{"meta":{"query_hash":"5aefc87fb3b1","filters":{"venue":"Procedia Computer Science"},"cohort_total":445,"direct_labels_cover":0,"predictions_cover":445,"exported":445,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/5aefc87fb3b1","api":"https://metacan.xera.ac/api/v1/cohort?venue=Procedia+Computer+Science"},"results":[{"id":"W1012347533","doi":"10.1016/j.procs.2015.05.135","title":"An Experimental Study Onthe Dehumidification Performance of a Low-flow Falling-film Liquid Desiccant Air-conditioner","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Adsorption and Cooling Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Natural Resources Limited","keywords":"Desiccant; Air conditioning; Liquid desiccant; Materials science; Volumetric flow rate; Moisture; Humidity; Environmental science; Regenerative heat exchanger; Air dryer; Inlet; Thermodynamics; Mechanics; Process engineering; Composite material; Mechanical engineering; Heat exchanger; Physics; Engineering","score_opus":0.021429397707816204,"score_gpt":0.24764123078406555,"score_spread":0.22621183307624934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1012347533","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981185,0.00011057333,0.0013881706,0.000011044322,0.000006742772,0.00005115217,0.00005729503,0.00003910171,0.00021738315],"genre_scores_gemma":[0.99256015,0.00021925202,0.005466449,0.000018359042,0.000007350963,0.000039283375,0.00013425134,0.000021528349,0.0015334969],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995739,0.000049315993,0.000035218345,0.00011050964,0.00016401787,0.00006704084],"domain_scores_gemma":[0.99927837,0.00026384628,0.00009109627,0.000094006624,0.00021357798,0.000059040176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062101154,0.00043740883,0.000599745,0.00024294107,0.00054014777,0.00032348366,0.0007698815,0.00048332658,0.0017583768],"category_scores_gemma":[0.0009038011,0.00020850463,0.0002475834,0.00024428242,0.00028949766,0.0005049699,0.00028008543,0.00047854398,0.00027988388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034356277,0.0002112219,0.000809677,0.00012062121,0.0000066116745,0.00005058397,0.00011713551,0.0006342231,0.9922978,0.000029046045,0.000035512898,0.0053440924],"study_design_scores_gemma":[0.000029021228,0.002493106,0.0046571027,0.000004786793,0.000018299028,0.0000419685,0.00003588883,0.002137981,0.99011874,0.0000047062126,0.000449567,0.000008893688],"about_ca_topic_score_codex":0.0028348821,"about_ca_topic_score_gemma":0.003702613,"teacher_disagreement_score":0.0028348821,"about_ca_system_score_codex":0.00046153698,"about_ca_system_score_gemma":0.00035181758,"threshold_uncertainty_score":0.005882323},"labels":[],"label_agreement":null},{"id":"W1035596560","doi":"10.1016/j.procs.2015.07.260","title":"Reducing Phase Cancellation Effect with ASK-PSK Modulated Stamp in Augmented UHF RFID Indoor Localization System","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Ultra high frequency; Ask price; Phase (matter); Telecommunications; Computer network; Physics","score_opus":0.008817598851492353,"score_gpt":0.2242047898534415,"score_spread":0.21538719100194914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1035596560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32640058,0.0013740037,0.66477764,0.0004918988,0.00021919118,0.00009261607,0.000073948424,0.0012946787,0.005275457],"genre_scores_gemma":[0.88072705,0.0004969242,0.11413105,0.00026449753,0.000099893885,0.00004319323,0.000087481356,0.000032836248,0.004117249],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954283,0.00008279453,0.000030151823,0.000072801195,0.00022553823,0.000045950266],"domain_scores_gemma":[0.9995485,0.00009918321,0.00012350945,0.000064904045,0.0001457165,0.000018051618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002472046,0.00047937056,0.00029242088,0.0003752541,0.00033030225,0.0004583846,0.00053961796,0.0005448685,0.0010835284],"category_scores_gemma":[0.00053760014,0.00016340891,0.00032678826,0.00038319424,0.00032776172,0.00094678835,0.00050430564,0.00042784528,0.0005932938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000912064,0.0000961512,0.005405718,0.00039744898,0.000085809894,0.00068353186,0.00036261414,0.011840534,0.77468103,0.004015775,0.0010971198,0.20042227],"study_design_scores_gemma":[0.000106690706,0.0019040409,0.0048682545,0.00005008062,0.00019356074,0.003471045,0.00017916627,0.18585293,0.7856025,0.001416732,0.016259082,0.0000959479],"about_ca_topic_score_codex":0.00023053639,"about_ca_topic_score_gemma":0.00038029635,"teacher_disagreement_score":0.0010835284,"about_ca_system_score_codex":0.00023377402,"about_ca_system_score_gemma":0.00022174956,"threshold_uncertainty_score":0.0036247373},"labels":[],"label_agreement":null},{"id":"W1221199048","doi":"10.1016/j.procs.2015.08.200","title":"Similarities of Frequent Following Patterns and Social Entities","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data science; Artificial intelligence; Computer security","score_opus":0.03095346345671893,"score_gpt":0.25249450785672184,"score_spread":0.2215410444000029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1221199048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47126004,0.0016966808,0.51141626,0.00047794843,0.00014714542,0.0005168244,0.004179614,0.000663522,0.00964195],"genre_scores_gemma":[0.86079186,0.00049444527,0.13257284,0.00007014718,0.00011871564,0.00033352303,0.0038212414,0.00004878361,0.0017483507],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9952296,0.00084533123,0.00079810456,0.0011673289,0.0017174159,0.00024217505],"domain_scores_gemma":[0.98325837,0.008422076,0.0032330516,0.0020394062,0.0026320142,0.0004150069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016884956,0.00040778783,0.0007109714,0.006341427,0.0006941778,0.0015780305,0.0011207542,0.0009402699,0.0021772336],"category_scores_gemma":[0.021673096,0.00029081333,0.0009822124,0.0066391192,0.00078933366,0.003790114,0.0013687029,0.0005998274,0.00048263435],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012173053,0.0005806268,0.20260693,0.0018606458,0.00081337016,0.0035069278,0.003487241,0.041535556,0.024728201,0.15229298,0.0071697393,0.5602004],"study_design_scores_gemma":[0.00010656699,0.00076871354,0.14481986,0.00027732033,0.0004304033,0.012402075,0.0030459685,0.47139877,0.015727622,0.31403723,0.03673055,0.00025497886],"about_ca_topic_score_codex":0.0012992227,"about_ca_topic_score_gemma":0.001533201,"teacher_disagreement_score":0.006341427,"about_ca_system_score_codex":0.0005586868,"about_ca_system_score_gemma":0.00050141773,"threshold_uncertainty_score":0.008929729},"labels":[],"label_agreement":null},{"id":"W1636364634","doi":"10.1016/j.procs.2015.08.317","title":"Resource Sharing in Mobile Cloud-computing with Coap","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Cloud computing; Resource (disambiguation); Distributed computing; Mobile cloud computing; Shared resource; Computer network; World Wide Web; Operating system","score_opus":0.022851172406376997,"score_gpt":0.24950081568923646,"score_spread":0.22664964328285947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1636364634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15845616,0.007379577,0.78704846,0.0027463888,0.00092068285,0.00073354883,0.0002842054,0.00209633,0.040334705],"genre_scores_gemma":[0.91767216,0.0008166487,0.07736605,0.00029537958,0.00011474905,0.00017820693,0.000102106744,0.00013086756,0.0033238747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968111,0.00076406903,0.00016024587,0.0005275559,0.0008923488,0.0008445286],"domain_scores_gemma":[0.9973048,0.0008414293,0.00016552466,0.000806121,0.0005610922,0.00032101013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026467394,0.0008613786,0.0014062338,0.00062753365,0.0022684615,0.0036014323,0.003103992,0.001153559,0.0027725168],"category_scores_gemma":[0.0051619196,0.0004245534,0.0007234108,0.0021737753,0.0014260026,0.0040452713,0.0043944055,0.0015521019,0.00060922164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016143038,0.0005099419,0.0038143776,0.0006106183,0.00036722238,0.0016508005,0.00076177926,0.4827863,0.014558507,0.31607953,0.019059729,0.158187],"study_design_scores_gemma":[0.00006427362,0.00011179828,0.00073094264,0.00004442739,0.00004961208,0.00038527156,0.0002812531,0.91334707,0.004715072,0.06715183,0.013066694,0.00005175496],"about_ca_topic_score_codex":0.01248777,"about_ca_topic_score_gemma":0.0074616107,"teacher_disagreement_score":0.01248777,"about_ca_system_score_codex":0.0021892649,"about_ca_system_score_gemma":0.0030065277,"threshold_uncertainty_score":0.024830163},"labels":[],"label_agreement":null},{"id":"W1644944000","doi":"10.1016/j.procs.2015.08.336","title":"Goal-driven Modeling for Confidence-based Patient Numeracy Assessment: C-PNA","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Numeracy; Artificial intelligence; Literacy","score_opus":0.18549703762201095,"score_gpt":0.4306981490131882,"score_spread":0.24520111139117726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1644944000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03723523,0.00024824368,0.95002645,0.0012332018,0.00007872285,0.0003181029,0.0006331413,0.0005588611,0.009668101],"genre_scores_gemma":[0.75316733,0.00026779517,0.24079734,0.00039933572,0.00004841776,0.0012075679,0.00067152205,0.000107136315,0.0033336093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976786,0.0011944104,0.00013464093,0.00042806187,0.00036159632,0.00020281064],"domain_scores_gemma":[0.98759717,0.009411296,0.00085672963,0.00039775606,0.0013367024,0.00040033247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004190993,0.0013807018,0.0011206906,0.0011692875,0.0006377198,0.0024599547,0.0026258181,0.002113206,0.0055650175],"category_scores_gemma":[0.020970633,0.0006886038,0.0015174752,0.0010828932,0.000954834,0.0015359349,0.0022868926,0.0029525468,0.0009165307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012948032,0.00015523081,0.0063017826,0.00012491574,0.000103863706,0.0001901904,0.00047289903,0.92823046,0.0004144398,0.035078906,0.0011382014,0.02765965],"study_design_scores_gemma":[0.000013713945,0.000028662733,0.00039434573,0.000019899095,0.000016449752,0.00003336443,0.000028834333,0.9861254,0.00010305853,0.01260707,0.00061607116,0.00001312656],"about_ca_topic_score_codex":0.02010845,"about_ca_topic_score_gemma":0.013290505,"teacher_disagreement_score":0.02010845,"about_ca_system_score_codex":0.002368401,"about_ca_system_score_gemma":0.0027514333,"threshold_uncertainty_score":0.039982855},"labels":[],"label_agreement":null},{"id":"W1648897910","doi":"10.1016/j.procs.2015.08.357","title":"M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Wearable computer; Support vector machine; Machine learning; Artificial intelligence; Vital signs; Raw data; Wearable technology; Real-time computing; Human–computer interaction; Embedded system; Medicine","score_opus":0.10115489345884701,"score_gpt":0.412445200886745,"score_spread":0.311290307427898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1648897910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055673413,0.0026028284,0.9255231,0.0010165221,0.0006730315,0.0003282652,0.0036377874,0.0077608125,0.0027841867],"genre_scores_gemma":[0.56844294,0.0015672841,0.41284764,0.00090420526,0.0003486125,0.0008849254,0.006715188,0.00031034547,0.007978816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994586,0.00014428109,0.000032255328,0.00015177639,0.00016451527,0.000048521848],"domain_scores_gemma":[0.9993063,0.0002835595,0.000042347354,0.000090468704,0.00024355187,0.00003388955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000951692,0.00090097403,0.00093482045,0.00089176936,0.0003645574,0.0007023431,0.0017336583,0.001219054,0.0018993139],"category_scores_gemma":[0.002611613,0.000249743,0.00083375495,0.00062584545,0.00022468227,0.00061356166,0.0008012738,0.0012777433,0.0011799004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007936237,0.00051163684,0.015940981,0.00029681073,0.0003549674,0.00038736823,0.00008542298,0.3174296,0.007941844,0.0036506553,0.031339522,0.62126756],"study_design_scores_gemma":[0.00002747464,0.00011968085,0.0014156282,0.000014062695,0.000024820984,0.000112608424,0.000009801113,0.98970956,0.001957948,0.0017796413,0.0048088343,0.000019867868],"about_ca_topic_score_codex":0.0089167375,"about_ca_topic_score_gemma":0.0072281663,"teacher_disagreement_score":0.0089167375,"about_ca_system_score_codex":0.0006473727,"about_ca_system_score_gemma":0.0007369347,"threshold_uncertainty_score":0.01772964},"labels":[],"label_agreement":null},{"id":"W1659087459","doi":"10.1016/j.procs.2015.08.389","title":"COC: An Ontology for Capturing Semantics of Circle of Care","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Access Control and Trust","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Ontology; Interoperability; Semantics (computer science); Upper ontology; Health care; World Wide Web; Information retrieval; Semantic Web; Programming language","score_opus":0.04231675702561785,"score_gpt":0.34446225539567077,"score_spread":0.3021454983700529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1659087459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008142202,0.0007659997,0.961822,0.0024360307,0.00041353612,0.0009450954,0.0053825835,0.0020381068,0.018054444],"genre_scores_gemma":[0.13219762,0.0015858493,0.8450604,0.0014815565,0.00031194763,0.001718924,0.0116065275,0.00071726687,0.005319864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99086964,0.0023595758,0.0018463613,0.0015110729,0.0027608394,0.00065254595],"domain_scores_gemma":[0.9882515,0.004128734,0.0015138778,0.0024604825,0.0027659463,0.00087951944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057468587,0.0010241376,0.0011086267,0.0061516655,0.0036572444,0.005643372,0.0027999682,0.0026981605,0.0030163215],"category_scores_gemma":[0.014340001,0.0011300725,0.003563486,0.0074709225,0.004350477,0.013152873,0.004789268,0.004069274,0.0010009292],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007428604,0.00009835091,0.0026729924,0.00056036765,0.00010962496,0.0007285444,0.0048687975,0.008069355,0.0017944355,0.9184055,0.016909586,0.045708295],"study_design_scores_gemma":[0.000056199722,0.00006749231,0.0021724289,0.000810171,0.00019235717,0.0016482227,0.003700383,0.049707044,0.0031387375,0.31385162,0.62446445,0.000190875],"about_ca_topic_score_codex":0.057037443,"about_ca_topic_score_gemma":0.040263657,"teacher_disagreement_score":0.057037443,"about_ca_system_score_codex":0.0050719697,"about_ca_system_score_gemma":0.011244374,"threshold_uncertainty_score":0.11341089},"labels":[],"label_agreement":null},{"id":"W1692844682","doi":"10.1016/j.procs.2015.07.295","title":"Ontology-based Sentiment Analysis Process for Social Media Content","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Sentiment analysis; Ontology; Social media; Process (computing); World Wide Web; Service (business); Content analysis; Customer service; Classifier (UML); Identification (biology); Data science; Artificial intelligence","score_opus":0.1419450223484216,"score_gpt":0.33566194448213704,"score_spread":0.19371692213371544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1692844682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025505524,0.000109318236,0.9645131,0.00072139956,0.0001336825,0.0008571596,0.0013651312,0.0022057854,0.004588857],"genre_scores_gemma":[0.21247858,0.00025988425,0.77762866,0.00018760026,0.0000878001,0.00091869885,0.003436386,0.00033088538,0.004671488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99629694,0.00074868416,0.0004517109,0.00057904853,0.001695947,0.0002276044],"domain_scores_gemma":[0.9953004,0.0016888997,0.00044110513,0.00034385113,0.0020923838,0.00013331525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003713879,0.0008366685,0.0006445733,0.0041546356,0.0016705317,0.0026508071,0.00081824855,0.00065034255,0.0032213265],"category_scores_gemma":[0.00929008,0.00036358438,0.002529269,0.0025176695,0.0007631959,0.0027259989,0.0015257748,0.0014306178,0.0019357639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040872625,0.00059390254,0.010806387,0.0009843142,0.00033897848,0.0011316041,0.007888303,0.016462322,0.08877134,0.045175113,0.021378618,0.80606043],"study_design_scores_gemma":[0.000061684594,0.00021414932,0.015285422,0.00024666166,0.000355491,0.0005320925,0.0053850873,0.70796514,0.09067725,0.0809777,0.09811577,0.00018353936],"about_ca_topic_score_codex":0.004880431,"about_ca_topic_score_gemma":0.005019719,"teacher_disagreement_score":0.004880431,"about_ca_system_score_codex":0.0017266367,"about_ca_system_score_gemma":0.0026015094,"threshold_uncertainty_score":0.019641101},"labels":[],"label_agreement":null},{"id":"W1964578722","doi":"10.1016/j.procs.2013.06.047","title":"Divide-and-Rule Scheme for Energy Efficient Routing in Wireless Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Wireless Routing Protocol; Routing protocol; Computer network; Wireless sensor network; Cluster analysis; Dynamic Source Routing; Zone Routing Protocol; Distributed computing; Probabilistic logic; Enhanced Interior Gateway Routing Protocol; Routing (electronic design automation); Link-state routing protocol; Artificial intelligence","score_opus":0.007832586569804276,"score_gpt":0.20788107551235177,"score_spread":0.2000484889425475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964578722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052837726,0.0023315637,0.9359294,0.00044666018,0.00035022432,0.00048609695,0.00012093622,0.0010785982,0.006418772],"genre_scores_gemma":[0.63858515,0.0012837196,0.35485137,0.00026487236,0.00015029185,0.0003744474,0.0002570444,0.000088321605,0.0041447612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986627,0.0003664781,0.00012962584,0.00020198834,0.00053906825,0.000100135374],"domain_scores_gemma":[0.99869967,0.00036942156,0.00013834804,0.000476367,0.0002181768,0.000097903634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016712324,0.00041826387,0.0010320762,0.00076983235,0.0009498902,0.00091871945,0.0023295858,0.00088592304,0.0011526385],"category_scores_gemma":[0.003066902,0.00023944929,0.0006075178,0.0010691182,0.00095991284,0.0014869176,0.00097397144,0.00096776977,0.0004331671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007930635,0.00045997545,0.0021799235,0.0005250943,0.00019630636,0.00081315055,0.0008141306,0.2447017,0.05852457,0.14546025,0.008515039,0.53701687],"study_design_scores_gemma":[0.00019180436,0.0013498326,0.0010610151,0.00006287982,0.00013547475,0.0019421895,0.00017835251,0.8722802,0.0313562,0.05438939,0.036906652,0.00014602774],"about_ca_topic_score_codex":0.0011029826,"about_ca_topic_score_gemma":0.0015996257,"teacher_disagreement_score":0.0023295858,"about_ca_system_score_codex":0.000536981,"about_ca_system_score_gemma":0.0008438882,"threshold_uncertainty_score":0.008838475},"labels":[],"label_agreement":null},{"id":"W1964648807","doi":"10.1016/j.procs.2014.07.022","title":"Investigative Support for Information Confidentiality Part II: Applications in Cryptanalysis and Digital Forensics","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Cryptanalysis; Confidentiality; Computer security; Information leakage; Covert channel; Plaintext; Covert; Cryptography; Encryption; Operating system","score_opus":0.009927375369455213,"score_gpt":0.21695500161126702,"score_spread":0.2070276262418118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964648807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008474248,0.0055657024,0.9503249,0.005863168,0.00062623475,0.00038354742,0.00006294983,0.0011092026,0.02759003],"genre_scores_gemma":[0.28511363,0.012529865,0.6812736,0.001395504,0.0017344967,0.0005246845,0.00018448422,0.0004398399,0.016803892],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99047905,0.004041094,0.00069443655,0.0008084026,0.003512676,0.0004643142],"domain_scores_gemma":[0.97276413,0.016789967,0.0015432773,0.006036853,0.002483578,0.00038226543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008656884,0.0009857006,0.0009426586,0.0034051957,0.002009885,0.0052896547,0.002295567,0.002878155,0.006658768],"category_scores_gemma":[0.019146876,0.00086997653,0.0017958636,0.0020137804,0.007849696,0.010157924,0.004945458,0.0054719,0.0017992303],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010870814,0.00021230562,0.00075167377,0.0006955951,0.00007147056,0.0005260762,0.0024391876,0.0060490347,0.013404631,0.7143596,0.007574215,0.2538075],"study_design_scores_gemma":[0.000051909716,0.00040107573,0.0011056318,0.0013623167,0.000076979515,0.0028408938,0.001044138,0.03847458,0.06453377,0.6631344,0.22679074,0.00018354131],"about_ca_topic_score_codex":0.00029873298,"about_ca_topic_score_gemma":0.00014513751,"teacher_disagreement_score":0.008656884,"about_ca_system_score_codex":0.001777811,"about_ca_system_score_gemma":0.0015071644,"threshold_uncertainty_score":0.045782566},"labels":[],"label_agreement":null},{"id":"W1964786728","doi":"10.1016/j.procs.2014.08.043","title":"Visualizing Information for Electromyographic Characterization","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Allison University","funders":"","keywords":"Computer science; Visualization; Data science; Human–computer interaction; Data visualization; Artificial intelligence","score_opus":0.006464410917255394,"score_gpt":0.23454342516808668,"score_spread":0.2280790142508313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964786728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019770347,0.0046186736,0.94897383,0.0028931494,0.00022006866,0.00025214616,0.003376253,0.011587479,0.00830803],"genre_scores_gemma":[0.18674655,0.004026221,0.80316824,0.00028174595,0.00019105671,0.00032781414,0.0021765046,0.00090734113,0.0021744883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991736,0.00037212484,0.00008761696,0.00008906159,0.00021832467,0.000059160855],"domain_scores_gemma":[0.99421126,0.004256771,0.0003019045,0.00049919775,0.0005464308,0.00018449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018920988,0.0016791279,0.000758994,0.0048762434,0.00076375616,0.0040968074,0.0010831559,0.0017278534,0.016155306],"category_scores_gemma":[0.011678846,0.0005490899,0.0007907139,0.0035001673,0.00063700025,0.003411736,0.0026477505,0.0013583214,0.0019091661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001087242,0.00019013455,0.005493654,0.002852466,0.00016672237,0.0028808224,0.0057733995,0.029475184,0.0551018,0.064525284,0.048786517,0.78366673],"study_design_scores_gemma":[0.00023060663,0.00050254446,0.009607117,0.0024891377,0.00030727457,0.0063064825,0.0035839018,0.34923482,0.06288575,0.24613275,0.31823915,0.0004804367],"about_ca_topic_score_codex":0.0015499605,"about_ca_topic_score_gemma":0.0021789176,"teacher_disagreement_score":0.016155306,"about_ca_system_score_codex":0.00058041164,"about_ca_system_score_gemma":0.00069437135,"threshold_uncertainty_score":0.054044843},"labels":[],"label_agreement":null},{"id":"W1965505293","doi":"10.1016/j.procs.2013.05.303","title":"Modeling the Evolution of Gene Regulatory Networks for Spatial Patterning in Embryo Development","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Developmental Biology and Gene Regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences; National Science Foundation","keywords":"Evolvability; Gene regulatory network; Vertebrate; Computer science; Evolutionary biology; Evolutionary developmental biology; Biology; Diversity (politics); Computational biology; Gene; Genetics","score_opus":0.007520716271316772,"score_gpt":0.21192800840660728,"score_spread":0.2044072921352905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965505293","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6704588,0.00093693816,0.3060663,0.0015013509,0.00009313155,0.000061839455,0.0004641957,0.00028117315,0.020136254],"genre_scores_gemma":[0.9701605,0.00043196208,0.024274638,0.000067247456,0.000025585792,0.000111728994,0.00011653943,0.000057426507,0.004754498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998779,0.000047262376,0.000004095372,0.000031871266,0.00001831056,0.000020568887],"domain_scores_gemma":[0.99946254,0.0003666172,0.0000743436,0.000024089133,0.000029638475,0.000042723386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043899135,0.00044223218,0.00040394242,0.0005385572,0.0004516672,0.0008810914,0.0009772638,0.0013523232,0.002057821],"category_scores_gemma":[0.002486829,0.00043338293,0.00058013346,0.0005266156,0.0012419784,0.00099899,0.00058531814,0.000729819,0.00025638944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010128251,0.00000534441,0.0005058609,0.000009356155,0.0000042844745,0.000022455255,0.000019393487,0.9884885,0.0003922465,0.0098573575,0.00006908583,0.0006160528],"study_design_scores_gemma":[0.000003501159,0.0000032539417,0.000121511635,0.0000015789088,0.0000021510482,0.000005405131,0.000006474032,0.99404365,0.00005695489,0.005618773,0.00013463189,0.0000020798134],"about_ca_topic_score_codex":0.014159607,"about_ca_topic_score_gemma":0.01237346,"teacher_disagreement_score":0.014159607,"about_ca_system_score_codex":0.0017947014,"about_ca_system_score_gemma":0.00085695414,"threshold_uncertainty_score":0.028154433},"labels":[],"label_agreement":null},{"id":"W1965632828","doi":"10.1016/j.procs.2010.04.166","title":"Object construction and destruction design patterns in Fortran 2003","year":2010,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Fluid Dynamics and Heat Transfer","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada)","funders":"Office of Naval Research; National Nuclear Security Administration; International Business Machines Corporation; Sandia National Laboratories; U.S. Department of Energy","keywords":"Computer science; Fortran; Leverage (statistics); Programming language; Software design pattern; Factory (object-oriented programming); Software engineering; Perspective (graphical); Object-oriented programming; Software; Theoretical computer science; Artificial intelligence","score_opus":0.0060726619298651485,"score_gpt":0.18821940527614225,"score_spread":0.18214674334627712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965632828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011721941,0.00029521977,0.96929884,0.0007776603,0.00010729158,0.000089621535,0.000104851955,0.008379254,0.009225362],"genre_scores_gemma":[0.082047574,0.0004425892,0.89472234,0.00068321,0.000055558372,0.0003657526,0.0005162173,0.0055911047,0.015575589],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99731356,0.00071454485,0.00037316224,0.00031304543,0.0010642346,0.00022142829],"domain_scores_gemma":[0.9964264,0.0012541236,0.00035427522,0.0012762552,0.00054523867,0.0001436209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033390294,0.0008948568,0.00042974215,0.0007966601,0.00076853053,0.0032586078,0.0017155821,0.0014676675,0.0045471517],"category_scores_gemma":[0.0075907465,0.0010292464,0.00090315024,0.0010955026,0.0018051577,0.0030460896,0.0018520128,0.002438762,0.0025247976],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000494056,0.00015225145,0.0032957254,0.000658594,0.00006293395,0.00087726023,0.004386228,0.017409593,0.022592358,0.47521314,0.029734194,0.4451236],"study_design_scores_gemma":[0.0001941753,0.0002060504,0.0010854609,0.00033957977,0.000054502456,0.0016893062,0.00032643176,0.028992586,0.03769042,0.15998955,0.7693113,0.0001206246],"about_ca_topic_score_codex":0.0013622008,"about_ca_topic_score_gemma":0.0015305891,"teacher_disagreement_score":0.0045471517,"about_ca_system_score_codex":0.0012214093,"about_ca_system_score_gemma":0.0014646238,"threshold_uncertainty_score":0.01765865},"labels":[],"label_agreement":null},{"id":"W1966489463","doi":"10.1016/j.procs.2014.07.037","title":"A Multicriterion Fuzzy Classification Method with Greedy Attribute Selection for Anomaly-based Intrusion Detection","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Defense Advanced Research Projects Agency","keywords":"Computer science; Intrusion detection system; Data mining; Greedy algorithm; Anomaly detection; Benchmark (surveying); Selection (genetic algorithm); Fuzzy logic; Feature selection; Machine learning; Artificial intelligence; Algorithm","score_opus":0.01800501104942439,"score_gpt":0.2616274691475112,"score_spread":0.24362245809808683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966489463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027207863,0.00054050575,0.97040725,0.00020726057,0.00010157652,0.00014411115,0.00010387473,0.00058277085,0.0007048496],"genre_scores_gemma":[0.4337497,0.0003932607,0.563388,0.0002049331,0.00015067984,0.0003731671,0.00040517416,0.000055420183,0.0012797294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997535,0.00062626484,0.00023745975,0.00054140994,0.0008650255,0.0001948576],"domain_scores_gemma":[0.99806005,0.00088884827,0.00015131965,0.00013684365,0.0006919186,0.0000709551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032220448,0.0011291185,0.0019100186,0.0032521093,0.0010033887,0.0013558833,0.0019643323,0.0012227404,0.0012078339],"category_scores_gemma":[0.004350909,0.00041276842,0.0018933852,0.0026942396,0.0005522167,0.0010551492,0.00074527966,0.0012808967,0.0004288907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048168786,0.00038289497,0.006781677,0.00019313495,0.00039459846,0.00023121249,0.00023680097,0.28465176,0.010819958,0.004821325,0.0036113746,0.6873935],"study_design_scores_gemma":[0.000020234904,0.0000865507,0.000694831,0.000011924267,0.000038024988,0.000079034944,0.000030189796,0.994615,0.0020688218,0.0017478405,0.0005862807,0.000021242877],"about_ca_topic_score_codex":0.0066291573,"about_ca_topic_score_gemma":0.003814032,"teacher_disagreement_score":0.0066291573,"about_ca_system_score_codex":0.0010928322,"about_ca_system_score_gemma":0.0015643002,"threshold_uncertainty_score":0.017040014},"labels":[],"label_agreement":null},{"id":"W1970004737","doi":"10.1016/j.procs.2010.04.147","title":"The recent developments in knowledge based neural modeling","year":2010,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial neural network; Extrapolation; Parametric model; Artificial intelligence; Microwave; Parametric statistics; Machine learning; Telecommunications","score_opus":0.013204683203760026,"score_gpt":0.22110761596449874,"score_spread":0.2079029327607387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970004737","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009195202,0.077191114,0.87631404,0.0058737798,0.00064291555,0.00003658811,0.00012480018,0.00028859248,0.03033297],"genre_scores_gemma":[0.39597994,0.18936242,0.38581964,0.0020709888,0.0034799872,0.00022133214,0.00056485867,0.00015037565,0.022350343],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99934846,0.00021216414,0.00004805702,0.000110560286,0.00024938583,0.00003147007],"domain_scores_gemma":[0.9984189,0.00093876297,0.000080208214,0.00014838365,0.00037301585,0.000040747058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014801325,0.00066094205,0.0007913344,0.00071879313,0.00022157395,0.0019367085,0.0016385183,0.0013659396,0.002536373],"category_scores_gemma":[0.0030629449,0.00033336584,0.00063423306,0.0013263589,0.0009141777,0.00232089,0.00087042135,0.0016785694,0.0008478416],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089801586,0.00011671748,0.0010371434,0.0009792871,0.0002006256,0.00022884854,0.00014117712,0.2665559,0.0018520635,0.24347512,0.0053680595,0.47995514],"study_design_scores_gemma":[0.000011476313,0.00004246712,0.0004303413,0.00021747052,0.0000479467,0.00011773698,0.000037020247,0.8191736,0.0013496662,0.1225666,0.055965625,0.000040039005],"about_ca_topic_score_codex":0.0028555077,"about_ca_topic_score_gemma":0.0018357674,"teacher_disagreement_score":0.0028555077,"about_ca_system_score_codex":0.00083152624,"about_ca_system_score_gemma":0.00066332205,"threshold_uncertainty_score":0.008485079},"labels":[],"label_agreement":null},{"id":"W1970709378","doi":"10.1016/j.procs.2014.07.089","title":"On Enhancing Network Reliability and Throughput for Critical-range based Applications in UWSNs","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Computer science; Throughput; Reliability (semiconductor); Range (aeronautics); Reliability engineering; Computer network; Telecommunications; Power (physics)","score_opus":0.012324991343390141,"score_gpt":0.24576227357164276,"score_spread":0.23343728222825263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970709378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40708938,0.0032434682,0.5793368,0.0005714567,0.00015457495,0.00013431435,0.00006455046,0.00073747244,0.008667938],"genre_scores_gemma":[0.9741158,0.0007211787,0.024413403,0.000038044607,0.000027200926,0.00002297818,0.000022474293,0.000017719673,0.0006211861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996618,0.00009346778,0.000021676384,0.000047166977,0.00011606025,0.000059858634],"domain_scores_gemma":[0.9992131,0.00030989895,0.000102266466,0.00009436821,0.00024256305,0.00003774255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005442978,0.00042651064,0.000323774,0.000508976,0.0003909188,0.0004204111,0.000599515,0.00023528496,0.0006023056],"category_scores_gemma":[0.0018709574,0.000118432145,0.00022537918,0.00044709974,0.00034089782,0.0010455875,0.0006180715,0.00033225567,0.00013597331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003903681,0.00017963922,0.0050632427,0.0004574714,0.00006336663,0.0004540551,0.00041740312,0.6063221,0.15196383,0.023930965,0.0020714707,0.20868608],"study_design_scores_gemma":[0.000009112807,0.00030037353,0.0009216517,0.000020872536,0.000029461984,0.00018044315,0.000104251376,0.9706259,0.022984566,0.0028848103,0.0019245809,0.000014115929],"about_ca_topic_score_codex":0.0022603578,"about_ca_topic_score_gemma":0.0022133663,"teacher_disagreement_score":0.0022603578,"about_ca_system_score_codex":0.0005630388,"about_ca_system_score_gemma":0.0005297743,"threshold_uncertainty_score":0.0044944882},"labels":[],"label_agreement":null},{"id":"W1971586190","doi":"10.1016/j.procs.2013.09.008","title":"An Optimal Energy Efficient and Minimum Delay Scheduling for Periodic WSN Applications","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Scheduling (production processes); Mathematical optimization; Distributed computing; Computer network; Real-time computing","score_opus":0.00736184947375489,"score_gpt":0.2260882832470825,"score_spread":0.2187264337733276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971586190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040268242,0.00041397542,0.95421714,0.00030489112,0.00006686873,0.00007755672,0.00012096923,0.00014863642,0.004381742],"genre_scores_gemma":[0.7629357,0.0007810582,0.23194832,0.00007427532,0.000057331894,0.00019122809,0.00021599742,0.00010799042,0.0036881228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999734,0.000074240575,0.000009750092,0.000053122403,0.000084466694,0.000044357337],"domain_scores_gemma":[0.9997116,0.00014490553,0.00005466099,0.000018743222,0.00004794262,0.0000221563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005542303,0.0006505282,0.00046924505,0.00034707537,0.00039263553,0.00064959296,0.000658997,0.0004992092,0.00140534],"category_scores_gemma":[0.0013586655,0.0003256894,0.00038016838,0.0005368305,0.00032075634,0.000716862,0.00043960378,0.00052863633,0.00016323589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056045836,0.000052067026,0.00018536772,0.00010166704,0.000013147203,0.000052464686,0.000043638007,0.9572877,0.0048516607,0.012959063,0.0011006593,0.023296405],"study_design_scores_gemma":[0.000007962419,0.000044802382,0.00006658807,0.0000046534687,0.0000046896753,0.000016154165,0.000015671076,0.99460185,0.0008453924,0.0038101918,0.00057863013,0.0000033954486],"about_ca_topic_score_codex":0.0023015311,"about_ca_topic_score_gemma":0.002844351,"teacher_disagreement_score":0.0023015311,"about_ca_system_score_codex":0.0006714541,"about_ca_system_score_gemma":0.0016017206,"threshold_uncertainty_score":0.0048717856},"labels":[],"label_agreement":null},{"id":"W1971600804","doi":"10.1016/j.procs.2014.05.462","title":"Implementation of A3ACKs Intrusion Detection System under Various Mobility Speeds","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"King Fahd University of Petroleum and Minerals; Acadia University","keywords":"Computer science; Intrusion detection system; Intrusion; Real-time computing; Computer security","score_opus":0.008381639985172057,"score_gpt":0.24689474044692405,"score_spread":0.238513100461752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971600804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85534817,0.00026223666,0.12600863,0.00018470178,0.00021002174,0.00040680263,0.00017260782,0.014546408,0.0028604376],"genre_scores_gemma":[0.9677432,0.000072156654,0.031020517,0.00004940118,0.000007942681,0.000102287464,0.00019791196,0.00004009262,0.0007664649],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992893,0.00013269446,0.000098046025,0.00016046879,0.00019718093,0.00012240079],"domain_scores_gemma":[0.99874383,0.00025363278,0.0001827263,0.000232804,0.0004391351,0.00014795213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079758145,0.00058333937,0.0005597333,0.0005555284,0.00037831822,0.00041073575,0.0011528521,0.0004041242,0.0008412801],"category_scores_gemma":[0.0020348073,0.00021786845,0.00025400645,0.00026713748,0.0002905869,0.00083206914,0.0004418719,0.00038815435,0.0001856078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044105942,0.0018977184,0.05909119,0.00080650457,0.0006135077,0.0020256427,0.0014301034,0.12042021,0.4449395,0.0065431744,0.008224134,0.34959763],"study_design_scores_gemma":[0.00031240605,0.0024199197,0.010945013,0.00003472529,0.00021659018,0.0007234153,0.00017779287,0.71662265,0.2628385,0.0008272633,0.004795601,0.00008610926],"about_ca_topic_score_codex":0.0012713223,"about_ca_topic_score_gemma":0.0006241885,"teacher_disagreement_score":0.0012713223,"about_ca_system_score_codex":0.00034979705,"about_ca_system_score_gemma":0.000513896,"threshold_uncertainty_score":0.0042181015},"labels":[],"label_agreement":null},{"id":"W1971633999","doi":"10.1016/j.procs.2014.08.003","title":"ICTH Preface 2014","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science","score_opus":0.0073022678958987045,"score_gpt":0.21101222778523512,"score_spread":0.2037099598893364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971633999","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022496085,0.013456629,0.0027443124,0.024545126,0.3542862,0.0003318277,0.005333115,0.00072311587,0.5963301],"genre_scores_gemma":[0.00725986,0.0043127956,0.0005714533,0.002370095,0.026479334,0.00011407048,0.0021127437,0.00027845445,0.9565012],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99939585,0.00006125337,0.000040589457,0.00011116268,0.00029717627,0.00009391623],"domain_scores_gemma":[0.9974663,0.00025366902,0.000104835555,0.00024745226,0.0012804097,0.00064723426],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010260557,0.0007580467,0.0005183465,0.0032210879,0.0016562594,0.0037141189,0.0010248711,0.0011198438,0.30919936],"category_scores_gemma":[0.0048755496,0.00023393032,0.00038155096,0.0020948101,0.00061442686,0.001548531,0.0020794077,0.0018876448,0.1544344],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058629746,0.000023647615,0.00015308354,0.00019862675,0.0000034963891,0.00004687978,0.000040776733,0.00010562991,0.00029357654,0.0050119995,0.9239198,0.07014381],"study_design_scores_gemma":[0.0000041127455,0.000015673897,0.00054683385,0.00012058654,0.0000020581808,0.000026417145,0.00003198521,0.000045732835,0.00012843031,0.001025229,0.99804974,0.0000032415876],"about_ca_topic_score_codex":0.0031227712,"about_ca_topic_score_gemma":0.0048463778,"teacher_disagreement_score":0.30919936,"about_ca_system_score_codex":0.0021889794,"about_ca_system_score_gemma":0.0019801247,"threshold_uncertainty_score":0.9853433},"labels":[],"label_agreement":null},{"id":"W1973412087","doi":"10.1016/j.procs.2013.09.051","title":"Context-based and Rule-based Adaptation of Mobile User Interfaces in mHealth","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"mHealth; Personalization; Computer science; Context (archaeology); Adaptation (eye); Mobile device; Human–computer interaction; Health care; Bridge (graph theory); User interface; Multimedia; World Wide Web; Medicine","score_opus":0.04052326193138458,"score_gpt":0.3778449191188347,"score_spread":0.33732165718745016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973412087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15410358,0.0020727138,0.8248045,0.00053672754,0.00020690224,0.0013303456,0.0002019633,0.0039004646,0.012842792],"genre_scores_gemma":[0.68510896,0.0008512266,0.31078297,0.0002594263,0.00005096951,0.00052152947,0.00022480183,0.00011101362,0.0020890685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971643,0.0012887912,0.00032984038,0.0004070461,0.0006715217,0.00013843355],"domain_scores_gemma":[0.99577785,0.002358925,0.00026289193,0.0005857859,0.0009078145,0.00010665289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002743743,0.0005378238,0.0005888366,0.0007152086,0.00054346706,0.0016848039,0.0014711006,0.0013160444,0.0009417506],"category_scores_gemma":[0.011337834,0.0004387213,0.0006133114,0.0005173459,0.00073029107,0.0014870316,0.0010527004,0.000913606,0.00046338516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008087775,0.0012008738,0.018223634,0.0013643015,0.0003568254,0.0023764665,0.006786575,0.08588297,0.08836453,0.010532697,0.0042841244,0.77981824],"study_design_scores_gemma":[0.0002331109,0.0012238047,0.03047916,0.0008006801,0.0008942111,0.0027038297,0.0023496624,0.8085262,0.08061868,0.023021236,0.048677824,0.00047169882],"about_ca_topic_score_codex":0.0042843553,"about_ca_topic_score_gemma":0.003984226,"teacher_disagreement_score":0.0042843553,"about_ca_system_score_codex":0.00042820518,"about_ca_system_score_gemma":0.00072647195,"threshold_uncertainty_score":0.014510453},"labels":[],"label_agreement":null},{"id":"W1974873444","doi":"10.1016/j.procs.2013.06.065","title":"Software Evolution as SaaS: Evolution of Intelligent Design in Cloud","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Software evolution; Computer science; Evolvability; Software engineering; Software development; Software; Software as a service; Cloud computing; Software system; Inheritance (genetic algorithm); Software construction; Data science; Programming language","score_opus":0.014486930275159293,"score_gpt":0.23873079679132056,"score_spread":0.22424386651616127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974873444","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1493249,0.0019404748,0.7990195,0.003883175,0.00024123534,0.00020372293,0.00009303653,0.00067464216,0.044619255],"genre_scores_gemma":[0.72671014,0.0013731392,0.2604624,0.00027585076,0.000057159377,0.0001318047,0.00012110788,0.00016366092,0.010704743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99891424,0.00052106444,0.00005302557,0.00014527018,0.00026557097,0.00010088804],"domain_scores_gemma":[0.9985757,0.0003565524,0.000214741,0.0004856341,0.00022597052,0.00014139821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014557303,0.0003261925,0.00031011342,0.00060150673,0.0009408013,0.0024905328,0.001029032,0.0008642211,0.0020646162],"category_scores_gemma":[0.004140174,0.0002564816,0.0006319829,0.0008815314,0.0016726396,0.0024661536,0.0017110679,0.0011867491,0.00040633033],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097603704,0.00012076244,0.0067366767,0.00020036288,0.0001069598,0.0004147416,0.001678272,0.12616396,0.010540865,0.73021275,0.0029683234,0.120758735],"study_design_scores_gemma":[0.00004194827,0.0002012703,0.0032228995,0.00013215838,0.00008293418,0.0008475267,0.0008471663,0.47386348,0.0066324477,0.42067283,0.09339393,0.00006148614],"about_ca_topic_score_codex":0.0027905963,"about_ca_topic_score_gemma":0.002088935,"teacher_disagreement_score":0.0027905963,"about_ca_system_score_codex":0.0014860026,"about_ca_system_score_gemma":0.0016956934,"threshold_uncertainty_score":0.010781765},"labels":[],"label_agreement":null},{"id":"W1975715938","doi":"10.1016/j.procs.2010.04.134","title":"Predictions of thermodynamic properties of energetic materials using COSMO-RS","year":2010,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energetic Materials and Combustion","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"Ministère de la Défense Nationale; National Science Council","keywords":"Computer science; Thermodynamics; Physics","score_opus":0.009002076868457503,"score_gpt":0.18762483114393108,"score_spread":0.17862275427547358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975715938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65849566,0.0022875695,0.2900397,0.0005072318,0.00020431782,0.00021521506,0.0047856704,0.0033741507,0.040090475],"genre_scores_gemma":[0.93794495,0.0009523187,0.05437294,0.00010017609,0.000053742853,0.00040241156,0.0025650496,0.0005382618,0.0030701759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998579,0.000045807174,0.000006754567,0.000015860682,0.00005603304,0.000017627915],"domain_scores_gemma":[0.99951303,0.00026109046,0.00004059166,0.000052879997,0.00010638856,0.000025996284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066933007,0.00070980605,0.0006869533,0.00091169035,0.00033765222,0.00042013763,0.0010337288,0.0007521771,0.0043097846],"category_scores_gemma":[0.0012351573,0.00033627488,0.00087953045,0.000673305,0.0003896867,0.0007429421,0.00041072263,0.00042773722,0.0008721714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009594682,0.000055590906,0.0016833019,0.00041425793,0.00004477637,0.00017488445,0.000046209094,0.9680751,0.008969904,0.012318002,0.0015061899,0.006615743],"study_design_scores_gemma":[0.000014064918,0.00003171486,0.00030465276,0.000009244253,0.000007980369,0.00003164767,0.000010181808,0.9944512,0.0023899053,0.0016789654,0.0010605054,0.000009969802],"about_ca_topic_score_codex":0.0010785131,"about_ca_topic_score_gemma":0.001012684,"teacher_disagreement_score":0.0043097846,"about_ca_system_score_codex":0.0003962519,"about_ca_system_score_gemma":0.0005682473,"threshold_uncertainty_score":0.014417648},"labels":[],"label_agreement":null},{"id":"W1976969478","doi":"10.1016/j.procs.2014.07.002","title":"FNC 2014 Preface","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Educational Robotics and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Data science","score_opus":0.006470212024715474,"score_gpt":0.21253428585992498,"score_spread":0.2060640738352095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976969478","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014600458,0.013354373,0.0061809,0.04231837,0.53192425,0.0003930687,0.006990851,0.0012229397,0.39615524],"genre_scores_gemma":[0.0058730966,0.005371843,0.0016516062,0.0040960396,0.056266002,0.00015805574,0.0036699648,0.00081157987,0.9221018],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991185,0.00009520579,0.000050526633,0.00016311722,0.0004811302,0.00009150299],"domain_scores_gemma":[0.99329466,0.00040510934,0.00015941597,0.0004100469,0.0044465116,0.0012842073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016888478,0.0010775949,0.0007338819,0.003829914,0.0021093225,0.004517952,0.0012091357,0.0014883158,0.27631888],"category_scores_gemma":[0.008837204,0.00028685573,0.0006168507,0.002015394,0.0005624351,0.0018065975,0.0019292195,0.0023705403,0.2051995],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027468084,0.000014120723,0.0000715305,0.00007516489,0.0000022695062,0.000023624587,0.000013758695,0.000118529286,0.00014910013,0.0015662058,0.9581447,0.039793503],"study_design_scores_gemma":[0.0000048781326,0.0000147763,0.00036554103,0.00012489241,0.00000280208,0.000035688212,0.000028028404,0.00010448524,0.00012973708,0.001499099,0.9976835,0.000006486055],"about_ca_topic_score_codex":0.009905403,"about_ca_topic_score_gemma":0.013080995,"teacher_disagreement_score":0.27631888,"about_ca_system_score_codex":0.003781078,"about_ca_system_score_gemma":0.0032183714,"threshold_uncertainty_score":0.92437875},"labels":[],"label_agreement":null},{"id":"W1977963540","doi":"10.1016/j.procs.2013.06.095","title":"Compressed Air Storage and Wind Energy for Time-of-day Electricity Markets","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Compressed air energy storage; Electricity; Energy storage; Renewable energy; Pumped-storage hydroelectricity; Wind power; Tariff; Computer science; Environmental economics; Automotive engineering; Environmental science; Distributed generation; Power (physics); Electrical engineering; Business; Economics; Engineering","score_opus":0.004359207612814838,"score_gpt":0.17211801758820303,"score_spread":0.1677588099753882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977963540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6522882,0.008388831,0.20105812,0.0039485157,0.0009087251,0.00020173218,0.0013436101,0.0006386126,0.13122368],"genre_scores_gemma":[0.99095154,0.00080899574,0.0037916831,0.000050759885,0.000037600217,0.000017279901,0.00011592424,0.000019717862,0.004206457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999107,0.000026891803,0.0000040009463,0.000010186575,0.00003398444,0.0000141247865],"domain_scores_gemma":[0.9998385,0.00008056452,0.00002242564,0.000011504748,0.00003364831,0.000013452004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002195605,0.00029708535,0.00031281527,0.00022540882,0.0002775172,0.00080699666,0.00052921486,0.00052600785,0.005917121],"category_scores_gemma":[0.0006607626,0.00012369781,0.00031898334,0.00039288492,0.0003134108,0.0011301145,0.00026004706,0.00043436253,0.0002600379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030866114,0.00009653482,0.0025479398,0.00018922352,0.000045421268,0.00033809504,0.000050271243,0.8436396,0.005133883,0.10667176,0.004834225,0.036144402],"study_design_scores_gemma":[0.000047813297,0.00007882149,0.001168322,0.000025535179,0.000014513819,0.0000760042,0.000052483352,0.9625075,0.0013834307,0.027868642,0.006759699,0.000017256147],"about_ca_topic_score_codex":0.009518628,"about_ca_topic_score_gemma":0.012430608,"teacher_disagreement_score":0.009518628,"about_ca_system_score_codex":0.0006934188,"about_ca_system_score_gemma":0.0007012546,"threshold_uncertainty_score":0.019794762},"labels":[],"label_agreement":null},{"id":"W1978062154","doi":"10.1016/j.procs.2014.07.065","title":"Power-aware Mapping for 3D-NoC Designs Using Genetic Algorithms","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Thompson Rivers University","funders":"","keywords":"Computer science; Scalability; Multi-core processor; Fitness function; Network on a chip; Genetic algorithm; Power consumption; Power (physics); Chip; Algorithm; Computer architecture; Embedded system; Parallel computing; Distributed computing","score_opus":0.043374633717403514,"score_gpt":0.2646851718614427,"score_spread":0.22131053814403917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978062154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08304996,0.00035183755,0.9089597,0.00018987172,0.00004416568,0.00012564461,0.000053034815,0.00071860093,0.0065072016],"genre_scores_gemma":[0.43959078,0.00024208332,0.557851,0.000082626924,0.000015520684,0.00026201698,0.00009771745,0.0001175682,0.0017407566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982184,0.00006329498,0.000006725463,0.000022708376,0.000060675684,0.000024792816],"domain_scores_gemma":[0.99965954,0.00018768941,0.00005297205,0.000028083517,0.00005927781,0.000012484979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004164669,0.00076708954,0.000395251,0.00092442165,0.0004015781,0.00046408028,0.0006094554,0.00065054646,0.0013821101],"category_scores_gemma":[0.0011750975,0.00037440046,0.0006277257,0.00054889556,0.00042015724,0.00042380032,0.00045375156,0.00048165457,0.00021495904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011453864,0.00003022663,0.00039829558,0.000039482064,0.000018085222,0.000041119933,0.000042899,0.9553491,0.0045826123,0.0036802525,0.00033466332,0.035471752],"study_design_scores_gemma":[0.000006710698,0.000020551468,0.00009497502,0.000005952174,0.0000067084397,0.000014951824,0.000012200704,0.9951742,0.0013002381,0.0027598152,0.0006000022,0.0000036902593],"about_ca_topic_score_codex":0.0028024323,"about_ca_topic_score_gemma":0.0035798384,"teacher_disagreement_score":0.0028024323,"about_ca_system_score_codex":0.0008222144,"about_ca_system_score_gemma":0.00088054576,"threshold_uncertainty_score":0.00596565},"labels":[],"label_agreement":null},{"id":"W1978996938","doi":"10.1016/j.procs.2012.06.175","title":"Distributed Policy-Based Management for Wireless Sensor Networks","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Access Control and Trust","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Wireless sensor network; Wireless; Computer network; Wireless network; Telecommunications; Distributed computing; Computer security","score_opus":0.016534440651443456,"score_gpt":0.297979195897964,"score_spread":0.2814447552465206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978996938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008590559,0.002093227,0.9791379,0.0011640713,0.00034601436,0.00032455692,0.000084229556,0.0013987758,0.0068606655],"genre_scores_gemma":[0.625067,0.0031203476,0.36120933,0.00041585867,0.00048793363,0.0007987475,0.00037951383,0.0001555482,0.008365732],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982804,0.0005371523,0.00016012067,0.00026805606,0.0006265523,0.00012763182],"domain_scores_gemma":[0.9987503,0.00054314133,0.00014867459,0.0002460948,0.00018059934,0.00013115031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027609249,0.00046718665,0.0006566838,0.00054557505,0.0010110447,0.0021118452,0.0019883541,0.0008847341,0.001459991],"category_scores_gemma":[0.0035259742,0.00028873634,0.00043767956,0.0009539838,0.0011331005,0.002595276,0.001766833,0.0014164209,0.00038386916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003044226,0.00038688967,0.0020831076,0.00050916837,0.00014910607,0.00054188084,0.00088445016,0.25881156,0.0119944,0.36904448,0.013281233,0.3420093],"study_design_scores_gemma":[0.0000530535,0.000089405396,0.0003397881,0.00006279552,0.00003640761,0.00017021914,0.00012571922,0.82334834,0.0036024002,0.13559705,0.036541294,0.000033548742],"about_ca_topic_score_codex":0.0022953006,"about_ca_topic_score_gemma":0.0019777685,"teacher_disagreement_score":0.0027609249,"about_ca_system_score_codex":0.0015602532,"about_ca_system_score_gemma":0.0020090041,"threshold_uncertainty_score":0.01460135},"labels":[],"label_agreement":null},{"id":"W1980804701","doi":"10.1016/j.procs.2012.04.040","title":"Measuring Gene Expression Noise in Early Drosophila Embryos: Nucleus-to-nucleus Variability","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"Noise (video); Computer science; Blastoderm; Biological system; Nucleus; Expression (computer science); Biology; Computational biology; Embryo; Cell biology; Artificial intelligence; Embryogenesis","score_opus":0.014430564874460513,"score_gpt":0.2245146065377121,"score_spread":0.2100840416632516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980804701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7488724,0.0004633389,0.24947643,0.00005539787,0.000010863162,0.00001748901,0.00018038882,0.00023630506,0.00068733393],"genre_scores_gemma":[0.9675157,0.00020010694,0.031802572,0.000017174287,0.000008555904,0.000022685606,0.00016955813,0.000047060217,0.00021643777],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996376,0.00006626597,0.000016775415,0.00010960248,0.00015242724,0.000017349255],"domain_scores_gemma":[0.9992705,0.00043516015,0.00010575859,0.000063059866,0.0000963722,0.000029170305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005076027,0.00028828744,0.0003306807,0.00071046554,0.00017282533,0.00028801794,0.00030006457,0.000242512,0.00024377798],"category_scores_gemma":[0.0014912738,0.0001691625,0.00021352222,0.0004322026,0.0004066126,0.00035002825,0.000267467,0.00037972015,0.00008271836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021312838,0.000039018687,0.016354514,0.00010118997,0.000058767415,0.00015625451,0.0002154986,0.02762406,0.92457753,0.0015001992,0.0000828239,0.029076988],"study_design_scores_gemma":[0.000016304508,0.00027378573,0.20214562,0.000022060272,0.00006796137,0.0006256839,0.00016651626,0.32855546,0.4608872,0.0061799684,0.00097278896,0.000086642365],"about_ca_topic_score_codex":0.0009942757,"about_ca_topic_score_gemma":0.0009464009,"teacher_disagreement_score":0.0009942757,"about_ca_system_score_codex":0.00045771737,"about_ca_system_score_gemma":0.00014672564,"threshold_uncertainty_score":0.003320992},"labels":[],"label_agreement":null},{"id":"W1981572337","doi":"10.1016/j.procs.2012.06.166","title":"Monitoring Winter Ice Conditions Using Thermal Imaging Cameras Equipped with Infrared Microbolometer Sensors","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Microbolometer; Permafrost; Snow cover; Remote sensing; Snow; Infrared; Environmental science; Thermal; Thermal infrared; Snowmelt; Computer science; Geology; Meteorology; Bolometer; Oceanography; Geomorphology; Optics; Telecommunications","score_opus":0.025142975837205952,"score_gpt":0.24452203689227742,"score_spread":0.21937906105507146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981572337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97899044,0.00013466741,0.017652337,0.000033684802,0.000024013802,0.00015151766,0.00078774465,0.0004936882,0.001731881],"genre_scores_gemma":[0.9553172,0.00019881864,0.041627593,0.000053002354,0.00002125478,0.00011479646,0.0011358208,0.00003957676,0.0014918391],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997075,0.000041645635,0.000014857919,0.00008142823,0.00010688167,0.00004759248],"domain_scores_gemma":[0.9995345,0.00008858094,0.000059371996,0.000030917534,0.0002410138,0.000045698907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002409009,0.00038815322,0.0003898446,0.0008289025,0.00027106568,0.00041808127,0.00044456235,0.00035259337,0.0012245015],"category_scores_gemma":[0.00048408224,0.00020613238,0.00019450374,0.0007080515,0.00009758941,0.00056443363,0.00023314875,0.00026610694,0.00039212062],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018398262,0.0012437649,0.11894449,0.00028170607,0.00020848688,0.0002609269,0.00034916145,0.012564629,0.73194027,0.00015089141,0.0020994449,0.13011643],"study_design_scores_gemma":[0.00018583775,0.0018815212,0.41367587,0.000037538877,0.000276183,0.0004277467,0.00042585892,0.19892547,0.3812938,0.00012576293,0.0026538826,0.000090470116],"about_ca_topic_score_codex":0.005327731,"about_ca_topic_score_gemma":0.01145434,"teacher_disagreement_score":0.005327731,"about_ca_system_score_codex":0.00035224596,"about_ca_system_score_gemma":0.00023769896,"threshold_uncertainty_score":0.010593414},"labels":[],"label_agreement":null},{"id":"W1986378259","doi":"10.1016/j.procs.2013.06.025","title":"Ad-centric Model Discovery for Prediciting Ads's Click-through Rate","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Click-through rate; Revenue; World Wide Web; The Internet; Search engine; Organic search; Service (business); Online advertising; Web search engine; Web search query","score_opus":0.02150087887796619,"score_gpt":0.25213489473268397,"score_spread":0.23063401585471777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986378259","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40153825,0.0030089756,0.57541484,0.0023275658,0.00022103617,0.00040206008,0.0074333088,0.0062002186,0.0034537795],"genre_scores_gemma":[0.9310801,0.00034460097,0.062300205,0.0001846313,0.00013419498,0.0001815762,0.0038919973,0.00005882286,0.0018238075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867815,0.00048146793,0.000107595086,0.00036348822,0.00021673618,0.00015245021],"domain_scores_gemma":[0.9920969,0.005716791,0.0006614889,0.0006013249,0.00070317695,0.00022024737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041092113,0.0018450217,0.0017962193,0.005079803,0.00064153253,0.0013852813,0.0015758288,0.0016892191,0.0018021882],"category_scores_gemma":[0.009443085,0.00075507205,0.0021655818,0.0032939874,0.00036998815,0.0013428017,0.000828233,0.0020393915,0.00091234595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080329174,0.001650231,0.10367463,0.00024698133,0.0008568824,0.00029412724,0.0001283258,0.68984133,0.001806109,0.005055387,0.0095043825,0.18613836],"study_design_scores_gemma":[0.000008079233,0.000025758673,0.0011392161,0.000004131643,0.000021396825,0.000023536308,0.000006687273,0.997592,0.00016001346,0.0008717259,0.0001418973,0.000005585348],"about_ca_topic_score_codex":0.016641505,"about_ca_topic_score_gemma":0.018003916,"teacher_disagreement_score":0.016641505,"about_ca_system_score_codex":0.0013804527,"about_ca_system_score_gemma":0.0016889217,"threshold_uncertainty_score":0.03308928},"labels":[],"label_agreement":null},{"id":"W1986401722","doi":"10.1016/j.procs.2013.09.114","title":"Tracking Method in Consideration of Existence of Similar Object around Target Object","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Object (grammar); Computer vision; Tracking (education); Artificial intelligence; Video tracking","score_opus":0.03204396204073693,"score_gpt":0.3107415780829116,"score_spread":0.27869761604217463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986401722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048562046,0.00028624246,0.99307185,0.000042548694,0.000060191494,0.000048907812,0.000016640759,0.0004098023,0.0012075531],"genre_scores_gemma":[0.24186833,0.00095644034,0.7490921,0.00019022494,0.00012744805,0.00022276865,0.00018975201,0.00013162993,0.0072213844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915504,0.00009363893,0.000042626292,0.00028432885,0.0003786165,0.000045760746],"domain_scores_gemma":[0.9993505,0.0001798129,0.00007298048,0.00007946116,0.00028274488,0.00003445543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009242385,0.0005069572,0.0008022006,0.0014518132,0.0006649864,0.0008924208,0.0010212832,0.0010833576,0.0014113298],"category_scores_gemma":[0.0018623424,0.00031731935,0.0006867484,0.0010721275,0.0004414995,0.0012711326,0.00082789105,0.00069527153,0.0006413381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018612182,0.00013412336,0.0053142495,0.00043520602,0.000220913,0.0004353312,0.00041491922,0.046023607,0.06434036,0.021129845,0.0043017804,0.85706365],"study_design_scores_gemma":[0.00007261565,0.00022786931,0.0051545785,0.000065240034,0.00020361679,0.0021794306,0.00010765345,0.92297703,0.04011163,0.009634656,0.019148922,0.000116789866],"about_ca_topic_score_codex":0.0030251315,"about_ca_topic_score_gemma":0.0022894691,"teacher_disagreement_score":0.0030251315,"about_ca_system_score_codex":0.0005032473,"about_ca_system_score_gemma":0.0010801891,"threshold_uncertainty_score":0.0060150623},"labels":[],"label_agreement":null},{"id":"W1990091694","doi":"10.1016/j.procs.2013.06.092","title":"Experimental Investigation on Phase Change Material based Thermal Energy Storage Unit","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Phase Change Materials Research","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Thermal energy storage; Phase-change material; Heat transfer fluid; Energy storage; Phase change; Work (physics); Heat transfer; Computer science; Thermal; Computer data storage; Process engineering; Tube (container); Scaling; Nuclear engineering; Materials science; Environmental science; Thermodynamics; Composite material; Physics","score_opus":0.08120777526118464,"score_gpt":0.2876418967486798,"score_spread":0.20643412148749515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990091694","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99656504,0.000112452086,0.0018982064,0.000042860738,0.000029713126,0.000051455358,0.00019106502,0.00006910822,0.0010402136],"genre_scores_gemma":[0.99613214,0.000105055784,0.0023116155,0.000017080907,0.000011040627,0.00006794403,0.00010183298,0.000015948479,0.0012373765],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994091,0.00009660201,0.000039223203,0.00012799125,0.00023743647,0.00008973256],"domain_scores_gemma":[0.99914825,0.00025898288,0.00008661877,0.00013484352,0.0003185456,0.00005280739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045010465,0.0003703181,0.0004200235,0.0002532312,0.00062320475,0.00038042144,0.0007700458,0.0007223048,0.0043106214],"category_scores_gemma":[0.0011203886,0.0002520586,0.00021731264,0.000334459,0.0006198945,0.00092294626,0.00042130213,0.0005148327,0.000620005],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005187484,0.0003002131,0.0012365187,0.00027861193,0.000010479715,0.00018547771,0.00026080653,0.0014191915,0.99127334,0.00042522562,0.00022866469,0.0038626804],"study_design_scores_gemma":[0.000036391146,0.0026731698,0.0035028243,0.000014365708,0.000016376423,0.00011951364,0.00020920779,0.0039965534,0.9878528,0.000068335445,0.001494708,0.000015799853],"about_ca_topic_score_codex":0.00037622004,"about_ca_topic_score_gemma":0.00041281426,"teacher_disagreement_score":0.0043106214,"about_ca_system_score_codex":0.00031886113,"about_ca_system_score_gemma":0.00025672404,"threshold_uncertainty_score":0.01442045},"labels":[],"label_agreement":null},{"id":"W1990547607","doi":"10.1016/j.procs.2014.05.031","title":"Naïve Creature with Fear and Desire Learning to Cross a Highway","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science","score_opus":0.007180505713796999,"score_gpt":0.2645851825076876,"score_spread":0.25740467679389056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990547607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8720852,0.00007306431,0.10970095,0.0008249305,0.000037844726,0.00013403819,0.00011297841,0.00013732482,0.01689359],"genre_scores_gemma":[0.9736302,0.000069217254,0.01962398,0.00020600526,0.000008106,0.00012775784,0.000058022117,0.000009018402,0.0062676566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998356,0.00005460983,0.000006252512,0.000048300488,0.00002053599,0.00003477494],"domain_scores_gemma":[0.999469,0.00017743492,0.00010979582,0.000075574055,0.00005400545,0.00011418777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035721593,0.00039161617,0.00034352575,0.00019536504,0.00039434174,0.0008668157,0.0010920111,0.0012691276,0.0032682521],"category_scores_gemma":[0.0016118967,0.00020344656,0.0004606384,0.00011882729,0.0012140382,0.0010009015,0.00092749676,0.00081288855,0.00029132536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006075525,0.0009636525,0.028228402,0.0003127691,0.0003193479,0.0021505326,0.0022057572,0.74831903,0.050997555,0.14017342,0.0024849088,0.023237025],"study_design_scores_gemma":[0.00014069518,0.0007696249,0.0043584593,0.000030581654,0.00008377588,0.00050636387,0.00044113558,0.9537053,0.0020660844,0.034153737,0.003679225,0.00006501583],"about_ca_topic_score_codex":0.0047042067,"about_ca_topic_score_gemma":0.002839354,"teacher_disagreement_score":0.0047042067,"about_ca_system_score_codex":0.00045839022,"about_ca_system_score_gemma":0.000584275,"threshold_uncertainty_score":0.01093334},"labels":[],"label_agreement":null},{"id":"W1990928257","doi":"10.1016/j.procs.2012.06.043","title":"Collaborate Social Network Services via Connectors","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; World Wide Web; Phone; Social network (sociolinguistics); The Internet; Service (business); Internet privacy; Social media","score_opus":0.010219543202651893,"score_gpt":0.23975164890377085,"score_spread":0.22953210570111895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990928257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025058057,0.00038135817,0.86387384,0.0020139236,0.0003932584,0.0011266612,0.0009149755,0.02510913,0.08112878],"genre_scores_gemma":[0.49166325,0.0015802174,0.42067444,0.0015109229,0.00044358673,0.0023650064,0.0053224876,0.0046098283,0.07183032],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954732,0.0015652763,0.00039424095,0.0006369869,0.001379447,0.0005508306],"domain_scores_gemma":[0.9963259,0.0009167809,0.00024996226,0.0012554785,0.00061036943,0.00064142223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055456418,0.0012725142,0.00083996233,0.0024193388,0.0021774098,0.0065869284,0.0017583501,0.0022437794,0.018073773],"category_scores_gemma":[0.007757967,0.0008172741,0.0016472545,0.0024825365,0.0023311067,0.011067029,0.011968577,0.002452763,0.008238478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045756614,0.00051559124,0.004894696,0.00051177625,0.00017669259,0.0018423962,0.0035448626,0.0067892186,0.013235407,0.77582294,0.043403804,0.14880505],"study_design_scores_gemma":[0.00022225847,0.00019721998,0.00155983,0.00021336833,0.00013383866,0.001221458,0.0017326412,0.07514708,0.015209305,0.36297578,0.54123235,0.00015491422],"about_ca_topic_score_codex":0.0033739689,"about_ca_topic_score_gemma":0.0029223396,"teacher_disagreement_score":0.018073773,"about_ca_system_score_codex":0.0013072167,"about_ca_system_score_gemma":0.002007034,"threshold_uncertainty_score":0.060462773},"labels":[],"label_agreement":null},{"id":"W1991612094","doi":"10.1016/j.procs.2013.09.055","title":"Enhancing Collection Tree Protocol for Mobile Wireless Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Protocol (science); Computer network; Tree (set theory); Wireless sensor network; Wireless; Wireless network; Mobile wireless; Wireless Application Protocol; Telecommunications","score_opus":0.011046636777057103,"score_gpt":0.2517754284890319,"score_spread":0.24072879171197478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991612094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0118911285,0.0040287543,0.97444236,0.00077857333,0.0005352662,0.00047792864,0.00015166677,0.0009883881,0.006705884],"genre_scores_gemma":[0.4145506,0.0077138036,0.5642964,0.0009779086,0.0007020722,0.0014126,0.00076983223,0.00024803123,0.009328675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991873,0.00025380784,0.00006937886,0.0000705789,0.00035127375,0.00006773215],"domain_scores_gemma":[0.99879897,0.00043572683,0.00013909278,0.00022058407,0.0003504203,0.000055259046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011401919,0.000551065,0.00051778153,0.00080325804,0.0007887866,0.000611116,0.0013442708,0.00071552664,0.0011765509],"category_scores_gemma":[0.0033803454,0.00019890281,0.00044330128,0.0013216591,0.0004650282,0.0018588807,0.0015589292,0.0011673588,0.00045064426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005634194,0.00019725985,0.0016186743,0.0015607896,0.00018809155,0.0013552761,0.00070057187,0.067493215,0.1267232,0.17520605,0.037510633,0.58688277],"study_design_scores_gemma":[0.000212958,0.0012552647,0.0015059413,0.0001886804,0.00024842547,0.0048950333,0.00030930436,0.5952418,0.06199026,0.081037566,0.2528866,0.00022820578],"about_ca_topic_score_codex":0.00089062,"about_ca_topic_score_gemma":0.0011251791,"teacher_disagreement_score":0.0013442708,"about_ca_system_score_codex":0.00043182887,"about_ca_system_score_gemma":0.0010084555,"threshold_uncertainty_score":0.0060299635},"labels":[],"label_agreement":null},{"id":"W1991769959","doi":"10.1016/j.procs.2012.06.151","title":"HAIKU: A Semantic Framework for Surveillance of Healthcare-Associated Infections","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University of New Brunswick; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canarie","keywords":"Computer science; Health care; Ontology; Scope (computer science); Vocabulary; Data science; Knowledge management","score_opus":0.021712531590591867,"score_gpt":0.31197461196890863,"score_spread":0.29026208037831674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991769959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007966413,0.0022263352,0.9519567,0.0020772554,0.00028015443,0.0010210389,0.0073074084,0.014378851,0.012785829],"genre_scores_gemma":[0.08104167,0.0027533337,0.8969085,0.0009433406,0.0001459432,0.00083877443,0.013451207,0.0006808541,0.0032363522],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963737,0.0009521513,0.00072330376,0.0006103432,0.0010315907,0.00030883716],"domain_scores_gemma":[0.9975253,0.0009338964,0.000322826,0.00043267623,0.00050429587,0.00028104344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004953821,0.0012907463,0.0013872881,0.00636294,0.0025851892,0.0060116453,0.0027128973,0.0026545704,0.0022249362],"category_scores_gemma":[0.0069588316,0.00082852546,0.0036796574,0.0043557226,0.0019477144,0.0076584634,0.005223626,0.0019015019,0.0011832973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035441222,0.0003921338,0.010834268,0.0024489076,0.00070228614,0.0024224531,0.004712232,0.040376414,0.0063285255,0.6556678,0.048444673,0.22731592],"study_design_scores_gemma":[0.000092738264,0.00010225746,0.0049842247,0.001239015,0.0005599403,0.0018158925,0.0018116953,0.17285089,0.0061607156,0.30028352,0.50985414,0.00024495364],"about_ca_topic_score_codex":0.03041889,"about_ca_topic_score_gemma":0.033672173,"teacher_disagreement_score":0.03041889,"about_ca_system_score_codex":0.0026089028,"about_ca_system_score_gemma":0.0078045814,"threshold_uncertainty_score":0.060483694},"labels":[],"label_agreement":null},{"id":"W1993837825","doi":"10.1016/j.procs.2014.05.383","title":"Supervised Discretization for Optimal Prediction","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Discretization; Artificial intelligence; Machine learning; Mathematical optimization; Mathematics","score_opus":0.010548499401469631,"score_gpt":0.22992879712260564,"score_spread":0.21938029772113601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993837825","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005236958,0.00018126625,0.9934714,0.00012820709,0.000035456294,0.000031622094,0.00008378745,0.00032558732,0.00050579716],"genre_scores_gemma":[0.33917087,0.0003196331,0.65609056,0.0002579317,0.00015108028,0.00039717616,0.0011205255,0.00019667186,0.0022955213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99691975,0.0015104308,0.00024725063,0.0005662477,0.00060395023,0.00015245676],"domain_scores_gemma":[0.99046445,0.00683147,0.0003711334,0.001077683,0.0010813223,0.00017399243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004571093,0.00068501564,0.001733466,0.0007686591,0.0006242614,0.0012548927,0.0014092791,0.0011870147,0.0021230935],"category_scores_gemma":[0.01688639,0.0005571656,0.001007354,0.00096077524,0.0010623301,0.0015646011,0.0014749096,0.0026435002,0.00059372996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002882751,0.00012652711,0.002177221,0.0001928528,0.00011176015,0.00008842523,0.00019213375,0.74504715,0.0018568668,0.045385014,0.0041361884,0.20039761],"study_design_scores_gemma":[0.0000075225257,0.000012709836,0.00008318927,0.000006513818,0.0000025400911,0.000009285836,0.000005769486,0.98471224,0.00028576094,0.0145433135,0.00032775095,0.0000033262568],"about_ca_topic_score_codex":0.0037864575,"about_ca_topic_score_gemma":0.0033099568,"teacher_disagreement_score":0.004571093,"about_ca_system_score_codex":0.0012389336,"about_ca_system_score_gemma":0.001698323,"threshold_uncertainty_score":0.024174571},"labels":[],"label_agreement":null},{"id":"W1994381500","doi":"10.1016/j.procs.2011.04.127","title":"Solvation structure and gelation ability of polyelectrolytes: predictions by quantum chemistry methods and integral equation theory of molecular liquids","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Surfactants and Colloidal Systems","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"National Research Council Canada; University of Alberta","keywords":"Solvation; Counterion; Acetonitrile; Aqueous solution; Electrolyte; Implicit solvation; Chemistry; Polyelectrolyte; Ion; Density functional theory; Dilution; Solvation shell; Thermodynamics; Physical chemistry; Computational chemistry; Polymer; Organic chemistry; Physics","score_opus":0.014804988590557037,"score_gpt":0.25286407592113475,"score_spread":0.2380590873305777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994381500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8950667,0.0010791776,0.10122842,0.00017774873,0.000016269652,0.00002091345,0.000083165236,0.00022028222,0.0021073853],"genre_scores_gemma":[0.9823613,0.000586502,0.016132269,0.00001517069,0.000009505424,0.00003388674,0.00012585086,0.000029643425,0.0007058886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999329,0.000016865564,0.0000034214247,0.000010074346,0.000029702376,0.0000069976213],"domain_scores_gemma":[0.9997738,0.00013148459,0.00003630241,0.00001665533,0.0000334473,0.000008273878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027675068,0.00025457644,0.00020967308,0.00029050326,0.00013669234,0.00020337907,0.00032742508,0.0004158968,0.000484077],"category_scores_gemma":[0.0007795755,0.00016830607,0.00034223244,0.00016243767,0.0005020354,0.00048115983,0.00021173793,0.0004647448,0.000142585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001700099,0.00012750045,0.0032281359,0.000305611,0.000043076918,0.00018751204,0.00018886145,0.628475,0.3300424,0.015964033,0.00035594593,0.020911818],"study_design_scores_gemma":[0.000009236041,0.000031761385,0.0007607065,0.000004693413,0.000003969006,0.000023751954,0.0000064139726,0.965456,0.031833004,0.0016669312,0.00019575864,0.000007727422],"about_ca_topic_score_codex":0.00093065656,"about_ca_topic_score_gemma":0.00043676345,"teacher_disagreement_score":0.00093065656,"about_ca_system_score_codex":0.00046640637,"about_ca_system_score_gemma":0.00023143209,"threshold_uncertainty_score":0.0033840537},"labels":[],"label_agreement":null},{"id":"W1994899504","doi":"10.1016/j.procs.2013.06.044","title":"AdAMAC: A New MAC Protocol for High Traffic Wireless Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Multiple Access with Collision Avoidance for Wireless; Access control; Wireless ad hoc network; Network packet; Throughput; Latency (audio); Wireless sensor network; Wireless network; Wireless; Wireless distribution system; Media access control; Wi-Fi array; Optimized Link State Routing Protocol; Telecommunications; Routing protocol","score_opus":0.013521014661544196,"score_gpt":0.24644697387558523,"score_spread":0.23292595921404102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994899504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011273661,0.0030097964,0.9772075,0.00051645923,0.0010661058,0.00034383233,0.00010228356,0.0026663987,0.0038139776],"genre_scores_gemma":[0.32938856,0.0035610986,0.6479386,0.0013932514,0.0009812663,0.0013704018,0.0004952087,0.00035058754,0.014521024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822074,0.00045563892,0.00015145027,0.00020403742,0.00081080914,0.00015723515],"domain_scores_gemma":[0.997424,0.00065860397,0.00045989084,0.00043076483,0.0008173642,0.00020937397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020916108,0.0009533391,0.0009769645,0.0010412128,0.0011826195,0.0015653028,0.0031863416,0.0011087138,0.0014797371],"category_scores_gemma":[0.004345075,0.000331772,0.00044961754,0.00084783474,0.0011835035,0.0025100878,0.002316024,0.0031718547,0.00073555676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010756141,0.000337191,0.0014839936,0.0010864503,0.00022773942,0.0008503912,0.0006459583,0.07703847,0.089180365,0.18714093,0.032936636,0.6079963],"study_design_scores_gemma":[0.00016460396,0.00082153856,0.00095221377,0.00013172638,0.00015728956,0.0025806818,0.00014470197,0.7314701,0.048134644,0.038783,0.17642799,0.00023156982],"about_ca_topic_score_codex":0.0006512611,"about_ca_topic_score_gemma":0.00070346915,"teacher_disagreement_score":0.0031863416,"about_ca_system_score_codex":0.000696523,"about_ca_system_score_gemma":0.001376503,"threshold_uncertainty_score":0.011061668},"labels":[],"label_agreement":null},{"id":"W1997338250","doi":"10.1016/j.procs.2013.09.309","title":"CaptchAll: An Improvement on the Modern Text-based CAPTCHA","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"CAPTCHA; Computer science; Task (project management); Implementation; Context (archaeology); Face (sociological concept); Human–computer interaction; Image (mathematics); The Internet; Simple (philosophy); User Friendly; World Wide Web; Artificial intelligence; Software engineering; Programming language","score_opus":0.01918704472592486,"score_gpt":0.2298191125447849,"score_spread":0.21063206781886004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997338250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057370225,0.001353359,0.9511108,0.0009981227,0.0008483449,0.00043992247,0.00016648154,0.021573512,0.017772408],"genre_scores_gemma":[0.1151878,0.002212862,0.83995545,0.0018479873,0.00094583334,0.0004322511,0.00077818596,0.00362309,0.035016503],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99570125,0.00056365505,0.00020233844,0.0005666032,0.0027314362,0.00023463387],"domain_scores_gemma":[0.99200726,0.0019709629,0.00036082824,0.0028223412,0.0025618833,0.0002766793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021466243,0.0018948492,0.0011404527,0.0025734885,0.0016202319,0.003146513,0.0037660222,0.003182154,0.020174924],"category_scores_gemma":[0.010557306,0.0007567995,0.0010528218,0.0023011577,0.0019658566,0.004683865,0.0031188105,0.0046361256,0.014009665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005452103,0.00039033842,0.00051428727,0.00045611057,0.00007344752,0.00039598864,0.00069453975,0.008212308,0.060633324,0.019296635,0.03322319,0.87556463],"study_design_scores_gemma":[0.00023071944,0.0016478698,0.001401114,0.00035821772,0.00021394645,0.004235568,0.00061034336,0.36032113,0.1671771,0.019046472,0.4443137,0.00044381112],"about_ca_topic_score_codex":0.0040292656,"about_ca_topic_score_gemma":0.0029911164,"teacher_disagreement_score":0.020174924,"about_ca_system_score_codex":0.00094448,"about_ca_system_score_gemma":0.001297426,"threshold_uncertainty_score":0.06749183},"labels":[],"label_agreement":null},{"id":"W1998593453","doi":"10.1016/j.procs.2013.09.060","title":"AID: An Energy Efficient Decoding Scheme for LDPC Codes in Wireless Body Area Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Low-density parity-check code; Computer science; Decoding methods; Energy consumption; Wireless; Algorithm; Bit error rate; Code (set theory); Power (physics); Real-time computing; Telecommunications; Electrical engineering","score_opus":0.016395528451132207,"score_gpt":0.25507090615628475,"score_spread":0.23867537770515254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998593453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07575808,0.0006981234,0.9171862,0.00020027759,0.00006241663,0.00010277699,0.000103436585,0.0009962727,0.004892344],"genre_scores_gemma":[0.6990443,0.00056549266,0.29506734,0.000113007096,0.000028681903,0.00010440758,0.00014722272,0.00005876121,0.0048708576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997665,0.00006284117,0.000011549693,0.000028731056,0.000105823405,0.000024505842],"domain_scores_gemma":[0.999716,0.00010419885,0.000034064713,0.000047578902,0.00008394472,0.000014183448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028557185,0.00033528398,0.0002742491,0.00032336125,0.0002903421,0.00038895552,0.00038842697,0.00040212262,0.0006180283],"category_scores_gemma":[0.0009562889,0.00010541851,0.00014364452,0.00039046572,0.00042138464,0.00035006463,0.00057356333,0.00044619903,0.00023192093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005860446,0.00014729347,0.0016593967,0.000408971,0.00006338554,0.0003118277,0.00037079398,0.38291892,0.19788939,0.04809378,0.003150246,0.3643999],"study_design_scores_gemma":[0.00003766292,0.00018611464,0.00040604256,0.000026374024,0.000019275454,0.00027422095,0.0000246322,0.90486294,0.0815487,0.008031738,0.004558442,0.000023913064],"about_ca_topic_score_codex":0.00084917364,"about_ca_topic_score_gemma":0.001289679,"teacher_disagreement_score":0.00084917364,"about_ca_system_score_codex":0.0003490933,"about_ca_system_score_gemma":0.00074200355,"threshold_uncertainty_score":0.0025328398},"labels":[],"label_agreement":null},{"id":"W2000362084","doi":"10.1016/j.procs.2013.09.216","title":"Shape from Endoscope Image based on Photometric and Geometric Constraints","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Image (mathematics); Artificial intelligence; Endoscope; Photometric stereo; Computer graphics (images)","score_opus":0.013324102100451072,"score_gpt":0.2588305781761224,"score_spread":0.2455064760756713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000362084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021372713,0.000118831864,0.9766337,0.00010683404,0.000022140253,0.000031835298,0.00007568411,0.000398494,0.0012396259],"genre_scores_gemma":[0.34699097,0.0007495819,0.6456229,0.00010023127,0.00006406406,0.00011791447,0.0009341303,0.00051575276,0.0049044546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995622,0.00004553234,0.000014280882,0.0000712708,0.00028248216,0.000024183242],"domain_scores_gemma":[0.9996007,0.00008265163,0.000055641663,0.0001278517,0.00010691878,0.000026222273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033060662,0.000678765,0.00069041416,0.0007157353,0.00027083707,0.00079332193,0.00068177056,0.0006970446,0.0014133111],"category_scores_gemma":[0.001605605,0.0005473762,0.0010088581,0.000659857,0.0006150077,0.001139693,0.0011403211,0.0010665003,0.00074960967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003526558,0.00006551829,0.0013287816,0.0002535712,0.00009484648,0.00030390295,0.00026506936,0.34708068,0.21086703,0.012373178,0.0022001495,0.42481458],"study_design_scores_gemma":[0.00002070287,0.000069054855,0.0013654958,0.00001486197,0.000023497869,0.000410796,0.000036936894,0.9398227,0.05140498,0.0038058127,0.0029666498,0.000058527232],"about_ca_topic_score_codex":0.002183004,"about_ca_topic_score_gemma":0.0021518993,"teacher_disagreement_score":0.002183004,"about_ca_system_score_codex":0.0003963728,"about_ca_system_score_gemma":0.00087205006,"threshold_uncertainty_score":0.0047280192},"labels":[],"label_agreement":null},{"id":"W2002724831","doi":"10.1016/j.procs.2010.04.200","title":"Efficient generated libraries for asynchronous derivative computation","year":2010,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. Department of Energy","keywords":"Computer science; Automatic differentiation; Computation; Asynchronous communication; Loop unrolling; Intrinsics; Parallel computing; Generator (circuit theory); Transformation (genetics); Program transformation; Code generation; Code (set theory); Programming language; Theoretical computer science; Compiler; Key (lock); Operating system","score_opus":0.016611026128836432,"score_gpt":0.2786232879182579,"score_spread":0.2620122617894215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002724831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025511911,0.00013482504,0.9324624,0.00012089292,0.00010917818,0.0001278469,0.00047635616,0.033181634,0.007875019],"genre_scores_gemma":[0.3044978,0.00021627362,0.66846687,0.00021879669,0.00010294548,0.0006206702,0.0024125904,0.01046223,0.013001848],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988237,0.00019531934,0.0000799619,0.00016575555,0.0006025756,0.00013259117],"domain_scores_gemma":[0.9972187,0.0008854005,0.00021684133,0.000876549,0.0007099269,0.000092551265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000952802,0.0009345703,0.0006566472,0.0010080419,0.0006796949,0.0017560073,0.0023142826,0.00087622885,0.011636771],"category_scores_gemma":[0.0052201063,0.0006442914,0.00087706995,0.0009938565,0.0008182715,0.0018330469,0.0017842309,0.0017155575,0.0044015544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016982123,0.000653533,0.003411284,0.00076136104,0.000110622444,0.0010091437,0.00081655633,0.18679918,0.09727705,0.29976737,0.038212914,0.36948273],"study_design_scores_gemma":[0.00034019863,0.00019192741,0.0005385528,0.000072856186,0.000049875867,0.00030753503,0.000048037567,0.749147,0.14501715,0.056041304,0.04817878,0.00006683567],"about_ca_topic_score_codex":0.00049997924,"about_ca_topic_score_gemma":0.00068789674,"teacher_disagreement_score":0.011636771,"about_ca_system_score_codex":0.0010242639,"about_ca_system_score_gemma":0.0015584239,"threshold_uncertainty_score":0.038928866},"labels":[],"label_agreement":null},{"id":"W2004316123","doi":"10.1016/j.procs.2014.05.141","title":"Efficient Data Structures for Risk Modelling in Portfolios of Catastrophic Risk Using MapReduce","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Variety (cybernetics); Computation; Big data; Risk assessment; Data science; Risk management; Data mining; Risk analysis (engineering); Artificial intelligence; Algorithm; Computer security; Finance","score_opus":0.1719838453484472,"score_gpt":0.37960404146384397,"score_spread":0.20762019611539675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004316123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032416306,0.0005077141,0.93954855,0.001359985,0.00016049798,0.0002965056,0.004840711,0.016028132,0.0048415954],"genre_scores_gemma":[0.39302787,0.0005486991,0.5929785,0.00038672282,0.00014058659,0.00057497504,0.008158396,0.0015677848,0.0026164767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803954,0.00038489312,0.00020675577,0.00039562918,0.000756512,0.00021664513],"domain_scores_gemma":[0.994911,0.0017347954,0.0003505991,0.0017324691,0.0009821,0.0002889553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003048561,0.0015072046,0.001244135,0.0016631475,0.0016134069,0.0044830763,0.0038682646,0.0010435754,0.0040294225],"category_scores_gemma":[0.011971517,0.0010202966,0.0025891943,0.0033714701,0.00093439396,0.0061701,0.002992732,0.0025925795,0.0015230306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059008,0.00044665227,0.008862243,0.00053433067,0.000390652,0.00043054073,0.0008040726,0.59639126,0.005366684,0.13591269,0.04936532,0.20090556],"study_design_scores_gemma":[0.00006385029,0.00003753053,0.0005760398,0.000023952129,0.000031033156,0.00008032678,0.00017037986,0.8640364,0.0029786474,0.12321924,0.00875355,0.00002907953],"about_ca_topic_score_codex":0.013079535,"about_ca_topic_score_gemma":0.017194767,"teacher_disagreement_score":0.013079535,"about_ca_system_score_codex":0.0022231203,"about_ca_system_score_gemma":0.0043987404,"threshold_uncertainty_score":0.026006758},"labels":[],"label_agreement":null},{"id":"W2005626131","doi":"10.1016/j.procs.2012.06.037","title":"Social Network Analysis of Kuwait Publicly-Held Corporations","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Corporate Finance and Governance","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interlock; Corporate governance; Centrality; Business; Collusion; Social network analysis; Interlocking; Accounting; Stock exchange; Industrial organization; Social network (sociolinguistics); Computer science; Finance","score_opus":0.028287566103638428,"score_gpt":0.23600792341190252,"score_spread":0.2077203573082641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005626131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963456,0.00009552827,0.00071880716,0.00014340902,0.0000035552036,0.000020274596,0.00075683586,0.00001644728,0.001899553],"genre_scores_gemma":[0.9976646,0.00007369741,0.0006321719,0.000008817376,0.0000042282154,0.00001980405,0.00097421015,0.0000020852704,0.0006203924],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996972,0.00011599077,0.000019123672,0.000053493593,0.00004917423,0.0000649993],"domain_scores_gemma":[0.9986815,0.0006150371,0.00033407143,0.00006397573,0.00020280843,0.000102570135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003283929,0.00013779542,0.00016480863,0.0036002249,0.0009456515,0.00077181525,0.0002466111,0.00032356262,0.0019590335],"category_scores_gemma":[0.0016762065,0.00008655559,0.00019482427,0.0035665587,0.00034279405,0.0006428071,0.00045503775,0.00017020981,0.00018748861],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053319754,0.0002999215,0.8317198,0.0003994509,0.00027375604,0.0017501098,0.018225402,0.025458585,0.008790548,0.019129407,0.0070382245,0.08638152],"study_design_scores_gemma":[0.000008474517,0.000081405306,0.8673886,0.00005617098,0.000077367724,0.00040354527,0.013512582,0.10538824,0.001080652,0.0032620511,0.008706455,0.000034486406],"about_ca_topic_score_codex":0.04982759,"about_ca_topic_score_gemma":0.061088514,"teacher_disagreement_score":0.04982759,"about_ca_system_score_codex":0.0012599209,"about_ca_system_score_gemma":0.00045495102,"threshold_uncertainty_score":0.09907508},"labels":[],"label_agreement":null},{"id":"W2006738362","doi":"10.1016/j.procs.2013.06.147","title":"Adaptive Energy Aware Cooperation Strategy in Heterogeneous Multi-domain Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Domain (mathematical analysis); Wireless sensor network; Energy (signal processing); Distributed computing; Computer network","score_opus":0.013822662728693435,"score_gpt":0.21768514224709357,"score_spread":0.20386247951840014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006738362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30451798,0.0005703184,0.6910503,0.00031020067,0.00006257268,0.00007936495,0.000017696071,0.00014733689,0.0032442568],"genre_scores_gemma":[0.98172635,0.0000746677,0.017537484,0.000039351053,0.000007221525,0.000027255728,0.000010644809,0.0000064855317,0.00057048706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963903,0.00014896819,0.000017842893,0.00006723801,0.000064363405,0.000062525505],"domain_scores_gemma":[0.99921167,0.00041075796,0.00010726601,0.0000722639,0.00010702374,0.000090979156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010151719,0.0004258923,0.00044819404,0.00046537406,0.00061850407,0.00049156253,0.0010367617,0.00050946523,0.0003056976],"category_scores_gemma":[0.0017111591,0.00017498742,0.00031033345,0.0003724841,0.00059517357,0.0009093012,0.00089216063,0.0002985146,0.00005723129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019317104,0.00009051492,0.0019736658,0.00008591531,0.000096165175,0.00056568696,0.00040660516,0.9140146,0.017188614,0.026821135,0.0007029542,0.03786108],"study_design_scores_gemma":[0.000012826335,0.000073613206,0.00024203249,0.000003586665,0.0000142168465,0.00007924576,0.000058758953,0.9926745,0.0013681117,0.0050205956,0.00044511515,0.000007401632],"about_ca_topic_score_codex":0.0014114543,"about_ca_topic_score_gemma":0.0012460954,"teacher_disagreement_score":0.0014114543,"about_ca_system_score_codex":0.00046162322,"about_ca_system_score_gemma":0.00047101785,"threshold_uncertainty_score":0.005368829},"labels":[],"label_agreement":null},{"id":"W2008325499","doi":"10.1016/j.procs.2014.11.086","title":"Top Down Bottom up Brain Models","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Sensemaking; Top-down and bottom-up design; Context (archaeology); Artificial intelligence; Human–computer interaction; Data science; Cognitive science; Software engineering","score_opus":0.018631950039026256,"score_gpt":0.24559155041018885,"score_spread":0.22695960037116258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008325499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022811,0.0011308405,0.9167461,0.0035512063,0.00014963205,0.000057875313,0.0005062858,0.00068032014,0.054366797],"genre_scores_gemma":[0.64281404,0.0015250371,0.32927543,0.0009142177,0.00013727325,0.00026177565,0.00062522554,0.00030014417,0.024146905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99957246,0.0001319131,0.000017634547,0.000117671065,0.0001082099,0.000052115178],"domain_scores_gemma":[0.9991326,0.0003430007,0.00006886197,0.00017936493,0.00019172486,0.00008453398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006529606,0.0008203549,0.00070079096,0.00090795144,0.00065550074,0.0028305303,0.0015688086,0.0010878433,0.008332787],"category_scores_gemma":[0.0020587652,0.00050432124,0.0014790702,0.0005827086,0.0015675118,0.0031729513,0.0014603887,0.0015886002,0.0019788786],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040352563,0.000024153662,0.0006840915,0.00013436892,0.00008150936,0.00010398745,0.0005644597,0.055428814,0.0021556555,0.90032965,0.0037002908,0.036752652],"study_design_scores_gemma":[0.000008883384,0.00002484116,0.00046732175,0.00003704101,0.000034597608,0.00008591702,0.00009363805,0.24409214,0.00085282075,0.74426514,0.010016919,0.00002077733],"about_ca_topic_score_codex":0.005670347,"about_ca_topic_score_gemma":0.00531082,"teacher_disagreement_score":0.008332787,"about_ca_system_score_codex":0.0013520869,"about_ca_system_score_gemma":0.00093704805,"threshold_uncertainty_score":0.02787596},"labels":[],"label_agreement":null},{"id":"W2012639041","doi":"10.1016/j.procs.2014.07.017","title":"Optimal Placement of RFID Antennas for Outdoor Applications","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Radio-frequency identification; Directional antenna; Identification (biology); Function (biology); Telecommunications; Antenna (radio)","score_opus":0.010072292569924538,"score_gpt":0.22291187497119316,"score_spread":0.21283958240126863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012639041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032566607,0.0005942254,0.9568216,0.0001668322,0.00008213918,0.000033173532,0.00006961508,0.0003543626,0.009311496],"genre_scores_gemma":[0.7394173,0.0007515245,0.25282082,0.00008387368,0.000052475945,0.00008500403,0.0001307524,0.00011425633,0.0065440764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996081,0.00013717385,0.000012603041,0.000074070915,0.00009962988,0.00006846612],"domain_scores_gemma":[0.999746,0.00009913539,0.000052300846,0.000032460302,0.00005431922,0.00001570599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030255082,0.0011111123,0.0007890351,0.00043298546,0.00038016262,0.0007760678,0.0005418742,0.0011617396,0.002082931],"category_scores_gemma":[0.00097351684,0.0005705433,0.00060004176,0.0006097938,0.00037585633,0.00060461886,0.0004925341,0.00040110614,0.0010999632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095311225,0.000039939358,0.00060974085,0.0000846977,0.000021441168,0.00017385269,0.000040099232,0.94203526,0.014588063,0.00405974,0.0014981795,0.036753632],"study_design_scores_gemma":[0.000021645426,0.00014245942,0.0004553752,0.0000148680465,0.000021030226,0.00015567192,0.00006347285,0.98502296,0.0068206755,0.0038458195,0.003416839,0.000019226209],"about_ca_topic_score_codex":0.0014809086,"about_ca_topic_score_gemma":0.0016958533,"teacher_disagreement_score":0.002082931,"about_ca_system_score_codex":0.00060926896,"about_ca_system_score_gemma":0.00049165974,"threshold_uncertainty_score":0.0069681406},"labels":[],"label_agreement":null},{"id":"W2012654131","doi":"10.1016/j.procs.2013.09.005","title":"Personalized Security Approaches in E-banking Employing Flask Architecture over Cloud Environment","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Cloud computing; Cloud computing security; Computer security; Security controls; Computer security model; Enterprise information security architecture; Architecture; Access control; Distributed System Security Architecture; Payment; Security service; Security information and event management; Control (management); Information security; World Wide Web; Operating system","score_opus":0.02557702156643895,"score_gpt":0.2274073820448885,"score_spread":0.20183036047844954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012654131","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13195199,0.0010570529,0.8399226,0.0016117885,0.00010828814,0.00036181416,0.000089008994,0.003895272,0.021002103],"genre_scores_gemma":[0.85487515,0.0007864073,0.1324886,0.00031020335,0.000039305778,0.00009471045,0.00015331413,0.00014008074,0.011112153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998845,0.00026245226,0.00010222801,0.0002006839,0.00031293381,0.0002766945],"domain_scores_gemma":[0.9993931,0.00009349168,0.000050921375,0.0002130142,0.00014116836,0.00010828028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001206653,0.00035070215,0.0004257476,0.0005939065,0.0012163242,0.0030845422,0.0012943989,0.0011836321,0.002576501],"category_scores_gemma":[0.0010834696,0.0003243604,0.0006372268,0.0004886546,0.001036294,0.00539568,0.0030977908,0.0014226597,0.000940153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008203756,0.00045616206,0.0065342267,0.00026745675,0.0001310192,0.0012141935,0.002062583,0.080517896,0.06065278,0.68408895,0.0075496975,0.15570465],"study_design_scores_gemma":[0.000080209036,0.00030117496,0.0023152858,0.00012413294,0.00017657004,0.0008848024,0.0012256006,0.7119437,0.052328758,0.16059051,0.06984421,0.00018509483],"about_ca_topic_score_codex":0.0038426497,"about_ca_topic_score_gemma":0.003241126,"teacher_disagreement_score":0.0038426497,"about_ca_system_score_codex":0.0014201731,"about_ca_system_score_gemma":0.001623759,"threshold_uncertainty_score":0.010304093},"labels":[],"label_agreement":null},{"id":"W2012773371","doi":"10.1016/j.procs.2012.06.021","title":"Ultrasonic Non-Destructive Testing (NDT) Using Wireless Sensor Networks","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nondestructive testing; Computer science; Wireless sensor network; Ultrasonic sensor; Wireless; Process (computing); Embedded system; Telecommunications; Computer network; Acoustics","score_opus":0.022137177880692655,"score_gpt":0.2431587889564914,"score_spread":0.22102161107579874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012773371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021846775,0.0066770273,0.9600888,0.0003945545,0.00027095823,0.00017974558,0.00006708855,0.0006838439,0.0097911805],"genre_scores_gemma":[0.49387157,0.014385294,0.47808692,0.00043463238,0.00025769495,0.00040315784,0.00023653687,0.00011964719,0.012204561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876595,0.0002413702,0.000050909704,0.00016601793,0.00073638227,0.000039369323],"domain_scores_gemma":[0.9991365,0.0004526861,0.00012897947,0.0001111825,0.00015053467,0.000020141295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005953409,0.0006492922,0.00038801008,0.0007653091,0.00024244425,0.0007282086,0.00079666125,0.00061478856,0.0007095656],"category_scores_gemma":[0.0012385814,0.00023030683,0.00035896138,0.0007055474,0.0005500852,0.0013247184,0.0009782452,0.0003978507,0.00038080005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013985111,0.00009603497,0.0047550965,0.0011767242,0.00009156872,0.00040877445,0.0002253407,0.016307417,0.3037972,0.012267424,0.002108653,0.65862596],"study_design_scores_gemma":[0.000058300517,0.0012531526,0.007713472,0.000431027,0.00021050418,0.0041167242,0.000396335,0.26228312,0.5303547,0.015993694,0.17703639,0.00015257607],"about_ca_topic_score_codex":0.00046670527,"about_ca_topic_score_gemma":0.0007960576,"teacher_disagreement_score":0.00079666125,"about_ca_system_score_codex":0.0003535395,"about_ca_system_score_gemma":0.00036460152,"threshold_uncertainty_score":0.0031485558},"labels":[],"label_agreement":null},{"id":"W2017155419","doi":"10.1016/j.procs.2011.09.050","title":"Quantum Theory-Inspired Search","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Engineering and Physical Sciences Research Council; European Commission","keywords":"Computer science; Quantum; Theoretical computer science; Quantum mechanics; Physics","score_opus":0.035995282752279215,"score_gpt":0.28006056234070886,"score_spread":0.24406527958842966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017155419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10944381,0.007257853,0.76360923,0.005836222,0.00063637213,0.00024629582,0.00037628366,0.00036615573,0.112227686],"genre_scores_gemma":[0.89992756,0.0024141287,0.08279989,0.000602512,0.00030047284,0.00027678796,0.00018924451,0.00009311881,0.013396392],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938536,0.00025613458,0.000027854696,0.00006793606,0.00019674003,0.00006600884],"domain_scores_gemma":[0.99883026,0.0007588954,0.00007638499,0.00012767581,0.00014342048,0.00006330063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008872083,0.0003128099,0.0009470825,0.0009213757,0.00074894703,0.0015591059,0.0011122598,0.0012294023,0.004528724],"category_scores_gemma":[0.003260529,0.00021275869,0.0007031109,0.0010571707,0.0019569267,0.0024695166,0.0011481822,0.0009554178,0.00041189895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018103987,0.000016754248,0.00018910323,0.00009349378,0.000024570287,0.000036301757,0.0000838112,0.033916373,0.0005535735,0.95495135,0.0014177308,0.008698836],"study_design_scores_gemma":[0.000026046904,0.000018697812,0.00018125228,0.000013914367,0.000009373559,0.00002932246,0.000029348845,0.33178973,0.00016923774,0.66518503,0.0025336032,0.000014349664],"about_ca_topic_score_codex":0.002519795,"about_ca_topic_score_gemma":0.0015373161,"teacher_disagreement_score":0.004528724,"about_ca_system_score_codex":0.0016965647,"about_ca_system_score_gemma":0.0010349562,"threshold_uncertainty_score":0.01515013},"labels":[],"label_agreement":null},{"id":"W2018248918","doi":"10.1016/j.procs.2014.08.074","title":"Efficient Private Information Retrieval for Geographical Aggregation","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Children's Hospital of Eastern Ontario; IBM (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information retrieval; Data science; World Wide Web","score_opus":0.012850529406597788,"score_gpt":0.24411863932706068,"score_spread":0.2312681099204629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018248918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023697728,0.0007022101,0.9670292,0.0011640571,0.00009996058,0.00031847338,0.0005264231,0.00093560264,0.005526269],"genre_scores_gemma":[0.7269734,0.00094082137,0.2598834,0.00057347445,0.0003407809,0.00064088544,0.0013966961,0.00015074604,0.009099846],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9941493,0.0015858151,0.0006068075,0.0008817464,0.001997716,0.00077859167],"domain_scores_gemma":[0.99059707,0.0028901983,0.0007319771,0.0046752538,0.0009324367,0.00017295437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033221329,0.00060273457,0.0018729647,0.0014458528,0.0020682593,0.0038090057,0.0020709638,0.0018415292,0.0031727822],"category_scores_gemma":[0.012612205,0.0005963177,0.0010773866,0.003281846,0.0016443118,0.008852353,0.0072067436,0.0018795496,0.0014693526],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012180633,0.00042251515,0.0022020799,0.00045252164,0.00020814306,0.00079325924,0.0021225447,0.12695844,0.033096522,0.50123537,0.024515508,0.3067751],"study_design_scores_gemma":[0.00015585087,0.0001532885,0.0006486421,0.000041079347,0.00010565883,0.0006219521,0.00053857715,0.6700769,0.016516851,0.2912706,0.019787831,0.000082736566],"about_ca_topic_score_codex":0.0019999128,"about_ca_topic_score_gemma":0.0013926851,"teacher_disagreement_score":0.0038090057,"about_ca_system_score_codex":0.0020053268,"about_ca_system_score_gemma":0.0026654343,"threshold_uncertainty_score":0.017569363},"labels":[],"label_agreement":null},{"id":"W2018517412","doi":"10.1016/j.procs.2013.06.045","title":"Energy Constrained Positioning in Mobile Wireless Ad hoc and Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Wireless sensor network; Wireless ad hoc network; Mobile ad hoc network; Wireless; Computer network; Energy (signal processing); Telecommunications","score_opus":0.0033340921202442597,"score_gpt":0.17669775311142488,"score_spread":0.1733636609911806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018517412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03141841,0.013435014,0.94650906,0.00045520443,0.00038516358,0.000115377516,0.00010758809,0.00040051877,0.00717361],"genre_scores_gemma":[0.77335113,0.017405389,0.19752075,0.00028804014,0.00048042802,0.00029605837,0.00031930406,0.000103832186,0.010235109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993586,0.00022663649,0.000031473748,0.00008899691,0.0002503599,0.00004394563],"domain_scores_gemma":[0.9992299,0.0004690228,0.000089935864,0.000095546595,0.00009760156,0.000018065597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005391591,0.00059332204,0.0007900609,0.0005030893,0.0003800673,0.0006247998,0.0006984813,0.00093647686,0.0013429634],"category_scores_gemma":[0.0022055663,0.0002923777,0.00024533813,0.0017414305,0.000573325,0.0011343176,0.0007884529,0.00053652795,0.0004714716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013146053,0.000069526875,0.0010298347,0.00043277224,0.00006311324,0.00025166763,0.00009575484,0.7706398,0.008367273,0.03418949,0.0029234607,0.1818059],"study_design_scores_gemma":[0.000024206229,0.0001817709,0.00088818034,0.000055040106,0.000023227787,0.0002476499,0.00006433957,0.9518019,0.0033136203,0.033205315,0.010162243,0.000032554286],"about_ca_topic_score_codex":0.0015545232,"about_ca_topic_score_gemma":0.001350455,"teacher_disagreement_score":0.0015545232,"about_ca_system_score_codex":0.0003436812,"about_ca_system_score_gemma":0.00033430057,"threshold_uncertainty_score":0.0044926405},"labels":[],"label_agreement":null},{"id":"W2018701378","doi":"10.1016/j.procs.2012.06.141","title":"Improving QoS in VANET Using MPLS","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Multiprotocol Label Switching; Quality of service; Vehicular ad hoc network; Computer network; Telecommunications; Wireless ad hoc network; Wireless","score_opus":0.011282234862883305,"score_gpt":0.21461526582487198,"score_spread":0.20333303096198868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018701378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11131476,0.003730528,0.84833795,0.00137623,0.00055654853,0.00017695873,0.00025603003,0.0048683244,0.029382732],"genre_scores_gemma":[0.88039196,0.0017309792,0.111722514,0.0002798285,0.00015529401,0.00005948116,0.00022640376,0.00016956213,0.005263955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995913,0.00011638855,0.00002089737,0.000042165324,0.00016262948,0.00006671285],"domain_scores_gemma":[0.9995179,0.00012576638,0.000037763715,0.00007152924,0.0002122173,0.000034868117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055691105,0.00051150844,0.0003409497,0.0007661584,0.00064072566,0.0011150775,0.0007577221,0.00046343755,0.0015864122],"category_scores_gemma":[0.0012791905,0.00019717337,0.00019250593,0.0007110701,0.00034400928,0.0010620094,0.0009212405,0.0006731982,0.00041559598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028405525,0.00019103824,0.0024311359,0.00034271122,0.000096064374,0.000577528,0.00036869073,0.5109089,0.08701972,0.051042452,0.011396773,0.33534092],"study_design_scores_gemma":[0.000017220502,0.000107301,0.00031141096,0.00002281346,0.000031268988,0.00018074585,0.00012926714,0.9539038,0.016113482,0.011584791,0.017563783,0.00003414215],"about_ca_topic_score_codex":0.0026350117,"about_ca_topic_score_gemma":0.0027131129,"teacher_disagreement_score":0.0026350117,"about_ca_system_score_codex":0.00059090526,"about_ca_system_score_gemma":0.00045844488,"threshold_uncertainty_score":0.0053070784},"labels":[],"label_agreement":null},{"id":"W2021418829","doi":"10.1016/j.procs.2011.08.033","title":"An evolutionary computation attack on one-round TEA","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Byte; Ciphertext; Computation; Encryption; Key (lock); Word (group theory); Theoretical computer science; Population; Algorithm; Mathematics; Computer security; Operating system","score_opus":0.09420885618557555,"score_gpt":0.32711132055310216,"score_spread":0.2329024643675266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021418829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39289272,0.00023397818,0.5904764,0.00061879406,0.00009060521,0.00009571888,0.00004809116,0.0004666855,0.015077011],"genre_scores_gemma":[0.95849955,0.000067027024,0.03825951,0.000093136376,0.000007854685,0.00004268187,0.000021871756,0.000033156866,0.0029752608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916935,0.0002608387,0.00003235745,0.00011443411,0.00028582604,0.00013711155],"domain_scores_gemma":[0.99903095,0.00044746004,0.00009736627,0.00029807125,0.00008774275,0.000038375198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007770875,0.00029443396,0.00041883285,0.0003186004,0.00049509545,0.00054901716,0.0005266357,0.000783182,0.001249053],"category_scores_gemma":[0.0030630424,0.0001380008,0.0004999371,0.00022971864,0.0008228974,0.0008221728,0.0010915238,0.0006290291,0.00019369238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067630043,0.000118788346,0.0048279874,0.00017104349,0.0002537726,0.001346562,0.00069278793,0.45004845,0.1128142,0.31560826,0.0015610325,0.111880794],"study_design_scores_gemma":[0.00003987253,0.00022565041,0.0010201597,0.000017483393,0.00003684541,0.00072582887,0.000058852413,0.9079065,0.037258394,0.048203655,0.0044814786,0.000025398773],"about_ca_topic_score_codex":0.00042525635,"about_ca_topic_score_gemma":0.00023097663,"teacher_disagreement_score":0.001249053,"about_ca_system_score_codex":0.00038301345,"about_ca_system_score_gemma":0.00033226484,"threshold_uncertainty_score":0.0041784644},"labels":[],"label_agreement":null},{"id":"W2021509062","doi":"10.1016/j.procs.2013.06.130","title":"Fast Polling Mechanism for Baseline BAN MAC (802.15.6) of Body Area Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Polling; Computer science; Baseline (sea); Computer network; Mechanism (biology)","score_opus":0.008078907823266729,"score_gpt":0.19427139570664362,"score_spread":0.1861924878833769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021509062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10670176,0.0024554548,0.87807363,0.00040175088,0.0008115739,0.0004907075,0.00015381303,0.005116248,0.0057950616],"genre_scores_gemma":[0.87613773,0.00066590356,0.11871504,0.00034336012,0.00024672906,0.00034889235,0.00027625437,0.00013447306,0.0031316786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988355,0.0003068202,0.00009412891,0.00016238136,0.00045798646,0.00014313933],"domain_scores_gemma":[0.9982331,0.00048768468,0.00022404212,0.0003631236,0.0005945151,0.000097538046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016045145,0.00039950604,0.00065309566,0.0008546872,0.00061152736,0.0006232671,0.001694684,0.0006297247,0.0011294569],"category_scores_gemma":[0.0028412286,0.00023057795,0.00040415523,0.00043476332,0.00043762405,0.0010242728,0.00062006316,0.0010000708,0.00023559654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023369521,0.0008665284,0.012630372,0.0012122694,0.00041600407,0.0011139845,0.00072675035,0.05448395,0.26243556,0.038063042,0.015948134,0.60976654],"study_design_scores_gemma":[0.0006233727,0.0029511026,0.014228784,0.00013617666,0.00051622145,0.003315303,0.00015785593,0.7879423,0.1218999,0.011918348,0.056068216,0.0002423917],"about_ca_topic_score_codex":0.0011166974,"about_ca_topic_score_gemma":0.0009671052,"teacher_disagreement_score":0.001694684,"about_ca_system_score_codex":0.00050326745,"about_ca_system_score_gemma":0.00085669494,"threshold_uncertainty_score":0.008485556},"labels":[],"label_agreement":null},{"id":"W2021921500","doi":"10.1016/j.procs.2014.05.129","title":"Exploring Rounding Errors in Matlab Using Extended Precision","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Rounding; Computer science; MATLAB; Floating point; Double-precision floating-point format; Decimal; Algorithm; Single-precision floating-point format; Computational science; Class (philosophy); Computation; Point (geometry); Arithmetic; Programming language; Artificial intelligence; Mathematics","score_opus":0.1226172758066115,"score_gpt":0.32507375397142,"score_spread":0.20245647816480852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021921500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033651195,0.0003607016,0.985829,0.00014435228,0.000108378525,0.000027106284,0.00008617022,0.0024997406,0.007579531],"genre_scores_gemma":[0.09868927,0.0013074509,0.8921371,0.00020023811,0.000112450405,0.00026280517,0.00032514686,0.0020800936,0.004885513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99614763,0.0010528194,0.0003405981,0.0003253514,0.0019685554,0.00016515232],"domain_scores_gemma":[0.99026835,0.005753044,0.0008012055,0.0010978433,0.001948002,0.0001314882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003568018,0.00126211,0.0008285026,0.001266123,0.0005615347,0.0029434243,0.0019061647,0.0007906009,0.013208327],"category_scores_gemma":[0.02351622,0.0006018396,0.0009077226,0.001156549,0.0013734428,0.0029496877,0.0022359276,0.002066527,0.003973564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050985714,0.00009089442,0.0018337106,0.0010305047,0.0001198258,0.0013662962,0.0010631485,0.27603596,0.015068953,0.39520094,0.018500943,0.289179],"study_design_scores_gemma":[0.00013322201,0.0002253401,0.0006280687,0.00070236454,0.00008157574,0.0010622849,0.00021668801,0.61839193,0.029243004,0.220598,0.1285527,0.00016485668],"about_ca_topic_score_codex":0.0011916965,"about_ca_topic_score_gemma":0.0010718037,"teacher_disagreement_score":0.013208327,"about_ca_system_score_codex":0.00057860935,"about_ca_system_score_gemma":0.0009661935,"threshold_uncertainty_score":0.044186294},"labels":[],"label_agreement":null},{"id":"W2022679375","doi":"10.1016/j.procs.2014.05.190","title":"Multi-scale Foreign Exchange Rates Ensemble for Classification of Trends in Forex Market","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CTS Forex (Canada); University of Calgary","funders":"","keywords":"Foreign exchange market; Computer science; Artificial intelligence; Foreign exchange; Machine learning; Naive Bayes classifier; Classifier (UML); Ensemble learning; Bayesian probability; Exchange rate; Econometrics; Support vector machine; Economics; Finance","score_opus":0.16535600801132466,"score_gpt":0.41411532142850954,"score_spread":0.24875931341718488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022679375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7301276,0.0016702433,0.26278865,0.0003111251,0.00029822384,0.0000888102,0.0006650863,0.0011594223,0.0028908895],"genre_scores_gemma":[0.96054804,0.00036862778,0.036874417,0.000039102095,0.000110306566,0.000036640828,0.00078971416,0.000019184152,0.0012141664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996637,0.00006348748,0.00003064478,0.00007863243,0.00010681677,0.000056761182],"domain_scores_gemma":[0.99944097,0.00014595296,0.00006490473,0.00007040701,0.00023466072,0.00004302503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011648934,0.00054141553,0.000889043,0.0016567893,0.0003466668,0.0005822945,0.00049582653,0.0005235002,0.0006494902],"category_scores_gemma":[0.0016168355,0.00017006835,0.00068233215,0.00095990556,0.0000842113,0.0007897188,0.00039120382,0.00059974875,0.00029289522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006440439,0.0005586881,0.04664318,0.000073779,0.0003622115,0.00026025326,0.00018101846,0.12579924,0.026248608,0.00087232015,0.005869447,0.7924873],"study_design_scores_gemma":[0.000005620004,0.00007955907,0.010084193,0.000007025755,0.000051627787,0.000045545938,0.00003869107,0.98583335,0.002989686,0.00034187417,0.0005093886,0.000013421075],"about_ca_topic_score_codex":0.003468061,"about_ca_topic_score_gemma":0.0038101657,"teacher_disagreement_score":0.003468061,"about_ca_system_score_codex":0.00023875148,"about_ca_system_score_gemma":0.00029591622,"threshold_uncertainty_score":0.0068957806},"labels":[],"label_agreement":null},{"id":"W2022686325","doi":"10.1016/j.procs.2014.08.023","title":"A Model for Privacy Compromisation Value","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Ottawa","funders":"","keywords":"Computer science; Incentive; Compromise; Information privacy; Internet privacy; Population; Value (mathematics); Computer security; Private information retrieval; Data science; Machine learning","score_opus":0.22143880321785245,"score_gpt":0.4101181923389419,"score_spread":0.18867938912108947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022686325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032996196,0.0006923164,0.8697339,0.013631804,0.00026413894,0.0005239876,0.0007793251,0.0004855716,0.0808927],"genre_scores_gemma":[0.8541434,0.0010914115,0.105991535,0.0022962505,0.0006582722,0.0011119664,0.0005553023,0.00024447995,0.03390741],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.980078,0.008510867,0.0010458261,0.003406131,0.004286652,0.002672603],"domain_scores_gemma":[0.95855695,0.023003772,0.0038741315,0.008904627,0.003988129,0.0016723953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016415754,0.0017963175,0.0015331549,0.00342891,0.0037671525,0.01251776,0.006188108,0.008820463,0.014431634],"category_scores_gemma":[0.049242925,0.0011898606,0.00280197,0.004312472,0.009482045,0.024702158,0.007949715,0.009403683,0.002206599],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004163184,0.000041616757,0.0006210525,0.000042476524,0.000032986678,0.00028246702,0.00034400605,0.010255584,0.00029449677,0.98011774,0.0020586078,0.005867317],"study_design_scores_gemma":[0.000042477892,0.00004315135,0.00020977316,0.000056667366,0.000053005435,0.00039730582,0.00015801397,0.08251277,0.0004021076,0.90606296,0.01002747,0.00003433118],"about_ca_topic_score_codex":0.0032889144,"about_ca_topic_score_gemma":0.0016414054,"teacher_disagreement_score":0.016415754,"about_ca_system_score_codex":0.006719927,"about_ca_system_score_gemma":0.0038413831,"threshold_uncertainty_score":0.08681589},"labels":[],"label_agreement":null},{"id":"W2024032348","doi":"10.1016/j.procs.2012.09.080","title":"Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":208,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Photovoltaic system; Grid-connected photovoltaic power system; Electric power system; Wavelet transform; Solar power; Electricity generation; Maximum power point tracking; Renewable energy; Power (physics); Reliability engineering; Wavelet; Artificial intelligence; Electrical engineering; Engineering","score_opus":0.03443551176320986,"score_gpt":0.23106682404862014,"score_spread":0.19663131228541028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024032348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25603008,0.00042437756,0.7411388,0.00016351181,0.00008012229,0.000019697265,0.00011358985,0.00036776866,0.0016619149],"genre_scores_gemma":[0.907573,0.0005921878,0.090903066,0.000016682417,0.00004510701,0.000018001374,0.0001914898,0.000023313954,0.00063714077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998977,0.000018866158,0.000009296824,0.000018770414,0.00004542317,0.000009898588],"domain_scores_gemma":[0.9998541,0.00007359984,0.000024988552,0.000011547159,0.000029684632,0.0000060052307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002600272,0.00033425787,0.00030928315,0.00050423737,0.000110144065,0.00032875192,0.0002510307,0.0003941138,0.00021421124],"category_scores_gemma":[0.0008933751,0.00015057158,0.00030740057,0.00080832624,0.00011092975,0.0005444055,0.00015380273,0.00043456545,0.00009899609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014631092,0.00011177105,0.0094234245,0.00011961291,0.00007930525,0.00036974027,0.00009220695,0.6603235,0.03773695,0.0032111953,0.0010936003,0.28729236],"study_design_scores_gemma":[0.0000015299357,0.000011808618,0.0010844137,0.0000017682522,0.0000039336,0.000018800172,0.000005094026,0.99678254,0.0016588707,0.00029928793,0.0001291445,0.0000028127295],"about_ca_topic_score_codex":0.0014525626,"about_ca_topic_score_gemma":0.0010582629,"teacher_disagreement_score":0.0014525626,"about_ca_system_score_codex":0.00012541044,"about_ca_system_score_gemma":0.0001287822,"threshold_uncertainty_score":0.0028882027},"labels":[],"label_agreement":null},{"id":"W2025856120","doi":"10.1016/j.procs.2012.06.126","title":"Multi-hop Interference-Aware Routing Protocol for Wireless Sensor Networks","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer network; Wireless sensor network; Network packet; Scheduling (production processes); Routing protocol; Distributed computing; Cluster analysis; Signal-to-interference-plus-noise ratio; Interference (communication); Key distribution in wireless sensor networks; Wireless; Wireless network; Power (physics); Telecommunications; Channel (broadcasting)","score_opus":0.03963815977384598,"score_gpt":0.3038966078442703,"score_spread":0.2642584480704243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025856120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007217734,0.009825306,0.97127515,0.000765512,0.0007359326,0.00022150548,0.00009869429,0.00082830054,0.009031866],"genre_scores_gemma":[0.4056424,0.018978598,0.5569658,0.0009572114,0.0007555705,0.0014502635,0.00094173156,0.00023485758,0.014073614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999158,0.0002664835,0.00007242809,0.00008874531,0.00037328005,0.000041091123],"domain_scores_gemma":[0.99945444,0.00020058107,0.00008170154,0.00008227004,0.00015889529,0.000022063528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007487868,0.0006436678,0.0007061347,0.0006327107,0.00069278263,0.0007728383,0.0012473963,0.0007804032,0.0008172574],"category_scores_gemma":[0.0014439552,0.0002346165,0.0004972417,0.0012374105,0.0004250991,0.0011257768,0.00087909476,0.0010581138,0.00053250155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026237874,0.00020378108,0.0009719469,0.0016316477,0.0002934713,0.00083913247,0.00047154637,0.19767107,0.055660572,0.15141408,0.024922824,0.56565744],"study_design_scores_gemma":[0.00008820478,0.00053257466,0.00083672954,0.00024969032,0.00021541191,0.0014402237,0.00015935201,0.7210032,0.025159972,0.10853369,0.14166239,0.00011860923],"about_ca_topic_score_codex":0.00046083867,"about_ca_topic_score_gemma":0.0006997242,"teacher_disagreement_score":0.0012473963,"about_ca_system_score_codex":0.00045843326,"about_ca_system_score_gemma":0.0007055639,"threshold_uncertainty_score":0.0039600134},"labels":[],"label_agreement":null},{"id":"W2026002911","doi":"10.1016/j.procs.2011.07.054","title":"OLSR-based Topology Discovery for Mobile Situational Awareness Systems","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"National Institute of Standards and Technology","keywords":"Computer science; Topology control; Topology (electrical circuits); Node (physics); Network topology; Mobile ad hoc network; Optimized Link State Routing Protocol; Wireless ad hoc network; Computer network; Distributed computing; Routing protocol; Routing (electronic design automation); Wireless network; Mathematics; Wireless","score_opus":0.03198087109937023,"score_gpt":0.26234842820811705,"score_spread":0.23036755710874682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026002911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044512045,0.001143883,0.9477189,0.00022349574,0.00008860624,0.00008887995,0.000103564926,0.0027304466,0.003390107],"genre_scores_gemma":[0.8011377,0.00096316054,0.19520909,0.000063444255,0.000079764286,0.0000729156,0.00028520403,0.0000960401,0.0020926932],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993082,0.00024244162,0.00003484076,0.00006449766,0.0003113065,0.0000387817],"domain_scores_gemma":[0.9994005,0.00017471422,0.000102431484,0.00014202851,0.00014286589,0.000037451715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005366678,0.00030948195,0.0004355328,0.0009166036,0.00032698616,0.00073635625,0.0006680564,0.00035630306,0.00059705845],"category_scores_gemma":[0.0013324204,0.0002201187,0.0002148351,0.0005722413,0.00031358656,0.001035038,0.0006344646,0.0003654778,0.0003300844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051178096,0.00016258487,0.0037449407,0.00032174308,0.00018184847,0.00072939147,0.0005534239,0.29395923,0.11114182,0.030325554,0.0050193607,0.55334824],"study_design_scores_gemma":[0.00001886406,0.00019021326,0.0017153473,0.000016716967,0.00003435871,0.00043446195,0.0001223089,0.9642198,0.018886717,0.0052951705,0.009028239,0.00003772224],"about_ca_topic_score_codex":0.0010284377,"about_ca_topic_score_gemma":0.0011430319,"teacher_disagreement_score":0.0010284377,"about_ca_system_score_codex":0.00024013474,"about_ca_system_score_gemma":0.00025333939,"threshold_uncertainty_score":0.0028381944},"labels":[],"label_agreement":null},{"id":"W2026004752","doi":"10.1016/j.procs.2012.06.058","title":"A Quantitative Approach for Intrusions Detection and Prevention based on Statistical N-Gram Models","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; n-gram; Gram; Intrusion detection system; Data mining; Artificial intelligence; Machine learning; Language model","score_opus":0.0388796804903905,"score_gpt":0.2874100422088104,"score_spread":0.24853036171841988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026004752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032782196,0.00013802273,0.9952904,0.00012932901,0.000027019298,0.00002377093,0.00006255094,0.0007360496,0.00031473456],"genre_scores_gemma":[0.4492874,0.00062796316,0.5463861,0.00036448165,0.00030483308,0.00030736267,0.00059608754,0.00028948908,0.0018361482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967163,0.0011794779,0.00024722258,0.00055530766,0.0011637899,0.00013778813],"domain_scores_gemma":[0.9901828,0.0067701847,0.000937999,0.0008989705,0.0010019583,0.00020804432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032133518,0.0018861163,0.0015531598,0.002772989,0.0005720993,0.001837111,0.0020399187,0.0015382041,0.0012730525],"category_scores_gemma":[0.0133330375,0.0008020446,0.0010830412,0.0013228939,0.0012653761,0.004717626,0.0014765039,0.0028185009,0.00087237667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031558156,0.0004148053,0.0045718537,0.00037727793,0.00037970443,0.00016452825,0.00020435784,0.7008161,0.019577235,0.053047784,0.0023969733,0.21773385],"study_design_scores_gemma":[0.0000036551753,0.000039238006,0.00021159234,0.0000070534943,0.0000094154775,0.000029786177,0.000005987559,0.9886502,0.00094851264,0.009767024,0.00031132685,0.000016180631],"about_ca_topic_score_codex":0.0029718974,"about_ca_topic_score_gemma":0.003148081,"teacher_disagreement_score":0.0032133518,"about_ca_system_score_codex":0.0011625295,"about_ca_system_score_gemma":0.0013179868,"threshold_uncertainty_score":0.016994},"labels":[],"label_agreement":null},{"id":"W2028918024","doi":"10.1016/j.procs.2011.07.066","title":"Cloud Services Testing: An Understanding","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Cloud computing; Computer science; Cloud testing; Task (project management); Pace; Service (business); Software; Work (physics); Test (biology); Data science; Cloud computing security; Computer security; Software engineering; World Wide Web; Systems engineering; Operating system","score_opus":0.08710239138076126,"score_gpt":0.2461313668587417,"score_spread":0.15902897547798045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028918024","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024053628,0.28309092,0.365852,0.115703285,0.0028121474,0.0003613678,0.00062781427,0.0006883549,0.2068105],"genre_scores_gemma":[0.45903102,0.33293238,0.14946705,0.021275666,0.0069501544,0.0005879407,0.00079459173,0.00049235957,0.028468834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960278,0.0013124958,0.0003837073,0.00062539853,0.0013344958,0.0003161001],"domain_scores_gemma":[0.9924706,0.0047180015,0.0004014617,0.00072448794,0.0013314316,0.00035395302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042015496,0.001215477,0.0008393143,0.006239647,0.0019178194,0.0106041,0.0027726407,0.005176272,0.0032321282],"category_scores_gemma":[0.0081227645,0.0007681568,0.00096275995,0.0043660393,0.010603289,0.019411149,0.00357859,0.005617708,0.001009559],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027942324,0.00007745386,0.001748618,0.00075433165,0.00002114835,0.00036841104,0.00452479,0.0024903489,0.0008152121,0.8709968,0.010785345,0.10738962],"study_design_scores_gemma":[0.0000100150755,0.00007687739,0.0024015552,0.0019402663,0.00001911696,0.0016043335,0.003968208,0.0130616585,0.00086334284,0.663443,0.31255472,0.00005690194],"about_ca_topic_score_codex":0.008723878,"about_ca_topic_score_gemma":0.003033764,"teacher_disagreement_score":0.0106041,"about_ca_system_score_codex":0.0056529376,"about_ca_system_score_gemma":0.0039530033,"threshold_uncertainty_score":0.04101509},"labels":[],"label_agreement":null},{"id":"W2029540594","doi":"10.1016/j.procs.2012.06.059","title":"On the Necessary Conditions for Covert Channel Existence: A State-of-the-Art Survey","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Covert channel; Covert; Computer science; Channel (broadcasting); Set (abstract data type); State (computer science); Computer security; Computer network; Algorithm; Programming language","score_opus":0.030249391190500477,"score_gpt":0.2665218395974583,"score_spread":0.23627244840695782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029540594","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0514521,0.14458245,0.69493157,0.012078966,0.0012151618,0.00033915977,0.00089101965,0.0005460127,0.09396355],"genre_scores_gemma":[0.6291342,0.21427365,0.14099394,0.0023201664,0.006310714,0.00068185356,0.0011145566,0.0003886849,0.0047823857],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9883682,0.003348068,0.0013978179,0.001665784,0.003975304,0.0012447444],"domain_scores_gemma":[0.84342426,0.13706236,0.0057744286,0.0057134363,0.006791949,0.0012335432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009711709,0.0017954354,0.004127634,0.0062066717,0.0026364217,0.006783404,0.0023701717,0.0045982595,0.0070361337],"category_scores_gemma":[0.05359917,0.001986748,0.00340589,0.005039988,0.008778275,0.020617979,0.0035307724,0.00576681,0.0020019952],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031139044,0.00042562815,0.0052325334,0.007419524,0.00026885988,0.0011969674,0.0008766183,0.035883117,0.004332197,0.79757255,0.010265498,0.13621509],"study_design_scores_gemma":[0.000061409046,0.0004092498,0.0026943134,0.0027026462,0.0002678354,0.0025117632,0.0009665423,0.082596876,0.0067520393,0.86443794,0.03632776,0.00027172104],"about_ca_topic_score_codex":0.0009946224,"about_ca_topic_score_gemma":0.0006648087,"teacher_disagreement_score":0.009711709,"about_ca_system_score_codex":0.0030857348,"about_ca_system_score_gemma":0.0032568055,"threshold_uncertainty_score":0.051361084},"labels":[],"label_agreement":null},{"id":"W2030165619","doi":"10.1016/j.procs.2013.06.040","title":"Low Sampling-rate Approach for ECG Signals with Compressed Sensing Theory","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Wireless; Wireless network; Compressed sensing; Computer network; Body area network; Wi-Fi; Noise (video); Telecommunications; Real-time computing; Artificial intelligence","score_opus":0.01900672927921186,"score_gpt":0.22100795649529273,"score_spread":0.20200122721608088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030165619","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018955822,0.00054089335,0.9957812,0.00024015298,0.00005971037,0.000030830208,0.000049504095,0.0000837543,0.0013183466],"genre_scores_gemma":[0.25642124,0.004350109,0.72999567,0.0004383795,0.00055300025,0.00032815654,0.00068663084,0.00014088031,0.0070859347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992687,0.0002048724,0.000034847424,0.00009297425,0.00035886889,0.000039654675],"domain_scores_gemma":[0.99889296,0.00074786256,0.00007150027,0.00006966602,0.00018887156,0.00002921153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141726,0.00094130926,0.00048587413,0.0007153395,0.0002998564,0.00072859286,0.0008034031,0.0010275278,0.0023918573],"category_scores_gemma":[0.0039024223,0.00023179247,0.000607228,0.00093564205,0.00067741016,0.0011454653,0.0009024561,0.0017365878,0.00074196747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025521003,0.000101677295,0.00096872624,0.0005910838,0.00009579777,0.00033452862,0.000310436,0.6120426,0.020420086,0.11024017,0.0057097077,0.24892987],"study_design_scores_gemma":[0.000007678861,0.000046799963,0.00013061105,0.000022829416,0.000009653022,0.00009508656,0.000015313706,0.9881209,0.0020206394,0.0075175776,0.0020008998,0.000011861626],"about_ca_topic_score_codex":0.0030312312,"about_ca_topic_score_gemma":0.0020315438,"teacher_disagreement_score":0.0030312312,"about_ca_system_score_codex":0.00050568086,"about_ca_system_score_gemma":0.0007067564,"threshold_uncertainty_score":0.008001506},"labels":[],"label_agreement":null},{"id":"W2031230831","doi":"10.1016/j.procs.2013.06.036","title":"Bloom Filter Supporting Distributed Policy-Based Management in Wireless Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Bloom filter; Wireless sensor network; Wireless; Computer network; Telecommunications; Distributed computing","score_opus":0.009695588339612165,"score_gpt":0.22855711934892467,"score_spread":0.2188615310093125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031230831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029892618,0.0005376159,0.96361125,0.0006184824,0.00007921574,0.00041110482,0.00019348213,0.0027817315,0.0018745537],"genre_scores_gemma":[0.5373622,0.00072940905,0.45564526,0.00044533765,0.00012793978,0.0007633352,0.0006681539,0.00012234056,0.0041359914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996899,0.0009058582,0.00049546646,0.00048284727,0.0009268484,0.00029005777],"domain_scores_gemma":[0.994027,0.0024788112,0.00070201105,0.001427288,0.0009875953,0.00037733177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004525879,0.00046232232,0.0011839534,0.001587742,0.002306988,0.00308383,0.0022583148,0.0014235087,0.0010704538],"category_scores_gemma":[0.0085777,0.00052217307,0.0005720676,0.0019233495,0.0011781675,0.0051211095,0.0024372486,0.0013467887,0.00037462325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016699226,0.0008310167,0.011685994,0.0005943507,0.0002510711,0.0009705984,0.002947649,0.14650014,0.058317635,0.2414482,0.012827829,0.52195567],"study_design_scores_gemma":[0.00013292962,0.00027703826,0.000995285,0.00008175728,0.000100811776,0.0003541367,0.0003354215,0.84938854,0.03822989,0.08722772,0.022766586,0.00010989806],"about_ca_topic_score_codex":0.0070133167,"about_ca_topic_score_gemma":0.005302853,"teacher_disagreement_score":0.0070133167,"about_ca_system_score_codex":0.0028053254,"about_ca_system_score_gemma":0.0046889186,"threshold_uncertainty_score":0.023935437},"labels":[],"label_agreement":null},{"id":"W2031723277","doi":"10.1016/j.procs.2011.07.041","title":"A Data Fusion Approach to Context-Aware Service Delivery in Heterogeneous Network Environments","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Context awareness; Context (archaeology); Ubiquitous computing; Adaptation (eye); Service (business); Key (lock); Context model; Human–computer interaction; World Wide Web; Data science; Computer security; Artificial intelligence; Object (grammar)","score_opus":0.09207404540182018,"score_gpt":0.24496586047412114,"score_spread":0.15289181507230096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031723277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034788863,0.00046799378,0.9948138,0.00018315404,0.000049520753,0.000037224094,0.000033452212,0.00013095624,0.00080500334],"genre_scores_gemma":[0.43505603,0.001380051,0.5605478,0.0002302911,0.00023450967,0.00021161893,0.000259204,0.000056482888,0.0020239765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981949,0.00044401435,0.00016766717,0.00032263785,0.0007337452,0.00013710748],"domain_scores_gemma":[0.9989524,0.00041949822,0.00010180196,0.00014783407,0.00032891327,0.000049583665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002703402,0.00062420726,0.0016055615,0.0016912975,0.0010266838,0.0020943666,0.0016367518,0.0011207807,0.0012455087],"category_scores_gemma":[0.004585298,0.00043993245,0.0013385898,0.002903111,0.00080273196,0.0027462295,0.002118135,0.0016333644,0.0003765955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003510646,0.00019260205,0.001738894,0.00028634633,0.00031688547,0.00047619254,0.0007892143,0.41840288,0.016140189,0.13159686,0.003562897,0.42614594],"study_design_scores_gemma":[0.000008415274,0.00004705248,0.000308439,0.000019087312,0.000043358395,0.000075624375,0.000080731756,0.9647798,0.0022785927,0.029660793,0.0026734737,0.000024573726],"about_ca_topic_score_codex":0.00509293,"about_ca_topic_score_gemma":0.0034901628,"teacher_disagreement_score":0.00509293,"about_ca_system_score_codex":0.0016672377,"about_ca_system_score_gemma":0.0011304155,"threshold_uncertainty_score":0.014297128},"labels":[],"label_agreement":null},{"id":"W2032687307","doi":"10.1016/j.procs.2014.08.061","title":"Synthesizing Population for Microsimulation-based Integrated Transport Models Using Atlantic Canada Micro-data","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Nova Scotia Department of Energy","keywords":"Computer science; Population; Microsimulation; Operations research; Mathematics; Transport engineering; Engineering","score_opus":0.16881553252585563,"score_gpt":0.349996064988079,"score_spread":0.18118053246222338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032687307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65892714,0.00016486873,0.32808146,0.00031115048,0.0000485777,0.00027262155,0.0013828469,0.00056033744,0.010251001],"genre_scores_gemma":[0.911891,0.00007022006,0.084975995,0.000028997565,0.0000053445347,0.00023199721,0.0009060123,0.00003499729,0.0018554599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987864,0.000028119532,0.0000062868035,0.00002718593,0.000030890067,0.000028841408],"domain_scores_gemma":[0.99929535,0.00039139247,0.000046615536,0.000034861016,0.00020408399,0.000027725431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004991005,0.00039522257,0.00036681627,0.0004939693,0.000535428,0.0005405168,0.0006326027,0.00045779833,0.0018069673],"category_scores_gemma":[0.0019393566,0.00023093459,0.0004889337,0.00046875753,0.0002938176,0.00022109678,0.000391604,0.00040058128,0.00010797766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075748885,0.000005439586,0.00097098947,0.000009481334,0.0000044700755,0.000012486879,0.000016852811,0.9948762,0.00018062134,0.0007894879,0.00007676669,0.0030496046],"study_design_scores_gemma":[0.0000029620585,0.0000050589547,0.00028931804,0.0000015938211,0.0000025349725,0.0000019761108,0.000012474644,0.99914074,0.00015417056,0.0002147441,0.0001727984,0.0000016610254],"about_ca_topic_score_codex":0.4670253,"about_ca_topic_score_gemma":0.3331094,"teacher_disagreement_score":0.5329747,"about_ca_system_score_codex":0.004048781,"about_ca_system_score_gemma":0.004207291,"threshold_uncertainty_score":0.92861384},"labels":[],"label_agreement":null},{"id":"W2033722220","doi":"10.1016/j.procs.2013.06.006","title":"The Evolution of Information Networks around Data-shifting Paradigms","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Bottleneck; The Internet; Participatory sensing; Process (computing); Architecture; Network architecture; Distributed computing; Key (lock); Data science; Computer network; Computer security; World Wide Web","score_opus":0.016785445079178625,"score_gpt":0.22491170255567278,"score_spread":0.20812625747649416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033722220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06840777,0.032575537,0.7112649,0.05942865,0.0014976169,0.00032994326,0.0005155231,0.0011338452,0.12484622],"genre_scores_gemma":[0.71629894,0.036863625,0.2107404,0.0066776546,0.0032164236,0.0007018086,0.00046194505,0.000395861,0.024643201],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99614453,0.001531525,0.00017560109,0.0008769248,0.00093613606,0.00033528105],"domain_scores_gemma":[0.99344903,0.0033495356,0.00046720073,0.0014220804,0.00078015705,0.00053197023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041493145,0.0006927355,0.00067401316,0.0025465968,0.00258484,0.011250993,0.0018161329,0.0036906062,0.002832853],"category_scores_gemma":[0.009379006,0.00079145987,0.000691272,0.0028041322,0.01010172,0.025813483,0.0050147194,0.0062916963,0.0011734846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021614036,0.0000135642385,0.00018741182,0.00006646509,0.000009222947,0.00006471858,0.0009171168,0.0031475483,0.00054794864,0.9782436,0.0015843872,0.015196408],"study_design_scores_gemma":[0.000013739558,0.000033473665,0.00025817094,0.00011296222,0.000010701909,0.0001784994,0.0006256723,0.014822634,0.00051108515,0.8710426,0.112355314,0.000035227615],"about_ca_topic_score_codex":0.0020321133,"about_ca_topic_score_gemma":0.0010957217,"teacher_disagreement_score":0.011250993,"about_ca_system_score_codex":0.0046549067,"about_ca_system_score_gemma":0.0018472045,"threshold_uncertainty_score":0.03377384},"labels":[],"label_agreement":null},{"id":"W2035478131","doi":"10.1016/j.procs.2013.06.017","title":"An Optimal Cross-Layer Scheduling for Periodic WSN Applications","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Scheduling (production processes); Time division multiple access; Integer programming; Schedule; Distributed computing; Mathematical optimization; Computer network; Algorithm; Mathematics","score_opus":0.014784944499326743,"score_gpt":0.280628044473647,"score_spread":0.2658430999743202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035478131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04842158,0.000518452,0.94541854,0.00026435102,0.000069254624,0.00006812373,0.00007988555,0.00017670052,0.004983117],"genre_scores_gemma":[0.8210891,0.0006651794,0.174433,0.00009541208,0.000058533136,0.00013241716,0.00017579203,0.00009558448,0.0032550865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966955,0.00011103565,0.000011920973,0.00006588685,0.00008856183,0.000053089203],"domain_scores_gemma":[0.9996538,0.00018263204,0.000055695375,0.000028172897,0.00005511432,0.00002472415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008765086,0.00056315254,0.00039438796,0.00028818822,0.00034378294,0.00065828825,0.0005631002,0.00043988758,0.0015798347],"category_scores_gemma":[0.0013276006,0.00033094338,0.0004222202,0.00041778525,0.00028990093,0.00074788684,0.0005265531,0.0005226486,0.00017640555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050884166,0.000050663653,0.00024651646,0.000083599814,0.00001787985,0.00006591661,0.000039635735,0.9587886,0.0046718237,0.009502277,0.00091420155,0.025568135],"study_design_scores_gemma":[0.000004580539,0.00004133394,0.000084944244,0.0000034465731,0.00000424352,0.000015917563,0.000013189971,0.99641657,0.0007398618,0.0021745819,0.0004989683,0.00000239645],"about_ca_topic_score_codex":0.0021126312,"about_ca_topic_score_gemma":0.0023627868,"teacher_disagreement_score":0.0021126312,"about_ca_system_score_codex":0.00056653784,"about_ca_system_score_gemma":0.0012218574,"threshold_uncertainty_score":0.0052850246},"labels":[],"label_agreement":null},{"id":"W2035634293","doi":"10.1016/j.procs.2013.06.116","title":"Freight Market Interactions Simulation (FREMIS): An Agent-based Modeling Framework","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scope (computer science); Focus (optics); Conceptual framework; Rationality; Agent-based model; Product (mathematics); Presentation (obstetrics); Conceptual model; Operations research; Artificial intelligence; Database","score_opus":0.031416026019904404,"score_gpt":0.2370781041559504,"score_spread":0.205662078136046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035634293","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057226843,0.0003067586,0.98180133,0.00047284603,0.00006013008,0.00012743505,0.0003466131,0.0011973517,0.009964956],"genre_scores_gemma":[0.34434935,0.0016819397,0.63834405,0.00027383902,0.0001504024,0.0010305336,0.0009792272,0.00039516127,0.0127954595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948776,0.00021151667,0.000038689366,0.00007925274,0.00013244379,0.000050173774],"domain_scores_gemma":[0.9995203,0.00025286205,0.000060663748,0.000040361312,0.00008136044,0.00004447653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012655116,0.000828888,0.0007723056,0.00076218817,0.0007719406,0.0020398418,0.0021207046,0.0017785312,0.0045635593],"category_scores_gemma":[0.0015460006,0.000518932,0.0012940883,0.0005861472,0.00085947814,0.0021483828,0.0015104947,0.0017122477,0.00081119896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024596584,0.00004054487,0.00044253236,0.000061027393,0.00005577829,0.00008167263,0.00012399929,0.8540972,0.00081955746,0.13178867,0.0016564692,0.010807925],"study_design_scores_gemma":[0.000011039168,0.000012015953,0.000053105778,0.000016666945,0.000013960194,0.000017241991,0.000014634736,0.9702115,0.00022536649,0.017953575,0.011461982,0.0000087215985],"about_ca_topic_score_codex":0.013962924,"about_ca_topic_score_gemma":0.010933353,"teacher_disagreement_score":0.013962924,"about_ca_system_score_codex":0.0013959536,"about_ca_system_score_gemma":0.0023993398,"threshold_uncertainty_score":0.027763307},"labels":[],"label_agreement":null},{"id":"W2038820425","doi":"10.1016/j.procs.2013.06.015","title":"Gossiping Based Distributed Plan Monitoring","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"","keywords":"Computer science; Gossip; Distributed computing; Plan (archaeology); Protocol (science); Process (computing); Information sharing; Real-time computing; Operating system","score_opus":0.018486898909161883,"score_gpt":0.2285711135837101,"score_spread":0.21008421467454821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038820425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056206618,0.00035217087,0.9352573,0.00013652391,0.000044932534,0.00020779805,0.000097763725,0.004566706,0.003130218],"genre_scores_gemma":[0.79871196,0.0001875707,0.19854946,0.00005384096,0.00003158573,0.00019327528,0.0002305795,0.00013634519,0.0019053995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885905,0.00027873408,0.000080128564,0.0002198438,0.00048141048,0.0000807889],"domain_scores_gemma":[0.99728,0.0011391711,0.00047848548,0.00059818814,0.00035108667,0.00015304702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001289464,0.0005911545,0.0005796943,0.00075639825,0.0005344091,0.0008562971,0.0011165257,0.0004573462,0.0010047155],"category_scores_gemma":[0.0033135137,0.00023906538,0.00028037291,0.0004690013,0.00067886466,0.0011704006,0.001172086,0.0006338197,0.00028927365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013101959,0.00035494985,0.0054199244,0.0004583719,0.0002455849,0.00068862224,0.0010412885,0.30567464,0.11300159,0.03542318,0.0040558507,0.53232586],"study_design_scores_gemma":[0.00006818613,0.0003313735,0.0013909446,0.000016234006,0.00006175526,0.00015374615,0.000114987255,0.9459344,0.031050432,0.016365359,0.004478257,0.000034315777],"about_ca_topic_score_codex":0.0014219114,"about_ca_topic_score_gemma":0.0016716048,"teacher_disagreement_score":0.0014219114,"about_ca_system_score_codex":0.00040565577,"about_ca_system_score_gemma":0.0009608975,"threshold_uncertainty_score":0.0068193674},"labels":[],"label_agreement":null},{"id":"W2040040060","doi":"10.1016/j.procs.2014.08.036","title":"Classification of Post-deployment Performance Diagnostic Techniques for Large-scale Software Systems","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University; University of Waterloo","funders":"","keywords":"Computer science; Software deployment; Task (project management); Scale (ratio); Field (mathematics); Software; Service (business); Data science; Risk analysis (engineering); Software engineering; Systems engineering; Operating system","score_opus":0.01095018948853029,"score_gpt":0.2416727533476307,"score_spread":0.2307225638591004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040040060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1556284,0.029374843,0.7831341,0.003645798,0.00055820274,0.0011557684,0.000821518,0.008595923,0.017085446],"genre_scores_gemma":[0.5780287,0.009969053,0.40596592,0.00038882112,0.00029526008,0.00041654307,0.0012151211,0.00041642832,0.0033041097],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99191815,0.0011694328,0.00082037336,0.0009419806,0.0046284883,0.0005215889],"domain_scores_gemma":[0.9506494,0.022988671,0.008588085,0.004722929,0.012308018,0.00074286934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00464481,0.0013386868,0.0006981638,0.006813615,0.0007154929,0.0020669647,0.0025447663,0.0010702952,0.0008341597],"category_scores_gemma":[0.037422195,0.00047313242,0.0010354987,0.004739407,0.00083723717,0.0027035577,0.0011407781,0.0015591304,0.0006447358],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026550572,0.00035410063,0.026030343,0.002037054,0.00010199236,0.0002421763,0.0011608084,0.008222271,0.015384093,0.0061184596,0.006160863,0.93392235],"study_design_scores_gemma":[0.0001715223,0.0025786674,0.16022536,0.0045865127,0.00086595793,0.0073901587,0.0049267914,0.5113291,0.13323785,0.03533076,0.13866812,0.00068914174],"about_ca_topic_score_codex":0.002127034,"about_ca_topic_score_gemma":0.0018116815,"teacher_disagreement_score":0.006813615,"about_ca_system_score_codex":0.0014781147,"about_ca_system_score_gemma":0.002002244,"threshold_uncertainty_score":0.024564385},"labels":[],"label_agreement":null},{"id":"W2040899808","doi":"10.1016/j.procs.2012.06.060","title":"The Role of DNS TTL Values in Potential DDoS Attacks: What Do the Major Banks Know About It?","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Denial-of-service attack; Computer security; Sophistication; Vulnerability (computing); Internet privacy; The Internet; World Wide Web","score_opus":0.00672458745591743,"score_gpt":0.2381971085736704,"score_spread":0.231472521117753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040899808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91487134,0.01185309,0.0150513165,0.023687119,0.00020051346,0.00006808133,0.00067367783,0.00016803225,0.03342677],"genre_scores_gemma":[0.99610245,0.0017968098,0.00090340443,0.0005047127,0.000095417476,0.000005424969,0.000073476906,0.000009672077,0.0005086418],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9958234,0.0017631695,0.0003500083,0.0003759276,0.0012482413,0.00043925797],"domain_scores_gemma":[0.9313415,0.031253137,0.023067908,0.0033505436,0.008507971,0.0024789213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005851445,0.00029898316,0.00048544703,0.0014467466,0.0010243683,0.004152024,0.0006545856,0.0019647896,0.00245487],"category_scores_gemma":[0.04117702,0.00027731617,0.00026946195,0.0014620335,0.0016844495,0.007720433,0.0010983527,0.0015357791,0.0010951156],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045727083,0.00021851037,0.7880754,0.00042638794,0.000105354084,0.000736367,0.005565793,0.0028042893,0.0035878306,0.006696982,0.004022134,0.18730368],"study_design_scores_gemma":[0.000026454718,0.0008730282,0.8351698,0.0012394052,0.00021108192,0.008517637,0.03561878,0.017627155,0.011035663,0.030745948,0.058688752,0.00024640726],"about_ca_topic_score_codex":0.0025217384,"about_ca_topic_score_gemma":0.002454911,"teacher_disagreement_score":0.005851445,"about_ca_system_score_codex":0.0012369073,"about_ca_system_score_gemma":0.0008291826,"threshold_uncertainty_score":0.030945778},"labels":[],"label_agreement":null},{"id":"W2043782910","doi":"10.1016/j.procs.2012.01.083","title":"Risk Analysis and Mitigation Strategy for System Design","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Risk analysis (engineering); Operations research","score_opus":0.019512306609396377,"score_gpt":0.24214315838402478,"score_spread":0.2226308517746284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043782910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018880876,0.00082291797,0.9548116,0.0027083308,0.00012710941,0.00041074643,0.00020081224,0.00046997462,0.021567633],"genre_scores_gemma":[0.778083,0.0012717987,0.20466124,0.000407421,0.000208089,0.00068810413,0.0003719932,0.00010879065,0.014199657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99746275,0.0010352373,0.00009894831,0.00033746765,0.0007930981,0.0002725199],"domain_scores_gemma":[0.99690044,0.0014724107,0.00030560791,0.00033430482,0.00087070465,0.00011644256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036727656,0.0020374844,0.00096615165,0.0018357009,0.00083738356,0.0027777292,0.0013164512,0.0016390153,0.010578372],"category_scores_gemma":[0.008351401,0.0004374691,0.0010885493,0.0005775438,0.0010545282,0.0023702336,0.0014892259,0.0016649987,0.0011476283],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015103463,0.00015201206,0.0028916767,0.00033148605,0.00021082193,0.00037316926,0.0002446811,0.66541785,0.0050455043,0.20376009,0.006607172,0.114814535],"study_design_scores_gemma":[0.000028864435,0.00021384652,0.0006909959,0.00011146303,0.000098602366,0.00016078468,0.00022338906,0.815485,0.0021813132,0.17256223,0.008209424,0.000034020282],"about_ca_topic_score_codex":0.0019380779,"about_ca_topic_score_gemma":0.0013370324,"teacher_disagreement_score":0.010578372,"about_ca_system_score_codex":0.0017577593,"about_ca_system_score_gemma":0.003801071,"threshold_uncertainty_score":0.03538817},"labels":[],"label_agreement":null},{"id":"W2045449341","doi":"10.1016/j.procs.2011.08.023","title":"An interactive simulation model of human drivers to study autonomous haulage trucks","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Haulage; Truck; Crusher; Computer science; Open-pit mining; Automotive engineering; Fuel efficiency; Productivity; Performance indicator; Excavator; Mining engineering; Civil engineering; Engineering; Mechanical engineering; Business","score_opus":0.04180204233569496,"score_gpt":0.2759615083255252,"score_spread":0.23415946598983023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045449341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5514769,0.00026309863,0.41977698,0.0006632892,0.000104878105,0.0002594357,0.0010254523,0.00069911504,0.02573088],"genre_scores_gemma":[0.9760918,0.00016957067,0.01539616,0.000046916,0.000025125499,0.00022387708,0.00030767045,0.000046403562,0.007692543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980503,0.00007008504,0.000008119086,0.00004125218,0.00003829127,0.000037271897],"domain_scores_gemma":[0.9994779,0.00028369366,0.00005159001,0.000030866555,0.000077796656,0.00007814645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034409377,0.0006803799,0.00050131616,0.000333631,0.0005804711,0.0008537641,0.001540614,0.0014096985,0.004427266],"category_scores_gemma":[0.0010395522,0.00045673846,0.00068939704,0.00032472765,0.00057201367,0.00066510873,0.0010923633,0.00084202987,0.0004726976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037136215,0.000031900992,0.0007979535,0.000007993354,0.000011361476,0.00004805751,0.00007750325,0.995455,0.00046152176,0.0020920243,0.00011503183,0.00086439156],"study_design_scores_gemma":[0.000008021345,0.0000116809315,0.00010279872,9.1851615e-7,0.0000028290374,0.0000049128093,0.000013875431,0.99916184,0.00006760563,0.00034603418,0.00027717324,0.000002384882],"about_ca_topic_score_codex":0.03445903,"about_ca_topic_score_gemma":0.015409938,"teacher_disagreement_score":0.03445903,"about_ca_system_score_codex":0.00081271114,"about_ca_system_score_gemma":0.0011137738,"threshold_uncertainty_score":0.06851691},"labels":[],"label_agreement":null},{"id":"W2045946715","doi":"10.1016/j.procs.2014.08.027","title":"Inter-domain Hybrid Metric as SLS Dissemination Mechanism in Green Optical Networks","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Routing (electronic design automation); Mechanism (biology); Metric (unit); Domain (mathematical analysis); Distributed computing; Computer network; Code (set theory); Static routing; Routing protocol","score_opus":0.00461038127854383,"score_gpt":0.2206191790881561,"score_spread":0.21600879780961227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045946715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17716989,0.0012872505,0.81169254,0.0005486296,0.00024145276,0.00015160028,0.00018679923,0.0019881488,0.006733622],"genre_scores_gemma":[0.9323207,0.00021595659,0.064815566,0.000060730814,0.000044226028,0.00005315775,0.00011751695,0.000062443265,0.002309574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989742,0.00033095342,0.000050055158,0.00010687012,0.0004660222,0.00007195031],"domain_scores_gemma":[0.9987657,0.00027980155,0.0001829795,0.00036137356,0.0003623343,0.00004785553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013706608,0.0003896759,0.00041122883,0.0009948849,0.00039443196,0.0010662011,0.0009060709,0.0004239692,0.00067178375],"category_scores_gemma":[0.0021559822,0.00012894058,0.00020691314,0.0007918231,0.00036488648,0.0016964581,0.0010329826,0.000567532,0.00015797296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075021555,0.00033967098,0.0052476157,0.00036223672,0.00022880851,0.0002856143,0.00047056394,0.21122259,0.14979266,0.07865069,0.0055030426,0.5471462],"study_design_scores_gemma":[0.0000510945,0.00061284844,0.0036922288,0.000030113863,0.000078094294,0.00035515337,0.00025423928,0.8834758,0.07559527,0.015990393,0.019768309,0.00009650055],"about_ca_topic_score_codex":0.00088939205,"about_ca_topic_score_gemma":0.0009327448,"teacher_disagreement_score":0.0013706608,"about_ca_system_score_codex":0.00066202704,"about_ca_system_score_gemma":0.00050631637,"threshold_uncertainty_score":0.007248819},"labels":[],"label_agreement":null},{"id":"W2048181093","doi":"10.1016/j.procs.2014.05.530","title":"Table Driven Hybrid Energy-aware and SLA-based Routing Mechanism over Optical Networks","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Routing table; Table (database); Computer network; Routing (electronic design automation); Connection (principal bundle); Node (physics); Distributed computing; Resource (disambiguation); Mechanism (biology); Routing protocol; Database","score_opus":0.00429532045464218,"score_gpt":0.1843575016297203,"score_spread":0.18006218117507813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048181093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15566133,0.0006226582,0.83096826,0.00039804465,0.00022758507,0.000145882,0.00025449262,0.004379546,0.0073421756],"genre_scores_gemma":[0.9152078,0.00016213449,0.08140647,0.00010600457,0.000055125878,0.000053932865,0.00026611198,0.00008702211,0.002655397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945503,0.00009883403,0.00004245289,0.00007792674,0.00024086345,0.000084880565],"domain_scores_gemma":[0.99941814,0.000120022145,0.00007368255,0.00019488765,0.00015379612,0.000039483795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006695572,0.0003853001,0.00043059155,0.0005724516,0.0005955603,0.0014063717,0.0014703206,0.00040245493,0.0012454612],"category_scores_gemma":[0.00076204335,0.00025448884,0.0002794293,0.0005836269,0.00027751137,0.0017095975,0.0010198962,0.0005184574,0.00032429548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012239541,0.00056611316,0.0037064129,0.00035421015,0.00022961685,0.00052987743,0.0004199361,0.25007647,0.25518528,0.06571498,0.010209315,0.41178387],"study_design_scores_gemma":[0.0000475676,0.00020089718,0.0009828729,0.000012119094,0.00004563877,0.0002250137,0.00008391806,0.9374263,0.04040576,0.013015303,0.007499073,0.000055625685],"about_ca_topic_score_codex":0.0012203495,"about_ca_topic_score_gemma":0.0017334067,"teacher_disagreement_score":0.0014703206,"about_ca_system_score_codex":0.0004451344,"about_ca_system_score_gemma":0.0006615168,"threshold_uncertainty_score":0.004166484},"labels":[],"label_agreement":null},{"id":"W2048889456","doi":"10.1016/j.procs.2013.06.069","title":"Towards a Real-time Error Detection within a Smart Home by Using Activity Recognition with a Shoe-mounted Accelerometer","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Activity recognition; Accelerometer; Histogram; Home automation; Set (abstract data type); Artificial intelligence; Real-time computing; Fuzzy logic; Smart environment; State (computer science); Machine learning; Computer vision; Human–computer interaction; Embedded system; Internet of Things; Algorithm","score_opus":0.03398339952496479,"score_gpt":0.2609805559961663,"score_spread":0.22699715647120153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048889456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3537939,0.0002736406,0.64217335,0.000167792,0.000061799496,0.00010540467,0.00008136448,0.0020900022,0.0012526782],"genre_scores_gemma":[0.87767833,0.0001227246,0.120987125,0.000062758336,0.000019131032,0.000045541336,0.00006377436,0.00001926507,0.0010014117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997522,0.000053093692,0.000017868379,0.00007514623,0.00008295123,0.000018712923],"domain_scores_gemma":[0.99967885,0.00011485948,0.000055178913,0.000044888813,0.00008357393,0.000022681714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003415118,0.0003447411,0.00039000015,0.0002905977,0.00014500694,0.00034115044,0.00037603322,0.0005236529,0.0007063622],"category_scores_gemma":[0.0008517547,0.00013533818,0.0002092006,0.00017005684,0.00021614203,0.00054940797,0.00027613502,0.00026213934,0.00031501314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011409292,0.0010217289,0.02957283,0.00029108443,0.00014788176,0.0005730404,0.0005474478,0.081547834,0.27716413,0.0017361393,0.0017618446,0.60449517],"study_design_scores_gemma":[0.00007666424,0.000952695,0.021530762,0.000034099005,0.000092273694,0.0005438905,0.0001420827,0.90127605,0.072408654,0.0010736607,0.0018209808,0.00004806575],"about_ca_topic_score_codex":0.0013734036,"about_ca_topic_score_gemma":0.0017828887,"teacher_disagreement_score":0.0013734036,"about_ca_system_score_codex":0.0001259949,"about_ca_system_score_gemma":0.00023311206,"threshold_uncertainty_score":0.0027308464},"labels":[],"label_agreement":null},{"id":"W2050129421","doi":"10.1016/j.procs.2013.06.112","title":"Agent-based Housing Market Microsimulation for Integrated Land Use, Transportation, Environment Model System","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Computer science; Environmental economics; Operations research; Transport engineering","score_opus":0.026365185909440984,"score_gpt":0.18899736129946865,"score_spread":0.16263217539002767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050129421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12616248,0.0004943967,0.8347006,0.0008588517,0.00023314216,0.0003298118,0.0024647568,0.0015516117,0.03320438],"genre_scores_gemma":[0.86228085,0.00031399124,0.11658805,0.00020370174,0.00006581479,0.0010007573,0.0018027165,0.0002675763,0.017476536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997844,0.0000889912,0.000010751819,0.00003758158,0.000043193522,0.00003517638],"domain_scores_gemma":[0.99913377,0.0005024525,0.00007632671,0.000036760346,0.00020125238,0.000049369366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060806354,0.0006870342,0.00097144046,0.00042842812,0.0005176451,0.00077609735,0.0012223242,0.0010800662,0.009946696],"category_scores_gemma":[0.0020847754,0.0004392377,0.0009304672,0.00039243422,0.0003601417,0.000568774,0.0009874683,0.0011749986,0.00085467537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011714018,0.000011247892,0.00023047053,0.000014349907,0.000011386677,0.000021892703,0.000011257249,0.9938399,0.00010457811,0.0038155257,0.00041006724,0.0015176245],"study_design_scores_gemma":[0.0000041089943,0.0000030397935,0.00003148894,0.0000012037215,0.0000018212662,0.0000018692313,0.0000025769205,0.99911934,0.000024305607,0.00052051625,0.00028859152,0.0000011788306],"about_ca_topic_score_codex":0.033346284,"about_ca_topic_score_gemma":0.02478439,"teacher_disagreement_score":0.033346284,"about_ca_system_score_codex":0.0010736111,"about_ca_system_score_gemma":0.0013456187,"threshold_uncertainty_score":0.066304386},"labels":[],"label_agreement":null},{"id":"W2057120045","doi":"10.1016/j.procs.2014.07.097","title":"A Comparison of Data Forwarding Schemes for Network Resiliency in Software Defined Networking","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Software-defined networking; Computer network; Software; Operating system","score_opus":0.06153752165118035,"score_gpt":0.335605941919964,"score_spread":0.27406842026878364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057120045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63491726,0.006145321,0.34292674,0.00075172435,0.0008052836,0.0005576026,0.0002476476,0.0018938627,0.011754518],"genre_scores_gemma":[0.9468837,0.00068641815,0.051079758,0.00006487058,0.00004789268,0.000063483865,0.0001238179,0.00003386249,0.0010163955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975514,0.00083357305,0.00017642576,0.00025858646,0.0008896785,0.00029033926],"domain_scores_gemma":[0.9931639,0.0034386972,0.00040402112,0.0011760725,0.0016198857,0.00019733795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004196527,0.0005546094,0.00072043866,0.002124946,0.0008435224,0.0011753528,0.0014060657,0.00084405387,0.0015737457],"category_scores_gemma":[0.008900988,0.00014833438,0.0005263426,0.0010293311,0.0006263625,0.0022155938,0.00083899853,0.00061455043,0.00016253929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0055193175,0.0010632834,0.0064249933,0.000955327,0.00035425345,0.00025347754,0.00042637534,0.22740597,0.0537474,0.06315126,0.005338406,0.63535994],"study_design_scores_gemma":[0.00016853516,0.0024880546,0.004137281,0.00011533968,0.0002584047,0.0004428651,0.00035169336,0.9254266,0.044625692,0.013815093,0.008079731,0.00009062686],"about_ca_topic_score_codex":0.0011559526,"about_ca_topic_score_gemma":0.00067518005,"teacher_disagreement_score":0.004196527,"about_ca_system_score_codex":0.0017781056,"about_ca_system_score_gemma":0.00081677036,"threshold_uncertainty_score":0.02219361},"labels":[],"label_agreement":null},{"id":"W2057562551","doi":"10.1016/j.procs.2013.06.144","title":"Towards a Distributed Plan Execution Monitoring Framework","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"","keywords":"Computer science; Scalability; Plan (archaeology); Distributed computing; Automaton; Context (archaeology); Runtime verification; Scale (ratio); Software engineering; Formal verification; Programming language; Theoretical computer science; Database","score_opus":0.0303077560838228,"score_gpt":0.2895329646253979,"score_spread":0.2592252085415751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057562551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015438766,0.00008362346,0.99551976,0.0003742363,0.000028524342,0.00009471633,0.00003576925,0.0011187069,0.0012008562],"genre_scores_gemma":[0.07599362,0.00021984045,0.92126685,0.00015720341,0.000074099175,0.0002806337,0.00015525195,0.00016427724,0.0016882491],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99590224,0.0010521866,0.0003435893,0.0006811498,0.0016676623,0.00035321718],"domain_scores_gemma":[0.9948573,0.0018394821,0.00059425726,0.0011746756,0.0010914273,0.0004427881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009015643,0.00071838533,0.00081964344,0.0015789587,0.0014475156,0.004269061,0.0032917478,0.0016481675,0.0024892928],"category_scores_gemma":[0.0080708815,0.00081509136,0.0019361574,0.0010205631,0.0031738728,0.0039042023,0.0038409873,0.0039652972,0.0006150784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009608341,0.00014138562,0.00088575087,0.00016592133,0.00008243506,0.000372991,0.00052814645,0.1580297,0.005163899,0.7740574,0.00305961,0.05741667],"study_design_scores_gemma":[0.000054347878,0.000046836056,0.00015785491,0.00008004162,0.0000584066,0.00012432002,0.00009642155,0.7640912,0.0037708539,0.20910034,0.022384122,0.000035316036],"about_ca_topic_score_codex":0.008180145,"about_ca_topic_score_gemma":0.007844695,"teacher_disagreement_score":0.009015643,"about_ca_system_score_codex":0.0024906264,"about_ca_system_score_gemma":0.00673465,"threshold_uncertainty_score":0.0476799},"labels":[],"label_agreement":null},{"id":"W2057924943","doi":"10.1016/j.procs.2011.08.041","title":"Multiple SOFMs Working Cooperatively In a Vote-based Ranking System For Network Intrusion Detection","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Intrusion detection system; Ranking (information retrieval); Data mining; Computer network; Artificial intelligence","score_opus":0.028366092891949184,"score_gpt":0.2185146057719605,"score_spread":0.1901485128800113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057924943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16684157,0.00064866245,0.81387097,0.0007058861,0.0007513817,0.000679172,0.00048984913,0.009985714,0.00602675],"genre_scores_gemma":[0.76665986,0.00010464953,0.22596087,0.0002506056,0.00029697167,0.00031446162,0.000999144,0.00021531462,0.005198191],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618405,0.0011845256,0.00031846098,0.00068020023,0.0010598523,0.0005728002],"domain_scores_gemma":[0.9940275,0.00182486,0.0003882477,0.00092495367,0.0023436584,0.00049080374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006097663,0.0010471202,0.0031802137,0.003019301,0.002057541,0.0020773318,0.0034338506,0.0014496048,0.0031879174],"category_scores_gemma":[0.009098702,0.0005996267,0.0009909973,0.002042131,0.0006372031,0.0024569621,0.0015722398,0.0013004922,0.0025293073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020532296,0.0011290997,0.014538475,0.00022426956,0.0004308372,0.00019604931,0.00042631186,0.041893538,0.027141146,0.004613205,0.016968394,0.89038545],"study_design_scores_gemma":[0.00015104315,0.00077049894,0.004507454,0.000018863808,0.000167095,0.00017761404,0.00020479214,0.9630858,0.020328617,0.0042603477,0.0062131775,0.00011473128],"about_ca_topic_score_codex":0.0037181918,"about_ca_topic_score_gemma":0.006259349,"teacher_disagreement_score":0.006097663,"about_ca_system_score_codex":0.0008237655,"about_ca_system_score_gemma":0.0014267599,"threshold_uncertainty_score":0.0322479},"labels":[],"label_agreement":null},{"id":"W2059370703","doi":"10.1016/j.procs.2013.06.091","title":"Modeling, Simulation and Control of Flat Panel Solar Collectors with Thermal Storage for Heating and Cooling Applications","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Solar Thermal and Photovoltaic Systems","field":"Energy","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Manitoba Hydro; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Manitoba Hydro","keywords":"Boiler (water heating); Thermal energy storage; Heating system; Thermal; Computer science; Environmental science; Storage tank; Transient (computer programming); Nuclear engineering; Solar air conditioning; Process engineering; Solar energy; Mechanical engineering; Meteorology; Waste management; Electrical engineering; Engineering; Thermodynamics","score_opus":0.020943307406029454,"score_gpt":0.2312814311813305,"score_spread":0.21033812377530103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059370703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57534176,0.0006893911,0.37729448,0.00035150634,0.00011770922,0.00026422352,0.0006511044,0.0013620622,0.043927778],"genre_scores_gemma":[0.9853976,0.00023321318,0.007885951,0.000015498063,0.000008935048,0.00008840301,0.00014050941,0.000040552823,0.006189322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999014,0.000022023687,0.0000042348556,0.0000142095305,0.000034480574,0.000023621704],"domain_scores_gemma":[0.99985886,0.000057625613,0.000018085815,0.000010145977,0.00004064945,0.000014609079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020444082,0.00052343006,0.00047988608,0.00025520637,0.000475558,0.00085449946,0.0007243369,0.00060150475,0.0024555414],"category_scores_gemma":[0.00040838265,0.0002799709,0.0005753954,0.00030475768,0.00032970012,0.0003918723,0.00030933297,0.00033662465,0.0003147868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034698423,0.000018219667,0.0003810415,0.000021774955,0.000008166457,0.000047692545,0.000026482976,0.9946877,0.0024335878,0.0006197715,0.0001308843,0.0015899556],"study_design_scores_gemma":[0.0000061819105,0.000016645045,0.00019882136,0.0000015976125,0.0000033001802,0.000005902523,0.000009001475,0.9987686,0.000628196,0.00010594557,0.0002538903,0.0000018337342],"about_ca_topic_score_codex":0.04459389,"about_ca_topic_score_gemma":0.03357237,"teacher_disagreement_score":0.04459389,"about_ca_system_score_codex":0.00078137097,"about_ca_system_score_gemma":0.0012797541,"threshold_uncertainty_score":0.088668644},"labels":[],"label_agreement":null},{"id":"W2060238489","doi":"10.1016/j.procs.2014.07.041","title":"WiLoVe: A WiFi-coverage based Location Verification System in LBS","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Location-based service; Embedded system; Computer network; Real-time computing; Computer security","score_opus":0.00466476254274714,"score_gpt":0.18267203983048508,"score_spread":0.17800727728773794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060238489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13887027,0.0006482201,0.7950603,0.00034626067,0.00021244673,0.0005864813,0.00040600303,0.057520397,0.0063496083],"genre_scores_gemma":[0.9139217,0.000117698415,0.08009425,0.00023351029,0.000048772843,0.00017633273,0.0006030226,0.00024195998,0.0045626233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988788,0.00025873628,0.000097768076,0.00023705621,0.00033032717,0.000197358],"domain_scores_gemma":[0.9988783,0.00019531156,0.00017064842,0.0004236464,0.00021458104,0.00011746841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007437861,0.0006357405,0.000800015,0.0007983939,0.00055742136,0.0008086008,0.002120517,0.0009237274,0.004486033],"category_scores_gemma":[0.0022802535,0.0003358478,0.00038394093,0.00043066405,0.000507122,0.002220822,0.0024075543,0.0007459423,0.0014814868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004243047,0.0009308293,0.017000044,0.0008346598,0.0002912525,0.0045747953,0.0009121562,0.04459956,0.19161408,0.02241169,0.035831835,0.6767561],"study_design_scores_gemma":[0.0004605939,0.0013686223,0.006387753,0.00009516389,0.00014245998,0.0020772994,0.00017059465,0.83292234,0.116001755,0.0054520597,0.03472067,0.00020072018],"about_ca_topic_score_codex":0.0017153199,"about_ca_topic_score_gemma":0.0011261582,"teacher_disagreement_score":0.004486033,"about_ca_system_score_codex":0.00045316195,"about_ca_system_score_gemma":0.0006740331,"threshold_uncertainty_score":0.015007257},"labels":[],"label_agreement":null},{"id":"W2060552609","doi":"10.1016/j.procs.2012.04.180","title":"Kepler for ‘Omics Bioinformatics","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Omics; Kepler; Bioinformatics; Data science; Computational biology; Biology","score_opus":0.16228822651608563,"score_gpt":0.39131302395183276,"score_spread":0.22902479743574713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060552609","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010296837,0.01672166,0.37341595,0.022214511,0.011310765,0.0013669052,0.14374413,0.22381136,0.19711791],"genre_scores_gemma":[0.083004214,0.010720138,0.52084845,0.006512753,0.0034197508,0.0027445655,0.20133045,0.04341087,0.12800878],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99544036,0.00113522,0.00044586376,0.0011940135,0.0012753352,0.0005092385],"domain_scores_gemma":[0.9932986,0.0014194495,0.0006893878,0.0019907008,0.0014927958,0.0011090882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057085073,0.0032025927,0.003404191,0.0038367913,0.001739621,0.0071499385,0.0040008575,0.0025174685,0.12053986],"category_scores_gemma":[0.010935332,0.0011353776,0.0028467702,0.007281181,0.0011721562,0.0043151774,0.006325759,0.0051481714,0.15837866],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018792821,0.00014036933,0.002134945,0.0035670095,0.00040358608,0.00084924354,0.0004953891,0.0041622403,0.0150142135,0.08430709,0.659332,0.2277146],"study_design_scores_gemma":[0.00022436354,0.00007165894,0.00086922257,0.0002126485,0.00008683707,0.00034452922,0.000059307036,0.0053106695,0.006533665,0.04335743,0.9428469,0.000082687635],"about_ca_topic_score_codex":0.0017016943,"about_ca_topic_score_gemma":0.0008128521,"teacher_disagreement_score":0.12053986,"about_ca_system_score_codex":0.0014939999,"about_ca_system_score_gemma":0.0040504253,"threshold_uncertainty_score":0.403246},"labels":[],"label_agreement":null},{"id":"W2062055860","doi":"10.1016/j.procs.2013.06.137","title":"Stability Visualizations as a Low-complexity Descriptor of Network Host Behaviour","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Host (biology); Stability (learning theory); Theoretical computer science; Human–computer interaction; Distributed computing; Machine learning","score_opus":0.02400791373947972,"score_gpt":0.25175426093237535,"score_spread":0.22774634719289563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062055860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28566492,0.0011750375,0.67995226,0.00079172716,0.00018649026,0.00022767128,0.005538711,0.021013845,0.005449298],"genre_scores_gemma":[0.773965,0.00056221423,0.21881235,0.00006346396,0.00011605056,0.00016094978,0.003564259,0.0010556184,0.0017000488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995608,0.00010251848,0.000051901046,0.0000664402,0.00017255529,0.000045796132],"domain_scores_gemma":[0.9961545,0.0019359054,0.00061031943,0.0003554868,0.00069513835,0.00024866348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009989709,0.00087944244,0.00045903825,0.004891757,0.00038002853,0.0021264611,0.00045238808,0.0005932084,0.0042383964],"category_scores_gemma":[0.00580593,0.00022652549,0.0003948053,0.0016682565,0.00034407244,0.0020866273,0.0011169731,0.00080225716,0.0006431506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027986318,0.0004012762,0.0541837,0.0016294784,0.0003082204,0.0015613348,0.004947927,0.12165691,0.23255703,0.04613649,0.032441955,0.50137705],"study_design_scores_gemma":[0.000060208462,0.00033015516,0.04960142,0.00015042136,0.00009673211,0.0010155689,0.0009159412,0.86460584,0.037993506,0.025424067,0.019626074,0.00018011683],"about_ca_topic_score_codex":0.0017478167,"about_ca_topic_score_gemma":0.0010665216,"teacher_disagreement_score":0.004891757,"about_ca_system_score_codex":0.00042094965,"about_ca_system_score_gemma":0.00029572356,"threshold_uncertainty_score":0.014178872},"labels":[],"label_agreement":null},{"id":"W2065774770","doi":"10.1016/j.procs.2013.06.020","title":"Experimental Evaluation of OpenStack Compute Scheduler","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; École de technologie supérieure","keywords":"Computer science; Cloud computing; Scheduling (production processes); Distributed computing; Operating system","score_opus":0.03153500671465286,"score_gpt":0.2826404459433564,"score_spread":0.25110543922870354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065774770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9780707,0.00016557667,0.018557098,0.0000593926,0.0001514735,0.00042754065,0.00029167449,0.0003966463,0.0018799676],"genre_scores_gemma":[0.98287535,0.0000750355,0.015294242,0.000034165107,0.00003087499,0.0003623124,0.0002371438,0.000072549694,0.0010182838],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980578,0.0006494438,0.00017201295,0.00034953305,0.0005422915,0.00022884907],"domain_scores_gemma":[0.9921715,0.0042581004,0.00065322604,0.0008171507,0.0017363955,0.00036353534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025163372,0.0005886697,0.00059370586,0.00045549625,0.0004955416,0.00060121133,0.00071811734,0.0005015193,0.0032795677],"category_scores_gemma":[0.007502538,0.00022818838,0.00034022902,0.0005259935,0.00051303365,0.000479096,0.00036007166,0.0004716741,0.00035336346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0344216,0.017452413,0.014591555,0.0019301596,0.0004511333,0.00038931903,0.001031304,0.18028788,0.6256647,0.0067142104,0.0030490945,0.1140167],"study_design_scores_gemma":[0.0022397495,0.0952075,0.034363665,0.00008179189,0.00043923367,0.00028181425,0.0007189465,0.3046608,0.54638296,0.0044266195,0.010969543,0.0002273625],"about_ca_topic_score_codex":0.00088108255,"about_ca_topic_score_gemma":0.0004740958,"teacher_disagreement_score":0.0032795677,"about_ca_system_score_codex":0.00055589015,"about_ca_system_score_gemma":0.0008974523,"threshold_uncertainty_score":0.013307869},"labels":[],"label_agreement":null},{"id":"W2066070678","doi":"10.1016/j.procs.2012.06.147","title":"Defense and Monitoring Model for Distributed Denial of Service Attacks","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Denial-of-service attack; Computer science; Application layer DDoS attack; Computer security; Trinoo; Wireless network; Network security; Computer network; Wireless; Telecommunications; The Internet","score_opus":0.028933538593184194,"score_gpt":0.26512156344809557,"score_spread":0.23618802485491136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066070678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096744455,0.0007901634,0.8765757,0.0019970364,0.00020670223,0.00024727412,0.0006618478,0.000781893,0.02199482],"genre_scores_gemma":[0.9562426,0.00052534934,0.026395991,0.00013177018,0.00007631133,0.00034155612,0.0002373807,0.000060741084,0.015988274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991352,0.00023937484,0.000049042363,0.00020841481,0.00019872995,0.00016923125],"domain_scores_gemma":[0.9984806,0.00071739947,0.00025847487,0.00006746005,0.00037399746,0.00010205194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014512347,0.0010558858,0.0010162091,0.0009732206,0.0006832116,0.0017465616,0.0021356023,0.0025626996,0.004474387],"category_scores_gemma":[0.0035376863,0.0004906443,0.0009780793,0.00064683025,0.0008660137,0.001671978,0.0012912916,0.0019382872,0.0006374351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005158798,0.000036429083,0.00060547394,0.000037068698,0.000016854188,0.0001333381,0.00008200984,0.97481763,0.0010298849,0.019798672,0.00049203116,0.002898859],"study_design_scores_gemma":[0.000004201449,0.000008348984,0.000045977868,0.0000022583342,0.0000031434795,0.0000108985505,0.000004930765,0.9984126,0.00005288509,0.0013024566,0.00014991491,0.0000023495932],"about_ca_topic_score_codex":0.009481225,"about_ca_topic_score_gemma":0.0036644624,"teacher_disagreement_score":0.009481225,"about_ca_system_score_codex":0.0019424721,"about_ca_system_score_gemma":0.0012695035,"threshold_uncertainty_score":0.018852055},"labels":[],"label_agreement":null},{"id":"W2067155330","doi":"10.1016/j.procs.2012.06.085","title":"Enhancing Public-Private Partnerships Through SMS Vouchers","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"ICT in Developing Communities","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mennonite Economic Development Associates","funders":"","keywords":"Voucher; Computer science; Scheme (mathematics); Computer security; World Wide Web","score_opus":0.10273881569244459,"score_gpt":0.2887315127788271,"score_spread":0.1859926970863825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067155330","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32883686,0.0010957599,0.20226413,0.02624973,0.0006558941,0.0043136063,0.00039557755,0.002213421,0.43397504],"genre_scores_gemma":[0.95451957,0.00035594404,0.025554338,0.00074678514,0.00015805665,0.00064869784,0.00009615875,0.00006282384,0.017857661],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98771065,0.008263443,0.0004002475,0.00046322314,0.0016172078,0.0015452792],"domain_scores_gemma":[0.9760341,0.012034309,0.0025869529,0.0029196974,0.0023460337,0.0040788585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012044198,0.00042153453,0.00033899397,0.0016704027,0.0020578357,0.003513715,0.0014915655,0.001576176,0.027916655],"category_scores_gemma":[0.026583137,0.00034886922,0.00042870306,0.0014581742,0.0013488355,0.0057505653,0.010708009,0.0011726124,0.0052371966],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005156751,0.0028310167,0.014127756,0.00094950886,0.000067829904,0.0006571501,0.006996901,0.008553628,0.0065810247,0.14268114,0.027607447,0.788431],"study_design_scores_gemma":[0.0011011151,0.0035415208,0.032434322,0.0011008509,0.00014195469,0.0015187592,0.019057432,0.036005285,0.0089729605,0.2645728,0.63134867,0.00020440052],"about_ca_topic_score_codex":0.00076013216,"about_ca_topic_score_gemma":0.0012966489,"teacher_disagreement_score":0.027916655,"about_ca_system_score_codex":0.0016918788,"about_ca_system_score_gemma":0.005295353,"threshold_uncertainty_score":0.093390465},"labels":[],"label_agreement":null},{"id":"W2071185898","doi":"10.1016/j.procs.2014.05.189","title":"Distance-based High-frequency Trading","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; High-frequency trading; Algorithmic trading; Finance","score_opus":0.07523439673813767,"score_gpt":0.35627225142322305,"score_spread":0.28103785468508535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071185898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081819035,0.00064526004,0.9133626,0.00014203318,0.00007612297,0.00004659521,0.00010664417,0.0006367659,0.0031649906],"genre_scores_gemma":[0.86926556,0.00026833592,0.12714788,0.000057392943,0.000054013097,0.00004861223,0.00019743515,0.000058210808,0.0029026044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991148,0.00016961752,0.00006494286,0.00019903391,0.0003902228,0.00006147846],"domain_scores_gemma":[0.9979317,0.001045526,0.0002617578,0.00028668856,0.00040664634,0.00006771572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009752032,0.000512231,0.0009728071,0.0012057137,0.0003657962,0.0012756215,0.0012687468,0.0006546461,0.001672266],"category_scores_gemma":[0.0035819563,0.00021225741,0.00043007673,0.0011722724,0.0005093752,0.0015396446,0.0008907299,0.000708864,0.00065961527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028622936,0.00020979156,0.00491571,0.000118157644,0.00010225984,0.00012739557,0.00011411381,0.4695301,0.007862366,0.015442449,0.0013133637,0.49997813],"study_design_scores_gemma":[0.0000047209037,0.000048881397,0.0008452585,0.0000048235243,0.000004992483,0.000058453687,0.000008844844,0.9930108,0.0013642586,0.004213057,0.00042602536,0.000009808957],"about_ca_topic_score_codex":0.0023746307,"about_ca_topic_score_gemma":0.0016244705,"teacher_disagreement_score":0.0023746307,"about_ca_system_score_codex":0.00062264723,"about_ca_system_score_gemma":0.0004233652,"threshold_uncertainty_score":0.005594313},"labels":[],"label_agreement":null},{"id":"W2072091972","doi":"10.1016/j.procs.2013.06.162","title":"The 1st International Workshop on Survivable and Robust Optical Networks (IWSRON 2013) Preface","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Operations research; Computer network; Telecommunications","score_opus":0.010038489206263011,"score_gpt":0.19656104955212242,"score_spread":0.1865225603458594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072091972","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01640286,0.037169468,0.20113283,0.037946396,0.3867945,0.00097226846,0.0019822845,0.0023108064,0.31528863],"genre_scores_gemma":[0.07242087,0.021967342,0.048058186,0.0034446125,0.051154777,0.00039878485,0.003331553,0.0012390055,0.7979849],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99921167,0.00013389289,0.00003049699,0.00019047101,0.0002929419,0.00014052074],"domain_scores_gemma":[0.99822074,0.0001538382,0.00004645482,0.00017362865,0.0008974767,0.000507885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020687615,0.0010224988,0.0007123792,0.0012965568,0.0012178991,0.0030332995,0.0014281097,0.001655286,0.062527455],"category_scores_gemma":[0.0022212595,0.0003250492,0.0008253263,0.00094657775,0.0005533419,0.0023861534,0.0028583778,0.002681256,0.020543009],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027039717,0.00015345817,0.00049876503,0.00039084538,0.000023147471,0.00023373314,0.0001856826,0.0023787431,0.006135953,0.016790614,0.6589601,0.3139786],"study_design_scores_gemma":[0.000030205028,0.00021425795,0.00088308996,0.0003019934,0.000020131385,0.0002747904,0.00016905821,0.0050621936,0.004242812,0.013941819,0.97482127,0.00003840642],"about_ca_topic_score_codex":0.0013466579,"about_ca_topic_score_gemma":0.0020543193,"teacher_disagreement_score":0.062527455,"about_ca_system_score_codex":0.0012015519,"about_ca_system_score_gemma":0.0015982434,"threshold_uncertainty_score":0.20917517},"labels":[],"label_agreement":null},{"id":"W2072190413","doi":"10.1016/j.procs.2014.07.012","title":"IDDR: Improved Density Controlled Divide-and-Rule Scheme for Energy Efficient Routing in Wireless Sensor Networks","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Computer science; Energy consumption; Wireless sensor network; Energy (signal processing); Computer network; Routing protocol; Scheme (mathematics); Protocol (science); Routing (electronic design automation); Residual; Selection (genetic algorithm); Distributed computing; Algorithm; Electrical engineering; Artificial intelligence; Mathematics","score_opus":0.006103479917729016,"score_gpt":0.20623585894178334,"score_spread":0.2001323790240543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072190413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059666593,0.0021673269,0.931175,0.0005227541,0.00034888048,0.00035370013,0.00015022402,0.0019843183,0.0036312914],"genre_scores_gemma":[0.70307714,0.0011939441,0.28951898,0.00029713043,0.00013035598,0.0002674707,0.00035050357,0.00010097117,0.0050635715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989405,0.00023108718,0.0000867575,0.00021155675,0.00043286762,0.00009714878],"domain_scores_gemma":[0.9991955,0.0002075582,0.00010814021,0.00021396657,0.00020679615,0.00006802817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008746681,0.00054210075,0.0010811775,0.00088119163,0.00090427103,0.00073958776,0.0030735368,0.00083761377,0.0009901135],"category_scores_gemma":[0.0019580715,0.00031443496,0.0005596269,0.001019011,0.0006373668,0.0014743893,0.0013616846,0.0008093085,0.00037391466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009141995,0.0005520926,0.0024489537,0.00053990964,0.00016077417,0.0007210578,0.00077133346,0.19431433,0.09495344,0.029466437,0.007444009,0.66771346],"study_design_scores_gemma":[0.00018291235,0.0009171424,0.0010037047,0.00004254456,0.0000944938,0.0013741631,0.00013244196,0.93966854,0.02967263,0.010528711,0.016262637,0.00012006273],"about_ca_topic_score_codex":0.0019884328,"about_ca_topic_score_gemma":0.0025554176,"teacher_disagreement_score":0.0030735368,"about_ca_system_score_codex":0.00061855244,"about_ca_system_score_gemma":0.00092694146,"threshold_uncertainty_score":0.0046257377},"labels":[],"label_agreement":null},{"id":"W2072404679","doi":"10.1016/j.procs.2013.06.061","title":"Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Accelerometer; Gesture; Tracking (education); Mobile phone; Computer vision; Artificial intelligence; Movement (music); Gesture recognition; Foot (prosody); Position (finance); Phone; Human–computer interaction; Acoustics; Telecommunications","score_opus":0.06685342729545717,"score_gpt":0.26673380139855246,"score_spread":0.19988037410309528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072404679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08167963,0.004848331,0.9058662,0.00018553389,0.0002604351,0.00019214419,0.0003269033,0.002303763,0.0043371427],"genre_scores_gemma":[0.5375093,0.0042483513,0.4516758,0.00025233897,0.00013547276,0.00024864986,0.0004492144,0.00009802722,0.005382921],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994179,0.00012770781,0.00004086874,0.00014459384,0.00022445954,0.000044454668],"domain_scores_gemma":[0.9995486,0.00014928891,0.000045777935,0.000068473935,0.00016177134,0.000026054147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003903314,0.0008497285,0.00055911695,0.0008295212,0.00024099393,0.0007015457,0.00055732415,0.0010400722,0.001789953],"category_scores_gemma":[0.0008261041,0.0004021717,0.00037369426,0.0007861452,0.00023367973,0.0006546751,0.0004066409,0.00033428657,0.0014783597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033139356,0.00009353412,0.0062038745,0.0006091921,0.00007351331,0.000275161,0.00019109897,0.0012537827,0.3823259,0.0007789804,0.0016685937,0.6061949],"study_design_scores_gemma":[0.0002340595,0.003664945,0.16706441,0.00077869615,0.0005614121,0.009140667,0.0006981518,0.18459517,0.5460433,0.0040613986,0.08269927,0.0004586029],"about_ca_topic_score_codex":0.0012224837,"about_ca_topic_score_gemma":0.0023913884,"teacher_disagreement_score":0.001789953,"about_ca_system_score_codex":0.00017642255,"about_ca_system_score_gemma":0.0002185611,"threshold_uncertainty_score":0.005988002},"labels":[],"label_agreement":null},{"id":"W2072973896","doi":"10.1016/j.procs.2014.08.016","title":"Central Routing Algorithm: An Alternative Solution to Avoid Mesh Topology in iBGP","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Border Gateway Protocol; Computer network; Routing protocol; Distributed computing; Topology (electrical circuits); Routing table; Node (physics); Interior gateway protocol; Network topology; Routing (electronic design automation); Enhanced Interior Gateway Routing Protocol; Static routing; Link-state routing protocol; Mathematics; Engineering","score_opus":0.007705807364607627,"score_gpt":0.2238357499847303,"score_spread":0.21612994262012267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072973896","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016607445,0.00057473895,0.9763899,0.00028750557,0.0001630825,0.00013507486,0.000039910658,0.0015065221,0.004295781],"genre_scores_gemma":[0.31681126,0.0004292673,0.67663103,0.00031794468,0.00010409124,0.00021205956,0.00025983318,0.0002347798,0.0049998118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882275,0.00036854405,0.00007807422,0.0002388891,0.00032223942,0.00016952865],"domain_scores_gemma":[0.99896276,0.00017838554,0.00015162879,0.00028316263,0.00035549418,0.00006858211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011631886,0.0005800472,0.0011192941,0.0014133077,0.0012257624,0.0015117856,0.0026312158,0.0011912924,0.0020549549],"category_scores_gemma":[0.0019935293,0.0002665732,0.0006647032,0.0012445428,0.00077985827,0.002281943,0.0018121772,0.0009844018,0.0006881521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007698718,0.00028796418,0.0030011407,0.00046802306,0.00024038267,0.0006227986,0.000674714,0.15427847,0.05461767,0.15209909,0.014834924,0.618105],"study_design_scores_gemma":[0.00022632255,0.00078605954,0.00091736665,0.000086798806,0.00016923316,0.0016553755,0.000303845,0.8802859,0.028048744,0.03713588,0.05026307,0.000121395235],"about_ca_topic_score_codex":0.0017016578,"about_ca_topic_score_gemma":0.001689655,"teacher_disagreement_score":0.0026312158,"about_ca_system_score_codex":0.0005820079,"about_ca_system_score_gemma":0.0015030146,"threshold_uncertainty_score":0.0068745017},"labels":[],"label_agreement":null},{"id":"W2073302373","doi":"10.1016/j.procs.2010.12.127","title":"Integrating web applications to provide an effective distance online learning environment for students","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Online and Blended Learning","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canada Research Chairs","funders":"","keywords":"Computer science; Web application; Distance education; World Wide Web; Online learning; Multimedia; Human–computer interaction; Mathematics education","score_opus":0.023503711471281354,"score_gpt":0.34804394269781846,"score_spread":0.32454023122653713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073302373","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4356348,0.0013886129,0.32804525,0.0054516876,0.0018898429,0.004489359,0.0016229582,0.116761535,0.104715884],"genre_scores_gemma":[0.50837916,0.0012367715,0.4003237,0.0021257377,0.0009947746,0.002802563,0.0033739898,0.0047801905,0.0759832],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986975,0.00039803196,0.00010209231,0.00017660203,0.00045357595,0.00017211025],"domain_scores_gemma":[0.9952695,0.0015625064,0.00023574124,0.0005715789,0.0009489054,0.0014116984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018591876,0.00086141966,0.00050492544,0.0015896449,0.0007210806,0.003615271,0.001952757,0.0010618849,0.0123888245],"category_scores_gemma":[0.006241466,0.0004130081,0.00055260403,0.0009226587,0.00016798638,0.0029916838,0.0031068516,0.0011460694,0.010385085],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042769447,0.0047710924,0.005743308,0.00043803614,0.00003917343,0.00066916255,0.0012253927,0.0011696442,0.014458708,0.0012178642,0.05014921,0.91969085],"study_design_scores_gemma":[0.0014209679,0.0078048697,0.0784551,0.0016352409,0.00049710606,0.0030707177,0.0042836573,0.066043615,0.05683942,0.015845455,0.76335984,0.0007439529],"about_ca_topic_score_codex":0.0003489447,"about_ca_topic_score_gemma":0.0005546871,"teacher_disagreement_score":0.0123888245,"about_ca_system_score_codex":0.0002710228,"about_ca_system_score_gemma":0.00086590636,"threshold_uncertainty_score":0.04144478},"labels":[],"label_agreement":null},{"id":"W2073636722","doi":"10.1016/j.procs.2014.08.002","title":"EUSPN-2014 Preface","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Academic Publishing and Open Access","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Data science","score_opus":0.05035270133866893,"score_gpt":0.3682944979763194,"score_spread":0.3179417966376505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073636722","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022247455,0.016464407,0.012125686,0.052868113,0.6242201,0.00046038223,0.0056649456,0.00080091564,0.28517073],"genre_scores_gemma":[0.015158963,0.012574475,0.0043953466,0.005450403,0.13657843,0.00030018526,0.0049997633,0.0008607496,0.8196817],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992731,0.00009655884,0.00005162225,0.00013579657,0.00037095958,0.00007202259],"domain_scores_gemma":[0.9937983,0.00055778475,0.00019448531,0.0003984666,0.0037390678,0.0013118535],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00180068,0.0008546591,0.0005384286,0.002543562,0.0012349054,0.0034155177,0.0008995445,0.0011870011,0.16487655],"category_scores_gemma":[0.008865118,0.0002241418,0.0006042145,0.0015576333,0.00049010303,0.0016528536,0.001975632,0.0026636727,0.10095676],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052865023,0.000039578405,0.00017416608,0.00017532078,0.000004695927,0.000069045185,0.000039485094,0.0003608753,0.00041007876,0.0049200915,0.92175305,0.072000764],"study_design_scores_gemma":[0.000006468316,0.000029656205,0.00067126466,0.00022392416,0.0000033736135,0.000062776555,0.000044233402,0.00017618602,0.0002590824,0.0029440944,0.99557084,0.000008048852],"about_ca_topic_score_codex":0.0027986772,"about_ca_topic_score_gemma":0.0032582285,"teacher_disagreement_score":0.9965845,"about_ca_system_score_codex":0.002280988,"about_ca_system_score_gemma":0.0024108547,"threshold_uncertainty_score":0.551567},"labels":[],"label_agreement":null},{"id":"W2074582715","doi":"10.1016/j.procs.2011.07.077","title":"An Adaptive Context-Aware and Event-Based Framework Design Model","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Event (particle physics); Publication; Context (archaeology); Leverage (statistics); Context model; Context awareness; Adaptation (eye); Ubiquitous computing; Data science; World Wide Web; Human–computer interaction; Artificial intelligence","score_opus":0.08759148297205874,"score_gpt":0.27435972195438035,"score_spread":0.18676823898232162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074582715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015161022,0.0002170126,0.993418,0.0002901613,0.000038099955,0.0001799688,0.0000732698,0.0007322304,0.0035352372],"genre_scores_gemma":[0.098368004,0.00079723593,0.89110374,0.00022445453,0.000067320885,0.00082655135,0.00050212967,0.00018268642,0.007927836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981993,0.00047680468,0.00023336752,0.0002874853,0.0006462106,0.00015687275],"domain_scores_gemma":[0.99915314,0.0002039386,0.0000822756,0.00015695243,0.00028166777,0.00012198782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032099704,0.00073765067,0.00063405477,0.0010322065,0.0009780187,0.003777419,0.00376972,0.0020497104,0.0028867193],"category_scores_gemma":[0.0030804672,0.0006209352,0.0013459648,0.0009166796,0.001027131,0.0038182174,0.002155775,0.0020452996,0.0012604306],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018146985,0.00022691996,0.0013671342,0.0004199229,0.00016123739,0.0006852462,0.001143456,0.2356627,0.011765359,0.6000792,0.008224899,0.14008248],"study_design_scores_gemma":[0.00007587993,0.00012331209,0.0002960739,0.00013577595,0.00014139162,0.00048055887,0.00014239826,0.81057215,0.004494878,0.07888788,0.10456612,0.00008361572],"about_ca_topic_score_codex":0.009825569,"about_ca_topic_score_gemma":0.008508804,"teacher_disagreement_score":0.009825569,"about_ca_system_score_codex":0.0015890532,"about_ca_system_score_gemma":0.003290761,"threshold_uncertainty_score":0.019536734},"labels":[],"label_agreement":null},{"id":"W2076589318","doi":"10.1016/j.procs.2011.07.081","title":"Using a Cloud-Hosted Proxy to support Mobile Consumers of RESTful Services","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Cloud computing; World Wide Web; Mobile Web; Mobile device; Web service; The Internet; Mobile computing; Mobile cloud computing; Proxy server; Mobile technology; Computer network; Operating system","score_opus":0.04701210453584197,"score_gpt":0.2697621088200591,"score_spread":0.2227500042842171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076589318","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67500526,0.0014105063,0.2944254,0.0012117547,0.000259406,0.00037071964,0.00010156594,0.008754343,0.018461056],"genre_scores_gemma":[0.97156966,0.0001628333,0.025210306,0.00012901299,0.00002933336,0.000031273656,0.00006247491,0.00012875661,0.002676428],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971026,0.000057784117,0.000031844018,0.00004132674,0.000064162305,0.00009465101],"domain_scores_gemma":[0.99889994,0.00016222389,0.00010199943,0.00040674015,0.0002562581,0.00017281329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040676072,0.0003141563,0.00046177837,0.0003514066,0.0007531967,0.0013937013,0.0010886893,0.00086538546,0.0013256902],"category_scores_gemma":[0.001396141,0.00024811082,0.00028081084,0.0004750999,0.00036004148,0.0016863411,0.001231356,0.0006680278,0.00046654578],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046566306,0.0013316872,0.022575684,0.00053333136,0.00020668608,0.007674096,0.0023569348,0.020194756,0.5586684,0.07243917,0.018743055,0.29061967],"study_design_scores_gemma":[0.00048244852,0.0007334994,0.007993208,0.000116949515,0.00041405222,0.003700732,0.00073246827,0.57640636,0.3447302,0.008351974,0.056142423,0.0001957352],"about_ca_topic_score_codex":0.0028657452,"about_ca_topic_score_gemma":0.0024554096,"teacher_disagreement_score":0.0028657452,"about_ca_system_score_codex":0.0005518566,"about_ca_system_score_gemma":0.0006651516,"threshold_uncertainty_score":0.005698085},"labels":[],"label_agreement":null},{"id":"W2078719365","doi":"10.1016/j.procs.2013.06.066","title":"Enhanced Adaptive SLA-aware Algorithms for Provisioning Shared Mesh Optical Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Computer science; Provisioning; Computer network; Service-level agreement; Distributed computing; Blocking (statistics); Path (computing); Optical mesh network; Shared resource; Quality of service; Algorithm; Telecommunications; Wireless mesh network","score_opus":0.013327034456118584,"score_gpt":0.23298537786562917,"score_spread":0.2196583434095106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078719365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035618722,0.00014925175,0.96256727,0.0000890285,0.000052509888,0.00005021105,0.000019969033,0.00040832878,0.0010446778],"genre_scores_gemma":[0.7804671,0.00010193021,0.21851113,0.000048023594,0.000035249526,0.00006268585,0.000057129593,0.000036888596,0.0006798488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940884,0.00015601901,0.00003749502,0.000082286606,0.00020207062,0.000113246],"domain_scores_gemma":[0.9990281,0.00037096627,0.0001763521,0.00014640977,0.00021691492,0.00006132012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008745086,0.00043797752,0.00048542948,0.00042922993,0.0004987545,0.0006661723,0.001198133,0.00046767472,0.0008819771],"category_scores_gemma":[0.0022354107,0.00024380285,0.00031592266,0.000458935,0.00034294196,0.0010464396,0.00096207316,0.0008711943,0.00015302244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002579807,0.00014758234,0.0015953828,0.000091996015,0.000058762565,0.000087941495,0.00014755377,0.7886508,0.015071933,0.01423787,0.0013347297,0.1783174],"study_design_scores_gemma":[0.000008835903,0.000024442774,0.00008082344,0.0000018992883,0.0000037939444,0.000013764332,0.000010214483,0.9973857,0.0008232116,0.0013273134,0.000315836,0.0000041387098],"about_ca_topic_score_codex":0.001709041,"about_ca_topic_score_gemma":0.0017436376,"teacher_disagreement_score":0.001709041,"about_ca_system_score_codex":0.0004964065,"about_ca_system_score_gemma":0.00095502235,"threshold_uncertainty_score":0.004624903},"labels":[],"label_agreement":null},{"id":"W2079099236","doi":"10.1016/j.procs.2012.06.125","title":"Fault Tolerant Wireless Sensor Networks using Adaptive Partitioning","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Retransmission; Computer science; Correctness; Wireless sensor network; Fault tolerance; Wireless; Distributed computing; Computer network; Wireless network; Error detection and correction; Real-time computing; Algorithm; Telecommunications","score_opus":0.024348219281551255,"score_gpt":0.2443531888415423,"score_spread":0.22000496955999105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079099236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055114836,0.0010111828,0.9408006,0.00014720658,0.00006298665,0.00007703329,0.000024602048,0.00046170523,0.0022999824],"genre_scores_gemma":[0.784026,0.00076894107,0.21313703,0.00006571712,0.000040664523,0.0001491246,0.00009364126,0.000064010834,0.0016548848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999686,0.00011249818,0.000019966237,0.000050289895,0.00010258404,0.000028613085],"domain_scores_gemma":[0.9995389,0.00019943502,0.00005886097,0.00010082144,0.00008291771,0.000019127318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036876395,0.00039633256,0.00038273423,0.00040629547,0.00039044247,0.00041575546,0.0009759501,0.00035036646,0.00053419947],"category_scores_gemma":[0.0011945268,0.00018426219,0.0002608628,0.00044931503,0.0003655989,0.0012360151,0.00082751946,0.00037459985,0.00014401412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028722466,0.00006265082,0.00085587765,0.00016278648,0.000076605545,0.00015457769,0.00032948438,0.731534,0.051282357,0.030217668,0.0015691173,0.18346769],"study_design_scores_gemma":[0.000020262998,0.00011639518,0.00034780943,0.000016845861,0.000020435617,0.00014191598,0.000055460467,0.9715144,0.009970588,0.013359615,0.0044181664,0.000018176248],"about_ca_topic_score_codex":0.0011291256,"about_ca_topic_score_gemma":0.0009658727,"teacher_disagreement_score":0.0011291256,"about_ca_system_score_codex":0.0004743415,"about_ca_system_score_gemma":0.00028988157,"threshold_uncertainty_score":0.0034416318},"labels":[],"label_agreement":null},{"id":"W2080298850","doi":"10.1016/j.procs.2013.09.023","title":"A Negotiation Protocol for Meeting Scheduling Agent","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Negotiation; Scheduling (production processes); Protocol (science); Distributed computing; Computer network; Mathematical optimization","score_opus":0.03685648417064298,"score_gpt":0.3002477802453971,"score_spread":0.26339129607475414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080298850","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005868668,0.00035634413,0.97399205,0.00072829024,0.00047617473,0.0010970902,0.0002022985,0.0011356017,0.016143426],"genre_scores_gemma":[0.22211733,0.00075886433,0.7523821,0.0004467877,0.00030916661,0.0034960276,0.0008807741,0.00023395974,0.01937505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99462366,0.002430457,0.00070753065,0.0005914792,0.0013606825,0.00028624825],"domain_scores_gemma":[0.9964624,0.0014603682,0.00028079035,0.00047641757,0.0010578005,0.00026222455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004757843,0.0007973482,0.00067598844,0.001103301,0.0022011653,0.002439103,0.0020842142,0.002485877,0.006942865],"category_scores_gemma":[0.008500449,0.00048522223,0.0009154284,0.001176687,0.0010931066,0.003394723,0.002351165,0.0022592258,0.0020095415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005787493,0.00032315604,0.0009184988,0.00086715666,0.00013308947,0.0018286606,0.0018106548,0.054605495,0.027895901,0.6769651,0.031769525,0.202304],"study_design_scores_gemma":[0.00044924725,0.0004280803,0.0004004175,0.00022934169,0.0001523543,0.0016271371,0.0004906201,0.51350665,0.0253073,0.14912218,0.30809513,0.00019155839],"about_ca_topic_score_codex":0.0012945905,"about_ca_topic_score_gemma":0.0007030898,"teacher_disagreement_score":0.006942865,"about_ca_system_score_codex":0.0010048159,"about_ca_system_score_gemma":0.0027805136,"threshold_uncertainty_score":0.02516216},"labels":[],"label_agreement":null},{"id":"W2081625086","doi":"10.1016/j.procs.2012.10.070","title":"Audition, The Game","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Stuttering Research and Treatment","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Stuttering; Video game; Relation (database); Flash (photography); Multimedia; Human–computer interaction; Psychology","score_opus":0.034542836470975426,"score_gpt":0.34815208448432367,"score_spread":0.3136092480133482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081625086","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09831668,0.011035947,0.39496398,0.0038832736,0.0034981638,0.0027853684,0.0026429875,0.024697717,0.45817587],"genre_scores_gemma":[0.48327327,0.0064597414,0.24355997,0.005338654,0.0006691375,0.0021203666,0.0026642242,0.0023794628,0.25353524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952745,0.000126025,0.000034822624,0.00008375562,0.00015827257,0.00006973487],"domain_scores_gemma":[0.9994041,0.00026763373,0.000041339266,0.000055762768,0.000080334845,0.00015093872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041489513,0.0010053244,0.00030341683,0.00064275996,0.00044438016,0.002195882,0.0009248895,0.00078462594,0.01665358],"category_scores_gemma":[0.002680667,0.00029860143,0.00044806785,0.00026619222,0.00050808507,0.0022437773,0.0017211328,0.00081430067,0.004185834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021806543,0.0008843112,0.0050142547,0.0017401484,0.00010995895,0.0010126001,0.0031993776,0.0037205413,0.03776677,0.062441953,0.138012,0.74391747],"study_design_scores_gemma":[0.00015338718,0.0008365903,0.004410601,0.00047136005,0.000084851315,0.0021152387,0.00074038276,0.008766652,0.0071870424,0.013513953,0.9616126,0.00010736517],"about_ca_topic_score_codex":0.0013608834,"about_ca_topic_score_gemma":0.0023994662,"teacher_disagreement_score":0.01665358,"about_ca_system_score_codex":0.0003521731,"about_ca_system_score_gemma":0.0006663472,"threshold_uncertainty_score":0.055711746},"labels":[],"label_agreement":null},{"id":"W2083775207","doi":"10.1016/j.procs.2013.09.007","title":"Analysis of Lightpath Re-provisioning in Green Optical Networks","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Provisioning; Computer science; Computer network; Routing (electronic design automation); Blocking (statistics); Resource (disambiguation); Distributed computing","score_opus":0.007035524918328535,"score_gpt":0.212644187886045,"score_spread":0.20560866296771646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083775207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32115465,0.0018120891,0.6505608,0.0007144613,0.00009712166,0.00013724722,0.00021020373,0.000279499,0.025033938],"genre_scores_gemma":[0.97277147,0.00055443624,0.022191275,0.000053218235,0.000023474771,0.000031395317,0.00006967057,0.00005680524,0.004248297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993253,0.00015321691,0.000016156537,0.00008105819,0.00025880997,0.0001654505],"domain_scores_gemma":[0.998789,0.0006712509,0.00015995678,0.00010127465,0.00023883388,0.000039761897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008181713,0.000635743,0.00045387572,0.00053965265,0.0004139182,0.0010039585,0.0008307616,0.00047234603,0.0022425232],"category_scores_gemma":[0.0021841521,0.00028485042,0.0004604946,0.00052268745,0.0005074595,0.0011632539,0.00044496913,0.00064598286,0.00013467405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033417604,0.000026008187,0.0007247017,0.00004724819,0.000020420526,0.000065148175,0.000023904677,0.9662829,0.0050248574,0.018907154,0.00029462777,0.008549674],"study_design_scores_gemma":[5.7585106e-7,0.00001112874,0.00042512623,0.0000034154884,0.0000062161735,0.00001922916,0.000010406718,0.9945064,0.0008584286,0.00387962,0.00027625306,0.0000032604216],"about_ca_topic_score_codex":0.005805991,"about_ca_topic_score_gemma":0.004363573,"teacher_disagreement_score":0.005805991,"about_ca_system_score_codex":0.0018834602,"about_ca_system_score_gemma":0.0010154248,"threshold_uncertainty_score":0.013665557},"labels":[],"label_agreement":null},{"id":"W2086338338","doi":"10.1016/j.procs.2012.01.069","title":"Applying Variable Coe_cient functions to Self-Organizing Feature Maps for Network Intrusion Detection on the 1999 KDD Cup Dataset","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Euclidean distance; Data mining; Dimension (graph theory); Intrusion detection system; Sample (material); Feature (linguistics); Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Intrusion; Euclidean geometry; Mathematics","score_opus":0.0149127544168876,"score_gpt":0.2271664706567321,"score_spread":0.2122537162398445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086338338","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81569445,0.001322021,0.15353818,0.00093552197,0.0004265849,0.00060816156,0.011482589,0.013089919,0.0029025483],"genre_scores_gemma":[0.7379955,0.00031243343,0.24042171,0.000103270424,0.00006667855,0.0005008528,0.018733557,0.0002982876,0.0015676584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885595,0.00036044762,0.00010701176,0.00021341216,0.00031314348,0.00014994986],"domain_scores_gemma":[0.99685353,0.0016586288,0.00016161188,0.00043750295,0.0007920675,0.00009666387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034207806,0.0015647396,0.0010668905,0.0047042244,0.0006861731,0.0011061413,0.0015146392,0.0010253178,0.00082960346],"category_scores_gemma":[0.008824708,0.00025643344,0.001338748,0.0035748242,0.0004193138,0.0008650664,0.00076223107,0.0012898983,0.0004992906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008386235,0.0011374729,0.026658963,0.00042452544,0.00046705018,0.00037269847,0.00029324103,0.28711903,0.004615175,0.0019185347,0.043764528,0.6323901],"study_design_scores_gemma":[0.00005878902,0.00010560086,0.009696887,0.000017829803,0.000030276167,0.000058464626,0.00010590807,0.98041683,0.004817372,0.0014143402,0.0032433209,0.000034459998],"about_ca_topic_score_codex":0.027200816,"about_ca_topic_score_gemma":0.0255692,"teacher_disagreement_score":0.027200816,"about_ca_system_score_codex":0.0013226608,"about_ca_system_score_gemma":0.0012019308,"threshold_uncertainty_score":0.054084957},"labels":[],"label_agreement":null},{"id":"W2088252348","doi":"10.1016/j.procs.2010.04.111","title":"Design of a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations","year":2010,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"Computer science; Crossover; Computation; Evolutionary computation; Evolutionary algorithm; Benchmark (surveying); Fitness function; Exploit; Architecture; Class (philosophy); Genetic architecture; Genetic algorithm; Theoretical computer science; Artificial intelligence; Gene; Machine learning; Algorithm; Biology; Genetics","score_opus":0.010310407754457884,"score_gpt":0.21874303848188967,"score_spread":0.2084326307274318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088252348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048412606,0.00016225493,0.94497997,0.00023716131,0.00003920878,0.000039260154,0.000058639893,0.00035764556,0.005713193],"genre_scores_gemma":[0.6814899,0.0004895702,0.30861038,0.00010354203,0.000030849817,0.00054092356,0.000170971,0.00017008794,0.008393803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988365,0.000025601468,0.000005692154,0.000030650182,0.000036918045,0.000017428592],"domain_scores_gemma":[0.9998872,0.000051297968,0.000013065278,0.000014055844,0.00001980389,0.000014452899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024156284,0.00038925023,0.0005954385,0.0002888652,0.00038119432,0.0007044852,0.0013197667,0.0008911892,0.0019237706],"category_scores_gemma":[0.0005109432,0.00041638254,0.0007890573,0.00029041103,0.00072846195,0.00073198247,0.0005145587,0.00069578394,0.00034271742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017099508,0.000015494079,0.0001701154,0.000022853454,0.000013127839,0.000056977497,0.000028678896,0.9666981,0.005538147,0.022116048,0.00014216601,0.0051810746],"study_design_scores_gemma":[0.00000919962,0.000010049683,0.000029616105,0.000002022093,0.000004856846,0.000011614502,0.000003817734,0.9945755,0.0006766211,0.0038803353,0.00079288747,0.0000033869503],"about_ca_topic_score_codex":0.0021471279,"about_ca_topic_score_gemma":0.0017750334,"teacher_disagreement_score":0.0021471279,"about_ca_system_score_codex":0.0005480617,"about_ca_system_score_gemma":0.0009082924,"threshold_uncertainty_score":0.0064356327},"labels":[],"label_agreement":null},{"id":"W2088446884","doi":"10.1016/j.procs.2014.08.015","title":"Forward Looking Emission Aware and SLA based Routing Mechanism","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Mechanism (biology); Routing (electronic design automation); Computer network","score_opus":0.005484127506711915,"score_gpt":0.20301954210825984,"score_spread":0.19753541460154792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088446884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030026186,0.00039572833,0.957382,0.0004354023,0.00038797065,0.00012177695,0.000108084896,0.0023123436,0.008830525],"genre_scores_gemma":[0.80829996,0.00041596976,0.18029048,0.0002871348,0.00015596516,0.00010845478,0.00026523054,0.00010971469,0.010067117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931073,0.00011375745,0.000046287896,0.00011934453,0.00028473273,0.00012507378],"domain_scores_gemma":[0.9992675,0.00012448292,0.000117827265,0.00015671285,0.00029228907,0.000041194315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090782513,0.0005347706,0.00052269764,0.00058573025,0.00069724227,0.001084199,0.0014896585,0.00074923574,0.001781433],"category_scores_gemma":[0.001136361,0.00023278668,0.00048679797,0.00038788415,0.00037302478,0.00154322,0.0011568508,0.0010828958,0.0004899678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005444876,0.00063526334,0.0018924583,0.0005536225,0.00015438872,0.0011430678,0.00041822076,0.2193312,0.27726293,0.11469222,0.013558689,0.36981335],"study_design_scores_gemma":[0.00006864585,0.00040782927,0.0011624114,0.000047130896,0.000086062755,0.00069935346,0.000118721415,0.88439417,0.06708022,0.023758113,0.02205821,0.00011914426],"about_ca_topic_score_codex":0.00085196894,"about_ca_topic_score_gemma":0.0008352793,"teacher_disagreement_score":0.001781433,"about_ca_system_score_codex":0.00039753984,"about_ca_system_score_gemma":0.00092797104,"threshold_uncertainty_score":0.005959511},"labels":[],"label_agreement":null},{"id":"W2090882202","doi":"10.1016/j.procs.2013.05.016","title":"Supervised Discretization with GK − τ","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Categorical variable; Discretization; Computer science; Discretization of continuous features; Maximization; Variable (mathematics); Probabilistic logic; Executable; Data mining; Machine learning; Loan; Artificial intelligence; Variables; Mathematical optimization; Mathematics; Discretization error","score_opus":0.007077395900725657,"score_gpt":0.20528238814462343,"score_spread":0.1982049922438978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090882202","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014057437,0.00009871667,0.9846283,0.00009989118,0.000030905027,0.0000343878,0.00008347119,0.00040385616,0.0005629488],"genre_scores_gemma":[0.33909523,0.00016347502,0.6572466,0.00018823183,0.00009406364,0.00022779017,0.00069156504,0.00020064211,0.0020923603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885917,0.0004794251,0.00011407371,0.00025324558,0.00020653244,0.00008766293],"domain_scores_gemma":[0.99735045,0.0013633156,0.00025432018,0.00052629644,0.00041691185,0.00008860876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018940298,0.00050504046,0.0010509132,0.0005874128,0.00043465002,0.0010294719,0.0010958157,0.0009694217,0.001709705],"category_scores_gemma":[0.0056586363,0.00033170084,0.0008459364,0.0008049986,0.0007897622,0.0011908041,0.0010151477,0.0011573159,0.0005171825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062090566,0.00015760383,0.0039553815,0.00034487294,0.00017246237,0.00019829815,0.00035164677,0.5726272,0.017155005,0.041947298,0.0048664,0.35760292],"study_design_scores_gemma":[0.000014139139,0.000048379814,0.00029429473,0.00001008889,0.000008542265,0.000040988798,0.000022621425,0.98326194,0.0027528424,0.012684081,0.00085335865,0.000008799442],"about_ca_topic_score_codex":0.0016392533,"about_ca_topic_score_gemma":0.0024016018,"teacher_disagreement_score":0.0018940298,"about_ca_system_score_codex":0.0006766349,"about_ca_system_score_gemma":0.0010126657,"threshold_uncertainty_score":0.01001668},"labels":[],"label_agreement":null},{"id":"W2091932138","doi":"10.1016/j.procs.2011.07.117","title":"Using RFID to Improve Hospital Supply Chain Management for High Value and Consignment Items","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"RFID technology advancements","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université du Québec à Montréal","funders":"","keywords":"Traceability; Consignment; Radio-frequency identification; Computer science; Supply chain; Supply chain management; Operations management; Computer security; Business; Software engineering; Marketing","score_opus":0.011991188605426042,"score_gpt":0.21554619571272157,"score_spread":0.20355500710729552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091932138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095379695,0.002281385,0.8902416,0.0013773753,0.00013249008,0.00013898195,0.00007002061,0.00207905,0.008299387],"genre_scores_gemma":[0.62809026,0.002256108,0.36318612,0.00028162458,0.00010614665,0.000075628435,0.00021267071,0.00007867676,0.0057126447],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993273,0.00019483591,0.00005666432,0.00012117521,0.00022792598,0.00007216166],"domain_scores_gemma":[0.9990208,0.00031002495,0.0002462077,0.00011144147,0.0002726347,0.000038951704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010265925,0.0005259178,0.00029528688,0.000731625,0.00042776336,0.0011645855,0.00079871435,0.00086828123,0.002145049],"category_scores_gemma":[0.0024636583,0.0001855393,0.00029479014,0.0012433201,0.00019043492,0.0023382984,0.00077982404,0.00039924603,0.0007727001],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002292382,0.00038363313,0.008361836,0.000503932,0.000085804204,0.0003528977,0.0006033411,0.054421734,0.062296316,0.013100067,0.0031706744,0.85649055],"study_design_scores_gemma":[0.00023921701,0.0020729622,0.01552604,0.00039463353,0.0004966818,0.0012707561,0.0014157948,0.5676276,0.23824924,0.02961854,0.14288104,0.00020753627],"about_ca_topic_score_codex":0.0010786498,"about_ca_topic_score_gemma":0.0011719251,"teacher_disagreement_score":0.002145049,"about_ca_system_score_codex":0.00038169321,"about_ca_system_score_gemma":0.0008432263,"threshold_uncertainty_score":0.0071759224},"labels":[],"label_agreement":null},{"id":"W2093827238","doi":"10.1016/j.procs.2014.07.073","title":"Determining Fuzzy Link Quality Membership Functions in Wireless Sensor Networks","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Link (geometry); Wireless sensor network; Fuzzy logic; Quality (philosophy); Wireless; Computer network; Data mining; Artificial intelligence; Telecommunications","score_opus":0.020213992656949432,"score_gpt":0.2542004213519967,"score_spread":0.2339864286950473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093827238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23233959,0.00010979274,0.7666071,0.000046957877,0.000009514732,0.00004554227,0.000015649604,0.00021386873,0.0006119777],"genre_scores_gemma":[0.91761965,0.000051928335,0.08206472,0.0000132746945,0.000003909857,0.000036836962,0.00002444782,0.000012600343,0.00017261095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991385,0.00024661084,0.000043922126,0.00011000294,0.0004007591,0.00006014457],"domain_scores_gemma":[0.99606603,0.002762548,0.00032403853,0.00019216102,0.00059508526,0.00006007581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025469856,0.00041937997,0.0003587941,0.00096807274,0.0004154724,0.00073621736,0.00060115603,0.0006613652,0.000371661],"category_scores_gemma":[0.011711721,0.00027283476,0.0003178574,0.00041297034,0.00058520044,0.00084603427,0.0004364773,0.0004674138,0.00009191611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020807804,0.00012059746,0.003450916,0.00009584409,0.00003553541,0.00010752645,0.0002793046,0.8715245,0.029472664,0.0051317867,0.0001528383,0.08942046],"study_design_scores_gemma":[0.000006291661,0.0000501356,0.00080579467,0.0000081408025,0.0000052503965,0.000025901538,0.000030728257,0.98822206,0.008972657,0.001756451,0.00010539148,0.000011180909],"about_ca_topic_score_codex":0.0016507212,"about_ca_topic_score_gemma":0.0010567443,"teacher_disagreement_score":0.0025469856,"about_ca_system_score_codex":0.0007248306,"about_ca_system_score_gemma":0.0003077102,"threshold_uncertainty_score":0.013469875},"labels":[],"label_agreement":null},{"id":"W2095088996","doi":"10.1016/j.procs.2011.07.030","title":"Towards Augmenting Federated Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Relay; Wireless sensor network; Scalability; Distributed computing; Robustness (evolution); Computer network; Grid; Software deployment; Network topology; Heuristic; Artificial intelligence","score_opus":0.020955738851921844,"score_gpt":0.2182611220020797,"score_spread":0.19730538315015786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095088996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046327394,0.0019778237,0.9452035,0.0006453996,0.00017300862,0.0000662336,0.000057313347,0.0009392543,0.0046100384],"genre_scores_gemma":[0.5469885,0.002071502,0.4463151,0.00029795055,0.0001295599,0.00014101225,0.00017943543,0.000063624575,0.0038133252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992762,0.00032748567,0.00003896196,0.00011135287,0.00017691868,0.000069157126],"domain_scores_gemma":[0.9987708,0.0004775586,0.00012202987,0.0003594489,0.00021567417,0.00005458331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016411149,0.00062826014,0.00072001514,0.00069552206,0.0003453243,0.00090538786,0.002110019,0.0012107879,0.0012117681],"category_scores_gemma":[0.003317748,0.0002454846,0.00047167254,0.00075463357,0.00051064545,0.0027880059,0.0023241718,0.0008903566,0.00038811882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041898314,0.00024000503,0.0015666778,0.00043268438,0.00011806291,0.00039389153,0.00038233,0.48202074,0.016997613,0.07050179,0.0036445567,0.42328265],"study_design_scores_gemma":[0.00002387679,0.00025409064,0.00025514644,0.000048539532,0.00003379704,0.00025331666,0.000085715576,0.93066555,0.0048505776,0.045793634,0.017721824,0.000013923742],"about_ca_topic_score_codex":0.00046053695,"about_ca_topic_score_gemma":0.00056565,"teacher_disagreement_score":0.002110019,"about_ca_system_score_codex":0.00034908083,"about_ca_system_score_gemma":0.0004344532,"threshold_uncertainty_score":0.008679092},"labels":[],"label_agreement":null},{"id":"W2098380006","doi":"10.1016/j.procs.2010.04.054","title":"Exploring utilisation of GPU for database applications","year":2010,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut de Cardiologie de Montréal; Nvidia","keywords":"Computer science; Acceleration; Central processing unit; CUDA; Parallel computing; Base (topology); General-purpose computing on graphics processing units; Algorithm; Computational science; Computer graphics (images); Operating system; Graphics","score_opus":0.07559545320925348,"score_gpt":0.2876882253438074,"score_spread":0.2120927721345539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098380006","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6240356,0.017295796,0.28354943,0.002061105,0.0004322114,0.00023848604,0.0007026387,0.006979029,0.06470571],"genre_scores_gemma":[0.85453945,0.0032253745,0.13557869,0.0002589992,0.00006104787,0.00009813875,0.0006885462,0.00037799627,0.0051717972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993513,0.00020292221,0.000037166657,0.000097050164,0.00017206237,0.0001394858],"domain_scores_gemma":[0.998965,0.0005062406,0.000028925018,0.00020695874,0.00024212248,0.00005071118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053914654,0.00093425595,0.00074927893,0.00074935757,0.0005236926,0.0021845035,0.0017062236,0.00072644354,0.0052399673],"category_scores_gemma":[0.002608795,0.00040551773,0.00042922483,0.002366348,0.00028558844,0.0020265607,0.00091056124,0.00064339244,0.0010694151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029023772,0.0004637136,0.015907738,0.001262529,0.0003862099,0.0015202583,0.0007228133,0.13488124,0.14716803,0.047262456,0.027340963,0.62018174],"study_design_scores_gemma":[0.00021379435,0.00071096234,0.0039858674,0.00014375392,0.00020402597,0.0008954868,0.00049407664,0.8335963,0.07288191,0.018885663,0.06792811,0.000060035323],"about_ca_topic_score_codex":0.00399003,"about_ca_topic_score_gemma":0.004323719,"teacher_disagreement_score":0.0052399673,"about_ca_system_score_codex":0.0007289224,"about_ca_system_score_gemma":0.0005239175,"threshold_uncertainty_score":0.017529428},"labels":[],"label_agreement":null},{"id":"W2104195238","doi":"10.1016/j.procs.2013.05.403","title":"A MapReduce Framework for Analysing Portfolios of Catastrophic Risk with Secondary Uncertainty","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Uncertainty quantification; Data science; Risk analysis (engineering); Machine learning","score_opus":0.028165541505720874,"score_gpt":0.3194657560178787,"score_spread":0.2913002145121578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104195238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007004643,0.00015720216,0.98612833,0.0001482395,0.000024580355,0.00014996261,0.00031774133,0.0035265486,0.0025427195],"genre_scores_gemma":[0.1472114,0.00031603978,0.84902364,0.000071859555,0.00003536314,0.00025870415,0.0008756131,0.00048373337,0.0017237635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994887,0.00009835613,0.00003417181,0.00007425252,0.00024173498,0.000062713334],"domain_scores_gemma":[0.9994967,0.0001540604,0.000034955076,0.00012854539,0.00011691083,0.00006890028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010255397,0.00095940224,0.0006850476,0.00086116494,0.0007624461,0.0017178769,0.0021369949,0.00051412405,0.002416021],"category_scores_gemma":[0.0018846161,0.0004342158,0.0015322384,0.000764606,0.0004988456,0.0012451854,0.0015545122,0.00106933,0.00063168607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020783476,0.00033998702,0.004048122,0.00043629252,0.0002888493,0.0006944184,0.00058923225,0.63537586,0.011890121,0.10485018,0.015275526,0.22600354],"study_design_scores_gemma":[0.00003519457,0.00003284527,0.0006349379,0.000020015204,0.000028152514,0.00016462636,0.000096370015,0.92079085,0.0034045402,0.058803294,0.015960094,0.000029032875],"about_ca_topic_score_codex":0.010282384,"about_ca_topic_score_gemma":0.0115773855,"teacher_disagreement_score":0.010282384,"about_ca_system_score_codex":0.00085481454,"about_ca_system_score_gemma":0.0019277345,"threshold_uncertainty_score":0.020445108},"labels":[],"label_agreement":null},{"id":"W2114026118","doi":"10.1016/j.procs.2012.06.044","title":"Ubiquitous Health Monitoring Using Mobile Web Services","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Vital signs; Interoperability; Telemedicine; Mobile device; Health care; Agile software development; Computer security; World Wide Web; Medicine","score_opus":0.06731576461723728,"score_gpt":0.45515278067110043,"score_spread":0.38783701605386317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114026118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08526578,0.0043444303,0.83619076,0.0017882874,0.0003984227,0.00050726545,0.000596958,0.018481525,0.0524265],"genre_scores_gemma":[0.84863335,0.0019954962,0.1301044,0.0007067379,0.00031999327,0.00023254112,0.00062994706,0.00024929852,0.017128313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934715,0.00018692386,0.000050942734,0.000097302574,0.0002514858,0.00006616187],"domain_scores_gemma":[0.99942905,0.00018044376,0.00006027548,0.00012330686,0.0001483605,0.000058584304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049495697,0.00045167,0.0003909356,0.0010215339,0.00034482774,0.0013779667,0.0006254495,0.0009231678,0.0025478362],"category_scores_gemma":[0.0013623695,0.0001765405,0.00030377516,0.00080748316,0.00022741708,0.0015071242,0.0013622306,0.00047497312,0.0012908009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073972804,0.00035403023,0.007231797,0.000530524,0.00014133206,0.0025283948,0.0006042525,0.007225575,0.07472962,0.024154367,0.020230997,0.8615293],"study_design_scores_gemma":[0.00026118694,0.00086524617,0.018149493,0.00053785363,0.00037676218,0.0066699125,0.0011097104,0.4733878,0.102052286,0.049959414,0.34641492,0.00021539365],"about_ca_topic_score_codex":0.0016134537,"about_ca_topic_score_gemma":0.0011550605,"teacher_disagreement_score":0.0025478362,"about_ca_system_score_codex":0.00024404495,"about_ca_system_score_gemma":0.00028717972,"threshold_uncertainty_score":0.008523345},"labels":[],"label_agreement":null},{"id":"W2161118616","doi":"10.1016/j.procs.2013.06.056","title":"LINK RECOMMENDER: Collaborative-Filtering for Recommending URLs to Twitter Users","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Recommender system; World Wide Web; Information overload; Information retrieval; Social media; Collaborative filtering; Focus (optics); Service (business)","score_opus":0.03329315568566538,"score_gpt":0.28868961767136064,"score_spread":0.25539646198569527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161118616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031348888,0.0015296016,0.95826495,0.00039638975,0.0002641049,0.0003885813,0.00082859595,0.003951955,0.0030269728],"genre_scores_gemma":[0.31262934,0.0016193008,0.67249507,0.00028155415,0.00042673777,0.00048221438,0.0018937591,0.00017838003,0.0099937115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836916,0.00046569906,0.00012098319,0.0003513101,0.00058082485,0.0001119818],"domain_scores_gemma":[0.9962838,0.0016811037,0.0002237516,0.00054331083,0.0011368605,0.0001312417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024273607,0.0010638731,0.0019883087,0.0028972297,0.0013923541,0.0010015587,0.002415442,0.0021379853,0.0036221053],"category_scores_gemma":[0.009229881,0.00064668787,0.0013659929,0.002956113,0.00038815063,0.0018126916,0.00077308645,0.0008924297,0.0029320244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010033337,0.0010771665,0.018988073,0.001071485,0.0009520723,0.0006268137,0.00057382486,0.104767144,0.024631424,0.008167457,0.03623336,0.8019078],"study_design_scores_gemma":[0.00010211699,0.00036134804,0.0028688733,0.00005800218,0.00022166228,0.0003460984,0.00009648687,0.97390103,0.007671762,0.0039543794,0.010330738,0.00008742571],"about_ca_topic_score_codex":0.020261955,"about_ca_topic_score_gemma":0.037486505,"teacher_disagreement_score":0.020261955,"about_ca_system_score_codex":0.00060805475,"about_ca_system_score_gemma":0.0010352619,"threshold_uncertainty_score":0.04028803},"labels":[],"label_agreement":null},{"id":"W2164569390","doi":"10.1016/j.procs.2015.05.031","title":"Modelling Multi-agent Systems with Category Theory","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Categorical variable; Computer science; Category theory; Constructive; Proof theory; Property (philosophy); Constructive proof; Theoretical computer science; Artificial intelligence; Machine learning; Mathematics; Mathematical proof; Programming language; Epistemology; Discrete mathematics; Process (computing); Pure mathematics","score_opus":0.051465289621392435,"score_gpt":0.24475976158846388,"score_spread":0.19329447196707145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164569390","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075262147,0.0014470832,0.96740234,0.0012821389,0.00012260194,0.000084958396,0.00008775647,0.0002639702,0.021783022],"genre_scores_gemma":[0.5667762,0.002231966,0.42030442,0.00046114004,0.0003094003,0.00047325646,0.0002639708,0.000098797274,0.009080924],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99779284,0.0011472881,0.0001362699,0.00020370755,0.0005459051,0.00017385208],"domain_scores_gemma":[0.9978167,0.0013159771,0.00018098147,0.0002644769,0.00026642362,0.00015541686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022556365,0.00062191125,0.00069261,0.0018949683,0.0011627747,0.0035470966,0.0019011879,0.0017367176,0.003825808],"category_scores_gemma":[0.00331207,0.0004407283,0.0013958884,0.0014838603,0.003958856,0.00452093,0.00381453,0.0021532623,0.0005566544],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005835627,0.000008501133,0.00012392722,0.000039160484,0.000013474108,0.00009921999,0.00031035495,0.016136589,0.00024817954,0.9796769,0.00032001827,0.003017811],"study_design_scores_gemma":[0.000013590506,0.000018980174,0.00008852747,0.000036539703,0.000010300896,0.000084677566,0.00013376048,0.0805306,0.00022657002,0.89957017,0.01927013,0.000016121696],"about_ca_topic_score_codex":0.0072535938,"about_ca_topic_score_gemma":0.004829729,"teacher_disagreement_score":0.0072535938,"about_ca_system_score_codex":0.0023183422,"about_ca_system_score_gemma":0.0018582889,"threshold_uncertainty_score":0.016820788},"labels":[],"label_agreement":null},{"id":"W2177776506","doi":"10.1016/j.procs.2015.10.030","title":"Video Foreground Detection in Non-static Background Using Multi-dimensional Color Space","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Foreground detection; Computer graphics (images); Space (punctuation); Color space; Background subtraction; Pixel; Image (mathematics)","score_opus":0.0785705464570195,"score_gpt":0.3329366249137926,"score_spread":0.2543660784567731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177776506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35577598,0.0006702449,0.6390395,0.00009987953,0.000088478315,0.000101405676,0.00012479347,0.0023171052,0.0017825856],"genre_scores_gemma":[0.6332162,0.00043407743,0.3647106,0.00007949061,0.000026341677,0.000035327772,0.00028642773,0.00007645229,0.001135084],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959177,0.00006916438,0.000019235475,0.00012349477,0.00013101958,0.00006527433],"domain_scores_gemma":[0.9994173,0.00017508479,0.00006780592,0.000076905206,0.00020158575,0.000061375315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062159833,0.0007799192,0.000624113,0.001183685,0.00031559693,0.00088884146,0.0007171134,0.00061729364,0.0006700637],"category_scores_gemma":[0.0013666924,0.00017300915,0.00037150533,0.00089563825,0.00036469765,0.0008125331,0.00044580386,0.00042401973,0.00028654904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013732058,0.00052364543,0.010627464,0.00034709286,0.00018088127,0.00044204373,0.0002705421,0.07506799,0.33845758,0.003124283,0.0014235278,0.5681617],"study_design_scores_gemma":[0.00003896248,0.00039821415,0.01085613,0.00001769844,0.000061461535,0.000530393,0.00009522073,0.820092,0.16561353,0.00074732833,0.0015080288,0.000041082545],"about_ca_topic_score_codex":0.0029179773,"about_ca_topic_score_gemma":0.003010631,"teacher_disagreement_score":0.0029179773,"about_ca_system_score_codex":0.00038529115,"about_ca_system_score_gemma":0.00040458926,"threshold_uncertainty_score":0.0058020353},"labels":[],"label_agreement":null},{"id":"W2191657759","doi":"10.1016/j.procs.2015.05.075","title":"Ubiquitous Tele-health System for Elderly Patients with Alzheimer's","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; Acadia University","keywords":"Wearable computer; Internet of Things; Computer science; Radio-frequency identification; Health care; Elderly people; Wireless; Ultra high frequency; Identification (biology); Wireless sensor network; Wearable technology; Telecommunications; The Internet; Ubiquitous computing; Computer security; Medical emergency; Embedded system; Medicine; World Wide Web; Human–computer interaction; Gerontology; Computer network","score_opus":0.04049713077978299,"score_gpt":0.2648527621645033,"score_spread":0.22435563138472034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191657759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69214356,0.008963795,0.18763046,0.0044710627,0.0009460664,0.002759402,0.0068227435,0.047266815,0.048996232],"genre_scores_gemma":[0.9441201,0.0016711035,0.03348138,0.0013103607,0.00025306205,0.0005793256,0.0019720444,0.00011915653,0.016493428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980325,0.000042123695,0.000028246712,0.000050306346,0.000047000853,0.000029092238],"domain_scores_gemma":[0.99965084,0.000065818414,0.000048522015,0.00005764473,0.000109997854,0.000067170484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036540083,0.0003000248,0.00049151614,0.0005552177,0.00028108244,0.00043107144,0.00051342964,0.0005517382,0.008756921],"category_scores_gemma":[0.00086634245,0.00009741731,0.00017068861,0.00027998825,0.00009346981,0.0005563203,0.00064930884,0.00030266333,0.0035426535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002158139,0.0011178582,0.045949828,0.0007282295,0.000093140996,0.0034153135,0.0015852153,0.001387241,0.057383202,0.0013313355,0.07497883,0.8098717],"study_design_scores_gemma":[0.0016811716,0.008421188,0.29029164,0.0010447998,0.0010544725,0.023732627,0.0048573925,0.12045719,0.10493801,0.004830854,0.4383598,0.00033089728],"about_ca_topic_score_codex":0.0006508637,"about_ca_topic_score_gemma":0.0008498471,"teacher_disagreement_score":0.008756921,"about_ca_system_score_codex":0.00016844897,"about_ca_system_score_gemma":0.00026743437,"threshold_uncertainty_score":0.029294789},"labels":[],"label_agreement":null},{"id":"W2199543770","doi":"10.1016/j.procs.2015.08.298","title":"RETRACTED: An adaptive predictor for system property forecasting","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Property (philosophy); Scientific publishing; Publishing; Process (computing); Data science; Work (physics); Scientific misconduct; Operations research; Law; Programming language; Medicine; Political science; Epistemology; Philosophy","score_opus":0.05561450297604993,"score_gpt":0.22186579423495723,"score_spread":0.1662512912589073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2199543770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055942863,0.0022627737,0.8781194,0.0041950042,0.0111843245,0.00034115955,0.0033703942,0.033088904,0.011495094],"genre_scores_gemma":[0.7450774,0.0011586684,0.19627817,0.0012714389,0.002583261,0.00043938245,0.0077890013,0.002582165,0.04282059],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868923,0.00034862495,0.000089641515,0.00026634603,0.0004806009,0.00012550436],"domain_scores_gemma":[0.99506205,0.0015475504,0.00025470497,0.0011023347,0.0017249254,0.00030835945],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0033727954,0.0013342586,0.0014860004,0.0013042676,0.0010506009,0.0024204624,0.0029632654,0.0019815369,0.02199749],"category_scores_gemma":[0.025647532,0.000530641,0.00081627246,0.001383682,0.0006277024,0.002792764,0.0027074919,0.0044319537,0.007598372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097443914,0.00020798313,0.011267051,0.00021904701,0.0001747233,0.0009403417,0.00020047052,0.24092712,0.0017377851,0.006645906,0.1456685,0.5910366],"study_design_scores_gemma":[0.000037740243,0.000059630795,0.0008738018,0.000020503952,0.000014882698,0.00006719538,0.000018684432,0.9859504,0.00086746865,0.0035656772,0.008506624,0.000017516832],"about_ca_topic_score_codex":0.011872964,"about_ca_topic_score_gemma":0.008027019,"teacher_disagreement_score":0.99801844,"about_ca_system_score_codex":0.0008281154,"about_ca_system_score_gemma":0.0027431585,"threshold_uncertainty_score":0.07358891},"labels":[],"label_agreement":null},{"id":"W2204506497","doi":"10.1016/j.procs.2015.10.010","title":"Rapid Deployment and Evaluation of Mobile Serious Games: A Cognitive Assessment Case Study","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cognitive Abilities and Testing","field":"Psychology","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; Canada Research Chairs","funders":"","keywords":"Computer science; Cognition; Software deployment; Human–computer interaction; Psychology","score_opus":0.110333359293838,"score_gpt":0.4143266470972648,"score_spread":0.3039932878034268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204506497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98863554,0.000084704516,0.0077257287,0.00015681176,0.000015364409,0.0009299002,0.00007393021,0.00009211334,0.002286032],"genre_scores_gemma":[0.97177863,0.00012370924,0.026304953,0.000078013276,0.000011648542,0.00040024993,0.000114046845,0.000027212034,0.0011614831],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9931564,0.004056421,0.00047942175,0.00051400467,0.0013164041,0.0004774024],"domain_scores_gemma":[0.9755239,0.01508302,0.0012679894,0.0017924515,0.004297709,0.0020349303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007360344,0.0009658861,0.0005408855,0.0013592073,0.0009842016,0.0015068701,0.0017441922,0.00158108,0.0007920355],"category_scores_gemma":[0.029689543,0.00033364436,0.00060838665,0.00069516985,0.0009756,0.0014037363,0.0016334085,0.0010333748,0.0004591754],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004213397,0.04122198,0.26928198,0.0020358993,0.0004708572,0.028086208,0.072878726,0.024649857,0.069997914,0.006569789,0.0065129083,0.4740805],"study_design_scores_gemma":[0.002721194,0.070690304,0.36353734,0.0012917587,0.0007577534,0.02839002,0.063443944,0.25918198,0.13501215,0.00849798,0.06555436,0.00092126674],"about_ca_topic_score_codex":0.004284083,"about_ca_topic_score_gemma":0.0075534373,"teacher_disagreement_score":0.007360344,"about_ca_system_score_codex":0.0013447906,"about_ca_system_score_gemma":0.0011287283,"threshold_uncertainty_score":0.038925648},"labels":[],"label_agreement":null},{"id":"W2218260584","doi":"10.1016/j.procs.2015.10.006","title":"Enhancing Reliability through Screening and Segmentation: An Online Video Subjective Quality of Experience Case Study","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada); Canada Research Chairs; University of Toronto","funders":"","keywords":"Computer science; Reliability (semiconductor); Segmentation; Quality (philosophy); Online video; Subjective video quality; Artificial intelligence; Multimedia; Machine learning; Image quality; Image (mathematics)","score_opus":0.1502959743851579,"score_gpt":0.4190598978200777,"score_spread":0.2687639234349198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2218260584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9295167,0.00013097875,0.06620263,0.0003455391,0.000022506016,0.0005402984,0.000094857656,0.00012247464,0.003024051],"genre_scores_gemma":[0.9604097,0.00008202363,0.038463224,0.00007138505,0.000029802703,0.00021013312,0.00006775496,0.00004409362,0.0006220564],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.97733486,0.015775947,0.0010917091,0.0011458332,0.0039381874,0.0007134786],"domain_scores_gemma":[0.8469999,0.112474695,0.008991527,0.010467672,0.01874443,0.0023218752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02217704,0.0006842242,0.00049849,0.0020365878,0.0014203719,0.0017899522,0.0017446189,0.0017567297,0.0011309034],"category_scores_gemma":[0.07518532,0.00030323752,0.00056679465,0.0013113999,0.0023106423,0.0022088543,0.0018536786,0.0009882154,0.00026637575],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028613661,0.0066279247,0.29319292,0.0025171451,0.00041309252,0.01579748,0.17554702,0.023644641,0.044007454,0.013310735,0.005269884,0.41681024],"study_design_scores_gemma":[0.0006851487,0.024054551,0.36365446,0.0012113418,0.0008058994,0.03219156,0.15255702,0.2338284,0.1344941,0.016530687,0.03899409,0.0009927717],"about_ca_topic_score_codex":0.0029128068,"about_ca_topic_score_gemma":0.0031131906,"teacher_disagreement_score":0.02217704,"about_ca_system_score_codex":0.0012497522,"about_ca_system_score_gemma":0.00088528806,"threshold_uncertainty_score":0.117284775},"labels":[],"label_agreement":null},{"id":"W2350668224","doi":"10.1016/j.procs.2016.04.259","title":"Forecasting Internal Temperature in a Home with a Sensor Network","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Real-time computing; Function (biology); Energy (signal processing); Detector; Motion sensors; Simulation; Telecommunications; Artificial intelligence; Statistics","score_opus":0.00596856043905191,"score_gpt":0.16821828416643742,"score_spread":0.1622497237273855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2350668224","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93333524,0.00025115878,0.062232066,0.0005458806,0.00009252031,0.00002331547,0.0008760333,0.00035810066,0.0022856463],"genre_scores_gemma":[0.99478406,0.00008780184,0.0043778406,0.000015993579,0.000021305897,0.000010178594,0.00031748437,0.000007958669,0.00037730543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998404,0.00004587527,0.0000071922545,0.000050303322,0.000029541297,0.000026697377],"domain_scores_gemma":[0.9996526,0.00018051098,0.000046579928,0.000027977063,0.00006378147,0.000028566488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003929482,0.00059019466,0.00039271152,0.00029425128,0.00021206382,0.0004551755,0.00040557902,0.00047568514,0.0005330947],"category_scores_gemma":[0.001403894,0.0002493357,0.00039145676,0.00048759885,0.00029009834,0.0007834324,0.00036399186,0.00049390714,0.00011102796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047209658,0.00001765042,0.0045185396,0.000007257311,0.000013331674,0.000024838486,0.000007691214,0.9915565,0.0003145278,0.00025615277,0.00032600955,0.0029101898],"study_design_scores_gemma":[0.0000026936739,0.0000072898697,0.00088690984,7.592127e-7,0.0000027774474,0.000002241171,0.000004045778,0.9986891,0.0001411743,0.00020980215,0.000051353756,0.0000018917709],"about_ca_topic_score_codex":0.021177951,"about_ca_topic_score_gemma":0.013380651,"teacher_disagreement_score":0.021177951,"about_ca_system_score_codex":0.0004857317,"about_ca_system_score_gemma":0.00028404608,"threshold_uncertainty_score":0.04210937},"labels":[],"label_agreement":null},{"id":"W2359695459","doi":"10.1016/j.procs.2016.04.114","title":"Conto: A Protégé Plugin for Configuring Ontologies","year":2016,"lang":"fr","type":"article","venue":"Procedia Computer Science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Plug-in; Abstraction; Domain (mathematical analysis); Ontology; Protégé; Data science; Domain knowledge; Data type; Information retrieval; Software engineering; Programming language; Semantic Web","score_opus":0.05554852715621697,"score_gpt":0.29373771207846333,"score_spread":0.23818918492224636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2359695459","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010607652,0.0005737956,0.46914873,0.00080526416,0.0004032623,0.0009845333,0.004015404,0.4955843,0.017877115],"genre_scores_gemma":[0.15672341,0.0022138897,0.5872043,0.0038549476,0.00032036664,0.0033358021,0.03529758,0.16742484,0.04362482],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865425,0.00024393066,0.00013856452,0.00020575215,0.00056663726,0.00019079406],"domain_scores_gemma":[0.9977132,0.0011838151,0.00014480096,0.00046061067,0.00023843355,0.00025914665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026358867,0.0019428466,0.00085309526,0.0022935432,0.00094545144,0.0026292794,0.0030588838,0.0017928309,0.012346905],"category_scores_gemma":[0.008486061,0.0025285063,0.0018967927,0.00097430043,0.0012272028,0.0052303127,0.005992119,0.0033154278,0.008561633],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020581086,0.0009865325,0.008027463,0.0026171205,0.0006332281,0.0047641513,0.004398823,0.018745115,0.02613808,0.061131615,0.5301591,0.34034064],"study_design_scores_gemma":[0.00036982767,0.0001447831,0.0056137405,0.0007015266,0.00014496506,0.0026963588,0.0004620629,0.09158631,0.022731466,0.044723343,0.8303427,0.00048297681],"about_ca_topic_score_codex":0.005373366,"about_ca_topic_score_gemma":0.0060111606,"teacher_disagreement_score":0.012346905,"about_ca_system_score_codex":0.0010307874,"about_ca_system_score_gemma":0.0015603831,"threshold_uncertainty_score":0.04130447},"labels":[],"label_agreement":null},{"id":"W2365419411","doi":"10.1016/j.procs.2016.04.270","title":"Counter-Measures against Stack Buffer Overflows in GNU/Linux Operating Systems","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Computer science; Operating system; Buffer overflow; Stack (abstract data type); Embedded system","score_opus":0.016102715669905183,"score_gpt":0.23521976477463857,"score_spread":0.2191170491047334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2365419411","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66422933,0.0066867955,0.28490984,0.002562911,0.0010273072,0.0008018528,0.00024332342,0.023506884,0.016031789],"genre_scores_gemma":[0.97022647,0.0003188673,0.02791961,0.00032285313,0.00006833637,0.00009799008,0.000081605685,0.0001271747,0.0008371227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625933,0.0009411048,0.0003214786,0.00047171317,0.0014930918,0.00051328307],"domain_scores_gemma":[0.987087,0.00357125,0.004387178,0.0016236136,0.002510189,0.00082079787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025340216,0.0011531091,0.0007259066,0.002614747,0.0009689,0.0013747162,0.0019398052,0.0012127154,0.0012676875],"category_scores_gemma":[0.015501779,0.00030368654,0.0003493407,0.0005447299,0.0010000522,0.0021932297,0.0021406433,0.0011238615,0.00045884817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032163304,0.0015510528,0.055453654,0.0019863872,0.0005465731,0.001520234,0.00233743,0.069080465,0.19955452,0.0194842,0.01556949,0.6296997],"study_design_scores_gemma":[0.0002757124,0.005067497,0.034461334,0.00082433334,0.00079742837,0.0020391897,0.0015374983,0.49193758,0.41759476,0.01681202,0.028276352,0.0003762685],"about_ca_topic_score_codex":0.001130738,"about_ca_topic_score_gemma":0.00082837784,"teacher_disagreement_score":0.002614747,"about_ca_system_score_codex":0.000787827,"about_ca_system_score_gemma":0.0014881281,"threshold_uncertainty_score":0.0134013295},"labels":[],"label_agreement":null},{"id":"W2376965755","doi":"10.1016/j.procs.2016.04.160","title":"Temperature Forecasts with Stable Accuracy in a Smart Home","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Degree (music); Set (abstract data type); Sample (material); Data set; Function (biology); Linear regression; Service (business); Regression; Econometrics; Statistics; Machine learning; Artificial intelligence; Mathematics","score_opus":0.004592946400927236,"score_gpt":0.17415509696534778,"score_spread":0.16956215056442053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2376965755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93134433,0.00017558524,0.065059446,0.00042283267,0.00005480575,0.000016991424,0.00047500787,0.0008139555,0.0016370107],"genre_scores_gemma":[0.991659,0.00004288916,0.00758671,0.000021736454,0.000018288469,0.000006304313,0.00026839465,0.00001794531,0.0003786831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972016,0.00007870049,0.000012573971,0.00007873413,0.00006531049,0.000044421107],"domain_scores_gemma":[0.9987276,0.0007066025,0.00015580373,0.00015265185,0.0002002961,0.000057018326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008979531,0.0005094055,0.00060747814,0.0002944657,0.00024070803,0.00048150326,0.000407624,0.0006521524,0.0006717025],"category_scores_gemma":[0.0038856277,0.00034751176,0.000293207,0.000403361,0.00040223054,0.0009387902,0.00045008794,0.000851095,0.00021386675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001715504,0.000031298787,0.0054980977,0.0000134346365,0.000017747678,0.000082939274,0.000039026745,0.9833329,0.0010142119,0.0005897451,0.00065835344,0.008550694],"study_design_scores_gemma":[0.000006830972,0.000012317874,0.0010762217,0.0000011885968,0.0000020488733,0.0000048685874,0.000006878799,0.9977799,0.0005673064,0.00047914052,0.000059334623,0.0000039764686],"about_ca_topic_score_codex":0.018656805,"about_ca_topic_score_gemma":0.0144218905,"teacher_disagreement_score":0.018656805,"about_ca_system_score_codex":0.0004581408,"about_ca_system_score_gemma":0.0003827906,"threshold_uncertainty_score":0.03709638},"labels":[],"label_agreement":null},{"id":"W2389169845","doi":"10.1016/j.procs.2016.04.116","title":"A Transaction Model for Executions of Compositions of Internet of Things Services","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Internet of Things; Database transaction; Computer security; World Wide Web; The Internet; Database","score_opus":0.012493972729100265,"score_gpt":0.23182024580741364,"score_spread":0.21932627307831337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2389169845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0089814905,0.00019615861,0.9857336,0.0003673235,0.00008713813,0.0002032141,0.00024652845,0.0005855204,0.0035990516],"genre_scores_gemma":[0.42942014,0.0006979317,0.55468947,0.00044612153,0.0004484266,0.0015699832,0.0017288503,0.0006444941,0.010354612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99162954,0.0020700872,0.0014999991,0.0011747439,0.0029563718,0.00066927064],"domain_scores_gemma":[0.9894817,0.0045171133,0.0010007601,0.0020714568,0.0024854161,0.0004435843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00571148,0.0014433202,0.0013266874,0.0018066543,0.0017322113,0.0042623878,0.0035193092,0.0028203991,0.0045783166],"category_scores_gemma":[0.0134670725,0.0010179703,0.0029292584,0.0019193087,0.0030932103,0.008652808,0.0022498886,0.0038936906,0.0017231576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028275044,0.00014371246,0.0016392152,0.0002371258,0.000075827986,0.0008902319,0.0007450466,0.14191915,0.0063365395,0.8218643,0.002571936,0.023294134],"study_design_scores_gemma":[0.000078305864,0.0001827565,0.00021211251,0.00007440639,0.00007867384,0.0004880067,0.00013830858,0.74123245,0.0035472598,0.24183227,0.012084613,0.000050830633],"about_ca_topic_score_codex":0.0054905107,"about_ca_topic_score_gemma":0.0039052872,"teacher_disagreement_score":0.00571148,"about_ca_system_score_codex":0.002276699,"about_ca_system_score_gemma":0.0033126415,"threshold_uncertainty_score":0.030205548},"labels":[],"label_agreement":null},{"id":"W2394987826","doi":"10.1016/j.procs.2015.08.226","title":"Automatic Detection of Polyp Using Hessian Filter and HOG Features","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Japan Society for the Promotion of Science","keywords":"Computer science; Artificial intelligence; Hessian matrix; AdaBoost; Computer vision; Feature (linguistics); Endoscope; Pattern recognition (psychology); Filter (signal processing); Support vector machine; Random forest; Mathematics","score_opus":0.0343980325590157,"score_gpt":0.28473177809324707,"score_spread":0.2503337455342314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394987826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22561069,0.0010056109,0.7678489,0.0001719838,0.00011663271,0.00011330972,0.00020467748,0.002055455,0.002872802],"genre_scores_gemma":[0.6320588,0.00051223155,0.36394748,0.00009367422,0.000050512015,0.00004726295,0.00034693285,0.000085404085,0.0028578031],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958163,0.000059334496,0.000018899229,0.00008380402,0.00020396244,0.000052326715],"domain_scores_gemma":[0.99967515,0.00008994505,0.000043132408,0.000025985131,0.00014160076,0.000024181223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046247835,0.00036552086,0.00045450887,0.0014222899,0.00021052545,0.00042067684,0.0003343586,0.0005651149,0.0007710385],"category_scores_gemma":[0.0007797134,0.0002772107,0.00049894094,0.00056473905,0.00021064714,0.0005865627,0.00031240517,0.00023273233,0.00039347858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036569466,0.00015970449,0.006454381,0.00016747841,0.00008651045,0.00022102342,0.00006160705,0.009810835,0.3183048,0.00079531147,0.0022042154,0.6613684],"study_design_scores_gemma":[0.000042191095,0.0003598944,0.038359396,0.0000308586,0.00008457027,0.0014016253,0.00007725241,0.7851411,0.16939935,0.00095113064,0.004053601,0.00009891641],"about_ca_topic_score_codex":0.002921192,"about_ca_topic_score_gemma":0.0036029883,"teacher_disagreement_score":0.002921192,"about_ca_system_score_codex":0.00026040408,"about_ca_system_score_gemma":0.0003533901,"threshold_uncertainty_score":0.005808294},"labels":[],"label_agreement":null},{"id":"W2436862416","doi":"10.1016/j.procs.2017.09.009","title":"A Comparative Analysis of the Performance of Scalable Parallel Patterns Applied to Genetic Algorithms and Configured for NVIDIA GPUs","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Parallel computing; Scalability; Algorithm; Computational science; Computer architecture; Operating system","score_opus":0.021249484756136673,"score_gpt":0.2718349738215539,"score_spread":0.2505854890654172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2436862416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9208462,0.0010550999,0.049654514,0.0004436362,0.00015303711,0.00018509901,0.0004371537,0.0026944694,0.024530686],"genre_scores_gemma":[0.9165596,0.0006106184,0.07842358,0.000092487106,0.000021024387,0.00017629913,0.00083387055,0.00037134287,0.0029113118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904436,0.00022514902,0.00006612302,0.00014803307,0.00038226778,0.00013410239],"domain_scores_gemma":[0.99729174,0.0010139858,0.00013454419,0.000500183,0.00090075744,0.00015862769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010140815,0.00051745813,0.0005068946,0.0010301174,0.00044743076,0.0008881446,0.0011037277,0.0005788835,0.0017288217],"category_scores_gemma":[0.005936582,0.0002161954,0.00032806903,0.0028008511,0.00042699455,0.00088665774,0.00043033407,0.00044686237,0.0004543536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002206898,0.00060196914,0.013500498,0.0005251858,0.00017877053,0.0005585127,0.00032456723,0.5583251,0.043163016,0.011185686,0.010151266,0.3592785],"study_design_scores_gemma":[0.00013481808,0.00081354845,0.010763647,0.00003467133,0.00005076995,0.00016230822,0.00021239596,0.95202583,0.026203563,0.002976789,0.00659298,0.000028710094],"about_ca_topic_score_codex":0.009198421,"about_ca_topic_score_gemma":0.006613397,"teacher_disagreement_score":0.009198421,"about_ca_system_score_codex":0.0010788548,"about_ca_system_score_gemma":0.0014360463,"threshold_uncertainty_score":0.018289745},"labels":[],"label_agreement":null},{"id":"W2479202071","doi":"10.1016/j.procs.2016.08.031","title":"A New Categorization Numerical Scheme for Mobile Robotic Computing Using Odor Data-set Recognition as a Case","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Categorization; Curse of dimensionality; Artificial intelligence; Set (abstract data type); Machine learning; Scheme (mathematics); Data mining; Generalization; Mobile robot; Pattern recognition (psychology); Robot","score_opus":0.05982937974222044,"score_gpt":0.3010800408299591,"score_spread":0.24125066108773865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2479202071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048980075,0.00006374694,0.9935607,0.00010727174,0.000039161303,0.000072087896,0.000021528951,0.0002773878,0.0009600447],"genre_scores_gemma":[0.09952159,0.00007132872,0.8984952,0.000080419,0.000027130762,0.00021568744,0.000066983885,0.000032634878,0.0014890924],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993667,0.00011933301,0.00008756355,0.00012763674,0.00026292788,0.00003580895],"domain_scores_gemma":[0.99929595,0.00018408806,0.00007060058,0.00019196662,0.00020746863,0.00004983665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009752533,0.00036282642,0.00047659336,0.0008614016,0.0007010985,0.00086294283,0.0015728081,0.0009581129,0.0024287088],"category_scores_gemma":[0.0028585559,0.0002021967,0.00053353846,0.00071003434,0.000920051,0.0014402706,0.0013597478,0.0008040323,0.0006157098],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016565557,0.00016269089,0.0017165034,0.00026599359,0.00005598946,0.00021755628,0.00036672296,0.29427668,0.08272198,0.21140142,0.0043042144,0.40434456],"study_design_scores_gemma":[0.000011389295,0.00005364104,0.00016900705,0.000011080809,0.0000051351726,0.00007203015,0.000015091314,0.97742647,0.0034461773,0.014679639,0.0040955367,0.000014926038],"about_ca_topic_score_codex":0.002009463,"about_ca_topic_score_gemma":0.0018732963,"teacher_disagreement_score":0.0024287088,"about_ca_system_score_codex":0.0009640235,"about_ca_system_score_gemma":0.0007918325,"threshold_uncertainty_score":0.008124828},"labels":[],"label_agreement":null},{"id":"W2490474310","doi":"10.1016/j.procs.2016.08.026","title":"Mining Collective Opinions for Comparison of Mobile Apps","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Mobile apps; Purchasing; World Wide Web; Product (mathematics); Sentiment analysis; Order (exchange); Revenue; Download; App store; Key (lock); Preference; Internet privacy; Artificial intelligence; Computer security","score_opus":0.03576990009422392,"score_gpt":0.3510896713947813,"score_spread":0.3153197713005574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2490474310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7217963,0.0038263993,0.24480711,0.0011683851,0.00057962205,0.0011717188,0.0113673275,0.002303877,0.012979358],"genre_scores_gemma":[0.90179753,0.0004398651,0.08588024,0.00011137644,0.0004133593,0.0005036602,0.008845423,0.000063441985,0.0019451795],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99583745,0.0007786623,0.0006504307,0.0011022604,0.0013624144,0.0002687082],"domain_scores_gemma":[0.99170446,0.004054284,0.0013538776,0.0005150913,0.0021165954,0.00025572086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028852138,0.0011941605,0.0012720822,0.010545438,0.00088975776,0.0017573669,0.0009875135,0.0012601616,0.0017167765],"category_scores_gemma":[0.013958703,0.0002621511,0.0014306287,0.005203653,0.0003878821,0.0018426138,0.0008632928,0.00079878396,0.0010962966],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013809871,0.0009557402,0.13633262,0.0014276726,0.0013621986,0.0013629314,0.0019470842,0.014008597,0.03500667,0.003314339,0.020230556,0.7826707],"study_design_scores_gemma":[0.0001315625,0.0011664444,0.18205403,0.00021609291,0.0009709798,0.001585456,0.0035961359,0.7521701,0.019269185,0.013420254,0.025247889,0.00017181992],"about_ca_topic_score_codex":0.002060551,"about_ca_topic_score_gemma":0.0032451255,"teacher_disagreement_score":0.010545438,"about_ca_system_score_codex":0.00071364164,"about_ca_system_score_gemma":0.0006050426,"threshold_uncertainty_score":0.01525861},"labels":[],"label_agreement":null},{"id":"W2498794836","doi":"10.1016/j.procs.2016.08.004","title":"Keynote I and Keynote II","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Queen's University","funders":"","keywords":"Computer science; Operations research; Mathematics","score_opus":0.024977476829267032,"score_gpt":0.18773087371966676,"score_spread":0.16275339689039972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498794836","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000438327,0.006740813,0.00139901,0.07299852,0.8355573,0.00025208454,0.000576972,0.00022733168,0.08180972],"genre_scores_gemma":[0.014504335,0.008043374,0.0009461273,0.070386626,0.5229636,0.0006411098,0.0008165988,0.00036598902,0.38133228],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978802,0.00021915509,0.00017524684,0.0005557917,0.0007681481,0.00040148693],"domain_scores_gemma":[0.99483633,0.0011252707,0.00036304598,0.00046122764,0.0019700848,0.0012440837],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024984847,0.0018949124,0.0014173472,0.002165897,0.0033149032,0.007366798,0.0023643575,0.009607524,0.1146174],"category_scores_gemma":[0.013956747,0.0005563574,0.001405322,0.0014455148,0.0011650244,0.005617074,0.0037021472,0.009846327,0.06702287],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009047442,0.00003107518,0.000060293045,0.00014014156,0.000007103522,0.000078993995,0.000016768312,0.000037901547,0.00035144403,0.008138041,0.9788229,0.0122247925],"study_design_scores_gemma":[0.000043824315,0.000071587405,0.000753676,0.00024396711,0.000020377945,0.00011606868,0.00006398634,0.00012506956,0.00052183407,0.011096592,0.98691666,0.000026336238],"about_ca_topic_score_codex":0.0016162999,"about_ca_topic_score_gemma":0.0017709723,"teacher_disagreement_score":0.8853826,"about_ca_system_score_codex":0.0034283495,"about_ca_system_score_gemma":0.002651479,"threshold_uncertainty_score":0.3834334},"labels":[],"label_agreement":null},{"id":"W2500304132","doi":"10.1016/j.procs.2016.08.071","title":"Experimental Analysis of Tools Used for Doxing and Proposed New Transforms to Help Organizations Protect against Doxing Attacks","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer science; Password; Computer security; Password cracking; Social engineering (security); Social media; Architecture; Data science; Internet privacy; World Wide Web; Password strength; One-time password","score_opus":0.026768661632086153,"score_gpt":0.2801936084668222,"score_spread":0.25342494683473604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500304132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9541529,0.00044926215,0.030088577,0.00026318332,0.00011590855,0.0012310223,0.00088850554,0.0027205138,0.010090069],"genre_scores_gemma":[0.96199685,0.00026143045,0.03102165,0.00010925943,0.000021172404,0.000797167,0.0012672885,0.0002770636,0.004248025],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9941466,0.0023967635,0.0006518369,0.0007966364,0.0015319638,0.00047623256],"domain_scores_gemma":[0.9435853,0.034741096,0.0039756414,0.0101871295,0.006150991,0.0013598753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046209437,0.0007562299,0.00042548223,0.0017036751,0.00057884486,0.0014418846,0.0009937277,0.0009310844,0.0049730116],"category_scores_gemma":[0.041568816,0.0003128788,0.00044844512,0.0009790512,0.0011475043,0.0028519903,0.0013329783,0.0012250381,0.0016748753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011604027,0.020783195,0.07465071,0.005489795,0.0003877717,0.0012089338,0.008370168,0.022725048,0.11214102,0.01807581,0.016923094,0.7076404],"study_design_scores_gemma":[0.0017736271,0.040272158,0.16467263,0.001396817,0.0013047952,0.0021741705,0.013001855,0.2869361,0.37589988,0.01949662,0.09255573,0.0005156789],"about_ca_topic_score_codex":0.0009566639,"about_ca_topic_score_gemma":0.0010152487,"teacher_disagreement_score":0.0049730116,"about_ca_system_score_codex":0.0007336413,"about_ca_system_score_gemma":0.00074911903,"threshold_uncertainty_score":0.024438143},"labels":[],"label_agreement":null},{"id":"W2502577124","doi":"10.1016/j.procs.2016.08.039","title":"Energy Aware Scheduling and Routing of Periodic Lightpath Demands in Optical Grid Networks","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Energy consumption; Computer network; Scheduling (production processes); Exploit; Anycast; Distributed computing; Routing (electronic design automation); Grid; Schedule; Flexibility (engineering); Mathematical optimization","score_opus":0.005502461691244937,"score_gpt":0.19581532462034776,"score_spread":0.19031286292910282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502577124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38210776,0.00073872105,0.6044042,0.00088434084,0.00013634609,0.00012363188,0.00023572898,0.00021476012,0.011154486],"genre_scores_gemma":[0.9713856,0.00019195555,0.026743557,0.000038396967,0.000017926237,0.000027446884,0.000044581975,0.000024340585,0.0015262121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998116,0.0000637924,0.0000074777895,0.00002922978,0.000042326163,0.000045536708],"domain_scores_gemma":[0.999613,0.0002402859,0.00006461745,0.000022336391,0.00003374274,0.000026102283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051073066,0.00034285846,0.00036560238,0.00026741857,0.0005133298,0.0006224651,0.0006118894,0.00045845707,0.0009910299],"category_scores_gemma":[0.0011431124,0.000244085,0.00018993641,0.00047680645,0.0003933198,0.00066118303,0.00037093624,0.0003576185,0.00007446521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006431127,0.000026140664,0.0003772473,0.00002667094,0.0000074116365,0.000046142795,0.000029576795,0.9808797,0.0017795971,0.00756997,0.00048384757,0.008709269],"study_design_scores_gemma":[0.0000041832454,0.000012584979,0.00008873867,0.0000012134748,0.0000017586551,0.000006371404,0.0000147996825,0.99689794,0.00029199195,0.0024787737,0.00019965098,0.00000199118],"about_ca_topic_score_codex":0.007012772,"about_ca_topic_score_gemma":0.010930201,"teacher_disagreement_score":0.007012772,"about_ca_system_score_codex":0.0010787022,"about_ca_system_score_gemma":0.0009490168,"threshold_uncertainty_score":0.013943911},"labels":[],"label_agreement":null},{"id":"W2502638135","doi":"10.1016/j.procs.2016.08.023","title":"Using Provenance and CoAP to track Requests/Responses in IoT","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Tracing; Transparency (behavior); Key (lock); Inference; Focus (optics); Provenance; The Internet; Internet of Things; Reliability (semiconductor); Component (thermodynamics); World Wide Web; Computer security; Data science; Artificial intelligence; Operating system","score_opus":0.2183518135412898,"score_gpt":0.42191312846589885,"score_spread":0.20356131492460905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502638135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18640676,0.0014267415,0.78329635,0.002067069,0.0003479265,0.0010163351,0.0018130639,0.014612675,0.009013046],"genre_scores_gemma":[0.8341252,0.0006476708,0.1615844,0.000207354,0.000083818144,0.00025391713,0.0011412425,0.00028792178,0.0016684714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937779,0.0020592955,0.0006811199,0.0009887858,0.002182485,0.00031042716],"domain_scores_gemma":[0.98139256,0.008585544,0.0018233444,0.004733509,0.0027455091,0.00071955635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064313863,0.0007527697,0.00083067967,0.002849594,0.0018864798,0.003110846,0.001246223,0.0013012412,0.00075258646],"category_scores_gemma":[0.018046297,0.0006358006,0.00041586033,0.0027501083,0.0013558716,0.0048473068,0.0029150804,0.0017174566,0.0004142004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035541249,0.0008063617,0.11832126,0.0017285233,0.0005722841,0.0040270747,0.014251352,0.1250328,0.09638506,0.06607237,0.013829347,0.5554195],"study_design_scores_gemma":[0.0000805089,0.0002756861,0.018928217,0.00031091028,0.00020188214,0.001406404,0.0027848817,0.7857586,0.07148582,0.075341366,0.043150198,0.0002755829],"about_ca_topic_score_codex":0.011329243,"about_ca_topic_score_gemma":0.0094000865,"teacher_disagreement_score":0.011329243,"about_ca_system_score_codex":0.0010456762,"about_ca_system_score_gemma":0.0019639856,"threshold_uncertainty_score":0.034012854},"labels":[],"label_agreement":null},{"id":"W2505410492","doi":"10.1016/j.procs.2017.03.019","title":"Let's Talk – Interoperability between University CRIS/IR and Researchfish: A Case Study from the UK","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Research Data Management Practices","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interoperability; Computer science; Variety (cybernetics); Productivity; Information system; Work (physics); Tracking (education); Engineering management; Process (computing); Knowledge management; Library science; World Wide Web; Sociology; Political science; Engineering","score_opus":0.14545214571071427,"score_gpt":0.37445918961819774,"score_spread":0.22900704390748347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505410492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83399475,0.005894223,0.007390545,0.08584633,0.0006436518,0.00045874756,0.0006157574,0.00017673742,0.06497926],"genre_scores_gemma":[0.9747191,0.0022043926,0.004211069,0.008679049,0.000083479055,0.00021665047,0.00022813427,0.00018952719,0.009468578],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9392131,0.042095866,0.0034126188,0.0027231353,0.006310161,0.006245139],"domain_scores_gemma":[0.9481641,0.031001888,0.00408562,0.004654368,0.0063609714,0.0057330974],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.028790457,0.0005266658,0.00081562396,0.0028692067,0.018485792,0.015144341,0.0028763344,0.0068410686,0.0064128926],"category_scores_gemma":[0.06360095,0.0009841565,0.0009992665,0.0067444877,0.010940062,0.016382527,0.012529495,0.0052316925,0.0016374126],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030277067,0.00034242283,0.036399428,0.0008810739,0.000071944516,0.028000409,0.8303287,0.0005605111,0.0011509609,0.03141336,0.031164875,0.039383475],"study_design_scores_gemma":[0.00004017537,0.00018941422,0.013600761,0.0011210394,0.000041052946,0.005330523,0.82933676,0.00041851288,0.0005951405,0.003352677,0.14586365,0.00011027738],"about_ca_topic_score_codex":0.121213384,"about_ca_topic_score_gemma":0.15499888,"teacher_disagreement_score":0.98485565,"about_ca_system_score_codex":0.020926697,"about_ca_system_score_gemma":0.012728662,"threshold_uncertainty_score":0.24101567},"labels":[],"label_agreement":null},{"id":"W2509362286","doi":"10.1016/j.procs.2016.08.243","title":"Modelling Functional Behavior of Event-based Systems: A Practical Knowledge-based Approach","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Consistency (knowledge bases); Conceptualization; Artificial intelligence; Set (abstract data type); Commonsense reasoning; Focus (optics); Event (particle physics); Unified Modeling Language; Machine learning; Human–computer interaction; Software; Programming language","score_opus":0.05602285586261897,"score_gpt":0.262393718664751,"score_spread":0.20637086280213202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509362286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032802527,0.00006700882,0.99398345,0.0002602807,0.000009432584,0.00006976652,0.000031083757,0.00018871736,0.0021099865],"genre_scores_gemma":[0.2403335,0.00048487287,0.7558427,0.00017078735,0.000047914047,0.0003833967,0.0002162674,0.00011806661,0.0024025186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99609214,0.0018659286,0.00033438293,0.0003700604,0.0011341524,0.00020340283],"domain_scores_gemma":[0.99414366,0.0039295317,0.0004293854,0.00091792154,0.00048057432,0.00009891011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006412872,0.0010702489,0.00057544914,0.0016152929,0.00095249934,0.00372042,0.003263543,0.0026473638,0.002693681],"category_scores_gemma":[0.010921611,0.0010047279,0.0016181952,0.0009029812,0.0040030414,0.00580385,0.0026341532,0.0027525078,0.0006375244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037979607,0.00011971695,0.0008895849,0.000201322,0.000041365136,0.00047338314,0.00075214973,0.3470416,0.003268721,0.62128013,0.0005479792,0.02534609],"study_design_scores_gemma":[0.000021855032,0.000049561248,0.00018625577,0.00011411764,0.000033751905,0.00015860779,0.00017204534,0.7392244,0.0026653016,0.24783486,0.009512725,0.00002662215],"about_ca_topic_score_codex":0.0035860639,"about_ca_topic_score_gemma":0.0026594326,"teacher_disagreement_score":0.006412872,"about_ca_system_score_codex":0.0017228813,"about_ca_system_score_gemma":0.0018514743,"threshold_uncertainty_score":0.033914924},"labels":[],"label_agreement":null},{"id":"W2522107234","doi":"10.1016/j.procs.2016.09.017","title":"A Review of Latest Web Tools and Libraries for State-of-the-art Visualization","year":2016,"lang":"en","type":"review","venue":"Procedia Computer Science","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Visualization; Web application; World Wide Web; Process (computing); Software; Web browser; Web modeling; Interactive visualization; Human–computer interaction; Multimedia; Web page; The Internet; Operating system","score_opus":0.0518667309054764,"score_gpt":0.3484296268695515,"score_spread":0.2965628959640751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522107234","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007115465,0.9763456,0.010836676,0.00057912647,0.00048835855,0.000052614258,0.00029630668,0.00049159816,0.010198267],"genre_scores_gemma":[0.0025363683,0.977794,0.014496131,0.0003777715,0.00041228023,0.000077888595,0.0004785977,0.00011386414,0.0037131668],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998926,0.000166128,0.00016443826,0.00014472236,0.00054092286,0.000057766214],"domain_scores_gemma":[0.9975666,0.0013125656,0.0001913634,0.00012984056,0.0007074314,0.00009229518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013495069,0.0012207731,0.0013322714,0.008170414,0.00050996715,0.002200917,0.0019387383,0.0014296958,0.008751301],"category_scores_gemma":[0.0034664054,0.0007105871,0.0011145736,0.008236079,0.0006197462,0.003655957,0.0009536766,0.0013898681,0.006757527],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036238318,0.00008162117,0.00022771186,0.015680451,0.0000404668,0.00015000996,0.00012248664,0.00042979128,0.002600845,0.0055988943,0.027356349,0.9476751],"study_design_scores_gemma":[0.0000069206135,0.00006094262,0.0006051743,0.004642735,0.00008780568,0.0010699371,0.00008366077,0.00050567684,0.0028560688,0.0030759794,0.98695964,0.00004546961],"about_ca_topic_score_codex":0.0014578647,"about_ca_topic_score_gemma":0.0014799181,"teacher_disagreement_score":0.008751301,"about_ca_system_score_codex":0.0005639645,"about_ca_system_score_gemma":0.0014900231,"threshold_uncertainty_score":0.029276013},"labels":[],"label_agreement":null},{"id":"W2540934986","doi":"10.1016/j.procs.2016.09.356","title":"Epidemiology-based Task Assignment Algorithm for Distributed Systems","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Context (archaeology); Population; Task analysis; Assignment problem; Distributed computing; Machine learning; Artificial intelligence; Medicine; Mathematical optimization","score_opus":0.035793028223503574,"score_gpt":0.27088973294342217,"score_spread":0.2350967047199186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2540934986","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013498429,0.00019444531,0.98341364,0.0003904302,0.00006247261,0.00013407893,0.000042893047,0.0002725256,0.0019910478],"genre_scores_gemma":[0.45244512,0.00033469492,0.5392297,0.00030317265,0.00010353913,0.0008990306,0.00028645233,0.00014451865,0.0062538544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908125,0.00031717317,0.000070771304,0.00020435924,0.0001849806,0.00014149665],"domain_scores_gemma":[0.9964006,0.0021285845,0.0004047285,0.00019813445,0.0006280474,0.00023995314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001809222,0.0010024799,0.0012327202,0.0008925012,0.0009969325,0.0012804491,0.0019850223,0.0014419237,0.0038642224],"category_scores_gemma":[0.008182324,0.00043472828,0.00061322545,0.00080217223,0.00078723,0.0016133639,0.0020929507,0.0014580674,0.00067554886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012233535,0.00011843767,0.00092432724,0.00009356583,0.0000324328,0.000049726143,0.00011194253,0.93301386,0.0013960447,0.013407277,0.0019858633,0.0487442],"study_design_scores_gemma":[0.000032156335,0.000026703343,0.000060938073,0.0000072690254,0.0000049681666,0.000017722932,0.000017651382,0.99271387,0.00022690548,0.006091002,0.00079700287,0.0000038156318],"about_ca_topic_score_codex":0.0029092003,"about_ca_topic_score_gemma":0.002476126,"teacher_disagreement_score":0.0038642224,"about_ca_system_score_codex":0.0015336454,"about_ca_system_score_gemma":0.0021507118,"threshold_uncertainty_score":0.012927115},"labels":[],"label_agreement":null},{"id":"W2586483973","doi":"10.1016/j.procs.2017.01.141","title":"Causal Analysis of Airline Trajectory Preferences to Improve Airspace Capacity","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"European Commission","keywords":"Computer science; Air traffic control; Workload; Trajectory; National Airspace System; Interdependence; Separation (statistics); Constraint (computer-aided design); Constraint programming; Range (aeronautics); Operations research; Air traffic management; Trajectory optimization; Free flight; Mathematical optimization; Simulation; Aerospace engineering","score_opus":0.02372190299875134,"score_gpt":0.2682412418537127,"score_spread":0.24451933885496133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586483973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21626168,0.00020147675,0.77801365,0.0006267318,0.000032340355,0.0001255877,0.0006811588,0.00024398878,0.0038132935],"genre_scores_gemma":[0.94939977,0.00014322455,0.04902382,0.00005573514,0.000023639164,0.000065841305,0.00030568882,0.000036482834,0.00094568875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858665,0.00068581954,0.000053519543,0.0002497389,0.00029227993,0.00013202679],"domain_scores_gemma":[0.986844,0.0092541985,0.0016417268,0.00075812015,0.0011529239,0.0003491427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029200632,0.00041085755,0.00043780787,0.0010175388,0.0003360462,0.00088376686,0.00068659044,0.0004288271,0.0035285067],"category_scores_gemma":[0.017735418,0.0003388006,0.0006020249,0.0014124372,0.00058505044,0.0016797626,0.0008199715,0.0009832025,0.000121903206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033453634,0.00019743123,0.017714286,0.00020505494,0.00016039386,0.00017039677,0.00032644699,0.8147148,0.0031070886,0.10855319,0.001326126,0.0531903],"study_design_scores_gemma":[0.000012086748,0.00004451742,0.0024060283,0.000010474862,0.000024844947,0.000012988524,0.00006469388,0.9696286,0.0007319205,0.026476175,0.0005754558,0.000012252312],"about_ca_topic_score_codex":0.008969884,"about_ca_topic_score_gemma":0.009043283,"teacher_disagreement_score":0.008969884,"about_ca_system_score_codex":0.0012897791,"about_ca_system_score_gemma":0.0014507662,"threshold_uncertainty_score":0.017835319},"labels":[],"label_agreement":null},{"id":"W2593477479","doi":"10.1016/j.procs.2017.01.182","title":"Vision Based Navigation for Omni-directional Mobile Industrial Robot","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing Municipal Science and Technology Commission; Science and Technology Commission of Shanghai Municipality","keywords":"Computer science; Mobile robot; Aerospace; Workspace; Robot; Industrial robot; Machine vision; Flexibility (engineering); Simulation; Artificial intelligence; Computer vision; Aerospace engineering; Engineering","score_opus":0.02425463359274197,"score_gpt":0.2694252506094591,"score_spread":0.24517061701671714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593477479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02634824,0.0017539077,0.9654677,0.0001378557,0.00015142799,0.000036314857,0.000025208046,0.0010603217,0.0050190743],"genre_scores_gemma":[0.66273016,0.0015132596,0.32666162,0.0002517064,0.00008630178,0.00011594284,0.00014515291,0.00004264107,0.008453294],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998838,0.0000146066,0.0000048273596,0.000029844163,0.000052874093,0.000014025627],"domain_scores_gemma":[0.9999441,0.000007923015,0.000009405198,0.000005603378,0.000027869843,0.0000050444282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011345476,0.00032276558,0.00022281051,0.00027323957,0.00021391916,0.00026078595,0.00039434922,0.00043115017,0.0007144571],"category_scores_gemma":[0.00017735525,0.000146019,0.00021009332,0.00019266274,0.00019101285,0.00029557332,0.0003092327,0.00029847393,0.00036505316],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020544139,0.00006686863,0.0013508654,0.00038247075,0.000046520858,0.00038233842,0.00024795218,0.05446218,0.24468532,0.009835837,0.004719959,0.68361425],"study_design_scores_gemma":[0.00009233906,0.0008223195,0.0034570578,0.000055789984,0.00007364988,0.0010881062,0.00012127812,0.8983297,0.059250444,0.0040957807,0.032543153,0.00007033111],"about_ca_topic_score_codex":0.0018850035,"about_ca_topic_score_gemma":0.0017238354,"teacher_disagreement_score":0.0018850035,"about_ca_system_score_codex":0.00017317107,"about_ca_system_score_gemma":0.00035872534,"threshold_uncertainty_score":0.0037480593},"labels":[],"label_agreement":null},{"id":"W2624166498","doi":"10.1016/j.procs.2017.05.089","title":"Extension of a Regularization Based Time-adaptive Numerical Method for a Degenerate Diffusion-Reaction Biofilm Growth Model to Systems Involving Quorum Sensing","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Bacterial biofilms and quorum sensing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Quorum sensing; Reaction–diffusion system; Regularization (linguistics); Biofilm; Extension (predicate logic); Degenerate energy levels; Diffusion; Biological system; Mathematical optimization; Distributed computing; Artificial intelligence; Mathematics; Mathematical analysis; Thermodynamics; Physics","score_opus":0.021963785953170913,"score_gpt":0.26927160153878893,"score_spread":0.247307815585618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624166498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0129850805,0.00011758741,0.9836536,0.00029019162,0.000099042234,0.00007679273,0.000033887496,0.00015399749,0.002589725],"genre_scores_gemma":[0.20919238,0.00024960726,0.78328496,0.00024668724,0.0001004635,0.00044760763,0.00010487599,0.0002342883,0.0061391843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953103,0.00017553575,0.000031139625,0.00006230411,0.00016635183,0.000033703567],"domain_scores_gemma":[0.99896765,0.0004198114,0.00012194165,0.00011822227,0.00027977617,0.00009267662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013287447,0.0005423297,0.00085112837,0.0005251361,0.00055606343,0.0005727327,0.001637606,0.0020599656,0.001437726],"category_scores_gemma":[0.0025321348,0.0004021625,0.0012358503,0.00040997544,0.0010517471,0.00058660295,0.0014760435,0.0014403332,0.0003975787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060258117,0.000099729696,0.0007517572,0.00013586438,0.000042885677,0.0002522472,0.0001925127,0.9076927,0.023210041,0.03900701,0.00079901284,0.027756073],"study_design_scores_gemma":[0.000004865517,0.000010771751,0.000035985362,0.0000032658713,0.0000017110857,0.000013425761,0.0000024792798,0.9977537,0.0003524828,0.001260782,0.00055505417,0.000005578641],"about_ca_topic_score_codex":0.0059051565,"about_ca_topic_score_gemma":0.0032847936,"teacher_disagreement_score":0.0059051565,"about_ca_system_score_codex":0.0005853552,"about_ca_system_score_gemma":0.0017875852,"threshold_uncertainty_score":0.011741579},"labels":[],"label_agreement":null},{"id":"W2624689822","doi":"10.1016/j.procs.2017.05.370","title":"On Continuous Queries in Stream Processing","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Correctness; Tuple; Workflow; Stream processing; Sequence (biology); Database; Distributed computing; Programming language","score_opus":0.014333403024423848,"score_gpt":0.2740356315100061,"score_spread":0.2597022284855822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624689822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00791,0.004810114,0.971202,0.0039158845,0.0005178432,0.00017798465,0.00021866342,0.00049774966,0.010749656],"genre_scores_gemma":[0.38921794,0.01061285,0.5722518,0.004693099,0.0064297263,0.00094571087,0.0013518477,0.0011073533,0.013389593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9752315,0.00784561,0.0026152148,0.0032103779,0.009609312,0.0014879943],"domain_scores_gemma":[0.94872516,0.036844507,0.0021266001,0.0049498198,0.006186154,0.0011678233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01755936,0.0018693018,0.0019051307,0.0033723086,0.0025024642,0.007711806,0.0035511437,0.0034365323,0.0038273751],"category_scores_gemma":[0.04400385,0.0012248999,0.00252259,0.0062115504,0.012028315,0.0198438,0.0073105497,0.009342989,0.0009450764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009525922,0.00004367349,0.0007076344,0.00019171233,0.00002641109,0.00029101572,0.0006110743,0.01376646,0.00073348574,0.9661975,0.0027591616,0.014576593],"study_design_scores_gemma":[0.00004045612,0.00006958226,0.00023988888,0.00010589032,0.00003105984,0.00027328872,0.00014070864,0.09256797,0.0010837483,0.88806325,0.017345257,0.000039003902],"about_ca_topic_score_codex":0.0062096287,"about_ca_topic_score_gemma":0.0021671474,"teacher_disagreement_score":0.01755936,"about_ca_system_score_codex":0.0045575025,"about_ca_system_score_gemma":0.002950309,"threshold_uncertainty_score":0.09286392},"labels":[],"label_agreement":null},{"id":"W2624775521","doi":"10.1016/j.procs.2017.05.366","title":"Baseline Synthesis and Microsimulation of Life-stage Transitions within an Agent-based Integrated Urban Model","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Microsimulation; Baseline (sea); Multinomial logistic regression; Population; Computer science; Simulation; Demography; Machine learning; Transport engineering; Engineering","score_opus":0.035892888603351866,"score_gpt":0.2928031734263001,"score_spread":0.2569102848229482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624775521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25604045,0.0003387277,0.70675576,0.0005650673,0.000077599565,0.0002452244,0.0031017382,0.0009863608,0.03188909],"genre_scores_gemma":[0.9364787,0.00017083621,0.056070134,0.000076554825,0.000016018554,0.00047069025,0.0011450227,0.000095424424,0.0054766578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999775,0.000079771205,0.000011644863,0.000050959392,0.000038957805,0.000043591866],"domain_scores_gemma":[0.99942696,0.00030150215,0.00006543371,0.000041173313,0.00013196134,0.000032917233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005388439,0.00044894294,0.00059625466,0.0003803814,0.00035021798,0.00067292654,0.0009336735,0.00064827007,0.004723971],"category_scores_gemma":[0.0015564752,0.00033177578,0.00068931555,0.00034601355,0.00039751086,0.0005768712,0.0008452921,0.0006601945,0.00034800707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008409522,0.0000058008413,0.00020909315,0.000009201853,0.000004274789,0.000009202687,0.000008956203,0.9967998,0.00012025808,0.0020649729,0.00006206233,0.0006979423],"study_design_scores_gemma":[0.000005102235,0.000008692895,0.000092139075,0.0000023001983,0.0000037961527,0.0000021061537,0.000007353842,0.9984269,0.00010366887,0.0010106508,0.00033524053,0.0000021208675],"about_ca_topic_score_codex":0.023897536,"about_ca_topic_score_gemma":0.012383254,"teacher_disagreement_score":0.023897536,"about_ca_system_score_codex":0.0012564397,"about_ca_system_score_gemma":0.0012602067,"threshold_uncertainty_score":0.047516882},"labels":[],"label_agreement":null},{"id":"W2752952491","doi":"10.1016/j.procs.2017.08.069","title":"TWINCLE : A Constrained Sequential Rule Mining Algorithm for Event Logs","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Event (particle physics); Data mining; Benchmark (surveying); Process mining; Workflow; Consistency (knowledge bases); Artificial intelligence; Database; Business process; Business process management; Work in process","score_opus":0.02904034743449188,"score_gpt":0.307797778905382,"score_spread":0.27875743147089016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752952491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01418247,0.00021463478,0.9804766,0.00024963907,0.000046556306,0.0002336886,0.0010205643,0.0030559334,0.00051982584],"genre_scores_gemma":[0.1021106,0.00017903435,0.8925324,0.00016890539,0.000051655214,0.00044307034,0.0034591295,0.00017945145,0.00087574794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779534,0.0004884159,0.0003486954,0.00057186285,0.00067483337,0.000120809695],"domain_scores_gemma":[0.9926502,0.004875078,0.0005595669,0.0006511699,0.001069352,0.00019462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027310278,0.0010371685,0.0016527033,0.0032544597,0.00080415024,0.0014616464,0.0028295075,0.0013267309,0.0021819614],"category_scores_gemma":[0.015617229,0.000693463,0.0012511637,0.003785445,0.00048601002,0.0024528354,0.0013463072,0.0019039913,0.00090968196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006384537,0.00065118365,0.0128127765,0.0005282322,0.0004285321,0.00062879256,0.00028356662,0.17572254,0.006275579,0.011428776,0.01601363,0.7745878],"study_design_scores_gemma":[0.000049381964,0.000064433625,0.0005193227,0.00001741523,0.00002921792,0.00024976698,0.000034749624,0.9859066,0.0016316461,0.008919222,0.0025597415,0.000018398818],"about_ca_topic_score_codex":0.0054037212,"about_ca_topic_score_gemma":0.00785796,"teacher_disagreement_score":0.0054037212,"about_ca_system_score_codex":0.00057458814,"about_ca_system_score_gemma":0.0024949627,"threshold_uncertainty_score":0.014443219},"labels":[],"label_agreement":null},{"id":"W2755654561","doi":"10.1016/j.procs.2017.08.353","title":"A Systematic Literature Review Comparing Primary and Community Health Care Indicators and Measurement Frameworks","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"Industry Canada","keywords":"Computer science; Standardization; Health care; Work (physics); Primary health care; Heuristics; Set (abstract data type); Data science; Process management; Knowledge management; Business; Political science","score_opus":0.059671499890606675,"score_gpt":0.4052190082258041,"score_spread":0.34554750833519743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755654561","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021033052,0.992473,0.0009153492,0.001132614,0.00021435533,0.00064389803,0.0013748852,0.000023000706,0.0011196209],"genre_scores_gemma":[0.020655321,0.9707318,0.004907398,0.00083277584,0.00008816958,0.0014589935,0.0011590717,0.000015294072,0.00015117916],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.97255075,0.009216556,0.010877559,0.0016978179,0.005143524,0.00051378174],"domain_scores_gemma":[0.8492417,0.11643865,0.016485821,0.002331819,0.014353597,0.0011484645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03034969,0.0015548088,0.0061810273,0.04046513,0.0014604297,0.004854777,0.0030198912,0.0020161653,0.0045162197],"category_scores_gemma":[0.1304615,0.0012950647,0.005992568,0.049054094,0.001848755,0.005413027,0.003082839,0.0016782666,0.00051829236],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014879243,0.000034017,0.003626666,0.85183233,0.0024302383,0.00013364073,0.0009246907,0.00023536939,0.00014221344,0.0018053539,0.005619769,0.13306691],"study_design_scores_gemma":[0.000065252556,0.000100865436,0.00774241,0.9400368,0.011468575,0.00027149648,0.001276882,0.00010940031,0.00017552834,0.00085015054,0.03785642,0.000046232704],"about_ca_topic_score_codex":0.021241345,"about_ca_topic_score_gemma":0.0631894,"teacher_disagreement_score":0.04046513,"about_ca_system_score_codex":0.0104329595,"about_ca_system_score_gemma":0.04410988,"threshold_uncertainty_score":0.16050643},"labels":[],"label_agreement":null},{"id":"W2756412720","doi":"10.1016/j.procs.2017.08.356","title":"Augmented Reality Based Brain Tumor 3D Visualization","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Augmented reality; Computer vision; Artificial intelligence; Pose; Orientation (vector space); Visualization; Face (sociological concept); Set (abstract data type); Calibration; Position (finance); Computer graphics (images)","score_opus":0.029449168388857052,"score_gpt":0.321884972794244,"score_spread":0.29243580440538697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756412720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07414486,0.0029899091,0.9033595,0.00053521706,0.0003954332,0.00013404356,0.0008765517,0.009246174,0.008318339],"genre_scores_gemma":[0.5569802,0.0035905566,0.42861223,0.0003883806,0.00024734883,0.00022226847,0.0012737776,0.0006254456,0.008059784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995648,0.00009876518,0.00002278253,0.00006822747,0.00020794029,0.000037535436],"domain_scores_gemma":[0.99952984,0.0001514389,0.000063337895,0.00011712429,0.00011004299,0.00002808756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026808764,0.00095288554,0.00055363023,0.0008431961,0.0001903086,0.0012408958,0.000703324,0.00082092074,0.0056657507],"category_scores_gemma":[0.0010358187,0.0004905889,0.0008830795,0.00045122718,0.00025766692,0.0007136991,0.0012327865,0.0007689097,0.0017474006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010781907,0.00019592402,0.002640341,0.00088465784,0.0002673743,0.0028688454,0.0009787768,0.038747575,0.28670958,0.004131823,0.015739974,0.64575696],"study_design_scores_gemma":[0.00025168614,0.0015294033,0.016100805,0.0003513165,0.00058941904,0.01850552,0.0005072756,0.50895786,0.30318716,0.0070023653,0.14248903,0.00052816165],"about_ca_topic_score_codex":0.00085775927,"about_ca_topic_score_gemma":0.0011000743,"teacher_disagreement_score":0.0056657507,"about_ca_system_score_codex":0.00018148903,"about_ca_system_score_gemma":0.00029100582,"threshold_uncertainty_score":0.0189538},"labels":[],"label_agreement":null},{"id":"W2767633796","doi":"10.1016/j.procs.2017.10.121","title":"Arabic Social Media Analysis and Translation","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Natural language processing; Normalization (sociology); Artificial intelligence; Arabic; Machine translation; Social media; Unavailability; Domain (mathematical analysis); Focus (optics); Modern Standard Arabic; Context (archaeology); Linguistics; World Wide Web","score_opus":0.023165091138060312,"score_gpt":0.2917618620317705,"score_spread":0.2685967708937102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767633796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20764796,0.0034506747,0.5440771,0.0058515472,0.0034426872,0.0020037687,0.043145046,0.021601124,0.16878018],"genre_scores_gemma":[0.5428882,0.002484203,0.3428852,0.0005963716,0.00076095964,0.0013673904,0.035676807,0.0018738189,0.07146707],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988456,0.00038780482,0.00010742692,0.00020804329,0.00036429465,0.000086891916],"domain_scores_gemma":[0.99810815,0.0004174469,0.000153016,0.00028153238,0.0009789312,0.000061007297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009113624,0.001252502,0.0003247221,0.0042607505,0.0013281032,0.0020231793,0.0003719669,0.00037518615,0.014432936],"category_scores_gemma":[0.004615332,0.0002058568,0.0006606788,0.0022474602,0.00044705562,0.0014478664,0.0012394334,0.00072578585,0.010695842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003394982,0.00018613828,0.0050279675,0.0007831753,0.00007950302,0.0009833726,0.0020784808,0.004558799,0.035505332,0.022520773,0.058034163,0.86990285],"study_design_scores_gemma":[0.000051524345,0.00018268883,0.020725843,0.00028105904,0.00010115163,0.001127168,0.004958603,0.13246842,0.13982809,0.028407777,0.6717025,0.00016521112],"about_ca_topic_score_codex":0.004082279,"about_ca_topic_score_gemma":0.003172817,"teacher_disagreement_score":0.014432936,"about_ca_system_score_codex":0.0008484012,"about_ca_system_score_gemma":0.001031088,"threshold_uncertainty_score":0.04828298},"labels":[],"label_agreement":null},{"id":"W2771263372","doi":"10.1016/j.procs.2017.11.446","title":"The role of organizational orientation and product attributes in performance for sustainability","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Winnipeg","funders":"","keywords":"Sustainability; Mindset; Context (archaeology); Product (mathematics); Computer science; Sustainability organizations; Value (mathematics); Perspective (graphical); Orientation (vector space); Process management; New product development; Knowledge management; Business; Marketing","score_opus":0.007238835334802982,"score_gpt":0.2239006734895653,"score_spread":0.2166618381547623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771263372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97845614,0.00013635108,0.0012829814,0.00034817422,0.000009899494,0.000024051105,0.000035977468,0.000011423234,0.019694954],"genre_scores_gemma":[0.9994166,0.000034805147,0.00022867184,0.000022177535,0.000003847591,0.0000032806777,0.000012520788,0.0000032559049,0.00027502608],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979753,0.00088565703,0.00008511535,0.00015289804,0.00058135507,0.0003196851],"domain_scores_gemma":[0.9788113,0.009007021,0.005532335,0.0010793159,0.0020448295,0.0035251235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031759404,0.00025780578,0.00017049862,0.0008967842,0.00072198577,0.0038065857,0.00033296467,0.0006522509,0.0030705172],"category_scores_gemma":[0.009526417,0.00010989354,0.00028426386,0.0011887929,0.0019875104,0.001549565,0.001846682,0.00079809804,0.0004472191],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035252774,0.0010267241,0.9289496,0.00009353062,0.000103329614,0.00018174855,0.0028549859,0.0019855888,0.002713776,0.012643535,0.0005550212,0.04853976],"study_design_scores_gemma":[0.00001340275,0.00037492582,0.98307806,0.000048776277,0.000036408623,0.00008688722,0.0050560185,0.0020092914,0.0009775867,0.0061984328,0.0020885265,0.00003168275],"about_ca_topic_score_codex":0.0020712367,"about_ca_topic_score_gemma":0.0025896206,"teacher_disagreement_score":0.0038065857,"about_ca_system_score_codex":0.0012888557,"about_ca_system_score_gemma":0.0012307614,"threshold_uncertainty_score":0.016796172},"labels":[],"label_agreement":null},{"id":"W2800911426","doi":"10.1016/j.procs.2018.04.079","title":"Pro-Environmental Potential in Activity-Travel Routine of Individuals: A Data Driven Computational Algorithm","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Computer science; Variety (cybernetics); Constraint (computer-aided design); Perception; Travel behavior; Relation (database); Public transport; Algorithm; Transport engineering; Data mining; Artificial intelligence; Psychology","score_opus":0.03055425635606926,"score_gpt":0.30857182839200875,"score_spread":0.27801757203593946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800911426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13143587,0.00012703876,0.86398774,0.0010196478,0.000040081293,0.00020568498,0.00034522708,0.00034326126,0.002495467],"genre_scores_gemma":[0.5657415,0.00009622511,0.4306222,0.00016016678,0.000040391744,0.00060752226,0.0006377587,0.000042666987,0.002051667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915004,0.00034688172,0.000063692394,0.00022174507,0.000116532276,0.00010106306],"domain_scores_gemma":[0.9940631,0.0048719333,0.00024268423,0.00018954165,0.0005055007,0.00012723914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023249546,0.00053795305,0.0009530309,0.00089655654,0.0007556374,0.0017662814,0.0016796299,0.0013561648,0.002012786],"category_scores_gemma":[0.008778657,0.00043917613,0.00087830774,0.0011430101,0.00080085127,0.0011659208,0.0013050715,0.0011486718,0.00022754051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011535241,0.000116961965,0.010960333,0.00005150551,0.00005412474,0.000067762536,0.00016574225,0.94276756,0.00033534315,0.00972285,0.00063189137,0.0350107],"study_design_scores_gemma":[0.0000066370744,0.000011469368,0.0003185381,0.0000047332355,0.000005276219,0.0000073042083,0.000019411003,0.99719536,0.000068519024,0.0022406816,0.00011930153,0.000002721713],"about_ca_topic_score_codex":0.017544355,"about_ca_topic_score_gemma":0.01264524,"teacher_disagreement_score":0.017544355,"about_ca_system_score_codex":0.0014229146,"about_ca_system_score_gemma":0.0023885418,"threshold_uncertainty_score":0.034884453},"labels":[],"label_agreement":null},{"id":"W2801210630","doi":"10.1016/j.procs.2018.04.066","title":"Investigation of the Impacts of Shared Autonomous Vehicle Operation in Halifax, Canada Using a Dynamic Traffic Microsimulation Model","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Nova Scotia Department of Energy","keywords":"Microsimulation; Computer science; TRIPS architecture; Operations research; Transport engineering; Service (business); Simulation; Business","score_opus":0.015391457140526428,"score_gpt":0.22727348909150108,"score_spread":0.21188203195097466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801210630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9790862,0.00011337998,0.010936884,0.00031492155,0.000014679379,0.000044667333,0.00041469466,0.00010142063,0.008973133],"genre_scores_gemma":[0.99807113,0.000049424492,0.00073414174,0.000008925204,0.0000011779173,0.000012183345,0.000105726795,0.000005604544,0.0010116876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997087,0.00007558288,0.0000064077294,0.0000409618,0.000055231423,0.00011314469],"domain_scores_gemma":[0.99936134,0.00020444013,0.00006982922,0.000025764792,0.0002581569,0.00008049982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037410134,0.00077774836,0.0003665109,0.00035614954,0.0007509717,0.001064879,0.0009072579,0.000493103,0.0012840867],"category_scores_gemma":[0.0009897004,0.00027281605,0.0004240852,0.00033467016,0.0006255416,0.0006083208,0.0006884404,0.00046158012,0.00007640279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055925007,0.00002376287,0.0051598595,0.000012319107,0.000017118153,0.00011323201,0.000033639128,0.99109006,0.00067383185,0.0015710051,0.00022336705,0.001025845],"study_design_scores_gemma":[0.0000052902233,0.00003030435,0.0017445278,0.0000019012916,0.000010935857,0.0000061060414,0.0001359265,0.9974355,0.0002714301,0.00016450636,0.00018857187,0.0000049773266],"about_ca_topic_score_codex":0.8044634,"about_ca_topic_score_gemma":0.63913906,"teacher_disagreement_score":0.19553661,"about_ca_system_score_codex":0.006963924,"about_ca_system_score_gemma":0.005350877,"threshold_uncertainty_score":0.3933763},"labels":[],"label_agreement":null},{"id":"W2801893945","doi":"10.1016/j.procs.2018.04.146","title":"Passenger Safety in Ride-Sharing Services","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer security; Computer science; Internet privacy; Order (exchange); Business; Telecommunications; Finance","score_opus":0.007511607910921255,"score_gpt":0.22439624526664015,"score_spread":0.2168846373557189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801893945","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08015858,0.018594723,0.0317088,0.045815203,0.0039683008,0.0007414893,0.0028586073,0.0040974845,0.81205684],"genre_scores_gemma":[0.7489651,0.01627086,0.014070535,0.013070934,0.0014943179,0.00032005413,0.00455332,0.0007491639,0.2005058],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99666315,0.0008515757,0.00013385652,0.0003576798,0.0013012728,0.00069247535],"domain_scores_gemma":[0.99667835,0.0005234646,0.0002695305,0.00023901109,0.0017754558,0.0005142112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019984886,0.00076438516,0.00025924674,0.001140784,0.0026825187,0.0052965484,0.0014921892,0.00236547,0.040183984],"category_scores_gemma":[0.0064350744,0.00029973776,0.00071119785,0.0013006019,0.00078190194,0.005098365,0.0051468136,0.001831347,0.013443361],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027604212,0.00034686763,0.020601414,0.0017926389,0.000059398673,0.0015184345,0.007476313,0.0020898136,0.003130841,0.059562698,0.35435885,0.5487867],"study_design_scores_gemma":[0.000015145006,0.00016569771,0.009795059,0.0009680915,0.00005648371,0.0014238453,0.007758386,0.0022966412,0.0015117951,0.009382077,0.9665562,0.0000706177],"about_ca_topic_score_codex":0.026908303,"about_ca_topic_score_gemma":0.01600838,"teacher_disagreement_score":0.040183984,"about_ca_system_score_codex":0.0031605333,"about_ca_system_score_gemma":0.0032620279,"threshold_uncertainty_score":0.13442886},"labels":[],"label_agreement":null},{"id":"W2802906475","doi":"10.1016/j.procs.2018.04.044","title":"Raptor code to mitigate Pilot contamination in Massive MiMo","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; MIMO; Channel (broadcasting); Code (set theory); Minimum mean square error; Scheme (mathematics); Real-time computing; Wireless; 3G MIMO; Algorithm; Telecommunications; Statistics; Mathematics","score_opus":0.011066844700544354,"score_gpt":0.234933393760945,"score_spread":0.22386654906040063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802906475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033681612,0.0012285375,0.9590352,0.00024977003,0.00012695047,0.00004666988,0.00008558353,0.0006299509,0.004915697],"genre_scores_gemma":[0.6749277,0.0012091937,0.31792083,0.00029329822,0.0001636653,0.00021650289,0.00019442204,0.000073170944,0.005001214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992095,0.00029632964,0.000027870308,0.00007916874,0.00030370284,0.00008338624],"domain_scores_gemma":[0.99874336,0.00055831973,0.00015423629,0.0002035455,0.00029640703,0.000044151395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063449424,0.0008382118,0.00066827075,0.00049494725,0.0003868524,0.0005337775,0.0007880409,0.0008272361,0.0011847267],"category_scores_gemma":[0.0027615249,0.00033501047,0.00035178705,0.00061823474,0.0007569113,0.0006182533,0.0009176614,0.0009049959,0.0006493822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005620111,0.00010343632,0.00078840554,0.00034112754,0.00016516204,0.0012029295,0.00032289114,0.67790395,0.054007895,0.07366919,0.0071080243,0.183825],"study_design_scores_gemma":[0.000026165522,0.00016152266,0.00016221688,0.000028205279,0.000025779895,0.00040936403,0.000018704277,0.97577035,0.01295668,0.008061318,0.0023511306,0.000028505006],"about_ca_topic_score_codex":0.00087131665,"about_ca_topic_score_gemma":0.0008850637,"teacher_disagreement_score":0.0011847267,"about_ca_system_score_codex":0.00021393731,"about_ca_system_score_gemma":0.000743242,"threshold_uncertainty_score":0.0039633512},"labels":[],"label_agreement":null},{"id":"W2883481734","doi":"10.1016/j.procs.2018.07.048","title":"Certain Investigations on Soft Lander for Lunar Exploration","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Patient Safety Institute","keywords":"Soft landing; Computer science; Aerospace engineering; Interfacing; Drop test; Kinematics; Drop (telecommunication); Marine engineering; Simulation; Arduino; Geology; Environmental science; Mechanical engineering; Physics; Engineering","score_opus":0.03935752766675044,"score_gpt":0.2618762590883082,"score_spread":0.22251873142155776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883481734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82355917,0.009980572,0.114853375,0.0017012826,0.00034625776,0.00016750974,0.00039576512,0.0008965411,0.048099536],"genre_scores_gemma":[0.95475227,0.005876537,0.02164932,0.00017984903,0.00006407824,0.000043503132,0.00029816787,0.00007398538,0.017062234],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983156,0.000021601803,0.000005026382,0.000034856184,0.00007850004,0.00002836951],"domain_scores_gemma":[0.9997911,0.000052896325,0.000015900801,0.000045746616,0.000062200386,0.000032214466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017111572,0.0003549615,0.00032246785,0.00044592432,0.00048645475,0.0007252531,0.0003849515,0.0005015647,0.006443842],"category_scores_gemma":[0.00046335868,0.00012795631,0.00037957178,0.0004988717,0.00029865553,0.00097268616,0.00048607006,0.00039043502,0.0010930976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004228511,0.00033147007,0.0119045,0.0019405793,0.000074068754,0.0019780311,0.0011187819,0.03343611,0.5581553,0.018026736,0.0063452106,0.36626643],"study_design_scores_gemma":[0.00008619779,0.0046636853,0.039887223,0.00047237196,0.00023234972,0.0037838896,0.005020729,0.2466281,0.45929217,0.019088864,0.2206724,0.00017201384],"about_ca_topic_score_codex":0.00082150096,"about_ca_topic_score_gemma":0.0010414147,"teacher_disagreement_score":0.006443842,"about_ca_system_score_codex":0.0002168817,"about_ca_system_score_gemma":0.00021714406,"threshold_uncertainty_score":0.021556735},"labels":[],"label_agreement":null},{"id":"W2889202450","doi":"10.1016/j.procs.2018.07.294","title":"Spark-based data analytics of sequence motifs in large omics data","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Tertiary Education Trust Fund","keywords":"Computer science; SPARK (programming language); Analytics; DNA sequencing; Genomics; Sequence motif; Data mining; Sequence (biology); Computational biology; Bioinformatics; Data science; Genome; DNA; Biology; Genetics; Gene","score_opus":0.12647893774525143,"score_gpt":0.3419665985896255,"score_spread":0.2154876608443741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889202450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12650393,0.0008453299,0.8583182,0.0006611087,0.00018460804,0.00038749923,0.0015797706,0.0094330255,0.00208645],"genre_scores_gemma":[0.39146084,0.00041539225,0.6011057,0.00015373135,0.00009474735,0.00027812156,0.004686172,0.00029681408,0.0015084033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871993,0.0001867531,0.000108025466,0.00033768968,0.00051276915,0.00013492293],"domain_scores_gemma":[0.9982863,0.0005550269,0.0001377267,0.00031542886,0.00046304677,0.00024249322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017289708,0.0009932583,0.0014251046,0.0017267023,0.0012709856,0.0012523768,0.0020985364,0.00059940846,0.0007026051],"category_scores_gemma":[0.004100866,0.00040739132,0.001242589,0.0030290615,0.0006299456,0.0015188508,0.0015420809,0.00094890024,0.0004942361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026795373,0.0013535391,0.02194173,0.0010011495,0.0007076684,0.0013114121,0.0011944998,0.28565153,0.07329198,0.020900214,0.02839156,0.5615751],"study_design_scores_gemma":[0.000104160004,0.00017002769,0.0016483933,0.000009707515,0.00003894113,0.00028683819,0.00021175592,0.96007055,0.0140099665,0.018723205,0.0047031185,0.000023299337],"about_ca_topic_score_codex":0.0045175618,"about_ca_topic_score_gemma":0.0056364993,"teacher_disagreement_score":0.0045175618,"about_ca_system_score_codex":0.00057903444,"about_ca_system_score_gemma":0.0021663597,"threshold_uncertainty_score":0.00914377},"labels":[],"label_agreement":null},{"id":"W2889214550","doi":"10.1016/j.procs.2018.08.017","title":"Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Computer science; Popularity; Data science; Analytics; Focus (optics); Variety (cybernetics); Data analysis; Simple (philosophy); Data mining; Artificial intelligence","score_opus":0.15349501197432974,"score_gpt":0.3518479565146497,"score_spread":0.19835294454031993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889214550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27635255,0.00069238705,0.6727447,0.0022894898,0.00021987373,0.0015109588,0.010042447,0.016669877,0.019477675],"genre_scores_gemma":[0.4893019,0.00025723194,0.4948524,0.00039442247,0.000043755856,0.00057283806,0.0077411323,0.00031345378,0.006522878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905163,0.00021263173,0.000056741686,0.0002850588,0.00027482497,0.00011913121],"domain_scores_gemma":[0.9983058,0.0008345898,0.00015719367,0.00019943988,0.00024276519,0.00026020972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007441453,0.0012364502,0.0007095902,0.002440158,0.0008998608,0.001618727,0.0014304509,0.0009047545,0.00440414],"category_scores_gemma":[0.005352027,0.00034135798,0.0008113365,0.001481839,0.00072106376,0.0017519223,0.0022566703,0.0011059464,0.0013115522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035015438,0.00149254,0.09811616,0.0014442829,0.0005631828,0.0041829194,0.005065577,0.1358547,0.038199537,0.09230294,0.050662607,0.568614],"study_design_scores_gemma":[0.000098045755,0.00023048303,0.015587347,0.00006539382,0.000043058975,0.0005736096,0.0014555701,0.9014501,0.0068728696,0.049178813,0.02437038,0.00007435094],"about_ca_topic_score_codex":0.010576913,"about_ca_topic_score_gemma":0.028295204,"teacher_disagreement_score":0.010576913,"about_ca_system_score_codex":0.00069130823,"about_ca_system_score_gemma":0.0009968076,"threshold_uncertainty_score":0.021030664},"labels":[],"label_agreement":null},{"id":"W2894994565","doi":"10.1016/j.procs.2018.10.167","title":"Resource Management Approach to an Efficient Wireless Sensor Network","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Redundancy (engineering); Computer science; Wireless sensor network; Energy consumption; Key distribution in wireless sensor networks; Wireless; Computer network; Efficient energy use; Embedded system; Real-time computing; Distributed computing; Wireless network; Telecommunications; Electrical engineering; Operating system","score_opus":0.011473793672601042,"score_gpt":0.22594808061185429,"score_spread":0.21447428693925324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894994565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0110553885,0.001050428,0.97344095,0.0010091026,0.00014666899,0.00015520064,0.00005088724,0.00022535908,0.012866045],"genre_scores_gemma":[0.5161692,0.0024952143,0.46092334,0.00041823674,0.00023789947,0.00059090223,0.00012205523,0.00010402181,0.018939162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995003,0.00015948783,0.000026607302,0.000086607,0.00017462822,0.00005234222],"domain_scores_gemma":[0.99972767,0.00010245535,0.000030392652,0.000040124312,0.00007353988,0.000025817439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067305035,0.000530765,0.00041184083,0.00042578307,0.0006215874,0.001167196,0.0011439817,0.0005377864,0.0024385345],"category_scores_gemma":[0.0011282773,0.00019824808,0.0002902815,0.0006822389,0.00057253917,0.001375517,0.00093558105,0.0008426319,0.00037518956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069167756,0.00012129738,0.0004194681,0.00025719046,0.00004849552,0.0003211017,0.0002166616,0.51263565,0.01256867,0.36518016,0.0066872556,0.10147495],"study_design_scores_gemma":[0.000017332148,0.000059582984,0.00012688896,0.000024851603,0.000017224384,0.000136199,0.00006194214,0.93735695,0.002942941,0.04227085,0.01697214,0.000013114447],"about_ca_topic_score_codex":0.0015876973,"about_ca_topic_score_gemma":0.0020526736,"teacher_disagreement_score":0.0024385345,"about_ca_system_score_codex":0.0009813065,"about_ca_system_score_gemma":0.0011713404,"threshold_uncertainty_score":0.00815773},"labels":[],"label_agreement":null},{"id":"W2899636024","doi":"10.1016/j.procs.2018.10.149","title":"An Approach for QoS-aware Service Composition with GraphPlan and Fuzzy Logic","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Fuzzy logic; Cloud computing; Quality of service; Web service; Set (abstract data type); Service (business); Rank (graph theory); Distributed computing; Artificial intelligence; World Wide Web; Programming language; Computer network; Operating system","score_opus":0.011552899602675606,"score_gpt":0.23825804769745218,"score_spread":0.22670514809477657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899636024","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004475146,0.0001062052,0.9932192,0.00012258432,0.000025863057,0.00012827871,0.00007576013,0.0006226081,0.0012243694],"genre_scores_gemma":[0.078136265,0.00015902455,0.92007416,0.0000873686,0.000023560193,0.00012917697,0.00027592573,0.00009378629,0.0010207018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989598,0.00021357383,0.00007475364,0.00023899548,0.00042149276,0.00009137676],"domain_scores_gemma":[0.99931264,0.000300132,0.00006322414,0.00008503164,0.00018199882,0.000056881792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013496927,0.00095067656,0.000712257,0.0023903502,0.0013387359,0.0013836233,0.0016319389,0.0009701345,0.002594489],"category_scores_gemma":[0.0023741696,0.00058117276,0.0017611319,0.00197281,0.0009146033,0.0020160829,0.001421608,0.0013905402,0.0005291674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024828402,0.00037394112,0.0019811115,0.0003531816,0.00015047066,0.00040208705,0.000414477,0.47012627,0.01885484,0.077434555,0.0050488766,0.4246119],"study_design_scores_gemma":[0.000019708776,0.000042853768,0.00012978028,0.000018424884,0.000029743107,0.00008738625,0.000060330192,0.9637779,0.0042883325,0.027808988,0.0037180355,0.000018480987],"about_ca_topic_score_codex":0.016822848,"about_ca_topic_score_gemma":0.016539799,"teacher_disagreement_score":0.016822848,"about_ca_system_score_codex":0.0018065331,"about_ca_system_score_gemma":0.0030202558,"threshold_uncertainty_score":0.03344983},"labels":[],"label_agreement":null},{"id":"W2903847404","doi":"10.1016/j.procs.2018.11.045","title":"Emotion in the Common Model of Cognition","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Cognition; Computer science; Common ground; Cognitive science; Cognitive psychology; Cognitive model; Psychology; Social psychology; Neuroscience","score_opus":0.03771965662044658,"score_gpt":0.2749673711273557,"score_spread":0.23724771450690912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903847404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16337019,0.008268435,0.5509631,0.03049862,0.0007495559,0.00012802151,0.00020375942,0.0003061482,0.24551214],"genre_scores_gemma":[0.9614444,0.0013269004,0.03138896,0.0007868841,0.0002761558,0.00009832496,0.00008381116,0.00006057406,0.0045340843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965229,0.0015054443,0.00020792226,0.0007708903,0.00064682134,0.00034607196],"domain_scores_gemma":[0.99748355,0.00080592337,0.00024970245,0.000619542,0.00050234015,0.0003388994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003392073,0.0007799374,0.0010366945,0.0022321735,0.0020519032,0.006498578,0.0017460579,0.0022667989,0.0029474616],"category_scores_gemma":[0.0040621627,0.0003548889,0.001770245,0.0012139977,0.014720803,0.015318433,0.004419444,0.0032563382,0.00043973373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016161812,0.000006690059,0.00024272942,0.000035173653,0.000013103459,0.000037623024,0.0015588711,0.0006172721,0.00022834724,0.9938758,0.00021669187,0.003151512],"study_design_scores_gemma":[0.000007571614,0.000017141625,0.00045262868,0.00003016815,0.000018311683,0.0000616651,0.00048681826,0.0026023146,0.000092512666,0.9927632,0.003456044,0.000011667686],"about_ca_topic_score_codex":0.0024516387,"about_ca_topic_score_gemma":0.0010894088,"teacher_disagreement_score":0.006498578,"about_ca_system_score_codex":0.0031756987,"about_ca_system_score_gemma":0.0013084095,"threshold_uncertainty_score":0.023041487},"labels":[],"label_agreement":null},{"id":"W2904474124","doi":"10.1016/j.procs.2018.11.102","title":"Generating Cognitive Context with Feature-Extracting Bidirectional Associative Memory","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Content-addressable memory; Associative property; Feature (linguistics); Context (archaeology); Limiting; Recall; Cognition; Pattern recognition (psychology); Artificial intelligence; Bidirectional associative memory; Machine learning; Artificial neural network; Cognitive psychology","score_opus":0.01576960875963367,"score_gpt":0.26103662526969895,"score_spread":0.24526701651006527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904474124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5028936,0.00037555394,0.4908766,0.00014069673,0.00013640971,0.00013714984,0.00018539958,0.001253434,0.004001227],"genre_scores_gemma":[0.931694,0.00008457997,0.06730014,0.00005263051,0.000015229803,0.00005722934,0.00009786067,0.000021410266,0.0006770945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998654,0.000021334556,0.000011393283,0.00004720827,0.0000319756,0.000022664626],"domain_scores_gemma":[0.99958175,0.00013302325,0.000057609097,0.00010023069,0.000095166484,0.000032173954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033010173,0.0005403988,0.00035317554,0.00036647686,0.00025094431,0.0004272342,0.00057690695,0.0003410683,0.001210065],"category_scores_gemma":[0.0016258113,0.00014777885,0.00028115508,0.00032861842,0.00029502687,0.0009782931,0.00091841543,0.000462726,0.00024268316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067095936,0.00037359263,0.0072367853,0.00029666445,0.00012616359,0.0004163726,0.00022822866,0.040003233,0.26619548,0.013637566,0.0013130171,0.66950184],"study_design_scores_gemma":[0.00006747398,0.00085168914,0.006267738,0.000034646946,0.00019808172,0.0006480746,0.00015952133,0.731407,0.22576784,0.029786766,0.0047391932,0.00007194661],"about_ca_topic_score_codex":0.00064164476,"about_ca_topic_score_gemma":0.0010967447,"teacher_disagreement_score":0.001210065,"about_ca_system_score_codex":0.00015610718,"about_ca_system_score_gemma":0.0003576606,"threshold_uncertainty_score":0.0040480494},"labels":[],"label_agreement":null},{"id":"W2904534786","doi":"10.1016/j.procs.2018.11.056","title":"Developing a macro cognitive common model test bed for real world expertise","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Macro; Test (biology); Cognition; Artificial intelligence; Programming language","score_opus":0.04543527759340191,"score_gpt":0.31308391821949677,"score_spread":0.26764864062609484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904534786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6140101,0.00016857432,0.34786436,0.00067349005,0.000194985,0.0037345816,0.0021275352,0.0039390572,0.02728733],"genre_scores_gemma":[0.80165565,0.00012375416,0.18313466,0.0003569443,0.000028660443,0.004249063,0.0039784145,0.000442947,0.0060298415],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975752,0.001009323,0.00016901587,0.00036734852,0.00065840327,0.00022067424],"domain_scores_gemma":[0.98758996,0.005866474,0.00057455053,0.0026365744,0.0024388675,0.000893587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00315904,0.0008138353,0.00050790055,0.0011170119,0.0005109885,0.0015782575,0.002889099,0.0011037681,0.005689807],"category_scores_gemma":[0.01627508,0.00046236464,0.000544074,0.0007534246,0.001205194,0.0025512255,0.0024466456,0.0015237472,0.0018391054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0059280223,0.01918895,0.054587234,0.0019496011,0.0004855607,0.0019997717,0.0072534494,0.26200482,0.07817455,0.09130212,0.04111841,0.43600753],"study_design_scores_gemma":[0.0015479955,0.010511843,0.033943422,0.00033325522,0.0002215483,0.00091990625,0.0024570765,0.71533835,0.10143673,0.053895816,0.079116225,0.00027788038],"about_ca_topic_score_codex":0.004747461,"about_ca_topic_score_gemma":0.0046480075,"teacher_disagreement_score":0.005689807,"about_ca_system_score_codex":0.0013087247,"about_ca_system_score_gemma":0.0014049447,"threshold_uncertainty_score":0.019034266},"labels":[],"label_agreement":null},{"id":"W2904949706","doi":"10.1016/j.procs.2018.11.060","title":"Why the Common Model of the mind needs holographic a-priori categories","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Embodied and Extended Cognition","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Rationality; Cognitive architecture; A priori and a posteriori; Architecture; Cognition; Ontology; Component (thermodynamics); Epistemology; Cognitive science; Common sense; Artificial intelligence; Philosophy; Psychology","score_opus":0.03736307977420122,"score_gpt":0.25884526818605147,"score_spread":0.22148218841185024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904949706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10927835,0.0040585385,0.59616625,0.059749752,0.0008421469,0.00009695326,0.0004026114,0.0005068321,0.22889857],"genre_scores_gemma":[0.922193,0.0007806139,0.06581825,0.0021197514,0.00023399387,0.000106421256,0.00020220607,0.00017506386,0.008370777],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99756444,0.001001886,0.00011390944,0.00054226,0.0005099165,0.0002676304],"domain_scores_gemma":[0.9954926,0.0017884352,0.00024245676,0.0014449065,0.00072071905,0.0003108756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041752574,0.00044404477,0.00066866825,0.0013295714,0.0021265673,0.0046489653,0.0013706355,0.0026808123,0.0052129105],"category_scores_gemma":[0.009608336,0.00056841277,0.0010819276,0.00068040553,0.01873263,0.017081754,0.0037213778,0.0035739306,0.0010419417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008699601,0.0000035362668,0.00010325462,0.000012475088,0.000004383292,0.000021806834,0.00043187998,0.00025635536,0.000093451956,0.9965288,0.00057676854,0.0019584217],"study_design_scores_gemma":[0.0000051600173,0.0000039080073,0.00008305772,0.00000846281,0.0000018138844,0.000032911914,0.000117026764,0.0005439482,0.00006146919,0.996327,0.0028104298,0.000004854794],"about_ca_topic_score_codex":0.0040892097,"about_ca_topic_score_gemma":0.003303391,"teacher_disagreement_score":0.0052129105,"about_ca_system_score_codex":0.0022511499,"about_ca_system_score_gemma":0.0016132969,"threshold_uncertainty_score":0.022081137},"labels":[],"label_agreement":null},{"id":"W2944959425","doi":"10.1016/j.procs.2019.04.099","title":"Analysis of Activity Location and Trip Mode Choice – A Study on Hierarchical Ordering","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nested logit; Computer science; Mode (computer interface); Logit; Mode choice; Operations research; Travel behavior; Work (physics); Logistic regression; Simulation; Machine learning; Transport engineering; Econometrics; Human–computer interaction; Mathematics; Public transport","score_opus":0.019591172751433534,"score_gpt":0.3185318900925721,"score_spread":0.2989407173411386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944959425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.830825,0.0003932618,0.15942061,0.00048119543,0.0000062636987,0.0001152758,0.0004338187,0.00006728301,0.008257311],"genre_scores_gemma":[0.97189033,0.00011479306,0.026654454,0.000019939464,0.000008639987,0.0000370105,0.00023707046,0.000020589605,0.0010171013],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9948613,0.0034019267,0.00013112194,0.0004857255,0.0007500787,0.0003697341],"domain_scores_gemma":[0.95872915,0.032993358,0.003466797,0.0021541892,0.0019172715,0.0007392692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044072294,0.00031600217,0.0005448332,0.0018474022,0.00074887206,0.0014382806,0.00093975937,0.00039426106,0.004139783],"category_scores_gemma":[0.026710846,0.0004845848,0.0010788727,0.0035555132,0.0014674118,0.0022250928,0.0011061829,0.00089403667,0.0002958849],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052585837,0.00050337403,0.49378848,0.00029814133,0.00041236304,0.0005071748,0.007676033,0.1345614,0.0034062902,0.24637088,0.0016067444,0.110343345],"study_design_scores_gemma":[0.000032658274,0.00025233845,0.20827447,0.00006209947,0.00008284017,0.00023817013,0.0027976441,0.6573635,0.00095135585,0.12614836,0.0037230505,0.00007354983],"about_ca_topic_score_codex":0.03738356,"about_ca_topic_score_gemma":0.026587782,"teacher_disagreement_score":0.03738356,"about_ca_system_score_codex":0.0023199916,"about_ca_system_score_gemma":0.0019400718,"threshold_uncertainty_score":0.07433194},"labels":[],"label_agreement":null},{"id":"W2945972880","doi":"10.1016/j.procs.2019.04.068","title":"Comparative Study on Range Free Localization Algorithms","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; King Fahd University of Petroleum and Minerals; Acadia University","keywords":"Computer science; Range (aeronautics); Centroid; Algorithm; Wireless sensor network; Node (physics); MATLAB; Scope (computer science); Position (finance); Artificial intelligence; Computer network","score_opus":0.016127823277977595,"score_gpt":0.24497356477435311,"score_spread":0.22884574149637552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945972880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11185576,0.05619098,0.77488303,0.0014897664,0.0009579415,0.00037347947,0.000802495,0.0029810932,0.050465364],"genre_scores_gemma":[0.6884496,0.026914705,0.27049527,0.000446759,0.00045082605,0.00026371583,0.0018708153,0.00063146855,0.010476863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959557,0.0011082849,0.0003073097,0.00048010732,0.0019024847,0.00024612926],"domain_scores_gemma":[0.9911708,0.005421762,0.00044430164,0.000709111,0.0021115078,0.00014253908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00261181,0.0011054792,0.0010079598,0.0045351023,0.0009851294,0.0021546716,0.0020200019,0.0014227823,0.004677589],"category_scores_gemma":[0.012963613,0.00029277935,0.0009067316,0.005012676,0.00056913815,0.0032769127,0.001058626,0.00060782593,0.001330211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010899123,0.00019834036,0.006589796,0.0018399815,0.00030378866,0.00032583374,0.00030907305,0.22466174,0.0062996685,0.028021686,0.010131636,0.72022843],"study_design_scores_gemma":[0.00017387391,0.0020973557,0.011185439,0.00050700596,0.00056916534,0.0034688662,0.0010755325,0.8633865,0.018403074,0.019849133,0.07906935,0.00021468043],"about_ca_topic_score_codex":0.002521741,"about_ca_topic_score_gemma":0.0014931962,"teacher_disagreement_score":0.004677589,"about_ca_system_score_codex":0.0010912472,"about_ca_system_score_gemma":0.0010239434,"threshold_uncertainty_score":0.015648127},"labels":[],"label_agreement":null},{"id":"W2946553955","doi":"10.1016/j.procs.2019.04.117","title":"Spatio-temporal Anomaly Detection in Intelligent Transportation Systems","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Anomaly detection; Data mining; Anomaly (physics); Intelligent transportation system; Scheme (mathematics); Temporal database; Data stream mining; Real-time computing","score_opus":0.009553193899687666,"score_gpt":0.22951632105431477,"score_spread":0.2199631271546271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946553955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18629313,0.00060392526,0.8108608,0.00035123262,0.00006357917,0.00003073972,0.00008306218,0.0007810806,0.00093244924],"genre_scores_gemma":[0.9398389,0.00014726435,0.05957798,0.000025029152,0.00003439012,0.000014037109,0.00006721549,0.000014639053,0.0002804877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890196,0.00030226767,0.00008645013,0.0002015062,0.0004300724,0.00007769801],"domain_scores_gemma":[0.9985563,0.0006234593,0.00028998667,0.00014099301,0.00032693436,0.00006237599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011331339,0.00032553825,0.00048707693,0.0016233092,0.00039184972,0.0007843344,0.000566506,0.00057714235,0.00029276428],"category_scores_gemma":[0.0027711394,0.00016791624,0.00036344453,0.001350906,0.0007357106,0.0010575933,0.0005417521,0.0005615298,0.00009266806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036381613,0.00022991616,0.02414237,0.00013876667,0.00019185334,0.0004994912,0.00041939886,0.5806876,0.041431103,0.020558843,0.0014034302,0.32993346],"study_design_scores_gemma":[0.0000024941585,0.000030115852,0.0020548962,0.000003390422,0.0000077088525,0.00006669029,0.000041858002,0.9896897,0.0026869844,0.004992896,0.0004150682,0.000008120194],"about_ca_topic_score_codex":0.004167083,"about_ca_topic_score_gemma":0.0023191597,"teacher_disagreement_score":0.004167083,"about_ca_system_score_codex":0.0007220313,"about_ca_system_score_gemma":0.0005496963,"threshold_uncertainty_score":0.008285642},"labels":[],"label_agreement":null},{"id":"W2946622927","doi":"10.1016/j.procs.2019.04.184","title":"IoT-based predictive maintenance for fleet management","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Predictive maintenance; Internet of Things; Architecture; Fleet management; Big data; Work (physics); Machine learning; Computer security; Data mining; Reliability engineering; Telecommunications","score_opus":0.005923858653000614,"score_gpt":0.2219247922871021,"score_spread":0.2160009336341015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946622927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11729836,0.00088286615,0.8688216,0.00042125897,0.00016799776,0.000108184024,0.00035050922,0.0035498387,0.008399447],"genre_scores_gemma":[0.9622892,0.00016918487,0.03570255,0.000045709927,0.000027193597,0.000038406288,0.0002199257,0.000044477867,0.0014632979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982893,0.000026366042,0.000010089219,0.000045904544,0.00006540262,0.000023239785],"domain_scores_gemma":[0.9997123,0.00007492376,0.00005436387,0.000056377983,0.00008066037,0.00002139173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029891817,0.0004177454,0.00042980645,0.00048799458,0.0003576291,0.000497451,0.0008241589,0.00041460592,0.0014374623],"category_scores_gemma":[0.0006619703,0.00016713735,0.0002891661,0.00041891268,0.00024909189,0.0007575181,0.000457993,0.00038871588,0.00031049532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024268297,0.00014610714,0.004908167,0.00014995689,0.00006300576,0.00026914175,0.00012866026,0.7140538,0.016278382,0.0059283003,0.004994708,0.25283703],"study_design_scores_gemma":[0.00000578297,0.000034478697,0.0009933846,0.0000068123795,0.000009832829,0.00004349198,0.000018359724,0.9934768,0.0017227236,0.0023160852,0.0013663054,0.0000059095946],"about_ca_topic_score_codex":0.002532282,"about_ca_topic_score_gemma":0.003607934,"teacher_disagreement_score":0.002532282,"about_ca_system_score_codex":0.0004225802,"about_ca_system_score_gemma":0.00035518457,"threshold_uncertainty_score":0.0050351024},"labels":[],"label_agreement":null},{"id":"W2972338638","doi":"10.1016/j.procs.2019.08.095","title":"Combined Reed-Solomon and Convolutional codes for IWSN based on IDWPT/DWPT Architecture","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Wireless sensor network; Wireless; Flexibility (engineering); Computer science; Architecture; Software deployment; Reliability (semiconductor); Channel (broadcasting); Wireless network; Coding (social sciences); Real-time computing; Computer network; Telecommunications; Software engineering","score_opus":0.004089746449024153,"score_gpt":0.19021281168700016,"score_spread":0.186123065237976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972338638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22633322,0.0011785898,0.7577848,0.0003383628,0.00011427473,0.0001174159,0.00020615975,0.001404903,0.012522343],"genre_scores_gemma":[0.8567898,0.0004140725,0.13703087,0.00007679539,0.000022036835,0.00004977129,0.00016501134,0.000034530778,0.0054171924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974865,0.000039898034,0.00001420194,0.000047167698,0.00011263298,0.000037485916],"domain_scores_gemma":[0.99972266,0.000060325514,0.000041122537,0.00004695947,0.000116657786,0.000012209615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024049608,0.00039594906,0.00024365785,0.0004393811,0.0002587674,0.00030847156,0.0004587491,0.00033938637,0.0010465542],"category_scores_gemma":[0.00056721026,0.0001107051,0.00021899998,0.00047649155,0.00031396048,0.0005375504,0.00032839947,0.0003068583,0.0003098492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007994468,0.0001747005,0.0036546546,0.00046405086,0.00014745945,0.00086586137,0.00034809246,0.25423828,0.3200748,0.06005206,0.0028630628,0.35631755],"study_design_scores_gemma":[0.000031822656,0.0003560159,0.0010383177,0.000042179727,0.00005411627,0.00077558827,0.00003953752,0.8800407,0.10202917,0.0076829363,0.007871952,0.000037631457],"about_ca_topic_score_codex":0.0024902439,"about_ca_topic_score_gemma":0.0036386738,"teacher_disagreement_score":0.0024902439,"about_ca_system_score_codex":0.0004679151,"about_ca_system_score_gemma":0.00062916166,"threshold_uncertainty_score":0.004951477},"labels":[],"label_agreement":null},{"id":"W2972395987","doi":"10.1016/j.procs.2019.08.073","title":"Simulating the charging of electric vehicles by laser","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Wireless Power Transfer Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Wireless power transfer; Laser; MATLAB; Wireless; Power (physics); Monochromatic color; Photovoltaic system; Automotive engineering; Maximum power transfer theorem; Electrical engineering; Charging station; Electric vehicle; Telecommunications; Physics; Optics; Engineering","score_opus":0.004807843750598792,"score_gpt":0.18801656838420286,"score_spread":0.18320872463360408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972395987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.725739,0.00023763132,0.21272565,0.00043950355,0.000119420176,0.00015455342,0.00048634043,0.00095865945,0.059139274],"genre_scores_gemma":[0.9841866,0.00011826252,0.009094406,0.000031462423,0.0000048725196,0.000064295215,0.000058492904,0.000029389425,0.0064121406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999081,0.000022522174,0.0000046797363,0.000011445041,0.000028108589,0.000025175814],"domain_scores_gemma":[0.9997454,0.00015511834,0.000021168735,0.0000167389,0.00004659545,0.000014850536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015411645,0.00022026566,0.00026037678,0.0002382896,0.00028613652,0.00060690375,0.00056247285,0.0007263446,0.005225137],"category_scores_gemma":[0.0005839128,0.00016318564,0.00036844297,0.00037458024,0.00033299642,0.0004454916,0.00038304485,0.00033466954,0.0003444538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059807422,0.000030274117,0.0006605715,0.000038962513,0.0000064087703,0.00008593866,0.000052532796,0.98887455,0.004017646,0.0026279842,0.00026574475,0.003279587],"study_design_scores_gemma":[0.000013385046,0.00002987247,0.0001666043,0.0000033090053,0.0000031837599,0.000012436724,0.0000268316,0.9970862,0.0015958261,0.0005140566,0.00054407754,0.0000041591356],"about_ca_topic_score_codex":0.0073691905,"about_ca_topic_score_gemma":0.004081195,"teacher_disagreement_score":0.0073691905,"about_ca_system_score_codex":0.0005661581,"about_ca_system_score_gemma":0.0004850171,"threshold_uncertainty_score":0.017479897},"labels":[],"label_agreement":null},{"id":"W2972500166","doi":"10.1016/j.procs.2019.08.059","title":"Performance Evaluation of CP-ABE Schemes under Constrained Devices","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Cloud computing; Encryption; Server; Cryptography; Key (lock); Computer security; Internet of Things; Access control; Delegation; Public-key cryptography; The Internet; Computer network; World Wide Web; Operating system","score_opus":0.02282997514148371,"score_gpt":0.26858123720653515,"score_spread":0.24575126206505143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972500166","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7968863,0.007684159,0.16203952,0.0017670041,0.00059465796,0.00082529767,0.0009992486,0.0013431383,0.027860655],"genre_scores_gemma":[0.97919184,0.0007119686,0.018657448,0.00007853423,0.0000324766,0.000085828586,0.00029429124,0.00002845998,0.0009192586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926537,0.00253856,0.00054152176,0.0005752895,0.0019967104,0.0016941375],"domain_scores_gemma":[0.98831743,0.006157266,0.001164449,0.0015554649,0.0022709563,0.0005344383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041216523,0.0011055366,0.0019298227,0.0009951437,0.0015508056,0.0024313685,0.0015374171,0.0016322822,0.0027350164],"category_scores_gemma":[0.011388837,0.00021392488,0.0006031282,0.0013343659,0.0010996887,0.0033538328,0.001864335,0.0009685299,0.0005481769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011050556,0.0014000473,0.0061736675,0.0016067824,0.0005777523,0.0009106373,0.00031441503,0.75264204,0.044185903,0.041070845,0.008130455,0.13193695],"study_design_scores_gemma":[0.0002557471,0.0016179738,0.0014025223,0.00006839628,0.000102406746,0.0007388377,0.0002308018,0.9677922,0.019284425,0.005788349,0.002644289,0.000074080155],"about_ca_topic_score_codex":0.0026530975,"about_ca_topic_score_gemma":0.0012746431,"teacher_disagreement_score":0.0041216523,"about_ca_system_score_codex":0.002603968,"about_ca_system_score_gemma":0.0024972137,"threshold_uncertainty_score":0.021797597},"labels":[],"label_agreement":null},{"id":"W2972517269","doi":"10.1016/j.procs.2019.08.075","title":"Thermodynamic analysis of the performance of sub-critical organic Rankine cycle with borehole thermal energy storage","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Organic Rankine cycle; Environmental science; Degree Rankine; Thermal energy storage; Fossil fuel; Borehole; Rankine cycle; Electricity; Process engineering; Greenhouse gas; Thermal efficiency; Nuclear engineering; Waste management; Waste heat; Thermodynamics; Mechanical engineering; Geology; Heat exchanger; Chemistry; Engineering; Electrical engineering","score_opus":0.002146346474994823,"score_gpt":0.17335082055857481,"score_spread":0.17120447408358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972517269","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998621,0.000080758335,0.00035133588,0.000009310269,0.000003275288,0.000007741114,0.0001105887,0.000016133052,0.00079984724],"genre_scores_gemma":[0.9996345,0.000023565002,0.00009074308,0.0000011261151,4.6320957e-7,0.0000019742988,0.000048682567,0.0000021617732,0.0001966904],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998901,0.000009448741,0.00000522038,0.000015945367,0.000048204594,0.00003111343],"domain_scores_gemma":[0.99991035,0.00002371455,0.000008460652,0.0000060628386,0.000039057315,0.000012251733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016606823,0.00023689281,0.00034805137,0.00029358687,0.00035746713,0.00031472289,0.00036046974,0.00015860869,0.0016151614],"category_scores_gemma":[0.00023383956,0.00008307234,0.00020130414,0.00035460872,0.00035942093,0.0003148108,0.00016173291,0.00016560998,0.00012567369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035795597,0.00057397044,0.037205305,0.00076777436,0.000126218,0.0006519469,0.00030445165,0.1491621,0.76384234,0.0020496985,0.0011820329,0.040554684],"study_design_scores_gemma":[0.00009716612,0.0020834177,0.07698203,0.000014184307,0.00009199187,0.00017991701,0.00048727554,0.26561654,0.65134174,0.00047858912,0.0025726336,0.00005454321],"about_ca_topic_score_codex":0.014066735,"about_ca_topic_score_gemma":0.0210623,"teacher_disagreement_score":0.014066735,"about_ca_system_score_codex":0.000684642,"about_ca_system_score_gemma":0.00047362692,"threshold_uncertainty_score":0.027969718},"labels":[],"label_agreement":null},{"id":"W2972635937","doi":"10.1016/j.procs.2019.08.044","title":"HoBAC: toward a Higher-order Attribute-Based Access Control Model","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Access Control and Trust","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Access control; Generalization; Role-based access control; Task (project management); Computer security; Control (management); Order (exchange); Distributed computing; Artificial intelligence","score_opus":0.0354221011042346,"score_gpt":0.3171247855236402,"score_spread":0.2817026844194056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972635937","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067392387,0.00024695066,0.9851521,0.0009048431,0.00008169222,0.00016820636,0.00018242735,0.00042232222,0.0061021545],"genre_scores_gemma":[0.46228752,0.0009368991,0.5244541,0.0007551738,0.00031848584,0.0008174234,0.00075422524,0.00016128799,0.009514947],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99070424,0.0027492396,0.0009109632,0.0012653372,0.003562982,0.00080726575],"domain_scores_gemma":[0.98879415,0.0036621229,0.0009660162,0.002986809,0.0027372113,0.00085368025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008760564,0.0007692541,0.0010455183,0.0021287978,0.001894531,0.007351103,0.0033195205,0.0022224446,0.0031664213],"category_scores_gemma":[0.009526296,0.000647426,0.0020389336,0.0025812935,0.004575721,0.010025424,0.0037255632,0.0051171463,0.0011308021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039325685,0.00006372101,0.00052403234,0.00006762856,0.000029186212,0.00012618386,0.0004624558,0.017052889,0.00091749645,0.9701001,0.0011556739,0.0094612865],"study_design_scores_gemma":[0.000045748766,0.00007610065,0.00023609497,0.00006302952,0.00005875535,0.00022960363,0.00017449076,0.30847868,0.0020246543,0.66105807,0.027506527,0.000048257603],"about_ca_topic_score_codex":0.008805297,"about_ca_topic_score_gemma":0.004278298,"teacher_disagreement_score":0.008805297,"about_ca_system_score_codex":0.00280929,"about_ca_system_score_gemma":0.0045967977,"threshold_uncertainty_score":0.04633081},"labels":[],"label_agreement":null},{"id":"W2972829814","doi":"10.1016/j.procs.2019.08.058","title":"Smart-AC: A New Framework Concept for Modeling Access Control Policy","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Access Control and Trust","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski; Cegep de Sept Iles","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Interconnectivity; Domain (mathematical analysis); Cloud computing; Access control; Security policy; Control (management); Computer security; Artificial intelligence","score_opus":0.029828970335194455,"score_gpt":0.3527781489901503,"score_spread":0.32294917865495587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972829814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013368644,0.00027623022,0.990298,0.0007270108,0.00009258264,0.000107086926,0.00017416921,0.0003185791,0.0066695334],"genre_scores_gemma":[0.18478365,0.0018450212,0.8016273,0.0005865165,0.00041138515,0.0010364525,0.0007601116,0.00028975753,0.008659848],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618644,0.0015864943,0.0003868008,0.00067381264,0.0008456394,0.00032079977],"domain_scores_gemma":[0.99742365,0.0010174044,0.00037837587,0.0005393686,0.00042313975,0.00021809552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004727283,0.0013633695,0.00081433094,0.0027504694,0.0012960952,0.005050261,0.0032871908,0.0026645951,0.0047402033],"category_scores_gemma":[0.0054012025,0.00068622635,0.0021243657,0.0023069484,0.004232458,0.010546557,0.0030221532,0.004132341,0.0010846247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013574045,0.000025681842,0.000336364,0.00007417764,0.00002137769,0.00008762613,0.00038019137,0.027212592,0.00040889982,0.95914686,0.0014076657,0.010885038],"study_design_scores_gemma":[0.000024280862,0.000066925924,0.00021758987,0.00019688347,0.00004535116,0.00023090687,0.00031357724,0.387888,0.0009421804,0.5106765,0.09934352,0.00005431344],"about_ca_topic_score_codex":0.011379934,"about_ca_topic_score_gemma":0.008194976,"teacher_disagreement_score":0.011379934,"about_ca_system_score_codex":0.0028433816,"about_ca_system_score_gemma":0.0036971308,"threshold_uncertainty_score":0.025000572},"labels":[],"label_agreement":null},{"id":"W2973177324","doi":"10.1016/j.procs.2019.08.060","title":"IoT Avatars: Mixed Reality Hybrid Objects for CoRe Ambient Intelligent Environments","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario College of Art and Design","funders":"Canada Research Chairs","keywords":"Computer science; Human–computer interaction; Avatar; Testbed; Mixed reality; Internet of Things; Virtual reality; Bandwidth (computing); Multimedia; Embedded system; World Wide Web; Telecommunications","score_opus":0.032445048877723204,"score_gpt":0.271749150027598,"score_spread":0.23930410114987477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973177324","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03936371,0.00083330565,0.92084116,0.00032843678,0.00035284463,0.00017160151,0.00015759781,0.0035338227,0.03441744],"genre_scores_gemma":[0.4916689,0.0009758695,0.46305034,0.00032841155,0.000098413555,0.00036115543,0.00052864774,0.0009160519,0.04207219],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966085,0.0001232889,0.000016136766,0.000051601222,0.0001137908,0.000034312478],"domain_scores_gemma":[0.9997836,0.000056008786,0.00001601818,0.00005810024,0.00003842994,0.000047965736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005155118,0.00068874,0.0003705056,0.00027682402,0.00037301957,0.001836712,0.00095447566,0.0008323642,0.008047827],"category_scores_gemma":[0.00090356305,0.0002894745,0.00047546145,0.00020100456,0.0005301238,0.0018789226,0.0027348227,0.0007049587,0.0022752124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009547558,0.00036314345,0.002369599,0.0012840183,0.0001408297,0.0024086572,0.007189623,0.013504092,0.29786882,0.20621973,0.02435552,0.4433412],"study_design_scores_gemma":[0.00015207805,0.0013623943,0.0031615982,0.00046030057,0.0001848799,0.0053393915,0.0025883627,0.14064766,0.1032978,0.05962261,0.6829568,0.00022622879],"about_ca_topic_score_codex":0.00018302318,"about_ca_topic_score_gemma":0.00028707652,"teacher_disagreement_score":0.008047827,"about_ca_system_score_codex":0.00015620055,"about_ca_system_score_gemma":0.0001576162,"threshold_uncertainty_score":0.026922643},"labels":[],"label_agreement":null},{"id":"W2980652599","doi":"10.1016/j.procs.2019.09.250","title":"A Predictive Workload Balancing Algorithm in Cloud Services","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; CloudSim; Workload; Cloud computing; Load balancing (electrical power); Distributed computing; Resource allocation; Algorithm; Operating system; Computer network","score_opus":0.004018425976200179,"score_gpt":0.2028276131764516,"score_spread":0.19880918720025142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980652599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051509596,0.0002829583,0.9427786,0.00024757028,0.000111811205,0.00009201313,0.000049170685,0.0017201328,0.0032081073],"genre_scores_gemma":[0.8457091,0.0001686091,0.15129592,0.00011101061,0.00005487065,0.00010305059,0.00013850347,0.00009984997,0.002319156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995524,0.000066853165,0.000021808328,0.00009892326,0.00020160493,0.00005838805],"domain_scores_gemma":[0.9995808,0.00013755157,0.00004555343,0.000056453955,0.00013687805,0.000042729007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000629981,0.0005794372,0.0006226597,0.0004136754,0.0007183911,0.0009985788,0.0010678296,0.00057089305,0.0010284568],"category_scores_gemma":[0.0018401694,0.0002747361,0.00027596523,0.0005426337,0.00043954139,0.0007988445,0.00060131383,0.00088120054,0.00039787043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020219947,0.00009593211,0.0011466607,0.000041957024,0.000025820393,0.00006996763,0.00008361221,0.8272462,0.009865574,0.006740366,0.0015694308,0.1529123],"study_design_scores_gemma":[0.0000045996885,0.000011577386,0.0000582164,0.0000015169595,0.0000015182158,0.0000074915583,0.000004385966,0.99810433,0.000754159,0.0007936204,0.00025659005,0.000002084364],"about_ca_topic_score_codex":0.0068110856,"about_ca_topic_score_gemma":0.0044400776,"teacher_disagreement_score":0.0068110856,"about_ca_system_score_codex":0.00068882527,"about_ca_system_score_gemma":0.0011735695,"threshold_uncertainty_score":0.013542891},"labels":[],"label_agreement":null},{"id":"W2991309845","doi":"10.1016/j.procs.2019.09.450","title":"Determinants of Pro-Environmental Activity-Travel Behavior Using GPS-Based Application and SEM Approach","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission; Acadia University","keywords":"Global Positioning System; Travel behavior; Computer science; Theory of planned behavior; Structural equation modeling; Intervention (counseling); Travel time; Simulation; Transport engineering; Control (management); Artificial intelligence; Psychology; Machine learning; Telecommunications","score_opus":0.024341711366118582,"score_gpt":0.2942990331544271,"score_spread":0.26995732178830856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991309845","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9737558,0.00007359705,0.023319852,0.00010985922,0.0000138371315,0.00015203285,0.0006315662,0.000098687866,0.0018448506],"genre_scores_gemma":[0.9845195,0.000072033705,0.014080051,0.000011300036,0.0000064514243,0.00016430458,0.00045815128,0.00000823202,0.0006800248],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99912924,0.0005513609,0.000041884585,0.00013924325,0.00008133179,0.000056846085],"domain_scores_gemma":[0.9983581,0.0012071898,0.0001426336,0.00009264623,0.00015020365,0.00004935984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010415243,0.0004666772,0.0003609665,0.0013453248,0.000283711,0.0006451913,0.00033907706,0.00034981675,0.0028851028],"category_scores_gemma":[0.003582399,0.00023117603,0.00096189545,0.0017571243,0.00021816861,0.0003903111,0.0004817868,0.0005233088,0.00027438978],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010296135,0.00046757876,0.9453999,0.00012278657,0.0003768221,0.00014201418,0.0014138162,0.012189486,0.0011494716,0.0018649325,0.0005246313,0.036245596],"study_design_scores_gemma":[0.000020107072,0.000779634,0.78463656,0.00007934616,0.00036482146,0.0001816638,0.0036956426,0.20495789,0.0011238117,0.0020882124,0.0020425175,0.000029825036],"about_ca_topic_score_codex":0.011178684,"about_ca_topic_score_gemma":0.009079278,"teacher_disagreement_score":0.011178684,"about_ca_system_score_codex":0.00035503833,"about_ca_system_score_gemma":0.000726991,"threshold_uncertainty_score":0.022227228},"labels":[],"label_agreement":null},{"id":"W2991568540","doi":"10.1016/j.procs.2019.11.079","title":"COMPETENCY QUESTIONS FOR BIOMEDICAL ONTOLOGY REUSE","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Reuse; Ontology; Interoperability; Domain (mathematical analysis); Scope (computer science); Process (computing); Ontology engineering; Upper ontology; Process ontology; Open Biomedical Ontologies; Software engineering; Data science; Semantics (computer science); Semantic interoperability; Knowledge management; Domain knowledge; World Wide Web; Suggested Upper Merged Ontology; Programming language","score_opus":0.010720130646637476,"score_gpt":0.2784180533142503,"score_spread":0.26769792266761283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991568540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037543487,0.000924271,0.9241932,0.016260812,0.0001703449,0.0005322301,0.00025874216,0.00028366118,0.019833224],"genre_scores_gemma":[0.4673743,0.00057303195,0.52444875,0.0020763373,0.0002409663,0.0006883604,0.0007047147,0.00015775867,0.0037357914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9592085,0.025154782,0.0042108498,0.0037889034,0.0063248854,0.00131219],"domain_scores_gemma":[0.8889947,0.07468751,0.0061964244,0.012133802,0.015674016,0.0023135582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034131616,0.0010166096,0.00089053944,0.0055547194,0.003347492,0.0049391384,0.0022383374,0.004363256,0.0029287348],"category_scores_gemma":[0.13604872,0.00074044464,0.0024063922,0.0024919666,0.0122719975,0.018833993,0.010508981,0.0046036337,0.0006402102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052276293,0.00013516059,0.0036547664,0.00032340857,0.00006875602,0.0005666413,0.0074601998,0.005956188,0.0013480132,0.89852136,0.0030040143,0.07890918],"study_design_scores_gemma":[0.000028876702,0.000041915304,0.0012311349,0.00034932565,0.00004537587,0.00050556177,0.0032642488,0.02580732,0.0021584474,0.9342074,0.032281365,0.000079019424],"about_ca_topic_score_codex":0.0097543765,"about_ca_topic_score_gemma":0.00400629,"teacher_disagreement_score":0.034131616,"about_ca_system_score_codex":0.004244849,"about_ca_system_score_gemma":0.0061675655,"threshold_uncertainty_score":0.18050742},"labels":[],"label_agreement":null},{"id":"W2997403877","doi":"10.1016/j.procs.2019.12.055","title":"Examination of the energy trading status of China and India and the prospect for cooperation","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Global Energy Security and Policy","field":"Energy","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"China; Energy security; Geopolitics; Diversification (marketing strategy); International trade; Politics; Business; Sanctions; Position (finance); Economy; Development economics; Economics; Political science; Renewable energy; Finance","score_opus":0.00585499531803204,"score_gpt":0.2098151180865152,"score_spread":0.20396012276848316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997403877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9772972,0.00038775275,0.00006890112,0.00075335044,0.0000062835024,0.000004590721,0.000099350924,0.000002778935,0.021379812],"genre_scores_gemma":[0.9985952,0.00018076769,0.000024106796,0.000031296637,0.00000261791,0.000001127593,0.000050779596,7.947307e-7,0.001113256],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997657,0.000044806446,0.000012993489,0.000021341604,0.00006132877,0.000093782335],"domain_scores_gemma":[0.999106,0.00019137889,0.00027180222,0.000043667267,0.00018564209,0.00020144071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051000924,0.00008271915,0.00009364217,0.0012720671,0.0007890433,0.0017032771,0.00035920425,0.00028709677,0.0029596922],"category_scores_gemma":[0.0008927556,0.0000717451,0.00015378073,0.002763854,0.0008153528,0.0011094692,0.00075135037,0.00038115945,0.00016273408],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001989006,0.000058384285,0.9171239,0.000060622973,0.000051894094,0.0014945057,0.008004115,0.00070599996,0.0013991419,0.03468909,0.0014901053,0.034723315],"study_design_scores_gemma":[0.000004925388,0.00003997225,0.974035,0.000032751745,0.000018737226,0.00029376918,0.014757808,0.00097537664,0.00030687053,0.001495114,0.008027133,0.000012494426],"about_ca_topic_score_codex":0.031218382,"about_ca_topic_score_gemma":0.05520423,"teacher_disagreement_score":0.031218382,"about_ca_system_score_codex":0.0013524946,"about_ca_system_score_gemma":0.0017173035,"threshold_uncertainty_score":0.06207335},"labels":[],"label_agreement":null},{"id":"W3004010991","doi":"10.1016/j.procs.2019.12.154","title":"Question Answering System to Support University Students’ Orientation, Recruitment and Retention","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Computer science; Focus (optics); Work (physics); Knowledge management; Data science","score_opus":0.01985168293819773,"score_gpt":0.2855224578674839,"score_spread":0.2656707749292862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004010991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11335047,0.0010658776,0.71279275,0.005253303,0.00056616485,0.0035066535,0.01838811,0.12917544,0.015901268],"genre_scores_gemma":[0.3640817,0.00061644,0.5765024,0.0023630268,0.00033920683,0.0020484033,0.037481926,0.0011746068,0.015392349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974492,0.0008942534,0.0004272502,0.00057442597,0.0004935676,0.00016133029],"domain_scores_gemma":[0.9919887,0.004041564,0.00068896083,0.0008691549,0.0019544014,0.00045717348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061131753,0.0007352522,0.00087025296,0.0032711988,0.001213455,0.0019501435,0.0016703136,0.0017751228,0.0069514113],"category_scores_gemma":[0.011259074,0.000307519,0.00089958485,0.0015495354,0.00033761747,0.0036656307,0.0019181272,0.0011952036,0.0046501216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018712064,0.0026091794,0.033733252,0.0017393044,0.00027672833,0.0010606138,0.004157977,0.004632003,0.06320262,0.02002329,0.10586641,0.7608274],"study_design_scores_gemma":[0.00042834683,0.0014814421,0.06446514,0.0006798752,0.00058107235,0.0016048155,0.0036395662,0.36300245,0.10124463,0.049877778,0.41261247,0.00038236016],"about_ca_topic_score_codex":0.0032143001,"about_ca_topic_score_gemma":0.0031362681,"teacher_disagreement_score":0.0069514113,"about_ca_system_score_codex":0.0010729751,"about_ca_system_score_gemma":0.0018431487,"threshold_uncertainty_score":0.032329917},"labels":[],"label_agreement":null},{"id":"W3016857857","doi":"10.1016/j.procs.2020.03.084","title":"Planning for Connected, Autonomous and Shared Mobility: A Synopsis of Practitioners’ Perspectives","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Standardization; Equity (law); Citizen journalism; Plan (archaeology); Service (business); Set (abstract data type); Knowledge management; Process management; Engineering management; Business; Marketing; Political science; World Wide Web","score_opus":0.025353460659327434,"score_gpt":0.25345690209277605,"score_spread":0.22810344143344863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016857857","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10191179,0.21571888,0.073266186,0.49261343,0.0065489262,0.00037104986,0.0002114609,0.00013650832,0.109221734],"genre_scores_gemma":[0.7261279,0.18797839,0.028035698,0.044589303,0.0017914723,0.0006026994,0.00013864135,0.00020289134,0.010532909],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9738724,0.01864265,0.0011970057,0.0014648645,0.002950807,0.0018722614],"domain_scores_gemma":[0.9659422,0.02711507,0.00087721716,0.0005929093,0.0036415753,0.0018310205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0343037,0.0010265406,0.00092620484,0.0043810927,0.009317229,0.0112813115,0.003170304,0.009351388,0.0030290931],"category_scores_gemma":[0.022385033,0.00093453645,0.000676436,0.0066571464,0.018433731,0.017465448,0.010932429,0.012766121,0.0005339058],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005148947,0.00008552652,0.0011108852,0.0024992712,0.000017780189,0.0018065148,0.7088898,0.0009940405,0.00069049187,0.19409445,0.019254584,0.07050519],"study_design_scores_gemma":[0.000009944894,0.00008288666,0.00059459906,0.004658879,0.000009995899,0.0010390384,0.55511683,0.0005142034,0.00023899865,0.031076437,0.40660903,0.000049145026],"about_ca_topic_score_codex":0.0081300745,"about_ca_topic_score_gemma":0.011133016,"teacher_disagreement_score":0.0343037,"about_ca_system_score_codex":0.010741658,"about_ca_system_score_gemma":0.0141212465,"threshold_uncertainty_score":0.18141747},"labels":[],"label_agreement":null},{"id":"W3033212464","doi":"10.1016/j.procs.2020.04.165","title":"Comparative Analysis of Convolution Neural Network Models for Continuous Indian Sign Language Classification","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Science and Engineering Research Board","keywords":"Computer science; Classifier (UML); Convolutional neural network; Artificial intelligence; Sign language; Sign (mathematics); Pattern recognition (psychology); Speech recognition; Natural language processing; Mathematics","score_opus":0.05605353777993627,"score_gpt":0.288091800585051,"score_spread":0.23203826280511475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033212464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7658236,0.00999727,0.20773388,0.0010003163,0.0003401776,0.00009144647,0.0005744517,0.0018238566,0.012614975],"genre_scores_gemma":[0.97198874,0.0013846246,0.022558795,0.00007807034,0.00003107687,0.000041947278,0.0005790325,0.0000509194,0.0032867277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954456,0.000099215846,0.000049283142,0.000089920955,0.00012711872,0.00008985623],"domain_scores_gemma":[0.9987835,0.00060602004,0.00007386744,0.00008946106,0.00040024362,0.000046842357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001664025,0.0009155937,0.00066353515,0.00081119704,0.00035252393,0.0008221113,0.00084716524,0.0008033641,0.0015622616],"category_scores_gemma":[0.003382986,0.00019896332,0.00063121004,0.0007089596,0.0002801773,0.001074534,0.00045629742,0.00081858155,0.0004188057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010802367,0.00025978932,0.0100866845,0.00024912815,0.00025496943,0.00017268027,0.00009358794,0.64921147,0.006859563,0.00336478,0.0025142487,0.32585287],"study_design_scores_gemma":[0.0000042359466,0.000068319736,0.0013044549,0.000012648492,0.000027085027,0.00002064409,0.000017234215,0.9965239,0.0014493328,0.00033089626,0.00023318903,0.0000080305745],"about_ca_topic_score_codex":0.02576473,"about_ca_topic_score_gemma":0.015934428,"teacher_disagreement_score":0.02576473,"about_ca_system_score_codex":0.0014130514,"about_ca_system_score_gemma":0.0011331846,"threshold_uncertainty_score":0.051229537},"labels":[],"label_agreement":null},{"id":"W3033735869","doi":"10.1016/j.procs.2020.04.304","title":"CoDeCoVe: A Novel System Level Co-Design &amp; Co-Verification Framework","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Correctness; Electronic system-level design and verification; Abstraction; High-level synthesis; Embedded system; Abstraction layer; Computation; System on a chip; Software; Programming language","score_opus":0.13334607221292663,"score_gpt":0.3229659653026943,"score_spread":0.18961989308976765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033735869","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010533833,0.00012287032,0.99630773,0.000053850395,0.000021058215,0.00009240166,0.000033660588,0.001343541,0.0009715649],"genre_scores_gemma":[0.11181218,0.0003454422,0.8837762,0.00017408948,0.000040277228,0.0005028451,0.00030136385,0.0006657408,0.0023819993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930034,0.0019872636,0.0003476986,0.0007917354,0.003326996,0.0005428725],"domain_scores_gemma":[0.99508286,0.0019178407,0.0004490177,0.0014868949,0.000930082,0.0001333306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005131141,0.0015134915,0.0012622698,0.0018680886,0.00091711385,0.002246421,0.0030374916,0.0016943589,0.0038792286],"category_scores_gemma":[0.009747604,0.0010448194,0.0024706293,0.0007216419,0.002223609,0.0026783694,0.0030507194,0.003257202,0.0010542605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002876277,0.00017332371,0.0019799685,0.0010314947,0.00031186594,0.0010681642,0.00053919683,0.3739083,0.03015427,0.28427407,0.0053581647,0.3009135],"study_design_scores_gemma":[0.00007459894,0.00022536963,0.00028534094,0.0001684398,0.000098503,0.0006550857,0.00004322461,0.8753457,0.030094927,0.053588968,0.039346427,0.000073362025],"about_ca_topic_score_codex":0.004733246,"about_ca_topic_score_gemma":0.005015457,"teacher_disagreement_score":0.005131141,"about_ca_system_score_codex":0.001332664,"about_ca_system_score_gemma":0.0050585344,"threshold_uncertainty_score":0.027136385},"labels":[],"label_agreement":null},{"id":"W3034073597","doi":"10.1016/j.procs.2020.04.159","title":"Green Networking: A Simulation of Energy Efficient Methods","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Virtualization; Green computing; Software-defined networking; Carbon footprint; Efficient energy use; Enhanced Data Rates for GSM Evolution; Distributed computing; Energy consumption; Telecommunications; Computer network; Cloud computing; Greenhouse gas; Operating system","score_opus":0.03582050960057844,"score_gpt":0.2941142548881957,"score_spread":0.2582937452876173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034073597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2201226,0.0015209519,0.6842774,0.0022143272,0.000467301,0.0003852356,0.0018284507,0.0011065224,0.088077195],"genre_scores_gemma":[0.8510269,0.00081570534,0.13075723,0.00031217944,0.00008066147,0.00064613076,0.00082783407,0.0003004171,0.0152330175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996526,0.00012693185,0.000017087494,0.00003780751,0.00009434132,0.00007120197],"domain_scores_gemma":[0.9983615,0.0011831025,0.00010432468,0.0000890432,0.00019558528,0.000066360306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006684446,0.0005959646,0.0007808808,0.00060233206,0.0008104908,0.0010280674,0.0013213394,0.0018715229,0.006262755],"category_scores_gemma":[0.0027865744,0.0003798867,0.0009336865,0.00081495935,0.0008654914,0.0008501893,0.00095188885,0.0010057118,0.00039944646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038801798,0.000039183367,0.0006261556,0.000034253215,0.000010615584,0.000037340993,0.000042324475,0.9762656,0.00037368797,0.019167533,0.0005890987,0.0027755282],"study_design_scores_gemma":[0.000009015781,0.0000068050017,0.000056177905,0.000006458866,0.0000021293006,0.000005377299,0.000008439727,0.9966918,0.00014580863,0.0022547725,0.00081031735,0.0000027828132],"about_ca_topic_score_codex":0.01868046,"about_ca_topic_score_gemma":0.008417569,"teacher_disagreement_score":0.01868046,"about_ca_system_score_codex":0.0011180275,"about_ca_system_score_gemma":0.0014210965,"threshold_uncertainty_score":0.03714347},"labels":[],"label_agreement":null},{"id":"W3090551381","doi":"10.1016/j.procs.2020.09.328","title":"Optimization of Spectrum Utilization Parameters in Cognitive Radio Using Genetic Algorithm","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Université du Québec à Chicoutimi","keywords":"Cognitive radio; Computer science; Spectrum management; Wireless; Wireless network; Genetic algorithm; Interference (communication); Transmission (telecommunications); Channel (broadcasting); Computer network; Optimization problem; Radio spectrum; Frequency allocation; Telecommunications; Mathematical optimization; Algorithm","score_opus":0.04418815036800695,"score_gpt":0.26420287145130117,"score_spread":0.22001472108329423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090551381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2001312,0.0010509184,0.7893933,0.000333921,0.00006518412,0.00013355602,0.000042616783,0.00030115002,0.008548075],"genre_scores_gemma":[0.9171142,0.00029977693,0.08127498,0.00009178218,0.000013644507,0.00012907873,0.00003733509,0.000025405163,0.0010136749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996444,0.00012524673,0.000013929091,0.000060191207,0.00007942156,0.000076894525],"domain_scores_gemma":[0.99943346,0.0003638735,0.0000733075,0.000017420944,0.000090210626,0.00002179781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008205856,0.00073942606,0.00075316365,0.0010072096,0.000405516,0.0009195164,0.000793554,0.001078639,0.00072675553],"category_scores_gemma":[0.0020805844,0.000343044,0.0005755474,0.0006858736,0.0005867732,0.0005217104,0.0004995003,0.00051831704,0.0000972075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028587216,0.000046100417,0.0005178609,0.000024772566,0.00002666284,0.000031464,0.000030391519,0.9839467,0.0011527461,0.0012689331,0.00017171726,0.0127539905],"study_design_scores_gemma":[0.000010609743,0.000026257367,0.00011759109,0.0000055671753,0.0000089618725,0.000008937283,0.000012132781,0.9988102,0.00033918867,0.00056280073,0.00009411423,0.0000037611921],"about_ca_topic_score_codex":0.0071677067,"about_ca_topic_score_gemma":0.005407585,"teacher_disagreement_score":0.0071677067,"about_ca_system_score_codex":0.0008314664,"about_ca_system_score_gemma":0.0013413581,"threshold_uncertainty_score":0.014251947},"labels":[],"label_agreement":null},{"id":"W3090702224","doi":"10.1016/j.procs.2020.09.202","title":"Predictive analytics on open big data for supporting smart transportation services","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Big data; Computer science; Open data; Data science; Analytics; Predictive analytics; Public transport; Open government; Government (linguistics); Computer security; World Wide Web; Data mining; Transport engineering; Engineering","score_opus":0.060751525410938416,"score_gpt":0.28085117084307,"score_spread":0.22009964543213156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090702224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20197561,0.007885542,0.69355595,0.022487374,0.0016304796,0.00084761553,0.032272756,0.016557962,0.022786744],"genre_scores_gemma":[0.84923637,0.0035157476,0.12002696,0.0011665095,0.00066372304,0.00023702535,0.023246864,0.00036795082,0.0015389088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809676,0.00047480757,0.00011904686,0.00032597122,0.00080345606,0.00017988506],"domain_scores_gemma":[0.99287635,0.0037152378,0.0005640346,0.001210979,0.0013075884,0.00032581927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023075864,0.0016067023,0.0009351083,0.00392955,0.00084303383,0.0030270133,0.0014394494,0.00091823866,0.0016059437],"category_scores_gemma":[0.016038513,0.00040802214,0.00092674105,0.0056465575,0.00095882517,0.0045685247,0.002561961,0.0024935473,0.0008256528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005877843,0.00063537544,0.056011792,0.00081142853,0.00047862326,0.0015966218,0.0011599801,0.4670336,0.0049645524,0.06711387,0.06776693,0.33183944],"study_design_scores_gemma":[0.000015460168,0.000038477294,0.0045369277,0.0001202393,0.000037051097,0.000080748796,0.0006343809,0.90691936,0.0013185979,0.07609275,0.010173024,0.000032871943],"about_ca_topic_score_codex":0.026057009,"about_ca_topic_score_gemma":0.026216976,"teacher_disagreement_score":0.026057009,"about_ca_system_score_codex":0.0015378819,"about_ca_system_score_gemma":0.0013322484,"threshold_uncertainty_score":0.05181068},"labels":[],"label_agreement":null},{"id":"W3091468429","doi":"10.1016/j.procs.2020.09.033","title":"Automatic Anode Rod Inspection in Aluminum Smelters using Deep-Learning Techniques: A Case Study","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Bell (Canada)","funders":"","keywords":"Anode; Computer science; Convolutional neural network; Deep learning; Artificial intelligence; Task (project management); Process (computing); Machine learning","score_opus":0.028578779481107822,"score_gpt":0.26057816769458236,"score_spread":0.23199938821347454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091468429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9334777,0.0010312104,0.058510985,0.0005834755,0.0000522532,0.00013505644,0.0007744659,0.0023814826,0.0030532961],"genre_scores_gemma":[0.9769744,0.00016789552,0.019857505,0.00004906219,0.000007719703,0.000016365537,0.00033047082,0.000048558482,0.0025481328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996542,0.000046853598,0.000021771935,0.00007934928,0.00014563854,0.000052184543],"domain_scores_gemma":[0.99938774,0.00021292469,0.000085586646,0.00007496669,0.00019216763,0.00004669985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005006128,0.00063635095,0.00048738325,0.0007697362,0.00035921676,0.00052907283,0.0008734263,0.001383292,0.0010758841],"category_scores_gemma":[0.00087259494,0.0002420643,0.0004905358,0.0005519126,0.00039746767,0.00049227715,0.00037343873,0.00040118108,0.0004233196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025625813,0.001108216,0.07703487,0.0013936672,0.00018654905,0.023235915,0.0008191376,0.32903373,0.1661357,0.0018560153,0.012444996,0.38418862],"study_design_scores_gemma":[0.00006260143,0.00057042675,0.019978162,0.000053102707,0.00005374065,0.0026729372,0.00041421593,0.8646953,0.105881914,0.0010355621,0.004530117,0.00005195065],"about_ca_topic_score_codex":0.005543812,"about_ca_topic_score_gemma":0.010438878,"teacher_disagreement_score":0.005543812,"about_ca_system_score_codex":0.00083226344,"about_ca_system_score_gemma":0.00051173934,"threshold_uncertainty_score":0.011023045},"labels":[],"label_agreement":null},{"id":"W3098021985","doi":"10.1016/j.procs.2020.10.024","title":"Deriving Access Control Models based on Generic and Dynamic Metamodel Architecture: Industrial Use Case","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Access Control and Trust","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Metamodeling; Architecture; Cloud computing; Distributed computing; Domain (mathematical analysis); Access control; Software engineering; Computer security","score_opus":0.09850395186740474,"score_gpt":0.30115007964918156,"score_spread":0.20264612778177682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098021985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06533301,0.00021350091,0.92202425,0.00043816277,0.0000402975,0.0005029059,0.00052857836,0.0014737366,0.009445633],"genre_scores_gemma":[0.32630655,0.00043193088,0.66772956,0.00011544858,0.000017719241,0.0003759775,0.0013860192,0.00023872685,0.0033980233],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982626,0.00051714125,0.00019726106,0.00027301922,0.0005783028,0.00017156015],"domain_scores_gemma":[0.99804616,0.0007276064,0.0001468652,0.0006728717,0.00034542222,0.00006119871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024789646,0.000630728,0.00038492715,0.0014534454,0.00073676795,0.002004242,0.0013390777,0.0015883439,0.0017381469],"category_scores_gemma":[0.004596893,0.00043547986,0.0015833902,0.0010787721,0.0012308805,0.002608971,0.001978113,0.001726584,0.0003419508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021028424,0.0006258183,0.009379522,0.0007069434,0.00014693462,0.0037402355,0.0026146236,0.34394845,0.029882628,0.44618934,0.0038563786,0.15869884],"study_design_scores_gemma":[0.000100052646,0.0002363998,0.0016124118,0.0002847838,0.00014760856,0.0014592344,0.0007779152,0.7871572,0.029547364,0.10509201,0.07350361,0.00008140501],"about_ca_topic_score_codex":0.0054625534,"about_ca_topic_score_gemma":0.0068154964,"teacher_disagreement_score":0.0054625534,"about_ca_system_score_codex":0.0012820069,"about_ca_system_score_gemma":0.001949869,"threshold_uncertainty_score":0.01311022},"labels":[],"label_agreement":null},{"id":"W3099413269","doi":"10.1016/j.procs.2020.10.056","title":"Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Coronavirus disease 2019 (COVID-19); Social media; Pandemic; Set (abstract data type); Sentiment analysis; Data set; Data science; Order (exchange); Dynamics (music); Social network (sociolinguistics); Social network analysis; Artificial intelligence; World Wide Web; Psychology","score_opus":0.19782410328593356,"score_gpt":0.3946554136890804,"score_spread":0.19683131040314686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099413269","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98306644,0.00022892925,0.003070344,0.0006992867,0.00009797193,0.00011834024,0.008819153,0.00019367148,0.0037058648],"genre_scores_gemma":[0.9865732,0.00025347815,0.0040440965,0.00009343056,0.00010434349,0.000121385034,0.007713952,0.000024514127,0.0010716458],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99947315,0.00012818759,0.00005323176,0.00010997369,0.00015635094,0.0000790983],"domain_scores_gemma":[0.9987488,0.0004502039,0.00027261555,0.00009719518,0.00029146447,0.00013969302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005844914,0.00033428465,0.00033496704,0.0014197093,0.00047880475,0.0007631108,0.00031636452,0.0005733706,0.00072264834],"category_scores_gemma":[0.0027967973,0.00011468956,0.00022398883,0.001197773,0.00023609959,0.0011606026,0.0007482486,0.0005686136,0.0006275062],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009277341,0.0005016357,0.8382304,0.00059511367,0.00019620502,0.0015352885,0.005271704,0.009541036,0.027408952,0.0023071324,0.017842812,0.0956421],"study_design_scores_gemma":[0.000024891699,0.0003575376,0.8741698,0.00010814605,0.00009600436,0.0005452286,0.008895746,0.0848571,0.009480844,0.0018408856,0.019529006,0.00009481025],"about_ca_topic_score_codex":0.0036048598,"about_ca_topic_score_gemma":0.0051347446,"teacher_disagreement_score":0.0036048598,"about_ca_system_score_codex":0.00032901907,"about_ca_system_score_gemma":0.00023476554,"threshold_uncertainty_score":0.0071677566},"labels":[],"label_agreement":null},{"id":"W3099494793","doi":"10.1016/j.procs.2020.10.033","title":"IoT mobile device Data Offloading by Small-Base Station Using Intelligent Software Defined Network","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Computer network; Base station; Server; Mobile device; Quality of service; Cellular network; Distributed computing; Operating system","score_opus":0.08066350520672587,"score_gpt":0.2801366555202493,"score_spread":0.19947315031352342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099494793","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043669444,0.0005787118,0.9492549,0.0001749682,0.00013256886,0.00018810396,0.000049628437,0.000979285,0.0049722856],"genre_scores_gemma":[0.8982406,0.0003902964,0.098045364,0.00015384423,0.00008866571,0.00017222983,0.00015969465,0.000037752383,0.002711515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955016,0.00008254034,0.000029182422,0.00010576377,0.0001615559,0.00007076112],"domain_scores_gemma":[0.9997024,0.00007359134,0.000039806462,0.000056153294,0.0000904364,0.000037523314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030436713,0.0007333992,0.00066252885,0.00043570198,0.00052454136,0.0008263801,0.0010586012,0.0004083953,0.0010611604],"category_scores_gemma":[0.0006547394,0.00010633382,0.00037278357,0.0005661074,0.00031082556,0.0007680184,0.0007570332,0.00048970117,0.00026277418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049907743,0.00065154757,0.0036043988,0.00032846179,0.00011656142,0.00048825098,0.00028445444,0.24493156,0.105745114,0.01662338,0.0053519327,0.6213753],"study_design_scores_gemma":[0.000027685,0.00019792609,0.0006572103,0.00001242267,0.000022199914,0.00012169615,0.000046771158,0.98255265,0.009440945,0.003385958,0.003519107,0.000015428757],"about_ca_topic_score_codex":0.0023116725,"about_ca_topic_score_gemma":0.002379426,"teacher_disagreement_score":0.0023116725,"about_ca_system_score_codex":0.00052157865,"about_ca_system_score_gemma":0.0005707102,"threshold_uncertainty_score":0.004596412},"labels":[],"label_agreement":null},{"id":"W3100371214","doi":"10.1016/j.procs.2020.10.070","title":"An Effective and Efficient Technique for Supporting Privacy-Preserving Keyword-Based Search over Encrypted Data in Clouds","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche; University of Manitoba","keywords":"Computer science; Encryption; Plaintext; Cloud computing; Keyword search; Information retrieval; Matching (statistics); Database; Computer security","score_opus":0.03024980591145811,"score_gpt":0.31985418431925433,"score_spread":0.28960437840779624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3100371214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06251638,0.0023931493,0.926937,0.0004133225,0.00013035267,0.0004082433,0.0005946692,0.0032067595,0.003400122],"genre_scores_gemma":[0.5139424,0.0015010453,0.4807621,0.00012758147,0.00011024906,0.00014984778,0.000710832,0.000101155754,0.002594766],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967437,0.0004673568,0.00034713408,0.00026382288,0.0018679702,0.00031007882],"domain_scores_gemma":[0.99675757,0.0007242483,0.00047499378,0.0013145464,0.00062797376,0.00010078712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012067708,0.0005394231,0.0010308214,0.0020196289,0.0011381241,0.0013346722,0.0010015512,0.00068894186,0.0013510268],"category_scores_gemma":[0.004249782,0.00033146626,0.00073452,0.0028292346,0.00064841716,0.0040308447,0.0016479746,0.0006733346,0.0010792135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016437917,0.00051309395,0.006083651,0.0010132198,0.0002849989,0.0016432933,0.0010446683,0.03376065,0.23451301,0.04954006,0.01507796,0.65488154],"study_design_scores_gemma":[0.00022566252,0.00070955156,0.003866917,0.0001373684,0.00026758682,0.008848009,0.0010480144,0.57307225,0.3405739,0.029783405,0.041262034,0.00020526965],"about_ca_topic_score_codex":0.0032518066,"about_ca_topic_score_gemma":0.0027423368,"teacher_disagreement_score":0.0032518066,"about_ca_system_score_codex":0.0006795805,"about_ca_system_score_gemma":0.00228184,"threshold_uncertainty_score":0.0064657927},"labels":[],"label_agreement":null},{"id":"W3111350020","doi":"10.1016/j.procs.2020.11.041","title":"Network attacks classification using Long Short-term memory based neural networks in Software-Defined Networks","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Russian Foundation for Basic Research","keywords":"Computer science; Term (time); Artificial neural network; Software; Long short term memory; Artificial intelligence; Data mining; Computer network; Recurrent neural network; Operating system","score_opus":0.04130806337973569,"score_gpt":0.25830613829470156,"score_spread":0.21699807491496587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111350020","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8141612,0.007551808,0.15547264,0.0014355797,0.0009291216,0.0003339254,0.008571719,0.004756413,0.006787581],"genre_scores_gemma":[0.95529515,0.0010843441,0.032815088,0.00017285606,0.00010061521,0.0001732031,0.007608047,0.000039830065,0.0027109534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956864,0.00006911254,0.000055204677,0.00012332281,0.00008654224,0.00009724101],"domain_scores_gemma":[0.9994838,0.00020326152,0.000059904054,0.00005768008,0.0001660742,0.000029232626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066358317,0.0013107917,0.0006895833,0.0012886359,0.00035570827,0.0007805571,0.0009582384,0.0010243322,0.0009608458],"category_scores_gemma":[0.0017812821,0.0002352136,0.0008519312,0.0009619192,0.00022446131,0.0010245333,0.0006077683,0.0012803558,0.00041384736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011792225,0.0011582398,0.020480532,0.0005052602,0.00042744793,0.00045776865,0.000116046154,0.44296664,0.009962157,0.0014507576,0.013570997,0.50772494],"study_design_scores_gemma":[0.000011267349,0.00014055063,0.002965238,0.000026128655,0.0000430725,0.00004495478,0.000032158816,0.9905095,0.004705954,0.00074532104,0.0007609407,0.000014999703],"about_ca_topic_score_codex":0.012864243,"about_ca_topic_score_gemma":0.012039336,"teacher_disagreement_score":0.012864243,"about_ca_system_score_codex":0.0010602343,"about_ca_system_score_gemma":0.0005751018,"threshold_uncertainty_score":0.025578737},"labels":[],"label_agreement":null},{"id":"W3129431246","doi":"10.1016/j.procs.2021.01.264","title":"Simulation of ground bearing pressure profile under hydraulic crane outrigger mats for the verification of 16-point combined loading","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"BIM and Construction Integration","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Outrigger; Finite element method; Computer science; Modular design; Structural engineering; Point (geometry); Bearing (navigation); Hydraulic pressure; Marine engineering; Mechanical engineering; Mathematics; Engineering; Artificial intelligence; Geometry","score_opus":0.016421453650551754,"score_gpt":0.23381779564716576,"score_spread":0.217396341996614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129431246","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98726857,0.000028752802,0.0077199703,0.000046619156,0.000016359088,0.00003219467,0.00022999817,0.00019347186,0.0044640386],"genre_scores_gemma":[0.9973563,0.000019336934,0.0017641935,0.0000033202427,9.301781e-7,0.00001617141,0.00006275537,0.000009686062,0.0007673074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998368,0.000023784873,0.000008175193,0.000021714199,0.000058704536,0.000050746625],"domain_scores_gemma":[0.99960476,0.00017322953,0.000051489496,0.00004236461,0.00007895616,0.000049212464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026661306,0.00048854237,0.00045070532,0.00050766586,0.00044761505,0.0005006216,0.00087903935,0.0010070364,0.0033036405],"category_scores_gemma":[0.0006445908,0.00026593904,0.00040840506,0.00041795615,0.00065465603,0.0004289747,0.00043288743,0.00041271705,0.0002621928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021411692,0.000107570326,0.0055188127,0.000097565215,0.000014548234,0.0006338759,0.00021301728,0.97159284,0.01446282,0.0008814018,0.00040426524,0.0058592497],"study_design_scores_gemma":[0.00001254941,0.00008710718,0.002480835,0.0000062438226,0.0000051028337,0.00002790314,0.00012058577,0.9936481,0.003322177,0.00008772509,0.000193996,0.0000076450815],"about_ca_topic_score_codex":0.009373651,"about_ca_topic_score_gemma":0.009766129,"teacher_disagreement_score":0.009373651,"about_ca_system_score_codex":0.00039378126,"about_ca_system_score_gemma":0.00064407376,"threshold_uncertainty_score":0.018638194},"labels":[],"label_agreement":null},{"id":"W3160463998","doi":"10.1016/j.procs.2021.03.024","title":"Configuration and Governance of Dynamic Secure SDN","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Research Council Canada","keywords":"Forwarding plane; Computer science; Routing control plane; Software-defined networking; Plane (geometry); Distributed computing; Segmentation; Computer network; Topology (electrical circuits); Artificial intelligence","score_opus":0.005947072007646617,"score_gpt":0.2150725801687481,"score_spread":0.20912550816110148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160463998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44625306,0.00031991533,0.5275421,0.00048496292,0.000074031705,0.0002375931,0.00012895482,0.0016105189,0.023348747],"genre_scores_gemma":[0.9820279,0.00007757032,0.016961413,0.000020077383,0.00000785564,0.000040143295,0.00007547613,0.000022403578,0.0007672019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986731,0.00042886223,0.000098230514,0.00021666814,0.00037776912,0.00020544777],"domain_scores_gemma":[0.99853015,0.00024547614,0.00023066025,0.00047740416,0.0003530514,0.00016320145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017297833,0.0003261356,0.00024393329,0.0005144704,0.00063826767,0.002078095,0.0007641584,0.0004875324,0.0012357597],"category_scores_gemma":[0.0027602213,0.00017179763,0.00019497723,0.00043522814,0.0010890819,0.0019671987,0.0015763749,0.0004941953,0.00020190254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041569036,0.000152626,0.015422325,0.00011358686,0.000061525665,0.0004213548,0.00066826865,0.62156606,0.018031243,0.2031585,0.0019000238,0.1380888],"study_design_scores_gemma":[0.000040799783,0.0001795069,0.0029032042,0.00003920329,0.00003582497,0.00014395556,0.00036055385,0.90683496,0.015132995,0.062999375,0.011297759,0.00003180516],"about_ca_topic_score_codex":0.0017583208,"about_ca_topic_score_gemma":0.0010491897,"teacher_disagreement_score":0.002078095,"about_ca_system_score_codex":0.0012730458,"about_ca_system_score_gemma":0.0012444285,"threshold_uncertainty_score":0.009236693},"labels":[],"label_agreement":null},{"id":"W3162321211","doi":"10.1016/j.procs.2021.03.068","title":"Assessment of the Traffic Enforcement Strategies Impact on Emission Reduction and Air Quality","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Air quality index; Enforcement; Traffic congestion; Air pollution; Transport engineering; Computer science; Environmental economics; Quality (philosophy); Speed limit; Business; Risk analysis (engineering); Engineering","score_opus":0.016449095741650818,"score_gpt":0.30641102061516245,"score_spread":0.28996192487351163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162321211","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98828924,0.0006023536,0.0034677275,0.00014075491,0.000026268344,0.00023950479,0.00032962643,0.00003637037,0.0068681506],"genre_scores_gemma":[0.99685967,0.0003326673,0.0020382085,0.0000176724,0.0000056625045,0.00007337466,0.00018469288,0.000003987529,0.00048402505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99845636,0.00046978224,0.00008952357,0.00013804619,0.00062674645,0.00021954424],"domain_scores_gemma":[0.9971973,0.0013806829,0.0004167254,0.00008674182,0.0008142795,0.00010427359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018915918,0.0008242292,0.0003314473,0.0013521044,0.00032438236,0.00095114857,0.00048622803,0.0006729516,0.0013700599],"category_scores_gemma":[0.003239361,0.00015759456,0.00072756,0.0008547731,0.0002847581,0.00080043584,0.00048810017,0.0004326546,0.00013839021],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020717683,0.0044652508,0.28920478,0.0022553436,0.001257875,0.001293041,0.0006505094,0.42133543,0.029644666,0.0069236234,0.0025260854,0.23837166],"study_design_scores_gemma":[0.00018829343,0.0130759,0.44336408,0.00036196414,0.0018803083,0.00030433934,0.004668259,0.48185778,0.040796027,0.004279907,0.009050688,0.0001724605],"about_ca_topic_score_codex":0.009048413,"about_ca_topic_score_gemma":0.0075730323,"teacher_disagreement_score":0.009048413,"about_ca_system_score_codex":0.0012822927,"about_ca_system_score_gemma":0.0012090817,"threshold_uncertainty_score":0.017991483},"labels":[],"label_agreement":null},{"id":"W3172923844","doi":"10.1016/j.procs.2021.05.016","title":"Incorporating Time Delays in the Mathematical Modelling of the Human Immune Response in Viral Infections","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Computer science; Mathematical model; Immune system; Differential equation; Population; Mathematics; Immunology; Biology; Statistics","score_opus":0.1710442038546805,"score_gpt":0.3767747710778812,"score_spread":0.2057305672232007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172923844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03384821,0.011074509,0.93060255,0.0020145737,0.00074432255,0.00010945314,0.00041371564,0.0001589575,0.02103366],"genre_scores_gemma":[0.7996724,0.02564758,0.1363984,0.0005999739,0.001153077,0.00036220392,0.0004508139,0.00013569363,0.035579797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995726,0.00016535641,0.000038288068,0.000061055805,0.000109569955,0.000053148844],"domain_scores_gemma":[0.99930334,0.0003831429,0.00015157058,0.00003677191,0.000090505026,0.000034648936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074783876,0.0008311103,0.0006012904,0.0008475819,0.00038057234,0.0013412022,0.0010373099,0.0014308563,0.0020883956],"category_scores_gemma":[0.0025533936,0.00029190557,0.0011344608,0.00070729747,0.0007650476,0.0015307233,0.0011287127,0.0014216034,0.00069221406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005810317,0.00006113995,0.0020805364,0.00036574563,0.000066690816,0.00069026765,0.00033515654,0.60217685,0.00735449,0.36400393,0.0019876703,0.02081956],"study_design_scores_gemma":[0.000017427254,0.000088081375,0.0005807149,0.00008283765,0.000052766158,0.00036385117,0.00006803675,0.8678689,0.001112546,0.112196386,0.017525466,0.000043018128],"about_ca_topic_score_codex":0.0042294078,"about_ca_topic_score_gemma":0.002270662,"teacher_disagreement_score":0.0042294078,"about_ca_system_score_codex":0.00080023345,"about_ca_system_score_gemma":0.00086045737,"threshold_uncertainty_score":0.00840956},"labels":[],"label_agreement":null},{"id":"W3184525714","doi":"10.1016/j.procs.2021.06.009","title":"Dynamical properties of spiking neural networks with small world topologies","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Network topology; Small-world network; Computer science; Random graph; Spiking neural network; Topology (electrical circuits); Artificial neural network; Dissipative system; Graph; Complex network; Theoretical computer science; Mathematics; Artificial intelligence; Physics; Combinatorics","score_opus":0.025178865135923646,"score_gpt":0.20900812003514907,"score_spread":0.1838292548992254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184525714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95246166,0.00016507972,0.043317337,0.0001642352,0.000013500817,0.00002255203,0.000068022426,0.000067301225,0.0037202002],"genre_scores_gemma":[0.9976513,0.00005216479,0.0019869443,0.0000070340707,0.0000035501089,0.000010335806,0.00003058245,0.000008556379,0.0002495345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998747,0.000039648345,0.000011028896,0.000020745867,0.000040351566,0.00001338237],"domain_scores_gemma":[0.9986326,0.00077635073,0.00026005867,0.0001095444,0.00013048144,0.00009088498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042837768,0.00015825896,0.00018911636,0.00061184156,0.00021829679,0.00058224896,0.00030090768,0.0003199659,0.00072223763],"category_scores_gemma":[0.0035504303,0.00017241712,0.0002488284,0.0002512414,0.000530415,0.000865703,0.00028901038,0.000268721,0.00007023745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015739507,0.000080805905,0.012892936,0.00014023353,0.00010160804,0.0008552514,0.0004758634,0.7568071,0.10062089,0.109588616,0.0005289023,0.01775031],"study_design_scores_gemma":[0.000010690781,0.000057859113,0.005258615,0.000010706198,0.000010584808,0.00030158157,0.00008078808,0.95537966,0.0045535853,0.033960447,0.0003548447,0.0000206608],"about_ca_topic_score_codex":0.00041799343,"about_ca_topic_score_gemma":0.000496275,"teacher_disagreement_score":0.00072223763,"about_ca_system_score_codex":0.00033953186,"about_ca_system_score_gemma":0.00012418402,"threshold_uncertainty_score":0.0024635196},"labels":[],"label_agreement":null},{"id":"W3202143479","doi":"10.1016/j.procs.2021.08.123","title":"Inferring the Number and Order of Embedded Topics Across Documents","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Order (exchange); Information retrieval; Data science; Theoretical computer science","score_opus":0.01020790319764575,"score_gpt":0.3080445696120024,"score_spread":0.29783666641435663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202143479","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44844505,0.0013651852,0.54277575,0.00048097462,0.00007462788,0.00021370352,0.0023845136,0.0015530442,0.0027070285],"genre_scores_gemma":[0.67168355,0.00061745476,0.32282704,0.00003858257,0.00010594843,0.00011229394,0.0029239708,0.00015131987,0.0015399337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986663,0.00024351894,0.00013790886,0.00057145394,0.00025094565,0.00012990301],"domain_scores_gemma":[0.99131596,0.0053822864,0.00087210373,0.0008043151,0.0013838332,0.00024141949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015503723,0.0006956477,0.0007274318,0.0058546383,0.001017456,0.0022464017,0.000686525,0.0009908803,0.0009468817],"category_scores_gemma":[0.011244746,0.00061545573,0.00066514267,0.0034694492,0.00058956695,0.0027015551,0.0010699191,0.0013413853,0.0008337227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011330977,0.0004740901,0.12080553,0.00069709664,0.00033092685,0.00050103554,0.0032145195,0.08622718,0.055698406,0.018787248,0.0075985026,0.7045324],"study_design_scores_gemma":[0.0000690894,0.00012563176,0.052783553,0.00010694807,0.00019347979,0.00045775864,0.0017464539,0.8503007,0.03222925,0.053249948,0.008633458,0.00010368046],"about_ca_topic_score_codex":0.0061705913,"about_ca_topic_score_gemma":0.009291489,"teacher_disagreement_score":0.0061705913,"about_ca_system_score_codex":0.00086311175,"about_ca_system_score_gemma":0.0016178355,"threshold_uncertainty_score":0.012269378},"labels":[],"label_agreement":null},{"id":"W3202441023","doi":"10.1016/j.procs.2021.09.022","title":"UHF RFID Spiral-Loaded Dipole Tag Antenna Conception for Healthcare Applications","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"RFID technology advancements","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Université Mohammed V de Rabat","keywords":"Computer science; Robustness (evolution); STRIPS; Planar; Ultra high frequency; Radio-frequency identification; Dipole antenna; Antenna (radio); Acoustics; Telecommunications; Physics; Artificial intelligence","score_opus":0.01505992437155617,"score_gpt":0.26517954072229816,"score_spread":0.250119616350742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202441023","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24072805,0.0010479401,0.7493853,0.00046076067,0.00018882744,0.00008237846,0.00013513409,0.00096677034,0.0070047043],"genre_scores_gemma":[0.88957226,0.00032938493,0.10628585,0.0001477218,0.000030238007,0.000050917,0.00009821868,0.0000425133,0.0034428437],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998584,0.000031837048,0.0000068087484,0.00003807958,0.00004633615,0.000018575416],"domain_scores_gemma":[0.9998135,0.000038483355,0.00004004976,0.000044793458,0.000050250565,0.000012966085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001741467,0.00036071107,0.00022227073,0.00018484361,0.00009389405,0.00041795475,0.00039050277,0.00078603806,0.0008756442],"category_scores_gemma":[0.00030057822,0.00016019137,0.0003387872,0.00018517525,0.0003100336,0.00037431606,0.00024988185,0.00016801794,0.00069278793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121712816,0.000022624272,0.0011352747,0.0001460778,0.000018582426,0.00029581337,0.0001274496,0.0067962143,0.95703393,0.0037049316,0.00052830705,0.030069096],"study_design_scores_gemma":[0.000028747148,0.00087544014,0.0037260766,0.000028321312,0.000060148064,0.003077573,0.000158141,0.07144525,0.8928444,0.0022061185,0.025496295,0.000053534364],"about_ca_topic_score_codex":0.00008347932,"about_ca_topic_score_gemma":0.00010943969,"teacher_disagreement_score":0.0008756442,"about_ca_system_score_codex":0.00030733962,"about_ca_system_score_gemma":0.00018555346,"threshold_uncertainty_score":0.0029293299},"labels":[],"label_agreement":null},{"id":"W3203079188","doi":"10.1016/j.procs.2021.08.160","title":"Revenue recognition in achieving consensus on analysts’ forecasts for revenue, operating income and net earnings: the role of implementing IFRS 15. Evidence from Poland","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Revenue; Earnings; Net income; Quarter (Canadian coin); Revenue recognition; Business; Novelty; Finance; Accounting","score_opus":0.017492476352752653,"score_gpt":0.24074179350549396,"score_spread":0.22324931715274132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203079188","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.974102,0.00039198244,0.0013270222,0.0014086601,0.00004111364,0.000056907626,0.00009431482,0.000055641078,0.022522295],"genre_scores_gemma":[0.998882,0.00005374296,0.0003486227,0.00006810879,0.000010758651,0.000008261675,0.000042608466,0.000004211843,0.0005816483],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98835224,0.0040105046,0.0010890827,0.0012032402,0.0031637354,0.002181135],"domain_scores_gemma":[0.9127424,0.022527402,0.049328078,0.0047510355,0.007121451,0.0035296835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011200744,0.00026827303,0.00028864687,0.0005789139,0.0006447109,0.0033194371,0.0008603029,0.0009449782,0.0028948628],"category_scores_gemma":[0.05043943,0.00021354928,0.00057595706,0.00050268136,0.001447373,0.0021288039,0.0022048957,0.0016413571,0.00058545347],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017855634,0.0007856191,0.83197695,0.00034641277,0.00044062847,0.00042437753,0.0038154626,0.0052127126,0.0030618454,0.01774793,0.0035014858,0.13090107],"study_design_scores_gemma":[0.00009294808,0.0009137464,0.9722205,0.0001890684,0.00023868209,0.00012500401,0.0047416547,0.0038181199,0.0039026223,0.0044236337,0.009274302,0.00005989344],"about_ca_topic_score_codex":0.009575413,"about_ca_topic_score_gemma":0.0073339483,"teacher_disagreement_score":0.011200744,"about_ca_system_score_codex":0.0023218498,"about_ca_system_score_gemma":0.0033271255,"threshold_uncertainty_score":0.05923587},"labels":[],"label_agreement":null},{"id":"W3203269860","doi":"10.1016/j.procs.2021.09.019","title":"Call Admission Control Optimization in 5G in Downlink Single-Cell MISO System","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Telecommunications link; Computer network; Optimization problem; Mobile broadband; Beamforming; Cellular network; Power control; Latency (audio); Telecommunications; Power (physics); Wireless; Algorithm","score_opus":0.00661323708368825,"score_gpt":0.1940132497629637,"score_spread":0.18740001267927545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203269860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42621127,0.003336039,0.5511675,0.0019571756,0.0001824616,0.00014841127,0.00024917745,0.00037383282,0.016374083],"genre_scores_gemma":[0.9932668,0.00021657604,0.005282636,0.00006202156,0.000027962986,0.000026329539,0.000034228768,0.0000124802045,0.0010710408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990513,0.00033775857,0.000026406231,0.00011273263,0.00015603445,0.00031577024],"domain_scores_gemma":[0.9984836,0.0010101424,0.00018383932,0.000028394314,0.0002007058,0.00009336635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014460484,0.00095043745,0.00127255,0.00063046784,0.00059913285,0.001584897,0.00070218014,0.0010494221,0.0015040024],"category_scores_gemma":[0.0024691795,0.0003244467,0.0004445507,0.0007648881,0.0009831481,0.00069160416,0.0007814592,0.0009076807,0.00009395397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057045774,0.000021302041,0.0005907125,0.000029306122,0.000015593427,0.00008312461,0.000021318096,0.9931311,0.00055557804,0.0028586434,0.0003117472,0.0023245285],"study_design_scores_gemma":[0.0000032212322,0.000010119083,0.00014660471,0.0000022019015,0.0000035371568,0.0000057258976,0.00001262952,0.9991027,0.00009552469,0.00057607883,0.00003902149,0.0000026913222],"about_ca_topic_score_codex":0.018535621,"about_ca_topic_score_gemma":0.0076912967,"teacher_disagreement_score":0.018535621,"about_ca_system_score_codex":0.0021379446,"about_ca_system_score_gemma":0.0016436864,"threshold_uncertainty_score":0.03685546},"labels":[],"label_agreement":null},{"id":"W3203975895","doi":"10.1016/j.procs.2021.09.232","title":"MAC Protocols for Industrial Delay-Sensitive Applications in Industry 4.0: Exploring Challenges, Protocols, and Requirements","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Université du Québec à Chicoutimi","keywords":"Computer science; Industrial Internet; Software deployment; Reliability (semiconductor); Internet of Things; Quality of service; Protocol (science); Low latency (capital markets); Latency (audio); Computer network; Computer security; Telecommunications; Power (physics)","score_opus":0.18645798879441725,"score_gpt":0.33196768523619763,"score_spread":0.14550969644178038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203975895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015176699,0.06151244,0.8902611,0.0037010938,0.0015575107,0.0007336442,0.00014990073,0.0010013478,0.025906263],"genre_scores_gemma":[0.38836467,0.07443914,0.51019347,0.0022778125,0.002415252,0.0024256946,0.000712742,0.0005891305,0.018582013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99687505,0.0008531388,0.0003036945,0.00026108045,0.001402419,0.00030462912],"domain_scores_gemma":[0.9952518,0.0016853185,0.00050951896,0.00061132363,0.0017795454,0.00016241807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004648897,0.0012374871,0.0008884011,0.0019130671,0.0013765588,0.0037459012,0.0021821058,0.0014215512,0.0020699245],"category_scores_gemma":[0.008147212,0.00069020625,0.0007088556,0.0017586639,0.0011554813,0.0043826923,0.0016623882,0.0031937794,0.0011214633],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040077916,0.00033755106,0.0024088374,0.0028693231,0.00016552887,0.0007686528,0.0014247337,0.06427326,0.045672473,0.48606083,0.020330349,0.37528768],"study_design_scores_gemma":[0.000061975494,0.0009032004,0.0025279892,0.0018988781,0.00024861106,0.002038117,0.0013520161,0.43322694,0.033965193,0.1654003,0.3581556,0.00022113256],"about_ca_topic_score_codex":0.0013207688,"about_ca_topic_score_gemma":0.00094765873,"teacher_disagreement_score":0.004648897,"about_ca_system_score_codex":0.0019806903,"about_ca_system_score_gemma":0.0020229444,"threshold_uncertainty_score":0.024586022},"labels":[],"label_agreement":null},{"id":"W3204771293","doi":"10.1016/j.procs.2021.08.206","title":"A Deep Learning-based Surrogate for the XRF Approximation of Elemental Composition within Archaeological Artefacts before Restoration","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Ministerio de Ciencia e Innovación; Ontario Ministry of Research, Innovation and Science; Universidad de Málaga","keywords":"Context (archaeology); Computer science; Task (project management); Artificial intelligence; Object (grammar); Deep learning; Composition (language); Archaeology; History; Linguistics","score_opus":0.013598746988688255,"score_gpt":0.24548316595115546,"score_spread":0.2318844189624672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204771293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12638174,0.000415195,0.8699037,0.00036984563,0.000073324794,0.000018488454,0.00023953682,0.0006876357,0.0019105382],"genre_scores_gemma":[0.88066787,0.00028037463,0.11314998,0.00011453584,0.000038800426,0.000043448035,0.0005921707,0.00010551416,0.005007188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997769,0.000048770085,0.000009409168,0.0000703877,0.000054821186,0.00003970478],"domain_scores_gemma":[0.99958533,0.00014730087,0.00006596347,0.000062161016,0.00011042388,0.000028726125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006930709,0.00046906882,0.0004848738,0.00041885258,0.00019153683,0.00071834016,0.0008423895,0.0011067326,0.0013026604],"category_scores_gemma":[0.0027767457,0.00027215487,0.00045259664,0.00042342054,0.0006618731,0.00075795245,0.00073558127,0.0009321776,0.0005458895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002595007,0.000070277965,0.003474885,0.00011726512,0.000038225066,0.00020842858,0.000084752006,0.8860041,0.018950045,0.0074279807,0.0015923276,0.08177216],"study_design_scores_gemma":[0.0000017588651,0.00001640554,0.00043098984,0.0000084937865,0.000002374571,0.000024792847,0.0000065542567,0.99602294,0.0017797342,0.0012401858,0.0004616543,0.000004050221],"about_ca_topic_score_codex":0.004392138,"about_ca_topic_score_gemma":0.0036625897,"teacher_disagreement_score":0.004392138,"about_ca_system_score_codex":0.0005544521,"about_ca_system_score_gemma":0.0007126909,"threshold_uncertainty_score":0.008733153},"labels":[],"label_agreement":null},{"id":"W3217311082","doi":"10.1016/j.procs.2021.10.028","title":"Baseline Accuracies of Forecasting COVID-19 Cases in Russian Regions on a Year in Retrospect Using Basic Statistical and Machine Learning Methods","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Russian Foundation for Basic Research","keywords":"Computer science; Coronavirus disease 2019 (COVID-19); Baseline (sea); Artificial intelligence; Machine learning; Data mining; Medicine","score_opus":0.4803249863692907,"score_gpt":0.49781754676368717,"score_spread":0.01749256039439645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217311082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9710669,0.0012111247,0.018948404,0.00021721188,0.00010664959,0.000024037787,0.004447335,0.0004784626,0.0034998364],"genre_scores_gemma":[0.990954,0.00019030948,0.0049372884,0.000008401718,0.000017869288,0.000009250134,0.003557486,0.00001870736,0.00030669625],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983846,0.00058547565,0.00017554473,0.00041898098,0.00026470766,0.00017070824],"domain_scores_gemma":[0.9939898,0.003039819,0.00055670546,0.0010085201,0.0012362222,0.00016889107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062571075,0.00059451425,0.00062112074,0.0016639876,0.00031179163,0.0013397456,0.0004969285,0.00068727287,0.0006846687],"category_scores_gemma":[0.013102946,0.0002003471,0.0006709798,0.00079216383,0.0003373869,0.0010902045,0.0007891922,0.000563868,0.0006115909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012669144,0.00015616318,0.49959525,0.00024743535,0.00051798474,0.00017151971,0.0005234087,0.38370943,0.00359813,0.002488045,0.0033141614,0.104411684],"study_design_scores_gemma":[0.000030464402,0.00040122514,0.33557078,0.00017943043,0.00016409419,0.00020475716,0.0005602885,0.64863616,0.0076318313,0.0030002713,0.0035241472,0.00009649269],"about_ca_topic_score_codex":0.013825953,"about_ca_topic_score_gemma":0.007483484,"teacher_disagreement_score":0.013825953,"about_ca_system_score_codex":0.00068205653,"about_ca_system_score_gemma":0.0005960529,"threshold_uncertainty_score":0.033091128},"labels":[],"label_agreement":null},{"id":"W4205686199","doi":"10.1016/j.procs.2021.11.008","title":"Treelength of Series-parallel Graphs","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Nationale de la Recherche; Ministry of Communications and Information, Singapore; Ministerul Cercetării şi Inovării; Providence Health Care","keywords":"Chordal graph; Combinatorics; Pathwidth; Indifference graph; Mathematics; Metric dimension; Cograph; Modular decomposition; Discrete mathematics; Outerplanar graph; Partial k-tree; Planar graph; Treewidth; Split graph; Clique-sum; Tree-depth; 1-planar graph; Graph; Line graph","score_opus":0.01713181673032898,"score_gpt":0.2728725849228167,"score_spread":0.2557407681924877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205686199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3816894,0.0016807129,0.56807065,0.0012377716,0.0001775289,0.00028714203,0.003146664,0.0021794871,0.04153069],"genre_scores_gemma":[0.7860078,0.001504103,0.19517726,0.0003694483,0.00019646264,0.00025650382,0.0035896902,0.0005313175,0.012367365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992982,0.00008640652,0.00004521417,0.00025623414,0.00021472143,0.000099186844],"domain_scores_gemma":[0.9978071,0.0009378764,0.0004151748,0.00034981666,0.00032006708,0.00017000483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031548052,0.0005185592,0.00049203343,0.0010364209,0.0007875019,0.0015432626,0.00096742465,0.0005395082,0.0050900658],"category_scores_gemma":[0.0037443938,0.00035425526,0.0006944236,0.0014459513,0.0010645265,0.0028425842,0.001156409,0.0011584967,0.0008693223],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007770767,0.00015052344,0.0055980003,0.0009536201,0.0000764972,0.00066648715,0.00087155006,0.18827507,0.04290732,0.5397765,0.016779082,0.20316833],"study_design_scores_gemma":[0.000042059182,0.0001345292,0.0017913447,0.000074081254,0.000043179414,0.0008447837,0.00019835196,0.22922681,0.0130388,0.7294903,0.025074366,0.000041394716],"about_ca_topic_score_codex":0.0017104221,"about_ca_topic_score_gemma":0.001665533,"teacher_disagreement_score":0.0050900658,"about_ca_system_score_codex":0.0014126833,"about_ca_system_score_gemma":0.0007823503,"threshold_uncertainty_score":0.017027915},"labels":[],"label_agreement":null},{"id":"W4206719352","doi":"10.1016/j.procs.2021.11.025","title":"The Speed and Threshold of the Biased Perfect Matching Game","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Dawson College; McGill University","funders":"","keywords":"Infinity; Matching (statistics); Computer science; Circuit breaker; Combinatorics; Mathematical economics; Algorithm; Discrete mathematics; Mathematics; Physics; Statistics; Mathematical analysis; Quantum mechanics","score_opus":0.016517971246204122,"score_gpt":0.2352522358121574,"score_spread":0.21873426456595327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206719352","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62078947,0.0015037452,0.3036654,0.0049478123,0.0002908187,0.0002515732,0.00076635944,0.0010924152,0.06669233],"genre_scores_gemma":[0.9626856,0.00067423837,0.028578313,0.0004940015,0.00012389934,0.00020142001,0.0002279588,0.00018490954,0.006829666],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966113,0.0011808595,0.00012448922,0.00059970253,0.0005833449,0.0009002841],"domain_scores_gemma":[0.9673162,0.025320368,0.0022100778,0.0018531968,0.0011667674,0.0021333408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005037459,0.00088941073,0.0016796795,0.001313478,0.0012283308,0.0044474,0.0028283428,0.0023493704,0.007547912],"category_scores_gemma":[0.048322074,0.0008860138,0.000990868,0.0008283281,0.0031848745,0.008493504,0.0027772565,0.003204097,0.0015081795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003050149,0.00023141294,0.008803634,0.00043268566,0.00020787031,0.00036691516,0.0009484691,0.14860035,0.022818306,0.7660656,0.008960621,0.039514035],"study_design_scores_gemma":[0.00022853675,0.00017952036,0.0013111995,0.000072970375,0.00007063465,0.00025453087,0.00013328256,0.45049307,0.0042806426,0.5406201,0.002287741,0.00006775089],"about_ca_topic_score_codex":0.0020360965,"about_ca_topic_score_gemma":0.0012037824,"teacher_disagreement_score":0.007547912,"about_ca_system_score_codex":0.0027208056,"about_ca_system_score_gemma":0.002239505,"threshold_uncertainty_score":0.026640952},"labels":[],"label_agreement":null},{"id":"W4210290154","doi":"10.1016/j.procs.2021.12.223","title":"A Parametric Multi-Agent Simulation Framework to Emulate Social Isolation During the Pandemic","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Pandemic; Isolation (microbiology); Social distance; Parametric statistics; Coronavirus disease 2019 (COVID-19); Lock (firearm); Population; Social isolation; Computer security; Simulation; Operations research; Medicine; Environmental health; Statistics; Engineering","score_opus":0.25389027607438985,"score_gpt":0.4425544491133872,"score_spread":0.18866417303899735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210290154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124420814,0.0005885136,0.8543758,0.0010163969,0.00018634548,0.00020949448,0.00052151154,0.00053677865,0.018144421],"genre_scores_gemma":[0.9233063,0.00036881032,0.072417766,0.000090526635,0.00005343609,0.00025910448,0.00026987705,0.000050255614,0.0031840245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956566,0.00025455662,0.000020393301,0.000044716893,0.00005368499,0.00006090394],"domain_scores_gemma":[0.9992567,0.00040520093,0.00008364003,0.000043263786,0.00011961849,0.00009167664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074616546,0.0006874314,0.00062623865,0.0004872678,0.00063112495,0.0009216829,0.0016121184,0.0013516723,0.0020252417],"category_scores_gemma":[0.0020360567,0.00035567352,0.000889498,0.0004271971,0.0006105987,0.0007054842,0.0013546041,0.00102525,0.00022104871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009051219,0.000008928621,0.00032157384,0.000007761486,0.000008047389,0.000026604332,0.000019707899,0.9954526,0.00013589479,0.0031541574,0.00010747456,0.0007483115],"study_design_scores_gemma":[0.000005108433,0.000009396581,0.00006117193,0.0000024143028,0.000002894424,0.0000063487805,0.0000096113545,0.99871266,0.00003391276,0.0008724298,0.00028093424,0.0000031924862],"about_ca_topic_score_codex":0.019938882,"about_ca_topic_score_gemma":0.0075535374,"teacher_disagreement_score":0.019938882,"about_ca_system_score_codex":0.0007630545,"about_ca_system_score_gemma":0.001223908,"threshold_uncertainty_score":0.03964567},"labels":[],"label_agreement":null},{"id":"W4210659552","doi":"10.1016/j.procs.2021.12.245","title":"Mental Health for Medical Students, what do we know today?","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Healthcare professionals’ stress and burnout","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Mindfulness; Psychological intervention; Mental health; Anxiety; Burnout; Exploratory research; Randomized controlled trial; Meditation; Intervention (counseling); Medical education; Psychology; Clinical psychology; Applied psychology; Medicine; Psychiatry","score_opus":0.04355800826645909,"score_gpt":0.4538914998549551,"score_spread":0.41033349158849597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210659552","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009066689,0.8142713,0.00049958465,0.1655596,0.004961372,0.00009403861,0.000491651,0.000034560642,0.0050211977],"genre_scores_gemma":[0.069121055,0.8757238,0.002389089,0.044246785,0.0054158415,0.00023700368,0.00035255298,0.000013896679,0.002500025],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990213,0.0005042179,0.000109571265,0.00006512035,0.00020099783,0.00009872776],"domain_scores_gemma":[0.99365884,0.0035218734,0.00085621444,0.00019954161,0.00082154264,0.0009419878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020274594,0.00025791297,0.0011730185,0.00092408084,0.00095568,0.0021122019,0.000453651,0.0018858417,0.009389489],"category_scores_gemma":[0.015313946,0.00022458719,0.0011358969,0.000924608,0.0008713171,0.0029062503,0.0011882806,0.0031666683,0.001034147],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022958114,0.000209262,0.007939384,0.038774498,0.00060713815,0.00012713388,0.0015041581,0.000089324974,0.00034906168,0.002956948,0.08807863,0.8591349],"study_design_scores_gemma":[0.00052695925,0.0011639289,0.07109271,0.2774385,0.00269161,0.0014419598,0.009325806,0.00021843021,0.00051992224,0.022299545,0.6131361,0.0001445696],"about_ca_topic_score_codex":0.005203478,"about_ca_topic_score_gemma":0.014452585,"teacher_disagreement_score":0.009389489,"about_ca_system_score_codex":0.00103869,"about_ca_system_score_gemma":0.0057062753,"threshold_uncertainty_score":0.031410992},"labels":[],"label_agreement":null},{"id":"W4210806007","doi":"10.1016/j.procs.2022.01.020","title":"Iterative Feedback Tuning Algorithm for Tower Crane Systems","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Control Systems and Identification","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Education and Research, Romania; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Corporation for National and Community Service","keywords":"Computer science; Tower; Algorithm; Iterative method; Control theory (sociology); Artificial intelligence; Control (management)","score_opus":0.009387033553152999,"score_gpt":0.20681719658248837,"score_spread":0.19743016302933536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210806007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016688544,0.00013792138,0.9796204,0.00004575446,0.000023413551,0.00004177999,0.000010247077,0.00046985457,0.002962077],"genre_scores_gemma":[0.810518,0.00012603364,0.18574631,0.00006930955,0.000025229408,0.00016567821,0.000054451044,0.00008086849,0.0032140724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964094,0.00007306292,0.000020855245,0.00007183832,0.00014648588,0.00004684212],"domain_scores_gemma":[0.99944156,0.0002267805,0.00007843613,0.00004402907,0.00019045218,0.000018719566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065358134,0.00060021365,0.0004898884,0.00042646108,0.0005518253,0.0005001902,0.0006745546,0.0006484641,0.0021027925],"category_scores_gemma":[0.0018703269,0.00023181613,0.00033499798,0.0002956996,0.00043581417,0.00039930386,0.00052758073,0.0006299711,0.00043322326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014661557,0.000052346888,0.0007918294,0.00013233922,0.000045237655,0.000086859895,0.00025153166,0.7597574,0.020690456,0.0063152756,0.0011505672,0.21057954],"study_design_scores_gemma":[0.000014171835,0.000070360205,0.00020928812,0.000007841657,0.0000059810445,0.00003517626,0.000010315314,0.9949785,0.0028311578,0.00081147027,0.0010172877,0.000008482686],"about_ca_topic_score_codex":0.005267444,"about_ca_topic_score_gemma":0.0031550333,"teacher_disagreement_score":0.005267444,"about_ca_system_score_codex":0.00042460262,"about_ca_system_score_gemma":0.000705084,"threshold_uncertainty_score":0.010473549},"labels":[],"label_agreement":null},{"id":"W4226047702","doi":"10.1016/j.procs.2022.01.310","title":"Modular Robotic Prefabrication of Discrete Aggregations Driven by BIM and Computational Design","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"BIM and Construction Integration","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Mitacs","keywords":"Prefabrication; Modular design; Computer science; Adaptability; Process (computing); Architecture; Design process; Engineering design process; Systems engineering; Distributed computing; Work in process; Engineering; Operating system","score_opus":0.008630916063125722,"score_gpt":0.19822316044246377,"score_spread":0.18959224437933805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226047702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037890505,0.000096144206,0.9523968,0.000070744936,0.000020125726,0.000069186375,0.000042725707,0.0003514489,0.009062405],"genre_scores_gemma":[0.5170905,0.00016286691,0.47940725,0.000024345201,0.0000076117512,0.00017645255,0.00014218136,0.00012208817,0.0028667583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991941,0.00020724718,0.000031319767,0.00012303382,0.00036532685,0.00007901832],"domain_scores_gemma":[0.99943143,0.00021246806,0.00006701001,0.00019107953,0.00006946567,0.000028603888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009904158,0.0006433523,0.00065249583,0.0006445346,0.0007045084,0.0014503822,0.0011475519,0.0006753098,0.0026405978],"category_scores_gemma":[0.0012497689,0.0004656128,0.001086263,0.00067175296,0.0014221105,0.0012424948,0.002037116,0.00087318465,0.00042916962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053460823,0.000070614806,0.00076724746,0.00015264789,0.00002606643,0.00021994232,0.00045269288,0.8466678,0.019225668,0.0851086,0.0005142763,0.04674096],"study_design_scores_gemma":[0.000013697811,0.00007831765,0.0006029167,0.000027292082,0.000015199547,0.000119662174,0.00012408261,0.95882165,0.007983127,0.02367302,0.008519505,0.000021537497],"about_ca_topic_score_codex":0.0017854671,"about_ca_topic_score_gemma":0.0021375166,"teacher_disagreement_score":0.0026405978,"about_ca_system_score_codex":0.0008774775,"about_ca_system_score_gemma":0.0011236751,"threshold_uncertainty_score":0.008833706},"labels":[],"label_agreement":null},{"id":"W4233934817","doi":"10.1016/j.procs.2013.09.038","title":"Preface","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science","score_opus":0.010812626487533903,"score_gpt":0.22989421154672743,"score_spread":0.21908158505919353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233934817","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021068738,0.013095156,0.024312934,0.02928406,0.3038323,0.0010206718,0.018385408,0.0027974583,0.6051651],"genre_scores_gemma":[0.0074866014,0.0061029345,0.005826413,0.0061570625,0.036252175,0.00045205775,0.011209653,0.001232646,0.9252804],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994661,0.000070114824,0.000034699227,0.000100670746,0.00028427798,0.00004413323],"domain_scores_gemma":[0.9948369,0.0008173484,0.00015932765,0.00050677167,0.003114224,0.00056545465],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009756632,0.001055657,0.0006284303,0.0029671057,0.0021928155,0.002454808,0.0011788248,0.0007376088,0.45398808],"category_scores_gemma":[0.011164652,0.00026966576,0.0005782953,0.0019104141,0.00045002482,0.002239377,0.0018198894,0.002605406,0.29718933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000321233,0.000028842385,0.00009826881,0.000105961866,0.0000020336386,0.00004218045,0.00003646009,0.0000919202,0.00021377305,0.0040829587,0.94680476,0.048460573],"study_design_scores_gemma":[0.000004463496,0.000017570646,0.00025918684,0.000109109926,0.000002368511,0.000049553648,0.0000496243,0.000053195203,0.00020344275,0.0040251273,0.99522024,0.0000061693304],"about_ca_topic_score_codex":0.0036031594,"about_ca_topic_score_gemma":0.004431547,"teacher_disagreement_score":0.5460119,"about_ca_system_score_codex":0.0013463513,"about_ca_system_score_gemma":0.0016440206,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4235919898","doi":"10.1016/j.procs.2015.08.303","title":"Preface","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.05185282223967664,"score_gpt":0.28583158695206984,"score_spread":0.2339787647123932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235919898","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020628422,0.013299789,0.023812104,0.030459186,0.30691892,0.0010359562,0.019314066,0.002856744,0.6002404],"genre_scores_gemma":[0.007352862,0.006175431,0.005772076,0.0065830066,0.037565127,0.00046917496,0.011777062,0.0012690776,0.92303616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994661,0.00007021801,0.000035144698,0.00010058438,0.00028376578,0.00004410386],"domain_scores_gemma":[0.99464744,0.0008689482,0.00016710683,0.0005101723,0.0032112766,0.0005950879],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009881707,0.0010509643,0.0006248513,0.003000392,0.002146123,0.0024602285,0.0011628434,0.00073744927,0.4641898],"category_scores_gemma":[0.011612173,0.0002708092,0.000569037,0.0019207753,0.00044396333,0.0022256582,0.0018168135,0.0026043963,0.30858642],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030193536,0.000027450369,0.00009263484,0.00010233169,0.0000018917431,0.000038659055,0.000033782308,0.00008702106,0.00019281569,0.0036859124,0.9496757,0.046031624],"study_design_scores_gemma":[0.0000045906695,0.000017296526,0.0002591451,0.00011388165,0.0000023308885,0.00004775205,0.00004900306,0.00005266957,0.00019196181,0.003981775,0.99527353,0.0000061259816],"about_ca_topic_score_codex":0.0035666467,"about_ca_topic_score_gemma":0.004504587,"teacher_disagreement_score":0.53581023,"about_ca_system_score_codex":0.0013463143,"about_ca_system_score_gemma":0.0016531164,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4240878014","doi":"10.1016/j.procs.2018.10.118","title":"Preface","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.01668297484814625,"score_gpt":0.2736355478952513,"score_spread":0.25695257304710506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240878014","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021790825,0.013638333,0.023780528,0.03250287,0.33221546,0.0010262328,0.017870354,0.0027300126,0.5740571],"genre_scores_gemma":[0.0081886025,0.0066407844,0.0063088895,0.0072058807,0.04404992,0.00050447334,0.012089276,0.0013203773,0.9136917],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994499,0.000074959054,0.000036551708,0.000106167274,0.0002860869,0.000046293284],"domain_scores_gemma":[0.9945134,0.0008783285,0.000173775,0.0005143228,0.0032985993,0.00062157813],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010136169,0.0010509774,0.00061432295,0.0028598092,0.0021485484,0.0024748247,0.0011469962,0.00076024595,0.43972802],"category_scores_gemma":[0.011963938,0.00026359296,0.0005718357,0.0018707055,0.00044705093,0.0022922112,0.0018329412,0.0026600992,0.28961295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031537616,0.000029291934,0.00009884172,0.000107801,0.0000020111222,0.00004188995,0.00003582754,0.00009463861,0.00019880958,0.0038608043,0.9484817,0.047016893],"study_design_scores_gemma":[0.000004685566,0.000018649283,0.0002738483,0.00011815498,0.0000023971204,0.000051644976,0.00005189474,0.00005538911,0.00019529274,0.004157087,0.99506456,0.0000064467467],"about_ca_topic_score_codex":0.0034660776,"about_ca_topic_score_gemma":0.004192422,"teacher_disagreement_score":0.560272,"about_ca_system_score_codex":0.0013714829,"about_ca_system_score_gemma":0.0016519616,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4241063860","doi":"10.1016/j.procs.2015.05.113","title":"The 5th International Conference on Sustainable Energy Information Technology (SEIT) Preface","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Energy (signal processing); Sustainable energy; Data science; Electrical engineering; Renewable energy","score_opus":0.011642455183533499,"score_gpt":0.21471164386906166,"score_spread":0.20306918868552817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241063860","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008580886,0.02813899,0.029907806,0.02198246,0.30709422,0.00044182135,0.001784159,0.0014770335,0.6005927],"genre_scores_gemma":[0.025872933,0.013119292,0.0061964,0.0018514169,0.014520488,0.000118103526,0.0023881278,0.00044207743,0.93549126],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994118,0.000059630256,0.000031179177,0.00008065005,0.00030350717,0.00011310085],"domain_scores_gemma":[0.99790215,0.00009607883,0.00004559126,0.00014177355,0.0012694162,0.00054505345],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016332943,0.0007823712,0.0006947837,0.002221083,0.0012426222,0.0048286975,0.001060004,0.0017166743,0.1319547],"category_scores_gemma":[0.0014724587,0.00022328136,0.0006295495,0.0015351722,0.00060447573,0.0021785786,0.0019323997,0.0021111006,0.065606266],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115352865,0.00015447891,0.0008733442,0.0003189569,0.0000145809045,0.00020774845,0.0000855469,0.0005733343,0.0040037576,0.0110915685,0.72709984,0.25546145],"study_design_scores_gemma":[0.00000635517,0.000067137196,0.0009953579,0.00012364775,0.000009794416,0.00013568555,0.000102933445,0.00071435183,0.0012772708,0.0027148593,0.9938403,0.000012429891],"about_ca_topic_score_codex":0.0025151824,"about_ca_topic_score_gemma":0.0037270712,"teacher_disagreement_score":0.86804533,"about_ca_system_score_codex":0.0009643648,"about_ca_system_score_gemma":0.0025720163,"threshold_uncertainty_score":0.44143248},"labels":[],"label_agreement":null},{"id":"W4244209064","doi":"10.1016/j.procs.2016.09.002","title":"Preface","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.01403050880067708,"score_gpt":0.24576280101516967,"score_spread":0.2317322922144926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244209064","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020520566,0.013566034,0.021782966,0.030443117,0.3080276,0.00101142,0.018650027,0.0027052166,0.60176164],"genre_scores_gemma":[0.007288838,0.0062684314,0.005525665,0.006622207,0.037642356,0.0004591003,0.011499651,0.0012155948,0.9234782],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99946433,0.00007096014,0.000035408993,0.00010112667,0.00028370926,0.000044550616],"domain_scores_gemma":[0.99473035,0.000842456,0.00016846508,0.0004965987,0.0031652704,0.000596742],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000989972,0.0010474155,0.00061399397,0.0030089081,0.0021644456,0.0024593568,0.0011618714,0.0007387122,0.45448855],"category_scores_gemma":[0.01156688,0.0002658711,0.0005592294,0.0019030654,0.00044515647,0.0022041972,0.0018200566,0.0025754306,0.30362132],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030444447,0.000027609492,0.00009588669,0.000103738756,0.0000018928238,0.000039869607,0.00003576193,0.00008592949,0.00018967468,0.0036956423,0.9492237,0.046469826],"study_design_scores_gemma":[0.000004429636,0.000017064445,0.00026153185,0.00011496383,0.0000022612141,0.000047713045,0.000050354953,0.00004928291,0.0001848914,0.0038246813,0.99543697,0.000005958403],"about_ca_topic_score_codex":0.0037019907,"about_ca_topic_score_gemma":0.004599558,"teacher_disagreement_score":0.5455115,"about_ca_system_score_codex":0.0013689988,"about_ca_system_score_gemma":0.0016980836,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4244465050","doi":"10.1016/j.procs.2015.07.168","title":"Preface","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science","score_opus":0.05185282223967664,"score_gpt":0.28583158695206984,"score_spread":0.2339787647123932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244465050","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020628422,0.013299789,0.023812104,0.030459186,0.30691892,0.0010359562,0.019314066,0.002856744,0.6002404],"genre_scores_gemma":[0.007352862,0.006175431,0.005772076,0.0065830066,0.037565127,0.00046917496,0.011777062,0.0012690776,0.92303616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994661,0.00007021801,0.000035144698,0.00010058438,0.00028376578,0.00004410386],"domain_scores_gemma":[0.99464744,0.0008689482,0.00016710683,0.0005101723,0.0032112766,0.0005950879],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009881707,0.0010509643,0.0006248513,0.003000392,0.002146123,0.0024602285,0.0011628434,0.00073744927,0.4641898],"category_scores_gemma":[0.011612173,0.0002708092,0.000569037,0.0019207753,0.00044396333,0.0022256582,0.0018168135,0.0026043963,0.30858642],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030193536,0.000027450369,0.00009263484,0.00010233169,0.0000018917431,0.000038659055,0.000033782308,0.00008702106,0.00019281569,0.0036859124,0.9496757,0.046031624],"study_design_scores_gemma":[0.0000045906695,0.000017296526,0.0002591451,0.00011388165,0.0000023308885,0.00004775205,0.00004900306,0.00005266957,0.00019196181,0.003981775,0.99527353,0.0000061259816],"about_ca_topic_score_codex":0.0035666467,"about_ca_topic_score_gemma":0.004504587,"teacher_disagreement_score":0.53581023,"about_ca_system_score_codex":0.0013463143,"about_ca_system_score_gemma":0.0016531164,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4249098611","doi":"10.1016/j.procs.2018.07.136","title":"Keynote II","year":2018,"lang":"es","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.014913121889228597,"score_gpt":0.2746807701088901,"score_spread":0.2597676482196615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249098611","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012170493,0.004411982,0.0049870997,0.0342621,0.21930835,0.000463133,0.0029962535,0.00097301934,0.73138106],"genre_scores_gemma":[0.0057981974,0.0019351862,0.0008148441,0.007840297,0.02411527,0.00020944506,0.0014797392,0.00037043347,0.95743656],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990382,0.00010288613,0.000046317484,0.0002110094,0.00044859876,0.00015296633],"domain_scores_gemma":[0.99761707,0.00032605452,0.00010172407,0.00030753974,0.0010401604,0.00060748734],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010392432,0.0009936213,0.00064209104,0.001607749,0.0019945675,0.0045419983,0.0015937391,0.0024606683,0.535204],"category_scores_gemma":[0.0064354856,0.00029498525,0.00075671944,0.001091398,0.0004973315,0.0031681,0.0030259744,0.0032048405,0.32774773],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007456664,0.000037701422,0.000067522094,0.00010041419,0.000003046344,0.000043994318,0.000025804537,0.00005472112,0.0004684288,0.0096603865,0.9490811,0.040382367],"study_design_scores_gemma":[0.000011261451,0.000024886192,0.00021581154,0.00009105579,0.00000356335,0.000044970602,0.000041321295,0.000047435362,0.0002876616,0.0040871357,0.9951395,0.000005435305],"about_ca_topic_score_codex":0.0015938636,"about_ca_topic_score_gemma":0.002110469,"teacher_disagreement_score":0.464796,"about_ca_system_score_codex":0.0015820663,"about_ca_system_score_gemma":0.0018249308,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4255024726","doi":"10.1016/j.procs.2013.06.122","title":"Preface","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science","score_opus":0.010812626487533903,"score_gpt":0.22989421154672743,"score_spread":0.21908158505919353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255024726","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021068738,0.013095156,0.024312934,0.02928406,0.3038323,0.0010206718,0.018385408,0.0027974583,0.6051651],"genre_scores_gemma":[0.0074866014,0.0061029345,0.005826413,0.0061570625,0.036252175,0.00045205775,0.011209653,0.001232646,0.9252804],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994661,0.000070114824,0.000034699227,0.000100670746,0.00028427798,0.00004413323],"domain_scores_gemma":[0.9948369,0.0008173484,0.00015932765,0.00050677167,0.003114224,0.00056545465],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009756632,0.001055657,0.0006284303,0.0029671057,0.0021928155,0.002454808,0.0011788248,0.0007376088,0.45398808],"category_scores_gemma":[0.011164652,0.00026966576,0.0005782953,0.0019104141,0.00045002482,0.002239377,0.0018198894,0.002605406,0.29718933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000321233,0.000028842385,0.00009826881,0.000105961866,0.0000020336386,0.00004218045,0.00003646009,0.0000919202,0.00021377305,0.0040829587,0.94680476,0.048460573],"study_design_scores_gemma":[0.000004463496,0.000017570646,0.00025918684,0.000109109926,0.000002368511,0.000049553648,0.0000496243,0.000053195203,0.00020344275,0.0040251273,0.99522024,0.0000061693304],"about_ca_topic_score_codex":0.0036031594,"about_ca_topic_score_gemma":0.004431547,"teacher_disagreement_score":0.5460119,"about_ca_system_score_codex":0.0013463513,"about_ca_system_score_gemma":0.0016440206,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4255590141","doi":"10.1016/j.procs.2021.07.002","title":"Preface","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.015677386445670866,"score_gpt":0.25792537255871106,"score_spread":0.2422479861130402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255590141","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018388537,0.011025502,0.02082123,0.027166631,0.31563827,0.0010194287,0.017766176,0.0027619223,0.60196203],"genre_scores_gemma":[0.0061144154,0.005144245,0.0049518547,0.005349207,0.035419453,0.00039265805,0.0106703825,0.0011229884,0.93083483],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999463,0.00006930707,0.000033931476,0.000094756375,0.00029479573,0.000044175667],"domain_scores_gemma":[0.99411595,0.0008824676,0.00017430245,0.0005238709,0.0036404517,0.00066291005],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009938225,0.0010433273,0.00063472014,0.0029422531,0.0020883589,0.0025059327,0.0011244061,0.00076401676,0.48360708],"category_scores_gemma":[0.011025024,0.00027792045,0.0005711488,0.0018825863,0.00041096704,0.002154152,0.001687504,0.0025559645,0.31354237],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031069663,0.0000265201,0.00008479248,0.000095808966,0.0000017849933,0.00003677431,0.000027059776,0.00008902478,0.00020781757,0.0030322988,0.95103025,0.045336712],"study_design_scores_gemma":[0.000004822429,0.000019415902,0.00025872048,0.00010719683,0.0000022508034,0.000045586075,0.000043469387,0.00005567226,0.00019872101,0.0031866503,0.9960716,0.0000058899645],"about_ca_topic_score_codex":0.0035931212,"about_ca_topic_score_gemma":0.0044524996,"teacher_disagreement_score":0.51639295,"about_ca_system_score_codex":0.0012841773,"about_ca_system_score_gemma":0.0016418291,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4285031979","doi":"10.1016/j.procs.2015.05.180","title":"Towards an Integrated Conceptual Design Evaluation of Mechatronic Systems: The SysDICE Approach","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Systems Engineering Methodologies and Applications","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Computer science; Mechatronics; Conceptual design; Software engineering; Human–computer interaction; Systems engineering; Management science; Artificial intelligence","score_opus":0.20470682996400758,"score_gpt":0.315425090509934,"score_spread":0.11071826054592643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285031979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00435083,0.00030090162,0.99174905,0.00032216613,0.000035193305,0.000120733064,0.000038199316,0.00021884838,0.0028641482],"genre_scores_gemma":[0.069834374,0.00044652342,0.9278394,0.00010566749,0.000020645713,0.00025631266,0.00015890914,0.00010570791,0.0012324951],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9913298,0.0041863783,0.00046554563,0.0005047314,0.0032689397,0.0002446102],"domain_scores_gemma":[0.99536234,0.001992391,0.00037497646,0.00093466253,0.0012012629,0.00013440663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008437617,0.0012311982,0.0010932789,0.002578486,0.00059734593,0.0049647484,0.0019644895,0.0018055385,0.0031574846],"category_scores_gemma":[0.01096693,0.0009909633,0.001541799,0.0012780877,0.0024050965,0.0044961465,0.003985138,0.0021430645,0.00048712548],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015355472,0.00034044543,0.0018471195,0.0012412178,0.00019239332,0.0002690519,0.0010574491,0.21151865,0.019470831,0.52163374,0.0020944704,0.24018106],"study_design_scores_gemma":[0.0000808299,0.00036967246,0.0005519635,0.0004481068,0.00011228416,0.00023511575,0.00043744245,0.8009313,0.014348611,0.13429184,0.048129987,0.000062864965],"about_ca_topic_score_codex":0.0013172852,"about_ca_topic_score_gemma":0.0014262933,"teacher_disagreement_score":0.008437617,"about_ca_system_score_codex":0.0017264853,"about_ca_system_score_gemma":0.0030217292,"threshold_uncertainty_score":0.044622898},"labels":[],"label_agreement":null},{"id":"W4285135671","doi":"10.1016/j.procs.2022.04.014","title":"Trend Prediction Of Stock Industry Index Based On Financial Text","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Index (typography); Stock market index; Data mining; Artificial intelligence; Deep learning; Stock (firearms); Machine learning; Stock market; Finance","score_opus":0.09261294565052652,"score_gpt":0.3565847755937169,"score_spread":0.26397182994319035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285135671","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8185651,0.0016044914,0.16172192,0.00066295953,0.0003531925,0.00010444334,0.007385575,0.0028772803,0.0067251395],"genre_scores_gemma":[0.95919275,0.0007635818,0.02986824,0.00006048871,0.00016021899,0.000056307093,0.0062610386,0.000042871157,0.0035944982],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998636,0.000012044258,0.000015491141,0.000043339798,0.00004558627,0.000019938254],"domain_scores_gemma":[0.9995926,0.00013168812,0.00007308378,0.00003043948,0.00015037462,0.000021861471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026831983,0.0007150999,0.00029734464,0.0019181746,0.00015576594,0.00040469592,0.00042283125,0.0003702521,0.0014403132],"category_scores_gemma":[0.0012982115,0.00013671149,0.00038440072,0.0014206634,0.00011308191,0.0011847741,0.0002807083,0.0005240686,0.0006758455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071952504,0.0004996018,0.06701025,0.00028088188,0.00016102387,0.00069057016,0.00012404169,0.1048447,0.037213795,0.0020394477,0.01385488,0.7725613],"study_design_scores_gemma":[0.000009524959,0.00005499655,0.009922452,0.00000962512,0.00002259522,0.0000404567,0.000014282819,0.9835233,0.004823754,0.00092721987,0.00064279034,0.000008841584],"about_ca_topic_score_codex":0.0042855563,"about_ca_topic_score_gemma":0.0058385767,"teacher_disagreement_score":0.0042855563,"about_ca_system_score_codex":0.0002881965,"about_ca_system_score_gemma":0.0003000354,"threshold_uncertainty_score":0.008521199},"labels":[],"label_agreement":null},{"id":"W4285744405","doi":"10.1016/j.procs.2022.03.011","title":"Pedestrian movement modelling for a commercial street considering COVID-19 social distancing strategies","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Social distance; Pedestrian; Microsimulation; Computer science; Pandemic; Distancing; Coronavirus disease 2019 (COVID-19); Transport engineering; Operations research; Simulation; Engineering; Medicine","score_opus":0.0432072576577256,"score_gpt":0.2797841035545718,"score_spread":0.2365768458968462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285744405","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92294395,0.00014870598,0.063792504,0.0002571355,0.00003515623,0.00007528423,0.00043634442,0.00012013373,0.012190744],"genre_scores_gemma":[0.99197704,0.00010513306,0.0056234114,0.000018749155,0.000005900246,0.00003837132,0.00015929044,0.000010366212,0.0020617484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998534,0.000050396888,0.000005605756,0.00002760229,0.000021381697,0.000041717976],"domain_scores_gemma":[0.9998541,0.000055116896,0.000025163774,0.000011529003,0.000028861417,0.000025237772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020288401,0.00045706838,0.00034670794,0.00033599144,0.0004598919,0.00068626076,0.00049496285,0.000744074,0.0015804071],"category_scores_gemma":[0.0004055154,0.00021687818,0.00056100613,0.00028662445,0.00041674665,0.00038431826,0.00064172887,0.00031328056,0.00016820677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039781073,0.000020191603,0.002344322,0.00001650386,0.00000963956,0.000121156205,0.000077613135,0.99251693,0.0010386048,0.0017962466,0.00013117724,0.0018879725],"study_design_scores_gemma":[0.000007367001,0.000049211918,0.0011744844,0.0000049198334,0.0000064966334,0.000022642782,0.00014456548,0.9972542,0.00031328606,0.00045895606,0.0005580439,0.0000059238837],"about_ca_topic_score_codex":0.03661276,"about_ca_topic_score_gemma":0.02260138,"teacher_disagreement_score":0.03661276,"about_ca_system_score_codex":0.00087391253,"about_ca_system_score_gemma":0.0009604613,"threshold_uncertainty_score":0.072799265},"labels":[],"label_agreement":null},{"id":"W4290928156","doi":"10.1016/j.procs.2022.07.023","title":"Live Sentiment Analysis Using Multiple Machine Learning and Text Processing Algorithms","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University; Thompson Rivers University","funders":"","keywords":"Computer science; Sentiment analysis; Naive Bayes classifier; Lexicon; Machine learning; Artificial intelligence; Support vector machine; Algorithm; Data stream mining; Data mining","score_opus":0.019950047655876543,"score_gpt":0.26987339386171944,"score_spread":0.2499233462058429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290928156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20238623,0.00072273967,0.77709556,0.0006082321,0.00061216217,0.0005937689,0.002027743,0.009299195,0.00665447],"genre_scores_gemma":[0.6045346,0.00034631157,0.38562727,0.00020194935,0.00034853411,0.0005064733,0.003220934,0.00028513643,0.0049289246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982651,0.0002636768,0.00015917579,0.00039061255,0.000768426,0.00015308223],"domain_scores_gemma":[0.99823064,0.00040068582,0.00018439125,0.00017510635,0.0009415481,0.00006759075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014092937,0.0013743935,0.0012518377,0.0027722416,0.00074274564,0.0013894433,0.0009881755,0.0008470656,0.0027872848],"category_scores_gemma":[0.0033835196,0.00035499045,0.00096620497,0.0020576105,0.00025994593,0.0024558029,0.00096735265,0.00090758037,0.0022945942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066393794,0.0005741963,0.020062545,0.00028726165,0.00038564808,0.00037418312,0.00024888036,0.030633477,0.054988276,0.0016449266,0.010257463,0.8798792],"study_design_scores_gemma":[0.000029465878,0.00025383657,0.008223341,0.000023646264,0.00006315136,0.00017111565,0.00020796045,0.9535137,0.029772146,0.0028410677,0.0048632273,0.00003737618],"about_ca_topic_score_codex":0.0017020662,"about_ca_topic_score_gemma":0.0022039518,"teacher_disagreement_score":0.0027872848,"about_ca_system_score_codex":0.00057608954,"about_ca_system_score_gemma":0.00048549703,"threshold_uncertainty_score":0.009324431},"labels":[],"label_agreement":null},{"id":"W4290928308","doi":"10.1016/j.procs.2022.07.126","title":"Protecting Routing Data in WSNs with use of IOTA Tangle","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cistel Technology (Canada); Norleaf Networks (Canada); Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Wireless sensor network; Routing (electronic design automation); Routing table; Computer security; Static routing; Node (physics); Sybil attack; Multipath routing; Routing protocol; Distributed computing","score_opus":0.048751754974251466,"score_gpt":0.25104825079667675,"score_spread":0.2022964958224253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290928308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49481034,0.0025027264,0.48391488,0.00060993,0.00022062231,0.00030276334,0.00015441827,0.009020787,0.008463467],"genre_scores_gemma":[0.95942974,0.00032083443,0.03752842,0.00012723547,0.0000150756105,0.000056815268,0.00007913818,0.00013514908,0.0023076434],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994093,0.000114223,0.000057090776,0.00011932262,0.0002217307,0.00007833343],"domain_scores_gemma":[0.9974832,0.0005301582,0.0005482715,0.0009913826,0.00034148665,0.000105507024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068096304,0.00048632512,0.00057368906,0.00079168845,0.0006131507,0.0008950598,0.00085501745,0.00054118154,0.00091243815],"category_scores_gemma":[0.002246484,0.00021652093,0.00027214526,0.0005376068,0.0007400037,0.0023663917,0.001214251,0.0005124743,0.0003161405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015848129,0.00043470488,0.012924552,0.0008539443,0.00028404078,0.004308116,0.0021333618,0.055432532,0.4849172,0.01634061,0.004678715,0.41610742],"study_design_scores_gemma":[0.00017464402,0.003493587,0.01037056,0.00020072421,0.00029021813,0.0069255796,0.000614007,0.39991474,0.5196299,0.019699717,0.03850558,0.00018081494],"about_ca_topic_score_codex":0.00029676518,"about_ca_topic_score_gemma":0.00037930498,"teacher_disagreement_score":0.00091243815,"about_ca_system_score_codex":0.00032881086,"about_ca_system_score_gemma":0.0003000185,"threshold_uncertainty_score":0.0036013722},"labels":[],"label_agreement":null},{"id":"W4290928373","doi":"10.1016/j.procs.2022.07.002","title":"Preface","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.01394729939811635,"score_gpt":0.24205116344327923,"score_spread":0.2281038640451629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290928373","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018594787,0.0107635865,0.021407371,0.027499743,0.314377,0.0010585703,0.018494144,0.002870381,0.6016696],"genre_scores_gemma":[0.006268791,0.0051035187,0.0052104536,0.005566692,0.035617016,0.00041410534,0.011193604,0.0011600748,0.92946565],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994535,0.00007082266,0.00003485141,0.000096988464,0.0002985954,0.000045263434],"domain_scores_gemma":[0.9938067,0.0009199315,0.00018323283,0.00055263104,0.0038458712,0.0006915088],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010205504,0.0010461749,0.00063001347,0.0029521775,0.0021146748,0.0025311222,0.0011245346,0.00076331705,0.48661104],"category_scores_gemma":[0.011427897,0.00028179484,0.00057162123,0.0018698117,0.00041344023,0.002172366,0.0017039274,0.0025701616,0.3178329],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031980446,0.00002706324,0.00008827516,0.00009592446,0.0000018019263,0.000037742884,0.000027321606,0.00008873661,0.00021030662,0.0030298568,0.9510504,0.04531055],"study_design_scores_gemma":[0.0000049337045,0.000019756286,0.00026235366,0.000108190136,0.0000022666627,0.000046873745,0.000044349297,0.00005569485,0.00020360638,0.0031995815,0.99604636,0.00000598856],"about_ca_topic_score_codex":0.0036097139,"about_ca_topic_score_gemma":0.0044401535,"teacher_disagreement_score":0.513389,"about_ca_system_score_codex":0.0012820985,"about_ca_system_score_gemma":0.0016681055,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4290928423","doi":"10.1016/j.procs.2022.07.013","title":"Cocoa Companion: Deep Learning-Based Smartphone Application for Cocoa Disease Detection","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Saskatchewan","funders":"University of Saskatchewan; Pennsylvania State University","keywords":"Computer science; Upload; Convolutional neural network; Deep learning; Artificial intelligence; Machine learning; Cloud computing; Pattern recognition (psychology); Operating system","score_opus":0.009285602062912532,"score_gpt":0.20152199585829794,"score_spread":0.1922363937953854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290928423","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51214933,0.0062498227,0.22267297,0.0013154078,0.0007776973,0.0013280017,0.025698889,0.19596161,0.033846263],"genre_scores_gemma":[0.895631,0.0009670424,0.068985276,0.00095908495,0.00007974821,0.00034631533,0.0123358425,0.00059174356,0.020104056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992216,0.000007414462,0.0000050392014,0.000026786887,0.000022439264,0.000016196898],"domain_scores_gemma":[0.999863,0.000037763566,0.000015115838,0.000015938187,0.0000444922,0.000023629538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012630525,0.00079835893,0.00035389783,0.00043351477,0.00010609246,0.00026418344,0.00064782816,0.0003741316,0.007894187],"category_scores_gemma":[0.0004810232,0.00016164935,0.00023285637,0.00023675126,0.000075273114,0.0003618792,0.0004564323,0.0002885715,0.0019499785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035636867,0.0009627411,0.033620957,0.0011989983,0.0003427772,0.0025298595,0.00031824145,0.011412778,0.11330157,0.0011427236,0.13058966,0.70101595],"study_design_scores_gemma":[0.00040060983,0.0020022613,0.063936405,0.00014732385,0.00021812749,0.0026101305,0.00026373274,0.7642749,0.097096555,0.001858162,0.06701051,0.0001814274],"about_ca_topic_score_codex":0.0046310457,"about_ca_topic_score_gemma":0.009933567,"teacher_disagreement_score":0.007894187,"about_ca_system_score_codex":0.00030669683,"about_ca_system_score_gemma":0.00025894213,"threshold_uncertainty_score":0.026408672},"labels":[],"label_agreement":null},{"id":"W4290928441","doi":"10.1016/j.procs.2022.07.032","title":"Fear of falling and risk factors in older adults","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Ecole Supérieure des Communications de Tunis; New York University","keywords":"Fear of falling; Falling (accident); Slowness; Balance (ability); Elderly people; Fall prevention; Physical medicine and rehabilitation; Computer science; Poison control; Injury prevention; Medicine; Gerontology; Medical emergency; Psychiatry","score_opus":0.016998073206708778,"score_gpt":0.3115753953902454,"score_spread":0.29457732218353666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290928441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9743931,0.013342985,0.00040026894,0.002262048,0.00012799972,0.000052953386,0.0004062798,0.000014986355,0.008999243],"genre_scores_gemma":[0.99439865,0.0039079753,0.00033037327,0.0004221869,0.000103440136,0.00002164118,0.00017221233,0.000001611444,0.0006417913],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99976844,0.000041174462,0.00002987774,0.000029947743,0.00009372858,0.00003683973],"domain_scores_gemma":[0.99949014,0.00006582623,0.00023573435,0.000012537123,0.00007975813,0.00011602892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024711975,0.0001897896,0.00027273127,0.00087456766,0.0004439394,0.0005388624,0.00017249095,0.00053164293,0.002023423],"category_scores_gemma":[0.0018537015,0.00010722077,0.00038992713,0.00066412473,0.00019956828,0.00040552352,0.0004074414,0.000637642,0.0001785794],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005967225,0.00022621392,0.97243226,0.000110277404,0.000056136443,0.00038169973,0.0008103145,0.00003979201,0.00021045457,0.00018149153,0.001224875,0.024266925],"study_design_scores_gemma":[0.0000031665732,0.0001384952,0.99590415,0.00013229945,0.000045606415,0.0011088682,0.00080027594,0.00015422904,0.000026451718,0.00033567185,0.0013389918,0.000011791678],"about_ca_topic_score_codex":0.0051412866,"about_ca_topic_score_gemma":0.0046066,"teacher_disagreement_score":0.0051412866,"about_ca_system_score_codex":0.00021753051,"about_ca_system_score_gemma":0.00028662008,"threshold_uncertainty_score":0.010222733},"labels":[],"label_agreement":null},{"id":"W4293193791","doi":"10.1016/j.procs.2022.03.050","title":"IPARS: An Image-based Personalized Advertisement Recommendation System on Social Networks","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Bipartite graph; Social media; Set (abstract data type); Graph; Information retrieval; Recommender system; Online advertising; Social network (sociolinguistics); Social graph; Rank (graph theory); World Wide Web; Machine learning; Artificial intelligence; The Internet; Theoretical computer science","score_opus":0.029236057362075313,"score_gpt":0.28003083154594755,"score_spread":0.25079477418387225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293193791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27821,0.0034477296,0.5832258,0.0013558626,0.0005538195,0.0024193532,0.015216914,0.089058094,0.026512325],"genre_scores_gemma":[0.4939275,0.0011430915,0.4600396,0.0007473863,0.00027399723,0.000590633,0.016066551,0.00047987088,0.026731344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996916,0.000044089167,0.00002406893,0.00008804779,0.000109932924,0.00004228054],"domain_scores_gemma":[0.9997508,0.000045872075,0.000035322435,0.000046252804,0.000091107104,0.000030722807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032133516,0.0012242473,0.0007988065,0.0019083876,0.0004933105,0.00048547014,0.0010237474,0.0007636153,0.0029658617],"category_scores_gemma":[0.0007286456,0.0003449183,0.0007930789,0.0011894403,0.00015740925,0.0013093001,0.0006044057,0.0006012993,0.0026724811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016365304,0.001116778,0.009729324,0.00045751926,0.00047213255,0.0008080163,0.00023315407,0.01605955,0.040534135,0.0019781736,0.079047434,0.84792733],"study_design_scores_gemma":[0.0002826388,0.0007257765,0.014146275,0.00003763004,0.0003283029,0.0008159595,0.00023511086,0.91655326,0.029580083,0.0028902686,0.03426691,0.00013780955],"about_ca_topic_score_codex":0.0157994,"about_ca_topic_score_gemma":0.028106846,"teacher_disagreement_score":0.0157994,"about_ca_system_score_codex":0.00044239152,"about_ca_system_score_gemma":0.00040508885,"threshold_uncertainty_score":0.031414866},"labels":[],"label_agreement":null},{"id":"W4296500587","doi":"10.1016/j.procs.2022.09.086","title":"System design of a text messaging program to support the mental health needs of non-treatment seeking young adults","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Center for Advancing Translational Sciences; National Institute of Mental Health; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Mobile phone; Mental health; Intervention (counseling); Computer science; Phone; Text messaging; Feature (linguistics); Multimedia; Applied psychology; Internet privacy; Psychology; Psychiatry","score_opus":0.032878217922663225,"score_gpt":0.35947726759445964,"score_spread":0.3265990496717964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296500587","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5260267,0.00070754485,0.407882,0.0013089281,0.0006048565,0.03211926,0.0014880908,0.017239016,0.012623626],"genre_scores_gemma":[0.6067114,0.00029953106,0.36802667,0.00054158835,0.00007225731,0.0140853105,0.0008479534,0.0002457162,0.009169598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924964,0.0003085555,0.00008033147,0.00021816957,0.00008308992,0.000060196366],"domain_scores_gemma":[0.99859554,0.0007312767,0.00010542703,0.00009605394,0.00022096695,0.0002507511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013336209,0.00074599055,0.00042884165,0.00047364028,0.00097089924,0.0014496123,0.0013950393,0.0011341799,0.007531781],"category_scores_gemma":[0.0031609926,0.00032591086,0.00040800247,0.00020846348,0.00031943925,0.0008336941,0.00088573823,0.00052528887,0.0012598645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00958921,0.015550128,0.023679456,0.0073669855,0.00057560584,0.002174258,0.009199414,0.020914087,0.2139954,0.008124608,0.018140018,0.67069083],"study_design_scores_gemma":[0.011094022,0.056296177,0.06545505,0.0014017497,0.003731485,0.0024396526,0.005989492,0.42458418,0.25519073,0.011700883,0.16141593,0.0007006283],"about_ca_topic_score_codex":0.0011591668,"about_ca_topic_score_gemma":0.0009671532,"teacher_disagreement_score":0.007531781,"about_ca_system_score_codex":0.00067755894,"about_ca_system_score_gemma":0.0016300248,"threshold_uncertainty_score":0.025196373},"labels":[],"label_agreement":null},{"id":"W4296500634","doi":"10.1016/j.procs.2022.09.083","title":"Night-time cardiac metrics from a wearable sensor predict intensity of next-day chronic pain","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chronic pain; Heart rate; Intensity (physics); Medicine; Wearable computer; Physical therapy; Physical medicine and rehabilitation; Computer science; Heart rate variability; Internal medicine; Blood pressure; Embedded system","score_opus":0.012473641979208605,"score_gpt":0.2174135918152787,"score_spread":0.20493994983607008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296500634","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922994,0.000654486,0.0054539074,0.00008001296,0.000035361278,0.000020495187,0.00036636725,0.000041253617,0.0010488078],"genre_scores_gemma":[0.9977628,0.00018379213,0.0016100354,0.000024408448,0.000025673635,0.000009911291,0.00015793741,0.000004299457,0.00022111138],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99977106,0.00008427253,0.000014961629,0.000049665156,0.00005343768,0.000026574893],"domain_scores_gemma":[0.9990639,0.00040747126,0.00027884098,0.00008045944,0.00010498374,0.00006445988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047181416,0.00027042886,0.00025812845,0.00041283193,0.000084298874,0.0004970152,0.00015938688,0.0002849186,0.0007454079],"category_scores_gemma":[0.0028185437,0.00010837275,0.00025174208,0.00038828407,0.00009048431,0.0002519351,0.00017876699,0.00034061293,0.0001780615],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061615044,0.0002174129,0.92535424,0.00013280682,0.0002818358,0.00007293568,0.00027805864,0.0010903372,0.023095706,0.00014611508,0.00042970973,0.048284702],"study_design_scores_gemma":[0.000005237201,0.00027747406,0.99314296,0.000014790297,0.00004391211,0.00014492233,0.000098199176,0.0046274704,0.0013276716,0.0001123163,0.00019619627,0.000008903507],"about_ca_topic_score_codex":0.0009771604,"about_ca_topic_score_gemma":0.0018304156,"teacher_disagreement_score":0.0009771604,"about_ca_system_score_codex":0.00010106206,"about_ca_system_score_gemma":0.000109789005,"threshold_uncertainty_score":0.0024952292},"labels":[],"label_agreement":null},{"id":"W4296500673","doi":"10.1016/j.procs.2022.09.081","title":"Developing, Deploying, and Evaluating Digital Mental Health Interventions in Spaces of Online Help- and Information-Seeking","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health; Vector Institute","funders":"NIH Office of the Director; National Institute of Mental Health; Klingenstein Third Generation Foundation; Health Resources and Services Administration; National Eating Disorders Association; National Institutes of Health; National Science Foundation","keywords":"Psychological intervention; Computer science; Mental health; The Internet; Internet privacy; Knowledge management; World Wide Web; Medicine; Nursing; Psychiatry","score_opus":0.0694147283908655,"score_gpt":0.4206303829672279,"score_spread":0.3512156545763624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296500673","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32571346,0.111567296,0.08286222,0.3271927,0.012192805,0.0340655,0.0007538212,0.00071788917,0.10493441],"genre_scores_gemma":[0.6379347,0.119107835,0.17588553,0.038446005,0.0022392692,0.020527175,0.00031938398,0.0001493613,0.0053906897],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.95641786,0.038039524,0.0014383973,0.00073558866,0.0019993447,0.0013692501],"domain_scores_gemma":[0.9158402,0.073600404,0.002903352,0.0015540338,0.0039857826,0.002116246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.053887803,0.00059622625,0.00089020596,0.0012602817,0.0032257468,0.0075302836,0.0031337438,0.0029700892,0.0036642062],"category_scores_gemma":[0.0824347,0.00033384038,0.0012141856,0.0013056142,0.004675332,0.0062997625,0.004687541,0.004712269,0.00066588505],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005677008,0.0060858107,0.010004878,0.030296825,0.0005248724,0.00044764383,0.042212266,0.0039284145,0.002099908,0.057157688,0.039012033,0.807662],"study_design_scores_gemma":[0.0022929872,0.016163645,0.031534865,0.11266673,0.0021920826,0.00062822556,0.21523026,0.008642919,0.012680512,0.13275257,0.46483225,0.00038289692],"about_ca_topic_score_codex":0.0045422316,"about_ca_topic_score_gemma":0.017133784,"teacher_disagreement_score":0.053887803,"about_ca_system_score_codex":0.008902889,"about_ca_system_score_gemma":0.027264563,"threshold_uncertainty_score":0.28498936},"labels":[],"label_agreement":null},{"id":"W4297415838","doi":"10.1016/j.procs.2022.09.345","title":"Multiple Models Fusion for Multi-label Classification in Speech Emotion Recognition Systems","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Utterance; Perspective (graphical); Speech recognition; Field (mathematics); Emotion classification; Emotion recognition; Process (computing); Artificial intelligence; Natural language processing; Machine learning","score_opus":0.19666255647349082,"score_gpt":0.34643608468821113,"score_spread":0.14977352821472031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297415838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08518324,0.0036080945,0.90018183,0.0010465668,0.00059484557,0.00019798908,0.00034720276,0.0051640263,0.0036761907],"genre_scores_gemma":[0.8145654,0.0006800637,0.17570984,0.00054499554,0.0003294704,0.00021630636,0.0010380251,0.00028602945,0.0066298824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978002,0.0007645741,0.00014759415,0.00060677197,0.00039826165,0.00028265998],"domain_scores_gemma":[0.9982262,0.0007658385,0.00011360334,0.00023663763,0.0005736976,0.00008402828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037320321,0.0017416576,0.0015661318,0.0011089895,0.0008149776,0.0018105514,0.0016203346,0.0017783527,0.0025796695],"category_scores_gemma":[0.0041851085,0.0005311017,0.001874934,0.0006221781,0.00048352103,0.0019334861,0.0020428298,0.0030555746,0.0025634994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012838758,0.00077306316,0.0037044238,0.00026314368,0.0005233052,0.00032469013,0.00051009445,0.1508374,0.036961593,0.0026212765,0.0070044515,0.79519266],"study_design_scores_gemma":[0.000011852064,0.00013343673,0.0008573011,0.000018491552,0.00006181994,0.000041039697,0.000078336285,0.98814476,0.006985094,0.0025715514,0.0010739145,0.000022374135],"about_ca_topic_score_codex":0.0030728825,"about_ca_topic_score_gemma":0.0033383712,"teacher_disagreement_score":0.0037320321,"about_ca_system_score_codex":0.0008830795,"about_ca_system_score_gemma":0.0006206618,"threshold_uncertainty_score":0.019737065},"labels":[],"label_agreement":null},{"id":"W4307178478","doi":"10.1016/j.procs.2022.09.418","title":"Use of modern technologies by public transport passengers during the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministerstwo Edukacji i Nauki","keywords":"Public transport; Pandemic; Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); European union; Sustainable transport; Purchasing; Business; Emerging technologies; Sample (material); Marketing; Regional science; Transport engineering; Computer science; Sustainability; Geography; Medicine; Engineering; Economic policy","score_opus":0.05812998021781502,"score_gpt":0.2846349444053883,"score_spread":0.22650496418757327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307178478","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99926895,0.000072201634,0.00003506686,0.000023175127,0.000001839797,0.0000073362817,0.0002730842,0.0000012416605,0.00031709607],"genre_scores_gemma":[0.99894434,0.00024949192,0.00007383053,0.000018324949,0.0000028345623,0.000011765225,0.00035578985,0.0000013182931,0.0003422792],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970716,0.00006701877,0.000034292443,0.000048936407,0.0000578746,0.000084612526],"domain_scores_gemma":[0.9991716,0.00014691,0.00038323528,0.000050539053,0.00017653612,0.00007129446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045819886,0.0002158753,0.00022692347,0.0006842672,0.00028859294,0.00066137593,0.0001727436,0.00034616233,0.0012600519],"category_scores_gemma":[0.0018026326,0.00019151966,0.00032913795,0.0008273188,0.00021946381,0.00069573754,0.0005812534,0.00037481205,0.0002750401],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001264314,0.00004778359,0.98627776,0.00006014103,0.000025096228,0.00011074083,0.0037107475,0.00012123718,0.0006953745,0.00003917488,0.00020949845,0.008576049],"study_design_scores_gemma":[0.0000018847239,0.00015799237,0.9919996,0.000019884765,0.000012894623,0.000115612835,0.0065783304,0.00012748888,0.00016203655,0.000013622267,0.0008034019,0.000007165558],"about_ca_topic_score_codex":0.014751938,"about_ca_topic_score_gemma":0.018267699,"teacher_disagreement_score":0.014751938,"about_ca_system_score_codex":0.0003486435,"about_ca_system_score_gemma":0.00030873076,"threshold_uncertainty_score":0.029332161},"labels":[],"label_agreement":null},{"id":"W4310784875","doi":"10.1016/j.procs.2022.11.088","title":"On the accuracy of Covid-19 forecasting methods in Russia for two years","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Russian Foundation for Basic Research","keywords":"Mean absolute percentage error; Statistic; Computer science; Coronavirus disease 2019 (COVID-19); Statistics; Artificial neural network; Population; Mean absolute error; Set (abstract data type); Simple (philosophy); Artificial intelligence; Econometrics; Mean squared error; Demography; Mathematics; Medicine","score_opus":0.4613923431627227,"score_gpt":0.5232448043944713,"score_spread":0.06185246123174859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310784875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9778346,0.0017475218,0.014883472,0.0004360438,0.00010562721,0.000016040076,0.0010772382,0.00024858298,0.0036510073],"genre_scores_gemma":[0.9958883,0.00024096185,0.0026243515,0.000013164322,0.000016115164,0.000005597414,0.0008396682,0.000015578977,0.000356225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986864,0.00046583748,0.00013975703,0.00033906611,0.00025031777,0.00011854377],"domain_scores_gemma":[0.9942478,0.0037771172,0.00050844613,0.0005324411,0.00083442783,0.00009976469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005083728,0.0007581331,0.000773312,0.0013877208,0.0003911011,0.0014187396,0.0005936748,0.0010279376,0.00059305015],"category_scores_gemma":[0.0128563745,0.00022166988,0.00072200096,0.00080591196,0.00036081322,0.0010001808,0.0008266494,0.00071054406,0.00033362687],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009628789,0.0001780307,0.19811995,0.00022529006,0.00057351164,0.00021850695,0.0003742017,0.6768482,0.0033417486,0.0021406668,0.0017220528,0.115294956],"study_design_scores_gemma":[0.000017233495,0.00026274545,0.07362461,0.000079101585,0.0001335515,0.00008599278,0.0002248959,0.9180656,0.0049842335,0.0011357735,0.00133928,0.000047029924],"about_ca_topic_score_codex":0.017609833,"about_ca_topic_score_gemma":0.00934055,"teacher_disagreement_score":0.017609833,"about_ca_system_score_codex":0.0007463287,"about_ca_system_score_gemma":0.0006406026,"threshold_uncertainty_score":0.03501469},"labels":[],"label_agreement":null},{"id":"W4312230037","doi":"10.1016/j.procs.2022.10.093","title":"State Transformation Combined Adaptive Robust Control for Motor Driven Joint with State Constraints and Input Saturation","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Zhejiang Provincial Ten Thousand Plan for Young Top Talents","keywords":"Control theory (sociology); Computer science; Bounded function; State (computer science); Transformation (genetics); Lyapunov function; Transient (computer programming); Control (management); Algorithm; Mathematics; Artificial intelligence; Nonlinear system","score_opus":0.013167163758591083,"score_gpt":0.1897548280015711,"score_spread":0.17658766424298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312230037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03173204,0.00025063285,0.96332884,0.000095089505,0.000041955915,0.000029369778,0.000017741462,0.00032225598,0.0041821073],"genre_scores_gemma":[0.97978187,0.00015350491,0.017337475,0.000038099235,0.0000229979,0.00007774533,0.000040194824,0.00002477602,0.0025233622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960893,0.00008196853,0.000022000726,0.00009946866,0.00013801943,0.000049665294],"domain_scores_gemma":[0.99961084,0.00013298704,0.000092596405,0.00003068633,0.00011557211,0.000017310167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005327635,0.0006867478,0.0006666359,0.00023471942,0.0003254796,0.00083587685,0.00076959614,0.00052196404,0.0011968742],"category_scores_gemma":[0.0008379814,0.00024299829,0.00040955358,0.0003386514,0.00058448024,0.00053703703,0.00087859144,0.0006334658,0.00017894672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002459821,0.00006842588,0.00062740984,0.00030796387,0.000081964194,0.00033640396,0.00029959177,0.86605984,0.037613172,0.024264183,0.0010730881,0.06902197],"study_design_scores_gemma":[0.000011941763,0.00008661837,0.00013669021,0.0000045964666,0.000007741289,0.000022964916,0.000009743479,0.9962453,0.0018758189,0.0009983671,0.0005939931,0.0000061985315],"about_ca_topic_score_codex":0.0037543543,"about_ca_topic_score_gemma":0.00208093,"teacher_disagreement_score":0.0037543543,"about_ca_system_score_codex":0.00035668354,"about_ca_system_score_gemma":0.0007667624,"threshold_uncertainty_score":0.007465005},"labels":[],"label_agreement":null},{"id":"W4312294756","doi":"10.1016/j.procs.2022.10.107","title":"A TD-Learning Based Bionic Cerebellar Model Controller For Humanoid Robots","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National University's Basic Research Foundation of China; Department of Education of Liaoning Province; Natural Science Foundation of Liaoning Province; Science and Technology Commission of Shanghai Municipality; China Postdoctoral Science Foundation","keywords":"Computer science; Humanoid robot; Robot; Process (computing); Artificial intelligence; Cerebellum; Reinforcement learning; Simulation; Neuroscience","score_opus":0.008869988027543456,"score_gpt":0.1988751749000063,"score_spread":0.19000518687246284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312294756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044048958,0.00070638687,0.93998504,0.00021712166,0.000251106,0.00010512222,0.00005450518,0.0015582956,0.013073555],"genre_scores_gemma":[0.9504053,0.0002412669,0.044468597,0.000084710264,0.000029457568,0.0001365483,0.000056117715,0.00003193968,0.0045460467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989736,0.000013455208,0.000008363314,0.000033415818,0.000034255296,0.000013162522],"domain_scores_gemma":[0.99986863,0.000024381014,0.00002392048,0.000012721238,0.00005862556,0.00001159803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020053268,0.00042492046,0.0003817864,0.00020937301,0.00040956473,0.00043547343,0.0007249508,0.00044814916,0.0022979819],"category_scores_gemma":[0.00043151283,0.00014223927,0.00026847338,0.00016488768,0.00033135127,0.00032688954,0.00038254765,0.00035249707,0.00033022763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037971395,0.00017349508,0.0011275796,0.00051037705,0.00011366925,0.0004983695,0.00026818528,0.6124622,0.0603742,0.012744012,0.00473472,0.30661348],"study_design_scores_gemma":[0.00005349025,0.00023518682,0.0003509012,0.000018553219,0.000025149135,0.00010091334,0.000014923465,0.9913703,0.0035902737,0.0009925844,0.0032342013,0.000013514125],"about_ca_topic_score_codex":0.0077443384,"about_ca_topic_score_gemma":0.0058652936,"teacher_disagreement_score":0.0077443384,"about_ca_system_score_codex":0.00040752164,"about_ca_system_score_gemma":0.0006339563,"threshold_uncertainty_score":0.015398502},"labels":[],"label_agreement":null},{"id":"W4312547917","doi":"10.1016/j.procs.2022.09.216","title":"Deep Learning based Currency Exchange Volatility Classifier for Best Trading Time Recommendation","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Currency; Deep learning; Volatility (finance); Artificial neural network; Artificial intelligence; Stochastic volatility; Machine learning; Foreign exchange market; Econometrics","score_opus":0.15744075782648428,"score_gpt":0.39727917009565156,"score_spread":0.23983841226916727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312547917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34110826,0.0026987235,0.63940567,0.0010865235,0.0004408483,0.00016618245,0.0012842203,0.003345388,0.010464163],"genre_scores_gemma":[0.88653034,0.0006818289,0.10118959,0.00024142968,0.00012743931,0.0000785847,0.0015798367,0.000041329717,0.009529634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997003,0.00003818774,0.00003368961,0.00006494108,0.00010277225,0.000060177816],"domain_scores_gemma":[0.9996525,0.00010359202,0.000029115272,0.000033649758,0.00015552799,0.000025671283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005745296,0.00042063577,0.00083299575,0.0009584182,0.00028568567,0.00087431393,0.0009086451,0.00076405023,0.002291883],"category_scores_gemma":[0.0011638879,0.00023214391,0.00046452312,0.0008154172,0.00010428124,0.00081345416,0.0003819828,0.001043992,0.00088386366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004968931,0.0007362326,0.011054521,0.00008638001,0.00017082457,0.00016580302,0.000053367818,0.15449235,0.00972075,0.0026224058,0.009089993,0.81131047],"study_design_scores_gemma":[0.000012898436,0.000048334667,0.00091911433,0.000007748466,0.000015323509,0.00002958073,0.0000100369225,0.9953961,0.00227383,0.000606579,0.00067324913,0.000007240951],"about_ca_topic_score_codex":0.007979683,"about_ca_topic_score_gemma":0.0102607105,"teacher_disagreement_score":0.007979683,"about_ca_system_score_codex":0.000580199,"about_ca_system_score_gemma":0.0006926974,"threshold_uncertainty_score":0.015866458},"labels":[],"label_agreement":null},{"id":"W4312557637","doi":"10.1016/j.procs.2022.10.099","title":"Effect Analysis of Elastic Components on Jumping Performance of Bionic Leg","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Science Foundation of Zhejiang Province; China Postdoctoral Science Foundation","keywords":"Bionics; Computer science; Component (thermodynamics); Gait; Jumping; Robot; Perspective (graphical); Simulation; Artificial intelligence; Physical medicine and rehabilitation; Physics","score_opus":0.005847799172682065,"score_gpt":0.19970931735834296,"score_spread":0.19386151818566089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312557637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9870336,0.0003345748,0.010712768,0.000032066477,0.000013253435,0.000011623105,0.00003975414,0.00007380901,0.0017485134],"genre_scores_gemma":[0.9990847,0.00008022106,0.00051952765,0.0000048320744,0.0000015288769,0.0000029980897,0.000024289933,0.0000066965003,0.00027509397],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988735,0.000019741363,0.0000058866867,0.000015451407,0.000040287592,0.000031378102],"domain_scores_gemma":[0.9994604,0.00029700532,0.00007001513,0.00003390486,0.00010378816,0.00003487546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020442464,0.00036227357,0.00023416617,0.00047461424,0.00019506761,0.0002427512,0.00018517824,0.00026349694,0.001235878],"category_scores_gemma":[0.00072101323,0.00011258214,0.00018186007,0.00021949773,0.00024649122,0.00024709548,0.00018622364,0.0001757486,0.00015533553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024303223,0.00034178837,0.01136729,0.00068346463,0.00010221573,0.0005621072,0.00021709454,0.17063542,0.7404305,0.0010441886,0.00039930176,0.071786314],"study_design_scores_gemma":[0.00003802082,0.0030743077,0.084191374,0.000064007254,0.00022516506,0.00022414331,0.00037403402,0.58872813,0.32116184,0.0005692342,0.0012732787,0.00007656129],"about_ca_topic_score_codex":0.0007712635,"about_ca_topic_score_gemma":0.0006897716,"teacher_disagreement_score":0.001235878,"about_ca_system_score_codex":0.000111103356,"about_ca_system_score_gemma":0.00008515887,"threshold_uncertainty_score":0.0041344166},"labels":[],"label_agreement":null},{"id":"W4312875629","doi":"10.1016/j.procs.2022.09.474","title":"Reconfigurable Intelligent Surfaces improved Spectrum Sensing in Cognitive Radio Networks","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Cognitive radio; Computer science; Wireless; Throughput; Node (physics); False alarm; Transmission (telecommunications); Spectral efficiency; SIGNAL (programming language); Computer network; Channel (broadcasting); Real-time computing; Telecommunications; Artificial intelligence","score_opus":0.015091598053909229,"score_gpt":0.23086042209726115,"score_spread":0.21576882404335193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312875629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4552801,0.0017768353,0.5252649,0.00030368165,0.0001448782,0.000038455262,0.000043973694,0.0005267173,0.016620534],"genre_scores_gemma":[0.9832623,0.00016015358,0.015784035,0.000028025559,0.000010617448,0.000009980392,0.0000086370455,0.000011727028,0.00072441314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997749,0.000056226334,0.000006225655,0.00003481869,0.00008144576,0.000046441724],"domain_scores_gemma":[0.9997234,0.00012699894,0.00005021227,0.000039254403,0.0000458229,0.00001436608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000224684,0.00041007652,0.00032658176,0.0003603014,0.00022051761,0.0006226241,0.00041268166,0.0004654386,0.00060234725],"category_scores_gemma":[0.0006209337,0.00018816594,0.00030521184,0.0003079356,0.00053004856,0.00055938214,0.0005101478,0.00028701164,0.00014762097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035583798,0.00014943587,0.0017434126,0.00019867055,0.00006056862,0.00045602696,0.00016397475,0.595964,0.2719746,0.027362924,0.0010427288,0.100527935],"study_design_scores_gemma":[0.0000145498725,0.0002594056,0.0010717042,0.000010827715,0.000023152807,0.00014728916,0.00005107527,0.9537743,0.036634434,0.0058960686,0.0020903477,0.000026804913],"about_ca_topic_score_codex":0.00045396047,"about_ca_topic_score_gemma":0.00043980876,"teacher_disagreement_score":0.0006226241,"about_ca_system_score_codex":0.00036637907,"about_ca_system_score_gemma":0.00018394148,"threshold_uncertainty_score":0.002658248},"labels":[],"label_agreement":null},{"id":"W4312988282","doi":"10.1016/j.procs.2022.09.473","title":"User Sentiment Analysis in Conversational Systems Based on Augmentation and Attention-based BiLSTM","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Sentiment analysis; Benchmark (surveying); Task (project management); Conversation; Artificial intelligence; Service (business); Mechanism (biology); Machine learning; Natural language processing","score_opus":0.01328517642429894,"score_gpt":0.2476918364515747,"score_spread":0.23440666002727575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312988282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28499094,0.002198402,0.69253594,0.001097591,0.0006453318,0.00023445081,0.0009478267,0.010012482,0.007337055],"genre_scores_gemma":[0.9105188,0.00042985726,0.081840664,0.00034247682,0.0001636305,0.00014909744,0.0015013898,0.00018339402,0.00487064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996712,0.0000732174,0.00002158372,0.00011294834,0.00006259016,0.000058469108],"domain_scores_gemma":[0.99957794,0.00014036744,0.000038244332,0.000040992552,0.00016917002,0.00003320267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007630301,0.0010547905,0.0006426549,0.00054092315,0.00039775285,0.0006154959,0.0008287252,0.0006632686,0.0020160328],"category_scores_gemma":[0.001896595,0.00032662658,0.0006278043,0.00038776232,0.00029998305,0.0014174384,0.0010038523,0.0011268303,0.0014108958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010082982,0.00062345754,0.007863879,0.00038703697,0.00021456978,0.00045118554,0.00079478783,0.071054295,0.11553671,0.0033220493,0.015068031,0.7836757],"study_design_scores_gemma":[0.000010584678,0.00010769224,0.00142693,0.0000140347165,0.000035524652,0.000053430325,0.00005678025,0.98511016,0.009095338,0.0024341955,0.001639865,0.000015437025],"about_ca_topic_score_codex":0.003620075,"about_ca_topic_score_gemma":0.005089877,"teacher_disagreement_score":0.003620075,"about_ca_system_score_codex":0.0005163188,"about_ca_system_score_gemma":0.0005625997,"threshold_uncertainty_score":0.0071980357},"labels":[],"label_agreement":null},{"id":"W4313038530","doi":"10.1016/j.procs.2022.11.103","title":"Reproduced correlations between integrity of white matter tracts and self-reported anxiety","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; National Research Center \"Kurchatov Institute\"","keywords":"White matter; Uncinate fasciculus; Fractional anisotropy; Cingulum (brain); Anxiety; Fasciculus; Diffusion MRI; Psychology; Corpus callosum; Lateralization of brain function; Superior longitudinal fasciculus; Medicine; Audiology; Clinical psychology; Neuroscience; Psychiatry; Magnetic resonance imaging; Radiology","score_opus":0.057215155383079114,"score_gpt":0.33287231757531144,"score_spread":0.27565716219223235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313038530","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956293,0.00042807695,0.0023316152,0.0000466006,0.000012212571,0.000021500964,0.00035677876,0.000026815169,0.0011471129],"genre_scores_gemma":[0.9990374,0.000056176013,0.00056183495,0.000007631953,0.000006599326,0.0000073845817,0.00018078533,0.000005026405,0.0001371413],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99883217,0.0003012908,0.0001887101,0.0003785631,0.00021344371,0.00008577866],"domain_scores_gemma":[0.9930488,0.0022278219,0.0026927048,0.0011613176,0.0006158912,0.00025341703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013890531,0.00029910775,0.00019575084,0.00072073017,0.00018737496,0.00052061625,0.00016649108,0.0002943492,0.001946821],"category_scores_gemma":[0.007330328,0.00014112295,0.0002408249,0.0004460353,0.0004985556,0.00033462935,0.0003739414,0.00035095934,0.00017345893],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000195563,0.000056178378,0.96999073,0.00007088945,0.00043403814,0.00026187324,0.00055773713,0.00023931717,0.016166648,0.00026914416,0.00013605616,0.011621818],"study_design_scores_gemma":[0.0000016701605,0.00007864731,0.99799323,0.0000042471875,0.00003528718,0.0004294229,0.00007123185,0.00018982193,0.000895456,0.00017116434,0.00012567516,0.00000427213],"about_ca_topic_score_codex":0.00089675555,"about_ca_topic_score_gemma":0.0020641221,"teacher_disagreement_score":0.001946821,"about_ca_system_score_codex":0.00014568312,"about_ca_system_score_gemma":0.00018473683,"threshold_uncertainty_score":0.0073460937},"labels":[],"label_agreement":null},{"id":"W4313201267","doi":"10.1016/j.procs.2022.11.166","title":"GWO-based Modeling of an Unstable Transport System","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education and Research, Romania; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Corporation for National and Community Service","keywords":"Computer science; Nonlinear system; Mathematical optimization; Metaheuristic; Process (computing); Nonlinear programming; Algorithm; Mathematics","score_opus":0.005219924248720919,"score_gpt":0.16756169386912045,"score_spread":0.16234176962039953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313201267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21444151,0.00038596196,0.7496575,0.0004339878,0.00009146114,0.00011466711,0.0004483503,0.0005261926,0.033900417],"genre_scores_gemma":[0.9874533,0.00013148467,0.007156297,0.000024163619,0.000011249382,0.0000865552,0.000094252006,0.000033340544,0.0050093234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998907,0.000029767478,0.0000050543285,0.000024605935,0.000027806314,0.000022154374],"domain_scores_gemma":[0.9999001,0.000029607203,0.000025936108,0.000007215039,0.000026080117,0.000010995642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021436223,0.0006396037,0.00064460095,0.00039654004,0.00039964792,0.0008889494,0.00055571186,0.000878075,0.002214914],"category_scores_gemma":[0.00034725593,0.00024829307,0.0005233964,0.0002732101,0.0006786845,0.0004964576,0.00077358173,0.0005194816,0.0003056527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018244678,0.000005208646,0.00019940302,0.000016019054,0.000006544393,0.00005782232,0.000022351905,0.9934462,0.0018975416,0.0031498254,0.00007603964,0.0011047962],"study_design_scores_gemma":[0.0000019600454,0.000006916357,0.00006272196,0.0000012922804,0.0000016658329,0.0000030187675,0.0000049353703,0.9991736,0.0001128849,0.00049198506,0.00013707069,0.0000018743365],"about_ca_topic_score_codex":0.014952716,"about_ca_topic_score_gemma":0.0065322644,"teacher_disagreement_score":0.014952716,"about_ca_system_score_codex":0.0005542429,"about_ca_system_score_gemma":0.0007935113,"threshold_uncertainty_score":0.029731393},"labels":[],"label_agreement":null},{"id":"W4315752436","doi":"10.1016/j.procs.2022.12.325","title":"ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"University of Johannesburg","keywords":"Computer science; Dual (grammatical number); Focus (optics); Product (mathematics); Mathematical optimization; Face (sociological concept); Operations research; Algorithm","score_opus":0.03316031751886156,"score_gpt":0.26871931573658003,"score_spread":0.23555899821771847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315752436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25089943,0.0011586776,0.7180908,0.0007418203,0.00016601509,0.00015746935,0.00052503275,0.00060724927,0.027653527],"genre_scores_gemma":[0.9868196,0.00020977833,0.007813875,0.0000280197,0.000014627083,0.00010029085,0.00009488367,0.00001278535,0.004906043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976224,0.000059173413,0.000016467631,0.00005292285,0.00007304225,0.000036109665],"domain_scores_gemma":[0.9997389,0.0001508353,0.000036113437,0.000007649251,0.00005552467,0.000011024758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043243973,0.0007518854,0.0007722619,0.00048613167,0.00039227394,0.0012454997,0.00071804726,0.0012233771,0.0026939604],"category_scores_gemma":[0.00067295576,0.00037038678,0.0006051989,0.00036641344,0.00035603347,0.00055524096,0.0004207805,0.00092658657,0.00019454287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003147245,0.000010643179,0.00023589349,0.000023134511,0.0000123820555,0.00006351543,0.00001783761,0.9953881,0.00041271237,0.0009715989,0.00011341351,0.0027193595],"study_design_scores_gemma":[0.0000022198196,0.000006581865,0.00006242919,0.0000019447293,0.0000027067388,0.0000033605754,0.000004605969,0.99962914,0.00006553164,0.00015477033,0.000065314205,0.0000013403481],"about_ca_topic_score_codex":0.01887611,"about_ca_topic_score_gemma":0.010976807,"teacher_disagreement_score":0.01887611,"about_ca_system_score_codex":0.00088096416,"about_ca_system_score_gemma":0.0007678838,"threshold_uncertainty_score":0.03753245},"labels":[],"label_agreement":null},{"id":"W4315778721","doi":"10.1016/j.procs.2022.12.286","title":"Development of a Digital Innovation Framework that is Renowned Globally","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Economic and Technological Innovation","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Analytic hierarchy process; Computer science; Digital transformation; Globe; Flexibility (engineering); Innovation management; Process management; Knowledge management; Industrial organization; Business; Operations research; Management; Economics; Engineering; World Wide Web","score_opus":0.09067803230037265,"score_gpt":0.2534656959704424,"score_spread":0.16278766367006975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315778721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029251317,0.009130081,0.6309464,0.027790379,0.0011719393,0.0014904256,0.00046264648,0.0005133954,0.29924348],"genre_scores_gemma":[0.40360758,0.0069836443,0.5575935,0.0025598358,0.0002548797,0.0013448719,0.000644212,0.00009149638,0.026919926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936651,0.0027316718,0.00048696873,0.00087612675,0.0017999649,0.00044028083],"domain_scores_gemma":[0.9951184,0.0016310072,0.0003610631,0.00053697446,0.0017654643,0.00058713066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009642632,0.0008207909,0.0005796338,0.006421832,0.0027500298,0.00944881,0.0017450001,0.0030621896,0.0032146059],"category_scores_gemma":[0.006689631,0.0002752337,0.0010784818,0.0045165303,0.007702179,0.008910592,0.006205675,0.0036169875,0.00075402076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006499414,0.00002700636,0.00055811217,0.00018961585,0.000009594059,0.00008076554,0.0012785089,0.0018950142,0.00034654335,0.94727737,0.0021587107,0.04617232],"study_design_scores_gemma":[0.00001984562,0.00011606218,0.002388285,0.0014746656,0.00004090424,0.0004670406,0.004626918,0.010557494,0.0010386751,0.58378893,0.3954056,0.00007550849],"about_ca_topic_score_codex":0.0077266744,"about_ca_topic_score_gemma":0.0060517513,"teacher_disagreement_score":0.011052358,"about_ca_system_score_codex":0.011052358,"about_ca_system_score_gemma":0.018357072,"threshold_uncertainty_score":0.08019084},"labels":[],"label_agreement":null},{"id":"W4315782982","doi":"10.1016/j.procs.2022.12.197","title":"Comparison of Energy-use Efficiency for Lettuce Plantation under Nutrient Film Technique and Deep-Water Culture Hydroponic Systems","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Innovations in Aquaponics and Hydroponics Systems","field":"Agricultural and Biological Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Hydroponics; Nutrient; Agricultural engineering; Energy (signal processing); Efficient energy use; Horticulture; Electrical engineering; Ecology; Statistics; Mathematics","score_opus":0.028070391347304074,"score_gpt":0.27181612137850153,"score_spread":0.24374573003119746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315782982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977088,0.0004501015,0.00077814533,0.000015405836,0.000008477312,0.000013334649,0.0002196145,0.000023121696,0.0007830724],"genre_scores_gemma":[0.99393386,0.0005636175,0.0024573235,0.000023682838,0.0000056955523,0.000047200254,0.0009899803,0.000030302337,0.0019482486],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99983346,0.000017858214,0.000019071163,0.00006063254,0.000041962183,0.000027000198],"domain_scores_gemma":[0.99976355,0.00004782647,0.000046225126,0.000031196636,0.000048021488,0.000063150575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015706515,0.000342864,0.00037828594,0.00027001204,0.00020672461,0.00035916676,0.00030842805,0.00019465401,0.0006514924],"category_scores_gemma":[0.00018245776,0.00009124612,0.00030019935,0.00026334453,0.00014003477,0.00036270157,0.0002849898,0.00049168995,0.00015197649],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014470432,0.000029030532,0.00075563276,0.000045872937,0.000007185308,0.000031906042,0.000029646253,0.00007777677,0.99692947,0.00001769278,0.000014456424,0.0019167346],"study_design_scores_gemma":[0.000023501563,0.0015130767,0.062111046,0.000018390027,0.000059117527,0.0001607338,0.00018514945,0.0011400414,0.9320899,0.000054559037,0.0026157848,0.000028554234],"about_ca_topic_score_codex":0.0026451603,"about_ca_topic_score_gemma":0.0037929977,"teacher_disagreement_score":0.0026451603,"about_ca_system_score_codex":0.00045936194,"about_ca_system_score_gemma":0.00023365769,"threshold_uncertainty_score":0.0052594543},"labels":[],"label_agreement":null},{"id":"W4318570632","doi":"10.1016/j.procs.2023.01.163","title":"Speech Emotion Classification using Ensemble Models with MFCC","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mel-frequency cepstrum; Disgust; Speech recognition; Emotion classification; Artificial intelligence; Convolutional neural network; Boosting (machine learning); Emotion recognition; Pattern recognition (psychology); Feature extraction; Anger; Psychology","score_opus":0.12613317575746266,"score_gpt":0.33915606181764285,"score_spread":0.2130228860601802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318570632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2915909,0.004507625,0.69368553,0.0005623178,0.00067074824,0.00016887746,0.0008135509,0.0027848128,0.005215684],"genre_scores_gemma":[0.92899,0.0009999971,0.06395937,0.0001156581,0.00018078422,0.00012116081,0.0013864236,0.00007925334,0.004167428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996131,0.0000732765,0.00002506515,0.00012859666,0.00009936345,0.00006067065],"domain_scores_gemma":[0.99944276,0.00017812096,0.000032407996,0.000049230024,0.0002703518,0.000027127031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097622245,0.0011215782,0.0010184252,0.0010053584,0.00031307215,0.0006706007,0.0006298228,0.00067982444,0.0011041453],"category_scores_gemma":[0.0014354993,0.0002884557,0.0012936365,0.0005232163,0.000133096,0.0007455183,0.00048700193,0.0011648994,0.0010287801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050569733,0.0003719157,0.008086698,0.000076289696,0.00036963078,0.00014996946,0.00014295864,0.2500483,0.022871966,0.0008365475,0.0049395766,0.71160036],"study_design_scores_gemma":[0.00000224664,0.000053918022,0.001663022,0.0000063142834,0.000033157132,0.000022196622,0.000012839084,0.9960008,0.001547531,0.00029188057,0.0003584086,0.00000775239],"about_ca_topic_score_codex":0.0050021703,"about_ca_topic_score_gemma":0.0043214555,"teacher_disagreement_score":0.0050021703,"about_ca_system_score_codex":0.00038717446,"about_ca_system_score_gemma":0.00030949846,"threshold_uncertainty_score":0.009946108},"labels":[],"label_agreement":null},{"id":"W4353004292","doi":"10.1016/j.procs.2023.01.302","title":"A Literature Review on Digital Twins in Warehouses","year":2023,"lang":"en","type":"review","venue":"Procedia Computer Science","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Exploit; Data warehouse; Data science; Warehouse; Database; Computer security; Business","score_opus":0.05386761178086455,"score_gpt":0.29607835214697004,"score_spread":0.2422107403661055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353004292","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020197382,0.99722147,0.00018232792,0.00030786608,0.00014687436,0.000011431414,0.000062730665,0.0000079922265,0.0018573281],"genre_scores_gemma":[0.00088459154,0.9982716,0.00024404471,0.00018506087,0.00008026172,0.00000862935,0.000055559147,0.0000017009012,0.00026853164],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993449,0.000131365,0.00013780662,0.000106980144,0.00023361597,0.000045256358],"domain_scores_gemma":[0.99721766,0.0017396434,0.000345534,0.000054535347,0.00055263244,0.0000898606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010006202,0.00097348535,0.001256657,0.009997408,0.000536098,0.0018718827,0.0009814126,0.0012481285,0.008973971],"category_scores_gemma":[0.00325407,0.0004869957,0.0013943466,0.013263319,0.00051259587,0.002527203,0.00089854264,0.0010781921,0.0018237785],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000658794,0.00008057484,0.0005332505,0.15362333,0.00021817393,0.0003681763,0.00042660852,0.0005509538,0.0008813869,0.007429288,0.040511373,0.795311],"study_design_scores_gemma":[0.000010720909,0.00008982432,0.002382581,0.087647334,0.00074385677,0.0012886125,0.00044497126,0.00012514871,0.00049556745,0.0027894964,0.9039452,0.000036809553],"about_ca_topic_score_codex":0.0037428876,"about_ca_topic_score_gemma":0.005920609,"teacher_disagreement_score":0.009997408,"about_ca_system_score_codex":0.0010582302,"about_ca_system_score_gemma":0.0038779061,"threshold_uncertainty_score":0.030020952},"labels":[],"label_agreement":null},{"id":"W4366149280","doi":"10.1016/j.procs.2023.03.077","title":"Maintenance Cost of Software Ecosystem Updates","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Software; Software maintenance; Software development; Ecosystem; Software engineering; Software analytics; Software construction; Operating system; Ecology","score_opus":0.016773421547049524,"score_gpt":0.2577916063472722,"score_spread":0.2410181848002227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366149280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9264377,0.0015162546,0.059237655,0.0007997404,0.0001165719,0.00013101583,0.0013427553,0.0009022784,0.009515896],"genre_scores_gemma":[0.98937464,0.00032326102,0.008137965,0.000025753414,0.000013469877,0.000031425032,0.0005496926,0.000085394146,0.0014584071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9976361,0.000448653,0.00018847818,0.00034528112,0.0010974377,0.0002839914],"domain_scores_gemma":[0.9880372,0.0071523357,0.0014373381,0.0011942144,0.0018155071,0.00036344162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024934225,0.00054894073,0.0005045333,0.0023950306,0.0005691856,0.0018362699,0.0014609569,0.0008985949,0.0023741426],"category_scores_gemma":[0.022113103,0.00035965673,0.0006096584,0.0017139185,0.0005976571,0.002832546,0.00090765517,0.000722814,0.00028203998],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051342184,0.00021359583,0.05106789,0.00034610956,0.00015279498,0.000850568,0.00038975204,0.78688365,0.007581998,0.022137497,0.0044949283,0.12536784],"study_design_scores_gemma":[0.000018829085,0.00020963908,0.015684092,0.000042354543,0.0001265991,0.00056975434,0.00025338284,0.9715897,0.00292496,0.0064573265,0.00208729,0.000036223475],"about_ca_topic_score_codex":0.0120795015,"about_ca_topic_score_gemma":0.0088559305,"teacher_disagreement_score":0.0120795015,"about_ca_system_score_codex":0.0025793407,"about_ca_system_score_gemma":0.0011156311,"threshold_uncertainty_score":0.024018407},"labels":[],"label_agreement":null},{"id":"W4366149840","doi":"10.1016/j.procs.2023.03.002","title":"Preface","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.02561450301341403,"score_gpt":0.2821545985616449,"score_spread":0.2565400955482309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366149840","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018538163,0.010775703,0.021798482,0.027660824,0.31859905,0.0010406859,0.017887793,0.0028434675,0.59754026],"genre_scores_gemma":[0.0062700845,0.0051148445,0.0051771854,0.0054483935,0.036386237,0.00040454033,0.010805107,0.0011614945,0.92923206],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994516,0.00007084056,0.000034910987,0.00009681427,0.00030100075,0.0000448656],"domain_scores_gemma":[0.9937861,0.000924513,0.00018378625,0.00055915944,0.0038580385,0.0006884118],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010153919,0.0010499171,0.00063590566,0.0029902477,0.0021074254,0.0025263857,0.0011277127,0.0007694997,0.48359236],"category_scores_gemma":[0.011506425,0.00028192654,0.00057742104,0.0018951596,0.0004157531,0.0021822844,0.0017083194,0.002589762,0.3149304],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031127653,0.000026586224,0.00008725638,0.00009466606,0.0000018059916,0.00003746721,0.00002696133,0.00008898508,0.00020899682,0.003037902,0.95115834,0.045199826],"study_design_scores_gemma":[0.0000049048567,0.000019663294,0.00026115213,0.00010793505,0.0000023064515,0.000047160167,0.000044160213,0.00005775066,0.0002059567,0.0032660821,0.99597687,0.0000060291827],"about_ca_topic_score_codex":0.0035886902,"about_ca_topic_score_gemma":0.0044187265,"teacher_disagreement_score":0.5164076,"about_ca_system_score_codex":0.0012882708,"about_ca_system_score_gemma":0.0016586604,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4366149846","doi":"10.1016/j.procs.2023.03.048","title":"Preliminary Results of EEBL System for the Smartphone VANET","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Vehicular ad hoc network; Embedded system; Smartphone application; Brake; Wireless ad hoc network; Telecommunications; Multimedia; Wireless; Automotive engineering","score_opus":0.011483480042802318,"score_gpt":0.21101227317187563,"score_spread":0.19952879312907332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366149846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8919567,0.0015767107,0.055177726,0.0009880438,0.00078875397,0.00065880554,0.0038687224,0.01316241,0.031822123],"genre_scores_gemma":[0.98227596,0.00025753438,0.0058053206,0.00017645909,0.000037153677,0.00010171399,0.0022207408,0.00015841074,0.008966756],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992061,0.00019712615,0.00006865536,0.00010840796,0.00024344942,0.00017630281],"domain_scores_gemma":[0.99855167,0.00020851928,0.000048343467,0.00014193197,0.0009321207,0.00011730497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078507466,0.00075887574,0.000582118,0.0006566175,0.0004334848,0.000844432,0.00071025296,0.0007502979,0.0080694845],"category_scores_gemma":[0.001747726,0.00015118775,0.0003428344,0.000343219,0.00029633657,0.00091263023,0.0006138272,0.00044630177,0.0028894409],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011402108,0.0026275855,0.0552622,0.0035547398,0.0006853289,0.0068279547,0.0028424116,0.13421844,0.30529258,0.004701856,0.09155279,0.38103205],"study_design_scores_gemma":[0.00076473376,0.012406309,0.060660142,0.00025381733,0.00064286095,0.002750416,0.0033161482,0.57171714,0.24331953,0.0015190442,0.10225252,0.00039738917],"about_ca_topic_score_codex":0.0058026426,"about_ca_topic_score_gemma":0.0039200205,"teacher_disagreement_score":0.0080694845,"about_ca_system_score_codex":0.00036988678,"about_ca_system_score_gemma":0.00036579938,"threshold_uncertainty_score":0.026995122},"labels":[],"label_agreement":null},{"id":"W4386857812","doi":"10.1016/j.procs.2023.08.244","title":"Graphs with constant balancing number","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Limits and Structures in Graph Theory","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Combinatorics; Constant (computer programming); Edge coloring; Graph; Graph coloring; Computer science; Upper and lower bounds; Mathematics; Exponential function; Discrete mathematics; Line graph; Graph power","score_opus":0.019963923861310913,"score_gpt":0.28054158056314554,"score_spread":0.26057765670183464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386857812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7759363,0.0012268854,0.1767406,0.0015003784,0.00015552629,0.00009282347,0.001054999,0.00078001566,0.042512484],"genre_scores_gemma":[0.9493668,0.00063431024,0.035582542,0.00037303296,0.00016188622,0.00013816403,0.0009842,0.00019143872,0.012567647],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99924433,0.00012193705,0.000028309134,0.00025128087,0.00019753454,0.00015662255],"domain_scores_gemma":[0.9972128,0.001224644,0.0004983199,0.00046343548,0.00027870497,0.0003221093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004139918,0.00060712936,0.0006235416,0.0011007163,0.0017784161,0.0016737563,0.0010101377,0.001074688,0.006127225],"category_scores_gemma":[0.004475111,0.0005336076,0.00051847025,0.0014782767,0.0013304366,0.0039209793,0.0012093333,0.0011216999,0.0008355957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006501038,0.00013846406,0.0053915908,0.00039182705,0.000083153376,0.0006191885,0.00074678345,0.05821841,0.037996702,0.83571094,0.008129983,0.051922817],"study_design_scores_gemma":[0.00007781797,0.00015413735,0.0034181818,0.000059747043,0.000047585094,0.0010930627,0.00029558263,0.08698352,0.008982829,0.8790695,0.019771367,0.00004661161],"about_ca_topic_score_codex":0.0011516332,"about_ca_topic_score_gemma":0.0011861224,"teacher_disagreement_score":0.006127225,"about_ca_system_score_codex":0.0013846357,"about_ca_system_score_gemma":0.0004619309,"threshold_uncertainty_score":0.02049768},"labels":[],"label_agreement":null},{"id":"W4387490074","doi":"10.1016/j.procs.2023.09.001","title":"Preface","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.02561450301341403,"score_gpt":0.2821545985616449,"score_spread":0.2565400955482309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387490074","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018538163,0.010775703,0.021798482,0.027660824,0.31859905,0.0010406859,0.017887793,0.0028434675,0.59754026],"genre_scores_gemma":[0.0062700845,0.0051148445,0.0051771854,0.0054483935,0.036386237,0.00040454033,0.010805107,0.0011614945,0.92923206],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994516,0.00007084056,0.000034910987,0.00009681427,0.00030100075,0.0000448656],"domain_scores_gemma":[0.9937861,0.000924513,0.00018378625,0.00055915944,0.0038580385,0.0006884118],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010153919,0.0010499171,0.00063590566,0.0029902477,0.0021074254,0.0025263857,0.0011277127,0.0007694997,0.48359236],"category_scores_gemma":[0.011506425,0.00028192654,0.00057742104,0.0018951596,0.0004157531,0.0021822844,0.0017083194,0.002589762,0.3149304],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031127653,0.000026586224,0.00008725638,0.00009466606,0.0000018059916,0.00003746721,0.00002696133,0.00008898508,0.00020899682,0.003037902,0.95115834,0.045199826],"study_design_scores_gemma":[0.0000049048567,0.000019663294,0.00026115213,0.00010793505,0.0000023064515,0.000047160167,0.000044160213,0.00005775066,0.0002059567,0.0032660821,0.99597687,0.0000060291827],"about_ca_topic_score_codex":0.0035886902,"about_ca_topic_score_gemma":0.0044187265,"teacher_disagreement_score":0.5164076,"about_ca_system_score_codex":0.0012882708,"about_ca_system_score_gemma":0.0016586604,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4387490710","doi":"10.1016/j.procs.2023.09.032","title":"Phenomenological Characteristics of Attention Bias Modification Apps: A Systematic Literature Review and Meta-Analysis","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Novelty; Systematic review; Attentional bias; Popularity; Computer science; Anxiety; Mental health; Cognitive psychology; Psychotherapist; Psychology; MEDLINE; Psychiatry; Social psychology","score_opus":0.15757968822449916,"score_gpt":0.3780138174667881,"score_spread":0.22043412924228892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387490710","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00519211,0.9915376,0.0009161856,0.00023865454,0.00010640494,0.00094857137,0.000662678,0.000021043368,0.0003768607],"genre_scores_gemma":[0.14832903,0.84190553,0.004025896,0.00094355264,0.00013862045,0.0033359907,0.001028402,0.00003250025,0.00026044957],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.98423344,0.0055283504,0.006484409,0.001185712,0.0022608228,0.00030720542],"domain_scores_gemma":[0.95129913,0.035753116,0.007913136,0.0013532274,0.0033617185,0.00031975572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023269974,0.002187733,0.010213724,0.0127761355,0.0008817544,0.0041074087,0.0018211572,0.001421992,0.0029678605],"category_scores_gemma":[0.070949756,0.0011257667,0.027133249,0.011201858,0.0010930441,0.0025292074,0.00246795,0.0014643555,0.00022335353],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095796055,0.000055704364,0.004946218,0.7233881,0.23659293,0.00013426103,0.00036226533,0.00023110915,0.00022013842,0.0002355622,0.00062070304,0.032254986],"study_design_scores_gemma":[0.00044984472,0.00032096475,0.008126943,0.14244117,0.84347874,0.00027228365,0.00027708444,0.00014893088,0.00022493447,0.0004957957,0.0037136462,0.000049666567],"about_ca_topic_score_codex":0.0048456118,"about_ca_topic_score_gemma":0.013595816,"teacher_disagreement_score":0.023269974,"about_ca_system_score_codex":0.0034039596,"about_ca_system_score_gemma":0.0066041653,"threshold_uncertainty_score":0.123064816},"labels":[],"label_agreement":null},{"id":"W4389483481","doi":"10.1016/j.procs.2023.10.433","title":"Deep Learning based Currency Trend Classification Trained on Technical Indicators based Generated Dataset","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Currency; Technical analysis; Foreign exchange market; Convergence (economics); Deep learning; Divergence (linguistics); Artificial intelligence; Index (typography); Moving average; Machine learning; Econometrics; Finance; Economics; Macroeconomics","score_opus":0.04395655796247519,"score_gpt":0.26182965790635815,"score_spread":0.21787309994388296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389483481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7666187,0.0015642017,0.21134354,0.0008701022,0.00039105827,0.00014301669,0.010306334,0.0031360774,0.005626912],"genre_scores_gemma":[0.94903183,0.000573972,0.032824613,0.00014063495,0.000076486336,0.00012326027,0.01453968,0.000049661317,0.0026398636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977356,0.000041283587,0.000022276261,0.0000669301,0.000052649797,0.000043320975],"domain_scores_gemma":[0.9992162,0.000260696,0.00010426761,0.00010704882,0.0002761235,0.0000357214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008338765,0.0008973509,0.00054084917,0.0015060396,0.00018296715,0.0006393992,0.00089357333,0.0006173771,0.0010305587],"category_scores_gemma":[0.0023418018,0.00022375405,0.0005683787,0.0014389224,0.00021437224,0.0009910166,0.0005879236,0.0012622987,0.0005611299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005502742,0.0007738132,0.05499081,0.00022438173,0.00022362678,0.00042151054,0.0001062718,0.5140783,0.008438295,0.0034226058,0.01564441,0.4011257],"study_design_scores_gemma":[0.000008813526,0.000046529785,0.0031756456,0.0000127754865,0.000014871458,0.000024800529,0.000014643787,0.99340546,0.0015488871,0.00096498465,0.0007745781,0.000007898691],"about_ca_topic_score_codex":0.004768478,"about_ca_topic_score_gemma":0.0048840335,"teacher_disagreement_score":0.004768478,"about_ca_system_score_codex":0.00060806517,"about_ca_system_score_gemma":0.0005117018,"threshold_uncertainty_score":0.00948149},"labels":[],"label_agreement":null},{"id":"W4389483684","doi":"10.1016/j.procs.2023.10.021","title":"FreCDo: A Large Corpus for French Cross-Domain Dialect Identification","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Support vector machine; Discriminative model; Classifier (UML); Identification (biology); Natural language processing; Domain (mathematical analysis); Word (group theory); Task (project management); Linguistics","score_opus":0.015374244764587817,"score_gpt":0.3110712238581493,"score_spread":0.29569697909356146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389483684","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.343623,0.011527774,0.04630568,0.0017493153,0.0015878269,0.0012695307,0.53949505,0.013385383,0.041056424],"genre_scores_gemma":[0.21400388,0.0013678193,0.04697345,0.00064457895,0.0003647603,0.001422697,0.7226179,0.0016439067,0.010960936],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99805176,0.0006411548,0.00018685896,0.00053780724,0.00039829005,0.00018400322],"domain_scores_gemma":[0.99564254,0.0019356109,0.00021704477,0.0006772989,0.0012926946,0.00023485869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016182924,0.0012850381,0.00072637707,0.007300265,0.0018286627,0.0016960099,0.0010643582,0.0015405839,0.01207978],"category_scores_gemma":[0.0069652307,0.00029383946,0.0006507486,0.0040097237,0.00071191083,0.0015148744,0.001787832,0.0009831191,0.007820887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009119414,0.00057031144,0.024762057,0.004294705,0.0002609617,0.00383251,0.005764667,0.0031975757,0.0313264,0.007928882,0.57133275,0.34581724],"study_design_scores_gemma":[0.00024730622,0.00022680097,0.09143102,0.0005912043,0.00012789885,0.0049725724,0.0033597401,0.008393181,0.012552559,0.0025231296,0.8753427,0.00023190874],"about_ca_topic_score_codex":0.027190791,"about_ca_topic_score_gemma":0.031381864,"teacher_disagreement_score":0.027190791,"about_ca_system_score_codex":0.0011784654,"about_ca_system_score_gemma":0.001432832,"threshold_uncertainty_score":0.05406505},"labels":[],"label_agreement":null},{"id":"W4389492788","doi":"10.1016/j.procs.2023.10.333","title":"Robotic Process Automation (RPA) using a heuristic method and the effective resistance of a graph","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Robotic Process Automation Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robot; Heuristic; Automation; Bipartite graph; Graph; Integer programming; Mathematical optimization; Process (computing); Software; Artificial intelligence; Algorithm; Theoretical computer science; Programming language","score_opus":0.01035238022947703,"score_gpt":0.2793350499916205,"score_spread":0.26898266976214347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389492788","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026689202,0.00019609083,0.96676695,0.00016839275,0.000039947343,0.00017730135,0.00006315336,0.00027180696,0.005627054],"genre_scores_gemma":[0.42881012,0.00045987137,0.5662682,0.00010619948,0.000044830795,0.00050372013,0.00016478657,0.00011530868,0.0035270022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992879,0.00039889407,0.00002121906,0.0001116893,0.00010503742,0.00007530441],"domain_scores_gemma":[0.99860054,0.0010542801,0.00013247435,0.00008846518,0.00008322781,0.000041050847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008418218,0.0012241178,0.0009851308,0.0016127127,0.00064031786,0.0011207098,0.0011080971,0.0011381209,0.002964672],"category_scores_gemma":[0.0021640407,0.00059128413,0.0012945278,0.0012581347,0.00119331,0.0011188164,0.00066752214,0.00086710806,0.0002923724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039761275,0.000055182954,0.00013915023,0.000106023086,0.000030277877,0.00006670226,0.00003471955,0.95781064,0.0022642354,0.014699872,0.0004265974,0.024326736],"study_design_scores_gemma":[0.000018259878,0.00008138882,0.00008596577,0.000014041209,0.0000148541685,0.000042335083,0.000021393429,0.98768395,0.0009257107,0.010207325,0.0008933699,0.000011356721],"about_ca_topic_score_codex":0.0036810962,"about_ca_topic_score_gemma":0.002468192,"teacher_disagreement_score":0.0036810962,"about_ca_system_score_codex":0.0011306239,"about_ca_system_score_gemma":0.0014185044,"threshold_uncertainty_score":0.009917855},"labels":[],"label_agreement":null},{"id":"W4390643712","doi":"10.1016/j.procs.2023.12.052","title":"Ordering of Solar Photovoltaic Panels using the MEREC-SPOTIS Hybrid Analytical Model","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Photovoltaic system; Renewable energy; Computer science; Solar energy; Warranty; Multiple-criteria decision analysis; Environmental economics; Concentrated solar power; Process engineering; Operations research; Electrical engineering; Mathematics; Economics; Engineering","score_opus":0.29314310612585204,"score_gpt":0.43548769536269694,"score_spread":0.1423445892368449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390643712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07446799,0.00033152621,0.899119,0.00040728808,0.000083248924,0.0002569268,0.00033030505,0.00022022461,0.024783349],"genre_scores_gemma":[0.83231723,0.00045247716,0.15387511,0.00010471503,0.000045188553,0.00047837922,0.00032790328,0.000045643894,0.012353346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991768,0.00034553235,0.000034997345,0.00009240714,0.00024746018,0.00010275527],"domain_scores_gemma":[0.9991721,0.00047288573,0.00009595445,0.000023632832,0.00019636386,0.000039019586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012359099,0.0008749757,0.0010657812,0.0015457997,0.00075781054,0.0020863425,0.0012695428,0.0010640271,0.004826688],"category_scores_gemma":[0.0020142558,0.00060186605,0.001454788,0.0015596197,0.0005176569,0.00090359396,0.00087161566,0.00087476266,0.00042038024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041265153,0.000046214383,0.0004922816,0.000069815316,0.000037021993,0.00006113612,0.000053545547,0.9777002,0.0005816581,0.011561135,0.00043223656,0.008923471],"study_design_scores_gemma":[0.0000030712315,0.000016725864,0.000057322293,0.0000039712518,0.0000053142294,0.0000063994335,0.0000108393515,0.99821824,0.00008078683,0.0013816603,0.00021189022,0.0000038134747],"about_ca_topic_score_codex":0.013529469,"about_ca_topic_score_gemma":0.014319757,"teacher_disagreement_score":0.013529469,"about_ca_system_score_codex":0.0017769684,"about_ca_system_score_gemma":0.0017657174,"threshold_uncertainty_score":0.026901484},"labels":[],"label_agreement":null},{"id":"W4392974616","doi":"10.1016/j.procs.2024.02.035","title":"Defect detection in additive manufacturing using image processing techniques","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Ministère des relations internationales et de la Francophonie","keywords":"Computer science; Image processing; Computer vision; Artificial intelligence; Image (mathematics)","score_opus":0.01436283497835114,"score_gpt":0.2544084405960031,"score_spread":0.24004560561765195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392974616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06486294,0.0009320833,0.92956907,0.00006513078,0.000042531177,0.000058326143,0.00008732457,0.0016963995,0.0026861888],"genre_scores_gemma":[0.41256967,0.00086937664,0.58334285,0.000046216057,0.000028886208,0.00007036309,0.00017774117,0.00012512239,0.0027697405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994248,0.00007185978,0.000022959839,0.00008364979,0.00036755158,0.000029108667],"domain_scores_gemma":[0.99950206,0.00019053073,0.00006959113,0.00007405401,0.00015355034,0.00001013268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003476072,0.0004614213,0.0002865597,0.0016330004,0.00015054143,0.0006952036,0.0005631762,0.00058128085,0.0011479853],"category_scores_gemma":[0.0008455427,0.00027182917,0.00045656238,0.00081569236,0.00033266706,0.00053670263,0.00034920248,0.00036882865,0.0005193125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015836334,0.00008316758,0.002419581,0.00044946163,0.00005675421,0.00019355898,0.00029388868,0.017883606,0.3466477,0.003695216,0.001408727,0.62671],"study_design_scores_gemma":[0.000024009974,0.00034043836,0.010392749,0.00008406888,0.00008256983,0.0011907822,0.00013024767,0.47737408,0.4963685,0.0026612445,0.011279554,0.00007180732],"about_ca_topic_score_codex":0.00067372195,"about_ca_topic_score_gemma":0.00062225154,"teacher_disagreement_score":0.0016330004,"about_ca_system_score_codex":0.00030669916,"about_ca_system_score_gemma":0.00025026215,"threshold_uncertainty_score":0.0038403869},"labels":[],"label_agreement":null},{"id":"W4392974630","doi":"10.1016/j.procs.2024.02.017","title":"Melt pool instability in surface polishing by laser remelting: preliminary analysis and online monitoring with K-means clustering","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Western University","funders":"Government of Canada","keywords":"Computer science; Polishing; Cluster analysis; Instability; Surface (topology); Laser; Data mining; Materials science; Artificial intelligence; Mechanics; Optics; Composite material; Physics","score_opus":0.01001674621095405,"score_gpt":0.22971218441587046,"score_spread":0.2196954382049164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392974630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78050613,0.00062316237,0.21569689,0.0001093503,0.000024886518,0.00015000449,0.0005260277,0.0013165501,0.0010469749],"genre_scores_gemma":[0.8928384,0.0002234755,0.10519637,0.000012771442,0.000010657881,0.00009454475,0.0006530366,0.000096901706,0.0008737796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995235,0.000035039757,0.000049865692,0.00013158737,0.00020698598,0.000052941912],"domain_scores_gemma":[0.99902296,0.00023296525,0.00014417132,0.00009210629,0.00046364486,0.000044118377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006860707,0.0005594613,0.00055538566,0.0023145757,0.0005463978,0.0006569261,0.00080790883,0.00064032245,0.0004892894],"category_scores_gemma":[0.0010094715,0.00031101616,0.0007295955,0.0016021675,0.0003911009,0.0006728544,0.00040307923,0.00051349326,0.00024594518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088079623,0.0006709098,0.060930397,0.00077439356,0.00020511022,0.0007283105,0.0018608591,0.15983544,0.3613569,0.0016367394,0.0028887673,0.40823135],"study_design_scores_gemma":[0.000013117681,0.0001699708,0.050709236,0.000017438966,0.000052377338,0.00015577165,0.00043436955,0.86621815,0.08003801,0.0006507961,0.0014500142,0.00009074407],"about_ca_topic_score_codex":0.009031365,"about_ca_topic_score_gemma":0.008053313,"teacher_disagreement_score":0.009031365,"about_ca_system_score_codex":0.0005103211,"about_ca_system_score_gemma":0.00046225355,"threshold_uncertainty_score":0.017957568},"labels":[],"label_agreement":null},{"id":"W4392974830","doi":"10.1016/j.procs.2024.01.164","title":"Failure prediction in the refinery piping system using machine learning algorithms: classification and comparison","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Mitacs","keywords":"Computer science; Refinery; Piping; Algorithm; Machine learning; Artificial intelligence; Mechanical engineering","score_opus":0.01893020031378206,"score_gpt":0.24038798959309945,"score_spread":0.2214577892793174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392974830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9528003,0.002606856,0.038611226,0.0004200665,0.00014647987,0.0000990624,0.00129836,0.0014119791,0.0026056976],"genre_scores_gemma":[0.9840925,0.00043682722,0.01262734,0.00004030803,0.000028591943,0.00003774968,0.0019829376,0.000026191341,0.0007275592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991622,0.00018596704,0.00010692968,0.00018957221,0.000224431,0.00013086053],"domain_scores_gemma":[0.9972524,0.001637342,0.00018574388,0.0001667222,0.0006507858,0.000107102656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020367613,0.0012695827,0.0010268751,0.0030094017,0.000387328,0.0008406634,0.0007346709,0.0011997696,0.0009798434],"category_scores_gemma":[0.0037955118,0.00017298969,0.0008053109,0.0012253532,0.00028396456,0.0008325962,0.0004365986,0.0006876336,0.0003841857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001194412,0.0007505722,0.06882609,0.0003507535,0.0003014147,0.0002052002,0.000086159955,0.6856509,0.002366287,0.00060828915,0.004175112,0.23548476],"study_design_scores_gemma":[0.000008834235,0.00014221408,0.009026919,0.000011261593,0.00002249647,0.000031507923,0.00003408574,0.9891862,0.001096323,0.00018206237,0.00024899244,0.000009040741],"about_ca_topic_score_codex":0.021269968,"about_ca_topic_score_gemma":0.008970041,"teacher_disagreement_score":0.021269968,"about_ca_system_score_codex":0.0011204969,"about_ca_system_score_gemma":0.0009007453,"threshold_uncertainty_score":0.042292356},"labels":[],"label_agreement":null},{"id":"W4392975176","doi":"10.1016/j.procs.2024.01.095","title":"Implementation of a Business Intelligence System in the Brazilian Nuclear Industry: An Action Research","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Mitacs","keywords":"Computer science; Action (physics); Business intelligence; Action research; Data science; Knowledge management; Engineering management; Process management; Management; Business","score_opus":0.1807196227601199,"score_gpt":0.41380082207359664,"score_spread":0.23308119931347673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392975176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98280185,0.0001982924,0.002937077,0.0022544945,0.000026865868,0.00060545705,0.00003555468,0.000020281452,0.011120153],"genre_scores_gemma":[0.9896791,0.0003614708,0.007695824,0.0003914267,0.000008871775,0.00021430667,0.000040987245,0.000008592155,0.0015994291],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.98973083,0.0076794513,0.00023914292,0.00047057372,0.0009825305,0.00089745026],"domain_scores_gemma":[0.9882047,0.006216518,0.0013640103,0.0008345371,0.0019924387,0.0013878138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014944824,0.0005065162,0.00044684132,0.0010730287,0.003928238,0.0032815074,0.0011012806,0.0015236144,0.00093322975],"category_scores_gemma":[0.014805551,0.00031006965,0.00038420822,0.00096171355,0.0038865306,0.0017588339,0.00254027,0.0019054245,0.00016142878],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064162683,0.011658306,0.12483531,0.001565419,0.00016096803,0.0036237433,0.4565701,0.0071909525,0.023858765,0.05056461,0.005135394,0.3141947],"study_design_scores_gemma":[0.0003729459,0.009351716,0.1878515,0.0015537153,0.0003250881,0.001008646,0.6262734,0.02277374,0.019390952,0.011701636,0.11915069,0.0002458943],"about_ca_topic_score_codex":0.025476137,"about_ca_topic_score_gemma":0.04319098,"teacher_disagreement_score":0.025476137,"about_ca_system_score_codex":0.007998249,"about_ca_system_score_gemma":0.01373814,"threshold_uncertainty_score":0.07903671},"labels":[],"label_agreement":null},{"id":"W4392975260","doi":"10.1016/j.procs.2024.01.122","title":"Remarks from an experimental study on human-robot collaborative assembly","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Robot; Human–computer interaction; Human–robot interaction; Simulation; Artificial intelligence","score_opus":0.020865164851379573,"score_gpt":0.3041768759877287,"score_spread":0.2833117111363491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392975260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93227565,0.00066663837,0.036871664,0.003033909,0.00057265756,0.0021507728,0.0017314032,0.00046745676,0.022229841],"genre_scores_gemma":[0.96733344,0.0003446925,0.019244505,0.0009150648,0.00015430721,0.0022352717,0.00086975144,0.0001136298,0.00878934],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.98761755,0.0062415027,0.0012003105,0.0017986158,0.0025004407,0.0006416876],"domain_scores_gemma":[0.9297201,0.054445595,0.0026248214,0.004947387,0.0069683064,0.0012937554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009711053,0.00095730805,0.00075347844,0.0006882695,0.002095466,0.0017188649,0.001901281,0.002196768,0.016355472],"category_scores_gemma":[0.04092713,0.000460625,0.0007836893,0.0006001178,0.0032849542,0.001928195,0.0021888826,0.0014126153,0.002306696],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03362495,0.053984582,0.07084406,0.011846795,0.0005995207,0.0060060257,0.13487062,0.012302199,0.40288058,0.017721266,0.025001517,0.23031782],"study_design_scores_gemma":[0.002445804,0.108988464,0.164697,0.0021664922,0.00092544645,0.0026767105,0.09681213,0.015141134,0.46940926,0.016355842,0.11926691,0.001114817],"about_ca_topic_score_codex":0.0018055299,"about_ca_topic_score_gemma":0.0017138403,"teacher_disagreement_score":0.016355472,"about_ca_system_score_codex":0.00070307206,"about_ca_system_score_gemma":0.0007479934,"threshold_uncertainty_score":0.05471456},"labels":[],"label_agreement":null},{"id":"W4392975354","doi":"10.1016/j.procs.2024.01.003","title":"An Optimization Model for Smart and Sustainable Distributed Permutation Flow Shop Scheduling","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Flow shop scheduling; Scheduling (production processes); Distributed computing; Permutation (music); Job shop scheduling; Mathematical optimization; Embedded system; Routing (electronic design automation)","score_opus":0.009929444823300281,"score_gpt":0.2397870947899102,"score_spread":0.22985764996660993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392975354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021547552,0.0003407509,0.9677222,0.00051389023,0.00007229608,0.00009368192,0.00037284303,0.00018802192,0.009148763],"genre_scores_gemma":[0.8706541,0.0007178927,0.113861226,0.00016586267,0.00007555562,0.0005404645,0.0004871248,0.00010562447,0.013392172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934846,0.0002346267,0.00002599716,0.00013183148,0.0001469747,0.00011207165],"domain_scores_gemma":[0.9993574,0.00038284407,0.00009532369,0.000023142415,0.00009874977,0.000042520624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011659431,0.0011085327,0.0012201187,0.0006713959,0.00058898673,0.0015214079,0.0013550093,0.0018854844,0.0036637743],"category_scores_gemma":[0.0017694175,0.00060862064,0.00097093475,0.0009538312,0.0007597006,0.0010795936,0.00097406795,0.001349298,0.0003979607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008520437,0.000006822197,0.00006223264,0.000013010267,0.0000050650224,0.000026132158,0.000006620313,0.9948895,0.00017675234,0.003584417,0.00015791632,0.0010629559],"study_design_scores_gemma":[0.0000043891014,0.0000074478294,0.00003080202,0.0000015652524,0.0000022232089,0.000005414439,0.0000033220877,0.9982456,0.00003303399,0.0014628622,0.00020141796,0.0000018904854],"about_ca_topic_score_codex":0.009104879,"about_ca_topic_score_gemma":0.005890264,"teacher_disagreement_score":0.009104879,"about_ca_system_score_codex":0.0015254958,"about_ca_system_score_gemma":0.0020328842,"threshold_uncertainty_score":0.018103778},"labels":[],"label_agreement":null},{"id":"W4396220496","doi":"10.1016/j.procs.2024.03.010","title":"Forecasting Implementation of Hybrid Time Series and Artificial Neural Network Models","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Series (stratigraphy); Time series; Artificial intelligence; Machine learning","score_opus":0.12604433748052923,"score_gpt":0.38762651837114037,"score_spread":0.26158218089061114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396220496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53668976,0.0006097382,0.4383117,0.00028164542,0.00025220637,0.0001442514,0.00034402506,0.0029216139,0.020445092],"genre_scores_gemma":[0.9253191,0.0001661465,0.07079949,0.000036920053,0.000021239206,0.0000763371,0.00026975185,0.00004700885,0.0032639524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996568,0.0000881511,0.000026522792,0.000057613754,0.00012544649,0.00004537664],"domain_scores_gemma":[0.9996269,0.00011287011,0.00002965446,0.000048539736,0.0001651183,0.000016819771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006907699,0.0005949277,0.00045759804,0.0004554218,0.0002455642,0.00086090434,0.000855519,0.00046091728,0.0016204857],"category_scores_gemma":[0.001159328,0.00024663864,0.00045598697,0.000626045,0.0001542214,0.0007866942,0.0004977536,0.0006730798,0.00036149906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000223339,0.00021490431,0.0038317088,0.00010620116,0.00015273626,0.00016112314,0.00011837262,0.87561727,0.011168394,0.0040607685,0.0008292398,0.103515945],"study_design_scores_gemma":[0.000008493222,0.000056605146,0.0006558411,0.0000044898106,0.000017321066,0.000015278356,0.000018763078,0.99467444,0.003290287,0.00046764428,0.0007825215,0.000008319969],"about_ca_topic_score_codex":0.012454198,"about_ca_topic_score_gemma":0.011570937,"teacher_disagreement_score":0.012454198,"about_ca_system_score_codex":0.00048760188,"about_ca_system_score_gemma":0.00055658066,"threshold_uncertainty_score":0.024763465},"labels":[],"label_agreement":null},{"id":"W4399209455","doi":"10.1016/j.procs.2024.05.150","title":"Blockchain Technology, Structure, and Applications: A Survey","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Becton Dickinson (Canada); University Canada West; Research & Development Corporation","funders":"","keywords":"Computer science; Blockchain; Data science; Computer security","score_opus":0.008604083588028532,"score_gpt":0.24447951176122573,"score_spread":0.2358754281731972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399209455","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005689711,0.9379702,0.018199306,0.00196046,0.00049196475,0.0000968121,0.000229573,0.0001872677,0.03517472],"genre_scores_gemma":[0.020401657,0.9655246,0.007288536,0.0003618785,0.0006811441,0.0000650154,0.0002958737,0.00003273067,0.005348555],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989945,0.00013695455,0.0000931746,0.00013729441,0.00056782155,0.00007023161],"domain_scores_gemma":[0.9985397,0.0008974022,0.00011569913,0.000097447664,0.0002707918,0.00007902006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009657121,0.0006899146,0.0008788895,0.0044148327,0.0008171904,0.002578194,0.00079010235,0.0014537369,0.0051044994],"category_scores_gemma":[0.0021341648,0.0005160738,0.00043708392,0.008608627,0.0010825127,0.0050251344,0.0011253303,0.0012700309,0.0022738965],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007470388,0.00015789508,0.0018395142,0.007668719,0.000040282583,0.0002526973,0.00036943454,0.0051934496,0.0028208103,0.11086234,0.02626872,0.8444515],"study_design_scores_gemma":[0.000012168206,0.00017793645,0.001150475,0.0025563063,0.000036180543,0.0013371452,0.00026580962,0.004336323,0.0015360592,0.06485426,0.9236895,0.000047898255],"about_ca_topic_score_codex":0.0012750948,"about_ca_topic_score_gemma":0.0012037371,"teacher_disagreement_score":0.0051044994,"about_ca_system_score_codex":0.0010171022,"about_ca_system_score_gemma":0.0017267918,"threshold_uncertainty_score":0.017076313},"labels":[],"label_agreement":null},{"id":"W4399249656","doi":"10.1016/j.procs.2024.05.059","title":"Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Royal Military College of Canada","funders":"","keywords":"Computer science; Probabilistic logic; Classifier (UML); Algorithm; Graph; Machine learning; Data mining; Artificial intelligence; Theoretical computer science","score_opus":0.01291702207499444,"score_gpt":0.2214312567230276,"score_spread":0.20851423464803318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399249656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047228234,0.00006906409,0.992963,0.000144132,0.000038543392,0.000044831824,0.00008543327,0.0009183216,0.0010137516],"genre_scores_gemma":[0.25369966,0.0002586227,0.7395938,0.0002452628,0.00012600771,0.0003370905,0.0011204805,0.00027692964,0.0043422366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937385,0.00010574129,0.000025831741,0.00015567275,0.00028029384,0.000058594873],"domain_scores_gemma":[0.99907136,0.00030946548,0.00010189616,0.00011797087,0.00035423122,0.00004505761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086102413,0.0006914094,0.00087132334,0.001556662,0.0006937644,0.00091226125,0.0018801572,0.0012103767,0.0029153272],"category_scores_gemma":[0.002906685,0.00030261083,0.0007095017,0.0009937685,0.0004627948,0.0018290953,0.00097507116,0.0015529911,0.001526106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009613219,0.0001534974,0.0019346952,0.00007623409,0.000049583956,0.00008597345,0.00007659073,0.50128275,0.0042704376,0.047033094,0.010220429,0.4347206],"study_design_scores_gemma":[0.0000040555037,0.000010427297,0.00008824027,0.000003376191,0.0000037302025,0.000013664385,0.00000565466,0.98845357,0.000563254,0.009709911,0.0011406259,0.0000035111989],"about_ca_topic_score_codex":0.0061567575,"about_ca_topic_score_gemma":0.0047524655,"teacher_disagreement_score":0.0061567575,"about_ca_system_score_codex":0.0012449304,"about_ca_system_score_gemma":0.0017071968,"threshold_uncertainty_score":0.01224184},"labels":[],"label_agreement":null},{"id":"W4399510106","doi":"10.1016/j.procs.2024.09.567","title":"Link Prediction in Bipartite Networks","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Galatasaray Üniversitesi; Providence Health Care","keywords":"Bipartite graph; Computer science; Recommender system; Benchmark (surveying); Link (geometry); Matching (statistics); Theoretical computer science; Graph; Task (project management); Heuristic; Artificial intelligence; Machine learning; Computer network; Mathematics","score_opus":0.008967276321014407,"score_gpt":0.23833898569584214,"score_spread":0.22937170937482773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399510106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13322896,0.0010021637,0.85360974,0.0007364268,0.00009153213,0.00012825408,0.00240858,0.0020278767,0.0067664967],"genre_scores_gemma":[0.8499036,0.000640318,0.13835563,0.00030915157,0.000094499344,0.00015321301,0.0049471566,0.00020489637,0.0053915735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871826,0.00042979507,0.000053604195,0.0004384817,0.00021314825,0.0001468425],"domain_scores_gemma":[0.993616,0.0040146355,0.00075625384,0.0007232277,0.0006724552,0.00021745409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017102641,0.0009545853,0.00080117414,0.0027520368,0.000919276,0.0013330671,0.0014464561,0.0016364456,0.0035718614],"category_scores_gemma":[0.01241627,0.000670919,0.00084858155,0.0027812754,0.0007650305,0.003677223,0.0011522197,0.0014913725,0.0010497016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024046123,0.00015069607,0.00716338,0.00019641113,0.00011013884,0.00012072968,0.000111100744,0.88259107,0.001791243,0.026441008,0.005159458,0.075924255],"study_design_scores_gemma":[0.0000047306403,0.000012612899,0.0005117357,0.000010573715,0.00001026714,0.000022224152,0.000013169434,0.9839087,0.0003978331,0.014628045,0.00047422785,0.0000058199134],"about_ca_topic_score_codex":0.020909714,"about_ca_topic_score_gemma":0.022286024,"teacher_disagreement_score":0.020909714,"about_ca_system_score_codex":0.0017684941,"about_ca_system_score_gemma":0.00093718286,"threshold_uncertainty_score":0.041576028},"labels":[],"label_agreement":null},{"id":"W4400420597","doi":"10.1016/j.procs.2024.06.047","title":"A New Comprehensive Mathematical Model for Heterogeneous Multi-service Hybrid Migration in Edge Computing","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Cloud computing; Edge computing; Enhanced Data Rates for GSM Evolution; Internet of Things; Distributed computing; Service (business); Feature (linguistics); Contrast (vision); Data science; Artificial intelligence; World Wide Web","score_opus":0.04783702600613527,"score_gpt":0.2962027372884056,"score_spread":0.24836571128227036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400420597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012318173,0.0011195476,0.9699793,0.0010444118,0.00019574561,0.00007348761,0.00017172503,0.00012737834,0.014970163],"genre_scores_gemma":[0.8273937,0.0038510896,0.135909,0.0008087602,0.00035144394,0.0005545111,0.0004496936,0.0002310684,0.030450733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951065,0.00014718932,0.00002430117,0.00009559851,0.00013104017,0.00009119212],"domain_scores_gemma":[0.99934286,0.00029415035,0.00009020598,0.000041954114,0.00018978666,0.000041016694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012536622,0.0012055587,0.000792165,0.0008680795,0.0007417275,0.0018322443,0.0018601198,0.0015327003,0.0026688653],"category_scores_gemma":[0.0027849637,0.00043981546,0.0014813358,0.00085381744,0.00089465064,0.0029348778,0.0013750121,0.0018759052,0.0005476223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022955193,0.00004130146,0.0005516827,0.00012077234,0.000034614728,0.00019637284,0.0001287513,0.7968746,0.0018297253,0.18938647,0.0038118388,0.007000942],"study_design_scores_gemma":[0.0000023797566,0.000007809682,0.000056781842,0.000008033939,0.0000059941813,0.000027243472,0.00001644839,0.9867927,0.00009912349,0.011961583,0.0010154877,0.000006382288],"about_ca_topic_score_codex":0.007452548,"about_ca_topic_score_gemma":0.0042090714,"teacher_disagreement_score":0.007452548,"about_ca_system_score_codex":0.0018109942,"about_ca_system_score_gemma":0.0014898046,"threshold_uncertainty_score":0.014818311},"labels":[],"label_agreement":null},{"id":"W4400420677","doi":"10.1016/j.procs.2024.05.188","title":"Preface","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.013962293402786761,"score_gpt":0.27515486982513393,"score_spread":0.26119257642234717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400420677","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001852347,0.010761407,0.021767695,0.028171076,0.31445807,0.0010546246,0.018721057,0.0028844003,0.6003294],"genre_scores_gemma":[0.006258158,0.0050713494,0.005262394,0.0056094527,0.035805237,0.0004136873,0.011235159,0.0011750077,0.9291696],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994491,0.00007219585,0.00003521274,0.0000976252,0.0003008086,0.00004499983],"domain_scores_gemma":[0.9937744,0.0009320601,0.00018525239,0.0005541553,0.0038646641,0.0006894385],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010305229,0.0010440577,0.0006287292,0.002985489,0.0021132906,0.002508991,0.0011308376,0.00076394324,0.48709103],"category_scores_gemma":[0.011589423,0.00028167665,0.0005714878,0.0018795308,0.0004158623,0.0021688195,0.0017087855,0.0025841517,0.31798682],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031235726,0.000026462181,0.000087182205,0.00009512817,0.000001781875,0.000036892674,0.000027460215,0.00008701541,0.0002077824,0.0030323148,0.95126927,0.0450976],"study_design_scores_gemma":[0.0000048894894,0.000019234058,0.00025808715,0.00010757812,0.0000022640995,0.00004605315,0.0000442157,0.000055530258,0.0002021897,0.0032441835,0.9960098,0.00000596057],"about_ca_topic_score_codex":0.0036219282,"about_ca_topic_score_gemma":0.0044715223,"teacher_disagreement_score":0.51290894,"about_ca_system_score_codex":0.0012903737,"about_ca_system_score_gemma":0.0016700602,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4400420714","doi":"10.1016/j.procs.2024.06.137","title":"Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Polytechnique Montréal","keywords":"Computer science; Reinforcement learning; Flexibility (engineering); Efficient energy use; Energy consumption; Anomaly detection; Reliability (semiconductor); Cyber-physical system; Adaptation (eye); Resource efficiency; Risk analysis (engineering); Distributed computing; Artificial intelligence","score_opus":0.005340990691195734,"score_gpt":0.21017058282273524,"score_spread":0.2048295921315395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400420714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067310765,0.00029241963,0.92806655,0.00033552878,0.00003497994,0.000030299101,0.000023336068,0.00057603716,0.003330066],"genre_scores_gemma":[0.9613276,0.00009032583,0.037534796,0.000059892427,0.000010969697,0.00002364565,0.000020467178,0.000024373106,0.00090785365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969935,0.00008645127,0.000013070225,0.00006358439,0.000085942505,0.000051606163],"domain_scores_gemma":[0.9992561,0.0003786656,0.00012001298,0.000070779715,0.00012654337,0.000047989648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000897079,0.00068323535,0.0004993161,0.00028135994,0.00022663048,0.0006757588,0.0006694851,0.00055191864,0.00087916845],"category_scores_gemma":[0.002411332,0.0002509321,0.00030467633,0.00018604775,0.0007521635,0.0010805678,0.0009623904,0.0010222261,0.0001580608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049834274,0.0000664264,0.0011494876,0.000042808344,0.000031617255,0.000037123187,0.000028529616,0.95297295,0.0041547343,0.004728277,0.0003367102,0.036401507],"study_design_scores_gemma":[0.0000025500692,0.000017076092,0.00008636878,0.0000022416846,0.0000026088596,0.0000042998845,0.0000021409078,0.99760985,0.0006091392,0.001557996,0.00010411637,0.0000016862903],"about_ca_topic_score_codex":0.0027712174,"about_ca_topic_score_gemma":0.0032864504,"teacher_disagreement_score":0.0027712174,"about_ca_system_score_codex":0.0007900071,"about_ca_system_score_gemma":0.00095704454,"threshold_uncertainty_score":0.0057318807},"labels":[],"label_agreement":null},{"id":"W4400420839","doi":"10.1016/j.procs.2024.06.008","title":"Supply Chain Transport Management, Use of Electric Vehicles, Review of Security and Privacy for Cyber-Physical Transportation Ecosystem and Related Solutions","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Supply chain; Computer science; Software deployment; Supply chain management; Computer security; Intelligent transportation system; Risk analysis (engineering); Process management; Engineering management; Business; Transport engineering; Marketing","score_opus":0.011479166442704457,"score_gpt":0.23595478670507344,"score_spread":0.22447562026236897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400420839","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004532972,0.93335766,0.011047197,0.006783609,0.0010864983,0.00003808062,0.00009695326,0.00006602143,0.042991057],"genre_scores_gemma":[0.059249245,0.9274429,0.003119198,0.0010397951,0.0006869959,0.000027093536,0.00015691495,0.000017348199,0.008260517],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99916196,0.00025096728,0.00006821649,0.000115048446,0.0003309386,0.00007284678],"domain_scores_gemma":[0.99839693,0.0009154022,0.00021680207,0.00007954464,0.0003445068,0.000046690402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077646464,0.00038377714,0.0003057935,0.0025043208,0.0006307163,0.0023553353,0.0005765801,0.0012227824,0.0045218524],"category_scores_gemma":[0.00233387,0.00019168646,0.00025236473,0.0047625373,0.0011249058,0.003913881,0.0007728464,0.00091429346,0.00087105605],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004030038,0.000060008922,0.001202018,0.009221472,0.00004217993,0.0002849971,0.00066247414,0.003741357,0.0009744029,0.27278036,0.04949572,0.6614948],"study_design_scores_gemma":[0.0000022655881,0.000052090993,0.0013469325,0.004658376,0.000028899616,0.00044151387,0.00086668093,0.0014359258,0.0008996427,0.04421425,0.9460326,0.000020901227],"about_ca_topic_score_codex":0.0035680549,"about_ca_topic_score_gemma":0.0042225034,"teacher_disagreement_score":0.0045218524,"about_ca_system_score_codex":0.0016964161,"about_ca_system_score_gemma":0.0024184415,"threshold_uncertainty_score":0.015127063},"labels":[],"label_agreement":null},{"id":"W4400420891","doi":"10.1016/j.procs.2024.06.051","title":"Computational Architecture of an Integrated Urban Model Considering Physical-Virtual Activity Spaces","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Dalhousie University","keywords":"Computer science; Flexibility (engineering); Microsimulation; Architecture; Process (computing); Traffic flow (computer networking); Land use; Transport engineering","score_opus":0.017537473052587564,"score_gpt":0.28931499717760734,"score_spread":0.27177752412501976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400420891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15170163,0.00022888926,0.80415857,0.0009306538,0.000105179526,0.00011423806,0.0009979726,0.0005558772,0.041207094],"genre_scores_gemma":[0.9099538,0.00023878316,0.07733606,0.0001507031,0.000046823872,0.00038609517,0.0006470812,0.00011459018,0.01112608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997819,0.00006849697,0.000008111252,0.000056830944,0.00004259812,0.00004214514],"domain_scores_gemma":[0.9997664,0.00009738351,0.000025543306,0.000025814596,0.000052414758,0.000032487635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025837935,0.0005208982,0.00070474134,0.00043310435,0.00071139476,0.001414878,0.0015088437,0.001242441,0.0041903304],"category_scores_gemma":[0.00091768627,0.0005118355,0.00082012895,0.00056912866,0.00084868015,0.0009732159,0.0016593144,0.00084409845,0.0004345895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007590771,0.00000901864,0.00029890196,0.000004645959,0.000005733832,0.000015291853,0.000013187908,0.99347323,0.0001261637,0.004990294,0.000119404955,0.0009364703],"study_design_scores_gemma":[0.0000027761,0.000003534773,0.00004695502,0.0000011755375,0.000002557895,0.0000030585427,0.000008204231,0.9982932,0.000036149053,0.0013086054,0.00029177056,0.0000018947975],"about_ca_topic_score_codex":0.030543316,"about_ca_topic_score_gemma":0.021044433,"teacher_disagreement_score":0.030543316,"about_ca_system_score_codex":0.0011951769,"about_ca_system_score_gemma":0.0019510476,"threshold_uncertainty_score":0.060731113},"labels":[],"label_agreement":null},{"id":"W4400420952","doi":"10.1016/j.procs.2024.06.058","title":"Digital twin (DT)-based predictive maintenance of a 6G communication network","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Predictive maintenance; Context (archaeology); Reliability engineering","score_opus":0.00897065244384339,"score_gpt":0.20806968226806882,"score_spread":0.19909902982422542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400420952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032876465,0.00045278057,0.95848227,0.00019871481,0.00018337535,0.000038213217,0.0000467552,0.00047123616,0.0072501497],"genre_scores_gemma":[0.92699677,0.00034971992,0.06905272,0.00007969701,0.0000619203,0.000026906582,0.00006877366,0.00003595448,0.0033275522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966824,0.000054285945,0.000017996883,0.00009428415,0.00013628574,0.000028899232],"domain_scores_gemma":[0.99950325,0.00016522956,0.00007122216,0.00010461205,0.00012308784,0.000032542637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004451276,0.00043118448,0.00040558635,0.00054219115,0.00046326194,0.000929791,0.00094965484,0.000492321,0.0018012988],"category_scores_gemma":[0.0021670063,0.00016248909,0.0002492558,0.0004465939,0.00042062232,0.001548113,0.00078518916,0.00059419015,0.0002580615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064661674,0.00007803905,0.0033306635,0.00023113521,0.000061041865,0.0004292738,0.00031819442,0.39769343,0.048244096,0.086280055,0.004746467,0.457941],"study_design_scores_gemma":[0.00000827642,0.0001072848,0.00045026705,0.000015410302,0.000027231106,0.00026941142,0.00002977155,0.9786568,0.0069094235,0.0094196685,0.004094071,0.00001236505],"about_ca_topic_score_codex":0.0017303086,"about_ca_topic_score_gemma":0.0013100124,"teacher_disagreement_score":0.0018012988,"about_ca_system_score_codex":0.00065293943,"about_ca_system_score_gemma":0.00042876782,"threshold_uncertainty_score":0.00602597},"labels":[],"label_agreement":null},{"id":"W4400421435","doi":"10.1016/j.procs.2024.06.069","title":"Triage Software Update Impact via Release Notes Classification","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Triage; Categorization; Classifier (UML); Software; Machine learning; Process (computing); Artificial intelligence","score_opus":0.023406008543741343,"score_gpt":0.30285513759217464,"score_spread":0.2794491290484333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400421435","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8110056,0.0031748982,0.15041828,0.0019801138,0.0013187446,0.0016285286,0.011033577,0.01108447,0.008355836],"genre_scores_gemma":[0.90410054,0.0008762661,0.07979779,0.00026503537,0.00042417392,0.00027741268,0.009877244,0.00017877966,0.0042028017],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9973828,0.00042004013,0.00039954664,0.0005931715,0.0010190262,0.0001853516],"domain_scores_gemma":[0.9783392,0.009673639,0.005405713,0.0013931502,0.0043496247,0.0008387136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026257345,0.001368551,0.0007434028,0.0058304337,0.0005111127,0.0016601075,0.0013273628,0.00094184035,0.0020449103],"category_scores_gemma":[0.024385586,0.000250078,0.0005408855,0.0021965203,0.0002359254,0.0016850446,0.0010316004,0.0019360957,0.002371884],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001342749,0.0008456918,0.40001556,0.00048380098,0.00020639242,0.00074174715,0.00053454435,0.017389296,0.008553639,0.00059174874,0.02629297,0.5430019],"study_design_scores_gemma":[0.00013144047,0.0017608071,0.2725135,0.0003623108,0.0003044429,0.0019316546,0.0017590765,0.6748571,0.024329524,0.003086428,0.018727899,0.00023575677],"about_ca_topic_score_codex":0.0047125416,"about_ca_topic_score_gemma":0.0071696616,"teacher_disagreement_score":0.0058304337,"about_ca_system_score_codex":0.00064449286,"about_ca_system_score_gemma":0.0009105045,"threshold_uncertainty_score":0.013886392},"labels":[],"label_agreement":null},{"id":"W4400421577","doi":"10.1016/j.procs.2024.06.088","title":"A Comparative Analysis of Time-Based and Hybrid Pricing Models for Electric Vehicle Charging","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Polytechnique Montréal","funders":"","keywords":"Computer science; Revenue; Electric vehicle; Duration (music); Queue; Battery (electricity); Queueing theory; Dynamic pricing; Charging station; Operations research; Automotive engineering; Power (physics); Simulation; Computer network; Finance; Business","score_opus":0.008420281563794318,"score_gpt":0.22141301690927118,"score_spread":0.21299273534547686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400421577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72269744,0.0021583,0.23384807,0.0013561997,0.0002244217,0.0001955515,0.000584785,0.00036780623,0.0385674],"genre_scores_gemma":[0.9871824,0.0004660822,0.009764492,0.00005727469,0.000028839924,0.000050695453,0.00012340979,0.000029593133,0.0022972678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913615,0.00046259494,0.000035096557,0.00006637102,0.00018566202,0.00011408531],"domain_scores_gemma":[0.996644,0.0024120898,0.00024249588,0.0001608398,0.00041774925,0.00012281208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019955062,0.0008502914,0.00072992337,0.0008569346,0.00034400274,0.001657677,0.0013076427,0.0010707522,0.0023556654],"category_scores_gemma":[0.0055952608,0.00029112294,0.0011209202,0.0009109605,0.00045080928,0.0017583291,0.00076588907,0.00080276065,0.00019267267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012694974,0.000073679796,0.0010932038,0.000035417994,0.000035127156,0.000035899626,0.000038583352,0.9776324,0.0002910063,0.014327883,0.0002897134,0.006020157],"study_design_scores_gemma":[0.000011212329,0.00006609716,0.00026039095,0.0000061855726,0.000013266615,0.000012186257,0.000026510023,0.99666876,0.00010719811,0.0024861386,0.00033368476,0.000008250849],"about_ca_topic_score_codex":0.010792155,"about_ca_topic_score_gemma":0.0052747824,"teacher_disagreement_score":0.010792155,"about_ca_system_score_codex":0.0018747547,"about_ca_system_score_gemma":0.0011292568,"threshold_uncertainty_score":0.021458685},"labels":[],"label_agreement":null},{"id":"W4401942373","doi":"10.1016/j.procs.2024.08.026","title":"Forecasting Future Behavior: Agents in Board Game Strategy","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Operations research; Artificial intelligence; Human–computer interaction","score_opus":0.06968261981308498,"score_gpt":0.3158349713752454,"score_spread":0.24615235156216042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401942373","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.903399,0.00013120178,0.08957548,0.0006586453,0.000019147645,0.0000884735,0.000101429556,0.00015358042,0.0058729555],"genre_scores_gemma":[0.98932505,0.000044029617,0.009743231,0.00002642867,0.0000028931076,0.000018382707,0.000045300683,0.000008213716,0.00078639673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999607,0.000214721,0.000014875864,0.0000794464,0.00004528373,0.00003870494],"domain_scores_gemma":[0.996664,0.0026237592,0.00027724155,0.00014050897,0.00016612433,0.00012841231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011211914,0.000456065,0.00029360256,0.0004080816,0.00030053296,0.001374737,0.0005371458,0.0007561651,0.0019207259],"category_scores_gemma":[0.011134234,0.00020518721,0.00027498874,0.00031278603,0.00044455496,0.0015060096,0.0005265323,0.0009264577,0.00024227927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058861735,0.0005729316,0.13748474,0.00010636727,0.0001963032,0.0003261726,0.0020487583,0.7196375,0.0044787806,0.028929075,0.0016179084,0.10401279],"study_design_scores_gemma":[0.000008680778,0.000050256665,0.006223971,0.000008306704,0.000011115778,0.000021493186,0.00021387731,0.985302,0.0005370505,0.0070030997,0.00061097124,0.000009227413],"about_ca_topic_score_codex":0.015381207,"about_ca_topic_score_gemma":0.009617515,"teacher_disagreement_score":0.015381207,"about_ca_system_score_codex":0.0007004561,"about_ca_system_score_gemma":0.0005610056,"threshold_uncertainty_score":0.030583382},"labels":[],"label_agreement":null},{"id":"W4401942397","doi":"10.1016/j.procs.2024.08.027","title":"Machine Learning for DoS Attack Detection in IoT Systems","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National d'Optique; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Internet of Things; Artificial intelligence; Computer security; Machine learning","score_opus":0.018260868701139948,"score_gpt":0.257598339447689,"score_spread":0.23933747074654907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401942397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18370198,0.0016193377,0.80667853,0.0010149064,0.00016060933,0.00016878165,0.000288845,0.0022740662,0.004092943],"genre_scores_gemma":[0.9007112,0.00037718503,0.09742822,0.00011081955,0.000056544228,0.00008003003,0.00030697053,0.000034634417,0.0008943355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909127,0.00030560378,0.000055833152,0.00012770113,0.00032192914,0.000097617616],"domain_scores_gemma":[0.9982576,0.0009950884,0.00022995034,0.00014776294,0.00032768204,0.000041904597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015322624,0.00064229086,0.00059602223,0.001297861,0.00042054054,0.00064661296,0.0005245931,0.00049713365,0.0008701278],"category_scores_gemma":[0.004226736,0.00018406709,0.00046231417,0.0007549354,0.00023306272,0.0009771874,0.0003936082,0.0009162308,0.00033598582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002014401,0.00043399705,0.020236395,0.00018352616,0.00014619553,0.00010772722,0.000058956168,0.4896214,0.009065881,0.0045218896,0.0040297033,0.47139287],"study_design_scores_gemma":[0.0000026615864,0.000040864074,0.0014308788,0.000009763703,0.0000061998107,0.000020271207,0.000009992212,0.995053,0.0015884438,0.0014554544,0.00037878467,0.0000037817592],"about_ca_topic_score_codex":0.0018479687,"about_ca_topic_score_gemma":0.0023699552,"teacher_disagreement_score":0.0018479687,"about_ca_system_score_codex":0.0006815763,"about_ca_system_score_gemma":0.0006412618,"threshold_uncertainty_score":0.00810343},"labels":[],"label_agreement":null},{"id":"W4401942401","doi":"10.1016/j.procs.2024.08.001","title":"Preface","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.013962293402786761,"score_gpt":0.27515486982513393,"score_spread":0.26119257642234717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401942401","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001852347,0.010761407,0.021767695,0.028171076,0.31445807,0.0010546246,0.018721057,0.0028844003,0.6003294],"genre_scores_gemma":[0.006258158,0.0050713494,0.005262394,0.0056094527,0.035805237,0.0004136873,0.011235159,0.0011750077,0.9291696],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994491,0.00007219585,0.00003521274,0.0000976252,0.0003008086,0.00004499983],"domain_scores_gemma":[0.9937744,0.0009320601,0.00018525239,0.0005541553,0.0038646641,0.0006894385],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010305229,0.0010440577,0.0006287292,0.002985489,0.0021132906,0.002508991,0.0011308376,0.00076394324,0.48709103],"category_scores_gemma":[0.011589423,0.00028167665,0.0005714878,0.0018795308,0.0004158623,0.0021688195,0.0017087855,0.0025841517,0.31798682],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031235726,0.000026462181,0.000087182205,0.00009512817,0.000001781875,0.000036892674,0.000027460215,0.00008701541,0.0002077824,0.0030323148,0.95126927,0.0450976],"study_design_scores_gemma":[0.0000048894894,0.000019234058,0.00025808715,0.00010757812,0.0000022640995,0.00004605315,0.0000442157,0.000055530258,0.0002021897,0.0032441835,0.9960098,0.00000596057],"about_ca_topic_score_codex":0.0036219282,"about_ca_topic_score_gemma":0.0044715223,"teacher_disagreement_score":0.51290894,"about_ca_system_score_codex":0.0012903737,"about_ca_system_score_gemma":0.0016700602,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4401942515","doi":"10.1016/j.procs.2024.08.014","title":"One Hop Routing Optimization Approach Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Hop (telecommunications); Machine learning; Routing (electronic design automation); Artificial intelligence; Computer network; Distributed computing","score_opus":0.030689540437905585,"score_gpt":0.2484530183927581,"score_spread":0.2177634779548525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401942515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0149606,0.0005722189,0.9793314,0.0002855611,0.00008641348,0.0000674769,0.00003879524,0.000255218,0.0044022524],"genre_scores_gemma":[0.6596936,0.0009274468,0.3303916,0.00023682255,0.00015031738,0.00029916997,0.00019381686,0.00010626995,0.008000961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972886,0.00009113004,0.000013151183,0.000055219105,0.00007706157,0.000034516568],"domain_scores_gemma":[0.9994418,0.00036064425,0.000058909667,0.00002141088,0.000100312944,0.00001694207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007068919,0.0007747067,0.000996969,0.0008153699,0.00043146883,0.0008166513,0.0009489575,0.00079255004,0.0019610696],"category_scores_gemma":[0.0011436265,0.00029202498,0.000531518,0.00064304826,0.0003588854,0.0006689694,0.0004449191,0.0008480977,0.00025972023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001179877,0.000028485341,0.00021575968,0.00003114826,0.000023003962,0.00002048295,0.000013521359,0.97149855,0.00024944064,0.0037639097,0.00054535794,0.02359865],"study_design_scores_gemma":[0.0000013156797,0.0000058573582,0.000018709792,0.0000014502597,0.0000017272132,0.0000027428498,0.0000020563537,0.998896,0.00004432704,0.00092031853,0.000104421044,0.0000010615782],"about_ca_topic_score_codex":0.003710315,"about_ca_topic_score_gemma":0.0029611152,"teacher_disagreement_score":0.003710315,"about_ca_system_score_codex":0.00080609205,"about_ca_system_score_gemma":0.0009630586,"threshold_uncertainty_score":0.0073774457},"labels":[],"label_agreement":null},{"id":"W4401942569","doi":"10.1016/j.procs.2024.08.013","title":"Classical Machine Learning and Large Models for Text-Based Emotion Recognition","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Natural language processing; Emotion recognition; Speech recognition; Machine learning","score_opus":0.04454859075435676,"score_gpt":0.3197422853566027,"score_spread":0.27519369460224596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401942569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029232908,0.0038549856,0.9597352,0.0011251606,0.00030533026,0.00011885361,0.0007303365,0.0021136717,0.0027833972],"genre_scores_gemma":[0.7153917,0.0027326576,0.26961425,0.00053044804,0.0006644109,0.0006025925,0.0018583499,0.00024389965,0.0083616655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989441,0.00042746187,0.00007199474,0.00025552706,0.0002453366,0.000055596523],"domain_scores_gemma":[0.9971282,0.0021014325,0.0001701455,0.0003057206,0.00025753563,0.00003696019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016128302,0.0012574315,0.00083046197,0.0010474115,0.00038286214,0.001166129,0.0011017654,0.0010170364,0.0030465384],"category_scores_gemma":[0.007462571,0.0003723898,0.00082615175,0.0012765245,0.00045041152,0.0018663753,0.00065330666,0.0019860712,0.002186886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005582951,0.00036927056,0.003823717,0.00037856583,0.00030252791,0.00025424775,0.00018407135,0.41339657,0.008375099,0.023304157,0.013054215,0.53599924],"study_design_scores_gemma":[0.0000066297516,0.000031141768,0.00050221174,0.000010936983,0.0000104746305,0.000024435578,0.000011498613,0.98810494,0.00083679927,0.009231598,0.0012202326,0.000009030573],"about_ca_topic_score_codex":0.0045571434,"about_ca_topic_score_gemma":0.0044769766,"teacher_disagreement_score":0.0045571434,"about_ca_system_score_codex":0.00080238056,"about_ca_system_score_gemma":0.0005402453,"threshold_uncertainty_score":0.010191679},"labels":[],"label_agreement":null},{"id":"W4401979865","doi":"10.1016/j.procs.2024.08.004","title":"Randomized protocols for resilient peer-to-peer networks","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"University of Ontario Institute of Technology","keywords":"Computer science; Peer-to-peer; Randomized controlled trial; Peer review; Computer network; Medicine","score_opus":0.024325032003986734,"score_gpt":0.3186379231953854,"score_spread":0.2943128911913987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401979865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009102369,0.0007344773,0.9865737,0.00052794226,0.00018392358,0.0002095843,0.00005184123,0.000787804,0.0018283624],"genre_scores_gemma":[0.37146577,0.001293112,0.6205566,0.00049350975,0.00031113235,0.0013982703,0.0002136986,0.00028358886,0.0039843423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925794,0.0033356063,0.00043214078,0.0010819326,0.0020607705,0.00051021855],"domain_scores_gemma":[0.983779,0.0106689995,0.0014266743,0.0026632403,0.0010713832,0.00039059517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064411927,0.0009529014,0.0014813592,0.0013100298,0.001835486,0.0021673536,0.0034917884,0.0019733189,0.0025849985],"category_scores_gemma":[0.02501619,0.0007314529,0.0009409282,0.0012725706,0.0030858344,0.0043389075,0.0029125302,0.0028053976,0.0006404012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020916211,0.00014732333,0.0004460568,0.0002911507,0.00012222306,0.00015009799,0.0001772798,0.5883093,0.005266654,0.3684061,0.0031492687,0.03332535],"study_design_scores_gemma":[0.00014369599,0.000115126626,0.000077365956,0.00003671574,0.000040219657,0.000105649255,0.000036770823,0.8380292,0.0031452265,0.1514851,0.0067374753,0.00004742456],"about_ca_topic_score_codex":0.0012498081,"about_ca_topic_score_gemma":0.0011968184,"teacher_disagreement_score":0.0064411927,"about_ca_system_score_codex":0.0021987024,"about_ca_system_score_gemma":0.0025888584,"threshold_uncertainty_score":0.03406465},"labels":[],"label_agreement":null},{"id":"W4401979921","doi":"10.1016/j.procs.2024.08.002","title":"Preface","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.013962293402786761,"score_gpt":0.27515486982513393,"score_spread":0.26119257642234717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401979921","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001852347,0.010761407,0.021767695,0.028171076,0.31445807,0.0010546246,0.018721057,0.0028844003,0.6003294],"genre_scores_gemma":[0.006258158,0.0050713494,0.005262394,0.0056094527,0.035805237,0.0004136873,0.011235159,0.0011750077,0.9291696],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994491,0.00007219585,0.00003521274,0.0000976252,0.0003008086,0.00004499983],"domain_scores_gemma":[0.9937744,0.0009320601,0.00018525239,0.0005541553,0.0038646641,0.0006894385],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010305229,0.0010440577,0.0006287292,0.002985489,0.0021132906,0.002508991,0.0011308376,0.00076394324,0.48709103],"category_scores_gemma":[0.011589423,0.00028167665,0.0005714878,0.0018795308,0.0004158623,0.0021688195,0.0017087855,0.0025841517,0.31798682],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031235726,0.000026462181,0.000087182205,0.00009512817,0.000001781875,0.000036892674,0.000027460215,0.00008701541,0.0002077824,0.0030323148,0.95126927,0.0450976],"study_design_scores_gemma":[0.0000048894894,0.000019234058,0.00025808715,0.00010757812,0.0000022640995,0.00004605315,0.0000442157,0.000055530258,0.0002021897,0.0032441835,0.9960098,0.00000596057],"about_ca_topic_score_codex":0.0036219282,"about_ca_topic_score_gemma":0.0044715223,"teacher_disagreement_score":0.51290894,"about_ca_system_score_codex":0.0012903737,"about_ca_system_score_gemma":0.0016700602,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4401980534","doi":"10.1016/j.procs.2024.08.008","title":"HoBACDSL: HoBAC-focused Access Control Domain Specific Language","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Access Control and Trust","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Domain (mathematical analysis); Access control; Domain-specific language; Control (management); Human–computer interaction; Programming language; Artificial intelligence; Computer security","score_opus":0.017029856606610102,"score_gpt":0.31419109950066787,"score_spread":0.29716124289405776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401980534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033906328,0.00050025765,0.9531456,0.0011428036,0.00037522812,0.00065965595,0.0037248568,0.026752757,0.010308275],"genre_scores_gemma":[0.10809327,0.0015825004,0.8427416,0.004028042,0.00040350796,0.002418192,0.013537645,0.007040255,0.020155013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948002,0.0016512085,0.0012330909,0.0006997477,0.0011646274,0.00045107192],"domain_scores_gemma":[0.9929133,0.003397567,0.00059936766,0.001438984,0.0012369236,0.00041390955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005995994,0.0012341788,0.0009858604,0.0017997185,0.0011316377,0.00552771,0.0025392408,0.0025477635,0.0073575745],"category_scores_gemma":[0.007701118,0.0014403667,0.0020914327,0.0013342878,0.0031493665,0.005159645,0.0036687613,0.0056078928,0.0061465977],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041807612,0.0002024887,0.0015933847,0.0015020864,0.00010294494,0.000959799,0.0033012214,0.010685023,0.01722545,0.7971754,0.07678124,0.090052955],"study_design_scores_gemma":[0.0001248764,0.00009860221,0.00044334176,0.00044861305,0.00007695217,0.0010695093,0.0003900866,0.05028671,0.019635107,0.13701865,0.7902477,0.00015984187],"about_ca_topic_score_codex":0.0052289064,"about_ca_topic_score_gemma":0.005603728,"teacher_disagreement_score":0.0073575745,"about_ca_system_score_codex":0.002126873,"about_ca_system_score_gemma":0.0049183103,"threshold_uncertainty_score":0.031710207},"labels":[],"label_agreement":null},{"id":"W4401980771","doi":"10.1016/j.procs.2024.08.030","title":"Design and Development of a Digital Twin Platform for Scenario-Based Testing of Road Vehicles","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Development (topology); Human–computer interaction; Simulation","score_opus":0.0517645003188639,"score_gpt":0.2402282934697728,"score_spread":0.18846379315090891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401980771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02412969,0.000063195876,0.9622392,0.000067196255,0.00005148171,0.0006634797,0.00012973507,0.0065365676,0.006119482],"genre_scores_gemma":[0.29351205,0.00012013437,0.69834805,0.00008322377,0.00001865015,0.000765647,0.0005555921,0.0007100461,0.0058865286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992329,0.00012926543,0.000053106716,0.00013556516,0.00036119384,0.00008798322],"domain_scores_gemma":[0.9991554,0.00014077319,0.00007850136,0.00016840745,0.00030778552,0.00014913573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011344623,0.00051683653,0.0003985463,0.0008855602,0.0002596482,0.0010960657,0.0022027872,0.00067347835,0.0067725056],"category_scores_gemma":[0.0018007613,0.00039731045,0.00041609406,0.0002853057,0.0005264861,0.000989035,0.001409763,0.00067042175,0.0020079596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063106325,0.0005654223,0.00849406,0.0008724775,0.00014795929,0.0016973834,0.0010941342,0.19037527,0.26885274,0.048720155,0.009746282,0.46880305],"study_design_scores_gemma":[0.00019076797,0.0012904642,0.0033501026,0.00013349515,0.0000868962,0.0011233933,0.00026855135,0.7696501,0.14221607,0.008037727,0.07354314,0.00010940276],"about_ca_topic_score_codex":0.0010992262,"about_ca_topic_score_gemma":0.001056317,"teacher_disagreement_score":0.0067725056,"about_ca_system_score_codex":0.00046189877,"about_ca_system_score_gemma":0.0012435414,"threshold_uncertainty_score":0.022656322},"labels":[],"label_agreement":null},{"id":"W4403411966","doi":"10.1016/j.procs.2024.09.136","title":"Research on Online English Teaching Platform Based On Cloud Computing Technology","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Cloud computing; Data science; World Wide Web; Multimedia; Software engineering; Operating system","score_opus":0.05001863147609095,"score_gpt":0.39238534007103193,"score_spread":0.342366708594941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403411966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95198584,0.00084180414,0.0044906484,0.0009098519,0.000096399766,0.00035728616,0.0001017373,0.00007182279,0.041144654],"genre_scores_gemma":[0.9797029,0.0014604066,0.006106849,0.00018393653,0.000040340652,0.00016163754,0.00012471741,0.000023477907,0.012195778],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990374,0.00028089967,0.000045106208,0.000102463826,0.00031440434,0.00021969399],"domain_scores_gemma":[0.9970425,0.0011366324,0.00023759932,0.0001526034,0.00081940927,0.0006113353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010456548,0.00020664245,0.00021425352,0.0010154843,0.0008721797,0.0016131587,0.0005168254,0.0002707036,0.0061952914],"category_scores_gemma":[0.0034225204,0.00011115317,0.00035506656,0.0010093133,0.00037898117,0.00251449,0.00049875764,0.00044337398,0.00096216117],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001758141,0.008928308,0.11127451,0.003465316,0.00012523062,0.0030121286,0.037487134,0.002131406,0.070328124,0.0420402,0.011671643,0.7077779],"study_design_scores_gemma":[0.0004817573,0.011192833,0.37835866,0.0024012132,0.0008354158,0.0041830963,0.16585697,0.032132737,0.12166083,0.011834689,0.27083698,0.00022482443],"about_ca_topic_score_codex":0.0031000434,"about_ca_topic_score_gemma":0.004122719,"teacher_disagreement_score":0.0061952914,"about_ca_system_score_codex":0.001102431,"about_ca_system_score_gemma":0.0030089954,"threshold_uncertainty_score":0.02072525},"labels":[],"label_agreement":null},{"id":"W4403768786","doi":"10.1016/j.procs.2024.10.218","title":"Poems, Pulses and Polygons: How Classical Arabic Poetry Resonates with Music and Geometry","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Language, Linguistics, Cultural Analysis","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Poetry; Computer science; Arabic; Geometry; Literature; Art; Linguistics; Philosophy; Mathematics","score_opus":0.023123902525975123,"score_gpt":0.21960690011371847,"score_spread":0.19648299758774335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403768786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7341465,0.0040700426,0.05991388,0.002383138,0.00032544212,0.00007898663,0.0002278279,0.0002478973,0.19860625],"genre_scores_gemma":[0.9881959,0.0004925542,0.006787377,0.00008664219,0.000055509565,0.000013879258,0.00005645689,0.00007000851,0.0042416495],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961936,0.00019622801,0.000011363332,0.00006493731,0.00007852247,0.000029606454],"domain_scores_gemma":[0.9990446,0.00055507204,0.0001249039,0.00010195711,0.000099069606,0.00007439279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048387237,0.00031634732,0.0001570674,0.001399813,0.0012960876,0.00426174,0.0002723158,0.000363492,0.0044334494],"category_scores_gemma":[0.004731128,0.00012210215,0.00016920686,0.0015308609,0.004952049,0.0038637782,0.0012087259,0.000638781,0.0005361113],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036597357,0.000036346104,0.020836575,0.000595249,0.000049680802,0.0013979791,0.29116186,0.002516563,0.013761746,0.45330116,0.006446008,0.2095309],"study_design_scores_gemma":[0.00005048368,0.00022402777,0.101440735,0.000568784,0.00007032835,0.0048099705,0.24276769,0.018520914,0.0051196693,0.27094007,0.3553147,0.00017261533],"about_ca_topic_score_codex":0.0013718748,"about_ca_topic_score_gemma":0.0024121248,"teacher_disagreement_score":0.0044334494,"about_ca_system_score_codex":0.0005085398,"about_ca_system_score_gemma":0.00034283413,"threshold_uncertainty_score":0.014831364},"labels":[],"label_agreement":null},{"id":"W4404539963","doi":"10.1016/j.procs.2024.10.357","title":"Moving Forward: A new internet-delivered program integrating life review therapy and self-compassion may lessen depression and anxiety in people facing life transitions","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Psychological Well-being and Life Satisfaction","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Université de Moncton","keywords":"Computer science; Anxiety; The Internet; Depression (economics); Compassion; Self-compassion; Psychotherapist; World Wide Web; Psychiatry; Psychology; Mindfulness","score_opus":0.02187780810848184,"score_gpt":0.3205338521782819,"score_spread":0.29865604406980006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404539963","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9860342,0.00078046246,0.0048048957,0.0013581703,0.00020067116,0.0012161725,0.00006681556,0.00039259202,0.0051459456],"genre_scores_gemma":[0.9448105,0.0012554768,0.04364244,0.0015862784,0.00018130541,0.0016572113,0.00016733617,0.000039765466,0.006659602],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99982136,0.00008506989,0.000010541354,0.000028504086,0.00002344217,0.000031164804],"domain_scores_gemma":[0.99960285,0.00013494412,0.00005656137,0.000031179603,0.000018789502,0.00015557632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061227067,0.0003027144,0.00031030268,0.00034592496,0.00042903607,0.00036879527,0.0004110758,0.00053894665,0.0049190503],"category_scores_gemma":[0.0013619524,0.00011854198,0.0006236456,0.00016436308,0.00020994953,0.0005645311,0.00078798016,0.00062724156,0.0005585284],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003228155,0.04274358,0.0054046605,0.0010960815,0.0001481822,0.00044840874,0.0020119182,0.00039495173,0.010911417,0.000395585,0.0047234944,0.92849356],"study_design_scores_gemma":[0.033498038,0.260789,0.5253772,0.0027028755,0.0027786763,0.006927489,0.012845358,0.015985258,0.030447861,0.0061396454,0.10208268,0.00042583133],"about_ca_topic_score_codex":0.00037371565,"about_ca_topic_score_gemma":0.0011544438,"teacher_disagreement_score":0.0049190503,"about_ca_system_score_codex":0.00015077573,"about_ca_system_score_gemma":0.00054447347,"threshold_uncertainty_score":0.016455889},"labels":[],"label_agreement":null},{"id":"W4404797443","doi":"10.1016/j.procs.2024.09.190","title":"SIR-SRGAN-ResNeXt: A New Super-Resolution GAN with Self-Interpolation Ranker and ResNeXt Generator","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Université du Québec à Chicoutimi","keywords":"Computer science; Generator (circuit theory); Interpolation (computer graphics); Algorithm; Physics; Telecommunications; Frame (networking); Quantum mechanics","score_opus":0.009607950074009754,"score_gpt":0.24980798241963115,"score_spread":0.2402000323456214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404797443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027861686,0.0011177481,0.9606978,0.00047671163,0.0001656772,0.0000877638,0.0003261791,0.0025350086,0.006731334],"genre_scores_gemma":[0.63780504,0.00086159277,0.33653483,0.0010754176,0.00011434247,0.00022409404,0.0013636999,0.00064534845,0.021375736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981254,0.000051618998,0.000005915165,0.000050469236,0.00005347702,0.0000259043],"domain_scores_gemma":[0.9997267,0.00012377511,0.000025777137,0.000045119687,0.00005841249,0.000020229487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005898123,0.0008399644,0.0005576539,0.00031963782,0.00014456929,0.000430004,0.0012912706,0.0007259619,0.0025006759],"category_scores_gemma":[0.0010257922,0.0003260094,0.00062528055,0.00021819443,0.00047535606,0.00087731786,0.0008029485,0.0013629281,0.00074729166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030119697,0.0001293482,0.0013333554,0.00016083849,0.00016612331,0.00034010792,0.00008132791,0.73291284,0.03464582,0.021077663,0.012160002,0.19669142],"study_design_scores_gemma":[0.000008957507,0.000043634987,0.00011972913,0.00000948063,0.000010871203,0.00008393267,0.0000034332245,0.9915479,0.0040833494,0.0024338826,0.0016461296,0.000008745751],"about_ca_topic_score_codex":0.0017429489,"about_ca_topic_score_gemma":0.0038680993,"teacher_disagreement_score":0.0025006759,"about_ca_system_score_codex":0.00050213205,"about_ca_system_score_gemma":0.00037736387,"threshold_uncertainty_score":0.008365631},"labels":[],"label_agreement":null},{"id":"W4404797449","doi":"10.1016/j.procs.2024.09.240","title":"Data Engineering and AI-Powered Skin Cancer Identification for Healthcare Applications","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Identification (biology); Health care; Cancer; Data science; Artificial intelligence; Medicine","score_opus":0.02895958354227395,"score_gpt":0.3321428602122025,"score_spread":0.30318327666992856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404797449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018916616,0.0061732144,0.9415238,0.0074826656,0.0006828549,0.0002597568,0.0032498832,0.0037806886,0.017930593],"genre_scores_gemma":[0.5272537,0.011393072,0.42796388,0.0027815,0.0006654614,0.0007543615,0.011120977,0.00065661117,0.01741051],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989818,0.00024257075,0.000083919396,0.00023552122,0.0003913641,0.00006488593],"domain_scores_gemma":[0.9977047,0.0008032998,0.000178579,0.0005506914,0.0006799538,0.00008286419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016875025,0.00067350146,0.0004995124,0.0011972927,0.000382256,0.0018255634,0.0013003981,0.0011362258,0.0063777],"category_scores_gemma":[0.0068348544,0.0003363717,0.00071855006,0.0015893505,0.0006237318,0.0019070513,0.0014442476,0.0014021838,0.0029948116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030056087,0.00017374073,0.0069305277,0.001095397,0.000121304685,0.0004846028,0.00020893214,0.09860647,0.02828308,0.05058153,0.048491627,0.7647222],"study_design_scores_gemma":[0.00003641324,0.00016191078,0.003969907,0.00026517594,0.000048824302,0.0007138295,0.00029213692,0.7088811,0.045037262,0.09857575,0.14195257,0.00006515276],"about_ca_topic_score_codex":0.0021767986,"about_ca_topic_score_gemma":0.0025119055,"teacher_disagreement_score":0.0063777,"about_ca_system_score_codex":0.0009629128,"about_ca_system_score_gemma":0.0015873837,"threshold_uncertainty_score":0.021335542},"labels":[],"label_agreement":null},{"id":"W4404797740","doi":"10.1016/j.procs.2024.09.669","title":"Privacy Preserving Genomic Data Imputation using Autoencoders","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Imputation (statistics); Data mining; Artificial intelligence; Machine learning; Missing data","score_opus":0.0728452314253215,"score_gpt":0.3266561473777632,"score_spread":0.2538109159524417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404797740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014245923,0.00023441702,0.9837054,0.00026817975,0.000031992655,0.00001745471,0.00013096078,0.00051011133,0.0008554632],"genre_scores_gemma":[0.76395357,0.00070411613,0.22839256,0.000786021,0.00011074244,0.00012849545,0.00090965553,0.000110357,0.004904538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849784,0.0004436991,0.00008753583,0.00039237275,0.00042554873,0.0001530121],"domain_scores_gemma":[0.9972657,0.0014635073,0.00023927103,0.000648048,0.0003187824,0.000064703345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018933222,0.0006049394,0.0010292096,0.00032293488,0.00043058625,0.0009490394,0.0015867354,0.0012209644,0.0009628403],"category_scores_gemma":[0.0043703318,0.0005016656,0.00097326783,0.000662765,0.0009924363,0.0016467449,0.0014821409,0.002218522,0.0004347872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024489267,0.00009783583,0.0016890424,0.00007955886,0.0001291422,0.00027206884,0.00011521888,0.8401714,0.0061521963,0.023532791,0.002422332,0.12509352],"study_design_scores_gemma":[0.0000074427985,0.000014519606,0.00012490597,0.0000051150823,0.0000063818825,0.000042104326,0.0000049597174,0.99068075,0.0016839337,0.0070534614,0.0003709418,0.0000055212895],"about_ca_topic_score_codex":0.003901966,"about_ca_topic_score_gemma":0.0042705475,"teacher_disagreement_score":0.003901966,"about_ca_system_score_codex":0.0008236329,"about_ca_system_score_gemma":0.0015909388,"threshold_uncertainty_score":0.010012984},"labels":[],"label_agreement":null},{"id":"W4404801440","doi":"10.1016/j.procs.2024.09.492","title":"Zero-Shot-Learning for Plant Species Classification","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Shot (pellet); Zero (linguistics); Artificial intelligence; Machine learning","score_opus":0.04391339696116745,"score_gpt":0.23256051856851748,"score_spread":0.18864712160735003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031029887,0.00093406247,0.96285725,0.00025305612,0.000086798274,0.00012895023,0.00042196718,0.0025622703,0.0017257554],"genre_scores_gemma":[0.6196603,0.0004461247,0.3718957,0.00051212637,0.00012441845,0.00031527004,0.0027379554,0.00022765121,0.004080472],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988123,0.00028629755,0.00006309674,0.0004147699,0.0002887051,0.00013481347],"domain_scores_gemma":[0.99822253,0.00095178577,0.000111544585,0.0003157524,0.0002972846,0.00010113414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017255414,0.00090143125,0.0014817301,0.0019396603,0.00083501154,0.0010313203,0.0031319715,0.0017234546,0.0029120455],"category_scores_gemma":[0.004425438,0.00046234432,0.001122415,0.0015426714,0.00125957,0.0022821939,0.002352972,0.001814956,0.0011310498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040738273,0.0005069228,0.0027496107,0.00044043997,0.00013996239,0.00021757206,0.0003770284,0.13867795,0.015071175,0.012667228,0.008590584,0.82015413],"study_design_scores_gemma":[0.000012321068,0.00007015632,0.00042974643,0.00001733018,0.000012927179,0.000060145212,0.00006111363,0.9745083,0.0040694387,0.019303704,0.0014376071,0.00001713419],"about_ca_topic_score_codex":0.006084185,"about_ca_topic_score_gemma":0.007018236,"teacher_disagreement_score":0.006084185,"about_ca_system_score_codex":0.0013227956,"about_ca_system_score_gemma":0.0012417939,"threshold_uncertainty_score":0.0120975375},"labels":[],"label_agreement":null},{"id":"W4404801468","doi":"10.1016/j.procs.2024.09.461","title":"Multi-Label Classification with Deep Learning and Manual Data Collection for Identifying Similar Bird Species","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Artificial intelligence; Data collection; Machine learning; Statistics","score_opus":0.1007911516650473,"score_gpt":0.355540649061696,"score_spread":0.2547494973966487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27881655,0.0014143105,0.6952279,0.0010396467,0.0007834354,0.000878131,0.0033028184,0.008070762,0.010466438],"genre_scores_gemma":[0.5578,0.00023335943,0.4281476,0.0006115472,0.00015981837,0.00042925953,0.00675626,0.0002890037,0.0055732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968727,0.00065509276,0.00022344169,0.0012026613,0.00067734334,0.00036873983],"domain_scores_gemma":[0.9946243,0.0015914032,0.00067540654,0.0015611034,0.001301545,0.00024623756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002923076,0.0014428454,0.0011440613,0.0038846203,0.001536749,0.001706468,0.0030820565,0.0022416199,0.0032225654],"category_scores_gemma":[0.0059352033,0.0005091868,0.0010173217,0.0024930313,0.0011285143,0.0034258545,0.0026351092,0.0026848477,0.0023686455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004828208,0.00089394156,0.027948823,0.0005281849,0.0001744987,0.0002869336,0.00051476475,0.030179877,0.024767125,0.0034905896,0.01692229,0.8938101],"study_design_scores_gemma":[0.000044304004,0.00022834878,0.010868373,0.00012280242,0.00006155837,0.00027055148,0.0006109322,0.94835484,0.017632147,0.011683364,0.010055945,0.000066897876],"about_ca_topic_score_codex":0.007133867,"about_ca_topic_score_gemma":0.020048402,"teacher_disagreement_score":0.007133867,"about_ca_system_score_codex":0.0016969611,"about_ca_system_score_gemma":0.0014921764,"threshold_uncertainty_score":0.015458882},"labels":[],"label_agreement":null},{"id":"W4404801487","doi":"10.1016/j.procs.2024.09.460","title":"Refining Bird Species Identification through GAN-Enhanced Data Augmentation and Deep Learning Models","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Refining (metallurgy); Identification (biology); Deep learning; Artificial intelligence; Machine learning; Ecology; Materials science; Biology","score_opus":0.07413222633854419,"score_gpt":0.31641886311521067,"score_spread":0.24228663677666648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1381615,0.000598639,0.8550517,0.00037704792,0.00011758047,0.000068130874,0.00037990714,0.0018147272,0.0034308105],"genre_scores_gemma":[0.76226854,0.00024280905,0.23187654,0.00043491332,0.000051823692,0.0000761411,0.0010457064,0.00021317837,0.0037904615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996903,0.00008991065,0.000011189289,0.00010691827,0.000055108132,0.000046615634],"domain_scores_gemma":[0.99934417,0.00032416498,0.000066265864,0.00012039408,0.00010906863,0.000035921425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009478881,0.00088662305,0.000549281,0.00069167826,0.00023951757,0.00065312954,0.00090370333,0.0007312431,0.0010828101],"category_scores_gemma":[0.0021672724,0.00034183738,0.0007613102,0.00042941124,0.00063307415,0.0010408533,0.0009190317,0.0012125439,0.0006060133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020757229,0.00014700745,0.009894768,0.00013255088,0.00009035789,0.00019827833,0.00018428893,0.74347234,0.03319925,0.007247981,0.0037428546,0.20148277],"study_design_scores_gemma":[0.0000019978715,0.00001729074,0.00044529277,0.0000066516,0.0000048134775,0.00003919023,0.000011636319,0.9945222,0.0024407955,0.0019284517,0.0005765309,0.0000051024836],"about_ca_topic_score_codex":0.0030262806,"about_ca_topic_score_gemma":0.0054234085,"teacher_disagreement_score":0.0030262806,"about_ca_system_score_codex":0.0005053383,"about_ca_system_score_gemma":0.00046959225,"threshold_uncertainty_score":0.0060173273},"labels":[],"label_agreement":null},{"id":"W4404801502","doi":"10.1016/j.procs.2024.09.488","title":"Subtype-MMCC: multimodal contrastive clustering approach for cancer subtype discovery with multi-omics data","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Cluster analysis; Information retrieval; Artificial intelligence; Data mining; Data science","score_opus":0.023187460103311953,"score_gpt":0.2720319823285792,"score_spread":0.24884452222526726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028367259,0.0005229765,0.96653426,0.00036872376,0.0000593149,0.0001372551,0.0007193993,0.002085673,0.0012050612],"genre_scores_gemma":[0.4474531,0.00040800788,0.5415875,0.00060216046,0.00011116678,0.00032742266,0.0038711634,0.00033250335,0.0053070327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994572,0.00011695918,0.000027006718,0.00020593582,0.00012724515,0.00006566787],"domain_scores_gemma":[0.99956614,0.00011103953,0.00004982342,0.00008916161,0.00013748843,0.000046315716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001104675,0.001019497,0.0007920593,0.0011863891,0.0007770901,0.00075595616,0.0020161374,0.0013053156,0.0012312914],"category_scores_gemma":[0.0019905667,0.00038395848,0.0014558925,0.0010021686,0.0005177833,0.0010579671,0.001361168,0.0018533752,0.00069939485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006036251,0.00034432972,0.013850468,0.00020877582,0.0004701907,0.000330152,0.00039864721,0.403552,0.024756514,0.017046172,0.015916806,0.5225224],"study_design_scores_gemma":[0.000010401549,0.000047410213,0.000994443,0.000010424549,0.000027658589,0.00006228135,0.000028243729,0.98516035,0.0025477847,0.00908864,0.00200313,0.000019288344],"about_ca_topic_score_codex":0.014533831,"about_ca_topic_score_gemma":0.027652739,"teacher_disagreement_score":0.014533831,"about_ca_system_score_codex":0.0013036074,"about_ca_system_score_gemma":0.0016994252,"threshold_uncertainty_score":0.028898418},"labels":[],"label_agreement":null},{"id":"W4404801508","doi":"10.1016/j.procs.2024.09.446","title":"From Data to Decisions : Exploring Data Analytics in HR for Agile Organizational Decision Making","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Agile software development; Analytics; Data science; Knowledge management; Data analysis; Process management; Management science; Data mining; Software engineering","score_opus":0.24804307298446507,"score_gpt":0.3426634907132106,"score_spread":0.09462041772874555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045235246,0.0017611161,0.9306981,0.0105734505,0.00011015796,0.0004136946,0.00065708446,0.0003731073,0.010178157],"genre_scores_gemma":[0.5420772,0.0011036115,0.45463866,0.00052022055,0.000076994365,0.00034525903,0.00058939547,0.00006258333,0.00058612647],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.98711234,0.009178716,0.0005803135,0.0008958112,0.0017722233,0.00046052595],"domain_scores_gemma":[0.9630494,0.03083603,0.0014851025,0.0028904816,0.0011398497,0.0005990776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014552862,0.0007427828,0.00068662415,0.0031199462,0.0016041289,0.011414749,0.0019610932,0.0015753163,0.0017388171],"category_scores_gemma":[0.035230637,0.00057130883,0.0011231718,0.0038569605,0.006879655,0.011512777,0.006487461,0.0034911912,0.000285229],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015879753,0.00027311503,0.010129993,0.00079755834,0.000113756534,0.00058430753,0.008431695,0.04920503,0.0017246542,0.79222363,0.0026296359,0.13372786],"study_design_scores_gemma":[0.000027389879,0.00007575711,0.0022798425,0.0006059977,0.000035055382,0.00013327766,0.0070791603,0.22439924,0.0019717414,0.7413855,0.021942692,0.00006433594],"about_ca_topic_score_codex":0.005648375,"about_ca_topic_score_gemma":0.006047218,"teacher_disagreement_score":0.014552862,"about_ca_system_score_codex":0.002838991,"about_ca_system_score_gemma":0.005079584,"threshold_uncertainty_score":0.07696378},"labels":[],"label_agreement":null},{"id":"W4404801525","doi":"10.1016/j.procs.2024.09.616","title":"A biomarker identification model from protein protein interaction network using natural language processing and graph convolutional network","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Graph; Identification (biology); Artificial intelligence; Natural language processing; Theoretical computer science","score_opus":0.01135384413387943,"score_gpt":0.25580281506799335,"score_spread":0.2444489709341139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050096825,0.0009778254,0.9416242,0.0012513633,0.000063529864,0.00010226595,0.0012533836,0.0022782618,0.002352378],"genre_scores_gemma":[0.78639495,0.0011640992,0.20054962,0.00058504054,0.0000949817,0.00043706893,0.002916142,0.00013520634,0.0077228965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999835,0.000026980391,0.000007740635,0.00007451742,0.0000321479,0.000023519793],"domain_scores_gemma":[0.99975985,0.00012648961,0.000035571964,0.000012733484,0.000050330123,0.00001516367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038226202,0.00092926493,0.000558229,0.0011822984,0.000319022,0.00062675483,0.0010896615,0.00091304036,0.0011339448],"category_scores_gemma":[0.0009443378,0.00040444158,0.0008732583,0.0009497635,0.00043734984,0.001114769,0.000563678,0.0009554633,0.00041119353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014199565,0.000132033,0.0025795116,0.00012858785,0.00010847987,0.00032075442,0.00006966156,0.89054763,0.0055608815,0.012870846,0.004068649,0.08347094],"study_design_scores_gemma":[0.000002652787,0.0000067029005,0.00011508614,0.0000023626508,0.000007454549,0.000013603704,0.0000015293035,0.9961521,0.00024468495,0.003253881,0.00019723056,0.0000026258926],"about_ca_topic_score_codex":0.017531505,"about_ca_topic_score_gemma":0.02228456,"teacher_disagreement_score":0.017531505,"about_ca_system_score_codex":0.0014367575,"about_ca_system_score_gemma":0.0013954465,"threshold_uncertainty_score":0.034858942},"labels":[],"label_agreement":null},{"id":"W4404801560","doi":"10.1016/j.procs.2024.09.613","title":"Using Auto-Encoders to Create Encodings for Three-Dimensional Protein Structure Information","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Encoder; Theoretical computer science; Artificial intelligence; Algorithm; Computer vision; Operating system","score_opus":0.011499858014108008,"score_gpt":0.2739601485216951,"score_spread":0.26246029050758707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021156738,0.00092954596,0.96739256,0.00040885946,0.0003461063,0.00007046935,0.001322128,0.004144553,0.0042289435],"genre_scores_gemma":[0.32269683,0.0016229941,0.663843,0.0003244878,0.00016961296,0.00025390196,0.0035597037,0.000498807,0.007030665],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996859,0.000047027028,0.000033971355,0.000079731995,0.00011804422,0.000035260677],"domain_scores_gemma":[0.99898964,0.0003679957,0.00010163842,0.00026958084,0.00022800635,0.00004307637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041930578,0.0005670805,0.00033818313,0.0007552696,0.00027721995,0.00081851956,0.0012247482,0.0005599927,0.0035408023],"category_scores_gemma":[0.0020605528,0.0002644652,0.0004272417,0.000961593,0.0005232991,0.0021578348,0.00086526835,0.0011255109,0.0014551454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027435127,0.00021279912,0.0022543613,0.0004189552,0.00007206825,0.000529876,0.00022905032,0.080585,0.035735752,0.13777426,0.017452573,0.72446096],"study_design_scores_gemma":[0.0000241404,0.00014535966,0.0006320724,0.0001113419,0.000051595773,0.000347078,0.000057204037,0.85567456,0.03961251,0.07234927,0.030945623,0.000049167338],"about_ca_topic_score_codex":0.0026052315,"about_ca_topic_score_gemma":0.004351601,"teacher_disagreement_score":0.0035408023,"about_ca_system_score_codex":0.0006789542,"about_ca_system_score_gemma":0.0008294292,"threshold_uncertainty_score":0.011845231},"labels":[],"label_agreement":null},{"id":"W4404801591","doi":"10.1016/j.procs.2024.09.632","title":"A heuristic method to solve an assignment problem using a random walk approximation","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Université du Québec à Chicoutimi","funders":"Université du Québec à Chicoutimi","keywords":"Computer science; Heuristic; Random walk; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Statistics","score_opus":0.020983853330012896,"score_gpt":0.3175615227836643,"score_spread":0.2965776694536514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404801591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009626696,0.00022377752,0.9872931,0.00014865557,0.000057588066,0.00012243124,0.000070313465,0.00035297498,0.0021044498],"genre_scores_gemma":[0.1704779,0.00026669662,0.8253782,0.0001843044,0.00006983887,0.00043488806,0.00039723812,0.00013292745,0.0026580752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990858,0.00039955782,0.00004184284,0.00017582053,0.00016280076,0.00013427227],"domain_scores_gemma":[0.99815387,0.0013938679,0.00011583921,0.00009306247,0.0001664052,0.00007698377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012022159,0.0013148217,0.0014118421,0.0014111928,0.00072591554,0.00096341054,0.0015059988,0.0013134718,0.0046221167],"category_scores_gemma":[0.0034781564,0.0006404973,0.0012124497,0.0017052385,0.00081882416,0.001115308,0.00085188745,0.001541766,0.0008291336],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000957083,0.00012968377,0.00032940748,0.00010706225,0.00004113596,0.00008071431,0.000051649076,0.9342814,0.0012470669,0.0097899325,0.0022418804,0.051604368],"study_design_scores_gemma":[0.000020725207,0.000038363876,0.000046771787,0.000008491546,0.000006677255,0.000020968868,0.000013469015,0.99564725,0.00021585022,0.0034541897,0.0005222066,0.0000050569056],"about_ca_topic_score_codex":0.007736332,"about_ca_topic_score_gemma":0.0071966867,"teacher_disagreement_score":0.007736332,"about_ca_system_score_codex":0.0008959937,"about_ca_system_score_gemma":0.00202324,"threshold_uncertainty_score":0.015462577},"labels":[],"label_agreement":null},{"id":"W4404804432","doi":"10.1016/j.procs.2024.09.513","title":"Temporal Graph Convolutional Network for Implicit Relation Prediction: Leveraging Timestamps and Confidence","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Timestamp; Graph; Relation (database); Theoretical computer science; Artificial intelligence; Data mining; Real-time computing","score_opus":0.014880243094255117,"score_gpt":0.24786705197039122,"score_spread":0.2329868088761361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404804432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31099716,0.0036977646,0.66827965,0.0017618148,0.00030464184,0.00009948252,0.0035600401,0.002513329,0.008786162],"genre_scores_gemma":[0.9446636,0.00085633143,0.047959488,0.00018954871,0.00009133238,0.000048491554,0.002536862,0.00008472092,0.0035697632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996451,0.0000696572,0.000020926118,0.00013764178,0.000081106875,0.00004543682],"domain_scores_gemma":[0.9985843,0.00064485706,0.00024150104,0.0002344162,0.00021093927,0.0000839674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085076224,0.0008682263,0.000525197,0.0013094704,0.00042272283,0.00086930196,0.0011843464,0.0008004504,0.0015951883],"category_scores_gemma":[0.0054667857,0.00026698672,0.0004918918,0.0015908018,0.00050359254,0.002958187,0.0010171623,0.0015013875,0.00055972533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063342875,0.00030722283,0.05367413,0.00034297508,0.00026719944,0.0003854952,0.0005162674,0.31592336,0.008481877,0.03786797,0.016354758,0.56524533],"study_design_scores_gemma":[0.00000679865,0.000035560435,0.0031679876,0.000033162567,0.000047386355,0.000086255546,0.000047501915,0.9718694,0.0018589277,0.020440377,0.0023885157,0.000018197392],"about_ca_topic_score_codex":0.011361313,"about_ca_topic_score_gemma":0.02386853,"teacher_disagreement_score":0.011361313,"about_ca_system_score_codex":0.0008725417,"about_ca_system_score_gemma":0.0007509317,"threshold_uncertainty_score":0.02259034},"labels":[],"label_agreement":null},{"id":"W4404835147","doi":"10.1016/j.procs.2024.09.405","title":"Hybrid Genetic Algorithms and Heuristics for Nonlinear Short-Term Hydropower Optimization: A Comparative Analysis","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Water resources management and optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TD Bank Group; Group for Research in Decision Analysis; Université du Québec à Chicoutimi","funders":"SINTEF Industri; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Term (time); Heuristics; Genetic algorithm; Nonlinear system; Algorithm; Mathematical optimization; Machine learning; Mathematics","score_opus":0.013664655095446702,"score_gpt":0.2462551010025786,"score_spread":0.2325904459071319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404835147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30691034,0.053999107,0.5769089,0.0009432028,0.00036866058,0.00032732688,0.00036757247,0.000938081,0.059236806],"genre_scores_gemma":[0.78478897,0.014884578,0.19670871,0.00020496245,0.00011324963,0.00014502708,0.00026305328,0.00012465162,0.002766687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988802,0.0005432455,0.000043708264,0.000083360675,0.00037487535,0.00007462724],"domain_scores_gemma":[0.99752647,0.0019345566,0.00012304819,0.000106894506,0.0002574113,0.000051545485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002119707,0.00078706123,0.0009845195,0.0026324412,0.00034110705,0.0012345656,0.00086071,0.0010192639,0.0015631261],"category_scores_gemma":[0.003772966,0.00028753834,0.000765456,0.0030413999,0.00040356637,0.0012043151,0.00046585916,0.0004984475,0.00018998822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003334121,0.0001941029,0.0023508212,0.00062134134,0.00041085645,0.00009622251,0.00006785956,0.75806063,0.0007480733,0.011957021,0.0011963974,0.22396322],"study_design_scores_gemma":[0.00007383937,0.0004756917,0.0022930887,0.00011648714,0.00021098508,0.00012601943,0.000120045996,0.9845003,0.0013942636,0.0054343133,0.005223667,0.000031325668],"about_ca_topic_score_codex":0.0043728678,"about_ca_topic_score_gemma":0.004142526,"teacher_disagreement_score":0.0043728678,"about_ca_system_score_codex":0.0008859628,"about_ca_system_score_gemma":0.0008293675,"threshold_uncertainty_score":0.011210203},"labels":[],"label_agreement":null},{"id":"W4404835218","doi":"10.1016/j.procs.2024.09.368","title":"A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Pattern recognition (psychology); Feature selection; Artificial intelligence; Feature (linguistics); Facial expression; Facial expression recognition; Selection (genetic algorithm); Expression (computer science); Morphology (biology); Facial recognition system; Programming language; Biology","score_opus":0.026430010025742516,"score_gpt":0.3020272920240673,"score_spread":0.2755972819983248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404835218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03586994,0.00014819582,0.96137935,0.00007752471,0.000046847443,0.00014281581,0.00013699352,0.0013836765,0.00081472116],"genre_scores_gemma":[0.33636224,0.00023953016,0.6554577,0.00015440705,0.00005641744,0.00048435447,0.0012822122,0.00030251103,0.005660683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964106,0.000046631347,0.000019823312,0.00009948836,0.00015046755,0.000042547785],"domain_scores_gemma":[0.9996991,0.00006272228,0.00002686064,0.000034012977,0.0001586749,0.000018777337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062274636,0.0006909213,0.00062518276,0.00091089495,0.0002754319,0.00035640347,0.00066994526,0.0003683348,0.0017460214],"category_scores_gemma":[0.0010591692,0.00016889913,0.0006976268,0.0005988822,0.00023800919,0.00035585312,0.00043484787,0.0004986324,0.00087116973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020862649,0.00018136989,0.0015693061,0.00005401102,0.00006257129,0.00014533785,0.000071764756,0.017625192,0.20970705,0.0011194113,0.0043566404,0.7648987],"study_design_scores_gemma":[0.000041317344,0.0002777731,0.009719981,0.000014160072,0.000068208734,0.0006217875,0.00007784083,0.8744231,0.10626233,0.0014691921,0.006977461,0.000046966168],"about_ca_topic_score_codex":0.0017598582,"about_ca_topic_score_gemma":0.0023957333,"teacher_disagreement_score":0.0017598582,"about_ca_system_score_codex":0.0002481073,"about_ca_system_score_gemma":0.0005301916,"threshold_uncertainty_score":0.0058410764},"labels":[],"label_agreement":null},{"id":"W4404835219","doi":"10.1016/j.procs.2024.09.409","title":"From Deep Learning to Interpretable and Explainable Deep Learning in Medical Image Computing: Balancing Innovation with Ethics and Responsibilities","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Artificial intelligence; Image (mathematics); Data science; Machine learning; Knowledge management","score_opus":0.017259412888506923,"score_gpt":0.3246125768113436,"score_spread":0.3073531639228367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404835219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091901205,0.003950954,0.8455198,0.04514483,0.00012718841,0.000114864284,0.00007784999,0.00015551574,0.01300782],"genre_scores_gemma":[0.88820195,0.0016437854,0.10660239,0.0012451637,0.00017726458,0.0001153849,0.00005282489,0.00005808322,0.00190327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99321884,0.004993198,0.00022282833,0.00040839025,0.00090226025,0.00025458547],"domain_scores_gemma":[0.9701819,0.02417998,0.0015436488,0.002399935,0.0012716558,0.00042289155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013376195,0.0004000659,0.00036717192,0.00090872427,0.00062767393,0.0036276267,0.0011556713,0.0018874641,0.0014186942],"category_scores_gemma":[0.039066393,0.0003777193,0.00042919372,0.0006291068,0.009288751,0.0057109063,0.0042016893,0.004110095,0.00017841908],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017014203,0.00008853315,0.004224881,0.00028196,0.00006396526,0.00020489252,0.0016710708,0.08233454,0.0019271869,0.8015173,0.0016594451,0.105856106],"study_design_scores_gemma":[0.00001564448,0.000032247794,0.0005695264,0.00012046874,0.000012911086,0.000056571113,0.00018199814,0.2140313,0.0018553378,0.77935517,0.0037520821,0.000016771435],"about_ca_topic_score_codex":0.0017173167,"about_ca_topic_score_gemma":0.0018871382,"teacher_disagreement_score":0.013376195,"about_ca_system_score_codex":0.002568993,"about_ca_system_score_gemma":0.0024809109,"threshold_uncertainty_score":0.07074088},"labels":[],"label_agreement":null},{"id":"W4404835225","doi":"10.1016/j.procs.2024.09.228","title":"Enhanced balancing with integrated resampling cascade and advanced analysis of ‘seizureDetect’ dataset key features","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Champlain Regional College","funders":"","keywords":"Computer science; Cascade; Key (lock); Resampling; Data mining; Computer architecture; Artificial intelligence; Computer security","score_opus":0.013847801655872493,"score_gpt":0.27905288206619405,"score_spread":0.26520508041032154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404835225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26172957,0.0004181558,0.729072,0.00026527786,0.0001478716,0.00028454245,0.0016616497,0.0038076187,0.0026133854],"genre_scores_gemma":[0.6892637,0.00015713071,0.30204287,0.000111496876,0.00010085467,0.00026435632,0.0058293384,0.0002351263,0.001995153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994129,0.00010288605,0.000041612282,0.00014938714,0.00021095129,0.000082213126],"domain_scores_gemma":[0.99924386,0.00017749242,0.000091165086,0.00012406151,0.00032226308,0.000041246014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013434994,0.0006046327,0.0005707651,0.0014394732,0.00039153456,0.0005866264,0.00053865596,0.00038996837,0.0011650976],"category_scores_gemma":[0.0036730762,0.00013213519,0.0005903806,0.00088204816,0.00020873875,0.0007298232,0.00075002585,0.00045801504,0.0005241797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010253565,0.00039986087,0.035471275,0.00022162078,0.00016530055,0.0005152213,0.00036775554,0.06858394,0.11769876,0.003526709,0.010655789,0.7613684],"study_design_scores_gemma":[0.00003137662,0.00028894324,0.037064996,0.000025880641,0.00005728318,0.00028193,0.00020169259,0.90898716,0.041548815,0.0038967894,0.0075612976,0.00005385359],"about_ca_topic_score_codex":0.0032315843,"about_ca_topic_score_gemma":0.0056539513,"teacher_disagreement_score":0.0032315843,"about_ca_system_score_codex":0.0002668651,"about_ca_system_score_gemma":0.00056582683,"threshold_uncertainty_score":0.0071052313},"labels":[],"label_agreement":null},{"id":"W4404835259","doi":"10.1016/j.procs.2024.09.199","title":"A Sketch of DSL and Code Generator for Accelerating Chatbot Development","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"AI in Service Interactions","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal College of Physicians and Surgeons of Canada","funders":"Ministry of Higher Education, Science, Research and Innovation, Thailand; Centre National pour la Recherche Scientifique et Technique","keywords":"Computer science; Digital subscriber line; Sketch; Chatbot; Generator (circuit theory); Programming language; Code generation; Code (set theory); World Wide Web; Operating system; Telecommunications; Algorithm; Key (lock)","score_opus":0.037880207669761794,"score_gpt":0.298303256302878,"score_spread":0.2604230486331162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404835259","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007520331,0.00026379616,0.9852223,0.00037298255,0.00011628498,0.00030543044,0.00027321643,0.009531895,0.0031621037],"genre_scores_gemma":[0.009855304,0.0007327913,0.9820807,0.00020251567,0.00004190941,0.00049353176,0.00059917755,0.0016966435,0.004297382],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99745315,0.00067610387,0.00035256165,0.00040734228,0.0009787413,0.00013215024],"domain_scores_gemma":[0.9972264,0.0010198375,0.00016034015,0.0007501155,0.0006227008,0.00022055922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002859721,0.0015825861,0.0006697786,0.0022600535,0.00079605187,0.0027722467,0.002427892,0.0017443192,0.013561256],"category_scores_gemma":[0.007704151,0.0016966639,0.0016078852,0.0011728208,0.001268556,0.0042508636,0.0026813976,0.0043571596,0.009247073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023423984,0.0003371867,0.0011739318,0.002268916,0.00006979575,0.0017124374,0.0020563442,0.028515425,0.04442091,0.41312334,0.037879653,0.46820778],"study_design_scores_gemma":[0.00013928556,0.00021557341,0.0003869416,0.00068664923,0.000061646475,0.0021513656,0.00019796696,0.12502368,0.034314655,0.065000944,0.7716386,0.00018260664],"about_ca_topic_score_codex":0.0021761297,"about_ca_topic_score_gemma":0.0017178048,"teacher_disagreement_score":0.013561256,"about_ca_system_score_codex":0.0012733411,"about_ca_system_score_gemma":0.0028095988,"threshold_uncertainty_score":0.045366883},"labels":[],"label_agreement":null},{"id":"W4404835266","doi":"10.1016/j.procs.2024.09.259","title":"The Effects of Artificial Intelligence on the Future of Employment: Looking for a Trend from a Literature Review","year":2024,"lang":"en","type":"review","venue":"Procedia Computer Science","topic":"Impact of AI and Big Data on Business and Society","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal College of Physicians and Surgeons of Canada","funders":"","keywords":"Computer science; Artificial intelligence; Data science","score_opus":0.10521195101425991,"score_gpt":0.4129905474590257,"score_spread":0.3077785964447658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404835266","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017093406,0.9973379,0.00008163705,0.0017452306,0.00014178595,0.0000023082468,0.000010603769,0.0000017187888,0.00050785695],"genre_scores_gemma":[0.0016700678,0.99755573,0.00009514421,0.00044415405,0.00014472866,0.000004009684,0.000010432674,0.0000010405572,0.00007464999],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988701,0.00034412026,0.00016740435,0.00016516427,0.0003888776,0.00006425743],"domain_scores_gemma":[0.99242157,0.0054255934,0.0006258726,0.00008379004,0.0012745016,0.00016872192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026430064,0.0005642424,0.0012432123,0.007374187,0.00059313804,0.0024960046,0.00077256176,0.001912398,0.00220071],"category_scores_gemma":[0.0068368167,0.00053592253,0.0007983266,0.010755412,0.001217429,0.0047609713,0.0008846675,0.0021840048,0.00058994663],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093358154,0.00005620697,0.0012827954,0.09496012,0.00036420955,0.00024121268,0.00081043894,0.00083465286,0.00046969674,0.037438348,0.031117404,0.8323316],"study_design_scores_gemma":[0.000022162794,0.0001180813,0.0061976947,0.13283175,0.0007096539,0.00089233776,0.0020874625,0.0004558698,0.00035530372,0.018549498,0.8377133,0.00006691244],"about_ca_topic_score_codex":0.004877162,"about_ca_topic_score_gemma":0.008975435,"teacher_disagreement_score":0.007374187,"about_ca_system_score_codex":0.00259773,"about_ca_system_score_gemma":0.005689556,"threshold_uncertainty_score":0.018847942},"labels":[],"label_agreement":null},{"id":"W4404835291","doi":"10.1016/j.procs.2024.09.319","title":"Towards an Ontology-Driven System For Building and Farming Greenhouses","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie; Academy of Scientific Research and Technology; Providence Health Care","keywords":"Computer science; Greenhouse; Ontology; Agriculture; Agricultural engineering; Ecology; Agronomy","score_opus":0.026293002022852553,"score_gpt":0.29136326067632873,"score_spread":0.26507025865347617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404835291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008238991,0.00008998375,0.97604567,0.00042473018,0.000056387773,0.0004113561,0.00041612764,0.009506811,0.004810063],"genre_scores_gemma":[0.06166686,0.0003087305,0.930238,0.00023033337,0.00002160978,0.00040267335,0.0021230548,0.0006823517,0.0043264],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99883693,0.00024424464,0.00017644957,0.00024229144,0.00039625427,0.000103772094],"domain_scores_gemma":[0.9990212,0.00026601617,0.000083118786,0.00024494264,0.00025689264,0.00012779658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002798379,0.0006516715,0.0006864651,0.0014914129,0.0012942762,0.0033503263,0.002094006,0.001739106,0.0028044386],"category_scores_gemma":[0.002962654,0.0006788643,0.0018448514,0.0010906009,0.00094984035,0.0047762785,0.0035104007,0.0018338575,0.001616386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044280017,0.0014504436,0.007774083,0.00141386,0.00041220096,0.003345108,0.0077910945,0.08780076,0.11394421,0.25484437,0.03199459,0.4887864],"study_design_scores_gemma":[0.00015418816,0.00014434637,0.0026324522,0.00035493067,0.00032246616,0.0010364468,0.0015740306,0.5976533,0.03618822,0.08175841,0.2779823,0.00019897887],"about_ca_topic_score_codex":0.009544152,"about_ca_topic_score_gemma":0.010528892,"teacher_disagreement_score":0.009544152,"about_ca_system_score_codex":0.0013974806,"about_ca_system_score_gemma":0.003351903,"threshold_uncertainty_score":0.018977225},"labels":[],"label_agreement":null},{"id":"W4404837934","doi":"10.1016/j.procs.2024.09.568","title":"Automating Software Documentation: Employing LLMs for Precise Use Case Description","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software Engineering Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Centre National pour la Recherche Scientifique et Technique","keywords":"Computer science; Documentation; Software; Software engineering; Software documentation; Data science; Programming language; Software development; Software development process","score_opus":0.04452629760129762,"score_gpt":0.31453133743875383,"score_spread":0.2700050398374562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404837934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072322637,0.00008798126,0.9873104,0.00015230544,0.000010105997,0.0003433792,0.00013324081,0.0026983942,0.0020318513],"genre_scores_gemma":[0.052628815,0.000101596306,0.9449503,0.00003714295,0.000006942165,0.00036855412,0.00042591122,0.00032765474,0.0011531665],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9910978,0.005132962,0.0007698478,0.00081075425,0.0019949183,0.0001937106],"domain_scores_gemma":[0.9743882,0.015488009,0.0019391931,0.0058893557,0.0020663086,0.00022900205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072208648,0.0009073746,0.0005132755,0.0052161505,0.0009077958,0.0033293986,0.0016883615,0.0012436173,0.003203977],"category_scores_gemma":[0.030026976,0.00086753746,0.0010369509,0.002169327,0.0011948398,0.003585062,0.0030906335,0.0015147496,0.001996235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013374975,0.00029990455,0.0048610237,0.0009872003,0.00006076523,0.0014663336,0.012309444,0.021238677,0.04568926,0.053998888,0.0050808717,0.85387385],"study_design_scores_gemma":[0.0001159936,0.00031997857,0.004609942,0.0016526367,0.0001303732,0.0038513117,0.0042771236,0.6317528,0.11097854,0.08184901,0.16022712,0.00023520837],"about_ca_topic_score_codex":0.0018713449,"about_ca_topic_score_gemma":0.0038516133,"teacher_disagreement_score":0.0072208648,"about_ca_system_score_codex":0.0012705178,"about_ca_system_score_gemma":0.0028207682,"threshold_uncertainty_score":0.03818804},"labels":[],"label_agreement":null},{"id":"W4404838064","doi":"10.1016/j.procs.2024.09.590","title":"A Survey for Educational Metaverse: Advances and Beyond","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Education and Learning Interventions","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"East China Normal University; National Natural Science Foundation of China","keywords":"Computer science; Metaverse; Data science; Human–computer interaction; Virtual reality","score_opus":0.02759012065459059,"score_gpt":0.337372138057835,"score_spread":0.3097820174032444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404838064","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041587204,0.97023803,0.0018652326,0.006970639,0.0008358523,0.000074015865,0.0006218711,0.00009390962,0.015141704],"genre_scores_gemma":[0.015923826,0.9755598,0.0023749976,0.002743891,0.00045064522,0.00011405175,0.00066104723,0.00005588784,0.002115796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957183,0.0014111085,0.00087301445,0.00037867067,0.001332462,0.0002863865],"domain_scores_gemma":[0.97636783,0.018060211,0.0018285721,0.0005017099,0.0025373823,0.0007043906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005022987,0.000722013,0.0011239396,0.013455428,0.00073922734,0.005172344,0.0009416604,0.0019775247,0.013339305],"category_scores_gemma":[0.022489378,0.00047094625,0.0011118116,0.015885126,0.0011960457,0.010605561,0.002552909,0.0020437215,0.0033199182],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000743815,0.00008774016,0.0032308756,0.03726577,0.0000770216,0.00017477428,0.0024010723,0.00016279805,0.00044568715,0.019519588,0.03265842,0.90390176],"study_design_scores_gemma":[0.00000861155,0.00008625667,0.0063160826,0.056976818,0.00011554024,0.00082464726,0.00395466,0.00009275919,0.0002950556,0.0039054346,0.92738426,0.000039791612],"about_ca_topic_score_codex":0.001923872,"about_ca_topic_score_gemma":0.003325175,"teacher_disagreement_score":0.013455428,"about_ca_system_score_codex":0.0022463088,"about_ca_system_score_gemma":0.0049766395,"threshold_uncertainty_score":0.044624448},"labels":[],"label_agreement":null},{"id":"W4404843995","doi":"10.1016/j.procs.2024.11.022","title":"Multi-Objective Optimization of RTAB-Map parameters using Genetic Algorithm for indoor 2D SLAM","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Genetic algorithm; Algorithm; Optimization algorithm; Artificial intelligence; Mathematical optimization; Machine learning","score_opus":0.01794851123854902,"score_gpt":0.24359509226064177,"score_spread":0.22564658102209276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404843995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11063163,0.00024494075,0.88417083,0.00012352318,0.000031220236,0.00009902015,0.00007006248,0.0006342075,0.0039946013],"genre_scores_gemma":[0.7673549,0.0001117271,0.23070592,0.000044057237,0.000008673928,0.00024367674,0.000109719615,0.00007886597,0.0013423827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974185,0.00009031164,0.000009739686,0.000041270112,0.000069186244,0.000047525624],"domain_scores_gemma":[0.99961627,0.00021133194,0.000055175722,0.000021305108,0.000078636425,0.000017223005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006802489,0.0009368316,0.0006114222,0.00081912073,0.0003830589,0.0007063275,0.00059730833,0.0008740932,0.0010649997],"category_scores_gemma":[0.0013219581,0.00036755172,0.00065057434,0.0005986945,0.0004232688,0.00043995844,0.0006468149,0.0005073272,0.00018725262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000146638195,0.00002113621,0.00022960923,0.000015707426,0.000010142125,0.00001822022,0.000021037187,0.9890754,0.0009990163,0.00043847127,0.00010442652,0.009052001],"study_design_scores_gemma":[0.000006560618,0.000022812774,0.000112402304,0.0000029662253,0.0000036770825,0.0000049456016,0.000010606882,0.99896264,0.0004583447,0.00028847303,0.00012351078,0.0000031152665],"about_ca_topic_score_codex":0.0060524796,"about_ca_topic_score_gemma":0.004851311,"teacher_disagreement_score":0.0060524796,"about_ca_system_score_codex":0.0006148779,"about_ca_system_score_gemma":0.001064239,"threshold_uncertainty_score":0.012034535},"labels":[],"label_agreement":null},{"id":"W4404844000","doi":"10.1016/j.procs.2024.11.015","title":"Terrain Recognition in Real-Time for a Legged Robot based on Ontology Information","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Computer science; Ontology; Terrain; Robot; Artificial intelligence; Human–computer interaction; Computer vision; Information retrieval; Data mining","score_opus":0.010463563626113573,"score_gpt":0.22505790751794202,"score_spread":0.21459434389182847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404844000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17675543,0.00023584312,0.8191073,0.00009852278,0.000047754816,0.00005314713,0.00014602818,0.0020343633,0.0015217009],"genre_scores_gemma":[0.8980626,0.00011915038,0.10036422,0.000033378856,0.000011448547,0.000046276284,0.00021724273,0.000022922583,0.0011226691],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999033,0.000009088082,0.0000069011935,0.000027411954,0.000038995335,0.0000143047055],"domain_scores_gemma":[0.9998772,0.000019537483,0.000026354544,0.000022430468,0.000042886546,0.000011552316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000989157,0.00036843732,0.00032792363,0.00054382073,0.00024154187,0.00033234627,0.00035603685,0.00028719532,0.0010026782],"category_scores_gemma":[0.00039989717,0.0001919383,0.0002467767,0.00037465757,0.00019388806,0.0006583309,0.00035550876,0.00023106764,0.00029522553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043527616,0.0001502088,0.010606728,0.0002930936,0.00007367434,0.0006721361,0.00039429052,0.062162742,0.23770039,0.0019273999,0.0018623917,0.68372166],"study_design_scores_gemma":[0.000034328925,0.00027184983,0.017051764,0.000034805937,0.00005364406,0.0005895745,0.0002543977,0.94141996,0.03499227,0.0024374402,0.0028195686,0.000040364273],"about_ca_topic_score_codex":0.002576718,"about_ca_topic_score_gemma":0.0035163742,"teacher_disagreement_score":0.002576718,"about_ca_system_score_codex":0.00014004063,"about_ca_system_score_gemma":0.0002667084,"threshold_uncertainty_score":0.0051234365},"labels":[],"label_agreement":null},{"id":"W4404844013","doi":"10.1016/j.procs.2024.11.013","title":"Prototype Design and Experimental Test for A Hydraulic-Driven Soft Robotic Arm","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Test (biology); Soft robotics; Simulation; Robotic arm; Human–computer interaction; Artificial intelligence; Robot","score_opus":0.0198993864411791,"score_gpt":0.25578122893138194,"score_spread":0.23588184249020283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404844013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67788064,0.00027189756,0.30101052,0.0006405127,0.00050532253,0.0038684946,0.00075130013,0.0043377695,0.0107336035],"genre_scores_gemma":[0.8529879,0.00012296735,0.1364077,0.00014602512,0.000033452063,0.0020425671,0.00025265888,0.000104356564,0.007902428],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921167,0.00009241883,0.00006304587,0.00016070073,0.00036978815,0.00010247857],"domain_scores_gemma":[0.99858105,0.00028109553,0.0001861958,0.0003270039,0.00040097887,0.0002237354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014053957,0.000593511,0.00041388068,0.0004805645,0.00040961785,0.0005079485,0.001912928,0.0013764481,0.0046073827],"category_scores_gemma":[0.0016191131,0.00034657048,0.00041035705,0.00014968032,0.00058841356,0.0006618822,0.00084549194,0.00076052453,0.0013118876],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042808714,0.0008127983,0.0011607199,0.0007453423,0.000039994513,0.0006641684,0.00040058244,0.0059809284,0.94586307,0.0022624847,0.0011704378,0.040471267],"study_design_scores_gemma":[0.00057554274,0.028556675,0.012103778,0.00015521102,0.00009022694,0.0014218237,0.0003285996,0.060730685,0.8596439,0.0013304827,0.034930393,0.00013272915],"about_ca_topic_score_codex":0.00025234715,"about_ca_topic_score_gemma":0.00029418003,"teacher_disagreement_score":0.0046073827,"about_ca_system_score_codex":0.0002474102,"about_ca_system_score_gemma":0.00088137854,"threshold_uncertainty_score":0.015413225},"labels":[],"label_agreement":null},{"id":"W4404844758","doi":"10.1016/j.procs.2024.11.021","title":"Running and Steering Gait Generation Based on Double-Leg 3D-SLIP Model for Bipedal Robots","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robot; Gait; Slip (aerodynamics); Simulation; Artificial intelligence; Physical medicine and rehabilitation; Aerospace engineering","score_opus":0.027195397449929895,"score_gpt":0.23967268354886986,"score_spread":0.21247728609893995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404844758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11344024,0.00024878728,0.87775105,0.000072832045,0.000057720204,0.000060594302,0.00007549861,0.00034906896,0.007944116],"genre_scores_gemma":[0.95740646,0.00016299018,0.0400857,0.000022702168,0.000009137581,0.00009085259,0.0000954651,0.000018901124,0.0021077257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999584,0.000005627434,0.0000027911456,0.000007948989,0.000018332114,0.000006891228],"domain_scores_gemma":[0.99994814,0.0000074504355,0.000012671418,0.000006577959,0.000017136777,0.000007980521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009894522,0.00034026502,0.00028561105,0.00029869768,0.0002442277,0.00029707857,0.0003428573,0.00028201015,0.0010717645],"category_scores_gemma":[0.00014922017,0.0001911546,0.0003571536,0.0001585479,0.00022023373,0.00021663925,0.00032913292,0.00024097625,0.00019017945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007318081,0.000055882712,0.0011703415,0.00014809119,0.000029586789,0.00028548975,0.00013082969,0.90314394,0.04621985,0.008742357,0.00066723966,0.039333116],"study_design_scores_gemma":[0.0000039671345,0.000040569525,0.00017381729,0.0000033736774,0.0000033301178,0.000018573037,0.000006560622,0.9980414,0.0008789047,0.00045096836,0.00037514113,0.0000032411745],"about_ca_topic_score_codex":0.0029341427,"about_ca_topic_score_gemma":0.0024826461,"teacher_disagreement_score":0.0029341427,"about_ca_system_score_codex":0.00017525961,"about_ca_system_score_gemma":0.00037197876,"threshold_uncertainty_score":0.0058341026},"labels":[],"label_agreement":null},{"id":"W4405112407","doi":"10.1016/j.procs.2024.11.171","title":"Primary Health Care Appointments and Hospital Stay: An Impact Analysis","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Computer science; Primary care; Primary health care; Health care; Family medicine; Medicine","score_opus":0.027142481602658884,"score_gpt":0.30596136344849284,"score_spread":0.278818881845834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405112407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8854079,0.0033152928,0.015771948,0.0019307985,0.00022041424,0.0008549131,0.083566934,0.00070983724,0.008221988],"genre_scores_gemma":[0.95596594,0.0010340018,0.010495812,0.00011162971,0.00016788204,0.0004114937,0.030200377,0.00010303327,0.0015098467],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99710613,0.0008270103,0.00024867943,0.00047562981,0.00090379134,0.0004387605],"domain_scores_gemma":[0.9878834,0.009090619,0.0011347256,0.00041453115,0.0010256801,0.0004509587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044328286,0.0009706038,0.0008808253,0.0074113305,0.00030716092,0.0016374165,0.0007994552,0.0007369832,0.0055194437],"category_scores_gemma":[0.013566194,0.00033723013,0.003783392,0.006235524,0.00042742625,0.00097452704,0.0014312739,0.0013320886,0.00070063263],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014299074,0.00083344773,0.8233558,0.0017884908,0.0027513483,0.0010290253,0.00042660983,0.0925667,0.000677912,0.004348095,0.011491612,0.059301116],"study_design_scores_gemma":[0.000081377104,0.0006312126,0.8017159,0.00028798982,0.0009385761,0.00044644315,0.00096314395,0.18193756,0.00069662207,0.0026146448,0.009614627,0.000071841896],"about_ca_topic_score_codex":0.025846353,"about_ca_topic_score_gemma":0.009947673,"teacher_disagreement_score":0.025846353,"about_ca_system_score_codex":0.0020877675,"about_ca_system_score_gemma":0.0018130091,"threshold_uncertainty_score":0.05139184},"labels":[],"label_agreement":null},{"id":"W4405112553","doi":"10.1016/j.procs.2024.11.088","title":"Evaluating Safe Region Sizes for Accuracy in Approximate Continuous Nearest Neighbour Queries","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Nearest neighbour; k-nearest neighbors algorithm; Data mining; Artificial intelligence; Pattern recognition (psychology); Information retrieval","score_opus":0.049995626381539336,"score_gpt":0.33551698660068136,"score_spread":0.28552136021914204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405112553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48791617,0.00677283,0.49344614,0.0007019395,0.0002741865,0.0005944576,0.0010437883,0.002882235,0.006368362],"genre_scores_gemma":[0.9132884,0.0006006098,0.084845155,0.000062323095,0.000046403675,0.00010845287,0.00056715007,0.00013071699,0.00035078175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98202366,0.0050482573,0.0013905321,0.0017427079,0.00888845,0.0009063945],"domain_scores_gemma":[0.87441623,0.09615579,0.006685711,0.009373214,0.012261377,0.0011076876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009403248,0.0010374695,0.001700499,0.0024806028,0.0010344199,0.0027813218,0.002730346,0.0018024859,0.0011660652],"category_scores_gemma":[0.112280704,0.0004197623,0.00063117937,0.002770768,0.0013450306,0.005134637,0.0020937857,0.0010787083,0.0004302553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034331544,0.00037282694,0.02382765,0.00051275396,0.000272691,0.00028744558,0.0007374132,0.7649071,0.012264427,0.008003461,0.0026685698,0.18271247],"study_design_scores_gemma":[0.000046182035,0.00065814157,0.004401048,0.00003759517,0.0000525285,0.00030747367,0.000432976,0.9784387,0.011884821,0.0027124896,0.000968082,0.000059947564],"about_ca_topic_score_codex":0.010789055,"about_ca_topic_score_gemma":0.006233548,"teacher_disagreement_score":0.010789055,"about_ca_system_score_codex":0.0023999722,"about_ca_system_score_gemma":0.0021134939,"threshold_uncertainty_score":0.049729705},"labels":[],"label_agreement":null},{"id":"W4405112666","doi":"10.1016/j.procs.2024.11.191","title":"BeComE: A Framework for Node Classification in Social Graphs","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Node (physics); Social network (sociolinguistics); Theoretical computer science; Artificial intelligence; World Wide Web; Social media","score_opus":0.032631046847118116,"score_gpt":0.3184168328245523,"score_spread":0.2857857859774342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405112666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008434177,0.0005859264,0.98154634,0.00071700214,0.00009792629,0.00021025415,0.002147306,0.0041661565,0.002094905],"genre_scores_gemma":[0.24698803,0.0012187436,0.73082525,0.00053460634,0.00032536237,0.00079528056,0.010698252,0.00071096316,0.007903637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989735,0.00033735385,0.000046344398,0.00030819207,0.00026306894,0.00007155386],"domain_scores_gemma":[0.9984642,0.0006762414,0.0001895867,0.00028038156,0.00027597096,0.00011357473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011586959,0.0015118833,0.00081886665,0.0037836777,0.0007479617,0.0017104596,0.0018838797,0.0016213553,0.0031468465],"category_scores_gemma":[0.0047617396,0.00047785477,0.0013540359,0.003128582,0.0007867785,0.004836313,0.001605699,0.0024744775,0.00195364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002649752,0.0005104327,0.011639177,0.0006051222,0.00030614925,0.00027846068,0.0005388961,0.25575644,0.0046305386,0.18524455,0.057471294,0.48275396],"study_design_scores_gemma":[0.00001047192,0.000038789603,0.0007473345,0.000025482672,0.000019511206,0.000069842594,0.000051953964,0.9067902,0.00050154096,0.08249891,0.009230836,0.000015047442],"about_ca_topic_score_codex":0.0064169597,"about_ca_topic_score_gemma":0.012266745,"teacher_disagreement_score":0.0064169597,"about_ca_system_score_codex":0.0011905619,"about_ca_system_score_gemma":0.00089903857,"threshold_uncertainty_score":0.012759209},"labels":[],"label_agreement":null},{"id":"W4405112933","doi":"10.1016/j.procs.2024.11.121","title":"From Signals to Emotion: Affective State Classification through Valence and Arousal","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Arousal; Valence (chemistry); Emotion classification; Emotion detection; Speech recognition; Affective computing; Emotion recognition; Artificial intelligence; Psychology; Social psychology; Physics","score_opus":0.045000500802349123,"score_gpt":0.34092356741064694,"score_spread":0.2959230666082978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405112933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6410728,0.0036807107,0.33260632,0.00080141076,0.0007051231,0.0004358897,0.0025784487,0.0016378836,0.016481336],"genre_scores_gemma":[0.9444994,0.0012253955,0.05018406,0.000103386876,0.00016675735,0.00015493795,0.0013588043,0.000053392254,0.0022538698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996184,0.000106768035,0.000027315638,0.00010028551,0.00010616189,0.000041003947],"domain_scores_gemma":[0.99960333,0.00015252903,0.000057224028,0.00003302294,0.0001285352,0.000025377927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056674174,0.0006658892,0.00040331003,0.0010465393,0.00019268644,0.0010391264,0.00027650487,0.00037966078,0.0019477977],"category_scores_gemma":[0.0020936073,0.00012761465,0.00057890004,0.0008001842,0.00022247298,0.0006193987,0.00035488402,0.0005643422,0.0011606114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010416026,0.00048052418,0.05594598,0.00047286553,0.0002468651,0.00021152616,0.0003799331,0.011921012,0.08560133,0.0020453532,0.0055576353,0.8360954],"study_design_scores_gemma":[0.00006924448,0.0015775978,0.37649828,0.00028831593,0.00045074252,0.0015314412,0.0008346486,0.5216625,0.07328967,0.010852933,0.012788874,0.00015576101],"about_ca_topic_score_codex":0.00060226914,"about_ca_topic_score_gemma":0.0006099043,"teacher_disagreement_score":0.0019477977,"about_ca_system_score_codex":0.00016289104,"about_ca_system_score_gemma":0.00016531651,"threshold_uncertainty_score":0.0065159798},"labels":[],"label_agreement":null},{"id":"W4405113179","doi":"10.1016/j.procs.2024.11.075","title":"Preface","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.013962293402786761,"score_gpt":0.27515486982513393,"score_spread":0.26119257642234717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113179","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001852347,0.010761407,0.021767695,0.028171076,0.31445807,0.0010546246,0.018721057,0.0028844003,0.6003294],"genre_scores_gemma":[0.006258158,0.0050713494,0.005262394,0.0056094527,0.035805237,0.0004136873,0.011235159,0.0011750077,0.9291696],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994491,0.00007219585,0.00003521274,0.0000976252,0.0003008086,0.00004499983],"domain_scores_gemma":[0.9937744,0.0009320601,0.00018525239,0.0005541553,0.0038646641,0.0006894385],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010305229,0.0010440577,0.0006287292,0.002985489,0.0021132906,0.002508991,0.0011308376,0.00076394324,0.48709103],"category_scores_gemma":[0.011589423,0.00028167665,0.0005714878,0.0018795308,0.0004158623,0.0021688195,0.0017087855,0.0025841517,0.31798682],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031235726,0.000026462181,0.000087182205,0.00009512817,0.000001781875,0.000036892674,0.000027460215,0.00008701541,0.0002077824,0.0030323148,0.95126927,0.0450976],"study_design_scores_gemma":[0.0000048894894,0.000019234058,0.00025808715,0.00010757812,0.0000022640995,0.00004605315,0.0000442157,0.000055530258,0.0002021897,0.0032441835,0.9960098,0.00000596057],"about_ca_topic_score_codex":0.0036219282,"about_ca_topic_score_gemma":0.0044715223,"teacher_disagreement_score":0.51290894,"about_ca_system_score_codex":0.0012903737,"about_ca_system_score_gemma":0.0016700602,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4405113218","doi":"10.1016/j.procs.2024.11.126","title":"NLP and Topic Modeling with LDA, LSA, and NMF for Monitoring Psychosocial Well-being in Monthly Surveys","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Information retrieval; Topic model; Data science","score_opus":0.028954047006875528,"score_gpt":0.35239906247632974,"score_spread":0.3234450154694542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12565571,0.0014451178,0.8668575,0.0010314212,0.00016497413,0.00062149594,0.0021020574,0.0013534065,0.00076823664],"genre_scores_gemma":[0.44988605,0.00057671417,0.54114604,0.00019523352,0.0002863148,0.0021115334,0.0050214813,0.00013020335,0.0006463763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9883173,0.008990856,0.0006579487,0.0012321202,0.000577839,0.00022382195],"domain_scores_gemma":[0.960045,0.036212638,0.0013422569,0.0011085111,0.001081015,0.00021064792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013897268,0.0013545338,0.0013125639,0.005271356,0.0012007275,0.0018940325,0.00100086,0.0013314049,0.000962235],"category_scores_gemma":[0.032518443,0.0005128332,0.0026718685,0.0034053137,0.0007182594,0.0020436514,0.0015324544,0.002246884,0.00052276516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009807694,0.0011735429,0.073660165,0.0011509581,0.0018288469,0.00050014514,0.0039017166,0.2295715,0.006686063,0.011516504,0.009085811,0.659944],"study_design_scores_gemma":[0.00004543816,0.00010263699,0.009152922,0.000059712245,0.00008475675,0.0000740245,0.00061716564,0.9733262,0.0009438326,0.013452661,0.0020843798,0.00005627766],"about_ca_topic_score_codex":0.008226607,"about_ca_topic_score_gemma":0.008845959,"teacher_disagreement_score":0.013897268,"about_ca_system_score_codex":0.0010015287,"about_ca_system_score_gemma":0.001567214,"threshold_uncertainty_score":0.07349664},"labels":[],"label_agreement":null},{"id":"W4405113355","doi":"10.1016/j.procs.2024.11.119","title":"SereniSens: a Multimodal AI Framework with LLMs for Stress Prediction through Sleep Biometrics","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Biometrics; Stress (linguistics); Sleep (system call); Speech recognition; Artificial intelligence; Operating system","score_opus":0.023259854594836,"score_gpt":0.2860154001024071,"score_spread":0.26275554550757113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030706141,0.0010700251,0.9442878,0.0006800064,0.00018981838,0.00026416784,0.001277663,0.017146744,0.004377605],"genre_scores_gemma":[0.5793881,0.00063634285,0.40657434,0.00076890906,0.00013646654,0.00066432956,0.0020621808,0.00045562876,0.009313776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997291,0.00007781331,0.000016698894,0.00009347835,0.00005363655,0.000029332778],"domain_scores_gemma":[0.9996872,0.00015238958,0.000029781635,0.000027753582,0.00006211414,0.00004079625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064162334,0.0007855301,0.00046054134,0.0006968198,0.00038410106,0.0007713499,0.0011096848,0.0006775093,0.0035593114],"category_scores_gemma":[0.0019479211,0.00024649734,0.0007588208,0.00027754286,0.0002940278,0.0008633742,0.0013206047,0.0009779335,0.00097333745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011177553,0.0007401445,0.015943356,0.00065825885,0.0005241652,0.000951717,0.0013585514,0.20895486,0.042257544,0.013957744,0.024283817,0.6892521],"study_design_scores_gemma":[0.000024509296,0.000120984274,0.001849541,0.000037429858,0.00004812693,0.00010222907,0.00010650354,0.9780253,0.0042860573,0.0074807866,0.007882612,0.000035932382],"about_ca_topic_score_codex":0.008725033,"about_ca_topic_score_gemma":0.013103581,"teacher_disagreement_score":0.008725033,"about_ca_system_score_codex":0.0005654864,"about_ca_system_score_gemma":0.0007920005,"threshold_uncertainty_score":0.017348528},"labels":[],"label_agreement":null},{"id":"W4405113430","doi":"10.1016/j.procs.2024.11.130","title":"TriageIntelli: AI-Assisted Multimodal Triage System for Health Centers","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Triage; Human–computer interaction; Artificial intelligence; Medical emergency; Medicine","score_opus":0.029949322307136778,"score_gpt":0.3456776759656986,"score_spread":0.31572835365856183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19973892,0.004031211,0.53624254,0.005483971,0.0015608618,0.0027483748,0.028064411,0.1921093,0.030020393],"genre_scores_gemma":[0.7914655,0.0012672518,0.17522222,0.0013488939,0.00031812087,0.001300284,0.021144642,0.00047147044,0.0074614934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996125,0.00008379734,0.00004485447,0.00010768199,0.0000961161,0.00005509076],"domain_scores_gemma":[0.9993325,0.00013318045,0.00011000102,0.00008568031,0.00020306588,0.00013557996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007457244,0.0010014146,0.0006409165,0.0015009264,0.0005343151,0.0009232057,0.0012735826,0.0007048354,0.0064878394],"category_scores_gemma":[0.0021397867,0.00023290544,0.0004188386,0.0009819906,0.00015679955,0.0012888126,0.0013902388,0.0008906408,0.0028105595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023994371,0.0008890513,0.04683889,0.0011249793,0.00039560447,0.00089867547,0.00079796714,0.042919543,0.024176832,0.005502115,0.23054329,0.6435135],"study_design_scores_gemma":[0.00023286938,0.0006979213,0.022231895,0.00018268265,0.00021343816,0.00047383012,0.00047051636,0.90294534,0.014451512,0.0049478775,0.05300499,0.00014713203],"about_ca_topic_score_codex":0.00688894,"about_ca_topic_score_gemma":0.005859675,"teacher_disagreement_score":0.00688894,"about_ca_system_score_codex":0.0010519505,"about_ca_system_score_gemma":0.001221268,"threshold_uncertainty_score":0.021704018},"labels":[],"label_agreement":null},{"id":"W4405113444","doi":"10.1016/j.procs.2024.11.080","title":"Application of machine learning in technological forecasting","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Economic and Technological Systems Analysis","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cégep de Rimouski; Université du Québec à Rimouski","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Data science; Industrial engineering","score_opus":0.017210108573362363,"score_gpt":0.2060497475140193,"score_spread":0.18883963894065695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105440445,0.008080594,0.86130494,0.003401146,0.00060078176,0.0002044022,0.0012500447,0.002171237,0.017546337],"genre_scores_gemma":[0.8899964,0.0030858577,0.10248002,0.00020030187,0.00034589227,0.00012353474,0.00090558507,0.000057388155,0.0028049746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999175,0.00037092087,0.00007152231,0.00014184868,0.00018301993,0.000057635596],"domain_scores_gemma":[0.9951632,0.0038993878,0.00023825753,0.00017258553,0.00046588006,0.000060691586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025831724,0.0007927842,0.0007540495,0.002914486,0.0004535407,0.0017388891,0.0007647812,0.0009666851,0.0017327187],"category_scores_gemma":[0.009597724,0.00032381227,0.0006733041,0.0030955651,0.00036487397,0.0012806521,0.0004959775,0.0011286663,0.00057323265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069000555,0.000100386125,0.010757885,0.00013227685,0.0001603141,0.000099964614,0.00006883841,0.7604155,0.00049470674,0.00640239,0.0025868241,0.21871197],"study_design_scores_gemma":[0.0000020490436,0.0000066688403,0.00059637486,0.000010782477,0.000005227423,0.0000067335077,0.0000108916865,0.99421614,0.0001463665,0.0043223873,0.00067145494,0.0000049772702],"about_ca_topic_score_codex":0.017007722,"about_ca_topic_score_gemma":0.009929849,"teacher_disagreement_score":0.017007722,"about_ca_system_score_codex":0.0013649429,"about_ca_system_score_gemma":0.00097356597,"threshold_uncertainty_score":0.03381741},"labels":[],"label_agreement":null},{"id":"W4405113448","doi":"10.1016/j.procs.2024.11.125","title":"Automatic Classification of Psychosocial Concerns: From Traditional Approach to Deep Learning","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mental Health Research Topics","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Deep learning; Psychosocial; Data science; Psychiatry; Medicine","score_opus":0.16782885634019237,"score_gpt":0.4229414030511663,"score_spread":0.25511254671097394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4258612,0.0049186563,0.5479252,0.0030861434,0.000316271,0.00062796887,0.0035619263,0.004825154,0.008877568],"genre_scores_gemma":[0.8851526,0.0011302113,0.10398611,0.00045854034,0.00017577321,0.00027246002,0.00401764,0.000063347674,0.0047433004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995165,0.00013971071,0.00003611887,0.00012309007,0.00013109944,0.000053507396],"domain_scores_gemma":[0.9993118,0.00027957594,0.000075985205,0.00007261741,0.00020470604,0.00005537956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008221129,0.0007700536,0.00040768,0.0018324265,0.00027972786,0.0008533547,0.0007940238,0.0006479785,0.0014655894],"category_scores_gemma":[0.0019296273,0.00013029209,0.0004596712,0.00091055,0.00026597403,0.0010780282,0.0009806686,0.0011077594,0.0007569603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023406776,0.0005990052,0.027494954,0.0002226935,0.00008619712,0.00016212843,0.00033791884,0.007920337,0.008216063,0.0013736172,0.010547741,0.94280535],"study_design_scores_gemma":[0.000054044733,0.00057385,0.06161637,0.00020011995,0.00015322788,0.0005503814,0.0010960253,0.8847422,0.01616896,0.020815065,0.013954706,0.00007503826],"about_ca_topic_score_codex":0.0021915596,"about_ca_topic_score_gemma":0.003797635,"teacher_disagreement_score":0.0021915596,"about_ca_system_score_codex":0.00048490663,"about_ca_system_score_gemma":0.00064589974,"threshold_uncertainty_score":0.0049028993},"labels":[],"label_agreement":null},{"id":"W4405113472","doi":"10.1016/j.procs.2024.11.078","title":"Design and development of a decision support platform for the evaluation of CSR performance measurement for manufacturing SMEs","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Quality and Supply Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de Rimouski; Université du Québec à Rimouski","funders":"","keywords":"Computer science; Corporate social responsibility; Decision support system; Manufacturing engineering; Process management; Artificial intelligence; Business","score_opus":0.1478914159138064,"score_gpt":0.3029062628883095,"score_spread":0.15501484697450313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12400464,0.00017001049,0.8143172,0.00092889305,0.00018042482,0.003478368,0.0014768535,0.04083627,0.014607352],"genre_scores_gemma":[0.4255406,0.00017779357,0.56348336,0.0004086163,0.000053473297,0.0018461929,0.0021668319,0.0006499584,0.0056732763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998738,0.00027086053,0.00015139225,0.00028811677,0.0003961916,0.00015534718],"domain_scores_gemma":[0.9978684,0.0006201618,0.00023641095,0.00029571413,0.0006128017,0.00036642855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025343685,0.00065578637,0.00057897647,0.0012372178,0.0005633203,0.0024202643,0.0017118745,0.00095489406,0.0043179095],"category_scores_gemma":[0.003939641,0.00043694148,0.0006702927,0.00070919207,0.00041561044,0.0016826006,0.0018130961,0.0009525874,0.0017684906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021434245,0.0027478202,0.04005264,0.0020514661,0.0005234338,0.0029092762,0.0030522437,0.11295221,0.14587003,0.04546009,0.02807356,0.6141639],"study_design_scores_gemma":[0.0004896646,0.00095283845,0.0153176235,0.00034782308,0.00024509476,0.0005157777,0.0008256855,0.81148225,0.08518233,0.0175607,0.06685577,0.00022442799],"about_ca_topic_score_codex":0.0024334004,"about_ca_topic_score_gemma":0.0014380347,"teacher_disagreement_score":0.0043179095,"about_ca_system_score_codex":0.000811054,"about_ca_system_score_gemma":0.002117477,"threshold_uncertainty_score":0.014444828},"labels":[],"label_agreement":null},{"id":"W4405113514","doi":"10.1016/j.procs.2024.11.076","title":"Preface","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.013962293402786761,"score_gpt":0.27515486982513393,"score_spread":0.26119257642234717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113514","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001852347,0.010761407,0.021767695,0.028171076,0.31445807,0.0010546246,0.018721057,0.0028844003,0.6003294],"genre_scores_gemma":[0.006258158,0.0050713494,0.005262394,0.0056094527,0.035805237,0.0004136873,0.011235159,0.0011750077,0.9291696],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994491,0.00007219585,0.00003521274,0.0000976252,0.0003008086,0.00004499983],"domain_scores_gemma":[0.9937744,0.0009320601,0.00018525239,0.0005541553,0.0038646641,0.0006894385],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010305229,0.0010440577,0.0006287292,0.002985489,0.0021132906,0.002508991,0.0011308376,0.00076394324,0.48709103],"category_scores_gemma":[0.011589423,0.00028167665,0.0005714878,0.0018795308,0.0004158623,0.0021688195,0.0017087855,0.0025841517,0.31798682],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031235726,0.000026462181,0.000087182205,0.00009512817,0.000001781875,0.000036892674,0.000027460215,0.00008701541,0.0002077824,0.0030323148,0.95126927,0.0450976],"study_design_scores_gemma":[0.0000048894894,0.000019234058,0.00025808715,0.00010757812,0.0000022640995,0.00004605315,0.0000442157,0.000055530258,0.0002021897,0.0032441835,0.9960098,0.00000596057],"about_ca_topic_score_codex":0.0036219282,"about_ca_topic_score_gemma":0.0044715223,"teacher_disagreement_score":0.51290894,"about_ca_system_score_codex":0.0012903737,"about_ca_system_score_gemma":0.0016700602,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4405113670","doi":"10.1016/j.procs.2024.11.118","title":"SentimentCareBot: Retrieval-Augmented Generation Chatbot for Mental Health Support with Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Chatbot; Sentiment analysis; Information retrieval; Natural language processing; Artificial intelligence; Data science; World Wide Web","score_opus":0.03687471595547576,"score_gpt":0.38637927840019193,"score_spread":0.34950456244471617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26646933,0.0010217524,0.6251646,0.001987922,0.0010128042,0.0033154015,0.0064152796,0.08222739,0.0123855565],"genre_scores_gemma":[0.64097303,0.00026264353,0.3328701,0.0010137376,0.0002144636,0.0019436161,0.007974622,0.00087910023,0.013868653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923027,0.00040806254,0.000043622247,0.0001372158,0.00012813823,0.000052691994],"domain_scores_gemma":[0.998256,0.0011061599,0.00009772539,0.0001532462,0.00026756758,0.000119358956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018775093,0.001186123,0.00046508803,0.0007064833,0.0004971656,0.0007941853,0.0011394238,0.00092550396,0.007081248],"category_scores_gemma":[0.005011649,0.000293914,0.0006202237,0.00023821645,0.0003100667,0.0013864489,0.0012868814,0.001074537,0.0023742726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045422837,0.0031494698,0.017829467,0.0024578886,0.0004221643,0.0016762511,0.0046515395,0.05021668,0.11695573,0.009219307,0.1078906,0.6809886],"study_design_scores_gemma":[0.00032647137,0.0014461259,0.0054260325,0.000083581246,0.00012229547,0.00032866027,0.00074659655,0.9310284,0.026550276,0.006706729,0.027109232,0.00012547677],"about_ca_topic_score_codex":0.0017847839,"about_ca_topic_score_gemma":0.0033183077,"teacher_disagreement_score":0.007081248,"about_ca_system_score_codex":0.0005654284,"about_ca_system_score_gemma":0.0005682685,"threshold_uncertainty_score":0.023689091},"labels":[],"label_agreement":null},{"id":"W4405113743","doi":"10.1016/j.procs.2024.11.109","title":"Intrusion Detection in IIoT Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski; Cégep de Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Intrusion detection system; Artificial intelligence; Machine learning; Data mining","score_opus":0.013935061854909878,"score_gpt":0.2450794846223368,"score_spread":0.23114442276742692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78730226,0.0023309279,0.193488,0.00088310865,0.00033417816,0.0003989889,0.0042563425,0.0071020606,0.0039039883],"genre_scores_gemma":[0.9218602,0.00039627822,0.06811427,0.00014187026,0.00006719818,0.0001260538,0.007982656,0.00006803478,0.0012434783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814975,0.00043871277,0.00019556654,0.00042866974,0.00058333424,0.00020395093],"domain_scores_gemma":[0.99779665,0.000983445,0.0003566322,0.00035696974,0.00041245768,0.00009386696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020193742,0.0011322086,0.0012175118,0.0027159075,0.0005050513,0.0013616852,0.0009561536,0.0010073641,0.00034886293],"category_scores_gemma":[0.0034986292,0.00022433807,0.00091487344,0.0014599076,0.0005204096,0.001561561,0.0011757677,0.0009651203,0.00035474738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017728055,0.0014152097,0.10676667,0.00065679534,0.00048196994,0.0012345102,0.00029042852,0.30750626,0.027449252,0.0033226982,0.01489359,0.5342098],"study_design_scores_gemma":[0.000024360888,0.00025906562,0.010710587,0.000034779445,0.00004276036,0.00039965272,0.00011605838,0.9654061,0.016890695,0.0027884003,0.003303622,0.000023759017],"about_ca_topic_score_codex":0.0033793962,"about_ca_topic_score_gemma":0.004174163,"teacher_disagreement_score":0.0033793962,"about_ca_system_score_codex":0.0007442275,"about_ca_system_score_gemma":0.0005781447,"threshold_uncertainty_score":0.010679603},"labels":[],"label_agreement":null},{"id":"W4405113866","doi":"10.1016/j.procs.2024.11.124","title":"Interactive Machine Learning Pedagogy: Developing a Web-Based Educational Platform for Clinical Predictive Modeling","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Université du Québec à Rimouski","funders":"","keywords":"Computer science; Machine learning; Human–computer interaction; Multimedia; Artificial intelligence","score_opus":0.07275786256837764,"score_gpt":0.42194292578354137,"score_spread":0.34918506321516374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020184806,0.00038561854,0.9112875,0.0014100214,0.0002263359,0.00070707145,0.0011331915,0.045444533,0.01922099],"genre_scores_gemma":[0.09951568,0.0010225684,0.8610525,0.0011492567,0.00029545472,0.0016446376,0.003842973,0.003444109,0.028032908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991708,0.00027787447,0.00006554858,0.00014688607,0.00022990469,0.00010904534],"domain_scores_gemma":[0.99713814,0.0018964944,0.0001003344,0.00027855148,0.0002519346,0.00033460592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018828282,0.0012611126,0.00051701587,0.0013133554,0.00036833057,0.002068356,0.0022706338,0.0012981242,0.02597362],"category_scores_gemma":[0.0067496873,0.0004273071,0.00097535644,0.0005681647,0.00051905145,0.0033226772,0.004013086,0.0015931224,0.013412945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005651949,0.0023091377,0.004222208,0.0013062384,0.00008579552,0.0018277263,0.002204685,0.01918373,0.03657723,0.03131071,0.07056135,0.8298461],"study_design_scores_gemma":[0.0005227192,0.0010420887,0.0055528004,0.0011874357,0.00011423336,0.0029358056,0.00094913226,0.19215514,0.068970345,0.08349504,0.642773,0.00030230155],"about_ca_topic_score_codex":0.00046636592,"about_ca_topic_score_gemma":0.0005376457,"teacher_disagreement_score":0.02597362,"about_ca_system_score_codex":0.00045318788,"about_ca_system_score_gemma":0.0014588708,"threshold_uncertainty_score":0.0868904},"labels":[],"label_agreement":null},{"id":"W4405113927","doi":"10.1016/j.procs.2024.11.094","title":"Application of Generative Artificial Intelligence in Minimizing Cyber Attacks on Vehicular Networks","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Generative grammar; Artificial intelligence; Computer security; Machine learning","score_opus":0.1429528841429706,"score_gpt":0.38131346463838195,"score_spread":0.23836058049541134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031926546,0.0003320567,0.9546659,0.00045608034,0.00003011648,0.00007139561,0.000018439774,0.00032163542,0.012177862],"genre_scores_gemma":[0.8515427,0.0003971074,0.14607695,0.00013639689,0.000021134802,0.00007229457,0.000051669256,0.00006576509,0.0016359255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852765,0.0006409347,0.00006760577,0.00020324804,0.00043199063,0.00012855775],"domain_scores_gemma":[0.9969098,0.0019022343,0.00031258614,0.0004884154,0.0002942011,0.00009277131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021768846,0.00052442664,0.00041925287,0.0012555867,0.0007646879,0.0018856908,0.0011244146,0.00079846964,0.0012431265],"category_scores_gemma":[0.006459171,0.00033850956,0.00075920054,0.0006558554,0.0028640127,0.0017402463,0.0023550326,0.0009707771,0.00020288228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034970923,0.000042033364,0.0025188266,0.0001136611,0.00005506276,0.00015895905,0.00047319336,0.64089566,0.0035842701,0.2664811,0.0005560924,0.08508622],"study_design_scores_gemma":[0.0000075777307,0.000057553,0.00044304668,0.00004795928,0.00002941769,0.000115034614,0.00013295357,0.80322826,0.0035268285,0.18741156,0.004979582,0.000020233027],"about_ca_topic_score_codex":0.0014162875,"about_ca_topic_score_gemma":0.001644192,"teacher_disagreement_score":0.0021768846,"about_ca_system_score_codex":0.0010803281,"about_ca_system_score_gemma":0.0011521402,"threshold_uncertainty_score":0.0115125775},"labels":[],"label_agreement":null},{"id":"W4407301939","doi":"10.1016/j.procs.2025.01.040","title":"EM-ACO-ARM: An Enhanced Multiple Ant Colony Optimization Algorithm for Adaptive Resource Management in Cloud Environment","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Ant colony optimization algorithms; Cloud computing; ANT; Resource (disambiguation); Distributed computing; Algorithm; Operating system; Computer network","score_opus":0.01010522958732076,"score_gpt":0.23234018267047735,"score_spread":0.22223495308315658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407301939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036495294,0.0008669681,0.9518003,0.00033958312,0.0001702557,0.00016981916,0.00009659123,0.0012846007,0.008776709],"genre_scores_gemma":[0.54641825,0.00047893086,0.44612554,0.00032795095,0.00008284192,0.000297982,0.0002471984,0.00018011287,0.005841198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953496,0.00013162967,0.000026475032,0.00007873418,0.00017121588,0.00005704048],"domain_scores_gemma":[0.999514,0.00019354714,0.00007281847,0.000050261202,0.00013636533,0.000033118573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055383577,0.00075981865,0.0009063269,0.00068062375,0.00045723506,0.00067830813,0.0013113081,0.00084922503,0.0012652364],"category_scores_gemma":[0.0015237398,0.0002974022,0.000688869,0.00080129365,0.00033324095,0.00059316715,0.00075168913,0.0008434971,0.000297798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011098123,0.00013125649,0.0015156452,0.00012334545,0.00012108971,0.00013557228,0.00005076925,0.86275977,0.0054418733,0.0044560246,0.0032052244,0.12194849],"study_design_scores_gemma":[0.000016964661,0.000030056603,0.00016392417,0.0000037402963,0.000009105443,0.00003238094,0.000004783597,0.9974567,0.00051847124,0.00056206924,0.001196213,0.000005555291],"about_ca_topic_score_codex":0.005928326,"about_ca_topic_score_gemma":0.00734079,"teacher_disagreement_score":0.005928326,"about_ca_system_score_codex":0.00042966678,"about_ca_system_score_gemma":0.001058815,"threshold_uncertainty_score":0.011787653},"labels":[],"label_agreement":null},{"id":"W4407919307","doi":"10.1016/j.procs.2025.01.335","title":"Innovative Swing Mechanism for Sustainable Energy Generation: Design, Performance, and IoT Integration","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NSCAD University","funders":"","keywords":"Computer science; Swing; Mechanism (biology); Internet of Things; Energy (signal processing); Mechanism design; Sustainable energy; Embedded system; Computer architecture; Industrial engineering; Renewable energy; Electrical engineering; Mechanical engineering","score_opus":0.011873954586898883,"score_gpt":0.2067400369826292,"score_spread":0.1948660823957303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407919307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56096125,0.0016068924,0.41928527,0.00040342353,0.00028406447,0.00028230512,0.00028020638,0.0010756945,0.015820947],"genre_scores_gemma":[0.96279186,0.00035643327,0.033159867,0.00003463053,0.000019710891,0.000072680734,0.00009741166,0.000032393153,0.0034349668],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998288,0.000017465021,0.0000114104705,0.000024489518,0.00009595453,0.000021845628],"domain_scores_gemma":[0.9998683,0.000017476374,0.000023038825,0.000016402135,0.000059044112,0.00001586867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027311558,0.0003440747,0.00021266825,0.00054195867,0.00019470356,0.00038003703,0.00046648085,0.0004025877,0.0017431928],"category_scores_gemma":[0.0003047998,0.00013685106,0.00029841787,0.00036773432,0.00018642422,0.0005965059,0.00032612006,0.00017252352,0.00041230948],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003326746,0.000117592346,0.0030977752,0.0006298898,0.00003685996,0.0004562624,0.0001565335,0.012664242,0.86527354,0.0075250207,0.0013078484,0.10840182],"study_design_scores_gemma":[0.00017706456,0.006836869,0.022347162,0.00018919597,0.00022370445,0.0024575973,0.0004849508,0.22012582,0.65572727,0.0073216558,0.08396152,0.00014722713],"about_ca_topic_score_codex":0.0001237463,"about_ca_topic_score_gemma":0.00030030755,"teacher_disagreement_score":0.0017431928,"about_ca_system_score_codex":0.00014175534,"about_ca_system_score_gemma":0.00018626767,"threshold_uncertainty_score":0.0058315992},"labels":[],"label_agreement":null},{"id":"W4407919452","doi":"10.1016/j.procs.2025.01.334","title":"Single-Axis Solar Tracking Systems: A Comprehensive Design and Performance Study","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NSCAD University","funders":"","keywords":"Computer science; Tracking (education); Tracking system; Simulation; Artificial intelligence; Kalman filter","score_opus":0.036568703461859316,"score_gpt":0.2572319493134649,"score_spread":0.22066324585160557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407919452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83668566,0.0026907052,0.14161998,0.00026605258,0.00011530139,0.00050055917,0.00056243996,0.001301243,0.016258158],"genre_scores_gemma":[0.98361397,0.00047359976,0.012642029,0.000018891977,0.000012382857,0.00007094657,0.00015738953,0.000038684422,0.0029720387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994673,0.000102494814,0.000031261836,0.00008337308,0.00025679663,0.000058850073],"domain_scores_gemma":[0.9993512,0.00012367552,0.00010831519,0.000053868287,0.0003301551,0.00003283357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006076759,0.00054414815,0.0006115446,0.00051457336,0.0003961369,0.0006879371,0.00052064954,0.00039698824,0.002822464],"category_scores_gemma":[0.000709872,0.00018867238,0.0004746875,0.00050340325,0.00014461314,0.0005455041,0.00024461639,0.00024426356,0.0006431996],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016135044,0.0006485503,0.021906568,0.0017567546,0.00030823814,0.00058847247,0.0004370398,0.4042793,0.1611022,0.0023809748,0.0047849277,0.4001934],"study_design_scores_gemma":[0.00012887554,0.00984766,0.03149208,0.00016743742,0.0003773034,0.0010644865,0.00040027764,0.81028724,0.12807174,0.0008716713,0.01718606,0.00010514173],"about_ca_topic_score_codex":0.002110706,"about_ca_topic_score_gemma":0.0024492282,"teacher_disagreement_score":0.002822464,"about_ca_system_score_codex":0.0006265145,"about_ca_system_score_gemma":0.00056129065,"threshold_uncertainty_score":0.009442091},"labels":[],"label_agreement":null},{"id":"W4407919697","doi":"10.1016/j.procs.2025.01.184","title":"Real time contaminants detection in wood panel manufacturing process using YOLO algorithms","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Mitacs","keywords":"Computer science; Process (computing); Algorithm; Real-time computing","score_opus":0.01993010311556979,"score_gpt":0.2562083574813149,"score_spread":0.2362782543657451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407919697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3642633,0.0020307007,0.62453014,0.00023042299,0.0001231159,0.00013243018,0.00033773284,0.004745835,0.0036064216],"genre_scores_gemma":[0.7522259,0.0005527331,0.24223876,0.00014639921,0.00003573179,0.00008002973,0.00111521,0.00012379678,0.0034814526],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996711,0.000039973125,0.000019720575,0.00012791401,0.00008396409,0.0000573446],"domain_scores_gemma":[0.99956673,0.00013703271,0.000089025794,0.0000445398,0.00014257453,0.00001999751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077498064,0.00073815626,0.0007116093,0.00111688,0.00029048856,0.0010047652,0.0007112771,0.0007385756,0.0009342938],"category_scores_gemma":[0.001253206,0.0002689499,0.0006601155,0.00044858962,0.0002644352,0.0006457093,0.0004802972,0.00042608986,0.00057088264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014291608,0.0004664678,0.015183266,0.00037788236,0.00018695851,0.0002542565,0.00018451235,0.226832,0.0952921,0.0010870046,0.0029688866,0.6557375],"study_design_scores_gemma":[0.00001606337,0.00018505931,0.00430036,0.000015522393,0.000031509273,0.00009139449,0.000044265806,0.9729267,0.021162165,0.00021537754,0.0009987872,0.000012775632],"about_ca_topic_score_codex":0.0059149596,"about_ca_topic_score_gemma":0.0068671526,"teacher_disagreement_score":0.0059149596,"about_ca_system_score_codex":0.00057177985,"about_ca_system_score_gemma":0.0007528401,"threshold_uncertainty_score":0.011761069},"labels":[],"label_agreement":null},{"id":"W4407919723","doi":"10.1016/j.procs.2025.01.191","title":"VR Games for Teaching Lean Manufacturing Tools: A Case Study of Stool Manufacturing","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Lean manufacturing; Manufacturing engineering; Industrial engineering","score_opus":0.03194047022853295,"score_gpt":0.317734211443989,"score_spread":0.28579374121545603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407919723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9820033,0.0002817356,0.009980155,0.0006267758,0.00004753822,0.00045448088,0.00008195949,0.00008678353,0.006437386],"genre_scores_gemma":[0.9644667,0.00085226813,0.026189499,0.00027026105,0.00002281473,0.00024740899,0.00009406559,0.00007798898,0.007778928],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99794346,0.0013011247,0.00006522397,0.00013913198,0.0002751576,0.0002759612],"domain_scores_gemma":[0.9971752,0.001936317,0.00014309573,0.00012834446,0.00014596968,0.00047105708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020957892,0.0009055707,0.0005909281,0.0009900079,0.0024695448,0.0020543085,0.0016900887,0.0023249988,0.0032092885],"category_scores_gemma":[0.0052876705,0.00034103045,0.0008051014,0.0007047457,0.0014763331,0.00108857,0.0020699352,0.0015919426,0.000768643],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023064918,0.056122202,0.03977111,0.0041095843,0.00038740292,0.12556604,0.31130207,0.030811017,0.053530455,0.029200718,0.013311502,0.33358142],"study_design_scores_gemma":[0.001484545,0.02890181,0.062053084,0.0016880499,0.000341006,0.05942283,0.4441507,0.06880268,0.06724056,0.015768677,0.2495689,0.00057730556],"about_ca_topic_score_codex":0.0019623619,"about_ca_topic_score_gemma":0.006820049,"teacher_disagreement_score":0.0032092885,"about_ca_system_score_codex":0.0008431299,"about_ca_system_score_gemma":0.0009136256,"threshold_uncertainty_score":0.011083722},"labels":[],"label_agreement":null},{"id":"W4407920021","doi":"10.1016/j.procs.2025.01.200","title":"Empowering SMEs in the Fourth Industrial Revolution: A Framework for Maintenance 4.0 Adoption","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Industrial Revolution; Knowledge management; Engineering management","score_opus":0.025838797916929057,"score_gpt":0.27089263725069984,"score_spread":0.24505383933377078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06826482,0.0073615317,0.4346916,0.11944646,0.0004625152,0.00069404405,0.00014989385,0.0003527786,0.36857638],"genre_scores_gemma":[0.8888358,0.004252675,0.09011994,0.0024877938,0.00015804746,0.00079289917,0.000069997826,0.000049248356,0.013233513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963934,0.002292465,0.00014903268,0.0002509382,0.0004224738,0.00049164076],"domain_scores_gemma":[0.9976948,0.0011801866,0.00029817378,0.000122283,0.00037018472,0.000334361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005164447,0.00091730914,0.000405989,0.0027095901,0.0027646916,0.0070709595,0.0019115582,0.0057869363,0.00405366],"category_scores_gemma":[0.0042659473,0.00048930733,0.0007989736,0.0016700897,0.008539286,0.0092365295,0.006908348,0.0038328276,0.0007144868],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059595154,0.000042601725,0.00046847734,0.00007715912,0.000004120692,0.0003128099,0.0025695546,0.0013289534,0.000265706,0.98733413,0.0009160011,0.006674491],"study_design_scores_gemma":[0.000029482995,0.00015395877,0.0015316423,0.0010703319,0.000027445922,0.0005239565,0.014173469,0.021464676,0.0007224272,0.83137196,0.1288545,0.00007625088],"about_ca_topic_score_codex":0.0048855795,"about_ca_topic_score_gemma":0.004822432,"teacher_disagreement_score":0.0070709595,"about_ca_system_score_codex":0.0055095092,"about_ca_system_score_gemma":0.0061271177,"threshold_uncertainty_score":0.03997451},"labels":[],"label_agreement":null},{"id":"W4407920072","doi":"10.1016/j.procs.2025.01.123","title":"Towards Efficient and Fine-Grained Traceability for a Live Lobster Supply Chain using Blockchain Technology","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blockchain; Traceability; Computer science; Supply chain; Data science; Computer security; Software engineering; Business","score_opus":0.009920315163678077,"score_gpt":0.25730761017105,"score_spread":0.24738729500737194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18621199,0.00031212665,0.803817,0.0009284012,0.000035609402,0.00047418522,0.00024353624,0.0011401917,0.00683694],"genre_scores_gemma":[0.858322,0.00031919766,0.13854763,0.00007276165,0.000007247061,0.00019968594,0.0003429933,0.00007135771,0.0021169912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976076,0.00096967025,0.00013727664,0.00030394463,0.0006953621,0.00028618332],"domain_scores_gemma":[0.99381036,0.0025991881,0.00065437023,0.0016601426,0.0010332516,0.00024266257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038762637,0.0005997942,0.0005158045,0.000995285,0.0011986278,0.003455042,0.0012993321,0.0015480962,0.0030642499],"category_scores_gemma":[0.010133013,0.0004882714,0.0006091192,0.0011545721,0.0016176654,0.0058073867,0.0028599468,0.0015620355,0.000658526],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004001324,0.00024114487,0.010551525,0.00031384671,0.000068220004,0.00046651778,0.00086202275,0.775541,0.02953102,0.086530715,0.0012306336,0.094263315],"study_design_scores_gemma":[0.000043830976,0.00013102595,0.0008499584,0.000062902625,0.000024172454,0.00008015073,0.00016674619,0.95192343,0.009198809,0.03284839,0.004644618,0.000025968226],"about_ca_topic_score_codex":0.01615651,"about_ca_topic_score_gemma":0.015483174,"teacher_disagreement_score":0.01615651,"about_ca_system_score_codex":0.0022167556,"about_ca_system_score_gemma":0.0053940583,"threshold_uncertainty_score":0.032124877},"labels":[],"label_agreement":null},{"id":"W4407920087","doi":"10.1016/j.procs.2025.01.105","title":"Artificial intelligence and consumer loyalty in e-commerce","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"RUDN University","keywords":"Computer science; Loyalty; Artificial intelligence; Data science; Marketing; Business","score_opus":0.028526312347211508,"score_gpt":0.3221080302527131,"score_spread":0.2935817179055016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97684735,0.0009148332,0.00037199463,0.0010191336,0.000022281905,0.000019894478,0.000020921714,0.000003207687,0.020780534],"genre_scores_gemma":[0.999005,0.00017798015,0.00006687839,0.0001297237,0.000012233474,0.000005020134,0.00001339337,9.27303e-7,0.0005888221],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99879336,0.00065858517,0.000066707165,0.000067177796,0.0002366461,0.00017749025],"domain_scores_gemma":[0.9943245,0.0030124732,0.0011354766,0.00012758395,0.00073656783,0.00066338724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012228475,0.00010271941,0.00021258998,0.0010598606,0.00090717454,0.0024937287,0.00021515344,0.0009319569,0.0036218285],"category_scores_gemma":[0.0043032025,0.0001248924,0.00031937557,0.0010696647,0.0010494886,0.00089427433,0.00070742087,0.0011128268,0.00019558778],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016388454,0.0009120174,0.951725,0.00009962259,0.00011162225,0.00025953073,0.0037449705,0.00027531033,0.0002740663,0.013262128,0.0006623871,0.02850947],"study_design_scores_gemma":[0.000013098803,0.00021783775,0.9822718,0.00009713798,0.00006358179,0.00034124614,0.007699688,0.0019094641,0.00011418787,0.004867928,0.0023838193,0.000020205509],"about_ca_topic_score_codex":0.0032574884,"about_ca_topic_score_gemma":0.003260735,"teacher_disagreement_score":0.0036218285,"about_ca_system_score_codex":0.0008187274,"about_ca_system_score_gemma":0.0005747004,"threshold_uncertainty_score":0.012116194},"labels":[],"label_agreement":null},{"id":"W4407920097","doi":"10.1016/j.procs.2025.01.118","title":"Design and Simulation of a Production Line for Plastic Recycling with Additive Manufacturing","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Production line; Production (economics); Line (geometry); Manufacturing engineering; Assembly line; Plastic waste; Process engineering; Industrial engineering; Mechanical engineering; Waste management","score_opus":0.015519913314936754,"score_gpt":0.2365327666402423,"score_spread":0.22101285332530557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48040313,0.0006217449,0.45123038,0.0006758449,0.00023659394,0.0006318839,0.001027971,0.0022326077,0.06293994],"genre_scores_gemma":[0.95464134,0.00021262778,0.03739636,0.00004529162,0.000007633601,0.0004609577,0.00028849364,0.000051546445,0.00689569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973744,0.00007388506,0.000012424565,0.000045007313,0.00008082171,0.000050461498],"domain_scores_gemma":[0.99958855,0.0002002049,0.000050676288,0.000025347268,0.00010105632,0.00003421214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041793167,0.00079067063,0.0007667676,0.0005081873,0.00074331247,0.001114863,0.00089390203,0.0013554908,0.0069520953],"category_scores_gemma":[0.00069162884,0.00042410745,0.00097391853,0.00037597297,0.00051323196,0.0003848682,0.00060431415,0.00061138836,0.00042836522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059526246,0.000033396867,0.000556186,0.00005016907,0.000009682934,0.00007707758,0.000027442054,0.99447775,0.0020152754,0.0007207067,0.00011187814,0.0018608661],"study_design_scores_gemma":[0.000019598867,0.000071885406,0.00016014942,0.0000051944658,0.000009978497,0.000010104094,0.00001945655,0.9975973,0.0012683157,0.00020519595,0.00062811427,0.0000047056074],"about_ca_topic_score_codex":0.009316678,"about_ca_topic_score_gemma":0.005191404,"teacher_disagreement_score":0.009316678,"about_ca_system_score_codex":0.0007907354,"about_ca_system_score_gemma":0.0012986494,"threshold_uncertainty_score":0.023257136},"labels":[],"label_agreement":null},{"id":"W4407920126","doi":"10.1016/j.procs.2025.01.109","title":"Design and Development of a Lean Robotic Cell for Concrete 3D Printing","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Innovations in Concrete and Construction Materials","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; 3D printing; Human–computer interaction; Development (topology); Manufacturing engineering; Composite material; Materials science","score_opus":0.016748376231142944,"score_gpt":0.22884333238603666,"score_spread":0.2120949561548937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07297295,0.0003211744,0.91438824,0.00014830749,0.00012533438,0.0004126811,0.00023947589,0.0017545455,0.009637294],"genre_scores_gemma":[0.31522632,0.00026947234,0.6792257,0.00004599097,0.0000138578325,0.00032332807,0.00023652322,0.00009671157,0.0045620603],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995173,0.000023890672,0.000020921787,0.00006016026,0.0003359338,0.000041761865],"domain_scores_gemma":[0.9997446,0.00003544555,0.000045414527,0.000044928645,0.00009536989,0.000034321343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041117315,0.00038978236,0.00039867437,0.00038647527,0.00039164958,0.0007879062,0.0011877596,0.00064256456,0.002204601],"category_scores_gemma":[0.00040198176,0.0003042209,0.00050818245,0.00026681676,0.00037043763,0.000456961,0.0005970374,0.0005302911,0.0010919486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012808753,0.00019486179,0.0016348113,0.00054206484,0.000028678862,0.0005921866,0.00026507265,0.14181104,0.7040876,0.015218442,0.0016498661,0.13384731],"study_design_scores_gemma":[0.00007338153,0.0012620254,0.0023376625,0.00006662394,0.00004452755,0.00088677456,0.000148134,0.42218885,0.50259,0.0023879292,0.06789545,0.000118659875],"about_ca_topic_score_codex":0.0009991989,"about_ca_topic_score_gemma":0.0016328077,"teacher_disagreement_score":0.002204601,"about_ca_system_score_codex":0.0005183924,"about_ca_system_score_gemma":0.0014058328,"threshold_uncertainty_score":0.007375121},"labels":[],"label_agreement":null},{"id":"W4407945992","doi":"10.1016/j.procs.2025.01.275","title":"Maintenance 4.0 in Mining Trucks: Data Digitalization and Advanced Protocols","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Mitacs","keywords":"Computer science; Truck; Data mining; Data science; Automotive engineering","score_opus":0.13384357824123533,"score_gpt":0.5086764434664153,"score_spread":0.37483286522517995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407945992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13490504,0.004390156,0.8026124,0.015227115,0.0006505914,0.001507562,0.00084323355,0.010922617,0.028941257],"genre_scores_gemma":[0.8701469,0.0020403352,0.11801942,0.0009936738,0.0002705583,0.0006124068,0.0009573651,0.00026901276,0.006690436],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996555,0.0010789367,0.00038902333,0.0005022161,0.0012019285,0.00027289812],"domain_scores_gemma":[0.9897384,0.0035445797,0.0014486592,0.0027767757,0.0019518788,0.00053973496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008645473,0.00046714945,0.0005267451,0.0019762104,0.0011862828,0.0043174154,0.0025170518,0.0015382678,0.0023338299],"category_scores_gemma":[0.016213493,0.0004923597,0.000353803,0.0015823428,0.001174684,0.006398705,0.0039424566,0.0019260955,0.0008678342],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012157629,0.0006006963,0.033983074,0.0006471219,0.00013946494,0.00086811034,0.0038768372,0.024016276,0.023361009,0.15193683,0.030253801,0.72910106],"study_design_scores_gemma":[0.00015513111,0.0010807979,0.019253291,0.0009978621,0.00016169404,0.0017078678,0.0033859867,0.579706,0.035793737,0.12071732,0.23673564,0.0003046808],"about_ca_topic_score_codex":0.0032334877,"about_ca_topic_score_gemma":0.0020092814,"teacher_disagreement_score":0.008645473,"about_ca_system_score_codex":0.0016520134,"about_ca_system_score_gemma":0.0023214875,"threshold_uncertainty_score":0.045722187},"labels":[],"label_agreement":null},{"id":"W4408337594","doi":"10.1016/j.procs.2025.02.280","title":"A Digital Transformation Project Portfolio Management Model for Underground Mines","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transformation (genetics); Digital transformation; Portfolio; Model transformation; Project portfolio management; Engineering management; Project management; Artificial intelligence; World Wide Web; Systems engineering; Finance","score_opus":0.015301218056274995,"score_gpt":0.24239454248944295,"score_spread":0.22709332443316796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408337594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063229784,0.00034613186,0.89587224,0.0014239906,0.00004992098,0.00028913672,0.00035194596,0.0003817529,0.038055107],"genre_scores_gemma":[0.7797858,0.00068052433,0.20074967,0.0001134285,0.000044982076,0.00054831215,0.00050354644,0.000075900556,0.01749789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854136,0.0005128032,0.00010695694,0.00026637057,0.00035380753,0.00021870837],"domain_scores_gemma":[0.9989963,0.00030734026,0.00018417386,0.00010602926,0.00022153469,0.0001846126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001995057,0.0006916398,0.00049149484,0.0012213099,0.0007888442,0.0039388724,0.0019739077,0.0013810529,0.0062455074],"category_scores_gemma":[0.0027594222,0.00033443957,0.00074867875,0.0015404893,0.00076201354,0.0032713914,0.0023318932,0.0012255032,0.0009193723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010644661,0.00020804636,0.0031038441,0.00007582827,0.000044817552,0.0003504684,0.00049340376,0.7109753,0.0009571218,0.21204811,0.0021971455,0.06943955],"study_design_scores_gemma":[0.0000223243,0.000057885783,0.00034737002,0.000022066171,0.000015645035,0.000062630774,0.00013333278,0.9502667,0.00021356583,0.04259114,0.0062525505,0.000014729959],"about_ca_topic_score_codex":0.0071199955,"about_ca_topic_score_gemma":0.005486272,"teacher_disagreement_score":0.0071199955,"about_ca_system_score_codex":0.0022527336,"about_ca_system_score_gemma":0.0024443923,"threshold_uncertainty_score":0.020893276},"labels":[],"label_agreement":null},{"id":"W4408338238","doi":"10.1016/j.procs.2025.02.240","title":"Task Generator 2.0: Integrating Interactive Technology with Personalized Task Generation","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"3v Geomatics (Canada)","funders":"","keywords":"Computer science; Task (project management); Generator (circuit theory); Human–computer interaction; Multimedia; Systems engineering; Power (physics)","score_opus":0.01309708990084671,"score_gpt":0.2565759540533312,"score_spread":0.24347886415248451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408338238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06838261,0.00039983107,0.81003803,0.00042736853,0.0002518821,0.0029260677,0.0012087239,0.108804844,0.007560738],"genre_scores_gemma":[0.25063887,0.00028982863,0.73186445,0.0004987234,0.00010129331,0.0030602987,0.0020848815,0.004123529,0.0073380372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898654,0.0003494135,0.00008524208,0.0002576866,0.00022715269,0.00009401485],"domain_scores_gemma":[0.9980008,0.001173317,0.00009831428,0.00035890573,0.00018199615,0.00018665797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019110615,0.0014909081,0.0005050453,0.0009739102,0.00022923881,0.0011920076,0.002041556,0.00095098093,0.006828558],"category_scores_gemma":[0.0066950386,0.00053524534,0.00061656744,0.00031466738,0.00044446552,0.0012358334,0.0021254658,0.0010520357,0.00263999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036561738,0.0026782015,0.009708664,0.0013185604,0.00038501108,0.0011933994,0.0019311032,0.020953763,0.08473238,0.006114889,0.034407217,0.8329207],"study_design_scores_gemma":[0.0020905535,0.005033379,0.0218506,0.00040318636,0.00047705043,0.0033698115,0.00038274145,0.6279403,0.17125829,0.031855233,0.13467939,0.0006594801],"about_ca_topic_score_codex":0.0010922004,"about_ca_topic_score_gemma":0.0014494101,"teacher_disagreement_score":0.006828558,"about_ca_system_score_codex":0.000312281,"about_ca_system_score_gemma":0.0008043668,"threshold_uncertainty_score":0.022843778},"labels":[],"label_agreement":null},{"id":"W4408338546","doi":"10.1016/j.procs.2025.02.094","title":"Artificial intelligence applied in adaptive manufacturing process monitoring: a state-of-the-art in the era of automation.","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada; Polytechnique Montréal","funders":"Mitacs","keywords":"Computer science; Automation; Process (computing); State (computer science); Artificial intelligence; Manufacturing engineering; Data science; Industrial engineering; Algorithm; Operating system; Mechanical engineering","score_opus":0.02072597934605534,"score_gpt":0.25827091849520106,"score_spread":0.23754493914914573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408338546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027251093,0.4171056,0.51415724,0.009297443,0.0015581707,0.00015802604,0.00025271127,0.00080027094,0.02941941],"genre_scores_gemma":[0.40213698,0.29314682,0.28935263,0.002574054,0.0024105457,0.00021142143,0.00059805333,0.00015671372,0.009412783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856204,0.00046050953,0.000119259676,0.00028353615,0.000526369,0.000048309033],"domain_scores_gemma":[0.9965738,0.0025722298,0.00021208434,0.00022836175,0.00033959217,0.0000740205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018586393,0.0006216129,0.00079358806,0.0018467694,0.0002797753,0.002889309,0.0010534283,0.0016374391,0.0012001073],"category_scores_gemma":[0.003995685,0.00024843443,0.00073480775,0.002555319,0.0012808687,0.0019763224,0.0009631778,0.0015677424,0.00050380814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009321917,0.00014923382,0.0044617197,0.0026000233,0.00027530594,0.00020559023,0.00031881273,0.017928611,0.0055408636,0.036206648,0.0064106,0.9258094],"study_design_scores_gemma":[0.000052168485,0.00072683487,0.016986402,0.0035254187,0.00034293142,0.0014573259,0.0010383272,0.36749536,0.014829324,0.2849194,0.30836684,0.00025960582],"about_ca_topic_score_codex":0.00071455044,"about_ca_topic_score_gemma":0.0006002483,"teacher_disagreement_score":0.002889309,"about_ca_system_score_codex":0.00058933406,"about_ca_system_score_gemma":0.00059289584,"threshold_uncertainty_score":0.009829521},"labels":[],"label_agreement":null},{"id":"W4409813933","doi":"10.1016/j.procs.2025.03.116","title":"Harnessing Large Language Models for Precision Topic Extraction and Technology Patent Nomination: A GPT-centric Methodology","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de Rimouski; Université du Québec à Rimouski","funders":"","keywords":"Computer science; Nomination; Patent analysis; Extraction (chemistry); Data science; Data mining","score_opus":0.04369533804685807,"score_gpt":0.3513519287723578,"score_spread":0.3076565907254998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409813933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019512601,0.0008461376,0.9744221,0.00092592544,0.0000819682,0.00019275548,0.0009293712,0.0013662202,0.0017229103],"genre_scores_gemma":[0.46305147,0.0015318893,0.52278745,0.00054076815,0.00064953975,0.0010187819,0.005334745,0.00043688016,0.0046484414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976399,0.0011662856,0.0001473533,0.00057191553,0.0003675486,0.00010700253],"domain_scores_gemma":[0.9876809,0.009922191,0.00080072175,0.0006981569,0.0007474538,0.00015060331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034737498,0.0013232362,0.0009218327,0.0039832573,0.0008704234,0.0027978534,0.001241963,0.0013095451,0.0019131701],"category_scores_gemma":[0.017463025,0.00050864473,0.0019096677,0.0034413897,0.0007572367,0.0041606,0.0018054586,0.0022155526,0.0021229954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004068278,0.00043002417,0.012071333,0.0010158602,0.0005602521,0.0007024991,0.0024491278,0.15131003,0.020057827,0.06597355,0.014941331,0.73008144],"study_design_scores_gemma":[0.000035263307,0.000081110054,0.001547195,0.00006244336,0.0001105443,0.00019597857,0.00023454175,0.93119174,0.004232899,0.053184006,0.009075004,0.000049372273],"about_ca_topic_score_codex":0.003541529,"about_ca_topic_score_gemma":0.0053707287,"teacher_disagreement_score":0.0039832573,"about_ca_system_score_codex":0.0010514681,"about_ca_system_score_gemma":0.002148942,"threshold_uncertainty_score":0.018371105},"labels":[],"label_agreement":null},{"id":"W4409814247","doi":"10.1016/j.procs.2025.03.080","title":"Reinforcement Learning-based Hybrid Routing Algorithms for Vehicular Ad Hoc Networks","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Telus (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Wireless ad hoc network; Routing (electronic design automation); Optimized Link State Routing Protocol; Vehicular ad hoc network; Computer network; Algorithm; Routing protocol; Distributed computing; Artificial intelligence; Telecommunications; Wireless","score_opus":0.00745590987846481,"score_gpt":0.2230018929799414,"score_spread":0.2155459831014766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0097871,0.00043087231,0.98772347,0.00013444069,0.00006211813,0.00004141142,0.000020190168,0.00038995533,0.0014104416],"genre_scores_gemma":[0.72356063,0.00049337273,0.2713472,0.00028263504,0.0001224359,0.0002753568,0.00014088616,0.00010708134,0.0036705278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992937,0.00027826003,0.00004146404,0.00012676761,0.00018126181,0.000078536345],"domain_scores_gemma":[0.99832684,0.0010545135,0.00017223696,0.000106323525,0.00026549172,0.000074625605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014726544,0.0009792141,0.0012729719,0.00063853414,0.00048218187,0.0007530664,0.0019505509,0.0010171846,0.0013318058],"category_scores_gemma":[0.0031252494,0.00038081114,0.00046429245,0.0006268194,0.0010085428,0.0012090724,0.0009899716,0.001209702,0.00032676864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049250073,0.000048993123,0.00036381267,0.000030381208,0.000037838014,0.000021747244,0.00002833196,0.9497286,0.0005443626,0.006598869,0.0006531734,0.041894644],"study_design_scores_gemma":[0.000011185704,0.00002488314,0.00002811523,0.0000025761246,0.0000029491873,0.0000070402916,0.0000032751266,0.9969494,0.00011258045,0.0025586223,0.00029652912,0.0000028478141],"about_ca_topic_score_codex":0.0043427777,"about_ca_topic_score_gemma":0.00293899,"teacher_disagreement_score":0.0043427777,"about_ca_system_score_codex":0.0010060818,"about_ca_system_score_gemma":0.0009046247,"threshold_uncertainty_score":0.0086349845},"labels":[],"label_agreement":null},{"id":"W4409814264","doi":"10.1016/j.procs.2025.03.113","title":"Using Machine Learning to Analyze and Detect Anomalies in SELinux Security Policies","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Computer security; Artificial intelligence; Security policy; Machine learning","score_opus":0.012482453141281073,"score_gpt":0.26694520520652004,"score_spread":0.254462752065239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26412722,0.00028355257,0.71981823,0.0006561808,0.000053135616,0.00016410435,0.0007938558,0.011844834,0.0022588873],"genre_scores_gemma":[0.8763592,0.00011680836,0.12185198,0.00009126085,0.000014614692,0.00006626382,0.00080424483,0.00015535473,0.0005402251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815756,0.0005189585,0.00015045489,0.0004052608,0.0006018887,0.0001659752],"domain_scores_gemma":[0.9930716,0.0033466863,0.0016915452,0.0009266416,0.00082697097,0.00013643876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019767005,0.00089674443,0.00057849963,0.0030900722,0.00052583084,0.0015308224,0.0009490634,0.00065011537,0.0007747432],"category_scores_gemma":[0.01078119,0.00032112413,0.00063764903,0.001194169,0.00079003687,0.0022118378,0.0008229629,0.0013401099,0.00031482373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002750343,0.0004239213,0.09108447,0.00022538888,0.00018993147,0.00048796862,0.0004885348,0.5498031,0.011311984,0.015257657,0.0047119264,0.32574022],"study_design_scores_gemma":[0.0000029835103,0.000021837148,0.0020647242,0.000010275471,0.0000072825724,0.00003965117,0.00004131398,0.9853618,0.0038916154,0.008035219,0.0005142336,0.000008931964],"about_ca_topic_score_codex":0.004789338,"about_ca_topic_score_gemma":0.0056121037,"teacher_disagreement_score":0.004789338,"about_ca_system_score_codex":0.0017458851,"about_ca_system_score_gemma":0.0016143756,"threshold_uncertainty_score":0.012667298},"labels":[],"label_agreement":null},{"id":"W4409814276","doi":"10.1016/j.procs.2025.03.118","title":"A Multimodal UAV-Based Pipeline for Precision Agriculture: Aerial Stress Detection with YOLO and High-Fidelity Disease Classification Using DeiT","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Pipeline (software); High fidelity; Artificial intelligence; Precision agriculture; Fidelity; Computer vision; Remote sensing; Agriculture; Operating system; Telecommunications; Geology","score_opus":0.016043306779412295,"score_gpt":0.23468131157397965,"score_spread":0.21863800479456735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11857848,0.00076972274,0.8433066,0.00053305825,0.00015452268,0.00020212996,0.0034802784,0.02454884,0.008426405],"genre_scores_gemma":[0.62573934,0.00043953885,0.35495046,0.0006874541,0.000051450603,0.00020483963,0.008293525,0.0007125509,0.008920926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990094,0.000008156264,0.0000033644267,0.00004418634,0.000019666786,0.000023675098],"domain_scores_gemma":[0.9998672,0.00003129881,0.000017710074,0.000028907934,0.000038806356,0.000015976328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017229567,0.00093911093,0.00042744476,0.0005679162,0.00021427109,0.0006656709,0.00058402057,0.00053348916,0.0029783782],"category_scores_gemma":[0.00056438305,0.00024044792,0.00041651714,0.00030774102,0.00018185661,0.0007961042,0.0008842714,0.00052837236,0.0014850554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063815893,0.00029419843,0.020115243,0.00053041335,0.0002080233,0.0007222721,0.00043768517,0.090535864,0.24121888,0.0032930677,0.026782487,0.61522377],"study_design_scores_gemma":[0.000029375922,0.00023388086,0.012087134,0.00007087484,0.00007204501,0.00033729564,0.00026425248,0.91912305,0.046831768,0.004099919,0.016805548,0.000044830285],"about_ca_topic_score_codex":0.004579571,"about_ca_topic_score_gemma":0.012168176,"teacher_disagreement_score":0.004579571,"about_ca_system_score_codex":0.00042740995,"about_ca_system_score_gemma":0.00041019008,"threshold_uncertainty_score":0.009963691},"labels":[],"label_agreement":null},{"id":"W4409814291","doi":"10.1016/j.procs.2025.03.058","title":"Transformer-Based Classification of Road Conditions Using Vehicular Sensor Data","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Transformer; Data mining; Real-time computing; Automotive engineering; Electrical engineering; Voltage","score_opus":0.05023077047132253,"score_gpt":0.32855820822985066,"score_spread":0.27832743775852814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39570507,0.00022506613,0.60025346,0.00009072359,0.000084672705,0.000061551546,0.00038566807,0.0011314512,0.0020622802],"genre_scores_gemma":[0.9886232,0.000076603814,0.010419752,0.000007789103,0.000009223345,0.0000119583065,0.0002865481,0.000012667716,0.00055235357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998678,0.000022689119,0.000008589712,0.0000393057,0.00003643659,0.000025190266],"domain_scores_gemma":[0.99981576,0.000064714186,0.000019314828,0.000017379558,0.00007240205,0.000010519563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028621455,0.0004983323,0.00039363958,0.00081678556,0.00010335894,0.000341943,0.00047413827,0.00024926366,0.0006039433],"category_scores_gemma":[0.00091613823,0.00013222656,0.00044223873,0.00047723998,0.0001673528,0.0005268016,0.00032245126,0.00033992474,0.0003569618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040160934,0.0002208425,0.025503451,0.00009707118,0.00011067274,0.00017418923,0.00010121123,0.5652849,0.030385451,0.002177841,0.0012830001,0.37425974],"study_design_scores_gemma":[0.0000026323748,0.00003666321,0.0025842797,0.000002304101,0.000010115755,0.00002937813,0.00001561054,0.9937936,0.0029095737,0.00045177704,0.00015935663,0.0000047070944],"about_ca_topic_score_codex":0.0054435898,"about_ca_topic_score_gemma":0.0055550328,"teacher_disagreement_score":0.0054435898,"about_ca_system_score_codex":0.0002882616,"about_ca_system_score_gemma":0.0002989726,"threshold_uncertainty_score":0.010823846},"labels":[],"label_agreement":null},{"id":"W4409814328","doi":"10.1016/j.procs.2025.03.107","title":"Automated UML Visualization of Software Ecosystems: Tracking Versions, Dependencies, and Security Updates","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Unified Modeling Language; Visualization; Software; Software engineering; UML tool; Programming language; Data mining","score_opus":0.010078912977670608,"score_gpt":0.2758635128138951,"score_spread":0.2657845998362245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12292284,0.0005627074,0.83792776,0.0010275805,0.00013166858,0.00014279915,0.0015317709,0.02905103,0.0067017875],"genre_scores_gemma":[0.48561388,0.0006293231,0.5075089,0.00012849984,0.000039382296,0.0001304341,0.0017435561,0.0020494144,0.0021566025],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99895704,0.00038561804,0.000095178424,0.00013854692,0.0003694553,0.00005420988],"domain_scores_gemma":[0.9931305,0.0029724576,0.0011154591,0.0012520967,0.0012386965,0.00029071403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025829438,0.00080232596,0.00034017095,0.004008054,0.0005817519,0.0029200658,0.0007482241,0.0008702559,0.0022385132],"category_scores_gemma":[0.0117670195,0.0005716932,0.0005199515,0.0015007686,0.00038593536,0.0026262226,0.0018265139,0.0010164928,0.0006583049],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006884039,0.0004039472,0.0665158,0.00077452126,0.00019337492,0.001556759,0.017819345,0.073122516,0.0719284,0.05295503,0.026991894,0.68705],"study_design_scores_gemma":[0.000080782636,0.0001606075,0.017064366,0.0003848668,0.00014564731,0.0008782443,0.0018830117,0.8308349,0.047231898,0.026475787,0.07466936,0.00019055376],"about_ca_topic_score_codex":0.0048530307,"about_ca_topic_score_gemma":0.0077385325,"teacher_disagreement_score":0.0048530307,"about_ca_system_score_codex":0.0007959252,"about_ca_system_score_gemma":0.001522494,"threshold_uncertainty_score":0.013660014},"labels":[],"label_agreement":null},{"id":"W4409814380","doi":"10.1016/j.procs.2025.03.091","title":"Cross-Domain Recommendation: Leveraging Semantic Alignment and User Clustering to Address Data Sparsity","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Cluster analysis; Information retrieval; Domain (mathematical analysis); Data mining; World Wide Web; Artificial intelligence","score_opus":0.04927657475991212,"score_gpt":0.32178045089358226,"score_spread":0.2725038761336701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07208314,0.0013363466,0.9217312,0.00030368415,0.00008562015,0.00013915187,0.00035472153,0.0013991857,0.0025669944],"genre_scores_gemma":[0.56748366,0.0006175154,0.42711678,0.0003102622,0.00011370564,0.000119608914,0.0015845842,0.00015833613,0.0024955352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971896,0.0009961035,0.00020238323,0.000749242,0.0006922256,0.00017054832],"domain_scores_gemma":[0.99528354,0.0015082266,0.00041779736,0.0014868941,0.0010996655,0.00020396605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025017706,0.00092977245,0.0019129117,0.0027500037,0.0010074688,0.0014683855,0.0017340181,0.0013037856,0.0009236408],"category_scores_gemma":[0.0077170804,0.00051700405,0.0011678821,0.005442885,0.0005435777,0.0034180977,0.0017304404,0.0014051368,0.0008885433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007461068,0.0010985844,0.03726788,0.00041835554,0.0009386861,0.0003982372,0.0011487232,0.18071336,0.023328988,0.014451071,0.010771299,0.72871876],"study_design_scores_gemma":[0.000032856333,0.00017481908,0.0041486486,0.000024307907,0.00011205795,0.00027976057,0.00025903122,0.9766118,0.005595761,0.0083464505,0.0043458375,0.000068670626],"about_ca_topic_score_codex":0.0077944743,"about_ca_topic_score_gemma":0.014042439,"teacher_disagreement_score":0.0077944743,"about_ca_system_score_codex":0.00060410507,"about_ca_system_score_gemma":0.0012162182,"threshold_uncertainty_score":0.015498221},"labels":[],"label_agreement":null},{"id":"W4409814381","doi":"10.1016/j.procs.2025.03.090","title":"WiFi-based Indoor Positioning using Low-cost Microcontrollers and Signal Fingerprinting","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Microcontroller; SIGNAL (programming language); Embedded system; Real-time computing","score_opus":0.004880569501374965,"score_gpt":0.2131132191013079,"score_spread":0.20823264959993293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09134415,0.00077582116,0.898733,0.00011966402,0.000115117095,0.00008793331,0.00011879147,0.004644403,0.0040610833],"genre_scores_gemma":[0.8410244,0.0003330626,0.15578401,0.00006106532,0.000036166966,0.000071558505,0.00012818782,0.000057465106,0.002504023],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995758,0.00008439938,0.00002639111,0.00010162737,0.00017440191,0.000037395643],"domain_scores_gemma":[0.9996364,0.00009388976,0.00007125522,0.00008570383,0.00009746394,0.000015296211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025465025,0.0005275083,0.00044440664,0.0006382795,0.00020761172,0.00038170052,0.0009194603,0.00045788693,0.001161796],"category_scores_gemma":[0.0010757738,0.00025768782,0.0002923771,0.00059929164,0.000174543,0.0007725739,0.00048125433,0.00024095303,0.00063549215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028212823,0.00016053213,0.013445986,0.0005136357,0.00013711255,0.0005395427,0.00017525794,0.14895615,0.1150473,0.0034123568,0.0025610954,0.7147689],"study_design_scores_gemma":[0.00004858855,0.0008868224,0.015075399,0.00008437126,0.00013380987,0.0020823898,0.00006744315,0.8621605,0.104112916,0.0018932262,0.013362044,0.000092508126],"about_ca_topic_score_codex":0.0015789932,"about_ca_topic_score_gemma":0.0017925826,"teacher_disagreement_score":0.0015789932,"about_ca_system_score_codex":0.00021350205,"about_ca_system_score_gemma":0.00018620731,"threshold_uncertainty_score":0.0038865805},"labels":[],"label_agreement":null},{"id":"W4409814415","doi":"10.1016/j.procs.2025.03.082","title":"Availability and Sustainability Aware Service Function Chains (SFC) Allocation and Embedding in Edge-Cloud Continuum","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Embedding; Enhanced Data Rates for GSM Evolution; Sustainability; Function (biology); Service (business); Distributed computing; Artificial intelligence; Operating system; Business","score_opus":0.006839667687790938,"score_gpt":0.242290229386668,"score_spread":0.23545056169887707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31357867,0.00064142526,0.6764953,0.0004005352,0.000060347502,0.00008101762,0.00007085979,0.0003566243,0.008315254],"genre_scores_gemma":[0.9711066,0.00011037436,0.028061956,0.000024860332,0.0000061931855,0.000018556246,0.000022980958,0.000011946253,0.00063655747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996892,0.00008599574,0.000009915968,0.000057411962,0.00008142954,0.000076092954],"domain_scores_gemma":[0.99968016,0.000104527244,0.000067033776,0.000038495837,0.00006423213,0.000045540968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004264324,0.0003483878,0.00027651121,0.00033098125,0.0005053186,0.0006662415,0.00047781676,0.00037118618,0.0008297785],"category_scores_gemma":[0.0008062864,0.000112205096,0.00022396534,0.00037291364,0.00037485512,0.0008159073,0.0006884673,0.00048187657,0.00011257602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001511445,0.00011217652,0.0030367465,0.0000852814,0.00002752378,0.00022338647,0.00009969446,0.88038987,0.025173748,0.01717113,0.001105888,0.07242339],"study_design_scores_gemma":[0.000004111346,0.00005703111,0.00064329314,0.0000064660685,0.000007429818,0.0000627636,0.000056955778,0.9897267,0.0037656568,0.004724872,0.0009379094,0.000006804876],"about_ca_topic_score_codex":0.0023385242,"about_ca_topic_score_gemma":0.0027012457,"teacher_disagreement_score":0.0023385242,"about_ca_system_score_codex":0.00069312996,"about_ca_system_score_gemma":0.0007460521,"threshold_uncertainty_score":0.0050290227},"labels":[],"label_agreement":null},{"id":"W4409814458","doi":"10.1016/j.procs.2025.03.025","title":"Comprehensive Review of Physiological Signal-Based Emotion Recognition: Methods, Challenges, and Insights on Arousal and Valence Dimensions","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Arousal; Valence (chemistry); Emotion recognition; SIGNAL (programming language); Speech recognition; Artificial intelligence; Human–computer interaction; Pattern recognition (psychology); Cognitive psychology; Neuroscience; Psychology; Physics","score_opus":0.10659079968974049,"score_gpt":0.37589319724641745,"score_spread":0.26930239755667695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814458","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000982166,0.9844773,0.010581643,0.0008187035,0.00058995606,0.00002614597,0.00015160473,0.000091586226,0.0022808772],"genre_scores_gemma":[0.005055691,0.98413634,0.007253673,0.0006679978,0.0010754641,0.000054394477,0.00033457993,0.000030735315,0.0013910645],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99937564,0.00013698483,0.00010353578,0.0001289411,0.00022817429,0.000026741598],"domain_scores_gemma":[0.9984748,0.00095646497,0.00011422811,0.000050570412,0.00036846747,0.00003535107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001423006,0.0009446902,0.001377137,0.0017497254,0.00020576948,0.0013064457,0.00084838626,0.00085976074,0.0028887773],"category_scores_gemma":[0.0026690164,0.00037071778,0.0006976809,0.0017588097,0.00037186992,0.0015102094,0.0005674855,0.0009566931,0.0020542685],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008213911,0.000044319346,0.00046687847,0.017960198,0.00014665407,0.00009880711,0.000112673086,0.0005621037,0.0067534903,0.0020208491,0.018861396,0.9528905],"study_design_scores_gemma":[0.000031790823,0.00057873526,0.009869083,0.012718495,0.0008165174,0.0026809007,0.00032658607,0.0032711225,0.007349736,0.0087499,0.95342976,0.00017736705],"about_ca_topic_score_codex":0.0007557174,"about_ca_topic_score_gemma":0.000792011,"teacher_disagreement_score":0.0028887773,"about_ca_system_score_codex":0.00034936966,"about_ca_system_score_gemma":0.0010017247,"threshold_uncertainty_score":0.00966388},"labels":[],"label_agreement":null},{"id":"W4409814470","doi":"10.1016/j.procs.2025.03.024","title":"Comprehensive Literature Review on Large Language Models and Smart Monitoring Devices for Stress Management","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Stress (linguistics); Human–computer interaction; Data science; Linguistics","score_opus":0.029331026114961814,"score_gpt":0.3495324001971202,"score_spread":0.3202013740821584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814470","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010221842,0.98681843,0.005548688,0.0013449215,0.00036932476,0.00003784356,0.000269809,0.00009067738,0.00449805],"genre_scores_gemma":[0.008577491,0.98245215,0.0057611302,0.0008703133,0.0005473989,0.000076787925,0.0004529641,0.000032430347,0.0012293188],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99940765,0.000200149,0.00010436071,0.000101015874,0.00015803281,0.000028868812],"domain_scores_gemma":[0.99338114,0.005750063,0.00022127193,0.00013167289,0.00046161734,0.000054207852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013202024,0.0010454197,0.0010400257,0.002605758,0.00034304857,0.0018089544,0.0009380141,0.0010656918,0.008755112],"category_scores_gemma":[0.0073628454,0.00040081126,0.0010632084,0.002789059,0.00046985506,0.0023738716,0.00088232383,0.0008893798,0.0018581827],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009592083,0.00006482991,0.000528477,0.059447985,0.00023275631,0.00024276196,0.0004294583,0.0011801502,0.001210624,0.008430132,0.030236712,0.89790016],"study_design_scores_gemma":[0.000027766508,0.00022041633,0.0033165086,0.050805517,0.0012335303,0.0013329373,0.00071610423,0.0026080688,0.0016996156,0.014609932,0.92334723,0.00008239499],"about_ca_topic_score_codex":0.001762029,"about_ca_topic_score_gemma":0.0023454954,"teacher_disagreement_score":0.008755112,"about_ca_system_score_codex":0.0005139386,"about_ca_system_score_gemma":0.002574458,"threshold_uncertainty_score":0.029288769},"labels":[],"label_agreement":null},{"id":"W4409814583","doi":"10.1016/j.procs.2025.03.037","title":"Immunity-Inspired Approaches to Cybersecurity: A Review","year":2025,"lang":"en","type":"review","venue":"Procedia Computer Science","topic":"Artificial Immune Systems Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Computer security; Immunity; Data science; Immunology; Medicine; Immune system","score_opus":0.11895654994341548,"score_gpt":0.30391041126900237,"score_spread":0.1849538613255869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814583","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001384699,0.997488,0.0004036404,0.00028587325,0.00018643489,0.000005345742,0.0000126974755,0.000008587594,0.001470984],"genre_scores_gemma":[0.00088951655,0.9980258,0.00035559796,0.00017005068,0.00016069732,0.000006697714,0.000019869954,0.0000018565388,0.00037000483],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998066,0.000034149216,0.000027624592,0.000039796032,0.00007400187,0.000017836934],"domain_scores_gemma":[0.9991165,0.0005888825,0.00007969357,0.000021509704,0.00014701445,0.00004645972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006209621,0.0010033948,0.0013063748,0.003135265,0.00043184284,0.0014866656,0.0010563784,0.0015706823,0.005250412],"category_scores_gemma":[0.001310665,0.00037245994,0.00061711745,0.003372124,0.00060616346,0.0019428597,0.00080104097,0.0015590087,0.001948431],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003922639,0.00010137746,0.00026620348,0.032673627,0.000116543735,0.00020061352,0.00014644914,0.0009719394,0.0012726975,0.013849457,0.022403771,0.92795813],"study_design_scores_gemma":[0.000010402242,0.00010726157,0.00076872867,0.011385533,0.00018255903,0.00094350363,0.000136984,0.0002661345,0.00042891252,0.0072764456,0.97845984,0.000033767286],"about_ca_topic_score_codex":0.001026169,"about_ca_topic_score_gemma":0.0015459313,"teacher_disagreement_score":0.005250412,"about_ca_system_score_codex":0.0006059645,"about_ca_system_score_gemma":0.001662447,"threshold_uncertainty_score":0.017564416},"labels":[],"label_agreement":null},{"id":"W4409814749","doi":"10.1016/j.procs.2025.03.002","title":"Preface","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.008178698928069883,"score_gpt":0.2725447053384898,"score_spread":0.2643660064104199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814749","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015116559,0.0088120885,0.011179461,0.031292308,0.3793145,0.00080243323,0.017669173,0.0022031234,0.54721516],"genre_scores_gemma":[0.005813105,0.0036773188,0.0024640695,0.0054773027,0.039657123,0.00029608805,0.009292957,0.0008963427,0.9324256],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993581,0.00008279629,0.000040252944,0.00011635486,0.00034498356,0.00005751645],"domain_scores_gemma":[0.99353164,0.0007414187,0.00018157033,0.00048613618,0.004256258,0.000803008],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011082069,0.0010620939,0.0005940011,0.0031011044,0.0022347122,0.0033018808,0.0010681715,0.00081651594,0.46652606],"category_scores_gemma":[0.009822461,0.00027747592,0.0006716477,0.001978788,0.00039092128,0.0020917372,0.0017792789,0.0027488964,0.25372672],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036435238,0.0000226298,0.00007690123,0.00009427672,0.00000220495,0.000032734333,0.000029373834,0.00006883246,0.00014763817,0.0022807962,0.9583868,0.038821425],"study_design_scores_gemma":[0.000006493031,0.000019278803,0.00028624575,0.000119960525,0.0000027451667,0.000028305863,0.0000443895,0.00004388252,0.00018234512,0.002188902,0.997072,0.0000055784512],"about_ca_topic_score_codex":0.005142357,"about_ca_topic_score_gemma":0.0057939203,"teacher_disagreement_score":0.53347397,"about_ca_system_score_codex":0.0016299124,"about_ca_system_score_gemma":0.0019467683,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4409814780","doi":"10.1016/j.procs.2025.03.054","title":"Developing Skeletal Activity Scheduler using Machine Learning","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Physical Activity and Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.05792746949389891,"score_gpt":0.3589306331328357,"score_spread":0.30100316363893675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046270955,0.0002762999,0.9473396,0.00026564865,0.0000998508,0.00009828475,0.00066108565,0.0024703187,0.0025178802],"genre_scores_gemma":[0.7004071,0.00035390118,0.2933046,0.00014792757,0.000100679375,0.00027421777,0.0018815573,0.00020481632,0.0033252311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997565,0.00004583587,0.000018011451,0.00009334016,0.000047292073,0.000038988495],"domain_scores_gemma":[0.99949205,0.00021610489,0.00006583897,0.000048671965,0.0001332734,0.00004398114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008026949,0.0006679772,0.00063419767,0.0008742935,0.0002815051,0.0005618551,0.0011170901,0.0005607821,0.002376686],"category_scores_gemma":[0.002627997,0.0004149147,0.00052661257,0.0006907564,0.00029261218,0.0009053063,0.0006563797,0.0008172613,0.0010940525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010913718,0.00018232765,0.012701952,0.000094200914,0.00006855825,0.000080576,0.00006628311,0.6926355,0.003438924,0.005119984,0.003627814,0.2818748],"study_design_scores_gemma":[0.0000032500764,0.000015762114,0.00064592535,0.000005305247,0.0000050949266,0.0000073088513,0.000008945695,0.99579006,0.0005771451,0.0024138864,0.00052443973,0.0000029359162],"about_ca_topic_score_codex":0.012225902,"about_ca_topic_score_gemma":0.015800474,"teacher_disagreement_score":0.012225902,"about_ca_system_score_codex":0.00068231334,"about_ca_system_score_gemma":0.0015400558,"threshold_uncertainty_score":0.024309456},"labels":[],"label_agreement":null},{"id":"W4409814790","doi":"10.1016/j.procs.2025.03.057","title":"Survey of Graph Neural Network Methods for Dynamic Link Prediction","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Link (geometry); Graph; Artificial neural network; Artificial intelligence; Data mining; Theoretical computer science; Computer network","score_opus":0.013952185234513307,"score_gpt":0.29750904300653686,"score_spread":0.28355685777202355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814790","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008009858,0.037135057,0.9441117,0.0016370097,0.00046962468,0.0000714247,0.0006355549,0.0012913881,0.0066383765],"genre_scores_gemma":[0.35974234,0.09073068,0.5213187,0.0016013501,0.0019787012,0.000400228,0.00404559,0.00089108327,0.019291287],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994937,0.00012537402,0.00004101225,0.00015709312,0.00013911336,0.000043659307],"domain_scores_gemma":[0.9986393,0.0008100433,0.000080537306,0.00012715423,0.00030058264,0.00004230073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012849761,0.0016872712,0.0012968583,0.0021264702,0.0004265005,0.0012298368,0.002602148,0.0013669742,0.002662077],"category_scores_gemma":[0.0040576085,0.0006756068,0.0011220792,0.002946457,0.0005890934,0.0022830172,0.00093729,0.002108598,0.0011319768],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091051406,0.000118537755,0.0025266719,0.0007382088,0.0002531888,0.00010398903,0.00007571049,0.37975556,0.0011451371,0.03263084,0.013797039,0.56876403],"study_design_scores_gemma":[0.0000051454576,0.000020115505,0.0004434977,0.00009614544,0.000038609676,0.000045075056,0.000016526441,0.97067505,0.00057360635,0.020797782,0.007272616,0.00001574163],"about_ca_topic_score_codex":0.0145484125,"about_ca_topic_score_gemma":0.013102076,"teacher_disagreement_score":0.0145484125,"about_ca_system_score_codex":0.0011841231,"about_ca_system_score_gemma":0.0011197665,"threshold_uncertainty_score":0.028927505},"labels":[],"label_agreement":null},{"id":"W4409814866","doi":"10.1016/j.procs.2025.03.030","title":"Generative AI in minimizing cyber-attacks: Developing the Vehicular Threat Intelligence Flowchart","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Flowchart; Generative grammar; Computer security; Artificial intelligence; Data science; Programming language","score_opus":0.04266459434306965,"score_gpt":0.3834927840791319,"score_spread":0.34082818973606227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044626263,0.00018259794,0.97858435,0.0008116021,0.00006219678,0.00016111598,0.00004953263,0.00095570256,0.0147302],"genre_scores_gemma":[0.18026865,0.00068138586,0.8111914,0.0003328403,0.000047636928,0.000356127,0.00020801974,0.00023980929,0.0066741663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984212,0.00075273373,0.00009134683,0.000221117,0.00040624404,0.00010750403],"domain_scores_gemma":[0.9952435,0.0031562506,0.00028613175,0.00043041588,0.00070663664,0.00017705688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035643869,0.0006429863,0.00023758634,0.0016122597,0.000709307,0.0028344523,0.0013593012,0.0011883418,0.0052476632],"category_scores_gemma":[0.008759603,0.00035169936,0.00069373735,0.0005393532,0.003129452,0.0027993985,0.0022188218,0.0015427939,0.0011393755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045821424,0.00009176472,0.001892856,0.00029837975,0.000026555368,0.0002306584,0.0016936839,0.07777769,0.004454573,0.73664755,0.0030415382,0.17379886],"study_design_scores_gemma":[0.000037795664,0.00012986443,0.0006129078,0.0005099126,0.000044626984,0.0002777074,0.0004486709,0.38574803,0.013224626,0.4820384,0.11685934,0.000068135494],"about_ca_topic_score_codex":0.0027711196,"about_ca_topic_score_gemma":0.0025478534,"teacher_disagreement_score":0.0052476632,"about_ca_system_score_codex":0.0015046463,"about_ca_system_score_gemma":0.0030135394,"threshold_uncertainty_score":0.018850505},"labels":[],"label_agreement":null},{"id":"W4409814878","doi":"10.1016/j.procs.2025.03.029","title":"A Comprehensive Literature Review on AI-Assisted Multimodal Triage Systems for Health Centers","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Triage; Data science; Medical emergency; Medicine","score_opus":0.024852149529769236,"score_gpt":0.3575423284688921,"score_spread":0.3326901789391229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409814878","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040605597,0.9956546,0.0009911576,0.0008056871,0.00021724083,0.00004498474,0.00021022838,0.000035680703,0.001634364],"genre_scores_gemma":[0.0026448055,0.9944898,0.0017182494,0.00046113672,0.00015978201,0.00005350654,0.00022280958,0.000008535017,0.0002414328],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.998926,0.00030419425,0.00030960553,0.0001324728,0.00028047856,0.00004722301],"domain_scores_gemma":[0.9929848,0.00563875,0.0004780479,0.00011621816,0.00070037675,0.000081908845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017324795,0.0009926037,0.0011682782,0.0049163946,0.00047503193,0.0017742492,0.0012088818,0.0013944104,0.008297607],"category_scores_gemma":[0.0084507195,0.00040539424,0.0018022525,0.0050977287,0.0004165445,0.001827381,0.0008334292,0.0009136051,0.0014960918],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009398274,0.00006494826,0.0005332158,0.1985205,0.00035712027,0.00018895409,0.00032012936,0.0009984922,0.00061980955,0.0031315724,0.029557817,0.7656135],"study_design_scores_gemma":[0.00003898719,0.000260822,0.0039692745,0.21258897,0.002899771,0.0012383505,0.00047757872,0.001054421,0.00082917925,0.0036984952,0.77286434,0.00007974348],"about_ca_topic_score_codex":0.003544723,"about_ca_topic_score_gemma":0.005428197,"teacher_disagreement_score":0.008297607,"about_ca_system_score_codex":0.0009845941,"about_ca_system_score_gemma":0.0051459908,"threshold_uncertainty_score":0.02775824},"labels":[],"label_agreement":null},{"id":"W4410252907","doi":"10.1016/j.procs.2025.04.497","title":"Developing Natural Language Processing Algorithms to Fact-Check Speech or Text","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Computer science; Natural language processing; Natural language; Speech recognition; Artificial intelligence; Algorithm","score_opus":0.02459784536166521,"score_gpt":0.31006999721146755,"score_spread":0.28547215184980235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410252907","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002627632,0.00012283829,0.99359834,0.0002659166,0.000038955906,0.00011684369,0.00024140993,0.0022449838,0.0007431752],"genre_scores_gemma":[0.045179565,0.0002665856,0.9515417,0.00019465652,0.00008150617,0.00015600896,0.001440409,0.00033023732,0.00080932333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954039,0.0019668934,0.00044670628,0.0011236268,0.00092412747,0.000134674],"domain_scores_gemma":[0.9519889,0.035084367,0.002690044,0.0050221616,0.004925132,0.00028939155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00891512,0.001221268,0.00094388763,0.005463667,0.0013186485,0.004044492,0.0024150845,0.0017134765,0.0055124257],"category_scores_gemma":[0.038808268,0.00065374543,0.0016120586,0.0023959272,0.0016412109,0.007980426,0.0021869463,0.002463266,0.0045637735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017412483,0.00028148846,0.006578485,0.001036168,0.00026099576,0.00032198537,0.0021365199,0.059269175,0.021555556,0.09080389,0.014779139,0.80280244],"study_design_scores_gemma":[0.00003405627,0.00011431135,0.0016276966,0.0002650861,0.00013251853,0.00041872094,0.0009367613,0.7585511,0.030337997,0.15790647,0.04956986,0.00010535617],"about_ca_topic_score_codex":0.003430546,"about_ca_topic_score_gemma":0.004720746,"teacher_disagreement_score":0.00891512,"about_ca_system_score_codex":0.0011554968,"about_ca_system_score_gemma":0.00220754,"threshold_uncertainty_score":0.047148287},"labels":[],"label_agreement":null},{"id":"W4410252960","doi":"10.1016/j.procs.2025.04.486","title":"Green IoT: AI-Powered Solutions for Sustainable Energy Management in Smart Devices","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Internet of Things; Green computing; Embedded system; Energy management; Energy (signal processing); Computer security; Operating system; Cloud computing","score_opus":0.012146461052092722,"score_gpt":0.25245328046235216,"score_spread":0.24030681941025944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410252960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042591713,0.023729905,0.77313673,0.012653509,0.0034350995,0.0004662819,0.0051424066,0.013737306,0.12510698],"genre_scores_gemma":[0.45753917,0.023539217,0.4509958,0.0031103594,0.0011954976,0.00066713145,0.01106929,0.0014353662,0.050448254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996942,0.000046673827,0.000016715998,0.000058488633,0.0001414403,0.000042455118],"domain_scores_gemma":[0.99971896,0.00008890521,0.000021841102,0.0000666942,0.00007222092,0.000031335207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000704111,0.00077125867,0.00039281632,0.0007839171,0.00049679255,0.0018740365,0.0010025855,0.0012054748,0.006962381],"category_scores_gemma":[0.0011325242,0.00028673158,0.00045455806,0.0013643698,0.0005502651,0.002792046,0.0015167269,0.0019218597,0.0023657265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028841253,0.00028969574,0.0035184326,0.00086447713,0.00010850275,0.00028768732,0.00021080115,0.06429352,0.0136550935,0.08076581,0.13647455,0.699243],"study_design_scores_gemma":[0.000059283553,0.00017085008,0.0030800654,0.00034309205,0.00004984108,0.00035028692,0.00020812878,0.37513345,0.022660552,0.14875315,0.44910723,0.00008412219],"about_ca_topic_score_codex":0.0025178995,"about_ca_topic_score_gemma":0.004418263,"teacher_disagreement_score":0.006962381,"about_ca_system_score_codex":0.0006803789,"about_ca_system_score_gemma":0.00074016204,"threshold_uncertainty_score":0.023291528},"labels":[],"label_agreement":null},{"id":"W4410252977","doi":"10.1016/j.procs.2025.04.580","title":"Harnessing Deep Learning for Crowdfunding Success Prediction: A Comparative Analysis on Kickstarter Dataset","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Ministry of External Affairs, India; Indian Council for Cultural Relations","keywords":"Computer science; Deep learning; Artificial intelligence; Machine learning; Data science","score_opus":0.024620767800887677,"score_gpt":0.28462909766672034,"score_spread":0.26000832986583267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410252977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8758597,0.0048609353,0.010427677,0.0035077678,0.00066501205,0.00040330822,0.08390193,0.00452369,0.015850062],"genre_scores_gemma":[0.75263256,0.0012763283,0.021636017,0.0005434054,0.00016941853,0.00044292706,0.21616727,0.00018675039,0.0069453944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981036,0.0006678228,0.00012779946,0.00029580187,0.000556058,0.000248923],"domain_scores_gemma":[0.9965989,0.0015766203,0.00025916696,0.00047364784,0.0007721213,0.00031960668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004020238,0.0014901827,0.00081493566,0.0035578387,0.00070375716,0.0009802819,0.0013289972,0.0012888488,0.0017575083],"category_scores_gemma":[0.006879485,0.00016319995,0.0006549144,0.0021927604,0.0005461999,0.0017297849,0.0019395873,0.0010718687,0.0012546291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018124997,0.002526059,0.15740965,0.0014149556,0.00072317023,0.0013324885,0.0010084006,0.10862065,0.0036435663,0.0035149506,0.29385868,0.42413494],"study_design_scores_gemma":[0.00037995944,0.0010886333,0.14293292,0.0007217869,0.00029994387,0.00056268746,0.004387964,0.70114064,0.011515389,0.007540879,0.12920791,0.00022121685],"about_ca_topic_score_codex":0.03204754,"about_ca_topic_score_gemma":0.06750506,"teacher_disagreement_score":0.03204754,"about_ca_system_score_codex":0.0015535176,"about_ca_system_score_gemma":0.0016306193,"threshold_uncertainty_score":0.063722014},"labels":[],"label_agreement":null},{"id":"W4410252979","doi":"10.1016/j.procs.2025.04.525","title":"AI-Powered Sustainability in Smart Cities","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hog Administrative Marketing Services (Canada); University Canada West","funders":"","keywords":"Computer science; Sustainability; Smart city; World Wide Web; Internet of Things","score_opus":0.004857088970409628,"score_gpt":0.21870857798747018,"score_spread":0.21385148901706055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410252979","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053046107,0.031215195,0.13293926,0.124669015,0.0023739133,0.00011340427,0.00022752378,0.0005411123,0.6548744],"genre_scores_gemma":[0.9065257,0.020246701,0.02584011,0.0063162623,0.0014179166,0.00015434553,0.00016602724,0.00015801191,0.039174885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992187,0.00033146754,0.000026612239,0.00008351379,0.00020536505,0.00013435868],"domain_scores_gemma":[0.99916625,0.0003893325,0.00008790909,0.00009137455,0.00016526697,0.00009983849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009247392,0.0004599377,0.00030934272,0.0011315396,0.0021102882,0.0055145593,0.00058584526,0.0022496928,0.0043005496],"category_scores_gemma":[0.0018778159,0.00024646654,0.00044510321,0.0014079115,0.0058081714,0.006595968,0.00489517,0.0022654266,0.0007235876],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004815253,0.000014847393,0.0003820038,0.00008001158,0.000006755431,0.000086168475,0.00057478395,0.005612218,0.00015458153,0.97161454,0.0045420653,0.016927231],"study_design_scores_gemma":[0.0000039816814,0.0000092736445,0.0002900691,0.00012894922,0.0000047588446,0.000072128816,0.001110576,0.0056887525,0.0003744937,0.84242105,0.14988345,0.000012660214],"about_ca_topic_score_codex":0.0031248636,"about_ca_topic_score_gemma":0.0031226375,"teacher_disagreement_score":0.0055145593,"about_ca_system_score_codex":0.0033562607,"about_ca_system_score_gemma":0.002072754,"threshold_uncertainty_score":0.024351478},"labels":[],"label_agreement":null},{"id":"W4410253109","doi":"10.1016/j.procs.2025.04.330","title":"Frameworks for AI Integration in HR and Workforce Adaptation","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"University Canada West","keywords":"Computer science; Adaptation (eye); Workforce; Engineering management; Data science; Software engineering","score_opus":0.017412271067976465,"score_gpt":0.2607380875517241,"score_spread":0.24332581648374765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410253109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009859333,0.0046572373,0.70777756,0.030803207,0.00049220846,0.00043233825,0.00007655402,0.00038657177,0.24551503],"genre_scores_gemma":[0.5999189,0.0040050563,0.3690486,0.0031092688,0.00046568585,0.0009830404,0.00014045801,0.00015734136,0.022171663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9899463,0.0063113673,0.00058020174,0.001024708,0.0014028825,0.00073445454],"domain_scores_gemma":[0.99063396,0.0054009114,0.00084181706,0.0013717676,0.0011012481,0.00065041333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015081472,0.00080491195,0.00041915837,0.0034502104,0.0030733687,0.010503687,0.0038199788,0.0049878135,0.007908949],"category_scores_gemma":[0.0147239715,0.00057638285,0.0013128782,0.0023342473,0.01849246,0.009217971,0.009296353,0.003940517,0.0011088866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003538627,0.000021520525,0.0002452709,0.00006058769,0.0000075333273,0.000057128465,0.0014364047,0.0026727917,0.00010560506,0.98305297,0.00064555724,0.011691232],"study_design_scores_gemma":[0.000010773174,0.00002508797,0.0004819417,0.00044644476,0.000016953803,0.00013330087,0.0024745949,0.0121551845,0.0002836025,0.90668255,0.07726466,0.000024992401],"about_ca_topic_score_codex":0.0064409883,"about_ca_topic_score_gemma":0.0057478454,"teacher_disagreement_score":0.015081472,"about_ca_system_score_codex":0.006354589,"about_ca_system_score_gemma":0.009019286,"threshold_uncertainty_score":0.07975936},"labels":[],"label_agreement":null},{"id":"W4410253156","doi":"10.1016/j.procs.2025.04.337","title":"Blockchain Models Applications: A Comparative Study on Security","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Computer science; Blockchain; Computer security; Data science","score_opus":0.020939547410602933,"score_gpt":0.2922800625226392,"score_spread":0.2713405151120363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410253156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82326674,0.008875467,0.050197553,0.0038425513,0.00014144863,0.00042474226,0.00042328308,0.0002964134,0.112531886],"genre_scores_gemma":[0.985584,0.0033727882,0.007353538,0.00009276742,0.000034027165,0.00007623994,0.00027097002,0.000067453315,0.0031481509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944648,0.002934843,0.00023250909,0.0002712203,0.001692131,0.00040443713],"domain_scores_gemma":[0.97623926,0.01665993,0.0011732416,0.002534898,0.0028793288,0.0005134258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062425816,0.00044162048,0.0004494735,0.002269853,0.0014441381,0.0034811425,0.00078822346,0.0016238604,0.004687661],"category_scores_gemma":[0.020785406,0.00026953412,0.0004988489,0.0033897301,0.001726274,0.008608347,0.002574435,0.0014267205,0.0006393179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001662866,0.0006103476,0.036448617,0.0014929792,0.00019473463,0.0010869675,0.0067302226,0.14287934,0.0046003405,0.60898775,0.0074978378,0.18780811],"study_design_scores_gemma":[0.00026916058,0.0030445154,0.02614115,0.002017917,0.00024349538,0.0019850868,0.0154381925,0.4658489,0.008726727,0.3067724,0.16927607,0.00023640227],"about_ca_topic_score_codex":0.003814773,"about_ca_topic_score_gemma":0.002941256,"teacher_disagreement_score":0.0062425816,"about_ca_system_score_codex":0.002295829,"about_ca_system_score_gemma":0.0019160368,"threshold_uncertainty_score":0.033014357},"labels":[],"label_agreement":null},{"id":"W4410253327","doi":"10.1016/j.procs.2025.04.266","title":"Enhancing Model Performance in Hybrid Class Imbalance Techniques","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Class (philosophy); Artificial intelligence","score_opus":0.00991858894615224,"score_gpt":0.25582980037837383,"score_spread":0.24591121143222158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410253327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27683294,0.0030653267,0.7088445,0.001088436,0.00030565896,0.00017964325,0.00048763596,0.0038565928,0.005339261],"genre_scores_gemma":[0.8648885,0.000617906,0.13046768,0.00032748643,0.0001681549,0.00015619252,0.0009723459,0.00015985382,0.0022418837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985366,0.00044086197,0.00009528954,0.0002927666,0.0004538633,0.00018055174],"domain_scores_gemma":[0.9977889,0.00097413274,0.00024060345,0.00032913385,0.00057585153,0.00009136545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045904308,0.0014361187,0.001340113,0.0019438462,0.0006242409,0.0020968844,0.0013730093,0.0011806053,0.00094629324],"category_scores_gemma":[0.0059496025,0.00035086597,0.001257664,0.0012472502,0.0004520221,0.002477733,0.001689422,0.0016463497,0.0008406314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007097461,0.00050730444,0.01596597,0.00020827103,0.00042431668,0.00021276136,0.00036867367,0.4556498,0.011701736,0.003545816,0.0068073077,0.50389826],"study_design_scores_gemma":[0.000015082795,0.0001450012,0.001500083,0.000021065294,0.000040891122,0.00006446816,0.00007023372,0.99081624,0.0032218746,0.0027494845,0.0013419575,0.000013507526],"about_ca_topic_score_codex":0.002706385,"about_ca_topic_score_gemma":0.0024022448,"teacher_disagreement_score":0.0045904308,"about_ca_system_score_codex":0.0006174919,"about_ca_system_score_gemma":0.00095268677,"threshold_uncertainty_score":0.024276853},"labels":[],"label_agreement":null},{"id":"W4410253330","doi":"10.1016/j.procs.2025.04.261","title":"Criteria for AI Adoption in HR: Efficiency vs. Ethics","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.06352430440781445,"score_gpt":0.4486952519537454,"score_spread":0.38517094754593095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410253330","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26422226,0.0052761296,0.21628344,0.054902222,0.00078990724,0.0025682887,0.00021463029,0.00018681971,0.45555627],"genre_scores_gemma":[0.9663586,0.00032798064,0.029662222,0.00091395085,0.00014956835,0.0005093842,0.000033849432,0.000054478583,0.0019900328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.86152786,0.08456457,0.01061976,0.0034520004,0.036498275,0.0033374468],"domain_scores_gemma":[0.6792406,0.22124529,0.033893913,0.013375521,0.045262124,0.006982625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09073751,0.00074815523,0.00109206,0.008065325,0.0043015727,0.016071877,0.0017534798,0.0047446797,0.0038490032],"category_scores_gemma":[0.20610255,0.00046256126,0.0010278084,0.004718443,0.027687913,0.0090999575,0.0061732703,0.0042880857,0.00078131654],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016113081,0.0003480364,0.037471782,0.0008787101,0.0001489702,0.0003169754,0.014794469,0.003061941,0.0013129854,0.85655963,0.0041085873,0.08083686],"study_design_scores_gemma":[0.00013750701,0.0007316299,0.06437581,0.0030842791,0.00012258175,0.0012065594,0.028683528,0.018440763,0.0032260113,0.81746674,0.06219695,0.000327624],"about_ca_topic_score_codex":0.0017439297,"about_ca_topic_score_gemma":0.0012551126,"teacher_disagreement_score":0.09073751,"about_ca_system_score_codex":0.0074144388,"about_ca_system_score_gemma":0.0077240537,"threshold_uncertainty_score":0.4798715},"labels":[],"label_agreement":null},{"id":"W4410258468","doi":"10.1016/j.procs.2025.04.586","title":"Balancing Innovation, Responsibility, and Ethical Consideration in AI Adoption","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hog Administrative Marketing Services (Canada); University Canada West","funders":"","keywords":"Computer science; Ethical responsibility; Knowledge management; Engineering ethics","score_opus":0.028749952235942774,"score_gpt":0.39724391045051277,"score_spread":0.36849395821457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410258468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28392622,0.011597496,0.14781311,0.27914983,0.001085017,0.00040058474,0.00001713425,0.00008128049,0.27592933],"genre_scores_gemma":[0.9876165,0.00093001156,0.0059455982,0.0037589145,0.00020862227,0.00010185314,0.0000033327663,0.000018496652,0.0014167209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8089597,0.1376045,0.0052038194,0.005249387,0.036124844,0.0068577183],"domain_scores_gemma":[0.74083906,0.20756662,0.016516339,0.0072607826,0.020216437,0.0076008514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13658918,0.00065459026,0.0010325207,0.0034849672,0.010587059,0.018611064,0.0018798895,0.012174866,0.0014798484],"category_scores_gemma":[0.14329086,0.0007418147,0.0007487729,0.002247749,0.074059024,0.018271616,0.016424071,0.013075184,0.00023598928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028541765,0.00008702195,0.004011052,0.00016300348,0.00003230809,0.0003932072,0.043969326,0.0011964259,0.00058632786,0.9193088,0.00089948886,0.029324446],"study_design_scores_gemma":[0.000028189124,0.00011841632,0.0030806279,0.00086288125,0.00003044299,0.00040470343,0.035174027,0.0021740384,0.0007862252,0.9233855,0.033856966,0.00009796949],"about_ca_topic_score_codex":0.0023790651,"about_ca_topic_score_gemma":0.0025183258,"teacher_disagreement_score":0.13658918,"about_ca_system_score_codex":0.011169918,"about_ca_system_score_gemma":0.022780843,"threshold_uncertainty_score":0.7223613},"labels":[],"label_agreement":null},{"id":"W4410258491","doi":"10.1016/j.procs.2025.04.565","title":"Exploring Cryptocurrency Acceptance Patterns: An In-depth Review of Influencing Factors from Adoption to Adaption for Human Resource Management","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Computer science; Cryptocurrency; Data science; Resource (disambiguation); Human resource management; Knowledge management; Risk analysis (engineering); World Wide Web; Business","score_opus":0.2458268543374308,"score_gpt":0.4139129694961517,"score_spread":0.1680861151587209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410258491","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042701926,0.94776124,0.0015724301,0.002578705,0.00015050222,0.00010597134,0.00021388577,0.000016045686,0.0048992806],"genre_scores_gemma":[0.12056823,0.87619025,0.0016750097,0.0006695957,0.000101779304,0.00010873956,0.00015469782,0.000013062672,0.0005186303],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970579,0.001319388,0.00056029466,0.0002547645,0.0006795489,0.0001280963],"domain_scores_gemma":[0.9630831,0.031953596,0.0024491125,0.0003179515,0.0019494656,0.00024674585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005324982,0.00037612588,0.00072956725,0.0036206928,0.00039695512,0.0021181542,0.00043836096,0.00069165824,0.0022241052],"category_scores_gemma":[0.018320171,0.00025945914,0.0011498076,0.005347208,0.0006420194,0.0022181766,0.00061195734,0.0009043653,0.00026572184],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001468102,0.0001391982,0.031290714,0.074360885,0.0007974023,0.00020854139,0.004915132,0.00051174813,0.00078598433,0.0075215395,0.0039413604,0.8753807],"study_design_scores_gemma":[0.00007675681,0.0011098993,0.29173377,0.19242145,0.0060167536,0.0018816742,0.019895885,0.0018257466,0.0029853783,0.007989717,0.47380283,0.00026020038],"about_ca_topic_score_codex":0.0049316986,"about_ca_topic_score_gemma":0.012214948,"teacher_disagreement_score":0.005324982,"about_ca_system_score_codex":0.0012807741,"about_ca_system_score_gemma":0.0043074936,"threshold_uncertainty_score":0.028161466},"labels":[],"label_agreement":null},{"id":"W4410774247","doi":"10.1016/j.procs.2025.03.218","title":"Deep Facial Feature Fusion and Voting Strategies for Enhanced Emotion Recognition","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"","keywords":"Computer science; Voting; Emotion recognition; Feature (linguistics); Fusion; Artificial intelligence; Facial expression; Speech recognition; Pattern recognition (psychology); Facial recognition system","score_opus":0.023816430863065563,"score_gpt":0.30815746117385595,"score_spread":0.2843410303107904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036805544,0.0004118528,0.9594134,0.00013801959,0.00011294973,0.000078784185,0.000096423064,0.0010515064,0.0018915667],"genre_scores_gemma":[0.7223121,0.00030175498,0.27244514,0.0002349614,0.000100531375,0.00013806332,0.00039069474,0.00014492279,0.0039317557],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992987,0.00013824594,0.00004239552,0.00020791884,0.00020017089,0.00011257079],"domain_scores_gemma":[0.99963295,0.00009790433,0.000043586922,0.00006551751,0.00013507319,0.00002503311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001104252,0.00093897583,0.0009805121,0.0007835474,0.0003201786,0.00072781625,0.0011264071,0.00059188576,0.0026155058],"category_scores_gemma":[0.0018038558,0.0002850425,0.00072466314,0.00051846285,0.00033685693,0.0011645195,0.0014393047,0.001049882,0.00087368145],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051348313,0.0002014391,0.0019502171,0.000083206876,0.00012225639,0.00010921161,0.00011937422,0.030274495,0.123344555,0.0054834615,0.0033804055,0.8344179],"study_design_scores_gemma":[0.000031094183,0.00022230519,0.0024741269,0.000020839123,0.00009917236,0.00019976324,0.000058902264,0.9210505,0.06357531,0.009059902,0.003167778,0.00004047525],"about_ca_topic_score_codex":0.0012083794,"about_ca_topic_score_gemma":0.0015772629,"teacher_disagreement_score":0.0026155058,"about_ca_system_score_codex":0.0004248267,"about_ca_system_score_gemma":0.00034290354,"threshold_uncertainty_score":0.008749783},"labels":[],"label_agreement":null},{"id":"W4410774290","doi":"10.1016/j.procs.2025.03.202","title":"ECASeg: Enhancing Semantic Segmentation with Edge Context and Attention Strategy","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Segmentation; Context (archaeology); Enhanced Data Rates for GSM Evolution; Artificial intelligence; Natural language processing; Human–computer interaction","score_opus":0.011686246188388124,"score_gpt":0.2645928048139291,"score_spread":0.25290655862554096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13077344,0.0063343723,0.81932,0.0008264522,0.0006674536,0.00034957027,0.0017097464,0.022146916,0.017872142],"genre_scores_gemma":[0.5967794,0.0015487206,0.37924907,0.0016860301,0.00029340573,0.00018821587,0.0058969613,0.0015102333,0.012848036],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995497,0.000054479842,0.000015177741,0.00018740838,0.00010685339,0.00008641813],"domain_scores_gemma":[0.9997507,0.00006833908,0.000014513574,0.000055988898,0.00007958491,0.00003079705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050699,0.0018931509,0.001363941,0.0019304776,0.00058227143,0.0012422985,0.0018817426,0.0016679309,0.0033323776],"category_scores_gemma":[0.0010369972,0.00040858067,0.0011309971,0.0016977562,0.00061016704,0.0020774317,0.0018010776,0.0013541804,0.0016744831],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092716573,0.00062579097,0.0022693607,0.00034852856,0.00021611045,0.00042954888,0.00026590104,0.0774017,0.057548806,0.0074129906,0.03134422,0.82121],"study_design_scores_gemma":[0.000068685775,0.0001976556,0.0018073174,0.000035775465,0.00012656824,0.00030116842,0.000105866755,0.9541527,0.021873323,0.009315085,0.011980292,0.000035498277],"about_ca_topic_score_codex":0.01478438,"about_ca_topic_score_gemma":0.02209057,"teacher_disagreement_score":0.01478438,"about_ca_system_score_codex":0.0008411638,"about_ca_system_score_gemma":0.0012928217,"threshold_uncertainty_score":0.029396594},"labels":[],"label_agreement":null},{"id":"W4410774436","doi":"10.1016/j.procs.2025.03.274","title":"SegAttnDetec: A Segmentation-Aware Attention-Based Object Detector","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Segmentation; Detector; Object (grammar); Computer vision; Artificial intelligence; Telecommunications","score_opus":0.009980041653944493,"score_gpt":0.27016885496507187,"score_spread":0.26018881331112736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039877295,0.0045767487,0.8954937,0.00086057937,0.0010263717,0.0005674269,0.0044698394,0.040047277,0.01308073],"genre_scores_gemma":[0.26589748,0.0014783646,0.68596345,0.0021486038,0.0003760112,0.000497404,0.016092746,0.0018936009,0.025652375],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906427,0.00006790458,0.000024009434,0.00039589376,0.0003048864,0.00014299677],"domain_scores_gemma":[0.9992798,0.00018091062,0.000047572703,0.00016909101,0.00022320483,0.000099445955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010486576,0.0024834764,0.0024103918,0.0036162797,0.0008854454,0.0021801093,0.004932756,0.0028179449,0.0042717396],"category_scores_gemma":[0.0022998215,0.0009365219,0.0015641713,0.002231302,0.0010255104,0.0020670532,0.0036512443,0.0023795383,0.0032189924],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005645836,0.000543064,0.0051725297,0.00046572974,0.00044211256,0.00028381954,0.00012563587,0.04520103,0.030834187,0.0072168075,0.08220948,0.826941],"study_design_scores_gemma":[0.00009165123,0.000259295,0.0022120264,0.00006769677,0.00015562055,0.0005217383,0.000049930037,0.9294781,0.02622991,0.01081743,0.030028807,0.00008777439],"about_ca_topic_score_codex":0.019879516,"about_ca_topic_score_gemma":0.03935394,"teacher_disagreement_score":0.019879516,"about_ca_system_score_codex":0.0017349609,"about_ca_system_score_gemma":0.0032877892,"threshold_uncertainty_score":0.039527595},"labels":[],"label_agreement":null},{"id":"W4410774452","doi":"10.1016/j.procs.2025.03.242","title":"An Intelligent Crime Surveillance Video System For Real-Time Applications","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Diabetes Action Canada","keywords":"Computer science; Real-time computing; Computer security; Artificial intelligence","score_opus":0.009655500179872163,"score_gpt":0.2802235906313833,"score_spread":0.27056809045151115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12092182,0.0015136818,0.65049595,0.0012577156,0.0011921754,0.001816582,0.020888204,0.14335293,0.058561042],"genre_scores_gemma":[0.64653075,0.0011334204,0.28327087,0.0012927745,0.0005322003,0.001553077,0.033439334,0.0016467611,0.030600792],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996499,0.000042297164,0.000023015875,0.00010162017,0.00013401831,0.000049156522],"domain_scores_gemma":[0.9995664,0.000044340086,0.00004183431,0.00006865019,0.00022598729,0.000052768566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047953898,0.0006873027,0.00056400045,0.0014161243,0.0003713941,0.00079166994,0.00081301323,0.0004704222,0.010478837],"category_scores_gemma":[0.0010183773,0.00022894415,0.0002647197,0.0005514238,0.00017327926,0.00090264704,0.0008192601,0.00060414796,0.0039730207],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013950644,0.0005252147,0.009957652,0.000424606,0.00019738123,0.0006378275,0.00038494304,0.0062597743,0.07858046,0.005289367,0.24281168,0.653536],"study_design_scores_gemma":[0.00033616275,0.0008991605,0.035230543,0.00025709902,0.00022327926,0.0015153935,0.0003515803,0.54882723,0.15010288,0.006299251,0.25575525,0.00020224591],"about_ca_topic_score_codex":0.0034450088,"about_ca_topic_score_gemma":0.0047476976,"teacher_disagreement_score":0.010478837,"about_ca_system_score_codex":0.0005772243,"about_ca_system_score_gemma":0.0005801619,"threshold_uncertainty_score":0.03505516},"labels":[],"label_agreement":null},{"id":"W4410774918","doi":"10.1016/j.procs.2025.03.180","title":"An Analysis of YOLOvX Deep Learning Models for Colon Cancer Detection","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"University Grants Commission","keywords":"Computer science; Deep learning; Artificial intelligence; Cancer; Machine learning; Medicine; Internal medicine","score_opus":0.012664761011906993,"score_gpt":0.32391272529476606,"score_spread":0.31124796428285906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774918","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7882577,0.00960689,0.18475555,0.0022701602,0.0002651509,0.00013238854,0.001423581,0.0023627847,0.010925718],"genre_scores_gemma":[0.9737626,0.0010048094,0.019404344,0.0002144261,0.000035514633,0.00004466933,0.0018902349,0.00006300028,0.0035804596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99965894,0.0000886147,0.000020281388,0.0000648212,0.00010225064,0.000065062806],"domain_scores_gemma":[0.9989819,0.0005706198,0.000077223296,0.000063191765,0.00026258637,0.00004455379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013416065,0.0007200018,0.0005683353,0.00064319436,0.00026631227,0.00071436766,0.0008087889,0.000666265,0.0014473874],"category_scores_gemma":[0.0033451193,0.00025872866,0.00047327855,0.00034575863,0.00024785716,0.00068334996,0.0005842625,0.0007012205,0.00029235985],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068664696,0.00015666407,0.015405993,0.00021248177,0.00015685752,0.00017904298,0.000058472626,0.85548425,0.0048189117,0.0044771163,0.004767129,0.11359655],"study_design_scores_gemma":[0.0000044328453,0.000041938514,0.00072046294,0.000009243074,0.000008423599,0.000014412157,0.000005840568,0.9978638,0.00069858105,0.00036345606,0.0002663491,0.000002984658],"about_ca_topic_score_codex":0.024666462,"about_ca_topic_score_gemma":0.016664656,"teacher_disagreement_score":0.024666462,"about_ca_system_score_codex":0.0013317912,"about_ca_system_score_gemma":0.0012408628,"threshold_uncertainty_score":0.04904574},"labels":[],"label_agreement":null},{"id":"W4410774956","doi":"10.1016/j.procs.2025.03.273","title":"Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical Investigation","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Benchmarking; Computer science; Deep learning; Empirical research; Artificial intelligence; Nano-; Machine learning; Embedded system; Computer architecture; Chemical engineering; Management","score_opus":0.037934668970654645,"score_gpt":0.3024132328936407,"score_spread":0.2644785639229861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410774956","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89983803,0.0035033792,0.069702074,0.0019654369,0.0005337985,0.00022680548,0.0036976652,0.004485358,0.016047413],"genre_scores_gemma":[0.96128905,0.0004309681,0.03075589,0.00030775854,0.000030780335,0.00011845951,0.0046828487,0.0003291148,0.002055144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999079,0.00034995022,0.000055151853,0.00018688226,0.00021542967,0.00011361735],"domain_scores_gemma":[0.99581134,0.0024664986,0.00014672289,0.0006037715,0.0008099683,0.00016168453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025220132,0.00091850944,0.00059095904,0.0006331387,0.0004086681,0.0008651657,0.0016213421,0.000966792,0.0027807704],"category_scores_gemma":[0.009698231,0.00039604664,0.0005176298,0.0008631158,0.0007620339,0.0017153086,0.00087328366,0.0019433589,0.0007602716],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051100075,0.0007410775,0.011239712,0.0003734061,0.00015120156,0.00012674066,0.00008453666,0.8833838,0.0014591588,0.0061036246,0.0280369,0.06778876],"study_design_scores_gemma":[0.00003833967,0.00012183707,0.0020852503,0.000025979747,0.000015252227,0.000025093657,0.00005453881,0.99130625,0.001608168,0.001916959,0.0027920315,0.000010331035],"about_ca_topic_score_codex":0.023433968,"about_ca_topic_score_gemma":0.029264746,"teacher_disagreement_score":0.023433968,"about_ca_system_score_codex":0.0017704488,"about_ca_system_score_gemma":0.0013168807,"threshold_uncertainty_score":0.046595156},"labels":[],"label_agreement":null},{"id":"W4413161366","doi":"10.1016/j.procs.2025.07.061","title":"Information Seeking and Sharing as Gratifications Explaining Mobile Social Media Use in Pre-, During and Post-Disaster Management","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"ICT in Developing Communities","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Social media; Uses and gratifications theory; Information sharing; Mobile device; Internet privacy; Mobile media; World Wide Web; Multimedia","score_opus":0.015402652444366179,"score_gpt":0.25114532266149553,"score_spread":0.23574267021712936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413161366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99116206,0.00012677116,0.0019745314,0.000508219,0.0000052395512,0.000040224317,0.00004285059,0.000006477923,0.0061336653],"genre_scores_gemma":[0.9989467,0.00010658362,0.00048367807,0.000017499608,0.0000028090708,0.000022678205,0.00001603782,0.0000018914398,0.00040211828],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99939907,0.00030251616,0.000027569495,0.000054922475,0.000076055345,0.00013994322],"domain_scores_gemma":[0.99515873,0.003694639,0.0005941125,0.00013729351,0.0002543811,0.00016087187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010090609,0.00038979447,0.0002193268,0.0010865673,0.0008014063,0.0022586992,0.00038737178,0.0006739278,0.0042585805],"category_scores_gemma":[0.0044356533,0.00026638922,0.00054052396,0.00087472895,0.0014072016,0.0014342825,0.0012900045,0.00076007243,0.00020249035],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022364632,0.0011412725,0.7417722,0.00043467624,0.000181771,0.0015059892,0.115533076,0.003784403,0.0035211847,0.06199245,0.00066034874,0.069249034],"study_design_scores_gemma":[0.00006169276,0.00071533566,0.6855769,0.00052102364,0.00027552282,0.0014702161,0.22608447,0.038559634,0.0024679506,0.034332283,0.009835361,0.00009956664],"about_ca_topic_score_codex":0.0035283442,"about_ca_topic_score_gemma":0.0039436286,"teacher_disagreement_score":0.0042585805,"about_ca_system_score_codex":0.0007436582,"about_ca_system_score_gemma":0.0012485273,"threshold_uncertainty_score":0.014246345},"labels":[],"label_agreement":null},{"id":"W4413161531","doi":"10.1016/j.procs.2025.07.062","title":"Integrating AI Tools to Enhance Learning Outcomes in Modern Education Systems","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"University Canada West","keywords":"Computer science; Artificial intelligence; Data science; Human–computer interaction","score_opus":0.008496543998217733,"score_gpt":0.32399155425911264,"score_spread":0.3154950102608949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413161531","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5971932,0.0029350796,0.24537711,0.011461272,0.0004976277,0.00059603865,0.00020834814,0.004346877,0.13738453],"genre_scores_gemma":[0.942921,0.0007490201,0.05100178,0.00038703944,0.00010007022,0.00016659207,0.00010251524,0.00010049791,0.004471424],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9941586,0.0034403116,0.00033445505,0.00040482188,0.0012158952,0.00044588416],"domain_scores_gemma":[0.98710024,0.008435939,0.0010318112,0.0011958973,0.0009801855,0.0012558476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007482769,0.0004229429,0.00037847928,0.0015310016,0.0010729138,0.007780503,0.0011372155,0.0010110857,0.008014677],"category_scores_gemma":[0.02459097,0.00016456051,0.00034372363,0.001093293,0.0017567999,0.005804876,0.0072553176,0.0012783665,0.0014826434],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003814147,0.0029858549,0.013366182,0.0010942366,0.00008204987,0.00031093103,0.009449919,0.008475228,0.0059877736,0.07424948,0.0060482216,0.8775686],"study_design_scores_gemma":[0.00057727774,0.006992203,0.07073917,0.0028567035,0.00044536224,0.0010802324,0.016220856,0.060675614,0.046288095,0.5456347,0.24811469,0.0003752079],"about_ca_topic_score_codex":0.00033097685,"about_ca_topic_score_gemma":0.00043954953,"teacher_disagreement_score":0.008014677,"about_ca_system_score_codex":0.0010772845,"about_ca_system_score_gemma":0.001543919,"threshold_uncertainty_score":0.039573133},"labels":[],"label_agreement":null},{"id":"W4413161850","doi":"10.1016/j.procs.2025.07.111","title":"Examining Critical Factors in Selecting AI Tools for Educational Success","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"University Canada West","keywords":"Computer science; Data science; Artificial intelligence","score_opus":0.049891907870359735,"score_gpt":0.34043064321462335,"score_spread":0.2905387353442636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413161850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8822975,0.026805414,0.013754511,0.010560799,0.0003260636,0.0022268468,0.00033400088,0.00013191215,0.06356289],"genre_scores_gemma":[0.9828871,0.005966869,0.009134028,0.00037031414,0.000033257595,0.00046768275,0.00010608494,0.00003197138,0.0010027096],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.954818,0.022051688,0.00614443,0.0015181005,0.013466889,0.0020009421],"domain_scores_gemma":[0.7418441,0.19591591,0.019131588,0.002531313,0.036164507,0.004412535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.053674605,0.0006719505,0.0008125548,0.007870147,0.0025598833,0.01374406,0.0013381611,0.0011179652,0.0033460874],"category_scores_gemma":[0.20742047,0.00048626098,0.00093379675,0.0058478178,0.0022679262,0.0076855854,0.0030096264,0.0016648679,0.00070862996],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057644484,0.0007469888,0.28381887,0.009472615,0.0005532071,0.0007447809,0.068534516,0.0007944897,0.0017262038,0.016175764,0.0060463846,0.61080974],"study_design_scores_gemma":[0.00018822198,0.0035451788,0.47402275,0.032364275,0.0025075115,0.0013302913,0.35290003,0.0043474417,0.011736883,0.019534787,0.09709813,0.00042444965],"about_ca_topic_score_codex":0.0026149822,"about_ca_topic_score_gemma":0.0047027427,"teacher_disagreement_score":0.053674605,"about_ca_system_score_codex":0.0047786315,"about_ca_system_score_gemma":0.011872382,"threshold_uncertainty_score":0.28386188},"labels":[],"label_agreement":null},{"id":"W4413162754","doi":"10.1016/j.procs.2025.07.045","title":"Establishing Criteria for Effective AI Adoption in Industry","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"University Canada West","keywords":"Computer science; Data science; Engineering management; Knowledge management","score_opus":0.03772070612234332,"score_gpt":0.32807753025161507,"score_spread":0.2903568241292718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413162754","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48449945,0.03741443,0.12261421,0.03576296,0.00076563074,0.0061475793,0.0010675365,0.00057543605,0.31115276],"genre_scores_gemma":[0.92231214,0.005000519,0.06720538,0.0011769547,0.00014444423,0.0017898583,0.00046915203,0.000068253794,0.0018332965],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9507874,0.016371265,0.010044614,0.0019375916,0.0181759,0.0026832845],"domain_scores_gemma":[0.81245565,0.091206335,0.025992181,0.0044708205,0.06056829,0.005306694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044046048,0.00079029734,0.0010585407,0.0122383,0.0031465206,0.010625543,0.0016791999,0.0023897737,0.003121186],"category_scores_gemma":[0.1450567,0.00049736846,0.0010296259,0.007356972,0.004171803,0.010135988,0.0054758484,0.0019965018,0.00096511067],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003684953,0.0006434209,0.22493334,0.0061704465,0.0003249092,0.0013581463,0.017852098,0.0044851736,0.0044511445,0.18730438,0.013598446,0.53850996],"study_design_scores_gemma":[0.00016862339,0.002588771,0.47254843,0.019578658,0.00068328466,0.0027345712,0.07631899,0.021040129,0.010698735,0.19435994,0.19879492,0.00048500934],"about_ca_topic_score_codex":0.0026434525,"about_ca_topic_score_gemma":0.003186946,"teacher_disagreement_score":0.044046048,"about_ca_system_score_codex":0.0046873763,"about_ca_system_score_gemma":0.008446789,"threshold_uncertainty_score":0.23294055},"labels":[],"label_agreement":null},{"id":"W4413332413","doi":"10.1016/j.procs.2025.07.186","title":"Defending federated learning systems against untargeted sybil attacks in non-IID environments","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Sybil attack; Computer security; Artificial intelligence; Computer network; Wireless sensor network","score_opus":0.0071075854289720615,"score_gpt":0.23253562677648074,"score_spread":0.22542804134750868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06849665,0.0002392766,0.9278714,0.0002620664,0.000059168204,0.000055396897,0.000030891173,0.0018395862,0.0011456521],"genre_scores_gemma":[0.9538395,0.000055825472,0.044661213,0.0001096775,0.000024247964,0.000034606706,0.000044148404,0.000054303153,0.0011766751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781054,0.00066289125,0.0001354148,0.0005347711,0.0005500446,0.00030643845],"domain_scores_gemma":[0.9928725,0.0025600358,0.0009247146,0.0021990736,0.0010968074,0.00034678486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039828033,0.0009775441,0.001450135,0.0009081323,0.00093789876,0.0021653087,0.0018645587,0.0014127977,0.00092996605],"category_scores_gemma":[0.014328366,0.0004193446,0.0006622706,0.0005403215,0.0014853015,0.0031417948,0.0036209403,0.0017949397,0.00051282044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038613158,0.00016519739,0.0045715785,0.000079699705,0.00013585981,0.0001890392,0.00021889189,0.8190072,0.008510403,0.017029729,0.0019751496,0.147731],"study_design_scores_gemma":[0.0000066718912,0.000039032635,0.00016885054,0.0000045972365,0.0000064526284,0.00004100678,0.000014601978,0.9911873,0.0020782049,0.0061996505,0.0002470131,0.0000067262013],"about_ca_topic_score_codex":0.0015123838,"about_ca_topic_score_gemma":0.0014383798,"teacher_disagreement_score":0.0039828033,"about_ca_system_score_codex":0.0011884521,"about_ca_system_score_gemma":0.0015124666,"threshold_uncertainty_score":0.021063268},"labels":[],"label_agreement":null},{"id":"W4413332417","doi":"10.1016/j.procs.2025.07.161","title":"Hybrid CNN-LSTM-GRU with Attention for Human Activity Recognition","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Artificial intelligence; Speech recognition; Activity recognition; Pattern recognition (psychology)","score_opus":0.027488652559972473,"score_gpt":0.27822942683999285,"score_spread":0.2507407742800204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21778853,0.0063439356,0.7526606,0.0009439104,0.00068480696,0.00020033139,0.0016472642,0.011507487,0.008223143],"genre_scores_gemma":[0.91701156,0.00070292846,0.0741703,0.00051021046,0.0001194818,0.00014473827,0.0013812952,0.00011865424,0.0058408375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996691,0.000054437995,0.0000137741135,0.00013305923,0.000050425533,0.00007919994],"domain_scores_gemma":[0.99979454,0.000071554496,0.000019480816,0.000029110106,0.00006664527,0.00001877956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005355561,0.0013523354,0.00086181785,0.000566749,0.00021609422,0.00048238097,0.0012246788,0.00087770744,0.001804164],"category_scores_gemma":[0.0010524562,0.00033238903,0.0006371867,0.0006217945,0.00020834584,0.00087045087,0.00076785963,0.00095511606,0.0008584686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050256413,0.00054985506,0.0041477266,0.00024326715,0.0003392937,0.00032044575,0.000097339944,0.30240136,0.024075396,0.001998131,0.009761958,0.65556264],"study_design_scores_gemma":[0.000007402144,0.00008520908,0.0009794065,0.000009779276,0.00003279675,0.000036627185,0.000010568657,0.99431604,0.0030620382,0.00084600307,0.00060514704,0.00000889288],"about_ca_topic_score_codex":0.016160551,"about_ca_topic_score_gemma":0.023647852,"teacher_disagreement_score":0.016160551,"about_ca_system_score_codex":0.00077336316,"about_ca_system_score_gemma":0.00069788145,"threshold_uncertainty_score":0.032132983},"labels":[],"label_agreement":null},{"id":"W4413332461","doi":"10.1016/j.procs.2025.07.188","title":"Enhancing Freeway Safety: LSTM-Based Detection of Traffic Anomalies","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Artificial intelligence; Computer security; Real-time computing","score_opus":0.003417175056187352,"score_gpt":0.19339504787557205,"score_spread":0.1899778728193847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33419943,0.00072806573,0.6504534,0.00065056304,0.00032252356,0.000060360177,0.001182348,0.0072601624,0.005143154],"genre_scores_gemma":[0.96174484,0.00018359674,0.034478504,0.00015037296,0.000051183975,0.00003475371,0.0011204163,0.00006113466,0.0021752797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975985,0.000035058467,0.000010694289,0.00008052117,0.000053935146,0.000059895],"domain_scores_gemma":[0.99970275,0.00009027995,0.00004642589,0.00003606911,0.00009850646,0.00002580621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038297437,0.00092069607,0.0003762962,0.0007545764,0.00021715794,0.00056781067,0.00079422956,0.0007542285,0.0012708107],"category_scores_gemma":[0.0014508788,0.00021650916,0.0005129539,0.0006049866,0.00025218335,0.0012843583,0.0008441938,0.0011167853,0.00057982164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032359784,0.00043793579,0.009956906,0.00011463855,0.0001247115,0.00019152918,0.00014731874,0.44413912,0.023741148,0.0014809978,0.008879169,0.5104629],"study_design_scores_gemma":[0.0000037035968,0.00003668793,0.0010085689,0.0000071999402,0.000012120556,0.000020366833,0.00001426479,0.9941128,0.003409593,0.00088525703,0.0004823211,0.000007175852],"about_ca_topic_score_codex":0.007153666,"about_ca_topic_score_gemma":0.009120641,"teacher_disagreement_score":0.007153666,"about_ca_system_score_codex":0.00053083984,"about_ca_system_score_gemma":0.000779509,"threshold_uncertainty_score":0.014224052},"labels":[],"label_agreement":null},{"id":"W4413332470","doi":"10.1016/j.procs.2025.07.148","title":"Preface","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University; Université du Québec à Chicoutimi","funders":"","keywords":"Computer science","score_opus":0.008178698928069883,"score_gpt":0.2725447053384898,"score_spread":0.2643660064104199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332470","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015116559,0.0088120885,0.011179461,0.031292308,0.3793145,0.00080243323,0.017669173,0.0022031234,0.54721516],"genre_scores_gemma":[0.005813105,0.0036773188,0.0024640695,0.0054773027,0.039657123,0.00029608805,0.009292957,0.0008963427,0.9324256],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993581,0.00008279629,0.000040252944,0.00011635486,0.00034498356,0.00005751645],"domain_scores_gemma":[0.99353164,0.0007414187,0.00018157033,0.00048613618,0.004256258,0.000803008],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011082069,0.0010620939,0.0005940011,0.0031011044,0.0022347122,0.0033018808,0.0010681715,0.00081651594,0.46652606],"category_scores_gemma":[0.009822461,0.00027747592,0.0006716477,0.001978788,0.00039092128,0.0020917372,0.0017792789,0.0027488964,0.25372672],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036435238,0.0000226298,0.00007690123,0.00009427672,0.00000220495,0.000032734333,0.000029373834,0.00006883246,0.00014763817,0.0022807962,0.9583868,0.038821425],"study_design_scores_gemma":[0.000006493031,0.000019278803,0.00028624575,0.000119960525,0.0000027451667,0.000028305863,0.0000443895,0.00004388252,0.00018234512,0.002188902,0.997072,0.0000055784512],"about_ca_topic_score_codex":0.005142357,"about_ca_topic_score_gemma":0.0057939203,"teacher_disagreement_score":0.53347397,"about_ca_system_score_codex":0.0016299124,"about_ca_system_score_gemma":0.0019467683,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4413332477","doi":"10.1016/j.procs.2025.07.168","title":"Adaptive and Efficient Data Retrieval in Distributed File Systems: A Metadata-Driven Approach","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Metadata; Information retrieval; Metadata repository; Data retrieval; Database; World Wide Web","score_opus":0.02986117336548021,"score_gpt":0.2653796244883331,"score_spread":0.23551845112285286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024420911,0.0010568167,0.9703803,0.00062366814,0.00005312176,0.00014310604,0.000075229415,0.001218616,0.0020282047],"genre_scores_gemma":[0.55414295,0.0010320806,0.44035375,0.00019842442,0.00016769528,0.00019690486,0.00024934896,0.0003184608,0.003340429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984249,0.000281849,0.00012916436,0.0002433823,0.0007858478,0.00013491973],"domain_scores_gemma":[0.99770504,0.00044633335,0.00022353634,0.00082284876,0.00070206844,0.00010027825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018936702,0.00042687045,0.0008174244,0.00079176837,0.000764245,0.0022588056,0.0030731673,0.00083443563,0.00061428454],"category_scores_gemma":[0.0037649793,0.00041666706,0.00045500524,0.0010728989,0.0010065137,0.0034742926,0.0018247232,0.0011562235,0.00035301817],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002781508,0.00037242987,0.0037442904,0.00049156667,0.00016311854,0.00025991243,0.00051183044,0.46420214,0.10138532,0.113924205,0.0065579023,0.30810916],"study_design_scores_gemma":[0.000024862551,0.000098878816,0.00054676394,0.000020238167,0.00003916389,0.00012906034,0.00011820417,0.94063526,0.025710318,0.02573888,0.0068910737,0.00004730243],"about_ca_topic_score_codex":0.002294305,"about_ca_topic_score_gemma":0.0031941358,"teacher_disagreement_score":0.0030731673,"about_ca_system_score_codex":0.0012949661,"about_ca_system_score_gemma":0.0019485478,"threshold_uncertainty_score":0.010014772},"labels":[],"label_agreement":null},{"id":"W4413332494","doi":"10.1016/j.procs.2025.07.183","title":"Developing a new IoT network topology for effective Greenhouse Monitoring and Control","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Greenhouse; Internet of Things; Topology control; Control (management); Topology (electrical circuits); Wireless sensor network; Network topology; Monitoring and control; Computer network; Distributed computing; Real-time computing; Computer security; Telecommunications; Artificial intelligence; Electrical engineering; Control engineering; Wireless network; Wireless; Key distribution in wireless sensor networks","score_opus":0.01136911671176341,"score_gpt":0.23795090056589172,"score_spread":0.2265817838541283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031138318,0.00032235374,0.9491321,0.0008638578,0.00039634455,0.00021952258,0.00017262365,0.0015470175,0.016207844],"genre_scores_gemma":[0.44295162,0.00087659195,0.54525864,0.0002507571,0.00013536835,0.00039856794,0.00063866633,0.00019809937,0.009291716],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997842,0.000043164517,0.00001672439,0.000048824655,0.00008359094,0.000023597995],"domain_scores_gemma":[0.99965227,0.000051053285,0.00003865936,0.00006247441,0.00014627996,0.00004928535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040824857,0.00033058046,0.00027729874,0.00054270274,0.0005303253,0.00092791044,0.0007515376,0.00041707774,0.0019422745],"category_scores_gemma":[0.0008054863,0.00022154626,0.0002565112,0.00042023376,0.0002736251,0.0018174493,0.00086752826,0.00050973875,0.00052306824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024378757,0.00023545159,0.0049735815,0.00045748986,0.000057014724,0.000973049,0.00048390563,0.28767523,0.18788297,0.096183985,0.017254623,0.4035789],"study_design_scores_gemma":[0.00004332101,0.00023206839,0.002078462,0.00005890236,0.000037220743,0.0005251292,0.00025634572,0.8711651,0.023830237,0.018216196,0.083505854,0.000051159055],"about_ca_topic_score_codex":0.0010948986,"about_ca_topic_score_gemma":0.0023188451,"teacher_disagreement_score":0.0019422745,"about_ca_system_score_codex":0.00048659506,"about_ca_system_score_gemma":0.0005765999,"threshold_uncertainty_score":0.006497562},"labels":[],"label_agreement":null},{"id":"W4413332499","doi":"10.1016/j.procs.2025.07.223","title":"GRU-Based Multi-Modal Human Activity Recognition","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Modal; Artificial intelligence; Activity recognition; Pattern recognition (psychology); Human–computer interaction","score_opus":0.04397431282124299,"score_gpt":0.3071591680633702,"score_spread":0.2631848552421272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15450166,0.0013361507,0.835554,0.00028540002,0.00023261881,0.00008490673,0.0007670417,0.003236888,0.0040013404],"genre_scores_gemma":[0.94593185,0.00034937196,0.050033584,0.00013799143,0.000059777678,0.0000621455,0.000548757,0.00004721445,0.0028292208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997712,0.000042194955,0.000010331152,0.000085307154,0.0000525701,0.000038350565],"domain_scores_gemma":[0.9998149,0.000052234464,0.00003130219,0.000025391142,0.00005799273,0.000018096389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026533147,0.0005789663,0.0006308348,0.0005348947,0.00012001476,0.00031915278,0.0007570978,0.00046162764,0.0012549835],"category_scores_gemma":[0.00096756476,0.00018302159,0.00042449252,0.00061539793,0.00023808953,0.0004200149,0.00051026547,0.00047728547,0.0007832242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031275392,0.00028164676,0.009909902,0.00018462517,0.00016571187,0.00032412334,0.00013299471,0.19824913,0.040030614,0.001915808,0.0043946644,0.74409807],"study_design_scores_gemma":[0.00000423709,0.0001156303,0.006076837,0.000016836959,0.000021980171,0.00013514054,0.000024842082,0.98410654,0.006750547,0.0017805476,0.00095051323,0.000016330163],"about_ca_topic_score_codex":0.0034860387,"about_ca_topic_score_gemma":0.006423919,"teacher_disagreement_score":0.0034860387,"about_ca_system_score_codex":0.00023665623,"about_ca_system_score_gemma":0.00023621079,"threshold_uncertainty_score":0.0069314837},"labels":[],"label_agreement":null},{"id":"W4413332502","doi":"10.1016/j.procs.2025.07.147","title":"Preface","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.008178698928069883,"score_gpt":0.2725447053384898,"score_spread":0.2643660064104199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332502","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015116559,0.0088120885,0.011179461,0.031292308,0.3793145,0.00080243323,0.017669173,0.0022031234,0.54721516],"genre_scores_gemma":[0.005813105,0.0036773188,0.0024640695,0.0054773027,0.039657123,0.00029608805,0.009292957,0.0008963427,0.9324256],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993581,0.00008279629,0.000040252944,0.00011635486,0.00034498356,0.00005751645],"domain_scores_gemma":[0.99353164,0.0007414187,0.00018157033,0.00048613618,0.004256258,0.000803008],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011082069,0.0010620939,0.0005940011,0.0031011044,0.0022347122,0.0033018808,0.0010681715,0.00081651594,0.46652606],"category_scores_gemma":[0.009822461,0.00027747592,0.0006716477,0.001978788,0.00039092128,0.0020917372,0.0017792789,0.0027488964,0.25372672],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036435238,0.0000226298,0.00007690123,0.00009427672,0.00000220495,0.000032734333,0.000029373834,0.00006883246,0.00014763817,0.0022807962,0.9583868,0.038821425],"study_design_scores_gemma":[0.000006493031,0.000019278803,0.00028624575,0.000119960525,0.0000027451667,0.000028305863,0.0000443895,0.00004388252,0.00018234512,0.002188902,0.997072,0.0000055784512],"about_ca_topic_score_codex":0.005142357,"about_ca_topic_score_gemma":0.0057939203,"teacher_disagreement_score":0.53347397,"about_ca_system_score_codex":0.0016299124,"about_ca_system_score_gemma":0.0019467683,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4413332503","doi":"10.1016/j.procs.2025.07.149","title":"Preface","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science","score_opus":0.008178698928069883,"score_gpt":0.2725447053384898,"score_spread":0.2643660064104199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332503","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015116559,0.0088120885,0.011179461,0.031292308,0.3793145,0.00080243323,0.017669173,0.0022031234,0.54721516],"genre_scores_gemma":[0.005813105,0.0036773188,0.0024640695,0.0054773027,0.039657123,0.00029608805,0.009292957,0.0008963427,0.9324256],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993581,0.00008279629,0.000040252944,0.00011635486,0.00034498356,0.00005751645],"domain_scores_gemma":[0.99353164,0.0007414187,0.00018157033,0.00048613618,0.004256258,0.000803008],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011082069,0.0010620939,0.0005940011,0.0031011044,0.0022347122,0.0033018808,0.0010681715,0.00081651594,0.46652606],"category_scores_gemma":[0.009822461,0.00027747592,0.0006716477,0.001978788,0.00039092128,0.0020917372,0.0017792789,0.0027488964,0.25372672],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036435238,0.0000226298,0.00007690123,0.00009427672,0.00000220495,0.000032734333,0.000029373834,0.00006883246,0.00014763817,0.0022807962,0.9583868,0.038821425],"study_design_scores_gemma":[0.000006493031,0.000019278803,0.00028624575,0.000119960525,0.0000027451667,0.000028305863,0.0000443895,0.00004388252,0.00018234512,0.002188902,0.997072,0.0000055784512],"about_ca_topic_score_codex":0.005142357,"about_ca_topic_score_gemma":0.0057939203,"teacher_disagreement_score":0.53347397,"about_ca_system_score_codex":0.0016299124,"about_ca_system_score_gemma":0.0019467683,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4413332524","doi":"10.1016/j.procs.2025.07.184","title":"Soil pH Prediction Using Deep Learning: An Ensemble Approach","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Ensemble learning; Deep learning; Artificial intelligence; Machine learning","score_opus":0.014574590381284768,"score_gpt":0.2350478643565754,"score_spread":0.22047327397529062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3201309,0.0018442202,0.67076224,0.00075539877,0.0001757453,0.000063461615,0.00084880576,0.0019287786,0.0034904943],"genre_scores_gemma":[0.9442862,0.00044539815,0.052521713,0.00012680827,0.000090568676,0.00004251638,0.0008995711,0.000036928093,0.0015503305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997714,0.00005285434,0.000014189727,0.00006854074,0.000050589268,0.000042459447],"domain_scores_gemma":[0.99941933,0.00023445275,0.000049136845,0.000060422924,0.0002018012,0.000034930305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009983911,0.0008713133,0.0008591695,0.0009806625,0.00025952817,0.00069437857,0.00092868827,0.00074027915,0.0006560114],"category_scores_gemma":[0.001532374,0.00034388684,0.0007578004,0.0007082988,0.00019251015,0.0009534718,0.00072259153,0.001107205,0.00025600326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014064873,0.00017441197,0.011277262,0.000042255706,0.00025013616,0.00007438037,0.000046324796,0.77747285,0.0033997786,0.000916494,0.0017334283,0.20447205],"study_design_scores_gemma":[0.0000020592931,0.0000122609035,0.00048081268,0.000002738171,0.00001047642,0.0000038919407,0.000003910909,0.99852437,0.00039120653,0.00045209995,0.00011303976,0.0000030510198],"about_ca_topic_score_codex":0.008339204,"about_ca_topic_score_gemma":0.010642761,"teacher_disagreement_score":0.008339204,"about_ca_system_score_codex":0.0004965189,"about_ca_system_score_gemma":0.00054501113,"threshold_uncertainty_score":0.016581297},"labels":[],"label_agreement":null},{"id":"W4413332626","doi":"10.1016/j.procs.2025.07.163","title":"A Comparative Analysis of Machine Learning Models for Behavioral Biometric Authentication using Keystroke Dynamics","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Keystroke dynamics; Computer science; Biometrics; Keystroke logging; Authentication (law); Artificial intelligence; Dynamics (music); Machine learning; Human–computer interaction; Computer security; Speech recognition; Password; S/KEY","score_opus":0.06482451492050345,"score_gpt":0.3407436140180518,"score_spread":0.27591909909754836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8368652,0.014133208,0.13410695,0.0018175005,0.0005409618,0.00020465463,0.0019459663,0.0023980679,0.007987486],"genre_scores_gemma":[0.97205395,0.0014669156,0.022483736,0.00014901548,0.00006734042,0.00009537507,0.0020439364,0.000051781495,0.0015880439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986456,0.0005645439,0.00011812319,0.00023034663,0.00030296142,0.00013847336],"domain_scores_gemma":[0.9948673,0.003474705,0.00028406747,0.00036928282,0.0008780811,0.00012657687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044210535,0.0014470408,0.0011662741,0.0021053236,0.00045195964,0.0012538509,0.0009145186,0.000958695,0.0012271339],"category_scores_gemma":[0.008836285,0.00024842354,0.0010599913,0.0011050844,0.0002874756,0.0014629428,0.0007300148,0.0010798404,0.000681987],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014220678,0.00066452764,0.03621927,0.00045151328,0.00060541433,0.00015011731,0.00015585082,0.6197398,0.0029584162,0.003201704,0.006641672,0.3277896],"study_design_scores_gemma":[0.000008351087,0.00018830715,0.0037900384,0.00003283704,0.000038905837,0.00002862544,0.00004111208,0.993923,0.0008091767,0.0006989776,0.00042657903,0.000014123709],"about_ca_topic_score_codex":0.013122865,"about_ca_topic_score_gemma":0.009622612,"teacher_disagreement_score":0.013122865,"about_ca_system_score_codex":0.0013364366,"about_ca_system_score_gemma":0.0010621515,"threshold_uncertainty_score":0.026092947},"labels":[],"label_agreement":null},{"id":"W4413332697","doi":"10.1016/j.procs.2025.07.162","title":"Game Theory: Cyber Deception Based on the Redundancy of Diversified Honeypots","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Honeypot; Computer science; Deception; Game theory; Redundancy (engineering); Computer security; Mathematical economics","score_opus":0.010242433820651169,"score_gpt":0.22324296968225327,"score_spread":0.21300053586160209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053474993,0.00064290134,0.9177576,0.0015069302,0.00015990762,0.0001659986,0.00011948644,0.000117523865,0.026054673],"genre_scores_gemma":[0.9576585,0.0005394712,0.034130346,0.00032372918,0.000106175714,0.00022348468,0.000039127943,0.00003678518,0.0069424296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99813867,0.0010968829,0.00005319487,0.00019078833,0.0002980877,0.00022242198],"domain_scores_gemma":[0.9960025,0.0025757665,0.0005101392,0.0002514253,0.00036809134,0.00029206902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021788555,0.0011258804,0.0010196539,0.0007764809,0.0006879371,0.0021321937,0.0020098686,0.00196749,0.0040347497],"category_scores_gemma":[0.007683822,0.00049394526,0.000903183,0.0005057867,0.0026515133,0.0030998618,0.0015019887,0.0020221053,0.00032960394],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000966965,0.00005986309,0.0007213307,0.00012763184,0.00008520253,0.00029771778,0.00014900298,0.4083097,0.003193498,0.57760394,0.00213764,0.0072178487],"study_design_scores_gemma":[0.000027701217,0.00005173571,0.00021210163,0.000017936083,0.00001722792,0.00009025407,0.0000334013,0.8618859,0.00021247819,0.13641378,0.001017671,0.000019788898],"about_ca_topic_score_codex":0.0021720186,"about_ca_topic_score_gemma":0.0011195723,"teacher_disagreement_score":0.0040347497,"about_ca_system_score_codex":0.0017726127,"about_ca_system_score_gemma":0.0010311091,"threshold_uncertainty_score":0.013497591},"labels":[],"label_agreement":null},{"id":"W4413363767","doi":"10.1016/j.procs.2025.07.152","title":"Towards effective and robust bank fraud detection thanks to machine learning","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Computer security","score_opus":0.008544032707416823,"score_gpt":0.24758545687467567,"score_spread":0.23904142416725885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413363767","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09607568,0.0019738025,0.8903756,0.002019091,0.00026447605,0.00020505866,0.00034241483,0.005465982,0.0032779067],"genre_scores_gemma":[0.70304745,0.0007118742,0.2920351,0.00089202,0.00018037652,0.00013609549,0.00079597585,0.00014809956,0.0020530834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963398,0.0014855975,0.00019412253,0.0005891579,0.0010373199,0.00035402866],"domain_scores_gemma":[0.9958941,0.0015508005,0.0006278104,0.0010108281,0.00076944346,0.0001470444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00519541,0.0014922937,0.001501978,0.0019200396,0.0006473368,0.0024889696,0.001691581,0.0023216798,0.0008958615],"category_scores_gemma":[0.01117174,0.0005811368,0.0010959728,0.0012913352,0.0013023237,0.002891695,0.0024081888,0.0032767728,0.0010414138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005232251,0.00063653354,0.010807323,0.0002100654,0.00023511641,0.00030931327,0.00018969648,0.5260368,0.018126352,0.010679559,0.01101808,0.421228],"study_design_scores_gemma":[0.000009588193,0.000043146643,0.00068197795,0.000018933473,0.000008565441,0.000060926825,0.000019958428,0.9876744,0.0051109996,0.0051240893,0.0012351546,0.000012223157],"about_ca_topic_score_codex":0.002244969,"about_ca_topic_score_gemma":0.0011490455,"teacher_disagreement_score":0.00519541,"about_ca_system_score_codex":0.0011052355,"about_ca_system_score_gemma":0.001336245,"threshold_uncertainty_score":0.027476251},"labels":[],"label_agreement":null},{"id":"W4413363931","doi":"10.1016/j.procs.2025.07.194","title":"Enhancing Healthcare with Digital Twins: A Comparative Approach Using AI and AI-Enhanced Digital Twins","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Artificial intelligence; Health care; Digital health; Data science; Human–computer interaction","score_opus":0.022049678158110264,"score_gpt":0.26448971148844075,"score_spread":0.24244003333033048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413363931","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9232551,0.0016285689,0.03587903,0.0020727355,0.00024313347,0.0008781835,0.0002898112,0.00015691262,0.03559657],"genre_scores_gemma":[0.95867294,0.00083951995,0.03678672,0.00046398237,0.000041189553,0.00036497443,0.00014044043,0.000032567714,0.0026576105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99409664,0.0041994704,0.00022780303,0.00035785526,0.0009188775,0.0001993856],"domain_scores_gemma":[0.9895449,0.006522456,0.0005916316,0.0010048844,0.0018434129,0.0004927024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077126566,0.00033450013,0.0004052304,0.0017874474,0.00098535,0.003011898,0.0006786205,0.00065500115,0.0031311233],"category_scores_gemma":[0.021848029,0.00017285383,0.000544935,0.0011037944,0.0013405349,0.0029854777,0.0028540213,0.0007801694,0.0003582496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004330664,0.005249998,0.08133194,0.0027991843,0.00065871235,0.0006207825,0.026534196,0.011176925,0.020136701,0.06602546,0.004700788,0.77643466],"study_design_scores_gemma":[0.0014878418,0.051410634,0.2500609,0.0039450307,0.0042053065,0.0032691858,0.1488884,0.11100318,0.10038504,0.08857414,0.23608555,0.0006847885],"about_ca_topic_score_codex":0.0022276742,"about_ca_topic_score_gemma":0.0030501077,"teacher_disagreement_score":0.0077126566,"about_ca_system_score_codex":0.0020470356,"about_ca_system_score_gemma":0.0020990868,"threshold_uncertainty_score":0.04078895},"labels":[],"label_agreement":null},{"id":"W4413363944","doi":"10.1016/j.procs.2025.07.193","title":"A Review of AIoT in Sustainable Agriculture: Advancing Soil Management with IoT Sensors","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Internet of Things; Agriculture; Sustainable agriculture; Agricultural engineering; Engineering management; World Wide Web","score_opus":0.0034916721255130343,"score_gpt":0.19718382338539803,"score_spread":0.19369215125988498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413363944","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022154678,0.99478394,0.0008939015,0.00070028595,0.00070426764,0.000012567158,0.000039502458,0.000012745719,0.0026311828],"genre_scores_gemma":[0.0012928607,0.9959572,0.00095896644,0.00052780967,0.00034794185,0.000015033518,0.000050136012,0.0000054168327,0.00084459333],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99955565,0.000092104536,0.000076886594,0.000083204664,0.00015772389,0.000034463093],"domain_scores_gemma":[0.9987238,0.0007378467,0.00013542348,0.00003363873,0.0003029366,0.00006643157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009399362,0.0009541875,0.0011156576,0.0027261346,0.0003960814,0.0016211979,0.0010149601,0.0017448026,0.004078965],"category_scores_gemma":[0.001760246,0.00041390615,0.0008423244,0.0045849783,0.00051191036,0.0024322732,0.00085145404,0.0014463554,0.0016554358],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007199237,0.000084936226,0.00040095957,0.043692857,0.00012130786,0.00023420397,0.00017069781,0.0011788915,0.0024108114,0.010818643,0.040098492,0.90071625],"study_design_scores_gemma":[0.0000050736626,0.00009577545,0.00078327366,0.009947122,0.00010695942,0.00040474036,0.00012063965,0.0002812531,0.00051699515,0.0029043993,0.98480797,0.000025786343],"about_ca_topic_score_codex":0.0017469508,"about_ca_topic_score_gemma":0.0026403063,"teacher_disagreement_score":0.004078965,"about_ca_system_score_codex":0.0008340378,"about_ca_system_score_gemma":0.0019445996,"threshold_uncertainty_score":0.01364547},"labels":[],"label_agreement":null},{"id":"W4413363956","doi":"10.1016/j.procs.2025.07.212","title":"Optimization of fuel transportation using a multi-product pipeline with intermediate pumping stations: gasoline, diesel, and Jet A-1","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Process Optimization and Integration","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Computer science; Diesel fuel; Gasoline; Pipeline (software); Jet fuel; Automotive engineering; Jet (fluid); Product (mathematics); Process engineering; Environmental science; Aerospace engineering; Waste management; Operating system","score_opus":0.012139619626164604,"score_gpt":0.2461936827127569,"score_spread":0.2340540630865923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413363956","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7574419,0.0006742222,0.22774465,0.00049952196,0.00005636661,0.00020229522,0.0005241052,0.00022588977,0.012631165],"genre_scores_gemma":[0.9656969,0.00013436005,0.0322687,0.000016958034,0.0000042779293,0.000073373485,0.00012688251,0.000018929786,0.0016597097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982005,0.000059350685,0.0000072943258,0.00003644875,0.000029291221,0.000047571153],"domain_scores_gemma":[0.999493,0.00032763733,0.00007061975,0.000015856951,0.000050680675,0.000042201405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066381914,0.0008344114,0.0006961099,0.0005898235,0.0004866133,0.0008904248,0.0005689299,0.0013437197,0.0018362594],"category_scores_gemma":[0.0012889096,0.0005570199,0.00069225597,0.0006924239,0.00049804733,0.0007588161,0.00053670106,0.000613419,0.00012853916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037849313,0.000027780663,0.0003786571,0.000026374919,0.000009169149,0.00004743399,0.000006418066,0.99692875,0.0006053809,0.00045459872,0.00004885749,0.0014287706],"study_design_scores_gemma":[0.000012136955,0.00008520773,0.00021904359,0.000002895448,0.000006778251,0.00000813744,0.0000134577085,0.99890316,0.00040983967,0.00022306453,0.00011362379,0.0000027177734],"about_ca_topic_score_codex":0.013297016,"about_ca_topic_score_gemma":0.008786602,"teacher_disagreement_score":0.013297016,"about_ca_system_score_codex":0.0012474178,"about_ca_system_score_gemma":0.001789286,"threshold_uncertainty_score":0.02643925},"labels":[],"label_agreement":null},{"id":"W4413364195","doi":"10.1016/j.procs.2025.07.154","title":"A Real-Time Indoor Object Detection and Distance Estimation System for Visually Impaired Individuals","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dawson College","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Visually impaired; Object (grammar); Estimation; Object detection; Real-time computing; Pattern recognition (psychology); Human–computer interaction","score_opus":0.01547813969452953,"score_gpt":0.28701283973825303,"score_spread":0.2715347000437235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413364195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.363752,0.0008535757,0.60783356,0.00032536732,0.00034231597,0.00042221407,0.0006692895,0.017911728,0.007889942],"genre_scores_gemma":[0.8151776,0.00035912718,0.17315684,0.00025035493,0.00005906272,0.00033979304,0.00040775514,0.00012918719,0.010120305],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997969,0.00002693453,0.000016410886,0.00006622258,0.000070348375,0.000023117953],"domain_scores_gemma":[0.999752,0.000039201055,0.00003137909,0.000027229291,0.000116539566,0.000033714812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002233546,0.00049471756,0.00046898253,0.00050010794,0.00020539782,0.0003389381,0.00069116824,0.0005785779,0.003992339],"category_scores_gemma":[0.0005550118,0.00018810765,0.00020259927,0.00021761497,0.00012790655,0.00036497207,0.00060574163,0.00026247196,0.0016995935],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085381756,0.00034065702,0.00786409,0.00052478316,0.000077671946,0.00105172,0.0005090854,0.0025066896,0.41988984,0.0006912764,0.008308572,0.55738175],"study_design_scores_gemma":[0.0004087951,0.006704664,0.12156032,0.00041038485,0.00072039623,0.017132835,0.0010204251,0.2665979,0.51317865,0.0019264854,0.06982299,0.0005161399],"about_ca_topic_score_codex":0.0010306087,"about_ca_topic_score_gemma":0.0014084553,"teacher_disagreement_score":0.003992339,"about_ca_system_score_codex":0.00017953037,"about_ca_system_score_gemma":0.00040113062,"threshold_uncertainty_score":0.013355732},"labels":[],"label_agreement":null},{"id":"W4413364377","doi":"10.1016/j.procs.2025.07.197","title":"CLIP: Centrality-Led ILP Controller Placement for Software-Defined Networking","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Centrality; Software-defined networking; Software; Controller (irrigation); Computer network; Operating system","score_opus":0.011765354915888347,"score_gpt":0.2580151565997801,"score_spread":0.24624980168389174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413364377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010108523,0.0002428178,0.9819902,0.0002728635,0.00010158262,0.00007737467,0.00007088412,0.0005969112,0.0065387697],"genre_scores_gemma":[0.535968,0.00043703915,0.4568796,0.00028381514,0.000117951124,0.00025334104,0.00028886,0.00035954593,0.0054117986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994265,0.00016302441,0.000019339539,0.00008890268,0.00019848385,0.00010387078],"domain_scores_gemma":[0.99890363,0.0006097873,0.00011702434,0.000073556876,0.0001986265,0.00009738395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093792885,0.0011279532,0.0005134984,0.0005909621,0.0005866762,0.0010414356,0.0011478219,0.0006921135,0.0048115263],"category_scores_gemma":[0.002794172,0.00033325367,0.0003782088,0.0006290798,0.0006502372,0.0010058461,0.0014496476,0.0013337891,0.000526847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005752285,0.000065896325,0.000397747,0.00009983297,0.000021727816,0.00006695173,0.00004750034,0.9296987,0.0033738501,0.01812852,0.003794588,0.044247214],"study_design_scores_gemma":[0.000011791562,0.00004001375,0.0000369039,0.0000072500156,0.000005592497,0.000018442475,0.000019414969,0.9928523,0.0008865226,0.0047788587,0.0013385735,0.000004371208],"about_ca_topic_score_codex":0.0031032541,"about_ca_topic_score_gemma":0.006174125,"teacher_disagreement_score":0.0048115263,"about_ca_system_score_codex":0.001318182,"about_ca_system_score_gemma":0.002635063,"threshold_uncertainty_score":0.016096175},"labels":[],"label_agreement":null},{"id":"W4413364391","doi":"10.1016/j.procs.2025.07.178","title":"Retrieve-Classify-Read: Passage Filtering via Subject Classification for University Question Answering","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Question answering; Subject (documents); Information retrieval; Artificial intelligence; Natural language processing; World Wide Web","score_opus":0.022977001796318477,"score_gpt":0.2571357960003268,"score_spread":0.23415879420400834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413364391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088576674,0.0012477381,0.8479666,0.000586505,0.00028450665,0.0011786132,0.0028664144,0.054171503,0.0031215092],"genre_scores_gemma":[0.38116562,0.000526413,0.597157,0.0003814061,0.00025025703,0.00063231273,0.01096343,0.0011046798,0.007818881],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985708,0.00058673305,0.00010702621,0.00040898132,0.0002389161,0.00008757078],"domain_scores_gemma":[0.99537,0.0025089174,0.00023992792,0.0008935862,0.0007410605,0.0002465218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032796906,0.0012963401,0.0010903869,0.002764071,0.0007745314,0.0014307179,0.0020527802,0.0017466057,0.005609877],"category_scores_gemma":[0.011299276,0.00031559065,0.0015379209,0.0012378613,0.0005712264,0.0024062344,0.0014614626,0.001540485,0.0039401352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010430069,0.00094529474,0.01276717,0.00057905517,0.00029615295,0.0003682858,0.0013298,0.029206881,0.03176753,0.0037375314,0.024351541,0.8936077],"study_design_scores_gemma":[0.00011576981,0.0008491046,0.008450267,0.00005355728,0.00019161409,0.00039996253,0.00036551344,0.92013425,0.03653907,0.0072544315,0.025546793,0.000099690515],"about_ca_topic_score_codex":0.013904609,"about_ca_topic_score_gemma":0.01265228,"teacher_disagreement_score":0.013904609,"about_ca_system_score_codex":0.0011292681,"about_ca_system_score_gemma":0.0015982304,"threshold_uncertainty_score":0.027647316},"labels":[],"label_agreement":null},{"id":"W4415223442","doi":"10.1016/j.procs.2025.08.217","title":"Climate Change Mitigation as a Complex Adaptive System - Energy System Transition for Low Carbon Emission Future","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Global Energy and Sustainability Research","field":"Energy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Climate change; Adaptability; Global warming; Work (physics); Adaptation (eye); Climate change mitigation; Energy system; Agency (philosophy); Greenhouse gas","score_opus":0.01403706624079713,"score_gpt":0.2613514697917727,"score_spread":0.2473144035509756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415223442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049460825,0.0018007541,0.8865924,0.0068120807,0.00030616776,0.00018188049,0.00024090467,0.00065202574,0.05395297],"genre_scores_gemma":[0.9229331,0.0013348646,0.06965959,0.0004480484,0.0001381507,0.0001498508,0.00011905451,0.00007749375,0.005139914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995795,0.00017203568,0.000013042373,0.00009631479,0.000089870766,0.000049309125],"domain_scores_gemma":[0.9996908,0.00011801812,0.000045803663,0.00003523402,0.000062756706,0.000047427387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006345069,0.0004949244,0.0002952171,0.0004096522,0.00067879667,0.001663221,0.00064027455,0.0008871484,0.0039014122],"category_scores_gemma":[0.0011720759,0.00015737128,0.00060513313,0.00050865783,0.0011655655,0.0023903672,0.0018652854,0.0014954126,0.0003826755],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049614944,0.000082045284,0.00357436,0.00024044202,0.00008614473,0.0002778175,0.0005137138,0.5579785,0.005278795,0.37953135,0.005013396,0.047373783],"study_design_scores_gemma":[0.000012646282,0.00010399309,0.0024505348,0.00009664496,0.000053870703,0.00015350858,0.0007024319,0.62414885,0.0011564826,0.3324474,0.03862404,0.000049662864],"about_ca_topic_score_codex":0.0040778304,"about_ca_topic_score_gemma":0.0048531573,"teacher_disagreement_score":0.0040778304,"about_ca_system_score_codex":0.0012149232,"about_ca_system_score_gemma":0.0012850864,"threshold_uncertainty_score":0.013051569},"labels":[],"label_agreement":null},{"id":"W4415223970","doi":"10.1016/j.procs.2025.08.219","title":"AI-Driven Agile Systems Engineering Approach for Managing Cross-System Interactions","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Anesthesiologists' Society; Ford Motor Company","keywords":"Agile software development; Adaptability; Process (computing); Automotive industry; Requirements engineering; Quality (philosophy); System of systems; Compromise; Product (mathematics)","score_opus":0.009596620925693189,"score_gpt":0.22982029254834685,"score_spread":0.22022367162265366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415223970","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0087021645,0.00013469109,0.98617715,0.00038104417,0.000020680303,0.00031200834,0.0000426341,0.0002824756,0.003947101],"genre_scores_gemma":[0.1741319,0.00024938292,0.82193327,0.00018802719,0.000018448884,0.00076877687,0.00016829559,0.0000792039,0.0024626208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99395293,0.00277669,0.0003754902,0.0006560514,0.0019172421,0.00032153985],"domain_scores_gemma":[0.99159193,0.004416598,0.0010188246,0.0011858647,0.0014576017,0.0003291823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006258712,0.0013322523,0.00059235643,0.0015305016,0.0011862037,0.0028020847,0.00259345,0.0012354372,0.0020407536],"category_scores_gemma":[0.008264064,0.0009834194,0.0015716502,0.0009294908,0.0021422766,0.002025525,0.0046501923,0.0022975511,0.00049689744],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015331214,0.0004776244,0.0072891735,0.001128253,0.00046949944,0.0020365934,0.0068414453,0.5568173,0.03036502,0.20543279,0.0026038554,0.18638523],"study_design_scores_gemma":[0.00006019766,0.00029703975,0.0006366911,0.00019017396,0.0001209548,0.00047815568,0.0011502865,0.89995533,0.008089292,0.06867107,0.020296704,0.000054112705],"about_ca_topic_score_codex":0.0029527517,"about_ca_topic_score_gemma":0.0046074437,"teacher_disagreement_score":0.006258712,"about_ca_system_score_codex":0.0014676584,"about_ca_system_score_gemma":0.0047270497,"threshold_uncertainty_score":0.03309965},"labels":[],"label_agreement":null},{"id":"W4415224278","doi":"10.1016/j.procs.2025.08.220","title":"Evaluating Technology Infusion Impacts on Electric Grid Modernization","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexibility (engineering); Electrification; Adaptability; Grid; Modernization theory; Electric power system; Portfolio; Emerging technologies","score_opus":0.09698709809915243,"score_gpt":0.41832585285432705,"score_spread":0.3213387547551746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415224278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9736024,0.00026325646,0.015524164,0.00019054972,0.000013609165,0.000083142186,0.00023724837,0.000034245037,0.01005146],"genre_scores_gemma":[0.99729496,0.00008512233,0.00228034,0.000007049358,0.000003164331,0.000020261117,0.0000713573,0.0000033867086,0.00023431143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99828315,0.0007607704,0.00006360874,0.00011982549,0.00059407955,0.00017849196],"domain_scores_gemma":[0.99077904,0.006123956,0.0014158111,0.0004503188,0.0009655241,0.00026531564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043238914,0.0006879974,0.00030985748,0.0017784041,0.00027514898,0.0021044991,0.00038462688,0.0006476346,0.0014859621],"category_scores_gemma":[0.01274637,0.00017845583,0.00047312788,0.0019979284,0.0007108878,0.0023357535,0.0014342006,0.0007827541,0.00010715515],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062832463,0.00024384806,0.08936437,0.00016482708,0.00018153507,0.00029351297,0.00023868252,0.81267464,0.0037860458,0.0304134,0.0005003034,0.061510436],"study_design_scores_gemma":[0.000042574502,0.0015085618,0.07823315,0.00007334876,0.00015675052,0.00013737727,0.0010202234,0.8832983,0.00806283,0.02494129,0.0024672446,0.0000582363],"about_ca_topic_score_codex":0.0014458335,"about_ca_topic_score_gemma":0.0017134064,"teacher_disagreement_score":0.0043238914,"about_ca_system_score_codex":0.0019850517,"about_ca_system_score_gemma":0.0006615716,"threshold_uncertainty_score":0.022867143},"labels":[],"label_agreement":null},{"id":"W4415224701","doi":"10.1016/j.procs.2025.08.197","title":"Design of an Autonomic Software System for Dragonfly’s Wind Tunnel","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Applied Physics Laboratory, Johns Hopkins University; Alberta Precision Laboratories; Langley Research Center; National Aeronautics and Space Administration","keywords":"Flexibility (engineering); Wind tunnel; Instrumentation (computer programming); Variety (cybernetics); Control system; Software; Systems design; Control (management); Atmosphere (unit)","score_opus":0.03329128485773344,"score_gpt":0.28708346390634476,"score_spread":0.25379217904861134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415224701","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1590914,0.00056724047,0.7978784,0.0005480435,0.00035410412,0.0011627974,0.00023588755,0.01998938,0.020172764],"genre_scores_gemma":[0.8142196,0.00021610942,0.17770457,0.00025553704,0.00005018931,0.00063824945,0.000391008,0.00049158774,0.006033093],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99953926,0.00008150931,0.000043205833,0.000115771225,0.00014506513,0.00007518433],"domain_scores_gemma":[0.9996164,0.00006401291,0.000032571475,0.000058072907,0.00012576891,0.0001032812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007427393,0.00050125027,0.00041148547,0.0004238856,0.0005564303,0.0013161534,0.0013402948,0.0004909669,0.0019938878],"category_scores_gemma":[0.0009741033,0.00027671666,0.00033270195,0.00020024473,0.00045013597,0.00066098874,0.00088020647,0.00077303016,0.00062302896],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013208521,0.00090107456,0.0190766,0.00087124837,0.0004480382,0.0027384371,0.002175828,0.30597308,0.22141187,0.051858056,0.023028532,0.3701964],"study_design_scores_gemma":[0.00018105636,0.00072451355,0.0035653426,0.00009337438,0.00014007586,0.0004510098,0.00023309104,0.9005422,0.04240014,0.004969359,0.046611242,0.00008856542],"about_ca_topic_score_codex":0.0022581008,"about_ca_topic_score_gemma":0.0016112376,"teacher_disagreement_score":0.0022581008,"about_ca_system_score_codex":0.0005138007,"about_ca_system_score_gemma":0.0011400714,"threshold_uncertainty_score":0.0066702366},"labels":[],"label_agreement":null},{"id":"W4415974165","doi":"10.1016/j.procs.2025.09.440","title":"Towards automatic extraction of UML class diagrams: Creation of an annotated dataset for training deep models","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Unified Modeling Language; Class diagram; Automation; Applications of UML; Schema (genetic algorithms); Software; Structuring","score_opus":0.045972145385984264,"score_gpt":0.3260006784343023,"score_spread":0.28002853304831804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23441379,0.0037759633,0.43713436,0.0020432072,0.0010265996,0.0016798856,0.25715247,0.042554148,0.020219686],"genre_scores_gemma":[0.15067239,0.0008123267,0.339808,0.00036520447,0.000092902534,0.002941581,0.49775928,0.0018021491,0.005746122],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99817026,0.0005560771,0.00019920658,0.00066907227,0.0002916748,0.000113696886],"domain_scores_gemma":[0.9931764,0.0038636057,0.0003503286,0.0009869756,0.0014054774,0.00021722629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023560566,0.0019595341,0.00071685994,0.00556691,0.00094319007,0.001439278,0.0016816924,0.00201662,0.005779827],"category_scores_gemma":[0.010151367,0.00065057556,0.0011404635,0.002862975,0.0007390537,0.0028187635,0.002014392,0.0024642914,0.005557678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005919612,0.0012580493,0.016564969,0.003979321,0.00020965628,0.002389447,0.0025338335,0.028505372,0.05183275,0.011893132,0.2749335,0.6053081],"study_design_scores_gemma":[0.00035831658,0.00041659107,0.029210176,0.0011144185,0.0002973366,0.0016336117,0.0033208232,0.42000046,0.079015404,0.0176795,0.446705,0.00024836272],"about_ca_topic_score_codex":0.008403698,"about_ca_topic_score_gemma":0.015977561,"teacher_disagreement_score":0.008403698,"about_ca_system_score_codex":0.0014445488,"about_ca_system_score_gemma":0.0023665628,"threshold_uncertainty_score":0.019335449},"labels":[],"label_agreement":null},{"id":"W4415974188","doi":"10.1016/j.procs.2025.09.549","title":"Content Safety and Response Quality in LLMs: A Data-Centric Refinement Approach","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Readability; Performance improvement; Quality management; Quality (philosophy); Coherence (philosophical gambling strategy); Natural language generation","score_opus":0.08154692548813655,"score_gpt":0.32179373135095823,"score_spread":0.24024680586282168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07253767,0.0015821902,0.8950091,0.0027523122,0.00024850454,0.0006894786,0.0026503562,0.02155419,0.0029761835],"genre_scores_gemma":[0.5558924,0.00048468477,0.42897066,0.0012792926,0.00020352784,0.0006604524,0.0062745307,0.0014330349,0.0048014307],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99487936,0.0023645523,0.00039418618,0.001127812,0.0010179807,0.00021609223],"domain_scores_gemma":[0.9813944,0.009295258,0.0010120185,0.004697044,0.003226258,0.00037500128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008375165,0.0016891613,0.0014693275,0.0018847102,0.0005670478,0.0019695866,0.0034520389,0.0016745413,0.0030805129],"category_scores_gemma":[0.027443811,0.0007457756,0.0018509941,0.0009924216,0.0014990646,0.0045119254,0.0030046843,0.00341549,0.002360961],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020398977,0.0007399185,0.01595411,0.0013763106,0.00040491598,0.0004567592,0.0012379137,0.36896637,0.052392833,0.014434635,0.019075539,0.5229208],"study_design_scores_gemma":[0.00008536003,0.00038156132,0.0014145547,0.0000624365,0.00011605255,0.00013094612,0.00015514714,0.9550723,0.01526056,0.019814143,0.0074477484,0.00005925892],"about_ca_topic_score_codex":0.007913541,"about_ca_topic_score_gemma":0.011628542,"teacher_disagreement_score":0.008375165,"about_ca_system_score_codex":0.0020764107,"about_ca_system_score_gemma":0.0029907238,"threshold_uncertainty_score":0.04429263},"labels":[],"label_agreement":null},{"id":"W4415974330","doi":"10.1016/j.procs.2025.10.037","title":"Proof and Application of Little’s Formula in Optimizing Bulk-service Multi-server Queue Systems","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Canadian Defence Academy","keywords":"Queue; Queueing theory; Bulk queue; Function (biology); Queueing system; Server; Service (business)","score_opus":0.010898476986794558,"score_gpt":0.24509230139954669,"score_spread":0.23419382441275213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009506836,0.0008560633,0.9712573,0.001307738,0.00019672581,0.000095766205,0.00013158465,0.0001771077,0.016470876],"genre_scores_gemma":[0.61980325,0.0021932381,0.36140832,0.0015059279,0.00049225107,0.0005873612,0.00021670805,0.00042166814,0.013371322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991247,0.0002617348,0.000030166817,0.000093922004,0.0003806788,0.00010887156],"domain_scores_gemma":[0.99628735,0.0025718817,0.00015475953,0.00016982702,0.00072840415,0.00008775684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037226016,0.0011271805,0.0012889673,0.000883109,0.00053740415,0.001150341,0.00177461,0.0013575462,0.004599936],"category_scores_gemma":[0.013018162,0.00047263363,0.0010741492,0.0010448761,0.0015735592,0.002395575,0.0014126227,0.0022314475,0.00069290114],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047441867,0.00009464166,0.00056887063,0.000247137,0.00003461437,0.00017150832,0.00020150901,0.35614163,0.0027228715,0.6004099,0.006923565,0.03243628],"study_design_scores_gemma":[0.00001745064,0.000031301584,0.00015922703,0.000043784887,0.000008935281,0.000030770956,0.000019178393,0.8730316,0.0005025758,0.12404706,0.002094766,0.000013254177],"about_ca_topic_score_codex":0.0042794417,"about_ca_topic_score_gemma":0.00263143,"teacher_disagreement_score":0.004599936,"about_ca_system_score_codex":0.0019825567,"about_ca_system_score_gemma":0.002901626,"threshold_uncertainty_score":0.019687295},"labels":[],"label_agreement":null},{"id":"W4415974379","doi":"10.1016/j.procs.2025.09.338","title":"KurtHGR: A Neural Maximal Correlation for Tabular Datasets","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Fondation du Risque; CNP Assurances","keywords":"Nonlinear system; Universality (dynamical systems); Curse of dimensionality; Bivariate analysis; Correlation; A priori and a posteriori; Feature selection; Pattern recognition (psychology); Covariance matrix; Feature (linguistics)","score_opus":0.01086153656386521,"score_gpt":0.26296386590555776,"score_spread":0.2521023293416925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04105397,0.001495723,0.9358587,0.00083225354,0.00017154503,0.00038772466,0.0042212973,0.013118922,0.0028598611],"genre_scores_gemma":[0.25800693,0.00063210976,0.7239059,0.0006828918,0.00015578361,0.001043304,0.011632224,0.0011517646,0.0027890576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739397,0.0012058002,0.00017056409,0.0005254765,0.00052988995,0.00017423577],"domain_scores_gemma":[0.99525696,0.002437426,0.000406283,0.001156103,0.00056504534,0.00017816636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006487673,0.0017927114,0.0020940315,0.0033572454,0.0010774374,0.002197688,0.0039114296,0.0021624896,0.0036330428],"category_scores_gemma":[0.025066804,0.0007128627,0.0016279124,0.0035485243,0.0011423315,0.0035861623,0.0037266146,0.0025928938,0.0018422725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009652261,0.00047494104,0.007838255,0.0007360859,0.0005072888,0.0003143607,0.00022668326,0.45804062,0.0031196936,0.036770597,0.04039429,0.45061198],"study_design_scores_gemma":[0.000042557553,0.000058054444,0.00045557896,0.000026906268,0.000013823728,0.0000467714,0.000021594056,0.9812843,0.00096519734,0.01525007,0.0018122,0.000022909064],"about_ca_topic_score_codex":0.0047295857,"about_ca_topic_score_gemma":0.006266923,"teacher_disagreement_score":0.006487673,"about_ca_system_score_codex":0.0014062371,"about_ca_system_score_gemma":0.0032297047,"threshold_uncertainty_score":0.03431046},"labels":[],"label_agreement":null},{"id":"W4415974466","doi":"10.1016/j.procs.2025.09.259","title":"Identifying Climate Anomalies with Simulated Antenna Data, Sensor Arrays, and Spiking Neural Networks","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Spiking neural network; Anomaly detection; Scalability; Artificial neural network; Pipeline (software); Key (lock); Encoding (memory); Anomaly (physics)","score_opus":0.029832221234130245,"score_gpt":0.2492679464635125,"score_spread":0.21943572522938226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44245765,0.00022059487,0.553219,0.0004373326,0.00006284055,0.000027148695,0.00040195117,0.00062076194,0.002552669],"genre_scores_gemma":[0.9723283,0.00008081897,0.02709305,0.000035346748,0.000015689018,0.000019826972,0.00018961521,0.000018121902,0.00021924147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978894,0.00008027539,0.000013733173,0.00005194855,0.00004470027,0.000020451538],"domain_scores_gemma":[0.9992137,0.00047228186,0.00010455013,0.0000897377,0.00009392446,0.000025742862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005293223,0.00036495432,0.00026449698,0.00041288865,0.00015581408,0.0005422961,0.00061441836,0.0004810594,0.00032802494],"category_scores_gemma":[0.003988908,0.00019781322,0.00030708482,0.0006327107,0.0004648152,0.00073471846,0.0005328686,0.0005216875,0.000065398184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045319382,0.00001839205,0.004289041,0.000020508345,0.000030032286,0.00003489858,0.000025508934,0.9764188,0.0022876116,0.0028562604,0.0001825276,0.01379104],"study_design_scores_gemma":[0.0000011351702,0.000004967013,0.00037811595,0.0000010385066,0.0000014194135,0.000005694051,0.0000035513283,0.99717206,0.00042060067,0.0019551597,0.00005427406,0.0000021090868],"about_ca_topic_score_codex":0.0027375903,"about_ca_topic_score_gemma":0.0031090968,"teacher_disagreement_score":0.0027375903,"about_ca_system_score_codex":0.00042816886,"about_ca_system_score_gemma":0.00034020338,"threshold_uncertainty_score":0.005443275},"labels":[],"label_agreement":null},{"id":"W4415974585","doi":"10.1016/j.procs.2025.09.640","title":"Exploring Learner-Action Timing in a Generative AI Supported EFL Ideathon: A KPT Study in Japan","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"Ritsumeikan Global Innovation Research Organization, Ritsumeikan University; Japan Science Society; Ritsumeikan University","keywords":"Usability; Generative grammar; Thematic analysis; Coding (social sciences); Interface (matter); Wilcoxon signed-rank test; Qualitative analysis; User interface","score_opus":0.14286898261938713,"score_gpt":0.3632496142660859,"score_spread":0.22038063164669874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99835974,0.000013430956,0.00075474853,0.000031162162,0.000002109403,0.00004848334,0.00001154252,0.000008046966,0.0007705908],"genre_scores_gemma":[0.9967753,0.000036711368,0.0019203874,0.000034733766,0.0000030014007,0.0001293055,0.000032078173,0.000015896301,0.0010526322],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9980228,0.0009528263,0.00010242294,0.00037587614,0.00027141123,0.00027474496],"domain_scores_gemma":[0.9942154,0.0034104248,0.00053072843,0.00045189552,0.00061225926,0.0007792392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057432665,0.00050933927,0.0006582651,0.0011644268,0.0025049928,0.0027636373,0.0010770849,0.0010662708,0.0019011626],"category_scores_gemma":[0.0097088525,0.0004520344,0.0004940816,0.0007912039,0.0027588094,0.00205237,0.0039327596,0.001068378,0.0003778657],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037111493,0.0016839703,0.056335557,0.00036048217,0.000031844913,0.0017254828,0.8953592,0.00046879225,0.013446002,0.0010272751,0.0002817747,0.028908484],"study_design_scores_gemma":[0.00016030425,0.0029945679,0.1726791,0.00018466063,0.00009066229,0.0009276853,0.79832536,0.0027250058,0.0077163265,0.0016803019,0.012354732,0.00016137458],"about_ca_topic_score_codex":0.0026128963,"about_ca_topic_score_gemma":0.005599945,"teacher_disagreement_score":0.0057432665,"about_ca_system_score_codex":0.0017475362,"about_ca_system_score_gemma":0.0017577918,"threshold_uncertainty_score":0.030373633},"labels":[],"label_agreement":null},{"id":"W4415974599","doi":"10.1016/j.procs.2025.09.258","title":"Intelligent Modeling of Soil Moisture Variability Using Remote Sensing and Spiking Neural Networks","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Adaptability; Artificial neural network; Spiking neural network; Precision agriculture; Merge (version control); Mean squared error; Water content; Process (computing)","score_opus":0.013033254079796773,"score_gpt":0.23788872262267108,"score_spread":0.2248554685428743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4361326,0.00030915378,0.5581466,0.0003054451,0.00004636447,0.00004118431,0.0002718385,0.00041556064,0.004331181],"genre_scores_gemma":[0.9842539,0.000093833005,0.014838328,0.00002504965,0.000008190759,0.000026122292,0.00008008598,0.000010799179,0.0006635933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991584,0.000022957103,0.0000056123595,0.000026011521,0.00001962863,0.000009868474],"domain_scores_gemma":[0.99982136,0.00009333909,0.000035615536,0.000015533033,0.000027538013,0.0000066074595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030249718,0.00030573984,0.00022863875,0.00023079437,0.00013967749,0.00041881643,0.00045880355,0.00037824523,0.00044256996],"category_scores_gemma":[0.0009246036,0.0001835177,0.00033714928,0.00031578806,0.00027934648,0.00060861453,0.00032557175,0.00034336105,0.00005954437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014684621,0.000009279821,0.0009194372,0.0000076066653,0.00001323968,0.000016644903,0.0000092977225,0.9905503,0.001647357,0.00092922855,0.00005123125,0.005831655],"study_design_scores_gemma":[4.8987414e-7,0.0000022935528,0.00015827166,5.791316e-7,8.6152625e-7,0.0000017619791,0.0000010514949,0.9992205,0.00015614723,0.00043162427,0.000025556932,8.02579e-7],"about_ca_topic_score_codex":0.004615549,"about_ca_topic_score_gemma":0.004682507,"teacher_disagreement_score":0.004615549,"about_ca_system_score_codex":0.0004086841,"about_ca_system_score_gemma":0.00027882334,"threshold_uncertainty_score":0.009177387},"labels":[],"label_agreement":null},{"id":"W4415974674","doi":"10.1016/j.procs.2025.09.441","title":"Assessing Machine Learning Models for Enhancing Intent Detection in Tourism Chatbots","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Higher Education, Science, Research and Innovation, Thailand; Centre National pour la Recherche Scientifique et Technique","keywords":"Random forest; Tourism; Support vector machine; Natural language understanding; Transformation (genetics); Sentiment analysis","score_opus":0.027839807335852507,"score_gpt":0.3014778020167092,"score_spread":0.2736379946808567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84949833,0.00273011,0.13785307,0.0009160609,0.00023482478,0.00034703081,0.0009679075,0.0038641847,0.003588537],"genre_scores_gemma":[0.9213841,0.00034115225,0.074525915,0.00012524548,0.000055279208,0.00014163264,0.0020443734,0.00007008102,0.0013121777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980399,0.0009855401,0.00013320433,0.00029554474,0.00032364993,0.0002221939],"domain_scores_gemma":[0.988108,0.009225936,0.00047190586,0.00046062833,0.0014600203,0.00027348957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057964697,0.0016546266,0.0009420808,0.002710736,0.0006979782,0.0012996421,0.0010146797,0.0014317954,0.00089430803],"category_scores_gemma":[0.014533946,0.0003176093,0.0009362765,0.001303091,0.00044201175,0.0015490974,0.000826987,0.001432306,0.0008168393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016296986,0.001441384,0.061359238,0.00086170074,0.0003356533,0.00026015262,0.00059025455,0.48739222,0.007798843,0.0014735496,0.009497423,0.42735994],"study_design_scores_gemma":[0.000010306463,0.00017412571,0.0037709547,0.000024595962,0.000021572363,0.000026561447,0.00011300178,0.9934974,0.001640215,0.00041023857,0.00030009056,0.000011018599],"about_ca_topic_score_codex":0.014351963,"about_ca_topic_score_gemma":0.01459576,"teacher_disagreement_score":0.014351963,"about_ca_system_score_codex":0.0012891048,"about_ca_system_score_gemma":0.0010490273,"threshold_uncertainty_score":0.030655086},"labels":[],"label_agreement":null},{"id":"W4415974692","doi":"10.1016/j.procs.2025.09.181","title":"Improving Nurse Scheduling Using a Random Forest Algorithm to Predict Employee Well-Being","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Aluminium Refining, Degassing and Filtering (Canada); Group for Research in Decision Analysis; Université du Québec à Chicoutimi","funders":"Mitacs","keywords":"Random forest; Scheduling (production processes); Overtime; Work (physics); Linear programming; Job shop scheduling","score_opus":0.03274423289670193,"score_gpt":0.344962055914178,"score_spread":0.3122178230174761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09059623,0.0005680275,0.9057248,0.00025445878,0.00009211202,0.00009899175,0.00028559,0.0013363791,0.0010433674],"genre_scores_gemma":[0.6424698,0.00034351114,0.35386348,0.00015325747,0.000116557225,0.00018826377,0.0012362488,0.00012603441,0.0015028024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995085,0.0001712548,0.000025222167,0.00013634677,0.00007870725,0.00007992895],"domain_scores_gemma":[0.99865806,0.00087170344,0.00010939989,0.000052966643,0.00024734729,0.000060449955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001598292,0.0008304312,0.0010001984,0.0008151947,0.00038351145,0.0004456029,0.00076773856,0.0005988237,0.0012273443],"category_scores_gemma":[0.0031053587,0.000351576,0.00078723894,0.0006340523,0.00014459727,0.00059812755,0.00032446155,0.0008153544,0.0004629741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025313764,0.0002689374,0.00828236,0.000069692825,0.00008484104,0.00005729687,0.000052790594,0.7799097,0.002458823,0.00080071075,0.0027064327,0.20505528],"study_design_scores_gemma":[0.000009792991,0.000038574035,0.0005336316,0.0000042399665,0.0000083678615,0.000008071635,0.0000056250733,0.998429,0.00027813384,0.00049271935,0.00018762756,0.0000041143867],"about_ca_topic_score_codex":0.017521668,"about_ca_topic_score_gemma":0.017348062,"teacher_disagreement_score":0.017521668,"about_ca_system_score_codex":0.0004718463,"about_ca_system_score_gemma":0.0014470366,"threshold_uncertainty_score":0.03483939},"labels":[],"label_agreement":null},{"id":"W4415974696","doi":"10.1016/j.procs.2025.10.061","title":"DeepRet: A Portable and Multimodal AI System for Enhancing Glaucoma Diagnosis in Resource-Limited Settings","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Glaucoma; Scalability; Bridge (graph theory); Healthcare system; Health care; Usability","score_opus":0.005393377797493066,"score_gpt":0.2648022505694321,"score_spread":0.259408872771939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20204395,0.0071292585,0.6119587,0.003191459,0.0011855838,0.0018100635,0.0055184374,0.12927441,0.03788814],"genre_scores_gemma":[0.6686509,0.00227764,0.30190888,0.004716505,0.00038952485,0.00065650226,0.0032376682,0.001805396,0.016356938],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999554,0.00008854137,0.000032572672,0.00009345235,0.00018447606,0.000047072695],"domain_scores_gemma":[0.9993563,0.00025504685,0.00006348866,0.00007074719,0.00016279561,0.00009155786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065497524,0.00061996304,0.0005013367,0.0008978268,0.00023710841,0.00076700764,0.0010422489,0.0007539665,0.008843124],"category_scores_gemma":[0.0021506893,0.00024962277,0.0003058722,0.0003411402,0.00026161998,0.0010134107,0.0016639172,0.0005943797,0.0026836428],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015883866,0.00053583266,0.008698878,0.00086829235,0.0001785969,0.0014753693,0.00037851607,0.0034275765,0.16151696,0.001837968,0.08401254,0.73548114],"study_design_scores_gemma":[0.0012301697,0.004199285,0.041860394,0.00067754055,0.00075784256,0.015260278,0.0006395858,0.41477254,0.26138216,0.009656942,0.24887122,0.00069201173],"about_ca_topic_score_codex":0.001455275,"about_ca_topic_score_gemma":0.0023924068,"teacher_disagreement_score":0.008843124,"about_ca_system_score_codex":0.0004553938,"about_ca_system_score_gemma":0.00061388017,"threshold_uncertainty_score":0.029583216},"labels":[],"label_agreement":null},{"id":"W4415974753","doi":"10.1016/j.procs.2025.09.598","title":"Navigating Responsible AI: A Systematic Review of Governance Mechanisms and Future Co-Governance Scenarios","year":2025,"lang":"en","type":"review","venue":"Procedia Computer Science","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Corporate governance; Dystopia; Key (lock); Balance (ability); Systematic review; Humanity","score_opus":0.026834119457595455,"score_gpt":0.39876417475775217,"score_spread":0.3719300553001567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974753","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011446627,0.9953367,0.0008419511,0.00095950667,0.00014462174,0.00021717229,0.00022707255,0.000009909886,0.0011185134],"genre_scores_gemma":[0.01743292,0.9787861,0.0021455623,0.000702415,0.000061010203,0.0004279028,0.00026628852,0.000010030752,0.00016785337],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.98544335,0.006441798,0.0045426413,0.0010031745,0.0022020275,0.0003670998],"domain_scores_gemma":[0.9070558,0.07545986,0.007715324,0.0019379486,0.0070662787,0.00076473615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019470088,0.0010791067,0.0026921968,0.015769878,0.0010078298,0.0040110303,0.0018161581,0.0019549134,0.003712641],"category_scores_gemma":[0.08814155,0.00087787054,0.0034586347,0.015557999,0.0017825791,0.0050105317,0.0024211877,0.0016808794,0.00044470752],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000976403,0.00003164712,0.0015986382,0.6769975,0.0022241222,0.00023074305,0.0018870849,0.0004440439,0.00025935876,0.006330707,0.0052250284,0.30467355],"study_design_scores_gemma":[0.000042372798,0.000099304816,0.0023567355,0.8625581,0.006115382,0.00036500648,0.0021759789,0.00012094775,0.0002458317,0.0031794272,0.12270042,0.000040555402],"about_ca_topic_score_codex":0.007564816,"about_ca_topic_score_gemma":0.02636979,"teacher_disagreement_score":0.019470088,"about_ca_system_score_codex":0.0038429562,"about_ca_system_score_gemma":0.029801015,"threshold_uncertainty_score":0.10296887},"labels":[],"label_agreement":null},{"id":"W4415974768","doi":"10.1016/j.procs.2025.09.260","title":"Enabling Real-Time, Explainable DDoS Mitigation via On-Premise Large Language Models and Flow Analysis","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Correctness; Denial-of-service attack; Intrusion detection system; Firewall (physics); Cloud computing; Flow network; Application layer DDoS attack; Network security; Botnet","score_opus":0.006116768476068697,"score_gpt":0.2345347288779298,"score_spread":0.2284179604018611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017899156,0.00008836577,0.9732982,0.00042245045,0.000030623785,0.00009021611,0.00018772388,0.0069965236,0.0009868317],"genre_scores_gemma":[0.5342223,0.00026393987,0.4604941,0.00044075013,0.00005717294,0.0001978528,0.00090638496,0.00063581066,0.0027816566],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991642,0.0002923846,0.000045424968,0.00019701471,0.00023575773,0.00006526534],"domain_scores_gemma":[0.9976459,0.0013419844,0.00022730803,0.00044622007,0.00027202096,0.000066640314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012275039,0.0008992511,0.0004323361,0.0007770449,0.00036942985,0.0016829259,0.0012356362,0.0008539488,0.0019005582],"category_scores_gemma":[0.0052996753,0.00031680637,0.0010036803,0.0002290922,0.0007457738,0.0026345868,0.0016597015,0.001904479,0.00089980045],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047864154,0.0009065859,0.01069047,0.00036174923,0.0002088726,0.0006375728,0.00097058987,0.38530603,0.062893555,0.046358924,0.008445979,0.48274103],"study_design_scores_gemma":[0.000008647863,0.000039554325,0.00031158119,0.000015336826,0.000016009291,0.00006422475,0.00003792738,0.97276884,0.01026948,0.0140350135,0.002417624,0.000015723768],"about_ca_topic_score_codex":0.0031529253,"about_ca_topic_score_gemma":0.004425438,"teacher_disagreement_score":0.0031529253,"about_ca_system_score_codex":0.0008518115,"about_ca_system_score_gemma":0.0015948806,"threshold_uncertainty_score":0.0064917207},"labels":[],"label_agreement":null},{"id":"W4415974815","doi":"10.1016/j.procs.2025.09.322","title":"Usability and Frustration in Using Adaptive Functions for Decision Making: A User Study","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; University of Victoria","keywords":"Usability; Apprehension; Perception; Web usability; Usability goals; Usability engineering; User experience design","score_opus":0.2808707280003,"score_gpt":0.4776284302839673,"score_spread":0.1967577022836673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993994,0.000025809944,0.000356075,0.000011034308,0.0000025101924,0.000050482122,0.000012894632,0.000008972643,0.00013284941],"genre_scores_gemma":[0.997633,0.000072736904,0.001622124,0.000038353417,0.000008477379,0.00019775503,0.000043212764,0.000009860114,0.00037448746],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967133,0.0019177698,0.00033347984,0.00031819107,0.0004650424,0.00025223132],"domain_scores_gemma":[0.97206885,0.02075214,0.001692143,0.0014153722,0.0027615167,0.0013099408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056733447,0.0005981791,0.00087138644,0.00096350105,0.00068353914,0.0010002737,0.00048110733,0.0006544811,0.001402007],"category_scores_gemma":[0.024303785,0.0004285897,0.00079588505,0.00038098652,0.00086920464,0.0008028203,0.00071432005,0.00075852615,0.00025271456],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006565823,0.022972597,0.5700628,0.002612577,0.0008187133,0.0029670894,0.20047408,0.0025937296,0.04710497,0.0006536078,0.00213978,0.14103425],"study_design_scores_gemma":[0.000602268,0.07594103,0.8291939,0.00022313627,0.0006468754,0.0035422347,0.05161702,0.016222393,0.01602161,0.0004307367,0.005118231,0.00044055263],"about_ca_topic_score_codex":0.00071465503,"about_ca_topic_score_gemma":0.0008101198,"teacher_disagreement_score":0.0056733447,"about_ca_system_score_codex":0.00037268334,"about_ca_system_score_gemma":0.00027012688,"threshold_uncertainty_score":0.030003905},"labels":[],"label_agreement":null},{"id":"W4415974865","doi":"10.1016/j.procs.2025.09.519","title":"Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Fondation du Risque; CNP Assurances","keywords":"Benchmark (surveying); Representation (politics); Focus (optics); Regression; Deep learning; External Data Representation; Latent variable; Big data","score_opus":0.017933237357502557,"score_gpt":0.308492226226615,"score_spread":0.2905589888691124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415974865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014607792,0.0002604088,0.98343605,0.00015624402,0.00004606221,0.00003812749,0.00007218407,0.000626212,0.0007568296],"genre_scores_gemma":[0.5678293,0.00031465915,0.4269961,0.0003507971,0.00013037927,0.00029845675,0.0008144175,0.00037192105,0.00289401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985758,0.0006455936,0.00006720853,0.00025430383,0.00034304653,0.00011390722],"domain_scores_gemma":[0.9952388,0.0026497804,0.0002817919,0.001069871,0.0005952186,0.00016459018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004946521,0.00083475857,0.0011700473,0.0008105356,0.0005671029,0.0012311256,0.0019702034,0.0011806452,0.0028534853],"category_scores_gemma":[0.015345269,0.00042239533,0.00084947265,0.0008885903,0.0011060459,0.002383458,0.0022262915,0.002654791,0.0010105729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003895019,0.00021774245,0.0041483142,0.0001562762,0.00011559141,0.00018336452,0.0001914349,0.65087,0.007209504,0.07150178,0.006421654,0.25859484],"study_design_scores_gemma":[0.000007662081,0.00002257438,0.00015938892,0.0000072302973,0.00000361219,0.000015781146,0.000007841172,0.9847015,0.0007888297,0.01370314,0.0005780496,0.000004393267],"about_ca_topic_score_codex":0.0022506304,"about_ca_topic_score_gemma":0.0032180042,"teacher_disagreement_score":0.004946521,"about_ca_system_score_codex":0.0008581529,"about_ca_system_score_gemma":0.0012274361,"threshold_uncertainty_score":0.026160002},"labels":[],"label_agreement":null},{"id":"W4416020257","doi":"10.1016/j.procs.2025.10.130","title":"LiteDHAZE: An Adversarial Dehazing Network for Robust Robotic Perception in Challenging Visual Conditions","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Adversarial system; Low latency (capital markets); Perception; Encoding (memory); Perspective (graphical); Feature (linguistics); Latency (audio); Generative adversarial network; Object (grammar); Robot","score_opus":0.01676636026257652,"score_gpt":0.3066416635815305,"score_spread":0.289875303318954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416020257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060312815,0.0009535196,0.93060577,0.00029099084,0.00015371329,0.0000938691,0.00021675974,0.0026330068,0.0047394307],"genre_scores_gemma":[0.76582557,0.0005567592,0.22353792,0.0004197992,0.00004336138,0.000093273455,0.0006118216,0.00028172243,0.008629927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988174,0.000017711782,0.000002958759,0.00003297624,0.00004433344,0.000020186155],"domain_scores_gemma":[0.9998085,0.00007694243,0.000021912494,0.000039993567,0.000037299797,0.000015313499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003701822,0.00073247263,0.00039456828,0.00027815424,0.00015366274,0.00035676855,0.0010433397,0.0005519415,0.001430955],"category_scores_gemma":[0.00082643476,0.00022820839,0.00031188322,0.00014473563,0.00041528017,0.00066286005,0.0010072802,0.00093838247,0.00038158754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023844326,0.00013517319,0.0012558289,0.0001592122,0.00014746236,0.00021396246,0.00007502091,0.68714184,0.055756316,0.0050328583,0.006848716,0.24299528],"study_design_scores_gemma":[0.000007432535,0.00006955799,0.00027061353,0.00000911882,0.000010607463,0.00008996684,0.0000089700525,0.985843,0.010477521,0.001581057,0.0016224345,0.000009724798],"about_ca_topic_score_codex":0.0016987603,"about_ca_topic_score_gemma":0.0034898145,"teacher_disagreement_score":0.0016987603,"about_ca_system_score_codex":0.0003518942,"about_ca_system_score_gemma":0.00030780703,"threshold_uncertainty_score":0.004787028},"labels":[],"label_agreement":null},{"id":"W4416020268","doi":"10.1016/j.procs.2025.10.138","title":"Design, Modeling and Experimental Investigation for a Helix-based Cable-Driven Soft Continuum Robot","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Robot; Kinematics; Curvature; Adaptability; Constant curvature; Feed forward; Constant (computer programming); Control theory (sociology)","score_opus":0.023440962143405533,"score_gpt":0.24821949547718405,"score_spread":0.2247785333337785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416020268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3079327,0.00028613195,0.68219,0.00024746932,0.000060755414,0.00026325844,0.00014054035,0.001062988,0.007816175],"genre_scores_gemma":[0.91115165,0.00015382949,0.08554764,0.000025577781,0.00000720564,0.00017014565,0.00006820916,0.000018469655,0.0028573289],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997696,0.00003741727,0.000009973272,0.000038949347,0.00012508637,0.000018909293],"domain_scores_gemma":[0.99966884,0.000069702575,0.00009117916,0.000052687625,0.000085726155,0.00003191162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047546867,0.00030558513,0.000248214,0.0002673538,0.0002610208,0.0003082167,0.00050506624,0.0005115862,0.0014118126],"category_scores_gemma":[0.00041135136,0.00017331957,0.00023764344,0.00012308528,0.00038577736,0.00034278916,0.00030514377,0.00029953895,0.00040316893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000445371,0.0002748906,0.0031807562,0.00074276334,0.00003236929,0.0007431991,0.0005086054,0.33790383,0.5733326,0.009579379,0.0011421357,0.07211407],"study_design_scores_gemma":[0.00007414842,0.0026739547,0.0029817976,0.00004837692,0.00003434349,0.00036862394,0.00013779147,0.8359406,0.14679375,0.0009864498,0.0099106785,0.00004944538],"about_ca_topic_score_codex":0.0005691965,"about_ca_topic_score_gemma":0.0005523165,"teacher_disagreement_score":0.0014118126,"about_ca_system_score_codex":0.00025281002,"about_ca_system_score_gemma":0.0005902869,"threshold_uncertainty_score":0.0047230124},"labels":[],"label_agreement":null},{"id":"W4416613396","doi":"10.1016/j.procs.2025.10.318","title":"Computational aspects of disks enclosing many points","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia, Innovación y Universidades","keywords":"Constant (computer programming); Convex polygon; Geodesic; Regular polygon; Polygon (computer graphics); Set (abstract data type); Time complexity; Position (finance)","score_opus":0.010886475354295324,"score_gpt":0.2656692797546746,"score_spread":0.25478280440037926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416613396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41073033,0.002154263,0.54332674,0.0024898092,0.00016912368,0.0002888134,0.0009701667,0.0012106745,0.038660083],"genre_scores_gemma":[0.74386066,0.00051303866,0.24772343,0.00011320083,0.00010153104,0.0001884502,0.001135215,0.0002386534,0.006125822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99825007,0.0004745525,0.00011702018,0.00038567453,0.00057963043,0.00019301796],"domain_scores_gemma":[0.9888376,0.008074609,0.0008552363,0.0012580076,0.000589852,0.00038476294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013933948,0.0007710588,0.0013361692,0.002135334,0.0016366676,0.0027317214,0.0035397627,0.0019524801,0.007936687],"category_scores_gemma":[0.017605832,0.00081205624,0.0008617144,0.002254699,0.0024701438,0.004495148,0.004215438,0.0013288644,0.0011115549],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008754245,0.00013610466,0.010374862,0.000535284,0.000082896804,0.0005556426,0.0007473403,0.7158922,0.0040401216,0.18984658,0.0051068026,0.0718067],"study_design_scores_gemma":[0.00006825753,0.00005829469,0.0007729957,0.000032558335,0.000018211687,0.00017540829,0.0001858478,0.9158728,0.0017865226,0.07688472,0.004125271,0.000019164148],"about_ca_topic_score_codex":0.0046259863,"about_ca_topic_score_gemma":0.004604713,"teacher_disagreement_score":0.007936687,"about_ca_system_score_codex":0.0013988095,"about_ca_system_score_gemma":0.0007791422,"threshold_uncertainty_score":0.026550889},"labels":[],"label_agreement":null},{"id":"W4416640364","doi":"10.1016/j.procs.2025.10.182","title":"Impact of Resampling techniques in Deep Learning based Intrusion Detection: A Comparative Study on NSL-KDD and UNSW-NB15","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Undersampling; Resampling; Oversampling; Benchmark (surveying); Deep learning; Feature (linguistics); Class (philosophy); Intrusion detection system","score_opus":0.019642336160717325,"score_gpt":0.31274055747998925,"score_spread":0.29309822131927193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92016923,0.0059092673,0.06420799,0.0010797448,0.00039054127,0.00030726875,0.0012175569,0.002395481,0.0043229293],"genre_scores_gemma":[0.95744944,0.00085972366,0.037947513,0.00023059975,0.00005350446,0.00008441535,0.0024553584,0.00007091976,0.0008485358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974975,0.0008575235,0.000256991,0.00045750075,0.0007176202,0.00021286638],"domain_scores_gemma":[0.99507976,0.0024105904,0.00042910842,0.00095807266,0.0009013854,0.0002211117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053912015,0.0014753881,0.0011515586,0.0014538324,0.0005382805,0.00090100017,0.0014600528,0.0009591733,0.00046421925],"category_scores_gemma":[0.0126330415,0.00025943763,0.0007076876,0.0008660978,0.0008654947,0.0019204371,0.0013534655,0.0012378024,0.0002854461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028658996,0.0019615488,0.062747985,0.0007229146,0.0007242239,0.00032022718,0.00030366474,0.4608869,0.011385958,0.0030363363,0.008571203,0.44647306],"study_design_scores_gemma":[0.00008723455,0.0010986669,0.01084551,0.000084975014,0.00010303111,0.0001959441,0.00023536984,0.96592104,0.016848467,0.0018541559,0.0026814267,0.000044122018],"about_ca_topic_score_codex":0.007773417,"about_ca_topic_score_gemma":0.008724934,"teacher_disagreement_score":0.007773417,"about_ca_system_score_codex":0.0012305622,"about_ca_system_score_gemma":0.0010359209,"threshold_uncertainty_score":0.028511703},"labels":[],"label_agreement":null},{"id":"W4416640414","doi":"10.1016/j.procs.2025.10.187","title":"Governance-as-Code: Managing Agentic AI with a Distributed Dual Proxy Gateway","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Verifiable secret sharing; Core (optical fiber); Proxy (statistics); Workflow; Enhanced Data Rates for GSM Evolution; Overhead (engineering); Inference; Spec#; Negotiation","score_opus":0.0050060048554148385,"score_gpt":0.23429507020013032,"score_spread":0.2292890653447155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12408883,0.00018367796,0.838831,0.0007381081,0.00011248512,0.0003893808,0.00006658424,0.027478533,0.008111382],"genre_scores_gemma":[0.68406796,0.000118476426,0.30694634,0.00038166955,0.000035176545,0.00026117242,0.00024774054,0.0015117028,0.006429739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985385,0.00048724707,0.00009094205,0.00026422256,0.00041434943,0.00020468365],"domain_scores_gemma":[0.99579537,0.00056220725,0.00033378907,0.0023531907,0.00037631305,0.0005790966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024182529,0.00044761866,0.00034732727,0.0005860081,0.0006675155,0.0019870969,0.0016975712,0.00082350377,0.0014065298],"category_scores_gemma":[0.0056027495,0.0004853057,0.00039178622,0.00043116912,0.002337421,0.002867415,0.0042871474,0.0018923464,0.00067896396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001225302,0.001219326,0.036167823,0.00038982203,0.00021584572,0.0022986531,0.0059650163,0.21024348,0.11116452,0.28578877,0.019457143,0.32586437],"study_design_scores_gemma":[0.00015468552,0.00033526082,0.002528719,0.00007675979,0.00008730135,0.00035859662,0.00047764706,0.8158012,0.058042236,0.06553532,0.056510936,0.00009139902],"about_ca_topic_score_codex":0.0019997747,"about_ca_topic_score_gemma":0.0017992398,"teacher_disagreement_score":0.0024182529,"about_ca_system_score_codex":0.0009036422,"about_ca_system_score_gemma":0.002122856,"threshold_uncertainty_score":0.012789071},"labels":[],"label_agreement":null},{"id":"W4416640425","doi":"10.1016/j.procs.2025.10.171","title":"Preface","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Publicity; Domain (mathematical analysis); Wish; Track (disk drive); Technical report; Special Interest Group","score_opus":0.010974833357541607,"score_gpt":0.25911692007914333,"score_spread":0.24814208672160173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640425","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026374164,0.022034712,0.010270873,0.061945785,0.2213013,0.0010399106,0.021577727,0.0039080842,0.6552842],"genre_scores_gemma":[0.012702475,0.012027018,0.005879133,0.01532246,0.029454643,0.0008166754,0.021980647,0.0021212313,0.8996957],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979182,0.00037849345,0.0001755695,0.00042610877,0.00087920314,0.00022238135],"domain_scores_gemma":[0.9923941,0.0010542561,0.00032551723,0.0007197041,0.004337274,0.001169027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021144203,0.0013773016,0.001100981,0.002986406,0.0040743495,0.005820669,0.0021993741,0.002265709,0.45244747],"category_scores_gemma":[0.015820522,0.0004505601,0.0008206582,0.0027383962,0.0011612262,0.0049206004,0.0034657104,0.004209787,0.32633063],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003665519,0.000018197192,0.00012917255,0.00015361577,0.0000028492163,0.00006515614,0.00015761556,0.000055521883,0.000101549034,0.0038129329,0.96366495,0.031801857],"study_design_scores_gemma":[0.0000036891224,0.000014716954,0.00019342684,0.0001303766,0.0000016588784,0.000079981925,0.00015533985,0.000020938687,0.000054694727,0.0014600249,0.99787974,0.000005425721],"about_ca_topic_score_codex":0.0036062906,"about_ca_topic_score_gemma":0.0031561193,"teacher_disagreement_score":0.45244747,"about_ca_system_score_codex":0.0027138342,"about_ca_system_score_gemma":0.0036799335,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4416640444","doi":"10.1016/j.procs.2025.10.174","title":"Comparative Analysis of Scalable IoT Topologies for Optimal and Precise Greenhouse Environment Monitoring","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Brock University; Acadia University","funders":"","keywords":"Wireless sensor network; Software deployment; Scalability; Greenhouse; Internet of Things; Precision agriculture; Environmental monitoring; SIGNAL (programming language)","score_opus":0.021666896016462232,"score_gpt":0.2530099131010064,"score_spread":0.23134301708454416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9025647,0.0015613035,0.07575979,0.0005438794,0.00010491711,0.00028602532,0.0010367259,0.00050977,0.01763288],"genre_scores_gemma":[0.987751,0.00046916326,0.010664001,0.000016841952,0.000008289752,0.00006741698,0.0003899857,0.000027607672,0.0006058667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962246,0.00010478469,0.000021669208,0.000051192124,0.00014323108,0.000056587076],"domain_scores_gemma":[0.9977616,0.0012482222,0.00026330666,0.00018919508,0.00042316123,0.0001144107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081193977,0.000424638,0.00030531545,0.0009816273,0.00029611826,0.00062730705,0.00042498932,0.00028059407,0.0013962786],"category_scores_gemma":[0.0029463014,0.00015066506,0.00029189276,0.0007746975,0.00024743614,0.0011805424,0.00033433244,0.00020024473,0.00018253832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004069778,0.00014942036,0.010749468,0.00046430234,0.00006125075,0.00045505562,0.0001659298,0.8572672,0.036190677,0.008395691,0.002269755,0.08342432],"study_design_scores_gemma":[0.000031137162,0.0011247426,0.017681696,0.00008978255,0.000082857936,0.00033224458,0.001070795,0.9523695,0.015682463,0.006530758,0.0049636574,0.000040319086],"about_ca_topic_score_codex":0.0011694117,"about_ca_topic_score_gemma":0.0022161189,"teacher_disagreement_score":0.0013962786,"about_ca_system_score_codex":0.0007277878,"about_ca_system_score_gemma":0.00041499262,"threshold_uncertainty_score":0.0052804947},"labels":[],"label_agreement":null},{"id":"W4416640475","doi":"10.1016/j.procs.2025.10.214","title":"Securing Inclusive Digital Environments: An Adaptive Approach to ISO 27001 for Assistive Technologies in SMEs","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Digital Accessibility for Disabilities","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University of Edmonton","funders":"Mitacs","keywords":"Key (lock); Information security; Compliance (psychology); Data Protection Act 1998; Information security management; Balance (ability); Digital transformation; Information technology","score_opus":0.018752172870408517,"score_gpt":0.3036027183132024,"score_spread":0.28485054544279387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53272766,0.0009869847,0.18707205,0.022235772,0.00019044832,0.003894091,0.00028767096,0.0006772467,0.25192812],"genre_scores_gemma":[0.904619,0.00049472135,0.08514631,0.00064349,0.000014648941,0.00085671037,0.0001357771,0.000047055863,0.008042272],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98901284,0.004607412,0.0009959681,0.0006303386,0.0038921463,0.00086130085],"domain_scores_gemma":[0.9907809,0.0027294545,0.0009264409,0.00080160494,0.004229137,0.00053241174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013276661,0.00044412122,0.00027545897,0.0023673512,0.003241143,0.005595563,0.0019877797,0.002125845,0.0012183401],"category_scores_gemma":[0.018087853,0.00024543828,0.0004075158,0.0020015256,0.0034447957,0.0025614752,0.004584789,0.0018782637,0.0003491532],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020697813,0.0009230744,0.05851256,0.0013450854,0.000041564777,0.0048552277,0.15171364,0.013559517,0.03534409,0.29649553,0.012930929,0.4240718],"study_design_scores_gemma":[0.00007956818,0.0011270465,0.114788316,0.0040524397,0.00017203626,0.0027748344,0.30838022,0.0351639,0.02836247,0.09029592,0.41439578,0.00040750683],"about_ca_topic_score_codex":0.03255349,"about_ca_topic_score_gemma":0.05832521,"teacher_disagreement_score":0.03255349,"about_ca_system_score_codex":0.011549524,"about_ca_system_score_gemma":0.027790638,"threshold_uncertainty_score":0.08379799},"labels":[],"label_agreement":null},{"id":"W4416640544","doi":"10.1016/j.procs.2025.10.206","title":"ESLS: A Vision-Based Emergency Safe Landing System for UAVs","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Chicoutimi","funders":"National Research Council Canada","keywords":"Software deployment; Inertial measurement unit; Search and rescue; Identification (biology); Descent (aeronautics); Range (aeronautics); Simultaneous localization and mapping","score_opus":0.006764661692847643,"score_gpt":0.23990986598064262,"score_spread":0.23314520428779498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28571004,0.00055358704,0.6473543,0.0003573906,0.0003172901,0.0003509122,0.0012603487,0.048010208,0.016085954],"genre_scores_gemma":[0.85570675,0.00015472458,0.13146664,0.00030803247,0.000053363798,0.0002443742,0.0021405185,0.00025916682,0.009666369],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998424,0.000017207609,0.000008214596,0.00003383808,0.0000675377,0.00003075421],"domain_scores_gemma":[0.9998964,0.000010854169,0.000015565236,0.000016460703,0.00003726738,0.000023319262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015528561,0.0004310045,0.00030310283,0.0004129406,0.00022229127,0.0003114654,0.00055624975,0.0003160281,0.0029146522],"category_scores_gemma":[0.00031095243,0.0001463506,0.00017846945,0.00014496119,0.00019466478,0.00041778327,0.0009690302,0.0003752935,0.0014047079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013524116,0.00044508363,0.008397219,0.00037816248,0.000106956046,0.001059573,0.0006516327,0.04771545,0.35018563,0.0037203028,0.03843885,0.5475487],"study_design_scores_gemma":[0.00046748773,0.0020202145,0.019976392,0.00011296525,0.00007166046,0.0011390027,0.0003317948,0.79553646,0.10953096,0.0018523808,0.06882438,0.00013633854],"about_ca_topic_score_codex":0.0023494612,"about_ca_topic_score_gemma":0.0018532878,"teacher_disagreement_score":0.0029146522,"about_ca_system_score_codex":0.00021636965,"about_ca_system_score_gemma":0.00042375285,"threshold_uncertainty_score":0.009750426},"labels":[],"label_agreement":null},{"id":"W4416640577","doi":"10.1016/j.procs.2025.10.178","title":"Sleep, Neuromodulation, and Avoiding Forgetting","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Sleep and Wakefulness Research","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Acadia University","keywords":"Forgetting; Construct (python library); Obstacle; Associative property; Work (physics)","score_opus":0.024082342175578468,"score_gpt":0.29530126899047,"score_spread":0.2712189268148915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08960117,0.0029730687,0.9013125,0.0011992276,0.00024729082,0.000035406374,0.00008111118,0.00080763723,0.0037426199],"genre_scores_gemma":[0.94368047,0.0018568723,0.051651407,0.00028742923,0.00015093823,0.00005422212,0.00009284989,0.00008158488,0.0021443127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99972945,0.000051623763,0.000028215121,0.00007530119,0.00007493419,0.00004044097],"domain_scores_gemma":[0.9984699,0.00059733243,0.00028158794,0.0003643678,0.00020548611,0.00008128471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082384056,0.0007554902,0.0006261685,0.00038220518,0.00035386253,0.0007630555,0.0012728561,0.00073196256,0.0009469832],"category_scores_gemma":[0.0042948015,0.00028984676,0.0006464057,0.00022486188,0.0015463524,0.0017815739,0.001247584,0.0012276892,0.00021772746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003723227,0.0001605517,0.0052944687,0.0007604233,0.0003706154,0.0004467645,0.00031080307,0.56140256,0.036867093,0.13446851,0.003039307,0.25650665],"study_design_scores_gemma":[0.000028977469,0.00035659707,0.0014878908,0.00008611211,0.000080190475,0.00028653248,0.000039620274,0.8064682,0.01307635,0.17517371,0.0028661364,0.000049678794],"about_ca_topic_score_codex":0.0012748089,"about_ca_topic_score_gemma":0.0014789265,"teacher_disagreement_score":0.0012748089,"about_ca_system_score_codex":0.00033355883,"about_ca_system_score_gemma":0.00064254954,"threshold_uncertainty_score":0.0043569207},"labels":[],"label_agreement":null},{"id":"W4416640682","doi":"10.1016/j.procs.2025.10.199","title":"TempHypE-GNN: Hyperbolic Graph Neural ODEs for Hierarchical Temporal Knowledge Graphs","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ode; Ordinary differential equation; Euclidean geometry; Graph; Artificial neural network; Knowledge graph; Hyperbolic geometry","score_opus":0.014853810731522685,"score_gpt":0.2836115977305752,"score_spread":0.2687577869990525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025149075,0.0008377328,0.95410377,0.00075468933,0.00019468874,0.000118860204,0.0030891355,0.0080811065,0.007670976],"genre_scores_gemma":[0.48739353,0.0009949214,0.47928262,0.001092523,0.000119681994,0.00029098982,0.012972953,0.0011637318,0.016689152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999708,0.00004748025,0.000019322584,0.00011930275,0.000077144745,0.000028738472],"domain_scores_gemma":[0.9994766,0.0001949773,0.000050149007,0.00012723244,0.00011730842,0.00003364399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005213127,0.0011475082,0.0006815974,0.00084050803,0.00038250754,0.0012114085,0.002414192,0.0012505042,0.0044036885],"category_scores_gemma":[0.0031909083,0.0005643038,0.0012326791,0.00082173664,0.0006099676,0.0025141742,0.0015681649,0.0018583994,0.0014451665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000760032,0.000053068245,0.0012441851,0.00016779272,0.00009483119,0.00012957327,0.00007988208,0.81664604,0.0024177274,0.026508467,0.011385654,0.14119676],"study_design_scores_gemma":[0.000004127006,0.0000071719655,0.00007629296,0.0000055981977,0.000004445138,0.000013987483,0.000006225172,0.987038,0.00039166588,0.011175776,0.0012725047,0.0000041284666],"about_ca_topic_score_codex":0.023452701,"about_ca_topic_score_gemma":0.040763065,"teacher_disagreement_score":0.023452701,"about_ca_system_score_codex":0.0016944227,"about_ca_system_score_gemma":0.0011741695,"threshold_uncertainty_score":0.04663235},"labels":[],"label_agreement":null},{"id":"W4416640732","doi":"10.1016/j.procs.2025.10.213","title":"RAG pipeline for private well contamination guidance: A comparative study of retrieval and generation strategies","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Centre Intégré de Santé et Services Sociaux de Chaudière-Appalache; Université du Québec à Rimouski","funders":"Mitacs","keywords":"Pipeline (software); Key (lock); Embedding; Variety (cybernetics); Threat model","score_opus":0.032369744556276996,"score_gpt":0.3133700143097117,"score_spread":0.2810002697534347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640732","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6204523,0.008545158,0.24799965,0.0018850559,0.00043641235,0.001724718,0.0045264172,0.08852263,0.0259077],"genre_scores_gemma":[0.8047613,0.0010634874,0.17898528,0.0004849451,0.00007442568,0.00024226731,0.006354166,0.0010479601,0.0069861338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967818,0.0016238034,0.00019435429,0.00052247924,0.00065723126,0.0002202188],"domain_scores_gemma":[0.9904365,0.0066453926,0.00028081567,0.0012214968,0.0011290902,0.00028673987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047798157,0.001218509,0.00094973046,0.0015695258,0.0005366144,0.0015040003,0.0014785926,0.0016757159,0.0061378884],"category_scores_gemma":[0.017882599,0.00032354565,0.00069498376,0.0007782825,0.00062974595,0.0032375047,0.0016087382,0.0011917856,0.00370156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033299292,0.0014210773,0.009756068,0.002920626,0.0002722093,0.0006095045,0.0021984421,0.06694966,0.041029144,0.0049195657,0.037161857,0.8294319],"study_design_scores_gemma":[0.00073208247,0.004737653,0.009185498,0.0002179129,0.00036885953,0.001114836,0.0023331365,0.85197127,0.06145754,0.0074236616,0.060177725,0.0002797323],"about_ca_topic_score_codex":0.008005137,"about_ca_topic_score_gemma":0.007260945,"teacher_disagreement_score":0.008005137,"about_ca_system_score_codex":0.000963534,"about_ca_system_score_gemma":0.0015211665,"threshold_uncertainty_score":0.02527839},"labels":[],"label_agreement":null},{"id":"W4416640766","doi":"10.1016/j.procs.2025.10.175","title":"A New Biotechnology Era: Computer Science Enabled Solutions to Environmental Challenges in Agriculture","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Bioeconomy and Sustainability Development","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Acadia University","funders":"","keywords":"Sustainability; Agriculture; Analytics; Productivity; Greenhouse gas; CRISPR; Greenhouse; Ecological footprint","score_opus":0.012869754916233751,"score_gpt":0.19924931349787323,"score_spread":0.1863795585816395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07260988,0.2065591,0.39794552,0.10869955,0.0042150435,0.00015203873,0.00063991616,0.002849052,0.20632996],"genre_scores_gemma":[0.56055087,0.21946529,0.16622369,0.015905175,0.0034889122,0.00019181806,0.00061444973,0.00042988942,0.033129934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995023,0.00012679673,0.000022915505,0.00007965633,0.00018479984,0.000083545776],"domain_scores_gemma":[0.99847513,0.00086369074,0.000102823826,0.00020441064,0.0001836697,0.00017028031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011928759,0.0004827579,0.00032927416,0.0014345556,0.0007369705,0.0034050778,0.00066741084,0.002031363,0.0061442647],"category_scores_gemma":[0.0025031525,0.00022643285,0.0003941493,0.001787724,0.001995252,0.008493287,0.002375409,0.0025170674,0.0013672516],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010895054,0.00009598818,0.0016526039,0.0007809215,0.000049855797,0.00023339658,0.0005121477,0.0074128523,0.01271854,0.52207094,0.03617359,0.41819018],"study_design_scores_gemma":[0.0000148196195,0.00011132356,0.00089099596,0.00046211912,0.000022549257,0.0003704188,0.00062224024,0.013598409,0.005817788,0.3815569,0.5964965,0.000035961242],"about_ca_topic_score_codex":0.00047808004,"about_ca_topic_score_gemma":0.0006510504,"teacher_disagreement_score":0.0061442647,"about_ca_system_score_codex":0.001186797,"about_ca_system_score_gemma":0.0013089104,"threshold_uncertainty_score":0.020554602},"labels":[],"label_agreement":null},{"id":"W4416640815","doi":"10.1016/j.procs.2025.10.217","title":"A Review on Sensor-based HAR Models Using GNN: AI in Healthcare","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Harrison McCain Foundation","keywords":"Transformative learning; Health care; Workspace; Healthcare system; Health professionals; Deep learning; Healthcare industry","score_opus":0.06821368816659576,"score_gpt":0.3385507640499174,"score_spread":0.2703370758833217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640815","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00085980975,0.9680706,0.025324617,0.0010465146,0.00069860753,0.000035300305,0.0001281942,0.000100907244,0.0037353172],"genre_scores_gemma":[0.009260042,0.9788785,0.009298696,0.0005291751,0.0005971202,0.0000547575,0.00020620185,0.000026780855,0.0011488363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964666,0.00009950894,0.000057471916,0.00008171289,0.00009756166,0.000017093716],"domain_scores_gemma":[0.99844813,0.0011408312,0.00009321481,0.00004587719,0.00024264611,0.000029267689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009073742,0.0012669107,0.0011180354,0.0015992998,0.00021886386,0.0012733502,0.001434751,0.0013215232,0.002545306],"category_scores_gemma":[0.0029685928,0.00049675396,0.000932728,0.0024385971,0.00046103288,0.0015402252,0.00062469085,0.001068824,0.0014670395],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008087569,0.00008149975,0.0008777923,0.027896833,0.00031740652,0.00023307276,0.00016256349,0.019176975,0.0016309419,0.01923379,0.02201397,0.90829426],"study_design_scores_gemma":[0.00001966052,0.00032467296,0.0025131565,0.017897336,0.00077023066,0.0012341673,0.00020486282,0.038340673,0.0021879582,0.03181276,0.9045428,0.00015167726],"about_ca_topic_score_codex":0.002792193,"about_ca_topic_score_gemma":0.0029858567,"teacher_disagreement_score":0.002792193,"about_ca_system_score_codex":0.0006253345,"about_ca_system_score_gemma":0.0012292385,"threshold_uncertainty_score":0.008514881},"labels":[],"label_agreement":null},{"id":"W570564009","doi":"10.1016/j.procs.2015.05.042","title":"ARCUN: Analytical Approach towards Reliability with Cooperation for Underwater WSNs","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Computer network; Routing protocol; Reliability (semiconductor); Wireless sensor network; Network packet; Throughput; Transmission (telecommunications); Channel (broadcasting); Routing (electronic design automation); Underwater acoustic communication; Underwater; Distributed computing; Wireless; Telecommunications; Power (physics)","score_opus":0.0432752474107003,"score_gpt":0.24887459161490294,"score_spread":0.20559934420420264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W570564009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002795281,0.0010470719,0.98941505,0.00034153013,0.00009692143,0.000033104978,0.000023441478,0.00012606109,0.0061214534],"genre_scores_gemma":[0.66573435,0.008697679,0.30374682,0.00068556226,0.00076431956,0.0005447254,0.00014681695,0.00041436055,0.019265272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993864,0.00021498525,0.000022336482,0.00007488884,0.00023775395,0.00006361592],"domain_scores_gemma":[0.99895775,0.0005148013,0.00012652621,0.00009287664,0.00027384504,0.000034132427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013056882,0.0010364797,0.00067164196,0.0008879056,0.00045528685,0.0007219377,0.0018350466,0.0007224057,0.002387968],"category_scores_gemma":[0.0032937857,0.00035792292,0.0008805911,0.0007135607,0.0008827005,0.0018001015,0.0013088805,0.0013639838,0.00054462376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024287921,0.000038750597,0.00038628632,0.0002597941,0.00005245631,0.00019824729,0.00023238386,0.7534723,0.003306065,0.21011522,0.0034084693,0.028505713],"study_design_scores_gemma":[0.0000019202996,0.000022434728,0.00004962484,0.000019505389,0.000008826405,0.000051625248,0.000027364671,0.97220707,0.00036474224,0.024630848,0.0026083256,0.00000768731],"about_ca_topic_score_codex":0.0025909892,"about_ca_topic_score_gemma":0.0014985193,"teacher_disagreement_score":0.0025909892,"about_ca_system_score_codex":0.0010624323,"about_ca_system_score_gemma":0.0009858763,"threshold_uncertainty_score":0.007988572},"labels":[],"label_agreement":null},{"id":"W590842113","doi":"10.1016/j.procs.2015.05.143","title":"Overload Management in Transmission System Using Particle Swarm Optimization","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Electric Power System Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Particle swarm optimization; Mathematical optimization; Transmission (telecommunications); Algorithm; Telecommunications; Mathematics","score_opus":0.016348471620368184,"score_gpt":0.21996243611533692,"score_spread":0.20361396449496874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W590842113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017959906,0.00043318793,0.9789083,0.00018927846,0.000074699514,0.000042772914,0.000019556515,0.0002149932,0.002157266],"genre_scores_gemma":[0.83568114,0.0007521007,0.1591543,0.00008889053,0.00015725454,0.00014575438,0.00009221062,0.000047400456,0.0038808887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998411,0.00005051729,0.000011655722,0.000024544439,0.000054104286,0.0000180276],"domain_scores_gemma":[0.9998776,0.000037114023,0.000027765696,0.000010592416,0.000036940502,0.000010033289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031350195,0.00066160347,0.0007636925,0.0003755621,0.00041048875,0.0005971108,0.00046720466,0.0006516193,0.00070175395],"category_scores_gemma":[0.00042212682,0.00023235893,0.0005663189,0.00038819725,0.00025555916,0.000556333,0.00044156893,0.00056922686,0.0001237502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039578168,0.000052476524,0.00064820354,0.000067141154,0.00006244943,0.00009895377,0.00006250449,0.9379592,0.0047109937,0.0052680685,0.0011083147,0.049922243],"study_design_scores_gemma":[0.000006090916,0.000026205586,0.00012822665,0.0000021501935,0.000008335496,0.000012387535,0.000004706439,0.998417,0.00029177946,0.0007035735,0.00039607956,0.0000034291052],"about_ca_topic_score_codex":0.0025667287,"about_ca_topic_score_gemma":0.0019073698,"teacher_disagreement_score":0.0025667287,"about_ca_system_score_codex":0.00029185828,"about_ca_system_score_gemma":0.0004218694,"threshold_uncertainty_score":0.0051036477},"labels":[],"label_agreement":null},{"id":"W597090566","doi":"10.1016/j.procs.2015.05.157","title":"A Fuzzy Decision Tree for Processing Satellite Images and Landsat Data","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Western University","funders":"","keywords":"Computer science; Decision tree; Satellite; Fuzzy logic; Remote sensing; Tree (set theory); Data mining; Artificial intelligence; Geology","score_opus":0.0646452796961345,"score_gpt":0.28928343866312173,"score_spread":0.22463815896698724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W597090566","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006380232,0.00027457307,0.99160635,0.00006779019,0.000050364615,0.000079862504,0.00012811647,0.0002905778,0.0011221276],"genre_scores_gemma":[0.10867097,0.00041014905,0.88870996,0.00007429939,0.000048557686,0.00020685518,0.00045011297,0.000027265893,0.0014017341],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992061,0.00019738803,0.000064899075,0.000113812275,0.0003707144,0.000047116755],"domain_scores_gemma":[0.9993888,0.00027889124,0.000043428336,0.00002945264,0.0002314832,0.000027807813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011971662,0.00055283925,0.000548051,0.0012122523,0.0005853786,0.0007773719,0.0008536003,0.0006391086,0.0018548277],"category_scores_gemma":[0.0023978117,0.00017104982,0.00090660155,0.0016431115,0.00023643071,0.00092811696,0.00035297323,0.000690274,0.0005696624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019916006,0.0001843562,0.0023652953,0.0003867826,0.00012176581,0.00021798527,0.00014687635,0.1985662,0.01450596,0.0223465,0.0053915433,0.75556755],"study_design_scores_gemma":[0.000015822012,0.000099299985,0.0007391798,0.000033359203,0.000027552056,0.000100349076,0.000030087216,0.98056215,0.0041158916,0.00848725,0.005772794,0.000016230293],"about_ca_topic_score_codex":0.0047442857,"about_ca_topic_score_gemma":0.003855448,"teacher_disagreement_score":0.0047442857,"about_ca_system_score_codex":0.0005861882,"about_ca_system_score_gemma":0.000982745,"threshold_uncertainty_score":0.009433329},"labels":[],"label_agreement":null},{"id":"W624337328","doi":"10.1016/j.procs.2015.05.024","title":"A Multidimensional Approach towards a Quantitative Assessment of Security Threats","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Information and Cyber Security","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer security","score_opus":0.05047732725231387,"score_gpt":0.3278531406094166,"score_spread":0.27737581335710276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W624337328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073371483,0.0007210323,0.97583926,0.0016393563,0.00009138241,0.00031328652,0.00019529786,0.00018214372,0.01368108],"genre_scores_gemma":[0.21610749,0.0009848052,0.77914065,0.00033528937,0.00011633875,0.0008833624,0.00023107749,0.000043650212,0.002157346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9894693,0.0051413625,0.0009998812,0.0010227077,0.0030298377,0.0003369009],"domain_scores_gemma":[0.9860254,0.0068146456,0.0022012321,0.0017437043,0.0026949234,0.00052008213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01011358,0.0021752992,0.0009749199,0.008617813,0.0013572979,0.0073132874,0.002339281,0.0018660297,0.0029984142],"category_scores_gemma":[0.017904134,0.0006930947,0.0022189398,0.0048269928,0.004789911,0.010265893,0.0043125395,0.0035356781,0.00050373515],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004418601,0.00026760672,0.006058676,0.00058085925,0.00017040904,0.00029455303,0.0031769995,0.03859924,0.0032528206,0.86355984,0.0027641486,0.08123059],"study_design_scores_gemma":[0.00002810931,0.0002976043,0.0037504898,0.00048132724,0.00010700737,0.00045424837,0.0030450162,0.1859966,0.0015170345,0.7765281,0.027644262,0.00015018757],"about_ca_topic_score_codex":0.0025273701,"about_ca_topic_score_gemma":0.0016882971,"teacher_disagreement_score":0.01011358,"about_ca_system_score_codex":0.0034477683,"about_ca_system_score_gemma":0.0030838007,"threshold_uncertainty_score":0.053486347},"labels":[],"label_agreement":null},{"id":"W641618483","doi":"10.1016/j.procs.2015.05.027","title":"Analysis on the Effect of Adopting Green SLA on Optical WDM Networks","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Wavelength-division multiplexing; Telecommunications; Computer network; Optoelectronics; Wavelength","score_opus":0.011429645940956044,"score_gpt":0.2266874865555787,"score_spread":0.21525784061462264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W641618483","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93434495,0.0012525477,0.047222555,0.0005333343,0.000098110104,0.000060824023,0.00013341435,0.00017654314,0.016177697],"genre_scores_gemma":[0.99738234,0.00027414112,0.0017800882,0.000029670697,0.000011063935,0.000006161766,0.000021268404,0.000009806852,0.00048534063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989496,0.000384038,0.000021948861,0.000080785285,0.000273894,0.00028986495],"domain_scores_gemma":[0.99484825,0.003875088,0.0004053906,0.00020925434,0.00056670065,0.00009544759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015220736,0.00037009522,0.00031565412,0.00045906976,0.0004393141,0.0007112311,0.0004938158,0.00035617978,0.0020120218],"category_scores_gemma":[0.004508904,0.00013081207,0.00044531072,0.00052406883,0.00054089096,0.0010406178,0.00039936593,0.00049462845,0.0001260259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044015484,0.00032210603,0.011869921,0.00031604528,0.00010264383,0.0003902223,0.00011246996,0.8780063,0.0348637,0.01737882,0.0010837282,0.055113822],"study_design_scores_gemma":[0.000014497536,0.00060554204,0.012064719,0.000038469287,0.00013669798,0.00013320247,0.00037809362,0.96169347,0.018576745,0.004618173,0.0017108415,0.000029516643],"about_ca_topic_score_codex":0.003378834,"about_ca_topic_score_gemma":0.0031514617,"teacher_disagreement_score":0.003378834,"about_ca_system_score_codex":0.0012854725,"about_ca_system_score_gemma":0.0005377987,"threshold_uncertainty_score":0.009326816},"labels":[],"label_agreement":null},{"id":"W7104265591","doi":"10.1016/j.procs.2025.09.508","title":"Enhancing veterinary education through the Langvet-IA Web Platform using AI","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Suite; Key (lock); Web application; Educational technology; E learning; Personalization; Interactive Learning; Open platform","score_opus":0.028584324753170756,"score_gpt":0.3495513235934307,"score_spread":0.32096699884026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104265591","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32418054,0.00094111083,0.5236778,0.0012294832,0.00033501312,0.0018000392,0.0007710215,0.044915047,0.102149785],"genre_scores_gemma":[0.55172354,0.0007471901,0.38742372,0.00056177605,0.00016287455,0.0010940363,0.0016448179,0.0015077025,0.055134267],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994599,0.0001474267,0.00003149784,0.000074118994,0.0001870416,0.00009997905],"domain_scores_gemma":[0.9986665,0.00056072057,0.0000874801,0.00014904143,0.00020303897,0.0003332693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007346649,0.00046926874,0.0002661034,0.0009559823,0.00040170364,0.0017143632,0.00078912906,0.00070164935,0.00875445],"category_scores_gemma":[0.0021923198,0.00015956587,0.00041452312,0.0003634208,0.00030729244,0.0017473225,0.002639306,0.0008235172,0.003689365],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011264607,0.004163517,0.007372174,0.002200337,0.000059989212,0.0020396372,0.0051333425,0.006457169,0.18304797,0.013557893,0.019599222,0.7552423],"study_design_scores_gemma":[0.00048119423,0.004231,0.05412136,0.0014649035,0.00020653642,0.0052345917,0.0039445884,0.1441134,0.18061481,0.031134684,0.5740014,0.0004515789],"about_ca_topic_score_codex":0.00051595864,"about_ca_topic_score_gemma":0.0007197044,"teacher_disagreement_score":0.00875445,"about_ca_system_score_codex":0.0003208077,"about_ca_system_score_gemma":0.00081773795,"threshold_uncertainty_score":0.029286563},"labels":[],"label_agreement":null},{"id":"W7106501654","doi":"10.1016/j.procs.2025.10.337","title":"Cops and Robbers on Token Graphs","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; British Columbia Institute of Technology","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Universidad Nacional Autónoma de México; Consejo Nacional de Ciencia y Tecnología","keywords":"Graph; Vertex (graph theory); Security token; Set (abstract data type); Integer (computer science)","score_opus":0.010810462332243493,"score_gpt":0.28424886563037566,"score_spread":0.2734384032981322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106501654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.376257,0.00030854924,0.5912141,0.0005488017,0.00010391567,0.0001998282,0.00045105067,0.0003455464,0.030571083],"genre_scores_gemma":[0.8894248,0.0003044405,0.088402405,0.00014495378,0.00006445278,0.00030075447,0.00046525116,0.00020395647,0.020689],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998379,0.0005243443,0.00007011434,0.00040918365,0.0002694358,0.0003479285],"domain_scores_gemma":[0.9962251,0.0016565056,0.00069355697,0.00038414248,0.00021882373,0.00082184724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009879274,0.0009493551,0.00097234006,0.0009897646,0.0012054611,0.0017446597,0.0013950744,0.0009497807,0.009624385],"category_scores_gemma":[0.0058315373,0.0004952904,0.0007088258,0.00072659174,0.0022850013,0.004539774,0.0019480676,0.0013174383,0.0007472289],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003103899,0.00006629395,0.0012984583,0.0001275643,0.00004020122,0.00034321923,0.00033564994,0.061689526,0.003327385,0.9171847,0.0028301983,0.012446469],"study_design_scores_gemma":[0.000059703732,0.00015501586,0.0010580217,0.00003890891,0.000034201494,0.00034154448,0.0003627436,0.25595516,0.0026659623,0.7287329,0.01055712,0.000038739854],"about_ca_topic_score_codex":0.0016459376,"about_ca_topic_score_gemma":0.0014976464,"teacher_disagreement_score":0.009624385,"about_ca_system_score_codex":0.0015579179,"about_ca_system_score_gemma":0.0006846879,"threshold_uncertainty_score":0.03219682},"labels":[],"label_agreement":null},{"id":"W7106608427","doi":"10.1016/j.procs.2025.10.265","title":"Transformer-based Human Action Recognition using Skeleton Heatmap","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Human skeleton; Skeleton (computer programming); Pipeline (software); Benchmark (surveying); Activity recognition; Generalization; Pattern recognition (psychology); Biometrics; Gaussian","score_opus":0.0592634990099064,"score_gpt":0.3298153361630602,"score_spread":0.2705518371531538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106608427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09151926,0.0009004826,0.87395847,0.00032812747,0.0003447872,0.00029502637,0.0044549606,0.021670267,0.0065285787],"genre_scores_gemma":[0.7811953,0.0006242434,0.1918477,0.00032590394,0.00015215622,0.000235016,0.0114331115,0.000602489,0.01358409],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963474,0.000049815055,0.000013845771,0.00016595883,0.0000862868,0.000049415135],"domain_scores_gemma":[0.99976724,0.000058859932,0.000017573344,0.00006655719,0.000063518404,0.000026268644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046206018,0.0012216932,0.00073858415,0.00091031974,0.00020964908,0.00059462304,0.00097613264,0.000467654,0.004704336],"category_scores_gemma":[0.0014629742,0.0002738258,0.0010020961,0.0007445705,0.00039835187,0.00070099335,0.00091724447,0.0007255016,0.0028885165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058369606,0.00027354938,0.0053166207,0.00013701471,0.00014598024,0.00016634511,0.00008301609,0.10571337,0.023020815,0.0016765499,0.023516886,0.8393662],"study_design_scores_gemma":[0.000026971924,0.00013398597,0.005424044,0.000018824408,0.00003322194,0.0002410846,0.000038084785,0.9729077,0.012874975,0.004223656,0.0040494422,0.000028033153],"about_ca_topic_score_codex":0.007332778,"about_ca_topic_score_gemma":0.013728607,"teacher_disagreement_score":0.007332778,"about_ca_system_score_codex":0.00044718734,"about_ca_system_score_gemma":0.0006012096,"threshold_uncertainty_score":0.015737534},"labels":[],"label_agreement":null},{"id":"W7106612763","doi":"10.1016/j.procs.2025.10.207","title":"Multi-label classification of evolving psychosocial concerns using prompt-based large language models","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mental Health via Writing","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Rimouski","funders":"","keywords":"Subcategory; Task (project management); Language model; Psychosocial; Language understanding","score_opus":0.11091024226595116,"score_gpt":0.43686324850655106,"score_spread":0.3259530062405999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106612763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44138566,0.0018468195,0.46098846,0.005093983,0.0014733456,0.0013817075,0.02778393,0.045036737,0.015009447],"genre_scores_gemma":[0.7300889,0.00038887464,0.23629913,0.0010482592,0.00024142668,0.0007017637,0.021079501,0.0007036475,0.009448623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987219,0.0005469228,0.00009356835,0.0003472018,0.00022429852,0.0000660514],"domain_scores_gemma":[0.9917665,0.004999731,0.00068720744,0.0006948937,0.0014114897,0.00044011252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017040864,0.0013302636,0.00038795976,0.0014774428,0.00045971217,0.001434775,0.0010685144,0.001256548,0.0051860414],"category_scores_gemma":[0.011915611,0.0002082636,0.00071748905,0.0007831044,0.00038651525,0.0026218772,0.0017752816,0.0021141334,0.0038110693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012691315,0.0009205231,0.058687642,0.0014190181,0.00009801576,0.0017702881,0.0062174695,0.014074913,0.037808184,0.006515002,0.08447417,0.78674567],"study_design_scores_gemma":[0.0002355006,0.0010686642,0.040418006,0.0005485481,0.00017927273,0.0020714363,0.009081562,0.7053279,0.05420944,0.07337244,0.11318158,0.00030563975],"about_ca_topic_score_codex":0.0017050593,"about_ca_topic_score_gemma":0.004698051,"teacher_disagreement_score":0.0051860414,"about_ca_system_score_codex":0.0009335908,"about_ca_system_score_gemma":0.0014538047,"threshold_uncertainty_score":0.017349064},"labels":[],"label_agreement":null},{"id":"W7106621733","doi":"10.1016/j.procs.2025.10.197","title":"NEMESIS: An Enhanced Hybrid Intrusion Detection System Leveraging Deep Q-Learning and Random Forest","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Intrusion detection system; Exploit; Hyperparameter; Reinforcement learning; Face (sociological concept); Deep learning; Network security","score_opus":0.005247709973411006,"score_gpt":0.2151965004196633,"score_spread":0.20994879044625228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106621733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088811964,0.0011459504,0.8756533,0.00072903937,0.00028271714,0.0003190537,0.0006258053,0.029003914,0.0034282736],"genre_scores_gemma":[0.6844855,0.00035160052,0.30673283,0.000893097,0.00009300162,0.00024339474,0.0011066225,0.00026394334,0.0058299783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968064,0.00006325822,0.000017689856,0.00009113815,0.00010821459,0.000039152193],"domain_scores_gemma":[0.9995395,0.00017042866,0.000052135827,0.00007115583,0.00012124221,0.00004546461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001218205,0.0007099271,0.0008636967,0.00080357963,0.00030988822,0.0005985441,0.0018793144,0.00094618823,0.0017489417],"category_scores_gemma":[0.0018933762,0.0003057212,0.0005147004,0.00041340123,0.0003923618,0.001517239,0.0011169858,0.00095904025,0.00073727255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010100681,0.0012789089,0.013411377,0.00029885504,0.00047127265,0.0005477512,0.00013955866,0.34409785,0.027163763,0.007270632,0.02188225,0.58242774],"study_design_scores_gemma":[0.000029009725,0.000097090786,0.0003883028,0.000004758869,0.000014855712,0.000056390792,0.0000038957987,0.99383587,0.0025574286,0.0015021133,0.001499863,0.000010444327],"about_ca_topic_score_codex":0.003601248,"about_ca_topic_score_gemma":0.004880315,"teacher_disagreement_score":0.003601248,"about_ca_system_score_codex":0.0005956778,"about_ca_system_score_gemma":0.0009398292,"threshold_uncertainty_score":0.007160604},"labels":[],"label_agreement":null},{"id":"W7106626838","doi":"10.1016/j.procs.2025.10.208","title":"Smart Data Transmission in IoT: AI Applications for Health and Air Quality Monitoring","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Software deployment; Data transmission; Transmission (telecommunications); Relevance (law); Volume (thermodynamics); Health care; Internet of Things; Resource allocation; Network congestion; Air quality index","score_opus":0.029732261525263377,"score_gpt":0.3364411516580445,"score_spread":0.30670889013278113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106626838","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046765544,0.005258114,0.92887264,0.0017363663,0.0005899969,0.0001469881,0.00021219139,0.0019231411,0.0144950915],"genre_scores_gemma":[0.76378053,0.005694735,0.21870139,0.0009867321,0.00057288393,0.00014370531,0.00035156286,0.00018168808,0.009586688],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998209,0.000030964256,0.000014965057,0.00003101249,0.00008943875,0.00001280614],"domain_scores_gemma":[0.99969053,0.000120117154,0.000034954955,0.000042261385,0.00009578959,0.000016415303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027702894,0.00037116124,0.00024956686,0.00046143075,0.0002586268,0.0006142269,0.00044312046,0.0005606774,0.002297453],"category_scores_gemma":[0.0007936523,0.000113941,0.0002101943,0.0006934375,0.00027606104,0.00084230275,0.0004055964,0.0003915917,0.00056517415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038152945,0.00024004153,0.0038857239,0.0005370185,0.000053249874,0.0005695739,0.00025654203,0.04101562,0.16181009,0.014267996,0.01154316,0.7654394],"study_design_scores_gemma":[0.00006632734,0.00039159134,0.0061010853,0.0001627811,0.00011551333,0.0015332971,0.00032771865,0.786918,0.10678525,0.026245428,0.0712715,0.00008150858],"about_ca_topic_score_codex":0.0005573943,"about_ca_topic_score_gemma":0.0007408325,"teacher_disagreement_score":0.002297453,"about_ca_system_score_codex":0.00022214952,"about_ca_system_score_gemma":0.00021497812,"threshold_uncertainty_score":0.0076857805},"labels":[],"label_agreement":null},{"id":"W7106634211","doi":"10.1016/j.procs.2025.10.196","title":"A Comparative Study to Feature Selection for Network Security, By using Deep Learning as an Embedded Model","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski; HEC Montréal; Université du Québec à Montréal","funders":"Université du Québec à Rimouski","keywords":"Singular value decomposition; Benchmark (surveying); Feature selection; Deep learning; Principal component analysis; Curse of dimensionality; Feature (linguistics); Component (thermodynamics); Selection (genetic algorithm)","score_opus":0.019881817058047146,"score_gpt":0.3185802084535893,"score_spread":0.29869839139554216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106634211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46743906,0.018246273,0.50227743,0.0013829828,0.00028106486,0.00022541302,0.00038586836,0.0015298782,0.008232011],"genre_scores_gemma":[0.9088886,0.0030030154,0.085587084,0.00009989728,0.00011992717,0.00006781703,0.0005738025,0.000063840365,0.0015959257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880254,0.0005503337,0.00007658857,0.00014521378,0.00036211207,0.00006327859],"domain_scores_gemma":[0.9960161,0.0028215176,0.00013586867,0.0003561472,0.0006101208,0.00006017974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021331862,0.0008397483,0.00064463937,0.0015171655,0.00025049443,0.00060120557,0.0003953337,0.0004903077,0.0013829251],"category_scores_gemma":[0.0062626665,0.0001329349,0.0005685694,0.0014488568,0.00030727632,0.0013836898,0.00045045343,0.0005869575,0.00021737978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015180293,0.00057243515,0.0096483445,0.00038904895,0.00044328911,0.00020594883,0.00009071308,0.1368029,0.009283148,0.0043080803,0.0052060154,0.831532],"study_design_scores_gemma":[0.000057701684,0.0014748102,0.007479886,0.00005042857,0.0001205845,0.0002445326,0.00007949485,0.9741216,0.009575683,0.0032374335,0.0035315012,0.000026369982],"about_ca_topic_score_codex":0.0018026455,"about_ca_topic_score_gemma":0.0015261357,"teacher_disagreement_score":0.0021331862,"about_ca_system_score_codex":0.00048151086,"about_ca_system_score_gemma":0.00029037753,"threshold_uncertainty_score":0.01128155},"labels":[],"label_agreement":null},{"id":"W7106640267","doi":"10.1016/j.procs.2025.10.184","title":"Machine Learning for Intrusion Detection in IIoT: A Comprehensive Review","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski; Cégep de Rimouski","funders":"","keywords":"Intrusion detection system; Scalability; False positive paradox; Industrial Internet; Deep learning; Supervised learning; Unsupervised learning","score_opus":0.013026074931653284,"score_gpt":0.26263416023220787,"score_spread":0.2496080853005546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106640267","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00057569204,0.990442,0.005948338,0.000591267,0.00033698807,0.000025545169,0.000043036267,0.0000508035,0.0019864247],"genre_scores_gemma":[0.00503898,0.98927677,0.00398262,0.0003082638,0.0005699333,0.000029980234,0.000109071654,0.000014745569,0.0006696446],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932694,0.00014554852,0.00009916766,0.00014547746,0.00024101109,0.000041950603],"domain_scores_gemma":[0.99770576,0.001679474,0.00016165074,0.000057353816,0.00034795376,0.00004776947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014343706,0.0011828882,0.001261029,0.0030249115,0.00032785328,0.0015922862,0.0012430578,0.0014609667,0.0027139024],"category_scores_gemma":[0.0031202948,0.000527355,0.0011265374,0.0035023165,0.00049761246,0.0023607935,0.0007705726,0.0013779295,0.0012632735],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000414838,0.00009473304,0.0007555129,0.013810275,0.00016522288,0.0001373948,0.00010651468,0.0038285067,0.00068566104,0.0073098266,0.016070787,0.95699406],"study_design_scores_gemma":[0.00002377402,0.00042096534,0.003533742,0.018294867,0.00075211696,0.0018864998,0.0002738964,0.015046445,0.0025670358,0.01844346,0.9386091,0.00014805958],"about_ca_topic_score_codex":0.0018169716,"about_ca_topic_score_gemma":0.0014785237,"teacher_disagreement_score":0.0030249115,"about_ca_system_score_codex":0.0006532039,"about_ca_system_score_gemma":0.0015602242,"threshold_uncertainty_score":0.00907886},"labels":[],"label_agreement":null},{"id":"W7117293298","doi":"10.1016/j.procs.2025.12.094","title":"Simulation-Based SCADA Model for Wood Panel Manufacturing","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Flexible and Reconfigurable Manufacturing Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SCADA; Manufacturing; Panel discussion; Computer-integrated manufacturing","score_opus":0.023169182161277165,"score_gpt":0.24313916325511317,"score_spread":0.219969981093836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117293298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34067595,0.000636269,0.59495443,0.00066466175,0.00019474843,0.0001822636,0.0021493,0.0033277804,0.057214577],"genre_scores_gemma":[0.9805762,0.00012772983,0.012417028,0.000029883244,0.000010516168,0.000088700945,0.00033444673,0.00006288135,0.0063525876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998554,0.000044186265,0.0000083597415,0.000028278644,0.00003547911,0.000028213024],"domain_scores_gemma":[0.9996965,0.0001111729,0.00003606496,0.00003260896,0.00009796239,0.000025689018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021766622,0.0005586809,0.000774844,0.00037112355,0.00048017592,0.0008381831,0.0010698293,0.0011085353,0.0069475216],"category_scores_gemma":[0.0006252195,0.00039761714,0.0006345272,0.0004555386,0.00035770427,0.0005209881,0.0003928819,0.00066589774,0.00068570435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016382497,0.000009611368,0.00015477798,0.0000074608597,0.000004861393,0.000025405041,0.0000073105202,0.99752706,0.0002561166,0.00083332305,0.00013378,0.0010239375],"study_design_scores_gemma":[0.0000043688074,0.0000050541253,0.00004974499,0.000001140177,0.0000022199383,0.00000294723,0.0000023447,0.9994529,0.00007564603,0.00023659492,0.0001657417,0.000001287644],"about_ca_topic_score_codex":0.03426955,"about_ca_topic_score_gemma":0.019875465,"teacher_disagreement_score":0.03426955,"about_ca_system_score_codex":0.0007436752,"about_ca_system_score_gemma":0.0009867831,"threshold_uncertainty_score":0.06814015},"labels":[],"label_agreement":null},{"id":"W7117299222","doi":"10.1016/j.procs.2025.12.108","title":"Designing a Reusable Pipeline Architecture for Cross-Domain Simulations","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Ministero dell'Istruzione e del Merito","keywords":"Workflow; Software deployment; Pipeline (software); Modular design; Reuse; Abstraction; Architecture; Cloud computing","score_opus":0.06846767344640158,"score_gpt":0.43314787407638433,"score_spread":0.36468020062998274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117299222","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065132338,0.00010420309,0.9865611,0.00021429895,0.000025905298,0.00018067633,0.00010012678,0.003524283,0.0027762277],"genre_scores_gemma":[0.11476137,0.00047681906,0.8801631,0.00009629186,0.000015449748,0.00038388246,0.001052287,0.00069348456,0.002357392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986191,0.00039567318,0.00018255612,0.00027075314,0.0003552916,0.00017651172],"domain_scores_gemma":[0.9976464,0.0005930683,0.00017216422,0.0009401924,0.0004919999,0.00015611698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034400716,0.0008927559,0.00052522076,0.0023344462,0.0008521342,0.0023886326,0.0020233728,0.0012771331,0.0036664493],"category_scores_gemma":[0.00526232,0.0010241444,0.0019698434,0.0013853032,0.0012158209,0.004415688,0.004195696,0.0016324283,0.0019352067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028566457,0.00033682282,0.008204374,0.0010406582,0.00027397188,0.0007469765,0.0019741463,0.32301065,0.03639043,0.28724438,0.008521881,0.33197],"study_design_scores_gemma":[0.00006642606,0.00016497307,0.0009672381,0.00024633083,0.00015598377,0.00030074918,0.00027095745,0.7984032,0.024239391,0.08950698,0.08559995,0.00007775013],"about_ca_topic_score_codex":0.0047171935,"about_ca_topic_score_gemma":0.0042684535,"teacher_disagreement_score":0.0047171935,"about_ca_system_score_codex":0.0012669566,"about_ca_system_score_gemma":0.0039042672,"threshold_uncertainty_score":0.018193007},"labels":[],"label_agreement":null},{"id":"W7117306328","doi":"10.1016/j.procs.2025.12.089","title":"Use of graph network and hybrid simulation to understand schedule resiliency and capacity under disruptions","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College Saint-Jean; University of Alberta","funders":"","keywords":"Interdependence; Schedule; Resource (disambiguation); Scheduling (production processes); Identification (biology); Workstation; Exploit; Supply chain","score_opus":0.1378214207620819,"score_gpt":0.37371096692076466,"score_spread":0.23588954615868277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117306328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3671365,0.0006037365,0.60322475,0.0007957225,0.00011300607,0.00019843626,0.0009778839,0.0011094656,0.025840504],"genre_scores_gemma":[0.9552258,0.00033658333,0.04164445,0.000053813543,0.000017631584,0.00013475632,0.00029880807,0.00007823025,0.0022097507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997507,0.000116010364,0.00000867707,0.000038681166,0.000041657,0.000044191547],"domain_scores_gemma":[0.99864405,0.001049147,0.00010730757,0.000059227645,0.000080524784,0.000059853664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005120443,0.00087036577,0.0005026876,0.0014387374,0.0004163859,0.00091775064,0.0007916877,0.0008341268,0.0021722866],"category_scores_gemma":[0.0020735147,0.00040154706,0.0007012309,0.000984399,0.0007714248,0.0009146529,0.0006936901,0.00053887395,0.00013781329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007241071,0.000004576234,0.00018989021,0.0000036146553,0.000005237649,0.000008005128,0.00000623556,0.9977763,0.00008234562,0.001366146,0.000031087304,0.0005193805],"study_design_scores_gemma":[0.0000016507626,0.0000028777733,0.000053326035,9.927238e-7,0.00000147159,0.0000014848357,0.000004682161,0.99871504,0.000036735793,0.0010997127,0.00008070434,0.0000012750363],"about_ca_topic_score_codex":0.034529366,"about_ca_topic_score_gemma":0.023564992,"teacher_disagreement_score":0.034529366,"about_ca_system_score_codex":0.002058348,"about_ca_system_score_gemma":0.0011298351,"threshold_uncertainty_score":0.06865674},"labels":[],"label_agreement":null},{"id":"W7117316833","doi":"10.1016/j.procs.2025.12.017","title":"Identification and Comparative Analysis of Legal and Contractual Provisions among Different Contract Types in Off-site Construction Projects","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Terminology; CLARITY; Construction contract; Pace; Consistency (knowledge bases); Identification (biology); Constructive; Contract management; Quickening","score_opus":0.03691916975233875,"score_gpt":0.3375415751914141,"score_spread":0.30062240543907537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117316833","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9837773,0.00025340472,0.005298872,0.00014052044,0.0000072801313,0.00012676672,0.00019946115,0.000009529744,0.010186882],"genre_scores_gemma":[0.99456525,0.00015171092,0.004049089,0.000015771084,0.0000022949669,0.000073627416,0.00029425332,0.0000123462905,0.0008356592],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98533857,0.0066697467,0.0014030972,0.0009108811,0.004906385,0.00077132747],"domain_scores_gemma":[0.93629366,0.038140655,0.012257757,0.0027940236,0.009581425,0.0009324499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012654167,0.00014738481,0.0002186311,0.0068422193,0.0018366387,0.0025602411,0.00078260794,0.00052080536,0.0022501668],"category_scores_gemma":[0.054805633,0.00024849933,0.00028528963,0.007703374,0.0029776052,0.003195109,0.0028868963,0.0007873294,0.00020730434],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005081189,0.00022873854,0.5745518,0.00071355613,0.00009279442,0.0010196699,0.14805797,0.0029056976,0.0060210354,0.06676701,0.0014938475,0.19763978],"study_design_scores_gemma":[0.000018304634,0.0002655938,0.7736806,0.00061447424,0.000082847444,0.00061475293,0.17899506,0.0084374305,0.0026872042,0.009875971,0.024651555,0.00007616341],"about_ca_topic_score_codex":0.011167257,"about_ca_topic_score_gemma":0.024113877,"teacher_disagreement_score":0.012654167,"about_ca_system_score_codex":0.0038070646,"about_ca_system_score_gemma":0.0039885864,"threshold_uncertainty_score":0.066922426},"labels":[],"label_agreement":null},{"id":"W884408679","doi":"10.1016/j.procs.2015.05.021","title":"Ad-ATMA: An Efficient MAC protocol for Wireless Sensor and Ad Hoc Networks","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Wireless ad hoc network; Multiple Access with Collision Avoidance for Wireless; Network packet; Wireless sensor network; Throughput; Ad hoc wireless distribution service; Latency (audio); Wireless; Wireless network; Vehicular ad hoc network; Optimized Link State Routing Protocol; Routing protocol; Telecommunications","score_opus":0.030468014931517556,"score_gpt":0.2868980690896219,"score_spread":0.2564300541581043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W884408679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054910006,0.007078959,0.97110724,0.00047047055,0.001263427,0.00047076633,0.0003074427,0.004171172,0.009639574],"genre_scores_gemma":[0.20131297,0.0079553835,0.761172,0.0009911376,0.0010075079,0.0018868096,0.0011966574,0.00049071887,0.023986837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988417,0.000281803,0.00010495919,0.000099180594,0.0006066099,0.00006581684],"domain_scores_gemma":[0.9991548,0.00021979399,0.00012498761,0.00012839204,0.00030788663,0.00006411822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010983042,0.00088734867,0.00068293884,0.0011654694,0.0008902301,0.0012110792,0.002334674,0.0009947418,0.003086954],"category_scores_gemma":[0.002137076,0.00032605135,0.00043010522,0.0012875604,0.0006327351,0.001825524,0.0012090886,0.001521089,0.0016458435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005664475,0.00033669468,0.00080269837,0.0016630283,0.00031831526,0.0006808055,0.00027798818,0.045715164,0.06500495,0.083922535,0.063317314,0.7373939],"study_design_scores_gemma":[0.00021010039,0.0008029499,0.00094195403,0.00020891019,0.00022372112,0.0020440163,0.000113085,0.37857246,0.048350155,0.03008697,0.5382745,0.00017119921],"about_ca_topic_score_codex":0.00054431596,"about_ca_topic_score_gemma":0.0008525573,"teacher_disagreement_score":0.003086954,"about_ca_system_score_codex":0.00046194106,"about_ca_system_score_gemma":0.000771201,"threshold_uncertainty_score":0.010326862},"labels":[],"label_agreement":null}]}