{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":10,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":10,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"2429ad173580","filters":{"venue":"Array"}},"results":[{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.03883518648875101,"gpt":0.3038080404009055,"spread":0.2649728539121545,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000319565,0.00007497244,0.0001133197,0.00002978057,0.0001495963,0.0001157308,0.0001671452,0.00003720144,0.000001346006],"category_scores_gemma":[0.00007927116,0.00008125526,0.00001037436,0.0001436443,0.00001341863,0.0004587184,0.000182609,0.00009625794,0.00000162947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003209515,"about_ca_system_score_gemma":0.00001151863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008005206,"about_ca_topic_score_gemma":0.004316716,"domain_scores_codex":[0.9991782,0.0001357016,0.0001067238,0.0003759715,0.00005718984,0.0001462291],"domain_scores_gemma":[0.9994867,0.0002007612,0.00004258522,0.0001783428,0.00005285846,0.00003868879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000005160778,0.00003528634,0.008049421,0.00002361048,0.000008869724,0.000002251976,0.004457066,0.000685566,0.03368117,0.0006232175,0.00000511613,0.9524233],"study_design_scores_gemma":[0.0005276013,0.0001132263,0.8364844,0.00004883575,0.000005843621,0.0000396432,0.0004463403,0.1503356,0.003931342,0.00312325,0.004695422,0.0002484338],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3420729,0.00659468,0.6490981,0.001117325,0.0001094974,0.0005765489,4.552523e-7,0.0001592289,0.0002712323],"genre_scores_gemma":[0.9262165,0.004379383,0.06920981,0.0000339031,0.00003851558,0.00006907986,0.000003544419,0.000008372955,0.00004086557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9521748,"threshold_uncertainty_score":0.3313493,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01874840495903099,"gpt":0.3021264838453107,"spread":0.2833780788862797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001039426,0.00007092115,0.00009907273,0.0001101085,0.0000759457,0.00004395279,0.0003421532,0.00004347465,7.217818e-7],"category_scores_gemma":[0.00001144715,0.00007160715,0.00002378219,0.0003289909,0.00002107471,0.0002406133,0.00005214939,0.00006856426,0.000003325087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005840056,"about_ca_system_score_gemma":0.00002469566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001219002,"about_ca_topic_score_gemma":0.00003981262,"domain_scores_codex":[0.9993118,0.00002662524,0.0001752188,0.0002712749,0.00005693837,0.0001581362],"domain_scores_gemma":[0.9994188,0.0001070376,0.00004576389,0.0003641501,0.00004169705,0.00002257879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006724082,0.00003987546,0.0007199374,0.0000597736,0.000008499549,0.000001281193,0.0001174672,0.001945183,0.03595981,0.8170916,0.006873981,0.1371759],"study_design_scores_gemma":[0.0006388833,0.0000992213,0.00145889,0.0005869715,0.00001344656,0.00001659665,0.00002169223,0.2523556,0.1936868,0.1585138,0.3920271,0.0005809764],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004326426,0.000205835,0.9944565,0.003491754,0.0001620317,0.0002781544,0.00000144714,0.0002184073,0.0007531691],"genre_scores_gemma":[0.9422841,0.00004981452,0.0537976,0.00188493,0.00008204632,0.0007677433,0.000005558434,0.000008012084,0.001120164],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9418515,"threshold_uncertainty_score":0.2920055,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4381325436","doi":"10.1016/j.array.2023.100300","title":"ResneSt-Transformer: Joint attention segmentation-free for end-to-end handwriting paragraph recognition model","year":2023,"lang":"en","type":"article","venue":"Array","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Artificial intelligence; Transformer; End-to-end principle; Encoder; Paragraph; Speech recognition; Pattern recognition (psychology); Pipeline (software); Computer vision; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04780665872640486,"gpt":0.2854302757035785,"spread":0.2376236169771737,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008972465,0.0002043415,0.000211728,0.0005296782,0.0003486599,0.0002336563,0.0005160399,0.000100753,0.00003281265],"category_scores_gemma":[0.0001511267,0.0002162521,0.0001911588,0.0009044745,0.00003675311,0.0008996633,0.00006116915,0.0001273782,0.0002746805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006093422,"about_ca_system_score_gemma":0.00005854707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002736013,"about_ca_topic_score_gemma":0.00006013066,"domain_scores_codex":[0.9980115,0.00006141504,0.0004721832,0.0005791216,0.0003986646,0.0004771665],"domain_scores_gemma":[0.9988216,0.0001499367,0.0001212016,0.0004874287,0.0002667667,0.0001530517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002920716,0.00009428817,0.000218999,0.000132919,0.00005098432,0.000008127256,0.001593624,0.0001315626,0.4157871,0.003426873,0.0134535,0.5650728],"study_design_scores_gemma":[0.001642867,0.0003695913,0.001296431,0.0003315855,0.00004663224,0.00002771019,0.0004335247,0.05482692,0.6896188,0.2492337,0.001361995,0.0008102132],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03958777,0.00001393171,0.9535091,0.002547037,0.0002092965,0.001029103,0.000112142,0.00131866,0.001672959],"genre_scores_gemma":[0.5202443,0.0001015284,0.4757597,0.001123356,0.0001570227,0.001601653,0.0004292923,0.000049069,0.0005340787],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5642626,"threshold_uncertainty_score":0.8818505,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293491576","doi":"10.1016/j.array.2022.100247","title":"Semi-supervised learning approach for localization and pose estimation of texture-less objects in cluttered scenes","year":2022,"lang":"en","type":"article","venue":"Array","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Artificial intelligence; Computer vision; Pose; Computer science; Segmentation; 3D pose estimation; Pattern recognition (psychology); Image warping; Histogram; RGB color model; Object (grammar); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01746004789136548,"gpt":0.221712630119083,"spread":0.2042525822277176,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002721659,0.00006757455,0.0001289289,0.0001193614,0.00008884088,0.00001413749,0.00003166743,0.00005288116,0.000006354896],"category_scores_gemma":[0.00003824012,0.00007054461,0.00002300851,0.0002115898,0.000006561491,0.00007105689,0.00001076434,0.0001057782,2.778023e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004374782,"about_ca_system_score_gemma":0.000008030886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004871447,"about_ca_topic_score_gemma":0.000004977579,"domain_scores_codex":[0.9994604,0.00005806603,0.0001846936,0.0001067442,0.00009812543,0.00009199245],"domain_scores_gemma":[0.9998254,0.00003631551,0.00003846876,0.00006364517,0.00001871119,0.00001752891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002852938,0.00001168911,0.0008707775,0.0001182232,0.000006448855,2.050582e-7,0.001118255,0.9697082,0.01642616,0.00002312986,0.00004961162,0.01163878],"study_design_scores_gemma":[0.0007213514,0.00007818626,0.0003191633,0.00002264195,0.000006036648,0.000004755463,0.001226024,0.9857016,0.01146068,0.0000398708,0.0003298034,0.00008995295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5571778,0.0001338682,0.4414625,0.000004418285,0.0002425969,0.0004017804,0.000005595566,0.00007354333,0.0004979041],"genre_scores_gemma":[0.999219,0.000003262445,0.0005638158,0.00000752699,0.00004623784,0.00007501604,0.00003869324,0.00001667022,0.00002979337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4420412,"threshold_uncertainty_score":0.2876725,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3021661706","doi":"10.1016/j.array.2020.100027","title":"Chordiogram image descriptor based on visual attention model for image retrieval","year":2020,"lang":"en","type":"article","venue":"Array","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Image retrieval; Computer vision; Pattern recognition (psychology); Visual Word; Precision and recall; Feature (linguistics); Salient; Image (mathematics); Image texture; Image segmentation","retraction":null,"screen_n_in":null,"score":{"opus":0.03144116415164915,"gpt":0.3073207896788298,"spread":0.2758796255271806,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002364545,0.0002082715,0.0002158757,0.00008766887,0.000150483,0.0002115025,0.0005846994,0.00008202669,0.000009138707],"category_scores_gemma":[0.0003048163,0.0001961444,0.000214858,0.0004763613,0.00005738934,0.001004082,0.00007275468,0.0001901295,0.00005228057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005048535,"about_ca_system_score_gemma":0.00006067273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001499179,"about_ca_topic_score_gemma":2.028097e-7,"domain_scores_codex":[0.9984447,0.00004213424,0.0002525335,0.0005820987,0.0003186885,0.0003598272],"domain_scores_gemma":[0.9990592,0.00008084784,0.0001022846,0.0003890287,0.0001856008,0.0001830162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003867085,0.0002197314,0.00005208057,0.0000838162,0.00001934149,0.0000207756,0.0002450577,0.00018998,0.9634905,0.002160556,0.009379862,0.02375155],"study_design_scores_gemma":[0.0005220342,0.0006306799,0.00005819682,0.00002025183,0.000009368972,0.000001035687,0.000007201955,0.6969395,0.2972223,0.002484835,0.001872802,0.0002317938],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001081105,0.00002207703,0.9940657,0.002913624,0.0001131348,0.0005308816,0.00001625322,0.0007183132,0.0005389139],"genre_scores_gemma":[0.3105022,0.000008344676,0.6859314,0.003206337,0.0001444074,0.00003274312,0.00001762122,0.00002835989,0.000128603],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6967494,"threshold_uncertainty_score":0.7998537,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415219894","doi":"10.1016/j.array.2025.100535","title":"Multi-scale signal-to-noise driven fusion of post-processing sequences for enhanced defect detectability in active infrared thermography","year":2025,"lang":"en","type":"article","venue":"Array","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Thermography; Visibility; Reliability (semiconductor); Metric (unit); Noise (video); Fusion; Infrared","retraction":null,"screen_n_in":null,"score":{"opus":0.007148226400120252,"gpt":0.2424168488029164,"spread":0.2352686224027961,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001715929,0.0001612227,0.0002457427,0.0003233958,0.00005218432,0.00001232649,0.0001863432,0.0001143797,0.00001640912],"category_scores_gemma":[0.00004181044,0.0001528979,0.0001684876,0.0006853361,0.00006428619,0.0001003308,0.0000176973,0.0001268121,2.353418e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003839008,"about_ca_system_score_gemma":0.00003322738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006331666,"about_ca_topic_score_gemma":0.0001857863,"domain_scores_codex":[0.9991642,0.00004009261,0.0002549081,0.0002255328,0.00007817739,0.000237055],"domain_scores_gemma":[0.9994477,0.0001750965,0.00004398685,0.0001925763,0.00009610767,0.00004450166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001015503,0.00004323903,0.001456249,0.000206557,0.00002406968,2.303549e-7,0.002331293,0.002173014,0.9719967,0.000002280722,0.000005269731,0.02165954],"study_design_scores_gemma":[0.0003281738,0.00009571092,0.03668474,0.000247438,0.00002409677,1.530379e-7,0.0005023419,0.003004788,0.9579291,0.0009910414,0.00002733625,0.0001651244],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7599684,0.0001200791,0.2384992,0.000008834371,0.00005078432,0.0005933833,0.0001379413,0.0001857414,0.0004356307],"genre_scores_gemma":[0.9846532,0.00001188795,0.01502302,0.00004012346,0.000008772855,0.0002321815,0.000007271394,0.00001725139,0.000006245285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2246849,"threshold_uncertainty_score":0.6234994,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.01078823703893809,"gpt":0.2715870794746342,"spread":0.2607988424356961,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001282344,0.0001377334,0.0001407355,0.0001010774,0.0002956948,0.00005767131,0.000596143,0.00004570258,0.000004426168],"category_scores_gemma":[0.00006017138,0.0001280945,0.00009580763,0.0006139751,0.00004677762,0.00004369863,0.00006370162,0.0002068018,0.00002312257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006133891,"about_ca_system_score_gemma":0.00006039078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005108738,"about_ca_topic_score_gemma":0.00001376328,"domain_scores_codex":[0.998849,0.00004823634,0.0001725052,0.0004513006,0.0001246303,0.0003543582],"domain_scores_gemma":[0.9990107,0.0002931205,0.00007032569,0.0005065779,0.00005070325,0.00006850823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003330112,0.00009742057,0.000132044,0.00003123581,0.00001951651,0.000002709249,0.0004594724,0.7808409,0.03656239,0.1217179,0.0008900966,0.05921302],"study_design_scores_gemma":[0.0008557681,0.0001252002,0.001054633,0.00004242162,0.00001725657,0.000003011133,0.00001977052,0.9151891,0.02687793,0.01482494,0.04068873,0.0003012517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01039676,0.00006498495,0.9831537,0.002512682,0.0002038339,0.000478898,0.000001254769,0.000341024,0.002846857],"genre_scores_gemma":[0.603365,0.000003983333,0.3948042,0.0007033878,0.0001079265,0.000216149,0.000009144461,0.00001433213,0.0007758553],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5929683,"threshold_uncertainty_score":0.5223542,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415535740","doi":"10.1016/j.array.2025.100538","title":"Mamba-caption: Long-range sequence modelling for efficient and accurate image captioning","year":2025,"lang":"en","type":"article","venue":"Array","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Moncton","funders":"University of Johannesburg","keywords":"Closed captioning; Security token; Convolutional neural network; Trigram; Embedding; Clipping (morphology); Decoding methods; Image (mathematics); Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.02984790941700368,"gpt":0.3121207670062762,"spread":0.2822728575892725,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002764828,0.0001074873,0.0001038558,0.00008956556,0.00032373,0.0001962194,0.0003421446,0.00003926375,0.00000418319],"category_scores_gemma":[0.00004564455,0.0001078303,0.00003786656,0.0002704368,0.00004862037,0.0001866668,0.0000789094,0.0001218707,0.00002542706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004117974,"about_ca_system_score_gemma":0.00004281929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001393465,"about_ca_topic_score_gemma":0.000006024338,"domain_scores_codex":[0.9990983,0.00003275898,0.0001656579,0.0003981962,0.0001003644,0.0002047241],"domain_scores_gemma":[0.9992341,0.0001680471,0.00006454988,0.0003821233,0.00009734483,0.00005388082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001575748,0.00008971311,0.00327863,0.000149325,0.0000349311,0.00000424362,0.001742318,0.730414,0.02888883,0.2156384,0.00019155,0.01955227],"study_design_scores_gemma":[0.0002305867,0.00001198734,0.003036645,0.00003865738,0.000007892429,0.000004347229,0.0000129622,0.9922223,0.0007573893,0.003129556,0.000432962,0.0001146959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09550691,0.0001101797,0.9004003,0.002583886,0.0001042435,0.0003051174,0.000004426,0.0001659034,0.0008190611],"genre_scores_gemma":[0.7688943,0.000006916083,0.2305865,0.0001843139,0.00002702429,0.0001076166,0.00000469418,0.000005722114,0.0001829032],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6733873,"threshold_uncertainty_score":0.4397193,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413363777","doi":"10.1016/j.array.2025.100472","title":"TCP DCERL+: Improving congestion control in mobile ad hoc networks","year":2025,"lang":"en","type":"article","venue":"Array","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lakehead University","funders":"","keywords":"Mobile ad hoc network; Computer network; Computer science; Network congestion","retraction":null,"screen_n_in":null,"score":{"opus":0.003495819249890806,"gpt":0.2093483220109973,"spread":0.2058525027611065,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040543,0.00015951,0.0002353132,0.0001314506,0.0001043418,0.0001365224,0.0005412924,0.0001180784,0.0000185891],"category_scores_gemma":[0.00003619038,0.000155647,0.00006843785,0.0005102744,0.00004357176,0.0002928998,0.00005282897,0.0002654544,0.0000345747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007076871,"about_ca_system_score_gemma":0.0001053302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000758272,"about_ca_topic_score_gemma":0.00009195046,"domain_scores_codex":[0.9986277,0.0001225973,0.0003002516,0.0004290202,0.0001380112,0.0003824725],"domain_scores_gemma":[0.9990569,0.0002650674,0.00008257804,0.0004472449,0.00007334985,0.00007488079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002799542,0.00004335675,0.0009472362,0.000005901176,0.0000164535,0.000009705142,0.00009640966,0.01988869,0.000371321,0.008286597,0.001014157,0.9692922],"study_design_scores_gemma":[0.003396347,0.0001495658,0.005786101,0.0001094789,0.00002296412,0.000005564567,0.00005529487,0.9179451,0.00007689099,0.001111784,0.0709886,0.000352316],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009953773,0.009836858,0.974277,0.001747334,0.001335965,0.0004703251,9.874645e-7,0.0002850565,0.002092734],"genre_scores_gemma":[0.9950753,0.0002183817,0.001625046,0.001941521,0.00009939793,0.0001780453,0.000002138402,0.000007398246,0.0008527202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9851216,"threshold_uncertainty_score":0.6347099,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7117753345","doi":"10.1016/j.array.2025.100661","title":"Multi-rate real-time simulation: Techniques, models, frameworks, and challenges","year":2025,"lang":"en","type":"article","venue":"Array","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Canada Foundation for Innovation; CMC Microsystems","keywords":"Key (lock); Work (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.1470139707843413,"gpt":0.4147768251278123,"spread":0.267762854343471,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001151502,0.0001329042,0.0002107184,0.0002246613,0.0002051337,0.0001463667,0.0003595795,0.0002500078,0.0001299141],"category_scores_gemma":[0.0004089277,0.0001085848,0.00005559438,0.0005253508,0.0000834094,0.0002790324,0.00008979644,0.0001825225,0.00007722747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002271307,"about_ca_system_score_gemma":0.0000342038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001958285,"about_ca_topic_score_gemma":0.000007405294,"domain_scores_codex":[0.998529,0.0001052111,0.0004293667,0.0004898594,0.0002865775,0.0001599448],"domain_scores_gemma":[0.9975652,0.001258932,0.0001189777,0.0007140998,0.0002750646,0.00006777153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004195587,0.0002563764,0.0008997035,0.00002512275,0.0000429707,0.000002429331,0.001083637,0.02780707,0.01198178,0.2342409,0.01032888,0.7132892],"study_design_scores_gemma":[0.0002031988,0.00002539484,0.004691547,0.00004956236,0.00001247944,7.326523e-7,0.0001592336,0.36285,0.002915988,0.4598371,0.1690294,0.00022532],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005222639,0.001192541,0.9237621,0.01158749,0.00007660007,0.0006461866,0.00001479073,0.0007346034,0.05676303],"genre_scores_gemma":[0.9021724,0.001508647,0.09008968,0.000569247,0.00004999306,0.00008293561,0.000005089441,0.00001304184,0.005508943],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8969498,"threshold_uncertainty_score":0.442796,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}