{"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"},"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001525727,0.0001452994,0.0002196938,0.00006477632,0.0000924136,0.00004196156,0.0000945272,0.00008083783,0.000004566948],"category_scores_gemma":[0.00006057746,0.0001463487,0.0000458086,0.00008434864,0.0000181422,0.0007994398,0.00001039649,0.0001609909,0.000004149241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006188132,"about_ca_system_score_gemma":0.00000522832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005542037,"about_ca_topic_score_gemma":0.000006404794,"domain_scores_codex":[0.999188,0.00003057392,0.0003434493,0.00007207193,0.00009168261,0.0002741879],"domain_scores_gemma":[0.9994894,0.0001145196,0.00008803302,0.0001367932,0.00008212399,0.00008911778],"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":[0.000007545291,0.000004607462,0.000009867006,0.00001422138,0.00001908267,1.586809e-7,0.0001837205,0.9003004,0.00001611249,0.00005770118,0.00001377393,0.09937277],"study_design_scores_gemma":[0.001222384,0.0001030627,0.00001645354,0.00002049491,0.00002462831,0.000005707963,0.0002220837,0.997406,0.000005036663,0.00003977162,0.000765876,0.0001684876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003686053,0.00001955451,0.9950251,0.000005607288,0.0002366265,0.0003851244,0.00001444725,0.0003328495,0.0002946644],"genre_scores_gemma":[0.8335677,0.000001918334,0.1658542,0.0001283054,0.0003043224,0.00003097728,0.00006318025,0.00003656934,0.00001281821],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8298816,"threshold_uncertainty_score":0.5967926,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001931951,0.00006653175,0.00009008434,0.00009248136,0.0001088657,0.0001288539,0.0001339959,0.00004594345,0.000002724805],"category_scores_gemma":[0.00005060925,0.00007613145,0.00002025493,0.000153295,0.00002206338,0.0008891735,0.00007981934,0.00006759851,0.00002351731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001997837,"about_ca_system_score_gemma":0.00006744918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007642888,"about_ca_topic_score_gemma":0.00001786657,"domain_scores_codex":[0.9993001,0.00002677834,0.0002894136,0.0001409097,0.0001021674,0.0001406894],"domain_scores_gemma":[0.9995244,0.0000684538,0.0001317697,0.0001694853,0.00007687535,0.00002908619],"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":[0.00004197255,0.0000734145,0.0005588438,0.0005591178,0.00001691526,0.000006146968,0.03749839,0.2093944,0.06353062,0.003152924,0.0006581429,0.6845091],"study_design_scores_gemma":[0.0004644602,0.00002175539,0.0002978694,0.00000917083,0.00000404104,0.00001246437,0.0003515895,0.9782315,0.01748269,0.001916997,0.001126658,0.00008074968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01427908,0.000163453,0.9841909,0.0003201327,0.0001305663,0.0001442501,0.000001083087,0.00004701805,0.0007235213],"genre_scores_gemma":[0.8402659,0.00007361,0.1586497,0.000340633,0.00001483111,0.00009205955,0.00001322596,0.00000764263,0.0005424385],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8259868,"threshold_uncertainty_score":0.3104551,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008973619,0.0001060518,0.0001359203,0.0001658692,0.001061896,0.0002681477,0.0005527697,0.0001263859,0.000156625],"category_scores_gemma":[0.000802782,0.0001103171,0.0001397431,0.0003262102,0.000209679,0.00081491,0.00009521354,0.0001034113,0.0008476929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001335214,"about_ca_system_score_gemma":0.0002247892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002380527,"about_ca_topic_score_gemma":0.000442058,"domain_scores_codex":[0.9985537,0.00001817914,0.0004611663,0.00014768,0.0003677865,0.0004514679],"domain_scores_gemma":[0.9985816,0.0004904008,0.0001676174,0.0001824603,0.0004545976,0.0001233533],"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":[0.0001117683,0.00007628335,0.00009199429,0.0003234487,0.00009300915,6.420796e-7,0.02942793,0.00150503,0.000360672,0.6143736,0.0658575,0.2877781],"study_design_scores_gemma":[0.0001747265,0.00002886905,0.00003928775,0.00001493461,0.00007663878,7.304707e-7,0.007613777,0.0395797,0.0002508832,0.0239844,0.9280328,0.0002032138],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006153481,0.0000118877,0.9536228,0.008291779,0.002105728,0.003216907,0.0003243685,0.0005255461,0.0257475],"genre_scores_gemma":[0.9558033,0.00004601611,0.03169208,0.002760609,0.002040894,0.002581233,0.0004586416,0.00003897166,0.004578236],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9496498,"threshold_uncertainty_score":0.9999303,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007684401,0.00008473296,0.0001228421,0.0002539938,0.00004553192,0.00006290938,0.0000499874,0.00003601462,0.00002723187],"category_scores_gemma":[0.000002064592,0.0000796569,0.00009628533,0.0003627129,0.00001101815,0.0003619789,0.0000023438,0.0001023221,0.00001756215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003038095,"about_ca_system_score_gemma":0.000004489547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004435878,"about_ca_topic_score_gemma":0.00001390661,"domain_scores_codex":[0.9995007,0.000003824137,0.0002359423,0.00006034927,0.0000976232,0.0001015533],"domain_scores_gemma":[0.9998448,0.00002769864,0.00001290075,0.00006320568,0.00001886317,0.00003251512],"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":[0.00001283654,0.00001681984,0.00007462547,0.0006669052,0.001878805,0.000002078538,0.01101292,0.8725704,0.00003452053,0.01626757,0.03718038,0.06028214],"study_design_scores_gemma":[0.00008309422,0.00002667864,0.0001873864,0.00001779082,0.0002334199,4.55543e-7,0.000153478,0.8678012,0.0001228879,0.00006691492,0.1312421,0.00006454979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007969501,0.0001391527,0.9799153,0.00003642372,0.0001563909,0.000237289,0.0002249732,0.006626232,0.01186734],"genre_scores_gemma":[0.9910716,0.00008912675,0.007881562,0.0000195246,0.0000327269,0.0001416819,0.0006342975,0.00000730835,0.0001221138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9902747,"threshold_uncertainty_score":0.3248314,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000570594,0.00004702515,0.00007588539,0.00008582752,0.0004596661,0.00008055745,0.0001051936,0.00003003841,0.00006731106],"category_scores_gemma":[0.001222864,0.00003379504,0.00005020988,0.0002545246,0.00006742233,0.0009877752,0.00002158533,0.00008381112,0.00002914589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003431375,"about_ca_system_score_gemma":0.00006822641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009456549,"about_ca_topic_score_gemma":0.0001927638,"domain_scores_codex":[0.9993242,0.00003048405,0.0002748473,0.00002697832,0.0001812189,0.0001622815],"domain_scores_gemma":[0.9994606,0.0002167372,0.000107363,0.0000859065,0.00009305116,0.00003630624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003821028,0.00001219032,0.00006413517,0.00009044924,0.00003062316,5.411705e-8,0.4446363,0.0005062279,0.00004203185,0.04824765,0.0007585923,0.5055736],"study_design_scores_gemma":[0.0004673447,0.00008336709,0.003840785,0.00007035155,0.00001904295,7.423243e-7,0.3697892,0.005243412,0.001913908,0.001034062,0.617416,0.0001217481],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4882077,0.00001725169,0.03970032,0.00139239,0.0006763794,0.0005502038,0.000001407494,0.00009564491,0.4693587],"genre_scores_gemma":[0.9985819,0.00003349883,0.0001950637,0.0003499869,0.00002660685,0.00001308788,0.000001122319,0.000001898983,0.0007967893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6166574,"threshold_uncertainty_score":0.3535427,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001014542,0.0002825889,0.0003638566,0.0001754405,0.0001818579,0.0003152441,0.00142609,0.0001212323,0.00005209557],"category_scores_gemma":[0.000151912,0.0002408228,0.0001357486,0.0004776798,0.0001687607,0.001390963,0.0007573186,0.0002507249,0.0002748842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001206408,"about_ca_system_score_gemma":0.0001405761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001516538,"about_ca_topic_score_gemma":2.881886e-7,"domain_scores_codex":[0.9976713,0.00009792754,0.0007497497,0.0003241102,0.0004853941,0.000671531],"domain_scores_gemma":[0.9983613,0.0001634961,0.0002000748,0.000933677,0.0001110534,0.0002303456],"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":[0.00007708069,0.003421343,0.000999919,0.001704725,0.0003165432,0.0001327284,0.1237142,0.005196542,0.0002551531,0.2943532,0.0003991775,0.5694294],"study_design_scores_gemma":[0.0002757036,0.0001108783,0.0002278484,0.00003445569,0.0000158317,0.0003510754,0.0002467842,0.9882944,0.0003055743,0.009557456,0.000244583,0.0003354421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03777476,0.00001729112,0.9471525,0.00001951194,0.0001228825,0.0007174028,0.000001210074,0.0005110192,0.0136834],"genre_scores_gemma":[0.2570168,5.929629e-7,0.7427953,0.00008599004,0.00002942599,0.00004840211,0.000001466747,0.00001163069,0.00001034837],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9830979,"threshold_uncertainty_score":0.982047,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002690178,0.0001203482,0.0001522718,0.0002373037,0.0003965663,0.00005481539,0.0001014434,0.00005387364,0.0004855168],"category_scores_gemma":[0.00002823459,0.0001244529,0.00006716653,0.0005527411,0.00001258807,0.000264056,0.00004489516,0.0002288217,0.0001468144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003070768,"about_ca_system_score_gemma":0.00003416404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006321318,"about_ca_topic_score_gemma":0.000001724066,"domain_scores_codex":[0.9989274,0.00003921748,0.000410545,0.00007151782,0.0003463624,0.000205023],"domain_scores_gemma":[0.9996117,0.00003745021,0.00007995928,0.0001884471,0.00002947366,0.00005299363],"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":[0.00002196476,0.00003318938,0.0003013834,0.0002928674,0.00004685455,0.00001095022,0.001697311,0.9542611,0.003621363,0.0001435929,0.004784097,0.03478537],"study_design_scores_gemma":[0.0003041073,0.00007161345,0.0002285033,0.00002520925,0.00001122044,0.00003932432,0.0004391678,0.9878122,0.001462122,0.00002301888,0.009423124,0.0001603983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9584519,0.00003589754,0.03179027,0.00001017686,0.002229666,0.0003412615,0.00001744233,0.001139354,0.005984019],"genre_scores_gemma":[0.9993298,8.438826e-7,0.0004510226,0.00004652249,0.00008341524,0.00004044324,0.000007519041,0.00001670938,0.00002369857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04087791,"threshold_uncertainty_score":0.5316069,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008078322,0.0001351325,0.0002152319,0.0005366942,0.0005578237,0.0002401763,0.002374854,0.00004366831,0.00004184322],"category_scores_gemma":[0.0002370178,0.0001331248,0.00007420864,0.001168358,0.00003321456,0.000957998,0.002008992,0.0002759109,0.00001148343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009226066,"about_ca_system_score_gemma":0.00006702463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001998716,"about_ca_topic_score_gemma":0.000009643749,"domain_scores_codex":[0.9986808,0.00004002984,0.0004334117,0.0002755359,0.000298876,0.0002713785],"domain_scores_gemma":[0.9982278,0.0001384908,0.0002712599,0.001277529,0.00004296303,0.00004199574],"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":[0.0001412787,0.001172251,0.009117218,0.0008519482,0.001615518,0.0000184458,0.007230658,0.1246318,0.008533042,0.08329624,0.006664365,0.7567273],"study_design_scores_gemma":[0.0004715898,0.0000752695,0.0001247416,0.0000057323,0.00004108562,0.000002648575,0.0002386244,0.980212,0.0004171626,0.0001802747,0.01805778,0.0001731143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002273274,0.00003788718,0.9971337,0.0008799319,0.00006496393,0.0004874584,0.00005508096,0.0007895619,0.0003241019],"genre_scores_gemma":[0.2596744,0.000003565087,0.7389923,0.0003336512,0.000004847439,0.000519328,0.0002309414,0.000008555696,0.0002324459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8555802,"threshold_uncertainty_score":0.542867,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003493065,0.0001473925,0.0002146631,0.0001667343,0.0005833029,0.0001589053,0.0004505148,0.00006214169,0.00001750707],"category_scores_gemma":[0.0002674192,0.0001222843,0.00002955903,0.0004447611,0.0001194397,0.001099062,0.0004478621,0.0001198043,0.000004040658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005932039,"about_ca_system_score_gemma":0.0002238646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002406015,"about_ca_topic_score_gemma":0.000006969882,"domain_scores_codex":[0.9989353,0.00005280039,0.0002757621,0.0002381742,0.0002273107,0.0002705972],"domain_scores_gemma":[0.99855,0.0002452111,0.0001380871,0.0002830091,0.0006827693,0.0001009049],"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":[0.001390578,0.0004946369,0.0005500606,0.0005400107,0.0009306354,0.000006324642,0.01684817,0.8005084,0.0001719168,0.09462494,0.0008486895,0.08308558],"study_design_scores_gemma":[0.00271895,0.0003357728,0.0004331729,0.00002536328,0.00002006435,0.000001401189,0.0003075794,0.9952591,0.0004950703,0.0002401218,0.00002312079,0.0001402909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003451929,0.0000174499,0.9965743,0.0001573347,0.00009011402,0.00114084,0.00002434798,0.00009139874,0.001558983],"genre_scores_gemma":[0.4081842,0.00001502398,0.5910259,0.0002194535,0.000006241884,0.00008884278,0.000005622416,0.000004010223,0.0004507568],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.407839,"threshold_uncertainty_score":0.4986608,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000157661,0.0001008453,0.00009133056,0.00007976538,0.0001032781,0.00004645107,0.0002533743,0.00008969916,0.00004942268],"category_scores_gemma":[0.00006421596,0.0000799606,0.00003504075,0.000185814,0.00009819344,0.0002750228,0.0002242943,0.0001384606,0.001229582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002147267,"about_ca_system_score_gemma":0.000006696883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005448592,"about_ca_topic_score_gemma":0.00001527568,"domain_scores_codex":[0.9992773,0.0000139627,0.0002117735,0.0001064292,0.0001824399,0.0002080939],"domain_scores_gemma":[0.9995592,0.00003747135,0.00003815969,0.0003385828,0.000003826329,0.00002273006],"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":[0.0004219317,0.0005367114,0.008859385,0.0003854164,0.0001963837,0.00001530187,0.01296014,0.2075918,0.04804453,0.01245197,0.02623889,0.6822975],"study_design_scores_gemma":[0.0008034477,0.0002315442,0.006828917,0.00008930187,0.00002032978,0.000003812801,0.0004573902,0.2244381,0.6592324,0.09420267,0.01323709,0.0004550159],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8756334,0.00000151165,0.04059462,0.001023906,0.0001653713,0.0002336636,0.000001961363,0.0005663871,0.0817792],"genre_scores_gemma":[0.993909,0.000001647459,0.004690225,0.000330429,0.000005390737,0.00003203978,7.737097e-7,0.000004376957,0.001026106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6818425,"threshold_uncertainty_score":0.9995481,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000306595,0.0001760611,0.0001642062,0.0001363214,0.000297121,0.0002971682,0.0004504114,0.00007340102,0.00001853066],"category_scores_gemma":[0.00003017471,0.000133327,0.00005480997,0.0009650165,0.00007397772,0.0006658947,0.0002040729,0.0002251118,0.0006122927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005473114,"about_ca_system_score_gemma":0.00007702522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008108764,"about_ca_topic_score_gemma":0.000006955921,"domain_scores_codex":[0.9985414,0.00001092014,0.0004187084,0.0002519697,0.0003918618,0.0003851802],"domain_scores_gemma":[0.9987986,0.000104112,0.0001806301,0.0006609883,0.0001217378,0.0001339002],"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":[0.0001758876,0.0008321149,0.003953236,0.0005596584,0.0002458821,0.00002207524,0.01407458,0.2595104,0.006482258,0.5363553,0.0394216,0.138367],"study_design_scores_gemma":[0.0003952999,0.00009324984,0.01220014,0.00004739995,0.0000120382,0.00002537252,0.0003439236,0.9714535,0.0002724711,0.0005826923,0.01434221,0.0002316385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1852695,0.000009328395,0.8016201,0.003591548,0.0003047078,0.0005644726,0.00001201467,0.000813333,0.007815085],"genre_scores_gemma":[0.9931445,0.000006506326,0.00576174,0.0007357798,0.0000560461,0.00006964519,0.00002553064,0.00001118944,0.0001890331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8078751,"threshold_uncertainty_score":0.7869987,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000786807,0.0001209822,0.000282177,0.0001938169,0.0001341302,0.0001469585,0.0001348272,0.00007182487,0.00002460527],"category_scores_gemma":[0.0001247774,0.0001074075,0.00005408822,0.0002016987,0.00002397123,0.0002518516,0.00001822441,0.0000823455,0.0006260336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004482598,"about_ca_system_score_gemma":0.00001048399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003077267,"about_ca_topic_score_gemma":0.000001728562,"domain_scores_codex":[0.9989854,0.00001423817,0.0005422753,0.0001364086,0.00004907695,0.0002726117],"domain_scores_gemma":[0.9991843,0.0001528327,0.0003623143,0.0002277554,0.00002600174,0.00004683987],"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":[0.00003919691,0.00001838806,0.001051503,0.000541529,0.00005657897,7.501739e-7,0.002420719,0.002712859,0.000003951716,0.9913027,0.001637103,0.0002147283],"study_design_scores_gemma":[0.005640294,0.002848668,0.007075927,0.001940667,0.00005997228,0.00007331554,0.01322547,0.5375056,0.0003869837,0.1515833,0.2778205,0.001839338],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9035216,0.0001116584,0.02126892,0.0003988238,0.001461859,0.001713513,0.0009842721,0.0004987108,0.07004062],"genre_scores_gemma":[0.9980884,0.000002616462,0.0002360777,0.0001143142,0.0001183134,0.00007986361,0.00007705815,0.00001793182,0.001265493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8397194,"threshold_uncertainty_score":0.8046604,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001498324,0.0004012594,0.0004940713,0.0005412383,0.001082057,0.0007269144,0.0009235629,0.0001797327,0.000003845903],"category_scores_gemma":[0.002052176,0.0003481367,0.0001516503,0.001308828,0.0001745068,0.00141605,0.0009527134,0.00108411,0.000001844185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001856184,"about_ca_system_score_gemma":0.000102995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002115928,"about_ca_topic_score_gemma":0.00001598345,"domain_scores_codex":[0.9971918,0.0001534937,0.001125756,0.0004806309,0.0002318497,0.0008164606],"domain_scores_gemma":[0.9972069,0.001350357,0.0004743328,0.0006530265,0.0002197685,0.00009561089],"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":[0.00004981647,0.00002155071,0.0010748,0.000497389,0.00004407667,0.000002478791,0.001920923,0.1140838,0.000004023255,0.001931709,0.00003312255,0.8803363],"study_design_scores_gemma":[0.0005565426,0.0001424196,0.0004389835,0.0003351994,0.00003815762,0.00004960347,0.000541286,0.9916141,0.0004511453,0.004431098,0.001048211,0.0003532334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002355395,0.0012709,0.9933007,0.0001783906,0.0002061432,0.001398241,0.000002280008,0.0004832911,0.0008046711],"genre_scores_gemma":[0.648395,0.0001418382,0.3505963,0.0003750635,0.0000369034,0.0003588477,0.000004524627,0.00001820575,0.00007337569],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.879983,"threshold_uncertainty_score":0.9998971,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.02561093,0.0006428914,0.001432178,0.001589438,0.002652175,0.002016851,0.007008723,0.0007820702,0.000968616],"category_scores_gemma":[0.007761365,0.0005036111,0.0007684474,0.004172873,0.0005644676,0.0017316,0.001015324,0.002385395,0.001202022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003248439,"about_ca_system_score_gemma":0.000980114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002892862,"about_ca_topic_score_gemma":0.0001458577,"domain_scores_codex":[0.9872722,0.001901263,0.003646256,0.001401685,0.004134037,0.001644576],"domain_scores_gemma":[0.9714418,0.02066615,0.0007627233,0.005340066,0.001225328,0.0005639063],"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":[0.0005294352,0.000695052,0.0004708274,0.0001701302,0.000987454,0.000003943988,0.004371129,0.02572108,0.00001139132,0.002301631,0.05530406,0.9094338],"study_design_scores_gemma":[0.001096629,0.0004250962,0.0001853184,0.0002676247,0.000340509,0.000002487652,0.005833644,0.6937121,0.0002509596,0.008529774,0.288986,0.0003698958],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001720652,0.0005812126,0.9767757,0.007422996,0.0005647329,0.002021078,0.003343284,0.00006028542,0.007510065],"genre_scores_gemma":[0.166986,0.007484975,0.8145508,0.002573353,0.0001648845,0.0003497708,0.0005508763,0.00006537641,0.007273955],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.909064,"threshold_uncertainty_score":0.9999446,"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)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003696386,0.0003503329,0.000684999,0.001103891,0.0002049223,0.0002720955,0.0006828213,0.0002834124,0.00002720806],"category_scores_gemma":[0.0001939282,0.0003368261,0.0001047614,0.001566294,0.0002002033,0.004276428,0.0002408009,0.0004665831,0.000006307803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001897393,"about_ca_system_score_gemma":0.0002957367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001245819,"about_ca_topic_score_gemma":8.496368e-7,"domain_scores_codex":[0.9962599,0.0005061856,0.002007404,0.000286926,0.0005506995,0.0003888979],"domain_scores_gemma":[0.9977479,0.0004385647,0.0008680454,0.0007292297,0.0001045821,0.0001116641],"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":[0.00005712395,0.0000491085,0.0003426619,0.001283057,0.0000408508,1.984683e-7,0.02597103,0.7080157,0.00002770792,0.004415783,0.00002298128,0.2597738],"study_design_scores_gemma":[0.0009518478,0.000119707,0.001635282,0.0009201724,0.00003854503,0.000003123688,0.001395528,0.9863506,0.005042681,0.002050097,0.001221549,0.0002708666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02422026,0.000100897,0.9478998,0.0001692321,0.0003812689,0.0006021675,0.000006694712,0.00002220957,0.02659743],"genre_scores_gemma":[0.7185408,0.00008309889,0.2807809,0.0005078746,0.00001268341,0.00002485358,0.000007584439,0.000005128549,0.00003707572],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6943206,"threshold_uncertainty_score":0.9999084,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002703278,0.00009087434,0.0001169731,0.00006172163,0.0001304755,0.0003954367,0.0004357316,0.00003885073,0.000007092111],"category_scores_gemma":[0.00004383269,0.00008225366,0.00005077571,0.0002515349,0.00001252152,0.001373127,0.0000292308,0.00005141753,0.0002025604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003760751,"about_ca_system_score_gemma":0.00006801588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001427393,"about_ca_topic_score_gemma":0.000002865057,"domain_scores_codex":[0.9991822,0.0000133427,0.0003168576,0.0001012427,0.0001601879,0.000226135],"domain_scores_gemma":[0.9994047,0.00008550996,0.0000904886,0.0002513878,0.0001079054,0.00006004377],"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":[0.00001807455,0.000102501,0.00002417284,0.0003554682,0.00005430021,0.000003997588,0.02095887,0.007686948,0.001219131,0.7534503,0.06028452,0.1558417],"study_design_scores_gemma":[0.0005411492,0.00006643868,0.0000531136,0.0000399842,0.000004321031,0.00002022611,0.00009604134,0.6899044,0.001770128,0.001711529,0.3056079,0.000184738],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002734298,0.00002013437,0.9540283,0.002199364,0.0002293626,0.0002930777,0.00000970123,0.0002379983,0.04024775],"genre_scores_gemma":[0.8683642,0.000001095967,0.1309991,0.0003167332,0.0001497,0.00002312305,0.00001308743,0.000003872255,0.0001290676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8656299,"threshold_uncertainty_score":0.3813205,"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)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004917423,0.0004506398,0.0005455948,0.0001259854,0.0001680134,0.0000716218,0.0002108826,0.0001683191,0.00001114729],"category_scores_gemma":[0.00004124378,0.0004107981,0.0002183269,0.0004714058,0.0003849402,0.0001961122,0.0003679489,0.0002994465,9.051427e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001031923,"about_ca_system_score_gemma":0.00009618136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002427895,"about_ca_topic_score_gemma":0.000102176,"domain_scores_codex":[0.9973138,0.00006137507,0.0009056483,0.0003636459,0.0004988098,0.000856708],"domain_scores_gemma":[0.9987827,0.0001430081,0.0003422691,0.0005225718,0.00003424188,0.0001752129],"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":[0.0003419351,0.000397947,0.1025304,0.001626866,0.0003999749,0.00001769042,0.001976195,0.8815688,0.002633625,0.0002012554,0.000003170679,0.008302143],"study_design_scores_gemma":[0.0008991907,0.0008149631,0.3119334,0.0002067965,0.0001681933,0.00001674152,0.001029158,0.6832352,0.001260147,0.00008884506,0.00001818255,0.0003291994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912729,0.00004752366,0.002643765,0.000115331,0.0002850609,0.002637967,0.00002713274,0.00004210528,0.002928235],"genre_scores_gemma":[0.9960998,0.00002465035,0.00353554,0.00005703455,0.00001922063,0.0001451106,0.0000161751,0.00002563538,0.00007682329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.209403,"threshold_uncertainty_score":0.9998344,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}