{"meta":{"query_hash":"7229bedfaaa1","filters":{"venue":"Information"},"cohort_total":168,"direct_labels_cover":0,"predictions_cover":168,"exported":168,"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/7229bedfaaa1","api":"https://metacan.xera.ac/api/v1/cohort?venue=Information"},"results":[{"id":"W1974664105","doi":"10.3390/info2010217","title":"Finding Emotional-Laden Resources on the World Wide Web","year":2011,"lang":"en","type":"article","venue":"Information","topic":"Music and Audio Processing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Emotive; Computer science; Information retrieval; Vocabulary; Interface (matter); Thumbnail; World Wide Web; Resource (disambiguation); Index (typography); Multimedia; Artificial intelligence; Image (mathematics); Linguistics","score_opus":0.039877062793162006,"score_gpt":0.22541186194331012,"score_spread":0.18553479915014812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974664105","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":[0.8565407,0.0049617225,0.07469003,0.0009876662,0.0001158697,0.0006512151,0.0026608433,0.006911827,0.052480154],"genre_scores_gemma":[0.8967287,0.0024734004,0.080586664,0.00059937174,0.00008427327,0.00021702249,0.0031013954,0.00053208455,0.015677184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963987,0.00010382243,0.000028768487,0.00007345744,0.00011710847,0.00003701257],"domain_scores_gemma":[0.99915135,0.0005056158,0.00007634567,0.00009004139,0.00011106217,0.00006565729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040772642,0.0005788222,0.0005174921,0.00212108,0.00068926235,0.0022912933,0.00036856625,0.0007259999,0.0031530452],"category_scores_gemma":[0.002903928,0.0001549964,0.00032795148,0.0017021106,0.0002701995,0.005005176,0.0014037618,0.00037214885,0.001958626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015670926,0.0006468362,0.01642016,0.003243498,0.00023517442,0.0027366353,0.008733717,0.0016973383,0.15226334,0.0055673486,0.030440662,0.7764482],"study_design_scores_gemma":[0.00024347445,0.0020355259,0.25475782,0.0016283531,0.0009323241,0.0077068387,0.044425186,0.11476752,0.24053094,0.040035117,0.29243016,0.0005068712],"about_ca_topic_score_codex":0.001216705,"about_ca_topic_score_gemma":0.0025541212,"teacher_disagreement_score":0.0031530452,"about_ca_system_score_codex":0.00027448477,"about_ca_system_score_gemma":0.0001551002,"threshold_uncertainty_score":0.010547996},"labels":[],"label_agreement":null},{"id":"W1991184457","doi":"10.3390/info3030290","title":"Review and Précis of Terrence Deacon’s Incomplete Nature: How Mind Emerged from Matter","year":2012,"lang":"en","type":"article","venue":"Information","topic":"Space Science and Extraterrestrial Life","field":"Physics and Astronomy","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":"Ontario College of Art and Design; University of Toronto; St. Michael's Hospital","funders":"","keywords":"Philosophy; Psychology; Epistemology","score_opus":0.010813491993651978,"score_gpt":0.2521474625741876,"score_spread":0.2413339705805356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991184457","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":[0.00027174206,0.92827904,0.00064749486,0.04307843,0.020667814,0.0000058877476,0.00003561911,0.000013010881,0.0070009218],"genre_scores_gemma":[0.0061109266,0.91317,0.00046354864,0.029875925,0.040682398,0.000021546542,0.00006525919,0.00003981202,0.009570595],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992944,0.00019567543,0.00007253752,0.00010951554,0.00027468448,0.00005317207],"domain_scores_gemma":[0.9946637,0.0037091023,0.00027159374,0.00012622215,0.0009912975,0.00023805052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001867005,0.0005463215,0.00088939286,0.002406129,0.0009256093,0.001919579,0.0010366514,0.0022488977,0.0029562945],"category_scores_gemma":[0.006580467,0.00030545142,0.00030530643,0.0028128403,0.003063398,0.0043168315,0.0010586169,0.004599728,0.0019013662],"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":[0.00004649557,0.000016582284,0.00017352111,0.0035410663,0.000041298645,0.0003430198,0.00087453454,0.00023601952,0.0005280779,0.04317527,0.79080784,0.16021621],"study_design_scores_gemma":[0.0000041065296,0.00001690631,0.00017171587,0.0009404427,0.000011526134,0.0004467927,0.0001152826,0.000023017787,0.00015353132,0.006348134,0.99175805,0.000010526158],"about_ca_topic_score_codex":0.0025739977,"about_ca_topic_score_gemma":0.0047759926,"teacher_disagreement_score":0.0029562945,"about_ca_system_score_codex":0.0017508017,"about_ca_system_score_gemma":0.002743169,"threshold_uncertainty_score":0.0127030015},"labels":[],"label_agreement":null},{"id":"W1997109122","doi":"10.3390/info3030391","title":"Information Flow and Health Policy Literacy: The Role of the Media","year":2012,"lang":"en","type":"article","venue":"Information","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Health policy; Public relations; Health literacy; HRHIS; Health care; International health; Key (lock); Health education; Health promotion; Citizen journalism; The Internet; Political science; Business; Health belief model; Internet privacy; Computer science; World Wide Web","score_opus":0.028682714316617026,"score_gpt":0.4245783632119528,"score_spread":0.3958956488953358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997109122","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":[0.18375207,0.01680805,0.009848204,0.3604718,0.0010442309,0.0001547657,0.00029670628,0.00013591013,0.4274882],"genre_scores_gemma":[0.97501874,0.007274352,0.0017729553,0.0077239224,0.00071573025,0.00008138338,0.00005702505,0.000043225355,0.0073126606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9894406,0.007823536,0.00037203598,0.00045081702,0.0010910924,0.0008220173],"domain_scores_gemma":[0.877193,0.11030209,0.0062585426,0.001535935,0.0026745524,0.0020357927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010919649,0.00031398862,0.00042378716,0.0035368588,0.00404871,0.02066347,0.0010763356,0.003753928,0.012649343],"category_scores_gemma":[0.04804687,0.0004442643,0.00046792915,0.0026868992,0.014235957,0.026471889,0.006175283,0.0034952592,0.0007113846],"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":[0.00020997282,0.00030389387,0.024880448,0.0015103834,0.00007797506,0.0008501147,0.25116777,0.00031559757,0.0007602337,0.5909834,0.015371101,0.11356903],"study_design_scores_gemma":[0.00011113496,0.00027126173,0.03243024,0.0057545295,0.00018589236,0.0014280285,0.2925274,0.001673917,0.0017433095,0.37016833,0.29351994,0.00018605818],"about_ca_topic_score_codex":0.0053361547,"about_ca_topic_score_gemma":0.0025097115,"teacher_disagreement_score":0.02066347,"about_ca_system_score_codex":0.0038964197,"about_ca_system_score_gemma":0.0046647284,"threshold_uncertainty_score":0.05774927},"labels":[],"label_agreement":null},{"id":"W1998143234","doi":"10.3390/info6010003","title":"The Kalman Filtering Blind Adaptive Multi-user Detector Based on Tracking Algorithm of Signal Subspace","year":2015,"lang":"en","type":"article","venue":"Information","topic":"Wireless Communication Networks 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 Calgary","funders":"National Natural Science Foundation of China","keywords":"Multiuser detection; Detector; Subspace topology; Code division multiple access; Kalman filter; Computer science; Interference (communication); Algorithm; Signal subspace; SIGNAL (programming language); Detection theory; Tracking (education); Control theory (sociology); Artificial intelligence; Telecommunications; Noise (video)","score_opus":0.07483947875312898,"score_gpt":0.3031580228323746,"score_spread":0.22831854407924562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998143234","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":[0.0020487723,0.0004610747,0.9963761,0.00004028319,0.000051228417,0.00002402544,0.000019072506,0.000332106,0.0006473199],"genre_scores_gemma":[0.26375344,0.0021007506,0.72762275,0.00028747035,0.00017942302,0.00019660879,0.00025308275,0.00009138251,0.0055150166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989618,0.00022576016,0.00005604479,0.00020361798,0.00048405246,0.0000685963],"domain_scores_gemma":[0.9992974,0.00025062694,0.000069836606,0.00006914579,0.0002851689,0.000027802724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008977789,0.0005595042,0.0011976759,0.00075697474,0.00056672987,0.00078906777,0.0009185524,0.001012019,0.0011171917],"category_scores_gemma":[0.0021081022,0.00032109502,0.0007523585,0.0011250183,0.0006745787,0.0017700933,0.0006764518,0.0010076233,0.0007203423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004558725,0.00016805892,0.003099982,0.0005405863,0.00029438408,0.00017861447,0.00023682145,0.108159505,0.090787485,0.0697219,0.005409951,0.7209469],"study_design_scores_gemma":[0.00005349816,0.00025131647,0.0008725717,0.000024082485,0.00006907737,0.00035856062,0.000024589059,0.9439697,0.03511922,0.010611646,0.008542205,0.00010355639],"about_ca_topic_score_codex":0.0019081843,"about_ca_topic_score_gemma":0.0016267147,"teacher_disagreement_score":0.0019081843,"about_ca_system_score_codex":0.00049499824,"about_ca_system_score_gemma":0.001040897,"threshold_uncertainty_score":0.004747987},"labels":[],"label_agreement":null},{"id":"W2041782663","doi":"10.3390/info4010094","title":"The Teleodynamics of Language, Culture, Technology and Science (LCT&amp;S)","year":2013,"lang":"en","type":"article","venue":"Information","topic":"Language and cultural evolution","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 College of Art and Design; University of Toronto","funders":"","keywords":"Sentience; Epistemology; Sociology; Philosophy","score_opus":0.004182609783519012,"score_gpt":0.26715438950123704,"score_spread":0.262971779717718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041782663","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":[0.030314459,0.051646948,0.0913335,0.051639143,0.002741502,0.00012977075,0.0005378709,0.0002852571,0.77137154],"genre_scores_gemma":[0.90915525,0.021534637,0.023764547,0.0046400744,0.002009031,0.00022380501,0.000141981,0.00007847204,0.03845223],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893886,0.00045951968,0.000047786012,0.00019685517,0.00025908125,0.00009793035],"domain_scores_gemma":[0.99903524,0.00055340957,0.00014515177,0.00009148691,0.0000930815,0.00008153],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010445481,0.00032181985,0.00026502882,0.0011172747,0.0015024121,0.0039431923,0.0005553574,0.0021340104,0.0056241793],"category_scores_gemma":[0.0019024968,0.00015947096,0.00021693442,0.0014025692,0.01654613,0.0047745877,0.002952032,0.002061881,0.0007661522],"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":[0.0000075667103,0.0000036358351,0.00018828336,0.00004180686,0.0000021730923,0.00006693313,0.001171858,0.00020276014,0.00013106757,0.984609,0.0028754189,0.0106995255],"study_design_scores_gemma":[0.000008073283,0.000024496105,0.0008392894,0.00014915627,0.000006806476,0.00049857836,0.00090113265,0.0010538645,0.0003146897,0.7141537,0.28202742,0.000022720149],"about_ca_topic_score_codex":0.0019540575,"about_ca_topic_score_gemma":0.0010850349,"teacher_disagreement_score":0.9984976,"about_ca_system_score_codex":0.0029785412,"about_ca_system_score_gemma":0.001760416,"threshold_uncertainty_score":0.021610975},"labels":[],"label_agreement":null},{"id":"W2114755385","doi":"10.3390/info6040669","title":"Extending Deacon’s Notion of Teleodynamics to Culture, Language, Organization, Science, Economics and Technology (CLOSET)","year":2015,"lang":"en","type":"article","venue":"Information","topic":"University-Industry-Government Innovation Models","field":"Business, Management and Accounting","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 College of Art and Design; University of Toronto; St. Michael's Hospital","funders":"","keywords":"Closet; Sociology; Epistemology; Philosophy; History","score_opus":0.009127377645134438,"score_gpt":0.20767879614620813,"score_spread":0.1985514185010737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114755385","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":[0.017547404,0.012477141,0.2481508,0.050186682,0.0019687281,0.00010043787,0.00031387692,0.00011526752,0.66913974],"genre_scores_gemma":[0.8748932,0.009841425,0.051040772,0.007939288,0.0021633098,0.00027739498,0.00016083599,0.00016021384,0.053523567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99838936,0.0007103449,0.00007187315,0.00027211366,0.00038122668,0.00017515592],"domain_scores_gemma":[0.9973111,0.001390035,0.0003077778,0.00042931314,0.00034166567,0.00022012572],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0021048498,0.0006852598,0.00061281945,0.002053898,0.0021961618,0.004628248,0.0011536501,0.0027034713,0.006094176],"category_scores_gemma":[0.004391415,0.00031584044,0.0007970522,0.0024566127,0.015108241,0.009116316,0.0040832697,0.004559073,0.0010325914],"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":[0.0000017190956,0.0000028977904,0.00003582618,0.000008167823,0.0000016510165,0.000013472847,0.00019897446,0.00027179858,0.00001643792,0.9975731,0.0006442095,0.0012318903],"study_design_scores_gemma":[0.000004105759,0.0000072232397,0.00006135422,0.000021786847,0.0000020278128,0.00004830361,0.000098644945,0.0007579198,0.0000315536,0.95724756,0.041713905,0.0000056963263],"about_ca_topic_score_codex":0.005393209,"about_ca_topic_score_gemma":0.0033129656,"teacher_disagreement_score":0.99780387,"about_ca_system_score_codex":0.0055084224,"about_ca_system_score_gemma":0.0023236522,"threshold_uncertainty_score":0.039966583},"labels":[],"label_agreement":null},{"id":"W2169679075","doi":"10.3390/info3010068","title":"What Is Information?: Why Is It Relativistic and What Is Its Relationship to Materiality, Meaning and Organization","year":2012,"lang":"en","type":"article","venue":"Information","topic":"Origins and Evolution of Life","field":"Physics and Astronomy","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario College of Art and Design; University of Toronto","funders":"National Development and Reform Commission","keywords":"Epistemology; Information theory; Meaning (existential); Materiality (auditing); Cybernetics; Information science; Context (archaeology); Computer science; Sociology; Cognitive science; Psychology; Mathematics; Philosophy","score_opus":0.019689746653877252,"score_gpt":0.25907605049820154,"score_spread":0.2393863038443243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169679075","genre_codex":"review","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":[0.015997307,0.5547466,0.06828147,0.25036445,0.007921635,0.00009117632,0.00041851727,0.00016418408,0.10201458],"genre_scores_gemma":[0.485369,0.41190156,0.03188633,0.03638215,0.024204886,0.00024846112,0.00032959128,0.00020769407,0.009470356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995613,0.002312505,0.00023932873,0.0005392456,0.00095172384,0.0003442789],"domain_scores_gemma":[0.9863789,0.009842786,0.0009711591,0.00076714455,0.0015287271,0.00051131024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006639197,0.000659438,0.0016376674,0.0050541516,0.0031491,0.0097164335,0.0017271059,0.0047813472,0.0026695787],"category_scores_gemma":[0.015550348,0.00049141847,0.0006690768,0.0054762224,0.035193436,0.023974238,0.002724761,0.0054200706,0.001007261],"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":[0.000013079128,0.000014899217,0.00054590777,0.0004612139,0.00002792447,0.00010638098,0.0018048852,0.00033621627,0.00013869855,0.963123,0.0074186586,0.026009098],"study_design_scores_gemma":[0.000003080473,0.0000141573155,0.0006036879,0.00062954787,0.000012585608,0.00023011534,0.0012397832,0.00031082198,0.0001493319,0.9118873,0.084887736,0.000031913194],"about_ca_topic_score_codex":0.00369961,"about_ca_topic_score_gemma":0.0017667877,"teacher_disagreement_score":0.0097164335,"about_ca_system_score_codex":0.0044205454,"about_ca_system_score_gemma":0.002856041,"threshold_uncertainty_score":0.035111845},"labels":[],"label_agreement":null},{"id":"W2190074293","doi":"10.3390/info6040811","title":"Information and Phylogenetic Systematic Analysis","year":2015,"lang":"en","type":"article","venue":"Information","topic":"Evolution and Paleontology Studies","field":"Earth and Planetary Sciences","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":"Shared Hierarchical Academic Research Computing Network; McMaster University; Fields Institute for Research in Mathematical Sciences","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McMaster University","keywords":"Cladogram; Character (mathematics); Synapomorphy; Phylogenetic tree; Measure (data warehouse); Autapomorphy; Taxon; Matrix (chemical analysis); Computer science; Mathematics; Cladistics; Biology; Data mining; Paleontology; Genetics; Geometry","score_opus":0.016284634819748012,"score_gpt":0.20813560741173126,"score_spread":0.19185097259198325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2190074293","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":[0.062330287,0.023982663,0.8518785,0.015575522,0.00038687894,0.00011918545,0.00060349784,0.00021440763,0.04490897],"genre_scores_gemma":[0.8671714,0.006686164,0.121895336,0.0013993332,0.00078348,0.00017525729,0.00023707967,0.00007652139,0.001575453],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9840905,0.009380848,0.0011787969,0.0019701507,0.0028753234,0.0005044338],"domain_scores_gemma":[0.9243243,0.061774075,0.0047267023,0.004814999,0.0036142173,0.0007457093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02331161,0.00075924175,0.0014196052,0.009423238,0.0019218936,0.0048326105,0.0012900984,0.001964854,0.0020072975],"category_scores_gemma":[0.06221263,0.0005177359,0.00096919446,0.00717855,0.020979151,0.011740695,0.0047966237,0.003210265,0.00020782482],"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":[0.000014211946,0.000005813091,0.0018897621,0.00012633106,0.000041031566,0.000037715647,0.00043564415,0.0048235096,0.00016232696,0.97255623,0.00033755993,0.0195698],"study_design_scores_gemma":[0.0000026818918,0.000011548766,0.0008986302,0.000071643364,0.000008943202,0.0000392738,0.000120199715,0.005376082,0.00012981439,0.99067104,0.0026533466,0.000016787275],"about_ca_topic_score_codex":0.001979778,"about_ca_topic_score_gemma":0.0014560756,"teacher_disagreement_score":0.02331161,"about_ca_system_score_codex":0.005384409,"about_ca_system_score_gemma":0.002207979,"threshold_uncertainty_score":0.123285055},"labels":[],"label_agreement":null},{"id":"W2237431699","doi":"10.3390/info7010015","title":"Information Extraction Under Privacy Constraints","year":2016,"lang":"en","type":"preprint","venue":"Information","topic":"Privacy-Preserving Technologies in Data","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mutual information; Random variable; Joint probability distribution; Information theory; Quantization (signal processing); Mathematics; Gaussian; Constraint (computer-aided design); Computer science; Probability density function; Function (biology); Algorithm; Statistics","score_opus":0.031579588226146246,"score_gpt":0.2906384200286676,"score_spread":0.2590588318025213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2237431699","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":[0.011413452,0.00060700893,0.9821504,0.0010766477,0.000035514175,0.00009195956,0.00031904803,0.00012038605,0.004185656],"genre_scores_gemma":[0.68694186,0.0026298559,0.30139923,0.00085904164,0.0003790197,0.00057206315,0.0011129072,0.00019242601,0.0059135575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9780441,0.011018662,0.0013678856,0.0035339752,0.004811386,0.0012239519],"domain_scores_gemma":[0.9355935,0.046705756,0.003733787,0.010450415,0.0029265927,0.000589979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014198625,0.0014748661,0.0030792507,0.0018497575,0.0015540384,0.0062497705,0.0029687225,0.0031704258,0.0022948736],"category_scores_gemma":[0.0673473,0.0011176191,0.0017245298,0.0055787694,0.0038241658,0.014363954,0.005507793,0.004231847,0.0008539716],"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":[0.00034647255,0.00009580834,0.0011909496,0.00051616185,0.00017475747,0.0006007229,0.00047870266,0.17800444,0.00418497,0.73964393,0.0033566942,0.07140635],"study_design_scores_gemma":[0.000036861496,0.00007483809,0.0003207707,0.000080474,0.0000472319,0.000478398,0.0001160708,0.3557567,0.005198362,0.6334599,0.0043904516,0.00003985397],"about_ca_topic_score_codex":0.00089011434,"about_ca_topic_score_gemma":0.00040892587,"teacher_disagreement_score":0.014198625,"about_ca_system_score_codex":0.0028173323,"about_ca_system_score_gemma":0.0030689468,"threshold_uncertainty_score":0.07509041},"labels":[],"label_agreement":null},{"id":"W2528338211","doi":"10.3390/info8010004","title":"A Symmetric Approach Elucidates Multisensory Information Integration","year":2016,"lang":"en","type":"article","venue":"Information","topic":"Multisensory perception and integration","field":"Psychology","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 Manitoba","funders":"","keywords":"Multisensory integration; Stimulus modality; Perception; Computer science; Matching (statistics); Sensory system; Point (geometry); Isolation (microbiology); Topology (electrical circuits); Communication; Cognitive science; Artificial intelligence; Mathematics; Psychology; Neuroscience; Biology; Geometry","score_opus":0.03068903993278252,"score_gpt":0.2940652249785713,"score_spread":0.26337618504578875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528338211","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":[0.090186395,0.002363778,0.7616208,0.004149872,0.00043188184,0.00004973398,0.00024273488,0.00034480504,0.14060998],"genre_scores_gemma":[0.9034277,0.0011869188,0.08576491,0.0006696953,0.0003906041,0.00008684376,0.00014546518,0.00010700521,0.008220782],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991271,0.0002437775,0.00004529783,0.00017402845,0.0003065456,0.00010333703],"domain_scores_gemma":[0.9989826,0.00031073298,0.00011981114,0.00026557394,0.00021470335,0.000106545776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013482884,0.00050081563,0.00064128,0.0015178149,0.0007682003,0.0023642315,0.001108387,0.0010007747,0.0058184867],"category_scores_gemma":[0.0024512294,0.00029344222,0.0008568313,0.00062219874,0.0036537154,0.0057215677,0.002969122,0.001396437,0.0010676719],"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":[0.000009628038,0.0000046164873,0.00007773454,0.000021049693,0.000004792003,0.000040398972,0.00007579657,0.0008794385,0.0013069947,0.9920087,0.00029620703,0.0052746558],"study_design_scores_gemma":[0.000004931679,0.000012719495,0.0001731605,0.000007751802,0.0000038816397,0.00008362841,0.000040402974,0.0096693,0.00059249526,0.9871595,0.0022459158,0.000006280804],"about_ca_topic_score_codex":0.0003350063,"about_ca_topic_score_gemma":0.0001775649,"teacher_disagreement_score":0.0058184867,"about_ca_system_score_codex":0.0009140482,"about_ca_system_score_gemma":0.0005549758,"threshold_uncertainty_score":0.01946479},"labels":[],"label_agreement":null},{"id":"W2564557061","doi":"10.3390/info7040073","title":"Symmetries, Information and Monster Groups before and after the Big Bang","year":2016,"lang":"en","type":"article","venue":"Information","topic":"Cosmology and Gravitation Theories","field":"Physics and Astronomy","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 Manitoba","funders":"","keywords":"Monster; Theoretical physics; Homogeneous space; Physics; String theory; Sporadic group; De Sitter universe; Symmetry (geometry); Group (periodic table); Universe; Abelian group; Pure mathematics; Mathematics; Quantum mechanics; Geometry","score_opus":0.0029479223706105886,"score_gpt":0.18695950757880467,"score_spread":0.18401158520819408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564557061","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":[0.8966825,0.0025033592,0.021395119,0.0026569113,0.0001872484,0.000021149284,0.00018173731,0.000086133805,0.076285854],"genre_scores_gemma":[0.9926091,0.00038471297,0.0017979337,0.00007651233,0.000064701635,0.000007654207,0.00004619733,0.000013672271,0.0049994476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998505,0.000042151813,0.000006327468,0.000025829031,0.00003238077,0.000042771568],"domain_scores_gemma":[0.99967074,0.00007952348,0.00008505765,0.000061611056,0.00003399009,0.00006909897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004460249,0.00018035357,0.0002698625,0.0006147678,0.0009798902,0.0012817669,0.00033297675,0.00064382766,0.002343004],"category_scores_gemma":[0.0011475426,0.00016163423,0.00033884807,0.00043909517,0.0031276406,0.0026573401,0.0008472052,0.0008353151,0.0001808485],"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":[0.000023321498,0.0000057507496,0.00046603673,0.000020293397,0.0000035492208,0.000060585644,0.00026207702,0.0011766426,0.00060630956,0.994722,0.00023857175,0.0024149069],"study_design_scores_gemma":[0.0000117456975,0.000033379267,0.002490505,0.000021868434,0.0000049611767,0.000100272875,0.00016346796,0.0047158333,0.00049456494,0.98858684,0.0033601096,0.000016376842],"about_ca_topic_score_codex":0.0029753805,"about_ca_topic_score_gemma":0.001685054,"teacher_disagreement_score":0.0029753805,"about_ca_system_score_codex":0.0011742443,"about_ca_system_score_gemma":0.000637869,"threshold_uncertainty_score":0.008519769},"labels":[],"label_agreement":null},{"id":"W2594976430","doi":"10.3390/info8010035","title":"Information and Symmetry: Adumbrating the Abstract Core of Complex Systems","year":2017,"lang":"en","type":"article","venue":"Information","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Variety (cybernetics); Intersection (aeronautics); Symmetry (geometry); Core (optical fiber); Epistemology; Cognitive science; Theoretical physics; Complex system; Computer science; Engineering ethics; Data science; Management science; Sociology; Physics; Psychology; Mathematics; Social science; Engineering; Philosophy; Artificial intelligence; Geometry","score_opus":0.021587025273003387,"score_gpt":0.2637814485386183,"score_spread":0.2421944232656149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594976430","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":[0.10164893,0.06658531,0.5844299,0.06726259,0.004435973,0.00007723955,0.0007103095,0.0008557213,0.17399399],"genre_scores_gemma":[0.89388144,0.024025353,0.06271957,0.0030891416,0.0056640934,0.00009039231,0.00033616365,0.00018540047,0.010008485],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985172,0.0005701271,0.00008153607,0.00020405774,0.0005106854,0.000116447365],"domain_scores_gemma":[0.99445903,0.0035289996,0.0004247686,0.00093934324,0.0003875955,0.00026017017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026643265,0.0004987605,0.0008743648,0.0023481436,0.0011520666,0.0051610945,0.0010261402,0.0016226433,0.004281025],"category_scores_gemma":[0.008315307,0.000307898,0.0006958234,0.0012834033,0.010770298,0.0102284225,0.0027250007,0.0028420663,0.0007329586],"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":[0.000011602916,0.0000042796264,0.00010366708,0.00005001952,0.000006033181,0.000029220068,0.00015162706,0.0009710085,0.00028095132,0.9867994,0.0016186176,0.009973721],"study_design_scores_gemma":[0.0000025617114,0.0000047002013,0.000065536704,0.000014547616,0.0000019740305,0.000027898212,0.00002703995,0.0020112486,0.000082226616,0.9935314,0.004226105,0.000004658699],"about_ca_topic_score_codex":0.00049430726,"about_ca_topic_score_gemma":0.00030326383,"teacher_disagreement_score":0.0051610945,"about_ca_system_score_codex":0.0012878984,"about_ca_system_score_gemma":0.0010034459,"threshold_uncertainty_score":0.014321446},"labels":[],"label_agreement":null},{"id":"W2767607030","doi":"10.3390/info8040142","title":"Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks","year":2017,"lang":"en","type":"article","venue":"Information","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":64,"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":"Universiti Malaysia Pahang; Trent University; Nottingham Trent University","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Restricted Boltzmann machine; Digit recognition; Deep learning; Boltzmann machine; Feature extraction; Arabic numerals; Pattern recognition (psychology); Feature (linguistics); Speech recognition; Numerical digit; Artificial neural network; Handwriting recognition; Arithmetic; Mathematics","score_opus":0.016477029754989284,"score_gpt":0.23759080170935984,"score_spread":0.22111377195437054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767607030","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":[0.043985143,0.0035591084,0.9414323,0.00048493646,0.00029188872,0.0000813854,0.00022113837,0.004181225,0.0057629216],"genre_scores_gemma":[0.6898305,0.0018723434,0.29613787,0.00043403308,0.00015175263,0.00014528113,0.0006514309,0.00014211993,0.010634673],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959725,0.00007236173,0.000028270668,0.00012643998,0.00012745317,0.000048239308],"domain_scores_gemma":[0.99969053,0.0000929297,0.00004856014,0.00005654572,0.00009033964,0.000021204867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040909767,0.0006319619,0.0008274241,0.00064251065,0.00023571067,0.00056295854,0.0010097692,0.00062111695,0.002115307],"category_scores_gemma":[0.0012438921,0.00030046163,0.00070886646,0.0006763016,0.00047616923,0.0010079171,0.0005696244,0.0009339845,0.0008325906],"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":[0.00026821467,0.0001278689,0.0017473415,0.00022737289,0.0001577742,0.00022333828,0.000070840586,0.3841435,0.029171184,0.016238717,0.005683495,0.5619404],"study_design_scores_gemma":[0.0000048789075,0.000024925832,0.0004637529,0.000009831894,0.000010116565,0.00007776516,0.000006211232,0.9888793,0.005405457,0.0037326582,0.0013698343,0.000015230493],"about_ca_topic_score_codex":0.0051711616,"about_ca_topic_score_gemma":0.0041478784,"teacher_disagreement_score":0.0051711616,"about_ca_system_score_codex":0.0006436316,"about_ca_system_score_gemma":0.0005827061,"threshold_uncertainty_score":0.010282099},"labels":[],"label_agreement":null},{"id":"W2767716445","doi":"10.3390/info8040141","title":"Rate Optimization of Two-Way Relaying with Wireless Information and Power Transfer","year":2017,"lang":"en","type":"article","venue":"Information","topic":"Energy Harvesting in Wireless Networks","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 Waterloo","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Relay; Power (physics); Computer science; Maximum power transfer theorem; Wireless; Optimization problem; Transformation (genetics); Computational complexity theory; Phase (matter); Wireless power transfer; Resource allocation; Mathematical optimization; Energy (signal processing); Convex optimization; Mathematics; Regular polygon; Algorithm; Telecommunications; Computer network; Physics","score_opus":0.005176552030399256,"score_gpt":0.1871109613372977,"score_spread":0.18193440930689844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767716445","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":[0.021747181,0.0012722843,0.9684021,0.00052181666,0.000081614206,0.00004833997,0.00009377448,0.00009393747,0.007738945],"genre_scores_gemma":[0.8397227,0.0033300617,0.14358287,0.000200672,0.00017204783,0.00029362825,0.00019742375,0.00015447276,0.01234603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999076,0.000402266,0.000038709215,0.0001629722,0.00020344346,0.00011662792],"domain_scores_gemma":[0.9982888,0.0012605573,0.00015112832,0.00008902186,0.00016152523,0.000049029237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019215397,0.0019011418,0.0014875415,0.0005601136,0.000397889,0.001728055,0.001434202,0.001632764,0.0024105876],"category_scores_gemma":[0.00391817,0.0006016842,0.0008374558,0.0011974603,0.0014267483,0.0020472023,0.0014991345,0.0013925524,0.0005995878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006776473,0.00004505005,0.00018840638,0.00014081599,0.000038354618,0.0001729361,0.000077681696,0.9405881,0.0035287845,0.042719375,0.00078489934,0.011647755],"study_design_scores_gemma":[0.00001103359,0.000027705637,0.000046149315,0.000006770884,0.000009087209,0.00004086342,0.000013317164,0.9898352,0.00077699934,0.008827281,0.00039647202,0.000009135511],"about_ca_topic_score_codex":0.0018192806,"about_ca_topic_score_gemma":0.0010970153,"teacher_disagreement_score":0.0024105876,"about_ca_system_score_codex":0.0012825828,"about_ca_system_score_gemma":0.00096422096,"threshold_uncertainty_score":0.010162175},"labels":[],"label_agreement":null},{"id":"W2770177099","doi":"10.3390/info8040156","title":"The Emperor of Strong AI Has No Clothes: Limits to Artificial Intelligence","year":2017,"lang":"en","type":"article","venue":"Information","topic":"Embodied and Extended Cognition","field":"Neuroscience","cited_by":98,"is_retracted":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":"Pleasure; Premise; Telos; Artificial general intelligence; Symbolic artificial intelligence; Human intelligence; Singularity; Epistemology; Curiosity; Intuition; Morality; Psychology; Artificial intelligence; Sociology; Aesthetics; Computer science; Social psychology; Philosophy; Mathematics; Artificial Intelligence System","score_opus":0.11484646189806128,"score_gpt":0.33158756090316643,"score_spread":0.21674109900510516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770177099","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":[0.024315545,0.03386774,0.08201954,0.30093044,0.0024033955,0.000034763623,0.000069912465,0.0003169647,0.5560417],"genre_scores_gemma":[0.90966386,0.01264382,0.021642411,0.02668333,0.0026356482,0.00018301593,0.000042739903,0.00026388647,0.026241248],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99558014,0.002327047,0.00014966192,0.00066541915,0.0009878206,0.00028991202],"domain_scores_gemma":[0.9811112,0.014684323,0.00054081454,0.0020323114,0.0010404908,0.000590879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074057225,0.0005404235,0.00084179244,0.0018962728,0.004115947,0.012227866,0.0019658136,0.0052843606,0.004350913],"category_scores_gemma":[0.016065363,0.0004863822,0.0007439577,0.0008862283,0.06675628,0.029503886,0.007550685,0.010572269,0.001258263],"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":[0.0000073664173,0.0000039696797,0.00006733429,0.000028847211,0.000004134736,0.000032043394,0.0014066742,0.00015167042,0.00004518102,0.9925679,0.0020242694,0.0036605773],"study_design_scores_gemma":[0.0000033358801,0.000003992499,0.00005114201,0.000043684428,0.0000019275315,0.000041691088,0.0005152983,0.0003266221,0.00004488684,0.9748891,0.024072273,0.0000059711665],"about_ca_topic_score_codex":0.0016154052,"about_ca_topic_score_gemma":0.001275738,"teacher_disagreement_score":0.012227866,"about_ca_system_score_codex":0.0032744342,"about_ca_system_score_gemma":0.0014992673,"threshold_uncertainty_score":0.039165676},"labels":[],"label_agreement":null},{"id":"W2775552709","doi":"10.3390/info8040161","title":"Can Computers Become Conscious, an Essential Condition for the Singularity?","year":2017,"lang":"en","type":"article","venue":"Information","topic":"Computability, Logic, AI Algorithms","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 Toronto","funders":"","keywords":"Consciousness; Active listening; Perception; Psychology; Human intelligence; Computer science; Cognitive science; Cognitive psychology; Artificial intelligence; Communication","score_opus":0.01743265544139592,"score_gpt":0.28302576506874766,"score_spread":0.2655931096273517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775552709","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":[0.1490847,0.025349485,0.10712402,0.25353405,0.002941687,0.00014469757,0.00036808418,0.00092080043,0.46053246],"genre_scores_gemma":[0.9670693,0.0051708026,0.009140819,0.007013541,0.00074237376,0.00008326479,0.000094256386,0.00014022573,0.010545313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99791366,0.00063071935,0.00010090566,0.00048500075,0.0005495485,0.00032011562],"domain_scores_gemma":[0.993853,0.0030944683,0.00042938985,0.0012559961,0.0008470054,0.0005202278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024483488,0.00033707524,0.0006073577,0.0006906916,0.00234003,0.005364235,0.00092066044,0.0031073093,0.0067479103],"category_scores_gemma":[0.014736061,0.00035323782,0.000498626,0.0004727609,0.021834586,0.016227068,0.003152888,0.0038931132,0.0016673085],"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":[0.000035240537,0.00001343963,0.00042998613,0.00012132937,0.000008992324,0.00019326316,0.0023118274,0.00021321788,0.0004981081,0.9787836,0.004397908,0.012993104],"study_design_scores_gemma":[0.000013140643,0.000017940158,0.00037094523,0.00006975513,0.0000059877934,0.00021853774,0.0006382328,0.000364049,0.00032917672,0.96421045,0.033748392,0.000013453803],"about_ca_topic_score_codex":0.0010060635,"about_ca_topic_score_gemma":0.0004607214,"teacher_disagreement_score":0.0067479103,"about_ca_system_score_codex":0.0013693171,"about_ca_system_score_gemma":0.0017231208,"threshold_uncertainty_score":0.022574008},"labels":[],"label_agreement":null},{"id":"W2796054262","doi":"10.3390/info9040083","title":"Thinking in Patterns and the Pattern of Human Thought as Contrasted with AI Data Processing","year":2018,"lang":"en","type":"article","venue":"Information","topic":"Language and cultural evolution","field":"Social Sciences","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":"St. Michael's Hospital; University of Toronto","funders":"","keywords":"Categorization; Cognitive science; Cognition; Conceptualization; Percept; Set (abstract data type); Information processing; Computer science; Psychology; Cognitive psychology; Artificial intelligence","score_opus":0.01693365809966886,"score_gpt":0.3127443733144873,"score_spread":0.29581071521481844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796054262","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":[0.38767996,0.0041668992,0.41189593,0.016372578,0.00024461737,0.00012542488,0.0002578134,0.00045125253,0.17880547],"genre_scores_gemma":[0.92170733,0.00079945923,0.07369367,0.00039088717,0.00005438373,0.00008342542,0.00006348354,0.000064712054,0.0031425296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99841356,0.0007190393,0.000113844406,0.00041424565,0.0002469443,0.00009227229],"domain_scores_gemma":[0.9960974,0.0024426305,0.00040579936,0.00062589266,0.00027879304,0.00014950923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002024546,0.00026750908,0.00024380632,0.0020671387,0.00084656733,0.0060555097,0.00066594157,0.00082950265,0.002096146],"category_scores_gemma":[0.0072857314,0.00026062166,0.00037729985,0.0015414174,0.016304841,0.008427354,0.0014707869,0.0010711622,0.00028822038],"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":[0.00007366726,0.00002797454,0.009868369,0.00026766438,0.000052371943,0.00028653498,0.02723444,0.0026992378,0.0062355744,0.8940849,0.0008714094,0.05829799],"study_design_scores_gemma":[0.000017034154,0.000050655937,0.0076609445,0.00010860458,0.000023109274,0.0005546951,0.008250391,0.0059018554,0.0021529226,0.9547467,0.020501556,0.000031499454],"about_ca_topic_score_codex":0.0020963107,"about_ca_topic_score_gemma":0.0013580711,"teacher_disagreement_score":0.0060555097,"about_ca_system_score_codex":0.0012058102,"about_ca_system_score_gemma":0.0008648451,"threshold_uncertainty_score":0.010706961},"labels":[],"label_agreement":null},{"id":"W2797003656","doi":"10.3390/info9040089","title":"Editorial for the Special Issue on “Wireless Energy Harvesting for Future Wireless Communications”","year":2018,"lang":"en","type":"article","venue":"Information","topic":"Energy Harvesting in Wireless 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":"Western University","funders":"Tomsk Polytechnic University","keywords":"Download; Wireless; Computer science; Wireless network; Telecommunications; Wireless sensor network; World Wide Web; Computer network","score_opus":0.010178240668414044,"score_gpt":0.23154000787053816,"score_spread":0.2213617672021241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797003656","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":[0.00004484119,0.002050409,0.00015251465,0.023911886,0.9721132,0.000015372145,0.000037174297,0.000048668153,0.0016258316],"genre_scores_gemma":[0.00041756747,0.0021340963,0.0001159898,0.027330028,0.95960885,0.000023842487,0.0000408753,0.000040780138,0.010288022],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967463,0.00043403317,0.00037572996,0.00061173446,0.0015421477,0.00029005876],"domain_scores_gemma":[0.98726475,0.004274212,0.0011152186,0.00038201097,0.00511484,0.0018488673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038513094,0.0030302815,0.003302548,0.002007951,0.0024132323,0.0055240835,0.0031098968,0.0128169125,0.019822452],"category_scores_gemma":[0.013879343,0.0010640012,0.0025437677,0.0009054793,0.0017915223,0.0035689417,0.001284776,0.015455018,0.0127864005],"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":[0.00005814944,0.000018356328,0.000031654403,0.00014092277,0.000016681668,0.00007178895,0.0000049324603,0.00002667175,0.00015369391,0.00024794944,0.994183,0.0050462172],"study_design_scores_gemma":[0.00006960454,0.00005153466,0.0004297177,0.00022009235,0.000049975788,0.00018114217,0.00002169027,0.00023837386,0.00023917221,0.00097108335,0.99750715,0.000020479687],"about_ca_topic_score_codex":0.0010689654,"about_ca_topic_score_gemma":0.0030327546,"teacher_disagreement_score":0.019822452,"about_ca_system_score_codex":0.0018343587,"about_ca_system_score_gemma":0.0021231833,"threshold_uncertainty_score":0.06631267},"labels":[],"label_agreement":null},{"id":"W2801771958","doi":"10.3390/info9050119","title":"Fast Identification of High Utility Itemsets from Candidates","year":2018,"lang":"en","type":"article","venue":"Information","topic":"Data Mining Algorithms 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":"Carleton University","funders":"Natural Science Foundation of Hubei Province","keywords":"Computation; Computer science; Identification (biology); Data mining; Tree (set theory); Set (abstract data type); Algorithm; Mathematics","score_opus":0.00928728821890769,"score_gpt":0.2380634091808697,"score_spread":0.228776120961962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801771958","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":[0.104784705,0.0010157152,0.8868128,0.00041067242,0.00009129148,0.0008404357,0.0015143142,0.0023322082,0.0021978568],"genre_scores_gemma":[0.38447788,0.0005789966,0.60598564,0.00015751472,0.000082809995,0.0005337916,0.00542419,0.0001895903,0.0025695064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813133,0.00026378082,0.0001892484,0.0003215364,0.0008431774,0.00025090546],"domain_scores_gemma":[0.9947221,0.002510687,0.00056296657,0.0006821664,0.0012424625,0.00027950446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019994273,0.0010980535,0.0019254732,0.004819131,0.0013508096,0.0020459383,0.0019875132,0.00102479,0.0019497938],"category_scores_gemma":[0.012812115,0.0006244598,0.0014729475,0.0054227165,0.0005078541,0.003337738,0.0020336893,0.001168276,0.0013545757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001123935,0.0005266097,0.022613145,0.0007598154,0.00019848123,0.0011388187,0.0008055281,0.04797343,0.016219744,0.023843076,0.017256942,0.8675405],"study_design_scores_gemma":[0.00014512283,0.00048994797,0.007103046,0.00016389304,0.0001389994,0.0018112531,0.000776721,0.892689,0.024613487,0.059346013,0.0126349665,0.00008760372],"about_ca_topic_score_codex":0.0015723086,"about_ca_topic_score_gemma":0.0027248776,"teacher_disagreement_score":0.004819131,"about_ca_system_score_codex":0.00066502125,"about_ca_system_score_gemma":0.0023671326,"threshold_uncertainty_score":0.010574102},"labels":[],"label_agreement":null},{"id":"W2802102969","doi":"10.3390/info9050113","title":"The Human Takeover: A Call for a Venture into an Existential Opportunity","year":2018,"lang":"en","type":"article","venue":"Information","topic":"Space Science and Extraterrestrial Life","field":"Physics and Astronomy","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":"Existentialism; Humanity; Autonomy; Computer science; Business; Sociology; Political science; Law","score_opus":0.02760747813801662,"score_gpt":0.3156566411345738,"score_spread":0.2880491629965572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802102969","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":[0.047396492,0.015490704,0.08128525,0.5152987,0.0037965276,0.00012617253,0.00008699733,0.0002557802,0.33626327],"genre_scores_gemma":[0.9350887,0.0033654135,0.012176597,0.027644994,0.0014163346,0.00016691898,0.000039640196,0.00021412739,0.019887272],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9880348,0.007819669,0.0002345366,0.0011829265,0.001367095,0.0013610355],"domain_scores_gemma":[0.988953,0.0054369564,0.00078341056,0.001776701,0.00085521094,0.0021947124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013442507,0.0009508106,0.00079500384,0.001541675,0.011700076,0.01686504,0.002347401,0.009442759,0.0074329586],"category_scores_gemma":[0.009630359,0.00057079754,0.0010406108,0.00084356015,0.12910873,0.031038871,0.02064115,0.014949768,0.00093518407],"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":[0.0000113114265,0.000014501101,0.00019004951,0.000034188477,0.0000053801386,0.00013157794,0.017021809,0.0001299182,0.00009615318,0.9756964,0.0033656913,0.0033030775],"study_design_scores_gemma":[0.000010944283,0.000028922914,0.00015812753,0.00015247826,0.0000061672304,0.00025036297,0.017661072,0.0003468381,0.00015243217,0.8186239,0.16257744,0.00003122324],"about_ca_topic_score_codex":0.0026660224,"about_ca_topic_score_gemma":0.0021477952,"teacher_disagreement_score":0.01686504,"about_ca_system_score_codex":0.0058654663,"about_ca_system_score_gemma":0.0054435357,"threshold_uncertainty_score":0.07109159},"labels":[],"label_agreement":null},{"id":"W2806477841","doi":"10.3390/info9060133","title":"A Machine Learning Filter for the Slot Filling Task","year":2018,"lang":"en","type":"article","venue":"Information","topic":"Topic Modeling","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 of Ottawa; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Classifier (UML); Artificial intelligence; USable; Relationship extraction; Natural language processing; Precision and recall; Filter (signal processing); Information extraction; Machine learning; Speech recognition; World Wide Web","score_opus":0.02177142452139717,"score_gpt":0.24031188179576599,"score_spread":0.21854045727436883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806477841","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":[0.037166946,0.00081936904,0.9426866,0.00055373536,0.00027792918,0.00040857232,0.0017852293,0.014282868,0.0020187688],"genre_scores_gemma":[0.21729966,0.00033641164,0.7666698,0.00046393913,0.00034859622,0.0008130048,0.006293677,0.0004953519,0.0072795493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997675,0.00039678763,0.00025239476,0.0007995999,0.0006228609,0.00025335336],"domain_scores_gemma":[0.99298483,0.0045434604,0.00028781613,0.0005916577,0.0014561706,0.0001360712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004772696,0.001744526,0.0019507916,0.0038855171,0.0017956644,0.0021686947,0.0020986253,0.0037949856,0.005125241],"category_scores_gemma":[0.0104269255,0.0005510424,0.0016048874,0.0028723096,0.0005495237,0.002705075,0.0009830344,0.0020939321,0.0052943053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009238926,0.00048628668,0.005734053,0.0004021507,0.00018529981,0.00031331897,0.00028331587,0.016045496,0.029372446,0.0045714984,0.027466059,0.91421616],"study_design_scores_gemma":[0.0001074432,0.00033621292,0.0045892755,0.000075532385,0.00016464265,0.00048180055,0.0001500533,0.91157186,0.047382414,0.007199036,0.027868481,0.00007324111],"about_ca_topic_score_codex":0.010263569,"about_ca_topic_score_gemma":0.010316349,"teacher_disagreement_score":0.010263569,"about_ca_system_score_codex":0.0013313853,"about_ca_system_score_gemma":0.002543913,"threshold_uncertainty_score":0.02524072},"labels":[],"label_agreement":null},{"id":"W2883218724","doi":"10.3390/info9070183","title":"AI to Bypass Creativity. Will Robots Replace Journalists? (The Answer Is “Yes”)","year":2018,"lang":"en","type":"article","venue":"Information","topic":"Ethics and Social Impacts of AI","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":"Toronto Metropolitan University; York University","funders":"","keywords":"Journalism; Citizen journalism; Creativity; Futures studies; Technical Journalism; Computer science; Robot; News media; Artificial intelligence; Sociology; Media studies; Public relations; Political science; World Wide Web; Law","score_opus":0.02777338728242658,"score_gpt":0.3876430849875173,"score_spread":0.35986969770509075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883218724","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":[0.007013494,0.078388296,0.01945289,0.70667094,0.011861423,0.00003768483,0.00010648848,0.0003334702,0.17613542],"genre_scores_gemma":[0.50937146,0.09427893,0.02948944,0.24405128,0.020105401,0.0002840524,0.00016140539,0.0005112329,0.10174686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9955924,0.002499592,0.00015070644,0.0005327276,0.0009386455,0.00028587607],"domain_scores_gemma":[0.98108715,0.0131164,0.0015481056,0.0014722333,0.001714593,0.001061533],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006024532,0.00047740267,0.00038062382,0.0011968252,0.0030723363,0.0067452355,0.00086685986,0.0052746,0.013419855],"category_scores_gemma":[0.021496298,0.0002557605,0.00054129295,0.0010462357,0.016755674,0.019111376,0.002745518,0.0043884907,0.0061080405],"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":[0.00008071945,0.000049845665,0.0010053226,0.00072415505,0.000041037445,0.00013813889,0.002718971,0.00031212723,0.00049741217,0.74825937,0.11591323,0.13025966],"study_design_scores_gemma":[0.000021552254,0.000053060703,0.00070551404,0.00055403716,0.000014419755,0.00025211668,0.0020163795,0.00034040475,0.0006143628,0.49272695,0.50267255,0.000028658245],"about_ca_topic_score_codex":0.0010703218,"about_ca_topic_score_gemma":0.00089952914,"teacher_disagreement_score":0.9969277,"about_ca_system_score_codex":0.0016896343,"about_ca_system_score_gemma":0.0017084692,"threshold_uncertainty_score":0.04489392},"labels":[],"label_agreement":null},{"id":"W2898492624","doi":"10.3390/info9110267","title":"Motivation Perspectives on Opening up Municipality Data: Does Municipality Size Matter?","year":2018,"lang":"en","type":"article","venue":"Information","topic":"E-Government and Public Services","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Perspective (graphical); Local government; Business; Open data; Political science; Public administration","score_opus":0.06115890129622779,"score_gpt":0.35173862857918314,"score_spread":0.29057972728295534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898492624","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":[0.9689584,0.0004566503,0.0051890025,0.0049117226,0.000064857886,0.00011558708,0.00018715028,0.00002469431,0.020092117],"genre_scores_gemma":[0.99818677,0.00011562702,0.00085482857,0.00015862279,0.000009800489,0.00006030359,0.000074142714,0.00001187173,0.00052810967],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9791268,0.014632662,0.00094268116,0.0009615737,0.0026721617,0.0016641335],"domain_scores_gemma":[0.9251852,0.047652546,0.012356913,0.002542115,0.006108077,0.0061550317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01954594,0.00023255397,0.0003876618,0.0017523426,0.0024074786,0.006000981,0.00080177607,0.0008695145,0.00461952],"category_scores_gemma":[0.072252624,0.00031801502,0.0004655267,0.002491098,0.003551564,0.004191815,0.004807254,0.0011981707,0.0003480313],"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":[0.00023594649,0.00019238281,0.6668615,0.000649463,0.00018821792,0.00086231506,0.23368888,0.0006044753,0.0014282641,0.03736612,0.00449854,0.05342393],"study_design_scores_gemma":[0.00003798937,0.00013678006,0.56120896,0.00066536386,0.000094062,0.00028758319,0.37036636,0.0013150468,0.00063728285,0.011478969,0.05367787,0.000093714945],"about_ca_topic_score_codex":0.01082464,"about_ca_topic_score_gemma":0.016897755,"teacher_disagreement_score":0.01954594,"about_ca_system_score_codex":0.0037693297,"about_ca_system_score_gemma":0.0049043954,"threshold_uncertainty_score":0.10337007},"labels":[],"label_agreement":null},{"id":"W2901196901","doi":"10.3390/info9110290","title":"Annotating a Low-Resource Language with LLOD Technology: Sumerian Morphology and Syntax","year":2018,"lang":"en","type":"article","venue":"Information","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Endowment for the Humanities; International Institute of Information Technology, Hyderabad; Bundesministerium für Bildung und Forschung; University of California, Los Angeles; Social Sciences and Humanities Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Sumerian; Syntax; Computer science; Resource (disambiguation); Annotation; Languages of Asia; Linguistics; Natural language processing; Artificial intelligence","score_opus":0.0029036296242360123,"score_gpt":0.22280456179319832,"score_spread":0.2199009321689623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901196901","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":[0.3565407,0.0026169375,0.44521323,0.0043783677,0.0005653943,0.0004887828,0.031293575,0.0055030617,0.15339996],"genre_scores_gemma":[0.6451112,0.0016074871,0.30191353,0.0006606661,0.00011771455,0.0006954488,0.02897847,0.0016005277,0.019314926],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998998,0.0003322668,0.00016625156,0.00026840175,0.00018537135,0.000049677372],"domain_scores_gemma":[0.9976622,0.00080478954,0.00027443518,0.0007329478,0.0004654239,0.00006026368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012896445,0.00044754657,0.0003839356,0.005719357,0.0018245911,0.0024430335,0.0006504457,0.00051586545,0.0071877483],"category_scores_gemma":[0.005074523,0.00032923368,0.0003330631,0.0050473367,0.0017431222,0.004414556,0.0036879492,0.0007902277,0.0018235762],"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":[0.00027137098,0.00012634121,0.035647437,0.003290666,0.00010988004,0.0028373932,0.04663411,0.00351502,0.05619928,0.23916782,0.03377233,0.57842845],"study_design_scores_gemma":[0.000011043371,0.00003306949,0.04154538,0.0009192476,0.00007453647,0.002097194,0.009791815,0.009911637,0.026723273,0.052144717,0.856631,0.000117053605],"about_ca_topic_score_codex":0.0046672863,"about_ca_topic_score_gemma":0.0067483964,"teacher_disagreement_score":0.0071877483,"about_ca_system_score_codex":0.0017522065,"about_ca_system_score_gemma":0.0016136043,"threshold_uncertainty_score":0.024045408},"labels":[],"label_agreement":null},{"id":"W2905655740","doi":"10.3390/info10010006","title":"A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection","year":2018,"lang":"en","type":"article","venue":"Information","topic":"Software Engineering Research","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 of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Entity linking; Information retrieval; Linked data; Word (group theory); Natural language processing; Named-entity recognition; Type (biology); Named entity; Cluster analysis; Artificial intelligence; Knowledge base; Task (project management); Semantic Web; Mathematics; Biology","score_opus":0.036744719496654005,"score_gpt":0.32420075310596663,"score_spread":0.2874560336093126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905655740","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":[0.17017396,0.005215295,0.80081695,0.001136942,0.0008278907,0.00054321816,0.0036296442,0.010251203,0.0074049183],"genre_scores_gemma":[0.39599502,0.0019880794,0.5877478,0.00035129787,0.00024272136,0.00035679515,0.008871814,0.00095368165,0.0034927903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941109,0.0028966798,0.000494813,0.0009807358,0.001270842,0.000246083],"domain_scores_gemma":[0.9793761,0.013167795,0.00078764703,0.0026511794,0.0034630504,0.00055423885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060799597,0.0024927196,0.0013548081,0.00412825,0.0009891589,0.0030626887,0.0016451495,0.0017600113,0.0022668885],"category_scores_gemma":[0.02820952,0.0006334465,0.001236627,0.0033323446,0.00062429503,0.008386907,0.0022504602,0.0024549272,0.0028718275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027947726,0.0012482534,0.018148886,0.0010778555,0.001032575,0.0002738325,0.0009734683,0.057549585,0.011619866,0.01079155,0.015768593,0.8787207],"study_design_scores_gemma":[0.000081542916,0.00044109815,0.003616187,0.00011937993,0.00015464005,0.0002692576,0.0005964507,0.9691075,0.008360748,0.010977169,0.0061576217,0.0001184995],"about_ca_topic_score_codex":0.010489171,"about_ca_topic_score_gemma":0.013654749,"teacher_disagreement_score":0.010489171,"about_ca_system_score_codex":0.0011184823,"about_ca_system_score_gemma":0.0020424898,"threshold_uncertainty_score":0.032154262},"labels":[],"label_agreement":null},{"id":"W2909591905","doi":"10.3390/info10010031","title":"The Universality of Experiential Consciousness","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Philosophy and Theoretical Science","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":"University of Toronto","funders":"","keywords":"Consciousness; Artificial consciousness; Psychology; Assertion; Social consciousness; Cognitive science; Epistemology; Philosophy; Computer science","score_opus":0.0065263713119567595,"score_gpt":0.26106782538795614,"score_spread":0.2545414540759994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909591905","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":[0.1017926,0.017849034,0.18742234,0.03514926,0.0011567702,0.0000977498,0.00024902986,0.0003435458,0.65593964],"genre_scores_gemma":[0.9841277,0.0021422887,0.006754701,0.0015597912,0.00052610977,0.00008111626,0.00007162241,0.000046334193,0.004690226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975078,0.0007762706,0.00013076687,0.00064235344,0.000571072,0.0003717323],"domain_scores_gemma":[0.9936646,0.0034140511,0.000388537,0.0013868182,0.0007215807,0.0004243447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032506753,0.00034482277,0.0005222786,0.0014130742,0.0017626953,0.0047369758,0.0010626287,0.0014386813,0.0050806683],"category_scores_gemma":[0.010080646,0.000306319,0.00056094065,0.0008547641,0.02654973,0.00968764,0.005571211,0.003479699,0.00047573049],"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":[0.000006930862,0.000006296885,0.000329354,0.000039449857,0.0000069938674,0.000052997715,0.0010136388,0.00010152458,0.00013555298,0.99320436,0.00037014126,0.0047326554],"study_design_scores_gemma":[0.000005552519,0.000008940342,0.0007037736,0.000040931576,0.0000045476063,0.00013091236,0.00035902875,0.00016650918,0.00013324013,0.98683935,0.011599631,0.0000076557635],"about_ca_topic_score_codex":0.0009857649,"about_ca_topic_score_gemma":0.00043999054,"teacher_disagreement_score":0.0050806683,"about_ca_system_score_codex":0.0016481188,"about_ca_system_score_gemma":0.0013131371,"threshold_uncertainty_score":0.01719141},"labels":[],"label_agreement":null},{"id":"W2913840322","doi":"10.3390/info10020043","title":"Efficient Security Scheme for Disaster Surveillance UAV Communication Networks","year":2019,"lang":"en","type":"article","venue":"Information","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Computer science; Scheme (mathematics); Computer security; Overhead (engineering); Computer network; Reliability (semiconductor); Network packet; Confidentiality; Exploit","score_opus":0.0025309717969516994,"score_gpt":0.18037119242019625,"score_spread":0.17784022062324456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913840322","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":[0.10822224,0.0008120183,0.8835067,0.00042567393,0.0001448733,0.0002693912,0.00011682791,0.0004455096,0.006056827],"genre_scores_gemma":[0.94268733,0.00026533566,0.0539739,0.000057417292,0.000037667465,0.0001345886,0.0001233503,0.000008500122,0.0027120109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994766,0.00012948108,0.000038765902,0.00008778976,0.00016922015,0.00009818493],"domain_scores_gemma":[0.9995664,0.000091476904,0.00008233302,0.00009923589,0.00013232265,0.000028252061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052655634,0.00034758932,0.000367125,0.00046271805,0.0007496518,0.0005565605,0.0005847502,0.00036626938,0.0016128271],"category_scores_gemma":[0.0008879517,0.000111082714,0.00018627211,0.00028033924,0.00037092296,0.0009321265,0.0009670094,0.00036907187,0.00023710479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011863684,0.00021089303,0.001862488,0.00035276395,0.0001018783,0.0003963313,0.0004325109,0.34841156,0.18691812,0.19399258,0.010044127,0.2560905],"study_design_scores_gemma":[0.00009261705,0.00039326726,0.0006537496,0.000030984385,0.00003436764,0.00034962775,0.000095985015,0.9512443,0.020598995,0.018154982,0.008311225,0.000039916846],"about_ca_topic_score_codex":0.0006631795,"about_ca_topic_score_gemma":0.00077243336,"teacher_disagreement_score":0.0016128271,"about_ca_system_score_codex":0.0008458014,"about_ca_system_score_gemma":0.0007689133,"threshold_uncertainty_score":0.006136775},"labels":[],"label_agreement":null},{"id":"W2917875619","doi":"10.3390/info10020073","title":"AI and the Singularity: A Fallacy or a Great Opportunity?","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fallacy; Human intelligence; Trustworthiness; Variety (cybernetics); Singularity; Artificial general intelligence; Epistemology; Wishful thinking; Field (mathematics); Skepticism; Artificial intelligence; Psychology; Sociology; Computer science; Social psychology; Philosophy; Mathematics","score_opus":0.01300203983681564,"score_gpt":0.23407058252422794,"score_spread":0.2210685426874123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917875619","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":[0.009847323,0.0355395,0.036818463,0.8210734,0.0051815836,0.000030199028,0.000059894588,0.00011021943,0.091339454],"genre_scores_gemma":[0.7922516,0.035596576,0.030943157,0.1091682,0.010605137,0.00016527425,0.00009829769,0.00026869774,0.020902988],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9896588,0.0053645307,0.0005128729,0.0012403054,0.002217303,0.0010062548],"domain_scores_gemma":[0.95962477,0.027879488,0.0026936717,0.0034942825,0.00326598,0.003041735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01579633,0.00063123624,0.0016560473,0.0026272521,0.0075140866,0.013977021,0.0024469877,0.008233286,0.010551295],"category_scores_gemma":[0.03334783,0.0004646272,0.0009806004,0.0023050385,0.066166066,0.04409677,0.011676099,0.015738757,0.0020730223],"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":[0.000016339618,0.000011022965,0.0001633014,0.000062484265,0.000007200455,0.000059380483,0.0011354223,0.00005952898,0.000040522787,0.98598063,0.0067024324,0.005761763],"study_design_scores_gemma":[0.0000050470053,0.000009394061,0.00006857296,0.00010545053,0.0000037388165,0.00008664335,0.0011893731,0.0001629365,0.000049141854,0.96526575,0.033045776,0.000008134023],"about_ca_topic_score_codex":0.0016239021,"about_ca_topic_score_gemma":0.0016681444,"teacher_disagreement_score":0.01579633,"about_ca_system_score_codex":0.0039923694,"about_ca_system_score_gemma":0.004324419,"threshold_uncertainty_score":0.08354002},"labels":[],"label_agreement":null},{"id":"W2941717757","doi":"10.3390/info10050156","title":"Exploring Whether Data Can be Represented as a Composite Unit in Form Processing Using the Manufacturing of Information Approach","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Data Quality and Management","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Relevance (law); Computer science; Representation (politics); Block (permutation group theory); Unit (ring theory); Quality (philosophy); Data mining; Simple (philosophy); Data quality; Information retrieval; Artificial intelligence; Mathematics; Engineering; Operations management","score_opus":0.49072795230219474,"score_gpt":0.41395952453904594,"score_spread":0.0767684277631488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941717757","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":[0.2552335,0.00077406934,0.7250807,0.0012756832,0.000052373827,0.0004969811,0.000263399,0.00032029205,0.016503053],"genre_scores_gemma":[0.61787295,0.00044088604,0.3798965,0.00008631121,0.000017517385,0.00015671343,0.00018100365,0.000028749708,0.001319451],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99641883,0.0019284614,0.00024553225,0.0004472709,0.00081512565,0.00014487178],"domain_scores_gemma":[0.98637563,0.008650578,0.0014486182,0.0019128426,0.0013985217,0.00021376168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044646505,0.00044028633,0.00050915265,0.0032850464,0.0006543997,0.004561391,0.0009076383,0.0008238382,0.0028513125],"category_scores_gemma":[0.01846197,0.00029227315,0.0011917533,0.003740552,0.0017252308,0.005544893,0.0014319475,0.00085435616,0.00031995767],"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":[0.0007293254,0.0004996627,0.050430868,0.0009132279,0.0002953472,0.0006466141,0.003912336,0.10298491,0.010610915,0.28603682,0.0022340682,0.5407058],"study_design_scores_gemma":[0.000090227106,0.00076930935,0.029321805,0.00030256686,0.00029818751,0.0006270808,0.004110905,0.774746,0.009359972,0.15375938,0.026511775,0.000102744976],"about_ca_topic_score_codex":0.0045978706,"about_ca_topic_score_gemma":0.004414225,"teacher_disagreement_score":0.0045978706,"about_ca_system_score_codex":0.002019646,"about_ca_system_score_gemma":0.0016297105,"threshold_uncertainty_score":0.023611665},"labels":[],"label_agreement":null},{"id":"W2947748448","doi":"10.3390/info10060184","title":"FPGA Implementation of Crossover Module of Genetic Algorithm","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Field-programmable gate array; Computer science; Travelling salesman problem; Genetic algorithm; Parallel computing; Software; Realization (probability); Computer architecture; Field (mathematics); Algorithm; Embedded system; Operating system; Mathematics; Artificial intelligence","score_opus":0.009378328181832317,"score_gpt":0.28732289737290556,"score_spread":0.27794456919107324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947748448","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":[0.14538847,0.0013970466,0.81496835,0.00032599378,0.00044756467,0.00018204344,0.00020977044,0.008085169,0.02899566],"genre_scores_gemma":[0.7948558,0.00048814717,0.19707212,0.00011118731,0.00007177512,0.00007184415,0.00020312493,0.00008063401,0.007045534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999879,0.000021681735,0.0000057819416,0.000021347852,0.00004736426,0.00002477476],"domain_scores_gemma":[0.9999217,0.000019729883,0.000010568928,0.000014184093,0.000028007502,0.000005775586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010794729,0.00024216702,0.00018671436,0.0003959319,0.00017520864,0.00032622393,0.0005171625,0.00023813768,0.003121318],"category_scores_gemma":[0.00025745772,0.000107695305,0.00015347164,0.00027548627,0.00010033861,0.00020492208,0.000106297826,0.00026610264,0.0005334032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000719196,0.000139525,0.0028606688,0.00046233574,0.00010873256,0.0009403619,0.00019460969,0.06463028,0.2849055,0.020369545,0.0077904635,0.61687887],"study_design_scores_gemma":[0.0003669438,0.0016837902,0.008860919,0.000117770636,0.00014290694,0.0034153853,0.00007484886,0.43639636,0.4633735,0.0041064676,0.08137475,0.000086378066],"about_ca_topic_score_codex":0.0011290299,"about_ca_topic_score_gemma":0.00082339393,"teacher_disagreement_score":0.003121318,"about_ca_system_score_codex":0.00022412598,"about_ca_system_score_gemma":0.00027792205,"threshold_uncertainty_score":0.01044184},"labels":[],"label_agreement":null},{"id":"W2950811391","doi":"10.3390/info10060208","title":"Latent Feature Group Learning for High-Dimensional Data Clustering","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Shenzhen University; China Postdoctoral Science Foundation","keywords":"Feature (linguistics); Pattern recognition (psychology); Cluster analysis; Artificial intelligence; Weighting; Computer science; Fitness function; Constraint (computer-aided design); Clustering high-dimensional data; Crossover; Data mining; Genetic algorithm; Mathematics; Machine learning","score_opus":0.02428100589534357,"score_gpt":0.2843044617072172,"score_spread":0.2600234558118736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950811391","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":[0.0043083206,0.000220885,0.99467975,0.00009467053,0.00002119771,0.00003127374,0.00004034136,0.00035784225,0.00024568598],"genre_scores_gemma":[0.2801504,0.00037843964,0.71586883,0.00024044936,0.00013904143,0.0003530794,0.000885537,0.00020771439,0.0017764813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974474,0.00076886045,0.0001477808,0.00072612934,0.0007056894,0.00020413657],"domain_scores_gemma":[0.9977811,0.0008269248,0.00029520984,0.00043896397,0.00054953265,0.00010828351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024284709,0.0010895665,0.0018738508,0.002763347,0.0015274079,0.0012113947,0.0023837867,0.0016776187,0.0014654786],"category_scores_gemma":[0.0058657415,0.00044747582,0.0015956023,0.00323175,0.0013208954,0.0025855114,0.001981604,0.0018317706,0.00092321273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018978877,0.00026285485,0.004668868,0.00030252387,0.00033394797,0.00015394545,0.0005106179,0.40147427,0.0067445645,0.02904566,0.006537904,0.549775],"study_design_scores_gemma":[0.00001479336,0.00004064674,0.00034158223,0.000011607189,0.000018179579,0.000043827098,0.00004923104,0.9790688,0.0011613042,0.017889038,0.0013413158,0.000019653193],"about_ca_topic_score_codex":0.0042124866,"about_ca_topic_score_gemma":0.0041458854,"teacher_disagreement_score":0.0042124866,"about_ca_system_score_codex":0.0012148242,"about_ca_system_score_gemma":0.0016183539,"threshold_uncertainty_score":0.012843132},"labels":[],"label_agreement":null},{"id":"W2964083623","doi":"10.3390/info10080246","title":"Text Filtering through Multi-Pattern Matching: A Case Study of Wu–Manber–Uy on the Language of Uyghur","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Natural Language Processing Techniques","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; National Natural Science Foundation of China; York University","keywords":"Computer science; Spelling; Word (group theory); Word2vec; Artificial intelligence; Vowel; Natural language processing; Matching (statistics); Population; Field (mathematics); Linguistics; Speech recognition; Mathematics; Statistics; Sociology","score_opus":0.019728807507551432,"score_gpt":0.2912128139284301,"score_spread":0.27148400642087867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964083623","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":[0.8106694,0.0018693715,0.17232178,0.0016169687,0.00012617566,0.00032543985,0.00061759073,0.0029833326,0.009469865],"genre_scores_gemma":[0.7455184,0.0005599078,0.2413029,0.00048889924,0.00009953234,0.00013199695,0.0010654372,0.00048246526,0.010350437],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991904,0.00031670302,0.00006779816,0.00017426834,0.00017491687,0.0000760083],"domain_scores_gemma":[0.99858785,0.0008129322,0.00010276406,0.00022304346,0.00021130827,0.00006223368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010300056,0.00040960708,0.00070009637,0.000999107,0.0012128697,0.0009895698,0.0005499704,0.00087215647,0.0014819722],"category_scores_gemma":[0.0037803468,0.00015688375,0.00051667844,0.0016646595,0.0008958414,0.0024302006,0.0010437763,0.00057490397,0.0006333215],"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":[0.0012355465,0.0007010256,0.039548766,0.0010448201,0.00024292721,0.01633137,0.016324824,0.02452381,0.0850091,0.05785019,0.026059143,0.7311285],"study_design_scores_gemma":[0.0002457219,0.001073605,0.041361105,0.00017874093,0.00022443885,0.010456535,0.011674178,0.61783713,0.10307547,0.043017585,0.170595,0.0002605312],"about_ca_topic_score_codex":0.01186669,"about_ca_topic_score_gemma":0.0135346195,"teacher_disagreement_score":0.01186669,"about_ca_system_score_codex":0.00045652717,"about_ca_system_score_gemma":0.0007022339,"threshold_uncertainty_score":0.023595214},"labels":[],"label_agreement":null},{"id":"W2967460745","doi":"10.3390/info10080257","title":"Visual Saliency Prediction Based on Deep Learning","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Visual Attention and Saliency Detection","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":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Memorial University of Newfoundland","funders":"Ministry of Higher Education and Scientific Research","keywords":"Artificial intelligence; Computer science; Deep learning; Convolutional neural network; Categorical variable; Pattern recognition (psychology); Segmentation; Transfer of learning; Saliency map; Kadir–Brady saliency detector; Pixel; Encoder; Computer vision; Image (mathematics); Machine learning","score_opus":0.004704822597397344,"score_gpt":0.22767142255383438,"score_spread":0.22296659995643703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967460745","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":[0.3012817,0.0021358193,0.6863009,0.00054317346,0.00017378393,0.00012889106,0.0008517383,0.0033485496,0.0052354485],"genre_scores_gemma":[0.9609368,0.0002607273,0.03627305,0.00009008175,0.00004602813,0.00003123095,0.00054940686,0.000041985495,0.0017706308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987125,0.000014588516,0.000005822012,0.00004295547,0.000034228902,0.00003113882],"domain_scores_gemma":[0.99971014,0.00009355175,0.000036149577,0.00002033022,0.00011773094,0.000022129343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029258625,0.0008589264,0.0005576988,0.0012016308,0.00019996827,0.0003918301,0.00070764334,0.00050956896,0.0012654467],"category_scores_gemma":[0.0013104179,0.00024000474,0.00048531336,0.0005270537,0.00024434392,0.000739593,0.00054360594,0.0006125546,0.0003002174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045394024,0.00030040526,0.0075683375,0.00020519157,0.00014737029,0.0002478685,0.00011379978,0.43428943,0.040741004,0.0033960138,0.007989646,0.504547],"study_design_scores_gemma":[0.0000043346295,0.000018551504,0.00079566234,0.0000037860784,0.000006767694,0.000013232645,0.0000041088947,0.9952779,0.0025206641,0.0011945856,0.00015789348,0.00000263297],"about_ca_topic_score_codex":0.009959751,"about_ca_topic_score_gemma":0.01110439,"teacher_disagreement_score":0.009959751,"about_ca_system_score_codex":0.0009001722,"about_ca_system_score_gemma":0.00045510373,"threshold_uncertainty_score":0.019803584},"labels":[],"label_agreement":null},{"id":"W2972799026","doi":"10.3390/info10090286","title":"Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Memorial University of Newfoundland","funders":"","keywords":"Discriminator; Computer science; Deep learning; Artificial intelligence; Convolutional neural network; Adversarial system; Image (mathematics); Convolution (computer science); Pattern recognition (psychology); Generative adversarial network; Artificial neural network; Computer vision; Detector; Telecommunications","score_opus":0.007330237573003794,"score_gpt":0.1956074419454287,"score_spread":0.1882772043724249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972799026","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":[0.21744667,0.00044677028,0.7789355,0.0003023103,0.000033333767,0.00006630374,0.00006949262,0.0008446877,0.0018549198],"genre_scores_gemma":[0.9239423,0.00015765273,0.07420354,0.000065804226,0.000011040809,0.000022022787,0.000094193885,0.000016167998,0.0014872551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960476,0.00009726883,0.000016569125,0.00011010394,0.000117561234,0.0000537601],"domain_scores_gemma":[0.9994393,0.00023867754,0.00011155749,0.0000968297,0.00008999504,0.00002371206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009805298,0.00066527614,0.0005397275,0.0006604925,0.00023395129,0.00054995366,0.00083315244,0.0008351507,0.00055302476],"category_scores_gemma":[0.0015853966,0.00030415712,0.00057755644,0.00029681952,0.00072235195,0.0008641597,0.0008736769,0.00087413733,0.0001907657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023388256,0.000109556044,0.003246918,0.00005042912,0.000091454785,0.00017893038,0.0000634812,0.84704566,0.020350555,0.0036296106,0.0005880202,0.124411516],"study_design_scores_gemma":[0.0000011340193,0.000020231404,0.0003972076,0.0000027863625,0.00000500027,0.00003320481,0.000003576341,0.9936724,0.005305982,0.00046257934,0.00009210672,0.0000038967332],"about_ca_topic_score_codex":0.0038064627,"about_ca_topic_score_gemma":0.0032448499,"teacher_disagreement_score":0.0038064627,"about_ca_system_score_codex":0.0009081327,"about_ca_system_score_gemma":0.00045840183,"threshold_uncertainty_score":0.0075686574},"labels":[],"label_agreement":null},{"id":"W2975249592","doi":"10.3390/info10100301","title":"Gender, Age and Subjective Well-Being: Towards Personalized Persuasive Health Interventions","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Psychological Well-being and Life Satisfaction","field":"Psychology","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Happiness; Subjective well-being; Psychological intervention; Psychology; Life satisfaction; Well-being; Affect (linguistics); Structural equation modeling; Cognition; Clinical psychology; Confirmatory factor analysis; Developmental psychology; Social psychology","score_opus":0.03695232586787422,"score_gpt":0.35401324020042285,"score_spread":0.3170609143325486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975249592","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":[0.8896339,0.017382456,0.014304746,0.044810228,0.000830712,0.00040917864,0.00016725267,0.00009403684,0.0323675],"genre_scores_gemma":[0.98212194,0.0047779647,0.008612636,0.0027247437,0.00014808778,0.00021080262,0.000040122417,0.000008782026,0.001354844],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99858594,0.0009247011,0.000052705545,0.00012609134,0.00017389322,0.00013664288],"domain_scores_gemma":[0.99552816,0.0027936108,0.00070820644,0.00009791016,0.0003427538,0.0005292726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006435788,0.00035868515,0.00029016362,0.0007714016,0.0006441606,0.00235835,0.0004414648,0.0010619492,0.004470198],"category_scores_gemma":[0.013634207,0.00022254766,0.00042839564,0.00040256293,0.00089883094,0.0021197028,0.0011153462,0.0013372437,0.00043393398],"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":[0.0007694938,0.002173243,0.3030775,0.00294212,0.0002212718,0.0010246019,0.07488752,0.0008992053,0.003916492,0.019368803,0.008845122,0.58187467],"study_design_scores_gemma":[0.00035037068,0.0035013368,0.64973253,0.010575486,0.0009691539,0.0025568085,0.13177837,0.006203528,0.0049060145,0.074164495,0.115060575,0.00020136054],"about_ca_topic_score_codex":0.0015517118,"about_ca_topic_score_gemma":0.0028795525,"teacher_disagreement_score":0.006435788,"about_ca_system_score_codex":0.0007232933,"about_ca_system_score_gemma":0.0019173083,"threshold_uncertainty_score":0.0340361},"labels":[],"label_agreement":null},{"id":"W2976432773","doi":"10.3390/info10100304","title":"Understanding Humans: The Extensions of Digital Media","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Media, Communication, and Education","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"University of Toronto","keywords":"Digital media; Computer science; Field (mathematics); Multimedia; Social media; Internet privacy; Extension (predicate logic); Electronic media; World Wide Web","score_opus":0.15558925372637925,"score_gpt":0.32541751169501365,"score_spread":0.1698282579686344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976432773","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":[0.10524906,0.023061866,0.17268163,0.0759875,0.0011213807,0.00015696826,0.00020066458,0.0003790575,0.6211619],"genre_scores_gemma":[0.9559712,0.0058242083,0.021122286,0.003160649,0.0004517044,0.000096040014,0.000076063145,0.00008835841,0.013209428],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99321353,0.0050050085,0.00013838,0.0006036865,0.0007108937,0.00032845928],"domain_scores_gemma":[0.9885389,0.007225786,0.0008192289,0.0020245956,0.0007336391,0.0006577346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007405197,0.0006176641,0.00036713216,0.0021687825,0.0035431061,0.012847487,0.001077286,0.0029181214,0.0055735046],"category_scores_gemma":[0.012441033,0.00049398176,0.00045259748,0.0011849992,0.04776061,0.04142508,0.007083891,0.004327658,0.00064750656],"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":[0.000020524467,0.000011411347,0.00085423334,0.000058276546,0.000005662193,0.000107331085,0.033879634,0.0003299751,0.0001927778,0.94932073,0.001656852,0.013562682],"study_design_scores_gemma":[0.0000074510567,0.000024911033,0.00063676556,0.00023016022,0.00000751408,0.0002693932,0.0161861,0.00088958914,0.00023594259,0.87020236,0.1112877,0.000022063976],"about_ca_topic_score_codex":0.0030880093,"about_ca_topic_score_gemma":0.0020033477,"teacher_disagreement_score":0.012847487,"about_ca_system_score_codex":0.002653433,"about_ca_system_score_gemma":0.0023065074,"threshold_uncertainty_score":0.039162874},"labels":[],"label_agreement":null},{"id":"W2988070392","doi":"10.3390/info10110347","title":"Can Message-Tailoring Based on Regulatory Fit Theory Improve the Efficacy of Persuasive Physical Activity Systems?","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Behavioral Health and Interventions","field":"Psychology","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":"York University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Promotion (chess); Health promotion; Intervention (counseling); Behavior change; Persuasive communication; Regulatory focus theory; Psychology; Health communication; Physical activity; Persuasive technology; Applied psychology; Orientation (vector space); Control (management); Public health; Persuasion; Medicine; Social psychology; Computer science; Physical therapy; Nursing; Communication","score_opus":0.031247037633325342,"score_gpt":0.34485978616526963,"score_spread":0.3136127485319443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988070392","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":[0.8649099,0.0029792383,0.10765896,0.005874536,0.00033557674,0.0024451895,0.0001429838,0.0012316074,0.014421948],"genre_scores_gemma":[0.9594382,0.000511987,0.0381162,0.0005592847,0.00006811478,0.00086964195,0.000047938764,0.00003632244,0.00035226363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993594,0.0047176844,0.00025341377,0.00046915232,0.0007232317,0.00024259745],"domain_scores_gemma":[0.9592063,0.034381956,0.00294423,0.0018270762,0.0010789279,0.0005614913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015021937,0.0007218544,0.0009796367,0.0014254411,0.0003705116,0.0015657664,0.0010163883,0.0016302041,0.0034428078],"category_scores_gemma":[0.06985572,0.00039402925,0.0014178366,0.0006287067,0.00085706037,0.003053144,0.0009639261,0.0010475048,0.00046535343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004343464,0.011006479,0.05023134,0.0025475326,0.00088026805,0.00013278043,0.003822793,0.014770482,0.0051180217,0.006652046,0.0019193324,0.89857554],"study_design_scores_gemma":[0.009369963,0.078105524,0.3891138,0.006829771,0.008346824,0.000928917,0.0066910987,0.3383846,0.027053757,0.10926467,0.025324244,0.00058683276],"about_ca_topic_score_codex":0.0009184981,"about_ca_topic_score_gemma":0.0008280259,"teacher_disagreement_score":0.015021937,"about_ca_system_score_codex":0.00088804605,"about_ca_system_score_gemma":0.0012376439,"threshold_uncertainty_score":0.07944453},"labels":[],"label_agreement":null},{"id":"W2988509849","doi":"10.3390/info10110352","title":"The Influence of Age, Gender, and Cognitive Ability on the Susceptibility to Persuasive Strategies","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Media Influence and Health","field":"Arts and Humanities","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":"Dalhousie University; University of Saskatchewan","funders":"","keywords":"Affect (linguistics); Psychology; Cognition; Trustworthiness; Developmental psychology; Persuasive technology; Social learning; Social psychology; Social cognitive theory; Pedagogy","score_opus":0.03929919794014691,"score_gpt":0.28302203833668577,"score_spread":0.24372284039653885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988509849","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":[0.9971144,0.0002189347,0.00042004706,0.000044014694,0.000010351289,0.000015818006,0.0000368832,0.0000037646093,0.0021357234],"genre_scores_gemma":[0.99918705,0.00007380558,0.000161858,0.000010530629,0.000004459578,0.0000071717036,0.000021104011,0.000001927427,0.0005321417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990578,0.00026810163,0.000117353455,0.00015849661,0.00026624315,0.00013196694],"domain_scores_gemma":[0.98992985,0.006623687,0.0015370975,0.0005293355,0.0007369808,0.0006430633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018638481,0.00028956062,0.00022253198,0.00080432725,0.0002687451,0.00094159716,0.00018607489,0.00029525225,0.0038332576],"category_scores_gemma":[0.0137476465,0.00013388798,0.00033460156,0.00027126505,0.00041022353,0.00053520425,0.00047834375,0.00035467293,0.0005027901],"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":[0.00042718695,0.0003170224,0.9514639,0.00008396654,0.00009423215,0.00039731336,0.008891945,0.00015855559,0.0037926792,0.00040636744,0.0001990368,0.033767868],"study_design_scores_gemma":[0.000009188542,0.0004889289,0.9906192,0.000031815787,0.000068612026,0.00067731587,0.004195929,0.0005539807,0.0012528904,0.0006528546,0.001425676,0.00002362113],"about_ca_topic_score_codex":0.0009978623,"about_ca_topic_score_gemma":0.0014388545,"teacher_disagreement_score":0.0038332576,"about_ca_system_score_codex":0.00013869471,"about_ca_system_score_gemma":0.0002587479,"threshold_uncertainty_score":0.012823522},"labels":[],"label_agreement":null},{"id":"W2989389353","doi":"10.3390/info10110344","title":"Investigation of the Moderating Effect of Culture on Users’ Susceptibility to Persuasive Features in Fitness Applications","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Collectivism; Persuasion; Psychology; Social psychology; Individualism; Persuasive technology; Ranking (information retrieval); Preference; Computer science; Political science; Artificial intelligence","score_opus":0.008614472297505598,"score_gpt":0.25960668522924124,"score_spread":0.2509922129317356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989389353","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":[0.99879324,0.000047685866,0.000234008,0.00004364107,0.0000046745186,0.000030089905,0.000021749844,0.0000026406929,0.00082229415],"genre_scores_gemma":[0.9992586,0.000041839983,0.00035879988,0.000029775652,0.0000045565002,0.00003538405,0.000022030445,0.000003375235,0.00024563255],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9936133,0.003850065,0.00064431183,0.00054343883,0.00096136495,0.0003875792],"domain_scores_gemma":[0.91564786,0.060209,0.01101867,0.004501196,0.006179072,0.0024442165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008169247,0.0003808803,0.00041169362,0.0006688757,0.0008770194,0.0022399034,0.0003833148,0.000565767,0.00274972],"category_scores_gemma":[0.044927366,0.00032807217,0.0006543903,0.00048638866,0.0008131213,0.0008981027,0.0012173041,0.0010762346,0.00034850076],"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":[0.0009448877,0.0006588244,0.93992364,0.00021399853,0.0003756615,0.00016650686,0.032703828,0.0001990782,0.002995819,0.00025998443,0.00015624338,0.021401538],"study_design_scores_gemma":[0.00003532906,0.0010720022,0.9767775,0.00007863055,0.00026561308,0.00017829597,0.017220818,0.0013928491,0.0018798084,0.00018269705,0.0008701555,0.00004633539],"about_ca_topic_score_codex":0.003794158,"about_ca_topic_score_gemma":0.003857043,"teacher_disagreement_score":0.008169247,"about_ca_system_score_codex":0.00045014606,"about_ca_system_score_gemma":0.00070897647,"threshold_uncertainty_score":0.043203652},"labels":[],"label_agreement":null},{"id":"W2997189740","doi":"10.3390/info11010029","title":"Importance Analysis of Components of a Multi-Operational-State Power System Using Fault Tree Models","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Independent Electricity System Operator; Uniwersytet Szczeciński","keywords":"Fault tree analysis; Dependability; Operational planning; State (computer science); Reliability engineering; Computer science; Tree (set theory); Electric power system; Metamodeling; Fault (geology); Operations research; Engineering; Power (physics); Mathematics","score_opus":0.13337220105093006,"score_gpt":0.34382566880156495,"score_spread":0.2104534677506349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997189740","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":[0.26489443,0.00015266926,0.731979,0.00010138029,0.00001239978,0.00008055977,0.00019519957,0.00019671423,0.0023876687],"genre_scores_gemma":[0.9571942,0.00008417895,0.041916974,0.000008053846,0.000007788848,0.000045233577,0.00014705984,0.000024332203,0.0005721145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989674,0.00030497232,0.000060763094,0.00015288404,0.00038946184,0.00012461038],"domain_scores_gemma":[0.99693644,0.002121176,0.0003546106,0.0001555225,0.00035042292,0.00008174479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015704683,0.00082445424,0.00061769114,0.0028490168,0.00045724466,0.0011829388,0.00060469756,0.00056926213,0.0012684956],"category_scores_gemma":[0.00579436,0.00035277728,0.0010057109,0.0014271275,0.00058612006,0.0017747959,0.00067230285,0.00071490335,0.00008873874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063370906,0.000028405135,0.005698902,0.0000502751,0.000053187654,0.0001903561,0.00015503849,0.9639349,0.0023518207,0.016191358,0.000110385256,0.011172075],"study_design_scores_gemma":[0.0000023100072,0.000018434563,0.001021899,0.0000045298016,0.000019414727,0.000023743723,0.000023270291,0.99190396,0.00062859757,0.0061621433,0.0001869604,0.000004848629],"about_ca_topic_score_codex":0.0068667526,"about_ca_topic_score_gemma":0.005020414,"teacher_disagreement_score":0.0068667526,"about_ca_system_score_codex":0.0012893676,"about_ca_system_score_gemma":0.00084479694,"threshold_uncertainty_score":0.013653576},"labels":[],"label_agreement":null},{"id":"W2999658533","doi":"10.3390/info11010044","title":"Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists","year":2020,"lang":"en","type":"article","venue":"Information","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":"National Research Council Canada","funders":"","keywords":"Computer science; Concatenation (mathematics); Reduction (mathematics); Data mining; Property (philosophy); Extension (predicate logic); Sequence (biology); Pattern search; Algorithm; Mathematics","score_opus":0.024125519468434935,"score_gpt":0.27403310736290215,"score_spread":0.2499075878944672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999658533","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":[0.07787043,0.0003856419,0.91720027,0.00037777342,0.00004035159,0.00023215909,0.0003061818,0.0017048876,0.0018824112],"genre_scores_gemma":[0.30863202,0.0003566861,0.6859761,0.00016368403,0.00006635487,0.00027265848,0.0012630657,0.00021274523,0.0030566526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718297,0.0006268384,0.00019770279,0.00037620778,0.0013809614,0.00023539933],"domain_scores_gemma":[0.987543,0.0074259187,0.0010141206,0.0022277758,0.0015259592,0.00026333999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028411262,0.00081716664,0.0012754318,0.0018106065,0.0008642536,0.0018357771,0.0024403445,0.00084708864,0.0027499357],"category_scores_gemma":[0.01893881,0.0007178847,0.0011819939,0.0021524162,0.00075830537,0.0044711637,0.0018592887,0.0013911016,0.0012638734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001207389,0.0005340993,0.013296755,0.0005631145,0.00015319708,0.0010191213,0.00081449817,0.119083315,0.048404973,0.04411391,0.0071264463,0.7636832],"study_design_scores_gemma":[0.00007190919,0.000593758,0.001826979,0.00008138562,0.00009520608,0.0007143398,0.00027482453,0.926644,0.02674662,0.034546446,0.008360384,0.00004412681],"about_ca_topic_score_codex":0.0013519698,"about_ca_topic_score_gemma":0.0033003201,"teacher_disagreement_score":0.0028411262,"about_ca_system_score_codex":0.00057071703,"about_ca_system_score_gemma":0.002476485,"threshold_uncertainty_score":0.0150254965},"labels":[],"label_agreement":null},{"id":"W3006845954","doi":"10.3390/info11030130","title":"Smali+: An Operational Semantics for Low-Level Code Generated from Reverse Engineering Android Applications","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Android (operating system); Correctness; Programming language; Operational semantics; Reverse engineering; Formal methods; Program analysis; Programmer; Static analysis; Software engineering; Semantics (computer science); Operating system","score_opus":0.028957399423620106,"score_gpt":0.2455302579281191,"score_spread":0.216572858504499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006845954","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":[0.0052094217,0.0001617685,0.9887911,0.00027510873,0.000074745556,0.00013846088,0.00027966424,0.0030659502,0.0020037182],"genre_scores_gemma":[0.182975,0.00064132526,0.80593604,0.00068457454,0.00019079221,0.0009179532,0.0014541114,0.002403892,0.0047962847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664634,0.00083907816,0.0004735663,0.00052662875,0.0011973295,0.0003169622],"domain_scores_gemma":[0.99584275,0.0020666604,0.0005187585,0.00076322776,0.00070250675,0.00010597737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028182182,0.0012569933,0.00053556426,0.0015754671,0.0008798179,0.0028337892,0.0019135408,0.0016748483,0.002897723],"category_scores_gemma":[0.0068228045,0.00089494855,0.0018954696,0.00062876334,0.0048535746,0.0041937623,0.0022530293,0.0031191083,0.001275851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018148897,0.00010336946,0.001443035,0.00084160885,0.00005592229,0.00078960904,0.0024485898,0.031168027,0.020929303,0.87301886,0.0057976195,0.06322256],"study_design_scores_gemma":[0.00009605146,0.000283544,0.00073319656,0.00045907614,0.0001229213,0.0014365462,0.0004497904,0.20948802,0.059029665,0.5605455,0.16715921,0.0001965416],"about_ca_topic_score_codex":0.002293133,"about_ca_topic_score_gemma":0.002128184,"teacher_disagreement_score":0.002897723,"about_ca_system_score_codex":0.0012526207,"about_ca_system_score_gemma":0.0020159734,"threshold_uncertainty_score":0.01490432},"labels":[],"label_agreement":null},{"id":"W3018676139","doi":"10.3390/info11050235","title":"Ethical Applications of Big Data-Driven AI on Social Systems: Literature Analysis and Example Deployment Use Case","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Digital Mental Health Interventions","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":"Carleton University","funders":"","keywords":"Social computing; Big data; Enabling; Data science; Software deployment; Computer science; Loneliness; Management science; Engineering ethics; Computer security; Sociology; Social media; Engineering; Psychology; Social psychology; World Wide Web","score_opus":0.1498387098822185,"score_gpt":0.4064267139084966,"score_spread":0.2565880040262781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018676139","genre_codex":"empirical","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":[0.54609215,0.120603725,0.077981316,0.047249764,0.00058196735,0.001957746,0.0011113558,0.00021552085,0.20420639],"genre_scores_gemma":[0.8783778,0.078841425,0.033394404,0.0032845496,0.00024954873,0.0011604757,0.00047748064,0.00008969363,0.0041246247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9883653,0.008420378,0.0007620792,0.00032194323,0.001807886,0.00032246683],"domain_scores_gemma":[0.9359031,0.05809947,0.0012358084,0.0009944959,0.003468215,0.00029888604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012653947,0.00043980082,0.0004392944,0.009269808,0.0035630744,0.004290358,0.0009168919,0.002222563,0.0023176672],"category_scores_gemma":[0.027503299,0.0003495962,0.0006079163,0.011866499,0.004010105,0.005076811,0.003156553,0.0016492836,0.00038824225],"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":[0.00017230977,0.0006899898,0.020023514,0.011896446,0.00021636666,0.008772156,0.13282153,0.007398396,0.0020963808,0.434331,0.026061362,0.3555206],"study_design_scores_gemma":[0.000105948784,0.00048673875,0.037184868,0.026260367,0.0003835544,0.0050595426,0.24588546,0.022775564,0.0035412165,0.13861114,0.5195199,0.0001857869],"about_ca_topic_score_codex":0.0071197064,"about_ca_topic_score_gemma":0.012814028,"teacher_disagreement_score":0.012653947,"about_ca_system_score_codex":0.0044323886,"about_ca_system_score_gemma":0.002947303,"threshold_uncertainty_score":0.066921294},"labels":[],"label_agreement":null},{"id":"W3047416267","doi":"10.3390/info11080386","title":"Predicting Acute Kidney Injury: A Machine Learning Approach Using Electronic Health Records","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Acute Kidney Injury Research","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":true,"ca_institutions":"Western University","funders":"","keywords":"Logistic regression; Emergency department; Medicine; Health records; Emergency medicine; Acute kidney injury; Metric (unit); Healthcare Cost and Utilization Project; Health care; Predictive modelling; Retrospective cohort study; Medical emergency; Machine learning; Intensive care medicine; Internal medicine; Computer science","score_opus":0.03163557583825543,"score_gpt":0.3278363932328936,"score_spread":0.29620081739463816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047416267","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":[0.7802931,0.0020870804,0.20897624,0.0017596461,0.00016368508,0.00045011658,0.0023776994,0.0011639172,0.0027285817],"genre_scores_gemma":[0.92566043,0.0005698337,0.07134236,0.00015192346,0.00012595257,0.00011929251,0.0016335696,0.00001420716,0.00038249017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997773,0.001052021,0.00026039543,0.00037825792,0.0004046792,0.00013163731],"domain_scores_gemma":[0.99406683,0.0038896962,0.0005799443,0.00040793436,0.0009248258,0.00013084627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042262822,0.00097235636,0.001016415,0.0037676091,0.00057913456,0.0013774165,0.0008420379,0.00090465485,0.00053193263],"category_scores_gemma":[0.014443915,0.0002890881,0.0009031834,0.0027337943,0.00016230311,0.001663119,0.0008465633,0.00095638324,0.00030097543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004854724,0.0012530658,0.34284404,0.00019174512,0.0010886783,0.00024565795,0.00023831862,0.23944525,0.0016937471,0.0007047225,0.0028784173,0.4089309],"study_design_scores_gemma":[0.000025650072,0.00023026555,0.030659093,0.00005852855,0.00017995854,0.0001088401,0.00012378495,0.9656025,0.0008499517,0.0015044247,0.0006290144,0.000027902057],"about_ca_topic_score_codex":0.013570713,"about_ca_topic_score_gemma":0.012242779,"teacher_disagreement_score":0.013570713,"about_ca_system_score_codex":0.00072773686,"about_ca_system_score_gemma":0.0010398395,"threshold_uncertainty_score":0.0269835},"labels":[],"label_agreement":null},{"id":"W3072329718","doi":"10.3390/info11080399","title":"Preventative Nudges: Introducing Risk Cues for Supporting Online Self-Disclosure Decisions","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Universität Duisburg-Essen; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Nudge theory; Internet privacy; SAFER; Risk perception; Perception; Private information retrieval; Computer science; Work (physics); Computer security; Psychology; Social psychology","score_opus":0.0357087967049944,"score_gpt":0.34625636217597666,"score_spread":0.3105475654709823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3072329718","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":[0.34389773,0.00077546155,0.6091439,0.0053569344,0.00037288826,0.0013333829,0.00023240164,0.0058384775,0.033048872],"genre_scores_gemma":[0.82569665,0.00020321383,0.1705647,0.00043899252,0.000053493295,0.00043034033,0.000086285276,0.000088015804,0.002438323],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99338114,0.004546761,0.00037328087,0.00061457336,0.0008199314,0.00026431764],"domain_scores_gemma":[0.9562079,0.028627086,0.005534808,0.0060752095,0.0015967297,0.0019581947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076562357,0.0012018821,0.0004862019,0.0009848938,0.0015747853,0.0034450511,0.001679287,0.0023338832,0.0056964085],"category_scores_gemma":[0.051992826,0.0005668865,0.00059717713,0.00034395963,0.0017656679,0.005479363,0.0049808915,0.0024393513,0.0007554927],"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":[0.0026626682,0.0059050363,0.053773426,0.0020477197,0.00038713895,0.0027086837,0.054652628,0.038385756,0.05079936,0.16472426,0.012128526,0.61182475],"study_design_scores_gemma":[0.0007919695,0.005760956,0.029418262,0.0028295652,0.0010226289,0.0029293639,0.019902043,0.3793595,0.06729553,0.29781407,0.19190045,0.0009756633],"about_ca_topic_score_codex":0.0005523673,"about_ca_topic_score_gemma":0.0010179805,"teacher_disagreement_score":0.0076562357,"about_ca_system_score_codex":0.00060973223,"about_ca_system_score_gemma":0.001447652,"threshold_uncertainty_score":0.040490508},"labels":[],"label_agreement":null},{"id":"W3082309692","doi":"10.3390/info11090426","title":"Exploring Neural Network Hidden Layer Activity Using Vector Fields","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Artificial neural network; Computer science; Projection (relational algebra); Representation (politics); Artificial intelligence; Debugging; Layer (electronics); Task (project management); Field (mathematics); Machine learning; Pattern recognition (psychology); Algorithm; Engineering; Mathematics","score_opus":0.14296523704079261,"score_gpt":0.2721613201989134,"score_spread":0.1291960831581208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082309692","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":[0.20843409,0.00038970148,0.7867051,0.0005479289,0.00005186519,0.000058334128,0.00051403866,0.0012945974,0.0020044222],"genre_scores_gemma":[0.8576788,0.00036333076,0.14007157,0.00004907534,0.000030730193,0.000074297284,0.0005125975,0.00010588735,0.0011136194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998491,0.000047458703,0.000007940666,0.000037806498,0.00003286177,0.000024722563],"domain_scores_gemma":[0.9993787,0.00041207953,0.00007227061,0.0000399257,0.000060383252,0.000036594163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006668224,0.0006008255,0.00033070525,0.0015765257,0.00028300995,0.0012027136,0.00050939195,0.00056940864,0.0016756855],"category_scores_gemma":[0.002410263,0.0003350152,0.0005010386,0.0010689619,0.0005123382,0.001513261,0.0006230141,0.0007335232,0.00019641193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028967205,0.00014128498,0.0079547195,0.00014685445,0.00007485342,0.00020428112,0.00051518076,0.78535104,0.017427437,0.02297768,0.0023448872,0.16257203],"study_design_scores_gemma":[0.000004824015,0.000016930955,0.00082555145,0.000009732759,0.0000039679367,0.000012770652,0.0000388277,0.98806816,0.001181721,0.009500083,0.000328994,0.000008457936],"about_ca_topic_score_codex":0.004657753,"about_ca_topic_score_gemma":0.004444755,"teacher_disagreement_score":0.004657753,"about_ca_system_score_codex":0.0005965829,"about_ca_system_score_gemma":0.0005231779,"threshold_uncertainty_score":0.0092612505},"labels":[],"label_agreement":null},{"id":"W3084636861","doi":"10.3390/info11090443","title":"Intelligent Adversary Placements for Privacy Evaluation in VANET","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversary; Eavesdropping; Vehicular ad hoc network; Computer security; Computer science; Pseudonym; Intelligent transportation system; Wireless ad hoc network; Computer network; Wireless; Engineering; Transport engineering; Telecommunications","score_opus":0.0235740385963057,"score_gpt":0.24510957078082768,"score_spread":0.221535532184522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084636861","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":[0.25191003,0.0010667623,0.74013746,0.00050021365,0.00013326856,0.0005891513,0.00017528633,0.0009816149,0.0045061503],"genre_scores_gemma":[0.94193137,0.00017558246,0.05718973,0.000057289126,0.000015262063,0.000094121904,0.000084758154,0.000024542891,0.00042734484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923138,0.004701391,0.0004015817,0.0006191317,0.0013616956,0.00060240756],"domain_scores_gemma":[0.97976905,0.012918853,0.0020615014,0.0023385794,0.0022905972,0.00062142604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00671559,0.0012690966,0.0010761516,0.0015830352,0.00094008184,0.0017862124,0.0016024978,0.0013019181,0.0008737397],"category_scores_gemma":[0.025644595,0.0004755874,0.00064779574,0.0009745836,0.0012468238,0.0027175017,0.002022445,0.00084178447,0.00020665937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009096973,0.00023852508,0.00799279,0.00017603711,0.00015110549,0.00022541723,0.00020565932,0.90281564,0.009324338,0.013765727,0.0007548592,0.063440256],"study_design_scores_gemma":[0.000024430004,0.00031173453,0.0005444722,0.000014703582,0.000030753952,0.00010604232,0.00009364406,0.99159086,0.0041232537,0.0028063871,0.0003356639,0.000018057002],"about_ca_topic_score_codex":0.0021425977,"about_ca_topic_score_gemma":0.0020388889,"teacher_disagreement_score":0.00671559,"about_ca_system_score_codex":0.0020072171,"about_ca_system_score_gemma":0.0015489336,"threshold_uncertainty_score":0.035515904},"labels":[],"label_agreement":null},{"id":"W3088061996","doi":"10.3390/info11100461","title":"Bots as Active News Promoters: A Digital Analysis of COVID-19 Tweets","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":39,"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 Bank of Canada; Simon Fraser University","funders":"","keywords":"Internet privacy; Dissemination; Coronavirus disease 2019 (COVID-19); Mainstream; Social media; Pandemic; Incentive; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); World Wide Web; Business; Computer science; Advertising; Public relations; Political science; Medicine; Telecommunications; Virology","score_opus":0.04662423116656165,"score_gpt":0.3509413842733422,"score_spread":0.30431715310678054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088061996","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":[0.97711617,0.0010647767,0.004812091,0.0008122577,0.0001357057,0.00019358247,0.007440706,0.0002964495,0.008128358],"genre_scores_gemma":[0.98138547,0.00051903445,0.0060036215,0.00039986902,0.00015634541,0.00019443236,0.0069260984,0.00012363754,0.0042915624],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987759,0.0003635363,0.000078073535,0.0002225693,0.00037394898,0.0001858528],"domain_scores_gemma":[0.9910704,0.0051242122,0.0020515474,0.0005338441,0.0008598665,0.00036008714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012392864,0.00047645115,0.00034787104,0.0046038697,0.00094150886,0.0012992102,0.0003246057,0.0005637758,0.0018439894],"category_scores_gemma":[0.007428098,0.00022683674,0.00033087778,0.0029122143,0.00048618054,0.0017845996,0.0008366187,0.0006565721,0.001263918],"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":[0.00093382894,0.00032461403,0.7987524,0.0013395937,0.0002751592,0.0020781213,0.015554378,0.0017741885,0.019769251,0.005259515,0.023838632,0.13010037],"study_design_scores_gemma":[0.000034185246,0.00023048268,0.8735307,0.0003058015,0.00021476012,0.0016902394,0.016056145,0.03232323,0.0056623463,0.003294454,0.06656447,0.00009312422],"about_ca_topic_score_codex":0.004112426,"about_ca_topic_score_gemma":0.0061448035,"teacher_disagreement_score":0.0046038697,"about_ca_system_score_codex":0.0005261477,"about_ca_system_score_gemma":0.00045161188,"threshold_uncertainty_score":0.008176982},"labels":[],"label_agreement":null},{"id":"W3094830184","doi":"10.3390/info11110504","title":"Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação para a Ciência e a Tecnologia; Ministério da Ciência, Tecnologia e Ensino Superior; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer network; Computer science; Geocast; Routing protocol; Network packet; Wireless Routing Protocol; Dynamic Source Routing; Distributed computing","score_opus":0.04004653720944708,"score_gpt":0.2589569634157565,"score_spread":0.21891042620630946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094830184","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":[0.106579386,0.0015887544,0.8842267,0.0003820842,0.0002670464,0.00034937932,0.00019917118,0.00086198165,0.0055454643],"genre_scores_gemma":[0.91401756,0.0009118417,0.08201631,0.00014810308,0.00003772868,0.00021808976,0.00025796774,0.000028386234,0.0023641372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998447,0.000028644276,0.000011096836,0.000017004902,0.00007411868,0.000024428748],"domain_scores_gemma":[0.9996246,0.00013933831,0.00007898086,0.000045066485,0.00008622282,0.000025713445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003735518,0.00028720463,0.00039569248,0.00043487587,0.00048991875,0.00037354484,0.00068621064,0.00027900818,0.00037575743],"category_scores_gemma":[0.00092475995,0.000107341475,0.0002483378,0.00041090517,0.0003379496,0.0005790725,0.000693981,0.0003860978,0.0000611227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008243909,0.00025190588,0.0043648933,0.0007121212,0.00021309621,0.0012874887,0.0006788692,0.31041422,0.21284917,0.1067537,0.012775956,0.3488741],"study_design_scores_gemma":[0.00014299671,0.0005008861,0.0012863697,0.000045158235,0.00012836662,0.000876406,0.00021397877,0.91668147,0.03898105,0.018128503,0.022946918,0.00006778136],"about_ca_topic_score_codex":0.0020513057,"about_ca_topic_score_gemma":0.0043271095,"teacher_disagreement_score":0.0020513057,"about_ca_system_score_codex":0.0004235612,"about_ca_system_score_gemma":0.0011110642,"threshold_uncertainty_score":0.0040786862},"labels":[],"label_agreement":null},{"id":"W3096267547","doi":"10.3390/info11110506","title":"A Multi-Objective Optimization Problem on Evacuating 2 Robots from the Disk in the Face-to-Face Model; Trade-Offs between Worst-Case and Average-Case Analysis","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Face (sociological concept); Parameterized complexity; Robot; Mathematical optimization; Trajectory; Algorithm; Artificial intelligence; Mathematics","score_opus":0.04580419657583967,"score_gpt":0.2778586777553245,"score_spread":0.23205448117948485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096267547","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":[0.024484716,0.00074054085,0.96856725,0.000929074,0.0000936097,0.00015368196,0.00026898252,0.00017973447,0.0045823855],"genre_scores_gemma":[0.5522602,0.0009758025,0.43591622,0.0006129313,0.00024863298,0.00075387856,0.0007149132,0.0003590513,0.008158273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722075,0.0012011713,0.000102911916,0.0007116874,0.000386347,0.0003770388],"domain_scores_gemma":[0.99323845,0.0051891822,0.00055398117,0.0003535782,0.00028266027,0.0003821579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047143204,0.0029019848,0.0034489103,0.00095426425,0.0010409585,0.002492237,0.0035290783,0.0042395657,0.005820895],"category_scores_gemma":[0.011645,0.0010255133,0.0025896342,0.0011234226,0.0018941901,0.0032490827,0.002738037,0.0037136055,0.0006955959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013415201,0.0000745461,0.00027578892,0.00017986876,0.00006501009,0.00010253668,0.00004845014,0.9753879,0.0004612285,0.013503332,0.0014431402,0.008324149],"study_design_scores_gemma":[0.000017114111,0.00006854732,0.00010671205,0.00002059104,0.00001243584,0.00003655414,0.000027116588,0.98742616,0.00020041528,0.011664518,0.0004076315,0.000012122605],"about_ca_topic_score_codex":0.0044777356,"about_ca_topic_score_gemma":0.0030115184,"teacher_disagreement_score":0.005820895,"about_ca_system_score_codex":0.0020033256,"about_ca_system_score_gemma":0.0017532044,"threshold_uncertainty_score":0.024932027},"labels":[],"label_agreement":null},{"id":"W3109231514","doi":"10.3390/info11110539","title":"Addressing Misinformation in Online Social Networks: Diverse Platforms and the Potential of Multiagent Trust Modeling","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Access Control and Trust","field":"Social Sciences","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":"McGill University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Misinformation; Computer science; Trustworthiness; Internet privacy; Order (exchange); Data science; Reflection (computer programming); Collective intelligence; World Wide Web; Computer security; Business","score_opus":0.06008268616445651,"score_gpt":0.3023274843960031,"score_spread":0.2422447982315466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109231514","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":[0.32954934,0.0022047744,0.6411621,0.01148905,0.00016784103,0.00020060853,0.00017311242,0.00024121879,0.014811991],"genre_scores_gemma":[0.9659963,0.00043060828,0.032452676,0.0001494671,0.00007357101,0.00004175026,0.000032824264,0.000016295402,0.0008064471],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931479,0.0047317953,0.00027571578,0.0006338043,0.00091577775,0.0002949227],"domain_scores_gemma":[0.9546884,0.031948816,0.0060931556,0.0037518297,0.0024458647,0.0010719914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009847503,0.0007366907,0.0008306614,0.0024668514,0.0018006851,0.005249645,0.0015897622,0.0024285563,0.0011337736],"category_scores_gemma":[0.04745381,0.00047535356,0.000814298,0.0016276932,0.0028138543,0.010777976,0.0032411737,0.002307055,0.00022948658],"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":[0.00045404557,0.00032542288,0.089093484,0.00044390833,0.00067539496,0.0016442295,0.014770822,0.29369175,0.0032963792,0.43929332,0.002732655,0.15357862],"study_design_scores_gemma":[0.000018342502,0.00010995306,0.005853951,0.00011928622,0.00008992988,0.00033021622,0.003071592,0.73744273,0.0010824451,0.24774356,0.004056942,0.00008108527],"about_ca_topic_score_codex":0.0043933103,"about_ca_topic_score_gemma":0.0037803934,"teacher_disagreement_score":0.009847503,"about_ca_system_score_codex":0.0016684129,"about_ca_system_score_gemma":0.00097034767,"threshold_uncertainty_score":0.0520792},"labels":[],"label_agreement":null},{"id":"W3110329197","doi":"10.3390/info11120555","title":"Correction: Díaz Ferreyra, N.E., et al. Preventative Nudges: Introducing Risk Cues for Supporting Online Self-Disclosure Decisions. Information 2020, 11, 399","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Privacy, Security, and Data Protection","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 de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Nudge theory; Psychology; Social psychology","score_opus":0.024788972259949936,"score_gpt":0.3357931364381321,"score_spread":0.31100416417818216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110329197","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":[0.000059808597,0.001124376,0.00032845294,0.11037138,0.88512355,0.000032977794,0.0014864795,0.00021116064,0.0012617777],"genre_scores_gemma":[0.012235718,0.012319069,0.0032605338,0.25065514,0.6031152,0.0006900306,0.003200813,0.0011052013,0.113418244],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9838038,0.0033359262,0.0025676512,0.0019886433,0.0069621284,0.0013418159],"domain_scores_gemma":[0.88322204,0.034297414,0.0059074583,0.0064278864,0.06633146,0.0038137883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014752361,0.0035263696,0.0039224676,0.005598233,0.0065136435,0.009374439,0.008672181,0.017523516,0.06710645],"category_scores_gemma":[0.22153978,0.002020522,0.0035310292,0.004388193,0.005802381,0.0042136707,0.0046494594,0.029817693,0.04023693],"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":[0.000011522052,0.0000023934601,0.000023788383,0.000093648116,0.000009072087,0.00006048782,0.00004766171,0.000011289867,0.000008380457,0.00033761375,0.998075,0.0013190535],"study_design_scores_gemma":[0.000097745025,0.000019667412,0.0009761397,0.001061404,0.0000730104,0.0003588301,0.0002903933,0.0002557963,0.00021181518,0.0015205847,0.9950641,0.00007057089],"about_ca_topic_score_codex":0.046445422,"about_ca_topic_score_gemma":0.03429606,"teacher_disagreement_score":0.06710645,"about_ca_system_score_codex":0.008157529,"about_ca_system_score_gemma":0.015581906,"threshold_uncertainty_score":0.22449344},"labels":[],"label_agreement":null},{"id":"W3111694987","doi":"10.3390/info11120579","title":"Release of the Fourth Season of Money Heist: Analysis of Its Social Audience on Twitter during Lockdown in Spain","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Communication and COVID-19 Impact","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Content analysis; Consumption (sociology); Period (music); Advertising; Qualitative analysis; Video on demand; Sociology; Qualitative research; Media studies; Psychology; Public relations; Business; Political science; Computer science; Multimedia; Social science; Art","score_opus":0.03998964241762884,"score_gpt":0.31751430559905663,"score_spread":0.27752466318142777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111694987","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":[0.99034286,0.0003044216,0.00017892387,0.0004826128,0.000039151873,0.00002451899,0.0004930391,0.000019641117,0.008114732],"genre_scores_gemma":[0.9939138,0.00047790774,0.00018119691,0.00014250766,0.000057491226,0.000053269916,0.0006061051,0.000035904868,0.004531725],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99932575,0.00023767173,0.000037361446,0.00007884835,0.00013900909,0.0001812932],"domain_scores_gemma":[0.9969021,0.0016773331,0.0005896428,0.00012244342,0.00044869125,0.00025969715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00131818,0.00024859127,0.00036735344,0.0025843778,0.0012370993,0.0031987529,0.00047488054,0.0010168789,0.0028014916],"category_scores_gemma":[0.004250492,0.0001600863,0.00017801329,0.0021986042,0.0009837764,0.0016072752,0.0019116702,0.000614788,0.00077199837],"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":[0.00090446667,0.00018613468,0.27865067,0.0010190778,0.00006496636,0.009461037,0.61117864,0.00035896062,0.0068813446,0.0026524405,0.011734398,0.076907866],"study_design_scores_gemma":[0.00001181208,0.000095563184,0.38916448,0.00033249022,0.000024589932,0.0005529378,0.56376106,0.0004833224,0.0010170266,0.00028702887,0.04422225,0.000047491136],"about_ca_topic_score_codex":0.013060541,"about_ca_topic_score_gemma":0.016061587,"teacher_disagreement_score":0.013060541,"about_ca_system_score_codex":0.0015558203,"about_ca_system_score_gemma":0.00073570496,"threshold_uncertainty_score":0.025969028},"labels":[],"label_agreement":null},{"id":"W3113990471","doi":"10.3390/info12010002","title":"The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"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":"Malware; Computer science; The Internet; Cluster (spacecraft); Phishing; Resource (disambiguation); Computer security; Population; World Wide Web; Demography","score_opus":0.013247295391695368,"score_gpt":0.2677901795738267,"score_spread":0.25454288418213133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113990471","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":[0.7906071,0.0015122608,0.008264557,0.0023175152,0.00017009607,0.00041748906,0.18656962,0.0007228894,0.00941841],"genre_scores_gemma":[0.81081605,0.0007538347,0.01540645,0.00022050101,0.000103682585,0.000272268,0.16930579,0.00008041754,0.003041041],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986719,0.00033142677,0.00015548065,0.00025941047,0.00035748855,0.00022426205],"domain_scores_gemma":[0.9975351,0.00057057966,0.000501084,0.000419924,0.0007842389,0.00018907695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011860789,0.00046613594,0.00042740014,0.004492232,0.00068192196,0.000938415,0.0009028861,0.00080964825,0.0010164329],"category_scores_gemma":[0.004574327,0.00013911046,0.0005733157,0.0055546723,0.0005060307,0.00087942864,0.0012669022,0.00042415192,0.00074202823],"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":[0.00045230877,0.0003895291,0.8071944,0.0011262721,0.00024253443,0.0030994029,0.0022100804,0.02548981,0.0033782865,0.005199646,0.081244364,0.069973335],"study_design_scores_gemma":[0.00007420811,0.0003018832,0.73262274,0.00046638885,0.00020789525,0.005058525,0.012071484,0.07891582,0.007955517,0.0029880025,0.15918088,0.00015662555],"about_ca_topic_score_codex":0.07311129,"about_ca_topic_score_gemma":0.08885406,"teacher_disagreement_score":0.07311129,"about_ca_system_score_codex":0.0015319379,"about_ca_system_score_gemma":0.0013541913,"threshold_uncertainty_score":0.1453715},"labels":[],"label_agreement":null},{"id":"W3119859168","doi":"10.3390/info12010028","title":"Robot Evacuation on a Line Assisted by a Bike","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Optimization and Search Problems","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":"Natural Sciences and Engineering Research Council of Canada; Carleton University","funders":"","keywords":"Robot; Mobile robot; Computer science; Line (geometry); Simulation; Wireless; Face (sociological concept); Constant (computer programming); Artificial intelligence; Mathematics; Telecommunications","score_opus":0.03134581304665471,"score_gpt":0.2795205945949723,"score_spread":0.2481747815483176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119859168","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":[0.41538432,0.00022761455,0.56802046,0.00048778605,0.00012301776,0.000167003,0.00013660906,0.001973276,0.01347997],"genre_scores_gemma":[0.89473647,0.0000856572,0.09470026,0.00006621627,0.0000144804535,0.0001308051,0.00021769863,0.000051915784,0.009996428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975866,0.000056988105,0.000014917944,0.00005613932,0.000036014684,0.000077353325],"domain_scores_gemma":[0.9995907,0.00013053452,0.00005381536,0.00007096301,0.00005557821,0.00009840304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030105404,0.0008358685,0.0008476809,0.00041534172,0.00097650615,0.00063741533,0.001199733,0.0013702511,0.0042729527],"category_scores_gemma":[0.0009475012,0.00030735251,0.00050316023,0.00028661024,0.00059869303,0.0010009963,0.0023171932,0.00091211,0.000903022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014554892,0.00028464568,0.0026131931,0.000225773,0.00007593096,0.0010028577,0.0006054576,0.8991849,0.020060109,0.01031966,0.002242088,0.061929926],"study_design_scores_gemma":[0.000046254652,0.0002616334,0.00044117664,0.000014357651,0.000013098524,0.00013565172,0.00017431169,0.9905884,0.0038129115,0.0021856425,0.002302183,0.000024332752],"about_ca_topic_score_codex":0.002867929,"about_ca_topic_score_gemma":0.0030419189,"teacher_disagreement_score":0.0042729527,"about_ca_system_score_codex":0.0003711766,"about_ca_system_score_gemma":0.0005351076,"threshold_uncertainty_score":0.014294505},"labels":[],"label_agreement":null},{"id":"W3127494928","doi":"10.3390/info12020071","title":"Business Models for Distributed-Simulation Orchestration and Risk Management","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Simulation Techniques and Applications","field":"Decision Sciences","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":"Response Biomedical (Canada); York University","funders":"","keywords":"Interoperability; High-level architecture; Orchestration; Computer science; Interface (matter); Software engineering; Process (computing); Business process; Abstraction; Systems engineering; Distributed computing; Process management; Engineering management; Engineering; Work in process; World Wide Web; Operating system; Operations management","score_opus":0.09958466514423102,"score_gpt":0.3872819012096154,"score_spread":0.2876972360653844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127494928","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":[0.0027608254,0.00022337321,0.9758976,0.0008669355,0.00010186007,0.00013389587,0.00018915739,0.00105693,0.018769419],"genre_scores_gemma":[0.21152106,0.00095671153,0.7693207,0.00037088626,0.00011069577,0.00063363207,0.0012133796,0.0005783484,0.015294501],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816626,0.00089052663,0.00014750025,0.00016735627,0.00051935297,0.00010898808],"domain_scores_gemma":[0.99883825,0.00043835083,0.00009495451,0.0002862075,0.0002346973,0.000107591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028312358,0.0009815285,0.00056446134,0.0011046638,0.0008982127,0.004268364,0.001787707,0.0019256602,0.00838939],"category_scores_gemma":[0.004155571,0.00046009113,0.0012011565,0.0009611226,0.0009867079,0.0025519258,0.002493323,0.002044341,0.0026073523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056477547,0.000102112856,0.00071732514,0.00010340042,0.000051997602,0.0001802455,0.00033681374,0.12657799,0.001971261,0.82886815,0.005487289,0.035546985],"study_design_scores_gemma":[0.000040710132,0.00003639147,0.00019130259,0.000104588216,0.000031108164,0.0001264749,0.00013981535,0.6523292,0.0020964255,0.23983435,0.105039135,0.000030478996],"about_ca_topic_score_codex":0.0037635108,"about_ca_topic_score_gemma":0.0027920536,"teacher_disagreement_score":0.00838939,"about_ca_system_score_codex":0.0015255895,"about_ca_system_score_gemma":0.0020990847,"threshold_uncertainty_score":0.028065264},"labels":[],"label_agreement":null},{"id":"W3187314870","doi":"10.3390/info12080317","title":"Design of Generalized Search Interfaces for Health Informatics","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ontology; Health informatics; Vocabulary; Interface (matter); Workflow; Domain (mathematical analysis); Controlled vocabulary; Information retrieval; Informatics; User interface; Set (abstract data type); Plug-in; Human–computer interaction; Data science; World Wide Web; Database; Public health; Engineering; Medicine; Programming language","score_opus":0.04229474773318576,"score_gpt":0.3360776097010882,"score_spread":0.29378286196790243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187314870","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":[0.014608789,0.00017325373,0.9743593,0.0003231544,0.000035149256,0.0006542211,0.00020843714,0.0071711903,0.0024664125],"genre_scores_gemma":[0.11167395,0.00017095277,0.882213,0.00034434962,0.000022108452,0.0010411199,0.0007206171,0.0010015947,0.0028122894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99464864,0.0024281223,0.0008138459,0.00078516424,0.0010776022,0.00024662243],"domain_scores_gemma":[0.9882719,0.0076091685,0.0005679146,0.0016946264,0.0014965505,0.00035975935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007063594,0.0011012182,0.0009077408,0.0017139316,0.00078100205,0.003992536,0.0024421273,0.0016046368,0.005992497],"category_scores_gemma":[0.023070848,0.00083154725,0.0011568106,0.0011330104,0.0014419119,0.0053543444,0.0036294835,0.0011487155,0.0016153221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020233702,0.00059914123,0.0076395907,0.0037469517,0.00042828333,0.0021384843,0.017999465,0.042146493,0.07201203,0.28798914,0.025802063,0.537475],"study_design_scores_gemma":[0.0006059423,0.00066263555,0.0030610939,0.000671211,0.0003575394,0.0016090531,0.0035296015,0.5749235,0.04827332,0.20269158,0.16336472,0.00024978942],"about_ca_topic_score_codex":0.0020221544,"about_ca_topic_score_gemma":0.002242103,"teacher_disagreement_score":0.007063594,"about_ca_system_score_codex":0.0009830283,"about_ca_system_score_gemma":0.001789637,"threshold_uncertainty_score":0.037356317},"labels":[],"label_agreement":null},{"id":"W3195400065","doi":"10.3390/info12090344","title":"VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Interpretability; Visual analytics; Computer science; Naive Bayes classifier; Random forest; Machine learning; Analytics; Predictive analytics; Support vector machine; Interactive visual analysis; Artificial intelligence; Visualization; Data mining; Decision tree; Data science","score_opus":0.04556151753470707,"score_gpt":0.3394224635250304,"score_spread":0.29386094599032336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195400065","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":[0.014312612,0.00080191554,0.8623392,0.0014302884,0.00027775124,0.00072516885,0.011079682,0.10388909,0.005144395],"genre_scores_gemma":[0.106386214,0.0006338308,0.8755533,0.0004854584,0.000119734876,0.0010362527,0.009999326,0.0033510402,0.0024348341],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984314,0.00059566845,0.00015968585,0.00027504255,0.0004414874,0.0000966451],"domain_scores_gemma":[0.99305713,0.0045708357,0.00046496993,0.0007886221,0.0008021546,0.00031628585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004373094,0.0018318885,0.000888794,0.0058641485,0.0007429923,0.003846934,0.0017654486,0.00095948216,0.012968067],"category_scores_gemma":[0.0171283,0.0005839429,0.001399369,0.0027996048,0.0006336026,0.0028618767,0.0038194784,0.0017627984,0.002778386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016613109,0.0004125142,0.011747442,0.0014813149,0.0003512792,0.0006626183,0.0027472938,0.017455442,0.015270453,0.032551166,0.14996083,0.7656983],"study_design_scores_gemma":[0.00054266775,0.00045686815,0.011093176,0.00082079525,0.00020575878,0.0008847106,0.0008858601,0.6589392,0.022896456,0.13509366,0.16781825,0.00036266577],"about_ca_topic_score_codex":0.0044697225,"about_ca_topic_score_gemma":0.0050157118,"teacher_disagreement_score":0.012968067,"about_ca_system_score_codex":0.00063930504,"about_ca_system_score_gemma":0.0013435789,"threshold_uncertainty_score":0.043382466},"labels":[],"label_agreement":null},{"id":"W3196761910","doi":"10.3390/info12090350","title":"The Relationship between Perceived Health Message Motivation and Social Cognitive Beliefs in Persuasive Health Communication","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Social cognitive theory; Cognition; Health communication; Context (archaeology); Social psychology; Applied psychology","score_opus":0.1437471242692505,"score_gpt":0.4358933791985975,"score_spread":0.292146254929347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196761910","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":[0.9958319,0.0001625673,0.00056755694,0.0002117921,0.000012214707,0.00004974723,0.000030614057,0.000007551611,0.003126078],"genre_scores_gemma":[0.99913615,0.00005536032,0.00033438898,0.000046243633,0.000010286232,0.000041023934,0.000023930737,0.000004009875,0.00034876863],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964759,0.0017130577,0.0002286252,0.00026665648,0.0010102792,0.00030546085],"domain_scores_gemma":[0.92568046,0.061355744,0.007316235,0.0010274547,0.002581441,0.0020387704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061836205,0.0003215248,0.0002805328,0.0011347266,0.0004283158,0.0019897202,0.00034953572,0.00081098254,0.004497026],"category_scores_gemma":[0.049996193,0.0003046403,0.0005429809,0.00042666213,0.0007005228,0.00078198285,0.00073414214,0.0014799506,0.00038370182],"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":[0.0010046043,0.0040591015,0.93014497,0.00031425126,0.0005679621,0.00012373594,0.011852466,0.00050432625,0.0024139066,0.00109059,0.0003572219,0.047566812],"study_design_scores_gemma":[0.000047371694,0.00068676635,0.99412704,0.00007510542,0.00014147755,0.00007588407,0.001809014,0.0013935909,0.0005449592,0.0005544186,0.00051913585,0.00002511677],"about_ca_topic_score_codex":0.0012227504,"about_ca_topic_score_gemma":0.00117245,"teacher_disagreement_score":0.0061836205,"about_ca_system_score_codex":0.00052891934,"about_ca_system_score_gemma":0.00048875686,"threshold_uncertainty_score":0.032702506},"labels":[],"label_agreement":null},{"id":"W3197905005","doi":"10.3390/info12090365","title":"Relationship between Perceived UX Design Attributes and Persuasive Features: A Case Study of Fitness App","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Innovative Human-Technology Interaction","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":true,"ca_institutions":"University of Saskatchewan; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Usability; Credibility; Psychology; Applied psychology; Social psychology; Exploratory research; User experience design; Computer science; Human–computer interaction","score_opus":0.06620759105077749,"score_gpt":0.31095668847428243,"score_spread":0.24474909742350492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197905005","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":[0.9987251,0.00004520091,0.0004693526,0.00006386855,0.0000017608555,0.00005136217,0.0000118694015,0.0000032632204,0.0006281195],"genre_scores_gemma":[0.9972193,0.00012296867,0.0019097042,0.00002984368,0.0000031459358,0.000079106045,0.000018296305,0.0000050681133,0.0006125272],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99651945,0.0020554555,0.00020430035,0.0002587327,0.0007043621,0.00025771416],"domain_scores_gemma":[0.97175705,0.02274439,0.0016840937,0.0007484107,0.0022932554,0.0007727903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005148992,0.00042026662,0.00042879235,0.0012663684,0.0019520192,0.0020774186,0.0007698412,0.0014865112,0.0016359915],"category_scores_gemma":[0.024270602,0.0003976543,0.000541026,0.0007510404,0.001182316,0.001674884,0.0010309657,0.0012038162,0.00023229979],"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":[0.00040509328,0.0049145357,0.4628807,0.0006830798,0.00013916321,0.017446214,0.4363052,0.00082489534,0.0069412896,0.0014507751,0.0009713694,0.067037724],"study_design_scores_gemma":[0.00012922719,0.004179987,0.48533466,0.00039100475,0.00028882938,0.015134504,0.46635678,0.008737739,0.0061671776,0.0009745105,0.012055141,0.00025044102],"about_ca_topic_score_codex":0.006286261,"about_ca_topic_score_gemma":0.0096829515,"teacher_disagreement_score":0.006286261,"about_ca_system_score_codex":0.00096403906,"about_ca_system_score_gemma":0.0010929265,"threshold_uncertainty_score":0.0272308},"labels":[],"label_agreement":null},{"id":"W3209669863","doi":"10.3390/info12110447","title":"Data-Driven Multi-Agent Vehicle Routing in a Congested City","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Traffic congestion; Routing (electronic design automation); Task (project management); Path (computing); Real-time data; Real-time computing; Floating car data; Computer network; Travel time; Transport engineering; Engineering; World Wide Web","score_opus":0.07407957448572013,"score_gpt":0.3320050505975773,"score_spread":0.2579254761118572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209669863","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":[0.70458573,0.00021641997,0.29104206,0.0005261883,0.00004204417,0.00008702678,0.00019588161,0.00027965085,0.003024899],"genre_scores_gemma":[0.9812306,0.000052016727,0.017959597,0.00001839001,0.000006007343,0.000038412785,0.000054053566,0.000012867578,0.00062809227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964035,0.00018586223,0.000013762609,0.000052713414,0.0000509603,0.000056376186],"domain_scores_gemma":[0.9978502,0.0012880944,0.00026973724,0.00011346722,0.00025779143,0.00022073822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013292802,0.0005184119,0.00089716626,0.0004325719,0.0006444947,0.00090859743,0.00095385883,0.00089303585,0.00073322514],"category_scores_gemma":[0.002819399,0.0006508199,0.00039885228,0.00059328985,0.0011884108,0.0008804405,0.0010020803,0.0007863906,0.000087918685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022912238,0.0000083844225,0.00021682645,0.000004546791,0.00000555154,0.00001869274,0.000009549794,0.9985777,0.0001505491,0.0004682127,0.000027433241,0.0004896258],"study_design_scores_gemma":[0.000006187608,0.000009095255,0.00008161006,5.59547e-7,0.0000017601984,0.0000019526087,0.00000704561,0.99945945,0.00006586257,0.00032865995,0.000036013626,0.000001837244],"about_ca_topic_score_codex":0.021582624,"about_ca_topic_score_gemma":0.01457507,"teacher_disagreement_score":0.021582624,"about_ca_system_score_codex":0.0011612804,"about_ca_system_score_gemma":0.0011505197,"threshold_uncertainty_score":0.042914033},"labels":[],"label_agreement":null},{"id":"W4200292339","doi":"10.3390/info13010008","title":"Interfaces for Searching and Triaging Large Document Sets: An Ontology-Supported Visual Analytics Approach","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Data Visualization and Analytics","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ontology; Visual analytics; Triage; Workflow; Analytics; Interface (matter); Information retrieval; Set (abstract data type); Domain (mathematical analysis); Data science; World Wide Web; Visualization; Data mining; Database","score_opus":0.030077955309139034,"score_gpt":0.35096850397299817,"score_spread":0.32089054866385913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200292339","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":[0.0067344294,0.0002644215,0.9836204,0.0008634064,0.000036988407,0.00033321837,0.0002483411,0.0044520544,0.0034466633],"genre_scores_gemma":[0.05198989,0.0003384766,0.9440031,0.00027616223,0.00003355824,0.00040410657,0.0005004803,0.0005989982,0.0018551578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951002,0.0022074152,0.00049585867,0.0007584034,0.0011583399,0.00027972006],"domain_scores_gemma":[0.98143077,0.010591387,0.00092020875,0.003339433,0.0027756726,0.0009425283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01076042,0.0012092981,0.0009077451,0.003973006,0.0011885607,0.010903819,0.0044901217,0.0019695696,0.006610609],"category_scores_gemma":[0.02804163,0.0010565363,0.0018225063,0.0021760808,0.0028512462,0.010487156,0.004385065,0.0024095934,0.0021622716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013050084,0.0006676009,0.0039700693,0.0024801577,0.00021307242,0.00086040655,0.02886124,0.021227624,0.0571145,0.20037091,0.029593557,0.65333587],"study_design_scores_gemma":[0.00034770687,0.00089318526,0.002979268,0.0017995811,0.00024203025,0.0013919818,0.009178901,0.3942665,0.06729246,0.26192865,0.2591867,0.00049297756],"about_ca_topic_score_codex":0.0035639345,"about_ca_topic_score_gemma":0.00337825,"teacher_disagreement_score":0.010903819,"about_ca_system_score_codex":0.0014971773,"about_ca_system_score_gemma":0.0019353007,"threshold_uncertainty_score":0.056907237},"labels":[],"label_agreement":null},{"id":"W4213441351","doi":"10.3390/info13030106","title":"Image Retrieval via Canonical Correlation Analysis and Binary Hypothesis Testing","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Image Retrieval and Classification Techniques","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":"McMaster University","funders":"McMaster University","keywords":"Canonical correlation; Pattern recognition (psychology); Artificial intelligence; Principal component analysis; Linear discriminant analysis; Computer science; Feature selection; Feature (linguistics); Binary number; Image retrieval; Convolutional neural network; Feature vector; Correlation; Mathematics; Image (mathematics)","score_opus":0.01672978815761795,"score_gpt":0.22792072792807624,"score_spread":0.2111909397704583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213441351","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":[0.011118245,0.0006418786,0.98450506,0.0002568912,0.00004935496,0.00015236287,0.00019589489,0.0009765439,0.0021038363],"genre_scores_gemma":[0.47719955,0.00090626476,0.51570904,0.00033937008,0.0003607441,0.0007297479,0.0016258134,0.0002135986,0.002915821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98921865,0.0048313714,0.00047834317,0.001729204,0.0032361348,0.00050624827],"domain_scores_gemma":[0.98889834,0.005730494,0.0012644407,0.0017342004,0.0021443637,0.00022819756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072671077,0.0014072104,0.0020834554,0.0053567397,0.0008763554,0.002531587,0.0018467781,0.0012425794,0.0030860428],"category_scores_gemma":[0.026703909,0.00041857615,0.0013648636,0.0054355897,0.0022442054,0.002928014,0.0025621317,0.0016205296,0.0017217146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047671454,0.00027065384,0.0038982297,0.00048791434,0.0003553966,0.00026658,0.00019476457,0.12863815,0.013215503,0.10843183,0.0109739285,0.7327904],"study_design_scores_gemma":[0.000030775907,0.00012966213,0.0011388342,0.000019250805,0.000033071112,0.0001398194,0.00004878652,0.94332045,0.004741015,0.04820871,0.002145543,0.000044081255],"about_ca_topic_score_codex":0.004143657,"about_ca_topic_score_gemma":0.0034505988,"teacher_disagreement_score":0.0072671077,"about_ca_system_score_codex":0.0013761511,"about_ca_system_score_gemma":0.0027486219,"threshold_uncertainty_score":0.038432598},"labels":[],"label_agreement":null},{"id":"W4214916274","doi":"10.3390/info13030124","title":"An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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; Artificial intelligence; Brain tumor; Modal; Feature (linguistics); Modality (human–computer interaction); Pattern recognition (psychology); Feature extraction; Process (computing); Magnetic resonance imaging; Embedding; Deep learning; Medical imaging; Image (mathematics); Modalities; Radiology; Medicine; Pathology","score_opus":0.019907245384279078,"score_gpt":0.31334287239915837,"score_spread":0.2934356270148793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214916274","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":[0.13273504,0.0023771925,0.85432774,0.00082454033,0.0002466895,0.0001229209,0.00052993896,0.0044782567,0.0043578558],"genre_scores_gemma":[0.83068216,0.0007944272,0.15684359,0.00082459615,0.00013068884,0.00009906073,0.0014141472,0.00011660457,0.009094621],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984264,0.000020452404,0.0000067877004,0.000057435616,0.000032748616,0.000039887746],"domain_scores_gemma":[0.9998367,0.00004126344,0.000017378992,0.000023099941,0.0000642651,0.000017242433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004358476,0.00092086795,0.0004988658,0.00051769515,0.00023732234,0.0005117901,0.0014753445,0.0009552581,0.0014896848],"category_scores_gemma":[0.0007350865,0.00032198834,0.00066283264,0.00037393908,0.00033195614,0.0008923855,0.000756661,0.001032234,0.00045228953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003936716,0.0003341852,0.0033777368,0.00012903188,0.0002261441,0.00027051143,0.00009005574,0.3156746,0.035513632,0.0037666734,0.009630947,0.6305929],"study_design_scores_gemma":[0.0000035611808,0.00003484306,0.0003160365,0.0000055853247,0.000018504797,0.000030297098,0.0000047709295,0.9953773,0.002811887,0.0009566539,0.00043556857,0.0000049703913],"about_ca_topic_score_codex":0.0092448285,"about_ca_topic_score_gemma":0.0117964335,"teacher_disagreement_score":0.0092448285,"about_ca_system_score_codex":0.0008786153,"about_ca_system_score_gemma":0.00057070504,"threshold_uncertainty_score":0.018382013},"labels":[],"label_agreement":null},{"id":"W4223904087","doi":"10.3390/info13040201","title":"A Framework for Online Public Health Debates: Some Design Elements for Visual Analytics Systems","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Misinformation and Its Impacts","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":"Western University","funders":"","keywords":"Public relations; Public health; Analytics; Computer science; Visual analytics; Internet privacy; Social media; Online participation; Data science; World Wide Web; Sociology; Political science; Visualization; The Internet; Medicine; Artificial intelligence","score_opus":0.18390358740060864,"score_gpt":0.42615472893283474,"score_spread":0.2422511415322261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223904087","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":[0.001115426,0.00024147303,0.9898691,0.0013419257,0.00007753167,0.0005585609,0.00047026237,0.0037425212,0.0025832138],"genre_scores_gemma":[0.015900474,0.00027816262,0.9798794,0.00026604932,0.00006221486,0.0009524199,0.0006992264,0.00065668166,0.0013054413],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9915387,0.004403765,0.0009856822,0.0012597333,0.001409175,0.00040290266],"domain_scores_gemma":[0.986192,0.007312809,0.0006635844,0.0024942327,0.0021304048,0.0012070618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016355004,0.0019872976,0.0015440314,0.006253465,0.0025499894,0.016754355,0.0058132177,0.0038163299,0.011504857],"category_scores_gemma":[0.026242072,0.0021004945,0.0047451123,0.00462115,0.005983531,0.016412282,0.0093856715,0.0056467177,0.004051472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005035707,0.00034773463,0.0023568743,0.0014989636,0.00016787155,0.00060160103,0.011694433,0.012653726,0.009341116,0.7208412,0.023959814,0.21603309],"study_design_scores_gemma":[0.00015780605,0.00016175689,0.00068466744,0.0007185073,0.00009071377,0.00044109227,0.0032571193,0.15492046,0.0056206533,0.55819553,0.27557388,0.00017771771],"about_ca_topic_score_codex":0.0065842457,"about_ca_topic_score_gemma":0.0051655867,"teacher_disagreement_score":0.016754355,"about_ca_system_score_codex":0.003343498,"about_ca_system_score_gemma":0.0023713782,"threshold_uncertainty_score":0.086494565},"labels":[],"label_agreement":null},{"id":"W4224278993","doi":"10.3390/info13040205","title":"Medical Knowledge Graph Completion Based on Word Embeddings","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Word2vec; Computer science; RDF; Knowledge graph; Terminology; Information retrieval; Word (group theory); Natural language processing; Semantics (computer science); Graph; Artificial intelligence; Theoretical computer science; Semantic Web; Embedding; Mathematics","score_opus":0.0161672622546258,"score_gpt":0.2530601875034533,"score_spread":0.23689292524882752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224278993","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":[0.083848506,0.0017731644,0.8957277,0.0011744751,0.00020646014,0.00029036548,0.0077811535,0.005925679,0.0032725052],"genre_scores_gemma":[0.5900214,0.0014445463,0.36093232,0.0006910027,0.0001948455,0.0004929561,0.040386416,0.0005295525,0.0053069494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912935,0.000139239,0.000078438105,0.00043990707,0.00014425765,0.00006875505],"domain_scores_gemma":[0.9986877,0.0005788027,0.00017734525,0.00022504354,0.00026825824,0.000062854895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005763983,0.0012658674,0.00065380963,0.0029102126,0.00038477228,0.0008352753,0.00092958286,0.00093863416,0.002755174],"category_scores_gemma":[0.0043344605,0.00035530614,0.001216582,0.0020834496,0.00070182054,0.0034004124,0.0012585147,0.001478209,0.0017233856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005350942,0.00029755503,0.015696276,0.0009023241,0.00031398778,0.0009029443,0.00088401843,0.1162316,0.0156334,0.026066262,0.030818539,0.79171795],"study_design_scores_gemma":[0.00005248659,0.00017671553,0.0035270462,0.00013442463,0.00012937565,0.00062517164,0.00041050953,0.90395826,0.009856975,0.062196262,0.018874485,0.00005822138],"about_ca_topic_score_codex":0.0076789437,"about_ca_topic_score_gemma":0.013016808,"teacher_disagreement_score":0.0076789437,"about_ca_system_score_codex":0.00071052444,"about_ca_system_score_gemma":0.0011753606,"threshold_uncertainty_score":0.015268505},"labels":[],"label_agreement":null},{"id":"W4281626616","doi":"10.3390/info13060290","title":"Contextualizer: Connecting the Dots of Context with Second-Order Attention","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Compute Canada","keywords":"Security token; Computer science; Sentence; Representation (politics); Theoretical computer science; Embedding; Computation; Transformer; Algorithm; Artificial intelligence","score_opus":0.0133513662740684,"score_gpt":0.22015760679256827,"score_spread":0.20680624051849986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281626616","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":[0.024362229,0.00038592107,0.96882844,0.0003846468,0.00009275497,0.00007126592,0.00009370882,0.002166467,0.0036145994],"genre_scores_gemma":[0.64218,0.0004857417,0.35080716,0.00030758767,0.00013979852,0.00015895226,0.00023563793,0.00036353566,0.005321584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945515,0.00019458303,0.000018808363,0.00020720626,0.000067236666,0.0000570486],"domain_scores_gemma":[0.99899465,0.00051169295,0.000085352585,0.00024485064,0.000099479614,0.000064006716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095617224,0.00088192546,0.00059292157,0.0009540079,0.00052992883,0.0014609165,0.0014422127,0.0008971803,0.005895558],"category_scores_gemma":[0.0041192397,0.000631109,0.000855209,0.00089901075,0.0010148813,0.004326313,0.0020761865,0.0013625531,0.0009277292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007668785,0.00022684001,0.0038170442,0.00034267866,0.0001968812,0.00031850487,0.0013035424,0.08013682,0.031632762,0.19848676,0.0075901,0.6751812],"study_design_scores_gemma":[0.00004303419,0.00020557796,0.0015120619,0.000038524275,0.00011110827,0.00019110672,0.000103940874,0.80534065,0.013693973,0.17094155,0.007777225,0.00004122828],"about_ca_topic_score_codex":0.0047550467,"about_ca_topic_score_gemma":0.006913493,"teacher_disagreement_score":0.005895558,"about_ca_system_score_codex":0.00085797015,"about_ca_system_score_gemma":0.00078942504,"threshold_uncertainty_score":0.01972258},"labels":[],"label_agreement":null},{"id":"W4289522513","doi":"10.3390/info13080368","title":"Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Data Visualization 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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information retrieval; Scope (computer science); Replica; Visualization; Selection (genetic algorithm); Construct (python library); Exploratory search; Visual analytics; Chart; Series (stratigraphy); World Wide Web; Data mining; Artificial intelligence","score_opus":0.02049080150419802,"score_gpt":0.3085334779593474,"score_spread":0.28804267645514936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289522513","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":[0.019415807,0.00038619377,0.915442,0.00055862684,0.000116464915,0.0010020529,0.0031429597,0.052567072,0.007368841],"genre_scores_gemma":[0.12084936,0.00029900164,0.8621832,0.00035758788,0.00007291131,0.0011684075,0.0045854785,0.003335348,0.007148747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977592,0.00087546394,0.00023713426,0.0004641411,0.00052146474,0.00014249612],"domain_scores_gemma":[0.9910513,0.0049917824,0.00039121116,0.0018456241,0.0012931174,0.0004270119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00393128,0.0022280198,0.0012066702,0.0047706245,0.0008276035,0.0039956905,0.0022544758,0.001191011,0.014218836],"category_scores_gemma":[0.018489096,0.0007308487,0.0014446918,0.0023468216,0.00097289745,0.006667658,0.0045739315,0.0013616671,0.0052865995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018626144,0.0009086977,0.006107903,0.0033024568,0.0002878338,0.0009825997,0.017055746,0.0036705777,0.09176307,0.039758213,0.069605455,0.76469475],"study_design_scores_gemma":[0.0005632271,0.0016961577,0.011451394,0.00086125144,0.00038431503,0.002010677,0.010855192,0.30353725,0.11365623,0.08689545,0.4671903,0.00089861057],"about_ca_topic_score_codex":0.0030096723,"about_ca_topic_score_gemma":0.005001974,"teacher_disagreement_score":0.014218836,"about_ca_system_score_codex":0.0005537822,"about_ca_system_score_gemma":0.00087480055,"threshold_uncertainty_score":0.04756677},"labels":[],"label_agreement":null},{"id":"W4292686778","doi":"10.3390/info13080400","title":"Optimized Screening for At-Risk Students in Mathematics: A Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Online Learning and Analytics","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 Alberta","funders":"","keywords":"Machine learning; Formative assessment; At-risk students; Artificial intelligence; Reading (process); Measure (data warehouse); Mathematics education; Random forest; Computer science; Mathematics; Data mining","score_opus":0.016662418817592645,"score_gpt":0.27403466722575204,"score_spread":0.2573722484081594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292686778","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":[0.4153994,0.0016043503,0.5730942,0.0035967366,0.00010396054,0.00047365561,0.0009800474,0.0017718782,0.002975841],"genre_scores_gemma":[0.8861989,0.00027626695,0.11099432,0.00025555462,0.00008905008,0.00017687408,0.00095944735,0.000038092476,0.0010115496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983078,0.00080765155,0.00011103276,0.0004014288,0.00019651442,0.00017556522],"domain_scores_gemma":[0.99229217,0.00553645,0.00066863524,0.0003053153,0.0009451172,0.000252306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004240562,0.0012481053,0.0015915933,0.0044573336,0.00064620515,0.0017510926,0.0013648395,0.001329108,0.0011700662],"category_scores_gemma":[0.013465057,0.00045236156,0.0010754581,0.0021513894,0.0005080039,0.0012966718,0.0010319473,0.0019005131,0.00049232616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006537018,0.0018130827,0.26407668,0.0001978126,0.00044643597,0.00032662018,0.0003906899,0.28389412,0.0020396404,0.0037028233,0.004535059,0.43792334],"study_design_scores_gemma":[0.000024618304,0.00020422522,0.0129467035,0.0000354941,0.00005917988,0.000070051945,0.0001155251,0.98073614,0.00045418972,0.00497248,0.00035813017,0.000023322349],"about_ca_topic_score_codex":0.009049332,"about_ca_topic_score_gemma":0.0076852906,"teacher_disagreement_score":0.009049332,"about_ca_system_score_codex":0.0010140033,"about_ca_system_score_gemma":0.0018839254,"threshold_uncertainty_score":0.022426486},"labels":[],"label_agreement":null},{"id":"W4293230743","doi":"10.3390/info13080394","title":"Saliency-Enabled Coding Unit Partitioning and Quantization Control for Versatile Video Coding","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Visual Attention and Saliency Detection","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":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Algorithmic efficiency; Coding (social sciences); Coding tree unit; Quantization (signal processing); Context-adaptive binary arithmetic coding; Algorithm; Computational complexity theory; Artificial intelligence; Computer vision; Data compression; Mathematics; Decoding methods; Statistics","score_opus":0.020206437837423905,"score_gpt":0.25793123693987624,"score_spread":0.23772479910245234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293230743","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":[0.020045953,0.0003201487,0.9783831,0.000050111932,0.000024484334,0.000027829026,0.00001441566,0.00019690639,0.0009370507],"genre_scores_gemma":[0.77987874,0.0002223507,0.21879742,0.000053938962,0.000039423805,0.000045128334,0.00006314734,0.00004876689,0.0008510513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970055,0.000050731676,0.000015912117,0.000054775926,0.00014282239,0.000035292913],"domain_scores_gemma":[0.99968183,0.00008233373,0.000046841804,0.00003452225,0.00013697558,0.000017610444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003422525,0.00045488705,0.0002825395,0.0006213363,0.0002963513,0.00037250752,0.0006529416,0.00021714924,0.000732732],"category_scores_gemma":[0.0011958515,0.00014298869,0.00022077323,0.00053634593,0.00035819257,0.00072132325,0.0005107864,0.00042889966,0.000102068814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027442657,0.000081591534,0.0015364427,0.0001302093,0.000035302644,0.00017227833,0.00026004124,0.20943105,0.1696888,0.040013414,0.0035111196,0.57486534],"study_design_scores_gemma":[0.000013797774,0.000080525046,0.0005666631,0.000008551209,0.000011879949,0.000108499444,0.000022949658,0.97010773,0.022482837,0.0047274306,0.0018535983,0.000015498956],"about_ca_topic_score_codex":0.0038712292,"about_ca_topic_score_gemma":0.0047776136,"teacher_disagreement_score":0.0038712292,"about_ca_system_score_codex":0.0006508618,"about_ca_system_score_gemma":0.0006304609,"threshold_uncertainty_score":0.0076974034},"labels":[],"label_agreement":null},{"id":"W4297477913","doi":"10.3390/info13100459","title":"Knowledge Graphs and Explainable AI in Healthcare","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Cape Breton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Knowledge graph; Health care; Leverage (statistics); Computer science; Transparency (behavior); Knowledge management; Artificial intelligence; Political science","score_opus":0.018159375831761665,"score_gpt":0.274958069201226,"score_spread":0.25679869336946437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297477913","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":[0.020550063,0.13718468,0.76235414,0.033281676,0.0006555105,0.0001843371,0.0009875803,0.00075407943,0.044047922],"genre_scores_gemma":[0.6363651,0.11171177,0.24008636,0.0042205835,0.00067026337,0.0004140841,0.0014797454,0.00013594078,0.004916245],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99780315,0.0012379991,0.00018309399,0.00029031813,0.00039532201,0.00009009877],"domain_scores_gemma":[0.99065304,0.0075702635,0.0005815231,0.0006579235,0.00044066337,0.00009663358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028937014,0.0007723781,0.00045605845,0.0033898123,0.0006302179,0.0031566487,0.00109973,0.0016841167,0.0032418577],"category_scores_gemma":[0.010501794,0.0003496064,0.0010595431,0.0025389257,0.003814166,0.0049688914,0.0017102438,0.0016881211,0.0003676039],"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":[0.000022573435,0.000022125892,0.0010464473,0.0014744386,0.00012342985,0.00030791733,0.0010006052,0.019961078,0.0003620043,0.87843746,0.003726232,0.09351575],"study_design_scores_gemma":[0.0000072474045,0.000016491578,0.00068592577,0.0010280182,0.00006650303,0.00020885149,0.00027423623,0.018784748,0.00039976134,0.91936356,0.05913436,0.000030229618],"about_ca_topic_score_codex":0.0053036003,"about_ca_topic_score_gemma":0.0038184333,"teacher_disagreement_score":0.0053036003,"about_ca_system_score_codex":0.002664282,"about_ca_system_score_gemma":0.001658632,"threshold_uncertainty_score":0.0193308},"labels":[],"label_agreement":null},{"id":"W4297538809","doi":"10.3390/info13100456","title":"Empirical Comparison between Deep and Classical Classifiers for Speaker Verification in Emotional Talking Environments","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"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; Speech recognition; Artificial intelligence; Support vector machine; Convolutional neural network; Mixture model; Hyperparameter optimization; Word error rate; Deep learning; Hyperparameter; Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.03425830924110825,"score_gpt":0.2881227611112637,"score_spread":0.2538644518701555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297538809","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":[0.8391321,0.012123472,0.13548471,0.00058922626,0.00044281414,0.00017715529,0.0018217555,0.0033833294,0.0068453667],"genre_scores_gemma":[0.95010775,0.0012127523,0.041970678,0.000118400545,0.00009595796,0.00009013969,0.003664939,0.00010961995,0.002629681],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953746,0.0015060806,0.00043932055,0.00088271155,0.0014522341,0.00034512932],"domain_scores_gemma":[0.9890676,0.0067842547,0.00053402025,0.0010871855,0.002252782,0.0002740629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071589886,0.0019229779,0.0010135933,0.0016223096,0.00047277083,0.001131856,0.0011715106,0.0014892359,0.0012293357],"category_scores_gemma":[0.0154526895,0.00034178866,0.00058063184,0.0006018144,0.0005400353,0.0021525472,0.0017846538,0.0011532536,0.0009162582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033706506,0.00055983046,0.02435911,0.00087759865,0.0007689407,0.0002481953,0.0003168581,0.22983594,0.018205123,0.0016858613,0.0075396313,0.71223223],"study_design_scores_gemma":[0.000046933303,0.0010660207,0.012022307,0.00013106554,0.00017999319,0.00025555003,0.00024539846,0.9574998,0.025703281,0.0011043948,0.0016818895,0.00006332139],"about_ca_topic_score_codex":0.003947774,"about_ca_topic_score_gemma":0.004463739,"teacher_disagreement_score":0.0071589886,"about_ca_system_score_codex":0.0009976741,"about_ca_system_score_gemma":0.0007472283,"threshold_uncertainty_score":0.03786081},"labels":[],"label_agreement":null},{"id":"W4297982274","doi":"10.3390/info13100466","title":"From Text Representation to Financial Market Prediction: A Literature Review","year":2022,"lang":"en","type":"review","venue":"Information","topic":"Stock Market Forecasting Methods","field":"Decision 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":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Leverage (statistics); Sentiment analysis; Financial market; Foreign exchange market; Market data; Data science; Artificial intelligence; Cryptocurrency; Social media; Finance; Computer science; Representation (politics); Business; Political science; World Wide Web; Exchange rate","score_opus":0.15594283576322182,"score_gpt":0.45725173313992906,"score_spread":0.30130889737670724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297982274","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":[0.00052384695,0.99472046,0.0021925073,0.00075525197,0.0002270991,0.00002531318,0.000101015066,0.000039926235,0.001414612],"genre_scores_gemma":[0.0038289516,0.99191177,0.0028553463,0.00028350053,0.00041603777,0.000030221861,0.00018010908,0.000011023983,0.00048288645],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951744,0.000120836106,0.000074113654,0.000118167816,0.00014566221,0.000023861872],"domain_scores_gemma":[0.99390805,0.0049086036,0.0002513418,0.0000916106,0.0007627161,0.00007775261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017548141,0.0010912784,0.0012026167,0.0040046615,0.00027221977,0.0016118952,0.0012002136,0.0011879276,0.004774438],"category_scores_gemma":[0.0066435416,0.00045252047,0.0010400588,0.0045731347,0.00048146636,0.002166065,0.00062842097,0.0010239069,0.002054112],"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":[0.000041854204,0.00007611455,0.0005845377,0.01834762,0.00015197387,0.000047805013,0.00008227622,0.00066723017,0.0004490885,0.002253744,0.01374943,0.9635483],"study_design_scores_gemma":[0.00007073592,0.0004219322,0.007299567,0.053304814,0.0019792216,0.0011611118,0.00048417452,0.0073148836,0.0029254833,0.017144486,0.9077375,0.00015613287],"about_ca_topic_score_codex":0.0023614732,"about_ca_topic_score_gemma":0.0024779306,"teacher_disagreement_score":0.004774438,"about_ca_system_score_codex":0.0004316046,"about_ca_system_score_gemma":0.0016947098,"threshold_uncertainty_score":0.015972137},"labels":[],"label_agreement":null},{"id":"W4313388814","doi":"10.3390/info14010026","title":"Non-Fungible Tokens (NFT): A Systematic Review","year":2022,"lang":"en","type":"review","venue":"Information","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":99,"is_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":"Popularity; Scope (computer science); Security token; Computer science; Psychology; Computer security","score_opus":0.020193329650679884,"score_gpt":0.28075429088676124,"score_spread":0.26056096123608136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313388814","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":[0.00041474984,0.9985202,0.00012472147,0.00025073934,0.00008043283,0.000116970165,0.00017441923,0.0000047464314,0.00031303678],"genre_scores_gemma":[0.005358624,0.9934994,0.0003623699,0.00029065897,0.000041102,0.00020317931,0.00013181522,0.000003675311,0.0001091721],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99321073,0.002177328,0.0021058065,0.0005267198,0.0017622935,0.0002170944],"domain_scores_gemma":[0.96489465,0.026133014,0.00491403,0.00057111704,0.0030901856,0.0003969627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008999073,0.0012099596,0.003900217,0.013127756,0.00078247074,0.0028941007,0.0016340191,0.0017160214,0.0054488434],"category_scores_gemma":[0.038989272,0.0008198059,0.0036848546,0.014029756,0.0009838312,0.0038759836,0.0017107186,0.0012214838,0.00048862107],"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":[0.000107676016,0.000021363783,0.0007048096,0.8851295,0.0020681128,0.00011711284,0.00033723513,0.00011516112,0.0001772787,0.00088831614,0.0040821917,0.10625126],"study_design_scores_gemma":[0.00007374644,0.00013263705,0.003147106,0.9044235,0.013649035,0.00061513623,0.00061537034,0.00007614693,0.00022586866,0.0007894952,0.07621681,0.0000352318],"about_ca_topic_score_codex":0.006263984,"about_ca_topic_score_gemma":0.022479534,"teacher_disagreement_score":0.013127756,"about_ca_system_score_codex":0.0032176203,"about_ca_system_score_gemma":0.017114434,"threshold_uncertainty_score":0.047592223},"labels":[],"label_agreement":null},{"id":"W4315647960","doi":"10.3390/info14010044","title":"A Shannon-Theoretic Approach to the Storage–Retrieval Trade-Off in PIR Systems","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Cryptography and Data Security","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":"National Science Foundation","keywords":"Codebook; Computer science; Code rate; Coding (social sciences); Theoretical computer science; Random access; Algorithm; Code (set theory); Scheme (mathematics); Mathematics; Decoding methods; Set (abstract data type); Statistics; Computer network","score_opus":0.013469915997715775,"score_gpt":0.22567219010190961,"score_spread":0.21220227410419384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315647960","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":[0.028166289,0.0019765487,0.939261,0.0014690025,0.00013978044,0.000095289,0.0000997184,0.00015841587,0.02863389],"genre_scores_gemma":[0.9056275,0.0026197436,0.08241062,0.00044900415,0.00043214703,0.0002751482,0.000071168964,0.00012262816,0.007991996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965557,0.0014248061,0.00018064021,0.0003418876,0.0010841576,0.00041282526],"domain_scores_gemma":[0.98928154,0.0074886256,0.0006111272,0.0015378665,0.0009192168,0.00016166978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039743683,0.0010840109,0.001228752,0.0014142321,0.0013124357,0.0039586397,0.0021659383,0.0020208738,0.005477986],"category_scores_gemma":[0.01676097,0.0006768849,0.00067080586,0.0014688057,0.0047459225,0.0070679504,0.0036463973,0.002541609,0.00083454256],"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":[0.000088344976,0.00004221362,0.00021059463,0.00022689876,0.000032645185,0.00015624071,0.00021872348,0.10137606,0.0049615735,0.877875,0.0010485445,0.013763181],"study_design_scores_gemma":[0.00002280094,0.0001770268,0.0002003686,0.00008297009,0.000029784263,0.0003202318,0.000116534844,0.5574122,0.005074034,0.43403068,0.0024517868,0.00008153402],"about_ca_topic_score_codex":0.0006005278,"about_ca_topic_score_gemma":0.00037614725,"teacher_disagreement_score":0.005477986,"about_ca_system_score_codex":0.002334325,"about_ca_system_score_gemma":0.0010580361,"threshold_uncertainty_score":0.021018684},"labels":[],"label_agreement":null},{"id":"W4316469706","doi":"10.3390/info14010054","title":"A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":340,"is_retracted":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":"Undersampling; Oversampling; Resampling; Random forest; Computer science; Machine learning; Class (philosophy); Data mining; Sampling (signal processing); Artificial intelligence; Bandwidth (computing)","score_opus":0.19159291031325643,"score_gpt":0.46640891991508493,"score_spread":0.2748160096018285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316469706","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":[0.11713507,0.0044004307,0.8744875,0.0006418643,0.00031940686,0.00040156962,0.00019897232,0.0010421517,0.0013730163],"genre_scores_gemma":[0.43252188,0.0017407681,0.56312215,0.00033239875,0.00028675914,0.00039414343,0.00077871565,0.00011167812,0.00071144884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99365425,0.0036451414,0.00040664818,0.0005727663,0.0014693597,0.00025176627],"domain_scores_gemma":[0.9855759,0.00946067,0.00082625897,0.0014329769,0.0024045636,0.00029959413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01416285,0.0010479335,0.0014594349,0.0025741765,0.00090169755,0.00096027687,0.0013227711,0.0010878682,0.0004298837],"category_scores_gemma":[0.027128503,0.00038564226,0.0014086115,0.0017612049,0.0007959108,0.0020609342,0.0013919452,0.0015734506,0.00017194905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016504909,0.00057133246,0.025128672,0.00053699367,0.00084841176,0.00011700659,0.0007477621,0.25071162,0.0061719473,0.010296615,0.004046755,0.6991723],"study_design_scores_gemma":[0.00006209952,0.0003193576,0.004182845,0.000058565536,0.00008130027,0.000082648636,0.00014923321,0.9869021,0.0028974782,0.0034997372,0.0017364961,0.000028014749],"about_ca_topic_score_codex":0.0056308312,"about_ca_topic_score_gemma":0.007916315,"teacher_disagreement_score":0.01416285,"about_ca_system_score_codex":0.00071674003,"about_ca_system_score_gemma":0.0017304058,"threshold_uncertainty_score":0.07490122},"labels":[],"label_agreement":null},{"id":"W4317743473","doi":"10.3390/info14020063","title":"Scale Abbreviation with Recursive Feature Elimination and Genetic Algorithms: An Illustration with the Test Emotions Questionnaire","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Mental Health Research Topics","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Feature (linguistics); Scale (ratio); Algorithm; Computer science; Machine learning; Reliability (semiconductor); Genetic algorithm; Variety (cybernetics); Test (biology); Feature selection; Artificial intelligence; Process (computing); Quality (philosophy); Data mining","score_opus":0.025481907774653895,"score_gpt":0.341766043024872,"score_spread":0.31628413525021815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317743473","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":[0.040234014,0.00031886034,0.95179284,0.00055825134,0.00013868087,0.00044017832,0.0003631371,0.0015093768,0.0046446365],"genre_scores_gemma":[0.094246835,0.00012549783,0.90232164,0.000119267046,0.000024296216,0.00048869016,0.00046657055,0.00024428894,0.0019628494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846184,0.0008585442,0.00012998721,0.00015670122,0.00034165807,0.000051337127],"domain_scores_gemma":[0.99722064,0.001739307,0.00014128016,0.00040361902,0.00046612875,0.000029081166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002123737,0.00070032256,0.00045446018,0.001075528,0.00035875014,0.00071863394,0.0007856389,0.00062450685,0.0041310657],"category_scores_gemma":[0.010755387,0.00018159341,0.0007548857,0.0015493593,0.00054437044,0.00061322254,0.00072737946,0.000843983,0.00079008087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031953157,0.00027468297,0.007064804,0.00027734763,0.00009255035,0.00044239155,0.0006286243,0.06303259,0.009785073,0.042338748,0.012636713,0.86310697],"study_design_scores_gemma":[0.00025144315,0.00036875534,0.0145443035,0.000078227335,0.00009399589,0.0010121318,0.000300362,0.87348187,0.014256553,0.042667564,0.052827373,0.000117437965],"about_ca_topic_score_codex":0.0027778482,"about_ca_topic_score_gemma":0.0033913753,"teacher_disagreement_score":0.0041310657,"about_ca_system_score_codex":0.00044480778,"about_ca_system_score_gemma":0.0005493176,"threshold_uncertainty_score":0.013819814},"labels":[],"label_agreement":null},{"id":"W4319598526","doi":"10.3390/info14020106","title":"Challenges Encountered and Lessons Learned when Using a Novel Anonymised Linked Dataset of Health and Social Care Records for Public Health Intelligence: The Sussex Integrated Dataset","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Public Health Research Programme; University of Sussex; Department of Health and Social Care; National Institute for Health and Care Research","keywords":"Bespoke; Timeline; Public health; Health care; Medical record; Medicine; Computer science; Data science; Geography; Business; Political science; Nursing","score_opus":0.4000898357453616,"score_gpt":0.4458689272431322,"score_spread":0.045779091497770596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319598526","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":[0.109382525,0.010881716,0.26549166,0.442863,0.009605633,0.004225517,0.12129743,0.0062669483,0.029985541],"genre_scores_gemma":[0.19078645,0.0047696154,0.6777059,0.022077605,0.0023506028,0.0021544972,0.09494682,0.0020159243,0.0031926013],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.8122066,0.13019344,0.017544447,0.012977186,0.024437394,0.0026408322],"domain_scores_gemma":[0.5248085,0.31398538,0.013055713,0.09052546,0.0491214,0.008503482],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.20056778,0.001375965,0.0017870748,0.008026863,0.0038493727,0.02345358,0.0069286046,0.0036358451,0.0030439356],"category_scores_gemma":[0.3315474,0.0011980726,0.0020742044,0.014047564,0.0051564868,0.018578302,0.012040482,0.0077155465,0.0021641036],"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":[0.0011844877,0.00053854624,0.1490479,0.007516427,0.0010968447,0.0042057885,0.043629162,0.013730946,0.005200097,0.1407379,0.34701452,0.28609744],"study_design_scores_gemma":[0.00015960811,0.00025632343,0.04087089,0.009510321,0.000259477,0.0043035117,0.055042405,0.026670942,0.004004873,0.13911435,0.7191887,0.00061859936],"about_ca_topic_score_codex":0.038060196,"about_ca_topic_score_gemma":0.04956679,"teacher_disagreement_score":0.20056778,"about_ca_system_score_codex":0.0057934066,"about_ca_system_score_gemma":0.012758784,"threshold_uncertainty_score":0.9858422},"labels":[],"label_agreement":null},{"id":"W4320496187","doi":"10.3390/info14020117","title":"Smart Contracts in Blockchain Technology: A Critical Review","year":2023,"lang":"en","type":"review","venue":"Information","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":323,"is_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":"Blockchain; Smart contract; Scope (computer science); Scripting language; Computer science; Automation; Business; Computer security; Data science; Engineering management; Engineering","score_opus":0.04055215058249103,"score_gpt":0.3347273428721651,"score_spread":0.2941751922896741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320496187","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":[0.000052464864,0.9989962,0.00006628209,0.00036208628,0.00013762196,0.000004998582,0.000008520295,0.0000023046528,0.00036955648],"genre_scores_gemma":[0.00051235553,0.9989298,0.000101959966,0.00022725743,0.00010085632,0.000008489257,0.000013239164,0.0000010705784,0.000105005485],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99884343,0.00029430797,0.00021240159,0.00014591773,0.00042516208,0.00007863268],"domain_scores_gemma":[0.9932287,0.0048538763,0.0004957998,0.000106545456,0.0011697904,0.00014526087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00315936,0.00093534344,0.001666968,0.0058730925,0.00062190596,0.002049172,0.0012877107,0.0021404692,0.00341304],"category_scores_gemma":[0.006747009,0.0005855868,0.0009668049,0.008743781,0.0010952872,0.0033119568,0.0010404984,0.0019971784,0.0012094175],"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":[0.0001064514,0.000076813136,0.00025799955,0.11739905,0.00024135485,0.0002481633,0.00033622474,0.00075417856,0.0006828055,0.01646559,0.0498448,0.8135865],"study_design_scores_gemma":[0.000016954808,0.00010979043,0.00073829555,0.05218868,0.00039352465,0.0005907694,0.00024192478,0.00015324983,0.00035766233,0.004277322,0.9408939,0.000038075188],"about_ca_topic_score_codex":0.0024404898,"about_ca_topic_score_gemma":0.0037853802,"teacher_disagreement_score":0.0058730925,"about_ca_system_score_codex":0.0017580054,"about_ca_system_score_gemma":0.005965543,"threshold_uncertainty_score":0.016708493},"labels":[],"label_agreement":null},{"id":"W4320920881","doi":"10.3390/info14020124","title":"Modeling and Moderation of COVID-19 Social Network Chat","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Hate Speech and Cyberbullying Detection","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":true,"ca_institutions":"Université Laval","funders":"Canadian Institutes of Health Research","keywords":"Misinformation; Computer science; Moderation; Conversation; Social media; Newspaper; PageRank; Hidden Markov model; Coronavirus disease 2019 (COVID-19); Casual; World Wide Web; Internet privacy; Artificial intelligence; Machine learning; Psychology; Computer security; Sociology; Communication","score_opus":0.023118616562597637,"score_gpt":0.2566452818126749,"score_spread":0.23352666525007723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320920881","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":[0.8686244,0.00014292184,0.12853186,0.0003219077,0.000029347468,0.00010457273,0.00039149067,0.0003950333,0.0014584814],"genre_scores_gemma":[0.9835934,0.00004661077,0.015280577,0.000018726465,0.000019795902,0.00008490302,0.0002894003,0.000029611112,0.0006369096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990262,0.0005254788,0.00003941956,0.00022463386,0.0000937379,0.00009062206],"domain_scores_gemma":[0.9892237,0.008952308,0.0006922843,0.00044703291,0.0004142532,0.00027038876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002695498,0.00045851804,0.0005170218,0.001226677,0.00060643425,0.0007274976,0.0007298378,0.00070626865,0.001069463],"category_scores_gemma":[0.012482861,0.00043954368,0.0004991985,0.0005109298,0.0005926724,0.0011101628,0.0010031138,0.0010904871,0.00031925534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011768946,0.00058399106,0.33648723,0.0003796498,0.0003545103,0.00096195564,0.011435356,0.48911673,0.019868918,0.019782292,0.0028285233,0.11702398],"study_design_scores_gemma":[0.000007996305,0.00003534315,0.010443935,0.0000109011835,0.000013694809,0.000037024154,0.00022569248,0.98545873,0.0007524909,0.0025727786,0.00042992475,0.000011461577],"about_ca_topic_score_codex":0.007705543,"about_ca_topic_score_gemma":0.011548168,"teacher_disagreement_score":0.007705543,"about_ca_system_score_codex":0.0006778955,"about_ca_system_score_gemma":0.0006026823,"threshold_uncertainty_score":0.015321374},"labels":[],"label_agreement":null},{"id":"W4321104661","doi":"10.3390/info14020127","title":"Subtask Segmentation Methods of the Timed Up and Go Test and L Test Using Inertial Measurement Units—A Scoping Review","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Inertial measurement unit; Test (biology); Segmentation; Market segmentation; Computer science; Artificial intelligence; Inertial frame of reference; Machine learning; Data mining; Business","score_opus":0.13402920437885266,"score_gpt":0.4497286622549308,"score_spread":0.3156994578760781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321104661","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":[0.0008603338,0.99253815,0.0026454832,0.0005410656,0.00041090933,0.0010503464,0.0005184379,0.00003457312,0.0014007705],"genre_scores_gemma":[0.010135375,0.97742724,0.007827374,0.00059696205,0.000303604,0.0023028231,0.00084422156,0.000036228357,0.0005262056],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9863301,0.003507526,0.0058235046,0.0011599798,0.002930835,0.00024813905],"domain_scores_gemma":[0.92680544,0.049642637,0.008211991,0.0016471699,0.013297249,0.00039540464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021615827,0.002471806,0.0055001457,0.033685148,0.0013971978,0.004635657,0.0029031262,0.002615466,0.0049436535],"category_scores_gemma":[0.07682949,0.0013877214,0.009147597,0.018377429,0.0016072777,0.004878359,0.0020978656,0.0016420683,0.0012890121],"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":[0.00024956866,0.000059641345,0.001190965,0.5921855,0.0026684965,0.0001332534,0.00086500234,0.00040508102,0.0005043664,0.0015433473,0.0053888457,0.3948059],"study_design_scores_gemma":[0.0000892641,0.00022284708,0.004918671,0.8926812,0.015250739,0.00047353512,0.0007611132,0.00044115397,0.0007951907,0.001644966,0.082623385,0.00009788412],"about_ca_topic_score_codex":0.014221257,"about_ca_topic_score_gemma":0.024056679,"teacher_disagreement_score":0.033685148,"about_ca_system_score_codex":0.0053525926,"about_ca_system_score_gemma":0.017188184,"threshold_uncertainty_score":0.11431682},"labels":[],"label_agreement":null},{"id":"W4321789461","doi":"10.3390/info14030148","title":"Multi-Dimensional Information Alignment in Different Modalities for Generalized Zero-Shot and Few-Shot Learning","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Discriminative model; Embedding; Artificial intelligence; Computer science; Benchmark (surveying); Pattern recognition (psychology); Space (punctuation); Visual space; Shot (pellet); Feature (linguistics); Feature vector; Class (philosophy); Modalities; Zero (linguistics); Machine learning; Geography","score_opus":0.044955854781808524,"score_gpt":0.2758490148283173,"score_spread":0.23089316004650878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321789461","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":[0.02652761,0.00093178137,0.96895546,0.00031483738,0.00009088301,0.00012276306,0.00026159247,0.001373372,0.0014216902],"genre_scores_gemma":[0.73130983,0.00070599944,0.2574488,0.00090750394,0.00025569974,0.00043439612,0.0023739152,0.0002455683,0.0063182022],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986848,0.00030626298,0.000060986797,0.00058731786,0.00022243803,0.00013820028],"domain_scores_gemma":[0.9986407,0.0005952493,0.00009802744,0.0003420065,0.0002208876,0.00010321051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015518308,0.0012650809,0.0020474868,0.0013064152,0.00071960705,0.0011682637,0.0033390676,0.0019505231,0.0031071422],"category_scores_gemma":[0.005049734,0.00055516156,0.0014244677,0.0014303324,0.0015800361,0.0035328583,0.002712317,0.002919959,0.001010029],"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":[0.00044171375,0.0006476785,0.0030833257,0.0003709698,0.0002497409,0.00023826126,0.0003619487,0.22679229,0.011973407,0.0204326,0.009638577,0.72576946],"study_design_scores_gemma":[0.000017106076,0.00008954416,0.00044617357,0.00002028936,0.000024794288,0.000094046365,0.000049631228,0.9735979,0.0025110778,0.02198813,0.0011362636,0.000024921354],"about_ca_topic_score_codex":0.005012614,"about_ca_topic_score_gemma":0.005685182,"teacher_disagreement_score":0.005012614,"about_ca_system_score_codex":0.0010818524,"about_ca_system_score_gemma":0.0012616562,"threshold_uncertainty_score":0.010394394},"labels":[],"label_agreement":null},{"id":"W4365149644","doi":"10.3390/info14040236","title":"Novel Task-Based Unification and Adaptation (TUA) Transfer Learning Approach for Bilingual Emotional Speech Data","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Emotion and Mood Recognition","field":"Psychology","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":"University of Sharjah","keywords":"Computer science; Transfer of learning; Task (project management); Adaptation (eye); Artificial intelligence; Machine learning; Field (mathematics); Speech recognition; Natural language processing","score_opus":0.1359338560340681,"score_gpt":0.33265770017506513,"score_spread":0.19672384414099703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365149644","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":[0.039430235,0.000471133,0.95150125,0.00031427114,0.00013746455,0.00024843897,0.00022221661,0.0054716896,0.002203281],"genre_scores_gemma":[0.5272746,0.00034003815,0.45906517,0.0005271274,0.00019384325,0.0009876795,0.0022779084,0.000640129,0.008693585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984484,0.00041415796,0.00012542421,0.0006107534,0.0002280909,0.00017323288],"domain_scores_gemma":[0.9983407,0.00067075866,0.00009117673,0.00033396477,0.00044612266,0.000117160795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029384925,0.0013663619,0.0013666385,0.0017507568,0.0010123439,0.0010519455,0.002449791,0.0015853068,0.003778268],"category_scores_gemma":[0.005808818,0.00053167687,0.0015308588,0.0017009006,0.00083562837,0.0027901519,0.0036865198,0.0026496134,0.0021309918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033660303,0.00041782577,0.002632145,0.00010627441,0.00014342675,0.0002258133,0.00049945596,0.06479213,0.015789753,0.003140546,0.0051182862,0.9067977],"study_design_scores_gemma":[0.000027327023,0.00012546047,0.0010031292,0.000010885209,0.00003177345,0.000102769016,0.00015796802,0.9839208,0.0058003976,0.0064089387,0.0023825746,0.00002789908],"about_ca_topic_score_codex":0.0039900225,"about_ca_topic_score_gemma":0.0032733046,"teacher_disagreement_score":0.0039900225,"about_ca_system_score_codex":0.0008360473,"about_ca_system_score_gemma":0.0012869831,"threshold_uncertainty_score":0.015540421},"labels":[],"label_agreement":null},{"id":"W4377108017","doi":"10.3390/info14050295","title":"Blockchain and Machine Learning: A Critical Review on Security","year":2023,"lang":"en","type":"review","venue":"Information","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Blockchain; Cryptocurrency; Computer science; Computer security; Mathematical proof","score_opus":0.04578074685288398,"score_gpt":0.3198991505148965,"score_spread":0.2741184036620125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377108017","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":[0.000061520266,0.9977798,0.00025881172,0.0008073734,0.00021051402,0.0000040640257,0.000008170488,0.0000042586075,0.00086546555],"genre_scores_gemma":[0.000922731,0.9979825,0.00022367423,0.00033060755,0.00024992126,0.000006607117,0.000014689192,0.0000023473158,0.0002668168],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99899393,0.00030377085,0.00014041632,0.00012118366,0.00037058335,0.000070083166],"domain_scores_gemma":[0.9941671,0.004565729,0.00029811967,0.00012491559,0.0007384112,0.000105786734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026111396,0.0009125086,0.0014111942,0.003599544,0.00051392464,0.0017094493,0.0009803169,0.0020969473,0.0039502294],"category_scores_gemma":[0.005712864,0.00045750474,0.00075013883,0.0045634857,0.0012717322,0.0035602855,0.0008736079,0.002530259,0.0015984328],"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":[0.000074005555,0.00007813601,0.00026602636,0.032265622,0.00015546952,0.00017879813,0.00017765256,0.0016395723,0.000424345,0.04735923,0.054603133,0.86277795],"study_design_scores_gemma":[0.000012852852,0.0000856867,0.0004308797,0.016205303,0.00011065273,0.00041585357,0.00009548318,0.0003391972,0.0003074112,0.015997592,0.96596843,0.000030726627],"about_ca_topic_score_codex":0.0017702956,"about_ca_topic_score_gemma":0.0021577536,"teacher_disagreement_score":0.0039502294,"about_ca_system_score_codex":0.0016632456,"about_ca_system_score_gemma":0.003227265,"threshold_uncertainty_score":0.013809204},"labels":[],"label_agreement":null},{"id":"W4380372961","doi":"10.3390/info14060329","title":"Condition Monitoring and Fault Detection in Small Induction Motors Using Machine Learning Algorithms","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":33,"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":"Induction motor; Electric motor; Fault detection and isolation; Process (computing); Fault (geology); Horsepower; Condition monitoring; DC motor; Computer science; Engineering; Control engineering; Artificial intelligence; Automotive engineering; Electrical engineering; Actuator; Voltage","score_opus":0.01703722491491046,"score_gpt":0.27254136948876084,"score_spread":0.2555041445738504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380372961","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":[0.116974704,0.00039373233,0.8792164,0.00021089893,0.000032906715,0.000045968605,0.000106903855,0.0018210514,0.0011974348],"genre_scores_gemma":[0.893834,0.0001368986,0.103831016,0.000059776918,0.000020510826,0.0000342914,0.00016401203,0.000049394963,0.0018701403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997874,0.000027185886,0.000013416904,0.000072831914,0.00006620553,0.000033039578],"domain_scores_gemma":[0.9991437,0.00043464356,0.00015758282,0.000066586384,0.00015061315,0.00004678093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004022963,0.00075288705,0.0007110281,0.0007539511,0.0002245022,0.0004984729,0.00073762576,0.00067622325,0.0012294849],"category_scores_gemma":[0.0017578563,0.00024774895,0.00028794297,0.0004878341,0.00037277324,0.0008171117,0.00043850095,0.000683293,0.0002896371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050743046,0.00021943469,0.007077128,0.0001400172,0.00005492961,0.00032238572,0.00008090185,0.5538045,0.041691497,0.0018859272,0.001998415,0.3922174],"study_design_scores_gemma":[0.000004589693,0.000032043194,0.0009418876,0.000004463935,0.000004193335,0.000022598737,0.000007324425,0.9929877,0.0047163824,0.0010259338,0.00024928112,0.0000036026631],"about_ca_topic_score_codex":0.0032296218,"about_ca_topic_score_gemma":0.004577978,"teacher_disagreement_score":0.0032296218,"about_ca_system_score_codex":0.00065447704,"about_ca_system_score_gemma":0.0004057685,"threshold_uncertainty_score":0.0064216256},"labels":[],"label_agreement":null},{"id":"W4382646338","doi":"10.3390/info14050278","title":"Deep Learning Pet Identification Using Face and Body","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","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":"Ontario Tech University","funders":"Mitacs","keywords":"Artificial intelligence; Biometrics; Identification (biology); Convolutional neural network; Computer science; Face (sociological concept); Pattern recognition (psychology); Preprocessor; Deep learning; Transfer of learning; Data pre-processing; Matching (statistics); Landmark; Facial recognition system; Machine learning; Medicine; Biology; Pathology","score_opus":0.01670222615576058,"score_gpt":0.2791895079160304,"score_spread":0.26248728176026986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382646338","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":[0.4791953,0.002163388,0.4850127,0.0007253862,0.00038000755,0.00013757372,0.00350951,0.009898603,0.018977562],"genre_scores_gemma":[0.90727955,0.00036525616,0.07607393,0.0002821302,0.00005563123,0.00005241569,0.0033527873,0.00008402245,0.012454255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997459,0.000029370136,0.000010151228,0.0001034535,0.000054656408,0.00005649102],"domain_scores_gemma":[0.99980944,0.000038467417,0.00003188234,0.00003603108,0.000069787355,0.000014434832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037531674,0.0007312357,0.0005346034,0.0009142957,0.00027040127,0.0005726778,0.000626788,0.0007897896,0.003035511],"category_scores_gemma":[0.00076873787,0.00023045707,0.0004976865,0.00056825846,0.00019273256,0.00083330495,0.00068500906,0.0005909035,0.0023523476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034850035,0.00037949844,0.030306129,0.00012229779,0.00016079759,0.0002942298,0.00008916959,0.06250234,0.034327555,0.0019059646,0.014164502,0.8553991],"study_design_scores_gemma":[0.0000076187716,0.000089066656,0.012745136,0.00003119365,0.000033498534,0.00024581118,0.00006657686,0.9647069,0.0153438505,0.00219009,0.0045180107,0.000022113874],"about_ca_topic_score_codex":0.0062378026,"about_ca_topic_score_gemma":0.008823565,"teacher_disagreement_score":0.0062378026,"about_ca_system_score_codex":0.0005165167,"about_ca_system_score_gemma":0.00036319045,"threshold_uncertainty_score":0.012403011},"labels":[],"label_agreement":null},{"id":"W4384557826","doi":"10.3390/info14070410","title":"Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection","year":2023,"lang":"en","type":"article","venue":"Information","topic":"AI in cancer detection","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial intelligence; Computer science; Random forest; Support vector machine; Convolutional neural network; Mammography; Feature selection; Pattern recognition (psychology); Classifier (UML); Breast cancer; Feature extraction; Artificial neural network; Machine learning; Cancer; Medicine","score_opus":0.005280063777756617,"score_gpt":0.2072418453120105,"score_spread":0.2019617815342539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384557826","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":[0.15358259,0.0034397156,0.83137923,0.0006799812,0.00020242752,0.00030184575,0.0013461638,0.0047184075,0.0043496657],"genre_scores_gemma":[0.64998066,0.0016795822,0.33849856,0.0004082193,0.00018729089,0.00025836434,0.002951728,0.00015690344,0.0058787866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996884,0.000034752717,0.000017788589,0.000090656016,0.00009530435,0.000073080395],"domain_scores_gemma":[0.9998254,0.000036091154,0.00002549723,0.000025442507,0.00007349497,0.000013982353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004777812,0.0010435276,0.0009202855,0.0015973184,0.00027223895,0.00059129426,0.0009178875,0.00057720864,0.0011019333],"category_scores_gemma":[0.0008252159,0.00031646102,0.00081899465,0.0011979293,0.00018921682,0.0006527633,0.0005831562,0.00044625494,0.00052076293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002745696,0.0002553967,0.0074033476,0.00014720456,0.00020765369,0.0002233042,0.00004837331,0.039477024,0.052326176,0.00089096406,0.0066522695,0.8920937],"study_design_scores_gemma":[0.000021473996,0.00012078426,0.007915117,0.000022859014,0.00010779391,0.00033194918,0.000040086576,0.9551617,0.030537667,0.0015928244,0.0041222014,0.000025573137],"about_ca_topic_score_codex":0.009202011,"about_ca_topic_score_gemma":0.012050481,"teacher_disagreement_score":0.009202011,"about_ca_system_score_codex":0.00071312534,"about_ca_system_score_gemma":0.0007139712,"threshold_uncertainty_score":0.018296897},"labels":[],"label_agreement":null},{"id":"W4384827994","doi":"10.3390/info14070415","title":"Multi-Class Skin Cancer Classification Using Vision Transformer Networks and Convolutional Neural Network-Based Pre-Trained Models","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Skin cancer; Computer science; Deep learning; Machine learning; Transfer of learning; Transformer; Pattern recognition (psychology); Cancer; Medicine; Engineering","score_opus":0.03908439578273524,"score_gpt":0.30278207540922575,"score_spread":0.2636976796264905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384827994","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":[0.6242873,0.0027465243,0.3564572,0.0005620852,0.00040441484,0.00036013007,0.00096980366,0.005358112,0.0088543985],"genre_scores_gemma":[0.95442563,0.0004056728,0.03988536,0.00016149187,0.00005241304,0.000072932264,0.0015027246,0.000044678192,0.0034490244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999678,0.000039728806,0.000017032155,0.00010932713,0.00008168794,0.00007421956],"domain_scores_gemma":[0.9996568,0.00008301073,0.000043275246,0.000050401446,0.00013414377,0.00003239102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006175131,0.0009776414,0.0005841817,0.0012293262,0.00026120202,0.00062469824,0.0009831407,0.0006314811,0.0012016543],"category_scores_gemma":[0.0012194802,0.00023744708,0.00089583156,0.00046336,0.00023959592,0.00085183507,0.00069172285,0.0010005673,0.00064290146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077309436,0.0008346999,0.027500622,0.0001933975,0.00027429077,0.00049188436,0.000107598615,0.19199972,0.02985368,0.0018153666,0.0071184165,0.7390373],"study_design_scores_gemma":[0.000008638109,0.00010752048,0.0031988404,0.000010622035,0.00003157047,0.00010784694,0.000029337192,0.98801196,0.0072017033,0.00063280016,0.00064831984,0.000010758298],"about_ca_topic_score_codex":0.008063631,"about_ca_topic_score_gemma":0.009264564,"teacher_disagreement_score":0.008063631,"about_ca_system_score_codex":0.00082907476,"about_ca_system_score_gemma":0.00077044254,"threshold_uncertainty_score":0.016033411},"labels":[],"label_agreement":null},{"id":"W4385498064","doi":"10.3390/info14080437","title":"IoTBChain: Adopting Blockchain Technology to Increase PLC Resilience in an IoT Environment","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Blockchain Technology Applications and Security","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":"Toronto Metropolitan University","funders":"","keywords":"Blockchain; Computer science; Computer security; Authentication (law); Resilience (materials science); Computer network","score_opus":0.00808726259810101,"score_gpt":0.24214763160753303,"score_spread":0.23406036900943203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385498064","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":[0.07504552,0.001059233,0.8745332,0.0018253223,0.0006427346,0.00051295245,0.0002832271,0.0053259577,0.040771823],"genre_scores_gemma":[0.73638064,0.0012095137,0.23729919,0.00047228707,0.00012576443,0.00043808986,0.00060437,0.00033744596,0.023132699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906176,0.00020853351,0.000054884153,0.00016552587,0.00037236963,0.00013684416],"domain_scores_gemma":[0.99846375,0.00030057025,0.00014901685,0.000458411,0.00039753976,0.00023074745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013053238,0.00045000188,0.00039988288,0.00076404697,0.0011814586,0.001488439,0.0012948539,0.0009846827,0.0059468285],"category_scores_gemma":[0.0024483174,0.00027798963,0.00034211465,0.00092549995,0.0010298495,0.003939817,0.0027890548,0.0011032284,0.0013293561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000813509,0.0006951858,0.0046347682,0.00062182953,0.00009616045,0.0018706452,0.0016213899,0.12897241,0.109665446,0.26075885,0.022496723,0.46775302],"study_design_scores_gemma":[0.000273587,0.0007068195,0.0009345295,0.00014460349,0.000070726055,0.00078309275,0.0002778596,0.6847355,0.06660086,0.10806579,0.13726795,0.00013875686],"about_ca_topic_score_codex":0.0028075033,"about_ca_topic_score_gemma":0.0035104642,"teacher_disagreement_score":0.0059468285,"about_ca_system_score_codex":0.0009332511,"about_ca_system_score_gemma":0.0016779556,"threshold_uncertainty_score":0.019894063},"labels":[],"label_agreement":null},{"id":"W4385544214","doi":"10.3390/info14080439","title":"Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Advanced Machining and Optimization Techniques","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Electrical discharge machining; Machinability; Taguchi methods; Machining; Automotive industry; Computer science; Enhanced Data Rates for GSM Evolution; Materials science; Titanium alloy; Surface roughness; Mechanical engineering; Orthogonal array; Algorithm; Alloy; Machine learning; Engineering; Metallurgy; Composite material; Artificial intelligence","score_opus":0.010769281374318809,"score_gpt":0.25904503083366087,"score_spread":0.24827574945934205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385544214","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":[0.6355319,0.00038240358,0.35636747,0.00013655658,0.000036552225,0.00012077089,0.00003200156,0.0003773578,0.0070150294],"genre_scores_gemma":[0.9700263,0.00007125788,0.028472615,0.00001831445,0.0000033788176,0.00004521213,0.000033267304,0.000007632211,0.0013219992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998908,0.000016055305,0.000011125789,0.000023300567,0.000042842126,0.000015941516],"domain_scores_gemma":[0.99972934,0.00014049906,0.000042144613,0.000011249742,0.00006787452,0.000008837006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004070557,0.0003402783,0.0003462838,0.00034645238,0.00018561275,0.00044471666,0.00042610205,0.00054939545,0.0007798475],"category_scores_gemma":[0.0008446633,0.00019597083,0.00048899197,0.00015661008,0.00016580179,0.00016708094,0.00018443147,0.00020900482,0.00008701556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015932135,0.00011076431,0.0027632935,0.00021338937,0.00004377875,0.00014624388,0.00012576989,0.85123074,0.037054103,0.0005739372,0.00025034285,0.107328266],"study_design_scores_gemma":[0.0000047719245,0.00014537414,0.00066856766,0.0000045374322,0.0000114512695,0.000016068749,0.000014341606,0.99448496,0.0044325916,0.000069237256,0.00014324937,0.000004806004],"about_ca_topic_score_codex":0.0038033882,"about_ca_topic_score_gemma":0.003683902,"teacher_disagreement_score":0.0038033882,"about_ca_system_score_codex":0.0003396471,"about_ca_system_score_gemma":0.0003251257,"threshold_uncertainty_score":0.007562518},"labels":[],"label_agreement":null},{"id":"W4385878021","doi":"10.3390/info14080462","title":"A Comprehensive Study of ChatGPT: Advancements, Limitations, and Ethical Considerations in Natural Language Processing and Cybersecurity","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":200,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Automatic summarization; Computer science; Field (mathematics); Artificial intelligence; Machine translation; Generative grammar; Natural language processing; Natural language generation; Data science; Architecture; Natural language","score_opus":0.14453183792913005,"score_gpt":0.43985728903190413,"score_spread":0.2953254511027741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385878021","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":[0.43680656,0.01413202,0.48212415,0.018346844,0.001089786,0.001749866,0.0046590855,0.01151255,0.029579146],"genre_scores_gemma":[0.7536975,0.0038011654,0.22386849,0.0023995996,0.0005176217,0.0012371391,0.0068496866,0.0014618702,0.006166924],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9764098,0.017233087,0.0008265635,0.0017974179,0.003388435,0.00034474485],"domain_scores_gemma":[0.8188453,0.14518876,0.003894861,0.016131453,0.013893669,0.0020459653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02403962,0.0007689313,0.00069358625,0.0024831316,0.0016575301,0.0034592703,0.0017995404,0.0014136482,0.0036450012],"category_scores_gemma":[0.10910091,0.00051404344,0.00064476376,0.0023986162,0.0027793942,0.00914988,0.004203056,0.003509367,0.0018059055],"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":[0.00095489796,0.00064031215,0.031022025,0.0032027182,0.00021294954,0.0006535411,0.014257684,0.023018727,0.014262288,0.036163513,0.028667942,0.8469434],"study_design_scores_gemma":[0.0002893974,0.0037017898,0.046317704,0.0031120258,0.0004659679,0.0049244617,0.014977193,0.42276478,0.07306832,0.11168464,0.3180792,0.00061451155],"about_ca_topic_score_codex":0.0036537952,"about_ca_topic_score_gemma":0.0046607275,"teacher_disagreement_score":0.02403962,"about_ca_system_score_codex":0.0025307431,"about_ca_system_score_gemma":0.003696639,"threshold_uncertainty_score":0.12713522},"labels":[],"label_agreement":null},{"id":"W4386461215","doi":"10.3390/info14090490","title":"Effects of Contractual Governance on IT Project Performance under the Mediating Role of Project Management Risk: An Emerging Market Context","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Outsourcing and Supply Chain Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Project governance; Corporate governance; Context (archaeology); Risk management; Mediation; Risk governance; Business; Project management; Project management triangle; Project risk management; Process management; OPM3; Knowledge management; Management; Economics; Political science; Computer science; Finance","score_opus":0.008652681756708597,"score_gpt":0.2237454553106165,"score_spread":0.21509277355390788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386461215","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":[0.9835745,0.0002492099,0.0027502608,0.0010361016,0.000010253918,0.00004399647,0.00002142892,0.0000072523217,0.012307079],"genre_scores_gemma":[0.9994174,0.000069420385,0.0002462446,0.000026393724,0.0000048184083,0.000008783844,0.000006682906,0.0000018197918,0.00021849648],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9902892,0.006495109,0.0003212716,0.0005387154,0.0013167361,0.0010390662],"domain_scores_gemma":[0.9461783,0.026097352,0.017555768,0.0022170353,0.00362069,0.0043307506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009071328,0.0002943798,0.00025774408,0.000753458,0.0014689309,0.003569695,0.0005496316,0.0006538336,0.0030802595],"category_scores_gemma":[0.02393486,0.00016170886,0.00030342376,0.00087314675,0.002864462,0.002759661,0.0030501366,0.0014064299,0.00019335987],"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":[0.00047417104,0.0014642972,0.8180751,0.00019455691,0.00018211406,0.0016506494,0.018601166,0.005326464,0.0016378657,0.07399587,0.0010952787,0.07730249],"study_design_scores_gemma":[0.00010272927,0.0010364895,0.8858555,0.00029950382,0.00018813513,0.00038718962,0.040416952,0.02314881,0.0017347488,0.03873274,0.008019531,0.00007765083],"about_ca_topic_score_codex":0.0041527343,"about_ca_topic_score_gemma":0.004282668,"teacher_disagreement_score":0.009071328,"about_ca_system_score_codex":0.0022349567,"about_ca_system_score_gemma":0.0046933554,"threshold_uncertainty_score":0.047974408},"labels":[],"label_agreement":null},{"id":"W4386525457","doi":"10.3390/info14090493","title":"Availability of Physical Activity Tracking Data from Wearable Devices for Glaucoma Patients","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Institute on Minority Health and Health Disparities; National Eye Institute; National Institutes of Health; Research to Prevent Blindness","keywords":"Biobank; Wearable computer; Activity tracker; Glaucoma; Physical activity; Computer science; Wearable technology; MEDLINE; Medicine; Physical medicine and rehabilitation; Bioinformatics; Ophthalmology","score_opus":0.03895440628559082,"score_gpt":0.30913632256844564,"score_spread":0.2701819162828548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386525457","genre_codex":"review","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":[0.019603591,0.9030775,0.0019143679,0.0034884484,0.0005284764,0.001681918,0.061914094,0.000054397828,0.007737325],"genre_scores_gemma":[0.1177707,0.827967,0.010316703,0.0031860473,0.0005000153,0.00512942,0.0341553,0.00006128618,0.0009134802],"study_design_codex":"systematic_review","study_design_gemma":"observational","domain_scores_codex":[0.9712659,0.007013842,0.016537536,0.0013610601,0.003420703,0.0004009753],"domain_scores_gemma":[0.7615164,0.18671155,0.03224706,0.003843057,0.014978144,0.0007037657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023380117,0.000718999,0.0044345,0.036474507,0.00091112545,0.00350245,0.0015493414,0.0021361043,0.0099322535],"category_scores_gemma":[0.13483867,0.0008451186,0.0044509503,0.037683126,0.0010012133,0.0046543367,0.0024769397,0.0010764484,0.0011724767],"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":[0.00048188076,0.00005096317,0.016505165,0.8502069,0.0036280693,0.00046808514,0.001320466,0.00012616465,0.0005221964,0.0008618805,0.007892472,0.11793581],"study_design_scores_gemma":[0.00022340522,0.00023764267,0.046274573,0.8503243,0.016630663,0.0010151505,0.0016929909,0.00013102354,0.0008099342,0.0010261597,0.08152715,0.000107004795],"about_ca_topic_score_codex":0.009619114,"about_ca_topic_score_gemma":0.020109188,"teacher_disagreement_score":0.036474507,"about_ca_system_score_codex":0.0020258047,"about_ca_system_score_gemma":0.010493674,"threshold_uncertainty_score":0.12364733},"labels":[],"label_agreement":null},{"id":"W4386713054","doi":"10.3390/info14090502","title":"PDD-ET: Parkinson’s Disease Detection Using ML Ensemble Techniques and Customized Big Dataset","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Boosting (machine learning); Deep learning; Parkinson's disease; Computer science; Decision tree; Machine learning; Movement assessment; Support vector machine; Population; Disease; Medicine; Psychology; Neuroscience; Internal medicine","score_opus":0.029335492460995954,"score_gpt":0.3049314146727496,"score_spread":0.27559592221175366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386713054","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":[0.6000971,0.007927402,0.27301687,0.0029752234,0.0018082027,0.0006867088,0.075648874,0.03042249,0.0074170954],"genre_scores_gemma":[0.7334976,0.0010102355,0.12097304,0.0007246164,0.00035285627,0.00037699234,0.13907413,0.00028257482,0.0037079968],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991443,0.00020537071,0.000084717605,0.0002919764,0.00015889629,0.00011474749],"domain_scores_gemma":[0.99911743,0.00023456955,0.00008000846,0.00027484127,0.00020830323,0.00008485173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016404454,0.002049644,0.0011371982,0.0022172935,0.00054213975,0.0008621513,0.0016454613,0.001402246,0.0009765164],"category_scores_gemma":[0.002461213,0.00033073837,0.0016682295,0.0011422575,0.00025355615,0.0013234774,0.0014893246,0.0014169499,0.0007434028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001081273,0.0015201182,0.0865364,0.00056236604,0.0018616727,0.001328326,0.00016890604,0.2738205,0.008790168,0.0017832473,0.108221486,0.5143255],"study_design_scores_gemma":[0.000073544754,0.0002691379,0.01556368,0.000051762123,0.00018647707,0.00043117153,0.00008798244,0.9671833,0.0046510054,0.002259773,0.009177931,0.00006438744],"about_ca_topic_score_codex":0.011821233,"about_ca_topic_score_gemma":0.019510841,"teacher_disagreement_score":0.011821233,"about_ca_system_score_codex":0.00070258207,"about_ca_system_score_gemma":0.00075681316,"threshold_uncertainty_score":0.023504853},"labels":[],"label_agreement":null},{"id":"W4387170333","doi":"10.3390/info14100533","title":"Evaluation of Smart Contract Vulnerability Analysis Tools: A Domain-Specific Perspective","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Government of Alberta","keywords":"Vulnerability (computing); Computer science; Domain (mathematical analysis); Risk analysis (engineering); Perspective (graphical); Computer security; Smart contract; Abstraction; Vulnerability assessment; Data science; Software engineering; Artificial intelligence; Business; Blockchain","score_opus":0.03591599558607542,"score_gpt":0.3006036500204747,"score_spread":0.26468765443439923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387170333","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":[0.75390136,0.0014558272,0.22150172,0.0012855278,0.00012941328,0.0015849796,0.0007932678,0.003598159,0.01574976],"genre_scores_gemma":[0.80911404,0.0009921032,0.18573184,0.00033160427,0.000039287566,0.00058662955,0.0011785675,0.00049661845,0.0015293577],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9825499,0.0070979805,0.0011099906,0.001157727,0.006883612,0.00120079],"domain_scores_gemma":[0.94282156,0.036331434,0.0040068743,0.0072555775,0.008580738,0.0010036726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013315816,0.0012216915,0.0006272547,0.004483244,0.0008077695,0.002885332,0.0020854461,0.001807105,0.0012503547],"category_scores_gemma":[0.044518225,0.0005406772,0.000934524,0.0020379808,0.001812457,0.0045811865,0.0028714438,0.0022381363,0.00037725686],"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":[0.0013898581,0.0038193353,0.062022585,0.0052026096,0.000753721,0.0021528103,0.008387211,0.33319104,0.08160038,0.07084257,0.012562523,0.41807538],"study_design_scores_gemma":[0.0002526355,0.0027571474,0.018982178,0.0014251143,0.0003644973,0.0015950726,0.004836933,0.7873973,0.12564313,0.020919543,0.035569385,0.00025701462],"about_ca_topic_score_codex":0.001925915,"about_ca_topic_score_gemma":0.0017733782,"teacher_disagreement_score":0.013315816,"about_ca_system_score_codex":0.0018536063,"about_ca_system_score_gemma":0.002612947,"threshold_uncertainty_score":0.07042158},"labels":[],"label_agreement":null},{"id":"W4387404441","doi":"10.3390/info14100546","title":"The Impact of Virtual Reality (VR) Tour Experience on Tourists’ Intention to Visit","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Virtual reality; Tourism; Psychology; Sense of presence; Immersion (mathematics); Destinations; Mixed reality; Realism; Avatar; Applied psychology; Computer science; Multimedia; Human–computer interaction; Geography","score_opus":0.03298933082707195,"score_gpt":0.3398190129415561,"score_spread":0.30682968211448414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387404441","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":[0.9978811,0.00005681239,0.000113709386,0.00004777882,0.0000023013033,0.0000062593867,0.000018562,0.00000312678,0.0018703945],"genre_scores_gemma":[0.99945134,0.00006177202,0.00012098897,0.0000144893165,0.0000019494782,0.000008395471,0.000021281978,0.0000016681116,0.0003181148],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99918574,0.0004228811,0.000045176323,0.00006851366,0.00015147905,0.00012614793],"domain_scores_gemma":[0.98979014,0.0075119906,0.0014411972,0.00022999635,0.00033907953,0.0006876112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093741505,0.00015914511,0.00015797431,0.0004834807,0.00033218504,0.0013470498,0.00021325081,0.000450166,0.0033995982],"category_scores_gemma":[0.007868878,0.00015655185,0.00052908796,0.00026293282,0.00062057667,0.0005551261,0.0008197509,0.00063203357,0.00020028444],"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":[0.0010160232,0.0011410303,0.90444326,0.00040220132,0.00026656402,0.00049420417,0.046116315,0.00085320655,0.004544355,0.00094359886,0.0003181019,0.039461207],"study_design_scores_gemma":[0.000015038553,0.0008598589,0.96905154,0.00007309184,0.00012248309,0.00016212545,0.027278326,0.00076101616,0.00052648457,0.00018779101,0.00093077164,0.000031526866],"about_ca_topic_score_codex":0.0055225804,"about_ca_topic_score_gemma":0.008338356,"teacher_disagreement_score":0.0055225804,"about_ca_system_score_codex":0.00035652105,"about_ca_system_score_gemma":0.0004268409,"threshold_uncertainty_score":0.011372805},"labels":[],"label_agreement":null},{"id":"W4387573037","doi":"10.3390/info14100557","title":"Exploring Blockchain Research in Supply Chain Management: A Latent Dirichlet Allocation-Driven Systematic Review","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Blockchain; Latent Dirichlet allocation; Supply chain; Traceability; Decentralization; Supply chain management; Transparency (behavior); Computer science; Knowledge management; Data science; Process management; Business; Topic model; Marketing; Economics; Computer security","score_opus":0.08593418660446979,"score_gpt":0.30354849304593484,"score_spread":0.21761430644146507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387573037","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":[0.0076141194,0.96492904,0.01596249,0.00516387,0.00033609444,0.0025115071,0.0015538827,0.00005635792,0.0018725429],"genre_scores_gemma":[0.097402595,0.8566139,0.035306815,0.0027081007,0.00026427564,0.0054940046,0.0016163577,0.00005027624,0.0005437638],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.96276313,0.023396324,0.006803691,0.0018421579,0.0046604928,0.0005342166],"domain_scores_gemma":[0.77765423,0.1965971,0.009383341,0.0035797937,0.0118065,0.0009790009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04683045,0.0011491921,0.003625485,0.030759266,0.0011754575,0.0045083435,0.0019100723,0.0018251801,0.0022940745],"category_scores_gemma":[0.14432463,0.0009598644,0.0040119695,0.025243135,0.0015650957,0.005141811,0.0034771913,0.0021170196,0.000423058],"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":[0.0002208289,0.00010813195,0.0052936506,0.5887292,0.0068495097,0.00075059664,0.0064375163,0.0029763523,0.001100176,0.013541074,0.0072574345,0.36673552],"study_design_scores_gemma":[0.00013147907,0.0002821184,0.004987854,0.8391806,0.015985072,0.0006800094,0.006411281,0.00322909,0.00088183157,0.017523656,0.110548414,0.000158591],"about_ca_topic_score_codex":0.005677505,"about_ca_topic_score_gemma":0.019125132,"teacher_disagreement_score":0.04683045,"about_ca_system_score_codex":0.005555578,"about_ca_system_score_gemma":0.033199385,"threshold_uncertainty_score":0.247666},"labels":[],"label_agreement":null},{"id":"W4387653383","doi":"10.3390/info14100569","title":"An AI-Based Framework for Translating American Sign Language to English and Vice Versa","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Hand Gesture 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":"Athabasca University","funders":"Faculty Professional Development Council, Pennsylvania State System of Higher Education; Pennsylvania State System of Higher Education","keywords":"Versa; American Sign Language; Computer science; Construct (python library); Artificial intelligence; Natural language processing; Sign language; Sign (mathematics); Deep learning; Image (mathematics); American English; Linguistics; Programming language","score_opus":0.011870552818291233,"score_gpt":0.289849091096539,"score_spread":0.27797853827824776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387653383","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":[0.006050571,0.0002197976,0.9798922,0.00039157382,0.00018756214,0.00017037416,0.0002443809,0.005597086,0.007246466],"genre_scores_gemma":[0.2068096,0.00032707996,0.77234554,0.0005619479,0.000096486205,0.00033931155,0.0011526799,0.0003172203,0.01805008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994005,0.00008947432,0.00005610278,0.00019171539,0.00019285377,0.000069338625],"domain_scores_gemma":[0.99952865,0.00010057532,0.000036971374,0.000055847104,0.00022224117,0.000055806748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078091054,0.0007479948,0.0004380959,0.0010044704,0.0006211498,0.0011536783,0.0011323116,0.0007247391,0.0054924483],"category_scores_gemma":[0.0016974865,0.0002571628,0.0008641075,0.0004956028,0.0008033541,0.0013885802,0.0011262331,0.0013299342,0.002426555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002373342,0.00034418298,0.0021255957,0.00031860877,0.00011250063,0.00064434437,0.0007178063,0.09043264,0.070188224,0.070089124,0.021608481,0.7431812],"study_design_scores_gemma":[0.00001929822,0.00012480351,0.00093171006,0.000063799715,0.00004650842,0.00024512436,0.0001767072,0.91980493,0.023284592,0.02423435,0.031019887,0.00004830851],"about_ca_topic_score_codex":0.019887041,"about_ca_topic_score_gemma":0.022166967,"teacher_disagreement_score":0.019887041,"about_ca_system_score_codex":0.0009442703,"about_ca_system_score_gemma":0.001874057,"threshold_uncertainty_score":0.039542556},"labels":[],"label_agreement":null},{"id":"W4388041141","doi":"10.3390/info14110590","title":"Predicting COVID-19 Hospital Stays with Kolmogorov–Gabor Polynomials: Charting the Future of Care","year":2023,"lang":"en","type":"article","venue":"Information","topic":"COVID-19 epidemiological studies","field":"Mathematics","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":"Ministerio de Ciencia e Innovación","keywords":"Concordance; Coronavirus disease 2019 (COVID-19); Robustness (evolution); Mathematics; Medicine; Statistics; Health care; Econometrics; Internal medicine; Biology; Disease","score_opus":0.07687479603413584,"score_gpt":0.37083003348893334,"score_spread":0.2939552374547975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388041141","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":[0.905148,0.0004426444,0.09046713,0.0012954008,0.0000803544,0.000055527376,0.00081833714,0.00027444866,0.0014180084],"genre_scores_gemma":[0.98601866,0.00012356549,0.013263958,0.000024022253,0.000024213918,0.000010226423,0.00032033032,0.0000065694107,0.00020842919],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996271,0.00012682805,0.000032054064,0.00008475054,0.00006955671,0.000059682818],"domain_scores_gemma":[0.99868363,0.0006430575,0.00026204775,0.000074560274,0.00024215702,0.00009460152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013835792,0.00037028297,0.00028045633,0.001098695,0.00025774728,0.00083984,0.00042403105,0.00043388162,0.00068505574],"category_scores_gemma":[0.004218015,0.00012182047,0.0004853485,0.00070177304,0.00022507337,0.000732175,0.00045151892,0.0006207813,0.00020940816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038483914,0.00019797127,0.6013689,0.00012072716,0.00011058481,0.00047466438,0.00046729605,0.23218095,0.0038324648,0.0036330754,0.0040120455,0.15321647],"study_design_scores_gemma":[0.0000044111366,0.00006610679,0.043569062,0.000015529393,0.000011603115,0.000077087694,0.0001775114,0.95356166,0.00052239595,0.0015272457,0.00045148787,0.000015845184],"about_ca_topic_score_codex":0.012220507,"about_ca_topic_score_gemma":0.010857037,"teacher_disagreement_score":0.012220507,"about_ca_system_score_codex":0.0007303049,"about_ca_system_score_gemma":0.0010963645,"threshold_uncertainty_score":0.024298728},"labels":[],"label_agreement":null},{"id":"W4388464957","doi":"10.3390/info14110604","title":"POSS-CNN: An Automatically Generated Convolutional Neural Network with Precision and Operation Separable Structure Aiming at Target Recognition and Detection","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Convolutional neural network; Benchmark (surveying); Artificial intelligence; Task (project management); Reinforcement learning; Pattern recognition (psychology); Deep learning; Field (mathematics); Machine learning","score_opus":0.015822272681247848,"score_gpt":0.23828038274097063,"score_spread":0.22245811005972277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388464957","genre_codex":"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":[0.15030736,0.002389817,0.7850042,0.00064579386,0.0007134356,0.00041954833,0.0035945217,0.027303284,0.029622108],"genre_scores_gemma":[0.6591543,0.0010916571,0.30975494,0.0006002952,0.000115126604,0.0003057715,0.01021344,0.0006436108,0.0181209],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998197,0.000015497733,0.000009450275,0.000057366527,0.00006968665,0.000028350918],"domain_scores_gemma":[0.9998292,0.00002440471,0.000020006613,0.0000450181,0.000067219386,0.000014071792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003488242,0.0011635908,0.00037609503,0.00054130406,0.00021631335,0.0004338035,0.001413863,0.00054257066,0.0028020751],"category_scores_gemma":[0.0007378141,0.00036107044,0.0005702627,0.00043460273,0.00036394468,0.0010815474,0.0005660944,0.00067205174,0.0009113368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057317765,0.000247199,0.004325444,0.00044564705,0.0002751334,0.00046929994,0.000053362837,0.3015279,0.08013636,0.013603992,0.040835287,0.5575072],"study_design_scores_gemma":[0.00003373371,0.00019515153,0.0011972712,0.000017185335,0.00004937423,0.00016696812,0.000007385444,0.9606315,0.025448104,0.0026951195,0.00953663,0.000021548045],"about_ca_topic_score_codex":0.0068896445,"about_ca_topic_score_gemma":0.010076807,"teacher_disagreement_score":0.0068896445,"about_ca_system_score_codex":0.00077793404,"about_ca_system_score_gemma":0.0009895406,"threshold_uncertainty_score":0.013699055},"labels":[],"label_agreement":null},{"id":"W4388832684","doi":"10.3390/info14110620","title":"Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Privacy-Preserving Technologies in Data","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; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Federated learning; Inference; Machine learning; Information leakage; Artificial intelligence; Vulnerability (computing); Bayesian inference; Data mining; Computer security; Bayesian probability","score_opus":0.06122179476991954,"score_gpt":0.33555038444060625,"score_spread":0.27432858967068674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388832684","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":[0.5198033,0.0011762653,0.46984097,0.0010180373,0.00009414924,0.00023567646,0.00019488466,0.0013894248,0.0062473454],"genre_scores_gemma":[0.9809363,0.00012488775,0.018323999,0.0000609012,0.000016527081,0.000040370414,0.00007027088,0.000029097968,0.00039773894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98982704,0.004372159,0.0003994711,0.0014044427,0.002719025,0.0012779929],"domain_scores_gemma":[0.9542166,0.025333583,0.0035169623,0.013536469,0.002553372,0.0008430079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012204746,0.00076106656,0.0015289248,0.0010412005,0.0011575801,0.0024807623,0.0018660182,0.0015467898,0.0011627643],"category_scores_gemma":[0.043376185,0.00032118565,0.00087623886,0.0010526914,0.0026644836,0.0064704875,0.0036337243,0.0022455202,0.00023222451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013865103,0.0005458064,0.009411511,0.00019268102,0.00025305786,0.00024687065,0.00026154533,0.81551987,0.0042519285,0.060364556,0.001772477,0.105793215],"study_design_scores_gemma":[0.000030410218,0.00032747764,0.0017914912,0.00003082222,0.00003475523,0.00019290103,0.00015192221,0.9635277,0.0055317646,0.027794732,0.0005641458,0.000021954014],"about_ca_topic_score_codex":0.00094483915,"about_ca_topic_score_gemma":0.0008880973,"teacher_disagreement_score":0.012204746,"about_ca_system_score_codex":0.0020690635,"about_ca_system_score_gemma":0.001852883,"threshold_uncertainty_score":0.06454569},"labels":[],"label_agreement":null},{"id":"W4390104081","doi":"10.3390/info15010009","title":"Blockchain and Business Process Management (BPM) Synergy: A Comparative Analysis of Modeling Approaches","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Blockchain Technology Applications and Security","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 Canada West","funders":"","keywords":"Blockchain; Business process management; Computer science; Business process; Transparency (behavior); Adaptability; Process (computing); Process management; Business process modeling; Business model; Data science; Work in process; Computer security; Business; Engineering; Operations management; Management","score_opus":0.03623104621613579,"score_gpt":0.2581160091880342,"score_spread":0.2218849629718984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390104081","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":[0.118019156,0.32704914,0.3845533,0.014867228,0.0004130707,0.00041296886,0.000338891,0.00038324093,0.153963],"genre_scores_gemma":[0.6686093,0.21688181,0.109167926,0.00058683363,0.00022674371,0.00043791562,0.0004195451,0.00013577228,0.00353409],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99057066,0.0055133845,0.00045534485,0.00041524673,0.0026915548,0.0003538564],"domain_scores_gemma":[0.9781349,0.017575491,0.0014256459,0.0007411868,0.0018330968,0.00028966446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012013648,0.00088064023,0.000941651,0.008849793,0.0011280032,0.00584519,0.0012369168,0.0016331185,0.0027847108],"category_scores_gemma":[0.016574483,0.0005655422,0.0013079927,0.014385986,0.002196352,0.008778179,0.0025459272,0.001560184,0.00039269918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022893403,0.00014111657,0.005684342,0.0042199316,0.00032244227,0.00021550011,0.006405239,0.021961447,0.0006696136,0.6926353,0.0020453571,0.26547077],"study_design_scores_gemma":[0.0001069582,0.00051396026,0.013823663,0.012582869,0.0008951239,0.0007496993,0.015574911,0.1470334,0.0028872148,0.5812257,0.22439772,0.00020883333],"about_ca_topic_score_codex":0.0032706482,"about_ca_topic_score_gemma":0.002894329,"teacher_disagreement_score":0.012013648,"about_ca_system_score_codex":0.0050813435,"about_ca_system_score_gemma":0.006685814,"threshold_uncertainty_score":0.063534975},"labels":[],"label_agreement":null},{"id":"W4390272643","doi":"10.3390/info15010016","title":"Towards Enhancing Automated Defect Recognition (ADR) in Digital X-ray Radiography Applications: Synthesizing Training Data through X-ray Intensity Distribution Modeling for Deep Learning Algorithms","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Advanced X-ray and CT Imaging","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 Laval","funders":"Canada Foundation for Innovation","keywords":"Digital radiography; Computer science; Artificial intelligence; Pipeline (software); Algorithm; Deep learning; Scalability; Throughput; Radiography; Machine learning; Computer vision; Database","score_opus":0.03703842255652953,"score_gpt":0.26759544609133623,"score_spread":0.2305570235348067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390272643","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":[0.020697009,0.00033473686,0.97647285,0.00015325255,0.000022895869,0.00003269891,0.000111212714,0.0016173387,0.0005579904],"genre_scores_gemma":[0.4851445,0.00085917505,0.5094128,0.00029030695,0.000043640583,0.00015346843,0.00096151035,0.00026836654,0.002866264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976724,0.00003747439,0.000014171114,0.000071316696,0.00008232939,0.000027450182],"domain_scores_gemma":[0.9994368,0.0002351748,0.000089712026,0.00008530815,0.00012903166,0.00002397957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064064784,0.00095781946,0.0005148,0.0006406881,0.00011563297,0.000852901,0.0010914899,0.000940467,0.0010903209],"category_scores_gemma":[0.0016989973,0.00040253915,0.00078234135,0.00042803443,0.0004314766,0.0007833473,0.00067126565,0.0010575948,0.0006699746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001016769,0.00014120937,0.0023422046,0.00022212893,0.00006771733,0.00010826514,0.000085666106,0.6849663,0.033845916,0.0035357568,0.0022185668,0.2723645],"study_design_scores_gemma":[0.000002037482,0.000021238198,0.0001790148,0.0000064391907,0.0000046842847,0.000023409635,0.0000057530606,0.99437606,0.004127021,0.0007981977,0.00045269777,0.0000034598002],"about_ca_topic_score_codex":0.002614466,"about_ca_topic_score_gemma":0.0027282836,"teacher_disagreement_score":0.002614466,"about_ca_system_score_codex":0.0005136994,"about_ca_system_score_gemma":0.000717461,"threshold_uncertainty_score":0.005198419},"labels":[],"label_agreement":null},{"id":"W4390738791","doi":"10.3390/info15010040","title":"Secure Genomic String Search with Parallel Homomorphic Encryption","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Cryptography and Data Security","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 of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Homomorphic encryption; Cryptography; Speedup; Theoretical computer science; Cryptographic primitive; Encryption; String (physics); Parallel computing; Instruction set; Distributed computing; Computer engineering; Cryptographic protocol; Algorithm; Computer network; Mathematics","score_opus":0.009144588783346355,"score_gpt":0.2141576374807494,"score_spread":0.20501304869740306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390738791","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":[0.14512697,0.0007637718,0.8331529,0.0006622927,0.00014421243,0.0001812654,0.00042607263,0.0048693265,0.014673274],"genre_scores_gemma":[0.6607599,0.00024577824,0.33228943,0.000208906,0.000040924187,0.00014015714,0.00062866864,0.00015972716,0.005526436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991875,0.00018228294,0.000061404055,0.00012477473,0.00033138104,0.000112630434],"domain_scores_gemma":[0.9992417,0.00020444838,0.000057284396,0.00034244705,0.0001211817,0.000032941996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062295265,0.000360294,0.00040542264,0.0005233844,0.00047820585,0.0011480066,0.0008895683,0.00062664464,0.0031105753],"category_scores_gemma":[0.0018673651,0.0001951405,0.0004769244,0.00073375116,0.00063118956,0.001831185,0.0012698367,0.00063896575,0.0009864531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013413534,0.00036862606,0.0043326872,0.00033057516,0.00012929691,0.0007771974,0.0005250707,0.18975155,0.08251694,0.20903824,0.017965034,0.49292335],"study_design_scores_gemma":[0.00017487374,0.0002814198,0.0008072142,0.000031021595,0.000030551197,0.0005033869,0.00009853285,0.8321041,0.07373949,0.07526161,0.016924229,0.000043573582],"about_ca_topic_score_codex":0.002025504,"about_ca_topic_score_gemma":0.0027598487,"teacher_disagreement_score":0.0031105753,"about_ca_system_score_codex":0.0009321359,"about_ca_system_score_gemma":0.0012793196,"threshold_uncertainty_score":0.010405958},"labels":[],"label_agreement":null},{"id":"W4390749945","doi":"10.3390/info15010037","title":"Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review","year":2024,"lang":"en","type":"review","venue":"Information","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Electric System Operator","keywords":"Downtime; Reliability engineering; Reliability (semiconductor); Computer science; Parametric statistics; Electric power system; Machine learning; Risk analysis (engineering); Engineering; Power (physics); Business","score_opus":0.013377639787438058,"score_gpt":0.2695372357019108,"score_spread":0.25615959591447274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390749945","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":[0.00020130828,0.9968945,0.0012890286,0.00021844181,0.0001443541,0.000008786572,0.000027106084,0.000014800633,0.00120165],"genre_scores_gemma":[0.0016701582,0.99643445,0.0011509049,0.00008685337,0.00017124026,0.000010541846,0.000045446086,0.0000042402703,0.00042611762],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99965143,0.00006884165,0.000059682905,0.00006473553,0.00013623433,0.00001905998],"domain_scores_gemma":[0.9986526,0.0008970116,0.00012093055,0.000033485197,0.00026586474,0.00003006378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009880832,0.000918292,0.0011826335,0.0028644553,0.00020666313,0.00096312293,0.000871871,0.0009259277,0.003207341],"category_scores_gemma":[0.0018383599,0.00037304484,0.00077312224,0.0037685307,0.000408598,0.001356836,0.00044439815,0.0011760219,0.0014603442],"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":[0.000036272533,0.00008495013,0.00033060223,0.02064465,0.00014695492,0.00010946005,0.000072353156,0.0019571092,0.0009777173,0.006286269,0.017216437,0.95213723],"study_design_scores_gemma":[0.000017034672,0.00024357067,0.0022108173,0.01212921,0.0004941296,0.0011320236,0.00011579457,0.0020777124,0.0012346051,0.007514137,0.9727588,0.000072081384],"about_ca_topic_score_codex":0.0014852074,"about_ca_topic_score_gemma":0.0016484351,"teacher_disagreement_score":0.003207341,"about_ca_system_score_codex":0.00045152902,"about_ca_system_score_gemma":0.0011729107,"threshold_uncertainty_score":0.0107296705},"labels":[],"label_agreement":null},{"id":"W4391029711","doi":"10.3390/info15010059","title":"An ART Tour de Force on Mental Imagery: Vividness, Individual Bias Differences, and Complementary Visual Processing Streams","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Visual perception and processing mechanisms","field":"Neuroscience","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":"","keywords":"Psychology; Mental image; Priming (agriculture); Perception; Cognitive psychology; Visual perception; Visual processing; Vernier scale; Cognition; Neuroscience; Cartography","score_opus":0.06795322578770087,"score_gpt":0.3447724229005194,"score_spread":0.27681919711281855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391029711","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":[0.989,0.0003278572,0.0016099653,0.00026306108,0.000011738903,0.000008571818,0.0000573552,0.000030688843,0.008690717],"genre_scores_gemma":[0.9969145,0.00016839165,0.00078797946,0.00005489205,0.0000108119275,0.000006701342,0.000034624507,0.000011429804,0.0020107003],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99993825,0.000019256295,0.0000016585128,0.000011306284,0.000020591973,0.000008941338],"domain_scores_gemma":[0.99958867,0.0002516757,0.000055274584,0.000041127107,0.000033671196,0.000029472692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021589136,0.00013132625,0.00008937669,0.00016276729,0.0001771141,0.00033080205,0.00008091422,0.00014385601,0.0056391223],"category_scores_gemma":[0.0014017206,0.00006719392,0.00012524994,0.000111964626,0.0004164778,0.00025907918,0.0005118106,0.00023323235,0.0001932994],"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":[0.0037641833,0.00021609868,0.15742509,0.000687621,0.00013162577,0.001277835,0.016574793,0.0022423686,0.38692504,0.009790744,0.0067951693,0.41416946],"study_design_scores_gemma":[0.000026138818,0.00055676006,0.958525,0.00006410332,0.00004799272,0.0016052506,0.0019893718,0.0036834076,0.020773916,0.0043324702,0.008357293,0.000038323826],"about_ca_topic_score_codex":0.0012833776,"about_ca_topic_score_gemma":0.002499947,"teacher_disagreement_score":0.0056391223,"about_ca_system_score_codex":0.00015902043,"about_ca_system_score_gemma":0.000107480155,"threshold_uncertainty_score":0.01886481},"labels":[],"label_agreement":null},{"id":"W4391099314","doi":"10.3390/info15010066","title":"Baseline Structural Connectomics Data of Healthy Brain Development Assessed with Multi-Modal Magnetic Resonance Imaging","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Nova Scotia Health Authority; St. Francis Xavier University; Memorial University of Newfoundland","funders":"National Institutes of Health; Canada Foundation for Innovation; Nova Scotia Research Innovation Trust","keywords":"Connectomics; Diffusion MRI; Magnetic resonance imaging; Baseline (sea); Tractography; Neuroimaging; Medicine; Functional magnetic resonance imaging; Neuroscience; Connectome; Psychology; Biology; Radiology; Functional connectivity","score_opus":0.07220338928432064,"score_gpt":0.3798657373353466,"score_spread":0.307662348051026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391099314","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":[0.98215216,0.00024205573,0.0041389023,0.00006285197,0.000009334185,0.000036466514,0.0122481445,0.00007764794,0.0010322601],"genre_scores_gemma":[0.9775729,0.00016436953,0.007176703,0.000033904787,0.000012580955,0.000167848,0.014068284,0.00005502398,0.0007483486],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99947435,0.00010259715,0.00006902652,0.00018366442,0.00011051686,0.00005984718],"domain_scores_gemma":[0.99716944,0.0006034897,0.00065996323,0.00054788525,0.00081500836,0.00020425646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010891929,0.00035805092,0.0003269098,0.0016249965,0.00041772134,0.00044958646,0.00032587734,0.00046264482,0.001962983],"category_scores_gemma":[0.0049308934,0.00013450554,0.00028854646,0.0009911985,0.00032410538,0.0004989599,0.00060749607,0.00042382497,0.00045344175],"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":[0.0005502995,0.00013674401,0.9395869,0.00011150485,0.00023725761,0.00041631286,0.0007291966,0.0009672414,0.016954921,0.000623988,0.0015172898,0.03816851],"study_design_scores_gemma":[0.0000050371273,0.00014977677,0.9946268,0.0000119999295,0.000035093675,0.0004541591,0.00014040027,0.0004443982,0.0021369576,0.00041748383,0.0015662663,0.0000116826],"about_ca_topic_score_codex":0.005499999,"about_ca_topic_score_gemma":0.011815993,"teacher_disagreement_score":0.005499999,"about_ca_system_score_codex":0.00025710667,"about_ca_system_score_gemma":0.0004361269,"threshold_uncertainty_score":0.010935962},"labels":[],"label_agreement":null},{"id":"W4391450256","doi":"10.3390/info15020081","title":"Gamification in Online Education: A Visual Bibliometric Network Analysis","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Educational Games and Gamification","field":"Psychology","cited_by":16,"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; Network analysis; Data science; Information retrieval; Engineering","score_opus":0.02138365802769898,"score_gpt":0.37906194365659296,"score_spread":0.357678285628894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391450256","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":[0.77490854,0.028335318,0.05768094,0.0044543534,0.00026277063,0.0022789186,0.04341105,0.0014129637,0.08725509],"genre_scores_gemma":[0.9351992,0.009603248,0.040960416,0.00009786297,0.00015759173,0.0015102519,0.010209719,0.000086556625,0.002175076],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9921673,0.0030220367,0.00091646024,0.0008388585,0.0027473692,0.00030785115],"domain_scores_gemma":[0.9655896,0.027662056,0.0030896254,0.0010722026,0.0022422648,0.00034430186],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0078246165,0.0007528827,0.0012572986,0.1414995,0.001424975,0.0059604333,0.0007669809,0.0007882633,0.0043035094],"category_scores_gemma":[0.0439879,0.00031385783,0.0016768315,0.1517179,0.0008087261,0.0036182476,0.0029971958,0.00054388866,0.0006608306],"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":[0.0003756197,0.00033157112,0.32772818,0.014510802,0.002822598,0.001050233,0.012544669,0.015983239,0.00333064,0.0433424,0.019799367,0.5581807],"study_design_scores_gemma":[0.00016230073,0.00045654023,0.55244386,0.006838184,0.0038419545,0.002825013,0.030930553,0.14072132,0.0036156937,0.06860767,0.1891809,0.0003760885],"about_ca_topic_score_codex":0.0066367923,"about_ca_topic_score_gemma":0.0064463615,"teacher_disagreement_score":0.8585005,"about_ca_system_score_codex":0.0021767148,"about_ca_system_score_gemma":0.0027301423,"threshold_uncertainty_score":0.041381},"labels":[],"label_agreement":null},{"id":"W4391679718","doi":"10.3390/info15020102","title":"Identifying Malware Packers through Multilayer Feature Engineering in Static Analysis","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Malware; Feature (linguistics); Malware analysis; Computer science; Static analysis; Computer security; Programming language","score_opus":0.009694447014749011,"score_gpt":0.27197905889975665,"score_spread":0.2622846118850076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391679718","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":[0.15435147,0.00035578499,0.84037095,0.00019903995,0.000047238907,0.00010881451,0.00024878976,0.0030759764,0.0012420283],"genre_scores_gemma":[0.66217285,0.00034133435,0.33359972,0.00011753416,0.0000458059,0.00012295632,0.0010165164,0.0002174235,0.002365889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994425,0.00008169075,0.000036492227,0.00014104722,0.00021591145,0.0000823906],"domain_scores_gemma":[0.9989322,0.0003977731,0.00018283799,0.00020856333,0.00023871165,0.00003992507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006676781,0.00084573973,0.00058886356,0.0022251213,0.00043136827,0.0010557837,0.0004681908,0.00054546766,0.000858404],"category_scores_gemma":[0.002835598,0.0002925274,0.00080057164,0.0007251878,0.00053006294,0.0016106295,0.0009975296,0.0008398884,0.00067612855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023836637,0.00029746993,0.02590947,0.00019496704,0.00010271739,0.0005378041,0.00040462732,0.09934991,0.16357137,0.006743815,0.0030361065,0.6996134],"study_design_scores_gemma":[0.000006117126,0.00024244573,0.010159716,0.00004090156,0.00004831623,0.00051949156,0.000115069335,0.91462445,0.060668793,0.008436914,0.0050955713,0.000042151492],"about_ca_topic_score_codex":0.0014552709,"about_ca_topic_score_gemma":0.002004801,"teacher_disagreement_score":0.0022251213,"about_ca_system_score_codex":0.00036999027,"about_ca_system_score_gemma":0.0005307703,"threshold_uncertainty_score":0.0035310388},"labels":[],"label_agreement":null},{"id":"W4391882779","doi":"10.3390/info15020115","title":"Leveraging Artificial Intelligence and Participatory Modeling to Support Paradigm Shifts in Public Health: An Application to Obesity and Evidence-Based Policymaking","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Physical Activity and Health","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; British Columbia Centre for Disease Control; Provincial Health Services Authority; Simon Fraser University; Michael Smith Health Research BC","keywords":"Paradigm shift; Perspective (graphical); Unintended consequences; Citizen journalism; Public health; Conceptual framework; Obesity; Relevance (law); Mental health; Psychology; Knowledge management; Data science; Sociology; Political science; Computer science; Medicine; Artificial intelligence; Social science; Epistemology","score_opus":0.367702395855594,"score_gpt":0.43001333206040493,"score_spread":0.06231093620481093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391882779","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":[0.12627773,0.0014228188,0.78893554,0.040940803,0.00030407106,0.0026863252,0.0005111597,0.00066916464,0.038252387],"genre_scores_gemma":[0.4466511,0.00061620283,0.54942054,0.0005068824,0.000045467914,0.0016397901,0.00022487888,0.000067893496,0.0008272651],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9383136,0.054826982,0.0013671317,0.0024311587,0.0020571067,0.0010039774],"domain_scores_gemma":[0.77693707,0.20365483,0.004908751,0.00803277,0.004376502,0.0020900846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08282891,0.0016978937,0.0010520879,0.00849527,0.00701921,0.009506344,0.0051388605,0.0038004825,0.004967736],"category_scores_gemma":[0.1053673,0.001077225,0.002765725,0.007540558,0.013182548,0.010076012,0.014423837,0.004635943,0.00031977714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044192508,0.0009935244,0.028324053,0.0028400726,0.0006566726,0.0017540234,0.119526796,0.14903273,0.0012502788,0.49058166,0.0043955008,0.2002027],"study_design_scores_gemma":[0.00028392227,0.0002264336,0.003756858,0.0013730506,0.00016608773,0.00021417273,0.048009492,0.2460657,0.0009584799,0.6631752,0.03559314,0.0001774432],"about_ca_topic_score_codex":0.028522594,"about_ca_topic_score_gemma":0.043108452,"teacher_disagreement_score":0.08282891,"about_ca_system_score_codex":0.012465263,"about_ca_system_score_gemma":0.018486774,"threshold_uncertainty_score":0.4380464},"labels":[],"label_agreement":null},{"id":"W4392238204","doi":"10.3390/info15030131","title":"Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University","funders":"York University","keywords":"Attribution; Data science; Authorship attribution; Survey research; Computer science; Psychology; Applied psychology; Natural language processing; Social psychology","score_opus":0.255335490359698,"score_gpt":0.44398692601731843,"score_spread":0.18865143565762044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392238204","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":[0.07190178,0.43029377,0.3960884,0.0363596,0.0058245393,0.00095356914,0.011591125,0.004818629,0.042168636],"genre_scores_gemma":[0.5361188,0.23209907,0.18807577,0.0038551711,0.00945791,0.0011173828,0.017774602,0.0013355357,0.010165783],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.94926345,0.022901552,0.006420431,0.007009729,0.013250786,0.0011540647],"domain_scores_gemma":[0.6294324,0.2923003,0.017968982,0.029159816,0.028041814,0.0030966545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047432207,0.001919125,0.002627963,0.024166448,0.0033970822,0.01235876,0.003784343,0.0031117734,0.005698478],"category_scores_gemma":[0.23356967,0.00080360175,0.0013127658,0.030376531,0.003285546,0.02341585,0.0040062363,0.0030278782,0.0041958434],"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":[0.00014115294,0.00014143859,0.03843274,0.004617826,0.00018040276,0.000082113744,0.0018622266,0.0030751014,0.00038566755,0.030378181,0.032604646,0.8880986],"study_design_scores_gemma":[0.00009473289,0.00023782052,0.051376875,0.013489205,0.00041634403,0.0031422246,0.010986881,0.07617706,0.0057323244,0.31283295,0.5249836,0.0005299714],"about_ca_topic_score_codex":0.001695486,"about_ca_topic_score_gemma":0.001433064,"teacher_disagreement_score":0.047432207,"about_ca_system_score_codex":0.0026345942,"about_ca_system_score_gemma":0.0038098542,"threshold_uncertainty_score":0.25084847},"labels":[],"label_agreement":null},{"id":"W4392510002","doi":"10.3390/info15030146","title":"Algorithm-Based Data Generation (ADG) Engine for Dual-Mode User Behavioral Data Analytics","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Taif University","keywords":"Computer science; Analytics; Dual mode; Mode (computer interface); Dual (grammatical number); Data analysis; Data mining; Human–computer interaction; Engineering","score_opus":0.10994547212387706,"score_gpt":0.36282674131657877,"score_spread":0.2528812691927017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392510002","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":[0.010639414,0.0001369445,0.9456722,0.00017258804,0.00007650102,0.00039412398,0.001992167,0.03945469,0.0014612658],"genre_scores_gemma":[0.12122949,0.00011699475,0.8671492,0.0002013774,0.0000336469,0.00048636275,0.007883658,0.0017230547,0.0011762001],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981121,0.0003066568,0.00023161824,0.000496443,0.00074675644,0.000106416584],"domain_scores_gemma":[0.99555004,0.0014847483,0.00020392612,0.0016361321,0.0009629886,0.0001622071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026879956,0.001183756,0.00067606376,0.0023584757,0.00049398764,0.002008313,0.002148391,0.0007258049,0.002942866],"category_scores_gemma":[0.010206106,0.00054128084,0.00096417964,0.0017980087,0.0006701554,0.002002486,0.0025210727,0.001602674,0.0022650948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010203267,0.00071967824,0.02281774,0.0005973807,0.00028355466,0.0007393203,0.0009030628,0.08624637,0.040746048,0.031914018,0.04843627,0.7655762],"study_design_scores_gemma":[0.00013545631,0.00016800353,0.0037375179,0.000051267023,0.00004369055,0.0004619926,0.00013724164,0.8715279,0.05584151,0.02944696,0.03837116,0.000077282035],"about_ca_topic_score_codex":0.002061353,"about_ca_topic_score_gemma":0.0019726604,"teacher_disagreement_score":0.002942866,"about_ca_system_score_codex":0.00075354776,"about_ca_system_score_gemma":0.0014527943,"threshold_uncertainty_score":0.014215648},"labels":[],"label_agreement":null},{"id":"W4393081802","doi":"10.3390/info15040173","title":"A Quantum-Safe Software-Defined Deterministic Internet of Things (IoT) with Hardware-Enforced Cyber-Security for Critical Infrastructures","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Internet of Things; Computer security; Computer science; Software; Critical infrastructure; Embedded system; Computer network; Operating system","score_opus":0.008351640772789982,"score_gpt":0.24354434395522664,"score_spread":0.23519270318243665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393081802","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":[0.05659467,0.000671115,0.9136427,0.0008114849,0.0002602477,0.00017762523,0.000078748664,0.00095333107,0.026810037],"genre_scores_gemma":[0.7705213,0.0006187489,0.22122094,0.00039254155,0.000081739185,0.00014440164,0.000108538996,0.000084612926,0.006827185],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994529,0.000093468385,0.000029373963,0.000073370196,0.00027955702,0.000071277755],"domain_scores_gemma":[0.9996879,0.00005764852,0.000046929905,0.000106253035,0.000067206165,0.000033996508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053017185,0.00028164338,0.00018547388,0.00021894777,0.0005653722,0.0011055771,0.00055660354,0.0005169705,0.0011148348],"category_scores_gemma":[0.000579451,0.00020533516,0.00041646074,0.00017289256,0.0012618641,0.0014423645,0.0010126482,0.00093779434,0.0002694031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016091386,0.00010020769,0.0014185474,0.00018540316,0.00003879041,0.00025519624,0.00029972033,0.050261974,0.070677735,0.81421846,0.0029376475,0.05944547],"study_design_scores_gemma":[0.00007499233,0.000551016,0.0010708437,0.0000999962,0.000072357296,0.00049517833,0.0001570746,0.634628,0.08555007,0.16564427,0.111577526,0.00007870754],"about_ca_topic_score_codex":0.001165802,"about_ca_topic_score_gemma":0.0011751082,"teacher_disagreement_score":0.001165802,"about_ca_system_score_codex":0.00079045363,"about_ca_system_score_gemma":0.0015186622,"threshold_uncertainty_score":0.0057350993},"labels":[],"label_agreement":null},{"id":"W4393352947","doi":"10.3390/info15040195","title":"Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Denial-of-service attack; Trinoo; Computer science; Computer security; Intrusion; Denial; Application layer DDoS attack; World Wide Web; The Internet; Geology; Psychology; Operating system","score_opus":0.014561523340992333,"score_gpt":0.22550587868003105,"score_spread":0.2109443553390387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393352947","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64100975,0.0018679803,0.104910046,0.0047289166,0.0011412535,0.0025219829,0.21757819,0.013057097,0.013184864],"genre_scores_gemma":[0.42519572,0.00071821857,0.13503253,0.00061285583,0.00021625051,0.00118673,0.43415368,0.00033696208,0.0025469773],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824286,0.00028950715,0.00017463005,0.00043647832,0.00064331555,0.0002133001],"domain_scores_gemma":[0.9973232,0.00045127864,0.0003167993,0.0007646078,0.0008825657,0.00026150365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015185385,0.00089548377,0.0005646102,0.0031979186,0.0010478287,0.0010839595,0.0016885881,0.0010925974,0.0005485226],"category_scores_gemma":[0.0040295394,0.00023261162,0.000729569,0.0024010523,0.000547467,0.0015691867,0.0013793783,0.0016474741,0.0005569925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013887587,0.0039616483,0.11663407,0.0016640369,0.00039025277,0.0015170444,0.00069513207,0.12892796,0.046218313,0.021257408,0.36995712,0.30738837],"study_design_scores_gemma":[0.00033024853,0.00084754406,0.119406015,0.00023465924,0.0001449721,0.001890909,0.0012074264,0.56020236,0.07493177,0.009785198,0.23080364,0.00021528636],"about_ca_topic_score_codex":0.006751017,"about_ca_topic_score_gemma":0.009938075,"teacher_disagreement_score":0.006751017,"about_ca_system_score_codex":0.0013568385,"about_ca_system_score_gemma":0.0015032686,"threshold_uncertainty_score":0.013423443},"labels":[],"label_agreement":null},{"id":"W4393993814","doi":"10.3390/info15040203","title":"Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Cultural Heritage Management and Preservation","field":"Arts and Humanities","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 Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Doors; Machine learning; Classifier (UML); Computer science; Random forest; Artificial intelligence; Context (archaeology); Engineering","score_opus":0.08642650201027106,"score_gpt":0.25259146515518893,"score_spread":0.16616496314491785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393993814","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":[0.047548372,0.0003193052,0.9483154,0.0002147153,0.0000655537,0.00008382047,0.00037473306,0.0017472828,0.00133088],"genre_scores_gemma":[0.6929146,0.0002243628,0.30145916,0.00013549539,0.00008646418,0.00029734726,0.001536431,0.00010419949,0.0032419092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942625,0.0001531773,0.000043343978,0.00018212298,0.00013431764,0.00006079692],"domain_scores_gemma":[0.9985536,0.00088078633,0.00011611869,0.0001112699,0.00031200078,0.000026333464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011400176,0.0009163098,0.000607228,0.0014281343,0.0003688517,0.0006519075,0.0009791398,0.0010292954,0.0026894957],"category_scores_gemma":[0.0033000878,0.00023509053,0.001284028,0.00082634785,0.00033651543,0.0006400769,0.00037916435,0.0011395058,0.00072495703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002676456,0.0002725689,0.00990329,0.00014886014,0.000115707066,0.00023892605,0.0001258439,0.49644408,0.006388127,0.0039041473,0.004331734,0.47785914],"study_design_scores_gemma":[0.000005146033,0.000022714696,0.00073968264,0.000007713678,0.000009029372,0.00002542429,0.00001135574,0.9963302,0.0009647902,0.0015099883,0.00036783566,0.0000060572897],"about_ca_topic_score_codex":0.004902732,"about_ca_topic_score_gemma":0.0042123804,"teacher_disagreement_score":0.004902732,"about_ca_system_score_codex":0.0007094507,"about_ca_system_score_gemma":0.00063577696,"threshold_uncertainty_score":0.00974834},"labels":[],"label_agreement":null},{"id":"W4396511291","doi":"10.3390/info15050253","title":"Transformer-Based Approach to Pathology Diagnosis Using Audio Spectrogram","year":2024,"lang":"en","type":"article","venue":"Information","topic":"AI in cancer detection","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Spectrogram; Computer science; Transformer; Speech recognition; Engineering; Electrical engineering","score_opus":0.020862403873731315,"score_gpt":0.26349126352855723,"score_spread":0.24262885965482592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396511291","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":[0.035842482,0.0005928474,0.95879865,0.00038508064,0.00008891468,0.00007226872,0.0002190496,0.0015268076,0.0024740286],"genre_scores_gemma":[0.777908,0.0009607003,0.21375069,0.00028806168,0.0001647904,0.00009128301,0.00056129944,0.000090031645,0.006185032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998429,0.000025426552,0.000010143039,0.00004434271,0.000049639224,0.000027585656],"domain_scores_gemma":[0.99976724,0.00008226024,0.00002468982,0.000021549142,0.00008175743,0.000022575943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035529226,0.0006088943,0.00041198495,0.0010877211,0.00019061497,0.0007348186,0.0007148008,0.0006539707,0.0015960443],"category_scores_gemma":[0.00094768003,0.00018707023,0.0006762294,0.0004394167,0.00028305638,0.0005682774,0.00063817913,0.00067772914,0.0009699506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003828839,0.00021497185,0.007664104,0.00016941667,0.00011970202,0.00055236963,0.0001548114,0.2200881,0.07669767,0.009184737,0.004818885,0.6799524],"study_design_scores_gemma":[0.0000076789065,0.00005705034,0.0009606472,0.000008072506,0.00002702511,0.00022013657,0.000029102443,0.9859993,0.007976797,0.0036724927,0.0010307754,0.0000108553495],"about_ca_topic_score_codex":0.0033792933,"about_ca_topic_score_gemma":0.0034313514,"teacher_disagreement_score":0.0033792933,"about_ca_system_score_codex":0.0003990723,"about_ca_system_score_gemma":0.00079702836,"threshold_uncertainty_score":0.0067192316},"labels":[],"label_agreement":null},{"id":"W4396857277","doi":"10.3390/info15050273","title":"Insights into Cybercrime Detection and Response: A Review of Time Factor","year":2024,"lang":"en","type":"review","venue":"Information","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Phishing; Cybercrime; Exploit; Computer security; Computer science; Identity theft; Hacker; Resilience (materials science); Blacklisting; Intrusion detection system; Malware; Process (computing); Scalability; Interoperability; Information exchange; The Internet; Telecommunications; World Wide Web","score_opus":0.017152337673056874,"score_gpt":0.2828954164624184,"score_spread":0.26574307878936154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396857277","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":[0.00030031297,0.99357575,0.0017928983,0.001849438,0.0004207894,0.000016857173,0.000023977665,0.000015350315,0.0020047363],"genre_scores_gemma":[0.0059179654,0.9902344,0.0016619733,0.00096525473,0.0007321384,0.000032504573,0.000038625545,0.000012554166,0.0004047363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99675035,0.0010606536,0.0004851506,0.00055313966,0.0009918071,0.00015895459],"domain_scores_gemma":[0.9544123,0.037849247,0.002498417,0.0007226479,0.004040349,0.00047704382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070335986,0.0021600986,0.0026766402,0.013321613,0.0010350001,0.0049216673,0.0022815284,0.0033868433,0.0037900729],"category_scores_gemma":[0.022015924,0.0011408895,0.0017355377,0.01179675,0.004249905,0.010876622,0.0018590558,0.003976866,0.0015212969],"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":[0.00012355078,0.00015764951,0.002155206,0.029743323,0.00027158772,0.00018392062,0.0011676494,0.002204766,0.0005009957,0.059500106,0.018034127,0.88595706],"study_design_scores_gemma":[0.00003212448,0.00042896494,0.0064364537,0.067620724,0.00062056497,0.0013203629,0.0028195449,0.0026593604,0.00084457936,0.062263414,0.85473853,0.00021528733],"about_ca_topic_score_codex":0.0048373826,"about_ca_topic_score_gemma":0.0044399914,"teacher_disagreement_score":0.013321613,"about_ca_system_score_codex":0.0043133507,"about_ca_system_score_gemma":0.00499275,"threshold_uncertainty_score":0.03719771},"labels":[],"label_agreement":null},{"id":"W4399335804","doi":"10.3390/info15060326","title":"A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence","score_opus":0.1830962904427462,"score_gpt":0.46855950476332586,"score_spread":0.28546321432057964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399335804","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":[0.6281984,0.0152454,0.33448926,0.0023697997,0.001389164,0.0011593656,0.0013703379,0.0049628313,0.010815385],"genre_scores_gemma":[0.78426874,0.0023444996,0.20700237,0.00047349939,0.00041733147,0.00043210367,0.002790772,0.00022305499,0.002047622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99178785,0.0040971646,0.0007074518,0.0011459368,0.0018684538,0.00039308684],"domain_scores_gemma":[0.9650896,0.024888465,0.0017002915,0.0033827496,0.0042100847,0.000728847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01476235,0.0017382842,0.0013926135,0.0031587759,0.0009821604,0.0018105798,0.0015675557,0.0016435807,0.0010954442],"category_scores_gemma":[0.038972158,0.00033984665,0.0018562645,0.0015587809,0.00088972936,0.0024484722,0.0017162741,0.0022081514,0.00082911825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036102415,0.0012753926,0.040363256,0.0012267699,0.0011029537,0.00013371551,0.0007767264,0.061293896,0.005083065,0.0033750029,0.009352901,0.872406],"study_design_scores_gemma":[0.00055405893,0.003773255,0.044731885,0.0006851967,0.00083455857,0.00037842538,0.0013532664,0.909516,0.015504405,0.011287787,0.0111678615,0.00021332131],"about_ca_topic_score_codex":0.003618854,"about_ca_topic_score_gemma":0.004669553,"teacher_disagreement_score":0.01476235,"about_ca_system_score_codex":0.0009462693,"about_ca_system_score_gemma":0.002474129,"threshold_uncertainty_score":0.07807171},"labels":[],"label_agreement":null},{"id":"W4399672865","doi":"10.3390/info15060354","title":"The Use of AI in Software Engineering: A Synthetic Knowledge Synthesis of the Recent Research Literature","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":18,"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":"Software development; Software engineering; Computer science; Social software engineering; Artificial intelligence; Personal software process; Software peer review; Software; Software quality; Engineering management; Software construction; Engineering","score_opus":0.20740942391871325,"score_gpt":0.4375526275545321,"score_spread":0.23014320363581886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399672865","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07756142,0.66777176,0.12931803,0.041862555,0.0023556198,0.00053396134,0.0022323541,0.00021767843,0.07814662],"genre_scores_gemma":[0.36670327,0.52061415,0.10066323,0.00515219,0.0013954907,0.00072137726,0.0018769008,0.00009793441,0.002775409],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99707794,0.0013538658,0.00037653802,0.00038561478,0.0007025364,0.0001034535],"domain_scores_gemma":[0.9797791,0.017015778,0.0006138318,0.00066629855,0.0017158707,0.00020920836],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006216527,0.0006881298,0.0007350343,0.02059042,0.0015909184,0.0054177633,0.0007753419,0.0011607662,0.0024718354],"category_scores_gemma":[0.013214525,0.0003333026,0.0006806173,0.015662083,0.00311691,0.0070229396,0.0021189586,0.0013600367,0.0003305234],"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":[0.00015983067,0.00015938166,0.0038854233,0.02316485,0.00024098706,0.0006234708,0.014008482,0.005415731,0.0028229419,0.2527098,0.012317799,0.6844913],"study_design_scores_gemma":[0.00004286067,0.0003121017,0.011095092,0.04891098,0.0004423381,0.0012417408,0.028961316,0.009740151,0.0034256766,0.34094697,0.55472666,0.00015402268],"about_ca_topic_score_codex":0.0018789986,"about_ca_topic_score_gemma":0.003271281,"teacher_disagreement_score":0.9937835,"about_ca_system_score_codex":0.0033235967,"about_ca_system_score_gemma":0.0046517244,"threshold_uncertainty_score":0.03287655},"labels":[],"label_agreement":null},{"id":"W4400118481","doi":"10.3390/info15070378","title":"Business Model Evolution in the Age of NFTs and the Metaverse","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Blockchain Technology Applications and Security","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 Canada West","funders":"","keywords":"Metaverse; Realm; Business model; Computer science; Perspective (graphical); Possible world; Knowledge management; Data science; Epistemology; Virtual reality; Business; Human–computer interaction; Marketing; Political science; Philosophy; Artificial intelligence","score_opus":0.007678820939963653,"score_gpt":0.21306098690954106,"score_spread":0.2053821659695774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400118481","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":[0.22958471,0.0065953773,0.47696593,0.03530255,0.0004754887,0.00017400458,0.00027411102,0.0008895955,0.2497383],"genre_scores_gemma":[0.90752906,0.0032768457,0.07665111,0.000734556,0.000104790575,0.00009741676,0.00024405237,0.00022942084,0.011132657],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9934303,0.003431288,0.00031456188,0.00063139,0.0017227095,0.0004697824],"domain_scores_gemma":[0.9873476,0.0052117924,0.0008435836,0.004218021,0.0016477894,0.0007312247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011915859,0.00043402892,0.0005873498,0.0023307921,0.002758336,0.013703322,0.0018048477,0.0029043977,0.004891228],"category_scores_gemma":[0.016473074,0.00047738355,0.00074096624,0.0024199418,0.010052972,0.0310913,0.008218287,0.004065227,0.0012179026],"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":[0.000028416584,0.000022338665,0.0007331513,0.00004123319,0.000007750095,0.0002000725,0.0034946098,0.001962006,0.00036250052,0.96822697,0.00069487846,0.024226075],"study_design_scores_gemma":[0.000011950678,0.00006615284,0.0004943694,0.00025250693,0.000011866976,0.00050504704,0.006129321,0.016870106,0.0010560679,0.8391007,0.13547024,0.000031644788],"about_ca_topic_score_codex":0.0031734188,"about_ca_topic_score_gemma":0.0027148963,"teacher_disagreement_score":0.013703322,"about_ca_system_score_codex":0.006254241,"about_ca_system_score_gemma":0.004445215,"threshold_uncertainty_score":0.063017845},"labels":[],"label_agreement":null},{"id":"W4401016221","doi":"10.3390/info15080435","title":"Survey on Knowledge Representation Models in Healthcare","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","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; Université du Québec à Chicoutimi","funders":"","keywords":"Health care; Representation (politics); Computer science; Knowledge management; Data science; Political science","score_opus":0.34555287616001806,"score_gpt":0.5487139623426153,"score_spread":0.2031610861825972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401016221","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":[0.011690225,0.59049267,0.30650797,0.017109321,0.00093333225,0.00045715514,0.0028362963,0.0011996264,0.06877343],"genre_scores_gemma":[0.08572148,0.7029986,0.19373892,0.0033074098,0.001123855,0.00046473183,0.0059058503,0.00021117133,0.0065280534],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99731183,0.000927073,0.00041800327,0.00031569847,0.00087946374,0.00014794641],"domain_scores_gemma":[0.9897893,0.008081715,0.000365143,0.0005950141,0.0010372441,0.00013146784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003939222,0.0009375526,0.001400501,0.005936208,0.00059701066,0.0042946874,0.0025725106,0.0015885192,0.0064669903],"category_scores_gemma":[0.01299758,0.00063494546,0.0016146471,0.012630056,0.0007765261,0.006697113,0.0013613935,0.0013620212,0.0019585367],"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":[0.00012176821,0.00016004054,0.0018498744,0.0068025477,0.00019638745,0.00022267326,0.00053418684,0.012842186,0.0006102095,0.16432387,0.035085857,0.77725035],"study_design_scores_gemma":[0.000041048,0.00022326979,0.002731613,0.0100837955,0.00040371026,0.001305725,0.0010025792,0.055461317,0.0019007728,0.18451507,0.74221236,0.00011879513],"about_ca_topic_score_codex":0.0053968113,"about_ca_topic_score_gemma":0.0031239397,"teacher_disagreement_score":0.0064669903,"about_ca_system_score_codex":0.0024877624,"about_ca_system_score_gemma":0.002922666,"threshold_uncertainty_score":0.021634221},"labels":[],"label_agreement":null},{"id":"W4401644781","doi":"10.3390/info15080491","title":"Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Supervised learning; Computer science; Artificial intelligence; Machine learning; Artificial neural network","score_opus":0.01014857225381052,"score_gpt":0.22289965936423545,"score_spread":0.21275108711042492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401644781","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":[0.00669687,0.0073987646,0.9783361,0.0031604164,0.00020783076,0.00008608756,0.00011500382,0.00044975046,0.0035491623],"genre_scores_gemma":[0.44650772,0.013667565,0.5306554,0.0021827766,0.002341179,0.00044875397,0.0005920449,0.00048520073,0.003119429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931851,0.0029338158,0.00029534832,0.0016933817,0.0017355491,0.00015674869],"domain_scores_gemma":[0.9668463,0.025025265,0.0013500635,0.0036090675,0.002574977,0.0005943977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009209053,0.001467333,0.0023680506,0.0019866251,0.0009480168,0.004382318,0.0028515998,0.0031941752,0.0012536698],"category_scores_gemma":[0.03058317,0.0009722595,0.0010827235,0.0017948381,0.0060741617,0.008920788,0.0036375436,0.006250967,0.0006532871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023287965,0.00027781352,0.006730206,0.0017374652,0.00053392467,0.000231992,0.0008760837,0.27670646,0.0033166667,0.27971187,0.011594016,0.41805068],"study_design_scores_gemma":[0.000019073253,0.00011804429,0.00083188625,0.00016569509,0.00003351188,0.00009790371,0.000085711166,0.6592934,0.0017567712,0.32877496,0.0087615065,0.0000615175],"about_ca_topic_score_codex":0.0020861132,"about_ca_topic_score_gemma":0.0016042389,"teacher_disagreement_score":0.009209053,"about_ca_system_score_codex":0.0020380614,"about_ca_system_score_gemma":0.0019000145,"threshold_uncertainty_score":0.048702717},"labels":[],"label_agreement":null},{"id":"W4402371396","doi":"10.3390/info15090552","title":"Understanding Telehealth Adoption among the Elderly: An Empirical Investigation","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Technology Use by Older Adults","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Telehealth; Empirical research; Business; Telemedicine; Psychology; Health care; Economics; Economic growth; Mathematics; Statistics","score_opus":0.0857069014269524,"score_gpt":0.3383130268242132,"score_spread":0.2526061253972608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402371396","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":[0.9992041,0.000055656357,0.00007759258,0.00009085352,7.730731e-7,0.000021505704,0.000034529785,5.151035e-7,0.00051455165],"genre_scores_gemma":[0.9991535,0.0002508867,0.00025871518,0.000060666356,0.0000025208965,0.000037099668,0.000053461597,6.1868616e-7,0.00018259652],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99868554,0.00049906416,0.0002104049,0.00012397469,0.0002793152,0.00020181003],"domain_scores_gemma":[0.98664176,0.008921162,0.0024753844,0.00044890263,0.0010556112,0.00045715613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004311573,0.00022542969,0.00033775042,0.0015504461,0.0010418658,0.0015211124,0.00051342644,0.00061570713,0.0018171628],"category_scores_gemma":[0.013302554,0.00031319662,0.0004954162,0.0018835524,0.00088744133,0.00228289,0.0012591449,0.00095873565,0.00017294154],"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":[0.000049013757,0.0010239949,0.9067738,0.00015643843,0.000039212053,0.00030591062,0.07229737,0.00011596839,0.00019582256,0.000926533,0.00018878772,0.017927257],"study_design_scores_gemma":[0.000016842258,0.00030290414,0.7574324,0.0002762982,0.00007972386,0.0002761424,0.23826687,0.0010320799,0.00021018345,0.00039249728,0.0016984772,0.000015561589],"about_ca_topic_score_codex":0.01274976,"about_ca_topic_score_gemma":0.019871991,"teacher_disagreement_score":0.01274976,"about_ca_system_score_codex":0.0011957063,"about_ca_system_score_gemma":0.0025350149,"threshold_uncertainty_score":0.025351107},"labels":[],"label_agreement":null},{"id":"W4402444791","doi":"10.3390/info15090560","title":"WGCAMNet: Wasserstein Generative Adversarial Network Augmented and Custom Attention Mechanism Based Deep Neural Network for Enhanced Brain Tumor Detection and Classification","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","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":"Athabasca University","funders":"","keywords":"Generative adversarial network; Mechanism (biology); Artificial neural network; Generative grammar; Artificial intelligence; Computer science; Adversarial system; Deep neural networks; Deep learning; Machine learning; Physics","score_opus":0.019716631065500513,"score_gpt":0.2489827490409387,"score_spread":0.2292661179754382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402444791","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":[0.083888024,0.0010947046,0.9000237,0.0005910675,0.00025083596,0.00016678487,0.00086264877,0.0069397767,0.006182554],"genre_scores_gemma":[0.7922266,0.0005311268,0.18929256,0.00060375634,0.00008119794,0.0002661267,0.0026102108,0.00035908082,0.014029278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975926,0.000047175126,0.000008432045,0.00006961056,0.00007605302,0.000039458642],"domain_scores_gemma":[0.9997478,0.00009084212,0.000032072923,0.000051765513,0.000059501162,0.000017993885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006231741,0.0010707261,0.00050151144,0.00035566455,0.00018752563,0.00043317807,0.0013736847,0.0006792591,0.0017297396],"category_scores_gemma":[0.0010999511,0.00030800313,0.0006369787,0.00031435164,0.00044748644,0.0007240781,0.0010404241,0.0014945932,0.00060235383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001870186,0.0001542257,0.0015364576,0.00008752279,0.00012337891,0.00018587813,0.00004406798,0.79177046,0.014218219,0.0044176676,0.009162374,0.17811276],"study_design_scores_gemma":[0.0000043686105,0.00003916317,0.00017576235,0.0000053472017,0.0000074476816,0.000026355894,0.000002484928,0.99456644,0.0033106965,0.000976588,0.000880255,0.000005083922],"about_ca_topic_score_codex":0.0063410094,"about_ca_topic_score_gemma":0.0089664515,"teacher_disagreement_score":0.0063410094,"about_ca_system_score_codex":0.00071568997,"about_ca_system_score_gemma":0.00074129814,"threshold_uncertainty_score":0.01260823},"labels":[],"label_agreement":null},{"id":"W4402455536","doi":"10.3390/info15090562","title":"Classification of Moral Decision Making in Autonomous Driving: Efficacy of Boosting Procedures","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Psychology of Moral and Emotional Judgment","field":"Neuroscience","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":"Boosting (machine learning); Computer science; Artificial intelligence; Psychology","score_opus":0.08117070459967349,"score_gpt":0.3244340891960425,"score_spread":0.243263384596369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402455536","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":[0.7536039,0.0010909192,0.23687112,0.0009802079,0.00024626506,0.00026672307,0.00013243158,0.0007153649,0.006093187],"genre_scores_gemma":[0.97630364,0.00010505379,0.02289645,0.000101903905,0.00004169293,0.000041573338,0.0000837605,0.000017269434,0.000408612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997124,0.0018423542,0.00011616008,0.0003528289,0.00037326818,0.000191438],"domain_scores_gemma":[0.9833545,0.012658326,0.0009974459,0.00084310764,0.0017025574,0.00044399404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011537087,0.00072076934,0.0009639371,0.0010598147,0.00054215756,0.0013767547,0.00088598725,0.0010273604,0.00088791444],"category_scores_gemma":[0.025695102,0.00024916505,0.00060624385,0.00053410203,0.0007789607,0.001103529,0.0007305364,0.0017236375,0.00044389305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00217872,0.00155264,0.11008222,0.0002439271,0.00036636833,0.0001441633,0.0008433044,0.306292,0.0065788324,0.006688054,0.004801433,0.56022835],"study_design_scores_gemma":[0.000027027958,0.00030877217,0.010945083,0.00003975555,0.00003485278,0.000037064765,0.00011548652,0.9802711,0.0021849617,0.005407636,0.00060784153,0.000020484065],"about_ca_topic_score_codex":0.0020401992,"about_ca_topic_score_gemma":0.0014634792,"teacher_disagreement_score":0.011537087,"about_ca_system_score_codex":0.0008382038,"about_ca_system_score_gemma":0.0011730945,"threshold_uncertainty_score":0.06101471},"labels":[],"label_agreement":null},{"id":"W4402585440","doi":"10.3390/info15090573","title":"Exploring Players’ Perspectives: A Comprehensive Topic Modeling Case Study on Elden Ring","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ring (chemistry); Psychology; Chemistry","score_opus":0.3131305822500731,"score_gpt":0.42431334809292287,"score_spread":0.11118276584284975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402585440","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":[0.97092664,0.000265382,0.021726493,0.0008875333,0.00003204653,0.00017301556,0.00030995006,0.00015429042,0.005524688],"genre_scores_gemma":[0.9800521,0.00017723326,0.014781876,0.000111270376,0.000027062617,0.000097373355,0.00037199512,0.00009042862,0.0042906823],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9951841,0.0031570708,0.00013879426,0.0005269631,0.00072380423,0.00026921908],"domain_scores_gemma":[0.98263377,0.013875254,0.0009986834,0.00053543324,0.0012252211,0.00073158316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046356022,0.0007720352,0.0005146438,0.0026913288,0.0032424848,0.0028197435,0.0011661057,0.0012797499,0.001271149],"category_scores_gemma":[0.014665963,0.00028819838,0.0005622941,0.0018803369,0.0013402541,0.0030199804,0.0014668432,0.0012433123,0.0005484225],"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":[0.0013752782,0.0013967856,0.15933122,0.001142909,0.00026496843,0.016732298,0.55131507,0.01837376,0.0230447,0.01553156,0.016290352,0.1952012],"study_design_scores_gemma":[0.00012850168,0.0014571114,0.113974445,0.00061443844,0.0002660478,0.007235378,0.41463986,0.26867136,0.019451283,0.011395613,0.16166778,0.00049824134],"about_ca_topic_score_codex":0.011049127,"about_ca_topic_score_gemma":0.028805241,"teacher_disagreement_score":0.011049127,"about_ca_system_score_codex":0.0021361948,"about_ca_system_score_gemma":0.0009508903,"threshold_uncertainty_score":0.024515688},"labels":[],"label_agreement":null},{"id":"W4403242607","doi":"10.3390/info15100618","title":"Gaussian Kernel Approximations Require Only Bit-Shifts","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Image and Signal Denoising Methods","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":"BP (Canada)","funders":"","keywords":"Bit (key); Kernel (algebra); Gaussian; Mathematics; Approximations of π; Applied mathematics; Statistical physics; Algorithm; Computer science; Physics; Discrete mathematics; Quantum mechanics","score_opus":0.01826701752609739,"score_gpt":0.28053961001575156,"score_spread":0.2622725924896542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403242607","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":[0.0057884105,0.00011270879,0.9924488,0.000035552795,0.000019010178,0.000015672895,0.000015357857,0.00041910057,0.0011454219],"genre_scores_gemma":[0.16251351,0.00031254595,0.8330932,0.000080364414,0.000026587517,0.00006584817,0.00010538196,0.00021589242,0.0035867386],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995402,0.00006195782,0.000028754646,0.00006725265,0.00026107917,0.000040855033],"domain_scores_gemma":[0.99939585,0.00015896797,0.00005894225,0.000219385,0.00014970034,0.00001700596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003983198,0.0005165504,0.00054238294,0.00031787707,0.0002368524,0.00087447814,0.0006129532,0.0006561167,0.0030963363],"category_scores_gemma":[0.001962897,0.00031981006,0.00043912863,0.00045847043,0.00042133528,0.0010904123,0.0005137339,0.0008651843,0.0024332046],"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":[0.00045307833,0.0000833049,0.0007644717,0.0002915938,0.00007606055,0.000115556184,0.00017599441,0.079598784,0.19823687,0.059351545,0.0026586747,0.65819407],"study_design_scores_gemma":[0.000027578577,0.0001515756,0.0008416793,0.000033736076,0.00004019806,0.00048182483,0.000049483755,0.84006715,0.12801462,0.015031417,0.015218243,0.000042610132],"about_ca_topic_score_codex":0.00159202,"about_ca_topic_score_gemma":0.0021840078,"teacher_disagreement_score":0.0030963363,"about_ca_system_score_codex":0.00041864358,"about_ca_system_score_gemma":0.0006758084,"threshold_uncertainty_score":0.010358274},"labels":[],"label_agreement":null},{"id":"W4403606092","doi":"10.3390/info15100638","title":"Voltage Deviation Improvement in Microgrid Operation through Demand Response Using Imperialist Competitive and Genetic Algorithms","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Smart Grid Energy Management","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":"Dalhousie University","funders":"Mitacs","keywords":"Microgrid; Voltage; Demand response; Genetic algorithm; Computer science; Algorithm; Imperialist competitive algorithm; Mathematical optimization; Control theory (sociology); Engineering; Mathematics; Machine learning; Artificial intelligence; Electrical engineering; Electricity; Control (management); Meta-optimization","score_opus":0.0077323876643767275,"score_gpt":0.22319939989326348,"score_spread":0.21546701222888676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403606092","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":[0.20586129,0.0010024642,0.7694108,0.00045234014,0.000096503325,0.00010241696,0.00005241718,0.0005489835,0.02247289],"genre_scores_gemma":[0.9718259,0.00016615429,0.026771044,0.00004448782,0.000015550117,0.000040079438,0.000026292182,0.000021601429,0.0010889325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972826,0.00008472518,0.000013990064,0.00004501836,0.00009384592,0.000034298402],"domain_scores_gemma":[0.9996655,0.00014986073,0.000055011882,0.000022658149,0.000088243294,0.00001868956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058129965,0.0006227725,0.0006686125,0.0004588978,0.00032788675,0.0008858737,0.0006627508,0.0005914484,0.00079288124],"category_scores_gemma":[0.0015597155,0.00021358002,0.00039570476,0.0004905066,0.00042718815,0.00050299143,0.00052401435,0.0004421108,0.000115989336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051951625,0.00004514686,0.00047397843,0.00003888849,0.000032467895,0.00003894958,0.00006540656,0.9541354,0.0017077801,0.004633517,0.00051028706,0.03826619],"study_design_scores_gemma":[0.000005710495,0.000025899157,0.000076086435,0.0000022460658,0.000004833879,0.000010171169,0.0000069364523,0.99862885,0.000316902,0.000670167,0.00024968942,0.0000025839465],"about_ca_topic_score_codex":0.00458832,"about_ca_topic_score_gemma":0.0031203346,"teacher_disagreement_score":0.00458832,"about_ca_system_score_codex":0.00056816445,"about_ca_system_score_gemma":0.0006226111,"threshold_uncertainty_score":0.009123206},"labels":[],"label_agreement":null},{"id":"W4403606135","doi":"10.3390/info15100634","title":"Promptology: Enhancing Human–AI Interaction in Large Language Models","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Natural language processing; Linguistics; Cognitive science; Psychology; Artificial intelligence; Philosophy","score_opus":0.015260533042689867,"score_gpt":0.29598498525756756,"score_spread":0.2807244522148777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403606135","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":[0.043650594,0.00039621358,0.9347134,0.0013687338,0.00012665571,0.0005553447,0.00025268033,0.01046688,0.008469389],"genre_scores_gemma":[0.31262827,0.0003811892,0.68016285,0.00043266974,0.00006737391,0.0005725963,0.000654429,0.0010192994,0.0040813033],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9914978,0.0062735644,0.0002896651,0.00078742893,0.000962835,0.00018861885],"domain_scores_gemma":[0.95997655,0.03144119,0.0011995282,0.0048441244,0.001672399,0.00086620176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009528375,0.0011917265,0.0006704777,0.0015103179,0.0010840069,0.004205767,0.0019181472,0.0017734641,0.008501571],"category_scores_gemma":[0.054497946,0.0005611318,0.0009304579,0.00083311246,0.0024826252,0.011110864,0.008288137,0.0022446571,0.0021121455],"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":[0.0010432849,0.0009367401,0.008750371,0.002644231,0.0001317169,0.0010631209,0.06809375,0.036942545,0.04191613,0.13380961,0.020324526,0.684344],"study_design_scores_gemma":[0.00039549993,0.0021170885,0.0046894965,0.0009862023,0.0002786399,0.0018896498,0.021282163,0.35918847,0.048522413,0.2830295,0.27722275,0.00039809998],"about_ca_topic_score_codex":0.00093873515,"about_ca_topic_score_gemma":0.001983118,"teacher_disagreement_score":0.009528375,"about_ca_system_score_codex":0.0010952136,"about_ca_system_score_gemma":0.0023689193,"threshold_uncertainty_score":0.050391436},"labels":[],"label_agreement":null},{"id":"W4403651495","doi":"10.3390/info15110663","title":"Impact of Digital Innovations on Health Literacy Applied to Patients with Special Needs: A Systematic Review","year":2024,"lang":"en","type":"review","venue":"Information","topic":"Mobile Health and mHealth Applications","field":"Health Professions","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":"Fundação para a Ciência e a Tecnologia","keywords":"Health literacy; Systematic review; Literacy; Psychology; Computer science; Medical education; Mathematics education; Medicine; MEDLINE; Pedagogy; Health care; Political science","score_opus":0.04445054626339247,"score_gpt":0.4667462457675202,"score_spread":0.42229569950412776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403651495","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":[0.0035780673,0.99460703,0.00014226855,0.00020128355,0.0001043863,0.0008497905,0.00018333981,0.000006971285,0.00032695424],"genre_scores_gemma":[0.030958543,0.966514,0.00067177,0.0004261369,0.00008857142,0.0010961185,0.00012715868,0.000004006661,0.000113796734],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99206984,0.0028939955,0.0028680519,0.00045708203,0.0014903933,0.00022068183],"domain_scores_gemma":[0.96933997,0.02408726,0.004032071,0.00034438667,0.0018532586,0.00034305322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0079206005,0.0010858404,0.0068646553,0.008112636,0.0007190966,0.0025920935,0.0012272566,0.0019562356,0.004137664],"category_scores_gemma":[0.036404192,0.00078849954,0.007178846,0.008138735,0.00088850857,0.0021262516,0.0015215136,0.0011154175,0.00021290887],"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":[0.00033251062,0.00006369599,0.0007984826,0.949725,0.0066527463,0.00010875686,0.0003042692,0.000059653492,0.0001284953,0.00013213519,0.00046715586,0.041227147],"study_design_scores_gemma":[0.0005693584,0.0006804354,0.005465937,0.90748006,0.0730235,0.0004340411,0.00061410334,0.00011365557,0.00027651366,0.00017270248,0.011126781,0.00004286149],"about_ca_topic_score_codex":0.005281625,"about_ca_topic_score_gemma":0.014873636,"teacher_disagreement_score":0.008112636,"about_ca_system_score_codex":0.003281487,"about_ca_system_score_gemma":0.010303077,"threshold_uncertainty_score":0.041888654},"labels":[],"label_agreement":null},{"id":"W4403978473","doi":"10.3390/info15110687","title":"Mitigating Bias Due to Race and Gender in Machine Learning Predictions of Traffic Stop Outcomes","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Policing Practices and Perceptions","field":"Social Sciences","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":"Nova Scotia Health Authority; St. Francis Xavier University","funders":"Nova Scotia Health Authority; Natural Sciences and Engineering Research Council of Canada; Compute Canada; Canada Foundation for Innovation; Nova Scotia Research Innovation Trust","keywords":"Race (biology); Law enforcement; Computer science; Machine learning; Artificial intelligence; Demographics; Selection bias; Enforcement; Omitted-variable bias; Feature selection; Point (geometry); Data science; Psychology; Statistics; Political science; Law; Mathematics; Sociology","score_opus":0.067869844401849,"score_gpt":0.37120577892663725,"score_spread":0.30333593452478824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403978473","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":[0.9430924,0.00031201047,0.049343947,0.0016702017,0.00015113952,0.000107758446,0.00083907525,0.0002934298,0.0041901283],"genre_scores_gemma":[0.98790526,0.00004013438,0.010527028,0.00018754494,0.00005498855,0.00004083956,0.00063549244,0.00003492866,0.0005737835],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9888876,0.008145882,0.00044480947,0.0011799717,0.00090623647,0.00043562325],"domain_scores_gemma":[0.94442344,0.040790647,0.0040805973,0.0074947956,0.0026620193,0.0005484902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027522307,0.00069713494,0.0006594386,0.0013245645,0.0012285344,0.0020887146,0.0010035139,0.0008887287,0.0013872114],"category_scores_gemma":[0.076937124,0.00025746922,0.00094503775,0.00093251426,0.001277455,0.0018053227,0.0015403787,0.0015813991,0.00070292666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053349783,0.00032530204,0.8793789,0.00007509762,0.00044744523,0.00015881498,0.0015725172,0.031169707,0.0017404354,0.0031520652,0.0031597312,0.078286536],"study_design_scores_gemma":[0.00011701166,0.0009237143,0.4096956,0.0002895539,0.00042938106,0.00039041028,0.003531472,0.5352421,0.013132515,0.022198021,0.01392244,0.00012778855],"about_ca_topic_score_codex":0.0109507935,"about_ca_topic_score_gemma":0.016329885,"teacher_disagreement_score":0.027522307,"about_ca_system_score_codex":0.0006482926,"about_ca_system_score_gemma":0.0015998915,"threshold_uncertainty_score":0.14555365},"labels":[],"label_agreement":null},{"id":"W4404024323","doi":"10.3390/info15110697","title":"Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review","year":2024,"lang":"en","type":"review","venue":"Information","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":109,"is_retracted":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":"Generative grammar; Narrative; Computer science; Linguistics; Natural language processing; Artificial intelligence; Philosophy","score_opus":0.044906160462432426,"score_gpt":0.35768750008523464,"score_spread":0.3127813396228022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404024323","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":[0.000090036425,0.9966145,0.00073827576,0.0007340266,0.00010258368,0.000012290481,0.000026859758,0.0000071482677,0.0016742933],"genre_scores_gemma":[0.001269022,0.99709773,0.00068126223,0.0004233946,0.000116972085,0.00002873109,0.000034184348,0.0000051277716,0.0003435226],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989839,0.00035382915,0.00016559001,0.00013756912,0.00030806923,0.000051068073],"domain_scores_gemma":[0.9925789,0.0063096955,0.00033937627,0.00016721142,0.0005285921,0.00007622255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027366178,0.0008499877,0.0013458732,0.0029787605,0.00050460617,0.0020060665,0.0012233066,0.001776389,0.00530661],"category_scores_gemma":[0.008447432,0.0004941947,0.001142653,0.0032373278,0.0011702708,0.0031196882,0.0011852747,0.0026986287,0.0017223966],"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":[0.00006548482,0.00007280461,0.00022984562,0.07311377,0.00024312842,0.00021897518,0.00037900847,0.0012625287,0.00068655954,0.06933888,0.02859382,0.82579523],"study_design_scores_gemma":[0.000014569422,0.0000842672,0.00035983694,0.030811131,0.00027408102,0.0007389785,0.00015694227,0.00029920283,0.00050389825,0.014915204,0.9518088,0.000033192075],"about_ca_topic_score_codex":0.0020420116,"about_ca_topic_score_gemma":0.0027504184,"teacher_disagreement_score":0.00530661,"about_ca_system_score_codex":0.0012150991,"about_ca_system_score_gemma":0.0035522322,"threshold_uncertainty_score":0.01775235},"labels":[],"label_agreement":null},{"id":"W4404495509","doi":"10.3390/info15110736","title":"Benchmarking for a New Railway Accident Classification Methodology and Its Database: A Case Study in Mexico, the United States, Canada, and the European Union","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Occupational Health and Safety Research","field":"Health Professions","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":"Division of Materials Research; Bundesanstalt für Materialforschung und -Prüfung","keywords":"Benchmarking; Accident (philosophy); European union; Database; Forensic engineering; Engineering; Political science; Business; Computer science; Management; International trade; Economics","score_opus":0.22677553706804962,"score_gpt":0.48602855461243366,"score_spread":0.259253017544384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404495509","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":[0.989926,0.00013980095,0.005937338,0.00042767954,0.000010534684,0.00017713195,0.00051335327,0.00005506289,0.0028131932],"genre_scores_gemma":[0.9890899,0.00015852087,0.009175396,0.000034073222,0.000006309678,0.00014566086,0.00065044645,0.000020485082,0.0007190831],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9926116,0.0041052317,0.00058716745,0.00051625597,0.0016766957,0.0005031051],"domain_scores_gemma":[0.98230135,0.008294038,0.0019133652,0.0017181516,0.005336647,0.00043630463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012080936,0.0001818632,0.00037301835,0.0026918321,0.0017823345,0.0023255122,0.0012404339,0.000836547,0.0010976029],"category_scores_gemma":[0.022093022,0.00020187808,0.00031885717,0.0080393,0.00065895804,0.0019366338,0.0011549515,0.00063060096,0.00012452494],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047176465,0.0016115349,0.76763284,0.00038189287,0.000083157356,0.002252658,0.019069405,0.011755302,0.0018743171,0.01068161,0.0040372936,0.18014833],"study_design_scores_gemma":[0.000071488445,0.0011479614,0.792701,0.00041607878,0.00016808513,0.0014264894,0.09910028,0.071927324,0.0043954602,0.0018529126,0.02668228,0.00011067429],"about_ca_topic_score_codex":0.21191023,"about_ca_topic_score_gemma":0.19167708,"teacher_disagreement_score":0.9903382,"about_ca_system_score_codex":0.009661781,"about_ca_system_score_gemma":0.004923744,"threshold_uncertainty_score":0.42135358},"labels":[],"label_agreement":null},{"id":"W4404640966","doi":"10.3390/info15120746","title":"A Comprehensive Analysis of Early Alzheimer Disease Detection from 3D sMRI Images Using Deep Learning Frameworks","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Neuroimaging; Computer science; Cognitive impairment; Preprocessor; Artificial intelligence; Cognition; Magnetic resonance imaging; Alzheimer's disease; Disease; Pattern recognition (psychology); Medicine; Neuroscience; Psychology; Pathology; Radiology","score_opus":0.019515412745288504,"score_gpt":0.3246575354460204,"score_spread":0.3051421227007319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404640966","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":[0.5065669,0.0060668387,0.47307786,0.0012371843,0.0001992765,0.00022799929,0.0045332676,0.0039799907,0.0041106134],"genre_scores_gemma":[0.9057716,0.0016414586,0.08258959,0.0002504696,0.00009757915,0.0001246919,0.0061127828,0.000096841846,0.0033150297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967206,0.00006991438,0.000018048926,0.00009434765,0.00008269129,0.000062981686],"domain_scores_gemma":[0.99968827,0.000087186854,0.000033683937,0.000045983958,0.0001172742,0.000027542515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012503155,0.0014196207,0.0007443428,0.0014855256,0.00028047638,0.00069938286,0.00079300447,0.00079098676,0.0008085978],"category_scores_gemma":[0.00199642,0.00037328817,0.0010908595,0.0006495713,0.00022771867,0.0005719692,0.00073619204,0.000727714,0.00070506043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056705956,0.0005804799,0.034660872,0.00032149174,0.00067602616,0.0008214995,0.00020842341,0.33038545,0.035101272,0.0027281477,0.015214692,0.57873464],"study_design_scores_gemma":[0.0000065044906,0.000083834966,0.006969355,0.000034131477,0.000046645524,0.00013281612,0.000020356516,0.9870171,0.0033249834,0.0013019588,0.0010440772,0.000018270655],"about_ca_topic_score_codex":0.013763004,"about_ca_topic_score_gemma":0.019933278,"teacher_disagreement_score":0.013763004,"about_ca_system_score_codex":0.0005842881,"about_ca_system_score_gemma":0.0009201435,"threshold_uncertainty_score":0.027365804},"labels":[],"label_agreement":null},{"id":"W4404720546","doi":"10.3390/info15120751","title":"Exploring the Impact of Image-Based Audio Representations in Classification Tasks Using Vision Transformers and Explainable AI Techniques","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Explainable Artificial Intelligence (XAI)","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Transformer; Computer science; Artificial intelligence; Computer vision; Audio visual; Speech recognition; Multimedia; Engineering; Electrical engineering","score_opus":0.08433699312779384,"score_gpt":0.3697730099991448,"score_spread":0.28543601687135095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404720546","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":[0.19482414,0.00062931166,0.79845583,0.0006661426,0.000080030586,0.00011976139,0.0001763065,0.0018144704,0.0032340786],"genre_scores_gemma":[0.87496704,0.00027901542,0.12294757,0.00011325017,0.00002757491,0.000042047523,0.00025045362,0.00008015368,0.0012929187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957496,0.00013620754,0.000021102825,0.00011052967,0.000104931816,0.00005227155],"domain_scores_gemma":[0.99809295,0.0012899821,0.00017006877,0.0001828334,0.00020089342,0.00006341917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011522647,0.00091180106,0.00033288312,0.00079448626,0.00018100135,0.001390023,0.0008355002,0.0007203528,0.0020584026],"category_scores_gemma":[0.005724433,0.00022443093,0.0006952487,0.00043871882,0.0005394015,0.002278629,0.00087736,0.0012772335,0.00041660332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060758647,0.00032759583,0.008393442,0.00029021665,0.00025161926,0.00029792858,0.00042160565,0.38061467,0.055072974,0.014461055,0.0015452949,0.537716],"study_design_scores_gemma":[0.000010081497,0.00011699153,0.0009953235,0.0000113103215,0.000029411429,0.000052102456,0.00005197516,0.98516953,0.008214819,0.0048771026,0.0004601941,0.000011071313],"about_ca_topic_score_codex":0.0040281867,"about_ca_topic_score_gemma":0.0032420966,"teacher_disagreement_score":0.0040281867,"about_ca_system_score_codex":0.0006536243,"about_ca_system_score_gemma":0.0004936871,"threshold_uncertainty_score":0.008009493},"labels":[],"label_agreement":null},{"id":"W4404999448","doi":"10.3390/info15120766","title":"Enabling Perspective-Aware Ai with Contextual Scene Graph Generation","year":2024,"lang":"en","type":"article","venue":"Information","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Contextual design; Artificial intelligence; Graph; Human–computer interaction; Perspective (graphical); Data science; Natural language processing; Object (grammar); Theoretical computer science","score_opus":0.012782248264085423,"score_gpt":0.2756536029919525,"score_spread":0.2628713547278671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404999448","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":[0.010283341,0.00030454976,0.9697028,0.00027434228,0.00006923902,0.000117111325,0.0007704973,0.015334703,0.0031434803],"genre_scores_gemma":[0.2199177,0.0003716232,0.76840377,0.00052578404,0.000054133285,0.00020025068,0.0046688626,0.0024973743,0.003360398],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995653,0.00010297989,0.000015225414,0.00016648594,0.00010730827,0.000042628304],"domain_scores_gemma":[0.9994498,0.00025590177,0.000025239624,0.00014027009,0.00008780079,0.000040959163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003390475,0.0014614057,0.00062653143,0.00084944034,0.0003932594,0.00090320973,0.0017735005,0.00097099406,0.006102355],"category_scores_gemma":[0.002110917,0.00046125773,0.0016575325,0.00059454254,0.00055666454,0.0017737309,0.0020394118,0.001623593,0.0021154569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042019115,0.0002491645,0.0016722339,0.00063843705,0.0001880657,0.0008326425,0.0008862926,0.29153255,0.06316943,0.02907657,0.030686432,0.580648],"study_design_scores_gemma":[0.000027824319,0.000043529682,0.00027310138,0.000022471488,0.000024799012,0.00014185227,0.00014669246,0.94909626,0.012254826,0.026538817,0.011404662,0.000025258314],"about_ca_topic_score_codex":0.00517215,"about_ca_topic_score_gemma":0.010925434,"teacher_disagreement_score":0.006102355,"about_ca_system_score_codex":0.0005818351,"about_ca_system_score_gemma":0.0006041379,"threshold_uncertainty_score":0.020414412},"labels":[],"label_agreement":null},{"id":"W4406381879","doi":"10.3390/info16010051","title":"Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors","year":2025,"lang":"en","type":"article","venue":"Information","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Knowledge management; Business; Human resource management; Critical success factor; Process management; Computer science","score_opus":0.005790616247711254,"score_gpt":0.22406022943550882,"score_spread":0.21826961318779756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406381879","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":[0.9799558,0.00083908305,0.0015969685,0.004394561,0.000026036216,0.0001760176,0.00006900756,0.000010419478,0.012932023],"genre_scores_gemma":[0.9987948,0.00030665798,0.00045847226,0.00010204599,0.000007763246,0.00006431389,0.000020860376,0.0000061032933,0.00023901231],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.98257554,0.007896567,0.0012224588,0.0011622143,0.004342731,0.002800508],"domain_scores_gemma":[0.8440823,0.11850188,0.020138362,0.0030477594,0.010619212,0.0036105162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021406954,0.00030503585,0.0004741559,0.00378611,0.0032738287,0.007635541,0.0011472283,0.0009758059,0.0027525262],"category_scores_gemma":[0.08532806,0.00045136872,0.0005355561,0.0042703385,0.0049663503,0.008088859,0.005233812,0.002456637,0.00021198275],"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":[0.0003741946,0.0005979548,0.59096706,0.00097561453,0.00015320376,0.0013144031,0.23740186,0.0011996746,0.0014494593,0.06473642,0.0016409864,0.09918926],"study_design_scores_gemma":[0.00001665444,0.00028884443,0.5222684,0.0012110199,0.00011821999,0.00045926488,0.44863805,0.0019443921,0.0010509245,0.007855887,0.016073778,0.00007458011],"about_ca_topic_score_codex":0.00823812,"about_ca_topic_score_gemma":0.007505649,"teacher_disagreement_score":0.021406954,"about_ca_system_score_codex":0.0040896987,"about_ca_system_score_gemma":0.008292002,"threshold_uncertainty_score":0.11321211},"labels":[],"label_agreement":null},{"id":"W4407156773","doi":"10.3390/info16020106","title":"From Data Silos to Health Records Without Borders: A Systematic Survey on Patient-Centered Data Interoperability","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Electronic Health Records Systems","field":"Health Professions","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":"Université du Québec à Rimouski; Université du Québec à Chicoutimi","funders":"","keywords":"Interoperability; Information silo; Health data; Survey data collection; Database; Health records; Data science; Computer science; Data mining; Engineering; World Wide Web; Silo; Health care; Statistics; Political science; Mathematics","score_opus":0.1900911032313819,"score_gpt":0.4937716957071274,"score_spread":0.3036805924757455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407156773","genre_codex":"empirical","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":[0.6693013,0.27910987,0.01469894,0.021223944,0.00038193664,0.0016472668,0.0040369476,0.00008930004,0.009510511],"genre_scores_gemma":[0.8831307,0.099863686,0.008399659,0.005017024,0.00009019531,0.0012282588,0.0016668179,0.00008884152,0.0005147319],"study_design_codex":"observational","study_design_gemma":"systematic_review","domain_scores_codex":[0.8994663,0.051337123,0.02724909,0.005619747,0.014686707,0.001641047],"domain_scores_gemma":[0.69803447,0.23161335,0.04132908,0.0093882475,0.017204247,0.0024305983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.084325135,0.00039953916,0.0015263603,0.007816931,0.0011833897,0.0034617041,0.0014502426,0.0011355593,0.0020037794],"category_scores_gemma":[0.19594096,0.00086617784,0.0019779482,0.012056637,0.0038548342,0.00970273,0.006824285,0.002246049,0.00023244269],"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":[0.00023407365,0.0001147624,0.348816,0.09383396,0.0017902622,0.0010100339,0.23642264,0.00036760143,0.0013515895,0.0120661985,0.007500378,0.29649252],"study_design_scores_gemma":[0.00010191142,0.0004867178,0.28469816,0.19819388,0.002645666,0.0035861824,0.3538246,0.0012613825,0.0017226001,0.008140072,0.14506535,0.00027355264],"about_ca_topic_score_codex":0.00642161,"about_ca_topic_score_gemma":0.010578156,"teacher_disagreement_score":0.084325135,"about_ca_system_score_codex":0.0034366124,"about_ca_system_score_gemma":0.01333539,"threshold_uncertainty_score":0.44595927},"labels":[],"label_agreement":null},{"id":"W4407349499","doi":"10.3390/info16020133","title":"blockHealthSecure: Integrating Blockchain and Cybersecurity in Post-Pandemic Healthcare Systems","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Blockchain Technology Applications and Security","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":"Sault College","funders":"","keywords":"Blockchain; Pandemic; Computer security; Healthcare system; Health care; Coronavirus disease 2019 (COVID-19); Internet of Things; Computer science; Business; Internet privacy; Political science; Medicine; Law","score_opus":0.00592396751186301,"score_gpt":0.24599891097674664,"score_spread":0.24007494346488364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407349499","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":[0.10886283,0.0017436366,0.8257774,0.007152108,0.00027202835,0.0006872666,0.00036829695,0.003090092,0.052046344],"genre_scores_gemma":[0.8356266,0.0013500263,0.14921652,0.0006144784,0.00007345999,0.00026939026,0.00045900347,0.00018358929,0.01220702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982028,0.00082783704,0.0000900462,0.00014917467,0.00047271088,0.00025726866],"domain_scores_gemma":[0.9977822,0.0008048944,0.00016594157,0.0006049168,0.00035336192,0.0002887387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033888828,0.0003795951,0.00030337865,0.0007778973,0.0013205601,0.002560156,0.0010278597,0.0015912517,0.005063363],"category_scores_gemma":[0.004726146,0.00028069603,0.0003845662,0.0007339442,0.0017793644,0.0058720014,0.004458265,0.0012847033,0.0009276391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004551101,0.0002715408,0.0071513606,0.00044962266,0.00007867937,0.0012001767,0.001870113,0.16760688,0.012701198,0.5729427,0.010959398,0.22431321],"study_design_scores_gemma":[0.00016287364,0.00055378955,0.0016452007,0.00041532813,0.00005935923,0.00077409804,0.0008476112,0.45976216,0.016899824,0.3585957,0.16016981,0.00011426132],"about_ca_topic_score_codex":0.0053064083,"about_ca_topic_score_gemma":0.0056454088,"teacher_disagreement_score":0.0053064083,"about_ca_system_score_codex":0.001513019,"about_ca_system_score_gemma":0.0042165527,"threshold_uncertainty_score":0.017922342},"labels":[],"label_agreement":null},{"id":"W4407416584","doi":"10.3390/info16020135","title":"PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Parkinson's Disease Mechanisms and Treatments","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":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Parkinson's disease; Artificial neural network; Fusion; Artificial intelligence; Computer science; Pattern recognition (psychology); Disease; Medicine; Internal medicine; Philosophy","score_opus":0.01087626989773867,"score_gpt":0.2688712801582042,"score_spread":0.25799501026046556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407416584","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":[0.33953825,0.0035995443,0.6382217,0.00082366576,0.00046937616,0.00023666673,0.0017115249,0.0081561385,0.0072432286],"genre_scores_gemma":[0.91566616,0.00042719315,0.07469344,0.0003756605,0.00009570817,0.00011146816,0.0021283184,0.000067102425,0.006434932],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978954,0.00001970103,0.000011664606,0.00007900684,0.000051581686,0.000048512877],"domain_scores_gemma":[0.9998567,0.000034282664,0.0000165906,0.000013765102,0.000056912326,0.000021849113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040797077,0.0009760081,0.0006802314,0.0008268255,0.00026950767,0.00054006925,0.00094254414,0.0007893554,0.001345533],"category_scores_gemma":[0.0005909306,0.00026541293,0.00060870737,0.0003659155,0.00021405076,0.00072340836,0.001056168,0.0007491251,0.00046991865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093098526,0.0007685509,0.012699304,0.0002311283,0.00046185075,0.000716771,0.00010699109,0.14558126,0.041501056,0.0016300795,0.009727618,0.7856445],"study_design_scores_gemma":[0.000019694382,0.00014592386,0.0030564119,0.000015233306,0.00005875621,0.00014954382,0.00001899348,0.987698,0.0067114946,0.0009885473,0.0011185365,0.000018917031],"about_ca_topic_score_codex":0.0074271597,"about_ca_topic_score_gemma":0.010429902,"teacher_disagreement_score":0.0074271597,"about_ca_system_score_codex":0.00066796667,"about_ca_system_score_gemma":0.0006885805,"threshold_uncertainty_score":0.014767826},"labels":[],"label_agreement":null},{"id":"W4407977025","doi":"10.3390/info16030175","title":"A Novel Comprehensive Framework for Detecting and Understanding Health-Related Misinformation","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Misinformation and Its Impacts","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":"Centre for International Governance Innovation; Balsillie School of International Affairs; University of Waterloo","funders":"","keywords":"Misinformation; Computer science; Data science; Psychology; Internet privacy; Computer security","score_opus":0.08747896870546143,"score_gpt":0.3697788809055521,"score_spread":0.28229991220009065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407977025","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":[0.08106361,0.0020679343,0.90202045,0.002959003,0.0002547097,0.00022205309,0.003293585,0.0021446426,0.005973962],"genre_scores_gemma":[0.69008166,0.0011777105,0.297805,0.00059032306,0.0002925074,0.00028187133,0.0050951913,0.00009075952,0.004584903],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994367,0.00017339525,0.000048952756,0.0001738856,0.00010613509,0.000060936985],"domain_scores_gemma":[0.99868864,0.00064830185,0.00018164127,0.00012507619,0.00029523272,0.0000610833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010862511,0.0009887321,0.00041589778,0.0022970757,0.0005166016,0.0013239955,0.0008561662,0.0009797862,0.0014221992],"category_scores_gemma":[0.004027188,0.00019517288,0.00071200076,0.0014428337,0.00053049286,0.0029819582,0.001565361,0.0013937803,0.00060559815],"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":[0.00037612687,0.0005209653,0.03298947,0.0009153875,0.00037225505,0.00059819897,0.0023950927,0.087527655,0.020901289,0.040710524,0.020983431,0.7917096],"study_design_scores_gemma":[0.000016579846,0.00014232662,0.008276115,0.0001363663,0.0001483419,0.0002720682,0.0007693833,0.90813917,0.005973326,0.062157415,0.013914802,0.00005406294],"about_ca_topic_score_codex":0.006991867,"about_ca_topic_score_gemma":0.01126813,"teacher_disagreement_score":0.006991867,"about_ca_system_score_codex":0.0007853938,"about_ca_system_score_gemma":0.0017330709,"threshold_uncertainty_score":0.013902307},"labels":[],"label_agreement":null},{"id":"W4408301970","doi":"10.3390/info16030215","title":"Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review","year":2025,"lang":"en","type":"review","venue":"Information","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Prostate cancer; Radiation therapy; Segmentation; Computer science; Artificial intelligence; Radiation treatment planning; Deep learning; Cancer; Software; Medical physics; Medicine; Internal medicine","score_opus":0.01967788407781012,"score_gpt":0.35868445584692243,"score_spread":0.3390065717691123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408301970","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":[0.001266312,0.9969207,0.0003870763,0.0002010072,0.00006708831,0.00022238947,0.0005474173,0.000017543005,0.00037056324],"genre_scores_gemma":[0.01508513,0.98160076,0.0016376908,0.0004564886,0.00006673651,0.00046877487,0.00051846856,0.000019894404,0.00014610318],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9938392,0.0017795269,0.0024179625,0.0005202824,0.0013134341,0.0001296365],"domain_scores_gemma":[0.97197443,0.021599077,0.0035150961,0.00038385252,0.0023357368,0.00019176552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073577557,0.0013082138,0.0051177386,0.008497126,0.0004886341,0.0023962907,0.0020486289,0.0015280137,0.005019316],"category_scores_gemma":[0.04014893,0.00081522734,0.008066069,0.0074411742,0.00074928184,0.002186785,0.0014091522,0.00093491806,0.0005647843],"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":[0.00034927437,0.0000258867,0.000696221,0.8718195,0.0049608015,0.000058760033,0.00015260422,0.00024304082,0.00020294642,0.00021747386,0.0019719377,0.11930156],"study_design_scores_gemma":[0.00036075697,0.00050282467,0.003733614,0.8975792,0.06338357,0.0004956562,0.00026382925,0.00035524732,0.0005251409,0.0004845348,0.032243606,0.00007203595],"about_ca_topic_score_codex":0.006588037,"about_ca_topic_score_gemma":0.017293733,"teacher_disagreement_score":0.008497126,"about_ca_system_score_codex":0.0029177442,"about_ca_system_score_gemma":0.0080615375,"threshold_uncertainty_score":0.038912},"labels":[],"label_agreement":null},{"id":"W4408529253","doi":"10.3390/info16030235","title":"AI Chatbots in Education: Challenges and Opportunities","year":2025,"lang":"en","type":"article","venue":"Information","topic":"AI in Service Interactions","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Data science; Knowledge management; Computer science","score_opus":0.03238469039645388,"score_gpt":0.2920414706660354,"score_spread":0.25965678026958156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408529253","genre_codex":"review","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":[0.03508001,0.4495352,0.054533724,0.34293568,0.00554725,0.0003301664,0.00019195324,0.0005209452,0.111325175],"genre_scores_gemma":[0.53351957,0.32433477,0.04904755,0.052706562,0.0038376292,0.0012712106,0.0002601125,0.00039987144,0.034622658],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.97811,0.014872331,0.0013314966,0.0013681161,0.003026211,0.001291799],"domain_scores_gemma":[0.9359196,0.050481044,0.002774073,0.0018531833,0.0061258515,0.002846188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02621386,0.00071148283,0.0007332234,0.002508931,0.006218893,0.01798899,0.0032406652,0.0073236767,0.005220398],"category_scores_gemma":[0.039461195,0.00073269114,0.00056137383,0.0027012378,0.012188206,0.034298036,0.008841924,0.0073635457,0.0016017442],"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":[0.00009459984,0.00016743876,0.00251202,0.007823471,0.00002492394,0.00078476267,0.048633408,0.00079871813,0.0005444185,0.5019534,0.03423622,0.40242663],"study_design_scores_gemma":[0.000013207628,0.00013275322,0.001322544,0.012868185,0.000021348207,0.000796006,0.046469405,0.0010909536,0.0006389263,0.06514185,0.8714328,0.0000720379],"about_ca_topic_score_codex":0.0036914216,"about_ca_topic_score_gemma":0.0043477104,"teacher_disagreement_score":0.02621386,"about_ca_system_score_codex":0.0072994772,"about_ca_system_score_gemma":0.010361225,"threshold_uncertainty_score":0.13863379},"labels":[],"label_agreement":null},{"id":"W4408576222","doi":"10.3390/info16030240","title":"Human-Centered Artificial Intelligence in Higher Education: A Framework for Systematic Literature Reviews","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Systematic review; Computer science; Political science; MEDLINE","score_opus":0.205313026369441,"score_gpt":0.4744675695037619,"score_spread":0.2691545431343209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408576222","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002192105,0.1418223,0.70433944,0.048619278,0.0020645799,0.09041208,0.0023749778,0.0016707291,0.0065045604],"genre_scores_gemma":[0.0133931795,0.015795734,0.8771664,0.0027249458,0.00028290818,0.08984328,0.0004260581,0.000077575925,0.0002899377],"study_design_codex":"systematic_review","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.21412767,0.688053,0.06131353,0.012499981,0.022350177,0.0016556752],"domain_scores_gemma":[0.1771329,0.72796524,0.029749501,0.032521494,0.028554624,0.004076207],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.59897405,0.007195038,0.014534683,0.075136304,0.007472248,0.020929832,0.013025356,0.011610554,0.00601565],"category_scores_gemma":[0.6159893,0.00538374,0.0135408845,0.037976783,0.02173569,0.016900433,0.020933587,0.009973085,0.0019920897],"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":[0.00040827692,0.0002671847,0.0027627603,0.35226488,0.007050884,0.00096681045,0.029783381,0.009367767,0.0009965114,0.27181676,0.018165171,0.30614963],"study_design_scores_gemma":[0.0013386884,0.00056459976,0.0014716503,0.379505,0.004957214,0.0007027684,0.009141623,0.016173104,0.001084891,0.4449534,0.13946185,0.0006451235],"about_ca_topic_score_codex":0.010951434,"about_ca_topic_score_gemma":0.018326733,"teacher_disagreement_score":0.40102595,"about_ca_system_score_codex":0.03598542,"about_ca_system_score_gemma":0.13851014,"threshold_uncertainty_score":0.49453646},"labels":[],"label_agreement":null},{"id":"W4408773747","doi":"10.3390/info16040257","title":"Optimizing Contextonym Analysis for Terminological Definition Writing","year":2025,"lang":"en","type":"article","venue":"Information","topic":"linguistics and terminology studies","field":"Arts and Humanities","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 Trois-Rivières","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Linguistics; Computer science; Natural language processing; Philosophy","score_opus":0.08820234775365367,"score_gpt":0.28615259384955966,"score_spread":0.197950246095906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408773747","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":[0.048007503,0.00090134464,0.9377979,0.00024531866,0.00010456662,0.00069129514,0.00091993937,0.0067407493,0.004591269],"genre_scores_gemma":[0.08125998,0.00031463354,0.9148439,0.000043938744,0.000021634698,0.0003336503,0.0012966326,0.0009009511,0.0009846294],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948343,0.0020084104,0.0007453185,0.0010062418,0.0012213846,0.0001843041],"domain_scores_gemma":[0.988765,0.006150131,0.00078821776,0.0015506569,0.0025155656,0.00023045643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004258449,0.0012473024,0.0012139393,0.005050058,0.0013938361,0.004471586,0.001103691,0.0006835356,0.00599488],"category_scores_gemma":[0.03087889,0.00074601726,0.0010713771,0.0049730847,0.0008469566,0.0066415593,0.0032883876,0.0013162938,0.003565003],"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":[0.0003430256,0.00018456473,0.0054053557,0.0017501399,0.00011689454,0.0004269273,0.004991636,0.005172419,0.0789752,0.018975575,0.008853542,0.8748047],"study_design_scores_gemma":[0.00023787709,0.0005991172,0.015288446,0.0007956301,0.00044670323,0.00268281,0.017198121,0.25572813,0.33588132,0.085012615,0.2856977,0.0004315819],"about_ca_topic_score_codex":0.0008912699,"about_ca_topic_score_gemma":0.002018816,"teacher_disagreement_score":0.00599488,"about_ca_system_score_codex":0.00081044174,"about_ca_system_score_gemma":0.0019017535,"threshold_uncertainty_score":0.022521138},"labels":[],"label_agreement":null},{"id":"W4409045115","doi":"10.3390/info16040284","title":"Applications of Generative Artificial Intelligence in Electronic Medical Records: A Scoping Review","year":2025,"lang":"en","type":"review","venue":"Information","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Generative grammar; Artificial intelligence; Computer science; Data science","score_opus":0.03886232435488321,"score_gpt":0.4194768122483831,"score_spread":0.38061448789349994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409045115","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":[0.00065925176,0.9951298,0.0009137176,0.0012181978,0.00023627037,0.0006085624,0.000231575,0.000014039741,0.0009886282],"genre_scores_gemma":[0.0065172813,0.9889438,0.002425253,0.00065622554,0.00013449964,0.00095649576,0.00021306635,0.000010545392,0.00014285662],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9659792,0.0133344205,0.013044654,0.0013978365,0.0058165086,0.00042738928],"domain_scores_gemma":[0.7609834,0.20424876,0.015089071,0.003706212,0.015404012,0.00056856987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03822288,0.0013180802,0.0037345383,0.027465029,0.0015173423,0.0052866098,0.0030225972,0.0033080822,0.005395046],"category_scores_gemma":[0.1674654,0.0013075778,0.0047770287,0.02522676,0.0024042479,0.00569951,0.0035158726,0.0020004618,0.00082601595],"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":[0.00007045838,0.0000327113,0.0007708571,0.79058653,0.0014671958,0.00016810201,0.0011688545,0.0002729356,0.00017264471,0.0024637368,0.0041940324,0.19863185],"study_design_scores_gemma":[0.000015862983,0.00004789064,0.0007618405,0.96330345,0.003099491,0.00018844649,0.0004301645,0.00009717126,0.00010549551,0.0007261959,0.031203805,0.000020167878],"about_ca_topic_score_codex":0.008214233,"about_ca_topic_score_gemma":0.020197999,"teacher_disagreement_score":0.03822288,"about_ca_system_score_codex":0.006201395,"about_ca_system_score_gemma":0.023315638,"threshold_uncertainty_score":0.20214432},"labels":[],"label_agreement":null},{"id":"W4409369980","doi":"10.3390/info16040303","title":"Automatic Detection of Camera Rotation Moments in Trans-Nasal Minimally Invasive Surgery Using Machine Learning Algorithm","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Nasal Surgery and Airway Studies","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Alberta; Universities Space Research Association","keywords":"Rotation (mathematics); Artificial intelligence; Computer science; Algorithm; Computer vision","score_opus":0.014782570227742951,"score_gpt":0.2591009969942557,"score_spread":0.24431842676651275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409369980","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":[0.53106534,0.00429186,0.45122042,0.0005367202,0.0007162837,0.00035272932,0.0021956766,0.004149102,0.005471834],"genre_scores_gemma":[0.90423167,0.001104212,0.0887491,0.00015842961,0.00012355892,0.000106913685,0.0028326174,0.00007909474,0.0026143692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952185,0.000051662882,0.000032278724,0.00017110443,0.00013363705,0.0000894479],"domain_scores_gemma":[0.9994423,0.00013373744,0.00010336784,0.000053723434,0.00022756081,0.000039288898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044675378,0.0010486207,0.0006684312,0.0012950143,0.00021293345,0.0005486139,0.00077874726,0.00066732086,0.001028302],"category_scores_gemma":[0.0019609446,0.00030684308,0.00058859756,0.0006040723,0.00024965682,0.0004466135,0.00047659152,0.00066852325,0.00063025724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008246622,0.00027966622,0.031319108,0.0003453542,0.00015203419,0.00075210584,0.00008500891,0.12157187,0.05339541,0.0006973141,0.010438949,0.7801386],"study_design_scores_gemma":[0.000014455644,0.00011687914,0.013799561,0.000046138095,0.000031510346,0.00036298294,0.00003560223,0.9674259,0.01636058,0.0004550942,0.0013283754,0.000022911283],"about_ca_topic_score_codex":0.0069265445,"about_ca_topic_score_gemma":0.009538159,"teacher_disagreement_score":0.0069265445,"about_ca_system_score_codex":0.00061734975,"about_ca_system_score_gemma":0.0006963667,"threshold_uncertainty_score":0.013772428},"labels":[],"label_agreement":null},{"id":"W4409546711","doi":"10.3390/info16040318","title":"Perspectives on Managing AI Ethics in the Digital Age","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":25,"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":"Engineering ethics; Psychology; Engineering","score_opus":0.037496778886922086,"score_gpt":0.40210792376182874,"score_spread":0.36461114487490665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409546711","genre_codex":"commentary","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":[0.015246482,0.026770838,0.036635477,0.5109977,0.0018547376,0.0000556152,0.00007515456,0.00006622696,0.40829775],"genre_scores_gemma":[0.84962237,0.028506385,0.02012595,0.07088739,0.003389557,0.00024055978,0.00007802269,0.00009394887,0.027055845],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98706913,0.008144997,0.0004330452,0.0009369695,0.0023232664,0.0010926112],"domain_scores_gemma":[0.98050565,0.013736162,0.0009862651,0.0012929564,0.0017905758,0.0016884453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017394107,0.0006603128,0.00047696897,0.002693097,0.008614619,0.022076169,0.0018454513,0.00954444,0.004229366],"category_scores_gemma":[0.013534358,0.00032569253,0.0005480562,0.00275826,0.07033972,0.018826438,0.0074941316,0.00965592,0.0006773469],"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":[0.0000031823681,0.0000055531077,0.0001224252,0.000029011753,0.000002217265,0.000050714203,0.0052144886,0.00018186656,0.000050960298,0.98755604,0.0023629367,0.0044205673],"study_design_scores_gemma":[0.000007822543,0.000012507829,0.00026233445,0.00042519646,0.0000051508523,0.00017908226,0.009441577,0.0007414709,0.00015911585,0.7852956,0.20345195,0.000018252545],"about_ca_topic_score_codex":0.009685554,"about_ca_topic_score_gemma":0.008784692,"teacher_disagreement_score":0.022076169,"about_ca_system_score_codex":0.014752755,"about_ca_system_score_gemma":0.011140199,"threshold_uncertainty_score":0.10703921},"labels":[],"label_agreement":null},{"id":"W4409667767","doi":"10.3390/info16050336","title":"Building a Cybersecurity Culture in Higher Education: Proposing a Cybersecurity Awareness Paradigm","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Information and Cyber Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Computer security; Internet privacy; Computer science","score_opus":0.01258313702816304,"score_gpt":0.27804042929977957,"score_spread":0.2654572922716165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409667767","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":[0.18373659,0.0029547529,0.29448608,0.15106039,0.0012367272,0.0008463664,0.00009112356,0.00029721137,0.3652907],"genre_scores_gemma":[0.91223556,0.0009815277,0.07696674,0.0032032111,0.00016324119,0.00042344813,0.000039026258,0.000032422013,0.0059549105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99426454,0.0034463971,0.00021072195,0.0005658668,0.00089860416,0.0006137768],"domain_scores_gemma":[0.9943045,0.0014084217,0.0008114702,0.0004718551,0.001080865,0.0019229108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007722782,0.00070897414,0.0003694033,0.0033297387,0.007031378,0.014529399,0.0024409739,0.003926229,0.0030481422],"category_scores_gemma":[0.004585988,0.0004766447,0.00068817637,0.0015358179,0.02675883,0.014221784,0.009668202,0.0051677874,0.0006528271],"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":[0.000013628883,0.00040474712,0.004616446,0.00016964469,0.000013185929,0.00020941549,0.040374387,0.0006151669,0.0006057304,0.9243033,0.002048472,0.026625866],"study_design_scores_gemma":[0.00004757094,0.00027740042,0.0049826424,0.0008437037,0.000030846026,0.00046885043,0.11101637,0.007258675,0.001090957,0.7844351,0.08945597,0.00009201706],"about_ca_topic_score_codex":0.0033653795,"about_ca_topic_score_gemma":0.003998789,"teacher_disagreement_score":0.014529399,"about_ca_system_score_codex":0.0073287254,"about_ca_system_score_gemma":0.015968246,"threshold_uncertainty_score":0.05317384},"labels":[],"label_agreement":null},{"id":"W4410501861","doi":"10.3390/info16050415","title":"Artificial Intelligence in SMEs: Enhancing Business Functions Through Technologies and Applications","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","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":"Université du Québec en Outaouais; Université du Québec à Trois-Rivières","funders":"","keywords":"Business intelligence; Business; Knowledge management; Process management; Computer science","score_opus":0.03562801299073627,"score_gpt":0.2822760577997157,"score_spread":0.24664804480897945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410501861","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":[0.16967097,0.62617147,0.023473842,0.05902717,0.00096978736,0.00041499862,0.0007694195,0.00019006256,0.11931228],"genre_scores_gemma":[0.70171905,0.27210233,0.016755527,0.006062589,0.0003059839,0.00031006883,0.00033194353,0.000031015206,0.002381482],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947678,0.0035254306,0.0005594989,0.0001947575,0.0007549156,0.00019758992],"domain_scores_gemma":[0.98877746,0.008315932,0.0009600916,0.00038280126,0.0012500163,0.00031370757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056602545,0.00033102953,0.00040318453,0.00350378,0.0008100576,0.0045563774,0.00044371156,0.001074008,0.0022539159],"category_scores_gemma":[0.016087916,0.00016174155,0.0005272512,0.0051928577,0.0013231114,0.0050334022,0.0022304836,0.0008625347,0.00045384152],"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":[0.00013601716,0.00012547994,0.021995641,0.045416538,0.0004167681,0.0013902863,0.012633072,0.0013457945,0.0039946283,0.09147877,0.011663426,0.80940354],"study_design_scores_gemma":[0.00009257533,0.00051758817,0.039869737,0.09664482,0.0010834566,0.002372382,0.037974898,0.0022000382,0.006319291,0.08799804,0.7248114,0.00011579457],"about_ca_topic_score_codex":0.001956378,"about_ca_topic_score_gemma":0.003798017,"teacher_disagreement_score":0.0056602545,"about_ca_system_score_codex":0.0013750951,"about_ca_system_score_gemma":0.003640166,"threshold_uncertainty_score":0.029934645},"labels":[],"label_agreement":null},{"id":"W4410518263","doi":"10.3390/info16050417","title":"A Novel Method for Community Detection in Bipartite Networks","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","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","funders":"Islamic Azad University","keywords":"Bipartite graph; Computer science; Computational biology; Artificial intelligence; Theoretical computer science; Biology; Graph","score_opus":0.014898281808755846,"score_gpt":0.31294387044681987,"score_spread":0.298045588638064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410518263","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":[0.0038215898,0.00016844552,0.9943796,0.00008830928,0.000054704564,0.00007936121,0.000115712515,0.00054085156,0.00075141987],"genre_scores_gemma":[0.12356151,0.0003035711,0.87107706,0.00025281496,0.00012580548,0.0003655542,0.0008662493,0.00022124322,0.0032261738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971818,0.0008115836,0.00014913241,0.00075093436,0.00087331666,0.00023321637],"domain_scores_gemma":[0.99656206,0.0014233941,0.00036195904,0.00042075166,0.0010159833,0.00021587987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021231207,0.00096965634,0.0011611981,0.0047980966,0.0015886523,0.001595739,0.0020876862,0.0016577162,0.002139675],"category_scores_gemma":[0.00880499,0.00062551,0.0014471483,0.0034167771,0.0012633007,0.0023671428,0.0024143378,0.0015565663,0.0011753623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003380026,0.00019835199,0.0065678144,0.00068308273,0.00028763435,0.00049403927,0.000683517,0.08409913,0.036111467,0.09915926,0.018213963,0.7531639],"study_design_scores_gemma":[0.00004409699,0.00008783309,0.0018657956,0.00006837083,0.00006025642,0.00087941805,0.0001313215,0.918186,0.009832264,0.05338453,0.015373627,0.000086519205],"about_ca_topic_score_codex":0.0042842804,"about_ca_topic_score_gemma":0.0040919427,"teacher_disagreement_score":0.0047980966,"about_ca_system_score_codex":0.00094126083,"about_ca_system_score_gemma":0.0016313678,"threshold_uncertainty_score":0.011228263},"labels":[],"label_agreement":null},{"id":"W4411077753","doi":"10.3390/info16060476","title":"Benchmarking Variants of Recursive Feature Elimination: Insights from Predictive Tasks in Education and Healthcare","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Benchmarking; Feature (linguistics); Health care; Computer science; Artificial intelligence; Machine learning; Computational biology; Business; Political science; Biology; Philosophy","score_opus":0.004059977449946719,"score_gpt":0.25534205161912366,"score_spread":0.2512820741691769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411077753","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":[0.1659609,0.05759393,0.75781035,0.00567726,0.00036683402,0.0008147748,0.0025453046,0.0012818476,0.007948868],"genre_scores_gemma":[0.67750794,0.009184636,0.30647868,0.00088463636,0.00024961022,0.00060958345,0.003957663,0.0002137515,0.0009134378],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98454595,0.010198682,0.001210007,0.0012824566,0.0024350912,0.0003277578],"domain_scores_gemma":[0.9361192,0.054293215,0.0016661006,0.0034884338,0.0041560675,0.0002769133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02616871,0.0010885933,0.0014181864,0.003970171,0.0004803613,0.0020920737,0.0016671981,0.0011451641,0.0010245304],"category_scores_gemma":[0.10016976,0.00033887685,0.0017027922,0.005197915,0.0009370074,0.002789303,0.0014246507,0.0016470389,0.00036930013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065009913,0.0004490394,0.03917558,0.0035528378,0.0010157537,0.00026851572,0.00060013204,0.18368998,0.0013984934,0.02208577,0.0091135325,0.7380002],"study_design_scores_gemma":[0.00027921493,0.0016890957,0.029487686,0.001666724,0.00078668364,0.00072496367,0.00083269866,0.8659325,0.005991875,0.06643454,0.025960803,0.00021311372],"about_ca_topic_score_codex":0.004468117,"about_ca_topic_score_gemma":0.005876072,"teacher_disagreement_score":0.02616871,"about_ca_system_score_codex":0.0010196742,"about_ca_system_score_gemma":0.0019359456,"threshold_uncertainty_score":0.13839501},"labels":[],"label_agreement":null},{"id":"W4411565490","doi":"10.3390/info16070520","title":"Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Mount Royal University","funders":"","keywords":"Genetic predisposition; Artificial intelligence; Machine learning; Computer science; Medicine; Disease; Pathology","score_opus":0.01084278504879178,"score_gpt":0.26936436875101133,"score_spread":0.25852158370221956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411565490","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":[0.6648719,0.0006588197,0.32902595,0.0023566613,0.000048857382,0.00014755088,0.0012918175,0.00034000762,0.0012585875],"genre_scores_gemma":[0.9753032,0.00012444526,0.023673786,0.00009113896,0.000037446185,0.000042604664,0.00042411152,0.000007593671,0.00029571523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995696,0.00026470664,0.000019776653,0.000083443745,0.000029889692,0.00003255815],"domain_scores_gemma":[0.99642533,0.002947637,0.00028446564,0.00014404405,0.00012955665,0.00006884496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018830613,0.0005512389,0.00038869958,0.0010404885,0.00018902044,0.00063928345,0.0005301862,0.0004949249,0.000983865],"category_scores_gemma":[0.0064850086,0.00018183806,0.0006933345,0.00044883642,0.0002061367,0.0004007188,0.0005336084,0.00086590427,0.0000925754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030219718,0.00040745427,0.2964759,0.00014000727,0.00047009913,0.0004549436,0.00045665234,0.56625056,0.0014070913,0.007941663,0.0018074322,0.12388602],"study_design_scores_gemma":[0.0000098564815,0.00004989801,0.012255815,0.000015651396,0.0000357491,0.000033664106,0.000051965155,0.98190355,0.00017985543,0.0052061314,0.00024888455,0.000009004975],"about_ca_topic_score_codex":0.006754062,"about_ca_topic_score_gemma":0.007253786,"teacher_disagreement_score":0.006754062,"about_ca_system_score_codex":0.00044907889,"about_ca_system_score_gemma":0.00062661653,"threshold_uncertainty_score":0.0134295225},"labels":[],"label_agreement":null},{"id":"W4411801306","doi":"10.3390/info16070549","title":"Large Language Models in Medical Chatbots: Opportunities, Challenges, and the Need to Address AI Risks","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Data science","score_opus":0.2297562887951659,"score_gpt":0.4405707767466559,"score_spread":0.21081448795149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411801306","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":[0.029234936,0.019121973,0.8916868,0.031643517,0.0009843361,0.0005939803,0.00091845467,0.007754521,0.018061496],"genre_scores_gemma":[0.37739506,0.010629966,0.58790255,0.008269788,0.00073226023,0.0016282082,0.0022476837,0.0019920839,0.009202377],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9826003,0.011717726,0.00096281915,0.0013476529,0.0028554744,0.00051598647],"domain_scores_gemma":[0.9319694,0.05399886,0.002414995,0.0056027393,0.0045311768,0.001482794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02394812,0.000971267,0.0011462409,0.001755546,0.0013232216,0.0076597105,0.0037672638,0.002892975,0.0069397427],"category_scores_gemma":[0.06647654,0.0009649856,0.001847629,0.0011307423,0.0048819785,0.015929412,0.0062179705,0.0049542184,0.0036580993],"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":[0.00068504334,0.0003075925,0.0074349195,0.00621746,0.00036895365,0.0009452657,0.009707674,0.05218862,0.013342597,0.40329787,0.035009965,0.47049406],"study_design_scores_gemma":[0.00017881453,0.00047104215,0.0021032211,0.003535779,0.00032211276,0.0011927782,0.0031966353,0.20240608,0.009880699,0.47823858,0.2982031,0.0002711724],"about_ca_topic_score_codex":0.0036915094,"about_ca_topic_score_gemma":0.004717845,"teacher_disagreement_score":0.02394812,"about_ca_system_score_codex":0.0032672116,"about_ca_system_score_gemma":0.004927282,"threshold_uncertainty_score":0.12665129},"labels":[],"label_agreement":null},{"id":"W4412197197","doi":"10.3390/info16070591","title":"Multimodal Artificial Intelligence in Medical Diagnostics","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":45,"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":"Natural Sciences and Engineering Research Council of Canada; Université de Moncton","keywords":"Artificial intelligence; Computer science","score_opus":0.013359651990892242,"score_gpt":0.3255027380438889,"score_spread":0.3121430860529967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412197197","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":[0.014522967,0.31597733,0.6061482,0.020858113,0.0010805979,0.00019283105,0.00077225297,0.001096895,0.0393509],"genre_scores_gemma":[0.45870614,0.24115722,0.2833288,0.006726929,0.0024249738,0.00039558343,0.0009490494,0.00022341854,0.006087828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984635,0.00087403564,0.00008834749,0.00018533277,0.00032684975,0.0000619954],"domain_scores_gemma":[0.9980088,0.0014853022,0.00010333916,0.00012485284,0.00023102845,0.000046693804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003057677,0.00071199157,0.0008397087,0.0016903381,0.0003465161,0.0028874387,0.00089826755,0.0015717893,0.0029647353],"category_scores_gemma":[0.0053374353,0.0003130259,0.00068398425,0.0016867011,0.0017528523,0.0023265998,0.0017194836,0.0018343995,0.00071723544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016157747,0.000074736,0.003236592,0.003418581,0.00033169214,0.00034044645,0.00049645617,0.038019665,0.0050208406,0.20804566,0.020150565,0.72070324],"study_design_scores_gemma":[0.000047921636,0.00026990508,0.004729229,0.0032020218,0.00029778117,0.0014165116,0.00048874755,0.15952455,0.008503553,0.6021921,0.21913351,0.00019421206],"about_ca_topic_score_codex":0.0011732382,"about_ca_topic_score_gemma":0.0011436015,"teacher_disagreement_score":0.003057677,"about_ca_system_score_codex":0.0011413933,"about_ca_system_score_gemma":0.00097548246,"threshold_uncertainty_score":0.01617074},"labels":[],"label_agreement":null},{"id":"W4412631349","doi":"10.3390/info16080633","title":"Indoor Positioning and Tracking System in a Multi-Level Residential Building Using WiFi","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Indoor and Outdoor Localization Technologies","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":"University of the Fraser Valley","funders":"","keywords":"Indoor positioning system; Tracking (education); Tracking system; Computer science; Real-time computing; Environmental science; Architectural engineering; Engineering; Artificial intelligence; Operating system; Kalman filter; Accelerometer","score_opus":0.020691550803158244,"score_gpt":0.25554493243445925,"score_spread":0.234853381631301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412631349","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":[0.63972896,0.00016008304,0.34407657,0.0002134411,0.00010661471,0.00019348024,0.00035312222,0.0065949364,0.008572768],"genre_scores_gemma":[0.9434055,0.000049234455,0.053340495,0.000048834496,0.000013394658,0.0000743655,0.000119607736,0.000022036553,0.0029264619],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970216,0.000047537196,0.000014004088,0.000070907605,0.000098170196,0.00006708603],"domain_scores_gemma":[0.9997812,0.000020515083,0.00003203468,0.000056988356,0.00007466486,0.000034660385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022580892,0.0003335782,0.00040210117,0.00038005822,0.0003727011,0.00038045098,0.0006759127,0.00042372735,0.0020298285],"category_scores_gemma":[0.00038392385,0.00012849945,0.00025545343,0.00044617502,0.00014692766,0.00041819073,0.0006552343,0.00022298373,0.0010223766],"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":[0.0008061125,0.000741714,0.07229269,0.0004891483,0.00016578595,0.0032326,0.0010246465,0.07277426,0.28075272,0.0033254868,0.007931409,0.55646336],"study_design_scores_gemma":[0.0001447092,0.0027276669,0.09268463,0.00008759137,0.00028926652,0.0028717807,0.0008421159,0.6812705,0.19205883,0.0010210128,0.025820276,0.00018163762],"about_ca_topic_score_codex":0.0043470254,"about_ca_topic_score_gemma":0.004672222,"teacher_disagreement_score":0.0043470254,"about_ca_system_score_codex":0.0002611144,"about_ca_system_score_gemma":0.00044033272,"threshold_uncertainty_score":0.008643448},"labels":[],"label_agreement":null},{"id":"W4412956487","doi":"10.3390/info16080665","title":"Deep Learning-Enhanced Ocean Acoustic Tomography: A Latent Feature Fusion Framework for Hydrographic Inversion with Source Characteristic Embedding","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Underwater Acoustics Research","field":"Earth and Planetary 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":"National Natural Science Foundation of China","keywords":"Hydrography; Inversion (geology); Embedding; Tomography; Geology; Fusion; Feature (linguistics); Computer science; Artificial intelligence; Oceanography; Seismology; Physics; Linguistics; Optics; Philosophy","score_opus":0.006801233717996004,"score_gpt":0.2306147961127694,"score_spread":0.22381356239477337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412956487","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":[0.0045013316,0.00015863372,0.99421304,0.00010100697,0.000023592316,0.00001052833,0.00009834052,0.00037911028,0.00051435234],"genre_scores_gemma":[0.49250612,0.0007110248,0.49914363,0.0003214002,0.00015171603,0.00013721129,0.0016977955,0.00028591216,0.00504519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971837,0.00007567543,0.000014102636,0.00007128411,0.00008246373,0.00003812341],"domain_scores_gemma":[0.999556,0.00017231816,0.00006811043,0.00006368474,0.000105161635,0.000034565142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006495766,0.00090792374,0.0005581824,0.00069310324,0.00023712311,0.0006567204,0.0012125728,0.0005802117,0.001749449],"category_scores_gemma":[0.0020490198,0.00032966523,0.0008528775,0.00095439656,0.0005114105,0.0012345832,0.0015843916,0.0015750771,0.0005332197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016554404,0.00012979309,0.0030849958,0.0001830244,0.00020313037,0.0001627436,0.0001444099,0.4942089,0.016937608,0.029553458,0.0057967897,0.4494296],"study_design_scores_gemma":[0.0000039353736,0.000012003763,0.00015435739,0.0000046314613,0.000008672693,0.000018350918,0.000007766726,0.99203205,0.001379726,0.0055402643,0.00083211187,0.0000060847137],"about_ca_topic_score_codex":0.0047043227,"about_ca_topic_score_gemma":0.0063721933,"teacher_disagreement_score":0.0047043227,"about_ca_system_score_codex":0.00046117592,"about_ca_system_score_gemma":0.0009551926,"threshold_uncertainty_score":0.009353876},"labels":[],"label_agreement":null},{"id":"W4413122761","doi":"10.3390/info16080676","title":"An Approximate Algorithm for Sparse Distributionally Robust Optimization","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Risk and Portfolio Optimization","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":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Robust optimization; Computer science; Algorithm; Mathematical optimization; Optimization algorithm; Mathematics","score_opus":0.04056409178548765,"score_gpt":0.34445253107084367,"score_spread":0.303888439285356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413122761","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":[0.0011509863,0.000049597787,0.9980646,0.00005821369,0.000011426304,0.000014757983,0.000011422535,0.00010228832,0.0005367228],"genre_scores_gemma":[0.19451496,0.00023304603,0.80130374,0.00020288986,0.00008447325,0.00023105038,0.00018786984,0.00015958546,0.0030823203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99932814,0.00023545015,0.00003401651,0.00011446189,0.0002291386,0.000058790498],"domain_scores_gemma":[0.998798,0.0007151257,0.000108091204,0.00015863002,0.00017269417,0.00004751074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001334945,0.00076976966,0.0012348472,0.00058487395,0.00037386068,0.0009901328,0.0012401413,0.0012803118,0.0035550236],"category_scores_gemma":[0.0040754564,0.00045533717,0.0006715533,0.0007648166,0.000765483,0.0013081795,0.0015724613,0.0018051639,0.0008623649],"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":[0.00006347221,0.000047619254,0.00031668594,0.00006877678,0.000036457262,0.000049426737,0.000043613123,0.84255606,0.0019474438,0.059042655,0.0019788872,0.09384888],"study_design_scores_gemma":[0.000006111236,0.000010602103,0.000014465537,0.000002483135,0.0000016756912,0.000010362602,0.0000024348794,0.9927671,0.00016703161,0.0066699353,0.0003456743,0.0000021604408],"about_ca_topic_score_codex":0.0021515612,"about_ca_topic_score_gemma":0.0020683953,"teacher_disagreement_score":0.0035550236,"about_ca_system_score_codex":0.0007797189,"about_ca_system_score_gemma":0.0012253906,"threshold_uncertainty_score":0.011892736},"labels":[],"label_agreement":null},{"id":"W4413721790","doi":"10.3390/info16090728","title":"On Information-Theoretic Scaling Laws for Wireless Networks","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scaling law; Wireless; Computer science; Wireless network; Law; Scaling; Theoretical computer science; Mathematics; Mathematical economics; Computer network; Political science; Telecommunications; Geometry","score_opus":0.011118458092443882,"score_gpt":0.25926324725690025,"score_spread":0.24814478916445637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413721790","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":[0.030002745,0.009655201,0.86417043,0.0044050915,0.0006192041,0.000090267895,0.00023712512,0.00035440273,0.09046554],"genre_scores_gemma":[0.859667,0.017733594,0.100921616,0.0022138667,0.0028337128,0.0005357688,0.00028124906,0.00038399574,0.015429267],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884474,0.00038780356,0.00005333545,0.00014856279,0.00044542982,0.00012003614],"domain_scores_gemma":[0.9926603,0.0055774865,0.00036416948,0.00062001095,0.00063032535,0.00014765801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023930431,0.0009816736,0.00077643833,0.0014307554,0.00084194884,0.002186629,0.0009337416,0.0012775948,0.0036486972],"category_scores_gemma":[0.014045403,0.0003962179,0.0006028575,0.0012593049,0.0034059258,0.004889942,0.0013963226,0.003611393,0.00082919677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006437331,0.000013595456,0.00010382037,0.000055722707,0.0000059573636,0.000055064593,0.000100398785,0.011093554,0.0005573276,0.9807344,0.001635931,0.005637773],"study_design_scores_gemma":[0.000005588915,0.000016323165,0.00012383366,0.00004358909,0.000004430219,0.000059272003,0.000028900424,0.09127365,0.00025282186,0.903867,0.0043126214,0.000012028901],"about_ca_topic_score_codex":0.0009318587,"about_ca_topic_score_gemma":0.00042812582,"teacher_disagreement_score":0.0036486972,"about_ca_system_score_codex":0.0015416882,"about_ca_system_score_gemma":0.00062324986,"threshold_uncertainty_score":0.012655735},"labels":[],"label_agreement":null},{"id":"W4413950888","doi":"10.3390/info16090765","title":"Toward Decentralized Intelligence: A Systematic Literature Review of Blockchain-Enabled AI Systems","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Blockchain Technology Applications and Security","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":"Vancouver Island University","funders":"","keywords":"Blockchain; Computer science; Computer security","score_opus":0.007754707267351123,"score_gpt":0.25365613012518096,"score_spread":0.24590142285782984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413950888","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":[0.000521908,0.9968166,0.0006570884,0.0007595046,0.000102693266,0.000052249747,0.00012994665,0.000008955977,0.00095107226],"genre_scores_gemma":[0.0043686433,0.99394315,0.00093543966,0.00039532455,0.000064071166,0.00006345421,0.00012714762,0.0000049967603,0.00009767245],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9964296,0.001183115,0.0011168679,0.00032390587,0.00080519926,0.00014121631],"domain_scores_gemma":[0.9719883,0.022547156,0.0022287883,0.00043621808,0.0024832496,0.0003163181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005112228,0.00094044144,0.0024721678,0.013644304,0.00068283663,0.0029965618,0.0012676137,0.0015186982,0.0047418457],"category_scores_gemma":[0.022592433,0.00064999366,0.0019647176,0.014226838,0.0011931179,0.003592667,0.0016496974,0.0015139717,0.0006308823],"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":[0.000093896466,0.000059476828,0.001084519,0.50732446,0.00092564325,0.00028836945,0.0011827082,0.0009861835,0.00052323367,0.009639558,0.010508908,0.46738297],"study_design_scores_gemma":[0.000035409834,0.00016266758,0.003410264,0.66485834,0.0036534453,0.0007389166,0.0011817549,0.00042245624,0.00036484297,0.00766115,0.31745076,0.00006000413],"about_ca_topic_score_codex":0.005196948,"about_ca_topic_score_gemma":0.014247503,"teacher_disagreement_score":0.013644304,"about_ca_system_score_codex":0.0020493309,"about_ca_system_score_gemma":0.015985744,"threshold_uncertainty_score":0.027036369},"labels":[],"label_agreement":null},{"id":"W4415282304","doi":"10.3390/info16100904","title":"Can We Trust AI Content Detection Tools for Critical Decision-Making?","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Government of Manitoba","funders":"","keywords":"Government (linguistics); Robustness (evolution); Precision and recall; Recall; Reliability (semiconductor); Content analysis","score_opus":0.03774558943092063,"score_gpt":0.3273950318151122,"score_spread":0.28964944238419155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415282304","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":[0.06283611,0.016343916,0.54584885,0.25366938,0.004540834,0.0010417826,0.0017172158,0.019634347,0.094367675],"genre_scores_gemma":[0.56948376,0.0039630234,0.39400214,0.018313715,0.0017209443,0.00090572354,0.0013162795,0.002969218,0.007325185],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.89395434,0.062168136,0.007032021,0.011296357,0.021866307,0.0036828972],"domain_scores_gemma":[0.39360562,0.4071891,0.035609808,0.08887563,0.061926734,0.012793041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12625623,0.002092443,0.0021420533,0.010798087,0.004293248,0.0334196,0.00563793,0.007781909,0.012260419],"category_scores_gemma":[0.5031828,0.0017947656,0.0013066947,0.0047928807,0.014797506,0.05855674,0.01206974,0.007894453,0.013528929],"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":[0.0012017157,0.00042501136,0.02881881,0.0029167808,0.00040336666,0.0006323291,0.023081234,0.0057410477,0.0048786504,0.17358376,0.09391791,0.6643994],"study_design_scores_gemma":[0.00027116964,0.00042010008,0.008054772,0.0035292576,0.00024324383,0.0009307205,0.011129239,0.040214423,0.0100901695,0.6074463,0.31717452,0.00049612956],"about_ca_topic_score_codex":0.005298702,"about_ca_topic_score_gemma":0.0035474168,"teacher_disagreement_score":0.12625623,"about_ca_system_score_codex":0.006201313,"about_ca_system_score_gemma":0.014912323,"threshold_uncertainty_score":0.6677147},"labels":[],"label_agreement":null},{"id":"W4415683709","doi":"10.3390/info16110937","title":"DefAn: Definitive Answer Dataset for LLM Hallucination Evaluation","year":2025,"lang":"en","type":"article","venue":"Information","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":"Ontario Tech University","funders":"King Fahd University of Petroleum and Minerals","keywords":"Benchmark (surveying); Benchmarking; Consistency (knowledge bases); Scope (computer science); Scale (ratio); Generative grammar","score_opus":0.03434413762232726,"score_gpt":0.3188867060780858,"score_spread":0.28454256845575854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415683709","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10982383,0.007917889,0.043905634,0.0037240263,0.0018423949,0.0035688467,0.7105834,0.08457306,0.034060907],"genre_scores_gemma":[0.072563246,0.000614227,0.046717294,0.0012175088,0.0001417747,0.0019539448,0.8673287,0.0008680713,0.008595258],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99548006,0.001837879,0.0005125175,0.0008565996,0.0011000737,0.00021287819],"domain_scores_gemma":[0.99199486,0.0039715148,0.00041885613,0.0016833195,0.001517246,0.0004142243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036120536,0.003385186,0.0010132804,0.0018635687,0.0010183685,0.0014586861,0.0033524083,0.003268928,0.01785914],"category_scores_gemma":[0.020047404,0.00042662694,0.0013987215,0.0013106888,0.0010095581,0.0031580732,0.003277986,0.0032310283,0.015120087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014590687,0.0009593742,0.0050774347,0.0032648847,0.00017786302,0.0005680969,0.00063985056,0.00797874,0.00487183,0.0028920285,0.85012114,0.121989645],"study_design_scores_gemma":[0.001960077,0.0017948697,0.017392004,0.0009000345,0.00019076046,0.0016373917,0.002321475,0.14153567,0.025339298,0.013350659,0.7932532,0.00032468734],"about_ca_topic_score_codex":0.0079481695,"about_ca_topic_score_gemma":0.015922336,"teacher_disagreement_score":0.01785914,"about_ca_system_score_codex":0.0016143756,"about_ca_system_score_gemma":0.0019939202,"threshold_uncertainty_score":0.059744716},"labels":[],"label_agreement":null},{"id":"W4416349845","doi":"10.3390/info16111008","title":"Balanced Neonatal Cry Classification: Integrating Preterm and Full-Term Data for RDS Screening","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Infant Health and Development","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Mel-frequency cepstrum; Neonatal respiratory distress syndrome; Respiratory distress; Cepstrum; Distress; Neonatal mortality","score_opus":0.08260823355175503,"score_gpt":0.43071969632333484,"score_spread":0.3481114627715798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416349845","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":[0.9291744,0.0015647296,0.062599845,0.000367983,0.00019362144,0.00031397934,0.0031078444,0.0008707637,0.0018069798],"genre_scores_gemma":[0.9460692,0.00038974802,0.045500353,0.00013290596,0.00011593624,0.00035503565,0.0059360797,0.00009348971,0.001407235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916184,0.00029553182,0.00007451086,0.00022866414,0.00012159407,0.000117827054],"domain_scores_gemma":[0.99892515,0.00043221208,0.00009909282,0.00014601304,0.00027613048,0.00012136197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002229239,0.0008975472,0.0008796888,0.0012523673,0.00024170292,0.0006017691,0.00046617855,0.00077065,0.0019465171],"category_scores_gemma":[0.0052473396,0.00015075617,0.00061140995,0.0005306071,0.00022367854,0.00052820466,0.0012686417,0.00057809777,0.0010038706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008048887,0.0009785341,0.21064827,0.00055876345,0.00047432675,0.00061872066,0.00053108955,0.014261798,0.06802332,0.0005586068,0.007499695,0.6877981],"study_design_scores_gemma":[0.0003813365,0.0040491913,0.51024336,0.00030759754,0.00083469314,0.0016358668,0.00139775,0.4357449,0.03289024,0.0030154698,0.009326642,0.00017282176],"about_ca_topic_score_codex":0.0028962742,"about_ca_topic_score_gemma":0.003240813,"teacher_disagreement_score":0.0028962742,"about_ca_system_score_codex":0.00025902534,"about_ca_system_score_gemma":0.0005882358,"threshold_uncertainty_score":0.011789501},"labels":[],"label_agreement":null},{"id":"W4416913708","doi":"10.3390/info16121058","title":"Beyond Accuracy: Explainable Deep Learning for Alzheimer’s Disease Detection Using Structural MRI Data","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Explainable Artificial Intelligence (XAI)","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":"Wilfrid Laurier University","funders":"Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Northern California Institute for Research and Education; Servier; BioClinica; University of Southern California; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; Alliance de recherche numérique du Canada; Eisai; Alzheimer's Association; U.S. Department of Defense","keywords":"Interpretability; Deep learning; Convolutional neural network; Neuroimaging; Magnetic resonance imaging; Kernel (algebra); Artificial neural network; Limit (mathematics)","score_opus":0.04876642285899911,"score_gpt":0.32868793085896414,"score_spread":0.279921507999965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416913708","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":[0.25486282,0.0040060133,0.7252855,0.0066324184,0.00016893908,0.00017330513,0.0015459928,0.0031924776,0.0041325595],"genre_scores_gemma":[0.93803567,0.00049067725,0.05905145,0.00044091034,0.00007933506,0.000058518206,0.0010132542,0.00008297261,0.00074733666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781895,0.0010797057,0.00013597136,0.00041709456,0.0004046276,0.00014366656],"domain_scores_gemma":[0.98423266,0.011776788,0.0011961532,0.0015267995,0.0010230178,0.00024453417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005677462,0.0012400133,0.00063025573,0.0014848341,0.0004251603,0.0018371859,0.0011850721,0.0014591146,0.0013011366],"category_scores_gemma":[0.029685182,0.0002852105,0.00071630446,0.000702482,0.0010843136,0.0027083047,0.0018106229,0.00224786,0.00024754563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075816043,0.00030824452,0.056184966,0.00053944846,0.0006084799,0.00041038916,0.00083289435,0.45118973,0.0038984441,0.034722846,0.008291809,0.44225463],"study_design_scores_gemma":[0.000018567467,0.00008353865,0.0024600865,0.00008799995,0.000059742597,0.000057508933,0.000043631695,0.95034575,0.002098265,0.043523826,0.0012027341,0.000018235518],"about_ca_topic_score_codex":0.0043485668,"about_ca_topic_score_gemma":0.0053327666,"teacher_disagreement_score":0.005677462,"about_ca_system_score_codex":0.0014229876,"about_ca_system_score_gemma":0.0011274215,"threshold_uncertainty_score":0.030025661},"labels":[],"label_agreement":null},{"id":"W4416941840","doi":"10.3390/info16121063","title":"ESG Communication Tactics and Reputational Capital on Social Media","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Corporate Identity and Reputation","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":"York University; Regional Municipality of Niagara; Niagara College","funders":"Schulich School of Business, York University; York University","keywords":"Categorization; Social media; Corporate communication; Dissemination; Reputation; Strategic communication; Corporate social responsibility","score_opus":0.015771093798016056,"score_gpt":0.22927334067523067,"score_spread":0.2135022468772146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416941840","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":[0.9942228,0.000071208146,0.0002550575,0.00013435894,0.000008015242,0.000015644264,0.00053393684,0.000011382496,0.0047475854],"genre_scores_gemma":[0.9989485,0.000035313282,0.00014324293,0.000011728551,0.000018193708,0.000008418083,0.00022252434,0.0000033871906,0.0006086701],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99890006,0.00034418117,0.000115305374,0.00015359798,0.00029860318,0.00018822125],"domain_scores_gemma":[0.9714947,0.014013069,0.0108664,0.0010378452,0.0017997787,0.0007881693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013462809,0.0002691402,0.00027367365,0.0029694128,0.0006934604,0.002857159,0.00035074743,0.00046387664,0.00374247],"category_scores_gemma":[0.013286322,0.00015220811,0.00020956335,0.0029987707,0.0005557399,0.0022867685,0.0014276848,0.00057700265,0.00073966075],"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":[0.00012541625,0.000097785734,0.97466135,0.00007008856,0.0000875655,0.00014159667,0.0034106679,0.00045018818,0.0011088288,0.000780215,0.00073156145,0.018334765],"study_design_scores_gemma":[0.0000031737989,0.000050151517,0.99141437,0.000021226862,0.00004132648,0.00009398694,0.0038302355,0.0021357064,0.00058652833,0.00033745472,0.0014639776,0.000021855752],"about_ca_topic_score_codex":0.0050341547,"about_ca_topic_score_gemma":0.008372469,"teacher_disagreement_score":0.0050341547,"about_ca_system_score_codex":0.00077395886,"about_ca_system_score_gemma":0.00027479164,"threshold_uncertainty_score":0.012519777},"labels":[],"label_agreement":null},{"id":"W4417196967","doi":"10.3390/info16121096","title":"A Data-Driven Topic Modeling Analysis of Blockchain in Food Supply Chain Traceability","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Food Supply Chain Traceability","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":"McGill University","funders":"","keywords":"Traceability; Supply chain; Latent Dirichlet allocation; Topic model; Food supply; Relation (database); Food safety; Scopus","score_opus":0.029167393641798316,"score_gpt":0.25124404740947914,"score_spread":0.22207665376768082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417196967","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":[0.37816358,0.031644378,0.55216724,0.005255998,0.00028799466,0.0021627857,0.021971174,0.0012951918,0.0070517454],"genre_scores_gemma":[0.85085654,0.005904686,0.12682052,0.0002828193,0.00015376134,0.002200933,0.011823741,0.0001046681,0.0018522703],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9935235,0.004082512,0.0006983649,0.0008456452,0.00064343854,0.0002065974],"domain_scores_gemma":[0.9498666,0.04434959,0.0020096353,0.0009957154,0.0024440554,0.00033446698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012298271,0.0007395856,0.0010621055,0.010342144,0.00081527274,0.0031224475,0.0009376639,0.0010632308,0.001941986],"category_scores_gemma":[0.041453328,0.00046938413,0.003270255,0.00870931,0.0005909019,0.0030246743,0.0018693116,0.0011004668,0.0007134781],"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":[0.0018818774,0.000592448,0.22520001,0.01921376,0.0044815694,0.0011877436,0.012533276,0.06482743,0.011093012,0.039338928,0.016340775,0.60330915],"study_design_scores_gemma":[0.0002810981,0.00072463247,0.08111805,0.0034697726,0.0039821714,0.00081091345,0.008415886,0.76464736,0.0059436634,0.075178236,0.055125844,0.0003024051],"about_ca_topic_score_codex":0.010250947,"about_ca_topic_score_gemma":0.013576396,"teacher_disagreement_score":0.012298271,"about_ca_system_score_codex":0.0011834212,"about_ca_system_score_gemma":0.003277849,"threshold_uncertainty_score":0.06504023},"labels":[],"label_agreement":null},{"id":"W4417493546","doi":"10.3390/info17010002","title":"Super Encryption Standard (SES): A Key-Dependent Block Cipher for Image Encryption","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Clair College","funders":"","keywords":"Encryption; Block cipher; Probabilistic encryption; Triple DES; Multiple encryption; Block cipher mode of operation; Deterministic encryption; Transposition cipher; Ciphertext","score_opus":0.005740996076622059,"score_gpt":0.24463605746742095,"score_spread":0.23889506139079889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417493546","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":[0.027272707,0.003046977,0.9462079,0.00048629948,0.00041413854,0.0004842839,0.00052252726,0.0025523026,0.019012881],"genre_scores_gemma":[0.45380735,0.004293865,0.5088669,0.00076810556,0.00027812348,0.00077990827,0.0021457924,0.0005922376,0.02846769],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99913174,0.00015264488,0.000091813905,0.000082321945,0.00046681322,0.00007466163],"domain_scores_gemma":[0.99924445,0.00012972819,0.00011243873,0.00022149591,0.00025207392,0.000039857347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008137965,0.0005134372,0.00041258353,0.00076728547,0.00032782266,0.0006828096,0.0007996244,0.0004921153,0.0030845585],"category_scores_gemma":[0.0015306431,0.00021594735,0.00046288254,0.00064263743,0.0006212797,0.0017223746,0.00092583155,0.0011622969,0.0020295987],"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":[0.00086573814,0.00015073743,0.001330635,0.0012054124,0.00014310866,0.00077642826,0.0002777307,0.02197726,0.2923474,0.2492722,0.034265522,0.39738786],"study_design_scores_gemma":[0.00018324821,0.0009841163,0.0014055053,0.00022388216,0.000107134496,0.0040192953,0.00009354503,0.17599036,0.4190126,0.036094014,0.36175025,0.00013601404],"about_ca_topic_score_codex":0.00034040763,"about_ca_topic_score_gemma":0.0005330251,"teacher_disagreement_score":0.0030845585,"about_ca_system_score_codex":0.00037256666,"about_ca_system_score_gemma":0.0009753421,"threshold_uncertainty_score":0.010318935},"labels":[],"label_agreement":null},{"id":"W7117800373","doi":"10.3390/info17010021","title":"AVI-SHIELD: An Explainable TinyML Cross-Platform Threat Detection Framework for Aviation Mobile Security","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Air Traffic Management and Optimization","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":"Software deployment; Android (operating system); Mobile device; Aviation; Overhead (engineering); Commercial aviation; Mobile computing","score_opus":0.006000935031184688,"score_gpt":0.2533912058490464,"score_spread":0.24739027081786172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117800373","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":[0.004476914,0.0005037996,0.7916737,0.00052498747,0.00015033386,0.00044036025,0.0088054,0.19049126,0.0029332023],"genre_scores_gemma":[0.15166523,0.0009803271,0.7763001,0.0014011863,0.00009784698,0.0019887602,0.03406015,0.027116302,0.0063901534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869007,0.00029597513,0.00014353785,0.0002579496,0.0005265821,0.000085847874],"domain_scores_gemma":[0.99792933,0.0012578168,0.00018460231,0.0003856534,0.00018686098,0.00005568887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016920762,0.0019288787,0.0005977092,0.0014328763,0.00045303928,0.0016943001,0.0037697451,0.0014222966,0.008788507],"category_scores_gemma":[0.010577039,0.0011017224,0.0022029222,0.00046440266,0.0011218452,0.0034593674,0.0030502412,0.0023506002,0.0028173418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011368204,0.00028813264,0.011033725,0.0041342746,0.00048174072,0.00152507,0.0015520644,0.27074376,0.038173087,0.112330556,0.24038608,0.31821468],"study_design_scores_gemma":[0.00014707867,0.00016646135,0.0014013728,0.0003093054,0.00008479997,0.00051587145,0.00010130505,0.7176279,0.029186996,0.05310171,0.19721445,0.00014274835],"about_ca_topic_score_codex":0.0073892605,"about_ca_topic_score_gemma":0.01557689,"teacher_disagreement_score":0.008788507,"about_ca_system_score_codex":0.0012055293,"about_ca_system_score_gemma":0.0016814652,"threshold_uncertainty_score":0.029400468},"labels":[],"label_agreement":null}]}