Aerial imagery; Artificial neural network; Image (mathematics); Pattern recognition (psychology); Computer vision; Geography; Cartography; Engineering; Transport engineering","score_opus":0.054571900585640216,"score_gpt":0.3023423917639086,"score_spread":0.24777049117826838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391170797","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":[0.5179284,0.00003078853,0.478521,0.0005795283,0.001143994,0.0011477597,0.000024963607,0.0004723814,0.00015115931],"genre_scores_gemma":[0.9751223,0.000026931153,0.023376398,0.00008802652,0.00047725008,0.00072266225,0.00005956704,0.000041299565,0.0000856219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969028,0.000202537,0.00048054138,0.0015953252,0.0004026089,0.00041616792],"domain_scores_gemma":[0.9984009,0.00010104373,0.0002603485,0.0009927131,0.00010366651,0.00014128152],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045768378,0.00039352104,0.00031127673,0.00023455644,0.00019736134,0.00011150342,0.00052721443,0.00028565826,0.00001595736],"category_scores_gemma":[0.000033185177,0.00039795742,0.000101609985,0.0004904701,0.000107066015,0.00024705732,0.0013503102,0.0012118173,0.00010667789],"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":[0.0007845456,0.000753398,0.1046546,0.0005322786,0.00011948601,0.00009185574,0.00044317392,0.8090389,0.06215215,0.010900479,0.000051347837,0.010477782],"study_design_scores_gemma":[0.0007780584,0.000071776805,0.1625549,0.00020889752,0.000029941304,0.000020514448,0.000005938098,0.8174336,0.002632022,0.015912598,0.000047016347,0.0003047472],"about_ca_topic_score_codex":0.00007478853,"about_ca_topic_score_gemma":0.00021986237,"teacher_disagreement_score":0.45719382,"about_ca_system_score_codex":0.0002796607,"about_ca_system_score_gemma":0.0001192657,"threshold_uncertainty_score":0.99984723},"labels":[],"label_agreement":null},{"id":"W4391256365","doi":"10.1016/j.spl.2024.110060","title":"A generalization bound of deep neural networks for dependent data","year":2024,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mathematics; Generalization; Artificial neural network; Deep neural networks; Upper and lower bounds; Artificial intelligence; Statistical physics; Applied mathematics; Mathematical analysis; Computer science","score_opus":0.03644504689136512,"score_gpt":0.30155558059884185,"score_spread":0.2651105337074767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391256365","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":[0.0016379167,0.00023631511,0.9943569,0.0020435904,0.00046556094,0.0007782378,0.0003202637,0.00015450675,0.000006762746],"genre_scores_gemma":[0.10629781,0.000011661022,0.89228743,0.0006955124,0.00013675446,0.00011327075,0.00043258403,0.00001824744,0.0000067514216],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983651,0.00006541572,0.00038967887,0.00068113307,0.00021560995,0.00028307937],"domain_scores_gemma":[0.9980122,0.000497192,0.000094737334,0.0012376244,0.00009390426,0.000064346816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003099366,0.00014303245,0.00015773711,0.0000477178,0.00010693659,0.00016310296,0.0010593857,0.00003752068,0.000004677795],"category_scores_gemma":[0.00010490692,0.0001416646,0.000033878045,0.00035338884,0.0001202531,0.00042767014,0.00038151367,0.00012106379,0.0000023584669],"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":[0.000010832127,0.000061272396,0.00015911671,0.00022397241,0.00003637557,0.0000071578315,0.00016214761,0.06446859,0.0017281144,0.7796797,0.0069122463,0.14655045],"study_design_scores_gemma":[0.00008068225,0.000024692881,0.00019543718,0.000006727148,0.000018817987,0.0000059854315,3.7032757e-7,0.81184006,0.00005898157,0.18680038,0.0008543771,0.00011351698],"about_ca_topic_score_codex":0.000013946697,"about_ca_topic_score_gemma":0.00009761564,"teacher_disagreement_score":0.74737144,"about_ca_system_score_codex":0.00007016685,"about_ca_system_score_gemma":0.00003138433,"threshold_uncertainty_score":0.57769144},"labels":[],"label_agreement":null},{"id":"W4391455314","doi":"10.1109/icfpt59805.2023.00065","title":"Journal Track Paper ICFPT 2023 : HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Quantization (signal processing); Floating point; Computer hardware; Overhead (engineering); Hardware acceleration; Deep learning; Binary number; Artificial neural network; Convolution (computer science); Computer engineering; FLOPS; Algorithm; Artificial intelligence; Field-programmable gate array; Parallel computing; Mathematics; Arithmetic","score_opus":0.04487813046640042,"score_gpt":0.29576256173267707,"score_spread":0.25088443126627663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391455314","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":[0.11638633,0.0007711052,0.87843096,0.0027265367,0.0004957415,0.00023882472,0.0000016200165,0.00039480085,0.0005540627],"genre_scores_gemma":[0.9522054,0.000600062,0.04623793,0.0002568464,0.00014631567,0.000014813989,0.0000029247124,0.000020200125,0.00051546126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847287,0.000059837334,0.00038900474,0.0003881971,0.0002520612,0.00043805366],"domain_scores_gemma":[0.9988986,0.00028567013,0.00016308426,0.00041508826,0.00008862272,0.00014893786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000304956,0.0001614916,0.0002098604,0.0001555755,0.00037593764,0.00013838995,0.00068733556,0.00005824468,0.00002405509],"category_scores_gemma":[0.00003031789,0.0001384855,0.000080115184,0.0014313185,0.00008372556,0.0007813091,0.00042057343,0.00031375233,0.00001642127],"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":[0.000011655791,0.00006174199,0.0007197369,0.000026702943,0.00002041203,0.00008618892,0.0004205625,0.9113205,0.010697845,0.01154443,0.0062078023,0.058882415],"study_design_scores_gemma":[0.00020601766,0.00007321972,0.002899454,0.000027652502,0.000005981019,0.00011656454,0.00009528973,0.99487334,0.0002972143,0.0005224518,0.00070998643,0.00017280527],"about_ca_topic_score_codex":0.000003837307,"about_ca_topic_score_gemma":0.0000032436078,"teacher_disagreement_score":0.8358191,"about_ca_system_score_codex":0.000016137092,"about_ca_system_score_gemma":0.00002179033,"threshold_uncertainty_score":0.5647275},"labels":[],"label_agreement":null},{"id":"W4391496273","doi":"10.1109/m2vip58386.2023.10413372","title":"Multi-Tracker Object Localizer: An Optimal Object Detector Based on Convolutional Neural Networks and Multi-Tracker Optimization Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Minimum bounding box; Object (grammar); Object detection; Computer vision; Intersection (aeronautics); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Process (computing); Algorithm; Image (mathematics); Engineering","score_opus":0.02455707431407405,"score_gpt":0.28231795263170273,"score_spread":0.25776087831762867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391496273","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":[0.0028885496,0.0000847555,0.9941473,0.0004284156,0.0003619162,0.0007215925,0.000022414775,0.0013110964,0.000033975477],"genre_scores_gemma":[0.26355112,0.000037708913,0.7346314,0.0009956723,0.00019345726,0.00019483537,0.0001464796,0.00005110295,0.00019821021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970469,0.00018903715,0.0004495274,0.0011461285,0.00044114867,0.00072729884],"domain_scores_gemma":[0.9981072,0.00039629373,0.00015230112,0.0008207445,0.00018980913,0.0003336836],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030377653,0.00041198707,0.00029784444,0.00024423865,0.00049781933,0.00023908759,0.0006699301,0.00019540609,0.00005831195],"category_scores_gemma":[0.00004970699,0.00038718522,0.000092780254,0.0014073366,0.00018877625,0.0009063988,0.00023954085,0.00039189684,0.000061963765],"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":[0.0000224828,0.00013638631,0.000189673,0.0000039305787,0.000009836664,0.000017669385,0.000032983557,0.95760006,0.00010652124,0.00018872178,0.00022712715,0.041464597],"study_design_scores_gemma":[0.0013172957,0.00019946578,0.004378951,0.000012389894,0.000011684128,0.000023807524,0.000020209996,0.99305266,0.00030189403,0.00001999684,0.00019934635,0.0004622793],"about_ca_topic_score_codex":0.000018555615,"about_ca_topic_score_gemma":0.000031308828,"teacher_disagreement_score":0.26066256,"about_ca_system_score_codex":0.00009568892,"about_ca_system_score_gemma":0.000067161214,"threshold_uncertainty_score":0.999858},"labels":[],"label_agreement":null},{"id":"W4391563775","doi":"10.1016/j.isprsjprs.2024.01.026","title":"Remote sensing image cloud detection using a shallow convolutional neural network","year":2024,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Calgary","funders":"","keywords":"Normalization (sociology); Convolutional neural network; Computer science; Pooling; Pixel; Artificial intelligence; Pattern recognition (psychology); Cloud computing; Ground truth; Computer vision; Remote sensing; Geology; Operating system","score_opus":0.019421744107670447,"score_gpt":0.277585381765491,"score_spread":0.2581636376578206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391563775","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":[0.11897652,0.0021009329,0.8760644,0.00037323,0.0021660423,0.00013878643,8.7192075e-7,0.00012577868,0.000053427793],"genre_scores_gemma":[0.5720257,0.00010221321,0.4264202,0.0001866841,0.0012315391,4.128905e-9,5.448968e-7,0.00002260581,0.000010480661],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780244,0.00018575294,0.00064685923,0.0004370294,0.00040211892,0.0005257847],"domain_scores_gemma":[0.998465,0.0003837513,0.00034857486,0.0002974137,0.0002663787,0.00023887557],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069105043,0.00027105384,0.000352336,0.00030519918,0.0004891049,0.00043814792,0.00023456615,0.00013190499,0.0000011782492],"category_scores_gemma":[0.000081628496,0.00024708718,0.00023787993,0.0014006344,0.00017212138,0.00066134415,0.0001662132,0.0008140591,0.0000030110684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028912462,0.000004418187,0.0000025541765,0.00002993949,0.000051878247,0.00042658465,0.00010890291,0.008314983,0.08465341,0.000072663344,0.000033053482,0.9062727],"study_design_scores_gemma":[0.00023553721,0.00007964662,0.000041586798,0.00036939466,0.00006159416,0.011827326,0.000035606477,0.96812403,0.0044368044,0.012242544,0.0022977265,0.00024819642],"about_ca_topic_score_codex":0.00018827576,"about_ca_topic_score_gemma":0.00007268969,"teacher_disagreement_score":0.95980906,"about_ca_system_score_codex":0.00013963447,"about_ca_system_score_gemma":0.000088753564,"threshold_uncertainty_score":0.99999815},"labels":[],"label_agreement":null},{"id":"W4391637182","doi":"10.32920/25190984.v1","title":"Classifying Negative Objects With Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lexicon; Object (grammar); Computer science; Point (geometry); Artificial intelligence; Artificial neural network; Image (mathematics); Space (punctuation); Term (time); Pattern recognition (psychology); Computer vision; Mathematics; Physics; Geometry","score_opus":0.0252899597102265,"score_gpt":0.27602857681440124,"score_spread":0.2507386171041747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391637182","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":[0.0010245467,0.0005266945,0.9765278,0.002762762,0.0008951334,0.0007054803,0.00000288461,0.0015361692,0.016018521],"genre_scores_gemma":[0.828795,0.00004484219,0.16826405,0.0008435828,0.00041193815,0.00032337967,0.000009530454,0.000047767164,0.0012598862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761397,0.000057395893,0.0002889379,0.0012548942,0.00030588475,0.00047890117],"domain_scores_gemma":[0.99794984,0.00026272514,0.00016880977,0.0013556898,0.00010518206,0.00015772783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008387954,0.000419677,0.00032371274,0.00012204533,0.00014956149,0.00051049126,0.0015227069,0.00020190267,0.000010866855],"category_scores_gemma":[0.000010862682,0.00031607202,0.00011084426,0.0007713283,0.00009821772,0.00019939103,0.0044262484,0.0016985636,0.000061680475],"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":[0.000008252321,0.000026643173,0.00003216405,0.00006683103,0.00007509737,0.00008645015,0.00036692477,0.8416717,0.000023461742,0.11235836,0.0024497584,0.04283434],"study_design_scores_gemma":[0.00008231639,0.000036919388,0.00010380543,0.00010793062,0.00001961997,0.000030134463,0.000017724655,0.9256499,0.000114946,0.07315718,0.00029077817,0.00038874394],"about_ca_topic_score_codex":0.000021764718,"about_ca_topic_score_gemma":0.000079766534,"teacher_disagreement_score":0.8277705,"about_ca_system_score_codex":0.000109482855,"about_ca_system_score_gemma":0.00011802642,"threshold_uncertainty_score":0.99992913},"labels":[],"label_agreement":null},{"id":"W4391637571","doi":"10.32920/25190984","title":"Classifying Negative Objects With Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lexicon; Object (grammar); Computer science; Point (geometry); Artificial intelligence; Image (mathematics); Artificial neural network; Space (punctuation); Term (time); Type (biology); Pattern recognition (psychology); Mathematics; Physics; Geometry; Astronomy","score_opus":0.0252899597102265,"score_gpt":0.27602857681440124,"score_spread":0.2507386171041747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391637571","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":[0.0010245467,0.0005266945,0.9765278,0.002762762,0.0008951334,0.0007054803,0.00000288461,0.0015361692,0.016018521],"genre_scores_gemma":[0.828795,0.00004484219,0.16826405,0.0008435828,0.00041193815,0.00032337967,0.000009530454,0.000047767164,0.0012598862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761397,0.000057395893,0.0002889379,0.0012548942,0.00030588475,0.00047890117],"domain_scores_gemma":[0.99794984,0.00026272514,0.00016880977,0.0013556898,0.00010518206,0.00015772783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008387954,0.000419677,0.00032371274,0.00012204533,0.00014956149,0.00051049126,0.0015227069,0.00020190267,0.000010866855],"category_scores_gemma":[0.000010862682,0.00031607202,0.00011084426,0.0007713283,0.00009821772,0.00019939103,0.0044262484,0.0016985636,0.000061680475],"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":[0.000008252321,0.000026643173,0.00003216405,0.00006683103,0.00007509737,0.00008645015,0.00036692477,0.8416717,0.000023461742,0.11235836,0.0024497584,0.04283434],"study_design_scores_gemma":[0.00008231639,0.000036919388,0.00010380543,0.00010793062,0.00001961997,0.000030134463,0.000017724655,0.9256499,0.000114946,0.07315718,0.00029077817,0.00038874394],"about_ca_topic_score_codex":0.000021764718,"about_ca_topic_score_gemma":0.000079766534,"teacher_disagreement_score":0.8277705,"about_ca_system_score_codex":0.000109482855,"about_ca_system_score_gemma":0.00011802642,"threshold_uncertainty_score":0.99992913},"labels":[],"label_agreement":null},{"id":"W4391640441","doi":"10.1109/tase.2024.3358894","title":"DL-DRL: A Double-Level Deep Reinforcement Learning Approach for Large-Scale Task Scheduling of Multi-UAV","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Alberta","funders":"Fundamental Research Funds for Central Universities of the Central South University; Central South University; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Scheduling (production processes); Artificial intelligence; Computation; Heuristic; Exploit; Distributed computing; Machine learning; Mathematical optimization; Algorithm","score_opus":0.03186650388898795,"score_gpt":0.27589179604690006,"score_spread":0.2440252921579121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391640441","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":[0.003203618,0.00006932872,0.9955741,0.00009087733,0.00022362484,0.00035123262,0.0000024319488,0.00044751778,0.000037264832],"genre_scores_gemma":[0.66830534,0.000018902609,0.33144775,0.000015357848,0.000012687896,0.0001362591,0.0000012884092,0.000008325318,0.00005407586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875265,0.0000056258773,0.00025454213,0.00039218063,0.0003170267,0.0002779746],"domain_scores_gemma":[0.9994393,0.00008472043,0.000046704317,0.00021845345,0.00012319269,0.00008761564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004722247,0.0001245805,0.00011750224,0.00035400095,0.00033492417,0.00016901924,0.0003029777,0.0000408292,0.0000014667961],"category_scores_gemma":[0.000010173164,0.00012257697,0.00004941012,0.0013102756,0.000049112747,0.0010399657,0.000007344893,0.00016538185,0.000003564582],"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":[0.000002167525,0.000020181162,4.344567e-7,0.00006293485,0.000005612074,1.3252492e-7,0.0007965622,0.95332897,0.02450139,0.0027283686,0.0000013164571,0.01855194],"study_design_scores_gemma":[0.0002824565,0.000034222954,0.000022919914,0.00004623014,0.0000078118655,0.000006431153,0.00007054961,0.97516793,0.023937793,0.000017267617,0.00027993674,0.00012645613],"about_ca_topic_score_codex":0.0000020300056,"about_ca_topic_score_gemma":0.0000011760899,"teacher_disagreement_score":0.6651017,"about_ca_system_score_codex":0.00007876884,"about_ca_system_score_gemma":0.000059957947,"threshold_uncertainty_score":0.4998544},"labels":[],"label_agreement":null},{"id":"W4391983449","doi":"10.1016/j.neucom.2024.127446","title":"Redundant co-training: Semi-supervised segmentation of medical images using informative redundancy","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Co-training; Computer science; Segmentation; Artificial intelligence; Training (meteorology); Redundancy (engineering); Pattern recognition (psychology); Machine learning; Training set; Image segmentation; Computer vision; Semi-supervised learning; Geography","score_opus":0.049704230010097744,"score_gpt":0.3515411510063972,"score_spread":0.30183692099629944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391983449","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":[0.09833204,0.00017180618,0.8983334,0.00089817017,0.0003448648,0.0002804333,0.0000037618142,0.00045371626,0.0011818424],"genre_scores_gemma":[0.8642725,0.000032941043,0.13511385,0.00032226226,0.00019513193,0.000011709109,0.000007304077,0.000022563605,0.000021736267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794614,0.000085008396,0.00055361696,0.00040944194,0.0006815858,0.00032422482],"domain_scores_gemma":[0.99888504,0.00042153412,0.00016511006,0.0003277604,0.0000731197,0.00012746986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003846876,0.00017324937,0.0002074331,0.00016762101,0.00020025302,0.00018252105,0.0007811235,0.000066961715,0.000012197124],"category_scores_gemma":[0.000069370406,0.00016858058,0.00008044232,0.0008852203,0.000099207326,0.0007797848,0.00034651862,0.0003480834,0.000016337257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006986007,0.00006444811,0.000147834,0.0003041475,0.000049001064,0.000104882274,0.012651539,0.023732578,0.07969822,0.035398338,0.0007962386,0.8470458],"study_design_scores_gemma":[0.00017755445,0.00004187409,0.00019154906,0.0002708268,0.0000074875566,0.00015726303,0.00015458047,0.9741801,0.02176382,0.0014689858,0.0014213382,0.00016458867],"about_ca_topic_score_codex":0.0000068257114,"about_ca_topic_score_gemma":3.5320323e-7,"teacher_disagreement_score":0.95044756,"about_ca_system_score_codex":0.00005189582,"about_ca_system_score_gemma":0.00020534384,"threshold_uncertainty_score":0.6874516},"labels":[],"label_agreement":null},{"id":"W4392033643","doi":"10.32920/25266754","title":"Motion Vector Extrapolation for Video Object Detection","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Extrapolation; Computer vision; Optical flow; Object detection; Motion vector; Latency (audio); Motion estimation; Detector; Low latency (capital markets); Motion detection; Real-time computing; Motion (physics); Pattern recognition (psychology); Telecommunications; Mathematics","score_opus":0.02856686301845925,"score_gpt":0.2949848587989118,"score_spread":0.26641799578045255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392033643","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":[0.0010846627,0.00023675285,0.992355,0.0010677603,0.0018940809,0.0012634782,0.000011645399,0.0011839562,0.00090266124],"genre_scores_gemma":[0.83581084,0.00001847571,0.16165492,0.00015123513,0.0005726084,0.0011509379,0.000031659663,0.000032162938,0.0005771737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984243,0.000029890361,0.00029200435,0.0008561916,0.00017747015,0.0002201386],"domain_scores_gemma":[0.9987544,0.00015291604,0.00014294873,0.0007741005,0.00011654134,0.000059140453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015992635,0.00021817535,0.00016415634,0.00016797794,0.00012914243,0.0002490829,0.0005320694,0.00016655034,0.0000067000933],"category_scores_gemma":[0.000035314813,0.00021313723,0.00016808299,0.00035893105,0.00001684429,0.00019730923,0.00071496837,0.0003926679,0.000085413456],"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":[0.000014562884,0.000057327863,0.000006601662,0.00034618517,0.00005736001,0.0000018031375,0.00020760225,0.031958926,0.02104251,0.2319538,0.0020516957,0.7123016],"study_design_scores_gemma":[0.00006387064,0.000027643922,0.00015429624,0.000031383468,0.000017447566,0.00000535976,0.000001862744,0.65315086,0.015105919,0.3277144,0.0035225207,0.00020442109],"about_ca_topic_score_codex":0.000016782124,"about_ca_topic_score_gemma":0.000053726075,"teacher_disagreement_score":0.83472615,"about_ca_system_score_codex":0.0001739887,"about_ca_system_score_gemma":0.000057200818,"threshold_uncertainty_score":0.8691484},"labels":[],"label_agreement":null},{"id":"W4392033975","doi":"10.32920/25266736.v1","title":"Deep Learning-Based Semantic Segmentation in Autonomous Driving","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Yorkville University","funders":"","keywords":"Segmentation; Computer science; Metric (unit); Intersection (aeronautics); Artificial intelligence; Task (project management); Sensitivity (control systems); Machine learning; Perception; Data mining; Engineering; Transport engineering","score_opus":0.014918846329199471,"score_gpt":0.2806587946254036,"score_spread":0.2657399482962041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392033975","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":[0.0075839143,0.00022006898,0.98720676,0.001874776,0.00040617364,0.0004960242,2.9162462e-7,0.0008845994,0.001327366],"genre_scores_gemma":[0.8497891,0.00002114837,0.14898306,0.0002064802,0.00006434559,0.0002731034,0.000021483585,0.000025075282,0.0006162406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982965,0.00007333138,0.00033893983,0.00081857626,0.00020065534,0.00027197239],"domain_scores_gemma":[0.99899423,0.00016409572,0.00012699378,0.0006188919,0.0000345236,0.000061277526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016189851,0.00022419497,0.00020128817,0.0002594442,0.00006688253,0.00024939678,0.00077671977,0.00012886827,0.000020803347],"category_scores_gemma":[0.000020589043,0.00022977832,0.00007938345,0.0005079209,0.000023953537,0.00010486237,0.0014925174,0.00094694144,0.00022804984],"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":[4.7115685e-7,0.00002401615,0.00046193658,0.0000658553,0.000005793036,0.000016629701,0.00017584972,0.95515066,0.00018325791,0.0070696464,0.000055332166,0.036790527],"study_design_scores_gemma":[0.00007829608,0.000013071235,0.00088288717,0.00010041403,0.000006589377,0.000002616815,0.000010015012,0.96521795,0.0006887476,0.03250916,0.00025933716,0.000230945],"about_ca_topic_score_codex":0.00003381518,"about_ca_topic_score_gemma":0.00022384092,"teacher_disagreement_score":0.84220517,"about_ca_system_score_codex":0.00023513111,"about_ca_system_score_gemma":0.00011665177,"threshold_uncertainty_score":0.9370088},"labels":[],"label_agreement":null},{"id":"W4392083683","doi":"10.32920/25266736","title":"Deep Learning-Based Semantic Segmentation in Autonomous Driving","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Yorkville University","funders":"","keywords":"Segmentation; Computer science; Metric (unit); Intersection (aeronautics); Artificial intelligence; Task (project management); Machine learning; Sensitivity (control systems); Perception; Data mining; Engineering","score_opus":0.014918846329199471,"score_gpt":0.2806587946254036,"score_spread":0.2657399482962041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392083683","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":[0.0075839143,0.00022006898,0.98720676,0.001874776,0.00040617364,0.0004960242,2.9162462e-7,0.0008845994,0.001327366],"genre_scores_gemma":[0.8497891,0.00002114837,0.14898306,0.0002064802,0.00006434559,0.0002731034,0.000021483585,0.000025075282,0.0006162406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982965,0.00007333138,0.00033893983,0.00081857626,0.00020065534,0.00027197239],"domain_scores_gemma":[0.99899423,0.00016409572,0.00012699378,0.0006188919,0.0000345236,0.000061277526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016189851,0.00022419497,0.00020128817,0.0002594442,0.00006688253,0.00024939678,0.00077671977,0.00012886827,0.000020803347],"category_scores_gemma":[0.000020589043,0.00022977832,0.00007938345,0.0005079209,0.000023953537,0.00010486237,0.0014925174,0.00094694144,0.00022804984],"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":[4.7115685e-7,0.00002401615,0.00046193658,0.0000658553,0.000005793036,0.000016629701,0.00017584972,0.95515066,0.00018325791,0.0070696464,0.000055332166,0.036790527],"study_design_scores_gemma":[0.00007829608,0.000013071235,0.00088288717,0.00010041403,0.000006589377,0.000002616815,0.000010015012,0.96521795,0.0006887476,0.03250916,0.00025933716,0.000230945],"about_ca_topic_score_codex":0.00003381518,"about_ca_topic_score_gemma":0.00022384092,"teacher_disagreement_score":0.84220517,"about_ca_system_score_codex":0.00023513111,"about_ca_system_score_gemma":0.00011665177,"threshold_uncertainty_score":0.9370088},"labels":[],"label_agreement":null},{"id":"W4392093232","doi":"10.1016/j.wasman.2024.02.028","title":"Smart-sight: Video-based waste characterization for RDF-3 production","year":2024,"lang":"en","type":"article","venue":"Waste Management","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Municipal solid waste; Cleaner production; Workflow; Process (computing); Computer science; Sorting; Food waste; Engineering; Waste management; Artificial intelligence; Database","score_opus":0.017079366420458243,"score_gpt":0.24993020127656998,"score_spread":0.23285083485611174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392093232","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":[0.00479951,0.00004734712,0.98371613,0.0076671992,0.0012570294,0.0012636526,0.000005296664,0.0005499983,0.00069383794],"genre_scores_gemma":[0.8809774,0.000076715995,0.104184054,0.0011782196,0.0009765704,0.0017868077,0.0001859873,0.000070652044,0.010563629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987011,0.000021692193,0.00021794136,0.0006194997,0.00019678143,0.000242986],"domain_scores_gemma":[0.99921024,0.000030164432,0.00006182532,0.00060580974,0.000042969325,0.000049006176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015100079,0.00014398455,0.00009649145,0.00017036058,0.00016251382,0.00021858832,0.0004159584,0.000026472773,0.0000059541107],"category_scores_gemma":[0.000005579395,0.00013784248,0.00006385573,0.00065982825,0.000019183504,0.00048510314,0.00015687008,0.000061863364,0.00008132186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000265422,0.00013792726,0.000009990301,0.0007423817,0.000088941044,0.000017354207,0.00029472058,0.02721318,0.035820037,0.36820298,0.008044045,0.55940187],"study_design_scores_gemma":[0.00017830284,0.000053562017,0.00007505545,0.000103066785,0.000029564257,0.00000206209,0.000023772174,0.640148,0.012289444,0.0032510622,0.34362444,0.00022168453],"about_ca_topic_score_codex":4.5829847e-7,"about_ca_topic_score_gemma":0.000001217625,"teacher_disagreement_score":0.8795321,"about_ca_system_score_codex":0.00007616422,"about_ca_system_score_gemma":0.000010715815,"threshold_uncertainty_score":0.5621053},"labels":[],"label_agreement":null},{"id":"W4392159079","doi":"10.1109/globecom54140.2023.10437780","title":"How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Queen's University","funders":"","keywords":"Slicing; Convergence (economics); Ran; Affect (linguistics); Computer science; Artificial intelligence; Psychology; Economics; World Wide Web; Computer network","score_opus":0.033440407813908515,"score_gpt":0.28020425536854554,"score_spread":0.24676384755463704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392159079","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":[0.086377345,0.00001944204,0.90356123,0.0074185245,0.000093201255,0.00052969425,0.00000101953,0.00063361024,0.0013659538],"genre_scores_gemma":[0.97340417,0.000027883474,0.025865253,0.000122045465,0.000022805256,0.00008767725,5.2776227e-7,0.0000055360883,0.00046407286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992934,0.00004388851,0.00012970186,0.00021430104,0.00012677834,0.0001919448],"domain_scores_gemma":[0.998885,0.00056285516,0.00007150707,0.00042764904,0.00003125474,0.000021754566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003058823,0.00007428636,0.000096125295,0.000076591714,0.00007278488,0.00003483399,0.0006942431,0.000023488601,0.0000023404011],"category_scores_gemma":[0.00008277034,0.000041546118,0.000031062966,0.0013088803,0.00007310491,0.00023931009,0.0002567298,0.00008974296,0.0000049949463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066649473,0.000055245655,0.019606477,0.00007678617,0.000014637921,0.000018897219,0.0018194155,0.0017527202,0.0679272,0.42849684,0.0035014802,0.47672364],"study_design_scores_gemma":[0.00013974076,0.00006741826,0.006827003,0.0000844209,0.0000034718507,0.000010580999,0.00029647368,0.523953,0.41112024,0.05267069,0.0045467108,0.00028020664],"about_ca_topic_score_codex":0.00002229376,"about_ca_topic_score_gemma":0.000097373224,"teacher_disagreement_score":0.88702685,"about_ca_system_score_codex":0.000012253402,"about_ca_system_score_gemma":0.000010699664,"threshold_uncertainty_score":0.16942015},"labels":[],"label_agreement":null},{"id":"W4392192981","doi":"10.3390/rs16050818","title":"Synthetic Data for Sentinel-2 Semantic Segmentation","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.04892338845933448,"score_gpt":0.32541814060770424,"score_spread":0.27649475214836977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392192981","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":[0.0022068825,0.00028462647,0.99363613,0.0025598202,0.00033824862,0.00026132137,0.000003915742,0.00041963294,0.00028941268],"genre_scores_gemma":[0.29917324,0.00004051517,0.6999496,0.0002563695,0.00024817386,8.3036944e-8,0.00004854969,0.000023569584,0.0002598882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899143,0.00002086849,0.00015008253,0.0005268069,0.00011672895,0.00019409721],"domain_scores_gemma":[0.99868184,0.00022801418,0.000033738197,0.0009820671,0.000035639387,0.000038700033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016565574,0.00008927017,0.00008157056,0.0000604504,0.00012307262,0.0002163605,0.00037785657,0.000024544328,8.830668e-7],"category_scores_gemma":[0.000043108917,0.00008730035,0.000031291547,0.00035451006,0.000018965402,0.0004516494,0.00023846162,0.000069208094,0.000052008872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010636056,0.0000031521665,2.073694e-7,0.000056007462,0.000011994099,0.0000129663795,0.00007676459,0.0007160999,0.03325542,0.0028043555,0.0016279035,0.96143407],"study_design_scores_gemma":[0.00004653675,0.0000042286165,0.0000026518176,0.00008262484,0.000014284654,0.00007842837,0.0000065026197,0.9757487,0.0030288773,0.010396531,0.01049381,0.000096825854],"about_ca_topic_score_codex":0.000003172535,"about_ca_topic_score_gemma":0.0000019415781,"teacher_disagreement_score":0.97503257,"about_ca_system_score_codex":0.00003020977,"about_ca_system_score_gemma":0.000025779682,"threshold_uncertainty_score":0.35600048},"labels":[],"label_agreement":null},{"id":"W4392203282","doi":"10.1088/1742-6596/2711/1/012015","title":"Comparative analysis of various models for image classification on Cifar-100 dataset","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Image (mathematics)","score_opus":0.10504830824397171,"score_gpt":0.35919997795837943,"score_spread":0.2541516697144077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392203282","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":[0.0014677,0.000059875943,0.99662495,0.0008449216,0.00010334562,0.00013176097,0.00044901884,0.00002121743,0.00029718815],"genre_scores_gemma":[0.9457435,0.00006871444,0.053881243,0.000048415506,0.000077018936,0.000015179398,0.00013109157,0.000004993931,0.000029848217],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989813,0.000038384493,0.0003983568,0.00021199574,0.00024469045,0.00012527996],"domain_scores_gemma":[0.9984504,0.00027551712,0.0003863892,0.00037983072,0.00045479852,0.000053073865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014248281,0.00012082641,0.0003675206,0.00017161532,0.000070064045,0.00015548553,0.00058975257,0.000027298922,0.000004224324],"category_scores_gemma":[0.000009973438,0.00010081,0.0001479504,0.0009847656,0.00010031062,0.0016483859,0.000058227342,0.00015817049,0.0000034472941],"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":[0.00003181392,0.000077668075,0.0000019689046,0.000020720767,0.00034106828,0.0000023121618,0.00088827044,0.03285328,0.012027528,0.93213916,0.0017080724,0.019908126],"study_design_scores_gemma":[0.0001059396,0.00025284008,0.00014648002,0.000046871824,0.00026602176,0.000005456383,0.00012169455,0.7284931,0.035350747,0.23305671,0.0020280476,0.00012607964],"about_ca_topic_score_codex":0.0000021465603,"about_ca_topic_score_gemma":0.0000052565297,"teacher_disagreement_score":0.9442758,"about_ca_system_score_codex":0.000035598918,"about_ca_system_score_gemma":0.0001512709,"threshold_uncertainty_score":0.41109124},"labels":[],"label_agreement":null},{"id":"W4392345381","doi":"10.1016/j.dib.2024.110264","title":"Unmanned aerial vehicle (UAV) images of road vehicles dataset","year":2024,"lang":"en","type":"article","venue":"Data in Brief","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Truck; Computer science; Drone; Artificial intelligence; Aerial image; Noise (video); Image processing; Vehicle tracking system; Categorization; Computer vision; Transport engineering; Segmentation; Image (mathematics); Engineering; Automotive engineering","score_opus":0.03568347827659771,"score_gpt":0.3167561627392809,"score_spread":0.2810726844626832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392345381","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":[0.2060249,0.010380538,0.6363205,0.028460542,0.003762545,0.0024932502,0.108949915,0.0019861327,0.0016216736],"genre_scores_gemma":[0.92937946,0.00030677018,0.056039046,0.00063238863,0.00040539374,0.000052493986,0.013086388,0.000032515323,0.000065556495],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987526,0.00004095498,0.0002585419,0.00056035933,0.00017818536,0.00020936292],"domain_scores_gemma":[0.99774486,0.00014986795,0.00004662041,0.0019969002,0.000013805304,0.00004795392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022420782,0.00009968817,0.00013372839,0.0000761679,0.000041048646,0.00011400322,0.002195069,0.00003492799,0.000013445901],"category_scores_gemma":[0.00005267175,0.00009803891,0.000015634541,0.00065832934,0.0000838018,0.0012962206,0.0014414433,0.00013406927,0.00007508859],"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":[0.00002644279,0.00018522349,0.00038501885,0.000105652194,0.000029813235,0.00012235733,0.00017216736,0.0006608439,0.028086202,0.04636815,0.58442515,0.33943298],"study_design_scores_gemma":[0.00058614835,0.00006763059,0.0117440475,0.00013562743,0.000015721575,0.000029399778,0.000018763674,0.26958323,0.014719844,0.010349789,0.6923223,0.00042748652],"about_ca_topic_score_codex":0.00018545405,"about_ca_topic_score_gemma":0.00005284939,"teacher_disagreement_score":0.7233545,"about_ca_system_score_codex":0.000016935099,"about_ca_system_score_gemma":0.00004412431,"threshold_uncertainty_score":0.40790212},"labels":[],"label_agreement":null},{"id":"W4392350016","doi":"10.18280/ts.410141","title":"Optimization of Deep Neural Networks for Enhanced Efficiency in Small Scale Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Deanship of Scientific Research, Princess Nourah Bint Abdulrahman University; Princess Nourah Bint Abdulrahman University","keywords":"Bespoke; Computer science; Artificial neural network; Deep learning; Artificial intelligence; Architecture; Latency (audio); Scale (ratio); Distributed computing; Process (computing); Computer architecture; Network architecture; Deep neural networks; Embedded system; Computer network; Telecommunications","score_opus":0.014373048596053952,"score_gpt":0.24631571792384846,"score_spread":0.2319426693277945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392350016","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":[0.034197338,0.00033727876,0.9641213,0.00032850335,0.00015130476,0.000609331,0.000003226512,0.00016376626,0.00008796878],"genre_scores_gemma":[0.89523,0.00001148592,0.10434838,0.00008672501,0.00008466252,0.00019891812,0.000011875558,0.000012955191,0.0000149865655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987967,0.000031466323,0.00037730508,0.00039729805,0.00011144344,0.0002858109],"domain_scores_gemma":[0.99940383,0.00021459421,0.00007504053,0.00020407257,0.000050602735,0.00005183705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001817775,0.00013257959,0.00015259472,0.00011723388,0.00006664034,0.00006751008,0.00045148173,0.00004597491,0.000014471345],"category_scores_gemma":[0.000005399097,0.00012966954,0.0000713016,0.0006197674,0.000044372155,0.00024627906,0.00007441482,0.00009680435,0.0000017738919],"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":[0.000009534003,0.000064996006,0.00002147168,0.000022026801,0.00000424873,0.0000011370395,0.00027010718,0.90621084,0.0028927887,0.0052057547,0.000013530252,0.08528355],"study_design_scores_gemma":[0.00028710527,0.00011983912,0.00017780601,0.000024509907,0.0000068666186,0.0000019660336,0.000011418701,0.99358183,0.0046766694,0.00092120835,0.00006394267,0.00012682876],"about_ca_topic_score_codex":0.000003403677,"about_ca_topic_score_gemma":0.000019943867,"teacher_disagreement_score":0.86103266,"about_ca_system_score_codex":0.000055180204,"about_ca_system_score_gemma":0.00002250087,"threshold_uncertainty_score":0.528777},"labels":[],"label_agreement":null},{"id":"W4392515398","doi":"10.3389/frobt.2024.1212070","title":"A survey on 3D object detection in real time for autonomous driving","year":2024,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object detection; Robustness (evolution); Artificial intelligence; Computer vision; Inference; Exploit; Monocular; Machine learning; Pattern recognition (psychology)","score_opus":0.013872789735296565,"score_gpt":0.26358666563178634,"score_spread":0.24971387589648977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392515398","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":[0.0041574975,0.00014401966,0.9944081,0.00037226966,0.00052104326,0.00022486274,0.0000031077423,0.0000813189,0.0000877979],"genre_scores_gemma":[0.7567678,0.00012705145,0.24247764,0.00011261882,0.00007178679,0.00005478403,0.000008178318,0.00001738926,0.000362747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993153,0.00003129684,0.00013383971,0.0002930334,0.000055065924,0.00017142529],"domain_scores_gemma":[0.999635,0.00014647016,0.00001980023,0.0001555377,0.000012364833,0.000030865558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002031504,0.00007826379,0.0001099556,0.0001416724,0.000050498922,0.000088025525,0.00014315553,0.00004376364,2.602225e-7],"category_scores_gemma":[0.00002293338,0.000078822966,0.00001816451,0.00038151277,0.00001815809,0.0001368037,0.000050391656,0.00012385295,0.0000027226029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017972417,0.00006737383,0.0076333196,0.000033540553,0.000014955499,0.000015878508,0.0003608599,0.45600423,0.0004247614,0.0064192484,0.0049533336,0.5240545],"study_design_scores_gemma":[0.0000999393,0.00005359339,0.016603142,0.000032425785,0.000001527601,0.0000010910977,0.000002071729,0.9767429,0.00007954953,0.005763379,0.0005299803,0.00009036905],"about_ca_topic_score_codex":0.000026152162,"about_ca_topic_score_gemma":0.00015672899,"teacher_disagreement_score":0.7526103,"about_ca_system_score_codex":0.000083203675,"about_ca_system_score_gemma":0.00002459354,"threshold_uncertainty_score":0.32143074},"labels":[],"label_agreement":null},{"id":"W4392592951","doi":"10.1049/ipr2.13048","title":"Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T","year":2024,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Toronto","funders":"Chongqing Medical University; China Postdoctoral Science Foundation; Natural Science Foundation of Chongqing; Chongqing University","keywords":"Segmentation; Computer science; Artificial intelligence; Upsampling; Deep learning; Image segmentation; Pattern recognition (psychology); Scale-space segmentation; Focus (optics); Feature (linguistics); Computer vision; Image (mathematics)","score_opus":0.00879415964833052,"score_gpt":0.28681128709989895,"score_spread":0.2780171274515684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392592951","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":[0.011447507,0.0019250931,0.9830161,0.0027271118,0.000051814026,0.00015939184,6.4998244e-7,0.00037478987,0.00029756353],"genre_scores_gemma":[0.43550143,0.000015003245,0.56400144,0.00030195032,0.00007301964,0.000042246556,0.0000017906797,0.0000151750755,0.000047955302],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896556,0.000021008063,0.00013830682,0.00046719902,0.00017558971,0.00023233835],"domain_scores_gemma":[0.9994743,0.00014822272,0.000061767045,0.00018914099,0.000056809255,0.00006976173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011377902,0.00013522428,0.00009936618,0.000061327875,0.0002448138,0.0007780876,0.00023071845,0.000035721758,0.0000028973875],"category_scores_gemma":[0.00003947686,0.00011604673,0.0000153419,0.0007355548,0.00006492406,0.001571486,0.000093445,0.00022267959,0.000016204987],"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":[0.000010992142,0.00002029955,0.00017172677,0.00017919292,0.000011280619,0.00006162072,0.0031602462,0.00085863733,0.05009372,0.015769787,0.00017263634,0.92948985],"study_design_scores_gemma":[0.0002664697,0.00007190689,0.00081712357,0.0011257103,0.000019813924,0.0002335698,0.00027405124,0.90957487,0.023073548,0.06323461,0.0008410452,0.0004672787],"about_ca_topic_score_codex":0.0000013933699,"about_ca_topic_score_gemma":0.000002239995,"teacher_disagreement_score":0.9290226,"about_ca_system_score_codex":0.00003478536,"about_ca_system_score_gemma":0.000050925144,"threshold_uncertainty_score":0.7503117},"labels":[],"label_agreement":null},{"id":"W4392682038","doi":"10.1088/1361-6560/ad3321","title":"Multi-scale adversarial learning with difficult region supervision learning models for primary tumor segmentation","year":2024,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Deep learning; Similarity (geometry); Jaccard index; Scale (ratio); Machine learning; Pattern recognition (psychology); Image (mathematics)","score_opus":0.13914332132571058,"score_gpt":0.3539512793568157,"score_spread":0.2148079580311051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392682038","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":[0.06097642,0.00028418182,0.93737733,0.0008844889,0.000094693314,0.00023879288,4.4863975e-7,0.00008814184,0.000055483048],"genre_scores_gemma":[0.91066915,0.00024248505,0.08829174,0.00018847868,0.00036229208,0.00009183432,0.00007394154,0.000011206203,0.00006886724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927545,0.00005019518,0.0001349081,0.00033970227,0.000050880648,0.00014887989],"domain_scores_gemma":[0.9995327,0.00026848415,0.000041765674,0.000088787776,0.00003888049,0.000029381728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011105633,0.00009398607,0.00014702434,0.00004642268,0.00009734121,0.00001290301,0.00011083186,0.00003073425,3.139253e-7],"category_scores_gemma":[0.000013834782,0.000064103035,0.000014085785,0.00029098973,0.000079376856,0.00022414532,0.00006482628,0.00020494968,7.6123143e-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":[0.00013371436,0.00011371153,0.0037892412,0.00021712272,0.000032789943,0.000012603113,0.0067617865,0.17871837,0.17138405,0.047861915,0.00019913411,0.59077555],"study_design_scores_gemma":[0.00082915695,0.00042730186,0.00027031192,0.000108918415,0.000010419588,0.000010558899,0.0002463951,0.986425,0.0002690744,0.010572805,0.0007240413,0.000106035404],"about_ca_topic_score_codex":0.000012462344,"about_ca_topic_score_gemma":0.000007731059,"teacher_disagreement_score":0.84969276,"about_ca_system_score_codex":0.000026731346,"about_ca_system_score_gemma":0.000017616294,"threshold_uncertainty_score":0.26140457},"labels":[],"label_agreement":null},{"id":"W4392856651","doi":"10.3390/jimaging10030071","title":"FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images","year":2024,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Mitacs","keywords":"Computer science; Artificial intelligence; Segmentation; Thresholding; Computer vision; Benchmark (surveying); Perspective (graphical); Image segmentation; Supervised learning; Pattern recognition (psychology); Image (mathematics); Artificial neural network","score_opus":0.016312325632423814,"score_gpt":0.31524018168984674,"score_spread":0.29892785605742295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392856651","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":[0.0034965517,0.0010397373,0.97400844,0.020435711,0.00066249433,0.00018036105,0.0000045434745,0.00010275086,0.00006940692],"genre_scores_gemma":[0.33433378,0.00014572433,0.6633413,0.0014393954,0.0005972425,0.00002214607,0.0000020234556,0.000024638926,0.00009375634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988932,0.000030079294,0.00039234775,0.00021634613,0.00024066138,0.00022735816],"domain_scores_gemma":[0.9988685,0.0004703593,0.00017902454,0.00023350895,0.0001644878,0.00008408605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028357373,0.00012130533,0.00016228226,0.00016768184,0.000119392236,0.0003993878,0.0005207621,0.000025651014,0.000008403203],"category_scores_gemma":[0.00009453562,0.000106181964,0.00014868981,0.00046360822,0.000027921364,0.0013719322,0.00008112736,0.0002709923,0.000008859221],"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":[0.000036975773,0.00014055564,0.001708394,0.00039798114,0.0001714006,0.00029304205,0.0024926176,0.007848125,0.099044174,0.03896226,0.06686876,0.7820357],"study_design_scores_gemma":[0.00061391323,0.000106957705,0.001105099,0.0009830032,0.00009232046,0.00050434127,0.00022203448,0.75106806,0.036404923,0.18834561,0.020134637,0.0004191126],"about_ca_topic_score_codex":7.6731186e-7,"about_ca_topic_score_gemma":3.43289e-7,"teacher_disagreement_score":0.7816166,"about_ca_system_score_codex":0.00007274143,"about_ca_system_score_gemma":0.00006528561,"threshold_uncertainty_score":0.43299747},"labels":[],"label_agreement":null},{"id":"W4392882805","doi":"10.1049/cvi2.12259","title":"Learnable fusion mechanisms for multimodal object detection in autonomous vehicles","year":2024,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Leverage (statistics); Convolutional neural network; Sensor fusion; Pooling; Concatenation (mathematics); Machine learning; Object detection; Computer vision; Pattern recognition (psychology)","score_opus":0.011553066510243331,"score_gpt":0.275363988140011,"score_spread":0.26381092162976766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392882805","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":[0.02102028,0.0001430208,0.9758599,0.00070048356,0.0010150704,0.00056226336,0.0000021542592,0.0006570531,0.000039801238],"genre_scores_gemma":[0.6949156,0.000016638658,0.3045565,0.00014200497,0.00017620897,0.000083191815,0.0000050260023,0.00001889546,0.000085935695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851406,0.000048266138,0.00026784049,0.00067042245,0.00017988854,0.00031951943],"domain_scores_gemma":[0.99920005,0.00024162578,0.000047498474,0.00039750256,0.000047075067,0.000066279244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024788117,0.00016787476,0.0001555924,0.00021627525,0.00017198607,0.00026203998,0.000467799,0.00009338438,0.000003071099],"category_scores_gemma":[0.0000065614886,0.00015859384,0.00009045642,0.00061198307,0.000016939406,0.00065051514,0.0003050885,0.00020054297,0.00007237738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015990623,0.00005949344,0.00000284751,0.00003395545,0.0000048659367,0.00001669537,0.00018252015,0.030731231,0.045903202,0.011197422,0.0005044285,0.9113473],"study_design_scores_gemma":[0.00026661635,0.00029361722,0.0003841786,0.0000713325,0.0000026269029,0.000022509417,0.000002630567,0.9413455,0.016271845,0.032643165,0.008522698,0.00017327067],"about_ca_topic_score_codex":0.000015032517,"about_ca_topic_score_gemma":0.000022715503,"teacher_disagreement_score":0.91117406,"about_ca_system_score_codex":0.0001275843,"about_ca_system_score_gemma":0.000035413097,"threshold_uncertainty_score":0.6467269},"labels":[],"label_agreement":null},{"id":"W4392902595","doi":"10.1109/tnnls.2024.3350715","title":"Zero-Shot Fault Diagnosis for Smart Process Manufacturing via Tensor Prototype Alignment","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Generalizability theory; Computer science; Artificial intelligence; Process (computing); Sample (material); Fault (geology); Domain (mathematical analysis); Robustness (evolution); Machine learning; Data mining; Pattern recognition (psychology); Mathematics","score_opus":0.021107569765753106,"score_gpt":0.2708766660322413,"score_spread":0.2497690962664882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392902595","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":[0.0112721585,0.0007408372,0.9832426,0.0005749017,0.0013610936,0.002069863,0.0000055474475,0.0006981447,0.000034879948],"genre_scores_gemma":[0.9937091,0.00015165818,0.0008896406,0.00011545345,0.00022800545,0.0041773124,0.000004146356,0.00005137951,0.00067332975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979842,0.00012204595,0.0003825894,0.0007797305,0.00024617018,0.00048532212],"domain_scores_gemma":[0.99877095,0.00058670936,0.000096434,0.0003267239,0.00005703525,0.00016216534],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023395088,0.00030700062,0.00027795398,0.00012989329,0.00074041216,0.00045981666,0.00033097368,0.00012359412,0.0000044243234],"category_scores_gemma":[0.0000036308543,0.00026452713,0.0001264644,0.00035924683,0.00004707283,0.00043093524,0.000005209725,0.00065536017,0.000010235745],"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":[0.00002459512,0.000036266214,0.000023249147,0.00016261057,0.00003895988,0.000007375701,0.0001426411,0.92580837,0.00006792964,0.00018815602,0.000116594805,0.07338328],"study_design_scores_gemma":[0.00020666156,0.00037655432,0.000029265819,0.00022522411,0.00003376778,0.000071699316,0.000030726984,0.9876874,0.0011437834,0.00012074395,0.009767136,0.00030705187],"about_ca_topic_score_codex":0.000023240606,"about_ca_topic_score_gemma":0.0000052470937,"teacher_disagreement_score":0.9824369,"about_ca_system_score_codex":0.000058010726,"about_ca_system_score_gemma":0.000014635949,"threshold_uncertainty_score":0.9999807},"labels":[],"label_agreement":null},{"id":"W4392906054","doi":"10.32920/25412848","title":"Understanding, Interpreting and Learning Representations in Deep Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Manitoba; York University","funders":"","keywords":"Computer science; Interpretability; Pooling; ENCODE; Artificial intelligence; Position (finance); Convolutional neural network; Representation (politics)","score_opus":0.05810391097385165,"score_gpt":0.31687392906330236,"score_spread":0.2587700180894507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392906054","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":[0.00222354,0.0007749459,0.9855671,0.0025663238,0.00044191052,0.0003424976,4.3667302e-7,0.00051581115,0.007567413],"genre_scores_gemma":[0.96407145,0.00011539039,0.03507581,0.00020696064,0.00008666749,0.0001064036,0.000008874056,0.00002457426,0.0003038794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812615,0.00013599372,0.00036141177,0.0009162185,0.00014465154,0.00031559827],"domain_scores_gemma":[0.998764,0.00048435692,0.00012141328,0.00052184536,0.000022833383,0.00008557911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024937923,0.00022600986,0.00021424613,0.00022709278,0.00015448655,0.000468831,0.00060004974,0.00014910239,0.000008041833],"category_scores_gemma":[0.00006682741,0.00022881612,0.000061764666,0.0005252071,0.00006792705,0.00016850622,0.0044661993,0.0020698989,0.0000064410906],"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":[0.0000015652055,0.000007538371,0.0010281756,0.000027151667,0.000010854655,0.000019577104,0.0008317846,0.8563858,0.0000071957675,0.12988488,0.00017034163,0.011625132],"study_design_scores_gemma":[0.00004969557,0.000009206892,0.00017828813,0.00008072042,0.000005853484,0.000014520683,0.00016682556,0.86659414,0.0000030985166,0.1326501,0.000060499504,0.00018704425],"about_ca_topic_score_codex":0.000048609927,"about_ca_topic_score_gemma":0.00019429183,"teacher_disagreement_score":0.9618479,"about_ca_system_score_codex":0.00017440277,"about_ca_system_score_gemma":0.000019927513,"threshold_uncertainty_score":0.933085},"labels":[],"label_agreement":null},{"id":"W4392906087","doi":"10.32920/25412848.v1","title":"Understanding, Interpreting and Learning Representations in Deep Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Manitoba; York University","funders":"","keywords":"Interpretability; Computer science; Pooling; ENCODE; Artificial intelligence; Position (finance); Convolutional neural network; Representation (politics)","score_opus":0.05810391097385165,"score_gpt":0.31687392906330236,"score_spread":0.2587700180894507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392906087","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":[0.00222354,0.0007749459,0.9855671,0.0025663238,0.00044191052,0.0003424976,4.3667302e-7,0.00051581115,0.007567413],"genre_scores_gemma":[0.96407145,0.00011539039,0.03507581,0.00020696064,0.00008666749,0.0001064036,0.000008874056,0.00002457426,0.0003038794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812615,0.00013599372,0.00036141177,0.0009162185,0.00014465154,0.00031559827],"domain_scores_gemma":[0.998764,0.00048435692,0.00012141328,0.00052184536,0.000022833383,0.00008557911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024937923,0.00022600986,0.00021424613,0.00022709278,0.00015448655,0.000468831,0.00060004974,0.00014910239,0.000008041833],"category_scores_gemma":[0.00006682741,0.00022881612,0.000061764666,0.0005252071,0.00006792705,0.00016850622,0.0044661993,0.0020698989,0.0000064410906],"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":[0.0000015652055,0.000007538371,0.0010281756,0.000027151667,0.000010854655,0.000019577104,0.0008317846,0.8563858,0.0000071957675,0.12988488,0.00017034163,0.011625132],"study_design_scores_gemma":[0.00004969557,0.000009206892,0.00017828813,0.00008072042,0.000005853484,0.000014520683,0.00016682556,0.86659414,0.0000030985166,0.1326501,0.000060499504,0.00018704425],"about_ca_topic_score_codex":0.000048609927,"about_ca_topic_score_gemma":0.00019429183,"teacher_disagreement_score":0.9618479,"about_ca_system_score_codex":0.00017440277,"about_ca_system_score_gemma":0.000019927513,"threshold_uncertainty_score":0.933085},"labels":[],"label_agreement":null},{"id":"W4392907847","doi":"10.32920/25412650.v1","title":"Smart Surveillance of Secondary Brain Tumours","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fluid-attenuated inversion recovery; Segmentation; Deep learning; Artificial intelligence; Computer science; Gadolinium; Image segmentation; Transfer of learning; Medicine; Pattern recognition (psychology); Magnetic resonance imaging; Radiology","score_opus":0.016418220854690496,"score_gpt":0.27498687968625385,"score_spread":0.2585686588315633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392907847","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":[0.008921288,0.0020032478,0.90107065,0.024060803,0.0018837966,0.0007193787,0.00008004881,0.0011286694,0.060132094],"genre_scores_gemma":[0.86563975,0.000081567974,0.127026,0.0010187833,0.00018789129,0.00012815504,0.00003345601,0.000038556052,0.0058458247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998374,0.000054448265,0.00036562077,0.0007522205,0.00022348203,0.00023026342],"domain_scores_gemma":[0.9980021,0.00028544513,0.00016391637,0.0013867065,0.00008452488,0.00007727285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023489098,0.00021697627,0.00030758212,0.000112283735,0.000033625092,0.00007813048,0.0015692804,0.0001226463,0.00004436364],"category_scores_gemma":[0.000027264081,0.00020248065,0.00013923539,0.00040169928,0.000069615126,0.00006853988,0.0040491265,0.0007212577,0.00012867985],"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":[0.0000048401207,0.000081005375,0.0006551115,0.0006035903,0.00007668094,0.000032679105,0.00029741976,0.0074828193,0.0014539468,0.7067227,0.16162261,0.12096664],"study_design_scores_gemma":[0.00016191478,0.00004600542,0.0065965257,0.00017707924,0.000009210368,0.000033389064,0.000010496288,0.09567808,0.0049674693,0.85737616,0.034165118,0.00077852956],"about_ca_topic_score_codex":0.00002839689,"about_ca_topic_score_gemma":0.00005767751,"teacher_disagreement_score":0.8567185,"about_ca_system_score_codex":0.00003922121,"about_ca_system_score_gemma":0.0002157492,"threshold_uncertainty_score":0.82569206},"labels":[],"label_agreement":null},{"id":"W4392908091","doi":"10.1109/jiot.2024.3373028","title":"A Lightweight Small Object Detection Method Based on Multilayer Coordination Federated Intelligence for Coal Mine IoVT","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Coal mining; Object detection; Latency (audio); Real-time computing; Cloud computing; Artificial intelligence; Distributed computing; Coal; Pattern recognition (psychology); Operating system","score_opus":0.032509395618520405,"score_gpt":0.3163988176578051,"score_spread":0.2838894220392847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392908091","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":[0.007825966,0.00010314311,0.9883304,0.0014954947,0.001602132,0.00032696433,0.0000023629807,0.00016487656,0.00014864118],"genre_scores_gemma":[0.75746673,0.0000072956386,0.24149436,0.0003361706,0.00019413035,0.00004036837,0.0000015393484,0.000020640327,0.00043879787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983874,0.00011000369,0.0005427966,0.00041605352,0.00028347393,0.00026029343],"domain_scores_gemma":[0.99822414,0.00083932874,0.00028836934,0.00021933236,0.000325014,0.0001038432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007523995,0.00020163473,0.00021938514,0.00033956105,0.00014106726,0.0004110504,0.00066045404,0.00009725461,0.000012215093],"category_scores_gemma":[0.00013199012,0.0001704757,0.0001951046,0.00042784147,0.000031925752,0.00059653056,0.00005000006,0.00047585167,0.000015683521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039473726,0.00030361512,0.000020832254,0.00015756971,0.00016599438,0.00006147604,0.0017932553,0.095235676,0.17861362,0.0052562086,0.0038660236,0.714131],"study_design_scores_gemma":[0.0001370134,0.00029361292,0.0000082429315,0.0001726874,0.000013241767,0.000112253954,0.0000063292537,0.63497573,0.35902005,0.002882973,0.0022682776,0.00010960261],"about_ca_topic_score_codex":0.000020612108,"about_ca_topic_score_gemma":0.000013253863,"teacher_disagreement_score":0.74964076,"about_ca_system_score_codex":0.00018375454,"about_ca_system_score_gemma":0.00007297384,"threshold_uncertainty_score":0.6951797},"labels":[],"label_agreement":null},{"id":"W4392928048","doi":"10.32920/25417270.v1","title":"Information Theoretic Measures Applied to Deep Learning Models","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mutual information; Computer science; Maximization; Artificial intelligence; Deep learning; Machine learning; Architecture; Information theory; Interaction information; Mathematics; Statistics","score_opus":0.025829266542905165,"score_gpt":0.25578156167396626,"score_spread":0.2299522951310611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392928048","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":[0.00019006453,0.000096477524,0.9054222,0.0013129092,0.00024604486,0.0006611435,0.000001216267,0.0011390597,0.090930894],"genre_scores_gemma":[0.82452023,0.000051573148,0.17361128,0.00084696425,0.00008368688,0.0005784252,0.0000181271,0.00001801173,0.00027168886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985863,0.000028967239,0.00033628664,0.000407728,0.0003709584,0.00026976765],"domain_scores_gemma":[0.99874777,0.00007810623,0.000102219594,0.00084196794,0.00010485163,0.00012511459],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021699848,0.00024002691,0.00019459103,0.00021322223,0.000114693146,0.0004858032,0.0012631799,0.000138333,0.000009411107],"category_scores_gemma":[0.000021264274,0.00021384447,0.00007296262,0.00045172384,0.000025352309,0.0003052803,0.0036538248,0.0008023116,0.00097095943],"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":[9.769805e-7,0.00000198039,1.4603292e-7,0.000019480356,0.0000047758635,1.4865067e-7,0.00067547616,0.45842,0.000014650911,0.45488185,0.00016333465,0.08581719],"study_design_scores_gemma":[0.000020273967,0.000005662103,0.000003164474,0.00001873923,0.0000053320373,0.0000014423772,0.000024859452,0.49163964,0.00016988713,0.5050791,0.0028708212,0.00016108804],"about_ca_topic_score_codex":0.0000055305777,"about_ca_topic_score_gemma":0.0000039684096,"teacher_disagreement_score":0.82433015,"about_ca_system_score_codex":0.0000898732,"about_ca_system_score_gemma":0.000056471206,"threshold_uncertainty_score":0.9998069},"labels":[],"label_agreement":null},{"id":"W4392928102","doi":"10.32920/25417270","title":"Information Theoretic Measures Applied to Deep Learning Models","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mutual information; Computer science; Maximization; Artificial intelligence; Deep learning; Machine learning; Architecture; Interaction information; Information theory; Data mining; Mathematics; Statistics","score_opus":0.025829266542905165,"score_gpt":0.25578156167396626,"score_spread":0.2299522951310611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392928102","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":[0.00019006453,0.000096477524,0.9054222,0.0013129092,0.00024604486,0.0006611435,0.000001216267,0.0011390597,0.090930894],"genre_scores_gemma":[0.82452023,0.000051573148,0.17361128,0.00084696425,0.00008368688,0.0005784252,0.0000181271,0.00001801173,0.00027168886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985863,0.000028967239,0.00033628664,0.000407728,0.0003709584,0.00026976765],"domain_scores_gemma":[0.99874777,0.00007810623,0.000102219594,0.00084196794,0.00010485163,0.00012511459],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021699848,0.00024002691,0.00019459103,0.00021322223,0.000114693146,0.0004858032,0.0012631799,0.000138333,0.000009411107],"category_scores_gemma":[0.000021264274,0.00021384447,0.00007296262,0.00045172384,0.000025352309,0.0003052803,0.0036538248,0.0008023116,0.00097095943],"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":[9.769805e-7,0.00000198039,1.4603292e-7,0.000019480356,0.0000047758635,1.4865067e-7,0.00067547616,0.45842,0.000014650911,0.45488185,0.00016333465,0.08581719],"study_design_scores_gemma":[0.000020273967,0.000005662103,0.000003164474,0.00001873923,0.0000053320373,0.0000014423772,0.000024859452,0.49163964,0.00016988713,0.5050791,0.0028708212,0.00016108804],"about_ca_topic_score_codex":0.0000055305777,"about_ca_topic_score_gemma":0.0000039684096,"teacher_disagreement_score":0.82433015,"about_ca_system_score_codex":0.0000898732,"about_ca_system_score_gemma":0.000056471206,"threshold_uncertainty_score":0.9998069},"labels":[],"label_agreement":null},{"id":"W4392943516","doi":"10.1109/icmla58977.2023.00301","title":"SAttisUNet: UNet-like Swin Transformer with Attentive Skip Connections for Enhanced Medical Image Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Image segmentation; Computer science; Transformer; Computer vision; Segmentation; Artificial intelligence; Electrical engineering; Engineering; Voltage","score_opus":0.015362371612935919,"score_gpt":0.29778672298784326,"score_spread":0.28242435137490735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392943516","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":[0.00950557,0.0000074228797,0.9807912,0.0064413603,0.00016833845,0.0008764375,0.000009964239,0.0006308898,0.0015687916],"genre_scores_gemma":[0.6245278,0.00011055689,0.36474028,0.0024007722,0.00020311803,0.0025173076,0.00018482009,0.000056239758,0.005259063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858093,0.000026028276,0.0002275134,0.00047030306,0.00034312208,0.00035208635],"domain_scores_gemma":[0.9990471,0.00033479178,0.00006097321,0.00030404804,0.000111797235,0.00014129619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014076586,0.00014643908,0.00014035586,0.00010016443,0.00027907075,0.00005870411,0.000392953,0.00005675872,0.00009408769],"category_scores_gemma":[0.000017097427,0.00011939015,0.00005988031,0.0011084209,0.00008497886,0.00053209974,0.000028062594,0.00010851282,0.00018640635],"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":[0.0001578108,0.0004660855,0.0002871518,0.00010968985,0.00029265668,0.000033209428,0.004122477,0.0053241244,0.35831162,0.23504074,0.038538493,0.35731593],"study_design_scores_gemma":[0.008051491,0.0011803319,0.006475418,0.00014020868,0.000104732746,0.00009816906,0.0015919181,0.33483407,0.5716688,0.031600334,0.042461388,0.0017931242],"about_ca_topic_score_codex":0.000010637936,"about_ca_topic_score_gemma":0.00020972766,"teacher_disagreement_score":0.61605096,"about_ca_system_score_codex":0.00004127079,"about_ca_system_score_gemma":0.00006800636,"threshold_uncertainty_score":0.4868589},"labels":[],"label_agreement":null},{"id":"W4392943519","doi":"10.1109/cac59555.2023.10450780","title":"Moving Object Segmentation by Combing Deep Learning and Geometric Constraints for Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Combing; Computer science; Segmentation; Artificial intelligence; Computer vision; Object (grammar); Deep learning; Image segmentation; Computer graphics (images); Geography; Cartography","score_opus":0.014489344281623903,"score_gpt":0.2754000120129523,"score_spread":0.26091066773132837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392943519","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":[0.041710053,0.000063612824,0.9565328,0.00026163066,0.000068706235,0.0003193589,8.149484e-7,0.00060872483,0.00043434885],"genre_scores_gemma":[0.89009625,0.00005094941,0.10910816,0.000092735325,0.000018714552,0.00010015386,0.00001532044,0.000011857459,0.00050583796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990901,0.000023711915,0.00016613875,0.00033782204,0.00010744902,0.00027479557],"domain_scores_gemma":[0.998986,0.00070726086,0.000082220045,0.00012364869,0.000034404264,0.00006646896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020756155,0.000093916504,0.00010000941,0.00019160815,0.00036506454,0.00013726643,0.00019206684,0.00002970589,0.0000064711394],"category_scores_gemma":[0.0000984266,0.00009947838,0.000024581088,0.0011272297,0.000040771796,0.00035873963,0.00016964205,0.000091031354,0.000017093918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.0863216e-7,0.000013851718,0.004229406,0.000015991685,0.00001279173,0.000001629396,0.0003069925,0.006595758,0.017068842,0.009929509,0.00050447,0.96131986],"study_design_scores_gemma":[0.00042110714,0.00006260979,0.0025021993,0.000011477811,0.000005689556,0.000011999308,0.00025853023,0.98667645,0.0057187965,0.0028119988,0.0012936438,0.00022552215],"about_ca_topic_score_codex":0.0000031564773,"about_ca_topic_score_gemma":0.0000029634014,"teacher_disagreement_score":0.98008066,"about_ca_system_score_codex":0.000042765616,"about_ca_system_score_gemma":0.000012338039,"threshold_uncertainty_score":0.40566105},"labels":[],"label_agreement":null},{"id":"W4393132109","doi":"10.1016/j.eswa.2024.123709","title":"OENet: An overexposure correction network fused with residual block and transformer","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Residual; Computer science; Transformer; Block (permutation group theory); Algorithm; Electrical engineering; Mathematics; Voltage","score_opus":0.01013189593889547,"score_gpt":0.24704922649607236,"score_spread":0.2369173305571769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393132109","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":[0.002619185,0.0034554752,0.98873025,0.0009497268,0.00023104704,0.0016611589,0.000009872481,0.0009703616,0.0013729311],"genre_scores_gemma":[0.96053153,0.00016079721,0.032150615,0.0001996655,0.0009842312,0.0049652737,0.00003426201,0.00006227305,0.0009113764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982659,0.000053975844,0.0002698778,0.00077890645,0.0002845974,0.00034669586],"domain_scores_gemma":[0.9987036,0.00013248286,0.000073821866,0.0008106276,0.00008933682,0.00019013848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000118730924,0.00024398515,0.00020047923,0.000081463135,0.0004387858,0.00035814845,0.00037987076,0.00008267299,0.0000023664945],"category_scores_gemma":[0.0000011919868,0.0001810013,0.000020784102,0.0010207404,0.0000921792,0.0006350478,0.000026211841,0.00020006052,0.00002444519],"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":[0.00023071787,0.0005720177,0.0027768489,0.00034107393,0.0005028728,0.00006937258,0.0111448,0.19900146,0.0062878104,0.56334454,0.067573495,0.14815502],"study_design_scores_gemma":[0.0011201727,0.0011365566,0.0025694869,0.0005693151,0.00007496048,0.0020757436,0.00081382686,0.308854,0.0011601539,0.0015307692,0.6785992,0.0014958109],"about_ca_topic_score_codex":0.00006499552,"about_ca_topic_score_gemma":0.00011546358,"teacher_disagreement_score":0.9579123,"about_ca_system_score_codex":0.000056901314,"about_ca_system_score_gemma":0.00009018752,"threshold_uncertainty_score":0.7381019},"labels":[],"label_agreement":null},{"id":"W4393150397","doi":"10.1609/aaai.v38i2.27901","title":"NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Lookup table; Image (mathematics); Computer science; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Algorithm; Arithmetic; Mathematics","score_opus":0.05887302667842721,"score_gpt":0.3264027947063941,"score_spread":0.2675297680279669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393150397","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":[0.010843185,0.000102783044,0.975299,0.008880477,0.00058823207,0.0010492794,0.00006535853,0.00020959982,0.0029621066],"genre_scores_gemma":[0.96418256,0.00004003296,0.034337357,0.00038730178,0.00017898303,0.00036202432,0.000006704641,0.000019753326,0.0004852704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99799114,0.0000089212135,0.0005223866,0.0006679759,0.0003928548,0.00041674022],"domain_scores_gemma":[0.9986311,0.00025600192,0.0002043857,0.00030124674,0.0005196286,0.00008763059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027528993,0.00024260793,0.00021951548,0.00011070142,0.00029946966,0.00041608684,0.0016548892,0.00006328201,0.00012846568],"category_scores_gemma":[0.00013512665,0.00018784733,0.00014681907,0.0006741967,0.0002837799,0.0006992869,0.00033686962,0.00024478504,0.00011394533],"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":[0.000016959393,0.000057169884,0.0000027997933,0.000051077783,0.000012230123,3.3661885e-7,0.00018325346,0.00015203786,0.14221987,0.8090293,0.0012173883,0.047057614],"study_design_scores_gemma":[0.000013572163,0.00010084621,0.000010079887,0.00008913224,0.000009815752,0.0000061295136,0.0000441791,0.27614623,0.35211492,0.37009117,0.0012283751,0.00014552259],"about_ca_topic_score_codex":0.000008152831,"about_ca_topic_score_gemma":0.000004955837,"teacher_disagreement_score":0.9533394,"about_ca_system_score_codex":0.0000652434,"about_ca_system_score_gemma":0.00008946944,"threshold_uncertainty_score":0.76601917},"labels":[],"label_agreement":null},{"id":"W4393308750","doi":"10.1007/s11554-024-01436-6","title":"Slim-neck by GSConv: a lightweight-design for real-time detector architectures","year":2024,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":546,"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":"National Natural Science Foundation of China","keywords":"Computer science; Detector; Code (set theory); Enhanced Data Rates for GSM Evolution; State (computer science); Convolutional code; Computer engineering; Real-time computing; Computer hardware; Artificial intelligence; Embedded system; Algorithm; Telecommunications; Programming language","score_opus":0.01309747618737339,"score_gpt":0.2808434033432951,"score_spread":0.2677459271559217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393308750","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":[0.006152376,0.0022544882,0.9866465,0.00298416,0.00017094029,0.0005009837,0.000012521976,0.00040378387,0.00087427645],"genre_scores_gemma":[0.027968725,0.00035153708,0.96868443,0.0001608773,0.0008291848,0.000065671615,0.0000041816447,0.00010445612,0.0018309392],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974264,0.00010200735,0.00082319207,0.0005559509,0.0005029178,0.0005895562],"domain_scores_gemma":[0.9975338,0.00077364687,0.0006047637,0.00041393156,0.00039329505,0.00028055036],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062102446,0.00035616977,0.0004999642,0.00033714095,0.00032154794,0.00090097444,0.0012022213,0.00010995578,0.000030702533],"category_scores_gemma":[0.00013068072,0.00028217142,0.00025739986,0.0008600556,0.000104997525,0.0012803087,0.0001444418,0.0004182281,0.00007434732],"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":[0.000072807394,0.000060450548,0.0000014930507,0.00018269126,0.000050711737,0.000074890915,0.0004064761,0.0005264783,0.84078777,0.000086723296,0.035223823,0.122525685],"study_design_scores_gemma":[0.0009851521,0.0008300459,0.00003208964,0.0012622625,0.00016218665,0.0013664364,0.000015629099,0.582661,0.36175737,0.033754185,0.016293874,0.0008798008],"about_ca_topic_score_codex":0.0000021013454,"about_ca_topic_score_gemma":1.7121192e-7,"teacher_disagreement_score":0.5821345,"about_ca_system_score_codex":0.00014228547,"about_ca_system_score_gemma":0.00039992694,"threshold_uncertainty_score":0.99996305},"labels":[],"label_agreement":null},{"id":"W4393372396","doi":"10.1109/tmi.2024.3383716","title":"Deep Generative Adversarial Reinforcement Learning for Semi-Supervised Segmentation of Low-Contrast and Small Objects in Medical Images","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Reinforcement learning; Artificial intelligence; Dice; Generalization; Contrast (vision); Image segmentation; Generative grammar; Machine learning; Pattern recognition (psychology); Pipeline (software); Computer vision; Mathematics","score_opus":0.011384335194320288,"score_gpt":0.27569448985521317,"score_spread":0.26431015466089286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393372396","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":[0.0028859372,0.00022822252,0.9932687,0.0025600158,0.0004386213,0.00042324138,0.0000018886249,0.00012328001,0.00007008959],"genre_scores_gemma":[0.9758325,0.0002572849,0.023066241,0.0004699191,0.000102623504,0.00020818581,0.0000064909464,0.000015932557,0.00004080601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834275,0.00008268345,0.00039488077,0.00041637954,0.0004974105,0.00026587857],"domain_scores_gemma":[0.9988274,0.000729398,0.00004855695,0.00014771413,0.000053029875,0.00019391665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039736874,0.00014997467,0.00018819785,0.00018941109,0.00013757062,0.000061949344,0.0002587234,0.00007239144,0.000053279924],"category_scores_gemma":[0.000064204294,0.00014166867,0.000066229506,0.00039482367,0.00013477539,0.00036717104,0.000008226428,0.0004407446,0.0000034549496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050684986,0.00011080754,0.000028325027,0.00016638631,0.000047223915,0.000056175715,0.0023884068,0.197314,0.0131734125,0.00086208276,0.00005403045,0.7857485],"study_design_scores_gemma":[0.00096904807,0.00004932959,0.000018333254,0.000253419,0.000014886037,0.00002254289,0.0001658266,0.97002,0.027964348,0.0003487682,0.0000480469,0.00012547195],"about_ca_topic_score_codex":0.000021028329,"about_ca_topic_score_gemma":0.00005443418,"teacher_disagreement_score":0.9729466,"about_ca_system_score_codex":0.000078246325,"about_ca_system_score_gemma":0.0001560398,"threshold_uncertainty_score":0.5777081},"labels":[],"label_agreement":null},{"id":"W4393388703","doi":"10.1007/s12530-024-09574-9","title":"Enhancing CNN structure and learning through NSGA-II-based multi-objective optimization","year":2024,"lang":"en","type":"article","venue":"Evolving Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Artificial intelligence; Multi-objective optimization; Mathematical optimization; Machine learning; Mathematics","score_opus":0.012615906655004222,"score_gpt":0.26042639883651003,"score_spread":0.2478104921815058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393388703","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":[0.0037160297,0.0033864826,0.9908137,0.00015707537,0.0006932554,0.00034600348,0.0000026454952,0.00071679184,0.00016805597],"genre_scores_gemma":[0.75481975,0.0000113519145,0.24452516,0.000038217575,0.00015953153,0.000038694034,0.0000062985814,0.00002129136,0.00037967283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986168,0.00013864055,0.00023956833,0.0005621352,0.00019434211,0.00024852288],"domain_scores_gemma":[0.9991153,0.00033329404,0.00009321526,0.00031108074,0.00009386155,0.00005326175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018792025,0.00017063,0.00016639453,0.000085167354,0.00054354756,0.00041087306,0.00026740748,0.00007782327,0.0000062653157],"category_scores_gemma":[0.00009592351,0.00015911087,0.00003383004,0.00060995243,0.00003287466,0.00083661184,0.00014942768,0.0003298659,0.0000093137005],"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":[9.596366e-7,0.000005930707,0.000081692146,0.00009776309,0.0000143838715,0.000006300408,0.0024526848,0.98623425,0.0051286328,0.004841783,0.00010819933,0.0010274284],"study_design_scores_gemma":[0.00011467994,0.0000360985,0.00009958729,0.00027639495,0.0000073014608,0.000021037402,0.00010642688,0.9966384,0.001290041,0.00019620836,0.001036534,0.00017728632],"about_ca_topic_score_codex":0.000052534066,"about_ca_topic_score_gemma":0.000009434515,"teacher_disagreement_score":0.75110376,"about_ca_system_score_codex":0.00011318469,"about_ca_system_score_gemma":0.00005603854,"threshold_uncertainty_score":0.6488353},"labels":[],"label_agreement":null},{"id":"W4393448579","doi":"10.5281/zenodo.5866189","title":"Backport Dataset","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science","score_opus":0.044243274655954415,"score_gpt":0.27832191114302374,"score_spread":0.23407863648706934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393448579","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":[0.0000012266916,0.000072299285,0.015562255,0.0007138718,0.00021831343,0.00052228034,0.9790359,0.00080556184,0.00306827],"genre_scores_gemma":[0.000012204282,0.00028966737,0.0010175841,0.0005933891,0.00016424546,2.5912428e-7,0.9969186,0.00076294254,0.00024110152],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99705637,0.00036566376,0.00037024327,0.0010046393,0.0006980358,0.000505043],"domain_scores_gemma":[0.9965516,0.000049913393,0.00027661724,0.002656054,0.00022619608,0.00023965615],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.0005594804,0.00026736106,0.00023382442,0.00030981825,0.0036052356,0.0010594567,0.007378349,0.00009709811,0.1162954],"category_scores_gemma":[0.00023901113,0.00030732912,0.00006602503,0.0012150407,0.00013173663,0.0004784394,0.010286661,0.00084410654,0.0139479],"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":[0.0000058108158,0.00009568876,1.4694846e-8,0.000029590305,0.000020730295,0.00003742282,0.000022051454,0.00008669884,0.000021231443,0.0016274898,0.98004705,0.01800624],"study_design_scores_gemma":[0.00016731546,0.00011457108,0.0000049744062,0.000008266838,0.000013578968,0.00023260983,0.000010597719,0.00016906332,0.00000828203,0.0003110009,0.99865067,0.00030908882],"about_ca_topic_score_codex":0.000016580623,"about_ca_topic_score_gemma":4.5176404e-7,"teacher_disagreement_score":0.1023475,"about_ca_system_score_codex":0.00022839657,"about_ca_system_score_gemma":0.000008965008,"threshold_uncertainty_score":0.9999775},"labels":[],"label_agreement":null},{"id":"W4393658308","doi":"10.5281/zenodo.4777564","title":"Backport Dataset","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Geography; Computer science","score_opus":0.044243274655954415,"score_gpt":0.27832191114302374,"score_spread":0.23407863648706934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393658308","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":[0.0000012266916,0.000072299285,0.015562255,0.0007138718,0.00021831343,0.00052228034,0.9790359,0.00080556184,0.00306827],"genre_scores_gemma":[0.000012204282,0.00028966737,0.0010175841,0.0005933891,0.00016424546,2.5912428e-7,0.9969186,0.00076294254,0.00024110152],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99705637,0.00036566376,0.00037024327,0.0010046393,0.0006980358,0.000505043],"domain_scores_gemma":[0.9965516,0.000049913393,0.00027661724,0.002656054,0.00022619608,0.00023965615],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.0005594804,0.00026736106,0.00023382442,0.00030981825,0.0036052356,0.0010594567,0.007378349,0.00009709811,0.1162954],"category_scores_gemma":[0.00023901113,0.00030732912,0.00006602503,0.0012150407,0.00013173663,0.0004784394,0.010286661,0.00084410654,0.0139479],"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":[0.0000058108158,0.00009568876,1.4694846e-8,0.000029590305,0.000020730295,0.00003742282,0.000022051454,0.00008669884,0.000021231443,0.0016274898,0.98004705,0.01800624],"study_design_scores_gemma":[0.00016731546,0.00011457108,0.0000049744062,0.000008266838,0.000013578968,0.00023260983,0.000010597719,0.00016906332,0.00000828203,0.0003110009,0.99865067,0.00030908882],"about_ca_topic_score_codex":0.000016580623,"about_ca_topic_score_gemma":4.5176404e-7,"teacher_disagreement_score":0.1023475,"about_ca_system_score_codex":0.00022839657,"about_ca_system_score_gemma":0.000008965008,"threshold_uncertainty_score":0.9999775},"labels":[],"label_agreement":null},{"id":"W4393864821","doi":"10.1109/jiot.2024.3362851","title":"Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Data Sets, Methods, and Challenges","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Overtaking; Computer science; Artificial intelligence; Rendering (computer graphics); Object detection; Segmentation; Obstacle; Automation; Field (mathematics); Context (archaeology); Human–computer interaction; Computer vision; Engineering","score_opus":0.10913541302129046,"score_gpt":0.38254957728192596,"score_spread":0.2734141642606355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393864821","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":[0.00022768189,0.0017364689,0.9927258,0.0037862214,0.0010766765,0.00018988842,0.0000046264504,0.00012423072,0.00012842401],"genre_scores_gemma":[0.25400335,0.00045262804,0.7450004,0.0003666218,0.0000805899,0.00001321293,0.000002383717,0.000021322365,0.00005945148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984418,0.000091381095,0.00042933508,0.00054279366,0.00023980318,0.00025488876],"domain_scores_gemma":[0.99792963,0.0010098297,0.00024598493,0.0006262671,0.00007695974,0.0001113354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012637144,0.00017770883,0.00021631975,0.00019374906,0.00009662682,0.00030206155,0.0017585931,0.00006229787,0.000007947705],"category_scores_gemma":[0.00014467411,0.00015377397,0.000073846124,0.00015412705,0.00004484482,0.00092022336,0.0003404415,0.00038228065,0.0000031973316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020916876,0.000058281235,0.0000017127504,0.00011244742,0.00007426571,0.000021187265,0.0010462834,0.002305945,0.0067038904,0.113579944,0.0010574942,0.87501764],"study_design_scores_gemma":[0.000082323124,0.00019550967,0.000006149535,0.00046146676,0.000017863198,0.00014526263,0.00002862729,0.8953094,0.027813267,0.06842528,0.0073772594,0.00013759587],"about_ca_topic_score_codex":0.000004332787,"about_ca_topic_score_gemma":0.000002099892,"teacher_disagreement_score":0.89300346,"about_ca_system_score_codex":0.00006734099,"about_ca_system_score_gemma":0.00006157921,"threshold_uncertainty_score":0.6270721},"labels":[],"label_agreement":null},{"id":"W4394597250","doi":"10.1109/wacv57701.2024.00381","title":"Defending Object Detection Models against Image Distortions","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Image (mathematics); Object detection; Object (grammar); Pattern recognition (psychology)","score_opus":0.02127055813271797,"score_gpt":0.26790182660237255,"score_spread":0.24663126846965458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394597250","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":[0.00084557675,0.00024921945,0.97796917,0.0005355363,0.00041225925,0.00011144908,0.0000017218293,0.0013136956,0.018561352],"genre_scores_gemma":[0.90426284,0.00004738293,0.09487081,0.000105543964,0.00008171872,0.0000715191,0.0000019033233,0.000010913816,0.00054734456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991911,0.000016334065,0.00013435287,0.00035660277,0.000118122756,0.0001834638],"domain_scores_gemma":[0.99939984,0.0001247071,0.00001848844,0.0003692664,0.000027232374,0.000060462455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000063646774,0.00008828601,0.00005851324,0.00009823616,0.0002173864,0.00020999268,0.00030008587,0.000026094014,0.000007519853],"category_scores_gemma":[0.0000123305035,0.000080224374,0.000059776765,0.0007159235,0.0000288159,0.0013384812,0.00011938322,0.0001212855,0.00017932407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.137709e-7,0.000025892497,0.0000029235655,0.0000193993,0.00001219953,0.000016479735,0.00026830833,0.0207207,0.079305224,0.6069963,0.001695307,0.29093635],"study_design_scores_gemma":[0.000019311705,0.000008704409,0.000015748223,0.0000106605885,0.000003283268,0.000012396892,0.000007236868,0.9199569,0.0091117695,0.06514143,0.005599739,0.00011281051],"about_ca_topic_score_codex":0.0000047619305,"about_ca_topic_score_gemma":0.000018568744,"teacher_disagreement_score":0.9034173,"about_ca_system_score_codex":0.000090912785,"about_ca_system_score_gemma":0.000020096288,"threshold_uncertainty_score":0.32714552},"labels":[],"label_agreement":null},{"id":"W4394597746","doi":"10.1109/wacv57701.2024.00332","title":"SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Object (grammar); Computer science; Object detection; Computer vision; Domain adaptation; Domain (mathematical analysis); Artificial intelligence; Labelling; Adaptation (eye); Pattern recognition (psychology); Mathematics; Optics; Physics","score_opus":0.0400239221683896,"score_gpt":0.3196846485735745,"score_spread":0.2796607264051849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394597746","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":[0.06462187,0.00022378852,0.9325896,0.00020294939,0.000627798,0.00078633835,0.000010564326,0.0007768253,0.00016028181],"genre_scores_gemma":[0.38489947,0.000022824956,0.6142855,0.00009401476,0.0002188249,0.00014267227,0.000022859002,0.000026318716,0.00028751374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982295,0.00005824413,0.0004036624,0.0006824053,0.0002936693,0.0003324947],"domain_scores_gemma":[0.99867964,0.00055466493,0.00012510656,0.0003608836,0.00020912591,0.00007058458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003189779,0.000197408,0.00013662822,0.00022113751,0.0005277032,0.00044562868,0.00027041684,0.00009267847,0.000008314365],"category_scores_gemma":[0.000047826354,0.00019790547,0.0000936997,0.0009933471,0.00004779306,0.001551075,0.00006258717,0.00015130456,0.00004490158],"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":[0.000047594105,0.000056821984,0.000087550325,0.00012497265,0.00005139056,0.00000972006,0.0013818515,0.49452904,0.09384239,0.058149222,0.000051531093,0.35166794],"study_design_scores_gemma":[0.00021600991,0.000064124084,0.0001822336,0.000038866914,0.000011897015,0.0000377128,0.000061920415,0.9472064,0.012589876,0.037939146,0.0014246919,0.00022711688],"about_ca_topic_score_codex":0.000026094447,"about_ca_topic_score_gemma":0.000039565017,"teacher_disagreement_score":0.45267737,"about_ca_system_score_codex":0.00022629932,"about_ca_system_score_gemma":0.00011437872,"threshold_uncertainty_score":0.807035},"labels":[],"label_agreement":null},{"id":"W4394625616","doi":"10.1109/wacv57701.2024.00733","title":"Re-VoxelDet: Rethinking Neck and Head Architectures for High-Performance Voxel-based 3D Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"","keywords":"Computer science; Voxel; Head and neck; Head (geology); Artificial intelligence; Computer vision; Geology; Medicine","score_opus":0.019001255693728084,"score_gpt":0.27066230289505483,"score_spread":0.25166104720132676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394625616","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":[0.20686647,0.00021591838,0.7895178,0.0019959428,0.00023463265,0.00031767084,0.0000014717032,0.00060957286,0.00024050093],"genre_scores_gemma":[0.8145792,0.00000993177,0.18439274,0.000612602,0.00012731041,0.00012323585,0.0000017364785,0.00001401993,0.0001392183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989771,0.000016859443,0.00016646979,0.00047139608,0.00012878055,0.00023939],"domain_scores_gemma":[0.9991947,0.00031332488,0.000033721095,0.0003664095,0.00003358491,0.000058274072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001304996,0.00013314221,0.00010244037,0.000104571,0.00025022673,0.00015885069,0.0002838505,0.000056556215,0.000003894563],"category_scores_gemma":[0.000018383584,0.000112205285,0.000029478733,0.00041148768,0.00004366269,0.00015555968,0.000081592596,0.00017581112,0.000009982105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025687923,0.00001776395,0.00009967127,0.00019198991,0.00001078544,0.0000028940472,0.00034413303,0.0336973,0.0074213534,0.020114645,0.00024177658,0.937832],"study_design_scores_gemma":[0.00017448384,0.00015839307,0.0006738976,0.000064619824,0.00000634271,0.000008314375,0.0000025292636,0.9073827,0.059598733,0.028136317,0.003616383,0.00017724793],"about_ca_topic_score_codex":0.000011058617,"about_ca_topic_score_gemma":0.00011210694,"teacher_disagreement_score":0.93765473,"about_ca_system_score_codex":0.000033261393,"about_ca_system_score_gemma":0.000027698528,"threshold_uncertainty_score":0.45755988},"labels":[],"label_agreement":null},{"id":"W4394625704","doi":"10.1109/wacv57701.2024.00225","title":"Training-Based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Representation (politics); Object detection; Object (grammar); Training set; Machine learning; Pattern recognition (psychology); Computer vision","score_opus":0.06895389448328801,"score_gpt":0.32495815696234687,"score_spread":0.25600426247905883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394625704","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":[0.0035641296,0.00008653148,0.99244004,0.00240294,0.00009975262,0.00053613406,0.0000037954985,0.0003637097,0.0005029452],"genre_scores_gemma":[0.8481424,0.0000120326595,0.15055814,0.00038967025,0.000045549925,0.0004749379,0.00000916611,0.000009437613,0.00035866845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990853,0.000013340551,0.00016572418,0.00043958516,0.00013654974,0.0001594791],"domain_scores_gemma":[0.99946,0.00014368724,0.000023733099,0.00028652936,0.00003294078,0.000053140335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011444835,0.00009532972,0.00007703877,0.00006637805,0.00012181721,0.000119306926,0.0001461849,0.000025885736,0.000004329579],"category_scores_gemma":[0.000012530113,0.00008390098,0.00004209816,0.00028865025,0.000017585886,0.000266631,0.000050606548,0.00005013273,0.0000037588138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015735552,0.000034080404,0.0000122161955,0.000055450982,0.000018292843,0.0000011400734,0.0007425148,0.20065792,0.023323914,0.043218017,0.00080735347,0.7311134],"study_design_scores_gemma":[0.00019568524,0.00006179117,0.00004515327,0.000012819401,0.000008179254,0.0000017556788,0.00003007426,0.9753331,0.009550934,0.0129300095,0.0017309014,0.0000996041],"about_ca_topic_score_codex":0.0000064404953,"about_ca_topic_score_gemma":0.000056732595,"teacher_disagreement_score":0.84457827,"about_ca_system_score_codex":0.000048403734,"about_ca_system_score_gemma":0.000033557106,"threshold_uncertainty_score":0.34213826},"labels":[],"label_agreement":null},{"id":"W4394693322","doi":"10.1016/j.patcog.2024.110493","title":"Incremental convolutional transformer for baggage threat detection","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"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":"Natural Sciences and Engineering Research Council of Canada; Department of Education and Knowledge; Higher Education Commission, Pakistan; Khalifa University of Science, Technology and Research","keywords":"Computer science; Artificial intelligence; Clutter; Segmentation; Machine learning; Pattern recognition (psychology); Computer vision; Radar","score_opus":0.03851290604190102,"score_gpt":0.2811092132493741,"score_spread":0.24259630720747308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394693322","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":[0.02608038,0.00013536765,0.97156954,0.0006495948,0.00043892418,0.0004565201,0.00006825054,0.00034758198,0.00025383363],"genre_scores_gemma":[0.9902011,0.0000431568,0.008533347,0.0002964206,0.00025084836,0.00051439786,0.00011423099,0.00001392505,0.00003255359],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992032,0.000018875338,0.00015310812,0.00032432025,0.00012452803,0.00017593335],"domain_scores_gemma":[0.99964434,0.00012438672,0.00002416644,0.000117968644,0.00004627275,0.000042849937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102323924,0.000097605065,0.00006517556,0.00007770152,0.00014385443,0.00009031779,0.00013800543,0.00004279568,0.00004692916],"category_scores_gemma":[0.0000050142226,0.00009710189,0.000074792064,0.00020368939,0.000020527097,0.0005745358,0.000015555255,0.0000944321,0.00025162727],"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":[0.000004742142,0.000019283389,0.000033709504,0.000031517557,0.0000124106255,0.0000013392751,0.000060281283,0.000011316046,0.021698961,0.0004918557,0.00020486083,0.97742975],"study_design_scores_gemma":[0.0010835174,0.00034381085,0.0037691104,0.00021496805,0.00007143455,0.00017718333,0.000039535345,0.5138597,0.322276,0.13672309,0.020690927,0.0007507613],"about_ca_topic_score_codex":0.000008828784,"about_ca_topic_score_gemma":0.000036755697,"teacher_disagreement_score":0.97667897,"about_ca_system_score_codex":0.000076810065,"about_ca_system_score_gemma":0.000015291833,"threshold_uncertainty_score":0.39597002},"labels":[],"label_agreement":null},{"id":"W4394745871","doi":"10.1145/3597503.3639191","title":"MultiTest: Physical-Aware Object Insertion for Testing Multi-sensor Fusion Perception Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Perception; Artificial intelligence; Sensor fusion; Object detection; Software; Intelligent decision support system; Object (grammar); Human–computer interaction; Computer vision; Pattern recognition (psychology)","score_opus":0.06424271818790957,"score_gpt":0.32376684748430207,"score_spread":0.2595241292963925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394745871","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":[0.036903545,0.000064147345,0.9600161,0.00033018668,0.00038625166,0.0008212071,0.000006423143,0.0013473514,0.00012484474],"genre_scores_gemma":[0.7813195,0.000006577173,0.21709967,0.00006980697,0.00032433993,0.00031832274,0.000013031729,0.00002061374,0.00082816964],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880177,0.00003076095,0.00020147329,0.000551602,0.00016393258,0.00025048942],"domain_scores_gemma":[0.99883854,0.0005244861,0.00004902229,0.0003683483,0.0001505916,0.00006901285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010580399,0.00015246915,0.00012860581,0.00009098122,0.0002310091,0.00024311693,0.00029810402,0.000048563124,0.000001818109],"category_scores_gemma":[0.000072516006,0.00012697674,0.000072623894,0.0006733318,0.000020533489,0.0005781333,0.00010972384,0.00011381756,0.0001484018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007902656,0.00023919033,0.00028868654,0.00033316916,0.000017802757,0.000007757929,0.0009715805,0.04484302,0.595327,0.02001645,0.0013902982,0.33655712],"study_design_scores_gemma":[0.00014262361,0.00006295879,0.0013220953,0.000088713066,0.000006810413,0.000014772342,0.0000525028,0.99510866,0.0008053101,0.0004013282,0.0018268134,0.00016741583],"about_ca_topic_score_codex":0.00003315806,"about_ca_topic_score_gemma":0.0000065454055,"teacher_disagreement_score":0.95026565,"about_ca_system_score_codex":0.00009723326,"about_ca_system_score_gemma":0.00002793869,"threshold_uncertainty_score":0.5177961},"labels":[],"label_agreement":null},{"id":"W4394758452","doi":"10.1007/s11633-023-1385-0","title":"Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications","year":2024,"lang":"en","type":"article","venue":"Machine Intelligence Research","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Segmentation; Computer science; Data science; Code (set theory); Artificial intelligence; Human–computer interaction; Programming language","score_opus":0.10877957879039397,"score_gpt":0.4174717492934924,"score_spread":0.30869217050309844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394758452","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":[0.03974805,0.0003108303,0.94943327,0.006397432,0.000119464064,0.0012290839,0.000028275377,0.0003754292,0.0023581802],"genre_scores_gemma":[0.98730177,0.00049634185,0.010563084,0.0002170875,0.00016237369,0.00056722533,0.000024337287,0.000028390323,0.0006394149],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969004,0.00025998816,0.00045760896,0.0008757003,0.0010172478,0.00048907485],"domain_scores_gemma":[0.99715674,0.001022048,0.00006645924,0.0012747237,0.0002293247,0.00025069327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010248771,0.00020334414,0.00019755212,0.0006528736,0.000359502,0.00024752345,0.001450829,0.000059753685,0.00006471669],"category_scores_gemma":[0.000034763034,0.00017325487,0.000077989134,0.0024540897,0.00023165888,0.00041895025,0.00046347143,0.0007856144,0.00020984838],"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":[0.000013373434,0.00014141937,0.00060958293,0.00011668945,0.00001915912,0.0000054226825,0.0023305092,0.002796759,0.02303032,0.49980974,0.0004659493,0.47066107],"study_design_scores_gemma":[0.000039628605,0.00032630417,0.0014060527,0.00016919491,0.000006102388,0.0000057617053,0.00007819669,0.579931,0.31632107,0.097955056,0.0035064411,0.0002551332],"about_ca_topic_score_codex":0.00032626494,"about_ca_topic_score_gemma":0.00014978422,"teacher_disagreement_score":0.9475537,"about_ca_system_score_codex":0.00025321177,"about_ca_system_score_gemma":0.00011099619,"threshold_uncertainty_score":0.70651287},"labels":[],"label_agreement":null},{"id":"W4394828356","doi":"10.1109/access.2024.3389497","title":"GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions","year":2024,"lang":"en","type":"review","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":541,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Transformer; Architecture; Natural language understanding; Emerging technologies; Natural language; Generative grammar; Artificial intelligence; Risk analysis (engineering); Engineering; Electrical engineering","score_opus":0.06809467507185797,"score_gpt":0.37677357841093434,"score_spread":0.3086789033390764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394828356","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":[7.752356e-8,0.9229065,0.06716212,0.0041957498,0.00058976084,0.0036820709,0.0000550074,0.0012128758,0.00019584445],"genre_scores_gemma":[0.000002431873,0.98933136,0.0033942983,0.000251355,0.0008213322,0.0059929336,0.000066806926,0.000070206224,0.000069295646],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99675655,0.00014433275,0.0007723565,0.0015299905,0.0003354818,0.00046126844],"domain_scores_gemma":[0.99788266,0.00018740344,0.00039795483,0.0012782254,0.00014781601,0.00010595735],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001413839,0.0007201479,0.001356137,0.00042563974,0.00043718866,0.00027089706,0.0020462098,0.0003424258,0.000003250241],"category_scores_gemma":[0.000009008593,0.0005661117,0.00035015066,0.001927746,0.00011076937,0.00058856804,0.00028789914,0.0010429096,0.000036994927],"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":[8.120308e-7,0.000034337256,2.8419056e-9,0.016167853,0.00010794547,0.000008837453,0.00005401745,0.00000788099,0.0000023887144,0.008031562,0.0005548553,0.9750295],"study_design_scores_gemma":[0.00006521576,0.00003293674,2.3415727e-7,0.012224465,0.0005089333,0.000113554575,0.000017557682,0.00015562684,0.000009990015,0.0025730054,0.9837721,0.0005263882],"about_ca_topic_score_codex":0.000002550233,"about_ca_topic_score_gemma":0.0000070056512,"teacher_disagreement_score":0.98321724,"about_ca_system_score_codex":0.00013718472,"about_ca_system_score_gemma":0.0001455179,"threshold_uncertainty_score":0.999679},"labels":[],"label_agreement":null},{"id":"W4394945038","doi":"10.1145/3642970.3655824","title":"SpeedyLoader","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Preprocessor; Pipeline (software); Data pre-processing; Asynchronous communication; Workflow; Artificial intelligence; Queue; Parallel computing; Machine learning; Computer engineering; Programming language; Database","score_opus":0.020444535794041232,"score_gpt":0.28568174829144083,"score_spread":0.2652372124973996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394945038","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":[0.000061215105,0.0002776164,0.91671884,0.0056920056,0.00016078552,0.000036668902,1.1737273e-7,0.0007174466,0.07633532],"genre_scores_gemma":[0.69699436,0.000026366819,0.27631402,0.0014983593,0.00018578114,0.00002234101,6.9398703e-7,0.000008350723,0.024949718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996639,0.0000018540513,0.0000427798,0.00015619217,0.000054451262,0.00008081256],"domain_scores_gemma":[0.99971116,0.0000373377,0.0000028218717,0.00021556659,0.0000064198257,0.000026714695],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000020401927,0.000031304033,0.000021709253,0.000023145956,0.000023390967,0.00008549353,0.00025517828,0.000009226693,0.000052375653],"category_scores_gemma":[0.0000015651076,0.000024301013,0.000023576575,0.00032081926,0.000008401434,0.00024372035,0.00007355886,0.00004222608,0.0012777026],"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":[2.7579109e-8,0.0000017899417,0.0000015565053,0.0000010221023,0.0000013989571,0.0000036270146,0.000015330192,0.00004064241,0.0002867733,0.86736304,0.015925536,0.11635925],"study_design_scores_gemma":[0.000017305454,0.0000067415035,0.00008517191,0.000004670534,0.0000011075596,0.000023631832,0.0000018529332,0.18556726,0.0028914248,0.12950537,0.6818129,0.000082607],"about_ca_topic_score_codex":6.799044e-7,"about_ca_topic_score_gemma":5.3659073e-7,"teacher_disagreement_score":0.7378577,"about_ca_system_score_codex":0.0000072629286,"about_ca_system_score_gemma":0.000008232602,"threshold_uncertainty_score":0.9994999},"labels":[],"label_agreement":null},{"id":"W4394998398","doi":"10.1145/3620665.3640356","title":"Atalanta: A Bit is Worth a “Thousand” Tensor Values","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; McMaster University; University of Toronto","funders":"","keywords":"Computer science; Speedup; Lossless compression; Encoder; Parallel computing; Computer hardware; Floating point; Quantization (signal processing); Efficient energy use; Algorithm; Computer engineering; Arithmetic; Data compression; Mathematics; Operating system; Engineering; Electrical engineering","score_opus":0.018187197459334885,"score_gpt":0.28244414491153763,"score_spread":0.26425694745220274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394998398","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":[0.004057237,0.00078310526,0.9762235,0.0066028493,0.00022846647,0.00019394963,0.000005077816,0.0014520636,0.010453694],"genre_scores_gemma":[0.6602814,0.0004150292,0.2975534,0.0052272324,0.00043530768,0.00016061655,0.0000059643226,0.00004112651,0.035879932],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990503,0.00001243882,0.00013197074,0.0004349294,0.00015269578,0.00021762836],"domain_scores_gemma":[0.99926406,0.00010731168,0.00001642863,0.0005174094,0.000020805173,0.00007400352],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005889451,0.000104132945,0.000088699264,0.000067083616,0.00008619299,0.00019054693,0.00050835527,0.000028691169,0.00008132289],"category_scores_gemma":[0.0000046472783,0.00007920389,0.000056373116,0.0006056632,0.000030609044,0.00038607596,0.00021299637,0.000106624946,0.001034762],"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":[0.0000037637767,0.00006213081,0.0006293736,0.000041271407,0.000047493213,0.000115048286,0.0009765307,0.00026111567,0.0018163678,0.52634877,0.23717107,0.23252708],"study_design_scores_gemma":[0.00014351445,0.00006334618,0.001124266,0.000058416892,0.000014295675,0.00016507557,0.00001896991,0.379003,0.0060411426,0.08982122,0.52310586,0.00044090184],"about_ca_topic_score_codex":0.0000061454057,"about_ca_topic_score_gemma":0.0000036758079,"teacher_disagreement_score":0.67867017,"about_ca_system_score_codex":0.000018816314,"about_ca_system_score_gemma":0.000026995811,"threshold_uncertainty_score":0.99974304},"labels":[],"label_agreement":null},{"id":"W4395069151","doi":"10.1007/s11263-024-02069-9","title":"Position, Padding and Predictions: A Deeper Look at Position Information in CNNs","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; York University; Toronto Metropolitan University; Vector Institute","funders":"","keywords":"Padding; Encoding (memory); Heuristics; Position (finance); Computer science; Convolutional neural network; Artificial intelligence; Boundary (topology); ENCODE; Filter (signal processing); Pattern recognition (psychology); Theoretical computer science; Algorithm; Computer vision; Mathematics; Computer security","score_opus":0.0060274247605354,"score_gpt":0.27680370564351053,"score_spread":0.2707762808829751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395069151","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":[0.05303664,0.00030976316,0.9400063,0.005177724,0.0012038004,0.00009754556,0.0000049289815,0.000058047444,0.00010525727],"genre_scores_gemma":[0.928387,0.00023474914,0.07026174,0.0006489663,0.0004227808,0.000006107973,0.00001545042,0.000006311962,0.000016889466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987685,0.000040718714,0.0005042896,0.00014870492,0.00042806347,0.000109724526],"domain_scores_gemma":[0.99919933,0.0001528606,0.00017187286,0.00011251094,0.0002976461,0.00006576351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002230217,0.00009860473,0.00010279014,0.00054095016,0.00006788075,0.00039285462,0.000404568,0.000046830457,0.000008940514],"category_scores_gemma":[0.0000112040025,0.00008903548,0.000059105645,0.00026670576,0.000026151843,0.0036387648,0.00025233283,0.00018856105,0.000025592224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017451937,0.00021982142,0.0011166445,0.000051098657,0.00017525916,0.0004358702,0.0032230048,0.096279174,0.004626879,0.14005348,0.014037897,0.7396063],"study_design_scores_gemma":[0.0005636207,0.00024883347,0.011123276,0.00061813026,0.000009518123,0.0029711605,0.000011207139,0.9442689,0.0004562532,0.022208821,0.017369436,0.00015085754],"about_ca_topic_score_codex":0.0000025524316,"about_ca_topic_score_gemma":0.0000025394188,"teacher_disagreement_score":0.87535036,"about_ca_system_score_codex":0.00028295434,"about_ca_system_score_gemma":0.000040384006,"threshold_uncertainty_score":0.3788306},"labels":[],"label_agreement":null},{"id":"W4395685235","doi":"10.18280/ijsse.140226","title":"Object Detection and Classification in Human Rescue Operations: Deep Learning Strategies for Flooded Environments","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Object detection; Deep learning; Object (grammar); Machine learning; Pattern recognition (psychology)","score_opus":0.011279291197368006,"score_gpt":0.2609719322956461,"score_spread":0.24969264109827807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395685235","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":[0.12102136,0.0006486083,0.87774295,0.0002976245,0.00017716415,0.000075740485,0.0000011596081,0.000020293486,0.000015126902],"genre_scores_gemma":[0.99242544,0.0007002723,0.0067279604,0.000011309872,0.00011068189,0.000009326353,0.000002741466,0.000005467882,0.0000067950523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994233,0.000014481606,0.00024864235,0.00012313736,0.00011979497,0.00007063338],"domain_scores_gemma":[0.9997771,0.000085756634,0.000029848348,0.00004347148,0.00003458197,0.000029255258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002019945,0.0000636803,0.000074027565,0.00015691061,0.00005887141,0.00017307563,0.00014104853,0.000032472624,7.836293e-7],"category_scores_gemma":[0.000030016427,0.000064474574,0.000024025285,0.00008456238,0.000012879431,0.0008579506,0.00003815568,0.00019709856,3.2537022e-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":[0.000027381982,0.00002604773,0.00006691421,0.00003185295,0.00005353399,0.000011948211,0.0019938038,0.7324481,0.10098329,0.12602812,0.0000011322103,0.038327876],"study_design_scores_gemma":[0.00022956205,0.000046082245,0.0033195284,0.00007024575,0.000004274486,0.00007371482,0.00009719797,0.9896031,0.0009405446,0.0035837984,0.0019635346,0.000068433634],"about_ca_topic_score_codex":0.000003921431,"about_ca_topic_score_gemma":0.000032672,"teacher_disagreement_score":0.87140405,"about_ca_system_score_codex":0.00006824103,"about_ca_system_score_gemma":0.000011849636,"threshold_uncertainty_score":0.26291966},"labels":[],"label_agreement":null},{"id":"W4395695919","doi":"10.1007/s11227-024-06136-3","title":"An enhanced object detection network for ship target detection in SAR images","year":2024,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Robustness (evolution); Synthetic aperture radar; Computer vision; Deep learning; Precision and recall; Pattern recognition (psychology)","score_opus":0.016356276126645614,"score_gpt":0.2822543687340377,"score_spread":0.26589809260739206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395695919","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":[0.22862214,0.0006472262,0.76957685,0.00025297658,0.0006235536,0.00017698888,4.8921214e-7,0.00008131076,0.000018478982],"genre_scores_gemma":[0.9108575,0.000041599113,0.087910324,0.00007947285,0.0010853005,0.0000040080668,4.3748878e-7,0.000016551396,0.000004824276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868,0.00018858832,0.00044662185,0.00019753168,0.00017871342,0.00030852164],"domain_scores_gemma":[0.9987225,0.0007374612,0.00012594255,0.0002514758,0.00010505801,0.000057573143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016230757,0.00012698503,0.00016973623,0.00013890644,0.00026915915,0.00014758598,0.00062845496,0.000048646678,0.0000013356339],"category_scores_gemma":[0.000050944127,0.00009399426,0.00009098092,0.000825245,0.000030225101,0.0009372362,0.00006540954,0.00041017222,0.0000034598831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000371602,0.000024654742,0.000036489113,0.000022245356,0.000016962229,0.000005526815,0.0011267699,0.36880624,0.19898766,0.00035503905,0.000053689135,0.43052757],"study_design_scores_gemma":[0.00020267046,0.00029028923,0.0009705277,0.00012471892,0.000015459897,0.00020559295,0.00011799655,0.79677254,0.17958471,0.021159178,0.00040874313,0.0001475502],"about_ca_topic_score_codex":0.000009766452,"about_ca_topic_score_gemma":0.00002899688,"teacher_disagreement_score":0.68223536,"about_ca_system_score_codex":0.0001087278,"about_ca_system_score_gemma":0.00004074225,"threshold_uncertainty_score":0.38329747},"labels":[],"label_agreement":null},{"id":"W4395959934","doi":"10.1088/978-0-7503-6244-3ch7","title":"Object detection by YOLOv1/YOLOv3 models","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Object (grammar); Computer vision; Cognitive science; Psychology","score_opus":0.02150686780017848,"score_gpt":0.23988689541527858,"score_spread":0.2183800276151001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395959934","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.0432857e-7,0.00073777855,0.5298267,0.00029536447,0.00026782206,0.00019932329,0.000008654974,0.0006510759,0.46801287],"genre_scores_gemma":[0.0046535186,0.0003599678,0.013806062,0.00048426606,0.00021193028,0.0000698691,0.000018320641,0.0000710197,0.98032504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982546,0.000006005405,0.0002933231,0.0008970281,0.00028350446,0.00026551925],"domain_scores_gemma":[0.99868774,0.00008041028,0.000108683715,0.00095837994,0.000060854374,0.000103907725],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000060870094,0.00034447602,0.00024464435,0.00013602451,0.000107222644,0.00013558858,0.00085486885,0.00028098846,0.00009696993],"category_scores_gemma":[0.0000020100747,0.0003166462,0.00015171667,0.000116532006,0.000053374082,0.00037400663,0.00042909864,0.00052261807,0.0016866644],"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":[0.0000011167207,0.0000040665304,1.25407205e-8,0.000009330505,0.000021534683,0.000005735735,0.000015354215,0.00082641403,0.0002518479,0.9307818,0.009438733,0.058644045],"study_design_scores_gemma":[0.000031760705,0.000030763204,5.025693e-8,0.000018501094,0.000013630993,0.000023140014,4.6848643e-7,0.10839791,0.0006751503,0.6156929,0.27484307,0.00027266552],"about_ca_topic_score_codex":0.000007123051,"about_ca_topic_score_gemma":0.000023240864,"teacher_disagreement_score":0.51602066,"about_ca_system_score_codex":0.000118300675,"about_ca_system_score_gemma":0.000027853595,"threshold_uncertainty_score":0.99992853},"labels":[],"label_agreement":null},{"id":"W4396229019","doi":"10.3390/rs16091567","title":"Ship Detection in Maritime Scenes under Adverse Weather Conditions","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Adverse weather; Environmental science; Oceanography; Remote sensing; Meteorology; Geology; Geography","score_opus":0.021102797059542697,"score_gpt":0.2789985816599827,"score_spread":0.25789578460044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396229019","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":[0.03813468,0.00018730419,0.95803857,0.0012796876,0.00028362122,0.00013798046,8.202438e-7,0.00041672905,0.0015206358],"genre_scores_gemma":[0.95009184,0.00003087842,0.049317166,0.00023853802,0.00010166612,8.2036934e-8,0.00000209482,0.000014939035,0.00020278075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918777,0.000043622076,0.00014021709,0.00032228176,0.000104420746,0.00020170734],"domain_scores_gemma":[0.999485,0.00013606764,0.000021456492,0.0002897386,0.000023452323,0.000044279757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008581943,0.000093128256,0.00007836505,0.00014666723,0.00010402701,0.0000717451,0.00011552947,0.000049798706,0.0000049693035],"category_scores_gemma":[0.000011704522,0.00009822089,0.000042380685,0.0007481035,0.000034472923,0.00031350777,0.000064588705,0.0001710363,0.00014809372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026571984,0.00001086306,0.0000045427823,0.000020195223,0.000011593471,0.00010612359,0.00025578964,0.0151481265,0.052235056,0.022777446,0.00007509239,0.90935254],"study_design_scores_gemma":[0.00006574129,0.0000069369216,0.00040006178,0.00009761801,0.000004114923,0.00011089451,0.000023737928,0.94678986,0.0029947113,0.04663576,0.0027484018,0.00012214482],"about_ca_topic_score_codex":0.000032672982,"about_ca_topic_score_gemma":0.00013263008,"teacher_disagreement_score":0.93164176,"about_ca_system_score_codex":0.00010800507,"about_ca_system_score_gemma":0.000025894778,"threshold_uncertainty_score":0.40053314},"labels":[],"label_agreement":null},{"id":"W4396598556","doi":"10.3390/electronics13091765","title":"Enhancing the Safety of Autonomous Vehicles in Adverse Weather by Deep Learning-Based Object Detection","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robustness (evolution); Object detection; Artificial intelligence; Computer science; Deep learning; Adverse weather; Object (grammar); Process (computing); Computer vision; Machine learning; Real-time computing; Pattern recognition (psychology); Geography","score_opus":0.004570992518559533,"score_gpt":0.22715555112752428,"score_spread":0.22258455860896476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396598556","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":[0.03997394,0.0061143176,0.9527183,0.0005221182,0.00007579326,0.00021201535,6.1078384e-7,0.00022259215,0.0001603196],"genre_scores_gemma":[0.99804884,0.00022142434,0.0014205845,0.00007373025,0.000023910541,0.000040427974,0.0000018495106,0.000013938172,0.00015531776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905884,0.00007905538,0.00020536676,0.0002629231,0.00012384944,0.00026993305],"domain_scores_gemma":[0.9993524,0.0002748303,0.000057911464,0.00027296547,0.000020283898,0.000021632695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026121753,0.00009571157,0.00009283054,0.00005886746,0.00010455008,0.000024675983,0.00033837618,0.000044185803,0.000004223714],"category_scores_gemma":[0.000023524086,0.00007798439,0.000050781848,0.0007145954,0.000031176296,0.00014533375,0.000047417536,0.0003885377,0.000016332822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028826536,0.00006185786,0.000084930994,0.000031061638,0.000025128898,0.00000459271,0.00089018425,0.2510904,0.19348234,0.012621933,0.00004918319,0.54162955],"study_design_scores_gemma":[0.00014612786,0.00012461704,0.00017503535,0.000024053801,0.0000065384143,0.000005209114,0.00002047878,0.74760103,0.21233952,0.0027963798,0.03663393,0.00012708915],"about_ca_topic_score_codex":0.000007028831,"about_ca_topic_score_gemma":0.0003474776,"teacher_disagreement_score":0.95807487,"about_ca_system_score_codex":0.00019822385,"about_ca_system_score_gemma":0.000112802576,"threshold_uncertainty_score":0.3180111},"labels":[],"label_agreement":null},{"id":"W4396628099","doi":"10.14778/3648160.3648165","title":"How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study","year":2024,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Deep learning; Geology; Data science; Computer science; Artificial intelligence","score_opus":0.03507132555401607,"score_gpt":0.2938870684634521,"score_spread":0.258815742909436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396628099","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":[0.9852037,0.0011170962,0.008478972,0.0037696678,0.00015476573,0.0008720263,0.0000029083897,0.00022493335,0.00017595543],"genre_scores_gemma":[0.9957573,0.000048929982,0.0035324423,0.00004887692,0.00006317537,0.00019937422,4.1751704e-7,0.000017241504,0.00033227314],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987418,0.000012160527,0.00018010048,0.0004800948,0.0003021219,0.00028370635],"domain_scores_gemma":[0.99956626,0.00003128398,0.000096983946,0.00016163108,0.00005576062,0.0000881095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020429157,0.00016598233,0.00015729896,0.000033900338,0.00024751454,0.00043300315,0.0007273314,0.00002695218,8.176409e-7],"category_scores_gemma":[0.00001014369,0.00012153139,0.000049542505,0.00032899893,0.00007711192,0.00072285015,0.0005962717,0.00019187787,5.596946e-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":[0.000052312356,0.0016676763,0.003202966,0.00018779315,0.00020366423,0.000008273506,0.171484,0.0042101517,0.42072338,0.22363251,0.00032000832,0.17430727],"study_design_scores_gemma":[0.0017350839,0.0015751983,0.00087081135,0.00020808914,0.000057031684,0.00008786005,0.03827235,0.65076137,0.2622132,0.039764624,0.0036747747,0.00077964267],"about_ca_topic_score_codex":0.000014656853,"about_ca_topic_score_gemma":0.0000072838566,"teacher_disagreement_score":0.6465512,"about_ca_system_score_codex":0.000060649792,"about_ca_system_score_gemma":0.000008531095,"threshold_uncertainty_score":0.4955906},"labels":[],"label_agreement":null},{"id":"W4396686374","doi":"10.1145/3629527.3651427","title":"Enhancing the Performance of Deep Learning Model Based Object Detection using Parallel Processing (Work In Progress Paper)","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Scalability; Workload; Object detection; SPARK (programming language); Deep learning; Latency (audio); Artificial intelligence; Real-time computing; Big data; Distributed computing; Data mining; Database; Pattern recognition (psychology); Operating system","score_opus":0.01633111645482004,"score_gpt":0.270952368690559,"score_spread":0.25462125223573895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396686374","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":[0.24024595,0.0005916338,0.7585745,0.00013539124,0.000035088306,0.00018500548,3.318039e-8,0.00017220627,0.000060219878],"genre_scores_gemma":[0.8079767,0.000011536316,0.19184814,0.00004771257,0.000024693638,0.000058268008,2.8925803e-7,0.000011147157,0.000021473183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893385,0.000045557925,0.00026810882,0.00032193158,0.0001889023,0.00024162547],"domain_scores_gemma":[0.9994975,0.000114059265,0.000078457226,0.00023920141,0.000045286048,0.000025499017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028261705,0.00011592459,0.000107822656,0.00009375745,0.0002171278,0.00011378056,0.00035094135,0.000041227344,0.0000018494347],"category_scores_gemma":[0.000012172482,0.00008304714,0.000037053756,0.0013625968,0.000052111074,0.00070341566,0.00009556372,0.00029219076,0.0000024461524],"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":[0.000005499488,0.000008363453,0.00022019724,0.000049751772,0.000001480067,6.709123e-7,0.00035219867,0.80806017,0.006983952,0.00023506144,1.9975421e-7,0.18408248],"study_design_scores_gemma":[0.00005879767,0.000021682461,0.00035837846,0.00016207028,0.000003902727,0.0000045136753,0.000027532526,0.9812927,0.017697837,0.00024202596,0.000028771728,0.0001017579],"about_ca_topic_score_codex":0.000003945517,"about_ca_topic_score_gemma":0.000032789525,"teacher_disagreement_score":0.5677308,"about_ca_system_score_codex":0.0000662192,"about_ca_system_score_gemma":0.00006846526,"threshold_uncertainty_score":0.3386564},"labels":[],"label_agreement":null},{"id":"W4396883665","doi":"10.2139/ssrn.4812540","title":"Automated Plan Generation for Prostate Radiotherapy Patients Using Deep Learning and Scripted Optimization","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Ottawa; McMaster University","funders":"","keywords":"Plan (archaeology); Computer science; Artificial intelligence; Deep learning; Prostate; Radiation therapy; Medical physics; Medicine; Radiology; Internal medicine; Geography; Archaeology","score_opus":0.015631078888990787,"score_gpt":0.278902755525507,"score_spread":0.2632716766365162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396883665","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":[0.03711997,0.0050741658,0.9557974,0.0002935806,0.0005043954,0.00078663207,0.0000044429325,0.00041326106,0.000006190935],"genre_scores_gemma":[0.5169436,0.013860174,0.46717808,0.0001415315,0.0009897951,0.00018340476,0.0002959499,0.00014759325,0.00025989374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976921,0.00010351099,0.00038011637,0.000547403,0.00020936182,0.0010674978],"domain_scores_gemma":[0.9991066,0.00003475411,0.0003812654,0.0001964051,0.00020712848,0.000073852556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051018695,0.00025452816,0.00021893905,0.00020591251,0.00044205293,0.00048774108,0.00034273474,0.00014868069,9.731564e-7],"category_scores_gemma":[0.000028879038,0.00024865923,0.00007381802,0.00025545643,0.000024617735,0.0002464146,0.00024969844,0.0018676745,8.373349e-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":[0.000015616673,0.000023973313,0.00012018723,0.00002620805,0.00010628447,6.5298906e-7,0.00026496087,0.9391222,0.0008070718,0.008762511,0.000034387132,0.05071593],"study_design_scores_gemma":[0.00042855748,0.00015894102,0.00001213849,0.00004760703,0.000032778094,0.000065962326,0.00001795998,0.95597094,0.00005566061,0.04276215,0.00021279292,0.00023452372],"about_ca_topic_score_codex":0.0000058786486,"about_ca_topic_score_gemma":0.000022669385,"teacher_disagreement_score":0.48861927,"about_ca_system_score_codex":0.0010438795,"about_ca_system_score_gemma":0.0007260524,"threshold_uncertainty_score":0.99999654},"labels":[],"label_agreement":null},{"id":"W4396941411","doi":"10.1109/access.2024.3401610","title":"Quantitative Analysis of Deep Learning-Based Object Detection Models","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object detection; Convolutional neural network; Artificial intelligence; Inference; Object (grammar); Deep learning; Context (archaeology); Generalization; Machine learning; Feature (linguistics); Task (project management); Pattern recognition (psychology)","score_opus":0.04573788830879514,"score_gpt":0.3491816719545718,"score_spread":0.3034437836457766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396941411","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":[0.038215674,0.00025904682,0.9606694,0.000091929534,0.00016011043,0.00010310472,0.0000019413071,0.00025761183,0.00024118533],"genre_scores_gemma":[0.99101186,0.000017074442,0.0088184085,0.00004964957,0.000019086574,0.000047340007,0.0000030936706,0.000008378077,0.000025135367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907994,0.000059420796,0.00018708492,0.00035153117,0.00018307933,0.00013893991],"domain_scores_gemma":[0.99909985,0.00036134853,0.000082927254,0.00032484156,0.000094115996,0.00003691825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011355493,0.00009034523,0.00015936233,0.0004164528,0.00008318585,0.0001450259,0.00062577083,0.00003453193,0.0000063781686],"category_scores_gemma":[0.000016848673,0.000084684936,0.00011999441,0.0040463577,0.00003544045,0.000931143,0.00006158745,0.00014045989,0.000011154153],"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":[0.0000042038355,0.00001237302,0.000044411463,0.000010159714,0.00009732323,0.0000021695755,0.00012468542,0.9649699,0.0022134962,0.0054016523,0.000007142499,0.02711249],"study_design_scores_gemma":[0.000037112553,0.00004147444,0.00039206687,0.000008074785,0.0000987638,4.4062594e-7,0.0000055375463,0.97467536,0.01738745,0.007188833,0.000077016964,0.000087850654],"about_ca_topic_score_codex":0.00003890217,"about_ca_topic_score_gemma":0.00015751303,"teacher_disagreement_score":0.95279616,"about_ca_system_score_codex":0.000036274923,"about_ca_system_score_gemma":0.000022516871,"threshold_uncertainty_score":0.34533513},"labels":[],"label_agreement":null},{"id":"W4399240157","doi":"10.7717/peerj-cs.2033","title":"Analysis of the performance of Faster R-CNN and YOLOv8 in detecting fishing vessels and fishes in real time","year":2024,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Université de Moncton","funders":"","keywords":"Fishing; Context (archaeology); Relevance (law); Computer science; Object detection; Fishery; Focus (optics); Data science; Marine fisheries; Fish <Actinopterygii>; Artificial intelligence; Geography; Pattern recognition (psychology); Political science; Archaeology","score_opus":0.013827480903453065,"score_gpt":0.2546168142399983,"score_spread":0.24078933333654526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399240157","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":[0.9551973,0.00007552774,0.044066876,0.00041193518,0.00007117933,0.00009883246,9.5273646e-7,0.000026254751,0.00005116023],"genre_scores_gemma":[0.9771354,0.000025840183,0.022769941,0.000037329482,0.000011718944,0.000004915103,1.3848202e-7,0.0000027004783,0.000011970737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879855,0.0000351091,0.0002641863,0.0004452219,0.00025688927,0.00020002153],"domain_scores_gemma":[0.99918765,0.00028404294,0.00007801658,0.00037088827,0.000044303462,0.000035088866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063624047,0.00008461493,0.00018530579,0.0003987674,0.00007889861,0.00012954707,0.0007833438,0.000021873298,7.020957e-7],"category_scores_gemma":[0.00002123337,0.00006455138,0.000027462727,0.004400519,0.00028441942,0.00086866575,0.0007705469,0.00012483247,4.9804964e-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":[0.0000067350006,0.000057947203,0.29902607,0.00016177137,0.000041823627,0.00000838307,0.011268804,0.14337724,0.048333127,0.0025754292,0.00001953277,0.49512315],"study_design_scores_gemma":[0.00003399433,0.000018044964,0.26399472,0.00006008287,0.000006287421,0.00000389597,0.000004667634,0.73323923,0.0024148847,0.00016708676,0.0000070767096,0.000050025665],"about_ca_topic_score_codex":0.0000362888,"about_ca_topic_score_gemma":0.000046395697,"teacher_disagreement_score":0.589862,"about_ca_system_score_codex":0.00002714984,"about_ca_system_score_gemma":0.00004365488,"threshold_uncertainty_score":0.2632329},"labels":[],"label_agreement":null},{"id":"W4399282665","doi":"10.1109/access.2024.3409057","title":"DNNSplit: Latency and Cost-Efficient Split Point Identification for Multi-Tier DNN Partitioning","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Latency (audio); Scalability; Inference; Computational complexity theory; Deep neural networks; Artificial neural network; Parallel computing; Distributed computing; Theoretical computer science; Artificial intelligence; Algorithm","score_opus":0.08664129922105507,"score_gpt":0.37987871610816076,"score_spread":0.2932374168871057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399282665","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":[0.031934496,0.00048493504,0.9643573,0.0012445113,0.00070392847,0.0008686214,0.000013654299,0.00031080644,0.00008174601],"genre_scores_gemma":[0.9697446,0.000059701917,0.028347805,0.00019538245,0.00015025263,0.0009664589,0.000012684076,0.000018978499,0.00050408766],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987794,0.000019367877,0.0002658452,0.0005529507,0.00013499006,0.00024743736],"domain_scores_gemma":[0.99919945,0.00013815639,0.0000699001,0.00041802507,0.00009262718,0.000081837046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020395144,0.0001184567,0.00010061153,0.0000883916,0.0002380908,0.0007208724,0.00054550596,0.000042564054,0.000005870449],"category_scores_gemma":[0.000025788784,0.000113145805,0.000045355988,0.00045688162,0.000047167472,0.0008738381,0.00013462506,0.000097859585,0.000045951412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020404848,0.0003078056,0.00082204246,0.0003704729,0.0000690559,0.000021953945,0.0016277808,0.068992145,0.018129392,0.45163006,0.008387016,0.44962186],"study_design_scores_gemma":[0.00016562162,0.0000128639185,0.0020195455,0.000049605114,0.000012451925,0.000009628237,0.000006972169,0.97015214,0.008000794,0.012052988,0.0073482385,0.00016917885],"about_ca_topic_score_codex":0.000007519,"about_ca_topic_score_gemma":0.0000077000595,"teacher_disagreement_score":0.9378102,"about_ca_system_score_codex":0.00006376713,"about_ca_system_score_gemma":0.00002954334,"threshold_uncertainty_score":0.69513893},"labels":[],"label_agreement":null},{"id":"W4399368811","doi":"10.21428/d82e957c.8a7c7c50","title":"STF: Spatio-Temporal Fusion Module for Improving Video Object Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Polytechnique Montréal","funders":"","keywords":"Fusion; Computer science; Computer vision; Artificial intelligence; Object (grammar); Computer graphics (images); Pattern recognition (psychology)","score_opus":0.01630375412020812,"score_gpt":0.26717820006595666,"score_spread":0.25087444594574854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399368811","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":[0.008229853,0.00012197625,0.9887165,0.00071417657,0.00050575385,0.00046333732,0.0000022141676,0.00086805265,0.00037814243],"genre_scores_gemma":[0.8493506,0.0000046193973,0.14929965,0.0001209352,0.0001819943,0.00018254029,0.0000063665484,0.000013092619,0.00084024604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990527,0.00001377706,0.00016523756,0.00044124998,0.00012450546,0.00020253692],"domain_scores_gemma":[0.9993771,0.00013062602,0.000035561363,0.00035384792,0.000050049533,0.00005282148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012830239,0.000101490456,0.000076480115,0.0000842844,0.00018346029,0.00017794273,0.00028891818,0.00004195148,0.000010439381],"category_scores_gemma":[0.000020611758,0.00008900408,0.0000632067,0.000464479,0.000013389993,0.0006412872,0.00013845025,0.00008789585,0.00005155615],"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":[0.000007069746,0.000017308554,0.000028202943,0.000045849898,0.0000052809173,0.000001916737,0.00007501924,0.0019522762,0.026853487,0.038256068,0.00088480435,0.9318727],"study_design_scores_gemma":[0.00008262909,0.0000703777,0.000105565174,0.00000812561,0.000003846169,0.000007270687,0.0000041241583,0.9268742,0.03861241,0.01774004,0.016369367,0.00012202709],"about_ca_topic_score_codex":0.000040715968,"about_ca_topic_score_gemma":0.0001659239,"teacher_disagreement_score":0.9317507,"about_ca_system_score_codex":0.00006568406,"about_ca_system_score_gemma":0.00003360642,"threshold_uncertainty_score":0.36294812},"labels":[],"label_agreement":null},{"id":"W4399623138","doi":"10.1007/s11760-024-03317-8","title":"Pixel U-Net: an improved version of U-Net for binary segmentation of wind turbine blades","year":2024,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Segmentation; Pixel; Computer science; Drone; Artificial intelligence; Block (permutation group theory); Image segmentation; Process (computing); Computer vision; Turbine; Engineering; Mathematics; Operating system","score_opus":0.015617810460821073,"score_gpt":0.29749917640564333,"score_spread":0.28188136594482227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399623138","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":[0.12631445,0.0014411487,0.87162954,0.00022275765,0.000034170913,0.00024702106,0.000011570816,0.00006410642,0.00003524657],"genre_scores_gemma":[0.8733046,0.000026576756,0.12648885,0.000048335354,0.00005398534,0.00001444259,0.00001715488,0.000010105004,0.00003597203],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924403,0.000018705625,0.0002138623,0.0002907733,0.000102529295,0.0001300722],"domain_scores_gemma":[0.9995114,0.00010544678,0.00010562053,0.00012670599,0.00010623092,0.000044567427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014671647,0.000095677366,0.00012130664,0.00008590677,0.00010053776,0.00009452531,0.0001752184,0.000032384607,0.000003544494],"category_scores_gemma":[0.000009547624,0.00008515746,0.00003058689,0.00031358347,0.00007722121,0.0013769662,0.000070315815,0.00006179213,7.1128164e-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":[0.000020633513,0.000027056136,0.000010645056,0.0002739557,0.0000050636086,7.7314195e-7,0.00049354334,0.00014624075,0.84174967,0.00014356905,0.000066534776,0.15706229],"study_design_scores_gemma":[0.00028418677,0.00032158097,0.00013570805,0.00018660055,0.0000243043,0.000006720423,0.0001510574,0.54218894,0.45132762,0.005040552,0.00021320245,0.00011953635],"about_ca_topic_score_codex":0.0000045529264,"about_ca_topic_score_gemma":7.3879096e-7,"teacher_disagreement_score":0.74699014,"about_ca_system_score_codex":0.000012240972,"about_ca_system_score_gemma":0.000050587874,"threshold_uncertainty_score":0.34726202},"labels":[],"label_agreement":null},{"id":"W4399728974","doi":"10.1109/syscon61195.2024.10553403","title":"Detection of Rail-track and Floodwater in UAV Imaging sensors Using Deep Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Track (disk drive); Computer science; Artificial intelligence; Computer vision; Deep learning; Remote sensing; Real-time computing; Geology","score_opus":0.012344270585135661,"score_gpt":0.26254355104311533,"score_spread":0.2501992804579797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399728974","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":[0.3920274,0.0002124621,0.6072892,0.00012303067,0.00004982654,0.000052759195,4.5482203e-8,0.00010036379,0.00014488345],"genre_scores_gemma":[0.95986813,0.000020245652,0.039990727,0.000021090143,0.000024076706,0.0000033557208,1.7063151e-7,0.0000066331404,0.000065567176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994086,0.000027666349,0.00013518344,0.00023362134,0.00006725589,0.00012767562],"domain_scores_gemma":[0.99974686,0.00007583403,0.000021742251,0.000115928924,0.000016270478,0.000023382641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000095891424,0.00006123702,0.00006701939,0.000111429974,0.000046115267,0.000058660473,0.00008856224,0.000016631873,0.0000034891877],"category_scores_gemma":[0.000008782918,0.00005558048,0.00001622651,0.0004078788,0.000025213436,0.0004027717,0.0000890708,0.00012449292,0.000005923937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002839502,0.000012345495,0.002421262,0.000043911175,0.0000062345116,0.000022780208,0.0010029777,0.09970273,0.4388877,0.0047623827,0.000003536927,0.45313132],"study_design_scores_gemma":[0.000047336554,0.00000586815,0.0009507975,0.00002091334,0.0000018230503,0.00004241565,0.00003607744,0.95833087,0.038684722,0.0014837636,0.00033080502,0.000064580585],"about_ca_topic_score_codex":0.000024423522,"about_ca_topic_score_gemma":0.000022295018,"teacher_disagreement_score":0.85862815,"about_ca_system_score_codex":0.000020834701,"about_ca_system_score_gemma":0.0000050805393,"threshold_uncertainty_score":0.22665063},"labels":[],"label_agreement":null},{"id":"W4399828401","doi":"10.32920/26052526.v1","title":"Implementation of a Cyclegan Model for MRI Image Translation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Translation (biology); Image (mathematics); Computer science; Artificial intelligence; Computer vision; Biology","score_opus":0.04660609839772886,"score_gpt":0.36906971240528225,"score_spread":0.3224636140075534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399828401","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":[0.0009797291,0.000088363624,0.9935738,0.0031414155,0.0001315527,0.0011656871,0.000073961244,0.0002302219,0.00061531365],"genre_scores_gemma":[0.23455133,0.000030462057,0.76462185,0.000064204934,0.000044639553,0.0004675044,0.000055603556,0.000015853686,0.0001485638],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887455,0.000010827105,0.00034406903,0.00048070276,0.0001433235,0.00014650966],"domain_scores_gemma":[0.9991469,0.000063105705,0.00013621205,0.000526722,0.00009267824,0.000034416033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010455984,0.00014052322,0.00015567474,0.00009740174,0.000035633573,0.0000665994,0.00051950815,0.00007316154,0.000005227338],"category_scores_gemma":[0.0000011608972,0.00013799632,0.00012841214,0.00016696563,0.00001856262,0.00016368445,0.00028693044,0.00015325277,0.00000617668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006836627,0.000038779555,0.000004991403,0.00048068125,0.00005049887,4.4781626e-7,0.002245979,0.2465065,0.012419449,0.51158094,0.0035807176,0.22308415],"study_design_scores_gemma":[0.0000775465,0.000008206172,0.000007306451,0.000012351336,0.000013878452,3.754373e-7,0.000009482628,0.65734416,0.006023036,0.3363164,0.00010136883,0.00008588539],"about_ca_topic_score_codex":0.000019565274,"about_ca_topic_score_gemma":0.00009779887,"teacher_disagreement_score":0.41083765,"about_ca_system_score_codex":0.000034381654,"about_ca_system_score_gemma":0.000093746436,"threshold_uncertainty_score":0.56273264},"labels":[],"label_agreement":null},{"id":"W4399828402","doi":"10.32920/26052526","title":"Implementation of a Cyclegan Model for MRI Image Translation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Translation (biology); Computer science; Image (mathematics); Artificial intelligence; Natural language processing; Image translation; Computer vision","score_opus":0.04660609839772886,"score_gpt":0.36906971240528225,"score_spread":0.3224636140075534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399828402","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":[0.0009797291,0.000088363624,0.9935738,0.0031414155,0.0001315527,0.0011656871,0.000073961244,0.0002302219,0.00061531365],"genre_scores_gemma":[0.23455133,0.000030462057,0.76462185,0.000064204934,0.000044639553,0.0004675044,0.000055603556,0.000015853686,0.0001485638],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887455,0.000010827105,0.00034406903,0.00048070276,0.0001433235,0.00014650966],"domain_scores_gemma":[0.9991469,0.000063105705,0.00013621205,0.000526722,0.00009267824,0.000034416033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010455984,0.00014052322,0.00015567474,0.00009740174,0.000035633573,0.0000665994,0.00051950815,0.00007316154,0.000005227338],"category_scores_gemma":[0.0000011608972,0.00013799632,0.00012841214,0.00016696563,0.00001856262,0.00016368445,0.00028693044,0.00015325277,0.00000617668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006836627,0.000038779555,0.000004991403,0.00048068125,0.00005049887,4.4781626e-7,0.002245979,0.2465065,0.012419449,0.51158094,0.0035807176,0.22308415],"study_design_scores_gemma":[0.0000775465,0.000008206172,0.000007306451,0.000012351336,0.000013878452,3.754373e-7,0.000009482628,0.65734416,0.006023036,0.3363164,0.00010136883,0.00008588539],"about_ca_topic_score_codex":0.000019565274,"about_ca_topic_score_gemma":0.00009779887,"teacher_disagreement_score":0.41083765,"about_ca_system_score_codex":0.000034381654,"about_ca_system_score_gemma":0.000093746436,"threshold_uncertainty_score":0.56273264},"labels":[],"label_agreement":null},{"id":"W4399864843","doi":"10.1007/s10489-024-05584-x","title":"Multi-level information fusion Transformer with background filter for fine-grained image recognition","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Transformer; Artificial intelligence; Pattern recognition (psychology); Filter (signal processing); Class (philosophy); Machine learning; Computer vision","score_opus":0.06846394179777275,"score_gpt":0.2971908414519767,"score_spread":0.22872689965420395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399864843","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":[0.00094703294,0.00003931984,0.9954165,0.0007139502,0.0001383849,0.0010976403,0.000051540246,0.00038195634,0.0012136537],"genre_scores_gemma":[0.2475753,0.00006162477,0.75034815,0.00051199103,0.00008252961,0.0010231012,0.00019189197,0.000022861279,0.00018256082],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872416,0.000008925493,0.00034576212,0.00040554494,0.00020098468,0.00031459634],"domain_scores_gemma":[0.99912137,0.00022798864,0.000069030284,0.0003648668,0.00013811658,0.00007861475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015672577,0.00020728847,0.00013358217,0.00013437275,0.00017493695,0.00029048952,0.0004729635,0.000070626804,0.00004093983],"category_scores_gemma":[0.000009932756,0.00016994473,0.000060729915,0.00068982405,0.00007090337,0.0016320996,0.000034002354,0.00017120555,0.00057672354],"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":[0.00004796178,0.000038322476,5.5061776e-7,0.00009586116,0.00001604886,0.000001361354,0.00075778965,0.00043102793,0.02323441,0.04497473,0.0010909309,0.929311],"study_design_scores_gemma":[0.00065300683,0.00034911558,0.00016026034,0.00023534284,0.000050085833,0.000063301835,0.0003066925,0.49516723,0.3737141,0.06273364,0.065428786,0.0011384395],"about_ca_topic_score_codex":0.0000044551225,"about_ca_topic_score_gemma":0.000018252122,"teacher_disagreement_score":0.9281726,"about_ca_system_score_codex":0.000055018514,"about_ca_system_score_gemma":0.000049630064,"threshold_uncertainty_score":0.7412807},"labels":[],"label_agreement":null},{"id":"W4399899568","doi":"10.1007/s10845-024-02417-z","title":"Dual visual inspection for automated quality detection and printing optimization of two-photon polymerization based on deep learning","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Process (computing); Artificial intelligence; Deep learning; Computer science; 3D printing; Dual (grammatical number); Visual inspection; Computer vision; Pattern recognition (psychology); Engineering; Mechanical engineering","score_opus":0.01845949410429593,"score_gpt":0.3186848398081004,"score_spread":0.3002253457038045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399899568","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":[0.20929298,0.00006403049,0.7899081,0.000067289235,0.00027417974,0.00016855348,3.1937975e-7,0.00020561772,0.000018935529],"genre_scores_gemma":[0.94952524,0.000043789074,0.050261054,0.00002080524,0.000118269214,0.000008040042,0.000002247256,0.000014908481,0.0000056792987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987902,0.000080916856,0.00057214906,0.00021348422,0.00020829061,0.00013492696],"domain_scores_gemma":[0.9988997,0.0003486981,0.0004848571,0.0000981498,0.00011696423,0.00005165636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045349743,0.000119754215,0.00016421973,0.00035072298,0.00015261456,0.00011922511,0.00011248299,0.000047521597,0.0000026051764],"category_scores_gemma":[0.000101346646,0.000110734545,0.00008365712,0.00025295507,0.000021567508,0.0003966521,0.000038935108,0.00021743457,6.8348095e-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":[0.00003636184,0.00002847726,0.000038621056,0.00006503925,0.000015595988,0.0000013506582,0.00015082856,0.87400615,0.021547567,0.00039672732,4.9101016e-7,0.103712775],"study_design_scores_gemma":[0.00010494681,0.0001472337,0.00027392362,0.00008178421,0.000009160017,0.000015964051,0.000023226441,0.5948566,0.40425378,0.0001443745,0.000029918892,0.000059080114],"about_ca_topic_score_codex":0.0000055173027,"about_ca_topic_score_gemma":0.0000032065675,"teacher_disagreement_score":0.7402322,"about_ca_system_score_codex":0.000112622736,"about_ca_system_score_gemma":0.00002456565,"threshold_uncertainty_score":0.45156237},"labels":[],"label_agreement":null},{"id":"W4400016384","doi":"10.1016/j.media.2024.103243","title":"CP-Net: Instance-aware part segmentation network for biological cell parsing","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CReATe Fertility Centre; University of Toronto","funders":"Temerty Faculty of Medicine, University of Toronto; Natural Sciences and Engineering Research Council of Canada; Vector Institute; Government of Ontario; Ontario Research Foundation","keywords":"Segmentation; Parsing; Computer science; Artificial intelligence; Market segmentation; Image segmentation; Pattern recognition (psychology); Feature (linguistics); Computer vision","score_opus":0.02439601499852344,"score_gpt":0.3100596373806054,"score_spread":0.2856636223820819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400016384","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":[0.0016182637,0.0010597673,0.9929779,0.0032564448,0.00023451359,0.00022419362,0.000011834363,0.000321592,0.00029545123],"genre_scores_gemma":[0.54230964,0.0010698085,0.449597,0.0035328446,0.0016632274,0.0005526075,0.00044101468,0.00003554184,0.0007983371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981016,0.0000831435,0.00037304888,0.0006400977,0.000398028,0.00040406044],"domain_scores_gemma":[0.998696,0.0005332747,0.000071967384,0.00041548038,0.00006807361,0.000215172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004261085,0.00015605081,0.00027236203,0.00011991772,0.00020960775,0.0001889125,0.00058241794,0.00010214201,0.0001672105],"category_scores_gemma":[0.00006617621,0.0001242187,0.00026387564,0.0026439899,0.00010657613,0.00035305606,0.0001450654,0.00020043459,0.000061979736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030098287,0.0003547511,0.0026171554,0.0001561529,0.0012124442,0.00028508523,0.0005899661,0.023003124,0.0030584906,0.020662954,0.15421963,0.7938101],"study_design_scores_gemma":[0.00017168628,0.000048536844,0.0002008663,0.000037332018,0.00025333802,0.000004192929,0.000025522275,0.94275105,0.00088891416,0.0074479026,0.047934335,0.00023633408],"about_ca_topic_score_codex":0.000004949731,"about_ca_topic_score_gemma":0.00002124878,"teacher_disagreement_score":0.9197479,"about_ca_system_score_codex":0.000058815785,"about_ca_system_score_gemma":0.000056285484,"threshold_uncertainty_score":0.5065492},"labels":[],"label_agreement":null},{"id":"W4400041381","doi":"10.3390/wevj15070285","title":"CTM-YOLOv8n: A Lightweight Pedestrian Traffic-Sign Detection and Recognition Model with Advanced Optimization","year":2024,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pedestrian; Computer science; Traffic sign recognition; Sign (mathematics); Pattern recognition (psychology); Artificial intelligence; Traffic sign; Engineering; Transport engineering; Mathematics","score_opus":0.011961789657816206,"score_gpt":0.22796185056720147,"score_spread":0.21600006090938526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400041381","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":[0.045118414,0.0012412709,0.95095015,0.0016158051,0.00012221062,0.00026556215,8.5659735e-7,0.00035630367,0.00032944727],"genre_scores_gemma":[0.89780784,0.00086217135,0.100541584,0.00015025711,0.00021857042,0.00004625067,0.0000022800123,0.0000300469,0.00034097148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855596,0.00006672293,0.0002995783,0.00043547017,0.0002754246,0.00036682212],"domain_scores_gemma":[0.9992563,0.00011430899,0.00013427607,0.00020104897,0.000117476135,0.00017655168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020931156,0.0001915327,0.00015568681,0.00047564448,0.00041533416,0.00045222192,0.0002524111,0.000053919706,0.000007999037],"category_scores_gemma":[0.000013927905,0.00016189618,0.000048728794,0.0024288644,0.000023869268,0.00136023,0.000032137887,0.0005617625,0.000012763593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003430567,0.000025758382,0.0000024432318,0.000008066105,0.000015310867,0.000019619622,0.00008210338,0.43150058,0.0042546974,0.000288804,0.00012648212,0.5636418],"study_design_scores_gemma":[0.00040395075,0.00023297619,0.000046762452,0.0000609665,0.000028586865,0.00067714474,0.000004024901,0.990885,0.002456933,0.0042736693,0.0007055378,0.00022444883],"about_ca_topic_score_codex":9.014487e-7,"about_ca_topic_score_gemma":0.000022984072,"teacher_disagreement_score":0.85268945,"about_ca_system_score_codex":0.00016186865,"about_ca_system_score_gemma":0.00011892715,"threshold_uncertainty_score":0.66019344},"labels":[],"label_agreement":null},{"id":"W4400041543","doi":"10.18280/ts.410347","title":"A Compare Research of Two Different Point Clouds 3D Object Detection Methods","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Xi'an Municipal Bureau of Science and Technology","keywords":"Point cloud; Computer science; Artificial intelligence; Robustness (evolution); Partition (number theory); Classifier (UML); Deep learning; Pattern recognition (psychology); Artificial neural network; Object detection; Computer vision; Data mining; Mathematics","score_opus":0.09885877208109599,"score_gpt":0.42537780850736173,"score_spread":0.32651903642626573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400041543","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":[0.055551793,0.0003554498,0.9423035,0.000473779,0.00021493618,0.0004327472,0.0000047697317,0.00023440759,0.00042865367],"genre_scores_gemma":[0.9035865,0.00001726925,0.09600328,0.000041810883,0.00012991155,0.00015712788,0.0000034500652,0.000013648944,0.000047018548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978524,0.00042431956,0.00036693722,0.00048802415,0.00051296013,0.0003553883],"domain_scores_gemma":[0.9986151,0.0006838551,0.00005590189,0.00042257636,0.00012374003,0.000098821096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096386933,0.00015013873,0.00019793004,0.0002174547,0.00016741747,0.00011427489,0.0006090615,0.00002764021,0.0000858251],"category_scores_gemma":[0.000014332829,0.00012581013,0.000096113065,0.0009959514,0.000093968614,0.00023471999,0.00026580022,0.0003245766,0.00002954612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030205225,0.00020269622,0.000060812887,0.000100794714,0.000066087654,0.000012379953,0.0011412645,0.004322156,0.3182054,0.12528567,0.0006078656,0.54996467],"study_design_scores_gemma":[0.00043075217,0.0002796,0.0016113558,0.00010393567,0.000018473516,0.000022195109,0.00005034007,0.77087134,0.17933823,0.042142916,0.004906532,0.00022432173],"about_ca_topic_score_codex":0.0000204303,"about_ca_topic_score_gemma":0.000028298564,"teacher_disagreement_score":0.8480347,"about_ca_system_score_codex":0.00013273531,"about_ca_system_score_gemma":0.000045167726,"threshold_uncertainty_score":0.51303875},"labels":[],"label_agreement":null},{"id":"W4400121095","doi":"10.59275/j.melba.2024-151b","title":"Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches","year":2024,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Initialization; Image registration; Artificial intelligence; Computer science; Subject (documents); Subject matter; Computer vision; Deep learning; Image (mathematics); Medical physics; Psychology; Medicine; Library science; Curriculum","score_opus":0.03619910164753889,"score_gpt":0.3430835035040612,"score_spread":0.3068844018565223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400121095","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":[0.07390275,0.0008678368,0.92171276,0.0032177207,0.000036064288,0.00015869756,0.0000061040196,0.00005211174,0.000045954173],"genre_scores_gemma":[0.9895644,0.000050973093,0.010048039,0.000042151856,0.00023166637,0.0000044949393,0.000036773716,0.000012179416,0.000009311977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981479,0.0004542578,0.0005562108,0.00022299853,0.0004055627,0.00021307265],"domain_scores_gemma":[0.99650437,0.0024841628,0.0005916346,0.00016876582,0.00012732457,0.00012376827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016075269,0.00017147067,0.00037164235,0.00057412306,0.00024020397,0.0001402419,0.00041897665,0.00003947229,0.000006020286],"category_scores_gemma":[0.00044530007,0.000111995985,0.00021261262,0.0018711542,0.00020884114,0.00035721168,0.00005456295,0.00065178063,0.0000011084693],"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":[0.00047568954,0.00040742682,0.099346526,0.00039951995,0.001975313,0.000056837787,0.0066873655,0.6642432,0.0037065495,0.003227077,0.0007803085,0.21869417],"study_design_scores_gemma":[0.00039973675,0.0005486896,0.015382705,0.00004819522,0.0003808247,0.00003866846,0.000045568195,0.98199975,0.00013214152,0.00066487415,0.00024079929,0.00011806711],"about_ca_topic_score_codex":0.000013852279,"about_ca_topic_score_gemma":0.0000016448661,"teacher_disagreement_score":0.91566163,"about_ca_system_score_codex":0.00006149748,"about_ca_system_score_gemma":0.00010100358,"threshold_uncertainty_score":0.45670637},"labels":[],"label_agreement":null},{"id":"W4400412808","doi":"10.1016/j.patrec.2024.07.005","title":"A sharper definition of alignment for Panoptic Quality","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canadian Institute of Steel Construction","keywords":"Metric (unit); Pixel; Function (biology); Isomorphism (crystallography); Panopticon; Object (grammar); Simple (philosophy); Quality (philosophy); Computer science; Segmentation; Artificial intelligence; Task (project management); Mathematics; Image (mathematics); Algorithm; Computer vision; Discrete mathematics; Combinatorics; Philosophy","score_opus":0.08185113704825331,"score_gpt":0.31417319968639534,"score_spread":0.23232206263814204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400412808","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":[0.071040295,0.000054129876,0.92039776,0.007435607,0.0002647114,0.0003927526,0.00008935509,0.00019089018,0.00013451642],"genre_scores_gemma":[0.91495985,0.00002428856,0.07924744,0.004914708,0.00013219829,0.0005655169,0.0001301084,0.000017336015,0.000008580823],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989208,0.000045990993,0.0003127452,0.00036421884,0.0001701047,0.00018613707],"domain_scores_gemma":[0.9992263,0.00033257157,0.00008519123,0.00026022483,0.00005096524,0.000044765042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017576598,0.00010895491,0.00011998454,0.000089156056,0.00005400363,0.00006672613,0.00022269724,0.000031059168,0.00003030436],"category_scores_gemma":[0.000016689055,0.000108530476,0.00010590193,0.00020102152,0.000031877193,0.0003268701,0.000050004724,0.00007080491,0.00013223341],"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":[0.000013812494,0.00012745599,0.0003050421,0.0004976933,0.00008382584,0.000008246822,0.00061291165,0.0001181517,0.13191113,0.012697762,0.004704866,0.8489191],"study_design_scores_gemma":[0.003959319,0.0007429938,0.014965396,0.00215613,0.00033165098,0.00015954758,0.00018858565,0.17016707,0.24524523,0.5312367,0.027410027,0.0034373195],"about_ca_topic_score_codex":0.0000073184956,"about_ca_topic_score_gemma":0.0000021981796,"teacher_disagreement_score":0.8454818,"about_ca_system_score_codex":0.0000463471,"about_ca_system_score_gemma":0.000011356344,"threshold_uncertainty_score":0.44257444},"labels":[],"label_agreement":null},{"id":"W4400645085","doi":"10.1109/iv55156.2024.10588523","title":"Gradual Batch Alternation for Effective Domain Adaptation in LiDAR-Based 3D Object Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Lidar; Alternation (linguistics); Computer science; Domain adaptation; Object detection; Object (grammar); Domain (mathematical analysis); Adaptation (eye); Artificial intelligence; Computer vision; Pattern recognition (psychology); Remote sensing; Mathematics; Geography","score_opus":0.015511695342540965,"score_gpt":0.2835943513703853,"score_spread":0.2680826560278443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400645085","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":[0.024380893,0.000063388,0.97284156,0.0006945973,0.00035322897,0.0010222318,0.0000016716858,0.00039346906,0.00024894663],"genre_scores_gemma":[0.8538857,0.0000030893975,0.14498037,0.0001091586,0.000092604416,0.0008697856,0.0000074692853,0.00001117438,0.000040659477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902666,0.000048701724,0.00017556587,0.00041987962,0.00015122753,0.00017795672],"domain_scores_gemma":[0.9991154,0.00055739784,0.000037974078,0.00020345839,0.00005229365,0.0000334856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002312666,0.00010712558,0.000085021034,0.00019747751,0.00008110482,0.00011796608,0.00019764145,0.000044059623,0.0000015688415],"category_scores_gemma":[0.000028470033,0.00010172282,0.00004885889,0.00078297517,0.000015028069,0.0005083598,0.000029114464,0.00010387306,0.000023434626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004050248,0.00006780774,0.00006386563,0.0000620982,0.000012892229,0.0000062410622,0.0006088428,0.06598296,0.022203956,0.09903129,0.0000553083,0.81186426],"study_design_scores_gemma":[0.000315461,0.00013190086,0.0011968216,0.000028084516,0.0000032698863,0.0000033175556,0.0000140536995,0.9243644,0.026540302,0.04604454,0.0012370218,0.000120855926],"about_ca_topic_score_codex":0.000040984436,"about_ca_topic_score_gemma":0.00040286843,"teacher_disagreement_score":0.8583814,"about_ca_system_score_codex":0.00017621717,"about_ca_system_score_gemma":0.00003758255,"threshold_uncertainty_score":0.4148136},"labels":[],"label_agreement":null},{"id":"W4400680152","doi":"10.1109/saner60148.2024.00044","title":"CEDAR: Continuous Testing of Deep Learning Libraries","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Science Foundation","keywords":"Computer science; Deep learning; Artificial intelligence","score_opus":0.021535581945717527,"score_gpt":0.2542780030835139,"score_spread":0.23274242113779636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400680152","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":[0.0023460903,0.0006881232,0.96925974,0.0004398212,0.000084429535,0.00006446172,1.750988e-7,0.0007982507,0.026318898],"genre_scores_gemma":[0.6059408,0.0000052335977,0.39316493,0.000053927328,0.00003641402,0.00000828483,4.870897e-7,0.0000061410556,0.00078374514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994162,0.000015687065,0.00013895564,0.00020752165,0.000089177636,0.00013250769],"domain_scores_gemma":[0.9992129,0.0004957696,0.000031677424,0.00019485655,0.000032607026,0.00003218608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005549654,0.00006071718,0.000080082624,0.000045553206,0.000066313994,0.000116529045,0.00033368668,0.0000188669,0.000015226154],"category_scores_gemma":[0.000070068156,0.000052073217,0.000024545105,0.00066752615,0.000037803875,0.00047828504,0.0001933103,0.00011595659,0.000042601405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.1573268e-7,0.000005248098,0.00048774292,0.000012924395,0.000004141365,0.000005182798,0.00015490045,0.0026076345,0.0028798264,0.53083986,0.00018589741,0.4628163],"study_design_scores_gemma":[0.00005345931,0.00006898733,0.0005108789,0.000043083946,0.000004484973,0.000028367276,0.00004778599,0.8931899,0.013598211,0.045052942,0.047241047,0.00016086253],"about_ca_topic_score_codex":0.0000034496124,"about_ca_topic_score_gemma":9.5334275e-7,"teacher_disagreement_score":0.89058226,"about_ca_system_score_codex":0.000005881263,"about_ca_system_score_gemma":0.000018024968,"threshold_uncertainty_score":0.21234842},"labels":[],"label_agreement":null},{"id":"W4400721789","doi":"10.1016/j.neunet.2024.106546","title":"Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation","year":2024,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":119,"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":"Department of Science and Technology of Liaoning Province; National Natural Science Foundation of China","keywords":"Computer science; Encoder; Segmentation; Artificial intelligence; Semantic gap; Image segmentation; Encoding (memory); Pattern recognition (psychology); Image (mathematics)","score_opus":0.011968647690954685,"score_gpt":0.28349962584325605,"score_spread":0.27153097815230137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400721789","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":[0.09588987,0.002070489,0.8807427,0.019781206,0.00047477824,0.00063297205,0.0000012078226,0.00020733383,0.00019938732],"genre_scores_gemma":[0.9973634,0.00014855244,0.0014657672,0.00062122557,0.00018856909,0.0001128572,0.000002959848,0.000016083512,0.00008057294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985531,0.00021910654,0.00028842184,0.0003426003,0.00030343578,0.00029331213],"domain_scores_gemma":[0.9983188,0.0010309387,0.00007311032,0.00047914396,0.00003932213,0.00005869677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061547407,0.00013717971,0.0001311062,0.000058805843,0.00027330068,0.00017120388,0.0006699357,0.00006243507,0.000023357868],"category_scores_gemma":[0.000039272134,0.00007484641,0.00004965146,0.0013674883,0.00018223445,0.00046439402,0.00018404955,0.00060088973,0.0000066137663],"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":[0.000025745785,0.00007971691,0.0003424202,0.00006965678,0.000053623644,0.0026113845,0.0017643289,0.7518527,0.00041089172,0.109272584,0.0054425937,0.12807436],"study_design_scores_gemma":[0.00014896033,0.000045563487,0.0000988037,0.00008015529,0.0000116984875,0.0018829633,0.00024395515,0.9951737,0.0001150466,0.0016161148,0.0004916535,0.000091420974],"about_ca_topic_score_codex":0.0001482176,"about_ca_topic_score_gemma":0.00055363076,"teacher_disagreement_score":0.9014735,"about_ca_system_score_codex":0.000033780667,"about_ca_system_score_gemma":0.000039992086,"threshold_uncertainty_score":0.3052148},"labels":[],"label_agreement":null},{"id":"W4400944150","doi":"10.1002/mp.17317","title":"Open access segmentations of intraoperative brain tumor ultrasound images","year":2024,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Magnetic resonance imaging; Ground truth; Segmentation; Computer science; Ultrasound; Surgical planning; Radiology; Artificial intelligence; Image segmentation; Medical physics; Computer vision; Neurosurgery; Medicine","score_opus":0.03667990389388491,"score_gpt":0.3911822921702972,"score_spread":0.3545023882764123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400944150","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":[0.0015710978,0.0000919401,0.98673606,0.008945585,0.00021464909,0.00024961025,0.000016914335,0.000110747904,0.002063394],"genre_scores_gemma":[0.9821515,0.000040961233,0.014656814,0.0023830736,0.000288264,0.000141194,0.000022227008,0.00001529071,0.00030069676],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99895436,0.000043716147,0.00019565946,0.00031357238,0.00034202868,0.0001506866],"domain_scores_gemma":[0.99840176,0.0010570778,0.000043165535,0.0003308277,0.00005243931,0.000114732895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014846635,0.00009167593,0.0001340221,0.000023474047,0.00008573458,0.0004341282,0.0024615163,0.000020399139,0.00009004208],"category_scores_gemma":[0.0001681781,0.00007642735,0.000035996913,0.00081587647,0.00015581772,0.001586189,0.0009995674,0.00018880948,0.000050136834],"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":[0.0000047645594,0.0003485216,0.00021014725,0.000091396476,0.00008463705,0.000060135844,0.0015047169,0.0007998202,0.013336415,0.5117367,0.13248536,0.33933738],"study_design_scores_gemma":[0.0006222991,0.00014059097,0.0014854316,0.0003318766,0.000029556484,0.000056930297,0.00006924172,0.030179303,0.43138635,0.52159864,0.013526498,0.00057326595],"about_ca_topic_score_codex":0.000018097438,"about_ca_topic_score_gemma":0.0000055183737,"teacher_disagreement_score":0.9805804,"about_ca_system_score_codex":0.00002789661,"about_ca_system_score_gemma":0.0001958403,"threshold_uncertainty_score":0.45741507},"labels":[],"label_agreement":null},{"id":"W4400945784","doi":"10.1109/itec60657.2024.10598994","title":"AI-Powered Video Monitoring: Assessing the NVIDIA Jetson Orin Devices for Edge Computing Applications","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Edge computing; Embedded system; Computer hardware; Computer graphics (images); Telecommunications","score_opus":0.04174176386121849,"score_gpt":0.37025983562022524,"score_spread":0.32851807175900677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400945784","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":[0.0006974666,0.0013501579,0.9852469,0.008963339,0.0005459784,0.0008397936,0.00000221326,0.0008838078,0.0014703599],"genre_scores_gemma":[0.78376734,0.000025020088,0.21407002,0.0005092285,0.00081398484,0.0005156319,0.0000044326994,0.000024081888,0.00027026728],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985586,0.0000343351,0.00028847234,0.0005850443,0.00018449666,0.00034904407],"domain_scores_gemma":[0.9978351,0.0011918356,0.00007393816,0.0007180465,0.000100889825,0.000080168145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027706785,0.00016804902,0.00012910547,0.00006880801,0.0006400506,0.0009543478,0.0011042518,0.000050493745,0.0000035513463],"category_scores_gemma":[0.000019663103,0.0001223578,0.000089354966,0.00095396827,0.000048952534,0.0007586683,0.0002951748,0.00021335116,0.00006533088],"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":[0.0000010672107,0.000047340323,0.00048509586,0.00007873598,0.00003619098,0.0000017573393,0.00042369394,0.0025573077,0.0038838359,0.45006156,0.004301077,0.53812236],"study_design_scores_gemma":[0.00011003362,0.000021400563,0.001687409,0.00008154422,0.000022414766,0.00001570358,0.00012809066,0.4672603,0.005000101,0.025643371,0.49972174,0.00030788514],"about_ca_topic_score_codex":0.0000045854936,"about_ca_topic_score_gemma":0.000004129568,"teacher_disagreement_score":0.78306985,"about_ca_system_score_codex":0.00006966419,"about_ca_system_score_gemma":0.00006959814,"threshold_uncertainty_score":0.92027974},"labels":[],"label_agreement":null},{"id":"W4401023793","doi":"10.24963/ijcai.2024/647","title":"Enhancing Semantic Clarity: Discriminative and Fine-grained Information Mining for Remote Sensing Image-Text Retrieval","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; National Research Council Canada; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Artificial neural network; Physics; Computer science; Artificial intelligence; Algorithm","score_opus":0.017503143301075046,"score_gpt":0.28564559695037506,"score_spread":0.2681424536493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401023793","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":[0.00755081,0.000081148835,0.9869538,0.003816476,0.00016272909,0.00037563554,0.0000029916644,0.00035070337,0.0007057012],"genre_scores_gemma":[0.28941646,0.000011819521,0.709889,0.0002859244,0.00007196582,0.0000017928717,0.00001127873,0.000009316168,0.00030244392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990594,0.0000214728,0.00026096235,0.00028731025,0.00013817778,0.00023271702],"domain_scores_gemma":[0.99899054,0.0005365853,0.00006125056,0.00024170375,0.0001093842,0.00006052322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024442506,0.00012737521,0.00012564407,0.00012387436,0.00021066118,0.00040910297,0.00015786577,0.000045883884,0.0000017142255],"category_scores_gemma":[0.00018421253,0.00011443235,0.00004137032,0.00049828837,0.00004520106,0.0018554023,0.00017655079,0.00012044136,0.0000129361315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017504595,0.0000051301718,0.0000013284772,0.00025212744,0.000024139828,0.000008100652,0.004855398,0.000087508226,0.028077075,0.06300184,0.0013582354,0.9023116],"study_design_scores_gemma":[0.00013841841,0.00003998697,0.000045163753,0.00009905404,0.000013914828,0.000037293816,0.00017307115,0.9542132,0.017550109,0.024748169,0.002780412,0.00016119637],"about_ca_topic_score_codex":0.0000081322305,"about_ca_topic_score_gemma":0.000024429684,"teacher_disagreement_score":0.9541257,"about_ca_system_score_codex":0.000040729115,"about_ca_system_score_gemma":0.000035372002,"threshold_uncertainty_score":0.46664155},"labels":[],"label_agreement":null},{"id":"W4401047682","doi":"10.1007/978-981-97-3559-4_13","title":"EnsUNet: Enhancing Brain Tumor Segmentation Through Fusion of Pre-trained Models","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Segmentation; Fusion; Artificial intelligence; Computer science; Brain tumor; Pattern recognition (psychology); Psychology; Philosophy; Linguistics","score_opus":0.018190283536057887,"score_gpt":0.2597213010172751,"score_spread":0.2415310174812172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401047682","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":[0.00012043657,0.015816946,0.97659254,0.00027419333,0.0007068707,0.001003098,0.000011656389,0.00012885962,0.0053454],"genre_scores_gemma":[0.9681405,0.0014641861,0.01828433,0.0006041423,0.0013174019,0.00019331065,0.00009610032,0.00014291711,0.0097571025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978373,0.000044738194,0.0007387894,0.00076602626,0.0003043688,0.000308775],"domain_scores_gemma":[0.9982469,0.0006593947,0.0003945761,0.00056965783,0.00007012529,0.000059352424],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025267142,0.0003989686,0.00058098946,0.00012901805,0.0000885857,0.00011781328,0.0003883093,0.0003271888,0.0000038791227],"category_scores_gemma":[0.00001372521,0.0003481591,0.000099381396,0.00021435827,0.00006741888,0.00023971363,0.00023304624,0.00054621533,0.0000024580718],"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":[0.000010752563,0.0000069486596,0.0000021264627,0.00023671535,0.000027513946,0.000016034584,0.00081830117,0.7991679,0.00056286727,0.18840191,0.0001195331,0.010629405],"study_design_scores_gemma":[0.00014676181,0.00006350811,0.0000027966116,0.001255957,0.000019419069,0.000041332758,0.000004288512,0.8159485,0.00012363205,0.18047762,0.0016121304,0.00030408992],"about_ca_topic_score_codex":0.000055347944,"about_ca_topic_score_gemma":0.00017365215,"teacher_disagreement_score":0.9680201,"about_ca_system_score_codex":0.000088725996,"about_ca_system_score_gemma":0.000034945213,"threshold_uncertainty_score":0.99989706},"labels":[],"label_agreement":null},{"id":"W4401170997","doi":"10.1016/j.media.2024.103287","title":"Image-level supervision and self-training for transformer-based cross-modality tumor segmentation","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fonds de Recherche du Québec - Santé; Compute Canada","keywords":"Computer science; Artificial intelligence; Segmentation; Modality (human–computer interaction); Image segmentation; Modalities; Pattern recognition (psychology); Deep learning; Machine learning; Computer vision","score_opus":0.0338594690691731,"score_gpt":0.35617742369687416,"score_spread":0.3223179546277011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401170997","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":[0.017764311,0.0002280106,0.9753992,0.005831924,0.000057029552,0.0002634846,0.000035474015,0.0003244739,0.000096102754],"genre_scores_gemma":[0.47129783,0.000055592383,0.5274105,0.0008157149,0.000096628464,0.00017126178,0.00008771434,0.000015716174,0.000049025773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981138,0.000056566565,0.00037429784,0.00063723983,0.0005090353,0.00030904994],"domain_scores_gemma":[0.9987191,0.0005343798,0.00004475843,0.00034690727,0.00011047381,0.0002443649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006703659,0.00016146693,0.0002627149,0.00021462532,0.00023242433,0.00040240356,0.00041468706,0.000058144702,0.00008398972],"category_scores_gemma":[0.00012617001,0.00013799475,0.0002308832,0.0015741877,0.0001373944,0.0008244794,0.000050787326,0.00016761333,0.00001330046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064822394,0.0007498877,0.0020766053,0.0011973397,0.0019131422,0.00030676043,0.005474543,0.0019725403,0.04493943,0.008115949,0.0024339634,0.930755],"study_design_scores_gemma":[0.0003398588,0.000038798844,0.0014955194,0.000020190113,0.00025078806,0.000006425066,0.000035323657,0.99105144,0.0027214831,0.0028301706,0.0010448874,0.00016510538],"about_ca_topic_score_codex":0.00002491044,"about_ca_topic_score_gemma":0.00002545126,"teacher_disagreement_score":0.98907894,"about_ca_system_score_codex":0.00004909472,"about_ca_system_score_gemma":0.00014073211,"threshold_uncertainty_score":0.56272626},"labels":[],"label_agreement":null},{"id":"W4401201671","doi":"10.48550/arxiv.2407.19394","title":"Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transformer; Training (meteorology); Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Engineering; Geography; Electrical engineering; Voltage","score_opus":0.11352214624892285,"score_gpt":0.24399012352745725,"score_spread":0.1304679772785344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401201671","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":[0.06808616,0.00007020729,0.92819715,0.00064667314,0.00045231855,0.0010822159,0.0002974009,0.00026969396,0.00089817465],"genre_scores_gemma":[0.9945395,0.000084485,0.0047402345,0.00013374345,0.000045644796,0.00002139412,0.00021780039,0.000025089612,0.00019212357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978092,0.00005881692,0.00025019396,0.0013573617,0.00007705292,0.00044737864],"domain_scores_gemma":[0.9986006,0.0003046994,0.00011263676,0.00077296706,0.00004685563,0.00016221155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002029602,0.00030251167,0.00028271717,0.0004081926,0.00016837294,0.000077512814,0.001129997,0.00026733498,0.000003536769],"category_scores_gemma":[0.000020468617,0.0003474852,0.00019434113,0.000894152,0.00009886339,0.00010815309,0.000738671,0.0008836189,0.000059522106],"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":[0.000029901943,0.00010248146,0.0000095648475,0.00006329096,0.000020136038,0.0000584931,0.00024463673,0.7969744,0.00006427255,0.19607586,0.00035885704,0.0059980974],"study_design_scores_gemma":[0.00048805794,0.0000583944,0.00020967494,0.00022282166,0.000041703355,0.0000023762423,0.00006944699,0.9549382,0.000085461266,0.040683933,0.002829812,0.0003701606],"about_ca_topic_score_codex":0.000022030681,"about_ca_topic_score_gemma":0.00032079802,"teacher_disagreement_score":0.9264533,"about_ca_system_score_codex":0.00032549514,"about_ca_system_score_gemma":0.00017863752,"threshold_uncertainty_score":0.9998977},"labels":[],"label_agreement":null},{"id":"W4401209463","doi":"10.1007/978-3-031-67751-9_9","title":"SnapSeg: Training-Free Few-Shot Medical Image Segmentation with Segment Anything Model","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Shot (pellet); Artificial intelligence; Computer vision; Segmentation; Image segmentation; Training (meteorology); Image (mathematics); Computer graphics (images)","score_opus":0.028926196218898727,"score_gpt":0.2819824265583252,"score_spread":0.2530562303394265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401209463","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":[0.000044649674,0.00031991772,0.9883343,0.0049661696,0.00064625725,0.0006349385,0.000011266407,0.00042392375,0.0046185683],"genre_scores_gemma":[0.0204596,0.00007806042,0.97511005,0.0030210367,0.0005035883,0.000073335075,0.00001820083,0.000077648285,0.0006584496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99423784,0.000023216007,0.0006118989,0.0021253487,0.0021971774,0.00080453465],"domain_scores_gemma":[0.99686176,0.00041100237,0.00026659458,0.001907198,0.00019203838,0.00036141614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075048534,0.0006518564,0.00051259657,0.00059596094,0.00034392302,0.00069249386,0.0045883227,0.00030318694,0.000027878134],"category_scores_gemma":[0.00006049091,0.00054298085,0.00011368948,0.00092308444,0.00084824325,0.0009866018,0.0021204327,0.0013866093,0.0000485479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000108096665,0.00004036579,0.000002836358,0.00008431781,0.000029300636,0.00033663542,0.0031432786,0.4313622,0.0007142335,0.104536034,0.00015637436,0.4595836],"study_design_scores_gemma":[0.00023323104,0.00009979601,0.000002197373,0.00046185503,0.0000119549495,0.00016615781,6.361933e-7,0.7343612,0.0007361147,0.26320678,0.00023588665,0.00048423262],"about_ca_topic_score_codex":0.0000055308333,"about_ca_topic_score_gemma":0.00010104601,"teacher_disagreement_score":0.45909938,"about_ca_system_score_codex":0.0004739796,"about_ca_system_score_gemma":0.0009366722,"threshold_uncertainty_score":0.99970216},"labels":[],"label_agreement":null},{"id":"W4401211761","doi":"10.1109/isca59077.2024.00073","title":"NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung","keywords":"Hash function; Computer science; Computation; Algorithm; Computer security","score_opus":0.028007022629605072,"score_gpt":0.2807838395485833,"score_spread":0.25277681691897824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211761","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":[0.01732326,0.00008651135,0.9754229,0.0037969784,0.0001943771,0.00040485125,0.000003891466,0.0014496675,0.0013175707],"genre_scores_gemma":[0.7220543,0.0000018046431,0.2765621,0.000960133,0.00012700746,0.00012916952,0.00001261015,0.000022722186,0.00013016994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840754,0.00003930365,0.00027504834,0.000652033,0.00028539545,0.00034068286],"domain_scores_gemma":[0.9987205,0.00043445546,0.00005945648,0.0005360302,0.00010519324,0.00014436696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000085832784,0.00021384201,0.0001591156,0.00013796958,0.00034897495,0.00086294493,0.00067307765,0.00004401043,0.00006708536],"category_scores_gemma":[0.00001519653,0.00016848235,0.000058652746,0.001144033,0.000054323416,0.0007906285,0.00013111038,0.00025259922,0.00014436162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032257372,0.0003145932,0.0010801066,0.00012291521,0.00009637706,0.00020582613,0.00065496727,0.3236851,0.009011932,0.32509577,0.006131249,0.3335689],"study_design_scores_gemma":[0.00024169707,0.00008135158,0.0005928077,0.00003557834,0.000009006151,0.00002303612,0.0000073965643,0.9923416,0.0023149285,0.001426256,0.0026893332,0.00023704297],"about_ca_topic_score_codex":0.000032485517,"about_ca_topic_score_gemma":0.00019738147,"teacher_disagreement_score":0.70473105,"about_ca_system_score_codex":0.000050141756,"about_ca_system_score_gemma":0.000204553,"threshold_uncertainty_score":0.8321398},"labels":[],"label_agreement":null},{"id":"W4401386967","doi":"10.1109/lra.2024.3440097","title":"DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":316,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Natural Science Foundation of China","keywords":"End-to-end principle; Computer science; Language model; End of history; Artificial intelligence; Political science","score_opus":0.008262373931495188,"score_gpt":0.2518810611154174,"score_spread":0.24361868718392224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401386967","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":[0.023653755,0.00008693798,0.96486735,0.0100214705,0.00037086292,0.0001973293,0.000005719584,0.00067755,0.00011903348],"genre_scores_gemma":[0.8169503,0.000007579738,0.18011692,0.0026305125,0.00008303758,0.000029332643,0.0000062404465,0.000018481122,0.00015759256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988326,0.000024322395,0.00022756388,0.00042906083,0.0001753979,0.00031105138],"domain_scores_gemma":[0.9993523,0.000097184544,0.000049312257,0.00036594787,0.00002060712,0.000114683135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012914054,0.00015743249,0.00013669583,0.000156074,0.0001597401,0.00035071452,0.00033855517,0.0000405293,0.0000068809372],"category_scores_gemma":[0.0000073952365,0.00015634696,0.000047139973,0.0003779884,0.000026269881,0.0006154322,0.00014926655,0.00015592786,0.000082158425],"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":[4.3456464e-7,0.000016550423,0.000018107077,0.00002385089,0.000015745167,0.000016586408,0.001581237,0.88140714,0.0603378,0.026470631,0.001981657,0.028130263],"study_design_scores_gemma":[0.000064363354,0.000010674755,0.00012198727,0.00004975606,0.000009062791,0.000016643093,0.000007797491,0.9962739,0.0013330466,0.0010417005,0.0008903808,0.00018073808],"about_ca_topic_score_codex":0.0000040086356,"about_ca_topic_score_gemma":0.000007716859,"teacher_disagreement_score":0.7932966,"about_ca_system_score_codex":0.00007136637,"about_ca_system_score_gemma":0.000024785024,"threshold_uncertainty_score":0.6375644},"labels":[],"label_agreement":null},{"id":"W4401399426","doi":"10.5194/tc-18-3471-2024","title":"The AutoICE Challenge","year":2024,"lang":"en","type":"article","venue":"˜The œcryosphere","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"Natural Environment Research Council; Sight Research UK; European Space Agency","keywords":"Computer science","score_opus":0.015085267022450364,"score_gpt":0.2596781636611139,"score_spread":0.24459289663866354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401399426","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":[0.0008087633,0.031660844,0.853361,0.067319445,0.0019741575,0.0006782519,0.0000035886508,0.0018033813,0.042390544],"genre_scores_gemma":[0.9733391,0.0007974494,0.005954976,0.00088210934,0.0005579551,0.00023505917,0.0000010170062,0.000031323645,0.018201057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989779,0.000046585694,0.00014504133,0.0003342423,0.00020401565,0.0002921859],"domain_scores_gemma":[0.9983767,0.0005147861,0.00003294884,0.0009923768,0.00003067701,0.000052504598],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017303255,0.000117717136,0.000068545916,0.0000055899673,0.0005279684,0.00032376018,0.0015297586,0.000038003684,0.000026226755],"category_scores_gemma":[0.0000063622706,0.00006355544,0.00006529219,0.0005049879,0.00009010319,0.00021945067,0.0003147786,0.0002664112,0.0009010141],"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":[9.928604e-7,0.000011118016,0.000003661241,0.0000062266763,0.00001447796,0.0000090650965,0.0002962087,0.00051342876,0.000050601833,0.582906,0.026940947,0.38924727],"study_design_scores_gemma":[0.000028812261,0.00001586062,0.00014894095,0.000011832997,0.0000040370387,0.000013937335,0.000020790649,0.10644987,0.000024565277,0.06060792,0.8325849,0.00008849101],"about_ca_topic_score_codex":0.000011703572,"about_ca_topic_score_gemma":0.00022679761,"teacher_disagreement_score":0.9725303,"about_ca_system_score_codex":0.000034136876,"about_ca_system_score_gemma":0.000043650536,"threshold_uncertainty_score":0.9998769},"labels":[],"label_agreement":null},{"id":"W4401408935","doi":"10.1145/3673038.3673154","title":"Murmuration: On-the-fly DNN Adaptation for SLO-Aware Distributed Inference in Dynamic Edge Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Inference; Latency (audio); Distributed computing; Personalization; Edge computing; Adaptation (eye); Artificial neural network; Enhanced Data Rates for GSM Evolution; Edge device; Reinforcement learning; Bandwidth (computing); Artificial intelligence; Cloud computing; Machine learning; Computer network","score_opus":0.03041338073792147,"score_gpt":0.2899150118096457,"score_spread":0.2595016310717242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401408935","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":[0.002054537,0.000045682038,0.99054974,0.006342278,0.00014443502,0.00056844985,0.000026763413,0.00014450791,0.00012361385],"genre_scores_gemma":[0.98828804,0.000026569016,0.0102156615,0.00030675798,0.000029732617,0.0004797821,0.00008451609,0.000008982043,0.00055995095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990598,0.000027387594,0.00020002633,0.0003781198,0.00014701276,0.00018762986],"domain_scores_gemma":[0.9987002,0.00082376896,0.000036687397,0.0003872078,0.0000143557945,0.000037803242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000092838774,0.000113989205,0.00007572458,0.00005415562,0.00011298079,0.00011822663,0.00043569008,0.0000385581,0.000015360592],"category_scores_gemma":[0.000043079923,0.00008746048,0.000035147623,0.00046710044,0.000030216377,0.000398326,0.00008436074,0.00011803034,0.000115782954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011397758,0.00010942861,0.00006557957,0.000023825025,0.000015127291,0.0000068140325,0.00047018865,0.14191768,0.0010644346,0.71754074,0.0025146788,0.13626012],"study_design_scores_gemma":[0.00009448675,0.00004070763,0.0007597963,0.000024239633,0.0000021420049,0.0000010989233,0.000024729574,0.9640758,0.00044355166,0.023412453,0.011012887,0.00010809503],"about_ca_topic_score_codex":0.000002522634,"about_ca_topic_score_gemma":0.000043259963,"teacher_disagreement_score":0.98623353,"about_ca_system_score_codex":0.00014219442,"about_ca_system_score_gemma":0.000037631016,"threshold_uncertainty_score":0.35665348},"labels":[],"label_agreement":null},{"id":"W4401626964","doi":"10.3390/machines12080557","title":"KCS-YOLO: An Improved Algorithm for Traffic Light Detection under Low Visibility Conditions","year":2024,"lang":"en","type":"article","venue":"Machines","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Visibility; Artificial intelligence; Robustness (evolution); Computer vision; Object detection; Block (permutation group theory); Cluster analysis; Benchmark (surveying); Algorithm; Channel (broadcasting); Pattern recognition (psychology); Mathematics","score_opus":0.011553135787856855,"score_gpt":0.2982213087616732,"score_spread":0.2866681729738163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401626964","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":[0.027233806,0.00016434716,0.9689197,0.0015212685,0.0006357726,0.000526555,0.000040761213,0.0009163437,0.00004145599],"genre_scores_gemma":[0.92782396,0.00000648816,0.07086319,0.00026145735,0.00034507344,0.00038349823,0.000039841005,0.000022880326,0.00025364308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888206,0.000036893874,0.0001941154,0.00056897517,0.000092738584,0.00022523495],"domain_scores_gemma":[0.9991365,0.00016458049,0.00003747711,0.0005121446,0.000060969534,0.000088314315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012559589,0.00014723487,0.000115989584,0.000082963175,0.00027945076,0.0001991861,0.00037268279,0.00006023619,0.000010078401],"category_scores_gemma":[0.000012593045,0.00012816196,0.00008775339,0.0004778069,0.000037381742,0.00062147714,0.00006440217,0.00013627438,0.000027094207],"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":[0.0000031241339,0.000099084704,0.0000029015955,0.000027100563,0.000016812106,0.0000015694293,0.00011997218,0.0059411735,0.019281805,0.006697285,0.0002284432,0.96758074],"study_design_scores_gemma":[0.00012293323,0.00009739306,0.00030999712,0.0000070723586,0.000009845496,0.000014364098,0.000004722814,0.96172464,0.0035528946,0.03128597,0.0027183418,0.00015182693],"about_ca_topic_score_codex":0.000007769221,"about_ca_topic_score_gemma":0.00010794919,"teacher_disagreement_score":0.9674289,"about_ca_system_score_codex":0.000053060925,"about_ca_system_score_gemma":0.000030254298,"threshold_uncertainty_score":0.5226293},"labels":[],"label_agreement":null},{"id":"W4401629926","doi":"10.1016/j.patcog.2024.110893","title":"Patient teacher can impart locality to improve lightweight vision transformer on small dataset","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Waterloo","funders":"Yunnan University","keywords":"Locality; Transformer; Computer science; Computer vision; Artificial intelligence; Engineering; Electrical engineering; Voltage","score_opus":0.02714832255672208,"score_gpt":0.2850006522337626,"score_spread":0.2578523296770405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401629926","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":[0.09486601,0.000047315618,0.8942562,0.0064836727,0.00069497887,0.00094618043,0.0017264686,0.00038926417,0.00058996474],"genre_scores_gemma":[0.98950076,0.000018816672,0.004071767,0.003915317,0.00024174231,0.00038725598,0.001780191,0.00002988983,0.000054264394],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984011,0.00006336745,0.00028092103,0.00071529683,0.00022615312,0.00031312893],"domain_scores_gemma":[0.9991318,0.000080854974,0.000041236875,0.0005233681,0.000043600725,0.00017911237],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001289857,0.00019452591,0.00012697295,0.00011251225,0.000107726744,0.0001738001,0.00029615263,0.00006363727,0.00007233871],"category_scores_gemma":[0.000008187523,0.00016634635,0.000061136714,0.00036495572,0.000018226001,0.00030201446,0.00007205697,0.00023598026,0.0012244503],"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":[0.000008437554,0.00009341818,0.000024739786,0.000023433055,0.00000952835,0.0000110622395,0.00023952084,0.000030591054,0.0017148943,0.000058879894,0.0069182967,0.9908672],"study_design_scores_gemma":[0.0012078036,0.0039550415,0.0030795299,0.0011625439,0.00012507584,0.0000919847,0.000078687044,0.16346787,0.25871685,0.025068795,0.5404092,0.0026366059],"about_ca_topic_score_codex":0.000045855046,"about_ca_topic_score_gemma":0.00012892822,"teacher_disagreement_score":0.9882306,"about_ca_system_score_codex":0.00010718939,"about_ca_system_score_gemma":0.00002311043,"threshold_uncertainty_score":0.9995532},"labels":[],"label_agreement":null},{"id":"W4401723357","doi":"10.1109/icsa-c63560.2024.00049","title":"Benchmarking Emerging Deep Learning Quantization Methods for Energy Efficiency","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Benchmarking; Quantization (signal processing); Computer science; Deep learning; Efficient energy use; Artificial intelligence; Electrical engineering; Engineering; Computer vision; Business","score_opus":0.022259017580566323,"score_gpt":0.35909970932392876,"score_spread":0.33684069174336245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401723357","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":[0.00003716531,0.0013053141,0.99499065,0.00051848905,0.0004413743,0.000112567934,9.863038e-8,0.0007137965,0.0018805274],"genre_scores_gemma":[0.10341539,0.000104970335,0.89550906,0.00013770707,0.00014707257,0.00008975408,0.0000060055304,0.000014925497,0.0005751268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989704,0.00006527199,0.00018895124,0.00043738398,0.000092049915,0.0002459682],"domain_scores_gemma":[0.99901307,0.0006286439,0.000040103456,0.00022295084,0.000049844533,0.000045408015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003344057,0.00010029094,0.00008815637,0.00012404694,0.00027069415,0.00017973459,0.00036447617,0.000033481763,0.000014049296],"category_scores_gemma":[0.000049795963,0.00009221416,0.00006173109,0.0009626074,0.000015315909,0.00039655692,0.00012896705,0.00008635508,0.000006863835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.5946173e-7,0.000003254406,0.000003018446,0.0000056782337,0.0000025185002,2.677215e-7,0.0000778478,0.027814914,0.0018044258,0.43565616,0.000041872077,0.53458977],"study_design_scores_gemma":[0.00003005413,0.000025267867,0.000007120402,0.000014506853,0.000003827179,0.0000040096606,0.000010278796,0.87687904,0.0029560423,0.018184636,0.10177796,0.00010727684],"about_ca_topic_score_codex":0.0000045080305,"about_ca_topic_score_gemma":0.0000036726235,"teacher_disagreement_score":0.8490641,"about_ca_system_score_codex":0.0000314026,"about_ca_system_score_gemma":0.000018343188,"threshold_uncertainty_score":0.37603843},"labels":[],"label_agreement":null},{"id":"W4401748255","doi":"10.1109/access.2024.3448304","title":"SPT-Swin: A Shifted Patch Tokenization Swin Transformer for Image Classification","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Lexical analysis; Artificial intelligence; Transformer; Computer vision; Pattern recognition (psychology); Electrical engineering; Engineering; Voltage","score_opus":0.05227880889700339,"score_gpt":0.3573828564111808,"score_spread":0.3051040475141774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401748255","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":[0.008923336,0.00012198803,0.9826997,0.0055924174,0.0006386493,0.00076504383,0.000018315872,0.0005630306,0.0006775475],"genre_scores_gemma":[0.95861787,0.000073833515,0.039433584,0.00047288896,0.00034380573,0.0006435683,0.000049055667,0.000032890606,0.00033248568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987254,0.000023849068,0.00027355138,0.0005236197,0.00018747794,0.00026610194],"domain_scores_gemma":[0.9991163,0.00017393433,0.000055925433,0.00046235055,0.00011934417,0.000072166935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012704587,0.00014549575,0.00011813866,0.00011425072,0.0001577809,0.00057641976,0.00095022155,0.00006974824,0.000014584314],"category_scores_gemma":[0.000014916016,0.0001371386,0.00007569554,0.001116786,0.00003283818,0.0023505825,0.000032860455,0.00012075785,0.00007429711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040317584,0.0001931392,0.00031246786,0.00033064088,0.00006403463,0.000015879185,0.0014292784,0.001628151,0.1175734,0.22126873,0.032425273,0.62471867],"study_design_scores_gemma":[0.00035724934,0.00005066631,0.0021934751,0.00005902223,0.000028818644,0.000012966723,0.000012411732,0.82008404,0.06478434,0.05912401,0.05288516,0.00040785436],"about_ca_topic_score_codex":0.000010202668,"about_ca_topic_score_gemma":0.000020853171,"teacher_disagreement_score":0.9496946,"about_ca_system_score_codex":0.00006387848,"about_ca_system_score_gemma":0.00006180379,"threshold_uncertainty_score":0.559235},"labels":[],"label_agreement":null},{"id":"W4401890562","doi":"10.1002/tee.24180","title":"<scp>EA</scp>‐<scp>YOLO</scp>: An Efficient and Accurate <scp>UAV</scp> Image Object Detection Algorithm","year":2024,"lang":"en","type":"article","venue":"IEEJ Transactions on Electrical and Electronic Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Feature (linguistics); Drone; Object detection; Artificial intelligence; FLOPS; Residual; Object (grammar); Algorithm; Computer vision; Pattern recognition (psychology)","score_opus":0.005197129946020283,"score_gpt":0.21870858590756725,"score_spread":0.21351145596154697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401890562","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":[0.082395725,0.004136115,0.9110987,0.00009930474,0.00030799443,0.00047743577,0.000011574951,0.0013368531,0.00013626587],"genre_scores_gemma":[0.986088,0.0020341764,0.010487281,0.000100694684,0.00024331437,0.00031522886,0.000009237696,0.000100972036,0.00062113575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963655,0.00007715684,0.00044953148,0.001217906,0.0004017807,0.0014881453],"domain_scores_gemma":[0.997373,0.0015387138,0.00007309083,0.00051649066,0.00008021602,0.0004185226],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029134454,0.0005516834,0.00038919863,0.00057207514,0.0005273566,0.00047617452,0.0004697279,0.00024089614,0.0000012960018],"category_scores_gemma":[0.00009880683,0.0005479812,0.00015719076,0.0025371069,0.00007057127,0.00078258186,0.000026349742,0.0014941855,0.00003433756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048422366,0.0004829924,0.0000031660704,0.0001555195,0.00029634708,0.000071615155,0.0012067154,0.3653974,0.11928503,0.007662661,0.00032189916,0.5051118],"study_design_scores_gemma":[0.00032253977,0.0006299071,0.00013760182,0.000036424528,0.000066551525,0.00032674323,0.000031060914,0.9347438,0.05063324,0.0009814183,0.011978169,0.000112512804],"about_ca_topic_score_codex":0.000015791902,"about_ca_topic_score_gemma":0.000014603099,"teacher_disagreement_score":0.90369225,"about_ca_system_score_codex":0.0003434298,"about_ca_system_score_gemma":0.00012661493,"threshold_uncertainty_score":0.99969715},"labels":[],"label_agreement":null},{"id":"W4401990922","doi":"10.1109/tim.2024.3451586","title":"TFIENet: Transformer Fusion Information Enhancement Network for Multimodel 3-D Object Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Transformer; Object detection; Fusion; Computer science; Sensor fusion; Artificial intelligence; Engineering; Pattern recognition (psychology); Electrical engineering; Voltage","score_opus":0.029310028732725823,"score_gpt":0.2653759985824064,"score_spread":0.2360659698496806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401990922","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":[0.0037293897,0.00009058439,0.9926376,0.00063218863,0.0012599294,0.0012068315,0.000013103807,0.0002563516,0.00017404208],"genre_scores_gemma":[0.96375936,0.0002880064,0.03437472,0.0004246598,0.000055541906,0.0010337315,0.0000094842335,0.000011993432,0.00004249122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985954,0.000031375606,0.0003635652,0.0003252547,0.0004298765,0.0002544981],"domain_scores_gemma":[0.9994769,0.000046345707,0.00006081226,0.0001961785,0.00012876045,0.00009097564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031233838,0.00018183804,0.00011254474,0.00017026567,0.0004539097,0.00020345795,0.00012451244,0.00006206305,0.000013207606],"category_scores_gemma":[0.0000018000845,0.0001743006,0.00008450443,0.0003948944,0.000024358096,0.0012859387,0.0000012315575,0.00013441622,0.000027899892],"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":[0.00005682906,0.00004858083,5.0110293e-7,0.000055156208,0.0000376879,1.213845e-7,0.0005762779,0.031069748,0.019539407,0.0015896168,0.00009667917,0.9469294],"study_design_scores_gemma":[0.0011947989,0.0005509824,0.000065322434,0.00013056169,0.00006109513,0.000010226483,0.000083641724,0.66913915,0.3058282,0.0021836786,0.020407282,0.00034506933],"about_ca_topic_score_codex":0.000016487513,"about_ca_topic_score_gemma":0.00008563809,"teacher_disagreement_score":0.96002996,"about_ca_system_score_codex":0.00025317963,"about_ca_system_score_gemma":0.00005888094,"threshold_uncertainty_score":0.7107772},"labels":[],"label_agreement":null},{"id":"W4402017616","doi":"10.3389/fdata.2024.1348030","title":"Sparse and Expandable Network for Google's Pathways","year":2024,"lang":"en","type":"article","venue":"Frontiers in Big Data","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Forgetting; Computer science; Leverage (statistics); Software deployment; Artificial intelligence; Task (project management); Deep learning; Multi-task learning; Machine learning; Lifelong learning; Modalities; Human–computer interaction; Data science; Software engineering; Systems engineering; Engineering","score_opus":0.09079329996318539,"score_gpt":0.2854376129015803,"score_spread":0.19464431293839493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402017616","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":[0.000234629,0.0071616857,0.9881646,0.00078056403,0.002852696,0.00031825254,0.00014835312,0.00016053478,0.00017866028],"genre_scores_gemma":[0.018481227,0.0009794596,0.9784088,0.00033730717,0.0010185759,0.00014180604,0.0002477242,0.000022402892,0.0003626843],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988091,0.000017928762,0.00015790031,0.0006251066,0.00008821233,0.00030174822],"domain_scores_gemma":[0.99862933,0.000106985,0.000026206888,0.0011622272,0.000011011221,0.00006424224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024031595,0.00010364003,0.00013108103,0.000063716216,0.00009278918,0.00016186168,0.0011575103,0.00004394393,6.3751605e-7],"category_scores_gemma":[0.000026246755,0.00009862686,0.00001527857,0.000502999,0.000045205456,0.0006115806,0.0007402736,0.00010948016,0.00000610783],"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":[0.000004537563,0.000010209679,0.00041412792,0.000021892405,0.00000862268,0.000008247194,0.00006560002,0.00057200855,0.000028932363,0.013559612,0.58278406,0.40252212],"study_design_scores_gemma":[0.000112215785,0.000014171301,0.00013138844,0.000037999045,0.000004222688,0.000004806432,0.000008721239,0.37828034,0.00002910318,0.061694395,0.5595736,0.0001090493],"about_ca_topic_score_codex":0.0000045444694,"about_ca_topic_score_gemma":0.000016632683,"teacher_disagreement_score":0.40241307,"about_ca_system_score_codex":0.000026853364,"about_ca_system_score_gemma":0.000042573047,"threshold_uncertainty_score":0.40218866},"labels":[],"label_agreement":null},{"id":"W4402158931","doi":"10.1109/icc51166.2024.10623100","title":"F<sup>2</sup>NAS: Flexible Federated Neural Architecture Search in Green Edge Computing","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Architecture; Computer architecture; Enhanced Data Rates for GSM Evolution; Operating system; Artificial intelligence","score_opus":0.02846614898148214,"score_gpt":0.2987501013488762,"score_spread":0.27028395236739405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402158931","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":[0.056555875,0.0003544008,0.9296321,0.007668451,0.00013146475,0.0004224533,0.0000025267734,0.0013947263,0.003837979],"genre_scores_gemma":[0.9562606,0.000007744872,0.04062918,0.0008549848,0.00021789469,0.000021931039,0.000009350338,0.0000264074,0.0019719165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798775,0.00010392354,0.00030458634,0.00074844604,0.0002784736,0.00057680457],"domain_scores_gemma":[0.99897385,0.00034860586,0.000021556729,0.00047231876,0.000050774543,0.00013287734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021955666,0.00021018174,0.00018589721,0.00025569202,0.00021240234,0.00042809726,0.0008576155,0.00007223715,0.000029199997],"category_scores_gemma":[0.000012211713,0.00018177155,0.00006518694,0.0021741434,0.000048272745,0.00039095245,0.0005773406,0.0006134545,0.00019967834],"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":[0.000008055216,0.00005998908,0.0004108776,0.00006649065,0.000017284,0.00012541315,0.0017630873,0.5126817,0.0014456896,0.07255564,0.0021301834,0.4087356],"study_design_scores_gemma":[0.0001380671,0.000034551944,0.00038294695,0.00004150952,0.0000016062123,0.000063836815,0.000035421646,0.98876166,0.001088287,0.005231806,0.0040110005,0.00020927397],"about_ca_topic_score_codex":0.000121943136,"about_ca_topic_score_gemma":0.000051261548,"teacher_disagreement_score":0.8997047,"about_ca_system_score_codex":0.000077009,"about_ca_system_score_gemma":0.00007868587,"threshold_uncertainty_score":0.7412429},"labels":[],"label_agreement":null},{"id":"W4402159071","doi":"10.1109/icc51166.2024.10622924","title":"On The Performance of Perception Systems of Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Perception; Human–computer interaction; Psychology","score_opus":0.018191211226323122,"score_gpt":0.25093871681759433,"score_spread":0.23274750559127122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402159071","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":[0.81543493,0.000118728654,0.18099746,0.0006969555,0.00014277696,0.00016686623,0.0000010713144,0.00012229593,0.0023189029],"genre_scores_gemma":[0.99731624,0.00002730857,0.0021107167,0.00004035147,0.000020093568,0.000020499534,2.9746337e-7,0.0000031370146,0.00046135267],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995553,0.000016213191,0.00013283009,0.00012163812,0.000108392036,0.00006564538],"domain_scores_gemma":[0.99944055,0.00017508624,0.00003313253,0.0003131114,0.000026417418,0.000011685714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009685672,0.0000432762,0.00005728931,0.00003136005,0.00003409985,0.000020358655,0.0003262639,0.000014945685,0.000008564569],"category_scores_gemma":[0.0000033823262,0.000025567024,0.000024355417,0.00025706526,0.00003553005,0.00012904372,0.000051134637,0.000053713167,0.00004324073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012995264,0.000014919475,0.000053866268,0.000047287194,0.0000048361644,2.0719392e-7,0.00020135136,0.018601576,0.01685022,0.94236666,0.0008687202,0.020989044],"study_design_scores_gemma":[0.00001666413,0.0000682507,0.0012803259,0.000051749932,0.0000017583525,0.0000035480257,0.000022521477,0.9907311,0.005962325,0.0010454045,0.0007785145,0.000037839938],"about_ca_topic_score_codex":0.00000679355,"about_ca_topic_score_gemma":4.422669e-7,"teacher_disagreement_score":0.9721295,"about_ca_system_score_codex":0.000015892889,"about_ca_system_score_gemma":0.00001537894,"threshold_uncertainty_score":0.1042593},"labels":[],"label_agreement":null},{"id":"W4402186871","doi":"10.32920/26871346.v1","title":"Unsupervised Domain Adaptation With Boundary-Aware GAN for Medical Image Segmentation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Domain adaptation; Adaptation (eye); Boundary (topology); Domain (mathematical analysis); Segmentation; Image (mathematics); Computer science; Artificial intelligence; Computer vision; Image segmentation; Pattern recognition (psychology); Mathematics; Psychology; Neuroscience; Mathematical analysis","score_opus":0.018857723017476912,"score_gpt":0.2953455631136179,"score_spread":0.276487840096141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402186871","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":[0.0013759712,0.00012676002,0.9854536,0.009301503,0.0003852432,0.0016225195,0.000036004454,0.0007248806,0.0009735278],"genre_scores_gemma":[0.06744144,0.00005133454,0.92840475,0.0008739504,0.00031467856,0.0018974529,0.0004326097,0.000057894096,0.0005258927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976326,0.00005196849,0.0003836277,0.000977881,0.0006500167,0.00030391192],"domain_scores_gemma":[0.99851143,0.0002043634,0.0001359229,0.00077714265,0.0001916306,0.00017948424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002553832,0.00030036917,0.0002455349,0.00012536084,0.00018465273,0.0004566674,0.000994415,0.00019759485,0.00004404634],"category_scores_gemma":[0.00001740003,0.0002441811,0.000101566126,0.000368829,0.00010088631,0.00025904085,0.0007193167,0.0004973898,0.0000627463],"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":[0.00020774866,0.00046075726,0.000062519066,0.0022172544,0.0004643021,0.00016898064,0.007510719,0.03587261,0.0033163123,0.5063491,0.025065195,0.4183045],"study_design_scores_gemma":[0.00053482014,0.0000989101,0.000044037748,0.00018507702,0.0000321707,0.000024021549,0.00017928885,0.72659826,0.0009984283,0.2676701,0.003219159,0.0004157557],"about_ca_topic_score_codex":0.000021792746,"about_ca_topic_score_gemma":0.0001682795,"teacher_disagreement_score":0.6907256,"about_ca_system_score_codex":0.000149533,"about_ca_system_score_gemma":0.00056849886,"threshold_uncertainty_score":0.9957417},"labels":[],"label_agreement":null},{"id":"W4402199097","doi":"10.1016/j.neucom.2024.128532","title":"Semi-supervised segmentation of medical images focused on the pixels with unreliable predictions","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Pixel; Artificial intelligence; Segmentation; Ground truth; Supervised learning; Pattern recognition (psychology); Function (biology); Image segmentation; Entropy (arrow of time); Cross entropy; Machine learning; Computer vision; Artificial neural network","score_opus":0.019728429781151545,"score_gpt":0.2722476784626602,"score_spread":0.25251924868150866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402199097","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":[0.029437622,0.00007400063,0.958015,0.010324401,0.0002460547,0.0003847068,0.0000028380869,0.0004740782,0.0010412738],"genre_scores_gemma":[0.97628206,0.000025083029,0.022582732,0.0007929026,0.00016787829,0.00006075023,0.0000026217774,0.000020973159,0.00006502428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984602,0.0000785731,0.00024082676,0.00039364325,0.0006141444,0.00021264768],"domain_scores_gemma":[0.99829715,0.0010936186,0.000066569024,0.00042112597,0.00005106764,0.000070452945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025084591,0.00012562337,0.00010807556,0.000075871605,0.00022856845,0.00012703676,0.0007274293,0.000038376504,0.000015783724],"category_scores_gemma":[0.0000458239,0.000081579325,0.00004357716,0.0009108902,0.0000695149,0.0002239435,0.00019071269,0.0003085316,0.000021736456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027825345,0.00032431824,0.0005997126,0.00020463484,0.00011542199,0.0001028143,0.0021106214,0.2503714,0.028960485,0.28302926,0.020534543,0.41361898],"study_design_scores_gemma":[0.00018867038,0.00014087446,0.00042686777,0.00022447557,0.000010204399,0.000035326164,0.000025417105,0.98325294,0.012083432,0.0013694832,0.0021372228,0.000105100735],"about_ca_topic_score_codex":0.000006259944,"about_ca_topic_score_gemma":0.0000012478404,"teacher_disagreement_score":0.9468444,"about_ca_system_score_codex":0.000022889926,"about_ca_system_score_gemma":0.000075847245,"threshold_uncertainty_score":0.33267084},"labels":[],"label_agreement":null},{"id":"W4402307376","doi":"10.1108/ijicc-01-2024-0020","title":"MFLD: lightweight object detection with multi-receptive field and long-range dependency in remote sensing images","year":2024,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Dependency (UML); Computer vision; Artificial intelligence; Range (aeronautics); Object (grammar); Field (mathematics); Object detection; Receptive field; Pattern recognition (psychology); Remote sensing; Geology; Mathematics","score_opus":0.013335156467270688,"score_gpt":0.2879970319881345,"score_spread":0.2746618755208638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402307376","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":[0.11718762,0.0020509313,0.8793674,0.00067167945,0.0005416708,0.00006932475,5.987729e-7,0.00003136938,0.00007936913],"genre_scores_gemma":[0.9121238,0.0008352568,0.08670965,0.000107766,0.00015754838,1.3757133e-7,3.7694096e-7,0.000009410446,0.00005605583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894327,0.00004602468,0.00035949217,0.00023642246,0.00027484828,0.00013994727],"domain_scores_gemma":[0.99907833,0.00036791494,0.000157876,0.00009353226,0.0002458916,0.000056481418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021094792,0.00012928828,0.00013794415,0.0002517074,0.00004988105,0.0002402367,0.00027406527,0.00004721983,0.0000017767587],"category_scores_gemma":[0.000053176725,0.000104757455,0.000039515293,0.00021367126,0.000042827673,0.00021795985,0.00014354347,0.00035663738,0.0000025557295],"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":[0.000027204358,0.000022433629,0.00083129056,0.0000101477035,0.00005819605,0.0002697575,0.0010997001,0.0022032086,0.0005939274,0.0004878786,0.000018292394,0.994378],"study_design_scores_gemma":[0.00041822344,0.00036732986,0.0050867796,0.001118414,0.000025561301,0.0032113523,0.00014123916,0.95642364,0.02704775,0.0049000042,0.00097464345,0.00028506824],"about_ca_topic_score_codex":0.000035051162,"about_ca_topic_score_gemma":0.00008263567,"teacher_disagreement_score":0.9940929,"about_ca_system_score_codex":0.000068588386,"about_ca_system_score_gemma":0.0000309876,"threshold_uncertainty_score":0.4271885},"labels":[],"label_agreement":null},{"id":"W4402351617","doi":"10.1109/ijcnn60899.2024.10651002","title":"SlideMLP: A Pure Multi-layer Perceptrons Method For Medical Image Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Image segmentation; Perceptron; Artificial intelligence; Computer vision; Layer (electronics); Segmentation; Pattern recognition (psychology); Scale-space segmentation; Image (mathematics); Artificial neural network; Materials science; Composite material","score_opus":0.03697561214061538,"score_gpt":0.3953447510018363,"score_spread":0.3583691388612209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402351617","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":[0.0001008482,0.00012246915,0.9843818,0.013521641,0.00023558718,0.00048932235,0.0000059418735,0.000608661,0.0005337604],"genre_scores_gemma":[0.0071499194,0.000022781545,0.9888265,0.0013123159,0.00014809768,0.0004188452,0.000012032384,0.000016696198,0.0020927947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987572,0.000047553996,0.00020714481,0.00048460742,0.00025625655,0.00024727194],"domain_scores_gemma":[0.9989864,0.00047132565,0.000023574503,0.00032814653,0.00005250209,0.00013804453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031049916,0.000117995536,0.000107166125,0.000065518405,0.00012310469,0.00015800484,0.00052024244,0.00006679426,0.00018993531],"category_scores_gemma":[0.00006284025,0.00009513266,0.000089594,0.0003711242,0.000028388597,0.00052711845,0.00015623112,0.00014976753,0.00017645911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036876954,0.00007933805,0.000008850241,0.000047472364,0.000032597865,0.000013948901,0.0007057342,0.00032979314,0.044639062,0.30339742,0.04111628,0.6096258],"study_design_scores_gemma":[0.00020198908,0.000025979001,0.000060832037,0.000012750309,0.000009352538,0.000029844556,0.000043784912,0.9659514,0.0031974404,0.0074296114,0.022908213,0.00012880583],"about_ca_topic_score_codex":0.0000050313834,"about_ca_topic_score_gemma":0.000023020202,"teacher_disagreement_score":0.9656216,"about_ca_system_score_codex":0.000049811148,"about_ca_system_score_gemma":0.00006990768,"threshold_uncertainty_score":0.38793972},"labels":[],"label_agreement":null},{"id":"W4402353346","doi":"10.1109/ijcnn60899.2024.10650367","title":"DfHM: A Hierarchical Approach for Matching Pairs of Images Using Graph Attention Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Matching (statistics); Pattern recognition (psychology); Graph; Theoretical computer science; Mathematics; Statistics","score_opus":0.02957311289921579,"score_gpt":0.2913987873105666,"score_spread":0.26182567441135085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402353346","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":[0.006562673,0.00039002832,0.991519,0.00039324252,0.0001770471,0.000389404,0.0000029312657,0.0003303675,0.00023534955],"genre_scores_gemma":[0.5236372,0.000009841568,0.4760843,0.00005994386,0.00008937009,0.00004619224,0.000007132255,0.0000116177425,0.00005440429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885297,0.000038213937,0.0002607601,0.0004337828,0.00014404398,0.0002702247],"domain_scores_gemma":[0.9993006,0.00021153071,0.00005485865,0.00033018424,0.000041418847,0.00006143017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017780064,0.0001254547,0.00014517695,0.00011004112,0.00012970869,0.000120451834,0.00042335497,0.00004829179,0.0000018976926],"category_scores_gemma":[0.000006201772,0.000106975705,0.00014854163,0.00067019626,0.000064861015,0.0004773806,0.00017413929,0.00017013404,6.6351237e-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":[0.000011702329,0.00008087175,0.00011303232,0.0001371085,0.000037856564,0.000003583146,0.0001280055,0.51585937,0.009929595,0.3671053,0.00079891837,0.105794646],"study_design_scores_gemma":[0.000077366414,0.00002595645,0.000118116885,0.000017413106,0.000010926332,0.000022196009,0.000010651793,0.9676714,0.00020713016,0.03163826,0.00008822251,0.00011235472],"about_ca_topic_score_codex":0.000011072332,"about_ca_topic_score_gemma":7.737365e-7,"teacher_disagreement_score":0.5170745,"about_ca_system_score_codex":0.000019196936,"about_ca_system_score_gemma":0.00001441267,"threshold_uncertainty_score":0.43623427},"labels":[],"label_agreement":null},{"id":"W4402387922","doi":"10.1109/sbac-pad63648.2024.00012","title":"Exploiting Processor Heterogeneity to Improve Throughput and Reduce Latency for Deep Neural Network Inference","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Latency (audio); Inference; Throughput; Artificial neural network; Deep neural networks; Artificial intelligence; Computer architecture; Computer network; Operating system; Wireless; Telecommunications","score_opus":0.03837820572122609,"score_gpt":0.33057654829377575,"score_spread":0.2921983425725497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402387922","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":[0.05216711,0.00050636416,0.9425695,0.0028531263,0.00033530797,0.0007629294,0.0000028609531,0.00056714704,0.00023564474],"genre_scores_gemma":[0.76143044,0.000024882884,0.23686549,0.00074364454,0.00030132162,0.00049264886,0.0000019862384,0.000016716884,0.00012287445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842334,0.00001631236,0.00024133966,0.00075400766,0.000120365476,0.00044465638],"domain_scores_gemma":[0.9990111,0.00032440174,0.00004197167,0.00039295113,0.00008309337,0.00014648378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013291572,0.00017259072,0.0001474714,0.000037208418,0.00022362414,0.0003069873,0.00049219636,0.000044571727,0.0000021958342],"category_scores_gemma":[0.000059298643,0.00014847927,0.000044542176,0.0006045627,0.000030227498,0.0007569355,0.00039421406,0.00012089502,0.00001591815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015029501,0.000034821114,0.0003092472,0.00017342117,0.000027414912,0.0000075767457,0.0007479889,0.024065746,0.010313717,0.19491208,0.0011448684,0.7682481],"study_design_scores_gemma":[0.000111195855,0.00014320205,0.00029239818,0.000040489325,0.0000085962465,0.000017467773,0.000015830174,0.9433235,0.0075946804,0.04500015,0.0031480475,0.00030444903],"about_ca_topic_score_codex":0.000004760427,"about_ca_topic_score_gemma":0.00002873987,"teacher_disagreement_score":0.91925776,"about_ca_system_score_codex":0.000025697735,"about_ca_system_score_gemma":0.000029476478,"threshold_uncertainty_score":0.6054809},"labels":[],"label_agreement":null},{"id":"W4402474854","doi":"10.1109/ccece59415.2024.10667286","title":"ResNet50V1.5 Evaluation for GPU Disaggregation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Computer graphics (images); Computational science; Parallel computing","score_opus":0.047666024013244226,"score_gpt":0.35455468899950704,"score_spread":0.3068886649862628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474854","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":[0.0005289036,0.00037332837,0.9886296,0.0057353145,0.0003046768,0.0005344072,0.0000014038272,0.00043819964,0.0034542114],"genre_scores_gemma":[0.7019357,0.000022140008,0.29489613,0.00039699086,0.000240811,0.0007403433,0.000024074572,0.000011612531,0.0017321964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999304,0.000017294293,0.000109542656,0.00028271746,0.00017254465,0.00011392782],"domain_scores_gemma":[0.9993938,0.00018610664,0.000018377448,0.00029685078,0.00007522668,0.000029595973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024103942,0.000055169352,0.000040369923,0.00004590945,0.000077307624,0.00012999387,0.00023675598,0.000022079566,0.0000184467],"category_scores_gemma":[0.00003627713,0.000046992838,0.000033410128,0.00037278864,0.000011161222,0.0004922316,0.000043629396,0.000036606583,0.000088977366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.6131033e-7,0.00000532988,0.0000037160053,0.000006341923,0.0000029074533,1.4392946e-7,0.000047922957,0.0017570505,0.0007659154,0.5877842,0.008643019,0.40098274],"study_design_scores_gemma":[0.000058361824,0.000013420004,0.000115361225,0.000009752016,0.0000047809044,0.0000019431038,0.0000024438182,0.7858755,0.0012451888,0.1596996,0.052916624,0.000057034278],"about_ca_topic_score_codex":0.0000012399463,"about_ca_topic_score_gemma":0.000005621996,"teacher_disagreement_score":0.7841184,"about_ca_system_score_codex":0.000048590446,"about_ca_system_score_gemma":0.00003797666,"threshold_uncertainty_score":0.19163123},"labels":[],"label_agreement":null},{"id":"W4402575256","doi":"10.1093/comjnl/bxae082","title":"Efficient object detector via dynamic prior and dynamic feature fusion","year":2024,"lang":"en","type":"article","venue":"The Computer Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Computation; Overhead (engineering); Detector; Artificial intelligence; Generator (circuit theory); Inference; Object detection; Pattern recognition (psychology); Object (grammar); Fusion; Process (computing); Algorithm","score_opus":0.005758767124131667,"score_gpt":0.24692753782367613,"score_spread":0.24116877069954445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402575256","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":[0.06978108,0.0033056296,0.9193738,0.0059392606,0.0011616962,0.00019718154,0.0000013315411,0.0002133446,0.000026657828],"genre_scores_gemma":[0.89425385,0.000200506,0.10461276,0.0004293911,0.00031878264,0.000008431113,8.261526e-7,0.000021062235,0.00015436842],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987864,0.0000986806,0.00019464092,0.00035055305,0.00026403504,0.0003056852],"domain_scores_gemma":[0.9990883,0.0002243792,0.00007832765,0.00043595265,0.00004977173,0.00012326294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029418126,0.0001772857,0.00013630773,0.00011381479,0.0005482323,0.0005409448,0.0008706545,0.0000503876,0.0000046268924],"category_scores_gemma":[0.000004168078,0.00011078281,0.00008576463,0.0004905403,0.00006754671,0.00014624363,0.00051332643,0.0006673794,0.000043055334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063546377,0.000023084445,0.00000448878,0.000015690764,0.000028129192,0.00008019543,0.0006095171,0.021770803,0.004638363,0.0017349343,0.00072559895,0.97036284],"study_design_scores_gemma":[0.00012830604,0.00006442,0.001580109,0.000065149165,0.000011752861,0.0028295112,0.0000038677513,0.9868018,0.0000551916,0.00587528,0.0024455788,0.00013903358],"about_ca_topic_score_codex":5.2687284e-7,"about_ca_topic_score_gemma":0.0000034864095,"teacher_disagreement_score":0.9702238,"about_ca_system_score_codex":0.00010548675,"about_ca_system_score_gemma":0.000046916663,"threshold_uncertainty_score":0.52163434},"labels":[],"label_agreement":null},{"id":"W4402632374","doi":"10.1007/978-3-031-71602-7_17","title":"Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); École de Technologie Supérieure; Concordia University","funders":"","keywords":"Computer science; Transformer; Artificial intelligence; Computer vision; Object (grammar); Electrical engineering; Voltage","score_opus":0.02028710733145838,"score_gpt":0.2601601459871975,"score_spread":0.2398730386557391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402632374","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":[0.00003045634,0.00039379176,0.99343175,0.001980596,0.0010274488,0.0012654277,0.000010030335,0.00025770612,0.0016027702],"genre_scores_gemma":[0.45535928,0.000109814515,0.5399998,0.0031380455,0.00055781787,0.00020788384,0.000040176452,0.00011309519,0.00047410582],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99630183,0.0000188336,0.0006476706,0.0017244264,0.0005814801,0.00072578306],"domain_scores_gemma":[0.99825984,0.00063691934,0.00013521468,0.0006708928,0.00015592005,0.00014123338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056956295,0.00051729137,0.00048171642,0.0009990551,0.00022062327,0.00038585972,0.0017790762,0.00026165246,0.0000078843805],"category_scores_gemma":[0.00004313182,0.0005050046,0.0001604053,0.0014029496,0.00044755297,0.0006715289,0.00033795572,0.0006734555,0.000018891253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008653525,0.000018838922,0.000018152468,0.00010819863,0.000009246601,0.00003197574,0.0010115716,0.30060297,0.00034898895,0.039165042,0.000021842814,0.6586545],"study_design_scores_gemma":[0.00024835562,0.00006661083,0.000007249601,0.00036739884,0.000006687592,0.000023997713,4.9767596e-7,0.7443005,0.0009636328,0.25206944,0.0015153312,0.00043031998],"about_ca_topic_score_codex":0.000014618902,"about_ca_topic_score_gemma":0.00018441748,"teacher_disagreement_score":0.65822417,"about_ca_system_score_codex":0.0004776552,"about_ca_system_score_gemma":0.00052144664,"threshold_uncertainty_score":0.9997402},"labels":[],"label_agreement":null},{"id":"W4402727002","doi":"10.1109/cvpr52733.2024.01010","title":"3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Diffusion; Object (grammar); Geometry; Computer vision; Artificial intelligence; Computer graphics (images); Mathematics; Physics","score_opus":0.007690117805056175,"score_gpt":0.23959648248888896,"score_spread":0.23190636468383277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727002","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":[0.008490735,0.00028747314,0.9862065,0.00067180855,0.0003841351,0.00021045197,9.0299125e-7,0.0015043342,0.002243676],"genre_scores_gemma":[0.9673651,0.00004413768,0.028964592,0.000202933,0.0002106224,0.000082429004,0.0000023526393,0.00001727225,0.0031105888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891365,0.000020922047,0.00011216183,0.0005049644,0.00023369018,0.00021458643],"domain_scores_gemma":[0.9993018,0.00009755712,0.000026393283,0.00045797415,0.000045894012,0.000070397255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000548997,0.00014324725,0.000091165966,0.00017362942,0.00023475138,0.00023590885,0.00030959415,0.00005709523,0.000031472493],"category_scores_gemma":[0.0000055170913,0.000100979734,0.00004088033,0.0016881707,0.0000307783,0.0005070036,0.00012949649,0.0002294444,0.000104723666],"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":[0.000016347922,0.000052786494,0.00007528936,0.00003903632,0.000027974831,0.000038162154,0.00016806561,0.0022655476,0.015381571,0.017713407,0.0018878285,0.962334],"study_design_scores_gemma":[0.00065396656,0.00086776935,0.014158306,0.00018826456,0.000048546954,0.0015627146,0.00009508452,0.7505235,0.09163334,0.012768392,0.1262412,0.0012588871],"about_ca_topic_score_codex":0.000023389686,"about_ca_topic_score_gemma":0.00012172811,"teacher_disagreement_score":0.96107507,"about_ca_system_score_codex":0.00006985847,"about_ca_system_score_gemma":0.000022624063,"threshold_uncertainty_score":0.4117834},"labels":[],"label_agreement":null},{"id":"W4402727033","doi":"10.1109/cvpr52733.2024.00540","title":"Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":221,"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; Computer vision; Operator (biology); Chemistry","score_opus":0.012581575313330073,"score_gpt":0.2913715012674099,"score_spread":0.2787899259540798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727033","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":[0.004093469,0.00086512446,0.99148285,0.0015531954,0.00010250991,0.00085170945,0.000006174906,0.00048620885,0.0005587743],"genre_scores_gemma":[0.7341884,0.00004889492,0.26411778,0.00041374302,0.000036591326,0.00063594215,0.000008738055,0.000014240358,0.00053570163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999075,0.000009438681,0.00017182973,0.00044300742,0.00009387282,0.00020683953],"domain_scores_gemma":[0.99928254,0.00018406077,0.000024086805,0.0003847532,0.00004850136,0.00007606865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015747298,0.00010208574,0.000086840264,0.0000622389,0.0002534954,0.00025211103,0.00031262252,0.000039143888,0.0000037664204],"category_scores_gemma":[0.0000060886655,0.000083750885,0.000031339918,0.00040752153,0.000041159532,0.0002107026,0.00018190537,0.000090447094,0.000046427565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029783153,0.000035587025,0.0000051512575,0.00006807136,0.000010589603,0.0000013757405,0.00029733183,0.037228104,0.004538707,0.8484466,0.0009381985,0.10842729],"study_design_scores_gemma":[0.000086445936,0.00002762329,0.000016776117,0.00001929342,0.000004738341,0.000014703221,0.000008849991,0.9417827,0.00050276594,0.027639704,0.02978929,0.000107072985],"about_ca_topic_score_codex":0.000001961918,"about_ca_topic_score_gemma":0.0000049198884,"teacher_disagreement_score":0.9045546,"about_ca_system_score_codex":0.000048188806,"about_ca_system_score_gemma":0.000035822944,"threshold_uncertainty_score":0.3415262},"labels":[],"label_agreement":null},{"id":"W4402727263","doi":"10.1109/cvpr52733.2024.02055","title":"ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Kootenay Association for Science & Technology","funders":"","keywords":"Benchmarking; Robustness (evolution); Computer science; Artificial intelligence; Artificial neural network; Pattern recognition (psychology)","score_opus":0.014782797466962727,"score_gpt":0.2578689268636341,"score_spread":0.24308612939667137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727263","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":[0.017062142,0.0005436164,0.9717234,0.0017874783,0.0012992221,0.00027911764,8.56211e-7,0.0010813798,0.0062228376],"genre_scores_gemma":[0.9278152,0.00006794119,0.069717735,0.00065607874,0.00073320925,0.00007190213,0.000004533768,0.00002826314,0.00090511277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830943,0.000058318634,0.00021870788,0.00070563215,0.00026172888,0.0004461508],"domain_scores_gemma":[0.9987202,0.0003803522,0.000036377736,0.0007329999,0.000025842724,0.00010420211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014106235,0.0002015607,0.00014193583,0.00008460985,0.00025682538,0.0003039438,0.00073558063,0.000051069972,0.00006483715],"category_scores_gemma":[0.00000933049,0.00016249443,0.000089925976,0.00097462244,0.000041199823,0.00034464293,0.0003526124,0.00023691801,0.00012784818],"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":[0.0000037240645,0.00003043932,0.000036569694,0.000014013068,0.000008357349,0.000047266174,0.000045053825,0.56842715,0.00066753064,0.09893181,0.006021604,0.32576647],"study_design_scores_gemma":[0.00005027275,0.000054694196,0.00041311927,0.00006531835,0.0000056402914,0.000036091627,0.000002331165,0.9880311,0.00034950412,0.003808618,0.006974085,0.0002092427],"about_ca_topic_score_codex":0.0000045072225,"about_ca_topic_score_gemma":0.000007417349,"teacher_disagreement_score":0.9107531,"about_ca_system_score_codex":0.000053906315,"about_ca_system_score_gemma":0.000025041387,"threshold_uncertainty_score":0.662633},"labels":[],"label_agreement":null},{"id":"W4402727857","doi":"10.1109/cvpr52733.2024.01436","title":"Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration; National Research Foundation of Korea; Ministry of Trade, Industry and Energy","keywords":"Lidar; Computer science; Fusion; Remote sensing; Radar; Radar imaging; Object (grammar); Sensor fusion; Computer vision; Artificial intelligence; Geology; Telecommunications","score_opus":0.011237402025327017,"score_gpt":0.24184916853293065,"score_spread":0.23061176650760362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727857","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":[0.026233539,0.00020001877,0.9694584,0.00070770486,0.00009126017,0.0002457373,0.0000022528247,0.00038025193,0.0026808607],"genre_scores_gemma":[0.8610809,0.00005022249,0.13829775,0.00015160289,0.000041041618,0.0000655067,0.0000023094572,0.000011922357,0.000298739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991886,0.000025164403,0.00011836637,0.0003788492,0.00012404485,0.00016498189],"domain_scores_gemma":[0.9995799,0.000066990535,0.000019382862,0.00025853707,0.000022635153,0.000052569074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007282877,0.0001068126,0.00008749777,0.00012453817,0.0001434168,0.00016916147,0.00015742918,0.000041849355,0.000025940082],"category_scores_gemma":[0.0000043825,0.00008239369,0.000016307657,0.00076140126,0.00005283769,0.0004066316,0.00009718699,0.00015382691,0.000020734027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025970377,0.00017386643,0.0003702181,0.000061009596,0.0000450333,0.00018232118,0.0020611528,0.015919527,0.027013121,0.26216003,0.00050845835,0.69147927],"study_design_scores_gemma":[0.00077512226,0.0004614551,0.012116768,0.00016814114,0.000029013321,0.0005668035,0.000100353165,0.9108173,0.012009973,0.044293433,0.017953854,0.0007077778],"about_ca_topic_score_codex":0.00009131132,"about_ca_topic_score_gemma":0.0005488959,"teacher_disagreement_score":0.89489776,"about_ca_system_score_codex":0.000053819414,"about_ca_system_score_gemma":0.000050792172,"threshold_uncertainty_score":0.3359917},"labels":[],"label_agreement":null},{"id":"W4402753685","doi":"10.1109/cvpr52733.2024.01844","title":"From SAM to CAMs: Exploring Segment Anything Model for Weakly Supervised Semantic Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Segmentation; Computer science; Natural language processing; Artificial intelligence; Information retrieval; Semantics (computer science); Programming language","score_opus":0.07928136067810239,"score_gpt":0.30955187744534246,"score_spread":0.23027051676724009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402753685","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":[0.04298711,0.00008778596,0.95220613,0.0029545317,0.00034760206,0.0006824777,0.000011259131,0.0006279776,0.000095134485],"genre_scores_gemma":[0.3814144,0.000018160888,0.6164527,0.00070607144,0.00015774391,0.00080339727,0.000015434534,0.000019082912,0.00041303385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987236,0.000012501623,0.00022692671,0.0005756968,0.00019388674,0.0002673327],"domain_scores_gemma":[0.9992217,0.00018942813,0.000020320678,0.0004155486,0.000039816812,0.0001131742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009673633,0.00013964252,0.00011334462,0.00009174741,0.00014204138,0.00026227793,0.00042545522,0.00002510164,0.000010120644],"category_scores_gemma":[0.000008999803,0.00013211605,0.000058412985,0.00039390623,0.0000072035477,0.0010310607,0.00019125732,0.0000727228,0.00008210533],"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":[0.00000980065,0.000049941475,0.000019459985,0.00006420063,0.000042948956,0.0000053616486,0.010932383,0.5302675,0.16576311,0.055869978,0.0036251934,0.23335017],"study_design_scores_gemma":[0.00009596115,0.000022818647,0.000016142609,0.000047095178,0.000008757028,0.0000010439848,0.00016043546,0.9621879,0.02121264,0.01550045,0.00058369327,0.00016310268],"about_ca_topic_score_codex":0.000031872776,"about_ca_topic_score_gemma":0.000020904998,"teacher_disagreement_score":0.4319204,"about_ca_system_score_codex":0.00011564554,"about_ca_system_score_gemma":0.00003514833,"threshold_uncertainty_score":0.5387536},"labels":[],"label_agreement":null},{"id":"W4402780320","doi":"10.1109/cvpr52733.2024.02435","title":"Learning Degradation-Independent Representations for Camera ISP Pipelines","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Degradation (telecommunications); Pipeline transport; Computer science; Environmental science; Telecommunications; Environmental engineering","score_opus":0.027459878569903666,"score_gpt":0.32377876576851045,"score_spread":0.2963188871986068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402780320","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":[0.00079383806,0.00018131729,0.9891286,0.0062936014,0.00027618528,0.00031368792,0.0000020378216,0.0007112969,0.0022994718],"genre_scores_gemma":[0.4839968,0.00008120846,0.47518277,0.0004720079,0.00034021502,0.0005497382,0.00004145965,0.000025676183,0.039310127],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990976,0.000018532366,0.00017861306,0.00039715128,0.00013853688,0.00016956215],"domain_scores_gemma":[0.9991085,0.00041968387,0.000029995375,0.00030651607,0.000082102946,0.000053216667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009775751,0.000083243714,0.000067660134,0.00007980788,0.0002115768,0.00022535775,0.00034064788,0.000025672653,0.000023959445],"category_scores_gemma":[0.000057096375,0.00007409136,0.000059401515,0.00048235778,0.000016563132,0.00052310777,0.00009437257,0.00011032615,0.00011248327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015053446,0.000020496896,0.00013667693,0.000016121141,0.00001603578,0.00000318528,0.00025929016,0.021771144,0.0027073373,0.7894818,0.013978865,0.17160752],"study_design_scores_gemma":[0.000090590926,0.000028114613,0.0002194654,0.0000109620405,0.000007511481,0.000014990076,0.00007045822,0.87399757,0.0025074256,0.026429784,0.09648145,0.00014167154],"about_ca_topic_score_codex":0.000014641837,"about_ca_topic_score_gemma":0.000021981275,"teacher_disagreement_score":0.85222644,"about_ca_system_score_codex":0.000025570873,"about_ca_system_score_gemma":0.00004101639,"threshold_uncertainty_score":0.3021358},"labels":[],"label_agreement":null},{"id":"W4402916137","doi":"10.1109/cvprw63382.2024.00804","title":"SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Adaptation (eye); Artificial intelligence","score_opus":0.046916175817896894,"score_gpt":0.30782840872153094,"score_spread":0.2609122329036341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916137","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":[0.0016839993,0.0001926665,0.99431854,0.0016657964,0.00026578183,0.0004977915,0.000007865687,0.00060092687,0.0007666053],"genre_scores_gemma":[0.7497925,0.000011293028,0.24927288,0.00016219298,0.000039540475,0.00014634021,0.00001198996,0.0000102693475,0.00055295986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989811,0.000016814618,0.00017136328,0.00043502203,0.00015208543,0.00024361206],"domain_scores_gemma":[0.9991383,0.00033592852,0.000019301038,0.00038341238,0.0000621949,0.00006088599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014752134,0.00010001469,0.000082996536,0.00007767014,0.00012629751,0.00017234951,0.00034576183,0.00003864495,0.0000056214267],"category_scores_gemma":[0.000012231882,0.0000832842,0.00006284173,0.00048982375,0.000008501962,0.00048023797,0.00010734973,0.0000687419,0.000051361825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020989269,0.000031010873,1.3731085e-7,0.000007933899,0.000002948435,7.9940344e-7,0.00018139441,0.14329112,0.00032639143,0.83045626,0.0008957014,0.02480418],"study_design_scores_gemma":[0.00010170868,0.000049175716,0.0000044909816,0.000011356047,0.000003278295,0.0000022393842,0.000021943128,0.8965657,0.0005672888,0.09223787,0.010336986,0.000097963355],"about_ca_topic_score_codex":0.0000026702376,"about_ca_topic_score_gemma":0.0000061392634,"teacher_disagreement_score":0.75327456,"about_ca_system_score_codex":0.000036831905,"about_ca_system_score_gemma":0.00003055786,"threshold_uncertainty_score":0.33962312},"labels":[],"label_agreement":null},{"id":"W4403024459","doi":"10.1109/pacrim61180.2024.10690208","title":"Improved Semi-Supervised Attention GAN for Semantic Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"WSP (Canada); Lakehead University","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Natural language processing","score_opus":0.02019679002532631,"score_gpt":0.2893361768450731,"score_spread":0.2691393868197468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403024459","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":[0.0045195012,0.000089843925,0.99111307,0.0023455156,0.000303528,0.0005848947,0.000002282702,0.0006224129,0.00041893683],"genre_scores_gemma":[0.78456664,0.0000213718,0.21194547,0.00041013854,0.00013539677,0.0003543712,0.000030591516,0.000016163396,0.0025198262],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992252,0.000011163544,0.00015432168,0.0003538476,0.000086028835,0.00016945416],"domain_scores_gemma":[0.9995113,0.0001020311,0.00002183709,0.00027911554,0.000041790616,0.000043941927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000083298495,0.000085551146,0.00006468419,0.00005704301,0.00009773128,0.00016647692,0.00024992766,0.00002781036,0.000011576033],"category_scores_gemma":[0.000005982464,0.00007568588,0.000060782964,0.00036487507,0.000011470036,0.0005407364,0.0000444538,0.00004875849,0.000057192887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045424667,0.000040613853,0.000047039703,0.00011297004,0.000025923859,0.0000018902372,0.00020234073,0.00063398154,0.61193544,0.14342307,0.005000266,0.23857191],"study_design_scores_gemma":[0.00014399925,0.00003772742,0.00010411111,0.000013983135,0.000008301135,0.000005257346,0.000016827642,0.97155213,0.015230668,0.010432303,0.0023480707,0.0001065918],"about_ca_topic_score_codex":0.000003232509,"about_ca_topic_score_gemma":0.0000064751157,"teacher_disagreement_score":0.9709182,"about_ca_system_score_codex":0.000036566817,"about_ca_system_score_gemma":0.000018914905,"threshold_uncertainty_score":0.30863807},"labels":[],"label_agreement":null},{"id":"W4403048090","doi":"10.1007/978-3-031-73471-7_7","title":"Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Shot (pellet); Human–computer interaction; Artificial intelligence; Computer graphics (images); Programming language; Natural language processing; Engineering drawing; Engineering","score_opus":0.015769344148611794,"score_gpt":0.2590442351092754,"score_spread":0.24327489096066357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403048090","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":[0.00020559876,0.0010197392,0.98992753,0.0016505136,0.00060322357,0.0006277149,0.0000037355874,0.0008298042,0.0051321182],"genre_scores_gemma":[0.1641314,0.0001274946,0.82969177,0.0011262301,0.00068409456,0.00009025731,0.00003302665,0.00016335961,0.0039523677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952251,0.00003250816,0.0005320585,0.002199018,0.0011971067,0.00081420894],"domain_scores_gemma":[0.99746746,0.00056893175,0.00027313858,0.0012822036,0.00019132634,0.00021694935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052773696,0.000656074,0.0005122799,0.0005285504,0.0005122418,0.00082094717,0.0030981237,0.0002597208,0.000021217911],"category_scores_gemma":[0.000040809802,0.00053174165,0.00009536609,0.0011229427,0.0005340126,0.0008191539,0.0017311858,0.0016551664,0.00014797597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051982843,0.000030310539,0.00004780967,0.0001018655,0.000018893475,0.0000796027,0.00073331717,0.13968332,0.0022236044,0.034017,0.0001410372,0.82291806],"study_design_scores_gemma":[0.00017988522,0.00028570674,0.000034123415,0.0009912235,0.000012671052,0.0001635823,3.398093e-7,0.91379446,0.0017811022,0.072154954,0.00973031,0.0008716352],"about_ca_topic_score_codex":0.000008439478,"about_ca_topic_score_gemma":0.000030667998,"teacher_disagreement_score":0.8220464,"about_ca_system_score_codex":0.00030097325,"about_ca_system_score_gemma":0.0002861338,"threshold_uncertainty_score":0.9997134},"labels":[],"label_agreement":null},{"id":"W4403067380","doi":"10.1007/978-3-031-72114-4_56","title":"Self-paced Sample Selection for Barely-Supervised Medical Image Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sample (material); Selection (genetic algorithm); Artificial intelligence; Image segmentation; Segmentation; Computer vision; Image (mathematics); Pattern recognition (psychology)","score_opus":0.01564007421340646,"score_gpt":0.28166476889811914,"score_spread":0.2660246946847127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403067380","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":[0.000021891543,0.00024870734,0.9932429,0.0026991589,0.0013078433,0.001173016,0.000017929615,0.0006819794,0.00060655014],"genre_scores_gemma":[0.0035803488,0.000085141764,0.9938231,0.0012651318,0.00077054946,0.0001500403,0.000033568853,0.000057878508,0.00023423652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959243,0.000023405511,0.0005396109,0.0017506192,0.001127226,0.0006348589],"domain_scores_gemma":[0.9972028,0.0012521658,0.00018094703,0.0008180803,0.00029675968,0.00024920565],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006218205,0.0004862842,0.00040137128,0.00056492875,0.00035962017,0.0005268432,0.002238717,0.00034461473,0.000035648005],"category_scores_gemma":[0.00014984224,0.00045803975,0.00015864831,0.0010165635,0.00029596567,0.0007217108,0.00079969823,0.0007483683,0.0000669598],"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":[0.000012659849,0.00006384361,0.00001629889,0.00022406364,0.000036705904,0.00002143278,0.0008102961,0.017076435,0.0014484377,0.12948361,0.0002619231,0.8505443],"study_design_scores_gemma":[0.00021217923,0.00010952549,0.00000405914,0.00012676367,0.000012365628,0.00004121862,1.2741613e-7,0.73502135,0.0021211216,0.25838706,0.0035933873,0.00037081738],"about_ca_topic_score_codex":0.000010702115,"about_ca_topic_score_gemma":0.000099718505,"teacher_disagreement_score":0.8501735,"about_ca_system_score_codex":0.0005060874,"about_ca_system_score_gemma":0.00058847567,"threshold_uncertainty_score":0.99978715},"labels":[],"label_agreement":null},{"id":"W4403073132","doi":"10.1007/s13369-024-09602-5","title":"Hybrid 3D Medical Image Segmentation Using CNN and Frequency Transformer Fusion","year":2024,"lang":"en","type":"article","venue":"Arabian Journal for Science and Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Université de Moncton","funders":"","keywords":"Fusion; Artificial intelligence; Transformer; Segmentation; Computer vision; Computer science; Pattern recognition (psychology); Materials science; Engineering; Electrical engineering; Voltage","score_opus":0.014628995643696246,"score_gpt":0.28218236540291497,"score_spread":0.26755336975921873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403073132","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":[0.18566743,0.0005244429,0.81237483,0.00090189854,0.0003693318,0.00008661824,8.780139e-7,0.00004907845,0.000025493458],"genre_scores_gemma":[0.7121918,0.00032927582,0.2872132,0.00009657416,0.00014194979,0.000008480298,3.8991038e-7,0.000008803947,0.000009497171],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990427,0.0000041068943,0.00015701432,0.00023197442,0.0003174621,0.0002467422],"domain_scores_gemma":[0.99956846,0.00005769953,0.00001985327,0.00008012948,0.000059273818,0.00021456505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005701661,0.00008197757,0.00006973368,0.00015829703,0.00038220244,0.00041595058,0.0002505379,0.000017129913,0.0000032844932],"category_scores_gemma":[0.000052412146,0.00006956867,0.00002107214,0.00038942433,0.00009242197,0.0018697302,0.000032276286,0.0001515169,0.0000011347504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018924792,0.000009174519,0.00004681988,0.000075200005,0.000009056273,0.000053981854,0.00074999564,0.0032737525,0.54962885,0.018523805,0.00007329986,0.4275542],"study_design_scores_gemma":[0.00010248448,0.0000386743,0.00015197652,0.000103917766,0.000005672755,0.0011086329,0.000014666506,0.9894403,0.0048273476,0.0034583441,0.00063690153,0.00011108778],"about_ca_topic_score_codex":0.0000013886744,"about_ca_topic_score_gemma":4.3038517e-7,"teacher_disagreement_score":0.98616654,"about_ca_system_score_codex":0.000051064733,"about_ca_system_score_gemma":0.000103333376,"threshold_uncertainty_score":0.4011021},"labels":[],"label_agreement":null},{"id":"W4403152519","doi":"10.1007/978-3-031-72111-3_50","title":"Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Computer vision; Domain (mathematical analysis); Segmentation; Artificial intelligence; Image segmentation; Image (mathematics); Computer graphics (images)","score_opus":0.021140401815408966,"score_gpt":0.2802780704294546,"score_spread":0.25913766861404564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403152519","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":[0.000035823796,0.0001912074,0.99362665,0.0017376083,0.00033034303,0.0028758424,0.000009067184,0.00028540925,0.0009080403],"genre_scores_gemma":[0.0025147733,0.000017135435,0.9951239,0.00096353225,0.00036545566,0.000537894,0.000012552997,0.000060579794,0.00040414682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99576044,0.000017439941,0.0004983488,0.0017778883,0.0012674195,0.0006784773],"domain_scores_gemma":[0.99811584,0.00027046475,0.00026950115,0.00085373095,0.0002792784,0.00021121623],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007197714,0.000506347,0.0004032156,0.00037712377,0.00039713542,0.0004932255,0.0019326257,0.00021934597,0.0000061508576],"category_scores_gemma":[0.00002319528,0.00041110182,0.00009006485,0.0005952788,0.0004537114,0.0007839671,0.0009166799,0.0007028924,0.000015344725],"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":[0.000029975721,0.000032579217,0.0000020248242,0.0001263953,0.000023461613,0.00005887003,0.0012117798,0.6933397,0.00087882497,0.1288032,0.000034694436,0.17545846],"study_design_scores_gemma":[0.0002003212,0.0002085538,3.92434e-7,0.00040980172,0.000008171436,0.000048740676,3.6369252e-7,0.70459473,0.0012785294,0.2927143,0.0001808647,0.00035521423],"about_ca_topic_score_codex":0.0000048226266,"about_ca_topic_score_gemma":0.00003762631,"teacher_disagreement_score":0.17510325,"about_ca_system_score_codex":0.00047238468,"about_ca_system_score_gemma":0.00074381556,"threshold_uncertainty_score":0.99983406},"labels":[],"label_agreement":null},{"id":"W4403278595","doi":"10.1109/fpl64840.2024.00019","title":"H2PIPE: High Throughput CNN Inference on FPGAs with High-Bandwidth Memory","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Throughput; Computer science; Field-programmable gate array; Bandwidth (computing); Inference; Memory bandwidth; Computer architecture; Embedded system; Parallel computing; Artificial intelligence; Computer network; Operating system","score_opus":0.016155840036476042,"score_gpt":0.26881333604974644,"score_spread":0.2526574960132704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403278595","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":[0.01384705,0.0001432177,0.96548265,0.007405052,0.00041508654,0.00030234872,0.0000033102967,0.0013203861,0.011080867],"genre_scores_gemma":[0.8976478,0.000055050252,0.09633325,0.0013641745,0.00017329304,0.00008454798,0.000005440043,0.000020958983,0.004315513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99837893,0.000029281504,0.00018571547,0.00073110877,0.00033686,0.0003381268],"domain_scores_gemma":[0.99850255,0.00035292903,0.00004049789,0.0009401663,0.000054013846,0.00010984202],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007690647,0.00021473438,0.00016643903,0.00008566481,0.00014929097,0.00023427744,0.0008067095,0.00005352674,0.00009145615],"category_scores_gemma":[0.000011270882,0.00014916214,0.000036336758,0.0009226957,0.000086942586,0.0006269118,0.00020595833,0.00026414197,0.0008139755],"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":[0.0000066810967,0.000042452622,0.000015614942,0.000012678802,0.00001632944,0.000040009836,0.00012698284,0.007987544,0.0003166581,0.93955725,0.005611372,0.046266418],"study_design_scores_gemma":[0.001559567,0.0017423,0.005425045,0.0005539148,0.000068697766,0.00015179816,0.000055828,0.20860548,0.07599668,0.5997896,0.103675194,0.0023758742],"about_ca_topic_score_codex":0.00005751685,"about_ca_topic_score_gemma":0.000035948357,"teacher_disagreement_score":0.88380075,"about_ca_system_score_codex":0.00006114061,"about_ca_system_score_gemma":0.00008524319,"threshold_uncertainty_score":0.999964},"labels":[],"label_agreement":null},{"id":"W4403334577","doi":"10.1145/3656019.3676950","title":"BOOM: Use your Desktop to Accurately Predict the Performance of Large Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Boom; Computer science; Artificial neural network; Deep neural networks; Deep learning; Artificial intelligence; Geology","score_opus":0.04491839197070661,"score_gpt":0.30777309590426044,"score_spread":0.2628547039335538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403334577","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":[0.14729416,0.00031107615,0.8491395,0.0019016644,0.00035678875,0.00036886477,0.0000033346962,0.00035033762,0.00027424443],"genre_scores_gemma":[0.9847263,0.00006324458,0.013130264,0.0012080049,0.0001435384,0.00007674653,0.0000026069808,0.000014397357,0.00063487014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869865,0.000038819064,0.0002714825,0.0003876561,0.0002151868,0.00038818023],"domain_scores_gemma":[0.99870723,0.00028935276,0.000046707206,0.00078493496,0.0000673064,0.00010445439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018252716,0.00014728084,0.00012393972,0.00005565487,0.00016772322,0.0002077412,0.0011119201,0.00004275935,0.000020386877],"category_scores_gemma":[0.000021684866,0.00009461504,0.00006401007,0.0010930555,0.00003934072,0.0009510425,0.0004802146,0.00022446174,0.000050551538],"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":[0.000028503388,0.00008748725,0.003632376,0.000043972883,0.00004928875,0.00001764659,0.00092683453,0.65613973,0.00073824235,0.13721591,0.012974259,0.18814576],"study_design_scores_gemma":[0.000055123328,0.00006690277,0.004195766,0.000013882142,0.000006035494,0.00001715171,0.0000100610305,0.98434854,0.00063380384,0.00016302403,0.010378011,0.000111716996],"about_ca_topic_score_codex":0.000006046082,"about_ca_topic_score_gemma":0.000017794144,"teacher_disagreement_score":0.83743215,"about_ca_system_score_codex":0.00002313548,"about_ca_system_score_gemma":0.000019626694,"threshold_uncertainty_score":0.38582894},"labels":[],"label_agreement":null},{"id":"W4403520169","doi":"10.1145/3691620.3695281","title":"In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Robot manipulator; Computer science; Artificial intelligence; Deep learning; Robot vision; Control engineering; Robot; Mobile robot; Engineering","score_opus":0.04952000702817206,"score_gpt":0.31697451831792717,"score_spread":0.2674545112897551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403520169","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":[0.03227173,0.00023235404,0.96441954,0.00017337633,0.00021289517,0.0004247645,1.531587e-7,0.0002454543,0.0020197157],"genre_scores_gemma":[0.8192853,0.0000074368804,0.18051039,0.000023378994,0.000027265454,0.000036894333,0.0000028062373,0.000018893641,0.000087673965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981723,0.000062993655,0.00060174847,0.00071754295,0.00020508573,0.00024032479],"domain_scores_gemma":[0.9987586,0.0003911762,0.00021209489,0.0005348207,0.000061803636,0.000041469284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023064215,0.00020456353,0.00029671774,0.00042130245,0.00003229766,0.000071838076,0.0006179549,0.00016124941,0.000003125353],"category_scores_gemma":[0.000052644456,0.00021257719,0.000054426386,0.00097530347,0.000020104088,0.00031655913,0.002065797,0.0008779028,0.000016091286],"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":[7.832951e-7,0.000021003543,0.00031699426,0.000064175016,0.0000020295533,0.000007101235,0.00021432202,0.9447681,0.00007586296,0.025986047,0.0000010039495,0.028542625],"study_design_scores_gemma":[0.000051849773,0.00001411345,0.0010260238,0.00017153117,0.0000028938314,0.0000017212046,0.000005108707,0.7758152,0.000054246324,0.22270866,0.000004977315,0.00014371282],"about_ca_topic_score_codex":0.00009313859,"about_ca_topic_score_gemma":0.00007236045,"teacher_disagreement_score":0.78701353,"about_ca_system_score_codex":0.0002050054,"about_ca_system_score_gemma":0.000055248784,"threshold_uncertainty_score":0.8668646},"labels":[],"label_agreement":null},{"id":"W4403583022","doi":"10.1016/j.jag.2024.104208","title":"Object detection in aerial images using DOTA dataset: A survey","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Geography; Object (grammar); Aerial survey; Cartography; Computer vision; Object detection; Artificial intelligence; DOTA; Remote sensing; Computer science; Object based; Aerial imagery; Pattern recognition (psychology); Biology","score_opus":0.033682450637837,"score_gpt":0.292415796209633,"score_spread":0.258733345571796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403583022","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":[0.29475704,0.000077552606,0.70339745,0.00041677486,0.0009200104,0.00017306367,0.000055648532,0.000042691358,0.00015976792],"genre_scores_gemma":[0.9775304,0.00012163247,0.02170229,0.00028143084,0.0002009267,0.0000052084997,0.00014458933,0.000006066196,0.0000074246345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988295,0.000026611,0.00055264885,0.00012753558,0.00035454886,0.000109159104],"domain_scores_gemma":[0.9992414,0.00013240613,0.00026425128,0.000113776994,0.00020362361,0.000044550754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060495926,0.00009668001,0.0001099804,0.00036161262,0.000051027775,0.00037065422,0.00030934738,0.000045655845,0.0000070830188],"category_scores_gemma":[0.000046939,0.000091025264,0.00002781419,0.00043846504,0.000021532733,0.0025561133,0.00007836938,0.00017615763,0.000011596956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039791377,0.00008583677,0.0014694872,0.00006317544,0.000120372184,0.000029753766,0.0018433814,0.07307651,0.03189524,0.032908686,0.0009537301,0.8571559],"study_design_scores_gemma":[0.001054271,0.00006541615,0.1250015,0.000105407526,0.000010803033,0.00021389854,0.000055462155,0.8367654,0.010895482,0.00784069,0.017725227,0.0002664198],"about_ca_topic_score_codex":0.00004104381,"about_ca_topic_score_gemma":0.00007977877,"teacher_disagreement_score":0.8568895,"about_ca_system_score_codex":0.000062829575,"about_ca_system_score_gemma":0.000073015384,"threshold_uncertainty_score":0.37119025},"labels":[],"label_agreement":null},{"id":"W4403857108","doi":"10.1088/2634-4386/ad8c78","title":"Unsupervised end-to-end training with a self-defined target","year":2024,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Grand Équipement National De Calcul Intensif; Agence Nationale de la Recherche; Canadian Institute for Advanced Research","keywords":"MNIST database; Computer science; Artificial intelligence; Unsupervised learning; Semi-supervised learning; Perceptron; Machine learning; Supervised learning; Deep learning; Labeled data; Hebbian theory; Pattern recognition (psychology); Multilayer perceptron; Artificial neural network","score_opus":0.019522746746219156,"score_gpt":0.21647302414908098,"score_spread":0.19695027740286183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403857108","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":[0.19035436,0.00023928245,0.8055882,0.0012560949,0.00026959536,0.00015986996,0.000001828277,0.0019883474,0.0001424357],"genre_scores_gemma":[0.7989521,0.00000802593,0.20067613,0.00017438039,0.00012895689,0.000011986472,0.0000019010405,0.000030364117,0.000016135438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986664,0.000016661677,0.00018242966,0.0005635143,0.00018199296,0.00038899147],"domain_scores_gemma":[0.99913496,0.0003260438,0.000019921546,0.00031108115,0.000023755532,0.00018421572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001344933,0.00021122686,0.00018363014,0.00015925201,0.00013528061,0.00023946114,0.00032264087,0.00003268722,0.00000328894],"category_scores_gemma":[0.00001925361,0.00019739757,0.000030915526,0.0009012068,0.000014883608,0.00017427398,0.0001915502,0.00029197737,0.0000126742125],"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":[0.0000075618436,0.00004255066,0.00015551284,0.0002452158,0.000083083745,0.00049933355,0.0059408634,0.7962441,0.017600132,0.053276476,0.00029506057,0.12561013],"study_design_scores_gemma":[0.000135069,0.00008607509,0.00089872855,0.00012317463,0.000007947635,0.00030721698,0.000008280327,0.98705167,0.00019057837,0.00014559488,0.010807171,0.00023851273],"about_ca_topic_score_codex":0.0000031186632,"about_ca_topic_score_gemma":5.5165793e-7,"teacher_disagreement_score":0.60859776,"about_ca_system_score_codex":0.000022299208,"about_ca_system_score_gemma":0.000036734673,"threshold_uncertainty_score":0.80496395},"labels":[],"label_agreement":null},{"id":"W4403888467","doi":"10.1007/978-3-031-73039-9_26","title":"Domain Generalization of 3D Object Detection by Density-Resampling","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Generalization; Resampling; Domain (mathematical analysis); Artificial intelligence; Object (grammar); Computer vision; Pattern recognition (psychology); Mathematics","score_opus":0.013477136404197699,"score_gpt":0.25189822309172627,"score_spread":0.23842108668752857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403888467","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":[0.00024466118,0.0007556043,0.9963473,0.00026765172,0.0011012708,0.00038806666,0.0000066475577,0.00021315811,0.000675613],"genre_scores_gemma":[0.16938958,0.000108248634,0.8289861,0.00057265034,0.00048379684,0.00002364405,0.000014989108,0.000058367317,0.00036259912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702513,0.000023115752,0.00051926484,0.0013284184,0.00068803213,0.00041601743],"domain_scores_gemma":[0.9980415,0.00029005058,0.00029935327,0.0010674393,0.00020460223,0.00009702323],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040679605,0.00038273513,0.0003780224,0.00059146783,0.00021504903,0.00023521075,0.0016477404,0.00024054579,0.000004944948],"category_scores_gemma":[0.000023152666,0.0003731807,0.000094025,0.001119168,0.00038606176,0.00041381083,0.00086441234,0.00056373357,0.000024706374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005255801,0.000022711492,0.000012342253,0.00007529993,0.000015917742,0.000019162273,0.00042987944,0.2191332,0.025695201,0.075648636,0.000033098324,0.6789093],"study_design_scores_gemma":[0.000099471894,0.00008163703,0.00000997637,0.00019395328,0.000009564322,0.00004716253,7.754315e-8,0.5364967,0.031521946,0.42827466,0.0028333191,0.00043152657],"about_ca_topic_score_codex":0.000016439957,"about_ca_topic_score_gemma":0.00008410707,"teacher_disagreement_score":0.67847776,"about_ca_system_score_codex":0.0002749972,"about_ca_system_score_gemma":0.00013054944,"threshold_uncertainty_score":0.999872},"labels":[],"label_agreement":null},{"id":"W4403936357","doi":"10.1016/j.image.2024.117223","title":"SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions","year":2024,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pediatric Oncology Group; Université du Québec en Outaouais","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Environmental science; Meteorology; Remote sensing; Geology; Geography","score_opus":0.026699268027414905,"score_gpt":0.31759403975660944,"score_spread":0.29089477172919453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403936357","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":[0.0021813181,0.0017647133,0.9932228,0.0008683377,0.000019913767,0.00038206796,0.0000021999524,0.0005392012,0.0010194671],"genre_scores_gemma":[0.87905604,0.00005227929,0.11988588,0.00006558821,0.00003908411,0.00046164414,0.000048652033,0.00003070191,0.00036013703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880815,0.00008158587,0.0003192702,0.00040115527,0.0001433535,0.000246475],"domain_scores_gemma":[0.99898225,0.00019365927,0.00011780688,0.00051554956,0.00014297376,0.00004777382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002978511,0.0001573946,0.00013061485,0.00020950208,0.0007565594,0.00043343482,0.0007677202,0.000074404714,0.000006281814],"category_scores_gemma":[0.000019539673,0.00016457276,0.000060596874,0.000715847,0.000088099994,0.0014406733,0.00017022008,0.00040019726,0.000020264046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010474301,0.00015077888,0.000022197422,0.00017321394,0.00001627746,0.000002173438,0.003356698,0.10419621,0.27369428,0.03893417,0.000121501704,0.57932204],"study_design_scores_gemma":[0.00014147886,0.000027797134,0.000050166305,0.00013545097,0.00000974787,0.0000059387376,0.000024679175,0.8970137,0.007456956,0.09374803,0.0012165239,0.00016953456],"about_ca_topic_score_codex":0.000007672496,"about_ca_topic_score_gemma":0.00008766762,"teacher_disagreement_score":0.87687474,"about_ca_system_score_codex":0.00013341183,"about_ca_system_score_gemma":0.000057856098,"threshold_uncertainty_score":0.67110825},"labels":[],"label_agreement":null},{"id":"W4403942789","doi":"10.3390/rs16214065","title":"A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Backbone network; Convolutional neural network; Computer vision; Pattern recognition (psychology); Telecommunications","score_opus":0.049922655573108286,"score_gpt":0.3100072635681523,"score_spread":0.260084607995044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403942789","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":[0.12821096,0.00007596507,0.86966723,0.00040092965,0.00032384833,0.00050648284,0.0000030569488,0.00076850946,0.000042986863],"genre_scores_gemma":[0.5888929,0.0000048056686,0.41070014,0.00012417807,0.00020298133,1.8548198e-7,0.000011818785,0.000028950739,0.000034080498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986183,0.000059498827,0.0002707081,0.00055680884,0.00012337432,0.0003713253],"domain_scores_gemma":[0.99915606,0.00015302241,0.000059912767,0.0004423201,0.000091666654,0.00009704893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018849313,0.00017538985,0.00015244047,0.00015937665,0.00029308084,0.00022724488,0.00018464669,0.000070526236,0.0000011179011],"category_scores_gemma":[0.000031608666,0.00018090422,0.00008596995,0.0008723607,0.00002493083,0.00036649537,0.00006748446,0.00015421545,0.0000066741086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008611747,0.0000062569693,1.5629251e-7,0.000025992429,0.000017362165,0.000014679898,0.00028950805,0.007111192,0.37948057,0.0002839927,0.000010732345,0.61275095],"study_design_scores_gemma":[0.00019888,0.00007089008,0.000004534093,0.00008098266,0.00001964935,0.0001414199,0.000017458131,0.93950045,0.052116964,0.0048321923,0.0028301703,0.0001864153],"about_ca_topic_score_codex":0.00006093572,"about_ca_topic_score_gemma":0.00013016703,"teacher_disagreement_score":0.93238926,"about_ca_system_score_codex":0.00015757949,"about_ca_system_score_gemma":0.000062980915,"threshold_uncertainty_score":0.73770595},"labels":[],"label_agreement":null},{"id":"W4404056949","doi":"10.1109/access.2024.3486937","title":"Corrections to “Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada”","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Thales (Canada); Royal Military College of Canada; Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Deep learning; Weather forecasting; Object (grammar); Remote sensing; Meteorology; Machine learning; Data science; Geography; Pattern recognition (psychology)","score_opus":0.02237418973964373,"score_gpt":0.2843275500148058,"score_spread":0.2619533602751621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404056949","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":[0.2543247,0.00007042133,0.74369466,0.0010426259,0.000349623,0.00040412412,0.0000023972464,0.000092838294,0.000018611949],"genre_scores_gemma":[0.99861884,0.0000059911877,0.000704971,0.000093034825,0.00005158076,0.00034190193,0.000002121065,0.000011440902,0.00017010921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992167,0.000032276137,0.0001828803,0.00032146554,0.00010029471,0.00014641025],"domain_scores_gemma":[0.99937594,0.00026832195,0.000053873646,0.00019898813,0.00005040601,0.000052445583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005843502,0.00009579626,0.00011094774,0.00014105655,0.00009496745,0.00011456136,0.00027302644,0.00003593022,0.0000015327041],"category_scores_gemma":[0.000025068646,0.000088557244,0.00002420096,0.0005051374,0.000019028112,0.00022980836,0.000036928206,0.00011496778,0.0000011559839],"study_design_candidate":"simulation_or_modeling","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":[0.000039041264,0.00010421257,0.0040829615,0.00014698593,0.000021591031,0.0000031099366,0.0006306431,0.32583907,0.04909085,0.0013469566,0.0006261723,0.6180684],"study_design_scores_gemma":[0.00012411473,0.0000390037,0.047082186,0.00003932056,0.0000063770713,9.851387e-7,0.000018788223,0.9285805,0.019723613,0.0003924776,0.0038748158,0.00011778145],"about_ca_topic_score_codex":0.04027066,"about_ca_topic_score_gemma":0.78567404,"teacher_disagreement_score":0.74540335,"about_ca_system_score_codex":0.00022827323,"about_ca_system_score_gemma":0.000133129,"threshold_uncertainty_score":0.96612024},"labels":[],"label_agreement":null},{"id":"W4404057431","doi":"10.1109/socc62300.2024.10737768","title":"Hardened-TC: A Low-cost Reliability Solution for CNNs Run by Modern GPUs","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Reliability (semiconductor); Computer science; Parallel computing; Physics","score_opus":0.017562399124814682,"score_gpt":0.2796168189760074,"score_spread":0.2620544198511927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404057431","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":[0.0010779914,0.00030411978,0.98938376,0.0063162083,0.0003195105,0.0007245068,0.000027342649,0.000800486,0.00104607],"genre_scores_gemma":[0.72727644,0.00006538598,0.26317123,0.0009213274,0.0002003157,0.0010055678,0.000042191135,0.000031932956,0.0072856243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862707,0.000019566927,0.00021459827,0.0006520416,0.0001649013,0.00032179974],"domain_scores_gemma":[0.9988933,0.00021952763,0.00003098549,0.0006867033,0.00007247706,0.00009696055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016781631,0.00013228458,0.000114390205,0.0000384542,0.00017234613,0.00016721501,0.00055657985,0.000060440016,0.000012609924],"category_scores_gemma":[0.00003156126,0.00011759221,0.00009316402,0.0003670232,0.000042221127,0.00052743114,0.0001767914,0.00011810266,0.00012364933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019398118,0.00020278039,0.000039248036,0.00011970019,0.00002494896,0.0000032675555,0.00033248097,0.012026243,0.02231695,0.12983678,0.27906093,0.5560173],"study_design_scores_gemma":[0.00009333716,0.000020333862,0.000022286826,0.000011542161,0.000004876539,0.0000035861472,0.0000013832748,0.83617926,0.0047125705,0.06625184,0.092561156,0.00013783334],"about_ca_topic_score_codex":0.000010884395,"about_ca_topic_score_gemma":0.000015819194,"teacher_disagreement_score":0.824153,"about_ca_system_score_codex":0.000109124136,"about_ca_system_score_gemma":0.00004818574,"threshold_uncertainty_score":0.47952712},"labels":[],"label_agreement":null},{"id":"W4404166222","doi":"10.1007/978-3-031-72855-6_7","title":"Sparse Refinement for Efficient High-Resolution Semantic Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Resolution (logic); Natural language processing; Pattern recognition (psychology); Computer vision; Information retrieval","score_opus":0.021684827526355067,"score_gpt":0.2712635171816526,"score_spread":0.24957868965529753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404166222","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":[0.000078162666,0.0005064058,0.9929868,0.0022386448,0.0021748913,0.001265287,0.000014696665,0.0002841718,0.00045091836],"genre_scores_gemma":[0.06884921,0.000054309352,0.9282077,0.0009240817,0.0006904813,0.00018098389,0.000036949186,0.000055406865,0.0010008668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99644166,0.000013306778,0.0005327371,0.0016688065,0.00074882695,0.0005946782],"domain_scores_gemma":[0.9979117,0.0003725871,0.00024983744,0.0011465879,0.000194553,0.00012472665],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048066635,0.0004427799,0.00034966398,0.00060268637,0.0003049953,0.00036828523,0.0016986537,0.00017426393,0.000007939734],"category_scores_gemma":[0.000027658227,0.00041160916,0.00012420339,0.0007158747,0.0003003423,0.00023898114,0.00087733864,0.0004268044,0.000093292154],"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":[0.0000043044865,0.00002334428,8.465949e-7,0.000055937868,0.000007805826,0.000010813501,0.00014438995,0.5595074,0.00052990095,0.17734791,0.000067724686,0.26229957],"study_design_scores_gemma":[0.00017234596,0.00010820267,0.000010389664,0.0002210224,0.000014699142,0.000016514161,8.246363e-8,0.7596406,0.0016476687,0.2359782,0.0018385053,0.00035177774],"about_ca_topic_score_codex":0.000009641188,"about_ca_topic_score_gemma":0.000043223652,"teacher_disagreement_score":0.26194778,"about_ca_system_score_codex":0.00056072057,"about_ca_system_score_gemma":0.00020187658,"threshold_uncertainty_score":0.9998336},"labels":[],"label_agreement":null},{"id":"W4404356575","doi":"10.1007/978-3-031-73503-5_5","title":"AIMSM - A Mechanism to Optimize Systems with Multiple AI Models: A Case Study in Computer Vision for Autonomous Mobile Robots","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Mobile robot; Mechanism (biology); Robot; Artificial intelligence; Human–computer interaction; Computer vision","score_opus":0.024680469177435754,"score_gpt":0.2870836643168783,"score_spread":0.26240319513944255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404356575","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":[0.0005918241,0.00028290902,0.98972315,0.0003904988,0.0014043897,0.0071129207,0.000020936306,0.00041944644,0.000053952688],"genre_scores_gemma":[0.390076,0.0000054630505,0.6077289,0.0006461875,0.00029892861,0.0010158909,0.0000044245944,0.000081239035,0.00014298799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940573,0.000050559334,0.0008864499,0.0031854862,0.00085236016,0.00096784823],"domain_scores_gemma":[0.9961251,0.0008681979,0.0002658939,0.0020720551,0.0003408708,0.00032786356],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007668026,0.00086186006,0.0009314339,0.0012443557,0.00035388288,0.0010501812,0.0027597763,0.00028477274,0.0000011457151],"category_scores_gemma":[0.000013008798,0.00073119515,0.00013189141,0.0014192632,0.00020516032,0.0009360855,0.0021134757,0.0009350052,0.000022424421],"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":[0.000020882164,0.00012240045,0.0000025068218,0.000052894862,0.000014731433,0.0015130148,0.0016087428,0.9214697,0.000017887154,0.009612715,0.000014155647,0.06555041],"study_design_scores_gemma":[0.00070194853,0.0016613334,0.0000014437218,0.0005671474,0.00001735485,0.0014627437,0.0000031069992,0.94352335,0.00003226691,0.051052526,0.0001668884,0.0008098913],"about_ca_topic_score_codex":0.00018476216,"about_ca_topic_score_gemma":0.00090559054,"teacher_disagreement_score":0.38948417,"about_ca_system_score_codex":0.00068624347,"about_ca_system_score_gemma":0.00039449576,"threshold_uncertainty_score":0.9999868},"labels":[],"label_agreement":null},{"id":"W4404792627","doi":"10.1007/978-3-031-72848-8_10","title":"JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Lidar; Tracking (education); Artificial intelligence; Computer vision; Remote sensing; Geography","score_opus":0.033986658081650606,"score_gpt":0.2916625514560327,"score_spread":0.2576758933743821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404792627","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":[0.00014444534,0.001597229,0.9897933,0.005132385,0.0011416109,0.0005552701,0.0000068131394,0.00023344302,0.0013955273],"genre_scores_gemma":[0.7586737,0.0001005222,0.23354883,0.0054009943,0.0008973127,0.000117023454,0.000026506752,0.00011915491,0.0011159526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623513,0.000041878935,0.00055707677,0.0016407211,0.00086214265,0.00066303904],"domain_scores_gemma":[0.99731314,0.000732458,0.00024959404,0.0014737711,0.00012396352,0.00010709809],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070896675,0.00048378875,0.00036681336,0.000607071,0.00032977175,0.00093156885,0.0034998565,0.00024492983,0.000009584129],"category_scores_gemma":[0.000045823414,0.00037492847,0.00013566916,0.0014587322,0.00067492033,0.0005719007,0.00084424525,0.0013720802,0.00006822399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006677741,0.00005149683,0.00007050596,0.000070607835,0.0000076039873,0.000099591154,0.00037755392,0.16657795,0.0010144896,0.03727792,0.00018703041,0.7942586],"study_design_scores_gemma":[0.00019032245,0.00006306185,0.00014474017,0.000939165,0.000008688482,0.000036590256,1.8669243e-7,0.72981477,0.002001237,0.2606883,0.005533062,0.000579873],"about_ca_topic_score_codex":0.000011578684,"about_ca_topic_score_gemma":0.00010224279,"teacher_disagreement_score":0.7936787,"about_ca_system_score_codex":0.00035748933,"about_ca_system_score_gemma":0.0002890016,"threshold_uncertainty_score":0.99987024},"labels":[],"label_agreement":null},{"id":"W4404821142","doi":"10.1007/978-3-031-72848-8_14","title":"SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for International Peace and Security; Université de Montréal","funders":"","keywords":"Computer science; Selective attention; Computer vision; Artificial intelligence; Transformer; Neuroscience; Electrical engineering; Cognition; Psychology; Voltage","score_opus":0.01851481912246042,"score_gpt":0.2735095589935203,"score_spread":0.2549947398710599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404821142","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":[0.000028173275,0.0004004107,0.99432755,0.0023440209,0.00070849736,0.0015073209,0.000041099065,0.00014682164,0.00049610593],"genre_scores_gemma":[0.0338276,0.00006820359,0.9639625,0.0007296977,0.0005156166,0.00020498599,0.000042414857,0.000044618082,0.0006043709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973199,0.000007713979,0.00036860057,0.001457727,0.00038790828,0.0004581943],"domain_scores_gemma":[0.99826443,0.000810467,0.00014254116,0.00039694257,0.00027226857,0.00011333208],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037854814,0.00037479933,0.00033736558,0.00042234207,0.00041092996,0.00038800773,0.0009269985,0.00020027315,0.0000017242785],"category_scores_gemma":[0.000017126154,0.00035018777,0.00014440864,0.0004098589,0.00039223506,0.0006129239,0.00021588067,0.0003772206,0.0000063879597],"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":[0.00003386256,0.000037332098,0.000005902959,0.00018121238,0.000025188358,0.0000047538306,0.00032792732,0.051396247,0.0037021658,0.44600058,0.00023220791,0.49805263],"study_design_scores_gemma":[0.00020701057,0.00021629478,0.000024253723,0.00021267054,0.000014077294,0.000036612248,6.281095e-8,0.5782924,0.0010326505,0.41760203,0.0020671587,0.00029473598],"about_ca_topic_score_codex":0.0000017750087,"about_ca_topic_score_gemma":0.000018679519,"teacher_disagreement_score":0.5268962,"about_ca_system_score_codex":0.00021598185,"about_ca_system_score_gemma":0.00016070502,"threshold_uncertainty_score":0.99989504},"labels":[],"label_agreement":null},{"id":"W4404838659","doi":"10.1016/j.neucom.2024.128998","title":"Depth-Wise Convolutions in Vision Transformers for efficient training on small datasets","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transformer; Artificial intelligence; Training (meteorology); Pattern recognition (psychology); Training set; Machine learning; Computer vision; Electrical engineering; Geography; Engineering; Voltage","score_opus":0.04759880997046345,"score_gpt":0.3174072731901771,"score_spread":0.26980846321971363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404838659","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":[0.03583696,0.00009142116,0.96013606,0.0020116903,0.00041751587,0.0006994104,0.000022121352,0.00041741625,0.00036741124],"genre_scores_gemma":[0.9646073,0.0000071325876,0.034565937,0.00055239396,0.00010924887,0.00008652458,0.00003473034,0.000023903764,0.00001283446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836,0.00004304718,0.00031134914,0.0006922568,0.00014892689,0.0004444451],"domain_scores_gemma":[0.99877095,0.00080890546,0.000042103555,0.00026672852,0.000018150391,0.00009317251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023455682,0.00016650016,0.00014894058,0.00020912298,0.00021952987,0.00013922741,0.00044831383,0.000060733903,9.428516e-7],"category_scores_gemma":[0.000033377823,0.00016614617,0.000079904836,0.00079793436,0.000032645443,0.00014688377,0.00009600254,0.00036234688,0.000022688377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008454338,0.00008862777,0.000011790006,0.000054032156,0.000005420351,0.00003056537,0.00080203987,0.28670722,0.0036126021,0.040347517,0.0006649986,0.66766673],"study_design_scores_gemma":[0.0002680843,0.00006350057,0.00072036433,0.00013408514,0.0000040888494,0.000015372314,0.000019639858,0.97846735,0.00044866747,0.0006602875,0.019036459,0.00016211007],"about_ca_topic_score_codex":0.0000025899417,"about_ca_topic_score_gemma":0.000020833244,"teacher_disagreement_score":0.92877036,"about_ca_system_score_codex":0.00006329863,"about_ca_system_score_gemma":0.000052414573,"threshold_uncertainty_score":0.6775244},"labels":[],"label_agreement":null},{"id":"W4404893039","doi":"10.1007/978-3-031-78110-0_29","title":"Transformer-Based RGB and LiDAR Fusion for Enhanced Object Detection","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Lidar; Computer vision; Transformer; RGB color model; Artificial intelligence; Fusion; Remote sensing; Electrical engineering; Geography; Engineering; Voltage","score_opus":0.013514235089094852,"score_gpt":0.2570924376651464,"score_spread":0.24357820257605153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404893039","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":[0.00016906706,0.0005660233,0.99512124,0.00087645056,0.0010947282,0.0010415856,0.00000974237,0.000300382,0.00082077004],"genre_scores_gemma":[0.5531175,0.00009409737,0.4448919,0.0009245536,0.00046145104,0.00013666124,0.000007311007,0.000055760993,0.0003107494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969919,0.000011277034,0.00039538098,0.0016111862,0.00047476517,0.00051553984],"domain_scores_gemma":[0.9981797,0.0005882198,0.00014515597,0.00079687935,0.00015117244,0.0001388979],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034483007,0.00044781473,0.00036733862,0.0005832052,0.0003559546,0.00036602624,0.0012207555,0.00025733167,0.0000035284065],"category_scores_gemma":[0.000028800467,0.00040844182,0.00013006099,0.00068253046,0.00039417567,0.00041439448,0.00023979112,0.00055832625,0.000015816988],"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":[0.0000112174075,0.000010828569,5.2343125e-7,0.00007725202,0.0000046371993,0.0000054182274,0.00018882242,0.016454104,0.013092983,0.007657853,0.0000024953952,0.96249384],"study_design_scores_gemma":[0.00026826427,0.00030843102,0.00000986677,0.00028961722,0.000013864182,0.000023315806,9.133742e-8,0.58107436,0.12302092,0.2914551,0.0030173166,0.0005188542],"about_ca_topic_score_codex":0.0000039697275,"about_ca_topic_score_gemma":0.00010619006,"teacher_disagreement_score":0.96197504,"about_ca_system_score_codex":0.00020578541,"about_ca_system_score_gemma":0.00022212157,"threshold_uncertainty_score":0.99983674},"labels":[],"label_agreement":null},{"id":"W4404909983","doi":"10.1007/978-3-031-78447-7_17","title":"MarUCOD: Unknown but Concerned Object Detection in Maritime Environments","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Computer science; Object (grammar); Computer vision; Artificial intelligence","score_opus":0.01298015442975818,"score_gpt":0.24083993394818096,"score_spread":0.2278597795184228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404909983","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":[0.00014260557,0.00082051,0.99280673,0.00056894956,0.0014597405,0.0006559315,0.0000066893376,0.00021468442,0.0033241857],"genre_scores_gemma":[0.68302006,0.0005528272,0.30459464,0.0022554395,0.0011937475,0.00017526849,0.000019452322,0.0001617897,0.0080268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995728,0.000032832053,0.0006005545,0.0020876408,0.0008148983,0.00073608034],"domain_scores_gemma":[0.9977585,0.00042660808,0.00021138975,0.0014022166,0.000041119947,0.00016014905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042648058,0.0005689192,0.00047962746,0.0006736548,0.00018506445,0.00037842928,0.0026026545,0.00034359162,0.000021986392],"category_scores_gemma":[0.00003168703,0.0005691345,0.000121808,0.0010078376,0.0006322993,0.00062162435,0.0016688925,0.0012173727,0.00026298183],"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":[0.000007649415,0.00003118428,0.000021780312,0.000033594264,0.000010317758,0.00018991811,0.0002099685,0.0529416,0.0020232252,0.027540449,0.000009005586,0.9169813],"study_design_scores_gemma":[0.00027509604,0.00012868855,0.00019644682,0.00032779857,0.000009191246,0.00011258853,1.2699915e-7,0.61030346,0.006556757,0.3707645,0.0105226915,0.00080264435],"about_ca_topic_score_codex":0.000027068081,"about_ca_topic_score_gemma":0.00029167754,"teacher_disagreement_score":0.91617864,"about_ca_system_score_codex":0.00081551744,"about_ca_system_score_gemma":0.00018722774,"threshold_uncertainty_score":0.999676},"labels":[],"label_agreement":null},{"id":"W4404980847","doi":"10.1080/19392699.2024.2434891","title":"Accurate foreign object detection for the coal preparation industry based on computer vision and deep learning algorithms","year":2024,"lang":"en","type":"article","venue":"International Journal of Coal Preparation and Utilization","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakes Environmental (Canada); University of Waterloo","funders":"Fundamental Research Funds for the Central Universities","keywords":"Robustness (evolution); Computer science; Object detection; Artificial intelligence; Algorithm; Minimum bounding box; Bounding overwatch; Deep learning; Feature extraction; Computer vision; Machine learning; Pattern recognition (psychology); Image (mathematics)","score_opus":0.03226031144744388,"score_gpt":0.3556750780591289,"score_spread":0.323414766611685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404980847","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":[0.008262418,0.00028537467,0.9890276,0.001102712,0.000778738,0.00036305262,0.0000029482073,0.0000686468,0.00010847064],"genre_scores_gemma":[0.9918895,0.00019870789,0.0070251077,0.00028154353,0.000488227,0.000032318265,0.000018913855,0.000011091608,0.000054619315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987312,0.00009731576,0.00040806443,0.00026505959,0.0003911184,0.00010726307],"domain_scores_gemma":[0.9985178,0.00062692544,0.00028634327,0.00011498361,0.00039592932,0.000057995716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046020496,0.00013548102,0.00011368473,0.00020095828,0.00024337201,0.00059361383,0.0002407591,0.00011210019,0.0000049577798],"category_scores_gemma":[0.00008167659,0.000100856,0.00006289948,0.0001787694,0.00004659404,0.001117009,0.000055922337,0.00028816678,0.0000019036478],"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":[0.0004637665,0.000058082704,0.00011283718,0.0000191128,0.00007079167,0.0000056407916,0.0005468699,0.64990544,0.00066397747,0.012705943,0.00037318296,0.33507437],"study_design_scores_gemma":[0.00042656608,0.00062927214,0.0014282834,0.00007122586,0.000020959125,0.00009710334,0.000029054008,0.98292965,0.001767449,0.0031297004,0.009373953,0.00009681013],"about_ca_topic_score_codex":0.0000017273217,"about_ca_topic_score_gemma":0.000009213962,"teacher_disagreement_score":0.9836271,"about_ca_system_score_codex":0.000072549,"about_ca_system_score_gemma":0.000055853034,"threshold_uncertainty_score":0.5724232},"labels":[],"label_agreement":null},{"id":"W4405137555","doi":"10.1016/j.eswa.2024.126034","title":"RQFormer: Rotated Query Transformer for end-to-end oriented object detection","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; End-to-end principle; Transformer; Artificial intelligence; Computer vision; Electrical engineering","score_opus":0.012589328320885667,"score_gpt":0.27383363220767726,"score_spread":0.2612443038867916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405137555","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":[0.0003811725,0.0010533719,0.98993397,0.0013667856,0.00035033489,0.004718371,0.000046167333,0.0012564909,0.0008933459],"genre_scores_gemma":[0.88482815,0.00006030867,0.064094044,0.00030409853,0.00052361225,0.048774764,0.00007550064,0.00007901809,0.001260505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978717,0.000033421875,0.00044066258,0.0008802752,0.00031040979,0.0004635291],"domain_scores_gemma":[0.9983638,0.0002545634,0.000081566024,0.00087496405,0.00019638544,0.00022873301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017276696,0.00027616436,0.0002427151,0.00025305603,0.00048419103,0.00024363663,0.00055576157,0.00008929911,0.0000071068976],"category_scores_gemma":[0.000008363517,0.00022744924,0.00009586853,0.0020600865,0.000053523436,0.0005792779,0.000032535616,0.00016189336,0.00017931008],"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":[0.000096020216,0.00026233582,0.000023329361,0.00030768767,0.00025547174,0.000006376498,0.003972805,0.0085232,0.15568142,0.42914426,0.008072966,0.39365414],"study_design_scores_gemma":[0.0003120806,0.00018666933,0.000045382287,0.00010269169,0.000019794423,0.00010774524,0.0001883573,0.09898562,0.017667856,0.0006873929,0.8812455,0.00045092218],"about_ca_topic_score_codex":0.000098303506,"about_ca_topic_score_gemma":0.00007944415,"teacher_disagreement_score":0.9258399,"about_ca_system_score_codex":0.00019581319,"about_ca_system_score_gemma":0.00012394195,"threshold_uncertainty_score":0.9275111},"labels":[],"label_agreement":null},{"id":"W4405317618","doi":"10.1049/icp.2024.3269","title":"Front Matter","year":2024,"lang":"en","type":"paratext","venue":"IET conference proceedings.","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; University of Limerick; Technological University Dublin; Trinity College Dublin; University of Galway; Ulster University; Orta Doğu Teknik Üniversitesi; University College Dublin; Queen's University Belfast; University of South Florida; Insight SFI Research Centre for Data Analytics; Science Foundation Ireland; Aberystwyth University; Munster Technological University; Dublin City University; Carnegie Mellon University","keywords":"Front (military); Geology; Oceanography","score_opus":0.02658151023303708,"score_gpt":0.28079801992448344,"score_spread":0.2542165096914464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405317618","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":[0.00005968167,0.0016575367,0.22355624,0.0050315196,0.0033080354,0.00067658286,0.00005379664,0.0005125788,0.76514405],"genre_scores_gemma":[0.026599025,0.0008866999,0.038618058,0.002226356,0.0015080727,0.00083033566,0.000073231044,0.00012220649,0.92913604],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971106,0.000007431522,0.0004431833,0.0013808637,0.00043111533,0.0006268278],"domain_scores_gemma":[0.9985929,0.00003071782,0.00024531758,0.0005526978,0.00038210722,0.00019625641],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009848673,0.0005444614,0.00048334716,0.00020046122,0.00014452446,0.0011846737,0.0025527948,0.00036819978,0.0058777807],"category_scores_gemma":[0.000010530745,0.00049472594,0.00014849358,0.00047616346,0.000104760475,0.0006497886,0.0009953304,0.0010225123,0.32541636],"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":[0.0000016206245,0.00001637997,0.000007384299,0.0001675601,0.000024497045,0.0000031166808,0.00032734388,0.000005653338,0.00022959989,0.02353186,0.9718871,0.0037978725],"study_design_scores_gemma":[0.00007299143,0.00003737977,0.000023336414,0.00036943663,0.000024459106,0.00003695639,0.000021036509,0.0071759885,0.0007442655,0.015144335,0.97567326,0.00067653944],"about_ca_topic_score_codex":0.000007612213,"about_ca_topic_score_gemma":0.0000016555058,"teacher_disagreement_score":0.3195386,"about_ca_system_score_codex":0.00011928413,"about_ca_system_score_gemma":0.00020807551,"threshold_uncertainty_score":0.9998522},"labels":[],"label_agreement":null},{"id":"W4405511023","doi":"10.1186/s12880-024-01523-x","title":"Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images","year":2024,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Vector Institute; Canada Research Chairs; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Ground truth; Computer science; Artificial intelligence; Segmentation; Cluster analysis; Pattern recognition (psychology); Pipeline (software); Classifier (UML); Sørensen–Dice coefficient; Deep learning; Image segmentation","score_opus":0.023173084489576164,"score_gpt":0.30729913843479456,"score_spread":0.2841260539452184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405511023","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":[0.042125195,0.0007619267,0.95325714,0.0033403009,0.00013275305,0.00027193298,0.0000015935101,0.000081075836,0.000028089447],"genre_scores_gemma":[0.7218254,0.00004844558,0.27733895,0.00046212593,0.00014999682,0.00012181636,0.000016184369,0.000013399862,0.000023619923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989596,0.000049703973,0.00027874872,0.00032166048,0.00021621598,0.00017404603],"domain_scores_gemma":[0.9991382,0.000573291,0.000028553439,0.00015150472,0.00002792746,0.000080557664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036757757,0.00008707904,0.0001146774,0.00011261632,0.00005180447,0.00008351082,0.00021136181,0.000023722861,0.000007008602],"category_scores_gemma":[0.00018961867,0.00008345076,0.00002850134,0.0002798726,0.000060716047,0.0005292254,0.00012426269,0.000087709945,0.0000016534144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009979355,0.000051839954,0.0044928743,0.00051927916,0.000008753112,0.00002254432,0.0015822827,0.0055722366,0.2701546,0.01149399,0.0011650121,0.7049266],"study_design_scores_gemma":[0.00025101987,0.000009224312,0.00086024526,0.000106270374,0.000002764546,0.00002104489,0.00006245861,0.9913126,0.0060598934,0.0011477774,0.00008409228,0.00008260692],"about_ca_topic_score_codex":0.000011739029,"about_ca_topic_score_gemma":0.00009072471,"teacher_disagreement_score":0.98574036,"about_ca_system_score_codex":0.000032748514,"about_ca_system_score_gemma":0.000074906944,"threshold_uncertainty_score":0.34030232},"labels":[],"label_agreement":null},{"id":"W4405825130","doi":"10.1007/978-981-96-0313-8_15","title":"A Novel Embryo Morphology Evaluation Based on Improved YOLOv8 Object Detection Model","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Morphology (biology); Computer science; Object (grammar); Embryo; Artificial intelligence; Biology; Zoology; Cell biology","score_opus":0.056116333148899696,"score_gpt":0.3195289805804924,"score_spread":0.2634126474315927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405825130","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":[0.00002383263,0.00013809737,0.9204116,0.0014468188,0.00034117515,0.0008915144,0.000019500445,0.00020985483,0.07651762],"genre_scores_gemma":[0.3530042,0.0007705316,0.6380774,0.00491945,0.00012172758,0.0007080165,0.00017839322,0.000047424797,0.0021729073],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977067,0.000030449162,0.0007427512,0.0005866141,0.0006351226,0.00029834104],"domain_scores_gemma":[0.99574184,0.0002813071,0.00037387703,0.0029200108,0.0005718496,0.00011113471],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013398073,0.00031664685,0.00025803116,0.0014523704,0.0005286629,0.0005118454,0.002797091,0.00019891994,0.000004380255],"category_scores_gemma":[0.000057150606,0.00032028186,0.00007215358,0.0008700785,0.00060768565,0.0037852926,0.001498426,0.00073762663,0.000097307435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064545634,0.00003486049,4.748125e-7,0.00002382793,0.0000054962024,1.9266744e-7,0.0004327449,0.14891164,0.00054170896,0.5597464,0.00009686013,0.29019937],"study_design_scores_gemma":[0.00030898213,0.00007877762,0.000053853593,0.00010451154,0.000012458409,0.00001974187,0.0000034033828,0.9589551,0.00006887427,0.03364064,0.006458736,0.00029493272],"about_ca_topic_score_codex":0.000007071244,"about_ca_topic_score_gemma":0.0000159386,"teacher_disagreement_score":0.81004345,"about_ca_system_score_codex":0.00045456906,"about_ca_system_score_gemma":0.00048279663,"threshold_uncertainty_score":0.9999249},"labels":[],"label_agreement":null},{"id":"W4406019798","doi":"10.53759/7669/jmc202505001","title":"Efficient and Accurate Traffic Sign Detection Leveraging YOLOv8: A Cutting-Edge Deep Learning Framework","year":2024,"lang":"en","type":"article","venue":"Journal of Machine and Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Computer science; Sign (mathematics); Deep learning; Artificial intelligence; Traffic sign; Computer vision; Mathematics","score_opus":0.012041333151956034,"score_gpt":0.268863493785661,"score_spread":0.25682216063370494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406019798","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":[0.39115033,0.003487094,0.60467374,0.00036792582,0.00018569117,0.000041113824,6.366556e-8,0.000068918416,0.000025142379],"genre_scores_gemma":[0.9628434,0.00009030855,0.036684666,0.000083819556,0.00027642792,6.825525e-7,1.2601996e-7,0.000011588428,0.000008976262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892545,0.000090694135,0.00035438524,0.00025134897,0.00016981285,0.00020832826],"domain_scores_gemma":[0.99889255,0.0006309016,0.00022156346,0.00009953248,0.000058173515,0.000097279124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053785485,0.00013322491,0.00017987627,0.0001598726,0.00035179482,0.00031447285,0.00020382222,0.000045317087,0.0000011222094],"category_scores_gemma":[0.000085205866,0.00011249644,0.00005730419,0.00044359086,0.000028432192,0.00016537422,0.00017915864,0.00075963174,0.0000020313043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004056324,0.000010034908,0.00007145807,0.000021108652,0.000014617418,0.000028388002,0.0011716041,0.36035347,0.00046877292,0.0007033784,0.0000038068042,0.6371493],"study_design_scores_gemma":[0.00012187209,0.00010842674,0.00077735767,0.00019666606,0.000015580514,0.0007357824,0.000073592986,0.9957968,0.00012530708,0.0013677903,0.0005665057,0.00011431118],"about_ca_topic_score_codex":0.0000013046899,"about_ca_topic_score_gemma":5.507536e-7,"teacher_disagreement_score":0.63703495,"about_ca_system_score_codex":0.000030859534,"about_ca_system_score_gemma":0.000018471192,"threshold_uncertainty_score":0.45874715},"labels":[],"label_agreement":null},{"id":"W4406048761","doi":"10.1167/jov.25.1.4","title":"Benchmarking the speed–accuracy tradeoff in object recognition by humans and neural networks","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"National Institutes of Health; National Eye Institute; York University; Gordon and Betty Moore Foundation; Alfred P. Sloan Foundation","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Inference; Convolutional neural network; Task (project management); Artificial neural network; Object (grammar); Benchmarking; Categorization; Recurrent neural network; Machine learning; Pattern recognition (psychology); Computation; Cognitive neuroscience of visual object recognition; Speedup; Deep learning; Block (permutation group theory); Algorithm","score_opus":0.016533822487182482,"score_gpt":0.2993192532275485,"score_spread":0.282785430740366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406048761","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":[0.47996238,0.002701766,0.5070605,0.008985634,0.0006096916,0.0002756877,0.0000010171192,0.000025649048,0.0003776248],"genre_scores_gemma":[0.9951921,0.00045575612,0.0036293285,0.0005887376,0.000109949455,0.0000015845355,0.000001116233,0.0000037552243,0.000017655351],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912095,0.00009329805,0.0003495536,0.00014457418,0.00014673821,0.00014487412],"domain_scores_gemma":[0.9990033,0.000496483,0.00024391232,0.00016756776,0.0000530353,0.00003571851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039009072,0.00008199878,0.00012562405,0.00010480362,0.00014667498,0.00011204079,0.00038307963,0.000039788316,0.0000029393539],"category_scores_gemma":[0.00004912133,0.000056665318,0.000045330427,0.0005089865,0.000033512257,0.0005624384,0.00009992408,0.00036653236,4.6431433e-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":[0.00003153604,0.000078240875,0.0011031944,0.0000068808813,0.000009636628,0.000013473338,0.0002455846,0.012461564,0.0026325565,0.00078059983,0.0069418624,0.9756949],"study_design_scores_gemma":[0.0007672465,0.0002524464,0.066356,0.0002617989,0.000016192977,0.000075803786,0.000052465442,0.9122109,0.00030434201,0.015552655,0.0039936113,0.00015653692],"about_ca_topic_score_codex":0.000003528386,"about_ca_topic_score_gemma":0.000008228258,"teacher_disagreement_score":0.9755383,"about_ca_system_score_codex":0.000035261175,"about_ca_system_score_gemma":0.000015484266,"threshold_uncertainty_score":0.23107447},"labels":[],"label_agreement":null},{"id":"W4406158156","doi":"10.1109/tits.2024.3520487","title":"Knowledge Guided Visual Transformers for Intelligent Transportation Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Norges Forskningsråd; The Research Council","keywords":"Computer science; Intelligent transportation system; Robustness (evolution); Cluster analysis; Crash; Artificial intelligence; Machine learning; Intelligent decision support system; Engineering; Transport engineering","score_opus":0.04017425846132747,"score_gpt":0.33257780099631606,"score_spread":0.29240354253498857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406158156","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":[0.0036836113,0.0007542469,0.983845,0.0003269078,0.0055825277,0.004271252,0.00031343647,0.00080665224,0.00041639354],"genre_scores_gemma":[0.99018574,0.00048806574,0.0022998825,0.00014014273,0.00010871281,0.0039035701,0.00018127573,0.00007053696,0.0026220605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99536467,0.00014424272,0.0019980879,0.001215975,0.00055583054,0.00072117144],"domain_scores_gemma":[0.99760693,0.0005439669,0.00034301527,0.0005523292,0.0006693301,0.0002844286],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004824682,0.00063892547,0.00070015364,0.0008350861,0.00064262893,0.00025230163,0.0007611536,0.00033646118,0.000017213068],"category_scores_gemma":[0.0000045483184,0.0006620684,0.0005506656,0.0019659891,0.00011435786,0.00077311427,5.269239e-7,0.00042278896,0.000095501135],"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":[0.00026763964,0.001151586,0.000057594847,0.0010861887,0.00049391284,0.000007378514,0.0031669997,0.8376791,0.0026471375,0.07365651,0.0016853955,0.07810057],"study_design_scores_gemma":[0.0027432444,0.0008802864,0.00033201562,0.0013473235,0.0006165811,0.000017268985,0.0027853386,0.75584435,0.11111569,0.0010369946,0.1212227,0.0020581807],"about_ca_topic_score_codex":0.00016543677,"about_ca_topic_score_gemma":0.0003634033,"teacher_disagreement_score":0.9865022,"about_ca_system_score_codex":0.0003870224,"about_ca_system_score_gemma":0.00025812752,"threshold_uncertainty_score":0.99958307},"labels":[],"label_agreement":null},{"id":"W4406207611","doi":"10.1109/jiot.2025.3527929","title":"Joint Task Partitioning and Resource Allocation in RAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Mobile edge computing; Computation offloading; Edge computing; Reinforcement learning; Markov decision process; Distributed computing; Server; Cloud computing; Computational resource; Resource allocation; Edge device; Mobile cloud computing; Enhanced Data Rates for GSM Evolution; Mobile computing; Computational complexity theory; Computer network; Artificial intelligence; Markov process","score_opus":0.012604748009515302,"score_gpt":0.25024992881957986,"score_spread":0.23764518081006455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406207611","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":[0.06768456,0.000080707105,0.929478,0.0013739122,0.00015051373,0.00013264564,2.617259e-8,0.000041858835,0.0010577796],"genre_scores_gemma":[0.9804033,0.000012126352,0.018380692,0.0009777113,0.000042852247,0.0000062035647,0.000001443571,0.000007217278,0.00016846634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985937,0.00012692332,0.0005416957,0.0002572739,0.00024784092,0.00023256015],"domain_scores_gemma":[0.9990387,0.00019358855,0.0003959038,0.00020243783,0.00010465763,0.00006472685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077765423,0.00012713214,0.00019082056,0.00028900686,0.00016140802,0.00016288146,0.00039551247,0.000050643568,0.0000039275046],"category_scores_gemma":[0.0001287156,0.00012545398,0.000055842833,0.0003428962,0.000042960266,0.00035344518,0.00012740486,0.0005990609,0.0000025889678],"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":[0.000021430824,0.000029548595,0.0005918242,0.000021691547,0.000012772554,0.000009419404,0.00094756787,0.978596,0.0042718975,0.0042451727,0.000281921,0.010970793],"study_design_scores_gemma":[0.0005233956,0.000100030535,0.00076469895,0.00067895633,0.000006449057,0.000021687669,0.000048894308,0.9839373,0.011294177,0.001483263,0.0010392786,0.00010187011],"about_ca_topic_score_codex":0.000019411707,"about_ca_topic_score_gemma":0.0000023410762,"teacher_disagreement_score":0.9127187,"about_ca_system_score_codex":0.00014330438,"about_ca_system_score_gemma":0.000040373783,"threshold_uncertainty_score":0.5115865},"labels":[],"label_agreement":null},{"id":"W4406254008","doi":"10.31436/iiumej.v26i1.3268","title":"4D Radar Imaging and Camera Fusion for Road Crossing Detection and Classification Using Deep Learning","year":2025,"lang":"en","type":"article","venue":"IIUM Engineering Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Radar; Deep learning; Object detection; Artificial neural network; Pattern recognition (psychology); Telecommunications","score_opus":0.011327685045094748,"score_gpt":0.25932034358683953,"score_spread":0.2479926585417448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406254008","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":[0.1317691,0.001020108,0.8665288,0.0002498928,0.00024711332,0.000094958225,1.4111744e-7,0.000082748236,0.000007141601],"genre_scores_gemma":[0.78149736,0.000049899536,0.21828519,0.000025765847,0.0001144151,0.000006711538,3.0154115e-7,0.000009234001,0.000011134106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993246,0.000016268072,0.00018486306,0.00020432896,0.000077543176,0.00019238812],"domain_scores_gemma":[0.9995887,0.00008430393,0.0000935904,0.00010390377,0.00006742807,0.00006204871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019665215,0.000100679485,0.000094139716,0.00016349198,0.0009123147,0.000626624,0.00011414123,0.000031253017,2.4233987e-7],"category_scores_gemma":[0.00006290504,0.00010712805,0.000024966726,0.0002539294,0.000032062504,0.00050976645,0.000072007955,0.00025821378,1.326474e-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":[0.0000049565706,0.000004776995,0.0003496419,0.000022669963,0.000007214294,0.0000015688524,0.00017391132,0.105895594,0.31876674,0.0011112799,0.000002898877,0.57365876],"study_design_scores_gemma":[0.00022403285,0.000010204828,0.0065189935,0.00006080126,0.000009456359,0.0001834942,0.00002687311,0.98844194,0.0025382899,0.0004274897,0.0014590247,0.00009937861],"about_ca_topic_score_codex":0.0000026560438,"about_ca_topic_score_gemma":9.562295e-7,"teacher_disagreement_score":0.88254637,"about_ca_system_score_codex":0.0000849037,"about_ca_system_score_gemma":0.00002604149,"threshold_uncertainty_score":0.7016879},"labels":[],"label_agreement":null},{"id":"W4406433534","doi":"10.1109/ic3i61595.2024.10828580","title":"Enhancing Autonomous Vehicle Navigation with Real-Time Object Detection Using Convolutional Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Object detection; Computer vision; Object (grammar); Pattern recognition (psychology)","score_opus":0.01135950399418019,"score_gpt":0.2517524045202488,"score_spread":0.2403929005260686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406433534","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":[0.18498147,0.00008106793,0.8131708,0.00014494105,0.00019924057,0.0001973362,7.127165e-7,0.0009007666,0.00032365267],"genre_scores_gemma":[0.94709986,0.000005319596,0.052315086,0.00006893734,0.00022993989,0.000032734006,0.000008098639,0.000019507546,0.00022051512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987477,0.000043715736,0.00021841357,0.0004935342,0.00019725457,0.00029940438],"domain_scores_gemma":[0.9993238,0.00015682855,0.000059185113,0.0003104219,0.00007065756,0.00007911638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012851176,0.00015063153,0.00011169891,0.000075466276,0.00028063735,0.00021482627,0.00023912641,0.000058578942,0.000014418893],"category_scores_gemma":[0.0000039732768,0.00013166593,0.000044283664,0.00088065397,0.000050022463,0.0009138147,0.000094151066,0.0001979633,0.000044397584],"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":[0.000013168944,0.000022361683,0.000064516564,0.000016725222,0.000025285042,0.000026924354,0.000123251,0.76665586,0.17721787,0.015975414,0.00003487968,0.039823726],"study_design_scores_gemma":[0.00008261348,0.000057148,0.00036675364,0.000035445308,0.0000096380145,0.00014558433,0.0000052250134,0.98695236,0.011204582,0.0008635163,0.00010693373,0.00017019789],"about_ca_topic_score_codex":0.000068441805,"about_ca_topic_score_gemma":0.000031025957,"teacher_disagreement_score":0.7621184,"about_ca_system_score_codex":0.00020538224,"about_ca_system_score_gemma":0.00007011216,"threshold_uncertainty_score":0.5369181},"labels":[],"label_agreement":null},{"id":"W4406457497","doi":"10.1109/tits.2025.3525542","title":"HSPFormer: Hierarchical Spatial Perception Transformer for Semantic Segmentation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Segmentation; Artificial intelligence; Computer science; Perception; Transformer; Computer vision; Pattern recognition (psychology); Natural language processing; Engineering; Psychology; Electrical engineering; Neuroscience","score_opus":0.02114758349039867,"score_gpt":0.29163457744551097,"score_spread":0.2704869939551123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406457497","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":[0.0053015966,0.00004280509,0.9890217,0.00076769385,0.0019170302,0.0022929679,0.00009863803,0.0003839379,0.00017362651],"genre_scores_gemma":[0.98466384,0.00015098305,0.012004513,0.00025831047,0.00006629121,0.0018355172,0.00008479852,0.000026047928,0.00090968044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976866,0.00006877401,0.0008451377,0.00065468776,0.00037388617,0.00037091697],"domain_scores_gemma":[0.998832,0.00026228052,0.00013508975,0.00044630276,0.00020852385,0.00011581626],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020507962,0.00029510408,0.00029695005,0.00041279334,0.00046438485,0.00012513054,0.00042674027,0.00015511824,0.000026700323],"category_scores_gemma":[0.0000020627658,0.0002970955,0.00026886698,0.0007597405,0.000064796885,0.0005436246,2.940104e-7,0.000254444,0.00005774588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003413786,0.0007035184,0.00008618164,0.0004795057,0.0002406136,0.0000029962644,0.0031041973,0.44537404,0.029437935,0.04516993,0.00047533045,0.47458437],"study_design_scores_gemma":[0.0030887655,0.0008548387,0.0020375673,0.0005509124,0.0003802279,0.000018134024,0.0014488613,0.772546,0.19317144,0.005745373,0.0187737,0.0013842094],"about_ca_topic_score_codex":0.00010370984,"about_ca_topic_score_gemma":0.00022339837,"teacher_disagreement_score":0.97936225,"about_ca_system_score_codex":0.00018530917,"about_ca_system_score_gemma":0.00008946406,"threshold_uncertainty_score":0.99994814},"labels":[],"label_agreement":null},{"id":"W4406857000","doi":"10.1109/jiot.2025.3534737","title":"Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Object detection; Edge computing; Object (grammar); Vehicle safety; Artificial intelligence; Automotive engineering; Engineering; Pattern recognition (psychology)","score_opus":0.012793723225063579,"score_gpt":0.2810222540667627,"score_spread":0.2682285308416991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406857000","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":[0.061360747,0.00029155798,0.93650806,0.00042703236,0.00090247596,0.00026110455,3.69376e-7,0.000066128814,0.00018254916],"genre_scores_gemma":[0.9518058,0.00012868755,0.047706526,0.00022204834,0.00006079163,0.000016518576,1.4232488e-7,0.000008339046,0.00005118978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984845,0.000058214377,0.0006489496,0.00034077917,0.00017302038,0.00029456004],"domain_scores_gemma":[0.99886024,0.00037798384,0.00030096315,0.0002213254,0.0001706698,0.00006881401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006754215,0.00015237361,0.0002416447,0.00030883198,0.00011389741,0.00014579667,0.00077827886,0.000073286225,0.0000017928494],"category_scores_gemma":[0.00017226081,0.00014782454,0.00009168177,0.0005008157,0.00004747394,0.0007423614,0.00018060763,0.00045460346,0.0000013005387],"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":[0.00022386409,0.0003916913,0.0015434035,0.00033833194,0.0001730542,0.000035198125,0.031636473,0.015141301,0.15768129,0.01885644,0.00037332592,0.77360564],"study_design_scores_gemma":[0.00045944608,0.00025329672,0.0005211179,0.0007688411,0.000020313633,0.00024118614,0.00017967031,0.54854685,0.42480084,0.0231389,0.0008184995,0.00025103264],"about_ca_topic_score_codex":0.000025049801,"about_ca_topic_score_gemma":0.000042097803,"teacher_disagreement_score":0.890445,"about_ca_system_score_codex":0.00020428313,"about_ca_system_score_gemma":0.00008140727,"threshold_uncertainty_score":0.602811},"labels":[],"label_agreement":null},{"id":"W4407241355","doi":"10.36227/techrxiv.173894988.81604432/v1","title":"Towards a Unified Evaluation Framework: Integrating Human Perception and Metrics for AI-Generated Images","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Perception; Computer science; Artificial intelligence; Data science; Computer vision; Psychology; Neuroscience","score_opus":0.07612113581608967,"score_gpt":0.39634644649946227,"score_spread":0.32022531068337257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407241355","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":[0.0024936413,0.00020603229,0.9906773,0.002388128,0.00033918567,0.0018031544,0.00002779209,0.00036306639,0.0017016986],"genre_scores_gemma":[0.27375418,0.00008667512,0.72375804,0.00061046466,0.00014295825,0.00092963333,0.00017145192,0.000012573706,0.0005340451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788004,0.00014299058,0.00042930094,0.0009455968,0.00034800518,0.0002540686],"domain_scores_gemma":[0.9976503,0.00030195297,0.00023304463,0.00083449006,0.00090826943,0.00007189176],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00077078195,0.00029149797,0.00029890603,0.00036257153,0.0003564162,0.00044016275,0.00076961966,0.00033399794,0.000015915199],"category_scores_gemma":[0.00047324595,0.00027263263,0.00009196541,0.0008955211,0.00004735516,0.00026403347,0.0011227691,0.00063426065,0.0000018343932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005642887,0.000058104248,0.000049617604,0.00013368919,0.000047216057,3.8803995e-7,0.00051414233,0.014098058,0.003521666,0.3871143,0.0025735311,0.59188366],"study_design_scores_gemma":[0.0001569306,0.000031699154,0.00043130433,0.00009254425,0.00004980545,0.0000010709538,0.000043917247,0.72894126,0.0014331134,0.26830655,0.00026342756,0.0002483795],"about_ca_topic_score_codex":0.000058570724,"about_ca_topic_score_gemma":0.000023625382,"teacher_disagreement_score":0.7148432,"about_ca_system_score_codex":0.00022426287,"about_ca_system_score_gemma":0.00022999453,"threshold_uncertainty_score":0.9999726},"labels":[],"label_agreement":null},{"id":"W4407583119","doi":"10.1002/sys.21800","title":"Two‐Stage Approach to Small‐Object Detection","year":2025,"lang":"en","type":"article","venue":"Systems Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Object detection; Computer science; Background subtraction; Artificial intelligence; Field (mathematics); Object (grammar); Computer vision; Deep learning; Video tracking; Real-time computing; Pattern recognition (psychology); Pixel","score_opus":0.012800169105986605,"score_gpt":0.22838873881994884,"score_spread":0.21558856971396223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407583119","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":[0.0036739847,0.00016253866,0.98973465,0.000036574405,0.0006992604,0.00043486708,6.9372635e-7,0.00061828457,0.004639126],"genre_scores_gemma":[0.93839705,0.0000016229935,0.059941914,0.000037916678,0.00010194174,0.00033309258,7.2188766e-7,0.000011605424,0.0011741169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991718,0.000014960264,0.0001758182,0.00031947173,0.00008931222,0.00022866002],"domain_scores_gemma":[0.99931496,0.000050709346,0.000027995296,0.00050866196,0.000032227443,0.00006545855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001234849,0.00011617925,0.00012505353,0.00017399684,0.000073992924,0.000095934105,0.0004418695,0.000032884556,1.3692416e-7],"category_scores_gemma":[0.000022635442,0.000122017496,0.000031919288,0.0009828276,0.0000034285922,0.0001280053,0.00013893285,0.000110728346,0.000019289006],"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":[5.711992e-7,0.0000073263177,0.0000092989085,0.000054393793,0.000007892036,7.0364206e-7,0.00004799855,0.8921166,0.014391024,0.089050785,0.000030145267,0.004283248],"study_design_scores_gemma":[0.00010298443,0.000008801465,0.00015746211,0.0000393268,0.0000023647551,0.00000787822,0.000015112903,0.9777629,0.0052146916,0.000042056934,0.016502524,0.00014389664],"about_ca_topic_score_codex":0.000029535877,"about_ca_topic_score_gemma":0.0000034266684,"teacher_disagreement_score":0.9347231,"about_ca_system_score_codex":0.00009425069,"about_ca_system_score_gemma":0.000013473245,"threshold_uncertainty_score":0.49757293},"labels":[],"label_agreement":null},{"id":"W4407626503","doi":"10.1016/j.neucom.2025.129681","title":"Diversity augmentation and multi-fuzzy label for semi-supervised semantic segmentation","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Department of Science and Technology of Shandong Province","keywords":"Artificial intelligence; Computer science; Diversity (politics); Segmentation; Fuzzy logic; Pattern recognition (psychology); Machine learning; Natural language processing; Sociology","score_opus":0.035396778923064205,"score_gpt":0.30491704014064286,"score_spread":0.26952026121757866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407626503","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":[0.22325408,0.00002859889,0.775151,0.0006555785,0.00014768071,0.00053539843,0.0000011471689,0.00016808079,0.00005842933],"genre_scores_gemma":[0.65633774,0.000011454111,0.3423452,0.0011172509,0.000033232853,0.00003585691,0.0000057803804,0.0000065899094,0.00010688857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990822,0.000037982554,0.00017388245,0.00041528099,0.00009661444,0.00019400907],"domain_scores_gemma":[0.99933857,0.00027336713,0.00008213736,0.00020116458,0.00006278647,0.000041949395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010198875,0.00010987945,0.000104564846,0.000082903156,0.0010525093,0.000067665336,0.00030173946,0.000030014688,3.04159e-7],"category_scores_gemma":[0.000024472649,0.00012286312,0.000026598535,0.000374225,0.000022793738,0.00029933552,0.00090363825,0.00007881985,0.0000021906098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003447597,0.00029436965,0.029120717,0.00030864554,0.000054970566,0.000006509007,0.0034587753,0.022665001,0.094373755,0.033543207,0.0012060653,0.81493354],"study_design_scores_gemma":[0.0012226583,0.000029709254,0.010687785,0.000025451067,0.000014195554,0.000002735716,0.00003427759,0.98153704,0.0031932855,0.0030103612,0.00012549045,0.00011700076],"about_ca_topic_score_codex":0.0000075133066,"about_ca_topic_score_gemma":0.000007367896,"teacher_disagreement_score":0.958872,"about_ca_system_score_codex":0.000034095858,"about_ca_system_score_gemma":0.000014209221,"threshold_uncertainty_score":0.80951566},"labels":[],"label_agreement":null},{"id":"W4407847668","doi":"10.1016/j.aei.2025.103224","title":"Deep learning-based rebar detection and instance segmentation in images","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Hunan University; Precast/Prestressed Concrete Institute; McGill University","keywords":"Rebar; Artificial intelligence; Segmentation; Deep learning; Computer science; Computer vision; Pattern recognition (psychology); Image segmentation; Engineering; Structural engineering","score_opus":0.002989233913601114,"score_gpt":0.21524582333234563,"score_spread":0.2122565894187445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407847668","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":[0.012591204,0.000140393,0.9864297,0.00007471717,0.00009226905,0.00018418177,2.5317314e-7,0.00024522643,0.00024208105],"genre_scores_gemma":[0.6350189,0.00008112949,0.3646852,0.00012129203,0.000003910587,0.000065130545,0.0000022887152,0.000005259398,0.000016906668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993673,0.0000071351237,0.000268335,0.00010523376,0.000082746534,0.00016922336],"domain_scores_gemma":[0.9995726,0.000099270204,0.0000706293,0.00019539995,0.000032210985,0.000029903304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007143365,0.000106616135,0.00009418935,0.00018192893,0.000066083325,0.000044789318,0.0001636254,0.000034600813,2.2945166e-7],"category_scores_gemma":[0.000048981034,0.000120323435,0.0000135688115,0.00065724284,0.0000143128,0.0008642051,0.000055851848,0.00018294479,0.0000027133922],"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":[0.0000017386886,0.000004020554,0.00009695607,0.000045648714,0.0000015350522,3.285198e-7,0.00012913404,0.8682869,0.0017932539,0.0018716005,9.83488e-7,0.12776789],"study_design_scores_gemma":[0.00033814213,0.000018843568,0.002594868,0.00005265919,0.0000015538069,0.0000014629296,0.000035695637,0.98383576,0.010757257,0.0003592967,0.0018935339,0.0001109071],"about_ca_topic_score_codex":7.685398e-7,"about_ca_topic_score_gemma":0.000004967059,"teacher_disagreement_score":0.6224277,"about_ca_system_score_codex":0.00008610429,"about_ca_system_score_gemma":0.000010892199,"threshold_uncertainty_score":0.49066472},"labels":[],"label_agreement":null},{"id":"W4408017677","doi":"10.1109/icmsci62561.2025.10893949","title":"Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Pedestrian; Computer science; Pedestrian detection; Artificial intelligence; Computer vision; Machine learning; Engineering; Transport engineering","score_opus":0.010362347399414119,"score_gpt":0.25302617466595323,"score_spread":0.24266382726653912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408017677","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":[0.032009322,0.000010718369,0.964704,0.0008292424,0.00017632481,0.00029410492,4.5616812e-7,0.00054553384,0.0014303253],"genre_scores_gemma":[0.7883907,7.7342605e-7,0.21042167,0.00016835169,0.000048950453,0.00020961941,0.0000029269604,0.00000649567,0.00075049413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924344,0.000017858114,0.00015488407,0.00033116128,0.000087127075,0.00016554545],"domain_scores_gemma":[0.9993409,0.00023343175,0.000076248754,0.00019634012,0.000117128366,0.000035955112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008644326,0.00009970857,0.00008639101,0.00012469784,0.00050040387,0.000093736446,0.00020477844,0.000039396473,0.000004132268],"category_scores_gemma":[0.000053201507,0.000086478794,0.00003836763,0.0007769867,0.000030326193,0.0002548296,0.000032736338,0.00015826107,0.0000039549736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005609222,0.000052581163,0.0029653674,0.000022583465,0.00002382741,5.4785994e-7,0.000049118244,0.13568316,0.010876229,0.08878611,0.000103550025,0.76138085],"study_design_scores_gemma":[0.0006623103,0.00012728736,0.0014120728,0.000018172288,0.000006528379,0.000005401798,0.000030230285,0.9425919,0.03814973,0.0038556873,0.013006808,0.00013384999],"about_ca_topic_score_codex":0.000037882015,"about_ca_topic_score_gemma":0.00018729805,"teacher_disagreement_score":0.8069088,"about_ca_system_score_codex":0.000104548846,"about_ca_system_score_gemma":0.00012672454,"threshold_uncertainty_score":0.38487527},"labels":[],"label_agreement":null},{"id":"W4408063720","doi":"10.1007/978-981-96-1483-7_26","title":"Deepfake Detection in Cancer Medical Imaging Using CNN Architectures","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; Artificial intelligence; Cancer detection; Medical imaging; Cancer; Machine learning; Medicine","score_opus":0.01664385121022629,"score_gpt":0.2952214120771828,"score_spread":0.2785775608669565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408063720","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":[0.000336713,0.0011246887,0.9940072,0.0018382269,0.0014180543,0.0004507845,0.000002547689,0.00017827647,0.0006435118],"genre_scores_gemma":[0.507338,0.00022425022,0.4857046,0.0052823084,0.0011066884,0.0000828684,0.0000035029902,0.000064217274,0.00019355703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958858,0.000049845505,0.0005893593,0.0016695762,0.0010674165,0.0007380257],"domain_scores_gemma":[0.99773574,0.0006001163,0.0002472183,0.0010957393,0.0001389621,0.00018225265],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055826176,0.0004860401,0.0004739894,0.0012557431,0.0002808273,0.00023424161,0.0029825822,0.00027219838,0.000018512528],"category_scores_gemma":[0.0001373085,0.00047172344,0.0001028117,0.0014990178,0.0005832033,0.0003144535,0.001535691,0.0013995095,0.000005541397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002906082,0.0000115574885,0.00009769106,0.000017894039,0.0000029572527,0.000050131628,0.0001160272,0.179832,0.00023610562,0.002279616,0.0000025967695,0.8173505],"study_design_scores_gemma":[0.00017134714,0.000014303243,0.00017328364,0.0005796001,0.0000047983744,0.00006681054,7.153504e-8,0.8734667,0.0016397203,0.122965485,0.00050989486,0.00040798917],"about_ca_topic_score_codex":0.00015208391,"about_ca_topic_score_gemma":0.0018123949,"teacher_disagreement_score":0.8169425,"about_ca_system_score_codex":0.00069883245,"about_ca_system_score_gemma":0.00087669794,"threshold_uncertainty_score":0.99977344},"labels":[],"label_agreement":null},{"id":"W4408110400","doi":"10.1007/978-3-031-82475-3_16","title":"AF Mask R-CNN: Attention Fused Mask R-CNN Mechanism for Instance Segmentation","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Mechanism (biology); Pattern recognition (psychology); Computer vision; Physics","score_opus":0.017521736767343567,"score_gpt":0.2669627276163726,"score_spread":0.24944099084902902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408110400","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":[0.000051639992,0.00017954982,0.9920729,0.0022074648,0.0020821341,0.0017552406,0.000022498407,0.00035647224,0.0012721312],"genre_scores_gemma":[0.027941262,0.000116939445,0.965836,0.0030566803,0.0004596077,0.00024324092,0.000045484765,0.000048948117,0.002251871],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99553233,0.000036048375,0.00071764714,0.002098689,0.0008560295,0.0007592515],"domain_scores_gemma":[0.9966897,0.00061384606,0.0005145723,0.0016165427,0.00041112534,0.00015422494],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048551138,0.00061926927,0.000528978,0.00072129554,0.0005318866,0.00044835822,0.0030208766,0.00033728045,0.000009348024],"category_scores_gemma":[0.000071047114,0.0006347528,0.00019962697,0.0010771811,0.00033017978,0.00093741366,0.00094530196,0.000601537,0.00003607535],"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":[0.000012818497,0.000034089604,0.0000095737905,0.00007900583,0.000015062844,0.000010692398,0.0002235789,0.017771462,0.0030218123,0.437734,0.00009234469,0.54099554],"study_design_scores_gemma":[0.00044584644,0.00011382502,0.000022765966,0.00028716508,0.00001558121,0.000011329682,3.7915538e-7,0.4733705,0.0040426366,0.5200114,0.0011240942,0.0005544733],"about_ca_topic_score_codex":0.000005062693,"about_ca_topic_score_gemma":0.000057861176,"teacher_disagreement_score":0.5404411,"about_ca_system_score_codex":0.0005499584,"about_ca_system_score_gemma":0.00039129562,"threshold_uncertainty_score":0.99961036},"labels":[],"label_agreement":null},{"id":"W4408111824","doi":"10.5220/0013108500003912","title":"Targeted Test Time Adaptation of Memory Networks for Video Object Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Adaptation (eye); Object (grammar); Segmentation; Test (biology); Artificial intelligence; Real-time computing; Psychology; Neuroscience","score_opus":0.012383887554284349,"score_gpt":0.26365019001290096,"score_spread":0.25126630245861664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408111824","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":[0.0007764252,0.00006740888,0.99657905,0.0005478458,0.000098297875,0.00059376226,0.0000033232402,0.00015739334,0.0011765179],"genre_scores_gemma":[0.47301662,0.000011063006,0.5227126,0.0010430942,0.000046103964,0.0002668713,0.000033873956,0.000008409584,0.0028613717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992734,0.000019716881,0.00023690025,0.00024321875,0.00008905449,0.00013771451],"domain_scores_gemma":[0.9987872,0.00064965006,0.00011349437,0.00029981893,0.00012488845,0.0000249217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011311678,0.00008061654,0.00010712204,0.000074450174,0.00009121218,0.000022449045,0.00030422406,0.000030891082,0.000009810115],"category_scores_gemma":[0.00006263806,0.000078266305,0.000043880336,0.00059019076,0.000023058778,0.00028376232,0.00006622219,0.000042850952,0.000008445732],"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":[0.00003329423,0.00014374396,0.00016841707,0.00004242267,0.000028899043,4.4205245e-7,0.00019350587,0.57533205,0.062119216,0.0667066,0.013209869,0.28202155],"study_design_scores_gemma":[0.0002823924,0.00004690934,0.00044253434,0.000009953733,0.000008120399,4.6314528e-7,0.000020723532,0.95882255,0.036199383,0.0035696304,0.0005235591,0.00007377348],"about_ca_topic_score_codex":0.000008154836,"about_ca_topic_score_gemma":0.000009332613,"teacher_disagreement_score":0.47386643,"about_ca_system_score_codex":0.00003217978,"about_ca_system_score_gemma":0.00004190129,"threshold_uncertainty_score":0.31916073},"labels":[],"label_agreement":null},{"id":"W4408132780","doi":"10.1007/s13177-025-00479-8","title":"YOLO-Based Approach for Multiple Vehicle Detection and Classification using UAVs in the Kurdistan Region of Iraq","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transport engineering; Data mining; Engineering","score_opus":0.1700818222853735,"score_gpt":0.4107110486973842,"score_spread":0.24062922641201068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408132780","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":[0.12699042,0.00023085557,0.87155133,0.00045253136,0.00018591876,0.00053879333,0.000005717819,0.0000065145514,0.00003789883],"genre_scores_gemma":[0.990466,0.000052263145,0.0093003055,0.000025853058,0.000056738012,0.00007175143,0.000008688459,0.0000054240927,0.0000129801265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980285,0.0002237615,0.0007475955,0.00019424327,0.0006692795,0.00013662915],"domain_scores_gemma":[0.9971464,0.00079633784,0.000350704,0.00019123909,0.0014838408,0.0000314425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015024928,0.0000779776,0.00014644045,0.0006292249,0.00011040163,0.00011663541,0.0007411845,0.000058995385,2.8147505e-7],"category_scores_gemma":[0.00012595742,0.00006213033,0.00007370301,0.00069459307,0.00010133977,0.00030010846,0.000009762135,0.00023360937,1.7849389e-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":[0.0011847037,0.0008856017,0.0253743,0.0004394326,0.00019313059,0.000027266893,0.0041315653,0.57674336,0.08835039,0.23099445,0.00014103792,0.07153472],"study_design_scores_gemma":[0.0006269171,0.00009944662,0.010416033,0.00017287697,0.000008762778,0.000018491099,0.0013831609,0.9735794,0.010866646,0.001806468,0.0009550628,0.00006670436],"about_ca_topic_score_codex":0.00006835565,"about_ca_topic_score_gemma":0.00005178173,"teacher_disagreement_score":0.86347556,"about_ca_system_score_codex":0.00016925622,"about_ca_system_score_gemma":0.00013565988,"threshold_uncertainty_score":0.25336015},"labels":[],"label_agreement":null},{"id":"W4408258337","doi":"10.1109/tencon61640.2024.10902794","title":"A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core Mobile Processors","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Multi-core processor; Regulator; Embedded system; Computer network; Parallel computing; Chemistry","score_opus":0.04034898995260314,"score_gpt":0.32411386773072753,"score_spread":0.2837648777781244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408258337","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":[0.0052185003,0.0006218977,0.9910279,0.0007265643,0.0001805039,0.0010161266,0.000009752386,0.0011180588,0.0000807187],"genre_scores_gemma":[0.5820643,0.0000070493634,0.41287518,0.0005945764,0.00010339475,0.0019671666,0.000010403569,0.000032428972,0.0023455676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873656,0.000009223828,0.00020123133,0.0006072271,0.00014403419,0.00030171388],"domain_scores_gemma":[0.9990048,0.00018356425,0.00003572824,0.00060031033,0.00007428486,0.00010130855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008011868,0.0001489408,0.00011923252,0.00007662513,0.00012544372,0.00013437147,0.0005686878,0.00005449437,0.000012260895],"category_scores_gemma":[0.000015747346,0.00012451144,0.00009343036,0.00054632284,0.000042890093,0.00021908735,0.000093734336,0.00008037353,0.00006813458],"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":[0.00006359234,0.00056158914,0.000071934424,0.0007021119,0.00009349913,0.00007712771,0.0007409036,0.4313343,0.019731484,0.17857163,0.027644075,0.3404078],"study_design_scores_gemma":[0.00018750945,0.000085503314,0.000009804307,0.000023829365,0.0000048697743,0.000011590791,0.0000030798976,0.91254586,0.018419843,0.003976647,0.06456427,0.00016716636],"about_ca_topic_score_codex":0.0000022145803,"about_ca_topic_score_gemma":0.000013420294,"teacher_disagreement_score":0.5781527,"about_ca_system_score_codex":0.000045624325,"about_ca_system_score_gemma":0.00008005745,"threshold_uncertainty_score":0.50774294},"labels":[],"label_agreement":null},{"id":"W4408291404","doi":"10.3390/electronics14061092","title":"Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Mechanism (biology); Computer science; Parathyroid gland; Scale (ratio); Artificial intelligence; Fusion; Pattern recognition (psychology); Medicine; Internal medicine; Parathyroid hormone; Physics; Philosophy; Geography; Calcium; Cartography","score_opus":0.008111736495930263,"score_gpt":0.24873391508921208,"score_spread":0.24062217859328183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408291404","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":[0.022757664,0.00035613048,0.9749004,0.00082660397,0.00017849891,0.00032058326,9.580804e-7,0.00030436896,0.0003547852],"genre_scores_gemma":[0.9560634,0.00021918592,0.04208999,0.0007002225,0.00004531011,0.0001239631,0.0000143712605,0.00001263938,0.0007309046],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988601,0.000059236496,0.00016777884,0.00042741044,0.00016552446,0.00031990203],"domain_scores_gemma":[0.9992364,0.00006576262,0.00007502301,0.0005203737,0.00005773396,0.00004469692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116814,0.00013645558,0.00010577999,0.00012673344,0.0002879558,0.000053302352,0.00036144833,0.00008211459,0.0000020447155],"category_scores_gemma":[0.000008409924,0.00013350202,0.00005810119,0.0007719243,0.00001319136,0.00015079851,0.000066571,0.00024041517,0.00005590104],"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":[0.00006516616,0.00036851157,0.00007721545,0.000010322485,0.000015532034,0.0000029139437,0.000033934226,0.0038945593,0.7150512,0.076156944,0.00045099368,0.20387277],"study_design_scores_gemma":[0.00059499225,0.00015359743,0.0006738181,0.000011248809,0.0000075359017,0.000001826933,0.0000013454671,0.8333117,0.13485119,0.015975796,0.014282285,0.0001347131],"about_ca_topic_score_codex":0.0000017156044,"about_ca_topic_score_gemma":0.000102461534,"teacher_disagreement_score":0.93330574,"about_ca_system_score_codex":0.00018562618,"about_ca_system_score_gemma":0.0000641576,"threshold_uncertainty_score":0.5444054},"labels":[],"label_agreement":null},{"id":"W4408327215","doi":"10.1016/j.engappai.2025.110415","title":"SlimDL: Deploying ultra-light deep learning model on sweeping robots","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Robot; Artificial intelligence; Deep learning","score_opus":0.01959378301907544,"score_gpt":0.2774699325464034,"score_spread":0.25787614952732796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408327215","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":[0.00078281685,0.00017554488,0.9963076,0.0006803828,0.0000858281,0.00041971327,8.9040213e-7,0.0005417803,0.0010054442],"genre_scores_gemma":[0.7859363,0.000050763145,0.21343228,0.000072286224,0.000042878655,0.0003722087,0.0000025779216,0.000018783017,0.00007189837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998402,0.000017646942,0.0005238249,0.0005267365,0.00020264047,0.00032716693],"domain_scores_gemma":[0.9984855,0.00035358465,0.0001349064,0.00081350975,0.00013375127,0.00007875493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018271475,0.0002004977,0.00020474209,0.00031648096,0.00025565564,0.00006750329,0.0010708782,0.00007738475,0.0000024222713],"category_scores_gemma":[0.00011642991,0.00023213141,0.000083966166,0.0016190214,0.000040018083,0.0002253688,0.00012448596,0.0003551242,0.000052899257],"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":[0.0000014081672,0.000030065266,0.000004124751,0.000011706954,0.000006113477,1.5398045e-7,0.00006983974,0.5544616,0.018284764,0.34701058,0.0000054653856,0.08011416],"study_design_scores_gemma":[0.000011261651,0.000014672623,0.000014148751,0.00004855116,0.0000064310643,9.360672e-7,0.00002316054,0.8189971,0.15663607,0.023401005,0.0006893007,0.00015739145],"about_ca_topic_score_codex":0.000004449856,"about_ca_topic_score_gemma":0.0000026320213,"teacher_disagreement_score":0.7851535,"about_ca_system_score_codex":0.00007564968,"about_ca_system_score_gemma":0.000035145036,"threshold_uncertainty_score":0.94660443},"labels":[],"label_agreement":null},{"id":"W4408520843","doi":"10.1109/tim.2025.3551917","title":"MIS-YOLOv8: An Improved Algorithm for Detecting Small Objects in UAV Aerial Photography Based on YOLOv8","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China; National Foundation for Science and Technology Development","keywords":"Aerial photography; Computer vision; Photography; Computer science; Artificial intelligence; Remote sensing; Computer graphics (images); Geography","score_opus":0.032915944029324,"score_gpt":0.27215504188394063,"score_spread":0.23923909785461664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408520843","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":[0.01505291,0.000009694395,0.9823374,0.00032815602,0.0007270234,0.0012537332,0.0000120319555,0.00013071185,0.00014830213],"genre_scores_gemma":[0.87806934,0.000008839091,0.12010306,0.0008776695,0.000025133002,0.0008866671,0.0000038143246,0.0000109894145,0.000014468248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858296,0.00008079944,0.00031881526,0.0005239826,0.00023078405,0.0002626399],"domain_scores_gemma":[0.99929875,0.00008641047,0.0000921064,0.0003227759,0.00011059993,0.00008935439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030168856,0.00019099959,0.00015506495,0.00040125893,0.0003242112,0.00009514228,0.00020867743,0.00006961293,0.0000036488561],"category_scores_gemma":[0.00000621572,0.00020234744,0.00007508433,0.0006215802,0.00003426245,0.00022023759,0.0000012583265,0.00016557678,0.0000011855669],"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":[0.00012845694,0.0003826687,0.0000144900505,0.000022099219,0.000023019378,4.804229e-7,0.00028858936,0.010088738,0.03573751,0.00014036272,0.0000069800544,0.9531666],"study_design_scores_gemma":[0.0038237735,0.0006523118,0.00029782095,0.000084289284,0.000027583119,0.0000014559512,0.00025110174,0.67244875,0.32141355,0.00049154804,0.00025121646,0.00025661086],"about_ca_topic_score_codex":0.000052144624,"about_ca_topic_score_gemma":0.000568308,"teacher_disagreement_score":0.95291,"about_ca_system_score_codex":0.00018749021,"about_ca_system_score_gemma":0.000094047646,"threshold_uncertainty_score":0.82514894},"labels":[],"label_agreement":null},{"id":"W4408686987","doi":"10.32388/1l3te6","title":"Partial Convolution Meets Visual Attention","year":2025,"lang":"en","type":"preprint","venue":"Qeios","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Convolution (computer science); Computer science; Artificial intelligence; Visual attention; Computer vision; Psychology; Neuroscience; Cognition","score_opus":0.02275257340443791,"score_gpt":0.31724891999294635,"score_spread":0.29449634658850843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408686987","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":[0.0046109725,0.00019282491,0.9882451,0.0028989476,0.0014566215,0.0005736444,0.00001260878,0.000504411,0.0015048361],"genre_scores_gemma":[0.9494593,0.000110435976,0.046440404,0.000502522,0.00054952124,0.0005239218,0.00011443881,0.0000139370095,0.0022854917],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984577,0.000073232026,0.00028609045,0.0006976217,0.00023016738,0.00025519708],"domain_scores_gemma":[0.9987838,0.00006481361,0.00018736978,0.00079631014,0.00009858454,0.00006906677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001069674,0.00019509805,0.00019590885,0.00009530955,0.00013948136,0.00009903589,0.0008574735,0.00018793554,0.00000962618],"category_scores_gemma":[0.000022348428,0.00021256968,0.0001166422,0.0002694165,0.000040043742,0.00017169042,0.0014983447,0.0003771071,0.0001303476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028542005,0.00045761766,0.0008580997,0.00028052196,0.00014794516,0.000022468197,0.0003086979,0.041128576,0.005889696,0.670834,0.025082221,0.25496164],"study_design_scores_gemma":[0.00038907173,0.000048324768,0.0038544103,0.0002137859,0.000048191487,0.000007054908,0.0000053090025,0.88313216,0.0030508195,0.046565983,0.06205408,0.00063083624],"about_ca_topic_score_codex":0.000012718312,"about_ca_topic_score_gemma":0.0000064376377,"teacher_disagreement_score":0.94484836,"about_ca_system_score_codex":0.00010776311,"about_ca_system_score_gemma":0.000113226,"threshold_uncertainty_score":0.866834},"labels":[],"label_agreement":null},{"id":"W4408696999","doi":"10.1109/itsc58415.2024.10920127","title":"3D Roadway Scene Object Detection with LiDARs in Snowfall Conditions","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada; Western University; National Research Council Canada; Research Canada","funders":"Transport Canada","keywords":"Lidar; Snow; Object detection; Computer science; Computer vision; Artificial intelligence; Object (grammar); Remote sensing; Pattern recognition (psychology); Geography; Meteorology","score_opus":0.010333725392808236,"score_gpt":0.2588569424647625,"score_spread":0.24852321707195424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408696999","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":[0.018043736,0.000113030874,0.9736,0.00059591606,0.00013952891,0.00023864281,0.0000017372184,0.00075149065,0.006515939],"genre_scores_gemma":[0.9513243,0.000022020242,0.04749278,0.00019105336,0.000048167527,0.000118424614,0.0000029922908,0.000010587681,0.00078969524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991444,0.000022123042,0.00013284289,0.00037680365,0.0001273443,0.00019651999],"domain_scores_gemma":[0.99946004,0.000102322134,0.000018953842,0.00033925386,0.000026949345,0.000052463638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006830234,0.00009756372,0.00007778475,0.00015218511,0.000082658094,0.00012452963,0.00026735602,0.00003261,0.000026126187],"category_scores_gemma":[0.0000055735663,0.00007884461,0.000022753742,0.0012739159,0.000035735775,0.0005245194,0.000067192326,0.00015907787,0.00017999657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017207485,0.00022559562,0.0013302146,0.000052511514,0.000053783468,0.00018847053,0.0010506402,0.046596553,0.037835907,0.2174539,0.002078686,0.69311655],"study_design_scores_gemma":[0.0005076038,0.0002889228,0.015258777,0.00014809174,0.000014744079,0.00030002565,0.000046599427,0.8929083,0.027639903,0.028234998,0.033990648,0.00066142925],"about_ca_topic_score_codex":0.00003029532,"about_ca_topic_score_gemma":0.000817534,"teacher_disagreement_score":0.9332805,"about_ca_system_score_codex":0.00007465637,"about_ca_system_score_gemma":0.00004582274,"threshold_uncertainty_score":0.321519},"labels":[],"label_agreement":null},{"id":"W4408791866","doi":"10.1109/jiot.2025.3554167","title":"BlockHybrid: Accelerating Object Detection Pipelines With Hybrid Block-Wise Execution","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Scholarship Council","keywords":"Computer science; Pipeline transport; Block (permutation group theory); Object (grammar); Parallel computing; Artificial intelligence","score_opus":0.013489212991605284,"score_gpt":0.25774793522633194,"score_spread":0.24425872223472667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408791866","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":[0.26531136,0.000108367945,0.73276937,0.0005178863,0.00053653296,0.00012209686,4.02873e-7,0.00009817237,0.00053581706],"genre_scores_gemma":[0.9494512,0.00003393548,0.049286075,0.00038811113,0.00018338476,0.000012245374,4.313243e-7,0.000011260661,0.00063333084],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99854577,0.000060298516,0.0005136289,0.00031383868,0.00030455407,0.00026188817],"domain_scores_gemma":[0.9986529,0.00012158104,0.0004933348,0.00032061886,0.0003328193,0.000078756486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000307414,0.00018102054,0.00021811208,0.0002514922,0.00019549114,0.00024269272,0.0008791408,0.000039314007,0.000004130883],"category_scores_gemma":[0.000048691716,0.00015020702,0.00009518465,0.00043953783,0.000063126776,0.0009947234,0.00013012261,0.0005069343,0.000005664738],"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":[0.00036611597,0.0004361871,0.0012454204,0.00012649599,0.0003772785,0.00018908158,0.0023768495,0.04189081,0.36117822,0.0053989124,0.016237058,0.57017756],"study_design_scores_gemma":[0.00060218247,0.00021853895,0.00020851004,0.00039638559,0.000035743804,0.0016023916,0.000037692884,0.27443972,0.7143431,0.005542768,0.0023312054,0.00024177707],"about_ca_topic_score_codex":0.00003286021,"about_ca_topic_score_gemma":0.000008382771,"teacher_disagreement_score":0.68413985,"about_ca_system_score_codex":0.00010382596,"about_ca_system_score_gemma":0.000076725766,"threshold_uncertainty_score":0.6125264},"labels":[],"label_agreement":null},{"id":"W4408793709","doi":"10.1109/icccit62592.2025.10928084","title":"Object Detection in Real-Time Surveillance Using Deep Learning-Based YOLO Framework","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Object (grammar); Deep learning; Computer vision; Real-time computing; Pattern recognition (psychology)","score_opus":0.012369011734545606,"score_gpt":0.28328442253670577,"score_spread":0.27091541080216014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408793709","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":[0.04586609,0.000038655842,0.9514202,0.00029760165,0.00011142895,0.00018600703,1.4191707e-7,0.00042052753,0.00165938],"genre_scores_gemma":[0.8599828,0.00001483005,0.13949694,0.00022432019,0.000023928125,0.000026618709,9.785385e-7,0.000007303275,0.00022231843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886066,0.00012712606,0.0002115192,0.00041846762,0.00011678351,0.000265469],"domain_scores_gemma":[0.99884826,0.00052604853,0.00007321158,0.0004641409,0.000051473955,0.00003689147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020173013,0.00011827214,0.00014691119,0.00016659524,0.00015899491,0.00006028275,0.00040797968,0.000090316054,0.0000139382455],"category_scores_gemma":[0.00012412918,0.00012152696,0.000039356135,0.0018197914,0.000031985768,0.00016972361,0.00011443075,0.0002751197,0.00003762251],"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":[0.000021488635,0.00006830296,0.01244306,0.000014627686,0.000008416085,0.0000065767586,0.00007562926,0.866053,0.018106671,0.011508489,0.000027744745,0.091666],"study_design_scores_gemma":[0.00013237438,0.000021276268,0.013037508,0.000023035234,0.0000011988119,0.0000014534662,0.0000044007256,0.97324413,0.0056303423,0.007396936,0.000375822,0.00013154052],"about_ca_topic_score_codex":0.00008845633,"about_ca_topic_score_gemma":0.00019918579,"teacher_disagreement_score":0.81411666,"about_ca_system_score_codex":0.00012765749,"about_ca_system_score_gemma":0.00004674713,"threshold_uncertainty_score":0.49557254},"labels":[],"label_agreement":null},{"id":"W4408987788","doi":"10.1007/s13042-025-02551-x","title":"MPKD-DCFI: multi-path knowledge distillation via dynamic contextual feature interaction","year":2025,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computational intelligence; Computer science; Feature (linguistics); Path (computing); Artificial intelligence; Pattern recognition (psychology)","score_opus":0.008220303562322107,"score_gpt":0.3132416437448676,"score_spread":0.3050213401825455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408987788","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":[0.02495787,0.0017799237,0.9687739,0.0027331447,0.000998343,0.000068030764,0.0000025353952,0.000044373628,0.00064190757],"genre_scores_gemma":[0.97435707,0.00046100025,0.022633275,0.000108602544,0.00011548614,0.0000021951723,0.000009493641,0.000008005185,0.0023048667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908584,0.00008937907,0.00032032374,0.00017947244,0.00021164761,0.00011336104],"domain_scores_gemma":[0.9988258,0.00024692997,0.00035819682,0.00010033385,0.0004094084,0.00005931541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017846201,0.00012612299,0.00015005551,0.00019581785,0.00010109321,0.00012739879,0.0004543407,0.00006036721,0.000005483395],"category_scores_gemma":[0.00014959238,0.00011284338,0.00006959201,0.00018164703,0.000043424032,0.00026566288,0.00017908086,0.000569562,0.000006404772],"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":[0.000100612386,0.000225107,0.010887638,0.000014249935,0.00019457664,0.000029158851,0.0008995978,0.0135171395,0.0034418332,0.016755972,0.00060443266,0.9533297],"study_design_scores_gemma":[0.0008337962,0.00009646199,0.015816262,0.00012676966,0.000021366666,0.00022685468,0.000044788274,0.916366,0.00012567974,0.0021828732,0.06403036,0.00012879312],"about_ca_topic_score_codex":0.000007715673,"about_ca_topic_score_gemma":0.000027035316,"teacher_disagreement_score":0.9532009,"about_ca_system_score_codex":0.000101679725,"about_ca_system_score_gemma":0.000037108824,"threshold_uncertainty_score":0.46016195},"labels":[],"label_agreement":null},{"id":"W4409018678","doi":"10.4271/2025-01-8023","title":"RGB2BEV-Net: A PyTorch-Based End-to-End Pipeline for RGB to BEV Segmentation Using an Extended Dataset for Autonomous Driving","year":2025,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Pipeline (software); Computer science; End-to-end principle; Artificial intelligence; RGB color model; Segmentation; Computer vision; Image segmentation","score_opus":0.0270288776538304,"score_gpt":0.3273894862916428,"score_spread":0.3003606086378124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409018678","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":[0.083434016,0.0003149873,0.807386,0.06153447,0.0015372634,0.026402196,0.006901368,0.010481946,0.0020077752],"genre_scores_gemma":[0.62175757,0.000009389803,0.36689034,0.00788266,0.00013938709,0.0022435992,0.0008919034,0.0000765295,0.00010864006],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945084,0.00013554696,0.0012564461,0.0022427153,0.0006463194,0.0012105651],"domain_scores_gemma":[0.9949221,0.0012885203,0.00029275284,0.002650339,0.00023540396,0.00061085995],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008006055,0.0007741032,0.0008418795,0.00047410306,0.0008859875,0.00030212637,0.0026156306,0.00043444725,0.000044400073],"category_scores_gemma":[0.00077473826,0.0007671525,0.00032731704,0.0018279923,0.00037112064,0.0009925837,0.00081354036,0.0005581499,0.000023286115],"study_design_candidate":"bench_or_experimental","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":[0.00026652738,0.00044741135,0.00001104713,0.000044300803,0.000021874532,0.000005364465,0.000022279095,0.0037578228,0.91854477,0.02345472,0.0078852475,0.045538638],"study_design_scores_gemma":[0.005372382,0.0064544533,0.4395176,0.00087425136,0.00044175654,0.00007633797,0.00014498473,0.0016052232,0.012134676,0.03760246,0.4917473,0.0040286006],"about_ca_topic_score_codex":0.000030563755,"about_ca_topic_score_gemma":0.023085859,"teacher_disagreement_score":0.9064101,"about_ca_system_score_codex":0.000637556,"about_ca_system_score_gemma":0.0003054348,"threshold_uncertainty_score":0.9994779},"labels":[],"label_agreement":null},{"id":"W4409257457","doi":"10.1109/tase.2025.3558929","title":"ESPPNet: An Efficient Progressive Spatial Pyramid Pooling Network for Real-Time Traffic Object Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Pooling; Pyramid (geometry); Object detection; Computer science; Artificial intelligence; Object (grammar); Computer vision; Real-time computing; Pattern recognition (psychology); Mathematics","score_opus":0.007541612524845799,"score_gpt":0.25238759928952353,"score_spread":0.24484598676467773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409257457","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":[0.12656522,0.000014313605,0.8714998,0.00016238862,0.00049907103,0.0005285773,0.0000026322014,0.00070463825,0.000023407038],"genre_scores_gemma":[0.94667244,0.000009776873,0.052918352,0.00004907499,0.00005478156,0.0002599734,0.0000010877493,0.000010320706,0.00002421541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860173,0.000015785072,0.0002230676,0.000506022,0.00028528928,0.00036811986],"domain_scores_gemma":[0.9991695,0.00014840077,0.00007074824,0.0003350725,0.00016989918,0.000106354106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042982315,0.0001578336,0.00013592969,0.00035790823,0.00076138345,0.0002416358,0.0003561338,0.000057122284,0.0000014135061],"category_scores_gemma":[0.000018333116,0.0001627728,0.000042128187,0.0017453507,0.000089194924,0.0006486838,0.0000049351092,0.00012288721,0.000004771732],"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":[0.000005384796,0.000024160829,1.6493816e-7,0.000009783554,0.00000382777,2.4697445e-7,0.00010582482,0.83709854,0.021638548,0.00045742202,0.0000034863524,0.14065261],"study_design_scores_gemma":[0.00019594784,0.00008637886,0.00032998275,0.000045009856,0.000010339174,0.000006690956,0.000010179886,0.9762367,0.022750402,0.00007730389,0.00009881594,0.00015224997],"about_ca_topic_score_codex":0.00000505504,"about_ca_topic_score_gemma":0.000006504055,"teacher_disagreement_score":0.8201072,"about_ca_system_score_codex":0.00015113043,"about_ca_system_score_gemma":0.000102854596,"threshold_uncertainty_score":0.66376823},"labels":[],"label_agreement":null},{"id":"W4409263494","doi":"10.1109/wacv61041.2025.00079","title":"VADet: Multi-Frame LiDAR 3D Object Detection Using Variable Aggregation","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lidar; Computer science; Frame (networking); Variable (mathematics); Object detection; Object (grammar); Computer vision; Artificial intelligence; Remote sensing; Pattern recognition (psychology); Computer network; Geology; Mathematics","score_opus":0.0213594945460207,"score_gpt":0.28630162824445765,"score_spread":0.26494213369843694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409263494","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":[0.0048224977,0.0000631421,0.9918023,0.00019452116,0.0003142022,0.00026275404,5.101991e-7,0.00041114693,0.0021289638],"genre_scores_gemma":[0.337091,0.000007739501,0.66123307,0.0004465797,0.000041009145,0.000031708027,9.96695e-7,0.000006269805,0.0011416576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906707,0.000038442704,0.00019131311,0.0003766542,0.00011316671,0.00021337837],"domain_scores_gemma":[0.9991832,0.000103061175,0.000074154916,0.0005127172,0.00008664083,0.000040269108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010786821,0.00010960113,0.00009922454,0.000122689,0.0002724097,0.00010662129,0.00038417068,0.000070407616,0.000011362421],"category_scores_gemma":[0.000046923018,0.000108824795,0.000031207237,0.001300013,0.00002305723,0.0005390108,0.00016994521,0.00013448592,0.000025475803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012340342,0.0002025504,0.000642056,0.000038316488,0.00004638729,0.0000031149339,0.00020965202,0.11772044,0.2254993,0.18258122,0.00017028622,0.47287434],"study_design_scores_gemma":[0.00019024813,0.000012766849,0.0003767097,0.000020742902,0.000007002942,0.000005721712,0.000006230731,0.9436434,0.041207455,0.009019033,0.00538937,0.00012130367],"about_ca_topic_score_codex":0.000057804522,"about_ca_topic_score_gemma":0.00002858134,"teacher_disagreement_score":0.82592297,"about_ca_system_score_codex":0.00011095589,"about_ca_system_score_gemma":0.000057656755,"threshold_uncertainty_score":0.4437746},"labels":[],"label_agreement":null},{"id":"W4409368133","doi":"10.1609/aaai.v39i4.32397","title":"L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Lidar; Fusion; Computer vision; Object (grammar); Computer science; Remote sensing; Artificial intelligence; Object based; Meteorology; Environmental science; Geography","score_opus":0.060215994674781066,"score_gpt":0.3033208690342145,"score_spread":0.24310487435943345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409368133","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":[0.022302238,0.00003824648,0.9630646,0.004867857,0.0006601055,0.0011918325,0.0000055494825,0.00021498618,0.0076545617],"genre_scores_gemma":[0.9728204,0.000047849477,0.02581406,0.00032162326,0.00008332063,0.00019669975,5.044795e-7,0.000013282397,0.00070224423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981935,0.000014022382,0.0005099206,0.00062434125,0.000301445,0.00035676733],"domain_scores_gemma":[0.99829143,0.0002101559,0.00033889685,0.0004263621,0.00067271956,0.00006043246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032970522,0.00023265058,0.00024270269,0.0001631986,0.00047065073,0.0001917611,0.0018569434,0.0001079714,0.000013394761],"category_scores_gemma":[0.00033248807,0.00018532621,0.000143835,0.001206972,0.00018618235,0.00038630216,0.00039121046,0.00026920435,0.00003053909],"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":[0.000060931012,0.00008562625,0.000024314217,0.00003350323,0.000009850174,6.542194e-8,0.00020900484,0.00034019604,0.10328969,0.60207325,0.00012865468,0.29374492],"study_design_scores_gemma":[0.00003256883,0.00013325835,0.00007189714,0.00014460014,0.000013994239,0.0000017711344,0.00011458145,0.13422723,0.62314606,0.24060251,0.0013478281,0.0001636864],"about_ca_topic_score_codex":0.000016462605,"about_ca_topic_score_gemma":0.000025691981,"teacher_disagreement_score":0.9505182,"about_ca_system_score_codex":0.00008255033,"about_ca_system_score_gemma":0.00009462566,"threshold_uncertainty_score":0.7557383},"labels":[],"label_agreement":null},{"id":"W4409370181","doi":"10.1609/aaai.v39i3.32278","title":"SSLFusion: Scale and Space Aligned Latent Fusion Model for Multimodal 3D Object Detection","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Scale (ratio); Space (punctuation); Computer science; Object (grammar); Artificial intelligence; Scale space; Computer vision; Fusion; Pattern recognition (psychology); Geography; Cartography; Image processing; Image (mathematics)","score_opus":0.04760777573300134,"score_gpt":0.2998149483334228,"score_spread":0.25220717260042147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409370181","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":[0.09960044,0.000024399054,0.8939371,0.004030108,0.00021995357,0.0009140957,0.0000038868934,0.000109360844,0.0011606516],"genre_scores_gemma":[0.9606463,0.00006450846,0.03850742,0.0001940761,0.000030494011,0.00013769831,4.078464e-7,0.0000084116855,0.00041071],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857956,0.000009232243,0.0003666614,0.0005485469,0.00022668278,0.00026931026],"domain_scores_gemma":[0.99883187,0.00011744869,0.00022956957,0.00027444283,0.00048460963,0.00006203186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024373445,0.00018821936,0.00020063696,0.0001155119,0.00040635988,0.00013155016,0.00087547983,0.000090264424,0.0000034017635],"category_scores_gemma":[0.00013988194,0.00014886595,0.00007894553,0.000607048,0.00016534181,0.00029836976,0.00045454345,0.00013384385,0.000005612474],"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":[0.00010470665,0.000090876616,0.00004983937,0.000042974465,0.0000073066144,4.6499952e-8,0.00056546397,0.0033856775,0.34882215,0.408105,0.000055112163,0.23877086],"study_design_scores_gemma":[0.000026229158,0.00005045101,0.000050588595,0.00006777826,0.0000074039517,8.353546e-7,0.00004024971,0.55754566,0.33175004,0.11035055,0.000026477963,0.00008372858],"about_ca_topic_score_codex":0.000019289453,"about_ca_topic_score_gemma":0.000036433557,"teacher_disagreement_score":0.86104584,"about_ca_system_score_codex":0.000047259025,"about_ca_system_score_gemma":0.000052353567,"threshold_uncertainty_score":0.60705775},"labels":[],"label_agreement":null},{"id":"W4409405979","doi":"10.1155/atr/8452511","title":"Research on Small Target Detection Algorithm for Autonomous Vehicle Scenarios","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province","keywords":"Computer science; Algorithm","score_opus":0.028298323402840626,"score_gpt":0.3286194816326162,"score_spread":0.30032115822977556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409405979","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":[0.03415579,0.00013184,0.96365625,0.0010162762,0.00050201674,0.00041914408,0.00000543218,0.000049474926,0.00006375246],"genre_scores_gemma":[0.52112716,0.00005155973,0.4783298,0.00016365814,0.000115576266,0.000066457695,0.0000055633564,0.000010942714,0.00012925232],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866736,0.0000497735,0.00048824615,0.00025775193,0.00027851906,0.00025837595],"domain_scores_gemma":[0.9983596,0.00037752494,0.00026312083,0.00025201388,0.0006729374,0.00007478693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046494216,0.000109763016,0.00017701281,0.00037532672,0.00025589005,0.000042794087,0.00044463214,0.00006563165,0.0000013316554],"category_scores_gemma":[0.00003128515,0.0001056999,0.00011128515,0.0009186157,0.000035500445,0.0005267209,0.0000063927737,0.00036692512,0.0000029098164],"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":[0.000095065094,0.00011567413,0.000016853402,0.000017733468,0.000019862684,0.0000071124837,0.00021749169,0.29329702,0.014824333,0.009964258,0.00006918938,0.6813554],"study_design_scores_gemma":[0.0066410773,0.00319206,0.044901192,0.00045085614,0.00007152289,0.000023129418,0.0006424716,0.31703845,0.33826196,0.2215341,0.0665841,0.00065905205],"about_ca_topic_score_codex":0.0000020415673,"about_ca_topic_score_gemma":0.000023995992,"teacher_disagreement_score":0.6806963,"about_ca_system_score_codex":0.00015552554,"about_ca_system_score_gemma":0.00012389183,"threshold_uncertainty_score":0.4310317},"labels":[],"label_agreement":null},{"id":"W4409669179","doi":"10.1038/s41598-025-92945-2","title":"The shallowest transparent and interpretable deep neural network for image recognition","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina; University of Wisconsin-Madison","keywords":"Computer science; Artificial intelligence; Image (mathematics); Artificial neural network; Pattern recognition (psychology); Deep neural networks; Computer vision","score_opus":0.019949481446896945,"score_gpt":0.27708295421198775,"score_spread":0.2571334727650908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409669179","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":[0.009511757,0.000741569,0.98037356,0.0022881264,0.005295415,0.00076702476,0.0000016236734,0.00014448326,0.0008764169],"genre_scores_gemma":[0.91829866,0.000048269678,0.07809349,0.00033644988,0.00012664848,0.00063807936,0.00004425042,0.000013889765,0.0024002527],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99845546,0.000031571544,0.00034876933,0.00067492115,0.00015160943,0.0003376656],"domain_scores_gemma":[0.99861324,0.0002380825,0.00014361493,0.0007989459,0.00014282014,0.00006327608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007462152,0.000106571926,0.000110865476,0.000043837004,0.0011317794,0.00075792975,0.00036028892,0.000028918974,0.0000023140808],"category_scores_gemma":[0.000072265604,0.00008059931,0.00006072012,0.0006178158,0.00021037743,0.000407368,0.00013714883,0.00008572022,0.000002954954],"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":[0.000030058913,0.00008771533,0.0009661053,0.00005915866,0.000043843702,0.000054412514,0.0004463263,0.007959078,0.0071581057,0.018453607,0.05191867,0.9128229],"study_design_scores_gemma":[0.00015983016,0.000031611533,0.0010040983,0.000055385433,0.000025832964,0.00011099318,0.000028132628,0.3270509,0.0027847632,0.54693556,0.12158101,0.00023190325],"about_ca_topic_score_codex":0.0000026020325,"about_ca_topic_score_gemma":0.000081705475,"teacher_disagreement_score":0.91259104,"about_ca_system_score_codex":0.000024911305,"about_ca_system_score_gemma":0.000036457448,"threshold_uncertainty_score":0.87048465},"labels":[],"label_agreement":null},{"id":"W4410036292","doi":"10.1007/978-3-031-88304-0_91","title":"Scoping Review of YOLO Applications in Emergency Vehicle Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Environmental science","score_opus":0.016821369897592992,"score_gpt":0.27630139902177314,"score_spread":0.25948002912418017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410036292","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000033011545,0.21989222,0.7750519,0.00010437717,0.000249882,0.0016477532,0.0000023790928,0.00003762806,0.0030105885],"genre_scores_gemma":[0.30747002,0.67517763,0.006797208,0.00081000105,0.0013668941,0.0031655885,0.00010602514,0.00013759435,0.0049690357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982521,0.000050881972,0.00081931043,0.0005233373,0.00014695372,0.00020740526],"domain_scores_gemma":[0.99851936,0.00033412545,0.00039952912,0.00062741083,0.00007925599,0.000040317263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024424467,0.00024335184,0.00056856615,0.00016847048,0.000083229,0.000027493474,0.0003913062,0.0002754695,0.0000041808375],"category_scores_gemma":[0.000029159348,0.0002305467,0.00007215389,0.00043944293,0.000040671002,0.000078734265,0.00015370436,0.00043756765,8.493293e-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":[0.000009548986,0.000050735096,0.00061760796,0.039755415,0.00005389362,0.000007589291,0.00011353322,0.3006512,0.00006826315,0.20335399,0.0001513175,0.45516694],"study_design_scores_gemma":[0.00036021209,0.00008559348,0.00012646113,0.2463804,0.000063048494,0.000020027657,0.0000019031281,0.6946597,0.00005281786,0.04883503,0.008397296,0.0010174796],"about_ca_topic_score_codex":0.000030003841,"about_ca_topic_score_gemma":0.0003879405,"teacher_disagreement_score":0.7682547,"about_ca_system_score_codex":0.000060464972,"about_ca_system_score_gemma":0.00004612992,"threshold_uncertainty_score":0.9401422},"labels":[],"label_agreement":null},{"id":"W4410049497","doi":"10.1145/3676151.3719373","title":"PreNeT: Leveraging Computational Features to Predict Deep Neural Network Training Time","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Training (meteorology); Artificial neural network; Artificial intelligence; Deep learning; Machine learning","score_opus":0.015845549813000157,"score_gpt":0.2646954906346127,"score_spread":0.24884994082161252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410049497","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":[0.0022119603,0.000117302625,0.98210984,0.004572082,0.00022613813,0.00032799673,0.0000011436707,0.00053200277,0.009901523],"genre_scores_gemma":[0.5422642,0.0000012299951,0.44854218,0.006335168,0.00019847497,0.00007430556,0.000012323823,0.000010583319,0.0025615445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986558,0.000042095657,0.0002205451,0.00048490078,0.00020373969,0.00039293067],"domain_scores_gemma":[0.99907476,0.00031716964,0.000046564077,0.0003810386,0.000065115906,0.000115381335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000111473295,0.00015466232,0.00015423924,0.000093248455,0.00029246212,0.00014382729,0.0007874624,0.00003997645,0.000022253398],"category_scores_gemma":[0.000021223688,0.00014999254,0.000051220486,0.0010362827,0.000027199329,0.0002767085,0.00035788896,0.00015458025,0.000054719036],"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":[0.0000032410574,0.000008623665,0.00016333204,0.0000018136947,0.000010540615,0.0000015645871,0.00019773887,0.84733903,0.000044370838,0.04267937,0.011908816,0.097641535],"study_design_scores_gemma":[0.00015583023,0.000022107999,0.011859073,0.000017585835,0.000004499188,0.000010682261,0.000009160259,0.94715214,0.000040816853,0.034717172,0.0058556017,0.00015533812],"about_ca_topic_score_codex":0.0000030261026,"about_ca_topic_score_gemma":0.000006934461,"teacher_disagreement_score":0.54005224,"about_ca_system_score_codex":0.000042309995,"about_ca_system_score_gemma":0.000048515984,"threshold_uncertainty_score":0.61165184},"labels":[],"label_agreement":null},{"id":"W4410087103","doi":"10.1109/tvt.2025.3566696","title":"MUFFIN-HGCN: Multi-Feature Fusion Hierarchical-GCN for 3D Object Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Fusion; Object detection; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Sensor fusion; Object (grammar); Computer science; Computer vision; Materials science","score_opus":0.01022992370560921,"score_gpt":0.2646882118269225,"score_spread":0.25445828812131327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410087103","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":[0.0044749565,0.00016743179,0.9851552,0.006936112,0.0006939901,0.0010391306,0.000012252948,0.001468738,0.00005214906],"genre_scores_gemma":[0.6658444,0.00010004199,0.33160695,0.00047054014,0.000026106178,0.0011194237,0.0000032308635,0.000024742427,0.0008045723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826,0.00005158399,0.00027921473,0.00081126904,0.00015588736,0.0004420262],"domain_scores_gemma":[0.99840575,0.00019116396,0.000090293244,0.0011018045,0.00014814189,0.000062828505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011701125,0.000265904,0.00026369316,0.0008020433,0.00069788075,0.000046720215,0.00086069957,0.0005247121,0.0000040423706],"category_scores_gemma":[0.000022388089,0.0002646341,0.00017974702,0.0022563667,0.00015286841,0.00019879399,0.000014840431,0.00082953676,0.000032770135],"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":[0.000049619943,0.00037202422,0.0000051757434,0.0000380403,0.000070618946,0.000008057556,0.000037459155,0.021211188,0.12463553,0.010521046,0.00018332538,0.8428679],"study_design_scores_gemma":[0.0013472238,0.00030689614,0.000074064774,0.0000640957,0.000056848352,0.000053170003,0.0000194825,0.5062271,0.44143593,0.012132277,0.03788675,0.0003961424],"about_ca_topic_score_codex":0.0000045627385,"about_ca_topic_score_gemma":0.00006775371,"teacher_disagreement_score":0.8424718,"about_ca_system_score_codex":0.00014391504,"about_ca_system_score_gemma":0.00006322242,"threshold_uncertainty_score":0.99998057},"labels":[],"label_agreement":null},{"id":"W4410351582","doi":"10.1016/j.eswa.2025.128127","title":"High performance point-Voxel feature set abstraction with mamba for 3D object detection","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"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":"Jilin Province Key R&D Plan Project; Education Department of Jilin Province; Ministry of Education; National Natural Science Foundation of China; Jiangsu University; Jilin Province Development and Reform Commission; Ontario Ministry of Natural Resources and Forestry","keywords":"Abstraction; Computer science; Feature (linguistics); Object (grammar); Set (abstract data type); Artificial intelligence; Point (geometry); Voxel; Pattern recognition (psychology); Computer vision; Mathematics; Programming language","score_opus":0.008977726881029904,"score_gpt":0.2536343444885778,"score_spread":0.2446566176075479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410351582","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":[0.0022175687,0.00031526684,0.99154663,0.0014936754,0.00019368833,0.0030451803,0.00001908013,0.00053430395,0.00063457584],"genre_scores_gemma":[0.83147115,0.00004684431,0.1479857,0.00027990475,0.00027259055,0.018084109,0.000058685215,0.00003115554,0.0017698604],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984282,0.000028186974,0.00027678805,0.0007019833,0.00022703159,0.000337786],"domain_scores_gemma":[0.9981364,0.00016620503,0.0002412082,0.0010963973,0.00027008695,0.00008971485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012216129,0.0002554047,0.00023431213,0.0001621932,0.00072283146,0.00016014784,0.0005377115,0.0001106919,0.000001219302],"category_scores_gemma":[0.000005857931,0.0002022455,0.000038370676,0.0011464407,0.000062368825,0.0006069875,0.000052176896,0.00020695369,0.000026625523],"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":[0.0009486865,0.0009991088,0.0011485021,0.001230642,0.0006818677,0.000008152174,0.0019924894,0.16903111,0.052959464,0.31198162,0.04451031,0.41450804],"study_design_scores_gemma":[0.002631968,0.0006510815,0.0041989367,0.000423682,0.00008245796,0.00032017694,0.00052405015,0.29968446,0.030609753,0.0011428846,0.65846366,0.0012668524],"about_ca_topic_score_codex":0.00006612902,"about_ca_topic_score_gemma":0.000059029328,"teacher_disagreement_score":0.843561,"about_ca_system_score_codex":0.00018333211,"about_ca_system_score_gemma":0.00010000012,"threshold_uncertainty_score":0.8247332},"labels":[],"label_agreement":null},{"id":"W4410372278","doi":"10.1007/978-3-031-89704-7_7","title":"Private, Efficient and Scalable Kernel Learning for Medical Image Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Privy Council Office","funders":"","keywords":"Computer science; Kernel (algebra); Scalability; Artificial intelligence; Computer vision; Operating system; Discrete mathematics; Mathematics","score_opus":0.008931695269164623,"score_gpt":0.2643238253037175,"score_spread":0.2553921300345529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410372278","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":[0.00009053125,0.00037240383,0.99605894,0.0013835764,0.00031053723,0.000539861,0.0000034154298,0.00017856719,0.0010621645],"genre_scores_gemma":[0.046385206,0.00010987938,0.9508143,0.0012276891,0.00023437994,0.00006350044,0.000013777259,0.000027130402,0.0011241228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99633193,0.00002858791,0.0004887468,0.0017042744,0.00084597163,0.00060048257],"domain_scores_gemma":[0.9969501,0.0013645338,0.00023605808,0.0009953096,0.00022440104,0.00022958763],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008327041,0.0003950052,0.0005721782,0.0009296732,0.000449079,0.00038561382,0.0024076162,0.00026669627,0.000012586312],"category_scores_gemma":[0.0003109022,0.00036343499,0.00016065736,0.0018249993,0.000697808,0.00022998806,0.0018486505,0.0007551759,0.0000068678055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053943654,0.00002992785,0.0001230825,0.000054599754,0.000055550267,0.000019605588,0.00013862166,0.3844925,0.00008349638,0.06438436,0.0000390487,0.5505738],"study_design_scores_gemma":[0.00018310099,0.000051232073,0.00011750966,0.000132604,0.000041487878,0.000011492095,5.401482e-8,0.9504599,0.00027809484,0.046043698,0.0023385359,0.00034229376],"about_ca_topic_score_codex":0.0000055573923,"about_ca_topic_score_gemma":0.00002396107,"teacher_disagreement_score":0.56596744,"about_ca_system_score_codex":0.00016155558,"about_ca_system_score_gemma":0.00032943368,"threshold_uncertainty_score":0.99988174},"labels":[],"label_agreement":null},{"id":"W4410540100","doi":"10.1007/978-3-031-91672-4_14","title":"Source-Free Domain Adaptation for YOLO Object Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Bell (Canada)","funders":"","keywords":"Computer science; Adaptation (eye); Domain adaptation; Domain (mathematical analysis); Object (grammar); Computer vision; Artificial intelligence; Mathematics; Psychology","score_opus":0.016516906295919693,"score_gpt":0.250469082906009,"score_spread":0.2339521766100893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410540100","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":[0.0000137064435,0.00026728594,0.9943066,0.001325781,0.001306971,0.001112385,0.000009273703,0.0003311099,0.0013268484],"genre_scores_gemma":[0.02515912,0.000033672786,0.97169614,0.0012518427,0.00055576384,0.00015314271,0.000009826202,0.000035690067,0.0011048197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966846,0.000029863802,0.00049709185,0.0016067926,0.00059123087,0.0005904261],"domain_scores_gemma":[0.99634236,0.001049166,0.00032723474,0.0018819562,0.0002864102,0.00011285002],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005340292,0.00045672053,0.00040286453,0.00075307733,0.00050324184,0.00031679455,0.003485371,0.00030315085,0.0000027246329],"category_scores_gemma":[0.00017022918,0.000458891,0.00016259326,0.00094423426,0.00036694726,0.00052224257,0.0011586576,0.000544958,0.0000126268515],"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":[0.000009122905,0.000011822322,0.0000017666349,0.000025075278,0.000006658115,0.0000023314549,0.00026854046,0.11748513,0.00037163106,0.035142764,0.000044715314,0.84663045],"study_design_scores_gemma":[0.00023675205,0.0001019124,0.000014844531,0.000102914426,0.000006310679,0.000014869249,1.5075459e-7,0.5397752,0.0014680647,0.44793132,0.010015979,0.0003316809],"about_ca_topic_score_codex":0.000008493519,"about_ca_topic_score_gemma":0.00024618974,"teacher_disagreement_score":0.84629875,"about_ca_system_score_codex":0.000400707,"about_ca_system_score_gemma":0.0003724189,"threshold_uncertainty_score":0.99978626},"labels":[],"label_agreement":null},{"id":"W4410616393","doi":"10.1016/j.dsp.2025.105343","title":"LP-YOLO: An improved lightweight pedestrian detection algorithm based on YOLOv11","year":2025,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Pedestrian detection; Computer science; Pedestrian; Artificial intelligence; Algorithm; Computer vision; Engineering; Transport engineering","score_opus":0.010695168668088643,"score_gpt":0.2548823107925834,"score_spread":0.24418714212449474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410616393","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":[0.0012732142,0.000065393964,0.99290884,0.0004633509,0.00009579908,0.00025606074,0.000005233966,0.0005013018,0.0044308146],"genre_scores_gemma":[0.9842031,0.000001005766,0.014810083,0.0005906926,0.00011343201,0.000043476295,0.000014985821,0.000015869025,0.00020735167],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985023,0.000026351237,0.0002678047,0.0006421089,0.00021507693,0.00034636247],"domain_scores_gemma":[0.9991273,0.00010535507,0.00012964511,0.00039415926,0.000117082745,0.00012645757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008656366,0.00021556916,0.00015939087,0.0001715698,0.00040379068,0.00083088846,0.000637703,0.00008706235,0.0000032702028],"category_scores_gemma":[0.000022639546,0.00020047442,0.000060544935,0.0010595167,0.000059771566,0.0018985664,0.00010318018,0.00022250388,0.00002045602],"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":[0.000013745215,0.000104008104,0.00002440124,0.0000134660295,0.0000029086702,0.0000036731913,0.000022567086,0.001179477,0.0017598944,0.00029223773,0.000013710627,0.99656993],"study_design_scores_gemma":[0.00030346957,0.00018636964,0.00012374259,0.000051193118,0.000005159607,0.0000039739666,0.00000858695,0.97458255,0.01352516,0.008263327,0.0027243607,0.00022212422],"about_ca_topic_score_codex":0.0000028978827,"about_ca_topic_score_gemma":0.0000029724495,"teacher_disagreement_score":0.9963478,"about_ca_system_score_codex":0.00009031777,"about_ca_system_score_gemma":0.00014132286,"threshold_uncertainty_score":0.81751096},"labels":[],"label_agreement":null},{"id":"W4410700982","doi":"10.1007/s13177-025-00507-7","title":"Intelligent Infrastructure for Enhancing Vulnerable Road User Safety using Machine Vision Technologies","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Intelligent transportation system; Computer science; Transport engineering; Engineering; Embedded system; Human–computer interaction; Computer security","score_opus":0.04340446660474166,"score_gpt":0.4046140619630252,"score_spread":0.3612095953582835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410700982","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":[0.012704475,0.0016690501,0.98128504,0.0016676933,0.0017690974,0.00069674425,0.000033452005,0.00007906578,0.00009540208],"genre_scores_gemma":[0.9596773,0.00080954575,0.03869158,0.00005599746,0.0001697286,0.000056137265,0.000025994936,0.000018782115,0.00049496634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964028,0.00018925186,0.0014369378,0.00038501516,0.001224013,0.00036198803],"domain_scores_gemma":[0.9947048,0.00073206506,0.0004949615,0.00037948388,0.0036094154,0.000079311634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002099069,0.00019418557,0.0003292809,0.0012312628,0.00032320994,0.00030752824,0.0020608183,0.00014860113,0.000012192045],"category_scores_gemma":[0.00032380724,0.0001663176,0.00018888336,0.0010468838,0.00010454084,0.0007920936,0.00008485732,0.00076808175,0.000005583987],"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":[0.0005540396,0.00026222115,0.0019496105,0.00018995935,0.0005222294,0.00005844982,0.0012403611,0.5461058,0.03967037,0.20672327,0.0017848251,0.20093884],"study_design_scores_gemma":[0.001523855,0.00049124856,0.0018360845,0.0022862207,0.000052880325,0.0001580707,0.0038389652,0.47801542,0.24079956,0.029435765,0.24096729,0.000594632],"about_ca_topic_score_codex":0.00008265582,"about_ca_topic_score_gemma":0.000102996884,"teacher_disagreement_score":0.9469728,"about_ca_system_score_codex":0.0005956154,"about_ca_system_score_gemma":0.00031780492,"threshold_uncertainty_score":0.67822343},"labels":[],"label_agreement":null},{"id":"W4410719240","doi":"10.1007/978-3-031-91575-8_7","title":"Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Similarity (geometry); Association (psychology); Identity (music); Motion (physics); Thermal; Artificial intelligence; Acoustics; Image (mathematics)","score_opus":0.022510191236795322,"score_gpt":0.289946035632893,"score_spread":0.2674358443960977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410719240","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":[0.00028559298,0.00020551833,0.9954603,0.0016126595,0.0008046239,0.00046511326,0.000006264751,0.00019770654,0.0009622619],"genre_scores_gemma":[0.08473596,0.000028778883,0.9125453,0.0018544102,0.00037730928,0.00002059345,0.000005224016,0.000023991875,0.0004084147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963978,0.000070674054,0.00050992944,0.0015431173,0.00087580027,0.00060267025],"domain_scores_gemma":[0.9972557,0.0009427224,0.00046818,0.0009436699,0.00027258391,0.000117189265],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001606609,0.00044245616,0.00045561232,0.00046860124,0.0005307423,0.00062527036,0.0018339908,0.00032900213,0.000006015355],"category_scores_gemma":[0.00020423066,0.00044933928,0.00010534015,0.0007799363,0.00016941172,0.0015796697,0.001733104,0.0010069243,0.0000057285897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044622648,0.000043447206,0.00028942825,0.00005693509,0.000026243251,0.000011204414,0.00085453916,0.118912295,0.013792742,0.03817806,0.0000091664415,0.8278215],"study_design_scores_gemma":[0.00027677807,0.00003266183,0.0021476827,0.00023706155,0.000021089747,0.000016820703,2.2403337e-7,0.88762826,0.007880778,0.100926764,0.00027171907,0.0005601715],"about_ca_topic_score_codex":0.000033661814,"about_ca_topic_score_gemma":0.00010106751,"teacher_disagreement_score":0.8272613,"about_ca_system_score_codex":0.00067359186,"about_ca_system_score_gemma":0.00025291654,"threshold_uncertainty_score":0.99979585},"labels":[],"label_agreement":null},{"id":"W4410719414","doi":"10.59075/67asf781","title":"Optimizing Convolutional Neural Networks for Real-Time Object Detection in Autonomous Vehicles","year":2025,"lang":"en","type":"article","venue":"˜The œcritical review of social sciences studies","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Object (grammar); Object detection; Computer vision; Real-time computing; Pattern recognition (psychology)","score_opus":0.04707215235764465,"score_gpt":0.37070865840226047,"score_spread":0.3236365060446158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410719414","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":[0.0012312524,0.124586694,0.83254355,0.035526272,0.00079533964,0.001917881,0.0000097129905,0.00020106729,0.0031882138],"genre_scores_gemma":[0.9625595,0.014143786,0.020368863,0.0021301622,0.00018098022,0.000542901,0.0000011723107,0.0000061729897,0.00006643692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850184,0.0001423052,0.00043480334,0.00035727324,0.00020529349,0.000358464],"domain_scores_gemma":[0.9974016,0.0021004525,0.0000996701,0.00015242948,0.00022134866,0.0000244887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010401307,0.00012353194,0.0003514755,0.000054049862,0.0009324668,0.000038581515,0.00068706175,0.000036879283,0.000001394497],"category_scores_gemma":[0.000447615,0.0000902861,0.0001249399,0.0013279457,0.0011741865,0.00023144737,0.0002955951,0.00012368255,0.0000014352062],"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":[0.000008646767,0.000074319585,0.000070209404,0.00090262527,0.00003408206,6.314902e-7,0.0003875381,0.0022501054,0.00046244933,0.82955605,0.0011524027,0.16510093],"study_design_scores_gemma":[0.00033383956,0.0001934928,0.0016101979,0.0017174063,0.000082001,0.0000033469576,0.0002885042,0.84216994,0.00014269906,0.14812815,0.004961412,0.00036899798],"about_ca_topic_score_codex":0.00001366666,"about_ca_topic_score_gemma":0.000036444304,"teacher_disagreement_score":0.96132827,"about_ca_system_score_codex":0.00010910432,"about_ca_system_score_gemma":0.00007768784,"threshold_uncertainty_score":0.7171875},"labels":[],"label_agreement":null},{"id":"W4410719651","doi":"10.1007/978-3-031-91838-4_4","title":"IPAdapter-Instruct: Resolving Ambiguity in Image-Based Conditioning Using Instruct Prompts","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Membrane Reactor Technologies (Canada)","funders":"","keywords":"Computer science; Ambiguity; Conditioning; Image (mathematics); Computer vision; Artificial intelligence; Computer graphics (images); Human–computer interaction; Multimedia; Programming language; Mathematics; Statistics","score_opus":0.022040909980660318,"score_gpt":0.27785654817346855,"score_spread":0.2558156381928082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410719651","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":[0.00082701613,0.00023172332,0.9940127,0.00047867952,0.00097075786,0.00080187654,0.000012116102,0.00024882905,0.002416295],"genre_scores_gemma":[0.11575659,0.0000137062425,0.8818741,0.0019874384,0.00022712696,0.000028606039,0.000011844084,0.00003670369,0.000063869236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951302,0.000066373126,0.0008628196,0.0021446492,0.0008812892,0.000914706],"domain_scores_gemma":[0.9966361,0.0006198635,0.00048992486,0.0017984004,0.00029081042,0.00016491213],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006146268,0.0006561093,0.0006719549,0.0014468061,0.00052833266,0.00048471472,0.003458115,0.00032698727,0.0000085728425],"category_scores_gemma":[0.00012770915,0.00070452405,0.000135821,0.0018488354,0.000931779,0.0011493802,0.0016140871,0.0013410816,0.000009248431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011235668,0.00004126029,0.00018883968,0.00010871879,0.000009433098,0.00017774399,0.00020227121,0.31757066,0.0020875107,0.03016761,0.000019981042,0.6494147],"study_design_scores_gemma":[0.00040334318,0.000046385183,0.0002754095,0.0010476827,0.0000067746137,0.000037037793,1.6194574e-7,0.8653374,0.0028940507,0.12893914,0.0003568314,0.0006558075],"about_ca_topic_score_codex":0.000043067328,"about_ca_topic_score_gemma":0.00014589392,"teacher_disagreement_score":0.64875895,"about_ca_system_score_codex":0.00089701137,"about_ca_system_score_gemma":0.0012042086,"threshold_uncertainty_score":0.99954057},"labels":[],"label_agreement":null},{"id":"W4410774290","doi":"10.1016/j.procs.2025.03.202","title":"ECASeg: Enhancing Semantic Segmentation with Edge Context and Attention Strategy","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Segmentation; Context (archaeology); Enhanced Data Rates for GSM Evolution; Artificial intelligence; Natural language processing; Human–computer interaction","score_opus":0.011686246188388124,"score_gpt":0.2645928048139291,"score_spread":0.25290655862554096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774290","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":[0.117566176,0.00010703477,0.8807976,0.0006518057,0.00018384006,0.00034370905,3.4703788e-7,0.00016303342,0.00018642044],"genre_scores_gemma":[0.85972714,0.000014578985,0.13961348,0.0004903794,0.000042345397,0.000048972317,0.000001008071,0.0000039499505,0.000058171798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984605,0.000018424631,0.0002045092,0.0007162771,0.000281006,0.000319285],"domain_scores_gemma":[0.9991348,0.000090493435,0.000101553305,0.0003666457,0.00020703705,0.00009946972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025290778,0.00014388152,0.00012525643,0.00019729102,0.0004411037,0.00040913103,0.00068583403,0.000024176883,7.150313e-7],"category_scores_gemma":[0.000011867285,0.00012414946,0.000015589461,0.0016912953,0.00027480186,0.001452524,0.00035688354,0.00010598682,0.000009684047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009975918,0.000084318846,0.0041457103,0.00010406658,0.000012650372,0.000010064704,0.00069154974,0.0025423737,0.03659494,0.13159482,0.00020068558,0.8240088],"study_design_scores_gemma":[0.00065916864,0.00022410964,0.025192013,0.00018713779,0.00001447673,0.00009444258,0.000077315366,0.9296728,0.03361857,0.009675394,0.00019621322,0.0003883835],"about_ca_topic_score_codex":0.000005746008,"about_ca_topic_score_gemma":0.000028465569,"teacher_disagreement_score":0.9271304,"about_ca_system_score_codex":0.000060631617,"about_ca_system_score_gemma":0.00021282461,"threshold_uncertainty_score":0.50626683},"labels":[],"label_agreement":null},{"id":"W4410774436","doi":"10.1016/j.procs.2025.03.274","title":"SegAttnDetec: A Segmentation-Aware Attention-Based Object Detector","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Segmentation; Detector; Object (grammar); Computer vision; Artificial intelligence; Telecommunications","score_opus":0.009980041653944493,"score_gpt":0.27016885496507187,"score_spread":0.26018881331112736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774436","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":[0.0107462425,0.00005142347,0.985351,0.0016416879,0.00081557146,0.0005375901,0.00000209005,0.0006068914,0.0002474512],"genre_scores_gemma":[0.64161426,0.0000032231787,0.3563684,0.0016641255,0.00008137823,0.00018057329,0.0000019795596,0.0000070152887,0.000079044476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99745023,0.000032314056,0.0003305416,0.0010627595,0.0005738105,0.0005503305],"domain_scores_gemma":[0.99814546,0.00023774656,0.00013981712,0.00091658527,0.00039318507,0.00016719007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034042797,0.00022350076,0.00017458838,0.00043785624,0.0007139086,0.00048547637,0.0025338845,0.00004588187,0.000004937572],"category_scores_gemma":[0.000050381055,0.00021710667,0.00007617997,0.004519233,0.00032557125,0.0011660465,0.000612002,0.0001784869,0.00007092094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019909005,0.00038503463,0.012577405,0.0002045029,0.000034134362,0.000019734141,0.0005283408,0.036031853,0.05356557,0.056732737,0.0027714886,0.8371293],"study_design_scores_gemma":[0.0003740022,0.000059890346,0.008289764,0.000055148474,0.0000060494704,0.000007636444,0.0000054053985,0.9582027,0.027729334,0.004412343,0.0005766351,0.000281083],"about_ca_topic_score_codex":0.000004695831,"about_ca_topic_score_gemma":0.0000072514877,"teacher_disagreement_score":0.9221709,"about_ca_system_score_codex":0.00018065171,"about_ca_system_score_gemma":0.00072784483,"threshold_uncertainty_score":0.8853353},"labels":[],"label_agreement":null},{"id":"W4410774956","doi":"10.1016/j.procs.2025.03.273","title":"Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical Investigation","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Benchmarking; Computer science; Deep learning; Empirical research; Artificial intelligence; Nano-; Machine learning; Embedded system; Computer architecture; Chemical engineering; Management","score_opus":0.037934668970654645,"score_gpt":0.3024132328936407,"score_spread":0.2644785639229861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774956","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":[0.034052894,0.000052503885,0.962682,0.00070359686,0.0006301651,0.0008137308,9.655388e-7,0.0005587692,0.0005053386],"genre_scores_gemma":[0.5365019,0.000020937323,0.4620371,0.00071935164,0.00030302053,0.00030765228,0.000008632138,0.000014929846,0.00008649007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969661,0.0000890774,0.00041061736,0.0013492401,0.0005486127,0.00063631363],"domain_scores_gemma":[0.99787474,0.00050029875,0.00021218472,0.0007563433,0.00039852908,0.00025791326],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009132569,0.00025515293,0.00025377527,0.00039363327,0.0009781013,0.0006440219,0.00205172,0.00008421638,6.0589537e-7],"category_scores_gemma":[0.000088823035,0.00024782144,0.000055798464,0.002444204,0.00025762827,0.0022103195,0.0005152299,0.00021862845,0.000016743586],"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":[0.000013110115,0.00008613063,0.0007689417,0.00007630305,0.000007650982,0.0000017977007,0.0015408847,0.72979593,0.008514391,0.13659494,0.0006129292,0.121987],"study_design_scores_gemma":[0.00017906867,0.00022365387,0.000746342,0.0000989243,0.0000051383695,0.0000065460736,0.0000072831317,0.9771058,0.0016010167,0.019298214,0.0004760919,0.00025191624],"about_ca_topic_score_codex":0.0000075342614,"about_ca_topic_score_gemma":0.0000022317583,"teacher_disagreement_score":0.50244904,"about_ca_system_score_codex":0.00021530801,"about_ca_system_score_gemma":0.00035151263,"threshold_uncertainty_score":0.9999974},"labels":[],"label_agreement":null},{"id":"W4410901236","doi":"10.1007/s00530-025-01855-w","title":"Weakly supervised free-space segmentation by fusing spatial priors and region features for auto-driving","year":2025,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Prior probability; Computer science; Artificial intelligence; Segmentation; Computer vision; Space (punctuation); Pattern recognition (psychology); Machine learning; Bayesian probability","score_opus":0.012284797830337656,"score_gpt":0.26033908115506116,"score_spread":0.2480542833247235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410901236","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":[0.006285669,0.00082186883,0.9880088,0.002318929,0.00083414617,0.0013666613,0.0000085061265,0.00025213152,0.000103277634],"genre_scores_gemma":[0.7307294,0.00006307146,0.2665949,0.000181183,0.00027507258,0.00063229626,0.000044582455,0.000027169783,0.0014523556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998779,0.000067945904,0.00025141533,0.00047118135,0.00016844051,0.00026201952],"domain_scores_gemma":[0.9988021,0.00041930116,0.00013406988,0.0004856193,0.00007958362,0.000079306956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013078957,0.00016370474,0.00019392022,0.000098650635,0.00027383797,0.00019957259,0.00042541535,0.000088385896,3.487679e-7],"category_scores_gemma":[0.00008786048,0.0001601797,0.000038156475,0.00030023904,0.000044314627,0.00034419828,0.00016641355,0.00010058462,0.000001899593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005223022,0.00013800427,0.0049245968,0.0004984161,0.00010192524,0.0000074737172,0.004697424,0.0032334889,0.32153335,0.009543812,0.08453893,0.5707303],"study_design_scores_gemma":[0.00081128196,0.00003028179,0.0015526026,0.0001328897,0.000014248699,0.000007924182,0.00014499339,0.9845846,0.005994703,0.00029730183,0.006237135,0.00019201395],"about_ca_topic_score_codex":0.00020787885,"about_ca_topic_score_gemma":0.000068905254,"teacher_disagreement_score":0.98135114,"about_ca_system_score_codex":0.00009055016,"about_ca_system_score_gemma":0.000037096772,"threshold_uncertainty_score":0.6531939},"labels":[],"label_agreement":null},{"id":"W4410915038","doi":"10.1007/978-3-031-91979-4_22","title":"MCUBench: A Benchmark of Tiny Object Detectors on MCUs","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Deep River Science Academy","funders":"","keywords":"Computer science; Benchmark (surveying); Detector; Object (grammar); Artificial intelligence; Computer vision; Computer graphics (images); Telecommunications; Cartography","score_opus":0.013762166117400245,"score_gpt":0.2538293027686813,"score_spread":0.24006713665128107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410915038","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":[0.000104632454,0.00031628006,0.9840136,0.00051049556,0.0011744061,0.00057909434,0.000010339974,0.00016539592,0.013125732],"genre_scores_gemma":[0.33522096,0.00012874746,0.66043466,0.0023764141,0.0004960555,0.00005287861,0.000009372061,0.00005223141,0.0012286528],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99594253,0.000037381666,0.0006708895,0.0017388309,0.0009654906,0.0006449021],"domain_scores_gemma":[0.9956182,0.0013039141,0.00044029072,0.0022381074,0.0002488676,0.00015059722],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043599383,0.0005711085,0.0006356628,0.0010586733,0.00024075827,0.0001458707,0.0042391187,0.0002961941,0.000020115656],"category_scores_gemma":[0.0001462072,0.00052506407,0.00019037788,0.0015507124,0.00066545524,0.00031690722,0.0014324266,0.00089949794,0.000025796853],"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":[0.0000089841615,0.000042435266,0.000031881715,0.00004743612,0.000013124313,0.000028380427,0.00021318413,0.058393024,0.00038205407,0.08440858,0.00010331795,0.8563276],"study_design_scores_gemma":[0.00048178626,0.0006665886,0.00037955845,0.0014157168,0.000023386316,0.000045080287,1.8265206e-7,0.45754352,0.020069888,0.5116291,0.0063241026,0.0014210623],"about_ca_topic_score_codex":0.000012036184,"about_ca_topic_score_gemma":0.000048676015,"teacher_disagreement_score":0.85490656,"about_ca_system_score_codex":0.0002816553,"about_ca_system_score_gemma":0.0005609447,"threshold_uncertainty_score":0.9997201},"labels":[],"label_agreement":null},{"id":"W4411221695","doi":"10.3389/fmed.2025.1581487","title":"Multi-class segmentation of knee MRI based on hybrid attention","year":2025,"lang":"en","type":"article","venue":"Frontiers in Medicine","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Class (philosophy); Computer science; Artificial intelligence; Medicine","score_opus":0.01279563470417875,"score_gpt":0.2847229472457299,"score_spread":0.2719273125415511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411221695","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":[0.00390027,0.00014295922,0.9896196,0.0045559406,0.00089568016,0.0003195132,0.0000012623705,0.000049761482,0.0005150183],"genre_scores_gemma":[0.43647492,0.00005107441,0.5611503,0.0016193683,0.000049012706,0.0000863326,0.000026141246,0.0000072029006,0.0005356133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911803,0.000050410083,0.0002651934,0.00026646059,0.00016711967,0.00013278972],"domain_scores_gemma":[0.99935615,0.000073558025,0.000103813814,0.00039072943,0.00004506251,0.00003068337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001889267,0.00008916444,0.00018934718,0.0003051411,0.000039733724,0.0000044788358,0.00030234287,0.000025466417,0.0000036870827],"category_scores_gemma":[0.000049470917,0.00008016243,0.000024840067,0.00068894133,0.000081399885,0.000106597305,0.000037104255,0.00010728951,0.000002279824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016024245,0.0006899928,0.064353034,0.00020721061,0.00006261533,0.00002494843,0.00036033572,0.095379,0.023773897,0.010537797,0.21749371,0.5869572],"study_design_scores_gemma":[0.0017025805,0.00010414039,0.013450443,0.00019054257,0.000010216935,4.1946367e-7,0.00005224661,0.9762488,0.0031845793,0.0032885794,0.0016921365,0.00007530361],"about_ca_topic_score_codex":0.000007648028,"about_ca_topic_score_gemma":0.00000308665,"teacher_disagreement_score":0.8808698,"about_ca_system_score_codex":0.000091334565,"about_ca_system_score_gemma":0.000025024958,"threshold_uncertainty_score":0.32689288},"labels":[],"label_agreement":null},{"id":"W4411336746","doi":"10.1109/jiot.2025.3579027","title":"SDANet: A Federated Efficient Remote Sensing Object Detection for Space-Air–Ground IoT","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Internet of Things; Object detection; Object (grammar); Space (punctuation); Remote sensing; Computer security; Artificial intelligence; Pattern recognition (psychology); Geography; Operating system","score_opus":0.015006749347826417,"score_gpt":0.2770697115627173,"score_spread":0.2620629622148909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411336746","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":[0.13810281,0.00008824936,0.85874367,0.0010409442,0.0013693518,0.00024058351,4.765878e-7,0.000089524954,0.000324382],"genre_scores_gemma":[0.8540549,0.000007729995,0.14466006,0.00045013326,0.00012514065,0.0000012521101,3.3248344e-7,0.000012307495,0.00068811426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860924,0.000065500455,0.00046955672,0.00031844227,0.00023311077,0.0003041604],"domain_scores_gemma":[0.998658,0.00025510282,0.00041823316,0.0002616968,0.0003275927,0.00007937006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043005898,0.00016747866,0.00023413886,0.00023986047,0.00026370832,0.00022867638,0.0005335811,0.00007936536,0.0000016984508],"category_scores_gemma":[0.000109544686,0.00015724624,0.00015397108,0.00046677946,0.000051449744,0.00020924189,0.00010476343,0.00039856686,0.000004895663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022635968,0.00008439973,0.000007593054,0.0000702162,0.00017818807,0.000029398894,0.0016490441,0.055142745,0.2565622,0.002236862,0.0027994525,0.6810135],"study_design_scores_gemma":[0.00040447898,0.000111273366,0.000036933616,0.0002093241,0.000016465066,0.0003244664,0.000029623041,0.8113432,0.17932014,0.006374356,0.0017060947,0.00012365192],"about_ca_topic_score_codex":0.00007352115,"about_ca_topic_score_gemma":0.000019600537,"teacher_disagreement_score":0.75620043,"about_ca_system_score_codex":0.00020239728,"about_ca_system_score_gemma":0.00007456588,"threshold_uncertainty_score":0.64123154},"labels":[],"label_agreement":null},{"id":"W4411399751","doi":"10.1016/j.patcog.2025.111975","title":"Refining pseudo-labels through iterative mix-up for weakly supervised semantic segmentation","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Training Program for Excellent Young Innovators of Changsha; Higher Education Discipline Innovation Project; Natural Science Foundation of Hunan Province; National Science Foundation","keywords":"Refining (metallurgy); Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Natural language processing; Computer vision; Materials science","score_opus":0.05057411921813759,"score_gpt":0.3192396139583518,"score_spread":0.26866549474021423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411399751","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":[0.046436135,0.000050852326,0.94948244,0.0021267426,0.0005064051,0.0005792548,0.000036985526,0.0002118604,0.00056931766],"genre_scores_gemma":[0.7322147,0.00006568358,0.25990146,0.0048734336,0.00017670146,0.0015124443,0.0004974512,0.000028044731,0.0007300817],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869525,0.00007426723,0.0003147292,0.0005066217,0.0001375726,0.0002715569],"domain_scores_gemma":[0.99900347,0.00032838405,0.00013065308,0.00031099352,0.00019084326,0.000035657533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012377748,0.00016709739,0.00016068202,0.000095217525,0.00027748087,0.00014398785,0.0003004813,0.00006147939,0.000017582],"category_scores_gemma":[0.000036777492,0.00017524706,0.00006817695,0.0004687941,0.00002587542,0.0008712492,0.00009097637,0.000111177535,0.00009158906],"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":[0.00001803076,0.00007556161,0.00054854265,0.000093825234,0.000039457384,0.0000016285627,0.0010431686,0.00008316644,0.026953967,0.0014899084,0.001452051,0.9682007],"study_design_scores_gemma":[0.00549497,0.00048659137,0.004131691,0.001197829,0.00019249196,0.00003102503,0.0005068163,0.39735878,0.3668959,0.21758662,0.004648601,0.0014686619],"about_ca_topic_score_codex":0.000015478516,"about_ca_topic_score_gemma":0.000026838945,"teacher_disagreement_score":0.966732,"about_ca_system_score_codex":0.00007226328,"about_ca_system_score_gemma":0.000028763287,"threshold_uncertainty_score":0.7146368},"labels":[],"label_agreement":null},{"id":"W4411505258","doi":"10.1007/978-3-031-95911-0_12","title":"FAST-AID Brain: Fast and Accurate Segmentation Tool Using Artificial Intelligence Developed for Brain","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Research Foundation; Northern California Institute for Research and Education; Velux Fonden; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Danmarks Grundforskningsfond; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; European Commission; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Innovationsfonden; Alzheimer's Association","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision","score_opus":0.05292207733568615,"score_gpt":0.32345717370596744,"score_spread":0.2705350963702813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411505258","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":[0.00010168734,0.00011847684,0.9934694,0.0039560758,0.00079071877,0.0012633657,0.000016450558,0.00013473489,0.00014909806],"genre_scores_gemma":[0.009367193,0.000022596843,0.9861034,0.003805062,0.00031759328,0.000059532664,0.000015066168,0.000027730815,0.00028179484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996561,0.000036199228,0.000677587,0.0016449104,0.0004789268,0.0006014094],"domain_scores_gemma":[0.99672884,0.0016730466,0.00035904354,0.00083345827,0.00029242918,0.00011316707],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060660084,0.0004909909,0.00042412922,0.00056200934,0.0005454525,0.0006335174,0.0018200795,0.00021898086,0.000004198751],"category_scores_gemma":[0.00022651792,0.00049538736,0.000081097336,0.00095797057,0.0005007853,0.00084891135,0.0011642704,0.00045202844,0.000005951133],"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":[0.000007906463,0.000009845904,0.0000029398143,0.00003498212,0.0000059987483,0.0000048743154,0.00029692546,0.09077664,0.0010286,0.080225654,0.000022045559,0.8275836],"study_design_scores_gemma":[0.00007593156,0.000058851445,0.000008713339,0.00018097513,0.0000058259498,0.000021196043,6.18309e-7,0.7008739,0.004484543,0.2933318,0.0005421791,0.00041545005],"about_ca_topic_score_codex":0.000005450847,"about_ca_topic_score_gemma":0.00005767705,"teacher_disagreement_score":0.8271681,"about_ca_system_score_codex":0.0002829237,"about_ca_system_score_gemma":0.00057671004,"threshold_uncertainty_score":0.9997498},"labels":[],"label_agreement":null},{"id":"W4411731341","doi":"10.3389/frai.2025.1601815","title":"A nnU-Net-based automatic segmentation of FCD type II lesions in 3D FLAIR MRI images","year":2025,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Segmentation; Fluid-attenuated inversion recovery; Computer science; Convolutional neural network; Cortical dysplasia; Artificial intelligence; Deep learning; Epilepsy; Preprocessor; Voxel; Pattern recognition (psychology); Lesion; Medicine; Magnetic resonance imaging; Radiology; Pathology","score_opus":0.023280973940952306,"score_gpt":0.30829262010913616,"score_spread":0.28501164616818386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411731341","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":[0.012581703,0.0002635389,0.98422164,0.00128426,0.0007172212,0.00045859316,0.000003095898,0.00009325129,0.0003767122],"genre_scores_gemma":[0.5477017,0.000051636023,0.45187148,0.00018632562,0.000013633637,0.000071120216,0.000005217258,0.0000066817997,0.00009215767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843675,0.00009245965,0.00061113044,0.0004045052,0.00018252288,0.00027263942],"domain_scores_gemma":[0.99906516,0.00016091806,0.00014457709,0.0005027148,0.00008587494,0.0000407267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002436469,0.00014337993,0.00023388317,0.0004603737,0.00011561973,0.00003336784,0.0007206347,0.000068210684,0.000014743502],"category_scores_gemma":[0.00011978159,0.00015469774,0.000042700012,0.0024628628,0.00014018737,0.00027866816,0.00016523048,0.00018372737,0.0000152831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038928614,0.00033579642,0.002236249,0.000050495422,0.000009617072,0.000007689436,0.0007283193,0.14685601,0.0046073925,0.026632085,0.002998231,0.8154992],"study_design_scores_gemma":[0.000048320486,0.00006926689,0.001061345,0.00012646799,0.0000050621693,3.6216485e-7,0.00019127876,0.8477068,0.09060321,0.059811026,0.00023276612,0.00014408321],"about_ca_topic_score_codex":0.000040850482,"about_ca_topic_score_gemma":0.00011343105,"teacher_disagreement_score":0.8153551,"about_ca_system_score_codex":0.00014500017,"about_ca_system_score_gemma":0.00014576192,"threshold_uncertainty_score":0.63083905},"labels":[],"label_agreement":null},{"id":"W4411769179","doi":"10.20944/preprints202506.2332.v1","title":"Context-Aware Feature Adaptation for Mitigating Negative Transfer in 3D LiDAR Semantic Segmentation","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"3v Geomatics (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Alliance de recherche numérique du Canada; Université Laval","keywords":"Adaptation (eye); Feature (linguistics); Context (archaeology); Lidar; Segmentation; Computer science; Semantic feature; Transfer (computing); Artificial intelligence; Remote sensing; Geography; Psychology; Linguistics","score_opus":0.0927996395565453,"score_gpt":0.35144459452757904,"score_spread":0.25864495497103374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411769179","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":[0.13551521,0.00008939077,0.8577285,0.0023732216,0.00040885952,0.0031486924,0.00006812726,0.00028557723,0.0003824211],"genre_scores_gemma":[0.9356323,0.00006301956,0.06061935,0.00046339622,0.000096307005,0.0023426185,0.00013331165,0.000025337731,0.0006243434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971301,0.00018493444,0.00058930955,0.0014092805,0.00029055492,0.00039583404],"domain_scores_gemma":[0.99776185,0.00057699665,0.0002458608,0.0010468316,0.0002815384,0.000086938504],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036556713,0.0003979767,0.00044428973,0.00023179172,0.00019610894,0.00006481817,0.001113895,0.00032345668,0.0000144613505],"category_scores_gemma":[0.00016626046,0.0004557147,0.00017822796,0.0005385056,0.00006383926,0.0004120201,0.00077811506,0.0008796443,0.00004006967],"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":[0.0003404668,0.0008451709,0.11529148,0.0032604688,0.00058636337,0.00003582769,0.06518146,0.50298506,0.02635128,0.057915498,0.00031866718,0.22688824],"study_design_scores_gemma":[0.0024954216,0.000061274695,0.06828242,0.0020376511,0.0001241227,0.0000074666573,0.0013326195,0.7196326,0.1461496,0.05734762,0.0010778925,0.0014512814],"about_ca_topic_score_codex":0.00009641005,"about_ca_topic_score_gemma":0.0003021069,"teacher_disagreement_score":0.8001171,"about_ca_system_score_codex":0.000286963,"about_ca_system_score_gemma":0.00024259799,"threshold_uncertainty_score":0.9997895},"labels":[],"label_agreement":null},{"id":"W4411949790","doi":"10.1109/iwcmc65282.2025.11059704","title":"Evaluating Real-Time Object Detection Models for Autonomous Vehicular Vision applications","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Object detection; Object (grammar); Machine vision; Pattern recognition (psychology)","score_opus":0.027990626999782546,"score_gpt":0.34645265623173366,"score_spread":0.3184620292319511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411949790","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":[0.001098068,0.000043131382,0.9894509,0.0007176307,0.000057471258,0.0015213704,0.0000018428735,0.00067923934,0.0064303814],"genre_scores_gemma":[0.26913264,0.000024395924,0.72444755,0.00031678603,0.00007011821,0.0031106556,0.000011743288,0.000015583033,0.002870549],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987572,0.000038061047,0.0002729673,0.00054908753,0.0001464833,0.00023623474],"domain_scores_gemma":[0.9985964,0.00031818965,0.00009165906,0.0007669925,0.00017659765,0.000050217528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028435138,0.00012623143,0.00013299669,0.00011476639,0.00044323076,0.00009264316,0.0005393414,0.00006475552,0.0000036817726],"category_scores_gemma":[0.000021635762,0.00012346976,0.00007979811,0.0007648287,0.000023567556,0.00045112186,0.00017913482,0.00007904974,0.000041304585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005345621,0.000045676956,0.0000010644821,0.000012892119,0.000010945683,9.636333e-8,0.000027585644,0.25232068,0.08304752,0.20029019,0.00025106038,0.46398696],"study_design_scores_gemma":[0.00017374953,0.000049577346,0.000029890343,0.0000075849034,0.000010334978,0.0000014630214,0.0000028463721,0.84410036,0.012615335,0.14077118,0.0021339087,0.00010376024],"about_ca_topic_score_codex":0.000012716297,"about_ca_topic_score_gemma":0.0000075627386,"teacher_disagreement_score":0.5917797,"about_ca_system_score_codex":0.00011838611,"about_ca_system_score_gemma":0.00007803362,"threshold_uncertainty_score":0.50349504},"labels":[],"label_agreement":null},{"id":"W4412352804","doi":"10.1109/tro.2025.3588454","title":"End-to-End 2D-3D Registration Between Image and LiDAR Point Cloud for Vehicle Localization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Lidar; Computer vision; Artificial intelligence; Computer science; Image registration; Cloud computing; End-to-end principle; Point (geometry); Remote sensing; Computer graphics (images); Image (mathematics); Geography; Mathematics; Geometry","score_opus":0.01924719162166084,"score_gpt":0.2795944582285054,"score_spread":0.2603472666068446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412352804","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":[0.00044348312,0.000016536571,0.9922565,0.0058575505,0.0003512997,0.00066320866,0.000027278462,0.00020653041,0.0001775788],"genre_scores_gemma":[0.65020716,0.000031400457,0.34819016,0.0007979814,0.000089151596,0.00011997776,0.000010253968,0.000018065486,0.00053582806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883276,0.000037266367,0.0003013767,0.0004370717,0.0001559238,0.00023559765],"domain_scores_gemma":[0.9988962,0.0002969058,0.00007472376,0.00050053955,0.00012788064,0.000103793674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012939583,0.00015626395,0.00016125356,0.00015305122,0.00041393717,0.00012645853,0.0002899947,0.00008182614,0.0000033314618],"category_scores_gemma":[0.0000115035855,0.00017210886,0.000054216212,0.0006965288,0.00006372575,0.00034458408,0.0000057696734,0.00015415337,0.000014173869],"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":[0.00001845402,0.00009319199,0.00002708345,0.000035116955,0.00002735805,8.8709294e-7,0.00016112719,0.8931079,0.0022606056,0.020534348,0.0007603731,0.08297356],"study_design_scores_gemma":[0.00081953517,0.00028506896,0.0004704718,0.00008035958,0.000109941095,0.0000049235396,0.000039109895,0.93131065,0.04511926,0.017270746,0.004076681,0.0004132776],"about_ca_topic_score_codex":0.000009231346,"about_ca_topic_score_gemma":0.000034712346,"teacher_disagreement_score":0.6497637,"about_ca_system_score_codex":0.000084357336,"about_ca_system_score_gemma":0.00005487947,"threshold_uncertainty_score":0.70183957},"labels":[],"label_agreement":null},{"id":"W4412460808","doi":"10.1167/jov.25.9.1837","title":"Investigating the Temporal Dynamics of LTM-to-VWM Reinstatement During Visual Search","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Dynamics (music); Computer science; Psychology","score_opus":0.018932635542161843,"score_gpt":0.3546003011529106,"score_spread":0.33566766561074873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412460808","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":[0.6352685,0.000047699432,0.35740936,0.006937262,0.000115968105,0.000117650234,4.5307848e-7,0.000010073811,0.00009301327],"genre_scores_gemma":[0.9488456,0.000022113434,0.05084191,0.00016330826,0.000044350792,0.0000018974637,3.9730156e-7,0.000004214018,0.00007620372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880093,0.00007368129,0.0004853581,0.00012288913,0.00037283683,0.0001442938],"domain_scores_gemma":[0.99905527,0.00014352109,0.0002824738,0.00023513523,0.00021175588,0.000071873736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005363101,0.00007100714,0.00013370597,0.00015506617,0.00016818364,0.00005140066,0.00068234705,0.000022588492,0.0000015546423],"category_scores_gemma":[0.000066850596,0.000048166556,0.000056679746,0.00078563276,0.00004338347,0.00028002154,0.00038044277,0.00024071588,0.0000021929739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001015457,0.00046033532,0.021725781,0.00019995066,0.00011779019,0.000039161223,0.00271781,0.27533424,0.27960917,0.08626468,0.0027802244,0.3306493],"study_design_scores_gemma":[0.0009761686,0.000725992,0.12891427,0.0010125993,0.00001964842,0.000059949743,0.00039425172,0.8064196,0.04081116,0.019238021,0.0012020845,0.00022623705],"about_ca_topic_score_codex":0.000005203869,"about_ca_topic_score_gemma":0.0000068545,"teacher_disagreement_score":0.5310854,"about_ca_system_score_codex":0.00011148688,"about_ca_system_score_gemma":0.000091825044,"threshold_uncertainty_score":0.19641751},"labels":[],"label_agreement":null},{"id":"W4412601528","doi":"10.2139/ssrn.5363643","title":"Lightweight Deep Learning for Visual Perception: A Survey of Models, Compression Strategies, and Edge Deployment Challenges","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Software deployment; Perception; Deep learning; Enhanced Data Rates for GSM Evolution; Computer science; Artificial intelligence; Computer vision; Human–computer interaction; Psychology; Software engineering; Neuroscience","score_opus":0.04220392858090016,"score_gpt":0.31403307458961083,"score_spread":0.27182914600871066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412601528","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":[0.011760009,0.03338872,0.9533751,0.0005651873,0.0001971271,0.00048604887,0.000004073822,0.00008010347,0.00014365432],"genre_scores_gemma":[0.9016652,0.08821854,0.009607381,0.000018004866,0.00015857941,0.00009486353,0.000029154486,0.000021864329,0.00018639503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971051,0.00030637096,0.0005045838,0.0006273173,0.0002914794,0.0011651007],"domain_scores_gemma":[0.9983891,0.0002878462,0.0005092835,0.00037398233,0.00034450038,0.000095258205],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0013035884,0.00030639174,0.00043924118,0.00017576276,0.00033964426,0.00016333941,0.00092803285,0.00021375077,0.000001264445],"category_scores_gemma":[0.000024405344,0.00028225346,0.00011880527,0.00017236572,0.00005368241,0.0004496761,0.0008158222,0.0025078552,5.6829066e-7],"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":[0.000087780696,0.00017071294,0.000098804034,0.00017470139,0.000206671,8.177963e-7,0.0008861255,0.113214746,0.00014151714,0.4348344,0.000027704024,0.45015603],"study_design_scores_gemma":[0.000482106,0.00034076744,0.002111189,0.0002011414,0.000037331727,0.000051645977,0.00052385574,0.5021735,0.000031451156,0.49341378,0.00032171846,0.0003115035],"about_ca_topic_score_codex":0.00005084676,"about_ca_topic_score_gemma":0.0008862892,"teacher_disagreement_score":0.94376767,"about_ca_system_score_codex":0.0004574554,"about_ca_system_score_gemma":0.0018756572,"threshold_uncertainty_score":0.999963},"labels":[],"label_agreement":null},{"id":"W4412604667","doi":"10.5753/reic.2025.6038","title":"Deep GreenAI: Effective Layer Pruning Method for Modern Neural Networks","year":2025,"lang":"en","type":"article","venue":"Revista Eletrônica de Iniciação Científica em Computação","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; Institute for Catastrophic Loss Reduction","keywords":"Pruning; Layer (electronics); Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Biology; Materials science; Nanotechnology; Botany","score_opus":0.019719820931589832,"score_gpt":0.3252183285404317,"score_spread":0.3054985076088419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412604667","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":[0.0016054302,0.0020235851,0.9910845,0.0012700703,0.0004259558,0.0024404607,0.000006704547,0.00076394685,0.00037931168],"genre_scores_gemma":[0.6138083,0.000034957626,0.38201612,0.0031726698,0.0002005936,0.0006045528,0.000017897084,0.00005325442,0.000091676346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99522907,0.00047302825,0.0009679546,0.0016268188,0.0004040817,0.0012990686],"domain_scores_gemma":[0.9939037,0.0031359536,0.00047178235,0.0018044676,0.00036785754,0.00031622863],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012806504,0.000578152,0.0008558691,0.00036775597,0.00076216,0.000535741,0.0027299314,0.00026845396,0.000004105855],"category_scores_gemma":[0.00029455114,0.0005890845,0.0004921622,0.002669403,0.0000914002,0.0004384471,0.0011704061,0.0007815837,0.000013653198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042062857,0.00013688182,0.00007980424,0.00007025356,0.00008184117,0.000009107549,0.00021431646,0.36946496,0.00024147212,0.16705333,0.0008080769,0.4617979],"study_design_scores_gemma":[0.00082937226,0.00014550157,0.0005676828,0.0001235922,0.00006789635,0.000032577285,0.000024735638,0.9710529,0.00010150063,0.013115633,0.01341755,0.00052109815],"about_ca_topic_score_codex":0.000003275008,"about_ca_topic_score_gemma":0.000011504716,"teacher_disagreement_score":0.6122028,"about_ca_system_score_codex":0.00036817882,"about_ca_system_score_gemma":0.00012483723,"threshold_uncertainty_score":0.9996561},"labels":[],"label_agreement":null},{"id":"W4412673678","doi":"10.1145/3731161","title":"Stochastic Preconditioning for Neural Field Optimization","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Stochastic optimization; Field (mathematics); Artificial neural network; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.01816744966289448,"score_gpt":0.2845887941968604,"score_spread":0.26642134453396593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412673678","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":[0.00020985972,0.00002799061,0.99162906,0.006965198,0.00037313905,0.0004315093,0.00000939672,0.00024793396,0.00010589305],"genre_scores_gemma":[0.667334,0.000028410446,0.3284852,0.0032270227,0.000029351104,0.0006118814,0.000013671584,0.000012011406,0.00025840485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992361,0.000018679395,0.00017454883,0.00030677367,0.000087225526,0.00017664558],"domain_scores_gemma":[0.99838465,0.0008039258,0.00004876579,0.0006344645,0.00008793486,0.000040238276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005635005,0.00010682785,0.00009120906,0.00022294206,0.00046869702,0.00006691001,0.0005656692,0.00006792248,0.000009536924],"category_scores_gemma":[0.000053094176,0.0001166706,0.000089677436,0.0008748717,0.000028424176,0.00032538985,0.000009556364,0.0001760661,0.000002428394],"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":[0.000008474968,0.000042971158,0.0000025739448,0.0000066166754,0.000013747539,9.110542e-8,0.000024438523,0.9281737,0.000020091757,0.040247943,0.00025010735,0.031209227],"study_design_scores_gemma":[0.00030407173,0.00009825145,0.000062107945,0.00002392496,0.000022805543,0.000002718908,0.000007824702,0.9262256,0.0008638591,0.07183689,0.00042172946,0.00013023338],"about_ca_topic_score_codex":0.000001914346,"about_ca_topic_score_gemma":0.000010726213,"teacher_disagreement_score":0.6671242,"about_ca_system_score_codex":0.000020698708,"about_ca_system_score_gemma":0.000028212798,"threshold_uncertainty_score":0.4757689},"labels":[],"label_agreement":null},{"id":"W4412712235","doi":"10.1016/j.knosys.2025.114127","title":"HA-U <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.svg\"> <mml:msup> <mml:mrow/> <mml:mn>3</mml:mn> </mml:msup> </mml:math> Net: A modality-agnostic framework for 3D medical image segmentation using nested V-Net structure and hybrid attention","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"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":"Scalable Vector Graphics; Mathematics; Computer science; World Wide Web","score_opus":0.01851962433704975,"score_gpt":0.27917060154379086,"score_spread":0.26065097720674113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412712235","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":[0.4801956,0.0005901659,0.51640797,0.00047329612,0.0012792583,0.0002420024,0.00012580321,0.00023106816,0.000454832],"genre_scores_gemma":[0.9478667,0.000044854372,0.049412057,0.00041791482,0.0007528637,0.0008502213,0.0004707721,0.00011305361,0.00007155615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960206,0.00029279664,0.0009659704,0.0011174774,0.00080749654,0.0007956427],"domain_scores_gemma":[0.99596614,0.001566167,0.00069077895,0.0011964736,0.0002205964,0.00035984564],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006698428,0.0004496637,0.00032858012,0.00023140406,0.0008783845,0.00064672984,0.0009637302,0.0005328502,0.00007684926],"category_scores_gemma":[0.00064281974,0.0005732632,0.00030497334,0.00082579465,0.00035908053,0.0007314936,0.0004929055,0.0006218251,0.00011767664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010220114,0.00013377737,0.000033532626,0.00096243445,0.0001519483,0.000082651124,0.0004234985,0.0049344786,0.0032002982,0.9842432,0.0018530779,0.0038788954],"study_design_scores_gemma":[0.0010193611,0.000205179,0.00009724406,0.0010233998,0.00021838395,0.0002534056,0.00017815632,0.98255306,0.011666902,0.0009572157,0.0013862585,0.00044142213],"about_ca_topic_score_codex":0.00013654547,"about_ca_topic_score_gemma":0.000095336814,"teacher_disagreement_score":0.98328596,"about_ca_system_score_codex":0.00006121288,"about_ca_system_score_gemma":0.0006300876,"threshold_uncertainty_score":0.9996719},"labels":[],"label_agreement":null},{"id":"W4412714434","doi":"10.1016/j.knosys.2025.114153","title":"Personally identifiable information detection in smart edge computing: A robust model evaluation","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo; University of New Brunswick; Saint Mary's University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Edge computing; Computer security; Artificial intelligence","score_opus":0.031419541592678984,"score_gpt":0.2878018561801413,"score_spread":0.2563823145874623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412714434","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":[0.011325171,0.00039294126,0.979842,0.00016543838,0.0011415636,0.0012414267,0.000002659446,0.00027328022,0.0056154854],"genre_scores_gemma":[0.99332976,0.0000020576724,0.005751232,0.00008950071,0.00007355415,0.00038083622,0.000025979502,0.000008712793,0.00033834745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980704,0.00023246615,0.0006183332,0.0004028837,0.00035602332,0.00031987263],"domain_scores_gemma":[0.9983316,0.00018589715,0.00022133409,0.0006114828,0.0005840597,0.00006563608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012778876,0.0001907965,0.00022420434,0.0005723664,0.00027023288,0.00030027196,0.00058697345,0.000115874005,0.0000014198332],"category_scores_gemma":[0.00010803134,0.00020699952,0.00006890522,0.0018657185,0.00003146249,0.0010927264,0.00011756994,0.00021097253,0.00011761475],"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":[0.0000087063245,0.00007140872,0.00019978499,0.00010782089,0.000007164355,2.4917068e-7,0.00039691382,0.96655566,0.00074013305,0.0071503557,0.0007946658,0.02396713],"study_design_scores_gemma":[0.000768916,0.000017713175,0.00058907474,0.00019036769,0.000012081465,0.0000019860179,0.000059036178,0.9944875,0.0010205909,0.0010107683,0.001664279,0.0001776587],"about_ca_topic_score_codex":0.00006406954,"about_ca_topic_score_gemma":0.00022742827,"teacher_disagreement_score":0.9820046,"about_ca_system_score_codex":0.00063268957,"about_ca_system_score_gemma":0.00047052628,"threshold_uncertainty_score":0.8441196},"labels":[],"label_agreement":null},{"id":"W4412937902","doi":"10.1038/s41598-025-06848-3","title":"Optimizing FCN for devices with limited resources using quantization and sparsity enhancement","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; King Khalid University; Soonchunhyang University; National Research Foundation","keywords":"Computer science; Quantization (signal processing); Algorithm","score_opus":0.029441423299576473,"score_gpt":0.28504763693910556,"score_spread":0.25560621363952907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412937902","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":[0.2548592,0.00018176147,0.7436858,0.0002499194,0.00043874813,0.0003759475,2.6863765e-7,0.0000692029,0.00013917316],"genre_scores_gemma":[0.62194216,0.0000037899797,0.3774951,0.00007405329,0.000014754315,0.000031992084,0.000006617445,0.0000042493193,0.0004272688],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861974,0.000018307359,0.000247509,0.000718115,0.00019277536,0.00020355507],"domain_scores_gemma":[0.9988878,0.00006541374,0.00023361054,0.0005900733,0.00017497162,0.00004815734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043722067,0.00009561912,0.00011104684,0.00014477524,0.0007152207,0.00043759993,0.00019277046,0.00002564367,9.845065e-7],"category_scores_gemma":[0.000026244348,0.00008314021,0.000020662146,0.00095646415,0.00011701817,0.00041367122,0.00016357235,0.00004087159,3.9458106e-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":[0.00015016894,0.0006943023,0.042630516,0.00062638137,0.0002601319,0.00018011325,0.008052405,0.27102238,0.39766768,0.09140523,0.008151805,0.1791589],"study_design_scores_gemma":[0.00036964883,0.00007897429,0.0014496229,0.00036463764,0.000064817345,0.00009449951,0.00013769288,0.77408016,0.11712808,0.022371056,0.08337669,0.00048414513],"about_ca_topic_score_codex":0.000005736554,"about_ca_topic_score_gemma":0.00002602277,"teacher_disagreement_score":0.5030578,"about_ca_system_score_codex":0.000034300338,"about_ca_system_score_gemma":0.000060157505,"threshold_uncertainty_score":0.55009717},"labels":[],"label_agreement":null},{"id":"W4413018251","doi":"10.1109/iv64158.2025.11097343","title":"Beyond Object Detection with Existence Maps for Anchor-Based Deep Learning Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"MIT Portugal","keywords":"Computer science; Artificial intelligence; Object detection; Deep learning; Object (grammar); Computer vision; Pattern recognition (psychology)","score_opus":0.02031067405672883,"score_gpt":0.2575333640588886,"score_spread":0.2372226900021598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413018251","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":[0.00078792113,0.00004871827,0.9905647,0.00040133158,0.000065046996,0.00043745185,6.548449e-7,0.00037986986,0.0073143295],"genre_scores_gemma":[0.6492302,0.0000037530738,0.34907305,0.00045161886,0.00001949415,0.00028316243,0.0000030751214,0.000006830638,0.00092882646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990853,0.000023394008,0.00013289835,0.00041569548,0.00011249028,0.00023019161],"domain_scores_gemma":[0.9991878,0.00021665165,0.00005982943,0.00038243041,0.000110167224,0.000043070344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000088966306,0.00011217407,0.00010336314,0.00008375962,0.00029814505,0.00007351683,0.00038718645,0.000037185302,0.0000013797917],"category_scores_gemma":[0.000016188675,0.0000953635,0.000038181817,0.0007040682,0.000033356126,0.00038424673,0.000060891834,0.000120488825,0.000004348673],"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":[0.00003368561,0.000030046986,0.000059744456,0.000017233195,0.000009555301,8.7190614e-7,0.000049496855,0.5432404,0.0032022018,0.27098435,0.000083162355,0.18228923],"study_design_scores_gemma":[0.00028820286,0.00009900019,0.000044089567,0.000011859957,0.0000053657136,0.0000017987726,0.00001383899,0.8986281,0.013705529,0.08561461,0.0014723492,0.00011526569],"about_ca_topic_score_codex":0.0000056431386,"about_ca_topic_score_gemma":0.00007932293,"teacher_disagreement_score":0.64844227,"about_ca_system_score_codex":0.000051596056,"about_ca_system_score_gemma":0.000043559943,"threshold_uncertainty_score":0.3888811},"labels":[],"label_agreement":null},{"id":"W4413061107","doi":"10.3390/electronics14142874","title":"Deep Learning for Computer Vision Application","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Public Safety Canada","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Artificial neural network; Deep neural networks; Machine learning; Human–computer interaction","score_opus":0.005766111807368011,"score_gpt":0.2752802122171564,"score_spread":0.26951410040978835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413061107","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":[0.00033139237,0.0012774519,0.99566877,0.0016084581,0.00007444481,0.00038437144,8.672984e-8,0.00025687183,0.0003981322],"genre_scores_gemma":[0.40292943,0.00043568405,0.59312636,0.0015066541,0.00020033235,0.0005981448,0.000030268,0.000020792082,0.0011523236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991915,0.000018976782,0.0001311766,0.00032194145,0.00007228154,0.00026407136],"domain_scores_gemma":[0.99935365,0.00015372317,0.000056992056,0.00034386388,0.00006544273,0.000026315078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009834432,0.00008163531,0.00008302689,0.00005424513,0.00022763765,0.00005342964,0.0004664022,0.00004193201,6.172407e-7],"category_scores_gemma":[0.00000984492,0.0000850778,0.000041200863,0.00046119894,0.00001335928,0.00015747691,0.00011229413,0.00015251219,0.000017476115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034495688,0.000017134307,0.00001717515,0.0000046836158,0.000004430395,5.7924396e-8,0.000013781987,0.028794695,0.0013427768,0.34569076,0.00064316334,0.6234679],"study_design_scores_gemma":[0.00011353586,0.00006559064,0.000102802434,0.000002965898,0.000002508939,0.0000010196554,5.27742e-7,0.665479,0.0010391903,0.031584647,0.30154908,0.00005913514],"about_ca_topic_score_codex":2.5145803e-7,"about_ca_topic_score_gemma":0.0000064453375,"teacher_disagreement_score":0.6366843,"about_ca_system_score_codex":0.000087657594,"about_ca_system_score_gemma":0.000037708654,"threshold_uncertainty_score":0.3469372},"labels":[],"label_agreement":null},{"id":"W4413074784","doi":"10.2139/ssrn.5355983","title":"Laqem: Transformer with Language-Augmented Queries and an Evolving Memory for Object Detection","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Transformer; Object (grammar); Artificial intelligence; Natural language processing; Electrical engineering; Engineering","score_opus":0.007646325371760996,"score_gpt":0.2715082334979097,"score_spread":0.2638619081261487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413074784","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":[0.026755838,0.003950166,0.9676232,0.0004994463,0.00017777177,0.00065661396,0.000007750838,0.00015565303,0.0001735573],"genre_scores_gemma":[0.96187896,0.002903006,0.03350236,0.00013015016,0.00039027416,0.00019465265,0.000017226203,0.000035758705,0.0009476194],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977021,0.000075286705,0.00027055587,0.0005285143,0.00019955414,0.001223981],"domain_scores_gemma":[0.99902284,0.00008972022,0.00021679502,0.00042033076,0.00016335658,0.00008697107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005808937,0.00027233362,0.00026256382,0.00017914141,0.00042869078,0.00021976934,0.0006150092,0.00013203202,0.0000015633514],"category_scores_gemma":[0.000021364227,0.00023459003,0.000087472894,0.00023810555,0.00005318181,0.0005714976,0.00007994394,0.0019265031,5.042719e-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":[0.0002444707,0.00011245557,0.00007854966,0.00016918281,0.00046344788,0.000005571517,0.002520667,0.00822283,0.0056887674,0.053233895,0.00003647654,0.92922366],"study_design_scores_gemma":[0.0042636483,0.0031941894,0.00050277746,0.00068708503,0.0005156289,0.0018391878,0.0067123356,0.18461478,0.031899262,0.76161,0.001917679,0.002243437],"about_ca_topic_score_codex":0.000065398315,"about_ca_topic_score_gemma":0.004082179,"teacher_disagreement_score":0.93512315,"about_ca_system_score_codex":0.000564739,"about_ca_system_score_gemma":0.0014227628,"threshold_uncertainty_score":0.9566304},"labels":[],"label_agreement":null},{"id":"W4413144393","doi":"10.1109/cvpr52734.2025.01797","title":"BootPlace: Bootstrapped Object Placement with Detection Transformers","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Alberta","funders":"","keywords":"Computer science; Transformer; Artificial intelligence; Engineering; Electrical engineering; Voltage","score_opus":0.007707733723410712,"score_gpt":0.2493951710990958,"score_spread":0.2416874373756851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413144393","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":[0.0040684137,0.000027764938,0.96757674,0.0013514895,0.0000778262,0.0003748598,3.670249e-7,0.00035863303,0.02616393],"genre_scores_gemma":[0.97041225,0.000019033694,0.025781026,0.00060183095,0.00001281209,0.00013746304,0.0000010379864,0.000005517115,0.0030290312],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991754,0.000015896343,0.0001345099,0.00031856404,0.00013830655,0.00021731855],"domain_scores_gemma":[0.99953586,0.000054798704,0.000029666187,0.00030020394,0.000032351025,0.00004712789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006135417,0.00011089079,0.000086943495,0.00008803927,0.00016714192,0.000050501698,0.0002928753,0.000030956086,0.000011500381],"category_scores_gemma":[0.0000023166522,0.000087458095,0.000029798082,0.0007372009,0.00003238128,0.0002650812,0.000033467397,0.00010041517,0.000018955592],"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":[0.00023316278,0.00023593356,0.00042287682,0.000056462937,0.00014398077,0.000009257718,0.00038309064,0.02827934,0.03290148,0.19968244,0.0035070372,0.7341449],"study_design_scores_gemma":[0.0038244417,0.00084149285,0.0041297204,0.000093610644,0.000062792475,0.000051865667,0.00037814537,0.3126327,0.53505534,0.011709833,0.13013749,0.0010825829],"about_ca_topic_score_codex":0.000008406442,"about_ca_topic_score_gemma":0.000119236225,"teacher_disagreement_score":0.9663438,"about_ca_system_score_codex":0.000060037954,"about_ca_system_score_gemma":0.00005171243,"threshold_uncertainty_score":0.35664377},"labels":[],"label_agreement":null},{"id":"W4413144756","doi":"10.1109/cvpr52734.2025.02553","title":"CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Modal; Computer science; Artificial intelligence; Object detection; Object (grammar); Correlation; Computer vision; Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.010974348546174052,"score_gpt":0.2662865451901367,"score_spread":0.25531219664396265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413144756","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":[0.0014631966,0.00013845912,0.9955838,0.00019842022,0.00012824124,0.0011498486,4.8881896e-7,0.0005745127,0.00076307193],"genre_scores_gemma":[0.68062896,0.000026940035,0.31242675,0.00023149519,0.000017262098,0.0005316266,0.0000072571065,0.000013521202,0.0061162305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989051,0.0000722314,0.00019915432,0.00045739548,0.00014136598,0.00022474863],"domain_scores_gemma":[0.999286,0.00012340207,0.000105274,0.00033448468,0.000101269536,0.000049548944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104094586,0.00015421181,0.00013443669,0.0000809731,0.00033515555,0.00008207757,0.00031424453,0.000063858184,0.00000752924],"category_scores_gemma":[0.000041344898,0.00012732507,0.000029901184,0.00083897886,0.00004073852,0.00045377054,0.000102618986,0.00027810966,0.000056874862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057403573,0.0002976387,0.0073999935,0.00005630342,0.000044871533,0.0000032895318,0.00027679247,0.111353055,0.015336654,0.007293402,0.00038714174,0.85749346],"study_design_scores_gemma":[0.00056827196,0.00009916465,0.0024542918,0.000036669233,0.000008788654,0.000006744061,0.000014993654,0.98071325,0.0062640705,0.000101074475,0.0095573235,0.0001753792],"about_ca_topic_score_codex":0.000038161354,"about_ca_topic_score_gemma":0.00013520147,"teacher_disagreement_score":0.86936015,"about_ca_system_score_codex":0.00006900239,"about_ca_system_score_gemma":0.00003743112,"threshold_uncertainty_score":0.51921654},"labels":[],"label_agreement":null},{"id":"W4413178337","doi":"10.1109/aiiot65859.2025.11105268","title":"Evaluating Real-Time Object Detection Models for Autonomous Vehicle Vision","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer vision; Object detection; Artificial intelligence; Object (grammar); Machine vision; Real-time computing; Pattern recognition (psychology)","score_opus":0.03457844501499408,"score_gpt":0.3529979519025499,"score_spread":0.3184195068875558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413178337","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":[0.010534786,0.000018153316,0.98009485,0.0006565709,0.00010174005,0.0005578234,7.097381e-7,0.0005315159,0.00750385],"genre_scores_gemma":[0.5934205,0.000007830885,0.40286702,0.00024432671,0.000036602167,0.00024910556,0.0000019595454,0.000008148207,0.0031644965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905396,0.000032508095,0.00019640847,0.0004002963,0.000113835275,0.00020300885],"domain_scores_gemma":[0.99902916,0.00029529238,0.000059944963,0.00047639877,0.00010376,0.00003546205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024552314,0.000090709815,0.00010046579,0.00007454435,0.00027637903,0.000073753465,0.0003746852,0.00004326287,0.000004044904],"category_scores_gemma":[0.000030266287,0.00008616651,0.00005229523,0.00048132805,0.000014370129,0.00048424804,0.00016118455,0.00005770584,0.000028143155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010266929,0.000028792418,0.0000017211579,0.000007486267,0.0000060597677,1.4450985e-7,0.00004552894,0.169151,0.16683088,0.07024756,0.0003697447,0.5933008],"study_design_scores_gemma":[0.00019341192,0.000079787125,0.00006562307,0.00000785887,0.000004401598,7.9639676e-7,0.0000023418795,0.86937416,0.027757205,0.10216197,0.00027577722,0.00007667987],"about_ca_topic_score_codex":0.000017194716,"about_ca_topic_score_gemma":0.000009899809,"teacher_disagreement_score":0.70022315,"about_ca_system_score_codex":0.00008959287,"about_ca_system_score_gemma":0.000053014828,"threshold_uncertainty_score":0.35137683},"labels":[],"label_agreement":null},{"id":"W4413211177","doi":"10.1007/978-3-031-94962-3_3","title":"Comparative Analysis of YOLOv8 and RT-DETR for Real-Time Object Detection in Advanced Driver Assistance Systems","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Object (grammar); Real-time computing; Information retrieval; Artificial intelligence","score_opus":0.029157463257096772,"score_gpt":0.3149333548097675,"score_spread":0.2857758915526707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413211177","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":[0.001234081,0.0005576819,0.9588219,0.00016461914,0.000127751,0.0013306154,0.0000635106,0.0000765796,0.03762324],"genre_scores_gemma":[0.69404143,0.0062335473,0.29736206,0.00019664348,0.000020193782,0.00042905172,0.00014382275,0.000010537691,0.001562709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818724,0.000047075704,0.00089434214,0.00039607182,0.0002695601,0.00020571258],"domain_scores_gemma":[0.99638176,0.00084262213,0.0006315985,0.0015797119,0.00050235627,0.00006194053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067693024,0.00021672172,0.0005759054,0.0020279118,0.00030820663,0.00019867628,0.0016360016,0.00011131078,7.687566e-7],"category_scores_gemma":[0.00003596902,0.00022898133,0.00006349696,0.002073302,0.00061400747,0.0032999215,0.0009657829,0.00024173019,0.000002157146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025251786,0.000044015243,0.00017759834,0.00011867213,0.000093741815,1.7787917e-7,0.002533044,0.03807695,0.00024236497,0.7431683,0.00007992936,0.21543996],"study_design_scores_gemma":[0.00029551686,0.000051870127,0.0061082644,0.00017627036,0.000038956692,0.0000019022374,0.00002882861,0.98356485,0.000051651445,0.0022055914,0.0072535593,0.00022273486],"about_ca_topic_score_codex":0.000025150752,"about_ca_topic_score_gemma":0.00009588787,"teacher_disagreement_score":0.9454879,"about_ca_system_score_codex":0.00020316271,"about_ca_system_score_gemma":0.00015188666,"threshold_uncertainty_score":0.93375874},"labels":[],"label_agreement":null},{"id":"W4413377297","doi":"10.3390/rs17162825","title":"Context-Aware Feature Adaptation for Mitigating Negative Transfer in 3D LiDAR Semantic Segmentation","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Alliance de recherche numérique du Canada; Université Laval","keywords":"Computer science; Lidar; Adaptation (eye); Feature (linguistics); Context (archaeology); Segmentation; Remote sensing; Artificial intelligence; Geography; Psychology","score_opus":0.02025623331067129,"score_gpt":0.2829156202674513,"score_spread":0.26265938695678004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413377297","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":[0.040695712,0.000069516944,0.9553364,0.0028156587,0.00013176806,0.00069490244,0.0000019750355,0.000110638604,0.0001434455],"genre_scores_gemma":[0.6379068,0.000007447039,0.36140996,0.00054031867,0.00002936259,4.795023e-7,0.000008395132,0.000007781558,0.00008944996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999036,0.00006220638,0.000214519,0.0003604695,0.0001080951,0.00021867406],"domain_scores_gemma":[0.9992139,0.00037521243,0.00005344005,0.0002144932,0.000113180075,0.000029811898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011332284,0.00012679613,0.00015034007,0.0001200841,0.00018821252,0.000067872854,0.00013391823,0.000065630986,2.5747667e-7],"category_scores_gemma":[0.000058066576,0.00013756841,0.000047071604,0.00067162846,0.00002773049,0.0003079252,0.000028552136,0.00015440976,0.000002076713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012370912,0.0000070258043,0.000009284869,0.00004054739,0.000010678476,0.0000038794847,0.002157383,0.011657482,0.016929237,0.0024104551,0.000044361215,0.9667173],"study_design_scores_gemma":[0.00049722544,0.000016451042,0.00015517649,0.0002480823,0.000009173261,0.000005094876,0.00047914073,0.96489745,0.024850283,0.008539568,0.00017978487,0.00012257013],"about_ca_topic_score_codex":0.000035416826,"about_ca_topic_score_gemma":0.00022898123,"teacher_disagreement_score":0.9665947,"about_ca_system_score_codex":0.00011216946,"about_ca_system_score_gemma":0.00005261128,"threshold_uncertainty_score":0.56098765},"labels":[],"label_agreement":null},{"id":"W4413385029","doi":"10.1016/j.cviu.2025.104482","title":"EntroFormer: An entropy-based sparse vision transformer for real-time semantic segmentation","year":2025,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Artificial intelligence; Segmentation; Computer science; Computer vision; Transformer; Pattern recognition (psychology); Engineering","score_opus":0.024558065701698877,"score_gpt":0.31328478899718215,"score_spread":0.28872672329548327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413385029","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":[0.0048067225,0.00002878659,0.9918359,0.0018466106,0.0002181559,0.0007759881,0.0000050804706,0.0002451384,0.00023761805],"genre_scores_gemma":[0.35774505,0.00013198997,0.64073926,0.001011423,0.000087738146,0.000048639307,0.000065866814,0.000022823258,0.00014720294],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848735,0.000060109818,0.000325058,0.000596364,0.00018427672,0.0003468125],"domain_scores_gemma":[0.9990714,0.00025487854,0.00009985047,0.00038158693,0.000064541106,0.00012772047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021511431,0.00021838657,0.00022583197,0.00024849613,0.0005299339,0.00042044674,0.0003375465,0.00006049573,0.000009643189],"category_scores_gemma":[0.000005093829,0.00019775354,0.000085785745,0.00041314852,0.00008276868,0.0012140668,0.00007298464,0.00009133303,0.000009064447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025176746,0.00042272123,0.00010302984,0.0002480142,0.00005698206,0.000011020884,0.00071971386,0.0022534966,0.43899035,0.3426364,0.009401243,0.20490524],"study_design_scores_gemma":[0.0015904036,0.00038888052,0.0001648011,0.00011703839,0.000022436156,0.0000045033566,0.000050529998,0.96573824,0.0067972033,0.023694454,0.0012015854,0.00022990243],"about_ca_topic_score_codex":0.00000237914,"about_ca_topic_score_gemma":0.0000022693264,"teacher_disagreement_score":0.96348476,"about_ca_system_score_codex":0.00017959179,"about_ca_system_score_gemma":0.000043742875,"threshold_uncertainty_score":0.80641556},"labels":[],"label_agreement":null},{"id":"W4413387550","doi":"10.1016/j.neucom.2025.131357","title":"Lightweight deep learning for visual perception: A survey of models, compression strategies, and edge deployment challenges","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Software deployment; Computer science; Perception; Deep learning; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Visual perception; Computer vision; Machine learning; Human–computer interaction; Psychology; Software engineering","score_opus":0.05001483670988554,"score_gpt":0.31432524430483766,"score_spread":0.2643104075949521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413387550","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":[0.09704109,0.0011536281,0.900503,0.00031541835,0.00010850978,0.0003731069,4.5654224e-7,0.0001674837,0.0003373162],"genre_scores_gemma":[0.96831125,0.00028114484,0.031219332,0.00007457228,0.000042414522,0.00003458519,0.0000048578927,0.000011875243,0.000019964124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986795,0.00014966531,0.0002967102,0.0005027559,0.0001322775,0.00023905617],"domain_scores_gemma":[0.99879116,0.00063159777,0.00014926678,0.00023393385,0.00014162512,0.000052425064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000238325,0.00014966528,0.000211615,0.000092791415,0.00029789974,0.000089650115,0.0003689056,0.000052470168,4.500787e-7],"category_scores_gemma":[0.000024643565,0.00014644439,0.000036213267,0.00030054926,0.00003798286,0.0003678061,0.0004168857,0.00017305555,5.768554e-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":[0.000025480233,0.000117843934,0.00063842954,0.000156586,0.000016391536,0.00000124033,0.00087871373,0.14807591,0.003211792,0.061930943,0.000058980375,0.7848877],"study_design_scores_gemma":[0.00029426583,0.00009321954,0.044118334,0.00006708925,0.0000050990657,0.0000032934606,0.00008400869,0.95077455,0.0002551054,0.0036132003,0.0005757892,0.00011606463],"about_ca_topic_score_codex":0.000013433249,"about_ca_topic_score_gemma":0.000019396637,"teacher_disagreement_score":0.8712702,"about_ca_system_score_codex":0.000019543191,"about_ca_system_score_gemma":0.0000391136,"threshold_uncertainty_score":0.59718287},"labels":[],"label_agreement":null},{"id":"W4413411867","doi":"10.1145/3721145.3730415","title":"CLOVER: A GPU-native, Spatio-graph-based Approach to Exact kNN","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of Toronto","funders":"","keywords":"Computer science; Graph; Parallel computing; Artificial intelligence; Theoretical computer science","score_opus":0.018425364620739695,"score_gpt":0.27480773514302087,"score_spread":0.2563823705222812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413411867","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":[0.0004938732,0.000023241835,0.8968279,0.0031919146,0.0000852605,0.0004599818,0.0000020206726,0.00034936023,0.09856645],"genre_scores_gemma":[0.433576,0.0000032589032,0.5570422,0.0057651144,0.000029692586,0.00024607492,0.000006988609,0.0000082833285,0.0033224113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998762,0.00003956278,0.0001928022,0.0005467158,0.00019105668,0.00026783173],"domain_scores_gemma":[0.99876606,0.00017253935,0.000047431662,0.0008239291,0.000086122585,0.0001039022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009313835,0.00014461628,0.0001393087,0.00020396058,0.00014233457,0.000085803025,0.0009569067,0.000040779287,0.000013269774],"category_scores_gemma":[0.00003100201,0.0001300735,0.00006413918,0.0020698681,0.000036060523,0.0002317313,0.00022699787,0.000115019386,0.000072995936],"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":[0.000006609303,0.00016956807,0.00046026267,0.000009137632,0.000011424322,7.3149846e-7,0.000059773476,0.024474125,0.00024563947,0.9194183,0.020985425,0.03415903],"study_design_scores_gemma":[0.0007564817,0.000086770095,0.0071109375,0.000027385613,0.0000116968695,0.0000020789284,0.000024854557,0.7862322,0.008394606,0.05437849,0.14242378,0.0005507579],"about_ca_topic_score_codex":0.000013768936,"about_ca_topic_score_gemma":0.0000124102635,"teacher_disagreement_score":0.86503977,"about_ca_system_score_codex":0.00005181418,"about_ca_system_score_gemma":0.00007025575,"threshold_uncertainty_score":0.53042436},"labels":[],"label_agreement":null},{"id":"W4413559141","doi":"10.1109/isie62713.2025.11124764","title":"SS-DeepSeg: An Efficient Deeplab with Smart Scaling for Robust Semantic Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Scaling; Segmentation; Artificial intelligence; Natural language processing; Mathematics","score_opus":0.019147987445090525,"score_gpt":0.2818605111163612,"score_spread":0.2627125236712706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413559141","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":[0.021730464,0.000021326101,0.97542125,0.0009265558,0.00015392392,0.0006607049,0.0000010453322,0.00028189598,0.0008028489],"genre_scores_gemma":[0.37538266,0.0000022048798,0.62305015,0.0006341134,0.000023263783,0.00018159948,0.000010078256,0.000008209135,0.00070770754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901396,0.000019986142,0.00017101117,0.00042818126,0.00013247419,0.00023437533],"domain_scores_gemma":[0.99919677,0.00013546208,0.000056403125,0.0004624954,0.00008929651,0.000059555052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011684541,0.00011217614,0.00010239453,0.00008486793,0.00025634735,0.000106407584,0.00040516938,0.000027924225,0.0000036656434],"category_scores_gemma":[0.000008639236,0.00009221535,0.000026521893,0.00060804613,0.000030004647,0.00020944221,0.00008296287,0.000056860754,0.00000906187],"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":[0.000021513886,0.00013690612,0.00041930456,0.000028903374,0.000017260807,0.0000016784079,0.00019248444,0.7869724,0.0015918197,0.17567424,0.00047622572,0.03446725],"study_design_scores_gemma":[0.00040807264,0.00006165609,0.00046425583,0.000021548816,0.000012165994,0.000005142966,0.00006967264,0.9901602,0.006293858,0.001862837,0.00050899375,0.0001315915],"about_ca_topic_score_codex":0.000007901632,"about_ca_topic_score_gemma":0.0000490002,"teacher_disagreement_score":0.35365218,"about_ca_system_score_codex":0.000049509348,"about_ca_system_score_gemma":0.000039571532,"threshold_uncertainty_score":0.3760433},"labels":[],"label_agreement":null},{"id":"W4413744590","doi":"10.1007/978-981-96-5754-4_28","title":"Comparative Analysis of Deep Learning Models for Image Classification: A Study on Synthetic Images of Bags","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Image (mathematics); Pattern recognition (psychology); Deep learning","score_opus":0.042612450313905384,"score_gpt":0.29941324235949823,"score_spread":0.25680079204559286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413744590","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":[0.00007495123,0.002959801,0.9894602,0.000052331085,0.00011610597,0.0014051645,0.00001763873,0.000034086795,0.00587977],"genre_scores_gemma":[0.99503154,0.00015539971,0.0037026003,0.000016791404,0.000063449865,0.0001894195,0.00002925658,0.000014902273,0.0007966579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980659,0.00011382546,0.0007258776,0.0006734726,0.00022581447,0.00019510911],"domain_scores_gemma":[0.99615586,0.0021747458,0.0006990036,0.00068983773,0.00023988912,0.000040687937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030044487,0.00031176815,0.001185628,0.00045808204,0.000099012366,0.00005727069,0.00047114902,0.00020595921,0.0000015687837],"category_scores_gemma":[0.00002994704,0.00027211537,0.00018058156,0.0005343609,0.000103309605,0.00008711721,0.00011619677,0.0003915696,2.401113e-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":[0.000021388376,0.000043255135,0.000065378175,0.0000656089,0.00037804258,0.0000011978444,0.0004742598,0.9354097,0.000020976304,0.055963624,0.000014301953,0.0075422646],"study_design_scores_gemma":[0.00018882906,0.00018027851,0.0001395372,0.00026722514,0.0003490187,9.931362e-7,0.000036406047,0.99281,0.000012446648,0.0056655724,0.00014903273,0.00020067139],"about_ca_topic_score_codex":0.000012472,"about_ca_topic_score_gemma":0.000053035994,"teacher_disagreement_score":0.99495655,"about_ca_system_score_codex":0.000048021702,"about_ca_system_score_gemma":0.000020645195,"threshold_uncertainty_score":0.9999731},"labels":[],"label_agreement":null},{"id":"W4413822039","doi":"10.1109/ojits.2025.3603968","title":"Roadside Fisheye Vision for Cooperative Perception in V2I-Assisted Automated Driving","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; McMaster University Medical Centre","funders":"","keywords":"Perception; Computer science; Computer vision; Artificial intelligence; Human–computer interaction; Psychology","score_opus":0.032128331180718804,"score_gpt":0.35596488762159517,"score_spread":0.3238365564408764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413822039","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":[0.10226223,0.0001010822,0.8946384,0.0005764289,0.0009857342,0.0012124289,0.0000052796536,0.00006353734,0.00015484051],"genre_scores_gemma":[0.980868,0.00006804692,0.018440342,0.000110667,0.000049529426,0.00012213047,0.00001566134,0.000012101378,0.00031355617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776816,0.00014005162,0.0013255044,0.00030533873,0.00025773482,0.00020323583],"domain_scores_gemma":[0.9980948,0.0002764583,0.00058718445,0.00026442873,0.0006967858,0.00008032281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074205856,0.0001680942,0.00039896969,0.0003355803,0.00014471226,0.0003357275,0.0010280233,0.000082473794,0.000004776663],"category_scores_gemma":[0.00003495864,0.00014948654,0.00011033819,0.00091258576,0.000026723828,0.0013407668,0.000013458638,0.00018516199,0.0000059097442],"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":[0.00028216068,0.00054469606,0.006594442,0.00015171526,0.000169084,0.000050012655,0.0047743213,0.8399338,0.07307939,0.017562948,0.009085532,0.04777186],"study_design_scores_gemma":[0.0024725546,0.0005867634,0.10600431,0.0027293118,0.000066308385,0.000057314788,0.0020540075,0.8639039,0.011729801,0.0010155694,0.008835765,0.00054440583],"about_ca_topic_score_codex":0.000052537747,"about_ca_topic_score_gemma":0.0003276092,"teacher_disagreement_score":0.8786057,"about_ca_system_score_codex":0.00020941504,"about_ca_system_score_gemma":0.00016732833,"threshold_uncertainty_score":0.60958844},"labels":[],"label_agreement":null},{"id":"W4413947069","doi":"10.1109/icra55743.2025.11127564","title":"AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Gaussian; Gaussian process; Computer graphics (images)","score_opus":0.012988149163677376,"score_gpt":0.27329727374853174,"score_spread":0.2603091245848544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413947069","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":[0.0024786484,0.000029675004,0.9749055,0.0036223845,0.00046971664,0.00045854092,0.0000015211109,0.0006290312,0.01740498],"genre_scores_gemma":[0.4354625,0.0000035973717,0.5626005,0.000347237,0.00005401024,0.000084428415,0.0000024504016,0.0000059873196,0.0014392724],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988474,0.000016391481,0.0002972371,0.000458523,0.00007125913,0.0003091841],"domain_scores_gemma":[0.9990427,0.00028040016,0.00010417073,0.00044193948,0.00006563099,0.000065139204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014650398,0.00013232583,0.00015016519,0.00011895763,0.0003591206,0.0001170518,0.00045086723,0.00006299912,0.000014292999],"category_scores_gemma":[0.00005365339,0.00013213439,0.0000661656,0.00051938306,0.00006713003,0.00040972733,0.00013145557,0.00010760454,0.000012701074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015929228,0.000012094822,0.00034950618,0.000009679622,0.000010891531,6.296145e-7,0.000028495047,0.0006436082,0.0023998476,0.5341073,0.00032520332,0.46211112],"study_design_scores_gemma":[0.0005283613,0.000028151566,0.0016282878,0.000078034594,0.000011917224,0.000050212693,0.000029131204,0.9113014,0.010332141,0.066712104,0.0090382,0.00026208608],"about_ca_topic_score_codex":0.0000033860495,"about_ca_topic_score_gemma":0.000012565074,"teacher_disagreement_score":0.91065776,"about_ca_system_score_codex":0.000075326556,"about_ca_system_score_gemma":0.00010860709,"threshold_uncertainty_score":0.5388284},"labels":[],"label_agreement":null},{"id":"W4413997976","doi":"10.18280/jesa.580710","title":"Leveraging UAV Imagery and Deep Learning for Automated Object Detection","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Object detection; Artificial intelligence; Deep learning; Computer science; Computer vision; Aerial imagery; Remote sensing; Geography; Pattern recognition (psychology)","score_opus":0.015228654520720683,"score_gpt":0.27280672529184335,"score_spread":0.25757807077112266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413997976","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":[0.046150837,0.0013708533,0.949408,0.00033444795,0.00039902117,0.00032363873,5.0221587e-7,0.0014751018,0.00053757493],"genre_scores_gemma":[0.8667215,0.00022474019,0.13214307,0.0001824402,0.00011046939,0.000053392512,0.0000011274645,0.000026923683,0.0005363344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982837,0.00021134161,0.0004976568,0.00038620998,0.00020184377,0.00041924434],"domain_scores_gemma":[0.99855846,0.000487973,0.00033222794,0.00028092368,0.00021908888,0.0001213214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048474432,0.00021550452,0.00027349,0.00032211235,0.0011906541,0.0006419601,0.00046191478,0.00006364471,0.0000030068954],"category_scores_gemma":[0.00031224132,0.00020364302,0.000102215185,0.00079035445,0.000076369906,0.0008539036,0.00021333071,0.00036910822,0.000011299759],"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":[0.00000877628,0.000019516647,0.00045528507,0.000090583206,0.00005030006,0.000018701458,0.00034947012,0.009459569,0.008556935,0.0017357863,0.00030113626,0.97895396],"study_design_scores_gemma":[0.00041565316,0.00007579563,0.09006498,0.00016061973,0.000025923651,0.00063589565,0.000048771693,0.89186573,0.0013615692,0.012956408,0.002190207,0.00019847271],"about_ca_topic_score_codex":0.0000043422774,"about_ca_topic_score_gemma":0.000004518097,"teacher_disagreement_score":0.9787555,"about_ca_system_score_codex":0.00018874397,"about_ca_system_score_gemma":0.00006410127,"threshold_uncertainty_score":0.91576695},"labels":[],"label_agreement":null},{"id":"W4414373498","doi":"10.1007/978-3-032-04927-8_12","title":"CENet: Context Enhancement Network for Medical Image Segmentation","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Segmentation; Context (archaeology); Feature (linguistics); Image segmentation; Key (lock); Boundary (topology); Code (set theory); Image (mathematics); Block (permutation group theory)","score_opus":0.013679759800947152,"score_gpt":0.2848460046431503,"score_spread":0.27116624484220314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414373498","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":[0.0000042658553,0.000621602,0.9911972,0.0033104247,0.0017531333,0.0012551078,0.000008741983,0.00015075995,0.0016987151],"genre_scores_gemma":[0.0035542478,0.00016436509,0.9874928,0.0066156443,0.0008814889,0.0001831818,0.000032060365,0.000026844207,0.00104939],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960556,0.000028109842,0.0006446947,0.0015449874,0.000994857,0.00073176535],"domain_scores_gemma":[0.99688834,0.001138047,0.00031555982,0.0011684333,0.00028831442,0.00020129699],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007209243,0.00045363657,0.0004643042,0.00029285398,0.00038799437,0.00028621464,0.0030848903,0.00027613452,0.00003909098],"category_scores_gemma":[0.000107427295,0.00043704527,0.00013313956,0.00064800715,0.00049620424,0.00043738238,0.0011225764,0.00054142217,0.000026758034],"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":[0.0000063668563,0.000019688463,0.000004350543,0.000029691726,0.000009668561,0.000008966813,0.00008680508,0.011052562,0.000105402134,0.06969356,0.0008936451,0.9180893],"study_design_scores_gemma":[0.0004330932,0.00013821677,0.00001022871,0.0004519207,0.000010324604,0.000013537861,1.1218754e-7,0.7014663,0.0028153402,0.27980515,0.014330904,0.000524856],"about_ca_topic_score_codex":0.000005677761,"about_ca_topic_score_gemma":0.00009679036,"teacher_disagreement_score":0.91756445,"about_ca_system_score_codex":0.00035664567,"about_ca_system_score_gemma":0.0006907742,"threshold_uncertainty_score":0.99980813},"labels":[],"label_agreement":null},{"id":"W4414404592","doi":"10.1109/jiot.2025.3612481","title":"A Novel Perception Entropy Metric for Optimizing Vehicle Perception With LiDAR Deployment","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Sichuan Province; National Natural Science Foundation of China","keywords":"Lidar; Entropy (arrow of time); Robustness (evolution); Ranging; Software deployment; Metric (unit); Particle swarm optimization; Point cloud; Perception","score_opus":0.018487809762708984,"score_gpt":0.2808870785035306,"score_spread":0.26239926874082165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414404592","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":[0.11490133,0.000044157707,0.8831509,0.0011501876,0.00033515153,0.00024551895,7.9936177e-7,0.000063042855,0.00010894923],"genre_scores_gemma":[0.5869596,0.000028061686,0.4122568,0.00038130753,0.00007172989,0.000023711553,6.353689e-7,0.000009058529,0.00026912274],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877197,0.00002745085,0.00038598094,0.0002877861,0.0002762532,0.00025057845],"domain_scores_gemma":[0.9989618,0.00011838403,0.00032601552,0.0002523355,0.00026383105,0.0000776515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029272583,0.00014485112,0.0001930978,0.00031876646,0.00013866481,0.00017503394,0.0007591547,0.000052646606,0.000011520784],"category_scores_gemma":[0.000028817236,0.00012041293,0.00012089176,0.0004284153,0.00004431004,0.0008590427,0.000093004994,0.00028390126,0.0000060359102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003069055,0.00044777064,0.00068159343,0.00007241773,0.00019853147,0.0000060207803,0.004033093,0.024031509,0.8053477,0.00988499,0.0038060907,0.15118338],"study_design_scores_gemma":[0.0026621087,0.0009179794,0.0033640568,0.0005191217,0.000103867416,0.0003895369,0.00036695745,0.9404585,0.043237947,0.004671404,0.0029028822,0.00040563243],"about_ca_topic_score_codex":0.000018755341,"about_ca_topic_score_gemma":0.0000016673936,"teacher_disagreement_score":0.916427,"about_ca_system_score_codex":0.00024446126,"about_ca_system_score_gemma":0.00005321122,"threshold_uncertainty_score":0.49102968},"labels":[],"label_agreement":null},{"id":"W4414405478","doi":"10.1109/iccworkshops67674.2025.11162172","title":"Efficient Adversarial Detection Frameworks for Vehicle-to-Microgrid Services in Edge Computing","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Science and Engineering Research Council","keywords":"Inference; Benchmark (surveying); Process (computing); Adversarial system; Evasion (ethics); Enhanced Data Rates for GSM Evolution; Edge computing; Edge device; Key (lock)","score_opus":0.007046615663747078,"score_gpt":0.2705852907643029,"score_spread":0.2635386751005558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414405478","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":[0.10171923,0.00003537952,0.89520574,0.0013608647,0.00057638186,0.0006250728,8.113949e-7,0.00020509791,0.00027144613],"genre_scores_gemma":[0.86055595,0.0000012204748,0.13759808,0.0016363077,0.00009782356,0.000064495485,0.0000011252475,0.000005275022,0.000039715822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989534,0.00002154038,0.00022678546,0.000431457,0.000085043364,0.00028179496],"domain_scores_gemma":[0.9991901,0.00028780664,0.000048749906,0.0003562894,0.00006690553,0.000050157993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001455,0.000105719024,0.00011951802,0.00015743377,0.00016986576,0.00007720877,0.00056977593,0.00010819122,0.0000010882617],"category_scores_gemma":[0.000018093488,0.000106901665,0.00004317081,0.0011252546,0.000013265844,0.00007091985,0.00031825318,0.00021224492,0.000016052421],"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":[0.00003086611,0.00008498604,0.00020928083,0.000031216226,0.0000072206785,6.668376e-7,0.0003161074,0.4857682,0.0065405946,0.030484347,0.00012575233,0.47640076],"study_design_scores_gemma":[0.00036538538,0.000027111297,0.0021115094,0.000042929223,0.0000025944971,5.925881e-7,0.000029042465,0.9836048,0.00710351,0.0028996924,0.0037037248,0.00010914254],"about_ca_topic_score_codex":0.000031050346,"about_ca_topic_score_gemma":0.00012933735,"teacher_disagreement_score":0.75883675,"about_ca_system_score_codex":0.00008333978,"about_ca_system_score_gemma":0.000021740532,"threshold_uncertainty_score":0.43593234},"labels":[],"label_agreement":null},{"id":"W4414596608","doi":"10.1007/s11554-025-01761-4","title":"WT-DETR: Wavelet-enhanced DETR for robust tiny object detection via multi-scale feature optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Chongqing","keywords":"Upsampling; Aliasing; Wavelet; Feature (linguistics); Context (archaeology); Convolution (computer science); Object detection; Field (mathematics); Representation (politics)","score_opus":0.01051958058838433,"score_gpt":0.2766373403406846,"score_spread":0.2661177597523003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414596608","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":[0.0011142697,0.00030051454,0.9966793,0.00082234055,0.00018460356,0.0003808682,0.000001799241,0.00014119019,0.0003750866],"genre_scores_gemma":[0.037555672,0.000108117485,0.9609841,0.00014499633,0.00020790065,0.00003312025,0.0000032830924,0.00002794528,0.00093486934],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983822,0.00005750208,0.00055477396,0.00037968723,0.00027238682,0.0003534541],"domain_scores_gemma":[0.9975916,0.00017401887,0.0008508144,0.00030393607,0.0009706905,0.00010889988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037528577,0.00022710804,0.00033843325,0.00031395763,0.0004727282,0.00033238292,0.0006267059,0.00013140455,0.0000044844537],"category_scores_gemma":[0.00016725626,0.00020715574,0.00015284219,0.0012112217,0.00005797327,0.0017436973,0.0001089906,0.0003555415,0.0000045171637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070097005,0.00009841933,0.000008515914,0.00011277145,0.000021637241,0.0000045721645,0.00014109479,0.07806613,0.48380575,0.0000054506504,0.0005673532,0.4370982],"study_design_scores_gemma":[0.00074068725,0.00008867185,0.0001384248,0.000203011,0.000039757113,0.0000812468,0.000023378483,0.84352547,0.15433706,0.00043070645,0.00021147564,0.00018011055],"about_ca_topic_score_codex":0.0000015441278,"about_ca_topic_score_gemma":0.000003366781,"teacher_disagreement_score":0.76545936,"about_ca_system_score_codex":0.00019906125,"about_ca_system_score_gemma":0.00018692332,"threshold_uncertainty_score":0.8447566},"labels":[],"label_agreement":null},{"id":"W4415209724","doi":"10.3390/s25206306","title":"Optimizing Detection Reliability in Safety-Critical Computer Vision: Transfer Learning and Hyperparameter Tuning with Multi-Task Learning","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"Trent University","keywords":"Hyperparameter optimization; Hyperparameter; Reliability (semiconductor); Generalizability theory; Transfer of learning; Focus (optics); Grid; Task (project management); SPARK (programming language)","score_opus":0.009235358507921223,"score_gpt":0.2703607519428755,"score_spread":0.2611253934349543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415209724","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":[0.30376878,0.000049839822,0.6951137,0.0005674541,0.000059934104,0.0001625858,1.6030955e-7,0.0001655167,0.0001119735],"genre_scores_gemma":[0.7772523,0.00002577968,0.22250926,0.00009073764,0.000018664516,0.000013989589,8.103099e-7,0.000010475748,0.000077990015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983801,0.00024626282,0.00027372682,0.000637027,0.00014065455,0.0003222107],"domain_scores_gemma":[0.99879843,0.0007878273,0.000025534178,0.00024594148,0.000068134475,0.00007414602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028514274,0.00017171576,0.00021530525,0.0001531889,0.0003473317,0.0001040755,0.00017219814,0.00008165804,0.0000018137066],"category_scores_gemma":[0.00010072879,0.00015843102,0.000035928642,0.00067896256,0.000137211,0.00031829992,0.00012549957,0.0006943241,0.0000045044726],"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":[0.00007315151,0.000053327974,0.0028200364,0.000032345964,0.000008611196,0.00002112284,0.00082541717,0.85722715,0.003729714,0.001307827,0.0000010470728,0.13390025],"study_design_scores_gemma":[0.00051612896,0.00014746231,0.0047564227,0.00010694851,0.0000075442817,0.000027818172,0.00008370283,0.9915596,0.0007815004,0.00015152092,0.0016718982,0.00018942461],"about_ca_topic_score_codex":0.000012603678,"about_ca_topic_score_gemma":0.000025992054,"teacher_disagreement_score":0.4734835,"about_ca_system_score_codex":0.00006893159,"about_ca_system_score_gemma":0.000017697828,"threshold_uncertainty_score":0.64606297},"labels":[],"label_agreement":null},{"id":"W4415377195","doi":"10.1016/j.imavis.2025.105770","title":"DynaGuide: A generalizable dynamic guidance framework for zero-shot guided unsupervised semantic segmentation","year":2025,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Zoo; University of Toronto","funders":"","keywords":"Segmentation; Pascal (unit); Modular design; Scalability; Diagonal; Feature (linguistics); Unsupervised learning; Prior probability","score_opus":0.023512155504163003,"score_gpt":0.3757815722765314,"score_spread":0.3522694167723684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415377195","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":[0.013074222,0.0005755778,0.9823614,0.002711086,0.0002961219,0.0005997028,0.0000030326307,0.00024768332,0.0001312068],"genre_scores_gemma":[0.25519738,0.000056127927,0.7430702,0.0013749423,0.000044808243,0.00003473505,0.000012697922,0.000013005863,0.00019615835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837613,0.000055643133,0.00044865758,0.00062876055,0.00014818544,0.00034263355],"domain_scores_gemma":[0.99860495,0.00049248576,0.00014970581,0.000535828,0.00014812377,0.00006887494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029954957,0.00019021556,0.00021995028,0.00012246666,0.000545218,0.00029470178,0.000509042,0.000071284085,0.0000024765516],"category_scores_gemma":[0.00010062908,0.0001903087,0.00006742634,0.0006857611,0.000048356575,0.00040790145,0.00038592302,0.000111320325,0.000005802515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035637917,0.00025077918,0.0010135723,0.0005543817,0.00009076326,0.00001672158,0.000859956,0.06165038,0.13061003,0.26912782,0.022191266,0.5135987],"study_design_scores_gemma":[0.00044382658,0.00003800467,0.00048699212,0.00020654923,0.000011444528,0.0000061238866,0.0000138000005,0.91234565,0.0034205103,0.08171375,0.0011336509,0.00017971873],"about_ca_topic_score_codex":0.000009040547,"about_ca_topic_score_gemma":0.0000031650748,"teacher_disagreement_score":0.85069525,"about_ca_system_score_codex":0.00005325621,"about_ca_system_score_gemma":0.00003808805,"threshold_uncertainty_score":0.77605635},"labels":[],"label_agreement":null},{"id":"W4415679831","doi":"10.3390/rs17213576","title":"SLA-Net: A Novel Sea–Land Aware Network for Accurate SAR Ship Detection Guided by Hierarchical Attention Mechanism","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Synthetic aperture radar; Segmentation; Focus (optics); Feature (linguistics); Perspective (graphical); Mechanism (biology); Fusion mechanism","score_opus":0.028067814948845988,"score_gpt":0.2934931146781641,"score_spread":0.2654252997293181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415679831","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":[0.012475694,0.00006102913,0.9835215,0.0022845208,0.0005471258,0.0006451461,0.0000067162673,0.0003302304,0.00012803198],"genre_scores_gemma":[0.53822577,0.000023206338,0.460051,0.0009452909,0.00032046696,6.31949e-7,0.000033847522,0.0000269872,0.00037282842],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983209,0.00006942069,0.00034103476,0.0006176749,0.00017894382,0.00047204367],"domain_scores_gemma":[0.99879044,0.00029253843,0.00015283273,0.00053710665,0.00013875215,0.0000883456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026954865,0.00020133906,0.00021034984,0.00008776551,0.00053867855,0.00014662038,0.00030571374,0.00013134288,4.1138668e-7],"category_scores_gemma":[0.00006762729,0.00021138799,0.000107197404,0.0007978337,0.000032895055,0.00023993306,0.00017297476,0.00023373797,0.000006481961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005389231,0.000031401025,0.000012238002,0.000058968508,0.000060130686,0.0000053285817,0.000059650472,0.022157667,0.17003943,0.013514862,0.0035500738,0.79045635],"study_design_scores_gemma":[0.00043015534,0.000028882803,0.00006861192,0.000088541245,0.000017697008,0.000030667343,0.000002986244,0.9255644,0.0070116366,0.062196158,0.0043703327,0.00018992108],"about_ca_topic_score_codex":0.000029825407,"about_ca_topic_score_gemma":0.000053760854,"teacher_disagreement_score":0.90340674,"about_ca_system_score_codex":0.00012154599,"about_ca_system_score_gemma":0.00004544811,"threshold_uncertainty_score":0.8620152},"labels":[],"label_agreement":null},{"id":"W4415707388","doi":"10.1109/jproc.2025.3623023","title":"Field-Programmable Gate Array Architecture for Deep Learning: Survey and Future Directions","year":2025,"lang":"","type":"article","venue":"Proceedings of the IEEE","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Field-programmable gate array; Interfacing; Dataflow; Variety (cybernetics); Architecture; Dataflow architecture; Reconfigurable computing; Data processing; Graphics; Emulation","score_opus":0.013111207611625122,"score_gpt":0.26348233010327854,"score_spread":0.25037112249165344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415707388","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":[0.16334967,0.010432177,0.74023044,0.06602707,0.006380053,0.006554684,0.00004134189,0.00057088456,0.006413678],"genre_scores_gemma":[0.92312074,0.0029566789,0.064272396,0.00089944224,0.0010419907,0.0006415774,0.0000029454714,0.000058985657,0.0070052254],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99815726,0.000028477696,0.0004080372,0.0006991885,0.00021054804,0.00049648125],"domain_scores_gemma":[0.99801785,0.00061122637,0.00040447403,0.00029786254,0.00056968344,0.00009888948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047522507,0.00029622362,0.00034645986,0.000121915844,0.0009789453,0.00024129232,0.0012109352,0.00017872483,0.00000288579],"category_scores_gemma":[0.00034846913,0.0002370413,0.00017249702,0.001714689,0.00018510666,0.00027118076,0.00029680447,0.00064528,0.0000010622645],"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":[0.0003357016,0.00022714812,0.010329793,0.0008236959,0.00023988869,1.3397145e-7,0.0026945916,0.0017854335,0.01832485,0.025551233,0.0051678387,0.9345197],"study_design_scores_gemma":[0.0013705678,0.0007524145,0.013534478,0.0004532062,0.00024411448,0.000032410633,0.0003839702,0.054115117,0.09663653,0.09480449,0.7367141,0.0009585829],"about_ca_topic_score_codex":0.00002589032,"about_ca_topic_score_gemma":0.00007410567,"teacher_disagreement_score":0.9335611,"about_ca_system_score_codex":0.00003698121,"about_ca_system_score_gemma":0.000054088094,"threshold_uncertainty_score":0.96662635},"labels":[],"label_agreement":null},{"id":"W4415715109","doi":"10.1145/3774505.3774517","title":"On-Device Dynamic DNN Inference through Spatial Sparsity Exploitation","year":2025,"lang":"en","type":"article","venue":"GetMobile Mobile Computing and Communications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Inference; Deep neural networks; Artificial neural network; Latency (audio); Mobile device; Key (lock); Edge device","score_opus":0.025545999540777284,"score_gpt":0.3361222237683081,"score_spread":0.3105762242275308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415715109","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":[0.03914414,0.0010001429,0.9512278,0.0020422966,0.00018316381,0.0006808092,0.000006123577,0.0004928002,0.005222753],"genre_scores_gemma":[0.9213875,0.00051689125,0.076710664,0.00085568003,0.000016713067,0.000295315,0.000028347284,0.0000103004695,0.00017859072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984025,0.00019943877,0.00038656936,0.0005476064,0.00016083336,0.00030309972],"domain_scores_gemma":[0.9949904,0.0019213393,0.00018009535,0.0026658692,0.0001732584,0.000069007096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002058249,0.00021404373,0.000227734,0.000107484884,0.0011141695,0.00016673288,0.0020285829,0.00007949599,0.0000037391487],"category_scores_gemma":[0.00010684499,0.00023006523,0.000061022813,0.00083493866,0.00021134081,0.00033555046,0.00168767,0.00039266757,0.000039158782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072082235,0.0003979291,0.00060478714,0.00003307664,0.000037128742,9.376093e-7,0.0014807511,0.026180865,0.00036114486,0.58755064,0.000662086,0.38268346],"study_design_scores_gemma":[0.00053238455,0.00016368496,0.0061716223,0.00022445963,0.000026071106,0.000006848151,0.00027793623,0.8342018,0.0002331849,0.12984572,0.027859101,0.00045716457],"about_ca_topic_score_codex":0.00006809242,"about_ca_topic_score_gemma":0.00009240032,"teacher_disagreement_score":0.88224334,"about_ca_system_score_codex":0.00009887194,"about_ca_system_score_gemma":0.00009096647,"threshold_uncertainty_score":0.9381788},"labels":[],"label_agreement":null},{"id":"W4415716426","doi":"10.1016/j.isprsjprs.2025.10.027","title":"MHFNet: Multimodal hybrid fusion framework for misaligned SAR-Optical ship detection","year":2025,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"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":"Sichuan Province Science and Technology Support Program; Aeronautical Science Foundation of China; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China; College of Family Physicians of Canada","keywords":"Benchmark (surveying); Bounding overwatch; Feature (linguistics); Code (set theory); Matching (statistics); Probabilistic logic; Feature matching; Fusion","score_opus":0.013962994602293225,"score_gpt":0.2909113406664863,"score_spread":0.27694834606419305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415716426","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":[0.06804183,0.00034395017,0.9296949,0.0008462141,0.0007105129,0.00023831226,7.5381024e-7,0.000048197653,0.00007534657],"genre_scores_gemma":[0.53883755,0.000054985707,0.46065274,0.00028866212,0.00014783141,5.985243e-8,3.4135383e-7,0.000007103627,0.00001075103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986671,0.00006911818,0.00046329724,0.00029117704,0.00020005328,0.00030923],"domain_scores_gemma":[0.998222,0.0008410438,0.00028381142,0.00027679667,0.00022800412,0.00014832271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042137856,0.00016859478,0.00027118312,0.0002551843,0.00033920287,0.00013803347,0.00026027794,0.0001184367,5.60451e-7],"category_scores_gemma":[0.00035654698,0.00014766844,0.00016268244,0.00059114717,0.00008747288,0.0002054155,0.00013466462,0.00040724638,7.759563e-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":[0.000086674314,0.000021257962,0.0000055428695,0.000021766306,0.000024749334,0.000017516211,0.00005414385,0.00021460255,0.048010133,0.0009176213,0.000027765507,0.95059824],"study_design_scores_gemma":[0.00081081997,0.00023224561,0.00020996762,0.0003473451,0.000058453636,0.0005901656,0.00006001813,0.59585124,0.25968394,0.13889536,0.0030328522,0.00022761729],"about_ca_topic_score_codex":0.000019770783,"about_ca_topic_score_gemma":0.0000103403345,"teacher_disagreement_score":0.9503706,"about_ca_system_score_codex":0.00005307418,"about_ca_system_score_gemma":0.000041907497,"threshold_uncertainty_score":0.60217446},"labels":[],"label_agreement":null},{"id":"W4415828691","doi":"10.5194/isprs-annals-x-1-w2-2025-27-2025","title":"A Feature-Driven Approach to Semantic Segmentation in Large-Scale 3D Urban Dataset","year":2025,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Segmentation; Feature (linguistics); Class (philosophy); Object (grammar); Point (geometry); Point cloud; Scale-space segmentation; Pattern recognition (psychology)","score_opus":0.030007167632519247,"score_gpt":0.3163260904820897,"score_spread":0.28631892284957045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415828691","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":[0.007873845,0.000028753904,0.9856964,0.00502663,0.00015811123,0.00046347003,0.00004027555,0.000030998744,0.000681553],"genre_scores_gemma":[0.91218096,0.000028502449,0.08452795,0.0031788494,0.000015419684,0.0000012939684,0.00003667222,0.0000023832367,0.000027998536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987508,0.000078588266,0.00031623765,0.0002447667,0.0003320284,0.00027757892],"domain_scores_gemma":[0.9991349,0.000084759595,0.0002085071,0.00040462942,0.0001051382,0.00006206391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060163916,0.00012176802,0.00016788447,0.00035281596,0.0003308783,0.0002328795,0.0005834955,0.000046941048,2.736015e-7],"category_scores_gemma":[0.00010235149,0.00009180711,0.000040988052,0.0023001996,0.00013463755,0.0005258887,0.00033644555,0.00012511585,0.0000022615716],"study_design_candidate":"simulation_or_modeling","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":[0.0000106202115,0.000027855136,0.00033100016,0.000031537405,0.0000067061314,1.2486038e-7,0.0015523949,0.015804064,0.00049513346,0.00014994126,0.0056581367,0.9759325],"study_design_scores_gemma":[0.00014149708,0.000028894989,0.0019289594,0.000060827144,0.0000035800977,0.0000037875873,0.0002760559,0.9865456,0.004135663,0.0005483255,0.006227175,0.00009962452],"about_ca_topic_score_codex":0.0075660646,"about_ca_topic_score_gemma":0.0039949,"teacher_disagreement_score":0.9758329,"about_ca_system_score_codex":0.0000150635005,"about_ca_system_score_gemma":0.00004979583,"threshold_uncertainty_score":0.99904263},"labels":[],"label_agreement":null},{"id":"W4415900716","doi":"10.28991/hij-2025-06-03-09","title":"Efficient Object Detection with an Optimized YOLOv8x Model","year":2025,"lang":"en","type":"article","venue":"HighTech and Innovation Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Robustness (evolution); Preprocessor; Object detection; Precision and recall; Quantization (signal processing); Computational complexity theory; Pattern recognition (psychology); Reliability (semiconductor)","score_opus":0.014431419444517582,"score_gpt":0.26457185905467817,"score_spread":0.2501404396101606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415900716","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":[0.119979545,0.000034017277,0.8783465,0.0008027246,0.00008110329,0.00010838308,3.5716133e-7,0.00009248036,0.00055489416],"genre_scores_gemma":[0.72982275,0.000015977024,0.26952085,0.00043231057,0.000033979253,0.000015125706,0.0000010932486,0.000003949151,0.00015393832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999233,0.000025248477,0.00023880799,0.00020855328,0.00014494182,0.00014943045],"domain_scores_gemma":[0.9992754,0.000020255267,0.00013748792,0.0002139461,0.0003091894,0.000043748492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020966135,0.00009149908,0.00009147775,0.0004421962,0.00040117314,0.00016234982,0.00021495816,0.000040009032,0.0000020619646],"category_scores_gemma":[0.000013293901,0.0000701432,0.0000117313,0.0022182076,0.000033756307,0.00032928423,0.00004518763,0.00022532283,0.0000012908664],"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":[0.00011804393,0.00010004145,0.00004128641,0.000006393758,0.000020100435,0.000004155483,0.0001231111,0.44678098,0.027893977,0.38557112,0.00025716788,0.13908364],"study_design_scores_gemma":[0.00059938716,0.000113109956,0.00024667854,0.000015606254,0.00000439681,0.000089057066,0.000010153817,0.97100914,0.009338948,0.017851988,0.000627893,0.00009362982],"about_ca_topic_score_codex":4.0635857e-7,"about_ca_topic_score_gemma":6.6401833e-7,"teacher_disagreement_score":0.60984325,"about_ca_system_score_codex":0.00004142956,"about_ca_system_score_gemma":0.00007624421,"threshold_uncertainty_score":0.308554},"labels":[],"label_agreement":null},{"id":"W4415968362","doi":"10.1109/lca.2025.3630094","title":"Disaggregated Speculative Decoding for Carbon-Efficient LLM Serving","year":2025,"lang":"","type":"article","venue":"IEEE Computer Architecture Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Decoding methods; Security token; Cache; Workload; Overhead (engineering); Latency (audio); Sequential decoding; Decoupling (probability)","score_opus":0.013340716557823408,"score_gpt":0.26462515500385364,"score_spread":0.2512844384460302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415968362","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":[0.08774961,0.0003428769,0.86863273,0.034997627,0.0051302514,0.0025003867,0.00002728332,0.00043901874,0.00018021873],"genre_scores_gemma":[0.6152608,0.00001061357,0.36626023,0.016297584,0.0016698831,0.00028249854,0.000017245982,0.00008310779,0.000118061784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99427974,0.000292977,0.0010564422,0.0022770276,0.00052004383,0.0015737495],"domain_scores_gemma":[0.9955929,0.0014626256,0.0005019396,0.0019175702,0.00022040094,0.00030457575],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032564352,0.0009737437,0.00085081416,0.00065911165,0.00091307587,0.00054057507,0.0027291046,0.00021474248,0.0000043529476],"category_scores_gemma":[0.00003825117,0.001003805,0.00052979914,0.0025085614,0.0003787023,0.00017078186,0.0010773183,0.0009875259,0.0000117085165],"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":[0.000057376306,0.00015776834,0.00007069314,0.00017439072,0.00021680989,0.000029223476,0.002036323,0.81328285,0.008588914,0.0027910415,0.0022692364,0.17032535],"study_design_scores_gemma":[0.0012161828,0.00012116317,0.00038460235,0.00071934087,0.00010478353,0.000033537188,0.000009434117,0.9831555,0.006383066,0.003040999,0.0039158715,0.00091552676],"about_ca_topic_score_codex":0.000021808737,"about_ca_topic_score_gemma":0.000020398282,"teacher_disagreement_score":0.5275112,"about_ca_system_score_codex":0.00042524905,"about_ca_system_score_gemma":0.00014960059,"threshold_uncertainty_score":0.99924123},"labels":[],"label_agreement":null},{"id":"W4415981885","doi":"10.48550/arxiv.2510.02990","title":"A Resource-Driven Approach for Implementing CNNs on FPGAs Using Adaptive IPs","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Field-programmable gate array; Convolutional neural network; Flexibility (engineering); Digital signal processing; Pooling; Convolution (computer science); Application-specific integrated circuit; Kernel (algebra); Resource (disambiguation); Artificial neural network","score_opus":0.15763243430213442,"score_gpt":0.34230574830793015,"score_spread":0.18467331400579573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415981885","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":[0.052118752,0.00007798198,0.9419908,0.00043826905,0.00019832807,0.0017104701,0.000063151616,0.00034096808,0.0030612662],"genre_scores_gemma":[0.45551527,0.000010559013,0.5417045,0.00067110074,0.0004712338,0.00085586734,0.00009788983,0.000042604923,0.000630988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970237,0.000101453305,0.000495013,0.0014291934,0.00026246737,0.0006881939],"domain_scores_gemma":[0.9973381,0.00035488332,0.0004533333,0.001597329,0.00014502685,0.000111309884],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002707017,0.0004071348,0.00042006248,0.0001980432,0.00053372263,0.000110200075,0.0018346331,0.00020227594,0.000002829774],"category_scores_gemma":[0.000057115605,0.0004288493,0.00024836,0.00048605612,0.00006110327,0.0001540785,0.0031793416,0.00070589577,0.000009869715],"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":[0.000086640095,0.00040281884,0.010335245,0.00032329114,0.00036789,0.000013197109,0.0020747029,0.8502486,0.00096630474,0.099294856,0.003792159,0.032094322],"study_design_scores_gemma":[0.00042755154,0.00007890032,0.0014057528,0.00022613723,0.00007307759,0.00000562508,0.000117558164,0.9741732,0.0012563171,0.00552886,0.016062833,0.0006441984],"about_ca_topic_score_codex":0.000018171891,"about_ca_topic_score_gemma":0.000006473096,"teacher_disagreement_score":0.40339652,"about_ca_system_score_codex":0.00023431658,"about_ca_system_score_gemma":0.00016591816,"threshold_uncertainty_score":0.99981636},"labels":[],"label_agreement":null},{"id":"W4416040400","doi":"10.1016/j.media.2025.103857","title":"Adaptive mix for semi-supervised medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; China Scholarship Council; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Overfitting; Segmentation; Perturbation (astronomy); Image segmentation; Pattern recognition (psychology); Image (mathematics)","score_opus":0.012633380039165325,"score_gpt":0.31778882447609685,"score_spread":0.30515544443693154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416040400","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":[0.0008362985,0.00014441753,0.9791366,0.01809895,0.000099971345,0.00033208367,0.0000104672,0.0001960911,0.0011451085],"genre_scores_gemma":[0.16156097,0.00029322557,0.82089645,0.013388305,0.00037212437,0.000981448,0.00025441806,0.00003065667,0.0022223867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99724674,0.00012885267,0.00050775433,0.0006922829,0.0010338445,0.00039055222],"domain_scores_gemma":[0.9977332,0.0008379684,0.00010827693,0.00070019625,0.00024622167,0.0003741436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063630147,0.00019156633,0.00040246896,0.00033467598,0.00023311835,0.00010258559,0.0013443872,0.00015021588,0.0005066588],"category_scores_gemma":[0.0008478597,0.0001689649,0.00033883334,0.003305118,0.00021856626,0.00047275296,0.00036405883,0.00026724345,0.000059526777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008247417,0.0008887241,0.0010405213,0.000106522435,0.0032219393,0.00020934656,0.000691979,0.00063781097,0.008255186,0.03936474,0.08416763,0.86133313],"study_design_scores_gemma":[0.0007142699,0.0000330707,0.00039818016,0.000026816844,0.0004363656,0.0000029332784,0.000066929235,0.98676515,0.003028306,0.0057326932,0.0026032066,0.00019210116],"about_ca_topic_score_codex":0.0000314175,"about_ca_topic_score_gemma":0.000083202736,"teacher_disagreement_score":0.9861273,"about_ca_system_score_codex":0.000082959166,"about_ca_system_score_gemma":0.00021867079,"threshold_uncertainty_score":0.6890189},"labels":[],"label_agreement":null},{"id":"W4416054896","doi":"10.59275/j.melba.2025-d1g3","title":"Investigating Demographic Bias in Brain MRI Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methods","year":2025,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Natural Sciences and Engineering Research Council of Canada; McDonnell Center for Systems Neuroscience; National Institutes of Health","keywords":"Segmentation; Metric (unit); Scale-space segmentation; Pattern recognition (psychology); Image segmentation; Field (mathematics)","score_opus":0.03975856399701808,"score_gpt":0.39768339274451264,"score_spread":0.35792482874749454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416054896","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":[0.10941187,0.002318716,0.8838187,0.003305891,0.00022033509,0.00081471045,0.0000012073177,0.00005956954,0.0000489981],"genre_scores_gemma":[0.6607477,0.00022326484,0.33865842,0.0001406148,0.000115658884,0.000045505512,0.000011960413,0.000023666704,0.000033160777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99341667,0.0033693684,0.0015767027,0.0005306837,0.00066174095,0.0004448112],"domain_scores_gemma":[0.9891569,0.0073724934,0.0026076408,0.00032346224,0.0003342431,0.00020524526],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0077475654,0.00042040384,0.000993389,0.0009899376,0.00057658367,0.00015474329,0.0013384763,0.00012078409,0.0000038949415],"category_scores_gemma":[0.0020365845,0.00032863114,0.00018600043,0.0015840747,0.00040705304,0.00030381628,0.0016681799,0.0043448987,3.180447e-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":[0.00009756805,0.00038629764,0.08879781,0.00043403608,0.0004067424,0.000018598626,0.042512897,0.45747533,0.0037781629,0.00023449009,0.000045611156,0.40581244],"study_design_scores_gemma":[0.0016920317,0.0004947281,0.005409052,0.0006955546,0.00014439155,0.00007327692,0.0046546455,0.98228264,0.00014237338,0.0035140256,0.00060931174,0.00028795938],"about_ca_topic_score_codex":0.00008132483,"about_ca_topic_score_gemma":0.000024797368,"teacher_disagreement_score":0.5513359,"about_ca_system_score_codex":0.000096338124,"about_ca_system_score_gemma":0.00012368264,"threshold_uncertainty_score":0.99991655},"labels":[],"label_agreement":null},{"id":"W4416129862","doi":"10.3390/s25226837","title":"DRC2-Net: A Context-Aware and Geometry-Adaptive Network for Lightweight SAR Ship Detection","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Synthetic aperture radar; Robustness (evolution); False positive paradox; Adaptability; Convolutional neural network; Scalability; Object detection; Feature (linguistics); Feature extraction","score_opus":0.015966186471773353,"score_gpt":0.2560217302181622,"score_spread":0.24005554374638885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416129862","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":[0.043191556,0.00044384238,0.95314825,0.0014842025,0.00042236852,0.00067254476,0.000005813769,0.00024647574,0.00038493116],"genre_scores_gemma":[0.96602786,0.0000413542,0.031835932,0.00070843863,0.00018651184,0.000072438364,0.000002851054,0.000013053164,0.0011115479],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988524,0.000045184446,0.00019340159,0.00048185507,0.00009811931,0.0003290519],"domain_scores_gemma":[0.9988895,0.00046535625,0.000085989894,0.00038845456,0.0000956144,0.00007505628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011713362,0.00015554987,0.00017849848,0.00010706058,0.0003417831,0.00006546022,0.00028896757,0.00008573429,0.0000014680533],"category_scores_gemma":[0.000036367128,0.00014980671,0.000057615714,0.00086427585,0.00006478395,0.00015819634,0.00012484135,0.00013981583,0.000010666663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014458658,0.00007000663,0.001082059,0.000051703133,0.0001244018,0.000008481004,0.00048776923,0.017767102,0.00064895,0.37388593,0.007178485,0.5985505],"study_design_scores_gemma":[0.0011150896,0.00027463387,0.0061765932,0.00010209653,0.00004740758,0.000021309339,0.00013350602,0.695284,0.010568415,0.12735415,0.15833217,0.0005906083],"about_ca_topic_score_codex":0.0000110623,"about_ca_topic_score_gemma":0.00008538738,"teacher_disagreement_score":0.9228363,"about_ca_system_score_codex":0.00005137837,"about_ca_system_score_gemma":0.000021832888,"threshold_uncertainty_score":0.6108941},"labels":[],"label_agreement":null},{"id":"W4416133563","doi":"10.1145/3774649","title":"SeeSSD: Computational Storage for Energy-Efficient Real-Time Object Detection","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Embedded Computing Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Object detection; Convolutional neural network; Firmware; Software; Pascal (unit); Object (grammar); Viola–Jones object detection framework; Feature extraction; Interface (matter)","score_opus":0.011886524082396717,"score_gpt":0.2699864684993832,"score_spread":0.25809994441698647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416133563","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":[0.004754402,0.00006420684,0.99095786,0.0003168875,0.0014520189,0.0008025425,0.000018720235,0.0010447031,0.0005886346],"genre_scores_gemma":[0.91475123,0.0000061742353,0.08390474,0.00016545843,0.00011322127,0.0002646392,0.000014531392,0.000028037586,0.00075199094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975602,0.00017731584,0.00060139375,0.0008480589,0.0003531238,0.00045991538],"domain_scores_gemma":[0.9967727,0.0014413516,0.00024123449,0.0011594052,0.00028065723,0.00010464299],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035707618,0.00030337588,0.00034703495,0.00040851018,0.0010148996,0.00022105027,0.0010696923,0.00013364841,0.0000023892048],"category_scores_gemma":[0.00003156904,0.00032637798,0.00019308401,0.0013559157,0.000053062347,0.00016534147,0.000040228202,0.00021935509,0.00002989091],"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":[0.000016120557,0.00012718595,8.201894e-7,0.000032108346,0.00005384736,0.00000117583,0.00016974684,0.94537556,0.0018248764,0.014509694,0.00017004696,0.037718795],"study_design_scores_gemma":[0.0005963221,0.000097418,0.000045656507,0.0001048328,0.000026375319,0.000019850102,0.00006397252,0.9920114,0.002002703,0.003366141,0.0013875258,0.000277758],"about_ca_topic_score_codex":0.000049276958,"about_ca_topic_score_gemma":0.0000060002067,"teacher_disagreement_score":0.9099968,"about_ca_system_score_codex":0.0003269186,"about_ca_system_score_gemma":0.00012229463,"threshold_uncertainty_score":0.9999188},"labels":[],"label_agreement":null},{"id":"W4416183714","doi":"10.1109/mipr67560.2025.00030","title":"Neural Network Structural Pruning and Acceleration in Frequency Domain","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pruning; Frequency domain; Inference; Acceleration; Discrete cosine transform; Computation; Artificial neural network; Set (abstract data type); Discrete Fourier transform (general); Domain (mathematical analysis)","score_opus":0.015948406212389327,"score_gpt":0.28169290854650064,"score_spread":0.2657445023341113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416183714","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":[0.39658287,0.0025438855,0.5794527,0.007751141,0.001051297,0.001141219,0.0000016703281,0.00017172798,0.011303498],"genre_scores_gemma":[0.8053409,0.00011438772,0.19277395,0.001174964,0.00017749691,0.000053515465,0.000004185227,0.0000102983995,0.00035032528],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972994,0.00018277351,0.00068301836,0.0009291435,0.00019547151,0.0007102346],"domain_scores_gemma":[0.99872017,0.00025504932,0.00016362539,0.0006672056,0.000076474294,0.00011746512],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025113582,0.0003296948,0.00031957607,0.00017993203,0.0005601937,0.0004558795,0.000721397,0.00014554129,0.000039600553],"category_scores_gemma":[0.000028229491,0.0003339923,0.00004826513,0.002357329,0.00016510476,0.0013208239,0.00063904526,0.0005009748,0.0000060408006],"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":[0.000010649158,0.000014620142,0.021209206,0.000021818329,0.000010609425,0.000009160052,0.00038133364,0.025509045,0.00073366374,0.86595684,0.0003226494,0.08582042],"study_design_scores_gemma":[0.00045679297,0.000038203256,0.06794165,0.00007219761,0.0000067735996,0.00001192332,0.000041461004,0.51377803,0.000050025654,0.41711068,0.00021971107,0.0002725295],"about_ca_topic_score_codex":0.00007712445,"about_ca_topic_score_gemma":0.00054839806,"teacher_disagreement_score":0.488269,"about_ca_system_score_codex":0.00012417701,"about_ca_system_score_gemma":0.0001078082,"threshold_uncertainty_score":0.9999112},"labels":[],"label_agreement":null},{"id":"W4416234899","doi":"10.1007/978-3-032-10466-3_23","title":"NNia-8: An 8-Core RISC-V Neural Network Inference Accelerator with Efficient Processing Elements and Memory Utilization","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Memory bandwidth; Artificial neural network; Computation; Edge device; Multi-core processor; Computational complexity theory; Bandwidth (computing); Memory management; Inference","score_opus":0.037425019230046326,"score_gpt":0.3000084682819254,"score_spread":0.2625834490518791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416234899","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":[0.0016318565,0.0006295404,0.9953443,0.0003071858,0.00040250117,0.0008099656,0.0000038123858,0.0002489752,0.0006218838],"genre_scores_gemma":[0.46383733,0.000055751058,0.53367156,0.0017199971,0.00041068505,0.00005626018,0.000018282362,0.00004103036,0.00018908129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99596703,0.000035712597,0.00054685934,0.0019130816,0.00079716614,0.00074017816],"domain_scores_gemma":[0.9974797,0.0002839485,0.00042427628,0.0012242324,0.00036522682,0.00022266577],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040563237,0.00058286247,0.0004586238,0.00041471582,0.0006707462,0.00071939133,0.0022629092,0.00020226721,0.0000061861074],"category_scores_gemma":[0.00003908612,0.0004976609,0.000039121896,0.00137716,0.00062277797,0.0008282602,0.0012719649,0.00070524955,0.0000037808359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000080368445,0.000023357046,0.0002160188,0.000034817298,0.000003834516,0.000013611502,0.00018501155,0.36447293,0.000033071614,0.005768536,0.000010215271,0.62923056],"study_design_scores_gemma":[0.0002452066,0.00016527729,0.00043477074,0.00042767232,0.0000119181495,0.00002556112,3.171845e-7,0.97691065,0.00015787019,0.020713257,0.00037260086,0.00053487683],"about_ca_topic_score_codex":0.0000037823156,"about_ca_topic_score_gemma":0.000076583856,"teacher_disagreement_score":0.6286957,"about_ca_system_score_codex":0.00017745457,"about_ca_system_score_gemma":0.000523846,"threshold_uncertainty_score":0.9997475},"labels":[],"label_agreement":null},{"id":"W4416251495","doi":"10.1109/ijcnn64981.2025.11229047","title":"Towards Efficient Pruning and Multi-Scale Feature Transformations to Uncover Medical Diseases","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Feature extraction; Medical imaging; Feature (linguistics); Pattern recognition (psychology); Pruning; Histogram; Feature engineering; Feature learning; Domain (mathematical analysis)","score_opus":0.010534747814540911,"score_gpt":0.2924497648493645,"score_spread":0.28191501703482363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416251495","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":[0.0044720303,0.0012490472,0.9381149,0.051012352,0.00046644817,0.0010467097,0.00004124214,0.00021580893,0.0033815037],"genre_scores_gemma":[0.7022044,0.0005106127,0.27786738,0.011231247,0.00014879857,0.0003182186,0.000017276567,0.000026471407,0.0076756063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742454,0.000082220344,0.0004481001,0.00081505976,0.00064659247,0.0005834963],"domain_scores_gemma":[0.9980541,0.00023261337,0.000061825434,0.00070915656,0.00015972539,0.0007826197],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018446552,0.00032553228,0.0003245074,0.00021816949,0.0006959662,0.0002795835,0.0009489736,0.00017936305,0.00009166317],"category_scores_gemma":[0.0001340653,0.00028922316,0.00010933074,0.0020740884,0.00019549626,0.00035535658,0.00067044114,0.0004056336,0.0000465745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039022823,0.0008104736,0.0007936645,0.00016995662,0.00006921314,0.000012130047,0.0029692454,0.065476954,0.00025976464,0.08493422,0.016354932,0.8281104],"study_design_scores_gemma":[0.0007482339,0.000050735285,0.009627288,0.00024075697,0.000054337823,0.000010819356,0.0001154905,0.9556604,0.0004243737,0.000649674,0.032077167,0.0003406996],"about_ca_topic_score_codex":0.000016412534,"about_ca_topic_score_gemma":0.00010959477,"teacher_disagreement_score":0.89018345,"about_ca_system_score_codex":0.000107848464,"about_ca_system_score_gemma":0.00044270649,"threshold_uncertainty_score":0.999956},"labels":[],"label_agreement":null},{"id":"W4416251711","doi":"10.1109/ijcnn64981.2025.11228615","title":"KDLight: A Lightweight Knowledge Distillation Framework for Medical Image Classification","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Generalization; Image (mathematics); Contextual image classification; Process (computing); Feature (linguistics); Adaptability","score_opus":0.024926740500778883,"score_gpt":0.3498878810235106,"score_spread":0.32496114052273173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416251711","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":[0.00012521996,0.0013080684,0.9163047,0.04739711,0.002022836,0.001817733,0.000015212096,0.00041270256,0.030596396],"genre_scores_gemma":[0.43911335,0.00065816706,0.54486954,0.0014168264,0.0010896345,0.001021484,0.00005463262,0.000048278802,0.011728062],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958039,0.00016548487,0.0011732442,0.0015438576,0.00054648676,0.0007670395],"domain_scores_gemma":[0.99419254,0.0026196674,0.00036555415,0.0017416284,0.00068230816,0.00039827768],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006054778,0.0004795076,0.00048738372,0.00030878818,0.0009266599,0.00040058568,0.0019670664,0.00067587703,0.00031717593],"category_scores_gemma":[0.0011827182,0.00044867402,0.00027793797,0.0028184112,0.00035833378,0.0007514233,0.00061049976,0.00060618157,0.0004627005],"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":[0.000024090603,0.0002517552,0.000064831125,0.000086088476,0.00003433059,8.7875964e-7,0.00016523953,0.000013140778,0.00022568462,0.79804486,0.0140949115,0.18699417],"study_design_scores_gemma":[0.00038372847,0.000052435516,0.0016642321,0.00026728015,0.000048867587,0.0000031004208,0.000019133471,0.5393559,0.0013739606,0.30370682,0.15278916,0.00033536225],"about_ca_topic_score_codex":0.000002524058,"about_ca_topic_score_gemma":0.000027254282,"teacher_disagreement_score":0.53934276,"about_ca_system_score_codex":0.00028129897,"about_ca_system_score_gemma":0.0007329949,"threshold_uncertainty_score":0.9997965},"labels":[],"label_agreement":null},{"id":"W4416275364","doi":"10.52783/tangence.19","title":"Benchmarking YOLOv4–YOLOv11 for Autonomous Driving: Small-Object Detection, Adverse Conditions and Confidence Calibration","year":2025,"lang":"","type":"article","venue":"Tangence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Calibration; Inference; Brier score; Benchmarking; Detector; Confidence interval; Benchmark (surveying); Throughput","score_opus":0.019635338260064365,"score_gpt":0.2798121024682078,"score_spread":0.26017676420814345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416275364","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":[0.023074659,0.0010085453,0.9697486,0.0016944713,0.0013668446,0.00198642,0.000044016648,0.0002896208,0.00078683504],"genre_scores_gemma":[0.9631596,0.00040703712,0.03361228,0.0007963096,0.00024713896,0.0007785591,0.000019207337,0.000024354033,0.000955496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99677324,0.00014094249,0.0007623166,0.001393456,0.00021812825,0.000711911],"domain_scores_gemma":[0.9969215,0.0010690653,0.00043968402,0.0009858786,0.00036101276,0.00022286909],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00038562634,0.0004336344,0.00039436002,0.0002831037,0.0015619852,0.00027798218,0.00089347496,0.0002304509,0.000044278415],"category_scores_gemma":[0.00028882368,0.0005196861,0.0001334329,0.001535055,0.0005056187,0.0010353716,0.00046435968,0.00035240338,0.00001623975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009338496,0.00031344395,0.0035883335,0.0004164585,0.00022344709,0.000025756162,0.0022034196,0.02190811,0.051428273,0.5669142,0.0016340924,0.35125107],"study_design_scores_gemma":[0.00062536786,0.0003009255,0.0092762755,0.00032097474,0.00015756888,0.000051746403,0.00014181896,0.92537457,0.01999686,0.03352671,0.00951674,0.0007104492],"about_ca_topic_score_codex":0.00005715123,"about_ca_topic_score_gemma":0.0004967889,"teacher_disagreement_score":0.94008493,"about_ca_system_score_codex":0.00021908969,"about_ca_system_score_gemma":0.00040923952,"threshold_uncertainty_score":0.99973786},"labels":[],"label_agreement":null},{"id":"W4416286402","doi":"10.1109/jstsp.2025.3633550","title":"MobiLLM: Enabling On-Device Fine-Tuning of Billion-Sized LLMs via Server-Assisted Side-Tuning","year":2025,"lang":"","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Mobile device; Backpropagation; Mobile computing; Quantization (signal processing); Mobile telephony; Computation; Server; Scheme (mathematics); Mobile processor","score_opus":0.03175548512140474,"score_gpt":0.30207165474353476,"score_spread":0.27031616962213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416286402","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":[0.21818979,0.004670132,0.77356905,0.0017497934,0.0007158859,0.000512613,0.0000019183465,0.00007199771,0.0005188416],"genre_scores_gemma":[0.95621437,0.00033937793,0.042083196,0.00041856038,0.000639461,0.000014801296,0.0000020072464,0.00005023267,0.00023801615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931973,0.00053567416,0.0032904842,0.0008507216,0.0011607998,0.00096498645],"domain_scores_gemma":[0.9912887,0.0014374927,0.0033236705,0.0006109086,0.0030810796,0.0002581379],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001497824,0.0006724199,0.0013890201,0.0017129439,0.00063342036,0.0003774682,0.0020627445,0.0004062456,0.000017516657],"category_scores_gemma":[0.000501221,0.00064093317,0.00029648928,0.010030935,0.00023400896,0.0016111743,0.00027075264,0.0024821598,0.0000026544058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033614755,0.0007127707,0.0010486307,0.00075222645,0.00017418624,0.00014324793,0.0015005564,0.16724767,0.26583812,0.00065333646,0.00008327803,0.56150985],"study_design_scores_gemma":[0.0043525565,0.0010582322,0.0028529314,0.014634525,0.00028473794,0.00032896825,0.00032538958,0.7597213,0.20087624,0.013551831,0.0008686389,0.0011446457],"about_ca_topic_score_codex":0.00002549355,"about_ca_topic_score_gemma":0.000044556986,"teacher_disagreement_score":0.73802453,"about_ca_system_score_codex":0.00057571445,"about_ca_system_score_gemma":0.0020772659,"threshold_uncertainty_score":0.99981916},"labels":[],"label_agreement":null},{"id":"W4416323687","doi":"10.1109/access.2025.3634156","title":"Superpixel-Guided Graph-Attention Boundary GAN for Adaptive Feature Refinement in Scribble-Supervised Medical Image Segmentation","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Adelaide Research and Innovation, University of Adelaide","keywords":"Segmentation; Feature (linguistics); Block (permutation group theory); Pattern recognition (psychology); Image segmentation; Context (archaeology); Boundary (topology); Pipeline (software); Residual","score_opus":0.034115597133233685,"score_gpt":0.3513687837173804,"score_spread":0.3172531865841467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416323687","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":[0.030302564,0.00013443641,0.95817304,0.008761356,0.00060612435,0.0011128273,0.000012258989,0.00015401388,0.0007433672],"genre_scores_gemma":[0.8244797,0.00019201271,0.16550943,0.0063488036,0.00025455267,0.002280696,0.00015504948,0.000035916873,0.000743853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982469,0.00007432221,0.0003622281,0.00059244654,0.0003837238,0.00034039142],"domain_scores_gemma":[0.99898136,0.00016533323,0.0000911006,0.00049769325,0.0001730582,0.00009148544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002891833,0.00018281298,0.0001991702,0.00025890185,0.00024304315,0.00024929072,0.0012538617,0.00010915368,0.000015167111],"category_scores_gemma":[0.00004859318,0.00017911894,0.00008516662,0.0012721203,0.00007848222,0.0012372123,0.00017949136,0.00021802414,0.000006678111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048145498,0.001575081,0.0099532865,0.000509222,0.0002650733,0.00009719086,0.0011736208,0.0052459147,0.15440385,0.13483083,0.27335894,0.41810554],"study_design_scores_gemma":[0.013381442,0.00042451304,0.06329009,0.000991973,0.00012750232,0.000036226305,0.0003706606,0.5950778,0.10879351,0.19620022,0.019446468,0.0018595924],"about_ca_topic_score_codex":0.000041760217,"about_ca_topic_score_gemma":0.00023112338,"teacher_disagreement_score":0.7941771,"about_ca_system_score_codex":0.00014267881,"about_ca_system_score_gemma":0.00015838967,"threshold_uncertainty_score":0.73042583},"labels":[],"label_agreement":null},{"id":"W4416335304","doi":"10.18280/isi.300918","title":"Image-Based Machine Learning Framework for LiDAR Data Analysis Accelerated on FPGA Platforms","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Field-programmable gate array; Data acquisition; Lidar; Feature (linguistics); Key (lock)","score_opus":0.0412345737675989,"score_gpt":0.31075359155427945,"score_spread":0.2695190177866805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416335304","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":[0.0044177235,0.00028260893,0.9898399,0.0007268161,0.000576823,0.0017663378,0.0004536773,0.00053316285,0.0014029525],"genre_scores_gemma":[0.698679,0.0001071659,0.29554975,0.0014381208,0.00011128888,0.00034581215,0.0036146715,0.000027939015,0.00012627148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995708,0.00010357018,0.0017892963,0.0009044945,0.0005880184,0.00090665283],"domain_scores_gemma":[0.993393,0.0013777261,0.0013438185,0.0028191647,0.0008631349,0.00020315377],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011062174,0.0006400936,0.0007929522,0.0015309207,0.0019110612,0.0019617944,0.0029858751,0.0004155525,0.00006262834],"category_scores_gemma":[0.0018837398,0.00063925766,0.00030919793,0.008251665,0.0002848625,0.008566752,0.00086335273,0.0009279299,0.0001404317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051903666,0.00021382849,0.0015966576,0.0009533629,0.0011933881,0.0000029499975,0.0019500777,0.31586948,0.0000902335,0.099463895,0.000804621,0.57734245],"study_design_scores_gemma":[0.00081289356,0.00022928424,0.0016113557,0.0004604086,0.00045785093,0.0000019112872,0.00013121498,0.9525386,0.0013881394,0.03246157,0.009338157,0.00056862977],"about_ca_topic_score_codex":0.00005017588,"about_ca_topic_score_gemma":0.000025222585,"teacher_disagreement_score":0.69429016,"about_ca_system_score_codex":0.00060202,"about_ca_system_score_gemma":0.00045025733,"threshold_uncertainty_score":0.9996059},"labels":[],"label_agreement":null},{"id":"W4416336843","doi":"10.1145/3777366","title":"A Survey of FPGA-based 3D CNN Accelerators and Hardware-aware Algorithmic Optimizations","year":2025,"lang":"en","type":"article","venue":"ACM Computing Surveys","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Convolutional neural network; Field-programmable gate array; Focus (optics); Software deployment; Key (lock); Deep learning; Action recognition; Action (physics); Resource (disambiguation)","score_opus":0.033401068999425215,"score_gpt":0.30149700936991886,"score_spread":0.26809594037049367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416336843","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":[0.021021785,0.00016553617,0.97741765,0.00044609295,0.00023804861,0.00030263804,0.000026879896,0.00025422042,0.00012712483],"genre_scores_gemma":[0.82258034,0.000011282909,0.17704062,0.00022455043,0.000019260055,0.000011984204,0.000048333346,0.000013391397,0.000050235747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976751,0.0007855538,0.00044879137,0.00057931355,0.00019296692,0.00031822774],"domain_scores_gemma":[0.99571717,0.002371763,0.0002097739,0.001199804,0.0004169409,0.00008454337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014651129,0.00020418546,0.00031204705,0.00020764615,0.0003183575,0.00011055633,0.0013458397,0.00007672569,0.0000041791563],"category_scores_gemma":[0.00063632795,0.00021679004,0.00004465495,0.0020570897,0.00010977047,0.00019447047,0.00086505624,0.00018752759,0.000004912565],"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":[0.000008110392,0.00027317798,0.15883945,0.000108151806,0.0001041983,0.0000073003325,0.00021760755,0.42254627,0.00028724823,0.0033483251,0.0026216682,0.4116385],"study_design_scores_gemma":[0.00029946177,0.000021846674,0.32112187,0.00004637713,0.000005879058,0.0000010605098,0.0000029239934,0.67726856,0.00059792696,0.0003427545,0.00012260782,0.00016869915],"about_ca_topic_score_codex":0.00026389366,"about_ca_topic_score_gemma":0.00009557876,"teacher_disagreement_score":0.80155855,"about_ca_system_score_codex":0.000039511826,"about_ca_system_score_gemma":0.00019031677,"threshold_uncertainty_score":0.8840441},"labels":[],"label_agreement":null},{"id":"W4416341683","doi":"10.1109/socc66126.2025.11235380","title":"SparHiXcel: A Cost-Effective Sparsity-Aware Convolutional Accelerator","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Field-programmable gate array; Convolutional neural network; Reduction (mathematics); Hardware acceleration; Exploit; Efficient energy use; Energy (signal processing); Deep learning; Filter (signal processing)","score_opus":0.025923447946621183,"score_gpt":0.3069476937123321,"score_spread":0.28102424576571094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416341683","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":[0.0012216332,0.00076985266,0.9651849,0.008497936,0.0016351589,0.00534698,0.00004784813,0.00045448466,0.016841203],"genre_scores_gemma":[0.95857066,0.00019476548,0.017221203,0.0058100107,0.0003542472,0.0021009576,0.000024089099,0.000027792137,0.015696287],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613553,0.00023270231,0.00062336534,0.001578597,0.00047468455,0.0009551188],"domain_scores_gemma":[0.99655306,0.00084819924,0.0002393273,0.0014273964,0.0005955126,0.00033651904],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025827804,0.0005388066,0.00052971655,0.00029045096,0.0010163971,0.0003935718,0.0018951866,0.000253022,0.00042233028],"category_scores_gemma":[0.000071281596,0.0005730076,0.00023842798,0.0029320235,0.00041330903,0.0009624727,0.0015651962,0.0006504494,0.000912605],"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":[0.00006615052,0.00048531216,0.0027756644,0.00006006591,0.00017293064,0.000019872858,0.00013015301,0.0030409726,0.00052198744,0.72746015,0.04875866,0.21650806],"study_design_scores_gemma":[0.0019239172,0.00017991425,0.020490998,0.00021168732,0.00010959752,0.00003490925,0.00006271699,0.56591934,0.0077978224,0.03628673,0.36590844,0.0010739313],"about_ca_topic_score_codex":0.000032729116,"about_ca_topic_score_gemma":0.00006212195,"teacher_disagreement_score":0.957349,"about_ca_system_score_codex":0.0005302531,"about_ca_system_score_gemma":0.00058742065,"threshold_uncertainty_score":0.9998653},"labels":[],"label_agreement":null},{"id":"W4416342822","doi":"10.1109/socc66126.2025.11235365","title":"Sparse Attention: A Co-Design Approach for Efficient Transformer Execution on Tensor Cores","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint John Regional Hospital; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exploit; Software; Inference; Pruning; Set (abstract data type); Component (thermodynamics); Minification; Tensor (intrinsic definition)","score_opus":0.05240302543642369,"score_gpt":0.31070684470831544,"score_spread":0.2583038192718918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416342822","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":[0.0005618193,0.00020950577,0.9741037,0.0033447216,0.00047152545,0.0053939805,0.000025859184,0.00027693025,0.015611941],"genre_scores_gemma":[0.60335445,0.00012910242,0.37715366,0.002019105,0.00020213715,0.0021999641,0.000054941094,0.000039038405,0.014847604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996393,0.00014298757,0.0007538952,0.0014540679,0.00044893858,0.00080706377],"domain_scores_gemma":[0.997716,0.00054733927,0.00019430519,0.0010861742,0.00027669387,0.00017949553],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048840663,0.00047997147,0.0004350641,0.00029851196,0.00090404,0.00024044044,0.0009959775,0.00020292414,0.00003764567],"category_scores_gemma":[0.000036690788,0.00043557398,0.00033651825,0.0014887083,0.00025859632,0.00023657993,0.000059531692,0.0002916885,0.00009566197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043383846,0.0014695888,0.000022010652,0.00013712357,0.00008470522,0.0000015579664,0.00018724421,0.4301697,0.0038234808,0.46050704,0.020641413,0.0825223],"study_design_scores_gemma":[0.001508669,0.00047580327,0.00041891437,0.000093927745,0.00008554336,0.000007274243,0.00007929378,0.97503984,0.009277743,0.0033201175,0.009239014,0.00045383134],"about_ca_topic_score_codex":0.000003846295,"about_ca_topic_score_gemma":9.3295495e-7,"teacher_disagreement_score":0.6027926,"about_ca_system_score_codex":0.00020438642,"about_ca_system_score_gemma":0.00017763663,"threshold_uncertainty_score":0.9998096},"labels":[],"label_agreement":null},{"id":"W4416364784","doi":"10.1148/ryai.240502","title":"Random Convolutions for Domain Generalization of Deep Learning–based Medical Image Segmentation Models","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Deutsche Forschungsgemeinschaft","keywords":"Segmentation; Convolution (computer science); Generalization; Dice; Pattern recognition (psychology); Image segmentation; Scale-space segmentation; Medical imaging","score_opus":0.02848134944934683,"score_gpt":0.32593589604278844,"score_spread":0.2974545465934416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416364784","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":[0.0016622714,0.0001955855,0.99442524,0.0025890847,0.00026456118,0.00056793395,0.0000037923855,0.00011436395,0.0001771486],"genre_scores_gemma":[0.60180104,0.00007055098,0.39715698,0.0004566596,0.00006567398,0.0003468049,0.00004291953,0.000008247209,0.000051097606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985097,0.00021260326,0.00052523485,0.00037200947,0.00014479845,0.00023566607],"domain_scores_gemma":[0.99840456,0.00085038436,0.00017303636,0.00029487585,0.00021712205,0.000060020604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004687149,0.000117763506,0.00022057572,0.00014994314,0.00025737507,0.000019633488,0.0005413072,0.00013121277,0.000031126263],"category_scores_gemma":[0.00030661505,0.000120107856,0.00008426085,0.0005989654,0.00029021886,0.0002159307,0.00006563821,0.00014101742,0.000010137176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043787022,0.00004565514,0.000017450657,0.000010031319,0.000011341435,5.993612e-7,0.0001065294,0.35262376,0.008352932,0.6126512,0.00008980203,0.026046861],"study_design_scores_gemma":[0.000105044586,0.00004041258,0.000009583379,0.000008397265,0.0000075545167,0.0000023249609,0.00002746034,0.68451506,0.029647427,0.28542972,0.00014013585,0.00006686923],"about_ca_topic_score_codex":0.0000064570336,"about_ca_topic_score_gemma":0.000032981297,"teacher_disagreement_score":0.6001388,"about_ca_system_score_codex":0.00005496634,"about_ca_system_score_gemma":0.00013902488,"threshold_uncertainty_score":0.4897856},"labels":[],"label_agreement":null},{"id":"W4416365769","doi":"10.1117/12.3092610","title":"An improved YOLOv11 framework for robust helmet detection with focus on small-scale target detection","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Focus (optics); Feature (linguistics); Object detection; Pooling; Residual; Feature extraction; Component (thermodynamics)","score_opus":0.01616501951215468,"score_gpt":0.26240429919542685,"score_spread":0.24623927968327217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416365769","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":[0.006212499,0.000107063985,0.98626757,0.0016164897,0.0012878017,0.0031919621,0.000016804,0.00069942203,0.00060038874],"genre_scores_gemma":[0.5299687,0.000023528399,0.46758384,0.0006484474,0.00028499865,0.00089225225,0.0000046138894,0.000042029464,0.0005515977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99587166,0.00016811698,0.00068683934,0.0020026104,0.0002863703,0.0009844236],"domain_scores_gemma":[0.9961667,0.000684367,0.0003998179,0.00200666,0.0004364384,0.0003060339],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003627905,0.0006420782,0.00050293375,0.00038437173,0.0013034609,0.00046548416,0.0011887527,0.0005104008,0.000025567935],"category_scores_gemma":[0.00009345697,0.0005891788,0.00019401456,0.0027338157,0.00017763415,0.0007935963,0.00019679748,0.00083773804,0.000026936572],"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":[0.0009601946,0.0006061116,0.000065763095,0.000067450936,0.00009068085,0.0000015068581,0.00015665896,0.08416457,0.029239213,0.023020836,0.00003627487,0.86159074],"study_design_scores_gemma":[0.0007244754,0.002732004,0.00038024262,0.00008568106,0.00006370657,0.000007863856,0.00006210898,0.65985864,0.27345547,0.06130948,0.0008192854,0.0005010375],"about_ca_topic_score_codex":0.000083188424,"about_ca_topic_score_gemma":0.0018894344,"teacher_disagreement_score":0.8610897,"about_ca_system_score_codex":0.00033843474,"about_ca_system_score_gemma":0.00015326464,"threshold_uncertainty_score":0.9999967},"labels":[],"label_agreement":null},{"id":"W4416373490","doi":"10.1016/j.neunet.2025.108327","title":"Bitscaling: Streamlining neural network compression via predictive multi-scale growth of mixed-precision networks","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Beijing Municipal Science and Technology Commission; Chinese Academy of Sciences; National Natural Science Foundation of China; Canadian Anesthesiologists' Society","keywords":"Quantization (signal processing); Scaling; Artificial neural network; Vector quantization; Constraint (computer-aided design); Data compression ratio; Data compression; Matching (statistics)","score_opus":0.01500735442718351,"score_gpt":0.2744722184364783,"score_spread":0.2594648640092948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416373490","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":[0.018526323,0.0021136943,0.9741334,0.0005159728,0.0028225582,0.0009283947,0.00000656284,0.0006633207,0.00028979592],"genre_scores_gemma":[0.9467458,0.00022301145,0.051273935,0.0006653138,0.000734267,0.00012554301,0.0000528778,0.000054569133,0.00012465489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951665,0.00044892333,0.0012619401,0.0013830834,0.00052700663,0.0012125602],"domain_scores_gemma":[0.9956463,0.0014586371,0.0006772338,0.001497709,0.0004028547,0.0003172751],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044278844,0.00064253126,0.00083776587,0.00022499445,0.00066227984,0.00014840157,0.0022930256,0.00044076802,0.000008645426],"category_scores_gemma":[0.00007094027,0.00059595617,0.00033810694,0.0028918881,0.0002773423,0.0008120695,0.0016217842,0.0012196687,0.00000436516],"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":[0.00010608247,0.00014095835,0.009863002,0.000015029925,0.000037422757,0.000008218567,0.000039296912,0.89356387,0.00019138551,0.0013011583,0.0030311157,0.091702476],"study_design_scores_gemma":[0.000956233,0.00017862968,0.015500278,0.00023735127,0.00005089943,0.000015544834,0.0000100619645,0.97994953,0.00042304792,0.0019996355,0.00024219725,0.00043655923],"about_ca_topic_score_codex":0.000029520505,"about_ca_topic_score_gemma":0.00003949931,"teacher_disagreement_score":0.9282195,"about_ca_system_score_codex":0.000086497916,"about_ca_system_score_gemma":0.000040004343,"threshold_uncertainty_score":0.99964917},"labels":[],"label_agreement":null},{"id":"W4416402485","doi":"10.1109/icicnct66124.2025.11232619","title":"Segmentation of Brain Tumors in MRI Images Utilizing the Modified U-Net Model","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Segmentation; Preprocessor; Pixel; Pattern recognition (psychology); Image segmentation; Fuzzy logic","score_opus":0.02917407155796866,"score_gpt":0.3123649511993728,"score_spread":0.28319087964140416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416402485","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":[0.009298147,0.0003284094,0.95697945,0.018719207,0.00010470886,0.00091393973,0.000006775412,0.000057934965,0.013591429],"genre_scores_gemma":[0.94019663,0.0001396065,0.05552449,0.0016917108,0.000015594815,0.000112971786,0.0000032199682,0.000009682784,0.0023060753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977801,0.00015884766,0.0007640242,0.00062049256,0.00028089934,0.00039566404],"domain_scores_gemma":[0.99802834,0.0006233022,0.00024117205,0.0009530482,0.00010554692,0.000048602793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004592964,0.00022980578,0.00027178152,0.0002102686,0.0002387459,0.000099007295,0.0012505761,0.00006561075,0.000010972636],"category_scores_gemma":[0.000055271474,0.00018788289,0.000084945255,0.0022522863,0.00024622047,0.0005748745,0.0005822188,0.0002845856,0.0000066517064],"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":[0.000011498565,0.00007272394,0.00007478066,0.000024792585,0.000008207604,8.0678035e-7,0.00058480556,0.6661391,0.0091117285,0.28604323,0.0015214293,0.036406867],"study_design_scores_gemma":[0.00036682546,0.000018469445,0.0008068152,0.00007958827,0.000008455362,0.0000011507434,0.00035950937,0.91851825,0.025135513,0.054536223,0.000028533714,0.000140669],"about_ca_topic_score_codex":0.000070695896,"about_ca_topic_score_gemma":0.00006950871,"teacher_disagreement_score":0.9308985,"about_ca_system_score_codex":0.00009468327,"about_ca_system_score_gemma":0.00018128399,"threshold_uncertainty_score":0.7661642},"labels":[],"label_agreement":null},{"id":"W4416429575","doi":"10.1109/iccad66269.2025.11241002","title":"Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Chinese Academy of Sciences","keywords":"Field-programmable gate array; Hummingbird; Limiting; Inference; Bandwidth (computing); Scheme (mathematics); Booster (rocketry); Unification","score_opus":0.022794582132595047,"score_gpt":0.3028497259613687,"score_spread":0.28005514382877367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416429575","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":[0.045802563,0.0005811472,0.92330515,0.0028287915,0.00024324059,0.00073842355,0.00001630516,0.0002555409,0.026228812],"genre_scores_gemma":[0.9091253,0.00012552112,0.044576965,0.010315853,0.000099190045,0.00012304592,0.0000036720514,0.000028680322,0.035601746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727327,0.000066525514,0.00046241825,0.0011994606,0.00027214462,0.00072615757],"domain_scores_gemma":[0.99803966,0.0001785637,0.000121718076,0.0013518375,0.00009924476,0.00020894558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017746839,0.00040796274,0.00035174383,0.00020385582,0.00045888932,0.000397678,0.0009290163,0.0001702671,0.00012907546],"category_scores_gemma":[0.000028444132,0.00037890577,0.000093338596,0.0008668668,0.00009135086,0.0005156495,0.0010808429,0.00042408693,0.00016959083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011995006,0.0009926964,0.00017364556,0.00014064532,0.00014652607,0.000040650684,0.0103074955,0.017401332,0.007578857,0.6859871,0.023043623,0.25406745],"study_design_scores_gemma":[0.0009527795,0.00007124474,0.0001507308,0.00008206497,0.000022931743,0.0000031507914,0.00036867795,0.9845534,0.0052075763,0.0036858853,0.0044853524,0.00041618448],"about_ca_topic_score_codex":0.000003869303,"about_ca_topic_score_gemma":0.00003573534,"teacher_disagreement_score":0.96715206,"about_ca_system_score_codex":0.000059819547,"about_ca_system_score_gemma":0.00010846904,"threshold_uncertainty_score":0.9998663},"labels":[],"label_agreement":null},{"id":"W4416444018","doi":"10.1016/j.softx.2025.102445","title":"SegEv: semantic segmentation performance verification and evaluation software","year":2025,"lang":"en","type":"article","venue":"SoftwareX","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Science and Technology Department of Henan Province; Natural Science Foundation of Chongqing; Chinese Aeronautical Establishment; Zhengzhou University; National Natural Science Foundation of China","keywords":"Segmentation; Visualization; Software deployment; Software; Modular design; Key (lock); Ground truth; Feature (linguistics)","score_opus":0.017213872339618342,"score_gpt":0.2895209092282574,"score_spread":0.27230703688863905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416444018","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":[0.14396687,0.00033977197,0.85392064,0.000628355,0.00016960686,0.00046452243,0.0000011967015,0.0003250859,0.00018397615],"genre_scores_gemma":[0.8931147,0.000119997,0.105887264,0.00037268567,0.000025527559,0.00021686935,0.000025545987,0.000006884062,0.00023052821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898356,0.00005176759,0.00018867898,0.00038266554,0.00022984117,0.0001634741],"domain_scores_gemma":[0.99913603,0.00012358713,0.00008465303,0.00046349285,0.00015329324,0.00003895416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024785093,0.0001084287,0.000089937275,0.00010555228,0.00025498195,0.00007844421,0.000291368,0.000045478242,0.0000067704314],"category_scores_gemma":[0.00007813092,0.000113117894,0.0000194998,0.0006013506,0.000040350245,0.00061242434,0.000116615796,0.000087554276,0.00003245516],"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":[0.000005189194,0.000030815572,0.012832736,0.000053536503,0.000012070175,2.9161367e-7,0.00023903979,0.0027211537,0.0015294302,0.0068526287,0.00079780177,0.9749253],"study_design_scores_gemma":[0.0011155412,0.00008323482,0.29037967,0.00013495657,0.000070378584,0.0000145294125,0.000050972747,0.6466648,0.015660299,0.0414942,0.0038520424,0.00047935607],"about_ca_topic_score_codex":0.0000030210576,"about_ca_topic_score_gemma":0.0000026114487,"teacher_disagreement_score":0.97444594,"about_ca_system_score_codex":0.000086439664,"about_ca_system_score_gemma":0.000073312855,"threshold_uncertainty_score":0.4612814},"labels":[],"label_agreement":null},{"id":"W4416460133","doi":"10.3390/electronics14234555","title":"Enhancing Self-Driving Segmentation in Adverse Weather Conditions: A Dual Uncertainty-Aware Training Approach to SAM Optimization","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Segmentation; Ambiguity; Adverse weather; Process (computing); Dual (grammatical number); Generalization; Adaptation (eye); Image segmentation","score_opus":0.011470164255887627,"score_gpt":0.2710303510084716,"score_spread":0.25956018675258397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416460133","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":[0.008822837,0.00015290042,0.9885771,0.00048392222,0.00007961498,0.00060894596,0.0000011700924,0.0002887076,0.0009847683],"genre_scores_gemma":[0.56158644,0.00009968733,0.43666178,0.0008020329,0.000046866408,0.00050974055,0.00005438217,0.000018340768,0.00022074611],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863243,0.000070069356,0.00028075045,0.00044991876,0.0001546596,0.00041217],"domain_scores_gemma":[0.99938065,0.00010925531,0.00008055538,0.00031534187,0.00005983892,0.000054346932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001961383,0.00014430303,0.0001433403,0.00022638512,0.00019226159,0.00005291669,0.00030740825,0.00005762656,0.0000044483886],"category_scores_gemma":[0.00003286743,0.00016583184,0.00003541459,0.0015061372,0.00001236255,0.00041521052,0.00009815473,0.00021142255,0.0000074625395],"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":[0.0000030761262,0.00006971288,0.000057088542,0.0000084653175,0.000012389501,7.9025546e-7,0.0016916697,0.9565338,0.0019180331,0.0338692,0.00009467193,0.005741098],"study_design_scores_gemma":[0.0004477584,0.00004770013,0.00018972294,0.000055394412,0.000010972012,0.000006461847,0.0004989727,0.99177206,0.0017463203,0.003203953,0.0017964398,0.00022424966],"about_ca_topic_score_codex":0.0000033469953,"about_ca_topic_score_gemma":0.0001046962,"teacher_disagreement_score":0.5527636,"about_ca_system_score_codex":0.0005259687,"about_ca_system_score_gemma":0.00023352014,"threshold_uncertainty_score":0.6762426},"labels":[],"label_agreement":null},{"id":"W4416478222","doi":"10.1145/3680207.3765592","title":"Demo: Split-and-Pipeline: Collaborative Large Model Inference on Edge Devices","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Testbed; Inference; Enhanced Data Rates for GSM Evolution; Scheme (mathematics); Edge device; Throughput; Data transmission; Edge computing","score_opus":0.021260142145487354,"score_gpt":0.32788836836324514,"score_spread":0.30662822621775776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416478222","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":[0.0026335157,0.0015027908,0.94118696,0.009111384,0.00030123504,0.00096497993,0.000054630542,0.0002265901,0.04401789],"genre_scores_gemma":[0.8744414,0.0011199525,0.10047602,0.00853738,0.00009006047,0.00017277942,0.0000074852196,0.000019053517,0.015135863],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99661607,0.00013155906,0.0006920975,0.0014260163,0.00035961994,0.00077462185],"domain_scores_gemma":[0.99671376,0.0008962244,0.00024767607,0.0012704288,0.0006152168,0.0002566786],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000299299,0.00051155617,0.00047745797,0.00022702706,0.00078900164,0.0004062623,0.0013309482,0.00020726272,0.000039089915],"category_scores_gemma":[0.00017261606,0.00047822882,0.000073925,0.0035124512,0.0002149781,0.0008430801,0.0012514495,0.0005135977,0.00017694908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026268324,0.00019961793,0.0003193239,0.000037997426,0.0000231494,0.0000023226537,0.00031388277,0.025527462,0.00008714153,0.89362955,0.005266679,0.074566625],"study_design_scores_gemma":[0.00054729695,0.000075244425,0.0005251527,0.00014812141,0.00003125757,9.905457e-7,0.00008043655,0.9295663,0.0010097574,0.048218783,0.019379338,0.00041731755],"about_ca_topic_score_codex":0.000006645985,"about_ca_topic_score_gemma":0.00015698138,"teacher_disagreement_score":0.90403885,"about_ca_system_score_codex":0.00010877744,"about_ca_system_score_gemma":0.0006404763,"threshold_uncertainty_score":0.99976695},"labels":[],"label_agreement":null},{"id":"W4416559751","doi":"10.1016/j.array.2025.100594","title":"A real-valued DCT-based spectral CNN architecture for efficient edge deep learning","year":2025,"lang":"en","type":"article","venue":"Array","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"University of Toronto Mississauga; Universiti Teknologi Malaysia","keywords":"MNIST database; Convolutional neural network; Discrete cosine transform; Reduction (mathematics); Benchmark (surveying); Convolution (computer science); Throughput; Deep learning","score_opus":0.010788237038938086,"score_gpt":0.27158707947463423,"score_spread":0.2607988424356961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416559751","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":[0.010396757,0.00006498495,0.9831537,0.0025126815,0.00020383392,0.00047889803,0.0000012547695,0.00034102402,0.0028468566],"genre_scores_gemma":[0.603365,0.0000039833326,0.39480418,0.0007033878,0.000107926484,0.000216149,0.000009144461,0.0000143321295,0.00077585527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998849,0.000048236343,0.00017250523,0.00045130056,0.00012463027,0.00035435817],"domain_scores_gemma":[0.99901074,0.00029312045,0.00007032569,0.0005065779,0.000050703253,0.00006850823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012823443,0.00013773338,0.00014073554,0.00010107738,0.00029569477,0.00005767131,0.00059614296,0.00004570258,0.000004426168],"category_scores_gemma":[0.000060171376,0.0001280945,0.00009580763,0.0006139751,0.000046777615,0.00004369863,0.00006370162,0.00020680178,0.000023122566],"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":[0.000033301123,0.00009742057,0.000132044,0.00003123581,0.00001951651,0.0000027092492,0.00045947245,0.78084093,0.036562387,0.12171788,0.0008900966,0.059213024],"study_design_scores_gemma":[0.0008557681,0.00012520017,0.0010546334,0.000042421623,0.000017256569,0.0000030111328,0.000019770523,0.9151891,0.026877929,0.014824941,0.040688727,0.0003012517],"about_ca_topic_score_codex":0.0000051087377,"about_ca_topic_score_gemma":0.000013763277,"teacher_disagreement_score":0.5929683,"about_ca_system_score_codex":0.000061338906,"about_ca_system_score_gemma":0.000060390776,"threshold_uncertainty_score":0.5223542},"labels":[],"label_agreement":null},{"id":"W4416674895","doi":"10.7717/peerj-cs.3388","title":"Towards optimal sparse CNNs: sparsity-friendly knowledge distillation through feature decoupling","year":2025,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Neural Network Applications","field":"Computer 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":"","keywords":"Pooling; Feature (linguistics); Decoupling (probability); Distillation; Artificial neural network; Convolutional neural network","score_opus":0.021106235920901165,"score_gpt":0.3079105817514071,"score_spread":0.28680434583050596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416674895","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":[0.01133058,0.0003693219,0.977956,0.0037171475,0.0016593323,0.0003605219,0.0000031659263,0.0005276187,0.004076332],"genre_scores_gemma":[0.38268948,0.000022464728,0.6162841,0.0004080498,0.00019293104,0.000024958292,0.000003450788,0.000007812111,0.00036676705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997045,0.000039105777,0.00031808583,0.0013201394,0.0005608423,0.00071681704],"domain_scores_gemma":[0.9977099,0.00017147971,0.00014877217,0.0013488482,0.0004490164,0.0001719941],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051086536,0.0002931795,0.00027625248,0.0002635836,0.0010310734,0.0006230677,0.0032898944,0.00008537537,0.000003767274],"category_scores_gemma":[0.000067980356,0.00028224732,0.00009760325,0.0042011207,0.00047522693,0.0020835656,0.0020831048,0.00032636276,0.00007912141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000994787,0.00018866238,0.0007375695,0.00003397725,0.000018109966,0.000014267389,0.0012914069,0.10610235,0.0011618746,0.55354387,0.010196442,0.32670152],"study_design_scores_gemma":[0.0002474681,0.0000564426,0.008032227,0.000059190726,0.000009244824,0.00002311395,0.000008390939,0.93954825,0.0030332794,0.012492333,0.0361503,0.00033976842],"about_ca_topic_score_codex":0.0000107423375,"about_ca_topic_score_gemma":0.0000099629315,"teacher_disagreement_score":0.8334459,"about_ca_system_score_codex":0.00022828915,"about_ca_system_score_gemma":0.00044739395,"threshold_uncertainty_score":0.999963},"labels":[],"label_agreement":null},{"id":"W4416677112","doi":"10.1109/ieid65666.2025.11255880","title":"Aero-engine damage detection and location method based on the CerberusDet-LS multi-task model","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Workload; Key (lock); Aviation accident; Generalization; Precision and recall; Pattern recognition (psychology)","score_opus":0.02724162851348924,"score_gpt":0.30579691130896586,"score_spread":0.2785552827954766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416677112","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":[0.0009933246,0.00014986303,0.9845058,0.01091446,0.00024120472,0.0012900822,0.000005120671,0.00022494885,0.0016752044],"genre_scores_gemma":[0.6199031,0.00005090141,0.37517643,0.0031394542,0.000035295583,0.00021377372,0.0000022553866,0.00001630857,0.0014625053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973692,0.00034128182,0.00048819205,0.0010586266,0.00030505206,0.0004376168],"domain_scores_gemma":[0.99652314,0.0012644569,0.00018883502,0.0015685016,0.00032743622,0.00012763117],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072043604,0.00038095482,0.00025946164,0.00022781685,0.0008729838,0.00027871755,0.00081532664,0.00016056577,0.000015486718],"category_scores_gemma":[0.00021338518,0.00029840527,0.000072593146,0.0026754383,0.000144451,0.00046293056,0.00033228088,0.0005172752,0.00003179031],"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":[0.000021093796,0.00012103073,0.00000772751,0.0000250155,0.000014146111,4.4975937e-7,0.00008625691,0.78115773,0.006933533,0.03886628,0.00015905873,0.17260765],"study_design_scores_gemma":[0.00054278877,0.00006689493,0.00055291556,0.00006560645,0.00004250436,0.0000014860714,0.000024312129,0.9738106,0.01802236,0.00608101,0.00052082725,0.00026867777],"about_ca_topic_score_codex":0.000033664073,"about_ca_topic_score_gemma":0.00007741856,"teacher_disagreement_score":0.6189097,"about_ca_system_score_codex":0.00015154861,"about_ca_system_score_gemma":0.0001619701,"threshold_uncertainty_score":0.99994683},"labels":[],"label_agreement":null},{"id":"W4416677651","doi":"10.1109/iccams65118.2025.11233924","title":"Advanced Liver Tumor Detection Using 3D Scan Segmentation With Dense Convolutional Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary; Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Convolutional neural network; Preprocessor; Pattern recognition (psychology); Dice; Sørensen–Dice coefficient; Contouring; Feature (linguistics); Image segmentation","score_opus":0.013678779270347321,"score_gpt":0.2611791970686935,"score_spread":0.2475004177983462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416677651","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":[0.052175477,0.00093227526,0.94383526,0.000245558,0.0007599525,0.0012293017,0.000005224158,0.00024429915,0.00057264743],"genre_scores_gemma":[0.7826005,0.00012495152,0.21512313,0.0009571703,0.00016067721,0.00012213213,0.000008385415,0.00002396344,0.0008790751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696404,0.00015939017,0.0006019356,0.0011488809,0.0004265047,0.0006992514],"domain_scores_gemma":[0.99794835,0.0002570556,0.00039236477,0.00075062097,0.00046912834,0.00018248944],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018370636,0.00043869493,0.0003202622,0.0002529668,0.001096128,0.00025060883,0.00054763915,0.000115070696,0.00007682275],"category_scores_gemma":[0.000020566884,0.000436716,0.000091067646,0.002578045,0.0003076562,0.0013482603,0.00032649055,0.0004173556,0.00002202047],"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":[0.00017390015,0.0001111925,0.00051688915,0.000026411475,0.00006163094,0.00001443992,0.00005882525,0.7865563,0.0055160453,0.00852503,0.00005454057,0.1983848],"study_design_scores_gemma":[0.0010680171,0.00016113496,0.003021576,0.00016651851,0.00009474435,0.00010314595,0.000060033322,0.9868403,0.0067606824,0.0006062861,0.00066359335,0.00045396393],"about_ca_topic_score_codex":0.00007543996,"about_ca_topic_score_gemma":0.0003224236,"teacher_disagreement_score":0.73042506,"about_ca_system_score_codex":0.0007272753,"about_ca_system_score_gemma":0.0003575357,"threshold_uncertainty_score":0.9998085},"labels":[],"label_agreement":null},{"id":"W4416727601","doi":"10.1109/igarss55030.2025.11244053","title":"Box-Supervised Instance Segmentation For Building Rooftop Delineation From High Spatial Resolution Remotely Sensed Imagery","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Segmentation; Scale (ratio); Image segmentation; Bounding overwatch; Building model; Minimum bounding box; Image resolution; Feature (linguistics)","score_opus":0.021935669007599728,"score_gpt":0.2905601193347037,"score_spread":0.26862445032710397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416727601","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":[0.042652573,0.0005685995,0.94525874,0.0063835667,0.0018496832,0.0025580109,0.00010639601,0.0004147987,0.0002076087],"genre_scores_gemma":[0.45509607,0.0001644334,0.54174787,0.0012987008,0.00049284106,0.00016839629,0.00022652473,0.000027551114,0.000777654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955208,0.0002296957,0.0013248523,0.0016762653,0.000508198,0.000740245],"domain_scores_gemma":[0.99628514,0.0010099771,0.00053047924,0.0012548048,0.00076062855,0.00015894722],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003596576,0.0005210656,0.0004931647,0.00031942886,0.001034139,0.0004641275,0.0008438537,0.00028413453,0.000042375596],"category_scores_gemma":[0.0002763998,0.0006011596,0.00021188572,0.0016943336,0.00013081501,0.0016206428,0.00036348592,0.00033118203,0.000029116467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003522479,0.0001484508,0.000121242796,0.00006157295,0.000059590406,0.000003146351,0.00021774707,0.045026716,0.17990609,0.02858079,0.0020774747,0.7434449],"study_design_scores_gemma":[0.0020347147,0.00008992489,0.0019516214,0.00019071979,0.00008924826,0.000001595169,0.00004690875,0.86621743,0.09950122,0.02725863,0.002135698,0.0004822769],"about_ca_topic_score_codex":0.0007826152,"about_ca_topic_score_gemma":0.0003476811,"teacher_disagreement_score":0.8211907,"about_ca_system_score_codex":0.0006144711,"about_ca_system_score_gemma":0.00032303086,"threshold_uncertainty_score":0.999644},"labels":[],"label_agreement":null},{"id":"W4416727784","doi":"10.1109/igarss55030.2025.11243006","title":"Benchmarking Cross-Domain Few-Shot Object Detection in Aerial Imagery","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Agence Nationale de la Recherche","keywords":"Object detection; Benchmarking; Context (archaeology); Variety (cybernetics); Object (grammar); Source code; Transfer of learning; Code (set theory)","score_opus":0.014246990722232638,"score_gpt":0.2991490533499821,"score_spread":0.2849020626277495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416727784","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":[0.10020812,0.000029916004,0.8843885,0.00035887788,0.0006007854,0.00021915331,3.947649e-7,0.00020141207,0.013992818],"genre_scores_gemma":[0.93822896,0.000008335114,0.060650468,0.0004298463,0.00012645742,0.000082867795,0.0000012990912,0.000005019781,0.00046675146],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989192,0.00004486445,0.00024197447,0.00041472647,0.00011333302,0.0002659012],"domain_scores_gemma":[0.9992726,0.00016182472,0.000049642127,0.0004476408,0.000034104014,0.000034204844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001930441,0.0001071153,0.00011344558,0.00015928605,0.00016448203,0.00016011245,0.00046468267,0.00005267648,0.000018584116],"category_scores_gemma":[0.00002890147,0.00010752781,0.000043949363,0.0011573149,0.000045633213,0.00046490505,0.00025618618,0.00015110474,0.000023096807],"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":[0.0000764561,0.0001886308,0.007326269,0.000032378524,0.000020770889,0.000035727073,0.00033784015,0.010806976,0.17752953,0.22419482,0.0014111868,0.5780394],"study_design_scores_gemma":[0.0025327776,0.00013724374,0.14143269,0.000105567204,0.00000859926,0.000043072836,0.000056745528,0.2794031,0.24486183,0.301695,0.028634356,0.0010890389],"about_ca_topic_score_codex":0.00003757507,"about_ca_topic_score_gemma":0.00023166463,"teacher_disagreement_score":0.83802086,"about_ca_system_score_codex":0.00010336292,"about_ca_system_score_gemma":0.000045233348,"threshold_uncertainty_score":0.43848568},"labels":[],"label_agreement":null},{"id":"W4416729431","doi":"10.1038/s41597-025-06322-9","title":"Balanced X-ray Security Dataset and Enhanced YOLO for Contraband Detection","year":2025,"lang":"en","type":"article","venue":"Scientific Data","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Department of Industrial and Systems Engineering, Hong Kong Polytechnic University; Hong Kong Polytechnic University","keywords":"Undersampling; Class (philosophy); Software deployment; Adaptability; Enhanced Data Rates for GSM Evolution; A priori and a posteriori; Economic shortage; Downgrade","score_opus":0.024705469303074025,"score_gpt":0.31039716708903015,"score_spread":0.2856916977859561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416729431","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":[0.0029537806,0.0001738346,0.98646533,0.0007242507,0.0010486218,0.000572574,0.007828195,0.000108680666,0.00012474993],"genre_scores_gemma":[0.9517825,0.000029578567,0.036976077,0.00043524947,0.00006970819,0.00015913937,0.009740324,0.000007592098,0.00079981476],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983691,0.000028532948,0.00018126672,0.0010417158,0.00012972181,0.00024966514],"domain_scores_gemma":[0.99713147,0.00018989979,0.00007247134,0.0024731741,0.000069172755,0.00006379347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005429435,0.00010044271,0.00011967271,0.00008458997,0.0005129928,0.00042154387,0.0017868648,0.000036197853,0.0000030467738],"category_scores_gemma":[0.00015723235,0.00009625244,0.000013467068,0.00066894636,0.0001812321,0.0009466975,0.0010292158,0.000081344115,0.000009012732],"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":[0.000053171832,0.00015961089,0.000032240583,0.00010351511,0.000045521923,0.0000015747282,0.00021924736,0.00016141983,0.17688403,0.034796793,0.44291312,0.34462976],"study_design_scores_gemma":[0.00080656464,0.000027299579,0.00038828532,0.000026733414,0.000020834617,0.0000025015654,0.000020624408,0.25023058,0.049852952,0.039289784,0.65909815,0.0002357293],"about_ca_topic_score_codex":0.000004718233,"about_ca_topic_score_gemma":0.00011920272,"teacher_disagreement_score":0.94948924,"about_ca_system_score_codex":0.000017793665,"about_ca_system_score_gemma":0.000048603724,"threshold_uncertainty_score":0.40649572},"labels":[],"label_agreement":null},{"id":"W4416750745","doi":"10.1109/iros60139.2025.11247126","title":"Synthetica: Large Scale Synthetic Data Generation for Robot Perception","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robotics; Rendering (computer graphics); Synthetic data; Robot; Pipeline (software); Inference; Task (project management); Object detection; Cognitive neuroscience of visual object recognition; Robustness (evolution)","score_opus":0.05758539366879605,"score_gpt":0.3331852470182795,"score_spread":0.27559985334948345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416750745","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":[0.0005603199,0.00038613373,0.97830254,0.013034418,0.0012791974,0.0019137985,0.00016773834,0.00024217906,0.0041136504],"genre_scores_gemma":[0.60111314,0.00037030305,0.3869484,0.0016477418,0.00050115836,0.00039129404,0.00028121192,0.000030891853,0.008715861],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960142,0.00013868594,0.00069925934,0.0021056037,0.00029397995,0.0007483148],"domain_scores_gemma":[0.9944298,0.00036540278,0.00017352548,0.0045784432,0.0003056142,0.00014719716],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007496735,0.0003603294,0.00035393285,0.00017795224,0.0010552239,0.0004859231,0.0027899563,0.00019644236,0.00023654223],"category_scores_gemma":[0.0001921213,0.0003677877,0.0001248728,0.0010041991,0.00015230985,0.0013758657,0.0017733201,0.00022195598,0.000216657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003154009,0.0008329353,0.000040335213,0.00012919425,0.00007032929,9.3784354e-7,0.00034843432,0.0068775928,0.043190487,0.19404252,0.10645065,0.64798504],"study_design_scores_gemma":[0.0003917691,0.00005399329,0.00015696608,0.000071267226,0.000103522056,0.000005630512,0.00006999328,0.92857647,0.0017733074,0.002837309,0.06562912,0.00033062528],"about_ca_topic_score_codex":0.000006868222,"about_ca_topic_score_gemma":0.00012564044,"teacher_disagreement_score":0.9216989,"about_ca_system_score_codex":0.00014883577,"about_ca_system_score_gemma":0.0001825153,"threshold_uncertainty_score":0.9998774},"labels":[],"label_agreement":null},{"id":"W4416859068","doi":"10.23977/cpcs.2025.090108","title":"Research on Optimization of Deep Learning in Handwritten Digit Recognition","year":2025,"lang":"","type":"article","venue":"Computing Performance and Communication systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"MNIST database; Convolutional neural network; Dropout (neural networks); Hyperparameter; Digit recognition; Pattern recognition (psychology); Deep learning; Overfitting; Perceptron","score_opus":0.06695506891178826,"score_gpt":0.34430754955971743,"score_spread":0.27735248064792917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416859068","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":[0.43485886,0.00735514,0.54065377,0.0010164576,0.0003687067,0.0015150061,0.0000021041842,0.00015698835,0.014072987],"genre_scores_gemma":[0.98809254,0.0064409235,0.004996722,0.00005300385,0.00004211542,0.00007283044,0.000029842977,0.000014991443,0.00025700123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655944,0.0010351931,0.0010313509,0.00054915505,0.00039888525,0.00042594617],"domain_scores_gemma":[0.9962879,0.0012759956,0.00047899017,0.0012237242,0.0006701637,0.00006327038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021490555,0.00021618504,0.00039623491,0.00084148155,0.0010197606,0.00031225852,0.0011374614,0.00017402538,0.0000022219997],"category_scores_gemma":[0.000099849596,0.0002424766,0.00004058213,0.0031137564,0.0002829537,0.0005746064,0.00090142793,0.0010243898,0.000016175296],"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":[0.000025765425,0.00008597006,0.0033995067,0.00018035315,0.000010096802,1.7869719e-7,0.0007797176,0.67038333,0.000013978701,0.003240793,0.000029648278,0.3218507],"study_design_scores_gemma":[0.0005415204,0.00016854811,0.0059774294,0.0031919065,0.0000070044994,0.000004415451,0.0008045424,0.98786473,0.00017542495,0.0002091999,0.00086745457,0.00018784606],"about_ca_topic_score_codex":0.000054113545,"about_ca_topic_score_gemma":0.000008265162,"teacher_disagreement_score":0.55323374,"about_ca_system_score_codex":0.0001647198,"about_ca_system_score_gemma":0.000087594875,"threshold_uncertainty_score":0.98879087},"labels":[],"label_agreement":null},{"id":"W4416862949","doi":"10.23977/jaip.2025.080319","title":"A Review of the Basic Applications of Machine Vision in Medical Image Segmentation","year":2025,"lang":"","type":"review","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Image segmentation; Scale-space segmentation; Medical imaging; Segmentation-based object categorization; Machine vision; Image processing","score_opus":0.0457293447663657,"score_gpt":0.43775138917076223,"score_spread":0.39202204440439653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416862949","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":[7.9287025e-7,0.50716287,0.48414534,0.006630712,0.0003686308,0.0014338203,0.0000131566085,0.000003686977,0.0002410196],"genre_scores_gemma":[0.00015741361,0.95542544,0.042903196,0.001155882,0.00019931511,0.000106890206,0.0000047585945,0.000019440522,0.000027684375],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9885808,0.0020778861,0.006513207,0.00056919525,0.001913743,0.0003451348],"domain_scores_gemma":[0.9762344,0.008035718,0.011674799,0.0013631516,0.0024977308,0.00019420558],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0067926887,0.00044644758,0.0018475802,0.00054265535,0.00017988558,0.0000730575,0.003779533,0.0002703474,0.00021460002],"category_scores_gemma":[0.010690506,0.0003231653,0.00085289596,0.006527846,0.000495534,0.0015978942,0.0008094871,0.0021042896,0.00002827769],"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":[0.000040756047,0.0008895343,0.0000013244911,0.0223184,0.00006718358,0.000010052234,0.00013723243,0.00019575703,0.000100517565,0.008327805,0.0002484597,0.967663],"study_design_scores_gemma":[0.0001628663,0.00066119496,0.0000077637615,0.45245963,0.0024768512,0.0011457345,0.0003810521,0.016291292,0.004333776,0.016929505,0.50441694,0.0007333904],"about_ca_topic_score_codex":0.00003385258,"about_ca_topic_score_gemma":0.000031062766,"teacher_disagreement_score":0.9669296,"about_ca_system_score_codex":0.00027204907,"about_ca_system_score_gemma":0.002567768,"threshold_uncertainty_score":0.99992204},"labels":[],"label_agreement":null},{"id":"W4416863425","doi":"10.23977/jeis.2025.100215","title":"Real-Time Pedestrian Detection System Based on YOLOv5-tiny","year":2025,"lang":"","type":"article","venue":"Journal of Electronics and Information Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian detection; Pedestrian; Cluster analysis; Frame (networking); Key (lock); Frame rate; Field (mathematics); Object detection","score_opus":0.0063521717523176945,"score_gpt":0.2516646783128207,"score_spread":0.245312506560503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416863425","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":[0.041771453,0.00028918678,0.9397403,0.0026359735,0.0009472107,0.00047751117,0.0000034307343,0.000055755187,0.014079187],"genre_scores_gemma":[0.99312127,0.0010974354,0.0051754653,0.00046640067,0.00007358306,0.0000061194633,5.9504265e-7,0.0000040282107,0.000055130684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969535,0.00007588311,0.0012282269,0.00025457554,0.0009397176,0.00054812763],"domain_scores_gemma":[0.99631685,0.00023911425,0.0015371955,0.0004847343,0.0011972255,0.00022489393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025195263,0.00021610266,0.00030828023,0.0012278482,0.0010022959,0.0009133665,0.0011217961,0.00009728395,0.0000041255635],"category_scores_gemma":[0.00028590646,0.0001911946,0.000101193444,0.003993233,0.00028294962,0.008553372,0.00013981595,0.0005246653,0.000025042713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036829986,0.00015316455,0.00008172201,0.00025169496,0.000030547628,0.000004164043,0.0006255474,0.1110569,0.039296895,0.2909883,0.00048303616,0.5566597],"study_design_scores_gemma":[0.00068400777,0.0009025068,0.0009707312,0.0002380261,0.000025207986,0.00007588349,0.00006987407,0.9727737,0.011341162,0.0006444275,0.012099343,0.0001751769],"about_ca_topic_score_codex":0.000003996303,"about_ca_topic_score_gemma":0.0000012251137,"teacher_disagreement_score":0.9513498,"about_ca_system_score_codex":0.00084466045,"about_ca_system_score_gemma":0.002213152,"threshold_uncertainty_score":0.8807614},"labels":[],"label_agreement":null},{"id":"W4416874671","doi":"10.1109/icoici65217.2025.11253661","title":"A Robust EFPN and CFF-Backbone for Small Object Detection in Visually Degraded Challenging Environments","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Object detection; Feature (linguistics); Pyramid (geometry); Software deployment; Feature extraction; Detector; Deep learning; Sensor fusion; Pattern recognition (psychology); Object (grammar)","score_opus":0.038267656276063135,"score_gpt":0.2658197169078288,"score_spread":0.22755206063176567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416874671","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":[0.041764162,0.0012423557,0.95289904,0.0014479922,0.00028428007,0.0018174056,0.0000024464118,0.00009301792,0.0004492712],"genre_scores_gemma":[0.93537855,0.0011582276,0.060682226,0.0004079567,0.00006413821,0.000541659,0.0000029637908,0.000025666852,0.001738602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722433,0.00009047549,0.00061478984,0.0012212167,0.0001656745,0.00068348576],"domain_scores_gemma":[0.998582,0.0004625522,0.00016837263,0.0006277516,0.00002744763,0.00013188629],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033377414,0.00037150315,0.00037007194,0.0003539588,0.00042983968,0.00015599925,0.0005241005,0.00018599667,0.000006617129],"category_scores_gemma":[0.000056204328,0.00041220442,0.000093339615,0.0009927191,0.00011398833,0.00040309184,0.0004898093,0.00031283335,0.000010168113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010731981,0.00045291617,0.0004651834,0.00016639325,0.0000691488,0.0000060086445,0.00058795896,0.043230318,0.021609241,0.02154651,0.000028711818,0.9117303],"study_design_scores_gemma":[0.0017831267,0.00022494666,0.007425086,0.00015032069,0.000035462366,0.000009262569,0.0001159363,0.96305525,0.018574333,0.006333329,0.0018394883,0.00045343162],"about_ca_topic_score_codex":0.00002898457,"about_ca_topic_score_gemma":0.00051573996,"teacher_disagreement_score":0.91982496,"about_ca_system_score_codex":0.00023332049,"about_ca_system_score_gemma":0.000052826268,"threshold_uncertainty_score":0.999833},"labels":[],"label_agreement":null},{"id":"W4416922229","doi":"10.1109/access.2025.3639410","title":"DSConvNet: A Lightweight Architecture for Extracting Image Features From Depthwise Separable Convolution Network for Edge Devices","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Convolution (computer science); Benchmark (surveying); Convolutional neural network; Block (permutation group theory); Image (mathematics); Feature (linguistics); Separable space; Enhanced Data Rates for GSM Evolution; Object (grammar)","score_opus":0.021854188176229895,"score_gpt":0.33183025417266554,"score_spread":0.3099760659964356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416922229","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":[0.0068003302,0.0017593431,0.98559386,0.0022310582,0.0011451322,0.001467472,0.00005067333,0.00030109464,0.0006510326],"genre_scores_gemma":[0.44981095,0.000055858676,0.5423986,0.0028453816,0.0017293467,0.0020719697,0.000097926466,0.000044428674,0.00094555435],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982498,0.000043544063,0.00031750792,0.0007278466,0.00012862883,0.0005326441],"domain_scores_gemma":[0.9973495,0.0014957446,0.00024058708,0.00066035765,0.00017007743,0.000083739615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015667587,0.00024247296,0.00027318415,0.00009084689,0.0005814466,0.00047504305,0.0015987088,0.00012472439,0.000003815929],"category_scores_gemma":[0.000059551843,0.00022529614,0.00013535233,0.00065587147,0.000049916445,0.001051053,0.00018693919,0.0002112789,0.000005573947],"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":[0.00053766294,0.0003498817,0.003082108,0.0004983804,0.00043215172,0.00001283976,0.0008439359,0.10011963,0.03874529,0.07607387,0.447994,0.33131027],"study_design_scores_gemma":[0.0021039818,0.00009111103,0.01018231,0.00037847637,0.00015830559,0.000009860592,0.000023318566,0.15933228,0.09449287,0.39833218,0.3338742,0.0010211118],"about_ca_topic_score_codex":0.000042409094,"about_ca_topic_score_gemma":0.0003513091,"teacher_disagreement_score":0.44319528,"about_ca_system_score_codex":0.000057454763,"about_ca_system_score_gemma":0.000080875645,"threshold_uncertainty_score":0.918731},"labels":[],"label_agreement":null},{"id":"W4416960425","doi":"10.1109/embc58623.2025.11252728","title":"UA-UNet: Uncertainty Aware Pseudo-Label Generation in Residual U-Net for Medical Image Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Interpretability; Segmentation; Image segmentation; Scale-space segmentation; Consistency (knowledge bases); Residual; Pattern recognition (psychology); Noise (video); Dice","score_opus":0.02356711719001247,"score_gpt":0.3324593845611456,"score_spread":0.3088922673711331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416960425","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":[0.010239629,0.00003169457,0.97685534,0.011312815,0.00016087321,0.0006176967,0.000005720251,0.00013159729,0.00064461585],"genre_scores_gemma":[0.21305023,0.000083786355,0.7762857,0.0062760166,0.00034648203,0.0010962419,0.00028649386,0.000018361896,0.0025566507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871844,0.00005329012,0.00031966675,0.00042979742,0.00024622155,0.00023260135],"domain_scores_gemma":[0.99923027,0.00020714093,0.00005769626,0.0003544046,0.00008819001,0.00006231697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026327017,0.000111986395,0.00011876855,0.0001104897,0.00013469098,0.000076044045,0.0004852387,0.00007896989,0.000029977718],"category_scores_gemma":[0.00007978954,0.00010513119,0.00002396601,0.00059329113,0.000039576298,0.0003904144,0.00014148372,0.00011038734,0.000012852936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003990066,0.00034109867,0.0005571251,0.000053185555,0.000026797945,0.000009758159,0.000341292,0.021191599,0.03834805,0.47037137,0.16193914,0.3067807],"study_design_scores_gemma":[0.00097410055,0.00003578013,0.0003332115,0.000013508018,0.0000037106322,0.0000021206129,0.000026480115,0.9740693,0.009934414,0.0123911025,0.002086491,0.0001297823],"about_ca_topic_score_codex":0.000038883696,"about_ca_topic_score_gemma":0.0005831705,"teacher_disagreement_score":0.9528777,"about_ca_system_score_codex":0.000103178216,"about_ca_system_score_gemma":0.00013873162,"threshold_uncertainty_score":0.42871258},"labels":[],"label_agreement":null},{"id":"W4416961726","doi":"10.1109/pst65910.2025.11268887","title":"Context-Aware Location De-Identification Using Denoising Diffusion","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Inpainting; Image (mathematics); Object (grammar); Consistency (knowledge bases); Pattern recognition (psychology); Probabilistic logic; Noise reduction; Digital image; Noise (video)","score_opus":0.02759573879075157,"score_gpt":0.31183504474726764,"score_spread":0.2842393059565161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416961726","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":[0.04174766,0.0010611974,0.95179373,0.0029606803,0.00083151605,0.0007913916,0.0000021021235,0.000254493,0.0005572403],"genre_scores_gemma":[0.9741311,0.00016344519,0.020566942,0.0013095883,0.00013016553,0.000044439035,0.000009849349,0.000021241643,0.0036232427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699986,0.00020097473,0.0008617552,0.0010278045,0.00032436068,0.00058521604],"domain_scores_gemma":[0.99725515,0.00026108607,0.00040475014,0.0013288701,0.00060084224,0.00014930539],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004313088,0.00032194637,0.0002740931,0.00032073067,0.0011689218,0.00059482193,0.0010863612,0.00020698727,0.000037475253],"category_scores_gemma":[0.00011294548,0.0003653871,0.0000996845,0.0029848225,0.00016916494,0.0010939421,0.0005677222,0.00030915914,0.000091649665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021580538,0.00029036746,0.002277755,0.00013736018,0.000041110354,0.0000038693956,0.0007006391,0.0466755,0.07459126,0.19849826,0.0009860056,0.6757763],"study_design_scores_gemma":[0.00032764248,0.000015439377,0.005575288,0.00031115158,0.00006037352,0.000014020898,0.00019588732,0.9575612,0.022695733,0.011210167,0.0017141683,0.00031890508],"about_ca_topic_score_codex":0.00016771897,"about_ca_topic_score_gemma":0.00008225216,"teacher_disagreement_score":0.9323834,"about_ca_system_score_codex":0.0007128984,"about_ca_system_score_gemma":0.00049608055,"threshold_uncertainty_score":0.99987984},"labels":[],"label_agreement":null},{"id":"W4416961998","doi":"10.1109/ist66504.2025.11268401","title":"3D Conditional Image Synthesis of Left Atrial LGE MRI from Composite Semantic Masks","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Segmentation; Pipeline (software); Pattern recognition (psychology); Endocardium; Image segmentation; Cardiac magnetic resonance; Training set","score_opus":0.010610323664335711,"score_gpt":0.26802811902156287,"score_spread":0.25741779535722714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416961998","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":[0.0067509734,0.00052899285,0.9769785,0.0059183757,0.0008605579,0.000741439,0.0003678564,0.00016230064,0.0076910383],"genre_scores_gemma":[0.77373517,0.00018759107,0.2236805,0.0005941868,0.00032627097,0.000020914029,0.000057442143,0.000020142077,0.0013777585],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99646044,0.00022960015,0.0011053035,0.0011228569,0.00054283114,0.0005389992],"domain_scores_gemma":[0.9948595,0.002832706,0.00044552668,0.0013961149,0.00029821147,0.00016791088],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002108951,0.0004203793,0.0007027388,0.00024027406,0.00041866326,0.00019136556,0.001688432,0.00018541634,0.0011427848],"category_scores_gemma":[0.0000927789,0.00044126567,0.0004211053,0.0009251393,0.00055549416,0.0006743927,0.00096975913,0.00034735835,0.0003354717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000607434,0.0015792316,0.007287632,0.00044419174,0.0023823385,0.000100496494,0.0007155434,0.046043802,0.16493548,0.63957846,0.033989813,0.10233557],"study_design_scores_gemma":[0.0015145506,0.000083163126,0.013127634,0.00041939697,0.00061134406,0.0000155822,0.000033502165,0.774901,0.10580483,0.09729003,0.005346393,0.00085260987],"about_ca_topic_score_codex":0.00012669853,"about_ca_topic_score_gemma":0.000040050207,"teacher_disagreement_score":0.7669842,"about_ca_system_score_codex":0.000100856516,"about_ca_system_score_gemma":0.00023940526,"threshold_uncertainty_score":0.9998039},"labels":[],"label_agreement":null},{"id":"W4416963043","doi":"10.1109/cisp-bmei68103.2025.11259273","title":"Hardware-Accelerated Pedestrian Detection and Low-Latency Video Streaming for Drone Applications","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Pedestrian detection; Drone; Object detection; Benchmark (surveying); Pedestrian; Pipeline (software); Software deployment; Video tracking; Enhanced Data Rates for GSM Evolution","score_opus":0.02246671417504733,"score_gpt":0.2878430025535817,"score_spread":0.2653762883785344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416963043","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":[0.005128994,0.0009982443,0.98536175,0.0023277884,0.00034160266,0.003755667,0.000026013853,0.00040437185,0.0016555588],"genre_scores_gemma":[0.9446127,0.00076852995,0.046272416,0.00044618733,0.00021114832,0.002919893,0.000023023445,0.000033609653,0.0047125025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969435,0.000060513943,0.00077567657,0.001382909,0.00018308657,0.0006543656],"domain_scores_gemma":[0.99754316,0.0005015902,0.00027663595,0.0010579,0.0003999308,0.00022076991],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00021236419,0.00041380562,0.00038392673,0.00028907388,0.0014290374,0.00050548,0.000732014,0.00021697691,0.000023853152],"category_scores_gemma":[0.00006818062,0.00045445995,0.00011296539,0.0023001789,0.00017456718,0.0008303646,0.00040516295,0.00029213857,0.000027973078],"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":[0.00003704316,0.00019418116,0.0001247045,0.00019280874,0.000060840626,7.086693e-7,0.00009069103,0.0012482591,0.024977239,0.042627674,0.0002848928,0.93016094],"study_design_scores_gemma":[0.001711753,0.00021336722,0.0025819603,0.00015407262,0.00014022269,0.0000135284,0.00010897194,0.8891499,0.033583216,0.03654891,0.035039585,0.00075448095],"about_ca_topic_score_codex":0.00006482605,"about_ca_topic_score_gemma":0.00019503174,"teacher_disagreement_score":0.9394837,"about_ca_system_score_codex":0.00015950539,"about_ca_system_score_gemma":0.00020717124,"threshold_uncertainty_score":0.99987096},"labels":[],"label_agreement":null},{"id":"W4417003382","doi":"10.1109/icce-asia67487.2025.11263738","title":"A Comprehensive Labeling Protocol and Real-time Inference Framework for Road Hazard Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Inference; Robustness (evolution); Software deployment; Protocol (science); Annotation; Component (thermodynamics); Scheme (mathematics)","score_opus":0.02990918452385403,"score_gpt":0.3548586765694692,"score_spread":0.32494949204561513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417003382","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":[0.0011486525,0.000041067087,0.89676565,0.0013118449,0.00021650032,0.099424146,0.0000057277334,0.00031355114,0.0007728369],"genre_scores_gemma":[0.04761404,0.00009448196,0.7062241,0.00080668746,0.00019615194,0.24352816,0.000002202579,0.000029563707,0.0015046239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972775,0.00010520155,0.00064910715,0.0011692455,0.00020912806,0.0005897997],"domain_scores_gemma":[0.99653536,0.0014972046,0.00028580296,0.0009507584,0.00056728785,0.00016356967],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015852282,0.0003910695,0.0004276123,0.00017807081,0.000834071,0.00040835104,0.00066015136,0.00027715927,0.000028775325],"category_scores_gemma":[0.0002361255,0.00039840696,0.000098053766,0.0013985534,0.00019792264,0.000536618,0.0006859202,0.0003868502,0.00005076517],"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":[0.0001580935,0.000121542354,0.000119940916,0.00034183112,0.000057712474,0.0000012679654,0.00022952525,0.002040635,0.022288341,0.16462882,0.00022696164,0.8097853],"study_design_scores_gemma":[0.00096595834,0.0003298725,0.0014797375,0.00045480992,0.000034753895,0.000005231275,0.000030789397,0.76558167,0.012210411,0.20664555,0.011791263,0.00046998056],"about_ca_topic_score_codex":0.000042084077,"about_ca_topic_score_gemma":0.000024225821,"teacher_disagreement_score":0.8093153,"about_ca_system_score_codex":0.00009939415,"about_ca_system_score_gemma":0.00017702661,"threshold_uncertainty_score":0.99984676},"labels":[],"label_agreement":null},{"id":"W4417031279","doi":"10.1016/j.aej.2025.11.042","title":"Edge-guided Multi-scale Attention Fusion Network for gastrointestinal tumor image classification","year":2025,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"Health Commission of Jiangxi Province; Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Discriminative model; Pattern recognition (psychology); Block (permutation group theory); Feature extraction; Pooling; Feature (linguistics); Noise (video); Fusion; Enhanced Data Rates for GSM Evolution","score_opus":0.022177792497603002,"score_gpt":0.2774107840346707,"score_spread":0.2552329915370677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417031279","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":[0.0048528626,0.00011662796,0.99271107,0.00080146117,0.00079609186,0.00041001197,0.000002328875,0.00021556884,0.00009400378],"genre_scores_gemma":[0.0309244,0.000031284657,0.9678188,0.000105571606,0.0005129661,0.00014083205,0.000009529223,0.000019386784,0.00043724588],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998686,0.000025531921,0.00041893782,0.00031471858,0.00014623089,0.0004085635],"domain_scores_gemma":[0.99896854,0.0001592258,0.0001792421,0.0003514656,0.00022091012,0.00012061762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024991384,0.00017566477,0.0001695085,0.00013528443,0.00033740475,0.00020203558,0.0005528592,0.000033194636,0.00000584685],"category_scores_gemma":[0.000101609214,0.00017639887,0.00012193084,0.0005066719,0.000021844136,0.00045737528,0.00010560064,0.00028994778,0.0000111576455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005577084,0.00024289578,0.005169831,0.0001354075,0.000054430602,0.00002734195,0.00006267962,0.33686277,0.5455082,0.04094393,0.044590052,0.026346676],"study_design_scores_gemma":[0.0007918605,0.00004469393,0.024603693,0.0002351977,0.000017470744,0.0004360683,0.000005511605,0.96535397,0.00058034185,0.0008498543,0.006910484,0.00017086383],"about_ca_topic_score_codex":7.129501e-7,"about_ca_topic_score_gemma":0.0000015196032,"teacher_disagreement_score":0.62849116,"about_ca_system_score_codex":0.00011378336,"about_ca_system_score_gemma":0.000056085122,"threshold_uncertainty_score":0.7193337},"labels":[],"label_agreement":null},{"id":"W4417052472","doi":"10.1109/atc67618.2025.11268782","title":"Feature Fusion and Knowledge-Distilled Multi-Modal Multi-Target Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pipeline (software); Feature (linguistics); Inference; Domain (mathematical analysis); Sensor fusion; Pattern recognition (psychology); Domain knowledge; Function (biology); Software deployment","score_opus":0.016984574615379968,"score_gpt":0.29630120835432605,"score_spread":0.2793166337389461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417052472","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":[0.003973679,0.004962568,0.9832908,0.0031304911,0.0015500034,0.0012401487,0.000008312519,0.00041127577,0.0014327365],"genre_scores_gemma":[0.6889072,0.00051586045,0.27821508,0.00037071045,0.00013595783,0.0001287315,0.0000057314114,0.00002367851,0.031697016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707717,0.00018296902,0.00042772418,0.0014708685,0.00019751437,0.00064374035],"domain_scores_gemma":[0.9979181,0.00027824688,0.0001870791,0.0010326174,0.00031580613,0.00026814462],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000234664,0.0005097877,0.00042928092,0.0002927645,0.0011099215,0.0002814325,0.00078261725,0.00037472244,0.000032088305],"category_scores_gemma":[0.000121018755,0.00048456338,0.00013792382,0.0019508854,0.00024272004,0.00057276175,0.0013067577,0.000683004,0.000108376116],"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":[0.00009065006,0.0009452677,0.00081803085,0.00016021902,0.00007646165,0.000009032188,0.0008184091,0.0021820138,0.035951626,0.008069625,0.0027953312,0.94808334],"study_design_scores_gemma":[0.0019112548,0.000077524885,0.0097918855,0.000092488204,0.00003727136,0.000015880272,0.000051169376,0.9314004,0.015703341,0.00078533974,0.039679684,0.00045373032],"about_ca_topic_score_codex":0.000024803248,"about_ca_topic_score_gemma":0.0005028943,"teacher_disagreement_score":0.94762963,"about_ca_system_score_codex":0.00016630038,"about_ca_system_score_gemma":0.00015832548,"threshold_uncertainty_score":0.9997606},"labels":[],"label_agreement":null},{"id":"W4417093832","doi":"10.1007/s11554-025-01816-6","title":"PRepDETR: a real-time detection transformer with partial convolutions and efficient cross-scale feature fusion","year":2025,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Object detection; Computation; Convolution (computer science); Feature extraction; Weighting; Feature (linguistics); Minimum bounding box; Pascal (unit); Fusion","score_opus":0.005182249866145906,"score_gpt":0.2678167437060388,"score_spread":0.2626344938398929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417093832","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":[0.22788532,0.00030116734,0.7683107,0.0011672538,0.00006685441,0.00027786105,0.0000029677385,0.00011846146,0.0018693851],"genre_scores_gemma":[0.724453,0.0003622028,0.27282175,0.00011032699,0.00020680272,0.000032742108,0.0000029221706,0.000034117227,0.0019761536],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984627,0.000058409878,0.00044206218,0.00036217962,0.00034647353,0.0003282181],"domain_scores_gemma":[0.9985399,0.00010348839,0.00044425434,0.00026168567,0.00050206727,0.00014860625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033645585,0.00020475237,0.0002984353,0.00022061839,0.00059125735,0.000385632,0.00035332938,0.000095937,0.0000076066817],"category_scores_gemma":[0.000029625282,0.00015944173,0.000081220474,0.00093009253,0.00020836765,0.0010397367,0.000052800202,0.00034074928,0.000007260619],"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":[0.00021828819,0.00014119549,0.00025924548,0.00009506103,0.000026302514,0.000020654135,0.00046443043,0.0036085704,0.909408,0.000053752345,0.00037194317,0.08533261],"study_design_scores_gemma":[0.0032607352,0.00067347917,0.0126549555,0.0012371441,0.00023495307,0.001536893,0.00008871002,0.6753891,0.30000117,0.0016054494,0.002605422,0.00071194716],"about_ca_topic_score_codex":0.000004873393,"about_ca_topic_score_gemma":0.0000027957851,"teacher_disagreement_score":0.6717806,"about_ca_system_score_codex":0.00011358143,"about_ca_system_score_gemma":0.00022029421,"threshold_uncertainty_score":0.6501845},"labels":[],"label_agreement":null},{"id":"W4417121803","doi":"10.1145/3779421","title":"Improved Image Classification using Lightweight Deep Neural Network Enhancements","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Convolutional neural network; Inference; Binary number; Pattern recognition (psychology); Artificial neural network; Contextual image classification; Deep learning; Binary classification","score_opus":0.022651556828293368,"score_gpt":0.2857864674172074,"score_spread":0.26313491058891403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417121803","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":[0.004479111,0.00081305887,0.9902752,0.0023638397,0.00086805894,0.0006184438,0.000002285467,0.0004319939,0.00014802064],"genre_scores_gemma":[0.94461876,0.00029331428,0.05425503,0.000117753916,0.000041286643,0.0003188629,0.0000024155333,0.000013509403,0.00033908512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984925,0.000043065862,0.00042874404,0.0005796448,0.00010254341,0.00035351235],"domain_scores_gemma":[0.99842364,0.0001144871,0.00014538114,0.0011384148,0.00012404387,0.000054050226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010059759,0.00020230252,0.00022787505,0.00037224955,0.0004768579,0.00010009025,0.00081462896,0.00019082136,0.0000053644053],"category_scores_gemma":[0.000014843866,0.00019248221,0.00004778284,0.001505756,0.00012079435,0.000248695,0.000052028383,0.00030368706,0.000014267622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029733239,0.00030195338,0.00022392433,0.000090637775,0.00019391757,0.0000063086463,0.000099426245,0.017887212,0.062021248,0.3038998,0.00016079214,0.61508507],"study_design_scores_gemma":[0.00022377499,0.00011313344,0.00004747718,0.00008196468,0.00003733168,0.00003433417,0.00011236089,0.94641036,0.026693955,0.018461026,0.0075135245,0.0002707415],"about_ca_topic_score_codex":0.000018252584,"about_ca_topic_score_gemma":0.000015424308,"teacher_disagreement_score":0.94013965,"about_ca_system_score_codex":0.00010332201,"about_ca_system_score_gemma":0.000024584024,"threshold_uncertainty_score":0.7849197},"labels":[],"label_agreement":null},{"id":"W4417150058","doi":"10.48550/arxiv.2503.16546","title":"A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Convolutional neural network; Inference; Feature learning; Feature extraction; Key (lock); Preprocessor; Big data; Natural language understanding; Feature (linguistics)","score_opus":0.13531040825514948,"score_gpt":0.3925663060686022,"score_spread":0.2572558978134527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417150058","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":[0.3394696,0.290287,0.32992646,0.019174607,0.0015720678,0.009587717,0.00022162661,0.0016483543,0.008112561],"genre_scores_gemma":[0.88398445,0.096152544,0.014205862,0.00035491752,0.0002940827,0.004476145,0.00012590247,0.00004516894,0.00036091855],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9963712,0.00067259726,0.00046442778,0.0015335947,0.00038388232,0.0005742671],"domain_scores_gemma":[0.995541,0.002251501,0.00017184735,0.0015789507,0.0003244444,0.00013228827],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005680869,0.00034897673,0.00042013137,0.0006544704,0.00045402237,0.00008303152,0.0013845554,0.0001832546,0.0000021535075],"category_scores_gemma":[0.00014409039,0.00036338335,0.0000683279,0.0013740791,0.00021906244,0.00021945621,0.0022500628,0.0017330017,0.000031739433],"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":[0.00004987111,0.0003185875,0.056387357,0.00041379436,0.000060514954,0.000012381284,0.0014281141,0.023555998,0.00009049637,0.024627458,0.00014807291,0.8929074],"study_design_scores_gemma":[0.00040639166,0.000082962426,0.84190035,0.0004777408,0.000009610731,0.000010451091,0.0001419466,0.012416026,0.0001573298,0.037836406,0.10581085,0.000749945],"about_ca_topic_score_codex":0.00023157064,"about_ca_topic_score_gemma":0.002247481,"teacher_disagreement_score":0.89215744,"about_ca_system_score_codex":0.00017089164,"about_ca_system_score_gemma":0.00011090808,"threshold_uncertainty_score":0.9998818},"labels":[],"label_agreement":null},{"id":"W4417168797","doi":"10.1016/j.eswa.2025.130732","title":"An uncertain boundary region-aware network for multi-scale liver tumor segmentation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Boundary (topology); Segmentation; Feature (linguistics); Context (archaeology); Key (lock); Reinforcement learning; Focus (optics)","score_opus":0.029757990334915246,"score_gpt":0.31708520237489907,"score_spread":0.2873272120399838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417168797","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":[0.00012104184,0.0014671413,0.9927022,0.0007325747,0.00018207758,0.0040689996,0.000016877219,0.0005485163,0.0001605558],"genre_scores_gemma":[0.09897418,0.00007780884,0.8542567,0.0016786591,0.0006352169,0.0421834,0.00019626945,0.000052814954,0.0019449577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981218,0.00007843856,0.00039673515,0.00080724235,0.00018829947,0.00040747796],"domain_scores_gemma":[0.99781793,0.00018297862,0.00022522073,0.0013563412,0.00026819602,0.00014930245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013909406,0.00024309653,0.0002440335,0.00009936836,0.00096702756,0.00024462742,0.0009467492,0.000070158734,0.0000014073985],"category_scores_gemma":[0.0000042568313,0.00021937693,0.00005885461,0.0010742322,0.00010042485,0.00054830796,0.00007996233,0.00011551145,0.000020627245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015778482,0.0014780622,0.003764813,0.0004323091,0.00023405132,0.000016394373,0.0036338351,0.1691042,0.005276872,0.5923883,0.13203816,0.091475256],"study_design_scores_gemma":[0.0008406651,0.000102549486,0.0004289002,0.00011767273,0.000019337805,0.000044855056,0.0005243468,0.7581404,0.00048804423,0.0014519606,0.2374037,0.00043758488],"about_ca_topic_score_codex":0.000114284725,"about_ca_topic_score_gemma":0.00012547924,"teacher_disagreement_score":0.5909363,"about_ca_system_score_codex":0.00017764057,"about_ca_system_score_gemma":0.00016960519,"threshold_uncertainty_score":0.8945932},"labels":[],"label_agreement":null},{"id":"W4417169891","doi":"10.1109/icecs66544.2025.11270647","title":"Systematic Framework Leveraging Early Assessment of DNN Reliability for Efficient and Reliable FPGA-Based Inference Deployment","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Inference; Software deployment; Python (programming language); Field-programmable gate array; Artificial neural network; Software; Reliability (semiconductor); Scheduling (production processes); Fault tolerance","score_opus":0.02121898758092157,"score_gpt":0.326227407700956,"score_spread":0.30500842012003443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417169891","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":[0.047591005,0.0005797847,0.9435062,0.002205119,0.0005169262,0.0051023886,0.000011257315,0.00015387108,0.00033348764],"genre_scores_gemma":[0.6366031,0.000030316429,0.36205578,0.00035385322,0.000014732994,0.0007719459,0.0000014473369,0.000014479917,0.00015431401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953856,0.00022662291,0.0016922439,0.0013997158,0.0006022492,0.0006935701],"domain_scores_gemma":[0.9905968,0.005623591,0.0007052568,0.002205074,0.0006675121,0.00020178982],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001485851,0.00046443494,0.0009800222,0.00023558713,0.00049949315,0.00027656966,0.0011231417,0.00019644514,0.000009580163],"category_scores_gemma":[0.00076112035,0.00042650226,0.00020123973,0.0015395773,0.00025581103,0.00023246338,0.00067912764,0.00042060876,0.0000030741512],"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":[0.000031778174,0.0006968404,0.005051879,0.02272749,0.00008908468,9.509728e-7,0.00022861901,0.58244556,0.00034301882,0.38643488,0.00007111453,0.0018787858],"study_design_scores_gemma":[0.0006360796,0.0002467248,0.0034578831,0.009163868,0.0001495599,6.1074724e-7,0.000044855457,0.9557624,0.004594327,0.025548482,0.000045144152,0.00035010776],"about_ca_topic_score_codex":0.000054054944,"about_ca_topic_score_gemma":0.0000034075692,"teacher_disagreement_score":0.58901215,"about_ca_system_score_codex":0.00042384656,"about_ca_system_score_gemma":0.0006882161,"threshold_uncertainty_score":0.9998187},"labels":[],"label_agreement":null},{"id":"W4417201564","doi":"10.1109/tits.2025.3639680","title":"SpaceFormer: Spatial Position Contextual Semantics Embedding for Multi-View 3D Object Detection","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Object detection; Embedding; Semantics (computer science); Object (grammar); Benchmark (surveying); Spatial contextual awareness; Transformer; Perception; Cognitive neuroscience of visual object recognition","score_opus":0.03785968974207379,"score_gpt":0.3204220058315956,"score_spread":0.28256231608952176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417201564","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":[0.0017421199,0.00084318523,0.9787824,0.00043204092,0.010906478,0.0061423415,0.000576187,0.0005253291,0.00004987602],"genre_scores_gemma":[0.98068386,0.0011607212,0.013566301,0.00023892,0.00024333206,0.0022120501,0.00009940315,0.00009334624,0.0017020851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99412286,0.00027138868,0.0023374904,0.0016187017,0.0007224373,0.00092711503],"domain_scores_gemma":[0.9963465,0.0005900463,0.0007759999,0.0010144504,0.0009877955,0.00028520895],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00056272664,0.0008956039,0.00094985386,0.00091126096,0.0015718971,0.00046424364,0.0007338157,0.00053973455,0.000032642492],"category_scores_gemma":[0.0000123128775,0.001003012,0.0007213607,0.0018917833,0.00019325236,0.0009785038,0.000001849351,0.0007699725,0.000114491406],"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":[0.00041149659,0.0008083219,0.000014834433,0.0009576878,0.00042780594,0.000006527177,0.0019242137,0.69862974,0.004281138,0.0034809262,0.000049990944,0.28900734],"study_design_scores_gemma":[0.0015492294,0.00057421916,0.00011922476,0.0013436563,0.0005548445,0.000018300321,0.00074696605,0.90372825,0.085007705,0.000102878665,0.0054383664,0.00081638835],"about_ca_topic_score_codex":0.00049289165,"about_ca_topic_score_gemma":0.0025404277,"teacher_disagreement_score":0.97894174,"about_ca_system_score_codex":0.0007245958,"about_ca_system_score_gemma":0.00030251427,"threshold_uncertainty_score":0.9997279},"labels":[],"label_agreement":null},{"id":"W4417225292","doi":"10.3390/fi17120568","title":"LSCNet: A Lightweight Shallow Feature Cascade Network for Small Object Detection in UAV Imagery","year":2025,"lang":"en","type":"article","venue":"Future Internet","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Object detection; Feature (linguistics); Feature extraction; Cascade; Deep learning; Aerial image; Enhanced Data Rates for GSM Evolution; Object (grammar); Edge device; Aerial imagery","score_opus":0.008747374177491662,"score_gpt":0.2471033950574188,"score_spread":0.23835602087992713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417225292","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":[0.006195524,0.001602046,0.9794537,0.0069571757,0.0027301281,0.00092668016,0.000007660816,0.00033486995,0.0017922194],"genre_scores_gemma":[0.68364495,0.0001793043,0.2888084,0.0053785783,0.0054335864,0.0013871789,0.00005770292,0.00006587041,0.0150444005],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99839866,0.000057751222,0.0003072213,0.0006578717,0.00011011668,0.00046836652],"domain_scores_gemma":[0.99892086,0.00021360161,0.00012311364,0.0006040437,0.00007331305,0.00006507557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017293642,0.00024185934,0.00024765526,0.00016532287,0.00009415032,0.00012667078,0.00096294336,0.00021368149,0.000005616413],"category_scores_gemma":[0.00003218574,0.00021945618,0.00013433599,0.0010015676,0.000027515262,0.00024334689,0.00025048308,0.00044308612,0.000019678855],"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":[0.00041985742,0.00031789078,0.0029705884,0.0002310709,0.00020796135,0.00012470025,0.0016506553,0.0057918048,0.007488311,0.16985503,0.33493066,0.47601148],"study_design_scores_gemma":[0.00072512,0.00011629368,0.008616835,0.00017608685,0.000019441222,0.000044211076,0.000022372757,0.06960859,0.011458109,0.020935932,0.8878423,0.00043469263],"about_ca_topic_score_codex":0.000019875935,"about_ca_topic_score_gemma":0.0020235125,"teacher_disagreement_score":0.6906453,"about_ca_system_score_codex":0.00013680288,"about_ca_system_score_gemma":0.000045279587,"threshold_uncertainty_score":0.8949163},"labels":[],"label_agreement":null},{"id":"W4417244214","doi":"10.1007/s13177-025-00575-9","title":"Innovative Convolutional Neural Networks Based on the Harris Hawk Optimization Algorithm for Ambulance Detection ","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Convolutional neural network; Preprocessor; Enhanced Data Rates for GSM Evolution; Deep learning; Artificial neural network; Smart city; Transfer of learning; Optimization algorithm; Precision and recall","score_opus":0.054947559730383726,"score_gpt":0.37002147353834575,"score_spread":0.315073913807962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417244214","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":[0.00049429166,0.00019273486,0.9921578,0.0042168377,0.0019440022,0.00082596514,0.000035255915,0.00003405325,0.000099048346],"genre_scores_gemma":[0.9772226,0.00007320329,0.021340057,0.00044116296,0.0004074493,0.00027601884,0.000049451675,0.00001418024,0.00017586096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969763,0.00027015197,0.00093061087,0.00030524004,0.0012442973,0.00027337502],"domain_scores_gemma":[0.9905773,0.0017412158,0.00047587437,0.0002730807,0.0068659154,0.00006664352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017160455,0.00015617724,0.00019777619,0.0007284165,0.0003101153,0.0002739194,0.0013576498,0.00009103708,0.000011627461],"category_scores_gemma":[0.00022716206,0.00012049945,0.00013586158,0.0015774001,0.00012363025,0.00045051152,0.000020763931,0.0005457354,0.0000030934928],"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":[0.000114438306,0.00008078084,0.00010521098,0.0000074294867,0.00008157196,0.0000056888994,0.00007071092,0.9235666,0.00010920309,0.035552707,0.0008237153,0.039481975],"study_design_scores_gemma":[0.0004278155,0.00014828837,0.00056902965,0.00013754534,0.000005825372,0.00000783529,0.000120044395,0.9915869,0.0019643188,0.0007552273,0.004184552,0.000092605136],"about_ca_topic_score_codex":0.000020024949,"about_ca_topic_score_gemma":0.00001051274,"teacher_disagreement_score":0.9767283,"about_ca_system_score_codex":0.00036958142,"about_ca_system_score_gemma":0.00022581567,"threshold_uncertainty_score":0.49138248},"labels":[],"label_agreement":null},{"id":"W4417248049","doi":"10.48550/arxiv.2504.06949","title":"Adaptive Computation Pruning for the Forgetting Transformer","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada","keywords":"Softmax function; Forgetting; Computation; Pruning; FLOPS; Transformer; Context (archaeology); Set (abstract data type)","score_opus":0.07684360389762872,"score_gpt":0.3255755501200715,"score_spread":0.24873194622244277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417248049","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":[0.0051487517,0.0003726909,0.98653316,0.0046897596,0.000602471,0.0016387647,0.000017344424,0.00027088093,0.0007261997],"genre_scores_gemma":[0.8160504,0.00007387003,0.18094698,0.0009246354,0.00027145163,0.0011334792,0.000033074346,0.000021150714,0.0005449739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848896,0.000042737563,0.00034218497,0.0006390456,0.00016235595,0.0003247058],"domain_scores_gemma":[0.9978729,0.0010332261,0.00023669667,0.0006346962,0.00017731094,0.00004517972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002370607,0.00023996281,0.00022237188,0.000069952504,0.000507086,0.00008203297,0.001212661,0.00012835582,0.0000011154132],"category_scores_gemma":[0.000045626548,0.00019459298,0.0001701654,0.00034010157,0.00005528024,0.00021946014,0.00041989007,0.000503982,0.000009471198],"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":[0.000031654967,0.00007716745,0.0035197507,0.00019487576,0.00023221121,0.0000020199045,0.0026535774,0.5323006,0.0004517149,0.06773637,0.0022122026,0.39058784],"study_design_scores_gemma":[0.0002859596,0.00003788453,0.006135926,0.00017527306,0.00006173251,0.0000021317478,0.00008234633,0.9528119,0.0014530096,0.030845432,0.0077516916,0.0003566999],"about_ca_topic_score_codex":0.00001351068,"about_ca_topic_score_gemma":0.000015118139,"teacher_disagreement_score":0.81090164,"about_ca_system_score_codex":0.00007881302,"about_ca_system_score_gemma":0.00013179645,"threshold_uncertainty_score":0.7935271},"labels":[],"label_agreement":null},{"id":"W4417249173","doi":"10.1109/mecon67253.2025.11276889","title":"Traffic Sign Classification Across Borders: Evaluating DenseNet201 on Belgium and Bangladesh Signs","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Signage; Traffic sign; Sign (mathematics); Transfer of learning; Advanced driver assistance systems; Deep learning","score_opus":0.06379162766729113,"score_gpt":0.38473943203579736,"score_spread":0.32094780436850623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417249173","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":[0.14302291,0.0011978468,0.83271766,0.014897492,0.00083188503,0.0019236881,0.000014403033,0.00047449776,0.0049196137],"genre_scores_gemma":[0.9629269,0.00030542372,0.03013798,0.0022234675,0.00018767136,0.00023045814,0.000011802337,0.00003287276,0.003943375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954544,0.00033624732,0.0008625612,0.001799345,0.00057497824,0.0009725129],"domain_scores_gemma":[0.99616575,0.001384568,0.00035650507,0.0015048705,0.00034665826,0.0002416558],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00083547435,0.0005273904,0.00042120233,0.00018275261,0.0013978247,0.0006973806,0.0010888465,0.00027878882,0.00005000063],"category_scores_gemma":[0.00021470117,0.0005459584,0.000119173244,0.0021565428,0.00039023525,0.0006303275,0.00050695695,0.0005641903,0.00012733007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055703582,0.0002741053,0.000114488656,0.000060050937,0.000050290066,0.0000031035888,0.0011124895,0.03713412,0.01070828,0.06640525,0.0040794346,0.8800027],"study_design_scores_gemma":[0.0010287124,0.00057615165,0.00446725,0.00018967438,0.000053977506,0.000008715568,0.0005852877,0.980222,0.002060064,0.0044587003,0.0057294006,0.00062006543],"about_ca_topic_score_codex":0.000010355169,"about_ca_topic_score_gemma":0.00007901022,"teacher_disagreement_score":0.9430879,"about_ca_system_score_codex":0.00020715565,"about_ca_system_score_gemma":0.00029486997,"threshold_uncertainty_score":0.99990225},"labels":[],"label_agreement":null},{"id":"W4417271794","doi":"10.1038/s41598-025-27441-8","title":"Pedestrian detection in aerial image based on convolutional neural network with attention mechanism and multi-scale prediction","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Henan Provincial Science and Technology Research Project","keywords":"Pedestrian detection; Convolutional neural network; Object detection; Pedestrian; Aerial image; Object (grammar); Deep learning; Pattern recognition (psychology)","score_opus":0.010071355899665365,"score_gpt":0.23849648965908704,"score_spread":0.22842513375942167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417271794","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":[0.20129983,0.0000139313615,0.79430574,0.00030964368,0.0032841868,0.0005171862,0.0000015691777,0.0001479408,0.00011993927],"genre_scores_gemma":[0.9617842,0.0000011522037,0.03764835,0.000058595175,0.0000772532,0.00011821122,0.00002489706,0.0000065620206,0.00028081966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998134,0.00007217781,0.00034148231,0.0008704704,0.0003081507,0.00027370176],"domain_scores_gemma":[0.99900603,0.000049008293,0.00018418809,0.00058727746,0.00010435993,0.00006910679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005860614,0.00013312696,0.0001228296,0.0002148967,0.00046969976,0.000268263,0.00014416198,0.000059918963,0.0000029087073],"category_scores_gemma":[0.00003007807,0.00012381744,0.000034029465,0.0012012437,0.0001477896,0.00045026522,0.00007878384,0.00015147186,0.0000023886278],"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":[0.0005161631,0.0012901835,0.06183069,0.00014648549,0.00004615721,0.00049832586,0.00029385745,0.47768945,0.35703665,0.02178989,0.003982318,0.07487984],"study_design_scores_gemma":[0.0005668844,0.00006769706,0.03250256,0.000053118947,0.000008030796,0.000058109938,0.000007043719,0.9463818,0.003654278,0.016135711,0.00043766754,0.00012706668],"about_ca_topic_score_codex":0.00001533577,"about_ca_topic_score_gemma":0.00021581515,"teacher_disagreement_score":0.76048434,"about_ca_system_score_codex":0.000096508644,"about_ca_system_score_gemma":0.000092084076,"threshold_uncertainty_score":0.5049129},"labels":[],"label_agreement":null},{"id":"W4417316549","doi":"10.5194/ica-abs-10-210-2025","title":"HOPE - High-resolution Operational Population Estimation with GNN","year":2025,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"European Space Agency","keywords":"Estimation; Population; Term (time); Identification (biology)","score_opus":0.007684798541313431,"score_gpt":0.24692463795554695,"score_spread":0.23923983941423352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417316549","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":[0.20921561,0.00003581355,0.77543616,0.012972534,0.00021238076,0.00041700478,0.0000038482635,0.000099369645,0.001607307],"genre_scores_gemma":[0.89663833,0.0000022675429,0.102891706,0.00022654109,0.000017636185,0.000012118466,0.000010556502,0.0000028532054,0.00019799259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99934715,0.00002107054,0.00019065567,0.00016521702,0.00018257165,0.00009335774],"domain_scores_gemma":[0.99925464,0.00008219273,0.00013608728,0.00043341226,0.00007570315,0.000017949835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007787138,0.000066381835,0.00006934813,0.000039954935,0.00017002186,0.0000365182,0.00043880267,0.000030394043,0.0000026094233],"category_scores_gemma":[0.00004072398,0.00004597932,0.000021514285,0.0003793577,0.000036402078,0.00041207133,0.00009288276,0.000078924306,0.000007026085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007828451,0.00005012125,0.00047242726,0.000007652479,0.000010147876,1.8719936e-7,0.000024778346,0.71135205,0.0014461183,0.2664712,0.000841853,0.019315632],"study_design_scores_gemma":[0.00024906395,0.000028058756,0.72729254,0.00006267485,0.000013382267,0.000003991406,0.0000025429783,0.21874537,0.0075489706,0.04517065,0.00077950733,0.00010325665],"about_ca_topic_score_codex":0.00010015126,"about_ca_topic_score_gemma":0.00003599825,"teacher_disagreement_score":0.7268201,"about_ca_system_score_codex":0.00003822711,"about_ca_system_score_gemma":0.00004364144,"threshold_uncertainty_score":0.18749821},"labels":[],"label_agreement":null},{"id":"W4417428384","doi":"10.1186/s13638-025-02546-w","title":"Robust incomplete multi-modal clustering with interpolation enhancement and dual-path contrastive optimization","year":2025,"lang":"en","type":"article","venue":"Journal on Wireless Communications and Networking","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Cluster analysis; Missing data; Interpolation (computer graphics); Context (archaeology); Residual; Process (computing); Pattern recognition (psychology); Spectral clustering","score_opus":0.03761237366089079,"score_gpt":0.28062838771888177,"score_spread":0.24301601405799098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417428384","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":[0.004225043,0.0011443574,0.9918578,0.002008154,0.00011803426,0.00025915285,8.934256e-7,0.000050911105,0.00033562607],"genre_scores_gemma":[0.7005418,0.0038868824,0.2951219,0.00032154738,0.000049089736,0.000035210425,0.0000046391447,0.000008415215,0.00003048685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893814,0.00013529068,0.00034223092,0.0002525071,0.00012522872,0.00020659997],"domain_scores_gemma":[0.9985995,0.00033239461,0.00028983768,0.00056631176,0.00012930894,0.00008260282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025654468,0.00015978218,0.00018889578,0.00013999491,0.0010154578,0.00030791387,0.00044900528,0.00004616039,0.0000011882536],"category_scores_gemma":[0.00000570377,0.00013919394,0.000023544026,0.00042052128,0.00011402181,0.00041694852,0.00051228376,0.0003969767,4.5130122e-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":[0.00011487469,0.00031517496,0.0029182187,0.000028977365,0.00016576512,0.000007891244,0.001100397,0.35493264,0.000994445,0.05389377,0.00010406609,0.58542377],"study_design_scores_gemma":[0.00062047516,0.00010248755,0.0009814645,0.00044047748,0.000015755992,0.00004612199,0.000056603363,0.99597037,0.000018924551,0.00031333696,0.0013018243,0.00013217589],"about_ca_topic_score_codex":0.000004511162,"about_ca_topic_score_gemma":0.000038129787,"teacher_disagreement_score":0.6967359,"about_ca_system_score_codex":0.00007914181,"about_ca_system_score_gemma":0.000036071688,"threshold_uncertainty_score":0.78101826},"labels":[],"label_agreement":null},{"id":"W4417438495","doi":"10.1109/tai.2025.3644335","title":"Fully Perturbed Self-Ensemble Framework Using Cascaded Parallel CNN-Transformer for Semisupervised Medical Image Segmentation","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Exploit; Transformer; Image segmentation; Deep learning; Labeled data; Medical imaging; Pattern recognition (psychology)","score_opus":0.0519471167204318,"score_gpt":0.3542854030578109,"score_spread":0.30233828633737914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417438495","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":[0.003438803,0.0002469981,0.98194355,0.0069182944,0.0034834591,0.003204347,0.000071226634,0.0004980509,0.00019526965],"genre_scores_gemma":[0.6033237,0.0008586749,0.39223525,0.0020989375,0.00029547978,0.0007498533,0.00000952201,0.00007653226,0.00035201904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935185,0.00029845472,0.0019121739,0.0018474623,0.0011073506,0.0013160692],"domain_scores_gemma":[0.9951291,0.002044443,0.0003157554,0.0012969091,0.0006632851,0.0005504931],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007078703,0.0008443571,0.00074650533,0.000542193,0.0020574047,0.000561879,0.0017028832,0.00082803913,0.000463995],"category_scores_gemma":[0.000109255874,0.0009334008,0.0006395668,0.002947497,0.00047444386,0.001126598,0.000012840419,0.0014961206,0.00022998133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000766204,0.002116143,0.0000014647001,0.00030231266,0.00035714582,0.000023944787,0.0035238534,0.2329149,0.06702163,0.028709924,0.0001963516,0.66406614],"study_design_scores_gemma":[0.00018711782,0.0002010567,0.0000014370818,0.00037193514,0.00020931121,0.000019074241,0.00037373274,0.64164865,0.32359928,0.03245132,0.000364814,0.0005722729],"about_ca_topic_score_codex":0.00007294757,"about_ca_topic_score_gemma":0.0001500249,"teacher_disagreement_score":0.6634939,"about_ca_system_score_codex":0.00058440055,"about_ca_system_score_gemma":0.0010157613,"threshold_uncertainty_score":0.9993117},"labels":[],"label_agreement":null},{"id":"W4417479546","doi":"10.5755/j01.itc.54.4.42676","title":"ACT-YOLO: An Efficient Multi-Module Fusion Object Detection Algorithm for Steel Surface Defect Detection","year":2025,"lang":"","type":"article","venue":"Information Technology And Control","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangxi Province; British Columbia Innovation Council","keywords":"Benchmark (surveying); Object detection; Feature (linguistics); Key (lock); Pattern recognition (psychology); Feature extraction; Fusion; Surface (topology)","score_opus":0.005897064819431543,"score_gpt":0.24506048991390417,"score_spread":0.23916342509447264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417479546","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":[0.05471963,0.00058845634,0.9392125,0.00082224107,0.0008942521,0.0028818906,0.00004380786,0.0007852218,0.00005201947],"genre_scores_gemma":[0.9583814,0.00011117864,0.0403005,0.00044848045,0.00004136776,0.0006078277,0.000015274463,0.000012637882,0.0000813138],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975942,0.00010827139,0.0008656522,0.00061934505,0.00020457269,0.000607966],"domain_scores_gemma":[0.9977033,0.00025822,0.00057030324,0.0008279143,0.00053307094,0.000107177475],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005596592,0.00040507325,0.0004280279,0.0009784711,0.0015001678,0.00024876412,0.00061778806,0.00076109497,0.0000031111779],"category_scores_gemma":[0.00016948182,0.0004265902,0.0001531665,0.0020724435,0.00027023617,0.0017974708,0.00022667249,0.00056869676,0.000033238935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010208512,0.00010868884,0.000046807374,0.00004069933,0.000054550605,3.1742107e-7,0.00014795155,0.02627431,0.008440864,0.0034832107,0.0000052074975,0.9612953],"study_design_scores_gemma":[0.00394527,0.00059476943,0.00089635776,0.000042721207,0.00009479346,0.000027019467,0.00015297915,0.9396817,0.048113387,0.0020055294,0.004096311,0.00034917705],"about_ca_topic_score_codex":0.000015333793,"about_ca_topic_score_gemma":0.00005847021,"teacher_disagreement_score":0.96094614,"about_ca_system_score_codex":0.00020476525,"about_ca_system_score_gemma":0.000110893685,"threshold_uncertainty_score":0.99981856},"labels":[],"label_agreement":null},{"id":"W4417509118","doi":"10.1109/icsit65336.2025.11294554","title":"Advanced Detection of Helmet Usage and Number Plates in Riders","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Law enforcement; Object detection; Human error; Data collection; Object (grammar); Automation","score_opus":0.009549717358566403,"score_gpt":0.27618738701021905,"score_spread":0.26663766965165264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417509118","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":[0.30677113,0.00057010236,0.6865357,0.0007759916,0.00025216088,0.00048278054,0.000002072104,0.000054265267,0.0045557916],"genre_scores_gemma":[0.9725178,0.0006074232,0.025392601,0.0002396792,0.000011275304,0.000044525605,5.075286e-7,0.000006889066,0.001179313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841404,0.00006332426,0.00049757375,0.00058824004,0.00013361989,0.00030319876],"domain_scores_gemma":[0.99883235,0.00039244143,0.00015047115,0.0004927895,0.0000696533,0.00006227125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016084341,0.00018791031,0.00027394816,0.00017441841,0.0001123692,0.000045834182,0.00034793947,0.00009238403,0.000027231015],"category_scores_gemma":[0.000058982634,0.00019816928,0.00004404102,0.001851027,0.00016857246,0.0006049018,0.0003406359,0.00020859134,0.000012918163],"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":[0.00006013555,0.00013577337,0.0060422705,0.00009487879,0.000024231058,0.0000036256731,0.00033636502,0.008934739,0.054950412,0.039267804,0.000056932022,0.89009285],"study_design_scores_gemma":[0.00232767,0.0001600115,0.046338543,0.00037260886,0.00003377933,0.000020563704,0.00028816648,0.50248504,0.37516052,0.06741725,0.004746749,0.0006490761],"about_ca_topic_score_codex":0.00006155837,"about_ca_topic_score_gemma":0.0003406304,"teacher_disagreement_score":0.88944376,"about_ca_system_score_codex":0.00005579219,"about_ca_system_score_gemma":0.00004475647,"threshold_uncertainty_score":0.8081109},"labels":[],"label_agreement":null},{"id":"W4417526804","doi":"10.1016/j.eswa.2025.130906","title":"APENet: Task-aware adaptation prototype evolution network for few-shot semantic segmentation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Leverage (statistics); Feature (linguistics); Focus (optics); Adaptation (eye); Ambiguity; Metric (unit); Query expansion; Semantic feature","score_opus":0.021938525889235966,"score_gpt":0.2911263125417948,"score_spread":0.2691877866525588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417526804","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":[0.00006643893,0.0009926679,0.98671967,0.0012062885,0.00021959355,0.00975758,0.000015177323,0.0004957697,0.00052680704],"genre_scores_gemma":[0.71699804,0.000040911804,0.19849813,0.00039992263,0.0004868453,0.08230491,0.0002632636,0.000041316853,0.000966673],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980525,0.00007147503,0.00047425847,0.00074078783,0.0002597404,0.00040128513],"domain_scores_gemma":[0.9980545,0.00022510838,0.00029418812,0.0009775767,0.00035388483,0.0000947646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018757167,0.000247815,0.00024384375,0.00013487677,0.00074849906,0.000170066,0.0006474512,0.00009891556,0.0000016124434],"category_scores_gemma":[0.000009747639,0.0002288462,0.000059806207,0.0014836877,0.00005394507,0.00047815708,0.00007526712,0.00011089275,0.000039269544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008428213,0.00019801623,0.0004692838,0.00020427893,0.00010317639,6.126373e-7,0.0005675735,0.203176,0.004186603,0.7612878,0.010491813,0.019230548],"study_design_scores_gemma":[0.0010988832,0.00020639146,0.00071509194,0.00021531638,0.000043478874,0.000018930845,0.00048692757,0.8458892,0.0007040492,0.012351688,0.13769083,0.00057920517],"about_ca_topic_score_codex":0.000078758014,"about_ca_topic_score_gemma":0.000062011946,"teacher_disagreement_score":0.78822154,"about_ca_system_score_codex":0.00026615494,"about_ca_system_score_gemma":0.00018664598,"threshold_uncertainty_score":0.9332077},"labels":[],"label_agreement":null},{"id":"W4417538516","doi":"10.48550/arxiv.2506.16531","title":"How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Domain adaptation; Snow; Domain (mathematical analysis); Sequence (biology); Noise (video); Object detection; Object (grammar); Point (geometry)","score_opus":0.077045986734562,"score_gpt":0.2940337026000955,"score_spread":0.21698771586553348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417538516","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":[0.021734543,0.00041508785,0.95482045,0.01802405,0.0003700029,0.0014475177,0.0028926,0.00022672568,0.00006900435],"genre_scores_gemma":[0.2589658,0.0013010618,0.71773416,0.006648702,0.0007718538,0.0026996832,0.0060955416,0.0000988699,0.0056843483],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99791163,0.00007650643,0.00028993157,0.0011751187,0.00019402165,0.00035278732],"domain_scores_gemma":[0.99761164,0.0003119883,0.00026439744,0.0015836713,0.00010805519,0.0001202619],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020677812,0.00032561732,0.00033605736,0.00010556256,0.0002623888,0.00025784317,0.0011170357,0.00021503796,0.0000037039206],"category_scores_gemma":[0.00007368941,0.00033560928,0.00010360715,0.00027555405,0.000083275125,0.00040117037,0.001396681,0.00037191066,0.000019923884],"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":[0.0004145043,0.000693234,0.028647648,0.0016864773,0.0009105871,0.00006573307,0.011494898,0.007694343,0.0034170118,0.106776126,0.5307161,0.30748332],"study_design_scores_gemma":[0.0013645393,0.00018321586,0.018687062,0.00039449165,0.00012439888,0.000015219193,0.00022761856,0.12917423,0.0019778884,0.07707193,0.7694968,0.0012826355],"about_ca_topic_score_codex":0.000040055373,"about_ca_topic_score_gemma":0.000053426673,"teacher_disagreement_score":0.30620068,"about_ca_system_score_codex":0.00007063475,"about_ca_system_score_gemma":0.00011398293,"threshold_uncertainty_score":0.9999096},"labels":[],"label_agreement":null},{"id":"W6884637092","doi":"10.1109/tim.2025.3548193","title":"SFC-YOLOv8: Enhanced Strip Steel Surface Defect Detection Using Spatial-Frequency Domain-Optimized YOLOv8","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Science and Technology Program of Hubei Province; National Natural Science Foundation of China","keywords":"STRIPS; Feature extraction; Boosting (machine learning); Robustness (evolution); Noise (video); Frequency domain; Strip steel; Sensitivity (control systems); Feature (linguistics)","score_opus":0.03034928841534202,"score_gpt":0.27111215715935366,"score_spread":0.24076286874401165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6884637092","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":[0.14319009,0.000056951983,0.8540766,0.00033618778,0.000861783,0.0007985157,0.000006216839,0.00020438344,0.000469251],"genre_scores_gemma":[0.9088401,0.00010047375,0.090543285,0.00030152884,0.000017247055,0.00012655542,0.0000018560423,0.000013303755,0.000055611967],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997941,0.00017700894,0.00045672563,0.0006093369,0.00050412054,0.00031181023],"domain_scores_gemma":[0.9989773,0.00007090172,0.00017469726,0.00045864852,0.00020443783,0.000113998416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033360365,0.00026403173,0.00022723968,0.00022037134,0.0006437998,0.00012666077,0.0002530598,0.00009701918,0.000017724398],"category_scores_gemma":[0.000007145906,0.00027771838,0.0001196528,0.0007412699,0.00007061387,0.0004896854,0.0000056706353,0.00024633997,0.000012795986],"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":[0.00013175736,0.0003012202,0.000025224641,0.00004156012,0.00016156567,0.0000012730227,0.00047852998,0.09453669,0.66738254,0.0015333308,0.000011614052,0.2353947],"study_design_scores_gemma":[0.0044174283,0.0003069662,0.00070901134,0.00015669652,0.00015068447,0.000013457019,0.00033986656,0.071799085,0.9159992,0.0052739736,0.0002701671,0.0005635111],"about_ca_topic_score_codex":0.00011729547,"about_ca_topic_score_gemma":0.00027595647,"teacher_disagreement_score":0.76565003,"about_ca_system_score_codex":0.0005546465,"about_ca_system_score_gemma":0.00013263088,"threshold_uncertainty_score":0.9999675},"labels":[],"label_agreement":null},{"id":"W6887716163","doi":"10.17611/dp/15468167","title":"Magnetotelluric Transfer Functions","year":2017,"lang":"en","type":"other","venue":"NSF Seismological Facility for the Advancement of Geoscience (SAGE)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Magnetotellurics; Transfer function; Field (mathematics); Transfer (computing); Interpretation (philosophy)","score_opus":0.036302071445477535,"score_gpt":0.28220067325196924,"score_spread":0.2458986018064917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6887716163","genre_codex":"methods","genre_gemma":"other","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":[0.000016189791,0.0016564403,0.9902345,0.0035105136,0.000885888,0.0019467839,0.0006575721,0.00030437097,0.0007877511],"genre_scores_gemma":[0.0014936249,0.002081824,0.022719922,0.0007193345,0.00010560652,0.00062286074,0.000044306846,0.00004205708,0.9721705],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969582,0.00007271097,0.00045069042,0.001185215,0.0006248881,0.00070828013],"domain_scores_gemma":[0.99657637,0.00044352675,0.0003180093,0.0024240995,0.00008247897,0.00015552655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004894435,0.00045062226,0.00052927627,0.000116526666,0.00076277775,0.00009356833,0.0035358705,0.0002388606,0.00026639074],"category_scores_gemma":[0.00013482067,0.00030415165,0.0003220076,0.00044453557,0.0010937083,0.00033118253,0.00048794327,0.00032367418,0.00008533115],"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":[0.000013847811,0.00013649926,0.000012626194,0.000043321976,0.000020117306,0.0000019196798,0.000018421431,0.00057668006,0.00014253522,0.0054616216,0.0001277133,0.9934447],"study_design_scores_gemma":[0.00031782596,0.0002806676,0.00014836193,0.000056397752,0.000038840055,0.00000537937,0.0000085535885,0.003592657,0.00012132979,0.002230243,0.992855,0.00034474666],"about_ca_topic_score_codex":0.00009037614,"about_ca_topic_score_gemma":0.000025973166,"teacher_disagreement_score":0.9930999,"about_ca_system_score_codex":0.00007023115,"about_ca_system_score_gemma":0.000086462074,"threshold_uncertainty_score":0.99994105},"labels":[],"label_agreement":null},{"id":"W6890007541","doi":"10.3389/ijph.2025.1607655.s001","title":"DataSheet1_Prevalence of COVID-19 Vaccine Hesitancy Among Healthcare Workers in Nigeria: A Systematic Review and Meta-Analysis.pdf","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Health care; Vaccination; Systematic review; Scale (ratio); MEDLINE; Quality (philosophy); Meta-analysis","score_opus":0.06733601379815127,"score_gpt":0.3508268472369761,"score_spread":0.28349083343882486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890007541","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":[2.5608477e-8,0.13909642,0.00028017405,0.0013172041,0.000012014988,0.0026817517,0.8565675,0.000040924013,0.0000039599317],"genre_scores_gemma":[0.0000087456665,0.003033793,0.00065803487,0.0031079378,0.00000912266,0.00347213,0.9895596,0.0000072224902,0.00014343156],"study_design_codex":"not_applicable","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99618745,0.0005748701,0.0013333209,0.0011362197,0.00041686944,0.00035124988],"domain_scores_gemma":[0.99373615,0.0010776069,0.0013045614,0.00345193,0.00016773696,0.00026202644],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035793724,0.00047049794,0.003321904,0.00045710462,0.00010249088,0.000074367854,0.0027673396,0.00020400426,0.010327697],"category_scores_gemma":[0.0021004896,0.00039494157,0.0004799165,0.0036906453,0.0000122157935,0.00036661362,0.0012102942,0.0004799228,0.00008030019],"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":[6.9690094e-7,0.000013774739,0.0000057571233,0.28256956,0.0027974788,0.000015928346,0.0000054003845,0.00006259771,8.948487e-9,0.00000420943,0.7145228,0.0000018071647],"study_design_scores_gemma":[0.00068598543,0.00011793345,0.00032335953,0.21034034,0.39470473,0.000057948975,0.000047741978,0.0008488555,0.0000032562727,0.00043062595,0.38980377,0.0026354445],"about_ca_topic_score_codex":0.0001134494,"about_ca_topic_score_gemma":0.0021132557,"teacher_disagreement_score":0.39190724,"about_ca_system_score_codex":0.0001316811,"about_ca_system_score_gemma":0.00031460603,"threshold_uncertainty_score":0.9998503},"labels":[],"label_agreement":null},{"id":"W6890336192","doi":"10.34943/ceb2dabd-b058-411c-bb2a-e56d3f2b892e","title":"Cascadia Basin Bottom Pressure Recorder Deployed 2011-09-23","year":2024,"lang":"en","type":"dataset","venue":"Ocean Networks Canada Society","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Abyssal plain; Abyssal zone; Ocean bottom; Structural basin; Seabed; Global Positioning System; Software deployment","score_opus":0.007414325371588321,"score_gpt":0.22062360548594304,"score_spread":0.21320928011435472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890336192","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":[0.000004776286,0.008562849,0.024493156,0.0038036746,0.0046692314,0.0009493575,0.9569387,0.0005421598,0.000036109162],"genre_scores_gemma":[0.00010836026,0.00501779,0.012624974,0.008372975,0.0028537845,0.00012229725,0.966536,0.00018680861,0.0041770046],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953351,0.00013656159,0.0006849566,0.001730921,0.0008649469,0.0012475268],"domain_scores_gemma":[0.9958455,0.0004410455,0.0003854842,0.0026232225,0.00015947728,0.00054529053],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00029201276,0.0008453275,0.00071110285,0.00005091135,0.00052182435,0.00037699952,0.003124623,0.00074177666,0.000063472275],"category_scores_gemma":[0.000018986688,0.0008439041,0.0004373626,0.001007577,0.00013968459,0.00031778368,0.00094495673,0.00257556,0.0000820895],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024006106,0.000021966174,0.000011704722,0.00009629732,0.00024115,0.00006879487,0.000017137143,0.009988418,1.503276e-7,0.00011049971,0.9882,0.0012414494],"study_design_scores_gemma":[0.00014239392,0.000019536505,0.000030465651,0.00009248418,0.00018383187,0.000040569346,0.0000098158025,0.079589225,0.0000011002825,0.0002672427,0.9188306,0.0007927825],"about_ca_topic_score_codex":0.11833545,"about_ca_topic_score_gemma":0.3704798,"teacher_disagreement_score":0.25214434,"about_ca_system_score_codex":0.0005146411,"about_ca_system_score_gemma":0.0018263058,"threshold_uncertainty_score":0.9997255},"labels":[],"label_agreement":null},{"id":"W6892428879","doi":"10.5281/zenodo.10123435","title":"Fig. 65 in A revision of Scipopus Enderlein including the subgenera Scipopus s. str., Phaeopterina Frey and Parascipopus subgen. nov. (Diptera, Micropezidae, Taeniapterinae)","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Spermatheca; Subgenus; Dorsum; Holotype; Head (geology)","score_opus":0.04954708025048606,"score_gpt":0.2817235714561964,"score_spread":0.23217649120571035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892428879","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":[0.11345462,0.02326046,0.28654268,0.013507605,0.0045490684,0.02072288,0.002927381,0.023794906,0.5112404],"genre_scores_gemma":[0.88852656,0.012599943,0.02363983,0.00049644476,0.0010700502,0.0000031793543,0.0010851641,0.019518938,0.05305992],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968039,0.00041305454,0.0005849353,0.0010497643,0.00055441057,0.0005939381],"domain_scores_gemma":[0.9977122,0.00006859353,0.00045172017,0.0014138205,0.00017694227,0.00017675676],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000777079,0.00038162738,0.0004303184,0.00066466833,0.0010905633,0.0006121396,0.0029401835,0.00016705613,0.00071699725],"category_scores_gemma":[0.000255579,0.00034735378,0.00008045209,0.0017958134,0.00034058304,0.00034994297,0.0044627488,0.0005124711,0.0009480031],"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":[0.000046573416,0.000107689506,0.000024000947,0.00018104585,0.000058161022,0.000053317675,0.00093810336,0.00010624535,0.001675562,0.006833186,0.1651492,0.8248269],"study_design_scores_gemma":[0.0004105634,0.00017438401,0.00017739118,0.0002980023,0.000012255734,0.00009682116,0.000037565802,0.0010408832,0.0002882733,0.00012403868,0.99699694,0.00034288722],"about_ca_topic_score_codex":0.00021139352,"about_ca_topic_score_gemma":0.000044139022,"teacher_disagreement_score":0.8318477,"about_ca_system_score_codex":0.0001743929,"about_ca_system_score_gemma":0.000010260844,"threshold_uncertainty_score":0.99989784},"labels":[],"label_agreement":null},{"id":"W6892714073","doi":"10.5281/zenodo.11822552","title":"icao annex 17 latest edition pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Civil aviation; Government (linguistics); Pretext; Work (physics); Subpoena; Liquation","score_opus":0.02641260658478312,"score_gpt":0.25336215713210863,"score_spread":0.22694955054732552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892714073","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":[0.0000010974354,0.0003488249,0.11217525,0.0018097989,0.0003217171,0.0004738812,0.00016307697,0.003649212,0.88105714],"genre_scores_gemma":[0.0007758376,0.0003875895,0.005577929,0.00038856026,0.0014003741,3.1221873e-7,0.0020413257,0.008278396,0.9811497],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980286,0.00013268953,0.00022943797,0.0008166567,0.00039417323,0.00039844954],"domain_scores_gemma":[0.9984129,0.000019717272,0.00015580832,0.001040433,0.00018610846,0.00018500014],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00022015655,0.00023868193,0.00018426144,0.00038965823,0.0007298542,0.0010819997,0.002260136,0.00015077213,0.031816833],"category_scores_gemma":[0.00010149217,0.0002545192,0.0000694839,0.0010385117,0.00013228437,0.0002558647,0.001982315,0.00046096486,0.21220335],"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":[0.0000018961841,0.000039447426,2.7020706e-8,0.000048219183,0.000022553508,0.000016794142,0.000072371164,0.000024122835,0.0001662975,0.01758388,0.96055025,0.021474143],"study_design_scores_gemma":[0.00011593173,0.000068547786,0.00000586721,0.00010621263,0.000011727284,0.000082376435,0.000008868681,0.0012229332,0.000039304825,0.0022486777,0.9958346,0.00025496274],"about_ca_topic_score_codex":0.0000059020126,"about_ca_topic_score_gemma":4.0668195e-7,"teacher_disagreement_score":0.18038653,"about_ca_system_score_codex":0.000117766846,"about_ca_system_score_gemma":0.0000039337983,"threshold_uncertainty_score":0.9999907},"labels":[],"label_agreement":null},{"id":"W6893665305","doi":"10.5281/zenodo.4689376","title":"Latreillia Roux 1830","year":2003,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Seta; Genus; Type species; Type (biology); Larva","score_opus":0.03452891714973229,"score_gpt":0.25104386471321677,"score_spread":0.2165149475634845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893665305","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":[0.0019479324,0.000118219505,0.73824346,0.0013158813,0.00013541504,0.00038931423,0.000020458267,0.0019213541,0.25590798],"genre_scores_gemma":[0.9631324,0.00013251453,0.0315166,0.00067943055,0.00014471177,1.400665e-7,0.00022950006,0.0012628839,0.0029018105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99852836,0.00021530944,0.00018280919,0.00045325825,0.00026632502,0.0003539584],"domain_scores_gemma":[0.99867076,0.000028963617,0.00007620558,0.00078627217,0.0002688773,0.0001689265],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003198298,0.000115750125,0.00009601973,0.00011349384,0.0017754072,0.00059510046,0.0015738832,0.000039391438,0.0019776346],"category_scores_gemma":[0.00025856655,0.00012308935,0.000040077142,0.00091842253,0.00008150817,0.0004606127,0.0007679146,0.00019929689,0.009852948],"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":[0.000005478664,0.00011165045,0.0000033962901,0.000012107926,0.000016194826,0.0000149661955,0.00038362324,0.0006194798,0.002967256,0.68249553,0.14828081,0.16508949],"study_design_scores_gemma":[0.00019239611,0.000057834928,0.00011677446,0.000005341205,0.0000024544638,0.000117304924,0.000021505064,0.0017453046,0.00092768454,0.0054288968,0.99124116,0.00014336577],"about_ca_topic_score_codex":0.0000014152023,"about_ca_topic_score_gemma":8.322743e-8,"teacher_disagreement_score":0.9611845,"about_ca_system_score_codex":0.00008318872,"about_ca_system_score_gemma":0.000002962918,"threshold_uncertainty_score":0.9995241},"labels":[],"label_agreement":null},{"id":"W6894039998","doi":"10.5281/zenodo.7078559","title":"R code for Demographic consequences of changing environmental periodicity","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Seventh Framework Programme; Horizon 2020 Framework Programme; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Vital rates; Akaike information criterion; Poisson distribution; Marmot; Negative binomial distribution; Poisson regression; Random effects model; Binomial (polynomial); Bayesian probability","score_opus":0.03260552868793427,"score_gpt":0.24199163618419667,"score_spread":0.2093861074962624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6894039998","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":[0.09785749,0.00025209246,0.8907048,0.0029050824,0.00014698933,0.001401196,0.0011715742,0.00096538104,0.0045954064],"genre_scores_gemma":[0.9942409,0.00003155598,0.0049234,0.00015349634,0.000028484577,5.654017e-7,0.00028181533,0.00022315672,0.0001166667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99881846,0.0001537824,0.00017373175,0.0003267744,0.00026672368,0.00026050388],"domain_scores_gemma":[0.99933374,0.00004075915,0.00012268692,0.0003708506,0.000060620798,0.000071314236],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040047505,0.00007847844,0.00009544206,0.00019490498,0.0031037177,0.0001287512,0.0013916093,0.000014986516,0.0009688505],"category_scores_gemma":[0.000057342517,0.00009096268,0.00004917465,0.00066891033,0.00022179923,0.0001899965,0.0016747416,0.00013354413,0.000087979075],"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":[0.00013092639,0.0006677264,0.00009686535,0.00010698085,0.00010608106,0.000016062762,0.007291684,0.009531016,0.26012254,0.34613848,0.036198687,0.33959293],"study_design_scores_gemma":[0.0003728055,0.00032610737,0.00027460145,0.000005715238,0.0000072132416,0.000114887756,0.00056457217,0.013409006,0.0039002283,0.0029510607,0.9779003,0.0001734663],"about_ca_topic_score_codex":0.0000011724918,"about_ca_topic_score_gemma":6.682023e-8,"teacher_disagreement_score":0.94170165,"about_ca_system_score_codex":0.0000783384,"about_ca_system_score_gemma":0.0000028138616,"threshold_uncertainty_score":0.9999444},"labels":[],"label_agreement":null},{"id":"W6901701814","doi":"10.60692/bwt7p-6jk30","title":"The Medical Segmentation Decathlon","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Polytechnique Montréal","funders":"","keywords":"Segmentation; Task (project management); Set (abstract data type); Image segmentation; Image (mathematics); Scale-space segmentation; Segmentation-based object categorization; Range (aeronautics)","score_opus":0.023730474420536943,"score_gpt":0.23379008309509763,"score_spread":0.2100596086745607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901701814","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":[0.021895522,0.000006152074,0.9735863,0.0012670866,0.0006126633,0.00039076887,0.000009638914,0.0003913381,0.0018405354],"genre_scores_gemma":[0.9967692,2.3303464e-7,0.001978652,0.00055700535,0.00004305687,0.0005332883,0.0000081574835,0.0000037592777,0.000106643565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984963,0.00009831867,0.00037761935,0.00012189528,0.0007189985,0.00018688299],"domain_scores_gemma":[0.9991174,0.00002997955,0.00021816966,0.0004968864,0.00005965991,0.000077951976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005103776,0.00008219038,0.00006953105,0.00006905309,0.0011184507,0.00019447331,0.0008909437,0.000022874578,0.000014918045],"category_scores_gemma":[0.000013497044,0.000059491078,0.000036569927,0.0004607874,0.000021174032,0.000689737,0.00040391373,0.00013394827,0.0002906896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011076766,0.000018456027,0.025625557,0.0001718803,0.00012500118,0.000034187105,0.20373322,0.038612746,0.000014345253,0.4700495,0.0110271545,0.2504772],"study_design_scores_gemma":[0.0019000152,0.00015369199,0.01266395,0.000036140802,0.000016111066,0.0008590452,0.022933012,0.8721031,0.00083179306,0.00024045815,0.087605536,0.00065715826],"about_ca_topic_score_codex":0.0000011483337,"about_ca_topic_score_gemma":1.1062286e-7,"teacher_disagreement_score":0.97487366,"about_ca_system_score_codex":0.0001529393,"about_ca_system_score_gemma":0.000049116923,"threshold_uncertainty_score":0.8602331},"labels":[],"label_agreement":null},{"id":"W6903070560","doi":"10.1016/j.ejrai.2025.100034","title":"Improving medical image segmentation with SAM2: analyzing the impact of object characteristics and finetuning on multi-planar datasets.","year":2025,"lang":"en","type":"article","venue":"European Journal of Radiology Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Segmentation; Dice; Sørensen–Dice coefficient; Object (grammar); Intersection (aeronautics); Image segmentation; Pattern recognition (psychology); Object detection; Scale-space segmentation","score_opus":0.03075355265644513,"score_gpt":0.3236239499293269,"score_spread":0.2928703972728818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6903070560","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":[0.09910025,0.00013145834,0.9000064,0.0005212816,0.00009522672,0.000087270586,0.000010852014,0.000011102187,0.000036126174],"genre_scores_gemma":[0.95571303,0.00014696374,0.0439148,0.0001234495,0.00008247453,0.0000010321203,0.0000073945375,0.0000072728844,0.0000035985913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984653,0.00045795413,0.0005754605,0.00019726822,0.00013531205,0.00016869245],"domain_scores_gemma":[0.9983724,0.0005931124,0.0005585409,0.00028948148,0.00011069724,0.00007579046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001145594,0.00012420223,0.00021504435,0.00014445282,0.00016641122,0.00004853393,0.00072989374,0.000022750162,0.0000064784963],"category_scores_gemma":[0.0004355346,0.00007593316,0.0000513152,0.0003598684,0.00028576146,0.0002084844,0.00009766001,0.0003856053,0.0000035123783],"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":[0.00035369257,0.00017245572,0.0034721575,0.000023017963,0.00021306107,0.0003791784,0.0010706893,0.008833349,0.057460323,0.011064141,0.00062090455,0.916337],"study_design_scores_gemma":[0.0007376726,0.004579612,0.089014456,0.0008220794,0.00019716944,0.003013713,0.0007122624,0.8438238,0.05225258,0.0038358895,0.00023609352,0.00077469763],"about_ca_topic_score_codex":0.0000076075085,"about_ca_topic_score_gemma":0.0000043081136,"teacher_disagreement_score":0.91556233,"about_ca_system_score_codex":0.000032814027,"about_ca_system_score_gemma":0.000095751784,"threshold_uncertainty_score":0.30964643},"labels":[],"label_agreement":null},{"id":"W6906631995","doi":"10.17605/osf.io/74mqn","title":"Raw and Supplementary data reported in \"Brain PDGFRβ+ cells exhibit distinctive diverse spatiotemporal reactive phenotypes after stroke without requiring KLF4 regulatory functions\"","year":2024,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Phenotype; Neurovascular bundle; Stroke (engine); PDGFRB; KLF4","score_opus":0.023745022621998818,"score_gpt":0.2776285371375377,"score_spread":0.2538835145155389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6906631995","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":[0.009839225,0.00011037784,0.17074661,0.0014623494,0.0020723063,0.0064973533,0.0024955957,0.0014492956,0.8053269],"genre_scores_gemma":[0.05467871,0.0001276079,0.02196909,0.00019399298,0.00031712846,0.0006898797,0.0005094397,0.00037731702,0.92113686],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9924586,0.00045843492,0.000872625,0.0050301324,0.00059929345,0.00058091513],"domain_scores_gemma":[0.989946,0.00036581836,0.0007300441,0.008625954,0.00007831458,0.00025385575],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0019784411,0.0006538709,0.00066662784,0.0004942348,0.00012508796,0.00019987884,0.0020551053,0.000357162,0.050484948],"category_scores_gemma":[0.00039509195,0.0007339126,0.0001210823,0.00047386487,0.00035938807,0.00072882255,0.008227229,0.0010247601,0.06624875],"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":[0.00046311933,0.0006864779,0.029421654,0.0005552059,0.0013133893,0.00072875386,0.0024990565,0.0007921099,0.006113628,0.0018785993,0.8858073,0.069740735],"study_design_scores_gemma":[0.0020228385,0.00001016804,0.019443337,0.0013856968,0.0005037147,0.00014084627,0.0005253282,0.013301594,0.004101874,0.009494398,0.94644,0.0026302114],"about_ca_topic_score_codex":0.00120693,"about_ca_topic_score_gemma":0.0023871476,"teacher_disagreement_score":0.14877751,"about_ca_system_score_codex":0.00042261984,"about_ca_system_score_gemma":0.0001915219,"threshold_uncertainty_score":0.99979407},"labels":[],"label_agreement":null},{"id":"W6920353461","doi":"10.6068/dp14ba7c4a75355","title":"Trend 2007 - 2011. Statistics Canada. CANSIM: Culture and Leisure - Film and Video | Country: Canada | Table: Film and video distribution, operating expenses, by North American Industry Classification System (NAICS) | Variable: Advertising, marketing and promotions, Motion picture and video distribution | Units: %, 2007-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-046.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Official statistics; Economic statistics; Population; Summary statistics; Descriptive statistics; Entertainment; Distribution (mathematics)","score_opus":0.014635240330403379,"score_gpt":0.2352434376031404,"score_spread":0.220608197272737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920353461","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":[0.000012293744,0.0049894345,0.00621892,0.000023742954,0.00022663553,0.00093677355,0.98738855,0.00011391894,0.000089757086],"genre_scores_gemma":[0.00043210844,0.0021712673,0.0011038267,0.00010834264,0.0001879189,0.000056319455,0.9953131,0.00008540689,0.00054173113],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955598,0.0005511173,0.000823379,0.0017159233,0.0006921668,0.0006576076],"domain_scores_gemma":[0.99601,0.00059144554,0.000960413,0.0015833795,0.00018808829,0.0006666979],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060494506,0.00071718416,0.00076815387,0.000050578394,0.0006614533,0.0005884387,0.0008414541,0.0004825395,0.00009206874],"category_scores_gemma":[0.00017751579,0.00068870524,2.6344165e-7,0.00032623138,0.0003906014,0.00077869935,0.00076006044,0.0010717179,0.0000022990419],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021287262,0.000025232684,0.00049946125,0.0004301065,0.00006977667,0.00003644663,0.0000044686617,0.000034251036,0.000005459857,0.00043900925,0.9974088,0.0010257096],"study_design_scores_gemma":[0.00044533212,0.00005722013,0.0004516997,0.00010508021,0.00018604343,0.00039531424,0.00027443454,0.04608255,8.0974296e-8,3.7616527e-7,0.9513216,0.0006802811],"about_ca_topic_score_codex":0.9544114,"about_ca_topic_score_gemma":0.9152917,"teacher_disagreement_score":0.0460872,"about_ca_system_score_codex":0.000320844,"about_ca_system_score_gemma":0.0018576706,"threshold_uncertainty_score":0.9995564},"labels":[],"label_agreement":null},{"id":"W6924105928","doi":"10.15468/dl.4jaf3e","title":"Occurrence Download","year":2025,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Download; Range (aeronautics); Agropyron cristatum; Training set","score_opus":0.013779637473514834,"score_gpt":0.23612429135340157,"score_spread":0.22234465387988675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6924105928","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":[0.0000047791623,0.000023929359,0.006495943,0.00059655507,0.0005510653,0.0003854946,0.99152404,0.00024346942,0.00017469806],"genre_scores_gemma":[0.0000019196916,0.00008922652,0.00008630567,0.0012483789,0.0000011982171,0.0000123149985,0.99856037,4.0535557e-9,2.944726e-7],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984598,0.00005176861,0.00038985006,0.0003657426,0.0004147077,0.000318101],"domain_scores_gemma":[0.9979224,0.000046580873,0.00030208056,0.0013178319,0.00027021513,0.0001408507],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013648326,0.00028020534,0.00024836717,0.000106298015,0.00037375253,0.0002358154,0.002139975,0.00025838337,0.00010825041],"category_scores_gemma":[0.00009462832,0.00029957044,0.00012932169,0.0009609147,0.00011285449,0.0018295157,0.0012919758,0.0003203925,0.029458705],"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":[0.0000054670154,0.00002184127,0.000045870896,0.00005930707,0.000011931323,9.4420557e-7,0.0000098580595,0.000067560475,1.6739069e-8,0.000007644514,0.9937201,0.006049423],"study_design_scores_gemma":[0.00016257042,0.000014058089,0.000014621424,0.0000027558076,0.000016103771,0.0000030066692,0.0000074263326,0.00000513801,0.0000029198145,0.0000158662,0.9995029,0.00025264474],"about_ca_topic_score_codex":0.000076895856,"about_ca_topic_score_gemma":0.00000561885,"teacher_disagreement_score":0.029350456,"about_ca_system_score_codex":0.0003039945,"about_ca_system_score_gemma":0.0002111478,"threshold_uncertainty_score":0.99994564},"labels":[],"label_agreement":null},{"id":"W6930760245","doi":"10.5281/zenodo.14511245","title":"decargroup/dkpy","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Selection (genetic algorithm); Order (exchange); Documentation; Inheritance (genetic algorithm); Process (computing); Matrix (chemical analysis)","score_opus":0.03134734046735241,"score_gpt":0.26433916627905907,"score_spread":0.23299182581170666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930760245","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":[0.0000011692729,0.00070808653,0.20679213,0.00080815784,0.00023680904,0.00040168833,0.00010632319,0.0049378895,0.78600776],"genre_scores_gemma":[0.0018971555,0.00076381554,0.020045614,0.00056783773,0.0011987056,3.1676248e-7,0.0014749337,0.024072679,0.94997895],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980843,0.00013588082,0.00020625217,0.00081257784,0.0003705936,0.00039041176],"domain_scores_gemma":[0.9983372,0.000015678572,0.00012444273,0.0012048049,0.00013849845,0.00017938852],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00021278515,0.00022847387,0.00018175201,0.00038853288,0.0006340948,0.0010766139,0.0029459381,0.00013171425,0.009228615],"category_scores_gemma":[0.000084402964,0.0002387289,0.0000731091,0.0010219283,0.00011481091,0.00016902194,0.002779105,0.00041736942,0.09150288],"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":[0.0000013044585,0.000025508623,2.2436536e-8,0.000043350075,0.000024336492,0.000014888591,0.00009029867,0.000008472971,0.000100268,0.06664102,0.8660991,0.06695142],"study_design_scores_gemma":[0.00010229601,0.00004868397,0.0000026916753,0.00007606257,0.000010188625,0.00008812782,0.000009114552,0.0007036858,0.000033705026,0.0030211976,0.99566364,0.00024059984],"about_ca_topic_score_codex":0.0000066699245,"about_ca_topic_score_gemma":3.9972466e-7,"teacher_disagreement_score":0.18674652,"about_ca_system_score_codex":0.000109697474,"about_ca_system_score_gemma":0.0000031430777,"threshold_uncertainty_score":0.99996036},"labels":[],"label_agreement":null},{"id":"W6931300343","doi":"10.5281/zenodo.4362440","title":"Liogenys rufoflava Moser 1918","year":2020,"lang":"en","type":"article","venue":"Open MIND","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Museum of Nature","funders":"","keywords":"Significant difference; Male genitalia; Female sex; Adult male","score_opus":0.07089534113286551,"score_gpt":0.31730607305978753,"score_spread":0.24641073192692203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931300343","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":[0.027808754,0.0000837481,0.9040875,0.0205344,0.00012134384,0.0007068022,0.000007461508,0.000025037425,0.046624955],"genre_scores_gemma":[0.46219194,0.000007979579,0.5344151,0.0021689632,0.00017254967,0.00004254647,0.0000054981683,0.000012520456,0.0009828869],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991952,0.000015105134,0.00012642462,0.00038745324,0.00010879249,0.00016701041],"domain_scores_gemma":[0.99931234,0.00003831858,0.000049144495,0.00043393465,0.00002552075,0.0001407399],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000049613427,0.000081253675,0.00009424462,0.0000135359805,0.000093209186,0.0002025253,0.0017962109,0.000025080199,0.0003330467],"category_scores_gemma":[0.000014784654,0.00007721211,0.000025372818,0.00049527717,0.000020608519,0.0004973646,0.00087769376,0.00008352206,0.0030753298],"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":[0.000008739991,0.00004484847,0.00028059824,0.0000019565089,0.000012558916,0.0000279842,0.0007298318,0.001461964,0.010611989,0.0073405104,0.011713346,0.9677657],"study_design_scores_gemma":[0.00026190528,0.00006244052,0.00049781904,0.0000054774664,0.0000047607555,0.000007916802,0.000011815344,0.06986157,0.020851597,0.0027360248,0.9054596,0.00023907555],"about_ca_topic_score_codex":0.000003948947,"about_ca_topic_score_gemma":0.000008390603,"teacher_disagreement_score":0.9675266,"about_ca_system_score_codex":0.000009291836,"about_ca_system_score_gemma":0.00002966097,"threshold_uncertainty_score":0.99770087},"labels":[],"label_agreement":null},{"id":"W6931538904","doi":"10.5281/zenodo.7468424","title":"Purification of SLC35F2 from Expi293FTM Cells","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Centre","funders":"European Commission","keywords":"Solubilization; Transporter; Transfection; Proteolysis; Cell culture; Recombinant DNA","score_opus":0.03375849317965125,"score_gpt":0.23888814292870755,"score_spread":0.2051296497490563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931538904","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":[0.041954346,0.00021545342,0.9138789,0.0027886145,0.00031945237,0.0009756716,0.00050269155,0.0018253706,0.037539486],"genre_scores_gemma":[0.98457134,0.00004318692,0.013480605,0.00018944612,0.00007769807,3.3618159e-7,0.0007408275,0.0004734873,0.00042306422],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99846786,0.00026994216,0.00023956866,0.00041865138,0.00039207534,0.00021188693],"domain_scores_gemma":[0.9986537,0.000043548032,0.00016980033,0.0008269934,0.00021774106,0.0000881795],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00029973767,0.000087794564,0.00010223187,0.00013264926,0.0018514849,0.0001899358,0.0022828144,0.000019854093,0.0033815443],"category_scores_gemma":[0.00006395676,0.000104550716,0.000039525657,0.00091094314,0.00007185054,0.00029320974,0.0023580026,0.00021022321,0.0012148853],"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":[0.000045148874,0.0004743812,0.0000041592243,0.00002204597,0.000041037783,0.00000959756,0.0034724423,0.008108492,0.2934169,0.1369173,0.19298424,0.36450428],"study_design_scores_gemma":[0.0002115318,0.000104808314,0.00020754835,0.0000033026095,0.0000040064556,0.000015295585,0.000120178054,0.0072053536,0.013041129,0.002993954,0.97596985,0.0001230493],"about_ca_topic_score_codex":0.000011670994,"about_ca_topic_score_gemma":6.621584e-8,"teacher_disagreement_score":0.942617,"about_ca_system_score_codex":0.00010449794,"about_ca_system_score_gemma":0.0000034566185,"threshold_uncertainty_score":0.9995628},"labels":[],"label_agreement":null},{"id":"W6939231492","doi":"10.60692/b9w37-vh070","title":"DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Object detection; Lidar; Deep learning; Adverse weather; Radar; Object (grammar); Ranging; Radar imaging","score_opus":0.08728564726637203,"score_gpt":0.27376275611744494,"score_spread":0.1864771088510729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939231492","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":[0.029485688,0.0000020022305,0.96842915,0.000029844095,0.00035380104,0.0004841426,0.000013357788,0.0010794009,0.0001226184],"genre_scores_gemma":[0.973977,1.7788975e-7,0.025684765,0.000041553474,0.00010347886,0.000087055334,0.0000581794,0.000009670017,0.00003806686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988647,0.00004131175,0.00038163643,0.00024312596,0.0002048972,0.00026431872],"domain_scores_gemma":[0.9987241,0.00003044693,0.00026096098,0.00081994897,0.000108242966,0.00005632275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038288042,0.0001191778,0.00012783805,0.00023512228,0.00043720874,0.00023632476,0.0007040452,0.000056897457,6.629772e-7],"category_scores_gemma":[0.000037843813,0.000112825546,0.000037393645,0.0008840844,0.000011786852,0.0022871816,0.0003429335,0.000087131964,0.00034139986],"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":[0.0001024964,0.000005437377,0.01211893,0.001269896,0.00016098858,0.000007818869,0.0866587,0.6439645,0.00043420072,0.0062176925,0.00040911508,0.2486502],"study_design_scores_gemma":[0.00026168188,0.000015994272,0.0015784932,0.000023268312,0.0000072225807,0.000025560064,0.0010491617,0.9949212,0.00047969414,0.000011986701,0.0014958857,0.00012985432],"about_ca_topic_score_codex":0.0000022121735,"about_ca_topic_score_gemma":2.6475456e-7,"teacher_disagreement_score":0.9444914,"about_ca_system_score_codex":0.00008622409,"about_ca_system_score_gemma":0.00001901691,"threshold_uncertainty_score":0.46008924},"labels":[],"label_agreement":null},{"id":"W6958034554","doi":"10.6068/dp15df2a00a4c73","title":"Trend 1980 - 2014. Energy Information Administration. International Energy Statistics: Petroleum | Country: Canada | Category: Consumption | Series: Consumption of Distillate Fuel Oil | Units: Barrels Per Day, 1980-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 004-015-006.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Petroleum; Energy consumption; Consumption (sociology); Energy policy; Energy (signal processing); Production (economics); Energy security; Administration (probate law); Agency (philosophy)","score_opus":0.023592696872415934,"score_gpt":0.2624497771546176,"score_spread":0.23885708028220165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958034554","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":[1.9565249e-7,0.0009916577,0.019505497,0.00002352407,0.0012313421,0.00018416185,0.9746235,0.00016419799,0.0032759362],"genre_scores_gemma":[0.0003979917,0.009493004,0.0016424445,0.00021502073,0.0002912838,0.000069626956,0.9856093,0.0000967052,0.002184632],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99569774,0.00029090513,0.0011514776,0.001184921,0.0011139193,0.0005610483],"domain_scores_gemma":[0.99243116,0.0005946769,0.0021325604,0.004379495,0.000106458276,0.00035566525],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033963157,0.00070595084,0.0007472443,0.00021689032,0.0002682662,0.0006151574,0.004615849,0.00042076956,0.0013063626],"category_scores_gemma":[0.0000508122,0.0007159703,0.000001586075,0.00006168785,0.0004729837,0.0022026254,0.0011569578,0.00045688232,0.00041507426],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007496998,0.00004215805,0.000016023889,0.00024388709,0.00015348801,0.000037499078,0.0000029661114,0.000102049074,0.000006007191,0.10212898,0.894385,0.0028070018],"study_design_scores_gemma":[0.00054047443,0.00007613702,0.000020626947,0.000040219045,0.00012340982,0.00016618015,0.000010992883,0.024229223,3.2410753e-7,0.000012279597,0.9740907,0.0006894547],"about_ca_topic_score_codex":0.7667538,"about_ca_topic_score_gemma":0.72549677,"teacher_disagreement_score":0.102116704,"about_ca_system_score_codex":0.0001867761,"about_ca_system_score_gemma":0.0012360981,"threshold_uncertainty_score":0.99960655},"labels":[],"label_agreement":null},{"id":"W6963797992","doi":"10.25318/1410014701-fra","title":"Enquête sur la population active (EPA), estimations de l'emploi selon la région métropolitaine de recensement basées sur les limites du recensement de 2001 et le Système de classification des industries de l'Amérique du Nord (SCIAN), moyennes mobiles de 3 mois, non désaisonnalisées","year":2020,"lang":"fr","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Population; Census; Context (archaeology); Research methodology","score_opus":0.026892345250041645,"score_gpt":0.28368236897682686,"score_spread":0.2567900237267852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6963797992","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":[0.044993997,0.00026957755,0.7640754,0.009158356,0.00014535584,0.0012557225,0.1799402,0.00013472853,0.000026700107],"genre_scores_gemma":[0.446562,0.005921234,0.17672426,0.0003998608,0.0003492868,0.0013712269,0.36835918,0.00014107878,0.00017187624],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99410504,0.0016995375,0.0011004151,0.0010090431,0.0007747806,0.0013112073],"domain_scores_gemma":[0.991428,0.004874063,0.0012371271,0.0006643224,0.0011227037,0.0006737564],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0014937627,0.0008050616,0.0006215222,0.00032720107,0.0013019035,0.00058645,0.00089091144,0.00059167045,0.000022672175],"category_scores_gemma":[0.005752285,0.00099423,0.00008976196,0.001124484,0.0003811614,0.00081281434,0.00024600828,0.0009185384,0.000004298694],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013277303,0.0006301353,0.02166464,0.00083998253,0.00019696067,0.0001601548,0.0037828565,0.04608231,0.0042805322,0.036345307,0.8323617,0.05352265],"study_design_scores_gemma":[0.0007439244,0.0002908758,0.38978493,0.0016514307,0.0004720399,0.00030912488,0.0021918004,0.48648876,0.005388019,0.013785153,0.097329915,0.001564027],"about_ca_topic_score_codex":0.25283584,"about_ca_topic_score_gemma":0.7142075,"teacher_disagreement_score":0.7350318,"about_ca_system_score_codex":0.009514159,"about_ca_system_score_gemma":0.0052509354,"threshold_uncertainty_score":0.9999983},"labels":[],"label_agreement":null},{"id":"W6964622344","doi":"10.3389/fpsyg.2022.1035494.s001","title":"Data_Sheet_1_Using latent profile analysis to uncover the combined role of anxiety sensitivity and test anxiety in students’ state anxiety.PDF","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Anxiety; Anxiety sensitivity; Test anxiety; Test (biology); Psychological intervention; Anxiety score","score_opus":0.016230026457753403,"score_gpt":0.26933653585545625,"score_spread":0.25310650939770285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6964622344","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":[0.0006810539,0.000092883805,0.000070030015,0.00012308815,0.000037932095,0.0012129403,0.9977214,0.000048910977,0.000011759263],"genre_scores_gemma":[0.0035646956,0.00015727764,0.00060706184,0.0004332413,0.000029472472,0.00056399056,0.9945738,0.00001970179,0.0000507612],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969249,0.00023175005,0.00050966156,0.0010827878,0.0007941336,0.00045676477],"domain_scores_gemma":[0.99665433,0.0005804103,0.00045081123,0.0020285097,0.00013541165,0.00015052006],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032262484,0.0003966766,0.0005711101,0.00026227313,0.00027041178,0.00022816537,0.001840044,0.00010806257,0.01819546],"category_scores_gemma":[0.00032985184,0.0003469323,0.00014387515,0.003336763,0.00001976728,0.00039471322,0.004803161,0.0006573137,0.00014841507],"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":[0.000010836636,0.00018182663,0.0029412417,0.00006351075,0.00011319256,0.0000106021525,0.00009526093,0.0063479417,0.000061652056,0.0000023568791,0.9879801,0.0021914449],"study_design_scores_gemma":[0.00026351894,0.00006706509,0.026120408,0.0001490028,0.000111797795,0.000010375104,0.000024249348,0.011661716,0.00015164372,0.000028730383,0.96094483,0.00046665317],"about_ca_topic_score_codex":0.00023280199,"about_ca_topic_score_gemma":0.0013941391,"teacher_disagreement_score":0.027035292,"about_ca_system_score_codex":0.00017112022,"about_ca_system_score_gemma":0.000106287785,"threshold_uncertainty_score":0.99989825},"labels":[],"label_agreement":null},{"id":"W6976590211","doi":"10.60692/2p9dw-1xh07","title":"DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Object detection; Lidar; Deep learning; Adverse weather; Radar; Object (grammar); Ranging; Radar imaging","score_opus":0.08728564726637203,"score_gpt":0.27376275611744494,"score_spread":0.1864771088510729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976590211","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":[0.029485688,0.0000020022305,0.96842915,0.000029844095,0.00035380104,0.0004841426,0.000013357788,0.0010794009,0.0001226184],"genre_scores_gemma":[0.973977,1.7788975e-7,0.025684765,0.000041553474,0.00010347886,0.000087055334,0.0000581794,0.000009670017,0.00003806686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988647,0.00004131175,0.00038163643,0.00024312596,0.0002048972,0.00026431872],"domain_scores_gemma":[0.9987241,0.00003044693,0.00026096098,0.00081994897,0.000108242966,0.00005632275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038288042,0.0001191778,0.00012783805,0.00023512228,0.00043720874,0.00023632476,0.0007040452,0.000056897457,6.629772e-7],"category_scores_gemma":[0.000037843813,0.000112825546,0.000037393645,0.0008840844,0.000011786852,0.0022871816,0.0003429335,0.000087131964,0.00034139986],"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":[0.0001024964,0.000005437377,0.01211893,0.001269896,0.00016098858,0.000007818869,0.0866587,0.6439645,0.00043420072,0.0062176925,0.00040911508,0.2486502],"study_design_scores_gemma":[0.00026168188,0.000015994272,0.0015784932,0.000023268312,0.0000072225807,0.000025560064,0.0010491617,0.9949212,0.00047969414,0.000011986701,0.0014958857,0.00012985432],"about_ca_topic_score_codex":0.0000022121735,"about_ca_topic_score_gemma":2.6475456e-7,"teacher_disagreement_score":0.9444914,"about_ca_system_score_codex":0.00008622409,"about_ca_system_score_gemma":0.00001901691,"threshold_uncertainty_score":0.46008924},"labels":[],"label_agreement":null},{"id":"W6979284107","doi":"","title":"LookWhere? Efficient Visual Recognition by Learning Where to Look and What to See from Self-Supervision","year":2025,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Security token; Computation; Task (project management); Pruning; Code (set theory); Feature extraction; Matching (statistics); Quadratic growth; Visualization","score_opus":0.020541996526571724,"score_gpt":0.19631286376699442,"score_spread":0.17577086724042268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979284107","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":[0.5025358,0.00013298845,0.4963166,0.00038563873,0.00007652983,0.0002014892,0.0000024773628,0.00016152638,0.0001869493],"genre_scores_gemma":[0.990479,0.00034905586,0.0076199444,0.0006446328,0.000024168556,0.0000025475924,0.000011697671,0.000009788562,0.0008591932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871916,0.00007694742,0.00011887265,0.00077345234,0.00006682516,0.00024475143],"domain_scores_gemma":[0.9991692,0.0001709464,0.000044729546,0.00033871544,0.000087622575,0.00018884103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008061789,0.00015026821,0.00013840749,0.00013970959,0.00026701795,0.00019525884,0.0004372776,0.00006614999,0.00002271202],"category_scores_gemma":[0.000014942219,0.00017720567,0.000035264802,0.0010012939,0.000023093731,0.0006726256,0.00054363615,0.00016261819,0.0003260349],"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":[0.00018951928,0.00063138007,0.0053041507,0.00005307321,0.00012750756,0.000103476224,0.0023851525,0.521866,0.02397028,0.022569766,0.009255437,0.41354424],"study_design_scores_gemma":[0.00093927485,0.0002544377,0.0018422214,0.00027921225,0.000052004667,0.0000023246687,0.00096450833,0.9556434,0.003305766,0.0067186034,0.029361287,0.000636966],"about_ca_topic_score_codex":0.00004939397,"about_ca_topic_score_gemma":0.000037306247,"teacher_disagreement_score":0.48869666,"about_ca_system_score_codex":0.00010992359,"about_ca_system_score_gemma":0.000025341598,"threshold_uncertainty_score":0.72262377},"labels":[],"label_agreement":null},{"id":"W6979919345","doi":"","title":"An Analysis and Benchmarking in Autoware.AI and OpenPCDet LiDAR-based 3D Object Detection Models","year":2022,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Object detection; Benchmarking; Artificial neural network; Bounding overwatch; Minimum bounding box; Point (geometry); Object (grammar)","score_opus":0.009626517038045262,"score_gpt":0.22809258432698382,"score_spread":0.21846606728893855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979919345","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":[0.9076595,0.00009278113,0.09154384,0.00019995263,0.00006142273,0.00030145192,0.000016450407,0.000076111864,0.000048537317],"genre_scores_gemma":[0.97489935,0.000084898835,0.022281528,0.00003675175,0.000010491836,0.000004193027,0.00025498777,0.000013614407,0.0024141734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986344,0.00011593298,0.00012772568,0.00068004086,0.00023384923,0.00020805396],"domain_scores_gemma":[0.99911803,0.00005168119,0.00021666876,0.00045279425,0.00007070192,0.00009011976],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016678208,0.0001935863,0.0003515977,0.00080108293,0.00033915805,0.00006554967,0.0005195606,0.00012865815,0.000028484299],"category_scores_gemma":[0.0000029130993,0.00025135127,0.00007346754,0.0012307448,0.000048013582,0.0008497418,0.00015253061,0.00029602993,5.511469e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046261444,0.00040156263,0.008360833,0.0003994188,0.00051690376,0.0001452753,0.18878035,0.47457355,0.01410995,0.002173915,0.000038179605,0.31003746],"study_design_scores_gemma":[0.00037606934,0.00016021151,0.022858556,0.000028030325,0.00018441588,0.000001652189,0.0072927596,0.96737003,0.00068598654,0.00068916474,0.000039468916,0.0003136383],"about_ca_topic_score_codex":0.017327989,"about_ca_topic_score_gemma":0.13170356,"teacher_disagreement_score":0.4927965,"about_ca_system_score_codex":0.00011045786,"about_ca_system_score_gemma":0.000060704526,"threshold_uncertainty_score":0.99999386},"labels":[],"label_agreement":null},{"id":"W6983049894","doi":"","title":"Learning to Segment Unseen Tasks In-Context","year":2024,"lang":"en","type":"dissertation","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Image segmentation; Inference; Process (computing); Set (abstract data type); Deep learning; Segmentation-based object categorization","score_opus":0.011213331341418921,"score_gpt":0.27207928408705784,"score_spread":0.2608659527456389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6983049894","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":[0.77172726,0.013100787,0.1078813,0.053214017,0.01183355,0.008054166,0.00015498587,0.008448439,0.025585499],"genre_scores_gemma":[0.90561813,0.00044291353,0.078662194,0.000303334,0.0001361269,0.0010249995,0.00024411725,0.00013724467,0.013430928],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99649996,0.000032791162,0.000821226,0.001417491,0.0005137155,0.0007148317],"domain_scores_gemma":[0.99760336,0.000051224215,0.00044793278,0.0015619118,0.00018964702,0.00014593957],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002039051,0.0006204069,0.00079865655,0.0023034182,0.00017359373,0.00009534521,0.0025789018,0.00090567785,0.000010512528],"category_scores_gemma":[0.00019249256,0.00065878616,0.00018986079,0.004011194,0.00014753472,0.00045172844,0.0006131384,0.0018189099,0.00031865487],"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":[0.00006212641,0.0002823991,0.001015054,0.0005614081,0.00031430824,0.00034127207,0.0051801996,0.010820723,0.010788284,0.16409346,0.008350595,0.7981902],"study_design_scores_gemma":[0.00078453764,0.00051580067,0.0011688977,0.002055003,0.000118747266,0.000041615953,0.001442319,0.004209364,0.02431634,0.01905921,0.9445496,0.0017385188],"about_ca_topic_score_codex":0.000051071656,"about_ca_topic_score_gemma":0.0016091164,"teacher_disagreement_score":0.93619907,"about_ca_system_score_codex":0.00036747198,"about_ca_system_score_gemma":0.0002759413,"threshold_uncertainty_score":0.99958634},"labels":[],"label_agreement":null},{"id":"W6995684506","doi":"","title":"Part-aware vehicle re-identification using harder negative aware triplet loss and attention embedded learnable resizer","year":2023,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Faculty of Graduate Studies and Research, University of Alberta; Mitacs","keywords":"Degree (music); Control system; Identification (biology); Control (management)","score_opus":0.02600705030119064,"score_gpt":0.2635148979337383,"score_spread":0.23750784763254765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995684506","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54929847,0.00030770426,0.20227349,0.24474934,0.0010147413,0.001420353,0.000078311714,0.0006487574,0.00020885764],"genre_scores_gemma":[0.09967673,0.0019275153,0.017475981,0.000046546287,0.00019333138,0.000011425121,0.0007628928,0.00012478986,0.87978077],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984191,0.000119546115,0.000047836464,0.00074394466,0.00037488915,0.000294682],"domain_scores_gemma":[0.99801344,0.00011786863,0.00068682746,0.0006775476,0.00039782617,0.000106490894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019006612,0.00023067658,0.00032128673,0.0002697872,0.00060667173,0.00008333367,0.0006262293,0.00023792064,0.000005931775],"category_scores_gemma":[0.000039697516,0.00031506506,0.00011474176,0.0009446373,0.000121367804,0.0008350206,0.00015935102,0.00031200293,0.00004514319],"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":[0.00046956012,0.00017525187,0.0019079463,0.0018857917,0.00050646346,0.00027648095,0.010851698,0.012070932,0.01677769,0.010946362,0.9235411,0.020590719],"study_design_scores_gemma":[0.0052016648,0.0003678723,0.06411993,0.0030942527,0.0010459716,0.000042335258,0.10190276,0.3654636,0.0041073486,0.002947656,0.44828278,0.0034238177],"about_ca_topic_score_codex":0.00003212875,"about_ca_topic_score_gemma":0.00053632405,"teacher_disagreement_score":0.8795719,"about_ca_system_score_codex":0.00013785888,"about_ca_system_score_gemma":0.00011837843,"threshold_uncertainty_score":0.99993014},"labels":[],"label_agreement":null},{"id":"W6996239981","doi":"","title":"Safety and efficacy of Burosumab in patients with X-linked hypophosphatemia: A systematic review","year":2025,"lang":"en","type":"article","venue":"PhilPapers (PhilPapers Foundation)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hypophosphatemia; Adverse effect; Bone pain; Joint pain; Renal function; Fibroblast growth factor 23; Vitamin D and neurology; Reabsorption","score_opus":0.00628741239874912,"score_gpt":0.23797730827183802,"score_spread":0.2316898958730889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996239981","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":[0.47151354,0.09030676,0.23199102,0.08314182,0.0026980885,0.07210429,0.00008350347,0.0031435126,0.045017477],"genre_scores_gemma":[0.986318,0.0036528849,0.006993841,0.0024538452,0.0000152365365,0.0002887901,0.00005540912,0.000028179846,0.00019384999],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99758714,0.00013726878,0.00091818033,0.0006500798,0.00039121558,0.0003161344],"domain_scores_gemma":[0.99780387,0.00051630236,0.00041340542,0.00096478616,0.00021468516,0.000086953376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033289488,0.0002946785,0.0007395544,0.00020481502,0.00019090092,0.00005929687,0.00072635256,0.00007226859,0.0000138065225],"category_scores_gemma":[0.00030370386,0.00024516936,0.00006425515,0.0017484997,0.00018032527,0.00063107535,0.00019257379,0.00021732763,0.00001945574],"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":[0.00085302745,0.0019788507,0.036424816,0.17231181,0.00080348173,0.000016532927,0.0011857882,0.002206181,0.00048996106,0.7061539,0.00022568676,0.07734994],"study_design_scores_gemma":[0.034775276,0.0012916361,0.7114008,0.21765438,0.0022669297,0.000026717384,0.00018854925,0.006814778,0.00045952917,0.016711967,0.004354977,0.0040544397],"about_ca_topic_score_codex":0.000008687122,"about_ca_topic_score_gemma":0.000016125248,"teacher_disagreement_score":0.689442,"about_ca_system_score_codex":0.00017380148,"about_ca_system_score_gemma":0.00010551878,"threshold_uncertainty_score":0.99977165},"labels":[],"label_agreement":null},{"id":"W6998841539","doi":"","title":"AVG 360 phone number @1_888_879_0163 AVG Antivirus Tech Support phone Number","year":2016,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Phone; Smart phone; Phone call; Service (business)","score_opus":0.014438993429098509,"score_gpt":0.2809418029817936,"score_spread":0.2665028095526951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6998841539","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":[0.000072648334,0.0000039967904,0.20055652,0.0005791864,0.00094246626,0.0014551372,0.00007477884,0.0014575374,0.79485774],"genre_scores_gemma":[0.0013688733,0.0006564615,0.032583363,0.00047117483,0.0005958876,0.0009611772,0.000047314774,0.00060716487,0.9627086],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9909244,0.00052644644,0.0011338419,0.004911723,0.0011227415,0.0013808004],"domain_scores_gemma":[0.9878651,0.00063177216,0.0011697299,0.009538818,0.00021802618,0.000576578],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017286682,0.0011361717,0.0011310409,0.00032396356,0.0003246625,0.00028823045,0.0057038935,0.0009085765,0.85326195],"category_scores_gemma":[0.0004403115,0.0011376943,0.00048771893,0.0010765383,0.00045644617,0.00065722206,0.005141199,0.0011694689,0.995006],"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":[0.000026664438,0.00041047495,0.0013844119,0.000078244804,0.00020480138,0.00009677439,0.00017043142,0.000029888091,0.0017826747,0.0055892244,0.9611323,0.029094115],"study_design_scores_gemma":[0.0008002559,3.3000708e-7,0.0002614552,0.0001876944,0.00006341595,0.0002534761,0.000007306302,0.00018914587,0.005403055,0.009239311,0.9823244,0.0012701524],"about_ca_topic_score_codex":0.00021154761,"about_ca_topic_score_gemma":0.000078778605,"teacher_disagreement_score":0.16797315,"about_ca_system_score_codex":0.00044003408,"about_ca_system_score_gemma":0.00036539728,"threshold_uncertainty_score":0.99967575},"labels":[],"label_agreement":null},{"id":"W7006259210","doi":"","title":"Synthesis of Imidazoles, 2-Pyrrolines and Pyrrolidines via Phospha-Münchnone Cycloadditions","year":2013,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cycloaddition; Intramolecular force; Nitrile; Imidazole; 1,3-Dipolar cycloaddition; Chloride","score_opus":0.011187423941393406,"score_gpt":0.23239480311528538,"score_spread":0.22120737917389197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006259210","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":[0.97040313,0.0025568563,0.00033800484,0.00023814276,0.0016318259,0.0025728338,0.0021834616,0.0013676044,0.018708121],"genre_scores_gemma":[0.97573733,0.0017774618,0.018748607,0.00015554862,0.000098440185,0.00129423,0.00045352412,0.00017847405,0.0015563965],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99554956,0.00016795276,0.0012379112,0.0015202977,0.0007465326,0.00077771983],"domain_scores_gemma":[0.99515086,0.00088525086,0.0011452949,0.0017035183,0.0006950732,0.0004199968],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003116488,0.0009023394,0.0010427525,0.00052417495,0.0010980603,0.00012229531,0.0018657274,0.0006536805,0.000112053094],"category_scores_gemma":[0.0004777194,0.0009411204,0.00031388752,0.0012933441,0.00016126115,0.0016916147,0.0004236049,0.0009795335,0.00019472379],"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":[0.00006053776,0.00043073297,0.000021962413,0.00053285004,0.00025269392,0.000028328559,0.000014092266,0.0003763642,0.13606799,0.34141245,0.00007611681,0.5207259],"study_design_scores_gemma":[0.00085450517,0.0001302011,0.0025480904,0.0009826282,0.0004190975,0.00014043592,0.00010286738,0.0042224154,0.75890243,0.20384796,0.025264734,0.0025846232],"about_ca_topic_score_codex":0.00016710495,"about_ca_topic_score_gemma":0.00027712862,"teacher_disagreement_score":0.62283444,"about_ca_system_score_codex":0.0001861119,"about_ca_system_score_gemma":0.000051106606,"threshold_uncertainty_score":0.99930394},"labels":[],"label_agreement":null},{"id":"W7013142093","doi":"","title":"Continuous Dropout","year":2018,"lang":"en","type":"article","venue":"UTS ePRESS (University of Technology Sydney)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dropout (neural networks); Overfitting; Feature (linguistics); Binary number; Artificial neural network; Binary classification; Pattern recognition (psychology)","score_opus":0.00855615106550311,"score_gpt":0.21491371122502856,"score_spread":0.20635756015952544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7013142093","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":[0.11481543,0.00009000504,0.8689888,0.007240613,0.00014826162,0.00018684211,0.0000045040592,0.00085628225,0.007669242],"genre_scores_gemma":[0.88902056,0.000021792926,0.10982148,0.00007120592,0.000020258956,6.0735726e-7,9.362287e-7,0.000005086996,0.0010380946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992097,0.0000144288915,0.000083035855,0.00036253818,0.000103315324,0.00022697209],"domain_scores_gemma":[0.9987595,0.000031346488,0.00012214258,0.0008891257,0.00014568723,0.00005216341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000044250064,0.00009499703,0.0001749994,0.00026422032,0.00022385568,0.0000074461805,0.0017653864,0.00015153059,0.000040548435],"category_scores_gemma":[0.000021998985,0.000112971116,0.000046940142,0.00079179415,0.0007655243,0.0002758937,0.00079246494,0.00014721541,0.0001679095],"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":[0.000018611414,0.00013843244,0.009406208,0.000010761348,0.00005934878,0.000057534588,0.00057675666,0.000021622645,0.01880194,0.7834409,0.010128755,0.17733912],"study_design_scores_gemma":[0.0023255313,0.000698468,0.008442827,0.00010895152,0.00007992053,0.00017329387,0.0011596604,0.049633913,0.08761318,0.23544553,0.6131495,0.0011692205],"about_ca_topic_score_codex":0.000027330216,"about_ca_topic_score_gemma":0.00003009828,"teacher_disagreement_score":0.7742051,"about_ca_system_score_codex":0.000022597958,"about_ca_system_score_gemma":0.000020656122,"threshold_uncertainty_score":0.46068284},"labels":[],"label_agreement":null},{"id":"W7018314500","doi":"","title":"Designing Efficient Neural Attention Systems Towards Achieving Human-level Sharp Vision","year":2019,"lang":"en","type":"article","venue":"NAIST Digital Library (Nara Institute of Science and Technology)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Feature (linguistics); Deep learning; Key (lock)","score_opus":0.017263353830875326,"score_gpt":0.24793699155279364,"score_spread":0.23067363772191832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018314500","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":[0.94565356,0.0004023823,0.046098083,0.0012944755,0.0004811271,0.0005975353,0.000017878867,0.00063439127,0.004820567],"genre_scores_gemma":[0.9897046,0.000014419711,0.009978838,0.00004968314,0.000024288573,0.000023497298,0.00000888025,0.000010719589,0.00018506203],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979158,0.0000075751736,0.00038859175,0.0007641513,0.000515474,0.00040839647],"domain_scores_gemma":[0.9988372,0.000022948434,0.00023193375,0.0006939493,0.00010537698,0.000108631466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019599072,0.0001989322,0.00025084527,0.0006267802,0.0004993678,0.0006444178,0.0017456122,0.000108359294,0.0000016150609],"category_scores_gemma":[0.00005315253,0.00017577547,0.000042587268,0.0032877058,0.0016293313,0.007781858,0.0012322016,0.0002233857,0.000030124198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013580095,0.0002729449,0.017543694,0.00015448904,0.00001589592,0.00003275671,0.00007120022,0.0051686084,0.18196875,0.5689319,0.0003205101,0.22550568],"study_design_scores_gemma":[0.0033598503,0.0029097921,0.059323296,0.002607916,0.000047388592,0.0009410246,0.00056545687,0.7419045,0.10486544,0.020226471,0.05976742,0.0034814219],"about_ca_topic_score_codex":0.0000017895767,"about_ca_topic_score_gemma":8.700778e-8,"teacher_disagreement_score":0.73673594,"about_ca_system_score_codex":0.000038956605,"about_ca_system_score_gemma":0.00014087498,"threshold_uncertainty_score":0.7167915},"labels":[],"label_agreement":null},{"id":"W7021310904","doi":"","title":"Novel model for Better Segmentation","year":2025,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Segmentation; Feature extraction; Preprocessor; Image segmentation; Leverage (statistics); Encoder; Pattern recognition (psychology); ENCODE","score_opus":0.01908837211324476,"score_gpt":0.27981926796715134,"score_spread":0.2607308958539066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021310904","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":[0.00034891273,0.0000152224075,0.99319476,0.0040319245,0.001169905,0.0007257307,0.000024415986,0.000019134639,0.00046996863],"genre_scores_gemma":[0.7802856,0.000011505198,0.20932421,0.0040471745,0.0003579205,0.00045070983,0.000023884168,0.000010622203,0.0054883873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988852,0.000018711253,0.00065855554,0.00004753853,0.0002772679,0.00011269454],"domain_scores_gemma":[0.99690557,0.00035255632,0.0016501751,0.00016657413,0.0009049026,0.000020253325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078483217,0.00006259752,0.00012432426,0.00012300066,0.00020822827,0.0002013591,0.0004904676,0.00005582114,6.22366e-8],"category_scores_gemma":[0.0003002069,0.00004592403,0.00014048727,0.00030607218,0.0000043505174,0.0019401142,0.000041358053,0.00007203859,0.0000019441468],"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":[0.000024589512,0.000033439268,0.00056902744,0.00007727645,0.00011838481,5.4889675e-9,0.0011014695,0.6253034,0.0018465439,0.3043571,0.058406074,0.0081627285],"study_design_scores_gemma":[0.0009043691,0.000015734113,0.0003421852,0.000043977227,0.00002319188,0.000002620863,0.00006203517,0.94887894,0.00067507953,0.0049512205,0.044043764,0.000056897145],"about_ca_topic_score_codex":7.1779135e-7,"about_ca_topic_score_gemma":6.135522e-7,"teacher_disagreement_score":0.7838706,"about_ca_system_score_codex":0.00042205313,"about_ca_system_score_gemma":0.00009248479,"threshold_uncertainty_score":0.19417106},"labels":[],"label_agreement":null},{"id":"W7023620980","doi":"","title":"OSM: An open set matting framework with OOD detection and few-shot learning","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Set (abstract data type); Feature (linguistics); Open set; Data set; Key (lock)","score_opus":0.03593202235646072,"score_gpt":0.3003150840878067,"score_spread":0.264383061731346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023620980","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":[0.98433924,0.00012282535,0.0009277439,0.00006225546,0.0009390915,0.001990399,0.00010472523,0.0020098148,0.009503913],"genre_scores_gemma":[0.95658034,0.00017330672,0.038058013,0.00023616552,0.00008969354,0.0006494166,0.0004067443,0.0003045488,0.0035017454],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953315,0.0004094618,0.00067558064,0.0020489497,0.0007102248,0.0008242661],"domain_scores_gemma":[0.9965225,0.0006049675,0.0007976744,0.0013407143,0.0003161721,0.00041799038],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008302273,0.00074792793,0.00065319886,0.00035386364,0.0028896122,0.0008193057,0.0023677812,0.000643054,0.000020278316],"category_scores_gemma":[0.00043850066,0.0007750941,0.000087625325,0.0018803702,0.00005653075,0.0028398929,0.00083470665,0.002755875,0.0001777959],"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":[0.00024796918,0.00015345513,0.0001118499,0.0003016328,0.00019945379,0.00011539474,0.00015055097,0.0033607816,0.02800488,0.13756041,0.000003261609,0.82979035],"study_design_scores_gemma":[0.0051523927,0.0049427534,0.023891462,0.0068294066,0.00083517487,0.0009022562,0.004891139,0.028392365,0.15200536,0.685698,0.0741479,0.012311792],"about_ca_topic_score_codex":0.00019395787,"about_ca_topic_score_gemma":0.0019947467,"teacher_disagreement_score":0.8174786,"about_ca_system_score_codex":0.00029444255,"about_ca_system_score_gemma":0.00004806554,"threshold_uncertainty_score":0.9995448},"labels":[],"label_agreement":null},{"id":"W7023955858","doi":"","title":"Potential for northern range expansion of the understory flora of temperate Québec: edaphic, climatic and biotic factors","year":2016,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Understory; Flora (microbiology); Temperate climate; Range (aeronautics); Temperate forest; Temperate rainforest; Vegetation (pathology)","score_opus":0.02872123640586042,"score_gpt":0.2875137970012527,"score_spread":0.25879256059539224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023955858","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":[0.9754278,0.0001957059,0.022245523,0.00021825213,0.00033996115,0.0013742915,0.000040582767,0.0000025869485,0.00015530991],"genre_scores_gemma":[0.99291116,0.000028537987,0.0058962405,0.000008614149,0.000029867224,0.000045147422,0.000036848138,0.00002079051,0.0010227999],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989941,0.000037750433,0.00032557762,0.00034038947,0.00016573934,0.00013642828],"domain_scores_gemma":[0.9986303,0.00015009094,0.0004748885,0.0005491714,0.00015718455,0.000038363643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103353064,0.00016376309,0.00027542535,0.000057683024,0.00015789918,0.00004081584,0.0010098176,0.00010433225,0.00001802835],"category_scores_gemma":[0.000031208758,0.00010369408,0.00007945466,0.00017553256,0.00007420539,0.00023295531,0.00015584643,0.00008234171,0.000004969532],"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":[0.00052461796,0.00042662115,0.009731503,0.0012064998,0.00041725105,0.0000026881733,0.019135924,0.0011589353,0.30852118,0.0018863581,0.0003309143,0.6566575],"study_design_scores_gemma":[0.012624987,0.0019468573,0.21494697,0.0077782082,0.0017667098,0.000031436597,0.009607095,0.059467528,0.6519624,0.027509904,0.0069553023,0.0054025664],"about_ca_topic_score_codex":0.00029049488,"about_ca_topic_score_gemma":0.008848086,"teacher_disagreement_score":0.65125495,"about_ca_system_score_codex":0.00003631965,"about_ca_system_score_gemma":0.00029379534,"threshold_uncertainty_score":0.49374396},"labels":[],"label_agreement":null},{"id":"W7024073219","doi":"","title":"Quel(s) rôle(s) pour l'agriculture urbaine à Montréal-Nord : perceptions et vision des résidents.","year":2014,"lang":"fr","type":"dissertation","venue":"EspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Ethnography; Urban environment; Informal settlements","score_opus":0.06061884879967888,"score_gpt":0.3432838858209143,"score_spread":0.2826650370212354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024073219","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":[0.036043923,0.0027497231,0.69255686,0.0050815907,0.0034488814,0.0018963786,0.0003767851,0.000745125,0.25710073],"genre_scores_gemma":[0.652484,0.000809332,0.12707889,0.0008337342,0.0014806456,0.0009259587,0.0030648613,0.00014021894,0.21318239],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.989523,0.0022629905,0.001526993,0.002503726,0.002989854,0.0011934437],"domain_scores_gemma":[0.989816,0.003816302,0.0010032939,0.0010444681,0.003381424,0.0009384924],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0038138693,0.0012825535,0.0008052172,0.0008057113,0.003321227,0.0038829742,0.0022938903,0.0020526838,0.000058627305],"category_scores_gemma":[0.0074511664,0.0012892399,0.00084207073,0.003010429,0.0024482955,0.0064371983,0.00059695763,0.0031929465,0.000692054],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.000096094525,0.001113855,0.00083859597,0.00031757387,0.00017255817,0.000117056035,0.0020261782,0.114349365,0.030948237,0.82469136,0.01527599,0.010053125],"study_design_scores_gemma":[0.002277213,0.00025082118,0.07188469,0.004427378,0.00029343815,0.0030335726,0.00072345755,0.092288375,0.010032703,0.33499205,0.476085,0.0037112986],"about_ca_topic_score_codex":0.00036355742,"about_ca_topic_score_gemma":0.0011720641,"teacher_disagreement_score":0.61644006,"about_ca_system_score_codex":0.005941904,"about_ca_system_score_gemma":0.007657883,"threshold_uncertainty_score":0.9999926},"labels":[],"label_agreement":null},{"id":"W7024264049","doi":"","title":"RELIEF, une démarche d'évaluation des processus de concertation : adaptation au contexte des organismes de bassin versant du Québec","year":2013,"lang":"fr","type":"other","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Chinese Dream; Social mobilization; Decentralization","score_opus":0.025498396186234434,"score_gpt":0.22779574300552055,"score_spread":0.20229734681928613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024264049","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":[0.096530475,0.0051593883,0.8748455,0.0006936933,0.00022763255,0.0007550152,0.000009430345,0.00007322864,0.021705644],"genre_scores_gemma":[0.84750915,0.0011844266,0.12343215,0.000035558445,0.00012890027,0.0000079097435,0.000017042737,0.000045399298,0.027639477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784917,0.00037748448,0.00041828194,0.00052024605,0.0004152332,0.00041955963],"domain_scores_gemma":[0.9978013,0.00034140912,0.0004624305,0.00046760397,0.0007488997,0.00017832675],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.00034109224,0.0003687409,0.0004049356,0.00016419134,0.0013155317,0.000049174025,0.0013213578,0.00029151337,0.00018121765],"category_scores_gemma":[0.00028052993,0.0003712076,0.00022744836,0.0008102151,0.0032615168,0.0006757198,0.00034153057,0.0003023954,0.00011351236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023658748,0.00086482195,0.012841057,0.004664553,0.00046251135,0.000014647265,0.1343566,0.3840341,0.047020625,0.13138056,0.0026339148,0.28149003],"study_design_scores_gemma":[0.0016528198,0.00023993562,0.19898987,0.0068066386,0.0005420591,0.00008860743,0.0014706233,0.7232629,0.019466061,0.0090133995,0.03780429,0.00066281157],"about_ca_topic_score_codex":0.084628075,"about_ca_topic_score_gemma":0.3402442,"teacher_disagreement_score":0.75141335,"about_ca_system_score_codex":0.006462762,"about_ca_system_score_gemma":0.0041027693,"threshold_uncertainty_score":0.9999846},"labels":[],"label_agreement":null},{"id":"W7024359418","doi":"","title":"Risk of mortality associated with sotalol and amiodarone for new-onset post-operative atrial fibrillation following CABG","year":2016,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sotalol; Amiodarone; Atrial fibrillation; Sinus rhythm; Cohort; Antiarrhythmic agent; Concomitant; Cohort study; Odds ratio; Dofetilide","score_opus":0.02925015130017615,"score_gpt":0.34357245888126253,"score_spread":0.31432230758108637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024359418","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":[0.943911,0.00011607528,0.052579854,0.000093732,0.00020666711,0.0022536018,0.00045850597,0.000007240619,0.00037332714],"genre_scores_gemma":[0.9301828,0.000030395098,0.0635115,0.000009684925,0.00020322422,0.00006194357,0.0017972046,0.00004053557,0.0041627064],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984948,0.00007967116,0.00037367738,0.00060901395,0.00023395654,0.00020887292],"domain_scores_gemma":[0.9980107,0.0005254444,0.00072572374,0.00042129404,0.00022958021,0.00008726552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027408992,0.00023505864,0.00042501063,0.000060693066,0.00025305574,0.0001624367,0.00050902274,0.00016140172,0.000012864831],"category_scores_gemma":[0.00026579495,0.00018496839,0.000121797275,0.00023529452,0.000030430498,0.00063837075,0.00010529785,0.00011171805,0.0000061582678],"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":[0.0013967855,0.00007862914,0.04291809,0.000045845078,0.002503644,0.0000079693755,0.005874818,0.0021619853,0.006549823,0.00094712933,0.0006206342,0.93689466],"study_design_scores_gemma":[0.023102405,0.00394603,0.84788424,0.0026582177,0.0035331114,0.000019571715,0.000937782,0.04392276,0.045589194,0.017878123,0.0057637137,0.0047648656],"about_ca_topic_score_codex":0.0001844844,"about_ca_topic_score_gemma":0.0028092002,"teacher_disagreement_score":0.9321298,"about_ca_system_score_codex":0.00005911581,"about_ca_system_score_gemma":0.00032852765,"threshold_uncertainty_score":0.75427926},"labels":[],"label_agreement":null},{"id":"W7024470536","doi":"","title":"2018 09 20 Roger Morin se rappelle de Kraft Hockey Ville a Roberval","year":2018,"lang":"fr","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Field hockey; Context (archaeology); Kraft paper; Moment (physics)","score_opus":0.011172031344335854,"score_gpt":0.21056865989472828,"score_spread":0.19939662855039242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024470536","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":[0.00017274823,0.00068438664,0.0008928169,0.0017584546,0.00094036944,0.0010520036,0.00010102966,0.00030270749,0.9940955],"genre_scores_gemma":[0.00032623071,0.0004730281,0.040987007,0.0008687576,0.0008288051,0.000084287116,0.000093323484,0.00013496862,0.9562036],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99599075,0.00022708102,0.0013036815,0.0006999745,0.00072953856,0.0010489683],"domain_scores_gemma":[0.99610335,0.00034395914,0.0011733976,0.0014238281,0.00048721445,0.00046823663],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00043292533,0.0007870315,0.00075524306,0.00012504662,0.00035108902,0.00021442331,0.0017020559,0.0007128175,0.40137795],"category_scores_gemma":[0.00013421931,0.00084669876,0.0003757739,0.000060447794,0.0005594569,0.0000023184302,0.00069067354,0.0005583406,0.13374503],"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":[0.00006050613,0.00014041875,0.0000074414324,0.00024599882,0.00008910444,0.00003294059,0.0006232303,0.008817912,0.000005961409,0.00091625925,0.9791465,0.00991373],"study_design_scores_gemma":[0.000748416,0.00030935337,0.000012328725,0.00031995898,0.00007849153,0.00037004237,0.00006102965,0.008033278,0.00011721185,0.0002167057,0.9889261,0.00080705225],"about_ca_topic_score_codex":0.0026110348,"about_ca_topic_score_gemma":0.0020959217,"teacher_disagreement_score":0.26763293,"about_ca_system_score_codex":0.00026817556,"about_ca_system_score_gemma":0.00018407575,"threshold_uncertainty_score":0.99939835},"labels":[],"label_agreement":null},{"id":"W7024497306","doi":"","title":"The settlement and adaptation needs of Korean newcomer and immigrant families in Toronto, pilot study","year":2001,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Immigration; Settlement (finance); Adaptation (eye); Ethnic group; Context (archaeology); Population","score_opus":0.007337019612224699,"score_gpt":0.1783397942106721,"score_spread":0.1710027745984474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024497306","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":[0.03779639,0.018383522,0.010855606,0.008054797,0.0005252191,0.0036044028,0.0005596469,0.00007782692,0.9201426],"genre_scores_gemma":[0.9434019,0.0060335724,0.004262097,0.00033386773,0.000047345806,0.000050724546,0.00000642842,0.00006321874,0.045800883],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986639,0.000055459444,0.00026257447,0.0002004925,0.00064143236,0.00017615364],"domain_scores_gemma":[0.9992241,0.00020822832,0.00020843926,0.0002673529,4.216676e-7,0.00009144736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001442481,0.00016679497,0.00021171139,0.000028173703,0.00008962877,0.000020413008,0.00027743843,0.000012722405,0.000007726688],"category_scores_gemma":[0.0000010995143,0.00013250654,0.0000073713863,0.00009869618,0.000083388804,0.00017267377,0.00022436818,0.00007534699,4.6831444e-10],"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":[0.00036014276,0.0003812672,0.07542637,0.0003411173,0.00035144985,0.00006284339,0.0031966472,0.0003158659,0.002339042,0.47470295,0.013766223,0.4287561],"study_design_scores_gemma":[0.004754752,0.003322655,0.26394886,0.0009818013,0.00014408944,0.00003161503,0.17734897,0.038182985,0.0031394898,0.0078013674,0.4980947,0.0022487103],"about_ca_topic_score_codex":0.08279832,"about_ca_topic_score_gemma":0.74901795,"teacher_disagreement_score":0.9056055,"about_ca_system_score_codex":0.0000095021105,"about_ca_system_score_gemma":0.00021524292,"threshold_uncertainty_score":0.9233094},"labels":[],"label_agreement":null},{"id":"W7024692672","doi":"","title":"Software Simulation of Numerically Controlled\\nMachining","year":2007,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Machining; Software; Field (mathematics); Simulation software; Work (physics)","score_opus":0.011703575477142182,"score_gpt":0.2505128945389811,"score_spread":0.23880931906183894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024692672","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":[0.36039412,0.00019177873,0.6373366,0.00020783154,0.0002957307,0.00068965333,0.000014724486,0.00022255145,0.0006469976],"genre_scores_gemma":[0.500652,0.00010528812,0.36392626,0.0000678364,0.00007563969,0.0000019793476,0.0003551737,0.000052556574,0.13476327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867386,0.00005087638,0.00024711376,0.00041459195,0.00037273252,0.00024084597],"domain_scores_gemma":[0.9980336,0.0002772547,0.00073831866,0.00052952056,0.00033543215,0.0000859115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001543825,0.00021061054,0.0005557963,0.00028738874,0.0001681459,0.000013739932,0.00091679994,0.00021173041,0.00003121821],"category_scores_gemma":[0.000042798365,0.00024428315,0.00021702974,0.00048592023,0.00006070483,0.00036288923,0.00010980586,0.00024249431,0.000019639177],"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":[0.0023503485,0.00061173463,0.0011079086,0.00077911204,0.000632704,0.00009074122,0.14129134,0.54947376,0.011658386,0.016490715,0.0011218991,0.27439135],"study_design_scores_gemma":[0.011246229,0.0009011683,0.014364472,0.0009470618,0.00063136715,0.000008633097,0.035186,0.9162503,0.006574343,0.008012704,0.003531918,0.0023458367],"about_ca_topic_score_codex":0.0011141935,"about_ca_topic_score_gemma":0.001684394,"teacher_disagreement_score":0.36677653,"about_ca_system_score_codex":0.000051619212,"about_ca_system_score_gemma":0.00007081351,"threshold_uncertainty_score":0.9961578},"labels":[],"label_agreement":null},{"id":"W7024920814","doi":"","title":"Trying to eat a sandwich while two girls go at it next to you","year":2023,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Code (set theory); Core (optical fiber); Goto; Action (physics)","score_opus":0.015099279232348186,"score_gpt":0.22781491569528364,"score_spread":0.21271563646293545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024920814","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":[0.000025201263,0.00008623954,0.0003660714,0.0022278598,0.00043875494,0.0011007625,0.00007488078,0.0010109365,0.9946693],"genre_scores_gemma":[0.00025012394,0.00009776572,0.022402499,0.0021493938,0.00025048864,0.00016110031,0.000039203875,0.00022462526,0.9744248],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976002,0.000050627732,0.00070723123,0.0005208002,0.00057345687,0.0005477029],"domain_scores_gemma":[0.9978041,0.00021231886,0.00040824251,0.0010847866,0.00014590712,0.00034466214],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00018434765,0.00042859197,0.0004712785,0.00017628097,0.000162267,0.00014577503,0.0013116305,0.00022402302,0.046830025],"category_scores_gemma":[0.00011146236,0.00044754107,0.00014997892,0.00008031644,0.000046577698,0.0000010631959,0.00080483896,0.0002450426,0.08808472],"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":[0.000020464782,0.000016742844,0.0000014838835,0.0000689934,0.000022734439,0.00001164045,0.00024734758,0.0066630244,0.000003099244,0.00013547034,0.98460627,0.008202736],"study_design_scores_gemma":[0.00030194837,0.00009526978,0.000006258422,0.00023017124,0.00001623065,0.000046524598,0.00002075741,0.0003150581,0.000026062302,0.000019212139,0.9984596,0.0004629137],"about_ca_topic_score_codex":0.00090794195,"about_ca_topic_score_gemma":0.0037080562,"teacher_disagreement_score":0.041254696,"about_ca_system_score_codex":0.00016458835,"about_ca_system_score_gemma":0.000030111916,"threshold_uncertainty_score":0.99979764},"labels":[],"label_agreement":null},{"id":"W7025151887","doi":"","title":"Trudeau Centre video (high bandwidth)","year":2005,"lang":"en","type":"other","venue":"TSpace (University of Toronto)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Terrorism; Conflict resolution; International relations; Ethnic group; Research centre; Field (mathematics)","score_opus":0.008189967661868705,"score_gpt":0.22047358765373087,"score_spread":0.21228361999186218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025151887","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":[0.000020114987,0.0014323217,0.15316373,0.0027062655,0.0002097892,0.00029801953,0.000024085612,0.00048619026,0.8416595],"genre_scores_gemma":[0.0040704254,0.0011515836,0.12430392,0.00007167215,0.00020230767,3.2844056e-7,0.000016252734,0.00008690594,0.8700966],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988679,0.00003810807,0.00007575141,0.0004981473,0.00024232481,0.00027771416],"domain_scores_gemma":[0.998555,0.000039389844,0.00028190314,0.0009486105,0.000039415325,0.00013568196],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000037025828,0.00022007014,0.00031882076,0.000056555953,0.00010024984,0.000014226169,0.0014140303,0.000198736,0.03253571],"category_scores_gemma":[0.0000036497374,0.0002798112,0.00011544876,0.00012275393,0.00009830677,0.0003532311,0.00039706947,0.00012846892,0.00026066124],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011540997,0.000090381895,0.000019510386,0.000036840975,0.00008692153,0.000020685806,0.0013667962,0.00010355191,0.00006579916,0.04351531,0.6446639,0.31001878],"study_design_scores_gemma":[0.00044491695,0.00002930845,0.0018846523,0.000073061114,0.000033445238,0.0000028750112,0.00020993045,0.0010923828,0.0000141238,0.00010405752,0.99575764,0.00035363407],"about_ca_topic_score_codex":0.034181714,"about_ca_topic_score_gemma":0.070894115,"teacher_disagreement_score":0.3510937,"about_ca_system_score_codex":0.00019914645,"about_ca_system_score_gemma":0.000047247726,"threshold_uncertainty_score":0.9999654},"labels":[],"label_agreement":null},{"id":"W7036813386","doi":"","title":"3.7 Chief Justice Hughes Delivered the Opinion of the Court for Gaines vs. Canada Case","year":2015,"lang":"en","type":"article","venue":"Blue Tiger Commons (Lincoln University - Missouri)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Economic Justice; State (computer science); Key (lock); Court decision","score_opus":0.03297592025223651,"score_gpt":0.2428214114725194,"score_spread":0.2098454912202829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036813386","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":[0.36584696,0.0009415499,0.51999897,0.10080215,0.0027359785,0.0044656578,0.0005403504,0.00041114155,0.0042572417],"genre_scores_gemma":[0.98927116,0.000020347221,0.008497582,0.0005179296,0.00014901102,0.000007933998,0.000009857964,0.000015337791,0.0015108088],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987599,0.00015806219,0.00019480452,0.00030526918,0.00028833997,0.0002936227],"domain_scores_gemma":[0.9975037,0.0006783441,0.00024119673,0.0010452496,0.00035247445,0.00017904006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021709937,0.00017117106,0.0001968196,0.00004741645,0.00085875497,0.000030669187,0.0017829124,0.000060855138,0.000003077208],"category_scores_gemma":[0.00009599856,0.00013036693,0.000101688915,0.0006350711,0.00023165725,0.00019251514,0.00061400625,0.00018600274,0.0000033935135],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048040116,0.000638313,0.0020392248,0.00019220123,0.00030198786,0.00046709273,0.008966685,0.12869827,0.00028401372,0.177975,0.67144763,0.008509203],"study_design_scores_gemma":[0.0018435251,0.00013181278,0.001027457,0.00006187481,0.00018708152,0.00036205698,0.0040711607,0.10945983,0.001025413,0.001679707,0.8796525,0.00049758406],"about_ca_topic_score_codex":0.043118507,"about_ca_topic_score_gemma":0.19934803,"teacher_disagreement_score":0.62342423,"about_ca_system_score_codex":0.00024765875,"about_ca_system_score_gemma":0.00060973334,"threshold_uncertainty_score":0.96325344},"labels":[],"label_agreement":null},{"id":"W7037460864","doi":"","title":"EFL learner and teacher perspectives on corrective feedback and their effect on second language learning motivation","year":2014,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Corrective feedback; Affect (linguistics); Test (biology); Second language; Second-language acquisition; Skepticism; Language proficiency; Language acquisition","score_opus":0.0025445979673621227,"score_gpt":0.15636163914601514,"score_spread":0.15381704117865302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7037460864","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035889056,0.001025945,0.0024165008,0.0010639192,0.00012964278,0.0004953963,0.000053027543,0.00007421522,0.9588523],"genre_scores_gemma":[0.7764352,0.00012219409,0.0005330573,0.0004932906,0.000118367025,0.000018247814,0.000006152549,0.000088028115,0.22218549],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986302,0.00013880199,0.00011433521,0.0004705336,0.00045434217,0.0001917793],"domain_scores_gemma":[0.9988119,0.0006373782,0.00018440917,0.00023282367,4.1967357e-7,0.0001330567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000013976607,0.0002616502,0.00026610034,0.000046031662,0.00013326558,0.000035257395,0.00019107533,0.00004570315,0.000035053537],"category_scores_gemma":[0.000009204824,0.00021352479,0.000017093458,0.00007085473,0.00008747142,0.00013564891,0.00014762848,0.00031420554,1.3069107e-8],"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":[0.0008615612,0.0001521544,0.015462605,0.00077303086,0.00064145087,0.00007200808,0.007274551,0.00094126235,0.013845588,0.27249736,0.05018956,0.63728887],"study_design_scores_gemma":[0.0040364717,0.0041326764,0.1848605,0.0027150887,0.00010143747,0.00006410996,0.021088658,0.048467733,0.07014834,0.0032563815,0.65730095,0.0038276692],"about_ca_topic_score_codex":0.00030562028,"about_ca_topic_score_gemma":0.0030666827,"teacher_disagreement_score":0.7405461,"about_ca_system_score_codex":0.0000096094445,"about_ca_system_score_gemma":0.00010906129,"threshold_uncertainty_score":0.87072885},"labels":[],"label_agreement":null},{"id":"W7038685290","doi":"","title":"Impact de l'aménagement forestier sur les populations de salamandres pourpres","year":2024,"lang":"fr","type":"other","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministère des Transports","keywords":"Environmental policy; Rail transportation; Environmental impact statement","score_opus":0.05906081126440393,"score_gpt":0.3337462352876654,"score_spread":0.27468542402326146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038685290","genre_codex":"methods","genre_gemma":"other","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":[0.0013394763,0.0050822482,0.84992224,0.0044181654,0.0005091526,0.00069017673,0.00003663955,0.00054879306,0.13745311],"genre_scores_gemma":[0.06268528,0.00027711486,0.18193392,0.00036744936,0.00077533745,0.00020404844,0.000023319579,0.00024957905,0.75348395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975725,0.00011315203,0.00039460033,0.0008095692,0.00027997684,0.0008302361],"domain_scores_gemma":[0.9982878,0.00017100634,0.00016760538,0.000990539,0.00008583909,0.00029719344],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00017805598,0.00049117557,0.00032833638,0.00026117737,0.00019969635,0.0004421414,0.0010123368,0.0002635119,0.004361417],"category_scores_gemma":[0.000083254614,0.00042905743,0.00028470234,0.0006266622,0.00013702334,0.00027322702,0.0005673363,0.00042270767,0.0016358861],"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":[0.000003823259,0.000112173104,0.009317868,0.000092707545,0.00012853395,0.0000390121,0.00025477255,0.030298049,0.000118447686,0.58493114,0.31682897,0.057874527],"study_design_scores_gemma":[0.00020185689,0.000095834344,0.0683223,0.00039955537,0.00016463535,0.000085824075,0.000020702044,0.24201931,0.000054480497,0.16690445,0.5209866,0.0007444356],"about_ca_topic_score_codex":0.0014751129,"about_ca_topic_score_gemma":0.0032921915,"teacher_disagreement_score":0.6679883,"about_ca_system_score_codex":0.00044654435,"about_ca_system_score_gemma":0.00023033569,"threshold_uncertainty_score":0.9998161},"labels":[],"label_agreement":null},{"id":"W7039100912","doi":"","title":"Kryžiaus kelio raiška Tytuvėnų bernardinų vienuolyno ansamblio sienų tapyboje: išsaugojimo būtinybė","year":2005,"lang":"en","type":"article","venue":"Laba (Lietuvos akademinių bibliotekų direktorių asociacija)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mural; Quarter (Canadian coin); Lithuanian; Theme (computing); Fountain; Square (algebra)","score_opus":0.016262508780012497,"score_gpt":0.2783189077115539,"score_spread":0.26205639893154137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039100912","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":[0.1739641,0.027369192,0.5220404,0.10940579,0.015120314,0.009963029,0.0008501447,0.01772129,0.123565726],"genre_scores_gemma":[0.7908927,0.007755401,0.14883655,0.008270415,0.0089579495,0.0012299278,0.0001549838,0.0005029877,0.03339906],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.990124,0.0003944183,0.0018930127,0.00271182,0.0022707335,0.0026060212],"domain_scores_gemma":[0.99263144,0.001243048,0.0011222735,0.0031735662,0.000633749,0.0011959574],"candidate_categories":["metaepi_narrow","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0014611899,0.0013077528,0.0013116399,0.0030764004,0.001243809,0.0010444949,0.004998581,0.0009344065,0.000307324],"category_scores_gemma":[0.00047714807,0.0013622356,0.0007011542,0.02195873,0.00045711864,0.0039238706,0.001552406,0.0018485554,0.002314344],"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":[0.000097854056,0.0013079061,0.005809317,0.00013403312,0.00046758348,0.00018403542,0.004708934,0.004170782,0.025075255,0.058325347,0.49905702,0.40066192],"study_design_scores_gemma":[0.0013858498,0.00022563447,0.0040820446,0.0001370509,0.00008972135,0.00013236805,0.000098766024,0.009006996,0.014432457,0.0047023427,0.96368974,0.0020170528],"about_ca_topic_score_codex":0.00006526703,"about_ca_topic_score_gemma":0.00006698338,"teacher_disagreement_score":0.61692864,"about_ca_system_score_codex":0.0006440495,"about_ca_system_score_gemma":0.0003896499,"threshold_uncertainty_score":0.9999925},"labels":[],"label_agreement":null},{"id":"W7042294478","doi":"","title":"Plan of Lock 5 old Welland Canal. [St. Catharines]","year":2016,"lang":"en","type":"other","venue":"Brock University Digital Repository (Brock University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Lock (firearm); Georeference; Digital mapping; Documentation; Object (grammar); National archives; Geographic information system","score_opus":0.00863011670222976,"score_gpt":0.18317562881214608,"score_spread":0.1745455121099163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042294478","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":[0.00042761906,0.00009690882,0.06270123,0.000013947585,0.0004590787,0.0005063115,0.0004306024,0.00082239317,0.9345419],"genre_scores_gemma":[0.0021374729,0.00012860076,0.00068450277,0.000019271942,0.00015488215,2.2383594e-7,0.00004081862,0.00010907199,0.99672514],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978428,0.000057907084,0.00023258376,0.0009672566,0.00041958244,0.00047987938],"domain_scores_gemma":[0.9975678,0.00012293909,0.0005975615,0.0011858472,0.00015907285,0.00036682351],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000001603492,0.00047685142,0.0005065601,0.0008511952,0.00022811246,0.00007952463,0.0025448436,0.00035592003,0.000027822713],"category_scores_gemma":[0.0000098379405,0.0005296777,0.00026454675,0.0008110559,0.00019354693,0.0008386123,0.0012700022,0.00026674208,0.00012456457],"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":[0.00005672357,0.00011891046,0.004374572,0.00007486992,0.00017760326,0.00024850463,0.000008322759,0.000059611255,0.0011468783,0.002408347,0.9903733,0.0009523615],"study_design_scores_gemma":[0.0006521982,0.00007343162,0.00027395843,0.00017928383,0.00006673041,0.000067736066,0.00016889987,0.000009337633,0.00019457456,0.00008985265,0.9976279,0.00059606135],"about_ca_topic_score_codex":0.00043977168,"about_ca_topic_score_gemma":0.0011106891,"teacher_disagreement_score":0.06218325,"about_ca_system_score_codex":0.00037596744,"about_ca_system_score_gemma":0.00024296698,"threshold_uncertainty_score":0.9997155},"labels":[],"label_agreement":null},{"id":"W7044041470","doi":"","title":"Upsampling Indoor LiDAR Point Clouds for Object Detection","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lidar; Upsampling; Point cloud; Object detection; Point (geometry); Object (grammar)","score_opus":0.01717261315515327,"score_gpt":0.23684411865244917,"score_spread":0.21967150549729592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7044041470","genre_codex":"empirical","genre_gemma":"other","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":[0.72318435,0.000072533774,0.27270094,0.0008717029,0.001208117,0.0011496262,0.00003828166,0.00067248754,0.00010195221],"genre_scores_gemma":[0.28468007,0.00038752265,0.23618546,0.000104590086,0.0004799954,0.000041808817,0.001079173,0.00019834311,0.47684306],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9986526,0.00003086887,0.00015489185,0.00059946906,0.00023122506,0.00033097842],"domain_scores_gemma":[0.9986262,0.00015273725,0.00034384753,0.0005608724,0.0002271079,0.00008928417],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014460589,0.00023065128,0.00032465666,0.00029540496,0.00041404585,0.000039648326,0.00090813084,0.00024401053,0.000006574393],"category_scores_gemma":[0.00002348839,0.00028983288,0.0002202414,0.0006196303,0.0000368506,0.00036531486,0.00014741512,0.0002446948,0.0000716294],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001352806,0.00045885346,0.00021233878,0.0025927906,0.00087815244,0.00010471725,0.39845514,0.015728945,0.1692989,0.023291795,0.0077506388,0.3798749],"study_design_scores_gemma":[0.008777773,0.002907328,0.019308079,0.00204073,0.0011348781,0.000047382408,0.30416152,0.27296442,0.25698358,0.10068307,0.024131676,0.0068595563],"about_ca_topic_score_codex":0.002457578,"about_ca_topic_score_gemma":0.018714149,"teacher_disagreement_score":0.4767411,"about_ca_system_score_codex":0.00010794133,"about_ca_system_score_gemma":0.00007415347,"threshold_uncertainty_score":0.99995536},"labels":[],"label_agreement":null},{"id":"W7066671337","doi":"","title":"Iuav Academics Abroad : una \"citta analoga\" per Venezia = an \"analogous city\" for Venice","year":2020,"lang":"it","type":"other","venue":"AirIuav (Università Iuav di Venezia)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); International market; Independence (probability theory); University campus","score_opus":0.03738417221160267,"score_gpt":0.2975248692837443,"score_spread":0.2601406970721416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066671337","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":[0.012728333,0.0046587735,0.8816776,0.018192243,0.0035512277,0.0070195855,0.0023299463,0.0027043314,0.06713794],"genre_scores_gemma":[0.4200161,0.006267197,0.19422881,0.018380592,0.0144867,0.0006766247,0.0054907175,0.004175792,0.33627743],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99112725,0.00040667507,0.0010006408,0.0039760973,0.0012281546,0.002261202],"domain_scores_gemma":[0.9924134,0.0006152603,0.0015456383,0.0033749896,0.00048329457,0.0015674203],"candidate_categories":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.00042123283,0.001733784,0.0018353552,0.00089797983,0.0014231366,0.00034377497,0.007089111,0.0018151037,0.0011755489],"category_scores_gemma":[0.00011887321,0.002243189,0.0010397356,0.0030083207,0.000734376,0.0017121243,0.001980805,0.0022952086,0.001424945],"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":[0.0012483143,0.0022426432,0.0030564098,0.0016654847,0.0038004385,0.0015390622,0.01001059,0.038905285,0.007623369,0.28009722,0.45782623,0.19198497],"study_design_scores_gemma":[0.0023281402,0.0009779147,0.0029318968,0.00045152448,0.000832346,0.0001123916,0.00042061997,0.06618913,0.00015835786,0.006142534,0.9167973,0.0026578626],"about_ca_topic_score_codex":0.00017786068,"about_ca_topic_score_gemma":0.0002495255,"teacher_disagreement_score":0.6874488,"about_ca_system_score_codex":0.0010147971,"about_ca_system_score_gemma":0.0008694716,"threshold_uncertainty_score":0.99987686},"labels":[],"label_agreement":null},{"id":"W7094744130","doi":"","title":"Feedback effects of chronic browsing on life-history traits of a large herbivore","year":2008,"lang":"en","type":"article","venue":"Corpus Université Laval (Université Laval)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ungulate; Herbivore; Reproduction; Litter; Roe deer; Population density; Population; Forage","score_opus":0.010314228942185337,"score_gpt":0.18766587483214275,"score_spread":0.17735164588995742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7094744130","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":[0.91607845,0.005815586,0.06452403,0.00072903297,0.0006233544,0.0008630972,0.000024830193,0.00047175735,0.01086985],"genre_scores_gemma":[0.99361736,0.001199798,0.0024095261,0.00013587427,0.00006917508,0.0000015470155,0.0000069650196,0.000031025294,0.002528711],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99806595,0.00011024846,0.00025268833,0.00061717187,0.00047333297,0.00048059755],"domain_scores_gemma":[0.9980228,0.0003158491,0.0004218446,0.0007582284,0.00020788926,0.00027338072],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000076382385,0.00030688886,0.00047562853,0.0003858056,0.0003315931,0.0000057025472,0.0014179661,0.00015534308,0.000012759153],"category_scores_gemma":[0.000028038394,0.0003642397,0.000257437,0.0010259306,0.00022760266,0.00055369525,0.00046704375,0.00031044587,0.000017659178],"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":[0.0006558927,0.002165743,0.0030889176,0.0009979059,0.00064925506,0.0017448263,0.039745435,0.0069959112,0.49610424,0.3677903,0.011156998,0.06890456],"study_design_scores_gemma":[0.022952354,0.0063782935,0.104132935,0.002054308,0.0007094487,0.00051487004,0.0015555735,0.036568303,0.610733,0.0018912212,0.20857643,0.003933318],"about_ca_topic_score_codex":0.00026251466,"about_ca_topic_score_gemma":0.0002616711,"teacher_disagreement_score":0.36589912,"about_ca_system_score_codex":0.0010277359,"about_ca_system_score_gemma":0.0005004464,"threshold_uncertainty_score":0.99988097},"labels":[],"label_agreement":null},{"id":"W7099315926","doi":"","title":"Proceedings of the 2003 Conference on New Interfaces for Musical Expression (NIME-03), Montreal, Canada LEMUR GuitarBot: MIDI Robotic String Instrument","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"MIDI; String (physics); Bridge (graph theory); Lemur; Musical instrument; Musical; Musical expression","score_opus":0.04265212416854661,"score_gpt":0.2440848143048019,"score_spread":0.20143269013625528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099315926","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":[0.7003266,0.000075540884,0.28291693,0.008208739,0.0005807349,0.0020629456,0.0000072866833,0.000172918,0.0056482963],"genre_scores_gemma":[0.9660465,0.000017248145,0.03212717,0.00027425538,0.000045466826,0.00004976821,6.3360096e-7,0.000008186718,0.001430768],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987911,0.0000072692837,0.000265579,0.00037406333,0.00029535775,0.00026660657],"domain_scores_gemma":[0.9991759,0.0000719166,0.00015494022,0.0003115761,0.00017205585,0.00011362022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037788865,0.00015105699,0.00016849197,0.000025489913,0.00019744893,0.000028459557,0.00094522367,0.000035309426,0.000008930459],"category_scores_gemma":[0.00004929032,0.00010153141,0.00003186942,0.0003614428,0.000051693303,0.00022647435,0.00028146344,0.00011867817,0.0000016157762],"study_design_candidate":"bench_or_experimental","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":[0.00012487578,0.00045495856,0.0021879391,0.00010670694,0.00006328822,0.000004851323,0.002072338,0.042815287,0.120519966,0.35133043,0.41492602,0.065393336],"study_design_scores_gemma":[0.0015860748,0.00048224576,0.0132139325,0.0004395094,0.000022914857,0.00003945695,0.0002857166,0.24596778,0.7161815,0.011949333,0.009059747,0.0007717945],"about_ca_topic_score_codex":0.021401994,"about_ca_topic_score_gemma":0.029280199,"teacher_disagreement_score":0.5956615,"about_ca_system_score_codex":0.00009793353,"about_ca_system_score_gemma":0.00036254572,"threshold_uncertainty_score":0.9884329},"labels":[],"label_agreement":null},{"id":"W7099319500","doi":"","title":"Annual Report Vision Values Strategic goals","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Annual report; Diversity (politics); Capital (architecture); Human capital; Service (business); Service provider; Strategic planning","score_opus":0.01884997781178611,"score_gpt":0.31362747466011887,"score_spread":0.29477749684833277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099319500","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":[0.13505168,0.000017238732,0.8231251,0.0031697305,0.00061543094,0.00024552617,0.0000036043446,0.0007966819,0.036974978],"genre_scores_gemma":[0.77682203,0.000003701822,0.21914293,0.00026497408,0.0001362231,0.000017322278,0.0000029973887,0.0000062627455,0.003603588],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991045,0.000012409019,0.0001830344,0.0003528577,0.0001704269,0.00017678534],"domain_scores_gemma":[0.9988623,0.00006574631,0.0000729769,0.00082658755,0.00008448506,0.000087893255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014781882,0.00008242784,0.00007636892,0.000032054482,0.00010981533,0.000053577816,0.0005782431,0.000044384025,0.00003952463],"category_scores_gemma":[0.000016070991,0.000066365894,0.000031411226,0.00028135788,0.000042407784,0.00052712223,0.00017660919,0.00016374484,0.00013752696],"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":[0.000001420612,0.00007744832,0.00024021814,0.0000020097352,0.0000040748237,0.00007869791,0.000081973165,0.0002822097,0.039647337,0.91870207,0.013657832,0.02722473],"study_design_scores_gemma":[0.0002815381,0.00014559994,0.0077436967,0.000007082321,0.000005528824,0.0009174422,0.00006404313,0.072756395,0.020574722,0.8180501,0.07888907,0.0005647805],"about_ca_topic_score_codex":0.000006220281,"about_ca_topic_score_gemma":0.000014835601,"teacher_disagreement_score":0.6417703,"about_ca_system_score_codex":0.0000055668515,"about_ca_system_score_gemma":0.000029367344,"threshold_uncertainty_score":0.27063227},"labels":[],"label_agreement":null},{"id":"W7099454653","doi":"","title":"Clinical Supervisor Addiction Services Vancouver Coastal Health Authority Pacific Spirit Community Health Centre","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Criminalization; Legalization; Public health; Decriminalization; Harm; Harm reduction; Cannabis; Public policy","score_opus":0.054432682953471014,"score_gpt":0.32762390287603427,"score_spread":0.27319121992256323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099454653","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":[0.028617151,0.0010555174,0.8704479,0.08146875,0.0030570277,0.0020691245,0.00015982818,0.0031550573,0.009969669],"genre_scores_gemma":[0.8932026,0.0010840485,0.09344862,0.009313144,0.0003322956,0.0000316421,0.000064075866,0.000031572872,0.002491985],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99686044,0.0008797391,0.00078272476,0.00053462904,0.00033367996,0.00060877256],"domain_scores_gemma":[0.9974016,0.00031152417,0.00030003983,0.0013715343,0.00010671993,0.00050857384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089256786,0.00022116378,0.0003781405,0.00006317307,0.0011157562,0.00009663898,0.0010645823,0.0000932859,0.0001703347],"category_scores_gemma":[0.000014054672,0.00020878675,0.000120609104,0.000673492,0.000091500435,0.00069927686,0.00048752496,0.0008319761,0.00022488358],"study_design_candidate":"not_applicable","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":[0.000012722008,0.0019579502,0.024482235,0.00013356688,0.00003794952,0.0000033451684,0.0027758714,0.00024831004,0.000006165721,0.030221874,0.32844943,0.6116706],"study_design_scores_gemma":[0.0015729587,0.0009384066,0.031313833,0.00012923722,0.00000896375,0.000023155271,0.0018699266,0.4163437,0.000022853023,0.0076475968,0.5393619,0.0007675192],"about_ca_topic_score_codex":0.00189062,"about_ca_topic_score_gemma":0.03638777,"teacher_disagreement_score":0.86458546,"about_ca_system_score_codex":0.00012160631,"about_ca_system_score_gemma":0.00007048446,"threshold_uncertainty_score":0.9811956},"labels":[],"label_agreement":null},{"id":"W7099779528","doi":"","title":"Achieving Data Quality in a Statistical Agency: A Methodological Perspective DATA DETECTIVES: UNCOVERING SYSTEMATIC ERRORS IN ADMINISTRATIVE DATABASES","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Consistency (knowledge bases); Data quality; General partnership; Data integrity; Public health; Information system; Health data; Systematic review","score_opus":0.5597639220459774,"score_gpt":0.5002279048689732,"score_spread":0.059536017177004186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099779528","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":[0.035882875,0.00020919845,0.9615559,0.00035114656,0.000041959207,0.0011042726,0.00023301579,0.00013866657,0.00048293886],"genre_scores_gemma":[0.52822345,0.00003481376,0.47145972,0.000078777695,0.000014187746,0.000101426434,0.00006579355,0.0000072589028,0.000014577325],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99487823,0.0018010753,0.0008373953,0.0016470904,0.00039109171,0.0004451258],"domain_scores_gemma":[0.98895085,0.0067964126,0.00023822166,0.0038121087,0.00006755213,0.00013487556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020119154,0.0002577176,0.00063516997,0.00014630548,0.00014814688,0.0000553207,0.0035523528,0.00005325055,0.000021747337],"category_scores_gemma":[0.0069736238,0.00021375356,0.000023093826,0.001160908,0.00020373803,0.0023360297,0.0037394778,0.00044165275,0.000025161775],"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":[0.00014751383,0.0014470698,0.014642279,0.0020479453,0.00013227868,0.0010187217,0.0111658005,0.004275756,0.00215227,0.9592131,0.00040842604,0.0033488574],"study_design_scores_gemma":[0.0014001561,0.0002913882,0.2523721,0.0014537779,0.000041298794,0.00040909857,0.009671369,0.69085085,0.0003024136,0.041577518,0.00005487041,0.0015751991],"about_ca_topic_score_codex":0.0010596551,"about_ca_topic_score_gemma":0.0023483858,"teacher_disagreement_score":0.91763556,"about_ca_system_score_codex":0.00022688645,"about_ca_system_score_gemma":0.00022088355,"threshold_uncertainty_score":0.8716617},"labels":[],"label_agreement":null},{"id":"W7101570573","doi":"10.1109/tvt.2025.3626427","title":"Enhancing BEV Perception Through Vehicle-Road Cooperative Systems: An Attention-Based Cross-View Fusion Approach","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Beijing Social Science Fund","keywords":"Sensor fusion; Perception; Feature (linguistics); Implementation; Field (mathematics); Channel (broadcasting); Active perception","score_opus":0.017950095172184148,"score_gpt":0.2963135318608911,"score_spread":0.27836343668870694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7101570573","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":[0.102645814,0.001267971,0.88786054,0.002308296,0.0015938634,0.002644285,0.00005082284,0.0014113042,0.00021709538],"genre_scores_gemma":[0.96669906,0.00068231224,0.029095363,0.00070479803,0.000088729765,0.0017875024,0.000040270286,0.000078888166,0.00082305266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99348354,0.0006073348,0.0014911741,0.0026275557,0.0006498289,0.001140554],"domain_scores_gemma":[0.9952522,0.00018104265,0.0004662503,0.0029674487,0.00094447174,0.00018861819],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00051580847,0.00093899184,0.0009881593,0.0011182358,0.0028527498,0.0005228025,0.001972844,0.001406796,0.000046646546],"category_scores_gemma":[0.000023195329,0.0010075668,0.0004093706,0.005964257,0.0011045646,0.0014377888,0.000033757315,0.0020670616,0.00023367957],"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":[0.00009781508,0.0021240665,0.000041327778,0.0004131343,0.00019381211,0.000021454442,0.0003097902,0.75352335,0.10892379,0.011295001,0.000033175776,0.123023264],"study_design_scores_gemma":[0.0017677683,0.0007575609,0.0002713758,0.00083907857,0.00025417216,0.00005865868,0.00062176835,0.9481801,0.04378913,0.0008195547,0.0016597102,0.0009811412],"about_ca_topic_score_codex":0.00013873265,"about_ca_topic_score_gemma":0.000066765875,"teacher_disagreement_score":0.86405325,"about_ca_system_score_codex":0.0008015339,"about_ca_system_score_gemma":0.0005371834,"threshold_uncertainty_score":0.9998896},"labels":[],"label_agreement":null},{"id":"W7104271069","doi":"10.5281/zenodo.17543325","title":"Real-time Object Detection System in the Environment","year":2025,"lang":"en","type":"peer-review","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Object detection; Focus (optics); Artificial neural network; Process (computing); Object (grammar); Edge detection; Key (lock); Enhanced Data Rates for GSM Evolution","score_opus":0.0275460619972599,"score_gpt":0.25826044767883827,"score_spread":0.23071438568157837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104271069","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":[0.000044514476,0.004611313,0.38269898,0.03615753,0.0009879901,0.007150066,0.0006887485,0.0041527892,0.5635081],"genre_scores_gemma":[0.05935561,0.13567,0.044878427,0.017146045,0.005906607,0.000047731177,0.05669984,0.01961585,0.6606799],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99688894,0.0008636784,0.0004333136,0.00077873335,0.00064311095,0.00039224405],"domain_scores_gemma":[0.9977639,0.000104943785,0.00024409531,0.0016041799,0.0002027915,0.000080092475],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011896293,0.00024786635,0.00029213683,0.00026286393,0.0014517906,0.00055356097,0.0038369242,0.000114354254,0.0007270055],"category_scores_gemma":[0.00019662935,0.00021773529,0.00009311621,0.0012442323,0.000081263075,0.00020911485,0.0020340006,0.00060285203,0.007057852],"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":[0.0000038647872,0.000058851478,2.5158917e-8,0.00059520453,0.00001783174,0.000014168096,0.00015152253,0.00021200813,0.00041306936,0.0034374453,0.8228026,0.17229338],"study_design_scores_gemma":[0.00012296562,0.000067779176,0.00001837609,0.00052417733,0.00002077252,0.000075598524,0.000021572963,0.0023842198,0.000058835714,0.000113930066,0.996402,0.00018973107],"about_ca_topic_score_codex":0.000024144376,"about_ca_topic_score_gemma":6.4500523e-7,"teacher_disagreement_score":0.33782056,"about_ca_system_score_codex":0.00055861555,"about_ca_system_score_gemma":0.0000087174985,"threshold_uncertainty_score":0.9998482},"labels":[],"label_agreement":null},{"id":"W7104279727","doi":"10.1016/j.neucom.2025.131966","title":"PDAViT: Pyramid dual-attention vision transformer","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; China Postdoctoral Science Foundation; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Pooling; Cascade; Transformer; FLOPS; Computational complexity theory; Pattern recognition (psychology); Convolutional neural network; Computation","score_opus":0.008683389836073502,"score_gpt":0.28262805898692683,"score_spread":0.27394466915085336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104279727","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":[0.040253915,0.000051198305,0.94896567,0.003890963,0.00045197053,0.00031088424,4.448446e-7,0.0005319652,0.0055430164],"genre_scores_gemma":[0.96404994,0.000012281651,0.033821627,0.0015698313,0.00009188119,0.000020980708,0.0000027729684,0.000011770517,0.0004189495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862415,0.000047791174,0.00029226995,0.00053059537,0.00018196691,0.00032325907],"domain_scores_gemma":[0.99917567,0.0001538898,0.000078478144,0.00047664176,0.00005871221,0.000056635872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012460363,0.0001481017,0.00013517139,0.0001335801,0.0003494842,0.0001278119,0.0005082461,0.00004890496,0.0000027883743],"category_scores_gemma":[0.000015193523,0.00015009048,0.00008194638,0.0009783296,0.00003521477,0.0003890883,0.00016391739,0.00022471992,0.000056737168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005373866,0.00010472987,0.00045086813,0.000032049633,0.000009757264,0.000013428358,0.000105067396,0.008140205,0.07133362,0.13420938,0.001766424,0.7838291],"study_design_scores_gemma":[0.0010142055,0.00012826617,0.044001307,0.00017434258,0.000020961905,0.000053633765,0.000015881087,0.8322645,0.015504338,0.028979726,0.07729044,0.0005524019],"about_ca_topic_score_codex":0.0000022360248,"about_ca_topic_score_gemma":0.0000014047272,"teacher_disagreement_score":0.923796,"about_ca_system_score_codex":0.000031072166,"about_ca_system_score_gemma":0.000025170491,"threshold_uncertainty_score":0.6120512},"labels":[],"label_agreement":null},{"id":"W7105614844","doi":"10.1109/lgrs.2025.3632353","title":"SFE-CapsNet: Spatial Feature Enhanced Capsule Networks for Remote Sensing Object Detection","year":2025,"lang":"","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Fuse (electrical); Object detection; Convolutional neural network; Feature extraction; Feature (linguistics); Pyramid (geometry); Pattern recognition (psychology); Process (computing); Visualization","score_opus":0.010237837115229693,"score_gpt":0.24797938268349362,"score_spread":0.23774154556826393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105614844","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":[0.058479838,0.0004910382,0.9194158,0.011843135,0.007879948,0.0014895838,0.0000068959757,0.00027326305,0.00012053009],"genre_scores_gemma":[0.6538304,0.00044395457,0.3337239,0.009692446,0.0013821328,3.9629745e-7,0.000006807558,0.00005569363,0.00086422486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940559,0.00026289304,0.0007928124,0.0025174008,0.0005799322,0.0017910402],"domain_scores_gemma":[0.99685425,0.00048666092,0.00057733187,0.0014000408,0.0003516023,0.0003301448],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00078799756,0.0008145146,0.0007378123,0.0004887111,0.0029302302,0.0008868555,0.00075936713,0.00046589368,7.7341406e-7],"category_scores_gemma":[0.00018689888,0.00084708043,0.00030340985,0.0027911197,0.0010567069,0.0007139401,0.00033593137,0.0010507616,0.0000072259904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039537605,0.000005977849,2.0120639e-7,0.00004157319,0.000021591653,0.000015349626,0.0002587219,0.0105225565,0.30278903,0.0000073212736,0.0004321217,0.685866],"study_design_scores_gemma":[0.0007405162,0.00014749219,0.00015432714,0.0006171095,0.00009907366,0.00015259556,0.000045591372,0.90423876,0.0896869,0.0010429457,0.0022984145,0.0007762849],"about_ca_topic_score_codex":0.0012073393,"about_ca_topic_score_gemma":0.0008894434,"teacher_disagreement_score":0.8937162,"about_ca_system_score_codex":0.00031795033,"about_ca_system_score_gemma":0.00021836079,"threshold_uncertainty_score":0.999398},"labels":[],"label_agreement":null},{"id":"W7106039670","doi":"10.2139/ssrn.5635890","title":"ECA-UNet: Efficient Channel Attention Enhanced U-Net for Image Segmentation","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Segmentation; Image segmentation; Kernel (algebra); Channel (broadcasting); Encoder; Scale-space segmentation; Image (mathematics); Segmentation-based object categorization; Deep learning","score_opus":0.011436818889386597,"score_gpt":0.29000598926496757,"score_spread":0.278569170375581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106039670","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":[0.009734337,0.0027828398,0.9770825,0.0031933112,0.0029146536,0.003559477,0.00007511085,0.00016884883,0.0004889568],"genre_scores_gemma":[0.86873674,0.028316123,0.08039074,0.0005354681,0.0031786181,0.0024359068,0.00039359558,0.0001775439,0.015835248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9894019,0.0003582712,0.0017439813,0.0019796574,0.00090369507,0.0056124884],"domain_scores_gemma":[0.9949111,0.00036674918,0.0020852715,0.0013118763,0.0010308813,0.00029417148],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0029201915,0.0009837159,0.00085482403,0.0005821957,0.001681015,0.0006754328,0.0027621754,0.00046540788,0.000017857345],"category_scores_gemma":[0.00010303247,0.0010716814,0.00087041623,0.0011614311,0.00017709105,0.0006182219,0.0010666741,0.0052706064,0.000080271035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004158208,0.00095706014,0.000009487967,0.00034243645,0.0009816088,0.000004622179,0.001155431,0.38633344,0.042954683,0.23313394,0.0005041383,0.33320734],"study_design_scores_gemma":[0.0031520566,0.0008134065,0.00008162591,0.0005583504,0.00038669593,0.00026724007,0.00089427177,0.462072,0.013881807,0.5152514,0.0012346224,0.0014065227],"about_ca_topic_score_codex":0.000017908846,"about_ca_topic_score_gemma":0.0001365068,"teacher_disagreement_score":0.89669174,"about_ca_system_score_codex":0.004852495,"about_ca_system_score_gemma":0.0053614,"threshold_uncertainty_score":0.99961865},"labels":[],"label_agreement":null},{"id":"W7106207709","doi":"","title":"CascadedViT: Cascaded Chunk-FeedForward and Cascaded Group Attention Vision Transformer","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Transformer; Efficient energy use; Software deployment; Feed forward; FLOPS; Energy consumption; Mobile device","score_opus":0.020780709626889656,"score_gpt":0.28938072068084153,"score_spread":0.26860001105395187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106207709","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":[0.71816534,0.0039404808,0.26263276,0.010352806,0.0013425244,0.0016982363,0.000025660996,0.0003555988,0.0014865978],"genre_scores_gemma":[0.98753756,0.0038271095,0.0026402774,0.0018495403,0.0003497911,0.0002144553,0.00003338652,0.00006888327,0.003478992],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9941071,0.0002877372,0.0013512949,0.0022347777,0.000636175,0.0013828833],"domain_scores_gemma":[0.99686295,0.00039775192,0.00041670082,0.001608005,0.0002214958,0.0004931222],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00059336727,0.00088470243,0.00084699324,0.00048096906,0.0013866046,0.00035015724,0.0013929997,0.00067442126,0.00006523109],"category_scores_gemma":[0.000070793634,0.00090368104,0.00035427086,0.002754793,0.00058422785,0.0020881717,0.00053010805,0.0012844758,0.00031882623],"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":[0.00039678917,0.0013109151,0.12723887,0.00080634607,0.0006402691,0.0001705074,0.0029427216,0.00069086516,0.23480383,0.060506836,0.0078489445,0.5626431],"study_design_scores_gemma":[0.004905501,0.00072464015,0.8146776,0.00093106704,0.0005828139,0.00026472108,0.00030386564,0.063827544,0.01694009,0.005084731,0.08949769,0.0022597327],"about_ca_topic_score_codex":0.00013663399,"about_ca_topic_score_gemma":0.00014417121,"teacher_disagreement_score":0.6874387,"about_ca_system_score_codex":0.00028693685,"about_ca_system_score_gemma":0.0001439787,"threshold_uncertainty_score":0.99991345},"labels":[],"label_agreement":null},{"id":"W7106564300","doi":"10.1016/j.trc.2025.105473","title":"Synthesizing perceived trajectory for placing cooperative roadside LiDAR: A hybrid simulation-deep learning framework","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Anhui Province; National University's Basic Research Foundation of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Trajectory; Artificial neural network; Probabilistic logic; Field (mathematics); Perception; Object detection; Multilayer perceptron; Radiance","score_opus":0.05212072811209508,"score_gpt":0.3804162262779189,"score_spread":0.3282954981658238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106564300","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":[0.08321858,0.00053733337,0.9095124,0.0030709852,0.000100854704,0.0009948789,0.000005800119,0.0024301421,0.00012903957],"genre_scores_gemma":[0.8827685,0.000236054,0.11564096,0.000047431065,0.000028331415,0.0010390795,0.000023829254,0.000022608396,0.00019317579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974304,0.00014584478,0.00049098866,0.0007972445,0.00044121826,0.00069434155],"domain_scores_gemma":[0.9950239,0.00360741,0.00012202851,0.000650234,0.00054532196,0.00005108958],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00065945584,0.00023500691,0.0002869944,0.0006170352,0.00137074,0.00015979355,0.0010512412,0.00016056029,0.000010439914],"category_scores_gemma":[0.0013871692,0.00024362712,0.00010602366,0.0017093854,0.00023191603,0.00057248457,0.000068390946,0.0009860594,0.000013046107],"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":[0.000041318202,0.00005427076,0.0013702587,0.000066838445,0.00005128218,0.00000996123,0.0013565856,0.8213477,0.0051288987,0.1036736,0.00020381207,0.06669546],"study_design_scores_gemma":[0.00052357896,0.00017870012,0.0038695412,0.00059658155,0.000027384518,8.8521574e-7,0.004539555,0.86198413,0.033613853,0.07185781,0.0222378,0.00057017675],"about_ca_topic_score_codex":0.000012357858,"about_ca_topic_score_gemma":0.000060693685,"teacher_disagreement_score":0.79954994,"about_ca_system_score_codex":0.00012810223,"about_ca_system_score_gemma":0.00011272868,"threshold_uncertainty_score":0.99992937},"labels":[],"label_agreement":null},{"id":"W7110028806","doi":"10.1109/bhi67747.2025.11269481","title":"Convolutional Occupancy Networks for Medical Imaging with Applications to the KiTS23 Challenge","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Dice; Inference; Pyramid (geometry); Feature (linguistics); Medical imaging; Convolutional neural network; Decision boundary; Pattern recognition (psychology); Voxel","score_opus":0.017352772878403293,"score_gpt":0.3066576132449205,"score_spread":0.2893048403665172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110028806","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":[0.000005127015,0.002937179,0.81208795,0.17536616,0.00040649192,0.0036807454,0.000018384686,0.00020520747,0.0052927486],"genre_scores_gemma":[0.70262325,0.0012667213,0.21720687,0.044933215,0.0027681785,0.022546142,0.000064213986,0.00008442818,0.00850701],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963743,0.000083676015,0.0006755061,0.0012912416,0.0006466088,0.00092866493],"domain_scores_gemma":[0.9957948,0.001381344,0.00017122307,0.0016860606,0.0005432616,0.00042331687],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00056505395,0.0004009393,0.00033824745,0.00014161732,0.0015715085,0.00023778103,0.0028716058,0.00013579825,0.000096329735],"category_scores_gemma":[0.00008066733,0.00028365926,0.00014182553,0.0023718372,0.00039406662,0.00031370332,0.0009140564,0.00052043184,0.000091278176],"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":[0.000030186216,0.00012992395,0.00014134233,0.000017475286,0.00004305193,9.810766e-7,0.000053381442,0.013657794,0.0000018066527,0.6996842,0.024236534,0.2620033],"study_design_scores_gemma":[0.0004571862,0.00004101554,0.00047258078,0.000086145104,0.00003619345,0.000013055716,0.000040983952,0.62519693,0.000007451962,0.009210611,0.3641908,0.00024704856],"about_ca_topic_score_codex":0.000012369372,"about_ca_topic_score_gemma":0.0002065824,"teacher_disagreement_score":0.7026181,"about_ca_system_score_codex":0.0001422147,"about_ca_system_score_gemma":0.0007068058,"threshold_uncertainty_score":0.99996156},"labels":[],"label_agreement":null},{"id":"W7115193798","doi":"10.1155/atr/5223257","title":"WHF‐YOLOv11: Traffic Cone Detection Algorithm in Complex Scenes Based on Wavelet Convolution and Hierarchical Feature Attention","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Henan Provincial Department of Transportation; U.S. Department of Transportation","keywords":"Minimum bounding box; Convolution (computer science); Pattern recognition (psychology); Feature (linguistics); Wavelet; Convolutional neural network; Benchmark (surveying); Wavelet transform; Feature extraction","score_opus":0.009418502446496868,"score_gpt":0.25829331601100686,"score_spread":0.24887481356451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115193798","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":[0.2571735,0.00013001902,0.74044627,0.0017695829,0.00020356778,0.00022537677,0.0000049787095,0.000035354686,0.000011361251],"genre_scores_gemma":[0.8761871,0.00012616851,0.12335757,0.0002306768,0.0000365884,0.000013607749,0.000027099206,0.0000071728377,0.0000140140955],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99875134,0.00007363015,0.00046780566,0.0002714658,0.00025670408,0.00017902869],"domain_scores_gemma":[0.9991354,0.00014797512,0.00031211152,0.00016330983,0.00017445692,0.000066800334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001892002,0.00014907793,0.00023867276,0.00038756168,0.00010755036,0.000028785109,0.00018066431,0.0000912628,0.0000012519755],"category_scores_gemma":[0.000019261808,0.00014519786,0.00007482693,0.0007560411,0.00006139524,0.00057614857,0.0000040981836,0.0003771194,7.2531174e-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":[0.00021997599,0.00016076681,0.0004328406,0.00003147657,0.0000127134435,0.00001778935,0.00013210472,0.2616358,0.024233786,0.0023826235,0.000029631576,0.71071047],"study_design_scores_gemma":[0.0027279637,0.00035501414,0.6081677,0.00018335717,0.000026051046,0.000018418883,0.000056303583,0.3825861,0.0019315623,0.0025887568,0.0011803057,0.00017846777],"about_ca_topic_score_codex":0.0000015882359,"about_ca_topic_score_gemma":0.000054521843,"teacher_disagreement_score":0.710532,"about_ca_system_score_codex":0.00008491187,"about_ca_system_score_gemma":0.000051303457,"threshold_uncertainty_score":0.59209967},"labels":[],"label_agreement":null},{"id":"W7116072254","doi":"10.3103/s1060992x25601708","title":"RCDINO: Enhancing Radar–Camera 3D Object Detection with DINOv2 Semantic Features","year":2025,"lang":"en","type":"article","venue":"Optical Memory and Neural Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Object detection; Object (grammar); Pattern recognition (psychology); Fusion; Sensor fusion; Baseline (sea)","score_opus":0.006259444506948935,"score_gpt":0.23468329687423592,"score_spread":0.228423852367287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116072254","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":[0.07383239,0.0008471997,0.9212682,0.0013378252,0.0004327849,0.00040400194,4.3295262e-7,0.00031295474,0.0015641795],"genre_scores_gemma":[0.9803126,0.00010083156,0.017677594,0.0011690094,0.00019727099,0.000056754554,0.0000020112361,0.000015156137,0.00046880363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829787,0.000074123556,0.00026044366,0.0006846879,0.0001848579,0.00049803423],"domain_scores_gemma":[0.99869746,0.00049558195,0.00007708164,0.0005163216,0.000056221637,0.00015734318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001328635,0.00026981134,0.00028358545,0.00009197675,0.00048620327,0.00019866877,0.00040157425,0.00012612599,0.0000039504544],"category_scores_gemma":[0.00003278857,0.0002151503,0.00005567872,0.00086372497,0.00017296859,0.0004316962,0.0002626291,0.00057821895,0.000002950431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023168158,0.000117341304,0.00021453144,0.00007048182,0.000077515884,0.00009461215,0.00011203657,0.054191433,0.015422597,0.027738094,0.0003530671,0.9013766],"study_design_scores_gemma":[0.000925545,0.00041546815,0.0060438565,0.00017555367,0.00008704557,0.0003034578,0.00003807313,0.9564757,0.031515665,0.0026884507,0.0006467323,0.0006844699],"about_ca_topic_score_codex":0.00000625712,"about_ca_topic_score_gemma":0.00006710464,"teacher_disagreement_score":0.9064802,"about_ca_system_score_codex":0.000032895055,"about_ca_system_score_gemma":0.00002033056,"threshold_uncertainty_score":0.8773575},"labels":[],"label_agreement":null},{"id":"W7116428891","doi":"10.23977/acss.2025.090408","title":"A U-Net Baseline for Left Atrial Tumor Segmentation: Performance Analysis and Limitations","year":2025,"lang":"","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dice; Segmentation; Generalization; Baseline (sea); Sørensen–Dice coefficient; Similarity (geometry); Function (biology); Pattern recognition (psychology)","score_opus":0.027625763511992227,"score_gpt":0.30177974665921614,"score_spread":0.2741539831472239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116428891","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":[0.012326946,0.03800365,0.94558674,0.0006423841,0.001457784,0.001809491,0.000048693688,0.000059338698,0.00006497626],"genre_scores_gemma":[0.8778704,0.011991442,0.108499795,0.0004660888,0.00063960376,0.00023325167,0.000040665323,0.00001767707,0.00024111777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964662,0.000293225,0.0012425544,0.0012054126,0.0002734766,0.00051911024],"domain_scores_gemma":[0.9954773,0.003049122,0.00046800566,0.0005712244,0.0002829575,0.00015138461],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00091182283,0.00041417446,0.0008434532,0.0006495254,0.0005419048,0.0005245723,0.00051301345,0.00009339109,0.000002595665],"category_scores_gemma":[0.000055165296,0.00041747882,0.0001828179,0.0021190569,0.00017986499,0.0013986741,0.00030861382,0.00020185753,0.0000020633488],"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":[0.00006779506,0.00006724913,0.008795744,0.0004867789,0.000268701,0.0000038086996,0.0005462168,0.5871605,0.00006186076,0.0062558753,0.00013859652,0.39614686],"study_design_scores_gemma":[0.0012887379,0.00025797592,0.0018459011,0.0003315395,0.00029269792,0.000015345426,0.00007338558,0.98507476,0.00008420494,0.0012657678,0.009081859,0.0003878381],"about_ca_topic_score_codex":0.000017089978,"about_ca_topic_score_gemma":0.00015626891,"teacher_disagreement_score":0.8655434,"about_ca_system_score_codex":0.00007156828,"about_ca_system_score_gemma":0.00009671983,"threshold_uncertainty_score":0.9998277},"labels":[],"label_agreement":null},{"id":"W7116784929","doi":"10.1007/s40747-025-02191-2","title":"GDA-RoadSeg: an improved road segmentation network with gated depthwise attention feature fusion","year":2025,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Convolution (computer science); Dependency (UML); Dilation (metric space); Block (permutation group theory); Feature (linguistics); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Task (project management)","score_opus":0.027410975418551198,"score_gpt":0.2917535347640734,"score_spread":0.2643425593455222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116784929","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":[0.021401796,0.0006146293,0.97337765,0.00054382806,0.0009710589,0.001747431,0.00000521949,0.0005974819,0.00074089714],"genre_scores_gemma":[0.95937896,0.00006724198,0.036747847,0.00039165423,0.0003396719,0.00031859073,0.00027608365,0.000034362794,0.0024455748],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975871,0.00022715007,0.00051225425,0.00084284984,0.00032480506,0.00050587585],"domain_scores_gemma":[0.99798733,0.00007782025,0.0003303758,0.001143018,0.00029716696,0.00016430004],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025711994,0.0003354887,0.00033587933,0.00015504262,0.0004911419,0.0003362186,0.00096752343,0.0001232021,0.000010577847],"category_scores_gemma":[0.000009967356,0.0002875742,0.000085246684,0.0014339605,0.000065498134,0.0005703284,0.00022840596,0.00025244264,0.000058119684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042822293,0.00096379116,0.009522677,0.00046217,0.0004596304,0.0000413293,0.001117595,0.27437356,0.17797433,0.09951915,0.054528356,0.3806092],"study_design_scores_gemma":[0.00059413764,0.00029034147,0.010200134,0.00033066762,0.000049791313,0.00004512252,0.0002421166,0.96098024,0.0022456741,0.001135259,0.0233354,0.00055109814],"about_ca_topic_score_codex":0.00009814613,"about_ca_topic_score_gemma":0.00019493345,"teacher_disagreement_score":0.9379772,"about_ca_system_score_codex":0.00021691323,"about_ca_system_score_gemma":0.000061245766,"threshold_uncertainty_score":0.9999576},"labels":[],"label_agreement":null},{"id":"W7116867560","doi":"10.21203/rs.3.rs-7339928/v1","title":"Lite-FARNet: A Light-weight Feedback Attention Residual Network for Efficient Multi-Class Segmentation in Complex Urban Scenes","year":2025,"lang":"","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Henan Provincial Science and Technology Research Project","keywords":"Segmentation; Software deployment; Inference; Adaptability; Context (archaeology); Residual; Key (lock); Representation (politics); Feature (linguistics)","score_opus":0.10063989054203243,"score_gpt":0.4087886899744186,"score_spread":0.30814879943238616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116867560","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":[0.030895447,0.0062670326,0.9179375,0.011612412,0.0020244021,0.028853305,0.0008164185,0.00054508215,0.0010483657],"genre_scores_gemma":[0.62436616,0.003942214,0.32779345,0.00051212916,0.0052612796,0.021212446,0.0047614696,0.0003300421,0.011820836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9855618,0.0026109596,0.0022087751,0.0040689316,0.0022988124,0.0032507526],"domain_scores_gemma":[0.99005425,0.0028672137,0.00078462675,0.0030830617,0.0026074315,0.0006033929],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004632854,0.001087776,0.0012209188,0.0015766298,0.0020730766,0.0011545279,0.0037965218,0.0008513624,0.00006358108],"category_scores_gemma":[0.00046926946,0.0011878994,0.0005625683,0.0063738604,0.00052814855,0.00047134372,0.004986156,0.0028072302,0.00017530842],"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":[0.00087808573,0.002837198,0.009172168,0.0038602103,0.0002175739,0.00004197826,0.002856192,0.8418124,0.0040074685,0.04508026,0.03637292,0.052863598],"study_design_scores_gemma":[0.0032733313,0.00042634262,0.038962018,0.003250366,0.000044354343,0.0000054256384,0.0003340936,0.9194372,0.0008266098,0.0050653983,0.027302066,0.0010727809],"about_ca_topic_score_codex":0.00019640238,"about_ca_topic_score_gemma":0.000606501,"teacher_disagreement_score":0.5934707,"about_ca_system_score_codex":0.0020784778,"about_ca_system_score_gemma":0.0011021963,"threshold_uncertainty_score":0.99988234},"labels":[],"label_agreement":null},{"id":"W7117121015","doi":"10.1016/j.array.2025.100652","title":"Green AI techniques for reducing energy consumption in AI systems","year":2025,"lang":"en","type":"article","venue":"Array","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Neuromorphic engineering; Inference; Transparency (behavior); Software deployment; Energy consumption; Key (lock); Efficient energy use; Artificial neural network","score_opus":0.018748404959030986,"score_gpt":0.3021264838453107,"score_spread":0.28337807888627975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117121015","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":[0.0004326426,0.00020583501,0.9944565,0.0034917544,0.0001620317,0.0002781544,0.00000144714,0.00021840734,0.00075316906],"genre_scores_gemma":[0.9422841,0.00004981452,0.0537976,0.00188493,0.00008204632,0.0007677433,0.000005558434,0.0000080120835,0.0011201644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993118,0.000026625245,0.00017521877,0.00027127485,0.00005693837,0.00015813622],"domain_scores_gemma":[0.9994188,0.000107037566,0.000045763893,0.00036415012,0.000041697054,0.00002257879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103942635,0.000070921145,0.00009907273,0.000110108536,0.000075945696,0.000043952794,0.0003421532,0.00004347465,7.217818e-7],"category_scores_gemma":[0.00001144715,0.00007160715,0.00002378219,0.00032899086,0.000021074706,0.00024061331,0.00005214939,0.00006856426,0.0000033250872],"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":[0.0000067240817,0.000039875456,0.00071993744,0.0000597736,0.000008499549,0.0000012811929,0.000117467214,0.001945183,0.035959806,0.8170916,0.0068739806,0.1371759],"study_design_scores_gemma":[0.00063888327,0.000099221295,0.0014588898,0.0005869715,0.000013446558,0.000016596654,0.00002169223,0.2523556,0.19368684,0.15851384,0.39202705,0.0005809764],"about_ca_topic_score_codex":0.00012190021,"about_ca_topic_score_gemma":0.000039812618,"teacher_disagreement_score":0.9418515,"about_ca_system_score_codex":0.000058400565,"about_ca_system_score_gemma":0.000024695659,"threshold_uncertainty_score":0.2920055},"labels":[],"label_agreement":null},{"id":"W7117306822","doi":"10.70063/techcompinnovations.v2i2.125","title":"A Self-Supervised Multi-Scale Fusion Framework for Accurate and Efficient Image Segmentation","year":2025,"lang":"","type":"article","venue":"Techcomp Innovations","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Segmentation; Image segmentation; Domain (mathematical analysis); Image fusion; Feature (linguistics); Annotation; Image (mathematics); Convolutional neural network; Key (lock)","score_opus":0.02729118494189251,"score_gpt":0.3335183243665714,"score_spread":0.3062271394246789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117306822","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":[0.018946353,0.000300276,0.966805,0.008497957,0.00067320897,0.0037527937,0.000091809525,0.0006833263,0.00024928804],"genre_scores_gemma":[0.17047778,0.00011638139,0.82681054,0.0011539102,0.000090427144,0.0010558501,0.00006472687,0.000030804153,0.00019958676],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684036,0.0000777545,0.0010269724,0.0011466086,0.00028791235,0.0006204089],"domain_scores_gemma":[0.99667484,0.0007134968,0.00042438332,0.0011413367,0.0009348186,0.00011115329],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00041434565,0.00044598643,0.00039209658,0.0006176112,0.0016184711,0.00045214093,0.0008577181,0.00026066593,0.000017673057],"category_scores_gemma":[0.0001816837,0.0004953879,0.00010272589,0.006158817,0.0002183312,0.0005386241,0.0007254389,0.00050943455,0.000028795626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050255512,0.0014651523,0.00040618723,0.00043853157,0.000113855494,0.000001642464,0.0019876743,0.0033225915,0.09418423,0.74261034,0.0012807063,0.15413885],"study_design_scores_gemma":[0.0014095822,0.00008254865,0.0023444463,0.00023031261,0.00007500937,0.0000048152374,0.00016927664,0.9466907,0.010317603,0.035730373,0.0024868369,0.0004585421],"about_ca_topic_score_codex":0.000008670625,"about_ca_topic_score_gemma":0.000009910672,"teacher_disagreement_score":0.9433681,"about_ca_system_score_codex":0.00022195218,"about_ca_system_score_gemma":0.00022148929,"threshold_uncertainty_score":0.9997498},"labels":[],"label_agreement":null},{"id":"W7117368275","doi":"10.1007/s10044-025-01559-x","title":"Partial derivative regularized knowledge transformation for deep neural networks generalization","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Overfitting; Regularization (linguistics); Jacobian matrix and determinant; Deep learning; Generalization; Deep neural networks; Artificial neural network; Pooling","score_opus":0.012757568290011675,"score_gpt":0.284981592304012,"score_spread":0.2722240240140003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117368275","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":[0.00072553335,0.0002579503,0.9963192,0.0015398961,0.000028537455,0.0008209109,0.000008803904,0.00011711349,0.00018208551],"genre_scores_gemma":[0.98212063,0.00007912791,0.01456938,0.00051570986,0.00008920592,0.0022917194,0.00023712187,0.0000073278384,0.00008974744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989164,0.00004797998,0.00034039665,0.000422079,0.00007187126,0.00020128436],"domain_scores_gemma":[0.9990918,0.00013418273,0.00012237609,0.00044192135,0.00014611208,0.000063574895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011744481,0.00013939188,0.0002148381,0.00021878026,0.0004344345,0.00012932412,0.00033059306,0.00005937253,0.000003889315],"category_scores_gemma":[0.000005651761,0.00013653681,0.00013464851,0.0021350707,0.00004481432,0.00025620934,0.00006668711,0.00006895266,0.0000019564363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052978617,0.00009441442,0.0015089833,0.000021693158,0.0002748606,5.344109e-8,0.00027497212,0.08079669,0.0005695222,0.21170634,0.00009415544,0.704653],"study_design_scores_gemma":[0.00026880775,0.000008167049,0.0027266433,0.0000025166314,0.0002563519,3.640773e-7,0.000013773389,0.98622817,0.00068582635,0.004905634,0.004775111,0.0001286282],"about_ca_topic_score_codex":0.000011107314,"about_ca_topic_score_gemma":0.00012625243,"teacher_disagreement_score":0.9817498,"about_ca_system_score_codex":0.000028619317,"about_ca_system_score_gemma":0.00001084855,"threshold_uncertainty_score":0.556781},"labels":[],"label_agreement":null},{"id":"W7117501352","doi":"10.1109/tc.2025.3649209","title":"Dual-Pronged Deep Learning Preprocessing on Heterogeneous Platforms With CPU, Accelerator and CSD","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Computers","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Preprocessor; Deep learning; Bottleneck; Data pre-processing; Dram; Central processing unit; Consistency (knowledge bases); Energy consumption","score_opus":0.016180984727127014,"score_gpt":0.25505046087766065,"score_spread":0.23886947615053364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117501352","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":[0.0875207,0.00027097008,0.90888184,0.0006482223,0.0010718616,0.0009203817,0.0000056642193,0.00047862332,0.0002017481],"genre_scores_gemma":[0.97068983,0.0002482284,0.027224446,0.0011290045,0.000095377756,0.00018217803,0.0000024058327,0.000060271126,0.0003682451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613154,0.00009808162,0.000638873,0.0017961303,0.0005074832,0.0008278809],"domain_scores_gemma":[0.9976176,0.0005606285,0.00028893995,0.0010354518,0.0001746306,0.00032273124],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00017416579,0.00071683875,0.0005486401,0.0005040985,0.001983452,0.0007413535,0.0007310561,0.00022395702,0.000014617189],"category_scores_gemma":[0.0000043596797,0.0006762507,0.0001526948,0.0016688938,0.0003311874,0.0008576521,0.000025738505,0.0012863618,0.000040048773],"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":[0.00017206788,0.0002641859,0.000015301266,0.00006094482,0.0001242258,0.000026645694,0.0006648172,0.59534085,0.00019599518,0.00019831426,0.000016921547,0.40291977],"study_design_scores_gemma":[0.001963556,0.0011896085,0.00014016527,0.0008288336,0.000116304385,0.00014725862,0.00007985354,0.9738649,0.019742504,0.00039117027,0.00072038115,0.00081543106],"about_ca_topic_score_codex":0.000009347802,"about_ca_topic_score_gemma":0.000044319386,"teacher_disagreement_score":0.8831691,"about_ca_system_score_codex":0.00025370944,"about_ca_system_score_gemma":0.00016680411,"threshold_uncertainty_score":0.9995689},"labels":[],"label_agreement":null},{"id":"W7117547354","doi":"10.1109/jsac.2025.3649622","title":"On-Device Machine Learning Model Adaptation for 6G Communications: From Standardization to Over-the-Air Prototyping Experiments Based on Beam Prediction","year":2025,"lang":"","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Qualcomm (Canada)","funders":"","keywords":"Standardization; Adaptation (eye); Benchmark (surveying); Inference; Wireless; Process (computing); Rapid prototyping; Data modeling","score_opus":0.058416790810631634,"score_gpt":0.3521063930030678,"score_spread":0.29368960219243617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117547354","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":[0.0045779133,0.0009900874,0.969403,0.018057464,0.0004230671,0.0053672846,0.0003044798,0.00022558546,0.00065111386],"genre_scores_gemma":[0.8765998,0.001558841,0.113283716,0.0042267554,0.0001164602,0.0036103702,0.00040003934,0.00007772103,0.0001262914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944263,0.00160547,0.0016882247,0.00084729644,0.00079227274,0.0006404351],"domain_scores_gemma":[0.9865374,0.0053705457,0.0011147208,0.0051951045,0.0015359415,0.0002462841],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001280254,0.00058779656,0.0005347329,0.0010715672,0.004429579,0.00052784523,0.00532396,0.00026276332,0.000008857941],"category_scores_gemma":[0.0014400419,0.000581779,0.00020783773,0.0043774187,0.00020326365,0.00072825124,0.00050381647,0.0027105615,0.000016298762],"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":[0.00055864843,0.0011722329,0.00016585468,0.000013254949,0.00009050392,3.5891472e-7,0.0012499152,0.9606842,0.0011612291,0.010191783,0.00067039265,0.024041632],"study_design_scores_gemma":[0.0020529898,0.0006966279,0.0013613289,0.0017739562,0.000081289996,0.0000029331977,0.00011845769,0.97238284,0.0012338542,0.0099022575,0.00998275,0.00041073456],"about_ca_topic_score_codex":0.000059016278,"about_ca_topic_score_gemma":0.00030900197,"teacher_disagreement_score":0.8720219,"about_ca_system_score_codex":0.001799499,"about_ca_system_score_gemma":0.0010543503,"threshold_uncertainty_score":0.99966335},"labels":[],"label_agreement":null},{"id":"W7117552630","doi":"10.18280/isi.301109","title":"Evaluating SOCAE-Driven Morphological Precision in Kidney Segmentation with a Deep ResNet269 Framework","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Pattern recognition (psychology); Image segmentation; Feature (linguistics); Key (lock)","score_opus":0.029432987138540236,"score_gpt":0.3224091535247187,"score_spread":0.29297616638617846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117552630","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":[0.08645513,0.00026092716,0.9070872,0.0007021568,0.0005069765,0.00220686,0.000014855068,0.00024336095,0.0025225393],"genre_scores_gemma":[0.6805146,0.00014823869,0.3171582,0.0013620713,0.000064863045,0.00058571197,0.00009804286,0.000015845591,0.000052465544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953093,0.0004614504,0.0018136025,0.0007061278,0.0008803836,0.0008291487],"domain_scores_gemma":[0.9963113,0.00075707916,0.0010257073,0.0010350308,0.00063142803,0.00023947575],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011188488,0.0005272679,0.000540093,0.00077568623,0.00081982644,0.000884977,0.0011058308,0.00045147754,0.000058684793],"category_scores_gemma":[0.0016067214,0.00049485837,0.00010855626,0.0042152563,0.00037642205,0.0064642015,0.0005820369,0.00083988055,0.00013650431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003568876,0.000139783,0.004141343,0.00045609538,0.000054870416,0.000012592036,0.010602563,0.25359476,0.000629807,0.025938667,0.00026627773,0.70380634],"study_design_scores_gemma":[0.0014851062,0.00057198846,0.010883404,0.0034369542,0.0000523017,0.00005570133,0.0008784773,0.9241509,0.0010538921,0.056339774,0.0005073011,0.0005842143],"about_ca_topic_score_codex":0.000040285515,"about_ca_topic_score_gemma":0.000027411079,"teacher_disagreement_score":0.70322216,"about_ca_system_score_codex":0.001279955,"about_ca_system_score_gemma":0.00056234316,"threshold_uncertainty_score":0.9997503},"labels":[],"label_agreement":null},{"id":"W7117576195","doi":"10.18280/ts.420637","title":"A Novel Deep Learning Architecture for Real-Time Autonomous Driving Perception with Fuzzy Graph-Cut and Metaheuristic Optimization","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deep learning; Architecture; Perception; Fuzzy logic; Metaheuristic","score_opus":0.009499671457316885,"score_gpt":0.23847433816217448,"score_spread":0.22897466670485758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117576195","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":[0.010384058,0.00013671476,0.98542476,0.0012413139,0.000089056266,0.002012222,0.0000120314235,0.000264852,0.00043498562],"genre_scores_gemma":[0.5255971,0.00015821557,0.47298327,0.00018504963,0.00014213013,0.00043065465,0.000061761464,0.00004167916,0.00040016265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996979,0.00013676586,0.000672837,0.0011985621,0.00035647256,0.00065636996],"domain_scores_gemma":[0.99824095,0.0005253257,0.0004056064,0.0004009365,0.00023928432,0.00018787147],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040364018,0.0005065108,0.00046930893,0.0003766066,0.0010794096,0.00040387566,0.00050414476,0.0001323832,0.000062834646],"category_scores_gemma":[0.000038717735,0.0004921463,0.00013347044,0.0010487842,0.00022929782,0.000466536,0.00021032023,0.00038532907,0.000004503769],"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":[0.000109300876,0.00016947475,0.00013140937,0.00009789888,0.00012539404,0.0000019988804,0.0010168991,0.87680715,0.015757628,0.005059605,0.00003669975,0.10068652],"study_design_scores_gemma":[0.0016464489,0.00062959397,0.0026001744,0.00021634303,0.00031966413,0.000034091725,0.00008649578,0.991489,0.00014326391,0.0018265904,0.00051245635,0.0004958712],"about_ca_topic_score_codex":0.000013999499,"about_ca_topic_score_gemma":0.000033068693,"teacher_disagreement_score":0.515213,"about_ca_system_score_codex":0.000159725,"about_ca_system_score_gemma":0.000121381585,"threshold_uncertainty_score":0.999753},"labels":[],"label_agreement":null},{"id":"W7117584784","doi":"10.18280/ts.420617","title":"Fine-Grained Identification of Urban Environmental Risks via Deep Image Segmentation and Uncertainty Modeling","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Segmentation; Image segmentation; Image (mathematics); Pattern recognition (psychology); Image processing","score_opus":0.018102450754592457,"score_gpt":0.26984816559722546,"score_spread":0.25174571484263303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117584784","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":[0.1939147,0.0006099429,0.803627,0.0005372131,0.00013626408,0.0010272616,0.000046716086,0.00004884703,0.000052029605],"genre_scores_gemma":[0.98381275,0.00017455906,0.015404022,0.00012938018,0.000086427775,0.00017209917,0.00011028122,0.000018456665,0.00009199555],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967865,0.0001680563,0.0012459431,0.0009054306,0.00048207978,0.00041203047],"domain_scores_gemma":[0.99856097,0.00015424164,0.00053205114,0.0005440075,0.000081422695,0.0001273005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047104614,0.00036460045,0.00033853785,0.00023339789,0.00043573216,0.00015606152,0.00057744415,0.00010084136,0.00009473816],"category_scores_gemma":[0.000013159239,0.00041327448,0.00012301597,0.00057304907,0.0002635018,0.0007496501,0.00029681812,0.0002166464,0.000013625153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100015015,0.0004621897,0.0009512288,0.00011578183,0.000120412355,0.0000021697065,0.001625111,0.31204888,0.51058716,0.004683549,0.00009803866,0.16920547],"study_design_scores_gemma":[0.0011527045,0.00011094788,0.0027760633,0.000054729895,0.00012993129,0.0000031085676,0.00017650027,0.96657604,0.023402028,0.0052807624,0.000046174504,0.0002909824],"about_ca_topic_score_codex":0.00003314535,"about_ca_topic_score_gemma":0.000016610622,"teacher_disagreement_score":0.7898981,"about_ca_system_score_codex":0.00022137114,"about_ca_system_score_gemma":0.00005107622,"threshold_uncertainty_score":0.9998319},"labels":[],"label_agreement":null},{"id":"W7117748108","doi":"10.1109/icceic67916.2025.11308258","title":"Yolov8s-Tsd: A traffic sign detection model based on feature extraction and feature enhancement","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Science and Technology Development Fund; National Natural Science Foundation of China","keywords":"Feature extraction; Feature (linguistics); Pattern recognition (psychology); Traffic sign; Sign (mathematics); Traffic sign recognition","score_opus":0.012411575470640455,"score_gpt":0.2746886275275344,"score_spread":0.26227705205689394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117748108","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":[0.004685672,0.00061285123,0.96881425,0.0205347,0.00066258945,0.0013462685,0.0000059303356,0.0003004349,0.003037293],"genre_scores_gemma":[0.8991083,0.00029452943,0.08113105,0.003429743,0.00011679468,0.00027297865,0.000008202017,0.000023953584,0.01561448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688727,0.00013551678,0.00036525773,0.0015468536,0.00045961983,0.0006055069],"domain_scores_gemma":[0.99800545,0.00028915744,0.00023631459,0.0010955398,0.00017512908,0.00019843888],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023089007,0.0005419799,0.0003532694,0.00035858943,0.0008038478,0.00033503756,0.00053222605,0.0004463494,0.000027461723],"category_scores_gemma":[0.000031208623,0.0005275675,0.000140836,0.001620179,0.00011740332,0.00063781004,0.00015448483,0.0009959997,0.00002964255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016072122,0.00029098158,0.0000018172049,0.000048786373,0.000021387945,0.0000029597845,0.00010297621,0.4357495,0.016803749,0.0037640666,0.005385673,0.5376674],"study_design_scores_gemma":[0.00078038254,0.000342492,0.00014269959,0.00013835677,0.00005977761,0.0000075405364,0.000030246032,0.95940846,0.03020125,0.0014995058,0.0069632,0.00042608593],"about_ca_topic_score_codex":0.000003585936,"about_ca_topic_score_gemma":0.000048205533,"teacher_disagreement_score":0.8944226,"about_ca_system_score_codex":0.0003292837,"about_ca_system_score_gemma":0.0001912715,"threshold_uncertainty_score":0.9997176},"labels":[],"label_agreement":null},{"id":"W7117760048","doi":"10.1109/rtss66672.2025.00026","title":"CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea","keywords":"Exploit; Scheduling (production processes); Granularity; Leverage (statistics); Transformer; Object detection; Timestamp","score_opus":0.014258093028246292,"score_gpt":0.2875467000280375,"score_spread":0.27328860699979124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117760048","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":[0.0022657677,0.000088158115,0.96565354,0.0064920015,0.0009225066,0.0032605403,0.000025186662,0.0005268228,0.02076547],"genre_scores_gemma":[0.6159951,0.00036513564,0.3042335,0.0028957406,0.0004923101,0.001977536,0.0000180058,0.000085790656,0.07393689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965991,0.00006971748,0.0008014538,0.001320318,0.00029992123,0.0009095134],"domain_scores_gemma":[0.99742246,0.0006092791,0.00013837007,0.0012256827,0.00032653994,0.00027768538],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034586468,0.00045707557,0.00048365182,0.00039112655,0.0007566576,0.00021675219,0.0011289963,0.00020403789,0.00014441994],"category_scores_gemma":[0.000069480324,0.000467201,0.00029403003,0.003140286,0.00010439375,0.0005962325,0.00015102347,0.00025240085,0.00048381346],"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":[0.00015674236,0.00017897662,0.0000058272503,0.00006264143,0.00006020608,0.0000011227714,0.0001958765,0.004231916,0.16261727,0.03326974,0.0053348625,0.7938848],"study_design_scores_gemma":[0.0013322517,0.00074614125,0.00022828786,0.00010458961,0.00010861822,0.000010882821,0.000022876857,0.6258659,0.2575291,0.018171301,0.09514195,0.0007381557],"about_ca_topic_score_codex":0.00005683396,"about_ca_topic_score_gemma":0.00033415077,"teacher_disagreement_score":0.79314667,"about_ca_system_score_codex":0.00023600984,"about_ca_system_score_gemma":0.00020540181,"threshold_uncertainty_score":0.999778},"labels":[],"label_agreement":null},{"id":"W7117785157","doi":"10.1016/j.eswa.2025.130945","title":"SSVA: Self-scanned visual attention for enhanced mask-free shadow removal","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Alfaisal University; Prince Sultan University","keywords":"Shadow (psychology); Benchmark (surveying); Channel (broadcasting); Mean squared error; Feature (linguistics); Pattern recognition (psychology); Shadow mask; Feature extraction","score_opus":0.00870717423581578,"score_gpt":0.2810863656059223,"score_spread":0.2723791913701065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117785157","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":[0.0005051487,0.0006847486,0.9868793,0.0027673726,0.00024157127,0.003992593,0.000015567504,0.0009978517,0.0039158403],"genre_scores_gemma":[0.5284509,0.000072462964,0.42584252,0.00069635955,0.00057302153,0.038945254,0.00009074989,0.00005836716,0.005270351],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99783766,0.000050040093,0.00048448288,0.00090342254,0.0002769068,0.00044750597],"domain_scores_gemma":[0.99734247,0.00023038291,0.000247592,0.0017511388,0.00029767983,0.00013075504],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016532137,0.00027258444,0.00029995915,0.00017328138,0.00062187185,0.00018094108,0.0011808601,0.00011016004,0.0000018859243],"category_scores_gemma":[0.00001923987,0.00024780032,0.000095247444,0.0013284091,0.000059695893,0.00039442803,0.00022820185,0.00012628456,0.00004668902],"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":[0.00004470731,0.0004121715,0.00005344077,0.00012955129,0.00013968372,0.0000021689466,0.0003952927,0.0013219556,0.037237454,0.9279135,0.017641135,0.014708983],"study_design_scores_gemma":[0.0036647979,0.00030507872,0.0005322105,0.000302213,0.00007913541,0.00012181914,0.00056934194,0.21468787,0.012647487,0.019026667,0.74675304,0.0013103159],"about_ca_topic_score_codex":0.00003070238,"about_ca_topic_score_gemma":0.000026437958,"teacher_disagreement_score":0.9088868,"about_ca_system_score_codex":0.00018919128,"about_ca_system_score_gemma":0.00013883541,"threshold_uncertainty_score":0.99999744},"labels":[],"label_agreement":null},{"id":"W7117794445","doi":"10.5220/0013888400004919","title":"Enhancing Optical Sensor Image Classification through Deep Learning with Convolutional Neural Network","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Convolutional neural network; Pattern recognition (psychology); Artificial neural network; Contextual image classification; Feature extraction","score_opus":0.01918857573128498,"score_gpt":0.28074893462272965,"score_spread":0.2615603588914447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117794445","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":[0.0035623102,0.0013261986,0.9708878,0.006469455,0.0007473165,0.0008557181,0.000001351895,0.00050637167,0.015643522],"genre_scores_gemma":[0.5485221,0.0002595239,0.44683674,0.00093447877,0.0005876237,0.00013440358,0.0000200486,0.00003398391,0.0026710546],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953061,0.00029927574,0.00093301205,0.0016058314,0.0006041853,0.0012516262],"domain_scores_gemma":[0.99664456,0.0011088972,0.00038240457,0.0010631613,0.00058257923,0.0002183935],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00036443584,0.00055873324,0.00050031446,0.00010460596,0.0015768656,0.00050835084,0.0010209241,0.0001979421,0.0001086333],"category_scores_gemma":[0.00013476456,0.00052267296,0.00015446496,0.0027674907,0.0006917996,0.0015462541,0.0006076624,0.0013401777,0.0002290978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106208485,0.0001623177,0.0011168723,0.00006287898,0.000075320604,0.000013450974,0.00022759881,0.19523035,0.0061223856,0.7282963,0.0006836505,0.06790271],"study_design_scores_gemma":[0.00068120175,0.00015130306,0.009328405,0.0001771841,0.00008584243,0.000051699968,0.00016885054,0.96739244,0.0016598537,0.008427187,0.011268756,0.0006072993],"about_ca_topic_score_codex":0.000009271566,"about_ca_topic_score_gemma":0.000052124306,"teacher_disagreement_score":0.7721621,"about_ca_system_score_codex":0.00028148963,"about_ca_system_score_gemma":0.00031413467,"threshold_uncertainty_score":0.99972296},"labels":[],"label_agreement":null},{"id":"W7118478460","doi":"10.18280/ijsse.151002","title":"A Hybrid Deep Learning Framework for Multimodal DeepFake Detection","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deep learning; Deep neural networks; Pattern recognition (psychology); Deep belief network; Statistical learning","score_opus":0.00569939637299001,"score_gpt":0.25541760402415437,"score_spread":0.24971820765116434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118478460","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":[0.010620547,0.002249605,0.98200333,0.0017599217,0.0030673472,0.00021195694,0.000008571143,0.000040052866,0.000038661143],"genre_scores_gemma":[0.9145599,0.0021141088,0.08248183,0.00012394386,0.0006720302,0.000009113607,0.0000025097913,0.00001475785,0.000021824682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823415,0.00003820861,0.0008030787,0.00029701844,0.0003487449,0.00027880794],"domain_scores_gemma":[0.9976898,0.0009213551,0.00044470566,0.00014587837,0.00067066686,0.00012761804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004541973,0.00022811584,0.00031360047,0.00036762396,0.00021892112,0.00021461226,0.00071602117,0.00012430218,0.000005653919],"category_scores_gemma":[0.0007156196,0.0002565284,0.00020769551,0.00025779102,0.000045582387,0.00068783376,0.00023416805,0.0009190962,0.0000011582363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039409715,0.000081539874,0.00007697219,0.00007684964,0.00034536418,0.00002717824,0.00082144456,0.43809694,0.001122523,0.10702883,0.000005510871,0.45192274],"study_design_scores_gemma":[0.0008478283,0.00012057536,0.00068702776,0.00047496948,0.00004427436,0.00021216898,0.0000347496,0.94448906,0.0023317097,0.03540247,0.015154818,0.00020032999],"about_ca_topic_score_codex":0.0000030799365,"about_ca_topic_score_gemma":0.000003496143,"teacher_disagreement_score":0.9039393,"about_ca_system_score_codex":0.00021201231,"about_ca_system_score_gemma":0.00005675732,"threshold_uncertainty_score":0.9999887},"labels":[],"label_agreement":null},{"id":"W7123361559","doi":"10.1109/icm66518.2025.11322486","title":"Efficient Pruning and Acceleration of Encoder-Based LLM Transformers on eFPGAs","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Encoder; Pruning; Transformer; Computation; Edge device; High memory; Offset (computer science); Decoding methods","score_opus":0.019883890929373114,"score_gpt":0.2851108188255527,"score_spread":0.26522692789617963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7123361559","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":[0.054428194,0.00017701414,0.92857313,0.0043358183,0.00020075637,0.0007796268,0.0000032224666,0.000071539405,0.0114307245],"genre_scores_gemma":[0.96665514,0.00004992103,0.031889327,0.000843135,0.000018604607,0.00004602872,0.0000023614464,0.000008478512,0.00048703712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981384,0.00006672367,0.0005188518,0.00064273743,0.00029171625,0.0003415164],"domain_scores_gemma":[0.998776,0.0003809235,0.00015173796,0.00046541847,0.00012705263,0.00009882791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021247906,0.00023664034,0.0002590456,0.00024867023,0.0003320831,0.00009319035,0.00039479745,0.00009327897,0.000026598938],"category_scores_gemma":[0.000025950352,0.0002275867,0.00007648809,0.0012502959,0.00018353526,0.00012780148,0.00007658525,0.00020582961,0.000006369777],"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":[0.000052844305,0.00037949128,0.00009303029,0.00009102397,0.000018344934,7.8549715e-7,0.0003478948,0.5716984,0.009328547,0.12607925,0.00012206696,0.2917883],"study_design_scores_gemma":[0.00064370723,0.00027254527,0.000917572,0.00016448337,0.00002188221,6.509211e-7,0.000041665695,0.9294866,0.06667044,0.0008467915,0.0007599364,0.00017372741],"about_ca_topic_score_codex":0.000010913496,"about_ca_topic_score_gemma":0.000015396095,"teacher_disagreement_score":0.9122269,"about_ca_system_score_codex":0.00007524844,"about_ca_system_score_gemma":0.00020493029,"threshold_uncertainty_score":0.9280716},"labels":[],"label_agreement":null},{"id":"W7123662747","doi":"10.1109/icet66147.2025.11321414","title":"Deployable AI for Public Safety: Weapon Detection in Challenging CCTV Scenarios","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Benchmarking; Robustness (evolution); Preprocessor; Deep learning; Training set; Task (project management); Object detection","score_opus":0.024216689157967177,"score_gpt":0.2841683868189687,"score_spread":0.25995169766100157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7123662747","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":[0.00072849466,0.0008494047,0.95400333,0.038044553,0.000972446,0.002027444,0.0000029532102,0.00037532786,0.0029960524],"genre_scores_gemma":[0.97023726,0.00051800854,0.022987409,0.002345993,0.0001414792,0.0007043745,0.000003685727,0.000034020006,0.0030277844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99648225,0.000103473176,0.00082843506,0.0012649208,0.00024571852,0.0010752042],"domain_scores_gemma":[0.9977523,0.000550046,0.00016904363,0.0011019545,0.00025686424,0.00016978514],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000572405,0.0003692525,0.00039809305,0.0006920413,0.00090774405,0.00037602382,0.0011316478,0.00022694933,0.000027972108],"category_scores_gemma":[0.00017636266,0.00041431954,0.00015565593,0.0034299635,0.00007423543,0.001435775,0.00057412597,0.0005107102,0.00004424412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045077748,0.00019747711,0.00011388259,0.00008226302,0.000023931829,0.000001649076,0.000114726616,0.025788553,0.0013204495,0.24737164,0.00035565693,0.7245847],"study_design_scores_gemma":[0.0010973468,0.00009367278,0.00035082363,0.00012092876,0.000013488753,0.0000048899096,0.00008430528,0.91035014,0.004566684,0.033040218,0.049905375,0.00037209658],"about_ca_topic_score_codex":0.000055331144,"about_ca_topic_score_gemma":0.0014464462,"teacher_disagreement_score":0.96950877,"about_ca_system_score_codex":0.00053268316,"about_ca_system_score_gemma":0.00026828388,"threshold_uncertainty_score":0.99983084},"labels":[],"label_agreement":null},{"id":"W7124144937","doi":"10.1109/auteee67053.2025.11322101","title":"A Lightweight Attention-Enhanced SegResNet for Medical Image Segmentation: Design, Evaluation, and CPU-Based Deployment","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"123 Certification (Canada)","funders":"","keywords":"Software deployment; Pipeline (software); Segmentation; Inference engine; Key (lock); Image segmentation; Feature (linguistics); Inference","score_opus":0.02698979421062599,"score_gpt":0.34359557727907003,"score_spread":0.31660578306844406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124144937","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":[0.0010937803,0.0011072054,0.95797026,0.032532327,0.00068661274,0.0057591754,0.000012661202,0.0002242666,0.0006137389],"genre_scores_gemma":[0.18869302,0.00033594467,0.796665,0.0057922434,0.00023811843,0.0054857596,0.000082889674,0.000044669854,0.0026623288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99495155,0.00044474192,0.0011036155,0.0015064188,0.0013264064,0.0006672941],"domain_scores_gemma":[0.99562454,0.0015201209,0.0003467166,0.00096977997,0.0011568153,0.00038202258],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018104902,0.00047897705,0.00043760616,0.00027648755,0.0008942928,0.00042904873,0.00094551593,0.00022759307,0.00052564236],"category_scores_gemma":[0.0003704761,0.00045767645,0.00015837731,0.0014244858,0.0003188416,0.0007313526,0.00033846914,0.00023654255,0.00004752625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039019895,0.0013687429,0.00025328115,0.00047195228,0.00044012076,0.000009510562,0.0004404305,0.009523067,0.04407697,0.0751313,0.05938288,0.80851156],"study_design_scores_gemma":[0.0040791556,0.00022922517,0.00059967017,0.0002356444,0.00021746474,0.0000043791815,0.00003827345,0.9112867,0.064042464,0.01628207,0.002541704,0.00044325803],"about_ca_topic_score_codex":0.0000065035542,"about_ca_topic_score_gemma":0.000036957874,"teacher_disagreement_score":0.9017636,"about_ca_system_score_codex":0.0002969076,"about_ca_system_score_gemma":0.0013048806,"threshold_uncertainty_score":0.9997875},"labels":[],"label_agreement":null},{"id":"W7124145953","doi":"10.1093/pasj/psaf147","title":"Moving object detection from multi-depth images with an attention-enhanced convolutional neural network","year":2025,"lang":"en","type":"article","venue":"Publications of the Astronomical Society of Japan","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Herzberg Institute of Astrophysics","funders":"National Astronomical Observatory of Japan; Japan Society for the Promotion of Science; Regional Studies Association; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Convolutional neural network; Object detection; Block (permutation group theory); Object (grammar); Focus (optics); Pattern recognition (psychology); Channel (broadcasting); Deep learning","score_opus":0.011929887346602544,"score_gpt":0.24695429192944923,"score_spread":0.23502440458284668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124145953","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":[0.44208804,0.00003121672,0.55536723,0.0021119805,0.00006294231,0.00022424692,0.000011386424,0.00006982687,0.00003314988],"genre_scores_gemma":[0.7691446,0.0000020892408,0.23052023,0.00005283438,0.000055394114,0.00008970046,0.00002070272,0.0000069769035,0.0001074635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987226,0.000075084,0.00036969245,0.000419193,0.00016214333,0.00025128783],"domain_scores_gemma":[0.99819416,0.00020994368,0.00036061284,0.0009506766,0.00021809361,0.000066505854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014150838,0.00014099166,0.00019349995,0.00003447863,0.00036459768,0.0000604118,0.0012774965,0.00006116097,0.000005685739],"category_scores_gemma":[0.000028978759,0.000116473704,0.00020564215,0.00076696486,0.00032622466,0.0006621506,0.0003542267,0.00020891668,0.0000019978309],"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":[0.000056491735,0.0007737537,0.35558784,0.000024566638,0.00038994127,1.5012817e-9,0.0003111589,0.48894256,0.079435304,0.022032281,0.0017613906,0.05068473],"study_design_scores_gemma":[0.00032715476,0.000026679738,0.68110466,0.000015549396,0.000020883088,1.8772907e-7,0.0000650408,0.3120984,0.005015311,0.001148962,0.00008904695,0.00008813258],"about_ca_topic_score_codex":0.00009737398,"about_ca_topic_score_gemma":0.000075417156,"teacher_disagreement_score":0.32705656,"about_ca_system_score_codex":0.00010026811,"about_ca_system_score_gemma":0.00010738757,"threshold_uncertainty_score":0.47496596},"labels":[],"label_agreement":null},{"id":"W7124148967","doi":"10.1109/dsins68311.2025.11330013","title":"CAH-Net: An Efficient Hardened Coordinate Attention Network for Medical Image","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Image (mathematics); Image processing; Visualization; Image quality; Feature (linguistics); Noise (video)","score_opus":0.011815532061224881,"score_gpt":0.3028972663146964,"score_spread":0.2910817342534715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124148967","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":[0.003958934,0.000704448,0.97087926,0.016022962,0.0024583917,0.0020821101,0.000016700793,0.00040241596,0.0034747643],"genre_scores_gemma":[0.5616531,0.00036191696,0.40662038,0.007158699,0.0016752289,0.001180391,0.000117853386,0.00008455878,0.021147879],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99517435,0.00022437498,0.0010161905,0.0016293795,0.00071202277,0.0012437066],"domain_scores_gemma":[0.9964517,0.00060260715,0.00028006732,0.00160875,0.0005066741,0.00055023574],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011484544,0.00047091415,0.00051920227,0.00015502062,0.0011514168,0.0004378466,0.002197539,0.00031695256,0.0002423962],"category_scores_gemma":[0.00017867403,0.0004678307,0.00028641886,0.0019392866,0.00036406488,0.00045407974,0.00089059887,0.00042579704,0.00013020044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015208968,0.0009392369,0.00021424613,0.00014092415,0.00012749442,0.000020377882,0.000102256236,0.14262766,0.0009592357,0.44940114,0.0745198,0.33079553],"study_design_scores_gemma":[0.001260181,0.00019644765,0.0011297179,0.00016349313,0.00006598341,0.000009973658,0.000018777788,0.9491571,0.00049230317,0.013735097,0.033327635,0.000443286],"about_ca_topic_score_codex":0.00002548901,"about_ca_topic_score_gemma":0.000085903484,"teacher_disagreement_score":0.80652946,"about_ca_system_score_codex":0.00015743247,"about_ca_system_score_gemma":0.00039116485,"threshold_uncertainty_score":0.9997773},"labels":[],"label_agreement":null},{"id":"W7124284762","doi":"10.5392/ijoc.2025.21.4.001","title":"Reconfigurable Multi-Precision Multipliers for Energy- Efficient CNN Acceleration for Visual AI in ICT Systems","year":2025,"lang":"en","type":"article","venue":"International Journal of Contents","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Multiplier (economics); Throughput; Efficient energy use; Overhead (engineering); Convolutional neural network; Logarithm; Edge device; Hardware acceleration","score_opus":0.036377087964785323,"score_gpt":0.3569165185121357,"score_spread":0.32053943054735035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124284762","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":[0.014247091,0.0002178359,0.98006344,0.0018593401,0.0030491494,0.0004510118,0.000009474509,0.000016580087,0.000086090055],"genre_scores_gemma":[0.9809498,0.000036033354,0.017694151,0.0005314452,0.00014900356,0.0001326473,0.000009685083,0.000008050043,0.00048919313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985641,0.000037737558,0.0006977094,0.00022430452,0.000301384,0.00017475754],"domain_scores_gemma":[0.9977302,0.00045497844,0.00041147883,0.0001366536,0.0012121159,0.000054585827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032288238,0.00011482978,0.00020362652,0.0003826222,0.000074796015,0.00017904233,0.00090695056,0.000059290196,0.0000012161269],"category_scores_gemma":[0.00023498645,0.00010487603,0.00013123032,0.00018847542,0.00001721153,0.00043024367,0.000056537447,0.00010677318,0.0000014376467],"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":[0.001487881,0.0010773464,0.0009384705,0.000036871385,0.0003371944,0.000015284102,0.0002970478,0.5056651,0.08343635,0.10671983,0.0065106056,0.29347798],"study_design_scores_gemma":[0.002827327,0.000119925186,0.0008052497,0.0002096865,0.000007824041,0.0000111344125,0.00003750933,0.97236544,0.008289327,0.0015841575,0.013640323,0.00010207569],"about_ca_topic_score_codex":0.000013331172,"about_ca_topic_score_gemma":0.000011845746,"teacher_disagreement_score":0.9667027,"about_ca_system_score_codex":0.00025330915,"about_ca_system_score_gemma":0.000080016296,"threshold_uncertainty_score":0.42767203},"labels":[],"label_agreement":null},{"id":"W7124865727","doi":"10.1109/icicml67980.2025.11333403","title":"Multimodal Remote Sensing Classification Algorithms for Vehicle-Road-Cloud Integration","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"123 Certification (Canada)","funders":"","keywords":"Hyperspectral imaging; Redundancy (engineering); Feature extraction; Remote sensing application; Feature (linguistics); Multispectral image; Channel (broadcasting); Spectral bands; Curse of dimensionality","score_opus":0.04653442943198563,"score_gpt":0.3270200904755079,"score_spread":0.28048566104352224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124865727","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":[0.0010743374,0.00021230151,0.9761545,0.014738947,0.002304043,0.002200054,0.0000133174135,0.0004151384,0.0028873724],"genre_scores_gemma":[0.30949703,0.00009867449,0.68303204,0.0013082968,0.0005200115,0.000021163587,0.000049798695,0.00002847673,0.005444511],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99642515,0.0001460297,0.00096785027,0.0014462519,0.00032578068,0.0006889617],"domain_scores_gemma":[0.9966384,0.00060112117,0.00038454743,0.0014605839,0.00075111,0.00016420918],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046404215,0.00044546416,0.0003915732,0.00020680061,0.00092340604,0.0004380368,0.0009018825,0.000296364,0.000010871484],"category_scores_gemma":[0.0002394578,0.00046058858,0.00022964586,0.0018687525,0.00019017803,0.00076427014,0.00030835645,0.00042692636,0.000073382296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030648458,0.000050445648,0.0000033433796,0.000022310463,0.000025347701,7.2385916e-7,0.00017174474,0.0010408111,0.011734081,0.05654834,0.0018646033,0.9285076],"study_design_scores_gemma":[0.0007621921,0.00007698617,0.00066833745,0.00014516481,0.000050515464,0.0000050869407,0.00011076414,0.9441219,0.0127076795,0.031294305,0.009661185,0.00039586038],"about_ca_topic_score_codex":0.00012658992,"about_ca_topic_score_gemma":0.00008253928,"teacher_disagreement_score":0.94308114,"about_ca_system_score_codex":0.00036980474,"about_ca_system_score_gemma":0.0002603716,"threshold_uncertainty_score":0.9997846},"labels":[],"label_agreement":null},{"id":"W7125790666","doi":"10.21428/594757db.f73769f4","title":"Topology-Driven Solver Selection for Stochastic Shortest Path MDPs via Explainable Machine Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Shortest path problem; Counterfactual thinking; Scheduling (production processes); Markov decision process; Selection (genetic algorithm); Robotics; Classifier (UML)","score_opus":0.012002911250557621,"score_gpt":0.26764819523130673,"score_spread":0.2556452839807491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125790666","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":[0.0005628929,0.000050611852,0.99605745,0.0013308036,0.00015561543,0.00043808753,8.425507e-7,0.00037936433,0.0010243602],"genre_scores_gemma":[0.8196554,0.0000050325407,0.17327031,0.00050800346,0.000074916235,0.00031425757,0.000011631382,0.0000100847,0.0061503635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990548,0.00002414317,0.00016471103,0.0003877113,0.00007414438,0.00029448525],"domain_scores_gemma":[0.9993158,0.00025081835,0.000054338507,0.00024104388,0.00009173468,0.00004625906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087042914,0.00011594043,0.0001254704,0.000088924935,0.00041556288,0.000045326047,0.00036479568,0.000054005985,0.000017229271],"category_scores_gemma":[0.00003631303,0.00011095862,0.000045750905,0.00051616627,0.000030635554,0.00024749734,0.00016124314,0.00016550657,0.000015947095],"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":[0.000018376339,0.00010335562,0.0013916779,0.0000138282885,0.00003237575,0.0000010080757,0.00011690989,0.51588184,0.0049244645,0.4187204,0.0035475197,0.055248264],"study_design_scores_gemma":[0.00018911118,0.00009658417,0.00014934839,0.0000059300432,0.000007999007,0.0000061280534,0.000009264072,0.9676384,0.00063735875,0.022837564,0.008310266,0.00011209878],"about_ca_topic_score_codex":0.00002558155,"about_ca_topic_score_gemma":0.000049619997,"teacher_disagreement_score":0.8227871,"about_ca_system_score_codex":0.00006723117,"about_ca_system_score_gemma":0.0000380849,"threshold_uncertainty_score":0.45247614},"labels":[],"label_agreement":null},{"id":"W7125820494","doi":"10.21428/594757db.ac00217d","title":"XGD: Explainable AI-Guided Knowledge Distillation with Feature Refinement for Semantic Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Pascal (unit); Segmentation; Hyperparameter; Distillation; Feature (linguistics); Feature extraction; Class (philosophy)","score_opus":0.017027600005312987,"score_gpt":0.3140735237984298,"score_spread":0.2970459237931168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125820494","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":[0.0010753549,0.00007650455,0.9840355,0.0076899976,0.00010360521,0.00085769675,0.0000018976826,0.00020699104,0.005952462],"genre_scores_gemma":[0.44908231,0.000017355776,0.5207706,0.0011717135,0.00007418473,0.00091353524,0.0000696714,0.000014224148,0.027886387],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991964,0.000016597884,0.00014680196,0.000352538,0.00009491205,0.00019274758],"domain_scores_gemma":[0.99925196,0.00010205102,0.00006131787,0.00038722574,0.00016182172,0.000035632616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077653254,0.000115368915,0.000102910686,0.000080576836,0.0002366221,0.000078825986,0.00025897642,0.000033332763,0.000004825735],"category_scores_gemma":[0.000012077533,0.00009114324,0.000027389666,0.0006421504,0.000017473018,0.00031429483,0.00008361972,0.000056382694,0.000010188038],"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":[0.000032539363,0.00013170748,0.0011211353,0.00013130264,0.000039671908,0.0000010939764,0.00023712755,0.009786761,0.00289545,0.7722333,0.14492825,0.06846164],"study_design_scores_gemma":[0.0021549722,0.00023180386,0.004414756,0.00013019453,0.00005546933,0.000009247925,0.000112991,0.7290201,0.03363149,0.039828837,0.18989268,0.0005174591],"about_ca_topic_score_codex":0.0000063579205,"about_ca_topic_score_gemma":0.00007910036,"teacher_disagreement_score":0.7324045,"about_ca_system_score_codex":0.00009572753,"about_ca_system_score_gemma":0.00005292962,"threshold_uncertainty_score":0.37167135},"labels":[],"label_agreement":null},{"id":"W7125922411","doi":"10.1109/isaics66888.2025.11350296","title":"Cotton color grading detection based on improved MobileNetV4","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"","keywords":"Grading (engineering); Pattern recognition (psychology); Color model; Visual inspection; Color image","score_opus":0.010946706541140312,"score_gpt":0.26626666808491284,"score_spread":0.2553199615437725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125922411","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":[0.005956831,0.00012412405,0.97614974,0.0027734346,0.0015828623,0.0014659554,0.0000036896547,0.0004576118,0.0114857415],"genre_scores_gemma":[0.97440183,0.000056944216,0.016883008,0.003177886,0.000121328405,0.0004362363,0.0000029084665,0.00002141025,0.0048984345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971783,0.00013116842,0.00055944006,0.0012249019,0.00027059266,0.00063556654],"domain_scores_gemma":[0.99740696,0.00064812263,0.00021582092,0.0013854145,0.0001748349,0.00016881879],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025231263,0.000389093,0.0003128252,0.00034178156,0.00074895716,0.00026757206,0.0009973573,0.0001932811,0.0000576949],"category_scores_gemma":[0.0000770829,0.00039643975,0.00016450386,0.0025878805,0.0001281297,0.00037173246,0.0002710888,0.00044374395,0.00011516349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001585229,0.0004631244,0.00007166028,0.000065832035,0.000036132686,0.0000040047867,0.00006360026,0.08947612,0.08439287,0.07007814,0.0010025054,0.75418746],"study_design_scores_gemma":[0.00063404767,0.000385588,0.00041077612,0.000068983114,0.000023837052,0.0000013436934,0.00001174094,0.86571455,0.11672195,0.004351039,0.01137797,0.00029818717],"about_ca_topic_score_codex":0.000032550386,"about_ca_topic_score_gemma":0.00007634252,"teacher_disagreement_score":0.968445,"about_ca_system_score_codex":0.00037889488,"about_ca_system_score_gemma":0.00018635392,"threshold_uncertainty_score":0.9998487},"labels":[],"label_agreement":null},{"id":"W7125942639","doi":"10.1109/smc58881.2025.11342542","title":"Lightweight and Dynamic Content-Augmented Object Detection for UAVs","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Object detection; Feature extraction; Software deployment; Feature (linguistics); Benchmark (surveying); Representation (politics); Pyramid (geometry); Edge detection","score_opus":0.018826963008763208,"score_gpt":0.2774377658271535,"score_spread":0.25861080281839033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125942639","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":[0.006394487,0.0018641773,0.9822589,0.0048441375,0.0009959139,0.0019219423,0.000010313608,0.00024286978,0.0014672581],"genre_scores_gemma":[0.94564325,0.000863017,0.03526936,0.0011858043,0.00005803259,0.0004922869,0.0000061687197,0.000018742126,0.016463364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781406,0.00005527544,0.00050329836,0.0009806845,0.0001495177,0.00049715245],"domain_scores_gemma":[0.9983614,0.00041242462,0.00016656949,0.00070274,0.00022962865,0.00012720974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012132067,0.00030530986,0.00030476437,0.0002142294,0.0006649043,0.00023459768,0.0005141968,0.00013795025,0.000013470795],"category_scores_gemma":[0.000053677886,0.00029206474,0.00011708992,0.0010338558,0.00013776199,0.0004982768,0.0003646752,0.00018768403,0.000017832775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009067487,0.00013531648,0.00007445747,0.000099747136,0.000103538936,0.0000013615712,0.00009837859,0.00016327716,0.051581137,0.086083904,0.00055914564,0.86100906],"study_design_scores_gemma":[0.0013713703,0.00025039696,0.001677184,0.00009106709,0.00007505059,0.000011448278,0.000053412976,0.9118496,0.044898786,0.018456653,0.020905154,0.0003598901],"about_ca_topic_score_codex":0.000015950209,"about_ca_topic_score_gemma":0.00023460289,"teacher_disagreement_score":0.94698954,"about_ca_system_score_codex":0.00015505282,"about_ca_system_score_gemma":0.000072784445,"threshold_uncertainty_score":0.99995315},"labels":[],"label_agreement":null},{"id":"W7126034583","doi":"10.1109/bibm66473.2025.11356079","title":"Multi-View Interactive Interference Training Based on Cross-Network Uncertainty for Semi-Supervised Medical Image Segmentation","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Complementarity (molecular biology); Concatenation (mathematics); Segmentation; Image segmentation; Consistency (knowledge bases); Interference (communication); Robustness (evolution)","score_opus":0.04642313025140784,"score_gpt":0.3753718903642657,"score_spread":0.3289487601128579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126034583","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":[0.0009995038,0.00017998004,0.9856668,0.006268998,0.0017261127,0.0028148473,0.000046885507,0.00031920176,0.0019776511],"genre_scores_gemma":[0.60157645,0.00011803013,0.3837767,0.011379004,0.0003237668,0.0013533015,0.00009538363,0.000043818745,0.0013335552],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944567,0.0003950023,0.0013714174,0.0019250271,0.0006866254,0.001165231],"domain_scores_gemma":[0.9929767,0.004245052,0.00043400508,0.0012543242,0.00066124887,0.00042866395],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011269113,0.0007126298,0.000732536,0.00023087757,0.0009027327,0.00066987314,0.0022248363,0.00032390154,0.00066091475],"category_scores_gemma":[0.00090501073,0.00068067794,0.00037041778,0.0017609919,0.00049367826,0.0010279955,0.00060281495,0.00090881984,0.00007256283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007012799,0.0007925156,0.00030242538,0.00029769703,0.00015344787,0.000014587191,0.0017474296,0.23364739,0.0014926305,0.012300302,0.003556802,0.7449935],"study_design_scores_gemma":[0.003292856,0.00036719724,0.00042364432,0.001590477,0.00005068036,0.0000035002745,0.00026947082,0.9874107,0.0018115136,0.00255887,0.001647242,0.00057384855],"about_ca_topic_score_codex":0.000024714456,"about_ca_topic_score_gemma":0.00012186977,"teacher_disagreement_score":0.7537633,"about_ca_system_score_codex":0.00052518444,"about_ca_system_score_gemma":0.0009684527,"threshold_uncertainty_score":0.99956447},"labels":[],"label_agreement":null},{"id":"W7126198474","doi":"10.21428/594757db.a7fcf8e0","title":"Balanced-Boundary IoU: A holistic algorithm towards improving segmentation evaluation metric","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Segmentation; Metric (unit); Boundary (topology); Scale-space segmentation; Intersection (aeronautics); Segmentation-based object categorization; Image segmentation; Object (grammar); Function (biology)","score_opus":0.030259294742450815,"score_gpt":0.3406812766666882,"score_spread":0.31042198192423737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126198474","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":[0.00072474,0.00045202865,0.9901633,0.0008573047,0.00047165417,0.00062543375,0.0000036328029,0.000311387,0.006390503],"genre_scores_gemma":[0.3152544,0.000047676025,0.68202263,0.0011374844,0.00009030006,0.0004014222,0.000037202884,0.000009342428,0.0009995312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985378,0.00007180994,0.00026414427,0.00047496764,0.0004105559,0.00024071544],"domain_scores_gemma":[0.99892944,0.00015748764,0.00010204069,0.00052328996,0.00023317583,0.00005455911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038048776,0.0001331995,0.0001273858,0.00028455484,0.00024387566,0.00019738018,0.0005363722,0.00004587294,0.00003767339],"category_scores_gemma":[0.000119799355,0.00012787973,0.000045911307,0.0022959586,0.000025908055,0.00064080465,0.00022731305,0.00011502347,0.000054986864],"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":[0.0000010549835,0.00002414032,0.000057624387,0.000007098041,0.000008130286,5.5791105e-7,0.000028797307,0.0013876038,0.0011497041,0.01633417,0.0008324416,0.9801687],"study_design_scores_gemma":[0.0003416389,0.000025044603,0.0032838113,0.000009851248,0.000024853052,0.0000035856513,0.000022067628,0.95277524,0.0038119361,0.037852738,0.0017030264,0.00014621769],"about_ca_topic_score_codex":0.00005557928,"about_ca_topic_score_gemma":0.000008230038,"teacher_disagreement_score":0.98002243,"about_ca_system_score_codex":0.00030192547,"about_ca_system_score_gemma":0.00024758236,"threshold_uncertainty_score":0.5214784},"labels":[],"label_agreement":null},{"id":"W7126213115","doi":"10.1109/mepcon66918.2026.11360113","title":"Real-time Insulator Defect Detection and Damage Assessment on Edge for UAV-Based Inspection","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Qatar University","keywords":"Insulator (electricity); Enhanced Data Rates for GSM Evolution; Software deployment; Focus (optics); Electric power transmission; Drone","score_opus":0.013602391988386744,"score_gpt":0.3044051534972436,"score_spread":0.29080276150885687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126213115","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":[0.07498405,0.000037726277,0.9130374,0.0014051131,0.0006446043,0.0021281193,0.0000112872,0.00056722545,0.0071844854],"genre_scores_gemma":[0.9369629,0.00008636753,0.060165405,0.0005988652,0.00014748007,0.0005514312,0.000009004968,0.000026518253,0.0014520356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749506,0.00013961305,0.00049727154,0.0011719414,0.00024242648,0.00045371987],"domain_scores_gemma":[0.99766356,0.0007969604,0.00022481021,0.000954485,0.00020736545,0.00015284038],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036240593,0.00036497475,0.00032575947,0.00036634147,0.0010133219,0.00028944586,0.00039188226,0.00018070238,0.00001603939],"category_scores_gemma":[0.00006489322,0.00037584052,0.00016991455,0.0014234976,0.0001293677,0.00044815213,0.00019697279,0.0002846686,0.000027996026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004012109,0.0010932846,0.00072105654,0.00028435414,0.00015863297,0.0000036165372,0.000099714634,0.026884064,0.25442752,0.2753266,0.0020220443,0.4385779],"study_design_scores_gemma":[0.0013320813,0.0007924578,0.018122448,0.00008571977,0.00006524617,0.0000017387632,0.000008715673,0.93038267,0.03910723,0.0064413175,0.0033175074,0.0003428837],"about_ca_topic_score_codex":0.000037602887,"about_ca_topic_score_gemma":0.00005641082,"teacher_disagreement_score":0.9034986,"about_ca_system_score_codex":0.0004751343,"about_ca_system_score_gemma":0.00023530895,"threshold_uncertainty_score":0.99986935},"labels":[],"label_agreement":null},{"id":"W7126251978","doi":"10.21428/594757db.d733a905","title":"Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Robustness (evolution); Lidar; Sensor fusion; Object detection; Dependability; Reliability (semiconductor); Redundancy (engineering); Key (lock)","score_opus":0.015166523698222316,"score_gpt":0.2683200038625602,"score_spread":0.2531534801643379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126251978","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":[0.0036292402,0.000025569361,0.9900298,0.0015954982,0.00032274777,0.0006508857,0.0000011890542,0.0004329935,0.003312082],"genre_scores_gemma":[0.43249506,0.000019613857,0.56429225,0.0007103878,0.00006591859,0.00027801484,0.0000026843009,0.000007241321,0.0021288246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989819,0.000029389843,0.00020072238,0.0004739273,0.00010016618,0.00021389645],"domain_scores_gemma":[0.99882233,0.00028533707,0.000054431996,0.00066774286,0.0001262685,0.00004388584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001180863,0.00010739584,0.00011378347,0.000084271,0.00029186544,0.00006441031,0.00048458428,0.00005855122,0.000008077071],"category_scores_gemma":[0.00007348218,0.00009637977,0.00006678259,0.0006978099,0.000031029027,0.0003061881,0.00021801224,0.00010640246,0.000024929832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032993994,0.00013364514,0.00023386296,0.000044238965,0.000014315755,6.6100176e-7,0.000102210004,0.07016143,0.028968172,0.06301878,0.0033523026,0.8339374],"study_design_scores_gemma":[0.00031874483,0.000087924986,0.0016215288,0.000016529953,0.000008606652,0.0000023490295,0.000010496793,0.9124235,0.018377827,0.018190185,0.04876725,0.00017509356],"about_ca_topic_score_codex":0.000012973278,"about_ca_topic_score_gemma":0.000052235708,"teacher_disagreement_score":0.842262,"about_ca_system_score_codex":0.00007947992,"about_ca_system_score_gemma":0.000040995743,"threshold_uncertainty_score":0.3930253},"labels":[],"label_agreement":null},{"id":"W7126432306","doi":"10.21428/594757db.48c945c1","title":"Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Reduction (mathematics); Enhanced Data Rates for GSM Evolution; Inference; Image (mathematics); Edge device; Pareto principle; Pattern recognition (psychology); Artificial neural network","score_opus":0.0393134585599059,"score_gpt":0.2932553994827262,"score_spread":0.2539419409228203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126432306","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":[0.105593674,0.00015232868,0.8920865,0.001098283,0.00013637968,0.00054418424,0.0000034412947,0.00018111445,0.00020411868],"genre_scores_gemma":[0.6875356,0.000005823072,0.3121283,0.00006327014,0.00007344216,0.00010295878,0.00000439053,0.00001041566,0.00007582753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986654,0.000043300824,0.000283525,0.0005774062,0.00013485014,0.00029555307],"domain_scores_gemma":[0.99927455,0.00023469889,0.00004030491,0.0003771281,0.000009040952,0.0000642608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002975762,0.00012898608,0.000120165845,0.000097061704,0.000112868096,0.00011434471,0.00028481983,0.000046880552,0.0000095454725],"category_scores_gemma":[0.000021963646,0.000118681746,0.00007030796,0.00034187155,0.00006419395,0.00019130719,0.00015515214,0.00012890973,0.000012716917],"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":[0.000013731739,0.00012303467,0.000069437425,0.00004523569,0.000016047145,0.00000674032,0.0006082336,0.8129254,0.031020403,0.085152596,0.0005305297,0.06948863],"study_design_scores_gemma":[0.00010779383,0.000026450869,0.00011564161,0.000030401243,0.0000063829802,0.000018035638,0.000048674698,0.98742735,0.0024519532,0.0014998931,0.0081099365,0.00015746098],"about_ca_topic_score_codex":0.00000481885,"about_ca_topic_score_gemma":7.013336e-7,"teacher_disagreement_score":0.5819419,"about_ca_system_score_codex":0.00019313897,"about_ca_system_score_gemma":0.000026317897,"threshold_uncertainty_score":0.48397014},"labels":[],"label_agreement":null},{"id":"W7127268960","doi":"10.1109/ccece64018.2025.11364391","title":"Improving UAV Thermal Object Detection: A Comparative Assessment of Detection Approaches","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Object detection; Preprocessor; Pipeline (software); Thermal infrared; Multispectral image; Histogram","score_opus":0.056706920003637154,"score_gpt":0.3179250980265276,"score_spread":0.26121817802289043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127268960","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":[0.034174424,0.00028549336,0.94453603,0.00026368684,0.00066968345,0.0012008395,0.0000027873743,0.00018611537,0.018680943],"genre_scores_gemma":[0.9443325,0.000017276641,0.054396737,0.0000916884,0.00011099856,0.000281613,0.0000011599842,0.000012322325,0.0007557096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969807,0.00028652174,0.0008408861,0.0010194936,0.00038367708,0.00048870937],"domain_scores_gemma":[0.9976167,0.00039810842,0.0005605305,0.0010646089,0.00025829792,0.000101772006],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038994165,0.000392772,0.00053065026,0.00029211497,0.00062583457,0.00017278618,0.0008716708,0.00016502907,0.000028648457],"category_scores_gemma":[0.000021423435,0.00038152072,0.00021291323,0.0024766608,0.0002738293,0.0007566598,0.0006395529,0.00054550444,0.000010368593],"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":[0.000043173466,0.0003655585,0.0002712374,0.00014511586,0.00015656068,0.0000011130534,0.0005869637,0.041142106,0.12789778,0.02744859,0.000009577923,0.8019322],"study_design_scores_gemma":[0.00038194668,0.00021976231,0.008313987,0.000044545595,0.000051488125,0.0000053670387,0.00026823318,0.7468001,0.24266683,0.00086750096,0.00013675187,0.0002434714],"about_ca_topic_score_codex":0.00009295735,"about_ca_topic_score_gemma":0.00025639808,"teacher_disagreement_score":0.9101581,"about_ca_system_score_codex":0.00029845632,"about_ca_system_score_gemma":0.00031096087,"threshold_uncertainty_score":0.9998637},"labels":[],"label_agreement":null},{"id":"W7127350421","doi":"10.1109/ccece64018.2025.11364514","title":"Comprehensive Analysis of Object Detection using RGB Camera for Autonomous Vehicles under Adverse Weather Conditions","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Adverse weather; Object detection; Generalization; Object (grammar); Weather prediction; RGB color model","score_opus":0.03311879209732461,"score_gpt":0.32313833880034204,"score_spread":0.29001954670301744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127350421","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":[0.17726044,0.00023323849,0.8202728,0.0003986871,0.00030246697,0.00096668565,0.000089605994,0.00011058369,0.00036547324],"genre_scores_gemma":[0.95707583,0.000047244972,0.041073676,0.00072726945,0.00004093662,0.00012507479,0.000021810447,0.000017979048,0.0008701813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754816,0.000121081546,0.0008006958,0.00086676393,0.00020876528,0.00045454287],"domain_scores_gemma":[0.9969424,0.00085675594,0.00045342333,0.0009826559,0.00066197355,0.00010276819],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010578378,0.00033071163,0.0006610509,0.00095820526,0.00069470645,0.000066093184,0.0005539676,0.00016113796,0.00007669624],"category_scores_gemma":[0.000022565278,0.00035859976,0.00059287244,0.0055014,0.00032228042,0.0004470166,0.0002535104,0.00018880746,0.000008062821],"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":[0.000029537288,0.000189792,0.00025499897,0.000043638265,0.0020072635,7.716084e-7,0.0002755156,0.8889703,0.050904553,0.042099826,0.000057738038,0.015166021],"study_design_scores_gemma":[0.000542507,0.000078066245,0.010099234,0.00003739525,0.002244652,0.0000024810633,0.0004130921,0.96100587,0.017310923,0.0068475916,0.0011142885,0.00030390828],"about_ca_topic_score_codex":0.0002410011,"about_ca_topic_score_gemma":0.0003008311,"teacher_disagreement_score":0.7798154,"about_ca_system_score_codex":0.00030668307,"about_ca_system_score_gemma":0.00025916225,"threshold_uncertainty_score":0.9998866},"labels":[],"label_agreement":null},{"id":"W7129600956","doi":"10.1109/iceconf65644.2025.11379554","title":"Design of Hardware Accelerator Structure on FPGA for Real-Time Image Classification through Optimised Convolution Layer","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Field-programmable gate array; Convolution (computer science); Key (lock); Loop unrolling; Deep learning; Hardware acceleration; Inference; Latency (audio); Gate array","score_opus":0.06347135336073978,"score_gpt":0.32606860156916295,"score_spread":0.2625972482084232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129600956","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":[0.0009469149,0.00008961557,0.9885078,0.0032554765,0.0004157102,0.0037392934,0.00012419894,0.00023660579,0.0026844006],"genre_scores_gemma":[0.30827788,0.00027815436,0.6867226,0.00069960253,0.0001345311,0.00039135639,0.00007922266,0.0000395899,0.003377113],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966214,0.00021435456,0.00094138796,0.0012759448,0.00036944848,0.0005774782],"domain_scores_gemma":[0.9958839,0.00084836234,0.0006229715,0.0016336655,0.0009023743,0.00010871466],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022717395,0.0004754115,0.0005525565,0.00016765647,0.000504689,0.0001943442,0.0012975426,0.00032238316,0.00020758306],"category_scores_gemma":[0.0001055691,0.0004620706,0.00017651227,0.0014105622,0.00024517401,0.0010570547,0.00021807337,0.00027341823,0.00004895551],"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":[0.0003850186,0.0002481502,0.0000056213303,0.00013636053,0.00008837801,7.103832e-7,0.00028437868,0.045965124,0.71706533,0.19045101,0.0266162,0.018753685],"study_design_scores_gemma":[0.0011588356,0.0002680644,0.00031801348,0.000111302914,0.00007012148,0.0000012862041,0.000038792205,0.6740194,0.30255565,0.01938157,0.0017227132,0.0003542493],"about_ca_topic_score_codex":0.000009118287,"about_ca_topic_score_gemma":0.0000015142238,"teacher_disagreement_score":0.62805426,"about_ca_system_score_codex":0.00027317597,"about_ca_system_score_gemma":0.00041901536,"threshold_uncertainty_score":0.9997831},"labels":[],"label_agreement":null},{"id":"W7130546369","doi":"10.1109/ictc66702.2025.11388697","title":"Regularizing Neural Networks for BEV Semantic Segmentation via Inter-class Hierarchy and Spatially-aware Weight Adjustment","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Object (grammar); Semantics (computer science); Key (lock); Hierarchy; Scale-space segmentation; Class (philosophy); Image segmentation","score_opus":0.012846817687259515,"score_gpt":0.269964650665877,"score_spread":0.25711783297861746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130546369","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":[0.0012833156,0.0018250113,0.9787061,0.012531551,0.002060356,0.0030959349,0.000009553706,0.00023090052,0.00025723124],"genre_scores_gemma":[0.8901114,0.000814291,0.10130976,0.0036620358,0.0005794043,0.00077361375,0.000079487014,0.000053198826,0.0026167925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960545,0.00018377115,0.0009951785,0.001530374,0.00030746538,0.0009287325],"domain_scores_gemma":[0.9972639,0.00077033084,0.0003803405,0.0010573254,0.0002710987,0.00025701805],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028413275,0.00058910577,0.0005394682,0.0002915139,0.0008332488,0.00040498175,0.0009284926,0.00022313854,0.000029463019],"category_scores_gemma":[0.000028715205,0.00059488363,0.00019213371,0.0011610973,0.00024876106,0.00095821475,0.0010820625,0.0004127426,0.0000058039036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101960824,0.00018360377,0.00039840752,0.00025971205,0.00015295553,0.000005655364,0.00032186368,0.03830951,0.0014250097,0.037501875,0.0028454124,0.91849405],"study_design_scores_gemma":[0.0013437095,0.0002762349,0.0006720847,0.00022357207,0.00015183435,0.000017381268,0.00004356718,0.9808033,0.0021392915,0.011540115,0.0022812677,0.0005076475],"about_ca_topic_score_codex":0.00003677533,"about_ca_topic_score_gemma":0.00024170877,"teacher_disagreement_score":0.9424938,"about_ca_system_score_codex":0.00026444497,"about_ca_system_score_gemma":0.0001078471,"threshold_uncertainty_score":0.99965024},"labels":[],"label_agreement":null},{"id":"W7130562406","doi":"10.1109/ictc66702.2025.11388538","title":"Unknown object detection by leveraging Segment Anything Model for labeling the OOD objects","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Bounding overwatch; Object detection; Object (grammar); Class (philosophy); Minimum bounding box; Pattern recognition (psychology)","score_opus":0.02304926885625906,"score_gpt":0.2791799452413219,"score_spread":0.2561306763850628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130562406","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":[0.0021899694,0.001782247,0.9863052,0.005344754,0.0008926368,0.0020071021,0.0000091183665,0.00031913503,0.0011498424],"genre_scores_gemma":[0.89737123,0.00022873617,0.09309682,0.003280486,0.0001322452,0.0005744389,0.000005320359,0.00003147592,0.005279223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673986,0.00011210678,0.0006914617,0.0012191399,0.00036331284,0.00087413867],"domain_scores_gemma":[0.99735713,0.000812902,0.00026246763,0.0011897759,0.00026383353,0.000113904025],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000709328,0.00043262198,0.00031716636,0.00014285161,0.0023828745,0.0005826532,0.0013203943,0.00014437067,0.000005667883],"category_scores_gemma":[0.00010145564,0.0003593444,0.00020002402,0.0015197309,0.00007752868,0.0007527553,0.0007054965,0.00051951234,0.00001069053],"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":[0.000021006466,0.000091695554,0.0000051654747,0.000066003784,0.00006980325,3.797592e-7,0.0016742201,0.65343,0.031250644,0.018697781,0.0012622536,0.29343104],"study_design_scores_gemma":[0.00053561677,0.00004898203,0.0000030003691,0.00010366681,0.000062182575,0.000004146321,0.00015227858,0.91946155,0.057072654,0.019877953,0.0023390634,0.00033889624],"about_ca_topic_score_codex":0.000032321546,"about_ca_topic_score_gemma":0.00020951746,"teacher_disagreement_score":0.8951813,"about_ca_system_score_codex":0.00037124185,"about_ca_system_score_gemma":0.00022530163,"threshold_uncertainty_score":0.99988586},"labels":[],"label_agreement":null},{"id":"W7130688382","doi":"10.1109/swc65939.2025.00083","title":"Real-Time FPGA-Based Object Detection with Bit-Width Adaptive Quantization","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Quantization (signal processing); Object detection; Inference; Latency (audio); Detector; Edge device; Software deployment; Toolchain","score_opus":0.01343076819399362,"score_gpt":0.25574244703851035,"score_spread":0.24231167884451674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130688382","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":[0.0060001984,0.00006632957,0.9768314,0.0011900738,0.0003017248,0.0013533003,0.0000057881416,0.0007079332,0.013543283],"genre_scores_gemma":[0.8077601,0.00007848264,0.18670517,0.00059743,0.00009218488,0.00018559337,0.000012488744,0.000035845056,0.0045326618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670994,0.00024284204,0.0005971295,0.0013480405,0.00046881728,0.0006332498],"domain_scores_gemma":[0.99713695,0.0004900225,0.00037211302,0.0013266328,0.0005106213,0.00016364391],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025046515,0.00047922437,0.00040122741,0.00044444657,0.00077089615,0.00026769354,0.00080493046,0.00018633253,0.00008391722],"category_scores_gemma":[0.000043302756,0.00043571525,0.00011771403,0.0052241012,0.00023324699,0.0008224506,0.00020942304,0.00032483702,0.0002569658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075539685,0.0006450567,0.00027826155,0.0000867822,0.00018399402,0.000018731178,0.0002474264,0.29099151,0.036586545,0.09492225,0.0010895658,0.5741945],"study_design_scores_gemma":[0.00078571314,0.00057412335,0.0010514797,0.00015429301,0.00006964538,0.0000053565414,0.000020756941,0.8703022,0.12197997,0.0037834947,0.0008251511,0.00044781674],"about_ca_topic_score_codex":0.00014416338,"about_ca_topic_score_gemma":0.0002919252,"teacher_disagreement_score":0.80175996,"about_ca_system_score_codex":0.00031571058,"about_ca_system_score_gemma":0.0005588981,"threshold_uncertainty_score":0.99980944},"labels":[],"label_agreement":null},{"id":"W7130694051","doi":"10.1109/swc65939.2025.00084","title":"Comparison of small object detection approaches in unmanned aerial vehicle (UAV) images","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Object detection; Benchmark (surveying); Convolutional neural network; Context (archaeology); Feature (linguistics); Deep learning; Aerial image; Generalization; Key (lock)","score_opus":0.06464606045802494,"score_gpt":0.3127073072439602,"score_spread":0.24806124678593522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130694051","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":[0.16541027,0.00068431307,0.8258801,0.00097504345,0.00064473733,0.00091131794,0.0000032864084,0.00012900098,0.0053619463],"genre_scores_gemma":[0.96997213,0.000052151838,0.029002955,0.0000823071,0.00009047303,0.00011159044,0.0000026825026,0.0000138441,0.00067189103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99709237,0.00023231593,0.0010196865,0.00089832104,0.00022987057,0.0005274381],"domain_scores_gemma":[0.9981251,0.0003837178,0.0003434938,0.0009529612,0.00011186463,0.00008290864],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003279162,0.0003149814,0.00059399643,0.00037268695,0.00021558195,0.00011684461,0.0010638642,0.00018804756,0.00001631568],"category_scores_gemma":[0.0000775006,0.0003359382,0.00013590461,0.0028315913,0.00024164368,0.0004092689,0.00062394526,0.00041570558,0.000021236458],"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":[0.00031070475,0.0015483178,0.013612917,0.00022831217,0.00006993071,0.0000029102855,0.0012839378,0.0997475,0.068748355,0.037923772,0.00031983858,0.7762035],"study_design_scores_gemma":[0.0007747725,0.00015527826,0.016003313,0.0000672321,0.000020884909,0.0000011200063,0.00019032668,0.64295715,0.3348643,0.004400298,0.000322798,0.0002425767],"about_ca_topic_score_codex":0.0001577082,"about_ca_topic_score_gemma":0.0008578757,"teacher_disagreement_score":0.80456185,"about_ca_system_score_codex":0.00014485714,"about_ca_system_score_gemma":0.0001401521,"threshold_uncertainty_score":0.9999093},"labels":[],"label_agreement":null},{"id":"W7130700679","doi":"10.1109/swc65939.2025.00031","title":"Benchmarking Few-Shot Learning Techniques for Steel Surface Defect Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Benchmarking; Focus (optics); Categorization; Implementation; Transfer of learning; Quality (philosophy)","score_opus":0.023760456386730897,"score_gpt":0.3058644463728425,"score_spread":0.2821039899861116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130700679","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":[0.004214284,0.00060931774,0.98136604,0.0008481041,0.000712193,0.0019877476,0.0000021532217,0.0009496161,0.009310541],"genre_scores_gemma":[0.73128873,0.00022255586,0.26179123,0.00036627473,0.00018077354,0.00028052359,0.0000043092527,0.00002666146,0.005838926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694526,0.00017553892,0.00062834256,0.0012475383,0.00025120043,0.000752127],"domain_scores_gemma":[0.9973033,0.0011324172,0.00029694007,0.00083810516,0.00031479678,0.000114428534],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00068173686,0.00040396498,0.00037469246,0.00020593293,0.0014221133,0.00039050812,0.00093406363,0.00023606513,0.000029998877],"category_scores_gemma":[0.00014370163,0.00043869083,0.000325725,0.0019777187,0.00011083786,0.00068631885,0.000597088,0.0005884083,0.000018773144],"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":[0.00004050613,0.00007950136,0.00038159822,0.00010814766,0.00006182853,0.0000010579944,0.00011850823,0.022769516,0.06212498,0.035055112,0.00047976465,0.8787795],"study_design_scores_gemma":[0.00027973388,0.00035247585,0.00032728928,0.00016621809,0.0000636214,0.000007372346,0.00004323867,0.6506365,0.2572779,0.0074643604,0.0828938,0.0004875089],"about_ca_topic_score_codex":0.000027753858,"about_ca_topic_score_gemma":0.000093471244,"teacher_disagreement_score":0.87829196,"about_ca_system_score_codex":0.00026642828,"about_ca_system_score_gemma":0.00013309572,"threshold_uncertainty_score":0.99987787},"labels":[],"label_agreement":null},{"id":"W7131061849","doi":"10.1109/iccvw69036.2025.00307","title":"Combining Transformers and CNNs for Efficient Object Detection in High-Resolution Satellite Imagery","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Object detection; Upsampling; Transformer; Feature extraction; Pattern recognition (psychology); Architecture; Feature (linguistics); Satellite imagery; Satellite image","score_opus":0.012528984115717906,"score_gpt":0.26196956538550664,"score_spread":0.24944058126978874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131061849","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":[0.067436464,0.0011532656,0.9264246,0.0015923376,0.0006646598,0.0016235104,0.000005496969,0.00012445805,0.00097517134],"genre_scores_gemma":[0.9748835,0.0006204794,0.023573548,0.00031403598,0.000035867117,0.00028089678,0.0000041244652,0.000014466244,0.00027306427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757576,0.00009262369,0.00063482515,0.0008976584,0.00017315013,0.0006259666],"domain_scores_gemma":[0.9987294,0.0005916314,0.00012306255,0.00035658476,0.00010012918,0.00009915651],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048414714,0.00028408857,0.00031936125,0.00042405317,0.0004925965,0.00017270693,0.00028760382,0.00014063719,0.0000029118528],"category_scores_gemma":[0.00004254471,0.0003055317,0.0000986754,0.0017875987,0.00017244418,0.00037756987,0.00012164406,0.0002735965,0.000005326553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001290439,0.00016278152,0.000120837525,0.00011784886,0.000021221473,0.0000014571169,0.00050978136,0.03705183,0.014476056,0.07003482,0.000019705734,0.8773546],"study_design_scores_gemma":[0.0015218116,0.0001713489,0.0062507764,0.00013618373,0.00003293397,0.0000051930983,0.00012620055,0.9562989,0.02507109,0.009122621,0.0009448249,0.00031812076],"about_ca_topic_score_codex":0.00008321036,"about_ca_topic_score_gemma":0.00029502483,"teacher_disagreement_score":0.9192471,"about_ca_system_score_codex":0.00023268024,"about_ca_system_score_gemma":0.00009476837,"threshold_uncertainty_score":0.9999397},"labels":[],"label_agreement":null},{"id":"W7131117072","doi":"10.1109/iccvw69036.2025.00736","title":"AgMIC: Agricultural Masked Image Consistency for Cross-Domain Segmentation","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Segmentation; Image segmentation; Pattern recognition (psychology); Context (archaeology); Consistency (knowledge bases); Thresholding; Domain (mathematical analysis); Field (mathematics)","score_opus":0.016144255668588306,"score_gpt":0.3210825643495453,"score_spread":0.304938308680957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131117072","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":[0.015748877,0.0003335494,0.9546327,0.00893057,0.0010810696,0.0027309107,0.000043503664,0.00026549128,0.016233338],"genre_scores_gemma":[0.26087108,0.00011405725,0.69777876,0.002371517,0.00022602537,0.0010422375,0.0000856894,0.000022594162,0.03748805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99694353,0.00008699235,0.0008630777,0.0011299477,0.0002631141,0.00071333797],"domain_scores_gemma":[0.9975849,0.0005166532,0.00030979016,0.0008595122,0.0005743379,0.00015484817],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024641998,0.00040568665,0.00036079413,0.00012474485,0.0010519206,0.00078032934,0.001081129,0.000147626,0.00008310122],"category_scores_gemma":[0.00007136185,0.00035360336,0.00027655033,0.0014112021,0.00037137367,0.0012793103,0.0004479668,0.00020622206,0.00012544385],"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":[0.00006494237,0.00027898274,0.00034598578,0.00017554646,0.0001103656,0.0000038081255,0.0005023793,0.00097248005,0.19689056,0.7143616,0.02508115,0.061212186],"study_design_scores_gemma":[0.011056955,0.0006550673,0.048155572,0.0004058271,0.00031510572,0.00008393963,0.0020654392,0.20783208,0.2051098,0.46072415,0.06039355,0.0032025254],"about_ca_topic_score_codex":0.000008572585,"about_ca_topic_score_gemma":0.00002098349,"teacher_disagreement_score":0.25685394,"about_ca_system_score_codex":0.0002349845,"about_ca_system_score_gemma":0.00017901858,"threshold_uncertainty_score":0.9998916},"labels":[],"label_agreement":null},{"id":"W7132872007","doi":"","title":"Enabling Versal AI Engines in the Galapagos Platform","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cloud computing; Field-programmable gate array; Layer (electronics); Encoder; Scale (ratio); Field (mathematics)","score_opus":0.030343301099535418,"score_gpt":0.3498316245489632,"score_spread":0.3194883234494278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132872007","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":[0.43391433,0.034057494,0.39734232,0.06275508,0.01997892,0.010880896,0.000032707707,0.0021379492,0.038900293],"genre_scores_gemma":[0.981019,0.0011417716,0.0038147266,0.0011921694,0.001073039,0.00059657724,0.00016865788,0.000108936576,0.010885088],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671155,0.00007238245,0.00057942094,0.0011465568,0.00071137416,0.0007786958],"domain_scores_gemma":[0.99756277,0.00066857657,0.0002376649,0.0012819014,0.00013705417,0.000112045615],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044117324,0.00059706444,0.00041227255,0.00038416695,0.00035669975,0.0006522933,0.002265379,0.0003221831,0.000056968824],"category_scores_gemma":[0.00008572509,0.00048482083,0.00020333234,0.0029115733,0.00007668955,0.0006432964,0.00022379274,0.0017633664,0.00067953736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017806402,0.00045942445,0.00010723986,0.0011851935,0.00018277316,0.00046401407,0.41976398,0.088582955,0.0057870806,0.35285404,0.015712893,0.11472232],"study_design_scores_gemma":[0.0008661116,0.00026342223,0.0010725464,0.0015232891,0.00021610291,0.00012936007,0.03427987,0.7813693,0.0042746165,0.028607303,0.14529629,0.0021017506],"about_ca_topic_score_codex":0.00012581285,"about_ca_topic_score_gemma":0.00039146596,"teacher_disagreement_score":0.6927864,"about_ca_system_score_codex":0.00016316964,"about_ca_system_score_gemma":0.00026966198,"threshold_uncertainty_score":0.9997603},"labels":[],"label_agreement":null},{"id":"W7132916138","doi":"","title":"Extending Data Flow Architectures for Convolutional Neural Networks to Object Detection and Multiple FPGAs","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Field-programmable gate array; Stratix; Convolutional neural network; Object detection; Speedup; Leverage (statistics); Reconfigurable computing; Inference; Data flow diagram","score_opus":0.042641800712502856,"score_gpt":0.3572060023033392,"score_spread":0.31456420159083637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132916138","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":[0.055666696,0.00228385,0.9348345,0.0008182285,0.0025701837,0.0033040035,0.00022193884,0.00025424905,0.000046376852],"genre_scores_gemma":[0.9251511,0.000115947194,0.06886487,0.00043280126,0.0010816006,0.00101708,0.0020087818,0.00011990077,0.0012079122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950635,0.00022712459,0.00063792337,0.002482298,0.0006040389,0.0009851452],"domain_scores_gemma":[0.99517894,0.0017509043,0.00052533,0.0019518731,0.00019491084,0.0003980652],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00051273673,0.00070696493,0.0005777034,0.00036541224,0.0022636089,0.0003353126,0.0022203755,0.00023580146,0.000067136985],"category_scores_gemma":[0.000491981,0.00082723587,0.00015432465,0.0012408483,0.000108812645,0.00026815737,0.0016069593,0.0010135061,0.000006067756],"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":[0.00034486424,0.000049343376,0.000039679722,0.00006402627,0.00005019639,0.0000038485846,0.0022637907,0.7989684,0.0016489594,0.00026419794,0.0003738215,0.1959289],"study_design_scores_gemma":[0.00064572366,0.00032754667,0.0018001609,0.000051647043,0.00009064709,0.00007343691,0.0007306609,0.98533505,0.00035096338,0.00059154484,0.009222063,0.00078056694],"about_ca_topic_score_codex":0.00014827585,"about_ca_topic_score_gemma":0.0011665268,"teacher_disagreement_score":0.8694844,"about_ca_system_score_codex":0.00022467194,"about_ca_system_score_gemma":0.00013384831,"threshold_uncertainty_score":0.99941784},"labels":[],"label_agreement":null},{"id":"W7132932490","doi":"","title":"Co-location of Deep Learning Jobs in GPU Clusters","year":2021,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Deep learning; Profiling (computer programming); Speedup; Scheduling (production processes); GPU cluster; Job shop scheduling; Training set","score_opus":0.01992238273421161,"score_gpt":0.34166815045869475,"score_spread":0.32174576772448316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132932490","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":[0.21545519,0.005562764,0.7690849,0.000991199,0.0009807744,0.0013204347,0.0000012581665,0.00014459889,0.006458887],"genre_scores_gemma":[0.97451,0.0010144069,0.01726914,0.000095078285,0.00011724025,0.00015637351,0.00036508,0.00006168124,0.0064110034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965204,0.00034554338,0.00088827324,0.0010998688,0.0005957071,0.00055020733],"domain_scores_gemma":[0.9966317,0.0004858752,0.0010678025,0.0009588475,0.0007118432,0.00014392802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033746415,0.0004508026,0.000638652,0.00036503983,0.00024714504,0.000108115564,0.0009891334,0.00035455142,0.000087684464],"category_scores_gemma":[0.00030996095,0.000573924,0.00012803836,0.0036276912,0.00010256744,0.00040151086,0.0001237527,0.0010278823,0.000075511554],"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":[0.00012424555,0.00040618592,0.0039688447,0.0011296994,0.00007777552,0.000039253926,0.084212735,0.6655116,0.024863983,0.009719077,0.00013292077,0.20981365],"study_design_scores_gemma":[0.0013054468,0.00027998205,0.041880783,0.0018301645,0.00008912617,0.000026650767,0.027190749,0.8910873,0.030962322,0.0007307913,0.003139707,0.0014769806],"about_ca_topic_score_codex":0.0001972818,"about_ca_topic_score_gemma":0.0009056212,"teacher_disagreement_score":0.7590548,"about_ca_system_score_codex":0.00022209004,"about_ca_system_score_gemma":0.000317399,"threshold_uncertainty_score":0.9996712},"labels":[],"label_agreement":null},{"id":"W7132944625","doi":"","title":"Comparative Study of Data Efficiency in Vision Transformer and ResNet-18 Architectures: Using CIFAR-10 and TinyImageNet","year":2024,"lang":"en","type":"dissertation","venue":"Trepo - Institutional Repository of Tampere University","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Transformer; RGB color model; Deep learning; Feature extraction; Machine vision; Pattern recognition (psychology)","score_opus":0.04936039154501471,"score_gpt":0.32843735968839627,"score_spread":0.27907696814338157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132944625","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":[0.9907435,0.00060083374,0.0050316667,0.000010484309,0.00013038586,0.0004982185,0.00004739732,0.000028095965,0.002909405],"genre_scores_gemma":[0.99396324,0.00003550501,0.005367595,0.0000020325995,0.000019201514,0.0000011125965,0.00009850252,0.000005781268,0.0005070004],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983521,0.000091506554,0.00035620562,0.00073181966,0.0003400926,0.00012828063],"domain_scores_gemma":[0.9988994,0.00012838635,0.0002452109,0.00053740776,0.0001273402,0.00006226868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011947021,0.00020375967,0.00035967102,0.00033938565,0.00030591962,0.000028767749,0.00065369473,0.000102617334,0.000003501908],"category_scores_gemma":[0.000012228233,0.00020830819,0.000036850513,0.00050445826,0.0002712892,0.00039415993,0.00022172654,0.00026162862,5.036898e-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":[0.007014135,0.0130839,0.010376202,0.0064526703,0.001979632,0.0035094852,0.19115894,0.29972324,0.35795185,0.042263035,0.0015412442,0.06494566],"study_design_scores_gemma":[0.010705609,0.004118263,0.22234036,0.0057567973,0.0017941484,0.0013696691,0.038604908,0.6627376,0.028458595,0.0036469537,0.016246524,0.0042205667],"about_ca_topic_score_codex":0.00032905024,"about_ca_topic_score_gemma":0.00066307915,"teacher_disagreement_score":0.36301437,"about_ca_system_score_codex":0.00008683527,"about_ca_system_score_gemma":0.00030286732,"threshold_uncertainty_score":0.8494562},"labels":[],"label_agreement":null},{"id":"W7132971677","doi":"","title":"Cross-Modal Data Fusion for 3D Instance Segmentation of Indoor Scenes","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Object (grammar); Fusion; 3d model; Scale-space segmentation; Pattern recognition (psychology); Sensor fusion","score_opus":0.08780173568384227,"score_gpt":0.43756894140895664,"score_spread":0.3497672057251144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132971677","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":[0.20268428,0.00085466896,0.78853947,0.00054808706,0.0026712473,0.0036161223,0.0005418313,0.00030863014,0.00023565927],"genre_scores_gemma":[0.26509032,0.003482087,0.6693678,0.00025254674,0.0012604564,0.0017274341,0.022803927,0.00038533407,0.035630114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955123,0.000088297435,0.0010647408,0.0018892833,0.0007869957,0.00065836514],"domain_scores_gemma":[0.99377656,0.00075953145,0.0016177847,0.0029086203,0.0007745258,0.00016298723],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005319194,0.00057317724,0.0006523269,0.00027364193,0.00066316273,0.00024298852,0.0033314282,0.0003667456,0.000038629936],"category_scores_gemma":[0.00028619848,0.00064823177,0.00014450279,0.0019013009,0.0001878909,0.0012674768,0.00090701174,0.0003759727,0.00008562195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013067328,0.0006807616,0.0017339946,0.0031089608,0.0002478709,0.000011635582,0.025187504,0.021772956,0.11294029,0.010925024,0.004371979,0.8177123],"study_design_scores_gemma":[0.0030554503,0.0005223347,0.015333262,0.0012639723,0.0002294571,0.0000083414625,0.0036068582,0.899405,0.060165804,0.003690553,0.010958751,0.0017602426],"about_ca_topic_score_codex":0.00011212271,"about_ca_topic_score_gemma":0.00039405376,"teacher_disagreement_score":0.877632,"about_ca_system_score_codex":0.00012996698,"about_ca_system_score_gemma":0.00041443773,"threshold_uncertainty_score":0.9995969},"labels":[],"label_agreement":null},{"id":"W7133007753","doi":"","title":"Tackling Resource Utilization In Deep Neural Network Accelerators","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Resource (disambiguation); Artificial neural network; Greedy algorithm; Bayesian probability; Bayesian network; Resource allocation; Scheduling (production processes)","score_opus":0.04158321525887822,"score_gpt":0.3535297073812612,"score_spread":0.311946492122383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133007753","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":[0.42103,0.025140207,0.503574,0.0047639064,0.009771702,0.008842667,0.00001700612,0.0018145404,0.025045974],"genre_scores_gemma":[0.9640337,0.0011934343,0.017134376,0.0016897975,0.0015880215,0.0013930572,0.0018464014,0.00035966767,0.010761506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99362373,0.00063471857,0.00121181,0.002075374,0.0010206534,0.0014336887],"domain_scores_gemma":[0.99610084,0.0005939955,0.0011244,0.0016999767,0.00017989903,0.00030089333],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00061418116,0.000870291,0.00078000955,0.0004576706,0.0013839822,0.0004149002,0.0025088664,0.0004053537,0.00075358077],"category_scores_gemma":[0.000131275,0.0011145358,0.00023549284,0.006430794,0.000087393244,0.00072280125,0.0008025805,0.0019994467,0.00006860968],"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":[0.000097061755,0.0001230283,0.0006916917,0.00006493206,0.000019938081,0.000043591783,0.010942991,0.9041638,0.00015952041,0.0072711543,0.0009759317,0.075446375],"study_design_scores_gemma":[0.0005694316,0.00014260797,0.0033194749,0.00012661402,0.00004495678,0.000018854635,0.0041947835,0.9274474,0.00025045394,0.001496831,0.061228678,0.001159912],"about_ca_topic_score_codex":0.00010042852,"about_ca_topic_score_gemma":0.00064149813,"teacher_disagreement_score":0.54300374,"about_ca_system_score_codex":0.0005319801,"about_ca_system_score_gemma":0.00021788065,"threshold_uncertainty_score":0.9999161},"labels":[],"label_agreement":null},{"id":"W7133025936","doi":"","title":"ENERGY MODELS FOR PRUNING NEURAL NETWORKS","year":2021,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Overfitting; Pruning; Entropy (arrow of time); Artificial neural network; Contextual image classification; Convolutional neural network; Feature (linguistics); Pattern recognition (psychology); Minification","score_opus":0.03586287556130653,"score_gpt":0.33163253050451375,"score_spread":0.2957696549432072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133025936","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":[0.0008332886,0.006721026,0.98542446,0.0010742224,0.0023327845,0.0008888633,0.0000063403236,0.00026631774,0.002452681],"genre_scores_gemma":[0.77163595,0.001739925,0.14606154,0.0016430604,0.0023696327,0.0023544792,0.0021125285,0.00033682302,0.071746044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99535096,0.0001533248,0.0007874493,0.0019622827,0.00050256925,0.0012433907],"domain_scores_gemma":[0.99561995,0.0006930375,0.00084435864,0.0017111981,0.0007628521,0.00036859012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018674425,0.0008424147,0.00080806453,0.00017179966,0.00097516377,0.00051669736,0.0017870378,0.0005756746,0.000038414077],"category_scores_gemma":[0.000042666397,0.0010057846,0.0004660886,0.001530589,0.00008106251,0.00079689943,0.0003842052,0.0006985446,0.000004932718],"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":[0.000041280517,0.000060808805,0.0000011986497,0.00006562701,0.000042593296,0.000009983906,0.0020651899,0.7855468,0.00028713205,0.10110174,0.00085401157,0.109923676],"study_design_scores_gemma":[0.00039936177,0.000104958526,0.0000097953525,0.00015371646,0.00009622864,0.000020612228,0.00086127705,0.98107463,0.0007698136,0.009155273,0.0064410493,0.0009133034],"about_ca_topic_score_codex":0.00009754215,"about_ca_topic_score_gemma":0.00023355948,"teacher_disagreement_score":0.8393629,"about_ca_system_score_codex":0.0001444202,"about_ca_system_score_gemma":0.00027088806,"threshold_uncertainty_score":0.99923927},"labels":[],"label_agreement":null},{"id":"W7133192529","doi":"10.18280/ijsse.151212","title":"An Interpretable Hybrid Framework for Principal Component Analysis–Extreme Gradient Boosting and a Single-Class Support Vector Machine for Zero-Day Penetration Detection","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Principal component analysis; Boosting (machine learning); Pattern recognition (psychology); Gradient boosting","score_opus":0.014179024385099444,"score_gpt":0.2642047096880211,"score_spread":0.2500256853029217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133192529","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":[0.05438383,0.0007163927,0.9417381,0.0010144033,0.0015212896,0.00048071507,0.0001009594,0.000033022843,0.000011246044],"genre_scores_gemma":[0.94584674,0.0003079421,0.05330754,0.00011628239,0.00032892133,0.00002590608,0.0000377755,0.000017298687,0.000011596044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772453,0.000054214433,0.0010770129,0.00046588166,0.00035381058,0.00032453003],"domain_scores_gemma":[0.9974069,0.00091267406,0.00061908254,0.00021905506,0.0006444967,0.00019777459],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008695321,0.00029265933,0.00048189305,0.00059640134,0.00025710254,0.00035723258,0.0005527854,0.00010761829,0.000004384316],"category_scores_gemma":[0.00035229028,0.00031840417,0.00025008907,0.00040746373,0.00005352383,0.00092677574,0.00016532291,0.00043104263,1.4592332e-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":[0.0019873301,0.0007782112,0.0012821272,0.00039842413,0.0034157587,0.000032366224,0.0033953865,0.6471935,0.040191077,0.15145408,0.000019685333,0.14985208],"study_design_scores_gemma":[0.00085947267,0.0004701972,0.0026424616,0.00028249345,0.00034840067,0.0001020333,0.000021548172,0.9792976,0.0040493086,0.008880337,0.0027913044,0.0002548482],"about_ca_topic_score_codex":0.00000865157,"about_ca_topic_score_gemma":0.00003258496,"teacher_disagreement_score":0.8914629,"about_ca_system_score_codex":0.00030813654,"about_ca_system_score_gemma":0.00006436239,"threshold_uncertainty_score":0.9999268},"labels":[],"label_agreement":null},{"id":"W7134301127","doi":"","title":"Вдосконалення моделі розпізнавання об'єктів на аерофотознімках з використанням глибокої згорткової нейронної мережі","year":2021,"lang":"en","type":"article","venue":"The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Cognitive neuroscience of visual object recognition; Set (abstract data type); Test set; Object (grammar); Pattern recognition (psychology); Aerial image; Object detection; Image (mathematics)","score_opus":0.06949051626543264,"score_gpt":0.33524346947687766,"score_spread":0.26575295321144504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134301127","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":[0.520118,0.0043527107,0.2335813,0.13863593,0.0070333043,0.0028585657,0.0009980707,0.0022408422,0.09018123],"genre_scores_gemma":[0.7557484,0.0003265877,0.02809795,0.0007308658,0.0005433307,0.000016210004,0.00029108665,0.000049386166,0.21419619],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942275,0.00046816884,0.00072262884,0.001573744,0.00214835,0.0008596062],"domain_scores_gemma":[0.99451387,0.0007145905,0.0005375146,0.0016884572,0.0022307588,0.00031483523],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010029606,0.000507456,0.0006184366,0.00042774438,0.0016138481,0.00044404648,0.0040273913,0.00024388176,0.0007695308],"category_scores_gemma":[0.00051383424,0.00045473556,0.0003810218,0.002778705,0.0017429444,0.0019148906,0.0023607982,0.00061380904,0.00032723762],"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":[0.00014949439,0.00068200397,0.00065044715,0.00007640187,0.00017738079,0.0001920839,0.0039524273,0.0026985423,0.015891034,0.9466107,0.020734847,0.008184622],"study_design_scores_gemma":[0.0011661459,0.00024165613,0.004330916,0.00006060487,0.000077257544,0.00022063497,0.0035061424,0.007500827,0.012492083,0.068450496,0.90097237,0.0009808786],"about_ca_topic_score_codex":0.0001062637,"about_ca_topic_score_gemma":0.00035903783,"teacher_disagreement_score":0.8802375,"about_ca_system_score_codex":0.00029874168,"about_ca_system_score_gemma":0.00092013326,"threshold_uncertainty_score":0.99979043},"labels":[],"label_agreement":null},{"id":"W7138908017","doi":"10.1109/globecom59602.2025.11432592","title":"Communication-Efficient Multi-Agent Collaborative Perception via Spatio-Temporal Heterogeneity","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Research and Development","keywords":"Perception; Redundancy (engineering); Representation (politics); Feature (linguistics); Software deployment; Active perception; Semantics (computer science); Percept","score_opus":0.027527944528389157,"score_gpt":0.3202792694150856,"score_spread":0.2927513248866965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7138908017","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":[0.035672273,0.0013369073,0.95207703,0.0059154807,0.00066534977,0.0022340058,0.00003192963,0.00033260067,0.0017344287],"genre_scores_gemma":[0.7934306,0.000488563,0.20242102,0.0012635402,0.00004415117,0.00034562865,0.0000867047,0.000021054475,0.001898695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957268,0.00066017377,0.0011077626,0.0013282443,0.0005021413,0.0006748266],"domain_scores_gemma":[0.9948942,0.00029394514,0.0004976178,0.00302737,0.0010261707,0.0002606824],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00050436327,0.00054960075,0.00048677874,0.0002823266,0.0015439311,0.0004007804,0.0022070338,0.0002306584,0.00020248619],"category_scores_gemma":[0.000058473957,0.00057526515,0.00020323576,0.003554028,0.00049556606,0.0005299541,0.0015935455,0.0005062733,0.0005122446],"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":[0.00017774235,0.0064687068,0.01228081,0.00016281464,0.00040936144,0.000010078243,0.0071707494,0.4692079,0.0056211064,0.10729513,0.010961735,0.38023385],"study_design_scores_gemma":[0.00088207243,0.000078037985,0.014712735,0.00008492847,0.000046085333,0.000003862847,0.00029195333,0.966806,0.002470737,0.0008298773,0.013274176,0.0005195637],"about_ca_topic_score_codex":0.00015404253,"about_ca_topic_score_gemma":0.0010227717,"teacher_disagreement_score":0.7577584,"about_ca_system_score_codex":0.0006786302,"about_ca_system_score_gemma":0.00040413506,"threshold_uncertainty_score":0.9997559},"labels":[],"label_agreement":null},{"id":"W7140074759","doi":"10.1093/jigpal/jzaf043","title":"Multimodal video and radar system for edge devices object detection","year":2025,"lang":"en","type":"article","venue":"Logic Journal of IGPL","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hyperion Technologies (Canada)","funders":"","keywords":"Object detection; Object (grammar); Enhanced Data Rates for GSM Evolution; Radar; Pipeline (software); Sensor fusion; Radar imaging; Modalities","score_opus":0.015604573686977742,"score_gpt":0.27467591400147673,"score_spread":0.25907134031449897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7140074759","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":[0.003951209,0.0010120375,0.9921115,0.0006724517,0.00044254662,0.00017897967,8.5397375e-7,0.00003883785,0.001591599],"genre_scores_gemma":[0.8971042,0.000030485875,0.102526024,0.0001762479,0.00011371791,0.000010347701,1.2925071e-7,0.0000029162009,0.000035914847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999327,0.00003627194,0.00027043128,0.00014380798,0.00009317901,0.0001293218],"domain_scores_gemma":[0.9991628,0.0002648771,0.0002344767,0.00015162802,0.00013966842,0.000046571473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023145128,0.000077546196,0.00015454194,0.00009763863,0.00014988534,0.000056240275,0.00032916697,0.000039376722,3.1466655e-7],"category_scores_gemma":[0.00004904827,0.00006010487,0.000058714846,0.00024850597,0.000023369885,0.00027665112,0.000067333924,0.00011146629,0.0000012500096],"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":[0.0001178064,0.00008329672,0.00018290615,0.00025456335,0.00009930291,0.000023304812,0.0002781246,0.0027562408,0.027455904,0.39615712,0.0007023044,0.57188916],"study_design_scores_gemma":[0.009218803,0.0025126957,0.02242423,0.0014627923,0.00034730064,0.002856648,0.0012323243,0.3862071,0.17101364,0.30420277,0.09714593,0.0013757802],"about_ca_topic_score_codex":0.0000022381194,"about_ca_topic_score_gemma":0.00000817783,"teacher_disagreement_score":0.893153,"about_ca_system_score_codex":0.00005865602,"about_ca_system_score_gemma":0.000033314893,"threshold_uncertainty_score":0.24510056},"labels":[],"label_agreement":null},{"id":"W7140311245","doi":"10.1109/iceace67491.2025.11439574","title":"Real-Time Defect Classification at the Edge: A Lightweight CNN Approach for Industrial-Scale Applications","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"123 Certification (Canada)","funders":"","keywords":"Feature (linguistics); Pattern recognition (psychology); Noise (video); Convolutional neural network; Identification (biology)","score_opus":0.042847889973421156,"score_gpt":0.2910810818401645,"score_spread":0.24823319186674336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7140311245","genre_codex":"methods","genre_gemma":"other","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":[0.00025545945,0.00051118777,0.88636917,0.013346197,0.0002978196,0.008944085,0.000060938357,0.0004367605,0.089778356],"genre_scores_gemma":[0.10105761,0.0019826621,0.37757856,0.0029098203,0.0029695737,0.06128838,0.0009719237,0.000216738,0.45102474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950677,0.00029826668,0.0011684281,0.0020651617,0.0004519656,0.0009484637],"domain_scores_gemma":[0.99319375,0.0017224714,0.0006034907,0.003760022,0.0004687444,0.00025152485],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0008287852,0.0006007444,0.0005612635,0.00024241085,0.002770201,0.0004592873,0.002987236,0.000503289,0.000114687515],"category_scores_gemma":[0.00007421662,0.0004648338,0.00053234014,0.0040947325,0.00055195467,0.00049334386,0.0011321343,0.00054218184,0.00051468954],"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":[0.000119813514,0.00086045824,0.00021516102,0.0000979018,0.00021788932,2.1784919e-7,0.00025175558,0.00288237,0.01827031,0.5307393,0.22717163,0.2191732],"study_design_scores_gemma":[0.0012361858,0.0000904507,0.0003804536,0.00003871032,0.00031767238,0.000008541253,0.00007077647,0.5310211,0.00781287,0.012281103,0.44607663,0.00066549855],"about_ca_topic_score_codex":0.00001969782,"about_ca_topic_score_gemma":0.000019879091,"teacher_disagreement_score":0.52813876,"about_ca_system_score_codex":0.0005996722,"about_ca_system_score_gemma":0.00050536817,"threshold_uncertainty_score":0.99978036},"labels":[],"label_agreement":null},{"id":"W7140313907","doi":"10.1109/gcwcn66157.2025.11448412","title":"A CNN-Transformer Hybrid for Precise Object Detection in UAV Aerial Imagery","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences North","funders":"","keywords":"Object detection; Object (grammar); Noise (video); Feature (linguistics); Edge detection; Drone","score_opus":0.013401826178034936,"score_gpt":0.28093299194051274,"score_spread":0.2675311657624778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7140313907","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":[0.012678344,0.0002802371,0.9751862,0.0017587013,0.002150811,0.0030084532,0.000015967846,0.00019362138,0.004727631],"genre_scores_gemma":[0.9531776,0.0002600564,0.04031777,0.00062171975,0.00037448297,0.0014095625,0.000006369867,0.000026340289,0.0038061389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968978,0.00008890379,0.0008395311,0.0011461166,0.00022587256,0.0008017536],"domain_scores_gemma":[0.99811757,0.0005895973,0.00014152243,0.00086027343,0.00017469606,0.00011636471],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036954138,0.00037193953,0.00041290492,0.0003988829,0.00037452357,0.0002566259,0.00085189333,0.00012608615,0.000051059982],"category_scores_gemma":[0.000109562694,0.00039307208,0.00024912582,0.0018358766,0.00013009417,0.0010499575,0.00015108977,0.00025460508,0.000054655247],"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":[0.000397253,0.00026425096,0.000027396403,0.00009718542,0.00002954493,0.000005304474,0.00015654013,0.0030226372,0.03668943,0.008607884,0.0016067909,0.9490958],"study_design_scores_gemma":[0.0025690696,0.0002419102,0.00049555174,0.00014223173,0.0000496955,0.000020267451,0.000026284117,0.48219898,0.44046143,0.04513709,0.028031556,0.0006259357],"about_ca_topic_score_codex":0.00005519578,"about_ca_topic_score_gemma":0.0003679285,"teacher_disagreement_score":0.9484699,"about_ca_system_score_codex":0.00025492988,"about_ca_system_score_gemma":0.00029620578,"threshold_uncertainty_score":0.9998521},"labels":[],"label_agreement":null},{"id":"W7143414974","doi":"10.1109/decon67170.2025.11447529","title":"Garbage Classification with Hardswish and Efficient Channel Attention: A GhostNetV2-Based Approach","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Channel (broadcasting); Identification (biology); Feature (linguistics); Key (lock)","score_opus":0.02226817420826212,"score_gpt":0.2556291106992981,"score_spread":0.23336093649103598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143414974","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":[0.0050120507,0.00067927217,0.9689203,0.007701051,0.00022997301,0.0016212036,0.000007709918,0.00029275005,0.015535682],"genre_scores_gemma":[0.9184961,0.000055773937,0.0745528,0.0012494032,0.00007263265,0.0004396176,0.000025192625,0.000023428433,0.005085096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657905,0.00013083662,0.00055167964,0.001651883,0.00047312764,0.0006134124],"domain_scores_gemma":[0.99754894,0.00017189218,0.00027633758,0.001453164,0.00032202998,0.00022766115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003240942,0.00044520551,0.00036661146,0.0002917876,0.00074682257,0.00045285875,0.0008224631,0.00015955107,0.00001547264],"category_scores_gemma":[0.000020989499,0.00039298434,0.000090617665,0.0025250204,0.00046323662,0.00028528288,0.00033770222,0.0003624859,0.000028424696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002201868,0.0022000605,0.0014383775,0.00052882184,0.0001641444,0.00001203646,0.00041167575,0.1939191,0.001683003,0.70243806,0.004109554,0.09287498],"study_design_scores_gemma":[0.0011528503,0.00012943111,0.014744877,0.00014881146,0.000068718444,0.000012748405,0.000112426984,0.98063004,0.00032477386,0.0004914008,0.0017683802,0.00041552994],"about_ca_topic_score_codex":0.000009142593,"about_ca_topic_score_gemma":0.0000073180977,"teacher_disagreement_score":0.913484,"about_ca_system_score_codex":0.0001353275,"about_ca_system_score_gemma":0.00025339483,"threshold_uncertainty_score":0.9998522},"labels":[],"label_agreement":null},{"id":"W7152496182","doi":"10.70675/7c6f2cc3z84cdz414cz8d9dz21c4268f8f86","title":"Ordonnancement dynamique pour l’inférence dans les réseaux de neurones profonds","year":2025,"lang":"fr","type":"dissertation","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Power consumption; Water equivalent; Booth's multiplication algorithm","score_opus":0.02967438622309004,"score_gpt":0.3012623420019579,"score_spread":0.27158795577886785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7152496182","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":[0.01741688,0.00068558526,0.9491626,0.0075175785,0.0010906134,0.0012101507,0.000027317008,0.00038231476,0.02250697],"genre_scores_gemma":[0.26185703,0.0026916668,0.4454723,0.0016930054,0.00041382757,0.002196588,0.0005205115,0.00009871533,0.28505635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99621034,0.0002049863,0.0007844222,0.0014291462,0.00043539607,0.0009357144],"domain_scores_gemma":[0.99718165,0.0003907545,0.00046718007,0.001261939,0.00046225145,0.00023624758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019523916,0.00069148885,0.00047980074,0.00019908203,0.0007733473,0.00025458055,0.002172437,0.00037062343,0.00012000353],"category_scores_gemma":[0.00011381086,0.0007214568,0.00020203313,0.0014017272,0.000119452685,0.0005220986,0.00040956205,0.0007696728,0.00012835437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002718101,0.00038111638,0.0025379946,0.00045558446,0.00005517809,0.000041813266,0.0021881044,0.057072792,0.016866522,0.7767364,0.0017710107,0.1418663],"study_design_scores_gemma":[0.00036153497,0.00014686632,0.014775182,0.000601009,0.00008942962,0.000032695592,0.0006551766,0.9168163,0.017330151,0.029876478,0.018346203,0.0009689846],"about_ca_topic_score_codex":0.0008975475,"about_ca_topic_score_gemma":0.005027453,"teacher_disagreement_score":0.8597435,"about_ca_system_score_codex":0.00039907332,"about_ca_system_score_gemma":0.0013769736,"threshold_uncertainty_score":0.99952364},"labels":[],"label_agreement":null},{"id":"W7156071715","doi":"10.1145/3789410.3789415","title":"SEA-YOLO: Thermal Imaging Object Detection via Spatial Edge Attention for Low-visibility Autonomous Driving","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Object detection; Leverage (statistics); Convolutional neural network; Enhanced Data Rates for GSM Evolution; RGB color model; SAFER; Deep learning; Cognitive neuroscience of visual object recognition; Object (grammar)","score_opus":0.008816821227879955,"score_gpt":0.2695869066586555,"score_spread":0.2607700854307755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7156071715","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":[0.104393646,0.00015582996,0.88751,0.0015279178,0.0024531803,0.0023157396,0.000007131119,0.00055784074,0.0010787054],"genre_scores_gemma":[0.97023976,0.000012992071,0.02735669,0.00045029385,0.0004904221,0.0004297966,0.000012845229,0.000035318153,0.00097187766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99567354,0.00022192733,0.0010061549,0.0018149315,0.00030668319,0.0009767412],"domain_scores_gemma":[0.99690694,0.00058158167,0.00040813338,0.0015045375,0.00042409138,0.00017474292],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00073318003,0.00051280524,0.0004466604,0.00026561378,0.0013781275,0.00045824188,0.0010827188,0.00017649074,0.000050941126],"category_scores_gemma":[0.0001290037,0.0005528146,0.00040979296,0.001264983,0.00021762236,0.0013082098,0.0007866541,0.00045672187,0.00006267286],"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":[0.000044313256,0.00020673868,0.0060107503,0.00009171669,0.000039436945,0.0000016323836,0.00009891446,0.0038814212,0.060239896,0.0012988797,0.00005881143,0.9280275],"study_design_scores_gemma":[0.00071251794,0.00008729406,0.0658857,0.000083343635,0.00006511675,0.000008099411,0.000019519859,0.88942003,0.036652308,0.0061096363,0.00050425716,0.00045216136],"about_ca_topic_score_codex":0.0002384292,"about_ca_topic_score_gemma":0.00061362726,"teacher_disagreement_score":0.92757535,"about_ca_system_score_codex":0.00062570756,"about_ca_system_score_gemma":0.00026784834,"threshold_uncertainty_score":0.9999219},"labels":[],"label_agreement":null},{"id":"W7160163621","doi":"10.1109/iccv51701.2025.02475","title":"Height-Fidelity Dense Global Fusion for Multi-Modal 3D Object Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Object detection; Object (grammar); Fusion; Sensor fusion; Pattern recognition (psychology)","score_opus":0.030260524686483607,"score_gpt":0.3276287719759858,"score_spread":0.2973682472895022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160163621","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":[0.009550596,0.0005861339,0.9821107,0.0012633705,0.0020531635,0.0022508416,0.000033655062,0.0004539254,0.0016976147],"genre_scores_gemma":[0.6482459,0.00012300849,0.34798288,0.0011767617,0.00019167614,0.0003119413,0.000006176057,0.000013446159,0.0019482569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99631786,0.00014851122,0.0008349999,0.0015453938,0.00032645193,0.0008267695],"domain_scores_gemma":[0.9972426,0.0003235643,0.00025911807,0.001412358,0.00054198975,0.00022032006],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003924462,0.00045952963,0.00040328267,0.000113195754,0.0012072417,0.00027082252,0.0011137286,0.00028885144,0.0000338732],"category_scores_gemma":[0.00018953437,0.00045981305,0.00029668148,0.0021118035,0.00015226417,0.0006707653,0.00088606426,0.0002906136,0.00008635984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018171665,0.00040462724,0.00033021162,0.00007646043,0.000052485415,0.0000030991957,0.00006716806,0.004969796,0.0066165533,0.051480215,0.0008388793,0.9349788],"study_design_scores_gemma":[0.0013354851,0.00016892386,0.0055943076,0.000051109884,0.00005848706,0.000015406402,0.000020509628,0.95122147,0.012401725,0.017495772,0.011202749,0.0004340771],"about_ca_topic_score_codex":0.00014521151,"about_ca_topic_score_gemma":0.0016046141,"teacher_disagreement_score":0.94625163,"about_ca_system_score_codex":0.00060094474,"about_ca_system_score_gemma":0.00028764503,"threshold_uncertainty_score":0.99978536},"labels":[],"label_agreement":null},{"id":"W7160307662","doi":"10.1109/aixset65682.2025.00011","title":"LOD-Net: Locality-Aware 3D Object Detection Using Multi-Scale Transformer Network","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Object detection; Object (grammar); Transformer; Artificial neural network; Noise (video)","score_opus":0.027400427988256783,"score_gpt":0.3016410555148936,"score_spread":0.27424062752663686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160307662","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":[0.002763552,0.0015526487,0.98896956,0.00061136513,0.0025227696,0.001547849,0.000008861113,0.00047958273,0.0015438346],"genre_scores_gemma":[0.7606306,0.0005355988,0.23437944,0.0014742811,0.0006667029,0.0001309088,0.000006546781,0.000057319543,0.0021186066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950945,0.00027999317,0.0011260421,0.0016630488,0.00045092183,0.0013855445],"domain_scores_gemma":[0.9973199,0.00029447261,0.00025991575,0.0015213579,0.00031454224,0.00028984403],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00045910748,0.0006639271,0.0006116556,0.0002278466,0.0015457073,0.0003479885,0.0012529829,0.00034666483,0.000098412755],"category_scores_gemma":[0.000014344944,0.00069481024,0.00033303257,0.004622521,0.0003289061,0.0010798066,0.00031980762,0.0009033621,0.0001203168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047442027,0.00031063255,0.0011440599,0.00013479884,0.00007877902,0.0000033214253,0.00027183793,0.22415584,0.0031608308,0.0017634534,0.00020394362,0.76872504],"study_design_scores_gemma":[0.00080986635,0.000068346315,0.0014069576,0.00023006964,0.00010944665,0.000017887385,0.00004343539,0.9699329,0.00887916,0.0027406018,0.015090267,0.00067105127],"about_ca_topic_score_codex":0.00019276318,"about_ca_topic_score_gemma":0.0013766792,"teacher_disagreement_score":0.768054,"about_ca_system_score_codex":0.0004323506,"about_ca_system_score_gemma":0.00033900005,"threshold_uncertainty_score":0.99975413},"labels":[],"label_agreement":null},{"id":"W7160456388","doi":"10.1109/iccit68739.2025.11491828","title":"A Composite Dual Attention Transformer for Query-Based Instance Segmentation in Autonomous Driving","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Transformer; Composite number; Dual (grammatical number); Pattern recognition (psychology)","score_opus":0.014534817728645871,"score_gpt":0.29234064572344637,"score_spread":0.27780582799480047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160456388","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":[0.049281787,0.0001647791,0.9398292,0.0063165464,0.000495554,0.002510802,0.000012538921,0.00016367956,0.0012251615],"genre_scores_gemma":[0.80384845,0.000037376893,0.19304524,0.0012395625,0.000038917216,0.00073958153,0.000037948463,0.0000167895,0.0009961574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997306,0.000091882706,0.0008677723,0.00091910636,0.00022745844,0.0005877686],"domain_scores_gemma":[0.99867433,0.00037024444,0.00020515762,0.0005103422,0.00015194838,0.00008796817],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002977464,0.00032171645,0.00033176914,0.0003776275,0.00040092666,0.0002168231,0.00047230348,0.00013236124,0.000014562683],"category_scores_gemma":[0.000013834183,0.0003675089,0.00016870232,0.0017497296,0.00010104953,0.00097639597,0.00005210979,0.00023675246,0.000014375341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018882478,0.000915324,0.009572975,0.00035378558,0.00006269341,0.0000065537197,0.0006745663,0.14521757,0.13204089,0.18394744,0.00047369467,0.5265457],"study_design_scores_gemma":[0.0025594272,0.00011521634,0.014330157,0.00032026065,0.000034802753,0.0000019758033,0.000056732875,0.9537223,0.021432787,0.005216427,0.0018098186,0.00040009507],"about_ca_topic_score_codex":0.00003078718,"about_ca_topic_score_gemma":0.0007038538,"teacher_disagreement_score":0.80850476,"about_ca_system_score_codex":0.00047336664,"about_ca_system_score_gemma":0.0003033242,"threshold_uncertainty_score":0.9998777},"labels":[],"label_agreement":null},{"id":"W7164025617","doi":"10.1109/icdabi67967.2025.11547663","title":"Enhancing Manifold Convexity in Deep MRI Image Clustering Using Adversarial Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Cluster analysis; Adversarial system; Pattern recognition (psychology); Image (mathematics); Deep learning; Manifold (fluid mechanics); Convexity; Nonlinear dimensionality reduction","score_opus":0.016578532787770203,"score_gpt":0.28870272389164253,"score_spread":0.2721241911038723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164025617","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":[0.013227895,0.00027218016,0.97564673,0.0007832258,0.0013674254,0.00074458733,6.883413e-7,0.00024230234,0.0077149374],"genre_scores_gemma":[0.72741663,0.00010147876,0.27079707,0.0004405793,0.00019415328,0.000024652216,0.0000017130168,0.000024300443,0.0009994295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99596936,0.00027865524,0.0010431241,0.0013377729,0.0003355881,0.0010355103],"domain_scores_gemma":[0.9980372,0.00046151306,0.00031292837,0.0008820599,0.00014589145,0.00016041783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006345392,0.0004536696,0.00052355684,0.00041868852,0.00071965647,0.00041040053,0.0011939189,0.00021482958,0.00013524202],"category_scores_gemma":[0.00014222087,0.00054784236,0.00015037907,0.0023811706,0.00013309564,0.001460473,0.0021110359,0.0010615627,0.00007811156],"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":[0.00007506381,0.00015551201,0.0016013314,0.00016306106,0.000049165923,0.00008471979,0.0014045552,0.864928,0.062962085,0.031051047,0.000034790482,0.037490662],"study_design_scores_gemma":[0.0009019968,0.000034318757,0.00057332224,0.00029019438,0.000026297617,0.000014764478,0.00027434924,0.98260874,0.011689305,0.0023832372,0.00073969044,0.0004637952],"about_ca_topic_score_codex":0.00028942426,"about_ca_topic_score_gemma":0.0011926906,"teacher_disagreement_score":0.71418875,"about_ca_system_score_codex":0.0005622138,"about_ca_system_score_gemma":0.00025146577,"threshold_uncertainty_score":0.9996973},"labels":[],"label_agreement":null}]}