{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":17,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":17,"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","author_layer_release":"2026-06-26"},"query_hash":"a768b8615a00","filters":{"venue":"Informatica"}},"results":[{"id":"W1749276991","doi":"10.15388/informatica.2014.20","title":"Adaptive Inverse Control Using an Online Learning Algorithm for Neural Networks","year":2014,"lang":"en","type":"article","venue":"Informatica","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"European Commission","keywords":"Computer science; Artificial neural network; Inverse; Process (computing); Algorithm; Scheme (mathematics); Control (management); Identification (biology); Adaptive control; Inverse problem; Basis (linear algebra); Artificial intelligence; Mathematics","authors":[{"name":"José Luis Calvo‐Rolle","is_ca":false},{"name":"Óscar Fontenla-Romero","is_ca":true},{"name":"Beatriz Pérez‐Sánchez","is_ca":false},{"name":"Bertha Guijarro‐Berdiñas","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01569817521235307,"gpt":0.2237472464309052,"spread":0.2080490712185522,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005498625,0.0005222445,0.0005751975,0.0003517929,0.0002564533,0.0005349623,0.0008402137,0.0009155811,0.001557672],"category_scores_gemma":[0.001390148,0.0002564371,0.0003807345,0.0003418037,0.000600711,0.0006786661,0.0007159065,0.001015338,0.0003369167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949506,"about_ca_system_score_gemma":0.000548803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002273205,"about_ca_topic_score_gemma":0.001809521,"domain_scores_codex":[0.9997054,0.00007606037,0.00001412893,0.00005537285,0.0001235425,0.00002544807],"domain_scores_gemma":[0.9996916,0.0001576038,0.00003702031,0.00002793043,0.00007613937,0.000009683433],"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":[0.00009541538,0.00009916956,0.0003239271,0.0001366522,0.00005856989,0.00007791796,0.00007801767,0.6800159,0.01747929,0.02283924,0.001100145,0.2776957],"study_design_scores_gemma":[0.000008362659,0.00003246033,0.00003702793,0.000003914976,0.000004190453,0.00001742785,0.000001725961,0.9967641,0.00101538,0.001426034,0.0006843713,0.000004996187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002422213,0.0001062221,0.9964795,0.0000443052,0.00002863315,0.00001481143,0.000002872052,0.0001096886,0.0007918582],"genre_scores_gemma":[0.373438,0.0003847984,0.6202809,0.0001346551,0.00014542,0.0003557397,0.00004471927,0.00007109614,0.005144614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002273205,"threshold_uncertainty_score":0.005210936,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3120059284","doi":"10.15388/20-infor442","title":"Deep Learning Model for Cell Nuclei Segmentation and Lymphocyte Identification in Whole Slide Histology Images","year":2021,"lang":"en","type":"article","venue":"Informatica","topic":"AI in cancer detection","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Information Technology Research Centre","keywords":"Artificial intelligence; Segmentation; Computer science; Pattern recognition (psychology); Perceptron; H&E stain; Deep learning; Ground truth; Pathology; Artificial neural network; Medicine","authors":[{"name":"Elzbieta Budginaitė","is_ca":false},{"name":"Mindaugas Morkūnas","is_ca":false},{"name":"Arvydas Laurinavičius","is_ca":false},{"name":"Povilas Treigys","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01067478874189173,"gpt":0.2412726812827887,"spread":0.230597892540897,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004685299,0.0006387788,0.0005043444,0.0004887214,0.0002155937,0.0006638615,0.001189581,0.001081186,0.002218178],"category_scores_gemma":[0.0007461598,0.0003955686,0.0008475485,0.0004503334,0.000318349,0.0005578503,0.0005041567,0.001090709,0.000854923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001181699,"about_ca_system_score_gemma":0.001112382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01490755,"about_ca_topic_score_gemma":0.01550296,"domain_scores_codex":[0.9998363,0.00002135455,0.000008432325,0.00005755072,0.00003902107,0.00003735024],"domain_scores_gemma":[0.9998216,0.00005335809,0.00002003193,0.00001572231,0.00007717626,0.00001209332],"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":[0.0002201252,0.0001191402,0.001744862,0.00008704174,0.00006498337,0.0001321368,0.00005785305,0.7915853,0.02173947,0.002950591,0.003605659,0.1776929],"study_design_scores_gemma":[0.000001979939,0.000008980903,0.0001388142,0.000002440984,0.000003143386,0.000007007574,0.000002230281,0.9976034,0.001573579,0.0004618225,0.0001942753,0.000002275159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08645009,0.0007270071,0.9058998,0.000525903,0.00009679239,0.00007411661,0.0005940207,0.003226655,0.002405653],"genre_scores_gemma":[0.7712077,0.0006084673,0.2091188,0.0003907729,0.00007840735,0.0002604705,0.001782609,0.0002116896,0.016341],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01490755,"threshold_uncertainty_score":0.02964151,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4381985213","doi":"10.31449/inf.v47i5.4361","title":"AI for Improving Justice Delivery: International Scenario, Potential Applications & Way Forward for India","year":2023,"lang":"en","type":"article","venue":"Informatica","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Software deployment; Context (archaeology); Economic Justice; Information and Communications Technology; Work (physics); Public domain; Productivity; China; Identification (biology); Political science; Business; Public relations; Economic growth; Engineering; Geography; Economics; Law","authors":[{"name":"Sunil Kumar Srivastava","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03877712204168753,"gpt":0.3684416431939235,"spread":0.3296645211522359,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002180615,0.0003763651,0.0002778643,0.002826694,0.002089058,0.006701152,0.001201575,0.002024785,0.00764025],"category_scores_gemma":[0.003126949,0.0001675723,0.0005609671,0.006128169,0.00179371,0.003838308,0.002192781,0.002287798,0.001732531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004201997,"about_ca_system_score_gemma":0.01073795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01178586,"about_ca_topic_score_gemma":0.01996679,"domain_scores_codex":[0.9983936,0.0005808067,0.0001046014,0.000113517,0.0004535902,0.0003538468],"domain_scores_gemma":[0.9955068,0.001868378,0.0003155289,0.0001524053,0.001454964,0.0007019255],"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":[0.0001253187,0.0004783207,0.009932657,0.00762408,0.00006668556,0.003202286,0.006694886,0.001956191,0.005479957,0.1838376,0.09300371,0.6875983],"study_design_scores_gemma":[0.00001680323,0.0003257598,0.02172663,0.006262763,0.00009247245,0.00173125,0.03237237,0.002971109,0.003448333,0.03344444,0.8975039,0.0001041009],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07148588,0.2485689,0.01171099,0.3329819,0.003142597,0.0003408204,0.001025223,0.000761432,0.3299824],"genre_scores_gemma":[0.6150335,0.3045213,0.02840547,0.01838987,0.0009726584,0.0001813029,0.0008819286,0.00009836607,0.03151561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01178586,"threshold_uncertainty_score":0.03048772,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392588996","doi":"10.31449/inf.v48i6.5234","title":"Machine Learning Algorithms for Transportation Mode Prediction: A Comparative Analysis","year":2024,"lang":"en","type":"article","venue":"Informatica","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"Applied Science Private University","keywords":"Computer science; Mode (computer interface); Machine learning; Algorithm; Artificial intelligence; Human–computer interaction","authors":[{"name":"Samer Murrar","is_ca":false},{"name":"Fatima Alhaj","is_ca":false},{"name":"Mahmoud H. Qutqut","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01641271836811846,"gpt":0.2679279128554686,"spread":0.2515151944873501,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01154882,0.001634009,0.001350157,0.008019296,0.000575716,0.002130904,0.001257636,0.00149662,0.001200588],"category_scores_gemma":[0.02932903,0.000314954,0.001375639,0.005834023,0.0003849091,0.003560746,0.0009321425,0.001384015,0.0005290567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456308,"about_ca_system_score_gemma":0.0009905749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007915165,"about_ca_topic_score_gemma":0.00472415,"domain_scores_codex":[0.9928393,0.002652518,0.0007902348,0.001105681,0.002352396,0.0002596813],"domain_scores_gemma":[0.9665001,0.02709358,0.0008396997,0.001540562,0.003794858,0.0002311798],"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":[0.000718711,0.0004372023,0.06093589,0.001342828,0.001505299,0.0001656586,0.0002274415,0.2118959,0.0009089951,0.007235281,0.007471381,0.7071553],"study_design_scores_gemma":[0.00004162521,0.0005714879,0.02504772,0.0004257237,0.0004371228,0.000271881,0.0003485148,0.9552655,0.002633426,0.006486809,0.008394358,0.00007578841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4722535,0.1290329,0.3586796,0.004085308,0.001176363,0.0004446154,0.003922754,0.003632555,0.02677242],"genre_scores_gemma":[0.8437978,0.01761191,0.1325848,0.0002738079,0.0002992669,0.0001453074,0.003711266,0.0001999086,0.001376044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01154882,"threshold_uncertainty_score":0.0610767,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406800622","doi":"10.31449/inf.v48i4.5977","title":"Exploring the Power of Dual Deep Learning for Fake News Detection","year":2025,"lang":"en","type":"article","venue":"Informatica","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Dual (grammatical number); Fake news; Power (physics); Computer science; Deep learning; Artificial intelligence; Internet privacy; Art; Physics; Literature","authors":[{"name":"Hounaïda Moalla","is_ca":false},{"name":"Dorsaf Sallami","is_ca":true},{"name":"Esma Aı̈meur","is_ca":true},{"name":"Bassem Ben Hamed","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0617939585889917,"gpt":0.3174265457077373,"spread":0.2556325871187456,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002361853,0.0007966813,0.0005149367,0.001047345,0.0003268216,0.00128975,0.0008778467,0.0009254294,0.001042373],"category_scores_gemma":[0.007818811,0.0002999013,0.00042005,0.0004278735,0.0006319288,0.002220558,0.001270613,0.001499832,0.0003388817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008059522,"about_ca_system_score_gemma":0.0006932922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00319254,"about_ca_topic_score_gemma":0.002855245,"domain_scores_codex":[0.9994658,0.0001960095,0.00002717602,0.0001209042,0.0001039723,0.00008612251],"domain_scores_gemma":[0.996406,0.002291903,0.0003218742,0.0003915589,0.0004021429,0.0001864381],"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":[0.001615938,0.001069386,0.05555099,0.0002129924,0.0003154026,0.0003254075,0.0004870426,0.3933724,0.0107017,0.01095858,0.003823132,0.5215669],"study_design_scores_gemma":[0.00001012273,0.000075426,0.000949552,0.00000737754,0.00001300521,0.0000245929,0.00003472381,0.9950653,0.001162819,0.002442727,0.0002085715,0.000005735653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6988866,0.001162001,0.2875701,0.001962215,0.0001090458,0.0001075073,0.0003042542,0.001267436,0.008630698],"genre_scores_gemma":[0.9798756,0.0001208673,0.018068,0.0001364212,0.00002705044,0.00001865255,0.000202845,0.00001490965,0.001535578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00319254,"threshold_uncertainty_score":0.01249081,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2135603567","doi":"10.15388/informatica.2011.343","title":"D-Optimal Designs: A Mathematical Programming Approach Using Cyclotomic Cosets","year":2011,"lang":"en","type":"article","venue":"Informatica","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Coset; Computer science; Mathematical theory; Order (exchange); Optimal design; Mathematical optimization; Binary number; Mathematics; Algorithm; Discrete mathematics; Arithmetic; Machine learning","authors":[{"name":"Mujahid N. Syed","is_ca":false},{"name":"Ilias Kotsireas","is_ca":true},{"name":"Pãnos M. Pardalos","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1000637344434517,"gpt":0.2711222823239481,"spread":0.1710585478804964,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002117254,0.001370528,0.001112731,0.001866807,0.0005892288,0.001715136,0.00105841,0.001092882,0.007105035],"category_scores_gemma":[0.00470543,0.0007278767,0.001233234,0.00192532,0.001716875,0.002367538,0.00129341,0.002211114,0.0007115439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218206,"about_ca_system_score_gemma":0.001352957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005486212,"about_ca_topic_score_gemma":0.0007228794,"domain_scores_codex":[0.999043,0.0004530761,0.00006187316,0.0001566095,0.0002340922,0.00005121107],"domain_scores_gemma":[0.9982466,0.001302652,0.0001546436,0.0001341077,0.0001238441,0.00003809721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004717442,0.00006565147,0.000214711,0.000266587,0.0000343742,0.00006213286,0.00006175478,0.2591796,0.001315028,0.6818444,0.002335781,0.05457278],"study_design_scores_gemma":[0.00004599155,0.0001000625,0.00006685511,0.0000701385,0.0000204495,0.0001058079,0.00004046461,0.557797,0.001331505,0.4312527,0.009144602,0.00002447608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002602863,0.0002426781,0.9912407,0.0002413395,0.00004399308,0.00004460114,0.00002910106,0.00002864189,0.005526134],"genre_scores_gemma":[0.1015006,0.001216881,0.8924423,0.0003452583,0.0001558685,0.0004488123,0.00009965382,0.00007912906,0.003711395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007105035,"threshold_uncertainty_score":0.02376866,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4280580087","doi":"10.31449/inf.v46i1.3015","title":"Automatic Fabric Inspection using GLCM-based Jensen-Shannon Divergence","year":2022,"lang":"en","type":"article","venue":"Informatica","topic":"Industrial Vision Systems and Defect Detection","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":"Horizon College and Seminary","funders":"Hong Kong Baptist University","keywords":"Divergence (linguistics); Computer science; Cluster analysis; Mutual information; Computation; Block (permutation group theory); Artificial intelligence; Kullback–Leibler divergence; Entropy (arrow of time); Matrix (chemical analysis); Hierarchical clustering; Pattern recognition (psychology); Algorithm; Computer vision; Mathematics; Combinatorics","authors":[{"name":"V Asha","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02137099783015454,"gpt":0.2199721732438528,"spread":0.1986011754136982,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001131366,0.0006881616,0.00099898,0.002295662,0.0003800242,0.0009766021,0.000796226,0.0006445349,0.0008340461],"category_scores_gemma":[0.003861279,0.0002935074,0.0008684116,0.001276216,0.0006506497,0.001180355,0.0009210334,0.000863723,0.0002934719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007900022,"about_ca_system_score_gemma":0.001005316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003651241,"about_ca_topic_score_gemma":0.003193225,"domain_scores_codex":[0.9987429,0.0001967317,0.00007032389,0.000193403,0.0006881066,0.0001085023],"domain_scores_gemma":[0.9982869,0.0007344101,0.0002675692,0.0001215482,0.0005146834,0.00007494269],"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":[0.0004189709,0.0001593398,0.004004914,0.0004638143,0.000166719,0.0004693626,0.0003284165,0.3137341,0.1295831,0.01107497,0.002906635,0.5366896],"study_design_scores_gemma":[0.000006463935,0.00004040064,0.001606469,0.000008222177,0.000009130139,0.0001344885,0.00002110725,0.9878181,0.007046352,0.002922595,0.0003636529,0.00002299949],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02461027,0.0001899518,0.9737833,0.00006874208,0.00001518538,0.00002891809,0.0000484602,0.0006008829,0.0006544188],"genre_scores_gemma":[0.5213918,0.0002820034,0.4768505,0.00008131479,0.00003359501,0.00008066236,0.0002757656,0.0001288846,0.0008753808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003651241,"threshold_uncertainty_score":0.007259965,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313495625","doi":"10.31449/inf.v46i4.3476","title":"OMCOKE: A Machine Learning Outlier-based Overlapping Clustering Technique for Multi-Label Data Analysis","year":2022,"lang":"en","type":"article","venue":"Informatica","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Fanshawe College","funders":"","keywords":"Cluster analysis; Outlier; Computer science; Anomaly detection; Data mining; Artificial intelligence; Unsupervised learning; Cluster (spacecraft); CURE data clustering algorithm; Machine learning; Pattern recognition (psychology); Correlation clustering","authors":[{"name":"Said Baadel","is_ca":true},{"name":"Fadi Thabtah","is_ca":false},{"name":"Joan Lu","is_ca":false},{"name":"Saida Harguem","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09572787538383581,"gpt":0.3254020660487998,"spread":0.229674190664964,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003666594,0.001700017,0.002212404,0.005173019,0.002474859,0.00203149,0.003123415,0.002310688,0.001463204],"category_scores_gemma":[0.01387932,0.0006111047,0.002288383,0.006134634,0.001356732,0.002792394,0.003123215,0.003194888,0.00126299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001308742,"about_ca_system_score_gemma":0.002433878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006416671,"about_ca_topic_score_gemma":0.00968244,"domain_scores_codex":[0.9950359,0.001055245,0.0004063966,0.001290105,0.00187504,0.0003373408],"domain_scores_gemma":[0.9935296,0.002071674,0.001015821,0.001213485,0.001962703,0.0002066495],"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":[0.0008094424,0.0005270476,0.01057865,0.0007358391,0.0008681375,0.000560323,0.001607437,0.1368389,0.01702804,0.01792519,0.01694467,0.7955762],"study_design_scores_gemma":[0.00006020974,0.0001294737,0.003223891,0.00008700452,0.00008904246,0.0004729251,0.0004503583,0.9419409,0.01299537,0.02686146,0.01356629,0.0001231273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006718704,0.0002461928,0.9897476,0.0001292226,0.00007155966,0.0001418813,0.0002104315,0.002277392,0.0004570169],"genre_scores_gemma":[0.1052391,0.0002115221,0.8908994,0.0001430278,0.00008341104,0.0003953573,0.001233596,0.0004845087,0.001310198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006416671,"threshold_uncertainty_score":0.01939106,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411959463","doi":"10.31449/inf.v49i23.8270","title":"Adaptive Strategy-Enhanced NSGA-II for Multi-Objective Optimization with Improved Convergence and Diversity Control","year":2025,"lang":"en","type":"article","venue":"Informatica","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Convergence (economics); Diversity (politics); Mathematical optimization; Control (management); Computer science; Mathematics; Artificial intelligence; Economics; Political science; Economic growth","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02154016471860877,"gpt":0.2727647767679153,"spread":0.2512246120493065,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00118457,0.001174275,0.0009043416,0.000806578,0.0005108789,0.0007083504,0.001188355,0.0007679957,0.001318907],"category_scores_gemma":[0.001490874,0.0004316542,0.0008861784,0.0007529911,0.0006258705,0.000469942,0.0008302017,0.001100151,0.0002361468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009161164,"about_ca_system_score_gemma":0.001663238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01319467,"about_ca_topic_score_gemma":0.01057178,"domain_scores_codex":[0.9992996,0.0002452744,0.00003960174,0.00007283903,0.0002626744,0.00007999299],"domain_scores_gemma":[0.9996064,0.0001524203,0.00005028676,0.00002524283,0.0001395265,0.00002606077],"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":[0.00003074194,0.00002789386,0.0003846888,0.00004978782,0.0000388636,0.00004621276,0.00004893694,0.9759006,0.001585321,0.002734176,0.000520554,0.01863239],"study_design_scores_gemma":[0.00001421915,0.00003297662,0.0001231131,0.000005589708,0.000007087785,0.000009662377,0.000005382901,0.9983307,0.0003016235,0.0006222461,0.0005422719,0.0000051807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05582045,0.000873831,0.9340104,0.0002368462,0.000112331,0.0001566957,0.00004914327,0.0005243169,0.008215999],"genre_scores_gemma":[0.8153794,0.0005546991,0.17779,0.0001894953,0.00004002229,0.0006452894,0.0002041899,0.00007408892,0.005122809],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01319467,"threshold_uncertainty_score":0.02623576,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407137171","doi":"10.31449/inf.v49i6.6642","title":"U-YOLOv3: A Model Focused on Underwater Object Detection","year":2025,"lang":"en","type":"article","venue":"Informatica","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Canadian Institute for Advanced Research; University of Minnesota","keywords":"Underwater; Object (grammar); Artificial intelligence; Computer science; Computer vision; Geology; Remote sensing; Oceanography","authors":[{"name":"Pratima Sarkar","is_ca":false},{"name":"Sourav De","is_ca":false},{"name":"Sandeep Gurung","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02210234791826161,"gpt":0.2536538662623501,"spread":0.2315515183440885,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004007108,0.0007028288,0.0006949539,0.0007117257,0.0003844575,0.0009993921,0.002398951,0.001144391,0.002502823],"category_scores_gemma":[0.0008065043,0.0003987263,0.001091417,0.0004971997,0.0006091729,0.001112173,0.0009693843,0.0008276374,0.001240864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214597,"about_ca_system_score_gemma":0.001192706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01699018,"about_ca_topic_score_gemma":0.01216544,"domain_scores_codex":[0.9996977,0.00003016173,0.00001054051,0.0001137854,0.0001044506,0.00004345659],"domain_scores_gemma":[0.9997626,0.00004637131,0.00003231946,0.00003098869,0.0001070968,0.00002063811],"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":[0.0006718413,0.0001716449,0.007430094,0.0003589727,0.0001497777,0.0004563624,0.0002507564,0.5334143,0.06335762,0.03315733,0.008748526,0.3518327],"study_design_scores_gemma":[0.00001016102,0.00007295729,0.0005903153,0.000009403989,0.00001441155,0.0001046901,0.00001598192,0.9882002,0.005284086,0.00212312,0.003560694,0.00001390422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0249614,0.0004824945,0.9656497,0.000304346,0.0001327126,0.0001060251,0.0002713557,0.002025378,0.006066581],"genre_scores_gemma":[0.553301,0.001161368,0.4129658,0.0005421768,0.0001186214,0.0003533234,0.001408626,0.0003943709,0.0297547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01699018,"threshold_uncertainty_score":0.0337826,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386415715","doi":"10.31449/inf.v47i8.4767","title":"LP SVM with A Novel Similarity function Outperforms Powerful LP-QP-Kernel-SVM Considering Efficient Classification","year":2023,"lang":"en","type":"article","venue":"Informatica","topic":"Neural Networks and 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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Radial basis function kernel; Artificial intelligence; Support vector machine; Kernel (algebra); Classifier (UML); Similarity (geometry); Pattern recognition (psychology); Kernel method; Computer science; Polynomial kernel; Tree kernel; Machine learning; Generalization; Mathematics; Algorithm; Discrete mathematics","authors":[{"name":"Rezaul Karim","is_ca":false},{"name":"Mahmudul Hasan","is_ca":true},{"name":"Amit Kumar Kundu","is_ca":false},{"name":"Ali Ahmed Ave","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03161360970452603,"gpt":0.2444218017831957,"spread":0.2128081920786696,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00150902,0.0006695039,0.001008907,0.0006518696,0.0003436506,0.001330396,0.0009847999,0.001081218,0.002685515],"category_scores_gemma":[0.003753031,0.0001399085,0.0004863701,0.0005836288,0.0004099364,0.001854323,0.0009155453,0.001205837,0.001111954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005548206,"about_ca_system_score_gemma":0.0007585171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001438311,"about_ca_topic_score_gemma":0.0009178027,"domain_scores_codex":[0.9986873,0.0002945658,0.0001197037,0.0002802392,0.0004835297,0.0001347664],"domain_scores_gemma":[0.9983907,0.0004790887,0.0001581378,0.0003052564,0.0005540877,0.0001127298],"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":[0.001641667,0.0009123079,0.007353717,0.0005201112,0.0002089495,0.0001917521,0.00007214479,0.2940874,0.03268334,0.008311382,0.01403369,0.6399837],"study_design_scores_gemma":[0.00001181425,0.000167417,0.0009347422,0.000005705769,0.00001579936,0.00003570387,0.0000122603,0.9932642,0.003787947,0.001071481,0.0006871162,0.000005835571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3655233,0.003540835,0.6124512,0.00103693,0.0005393179,0.0001459228,0.0002971794,0.005249033,0.01121643],"genre_scores_gemma":[0.9595274,0.0002084466,0.03707964,0.0001120255,0.0001143655,0.00003280025,0.0005200197,0.0001023608,0.00230291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002685515,"threshold_uncertainty_score":0.00898397,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386174621","doi":"10.15388/23-infor524","title":"Quasi-Owen Value for Games on Augmenting Systems with a Coalition Structure","year":2023,"lang":"en","type":"article","venue":"Informatica","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institute on Governance","funders":"Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology; Nanjing University; Nanjing University of Information Science and Technology; National Natural Science Foundation of China","keywords":"Core (optical fiber); Axiom; Stochastic game; Extension (predicate logic); Mathematical economics; Monotonic function; Value (mathematics); Computer science; Scheme (mathematics); Rationality; Axiomatic system; Mathematics","authors":[{"name":"Jie Tang","is_ca":true},{"name":"Zijun Li","is_ca":false},{"name":"Fanyong Meng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03020988972453831,"gpt":0.2264724172038449,"spread":0.1962625274793066,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003070806,0.0005926123,0.0005298036,0.001448025,0.0009597282,0.002957997,0.000798038,0.001056668,0.005734758],"category_scores_gemma":[0.007730837,0.0002579774,0.000796676,0.001121677,0.003464176,0.00757088,0.002097052,0.002299892,0.0005255826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001719447,"about_ca_system_score_gemma":0.0009200366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006661561,"about_ca_topic_score_gemma":0.0006683701,"domain_scores_codex":[0.9979925,0.001015585,0.0001456986,0.0002833352,0.0003847222,0.0001781146],"domain_scores_gemma":[0.9969284,0.001871558,0.0003525392,0.0003682029,0.0003135889,0.0001657771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00000474662,0.000004334382,0.00006393055,0.00001339056,0.000002832558,0.000007096373,0.00003786193,0.00215032,0.0001516441,0.9947855,0.000143314,0.002634886],"study_design_scores_gemma":[0.000004116024,0.00001733248,0.0001080901,0.00001639295,0.000003306142,0.0000222437,0.00003069914,0.02050819,0.0002156097,0.9765003,0.002566318,0.000007469011],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07188387,0.0006581703,0.8645594,0.0009208221,0.0001448472,0.0001282981,0.0001893837,0.00006614786,0.06144909],"genre_scores_gemma":[0.8060225,0.0009125329,0.1761673,0.0003582897,0.0002009973,0.0004544203,0.0003239778,0.00006392479,0.01549598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005734758,"threshold_uncertainty_score":0.01918471,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415548626","doi":"10.31449/inf.v49i6.7534","title":"A Hybrid Deep Learning Approach for Analyzing and Detecting the Malware in Software Defined Networks","year":2025,"lang":"","type":"article","venue":"Informatica","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Malware; Deep learning; Convolutional neural network; Perceptron; Artificial neural network; Feature (linguistics); Feature extraction; Software","authors":[{"name":"Vasantharaj Karunakaran","is_ca":false},{"name":"Angelina Geetha","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00899564199833745,"gpt":0.2428628945178413,"spread":0.2338672525195038,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005645388,0.0008961068,0.0003768175,0.001210426,0.0002788239,0.0006089404,0.0007999525,0.0006755595,0.0006138292],"category_scores_gemma":[0.001051924,0.0002795348,0.0004627471,0.0006053945,0.000358369,0.001284511,0.0008296469,0.0008746281,0.0001960329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008828877,"about_ca_system_score_gemma":0.0007555629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005729562,"about_ca_topic_score_gemma":0.009634394,"domain_scores_codex":[0.9996911,0.00005469852,0.00001832166,0.00007730569,0.00009911633,0.00005935107],"domain_scores_gemma":[0.9996359,0.0001342238,0.00004483007,0.0000336194,0.0001261261,0.00002532711],"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":[0.0002406346,0.0003479526,0.006916436,0.0001301704,0.0001809115,0.0002023158,0.0001193019,0.5090764,0.02133406,0.00520144,0.00231276,0.4539376],"study_design_scores_gemma":[0.000001702192,0.00002403839,0.0002708464,0.000003898515,0.000006851124,0.00001609293,0.00000726435,0.9961758,0.00216724,0.001098083,0.0002246366,0.000003610537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1087061,0.0008816895,0.8853487,0.0004610508,0.00007580344,0.000067399,0.0002251957,0.001861674,0.002372386],"genre_scores_gemma":[0.8333114,0.0004499804,0.1611838,0.0002741144,0.00004596836,0.00007716694,0.0004124232,0.00004506064,0.00420003],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005729562,"threshold_uncertainty_score":0.01139241,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7116631028","doi":"10.31449/inf.v49i28.9994","title":"Research on HSE Risk Assessment Method for Multi-source Heterogeneous Data Driven by Transformer-FL Framework","year":2025,"lang":"","type":"article","venue":"Informatica","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Anhui Provincial Department of Education","keywords":"Risk assessment; Residual; Context (archaeology); Data-driven; Workstation; Data integration; Risk management","authors":[{"name":"Wenlei Sun","is_ca":true},{"name":"Min Yang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2381653448924984,"gpt":0.5550843463709467,"spread":0.3169190014784483,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003501175,0.001563532,0.001279646,0.003213132,0.000517234,0.00189499,0.00221977,0.001187984,0.001791364],"category_scores_gemma":[0.008472094,0.0004389363,0.002000527,0.001918119,0.0008513425,0.004440168,0.002403717,0.00207186,0.0004711207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294728,"about_ca_system_score_gemma":0.001882928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008262481,"about_ca_topic_score_gemma":0.006036695,"domain_scores_codex":[0.9972317,0.0006085414,0.0002408708,0.0007259117,0.0009569867,0.000236027],"domain_scores_gemma":[0.9968509,0.001264614,0.000332231,0.0004223333,0.0009894323,0.0001404651],"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":[0.0002138277,0.0002038911,0.01491566,0.0004013275,0.000446186,0.0004638763,0.0003094923,0.3858773,0.008533316,0.01801074,0.006013478,0.5646108],"study_design_scores_gemma":[0.000009773165,0.00005077929,0.001485222,0.00003141196,0.00005237526,0.0001984254,0.00008582447,0.9801371,0.003384582,0.01276987,0.001766052,0.0000286031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01121651,0.0008377949,0.9854232,0.0003927158,0.00004817826,0.00006293332,0.0001976592,0.0008703804,0.0009505812],"genre_scores_gemma":[0.5818654,0.001688224,0.4081412,0.0006140294,0.0002417302,0.000212778,0.002206421,0.0002697308,0.004760535],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008262481,"threshold_uncertainty_score":0.01851624,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4416571004","doi":"10.31449/inf.v49i12.8933","title":"Models And Methods of Analysing Infrastructure Performance in Cloud Environments Based on Process Optimisation Methods","year":2025,"lang":"","type":"article","venue":"Informatica","topic":"Cybersecurity and Information Systems","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Cloud computing; Flexibility (engineering); Downtime; Process (computing); Scalability; Automation; Artificial neural network; Resource (disambiguation)","authors":[{"name":"Pavlo Kudrynskyi","is_ca":false},{"name":"O. S. Zvenihorodskyi","is_ca":true},{"name":"Y. Bai","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01804675014440299,"gpt":0.3352531296097221,"spread":0.3172063794653192,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001599505,0.001481451,0.0009995222,0.00140692,0.0004618359,0.002201791,0.001559962,0.001478478,0.001473876],"category_scores_gemma":[0.003885477,0.0005858408,0.001653678,0.00183591,0.001047895,0.001744514,0.001038143,0.00162745,0.0003695734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001534901,"about_ca_system_score_gemma":0.001612145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01126049,"about_ca_topic_score_gemma":0.00423386,"domain_scores_codex":[0.9988841,0.0004155707,0.00005846588,0.0001658935,0.0003714721,0.0001043817],"domain_scores_gemma":[0.9984183,0.001053174,0.0001965709,0.0001039089,0.0001968723,0.00003109108],"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":[0.00001191682,0.00002160902,0.0003349221,0.00006360531,0.00003116596,0.00002331884,0.00003333358,0.9773006,0.0004380612,0.01344516,0.0001738218,0.008122461],"study_design_scores_gemma":[0.000003859102,0.00001234998,0.0001139771,0.00001104892,0.000006360327,0.000008921273,0.000007944406,0.9926009,0.0002403071,0.006341464,0.0006458809,0.000007105127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008003082,0.0007766931,0.9846523,0.0002330681,0.00006582677,0.0001082973,0.0001121316,0.0002007388,0.005848036],"genre_scores_gemma":[0.703302,0.004252715,0.2826503,0.0001491376,0.0001884209,0.0008465546,0.0004120486,0.0001973814,0.008001466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01126049,"threshold_uncertainty_score":0.02238989,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1690786369","doi":"10.15388/informatica.2005.096","title":"Transcoding Aspects for Image Tele-Collaboration","year":2005,"lang":"en","type":"article","venue":"Informatica","topic":"Distributed and Parallel Computing Systems","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":"Transcoding; Computer science; Grid; Multimedia; World Wide Web; Human–computer interaction; Computer network","authors":[{"name":"Sabah Mohammed","is_ca":true},{"name":"Jinan Fiaidhi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01073778383398274,"gpt":0.256415065618308,"spread":0.2456772817843253,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006770564,0.0005356235,0.0003557241,0.0005906828,0.0005105729,0.002166342,0.0007370362,0.0008450348,0.007118429],"category_scores_gemma":[0.003929343,0.0002150884,0.0005199963,0.0006873669,0.001139054,0.003061079,0.001239737,0.001199278,0.001099208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004572534,"about_ca_system_score_gemma":0.0002946997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008533107,"about_ca_topic_score_gemma":0.0007574502,"domain_scores_codex":[0.9995024,0.000136877,0.0000316539,0.00007318305,0.0002048664,0.00005103897],"domain_scores_gemma":[0.9982311,0.000647893,0.00007457234,0.0005058583,0.0004738231,0.00006666173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004162042,0.0001099816,0.0006912185,0.0004602063,0.00005510362,0.001740131,0.002321805,0.01953634,0.1915001,0.40243,0.00514476,0.3755942],"study_design_scores_gemma":[0.00007189823,0.0002635641,0.001750186,0.0001388622,0.0001326195,0.00714061,0.001987745,0.3212421,0.2001228,0.3453399,0.1216717,0.000138001],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02521894,0.0008056245,0.9495797,0.0005128825,0.0001929185,0.0000880654,0.00007364106,0.0008262306,0.02270195],"genre_scores_gemma":[0.5345153,0.001709903,0.4486598,0.0002553927,0.0002479485,0.0001167979,0.0002609736,0.0004668262,0.01376699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007118429,"threshold_uncertainty_score":0.02381355,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417443697","doi":"10.31449/inf.v49i22.10269","title":"Dynamic Heterogeneous Graph Neural Network with Carbon-Sensitive Dual Attention for Lifecycle Carbon Footprint Assessment of Engineering Projects","year":2025,"lang":"","type":"article","venue":"Informatica","topic":"Environmental Impact and Sustainability","field":"Environmental 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":"Emissions Reduction Alberta; Transport Canada","funders":"","keywords":"Adaptability; Graph; Artificial neural network; Carbon footprint; Node (physics); System lifecycle; Inference; Dual (grammatical number)","authors":[{"name":"Xianjun Wu","is_ca":false},{"name":"Q. Liu","is_ca":false},{"name":"Jianming Xu","is_ca":true},{"name":"Shi Cheng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.004675114388830819,"gpt":0.2336901267083081,"spread":0.2290150123194773,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006301606,0.001157952,0.0006813886,0.0008293442,0.0003198372,0.0006772808,0.001162752,0.0009384191,0.001028389],"category_scores_gemma":[0.001989408,0.0003036168,0.0008223057,0.0008296469,0.0003045419,0.001199163,0.0006246914,0.001152695,0.0002535114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001052878,"about_ca_system_score_gemma":0.0007244932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02184832,"about_ca_topic_score_gemma":0.02018392,"domain_scores_codex":[0.9996964,0.00006766721,0.00001595055,0.0001174401,0.00004174709,0.00006085593],"domain_scores_gemma":[0.9995533,0.0002090827,0.00005023002,0.00003901784,0.0001229114,0.00002537389],"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":[0.0002922272,0.0002149012,0.006308379,0.00008827748,0.0001725051,0.00009701489,0.00004562904,0.7919486,0.003358094,0.001412783,0.002979293,0.1930823],"study_design_scores_gemma":[0.000004803783,0.00002406438,0.0006453446,0.000003565316,0.00001981607,0.000007517201,0.000006776028,0.9976256,0.000498636,0.00101765,0.0001415519,0.000004682316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4138366,0.003721275,0.5693508,0.001526127,0.0004398924,0.0001216819,0.00121056,0.003770784,0.006022237],"genre_scores_gemma":[0.9706283,0.0003712613,0.02543452,0.0002194394,0.00007120729,0.00005746899,0.0009881003,0.00004553053,0.002184374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02184832,"threshold_uncertainty_score":0.04344231,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}