{"meta":{"query_hash":"4dfd76f1328d","filters":{"venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference"},"cohort_total":51,"direct_labels_cover":0,"predictions_cover":51,"exported":51,"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/4dfd76f1328d","api":"https://metacan.xera.ac/api/v1/cohort?venue=Proceedings+of+the+...+International+Florida+Artificial+Intelligence+Research+Society+Conference"},"results":[{"id":"W3160036765","doi":"10.32473/flairs.v34i1.128474","title":"Confusion detection using cognitive ability tests","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Confusion; Memorization; Cognition; Computer science; Support vector machine; Artificial intelligence; Cognitive psychology; Orientation (vector space); Psychology; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.23939369072749378,"score_gpt":0.4125526449569652,"score_spread":0.17315895422947142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160036765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98795044,0.000023391052,0.0043096025,0.001848832,0.0011719583,0.0003706221,0.00003480903,0.000051942698,0.0042384192],"genre_scores_gemma":[0.9984805,0.00009098292,0.0005316973,0.00019028402,0.00035490838,0.00002494513,0.0000013908317,0.000016390664,0.00030891577],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99642766,0.00009509473,0.0005708677,0.0007710458,0.001644272,0.0004910523],"domain_scores_gemma":[0.9939618,0.0011291418,0.00024110191,0.00017685752,0.0043757576,0.00011530508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014626471,0.0002016066,0.00022382375,0.00009178733,0.00058109406,0.0004900055,0.0012802802,0.00013679294,0.0002708333],"category_scores_gemma":[0.006148138,0.00016587439,0.00028063217,0.0010039549,0.0010047591,0.00055149995,0.0010926804,0.0008301278,0.000033491175],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070544054,0.00014401566,0.000562349,0.00004847317,0.000023327853,0.0000015344838,0.0016264233,0.000044897944,0.96561164,0.020522522,0.00007923728,0.011265059],"study_design_scores_gemma":[0.000044941797,0.000060397673,0.00026380847,0.00025962814,0.00000876575,0.000026815575,0.004525518,0.06571788,0.8934524,0.03530254,0.00018991508,0.00014739383],"about_ca_topic_score_codex":0.000119549884,"about_ca_topic_score_gemma":0.000029689727,"teacher_disagreement_score":0.07215922,"about_ca_system_score_codex":0.00024871927,"about_ca_system_score_gemma":0.00037409368,"threshold_uncertainty_score":0.736034},"labels":[],"label_agreement":null},{"id":"W3160310472","doi":"10.32473/flairs.v34i1.128502","title":"Multilingual Automatic Term Extraction in Low-Resource Domains","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Task (project management); Term (time); Artificial intelligence; Resource (disambiguation); Raw data; Sequence labeling; Sequence (biology); Domain (mathematical analysis); Artificial neural network; Natural language processing; Deep learning; Information extraction; Machine learning; Engineering","score_opus":0.08710697055732607,"score_gpt":0.4004800386628963,"score_spread":0.3133730681055702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160310472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8505075,0.00008193103,0.13532215,0.0063548163,0.0005591424,0.0005470475,0.0000080201,0.00023532445,0.0063840486],"genre_scores_gemma":[0.9739368,0.00013060796,0.025188362,0.00008563096,0.00016451206,0.00006448942,0.0000028288935,0.000014117333,0.00041265215],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961391,0.00005852997,0.0007726431,0.0007010722,0.001793482,0.0005351656],"domain_scores_gemma":[0.9961059,0.000489856,0.00028781226,0.0003944755,0.0026197105,0.00010224884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020783371,0.00020419352,0.00027449653,0.00022212458,0.00026464788,0.0005162154,0.0031892771,0.00014106295,0.00008844407],"category_scores_gemma":[0.0017622143,0.00017904733,0.00030410153,0.0017938617,0.000421635,0.0010611374,0.0014547565,0.0008989691,0.000023691831],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002526952,0.00048025954,0.0025781132,0.00013117168,0.00010840782,0.000011412711,0.006400238,0.0003392325,0.50172347,0.34442088,0.0005724651,0.14320908],"study_design_scores_gemma":[0.000037715126,0.000023528431,0.0005776777,0.0002894874,0.0000044572,0.000010968437,0.0026127663,0.19104607,0.715722,0.089251384,0.00026952778,0.00015440118],"about_ca_topic_score_codex":0.000068458125,"about_ca_topic_score_gemma":0.000078039324,"teacher_disagreement_score":0.25516948,"about_ca_system_score_codex":0.00044391546,"about_ca_system_score_gemma":0.00039742416,"threshold_uncertainty_score":0.73013383},"labels":[],"label_agreement":null},{"id":"W3160487084","doi":"10.32473/flairs.v34i1.128379","title":"Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Bayesian Methods and Mixture Models","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Agence Nationale de la Recherche; Equipex","keywords":"Cluster analysis; Inference; Artificial intelligence; Entropy (arrow of time); Mixture model; Unsupervised learning; Computer science; Categorization; Pattern recognition (psychology); BETA (programming language); Kullback–Leibler divergence; Machine learning; Algorithm; Mathematics; Physics","score_opus":0.11354511964787596,"score_gpt":0.3514613912485156,"score_spread":0.23791627160063966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160487084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072086183,0.00005978679,0.9799408,0.008205066,0.00042407203,0.00020922249,0.000021689602,0.000041716696,0.003889047],"genre_scores_gemma":[0.75647,0.00009456096,0.24242142,0.00018243837,0.00014047917,0.000028889315,0.0000070261567,0.0000128727015,0.0006423179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961953,0.00009940042,0.00067935267,0.0006078454,0.0019684678,0.00044959775],"domain_scores_gemma":[0.99227726,0.00066385925,0.0003476537,0.0002957641,0.006290858,0.00012458072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023690066,0.00020504264,0.00030669756,0.00014220978,0.0003190063,0.00031664694,0.0026373551,0.00017661514,0.00012297138],"category_scores_gemma":[0.0018216524,0.00017083746,0.0003946414,0.0012550843,0.0003925098,0.0005306484,0.001005578,0.00092564174,0.000008976924],"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":[0.0000259817,0.00011492049,0.00020888227,0.000059859452,0.000080759884,6.408471e-7,0.0015729078,0.0058600185,0.12534969,0.8593895,0.0007101502,0.006626724],"study_design_scores_gemma":[0.000035578796,0.00002478917,0.000022827831,0.00008333425,0.0000060406123,0.0000013517183,0.00020432351,0.5163112,0.27326256,0.20979807,0.00016419623,0.000085670334],"about_ca_topic_score_codex":0.000042675736,"about_ca_topic_score_gemma":0.000005380864,"teacher_disagreement_score":0.7492614,"about_ca_system_score_codex":0.00014128734,"about_ca_system_score_gemma":0.0011213229,"threshold_uncertainty_score":0.696655},"labels":[],"label_agreement":null},{"id":"W3160543801","doi":"10.32473/flairs.v34i1.128339","title":"An Exploration On-demand Article Recommender System for Cancer Patients Information Provisioning","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Provisioning; Recommender system; Baseline (sea); Cancer; Knowledge management; World Wide Web; Medicine","score_opus":0.158861643164401,"score_gpt":0.3893944126586932,"score_spread":0.2305327694942922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160543801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14022398,0.000031609015,0.8347767,0.015345321,0.0028515903,0.0017923133,0.000061959494,0.0002515856,0.004664911],"genre_scores_gemma":[0.99073344,0.00006125857,0.008418844,0.00014203809,0.00019833707,0.00037297842,0.000010045583,0.0000090136855,0.00005402687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975582,0.00004555496,0.0006014374,0.0003776753,0.0010855431,0.00033159423],"domain_scores_gemma":[0.99351746,0.00019252225,0.0002843402,0.00024018419,0.005674896,0.000090616086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017173012,0.00014273865,0.00017070246,0.00009640665,0.00044618247,0.0009862044,0.0015242219,0.00009708464,0.000014629483],"category_scores_gemma":[0.00041766115,0.00011291236,0.00015229048,0.000535586,0.00008268312,0.003554046,0.00039342497,0.00029781697,0.000009141509],"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":[0.000049593735,0.00022386802,0.0009941212,0.0001793336,0.00006563751,1.1928078e-7,0.0062029804,0.00017853125,0.017232988,0.8978759,0.004046939,0.07294997],"study_design_scores_gemma":[0.000086618806,0.00019423812,0.0001431636,0.00037805023,0.000004823206,0.000001151138,0.0068591437,0.24565884,0.6927763,0.05162973,0.002100978,0.00016696038],"about_ca_topic_score_codex":0.00008440596,"about_ca_topic_score_gemma":0.000012852986,"teacher_disagreement_score":0.85050946,"about_ca_system_score_codex":0.00032046586,"about_ca_system_score_gemma":0.0002461209,"threshold_uncertainty_score":0.95099914},"labels":[],"label_agreement":null},{"id":"W3160648873","doi":"10.32473/flairs.v34i1.128479","title":"Performance Metrics for State-Based Imitation Learning","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Anomaly Detection Techniques and Applications","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":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Perceptron; Artificial neural network; Machine learning; Imitation; Domain (mathematical analysis); State (computer science); Multilayer perceptron; Long short term memory; Layer (electronics); Recurrent neural network; Algorithm","score_opus":0.15105616236172434,"score_gpt":0.3704275475126838,"score_spread":0.21937138515095947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160648873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11160116,0.000025157824,0.8782082,0.0065593147,0.0003490965,0.0004549766,0.000010340224,0.00011734067,0.0026744045],"genre_scores_gemma":[0.9489668,0.00015979473,0.049623016,0.00008221294,0.00010271306,0.00020512076,0.000004228782,0.000010040109,0.0008460616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99783427,0.000020466725,0.0004154211,0.0004325763,0.0009734915,0.00032376268],"domain_scores_gemma":[0.99317884,0.0004336369,0.00020686854,0.00018755553,0.0059249164,0.00006815349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015695317,0.0001186304,0.0001335608,0.00012378689,0.000543406,0.00043814097,0.0017042033,0.00007552828,0.000027674516],"category_scores_gemma":[0.0010792667,0.00010427069,0.00021893976,0.0015306644,0.00021038823,0.00051083363,0.00045677653,0.00044412582,0.000011266995],"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":[0.00003789375,0.00016788296,0.0017512296,0.00013176966,0.00006191135,1.9925058e-7,0.0013616116,0.001721919,0.17616467,0.6794975,0.0016390071,0.1374644],"study_design_scores_gemma":[0.000020526037,0.000056910365,0.00012350453,0.000038254704,0.000002562124,0.000001149064,0.0005783896,0.4304607,0.5330801,0.033378385,0.0021843864,0.00007512879],"about_ca_topic_score_codex":0.000021171281,"about_ca_topic_score_gemma":0.0000038830726,"teacher_disagreement_score":0.8373656,"about_ca_system_score_codex":0.00018237821,"about_ca_system_score_gemma":0.00040980466,"threshold_uncertainty_score":0.42520356},"labels":[],"label_agreement":null},{"id":"W3160789541","doi":"10.32473/flairs.v34i1.128367","title":"Using Deep Learning algorithms to detect the success or failure of the Electroconvulsive Therapy (ECT) sessions","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Blood Pressure and Hypertension Studies","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Electroconvulsive therapy; Major depressive disorder; Electroencephalography; Mental health; Depression (economics); Health professionals; Session (web analytics); Psychology; Psychiatry; Magnetic resonance imaging; Mental healthcare; Health care; Medicine; Computer science; Cognition","score_opus":0.21262297420695128,"score_gpt":0.4176508145072,"score_spread":0.2050278403002487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160789541","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9372151,0.0013746764,0.0032997618,0.053422105,0.0008904951,0.0015561682,0.000020073005,0.0000459397,0.0021756813],"genre_scores_gemma":[0.9952501,0.000832719,0.0019297744,0.0006305681,0.00040201427,0.000060987313,0.0000010944021,0.000019968053,0.0008727646],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99698895,0.00008196821,0.00050498283,0.0003970661,0.0015998966,0.00042713844],"domain_scores_gemma":[0.99195856,0.00070393237,0.00022527226,0.00024434552,0.006774592,0.00009330479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012507715,0.00018750064,0.0003733968,0.00006703609,0.00080779847,0.00014962998,0.0011631264,0.000113366994,0.00023269352],"category_scores_gemma":[0.0024453762,0.00009141127,0.000363276,0.0012756474,0.0006107995,0.0001510274,0.0009545566,0.0011022976,0.00000617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041357626,0.00013912441,0.0033717207,0.00009711983,0.0008382006,0.0000031351733,0.008423677,0.0001389175,0.9544232,0.008394202,0.0022908743,0.02146628],"study_design_scores_gemma":[0.000099376484,0.00015281313,0.0007995661,0.00045423905,0.00009267467,0.00004705759,0.02128927,0.008401048,0.9603813,0.003738236,0.0044255224,0.000118855336],"about_ca_topic_score_codex":0.00019268776,"about_ca_topic_score_gemma":0.00010196128,"teacher_disagreement_score":0.058035012,"about_ca_system_score_codex":0.000074922165,"about_ca_system_score_gemma":0.0006247148,"threshold_uncertainty_score":0.6213014},"labels":[],"label_agreement":null},{"id":"W3161484751","doi":"10.32473/flairs.v34i1.128508","title":"Representing Time Series Data in Intelligent Training Systems","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Time Series Analysis and Forecasting","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":"Lockheed Martin (Canada)","funders":"","keywords":"Dynamic time warping; Computer science; Embedding; Simple (philosophy); Euclidean distance; Time series; Representation (politics); Series (stratigraphy); Artificial intelligence; Machine learning; Data mining","score_opus":0.2591728554641427,"score_gpt":0.37115377840400965,"score_spread":0.11198092293986694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161484751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50549704,0.0023165806,0.2966237,0.06817406,0.008689521,0.0027394844,0.00024160328,0.00059389125,0.11512411],"genre_scores_gemma":[0.9876186,0.00030889577,0.009354768,0.000037688314,0.00037667697,0.000028122857,0.0000140586335,0.00001637857,0.0022448155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568295,0.00006723838,0.0009384752,0.00093037676,0.0017526426,0.00062833895],"domain_scores_gemma":[0.9954568,0.00039302817,0.00031855528,0.0007469473,0.0029765463,0.0001080986],"candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.004541498,0.00019627089,0.0003458339,0.00014827179,0.00035208557,0.0013089613,0.005832138,0.00010742254,0.00012351331],"category_scores_gemma":[0.0027028392,0.00016664564,0.0002083471,0.0019029321,0.00037979826,0.0017745842,0.0051705264,0.00072292285,0.000038974387],"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":[0.00003498024,0.00016449772,0.0018786159,0.00013241057,0.00023046072,0.000011063742,0.011367431,0.0013292839,0.08760439,0.85067075,0.0018217065,0.044754416],"study_design_scores_gemma":[0.000030238205,0.000032158237,0.00011440729,0.0004562918,0.000008991299,0.00003326341,0.02257841,0.8319176,0.10872154,0.032985676,0.0028866294,0.00023478939],"about_ca_topic_score_codex":0.0002873778,"about_ca_topic_score_gemma":0.000053481446,"teacher_disagreement_score":0.83058834,"about_ca_system_score_codex":0.00017846296,"about_ca_system_score_gemma":0.00049069186,"threshold_uncertainty_score":0.9997278},"labels":[],"label_agreement":null},{"id":"W3162322285","doi":"10.32473/flairs.v34i1.128427","title":"Ensemble-based Semi-Supervised Learning for Hate Speech Detection","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Hate Speech and Cyberbullying Detection","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 Regina","funders":"","keywords":"Leverage (statistics); Computer science; Ensemble learning; Artificial intelligence; Labeled data; Voice activity detection; Machine learning; Supervised learning; Natural language processing; Speech recognition; Speech processing; Artificial neural network","score_opus":0.10150214186858707,"score_gpt":0.3376627144692195,"score_spread":0.23616057260063245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162322285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22616935,0.000049608818,0.75980127,0.0074840332,0.0017905044,0.0007203536,0.000007419265,0.00018197212,0.0037954927],"genre_scores_gemma":[0.98179513,0.000081211976,0.016413737,0.000112641974,0.0003562587,0.00012629743,0.000004369744,0.000018587272,0.0010917821],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967236,0.00005457744,0.0005132316,0.00068128796,0.001457823,0.0005695153],"domain_scores_gemma":[0.9931443,0.00045023335,0.0002049895,0.00025631278,0.0058204494,0.00012374845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022272356,0.00019734522,0.00021425345,0.00013224466,0.00070532825,0.00082309433,0.0019460723,0.0001622678,0.000055467575],"category_scores_gemma":[0.001869183,0.00017605537,0.000389223,0.0012105345,0.00023563887,0.0006266777,0.00057579693,0.00076716265,0.000031786465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007353141,0.00009520501,0.00013936697,0.00009288476,0.000066774475,0.0000013824273,0.00090673263,0.00062126626,0.7946385,0.065905906,0.00034040585,0.13711804],"study_design_scores_gemma":[0.00005462334,0.00008437897,0.000024324781,0.000089922505,0.0000052271257,0.0000076928,0.0010519607,0.3283182,0.63841665,0.030379947,0.0014484542,0.000118624135],"about_ca_topic_score_codex":0.0000852781,"about_ca_topic_score_gemma":0.00004035001,"teacher_disagreement_score":0.7556258,"about_ca_system_score_codex":0.00025511815,"about_ca_system_score_gemma":0.00044762128,"threshold_uncertainty_score":0.7937117},"labels":[],"label_agreement":null},{"id":"W3163052523","doi":"10.32473/flairs.v34i1.128478","title":"One game show, two boys, two aces, three prisoners - what’s an AI to do?","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Counterintuitive; Simple (philosophy); Representation (politics); Pearl; Dice; Mathematical economics; Psychology; Computer science; Epistemology; Mathematics; Statistics; Philosophy; Law","score_opus":0.16881770852735323,"score_gpt":0.4016206498379394,"score_spread":0.23280294131058615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163052523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37742943,0.00036694476,0.5385489,0.07347769,0.004902626,0.0017059781,0.000035552708,0.00032800087,0.003204887],"genre_scores_gemma":[0.9525094,0.00020108809,0.045206755,0.0007361076,0.00085167,0.00012924912,0.0000057789375,0.000030371131,0.00032956194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9926272,0.00010646585,0.00096578477,0.0015595268,0.003680316,0.0010607222],"domain_scores_gemma":[0.9889233,0.00052293856,0.00026470944,0.0008863557,0.008942704,0.00045999515],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.004065016,0.00037244512,0.0004654596,0.00021199121,0.0004741407,0.0037678606,0.007669947,0.00015391727,0.00023088607],"category_scores_gemma":[0.0017262272,0.00034627024,0.00040262315,0.0023407326,0.00071541866,0.0032212592,0.004807475,0.0013136092,0.00010955089],"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":[0.00006778554,0.0006414169,0.001415923,0.00006947954,0.00016633478,0.0000042878632,0.008854099,0.0012396568,0.07595581,0.7024374,0.001361618,0.2077862],"study_design_scores_gemma":[0.00011412468,0.00021469449,0.0006549959,0.00030623554,0.000012010616,0.000016219465,0.0053470396,0.3036405,0.28053105,0.40722102,0.0014877544,0.00045430867],"about_ca_topic_score_codex":0.00042078056,"about_ca_topic_score_gemma":0.00035081865,"teacher_disagreement_score":0.57508,"about_ca_system_score_codex":0.0006094086,"about_ca_system_score_gemma":0.001098748,"threshold_uncertainty_score":0.9998989},"labels":[],"label_agreement":null},{"id":"W3163104826","doi":"10.32473/flairs.v34i1.128490","title":"Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Mixture model; Robustness (evolution); Cluster analysis; Class (philosophy); Computer science; Artificial intelligence; Synthetic data; Machine learning; Pattern recognition (psychology); Data mining","score_opus":0.16453687586872798,"score_gpt":0.3525063937222555,"score_spread":0.18796951785352753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163104826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04836265,0.00004033152,0.9182959,0.013848341,0.00043339853,0.00037307944,0.00001923555,0.00014062175,0.018486438],"genre_scores_gemma":[0.93034375,0.000047944024,0.06723307,0.00033268833,0.00013083573,0.000040329298,0.0000072368694,0.000020059284,0.0018441009],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957889,0.00007680299,0.0005286589,0.0007446961,0.0022514025,0.0006095794],"domain_scores_gemma":[0.99258894,0.00046091605,0.00023594125,0.00028379506,0.006241781,0.00018862652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001999428,0.000248361,0.00026172402,0.00012630194,0.0005923365,0.0009731233,0.0026396138,0.00012489282,0.00023285933],"category_scores_gemma":[0.0014019959,0.00019950845,0.00025090226,0.001250282,0.0008029864,0.0008235262,0.00078566093,0.0013078932,0.000033493612],"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":[0.000093924165,0.0001899876,0.0011101888,0.00008553512,0.00014506112,0.000006600214,0.006294235,0.04268352,0.22771706,0.69464576,0.0007883244,0.02623983],"study_design_scores_gemma":[0.00010843741,0.000050106097,0.00007136372,0.00020263248,0.000006381948,0.000011782453,0.0047791186,0.7820833,0.1941757,0.017921565,0.0003941704,0.00019545543],"about_ca_topic_score_codex":0.000037427464,"about_ca_topic_score_gemma":0.000020409761,"teacher_disagreement_score":0.8819811,"about_ca_system_score_codex":0.00019763538,"about_ca_system_score_gemma":0.001352792,"threshold_uncertainty_score":0.93838507},"labels":[],"label_agreement":null},{"id":"W3163694547","doi":"10.32473/flairs.v34i1.128506","title":"Covid-19 News Clustering using MCMC-Based Learing of finite EMSD Mixture Models","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Bayesian Methods and Mixture Models","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":"Concordia University","funders":"","keywords":"Markov chain Monte Carlo; Cluster analysis; Computer science; Mixture model; Multinomial distribution; Artificial intelligence; Bayesian probability; Machine learning; Generative model; Flexibility (engineering); Task (project management); Dirichlet distribution; Statistical model; Data mining; Generative grammar; Mathematics; Statistics; Engineering","score_opus":0.28698765060979586,"score_gpt":0.4139066479645853,"score_spread":0.12691899735478945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163694547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013032485,0.0001077558,0.97673887,0.006628909,0.00061202643,0.00027354775,0.000016650292,0.000045922217,0.0025438585],"genre_scores_gemma":[0.737291,0.00013261831,0.26168132,0.00045199948,0.00018622926,0.000018948162,0.0000020046166,0.000016877322,0.00021900567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99616027,0.0000967507,0.0007611437,0.00072613824,0.0017022868,0.00055339595],"domain_scores_gemma":[0.9945044,0.0007019187,0.00036048787,0.00043568312,0.003716592,0.00028089687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026337178,0.00023542024,0.00035332446,0.00016551091,0.0003923948,0.00046605646,0.0031977212,0.0001859592,0.000076601245],"category_scores_gemma":[0.0026301546,0.00020334471,0.00040744574,0.0013139448,0.0004629721,0.0008331793,0.0017589801,0.0007945975,0.0000032241974],"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":[0.0000599197,0.00014600602,0.00023035872,0.00034446927,0.0001116279,0.000004080975,0.0052611385,0.02864238,0.24830979,0.69686574,0.00049421657,0.019530289],"study_design_scores_gemma":[0.000039703154,0.000023266208,0.0000039491692,0.00016455652,0.0000063419716,0.000005958861,0.0008005698,0.57790697,0.22414786,0.19661303,0.0001689203,0.0001188603],"about_ca_topic_score_codex":0.00042627967,"about_ca_topic_score_gemma":0.000053120137,"teacher_disagreement_score":0.72425854,"about_ca_system_score_codex":0.00033624715,"about_ca_system_score_gemma":0.0018634936,"threshold_uncertainty_score":0.8292157},"labels":[],"label_agreement":null},{"id":"W4225371371","doi":"10.32473/flairs.v35i.130731","title":"Pedestrian Traffic Prediction using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Pedestrian; Artificial intelligence; Artificial neural network; Event (particle physics); Traffic flow (computer networking); Deep learning; Dual (grammatical number); Pedestrian detection; Machine learning; Engineering; Transport engineering","score_opus":0.10451265099685529,"score_gpt":0.3240030754447231,"score_spread":0.21949042444786782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225371371","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9273607,0.00015192191,0.04883696,0.0013506253,0.0031213653,0.0010131849,0.000055858007,0.0021839978,0.015925396],"genre_scores_gemma":[0.99833,0.00030300516,0.00078363006,0.000018513776,0.000247553,0.00011144097,0.0000068957597,0.000024653747,0.0001743287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758923,0.000028635379,0.00042538487,0.0002779689,0.0013227861,0.00035597864],"domain_scores_gemma":[0.9990597,0.00007775074,0.00009237944,0.00010490617,0.00059735245,0.00006793278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015005865,0.00014323366,0.00013730302,0.00017529824,0.0006532845,0.00017630533,0.0011710266,0.00006236983,0.00021809804],"category_scores_gemma":[0.0001749633,0.00013978723,0.00018992476,0.00074232375,0.00022066376,0.00032033524,0.00055379194,0.0010481112,0.0000063121047],"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":[0.00012035496,0.00024524448,0.0012807043,0.00026937496,0.0004060077,0.0000015689726,0.0090422835,0.55329466,0.17536192,0.12279386,0.019775743,0.117408276],"study_design_scores_gemma":[0.000033409913,0.00006782755,0.00007885779,0.00004117029,0.000012402411,0.000006152821,0.011174012,0.95368856,0.025715806,0.002674024,0.0063871457,0.00012064929],"about_ca_topic_score_codex":0.000035776353,"about_ca_topic_score_gemma":0.0000059462604,"teacher_disagreement_score":0.40039387,"about_ca_system_score_codex":0.00048359224,"about_ca_system_score_gemma":0.00006389717,"threshold_uncertainty_score":0.57003576},"labels":[],"label_agreement":null},{"id":"W4225373816","doi":"10.32473/flairs.v35i.130660","title":"Protein-Protein Interaction Extraction using Attention-based Tree-Structured Neural Network Models","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","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; Tree (set theory); Artificial intelligence; Task (project management); Artificial neural network; Machine learning; Natural language processing; Phrase; Recurrent neural network; Tree structure; Data structure; Mathematics","score_opus":0.15386596719488524,"score_gpt":0.38203420285943096,"score_spread":0.22816823566454572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225373816","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9835955,0.00006593693,0.012373257,0.0020332292,0.00082251686,0.00051434565,0.000026280448,0.000022997108,0.00054597406],"genre_scores_gemma":[0.9949263,0.0000111235995,0.0038483741,0.00006621629,0.00055362604,0.00015992239,0.000024281835,0.000017803917,0.00039239405],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975178,0.00007362838,0.00043125983,0.00046614683,0.001112803,0.00039832466],"domain_scores_gemma":[0.9983742,0.000050903403,0.0002728646,0.0001658762,0.0010640258,0.000072100265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001282805,0.00016535025,0.00015228559,0.00006863941,0.0006683978,0.00013415549,0.0010644911,0.00012804942,0.00011932188],"category_scores_gemma":[0.0003980929,0.00014315761,0.0002633297,0.0004308269,0.0004358038,0.000036108297,0.00065287587,0.0007209345,0.0000018982668],"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":[0.00032199651,0.00008075459,0.00026067471,0.000028850483,0.00006687319,3.8917065e-7,0.0001613991,0.019488398,0.96222097,0.0072107315,0.00071481604,0.009444155],"study_design_scores_gemma":[0.000072988994,0.0002292806,0.00007792407,0.00008003163,0.000010324244,0.0000086408445,0.003676583,0.4964831,0.47745436,0.020410633,0.001325206,0.00017093857],"about_ca_topic_score_codex":0.0001584005,"about_ca_topic_score_gemma":0.00002512412,"teacher_disagreement_score":0.4847666,"about_ca_system_score_codex":0.0001910848,"about_ca_system_score_gemma":0.00024828158,"threshold_uncertainty_score":0.5837798},"labels":[],"label_agreement":null},{"id":"W4225383538","doi":"10.32473/flairs.v35i.130643","title":"Learning to Rank with BERT for Argument Quality Evaluation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Software Engineering Research","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":"Polytechnique Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Argument (complex analysis); Leverage (statistics); Ranking (information retrieval); Rank (graph theory); Computer science; Learning to rank; Pairwise comparison; Artificial intelligence; Quality (philosophy); Machine learning; Representation (politics); Task (project management); Mathematics; Epistemology; Political science; Engineering","score_opus":0.17335816357690312,"score_gpt":0.4124483342358819,"score_spread":0.23909017065897878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225383538","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69259375,0.000039345476,0.2813055,0.020347102,0.0012014079,0.002986661,0.000028533694,0.00017512834,0.0013226051],"genre_scores_gemma":[0.9849531,0.000011953132,0.0128946025,0.000080885926,0.00016094821,0.0013229377,0.000004408199,0.000017499919,0.00055364234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937664,0.000091449176,0.000474417,0.0006544385,0.0044419724,0.0005712755],"domain_scores_gemma":[0.99270743,0.001428033,0.00016835582,0.0002881402,0.005266827,0.00014120305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010700533,0.00016182198,0.00019298935,0.00019192841,0.00084299874,0.00044193122,0.0038509471,0.00004507584,0.00012928758],"category_scores_gemma":[0.004979629,0.00013469139,0.00018004583,0.0014180386,0.00017885896,0.00042226136,0.0020176293,0.0008378072,0.000012048033],"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":[0.0006095431,0.00042005233,0.009612994,0.00019889306,0.00031485505,7.0036833e-7,0.026315622,0.046913322,0.13756676,0.670898,0.0060118646,0.10113735],"study_design_scores_gemma":[0.00023235843,0.0010574885,0.0022476527,0.00013834157,0.000014353231,0.000008590004,0.012978102,0.70260054,0.21286334,0.060355946,0.0070519736,0.0004513247],"about_ca_topic_score_codex":0.0001424329,"about_ca_topic_score_gemma":0.000008892461,"teacher_disagreement_score":0.6556872,"about_ca_system_score_codex":0.0008449698,"about_ca_system_score_gemma":0.0005887208,"threshold_uncertainty_score":0.7156082},"labels":[],"label_agreement":null},{"id":"W4225384347","doi":"10.32473/flairs.v35i.130629","title":"Estimating Automobile Crash Characteristics from Images using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Crash; Collision; Artificial intelligence; Deep learning; Computer science; Machine learning; Simulation; Engineering; Computer security","score_opus":0.06565464327735158,"score_gpt":0.3190429977632977,"score_spread":0.2533883544859461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225384347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9708507,0.000070548325,0.02474676,0.00060210517,0.0012254512,0.00028511666,0.00007153165,0.00029772724,0.0018500265],"genre_scores_gemma":[0.9917303,0.000060057962,0.0076605985,0.0000193327,0.00028926958,0.000075107666,0.00001086853,0.000030881547,0.00012362399],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771506,0.000030235067,0.00052890764,0.00033473974,0.00094617496,0.0004449075],"domain_scores_gemma":[0.99861765,0.0002705334,0.00016514635,0.00014275181,0.0007437642,0.000060132283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013583223,0.00017687138,0.0002237132,0.000102674356,0.0008931436,0.00015714603,0.0016208509,0.000105873056,0.0006304391],"category_scores_gemma":[0.0005178555,0.00017350123,0.00017101232,0.0005210078,0.00043078788,0.00027458352,0.0010864602,0.0016860861,0.000017553137],"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":[0.000072146475,0.00016510539,0.013286453,0.00013819693,0.00041649566,0.000004101414,0.008513815,0.1083799,0.70783895,0.05435707,0.0008288834,0.10599886],"study_design_scores_gemma":[0.000023465687,0.00003231558,0.00050016184,0.000049573588,0.00001078913,0.0000051274287,0.004534036,0.85359675,0.114210956,0.026458152,0.0004216828,0.0001569735],"about_ca_topic_score_codex":0.0001160867,"about_ca_topic_score_gemma":0.0000035656078,"teacher_disagreement_score":0.74521685,"about_ca_system_score_codex":0.00047368385,"about_ca_system_score_gemma":0.000105743966,"threshold_uncertainty_score":0.7325299},"labels":[],"label_agreement":null},{"id":"W4225387407","doi":"10.32473/flairs.v35i.130696","title":"The Place of Quasi Topological Structure in the Mathematical Theory of Categorization","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Fuzzy and Soft Set Theory","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Mathematical structure; Mathematical theory; Categorization; Category theory; Computer science; Point (geometry); Frame (networking); Topological space; Bridging (networking); Mathematics; Topology (electrical circuits); Artificial intelligence; Pure mathematics; Physics; Geometry","score_opus":0.26424786961666635,"score_gpt":0.43531848402027895,"score_spread":0.1710706144036126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225387407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97862077,0.000095492964,0.0026206851,0.010686329,0.0006159513,0.000645586,0.00007263142,0.000009113598,0.0066334372],"genre_scores_gemma":[0.9991191,0.00006000559,0.00015513906,0.00006542694,0.00009220031,0.000049823353,0.000001788864,0.0000065297354,0.00044996088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99315274,0.00051095994,0.0011290282,0.00036342372,0.004505427,0.00033845069],"domain_scores_gemma":[0.9870319,0.009708677,0.00053010695,0.00035585888,0.0023312652,0.000042200405],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.021813558,0.0001303156,0.0002651301,0.00012293934,0.00064599945,0.0002000013,0.0056977393,0.000084950036,0.0007349793],"category_scores_gemma":[0.0145585025,0.00006420879,0.0002626987,0.001533692,0.0019347722,0.00021377811,0.0012234541,0.0009392921,0.0000063093244],"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":[0.00024323192,0.00013805994,0.00076982257,0.000016586924,0.00002555846,2.3356634e-7,0.012736108,0.00032662478,0.01057929,0.96980447,0.0006801128,0.0046798927],"study_design_scores_gemma":[0.000029465698,0.00011665313,0.000361453,0.000021762877,0.0000042812694,0.0000045473257,0.10304927,0.009093813,0.03443889,0.8524564,0.00036605212,0.000057438727],"about_ca_topic_score_codex":0.000044844975,"about_ca_topic_score_gemma":0.000019310293,"teacher_disagreement_score":0.117348105,"about_ca_system_score_codex":0.000118466465,"about_ca_system_score_gemma":0.00028221376,"threshold_uncertainty_score":0.9996819},"labels":[],"label_agreement":null},{"id":"W4225399182","doi":"10.32473/flairs.v35i.130545","title":"Preliminary Thoughts on Defining f(x) for Ethical Machines","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Normative; Ethical issues; Ethical theories; Engineering ethics; Ethical theory; Computer science; Epistemology; Management science; Philosophy; Engineering","score_opus":0.241515362300319,"score_gpt":0.46865535222575816,"score_spread":0.22713998992543916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225399182","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41357738,0.00014629649,0.0013733661,0.43924662,0.0067496174,0.002901104,0.000345925,0.00019867114,0.13546103],"genre_scores_gemma":[0.9942763,0.00018139428,0.0009902975,0.0009669255,0.0012821859,0.00025562965,0.0000060061698,0.00002139009,0.0020199069],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99521506,0.00014773227,0.00046871864,0.0004462819,0.0030853713,0.0006368616],"domain_scores_gemma":[0.99351615,0.002257832,0.00024200573,0.0001403727,0.0036893985,0.00015426015],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009834615,0.00015808862,0.00021232833,0.000111308305,0.0038885765,0.0004875049,0.0025214069,0.000237731,0.00024110902],"category_scores_gemma":[0.008336889,0.00013739882,0.00038310327,0.0007108013,0.0013344103,0.00031319712,0.0008821791,0.002291492,0.000012460854],"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":[0.00024871575,0.00012359832,0.00020077835,0.000026738599,0.000053621,2.6748316e-7,0.031494338,0.000109525536,0.0028311168,0.94685787,0.0113146035,0.0067388355],"study_design_scores_gemma":[0.00006801974,0.0005032509,0.00011155659,0.000112303074,0.000014571376,0.0000011409899,0.069882326,0.00921574,0.018167507,0.86669654,0.03498803,0.00023900857],"about_ca_topic_score_codex":0.00083260477,"about_ca_topic_score_gemma":0.00012580339,"teacher_disagreement_score":0.5806989,"about_ca_system_score_codex":0.00049148727,"about_ca_system_score_gemma":0.00095653924,"threshold_uncertainty_score":0.9980636},"labels":[],"label_agreement":null},{"id":"W4225401927","doi":"10.32473/flairs.v35i.130667","title":"Integration of Multivariate Beta-based Hidden Markov Models and Support Vector Machines with Medical Applications","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Text and Document Classification Technologies","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Discriminative model; Support vector machine; Artificial intelligence; Computer science; Fisher kernel; Pattern recognition (psychology); Kernel (algebra); Generative model; Machine learning; Multivariate statistics; Decision boundary; Kernel method; Generative grammar; Mathematics; Kernel Fisher discriminant analysis","score_opus":0.11285070093023435,"score_gpt":0.3615773986869301,"score_spread":0.24872669775669576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225401927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06164354,0.000058041092,0.8946281,0.037837267,0.0003371715,0.0013217255,0.000064910884,0.00020416222,0.003905063],"genre_scores_gemma":[0.980639,0.000048336708,0.018542757,0.00006423431,0.000041669115,0.00041590715,0.000007669147,0.00000852583,0.00023192227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967593,0.000036307116,0.00047615843,0.00043695714,0.0020435348,0.00024775564],"domain_scores_gemma":[0.9974758,0.00036837065,0.00029621023,0.00025749113,0.0015261347,0.00007599542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017322456,0.00013819382,0.000182102,0.00016270345,0.0003985953,0.00018875496,0.0029574158,0.000071446084,0.00015742399],"category_scores_gemma":[0.00034209568,0.00010172864,0.00010445317,0.00087815087,0.0006705998,0.0005244341,0.0012873429,0.00057661755,0.0000021451065],"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":[0.000045025092,0.00012725046,0.00027350767,0.000028302096,0.00003959802,1.9499781e-7,0.0010972697,0.000072236944,0.021775952,0.9019112,0.00030306107,0.0743264],"study_design_scores_gemma":[0.00008659988,0.00019363014,0.00031783938,0.000066094064,0.000008637675,0.0000055728483,0.002588348,0.66946346,0.18361858,0.14297073,0.0005275344,0.00015301774],"about_ca_topic_score_codex":0.00012144764,"about_ca_topic_score_gemma":0.000013960568,"teacher_disagreement_score":0.91899544,"about_ca_system_score_codex":0.00013166423,"about_ca_system_score_gemma":0.00043766867,"threshold_uncertainty_score":0.5495663},"labels":[],"label_agreement":null},{"id":"W4225424639","doi":"10.32473/flairs.v35i.130561","title":"Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Bayesian Modeling and Causal Inference","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 Guelph","funders":"","keywords":"Inference; Nat; Bayesian network; Discretization; Dynamic Bayesian network; Variable elimination; Computer science; Approximate inference; Focus (optics); Tree (set theory); Treewidth; Fiducial inference; Bayesian inference; Exponential family; Algorithm; Frequentist inference; Mathematics; Artificial intelligence; Bayesian probability; Theoretical computer science; Machine learning; Combinatorics; Graph; Physics","score_opus":0.06755913222578512,"score_gpt":0.34351136169060514,"score_spread":0.27595222946482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225424639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022204397,0.000049912567,0.9689181,0.005949042,0.0006151302,0.00082046166,0.00004818733,0.00010496636,0.0012898119],"genre_scores_gemma":[0.9795471,0.000103197766,0.019014273,0.00014338407,0.00013097648,0.00051518437,0.000018585346,0.000027094438,0.00050023134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99569994,0.000059790767,0.00065489975,0.0008452093,0.0019884468,0.00075169076],"domain_scores_gemma":[0.9954492,0.0005079967,0.00036416127,0.0003747747,0.0031518037,0.00015210581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025501803,0.00027746262,0.00029638136,0.00016673944,0.0012474753,0.00073999463,0.0047437632,0.00008066689,0.00008696275],"category_scores_gemma":[0.00045082334,0.00022551966,0.00024956753,0.0012365064,0.0004290636,0.0009709169,0.0014612337,0.0010638895,0.0000037235177],"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":[0.00023198815,0.00026592825,0.00032187795,0.00007119438,0.00012985352,0.0000010483132,0.0022551022,0.064297855,0.012934716,0.8928898,0.0011955019,0.025405146],"study_design_scores_gemma":[0.000074596595,0.00024250714,0.00002527579,0.00008588098,0.000010050747,0.000010850753,0.0014860447,0.8481603,0.017561661,0.13179313,0.0003123735,0.00023732438],"about_ca_topic_score_codex":0.000115109026,"about_ca_topic_score_gemma":0.000023965056,"teacher_disagreement_score":0.9573427,"about_ca_system_score_codex":0.00042063522,"about_ca_system_score_gemma":0.0006331647,"threshold_uncertainty_score":0.95946974},"labels":[],"label_agreement":null},{"id":"W4225425583","doi":"10.32473/flairs.v35i.130850","title":"Learning Automata with Artificial Reflecting Barriers in Games with Limited Information","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Optimization and Search Problems","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":"Carleton University","funders":"","keywords":"Nash equilibrium; Computer science; Game theory; Reinforcement learning; Fictitious play; Complete information; Perfect information; Learning automata; Mathematical economics; Point (geometry); Saddle point; Artificial intelligence; Automaton; Mathematics","score_opus":0.09331720338213315,"score_gpt":0.3458034193125344,"score_spread":0.2524862159304012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225425583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7212794,0.000028139038,0.21078044,0.03778448,0.0010595494,0.0026667058,0.000031663578,0.0005220464,0.025847588],"genre_scores_gemma":[0.9908398,0.000037778183,0.008495878,0.00013188824,0.00005631296,0.00018840881,0.0000065452214,0.000012584044,0.00023080061],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99582106,0.00009778232,0.00058274536,0.00043349582,0.002508427,0.00055651803],"domain_scores_gemma":[0.9968515,0.00026677613,0.00031390894,0.00021098464,0.0022275844,0.00012923536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003353915,0.00017685042,0.00019187537,0.0003605072,0.00094922073,0.00082051085,0.0025829356,0.000058257952,0.00013881728],"category_scores_gemma":[0.0009345421,0.00013611678,0.00008933832,0.0026362645,0.0003722953,0.0018846503,0.0013462539,0.0014004044,0.0000100834],"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":[0.000575042,0.00021348498,0.008321437,0.00011034206,0.00013815615,0.000003470503,0.047433775,0.11095051,0.013345167,0.7679871,0.0007758148,0.05014572],"study_design_scores_gemma":[0.00012809064,0.0005612353,0.0002162333,0.00014044873,0.0000038913045,0.000016759524,0.04064172,0.91164166,0.028391795,0.016242277,0.0017669266,0.00024898362],"about_ca_topic_score_codex":0.00021835837,"about_ca_topic_score_gemma":0.00003426434,"teacher_disagreement_score":0.8006911,"about_ca_system_score_codex":0.00042262624,"about_ca_system_score_gemma":0.000732037,"threshold_uncertainty_score":0.7912205},"labels":[],"label_agreement":null},{"id":"W4225428113","doi":"10.32473/flairs.v35i.130688","title":"Unsupervised Neural Network for Data-Driven Corrosion Detection of a Mining Pipeline","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institute of Mining, Metallurgy and Petroleum","keywords":"Corrosion; Pipeline transport; Artificial neural network; Pipeline (software); Computer science; Representation (politics); Data mining; Artificial intelligence; Engineering; Materials science; Environmental engineering","score_opus":0.18069879283595278,"score_gpt":0.3621233291969468,"score_spread":0.181424536360994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225428113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9110462,0.000073965144,0.08395809,0.00060735096,0.0014813759,0.001079586,0.00023329825,0.00025454766,0.0012655668],"genre_scores_gemma":[0.95020384,0.00004051734,0.04921114,0.000013811123,0.00028666467,0.0001692322,0.00002250742,0.000029579247,0.000022697346],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771994,0.00002587384,0.00054681354,0.0003487145,0.0009906208,0.00036805528],"domain_scores_gemma":[0.9974185,0.0005080413,0.00017297511,0.00024512113,0.00160378,0.00005157948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020021375,0.00015154213,0.00020978918,0.000095214375,0.0003741837,0.00008002794,0.0023033677,0.000060768278,0.000056990313],"category_scores_gemma":[0.00095529685,0.00014386364,0.000147514,0.0006878252,0.00028561367,0.0003075325,0.0012938904,0.0005629003,9.1227923e-7],"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":[0.00018636312,0.00006547577,0.0017291991,0.00019088967,0.000060873776,2.028266e-7,0.0016838188,0.009299682,0.9326341,0.03861792,0.0043119877,0.011219478],"study_design_scores_gemma":[0.00003682606,0.00010879156,0.00006945171,0.00008438626,0.00001117665,0.0000038598023,0.0021512164,0.74140686,0.19439967,0.061468832,0.00014666768,0.00011228584],"about_ca_topic_score_codex":0.000089631445,"about_ca_topic_score_gemma":0.000018566105,"teacher_disagreement_score":0.73823446,"about_ca_system_score_codex":0.00026687357,"about_ca_system_score_gemma":0.0001013909,"threshold_uncertainty_score":0.5866589},"labels":[],"label_agreement":null},{"id":"W4229048432","doi":"10.32473/flairs.v35i.130724","title":"Vehicle Traffic Estimation Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Mean absolute percentage error; Traffic flow (computer networking); Computer science; Artificial neural network; Convolutional neural network; Mean squared error; Deep learning; Word error rate; Statistics; Meteorology; Artificial intelligence; Geography; Mathematics","score_opus":0.08944595839567647,"score_gpt":0.3304626479577519,"score_spread":0.2410166895620754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229048432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9091262,0.000090574504,0.07746136,0.0013000225,0.0014716788,0.0006648109,0.000015679852,0.0013919133,0.008477762],"genre_scores_gemma":[0.9974538,0.0001205007,0.0020439622,0.00002235789,0.00011403857,0.00008899602,0.0000042017555,0.000020690537,0.00013146289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979321,0.000022606926,0.00035475465,0.0002292302,0.0011566767,0.00030467034],"domain_scores_gemma":[0.99916846,0.00007979255,0.00008176338,0.000090556976,0.00052684225,0.000052561383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012728424,0.00012071759,0.00011871162,0.000138966,0.0005565907,0.00016069466,0.0010419065,0.000047734447,0.00018085295],"category_scores_gemma":[0.00017023656,0.0001181507,0.00014877644,0.000655469,0.00018866899,0.00031281987,0.00050981645,0.00082353904,0.000008361297],"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":[0.000035464232,0.00008671193,0.00017176503,0.000095158204,0.000121352736,5.013037e-7,0.003909544,0.7009143,0.10229028,0.083280794,0.0051450254,0.10394908],"study_design_scores_gemma":[0.000022589073,0.000039178685,0.00004009322,0.000031911095,0.000007531261,0.0000028975815,0.0064901765,0.9457253,0.04201769,0.003771271,0.0017475336,0.00010381326],"about_ca_topic_score_codex":0.00002672543,"about_ca_topic_score_gemma":0.0000035323917,"teacher_disagreement_score":0.244811,"about_ca_system_score_codex":0.0004076099,"about_ca_system_score_gemma":0.000046600708,"threshold_uncertainty_score":0.48180458},"labels":[],"label_agreement":null},{"id":"W4229048469","doi":"10.32473/flairs.v35i.130722","title":"Generative Adversarial learning with Negative Data Augmentation for Semi-supervised Text Classification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Discriminator; Generator (circuit theory); Computer science; Generative grammar; Manifold (fluid mechanics); Representation (politics); Boundary (topology); Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Mode (computer interface); Generative model; Decision boundary; Matching (statistics); Mixing (physics); Key (lock); Power (physics); Machine learning; Mathematics; Statistics; Physics; Support vector machine","score_opus":0.214596084131797,"score_gpt":0.3702213578985494,"score_spread":0.15562527376675236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229048469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16708529,0.000025893016,0.7971298,0.020768614,0.0036492723,0.0031350902,0.00023851467,0.00022291557,0.007744633],"genre_scores_gemma":[0.98072636,0.000021405778,0.017867012,0.000076306,0.0003284056,0.00041703647,0.00005134074,0.000016812206,0.0004953416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996314,0.00006590647,0.00045153673,0.0007581469,0.0020299247,0.00038048494],"domain_scores_gemma":[0.9959128,0.00060594955,0.00034825443,0.00034220197,0.0027083452,0.00008245148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023105463,0.00017097814,0.000173313,0.00013719189,0.00093874085,0.0005804647,0.0039098724,0.000053697186,0.000055836772],"category_scores_gemma":[0.0014036116,0.00014211448,0.000115005285,0.0010857785,0.00042566744,0.0017573911,0.0020737152,0.0006851821,0.000007876832],"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":[0.0006901989,0.00027574223,0.00044440065,0.000068202935,0.00029535627,5.9076797e-7,0.017294005,0.0036466126,0.14590317,0.696938,0.005481457,0.12896226],"study_design_scores_gemma":[0.00014591978,0.00039570648,0.00007785138,0.000043407916,0.000011353453,0.0000052783976,0.019569797,0.778241,0.14624836,0.053568833,0.0015225939,0.00016990509],"about_ca_topic_score_codex":0.000096352705,"about_ca_topic_score_gemma":0.000021775002,"teacher_disagreement_score":0.8136411,"about_ca_system_score_codex":0.0004939458,"about_ca_system_score_gemma":0.00049418537,"threshold_uncertainty_score":0.726558},"labels":[],"label_agreement":null},{"id":"W4248507868","doi":"10.32473/flairs.v34i1.128751","title":"Committee Listings","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nuclear Physics; Russian Academy of Sciences; Philips Research Americas; University of North Carolina at Charlotte; University of Colorado Boulder; Slovenská technická univerzita v Bratislave; University of Nicosia; Lomonosov Moscow State University; University of Texas at El Paso; International Institute for Applied Systems Analysis; Technological University Dublin; Nanjing University; Agricultural University of Athens; Los Alamos National Laboratory; National and Kapodistrian University of Athens; Universidade Federal do Rio Grande do Sul; Centre National de la Recherche Scientifique; Simon Fraser University; Université de Bretagne Occidentale; Radford University; Tsinghua University; Universität Wien; Università degli Studi di Milano-Bicocca; Université du Québec en Outaouais; University College Cork; Illinois State University; Aalborg Universitet; Indiana University Bloomington; International Science and Technology Center; Cardiff University; Tennessee Tech University; Office of Naval Research; Universität Trier; Florida Institute of Technology; Université du Québec à Trois-Rivières; Montana State University; Universitetet i Bergen; University of Miami; University of Texas at Arlington; University of South Carolina; Cyprus University of Technology; University of Regina; University of Louisiana at Lafayette; Middlesex University; Army Research Laboratory; Bradley University; Drexel University; Washington State University; Universidade Federal de São João del-Rei; University of Ottawa; University of Maryland, Baltimore County; DePaul University; Kennesaw State University; Dana-Farber/Harvard Cancer Center; University of Cyprus; Samsung; University of Southern California; University of Memphis; University of Manchester; University of Hartford; Universidade do Porto; Northwestern University; University of Central Florida; Tulane University; Concordia University; Central Connecticut State University; Université du Québec à Montréal; Korea University; Univerzita Karlova v Praze; Universiteit Gent; Universidade Federal de Pelotas; University of Pittsburgh; Clemson University; Massachusetts Institute of Technology","keywords":"Business","score_opus":0.25261366662803475,"score_gpt":0.3352411544128727,"score_spread":0.08262748778483797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248507868","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17109235,0.00047811723,0.0008413898,0.0150979245,0.0057553477,0.0003464987,0.00035226566,0.000055337347,0.80598074],"genre_scores_gemma":[0.92235696,0.00044297017,0.0012051567,0.00018453614,0.000346369,0.000028838673,0.0000041074054,0.000013838676,0.075417206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998013,0.000007559551,0.00069585734,0.00055845024,0.00030406844,0.00042102227],"domain_scores_gemma":[0.9975904,0.00016832176,0.00029669428,0.00020716673,0.0016567629,0.00008064539],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00220029,0.00013616055,0.00028287384,0.00009116087,0.00040647842,0.00042561025,0.0014145564,0.00008651198,0.0045692995],"category_scores_gemma":[0.0015518231,0.00013121111,0.00030999153,0.00061642175,0.0006966458,0.0003721643,0.0010355401,0.00043152706,0.0016276893],"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":[0.00001130267,0.000073076975,0.003412803,0.000029934976,0.00010480752,4.685077e-7,0.0026312463,0.000013741302,0.0027736407,0.9147744,0.07525516,0.00091938116],"study_design_scores_gemma":[0.000095141215,0.000039165516,0.00082771806,0.00015678938,0.000009293025,0.0000070671945,0.03206221,0.0147682,0.1173019,0.68230623,0.15204649,0.00037979637],"about_ca_topic_score_codex":0.00023516403,"about_ca_topic_score_gemma":0.000016989467,"teacher_disagreement_score":0.75126463,"about_ca_system_score_codex":0.00020416832,"about_ca_system_score_gemma":0.00010873437,"threshold_uncertainty_score":0.9991497},"labels":[],"label_agreement":null},{"id":"W4375858571","doi":"10.32473/flairs.36.133256","title":"Identifying Protein-Protein Interaction using Tree-Transformers and Heterogeneous Graph Neural Network","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial neural network; Transformer; Graph; Artificial intelligence; Theoretical computer science; Engineering; Electrical engineering","score_opus":0.17353665750856861,"score_gpt":0.3955412738069915,"score_spread":0.22200461629842289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99503124,0.000101271144,0.002444308,0.0012750169,0.00043299232,0.00036021305,0.000009754471,0.000033325014,0.0003118611],"genre_scores_gemma":[0.9976125,0.00020843788,0.0013887961,0.000032092605,0.00041036136,0.00005400016,0.000008460977,0.000015889225,0.0002695001],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980675,0.00003227305,0.00036088817,0.00041796232,0.00067129376,0.0004500667],"domain_scores_gemma":[0.9989368,0.000047851023,0.00013101856,0.00010134792,0.0006957811,0.000087237924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011839436,0.00015460097,0.00014871461,0.0000813055,0.00034950892,0.00018671743,0.000665562,0.00015928279,0.000019471567],"category_scores_gemma":[0.000451918,0.00012557508,0.00018568788,0.0005297845,0.0006863251,0.000029867808,0.00050712225,0.0003917931,0.0000054461],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012618802,0.000023643923,0.0004614107,0.00006792822,0.00009856176,7.5149785e-7,0.0004544057,0.00025150366,0.9669683,0.002910907,0.00040139793,0.02823502],"study_design_scores_gemma":[0.00006241524,0.0001884231,0.00018842291,0.00029060902,0.000011725491,0.000014779055,0.0053326003,0.05059383,0.92030925,0.021980468,0.00082667486,0.00020079428],"about_ca_topic_score_codex":0.000107800384,"about_ca_topic_score_gemma":0.00003647337,"teacher_disagreement_score":0.05034233,"about_ca_system_score_codex":0.00004644376,"about_ca_system_score_gemma":0.00008517911,"threshold_uncertainty_score":0.5120804},"labels":[],"label_agreement":null},{"id":"W4375858679","doi":"10.32473/flairs.36.133203","title":"Further Thoughts on Defining f(x) for Ethical Machines: Ethics, Rational Choice, and Risk Analysis","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Ethics and Social Impacts of AI","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Consequentialism; Utilitarianism; Deontological ethics; Normative; Perspective (graphical); Ethical theory; Epistemology; Rational agent; Normative ethics; Management science; Engineering ethics; Computer science; Risk analysis (engineering); Sociology; Economics; Artificial intelligence; Philosophy; Business; Engineering","score_opus":0.2636580989225152,"score_gpt":0.4931448676318907,"score_spread":0.2294867687093755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67991376,0.00006009201,0.0026344706,0.29785147,0.0014679893,0.0012249114,0.00025516693,0.00017500724,0.01641713],"genre_scores_gemma":[0.99413425,0.0019294395,0.0008822619,0.0005555645,0.00125869,0.00008189428,0.000013392595,0.000018232959,0.0011262883],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99570066,0.00018418595,0.00048267818,0.0004930333,0.0025601345,0.00057930715],"domain_scores_gemma":[0.98703396,0.0070656766,0.00026984126,0.00012915047,0.005317797,0.00018357491],"candidate_categories":["metaresearch","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.014718451,0.00016967684,0.0002680573,0.00025354096,0.002303334,0.00078462827,0.001241998,0.00055390864,0.00007744353],"category_scores_gemma":[0.028814165,0.00013766902,0.00041149047,0.001894628,0.00182795,0.00036159373,0.0003259065,0.0024289775,0.000025920977],"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":[0.000055596876,0.000045739856,0.008949739,0.00003269175,0.0004336964,1.0612461e-7,0.0505867,0.00016633816,0.0008840735,0.9320668,0.002948043,0.0038305048],"study_design_scores_gemma":[0.00008996724,0.00012969825,0.005616405,0.00015623837,0.00012960448,1.8152e-7,0.04516143,0.045845713,0.004823224,0.8883893,0.009362099,0.0002961737],"about_ca_topic_score_codex":0.0025760087,"about_ca_topic_score_gemma":0.002706788,"teacher_disagreement_score":0.31422046,"about_ca_system_score_codex":0.00018056924,"about_ca_system_score_gemma":0.00072694506,"threshold_uncertainty_score":0.99987245},"labels":[],"label_agreement":null},{"id":"W4375858688","doi":"10.32473/flairs.36.133140","title":"Using Bidirectional Associative Memory Neural Networks to Solve the N-bit Task","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks 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":"University of Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Content-addressable memory; Task (project management); Bidirectional associative memory; Identifier; Associative property; Artificial intelligence; Arithmetic; Mathematics","score_opus":0.21907951948329998,"score_gpt":0.3981573109817071,"score_spread":0.1790777914984071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858688","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43348455,0.00009824832,0.41698745,0.13139664,0.0062828036,0.0022324123,0.00007531956,0.00056960667,0.008872968],"genre_scores_gemma":[0.994809,0.00008285536,0.0028330758,0.00044144332,0.0009293069,0.00015187645,0.0000028693232,0.000016698357,0.00073287595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658805,0.000037228732,0.00048779833,0.00057466514,0.0016602932,0.00065193936],"domain_scores_gemma":[0.9961051,0.000771793,0.00022208711,0.00028551885,0.0024779618,0.00013753884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024229267,0.00018993871,0.00018651727,0.0001228066,0.0010543386,0.0006937868,0.0040229405,0.00010019797,0.0000334684],"category_scores_gemma":[0.00058314815,0.000131614,0.00028473194,0.0030613164,0.00041544085,0.00051999075,0.0019918852,0.00081777887,0.00005520564],"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":[0.00003816669,0.00010617841,0.0005956988,0.000018726978,0.00018503137,0.0000010464573,0.0054642195,0.044363424,0.06565739,0.81560403,0.034083024,0.033883035],"study_design_scores_gemma":[0.00001895113,0.00003310069,0.00043340807,0.000052241932,0.0000061238275,0.0000037737127,0.0020218124,0.9300273,0.019708982,0.04650579,0.0010421067,0.00014639151],"about_ca_topic_score_codex":0.00019546926,"about_ca_topic_score_gemma":0.000027130938,"teacher_disagreement_score":0.88566387,"about_ca_system_score_codex":0.00025414632,"about_ca_system_score_gemma":0.00018175258,"threshold_uncertainty_score":0.8109227},"labels":[],"label_agreement":null},{"id":"W4375858707","doi":"10.32473/flairs.36.133373","title":"Using Knowledge Graph Embedding for Fault Detection","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Automotive industry; Fault detection and isolation; Automotive engineering; Fault (geology); Electric vehicle; Computer science; Engineering; Business; Artificial intelligence; Power (physics); Actuator","score_opus":0.2955318834200215,"score_gpt":0.42575329626852526,"score_spread":0.13022141284850375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90626514,0.00006854981,0.08279327,0.00023713532,0.005656752,0.0010717249,0.000042405587,0.00036540578,0.0034995931],"genre_scores_gemma":[0.9981376,0.00012752738,0.0004807806,0.000004162642,0.0008834288,0.00011627794,0.0000023496293,0.00003029789,0.00021756015],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980057,0.000015702539,0.0004997854,0.00029304915,0.0007454865,0.00044029177],"domain_scores_gemma":[0.99736,0.0002802955,0.00009633737,0.00011052049,0.0020810496,0.00007183096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021645434,0.00015846339,0.00018755971,0.00026467402,0.00044504576,0.00024991037,0.0006769999,0.00018408078,0.000025913347],"category_scores_gemma":[0.0005017546,0.000135776,0.00031055292,0.0014785086,0.00016674548,0.0003232768,0.0001946564,0.00047499404,0.00003809201],"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":[0.000057758738,0.000022201724,0.000039856313,0.00016156332,0.00010313982,1.2328336e-7,0.0019942021,0.015546507,0.9253557,0.018314693,0.0034000145,0.035004277],"study_design_scores_gemma":[0.000031676154,0.000029882596,0.00000983718,0.00012194362,0.000004722878,0.0000019374918,0.002874973,0.509641,0.47232255,0.012633668,0.0022320016,0.000095833224],"about_ca_topic_score_codex":0.000058435817,"about_ca_topic_score_gemma":0.000012018395,"teacher_disagreement_score":0.4940945,"about_ca_system_score_codex":0.00028003837,"about_ca_system_score_gemma":0.000069043825,"threshold_uncertainty_score":0.5536785},"labels":[],"label_agreement":null},{"id":"W4375858784","doi":"10.32473/flairs.36.133365","title":"Towards binary encoding in Bidirectional Associative Memories","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks 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":"University of Ottawa","funders":"","keywords":"Encoding (memory); Computer science; Associative property; Binary number; Cognition; Bidirectional associative memory; Task (project management); Recall; Artificial intelligence; Content-addressable memory; Transmission (telecommunications); Function (biology); Artificial neural network; Pattern recognition (psychology); Cognitive psychology; Psychology; Neuroscience; Arithmetic; Mathematics; Biology; Engineering","score_opus":0.1746696167332556,"score_gpt":0.3893567369444212,"score_spread":0.2146871202111656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858784","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86641556,0.00007756673,0.012753525,0.08088115,0.0033723135,0.0012571226,0.000053298136,0.000455254,0.034734186],"genre_scores_gemma":[0.99633974,0.00032242533,0.0020132707,0.000074921714,0.00032368876,0.00014031274,0.0000031017896,0.000009719342,0.0007728509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99703395,0.000032153515,0.00047307202,0.0004949331,0.0014667455,0.0004991707],"domain_scores_gemma":[0.99743056,0.00046700868,0.00017390413,0.00017400217,0.001676628,0.00007786888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023044695,0.0001474457,0.00018095037,0.00021457672,0.00040768596,0.0003884975,0.002791543,0.000095982134,0.00004361278],"category_scores_gemma":[0.00071698526,0.00012116209,0.00019316313,0.003259886,0.00035755787,0.0007178244,0.0014070473,0.00064570765,0.000060842525],"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":[0.000014788361,0.00008303605,0.0018629453,0.00002240315,0.000044349195,0.0000010785266,0.0038085054,0.0004851157,0.059650682,0.9126794,0.0061801947,0.015167515],"study_design_scores_gemma":[0.000042694075,0.000053253196,0.003867244,0.00016738402,0.000003051327,0.0000027909452,0.004178975,0.3703371,0.22093321,0.3985571,0.0016454011,0.00021182587],"about_ca_topic_score_codex":0.00015111991,"about_ca_topic_score_gemma":0.00003081949,"teacher_disagreement_score":0.5141223,"about_ca_system_score_codex":0.00028431116,"about_ca_system_score_gemma":0.00028156635,"threshold_uncertainty_score":0.5187428},"labels":[],"label_agreement":null},{"id":"W4375858861","doi":"10.32473/flairs.36.133230","title":"Biogeography-based optimization for feature selection","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","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":"University of Regina","funders":"","keywords":"Cluster analysis; Data mining; Computer science; Cluster (spacecraft); Selection (genetic algorithm); Feature selection; Biogeography; Feature (linguistics); Machine learning; Artificial intelligence; Ecology; Biology","score_opus":0.13065043082825348,"score_gpt":0.38871743987603846,"score_spread":0.258067009047785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009455058,0.000017718798,0.97035784,0.017142462,0.0009523315,0.0010153692,0.000050630963,0.0003057502,0.00070282444],"genre_scores_gemma":[0.7509341,0.00017019153,0.24669243,0.00013735163,0.0005785044,0.0005112389,0.000029575096,0.000041813757,0.0009047543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99645764,0.000028244674,0.00039192813,0.0006402828,0.0018130054,0.0006688824],"domain_scores_gemma":[0.99369574,0.000523844,0.00018207781,0.00023419429,0.0052504805,0.00011365754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024156729,0.00018232121,0.00018371434,0.00039032908,0.0006222587,0.00060361385,0.0033431596,0.000147894,0.000025702335],"category_scores_gemma":[0.0012980569,0.000153489,0.0003141644,0.0037113565,0.00039629015,0.0007351465,0.0008564305,0.0005657099,0.000020449308],"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":[0.00022001012,0.00025436762,0.0013469037,0.0003557919,0.00024176297,9.339437e-7,0.0023281693,0.15230006,0.16488247,0.57057273,0.016716104,0.09078072],"study_design_scores_gemma":[0.000046346864,0.00008612353,0.000082691484,0.0000707466,0.000002643204,0.0000011781431,0.0004667092,0.76033014,0.19914109,0.038951255,0.00070255896,0.0001185114],"about_ca_topic_score_codex":0.000045486653,"about_ca_topic_score_gemma":0.000005800894,"teacher_disagreement_score":0.7414791,"about_ca_system_score_codex":0.00022999075,"about_ca_system_score_gemma":0.0003421664,"threshold_uncertainty_score":0.62591},"labels":[],"label_agreement":null},{"id":"W4376958591","doi":"10.32473/flairs.36.133320","title":"Multi-hop Question Generation without Supporting Fact Information","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","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":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Lethbridge","keywords":"Hop (telecommunications); Computer science; Computer network","score_opus":0.22374006884096853,"score_gpt":0.4126515714519567,"score_spread":0.18891150261098816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376958591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37793523,0.0000092899945,0.60830635,0.009529697,0.0017347202,0.00064609456,0.000012810422,0.00028855316,0.0015372153],"genre_scores_gemma":[0.977041,0.00008986005,0.021958496,0.0000935608,0.00035243784,0.00007161538,0.000012784348,0.000009601054,0.00037063519],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99646413,0.000038378297,0.0007678946,0.0004288607,0.0017816001,0.0005191471],"domain_scores_gemma":[0.9957394,0.00014363565,0.00034846432,0.0002619855,0.0034065533,0.0000999625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040130387,0.00016970998,0.0001704313,0.00023492782,0.00046299864,0.0008782198,0.0025007105,0.00012569784,0.000029378463],"category_scores_gemma":[0.0018351065,0.00014279324,0.00017890007,0.0011579102,0.00020368252,0.0025941997,0.0010759726,0.0005551967,0.00015096113],"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":[0.000020583548,0.00007016547,0.0042988476,0.00009962611,0.00007270377,5.0221325e-7,0.012765202,0.0031982246,0.2556584,0.6302838,0.002818596,0.090713404],"study_design_scores_gemma":[0.000034642784,0.0000262968,0.00041629624,0.000078104975,0.0000027929898,0.0000024261503,0.0022572489,0.8332623,0.14447096,0.018895464,0.00042657263,0.00012690596],"about_ca_topic_score_codex":0.00021991595,"about_ca_topic_score_gemma":0.000017124865,"teacher_disagreement_score":0.83006406,"about_ca_system_score_codex":0.00024824624,"about_ca_system_score_gemma":0.0002907942,"threshold_uncertainty_score":0.84686935},"labels":[],"label_agreement":null},{"id":"W4376958607","doi":"10.32473/flairs.36.133328","title":"Towards a multi-modal Deep Learning Architecture for User Modeling","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Multimodal Machine Learning 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":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Modal; Convolutional neural network; User modeling; Representation (politics); Feature (linguistics); Feature learning; Machine learning; Architecture; Human–computer interaction; User interface","score_opus":0.16179346554749333,"score_gpt":0.40432547208867836,"score_spread":0.24253200654118504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376958607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12088357,0.00001851446,0.862153,0.014503742,0.00046345702,0.00080406957,0.0000122265565,0.00030212497,0.00085932773],"genre_scores_gemma":[0.9251984,0.00007259184,0.07347722,0.00006500505,0.00029300488,0.0003863071,0.0000076981305,0.000027359329,0.0004724064],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964392,0.00004212186,0.0005637996,0.0007536059,0.00149364,0.0007076304],"domain_scores_gemma":[0.9956569,0.000510332,0.0002039103,0.00030471766,0.0031838198,0.00014029973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028736927,0.00022816926,0.00023444438,0.00024425826,0.00079887063,0.0006153047,0.0044365535,0.0001477999,0.000024322773],"category_scores_gemma":[0.002624923,0.00018881848,0.00036859125,0.0016253799,0.0002990125,0.0004725218,0.0017140984,0.0011775737,0.00006694995],"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":[0.000051351497,0.000118319,0.0009440446,0.0001176528,0.00012032072,3.521524e-7,0.009741803,0.10441184,0.07089645,0.7246037,0.00042542192,0.08856871],"study_design_scores_gemma":[0.000058373716,0.000049857226,0.00025500768,0.000075256656,0.0000050366166,0.0000023416312,0.0017948681,0.88073087,0.023089286,0.09296522,0.0008043615,0.0001695287],"about_ca_topic_score_codex":0.00042554797,"about_ca_topic_score_gemma":0.000028603656,"teacher_disagreement_score":0.80431485,"about_ca_system_score_codex":0.00018468389,"about_ca_system_score_gemma":0.0002510227,"threshold_uncertainty_score":0.8244294},"labels":[],"label_agreement":null},{"id":"W4377018759","doi":"10.32473/flairs.36.133326","title":"Improving Word Embedding Using Variational Dropout","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Dropout (neural networks); Word (group theory); Computer science; Word embedding; Artificial intelligence; Overfitting; Orthogonality; Natural language processing; Embedding; Curse of dimensionality; Inference; Machine learning; Mathematics; Artificial neural network","score_opus":0.19908569463833295,"score_gpt":0.3984730303623926,"score_spread":0.19938733572405967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377018759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24841155,0.000022422004,0.73891956,0.0070110364,0.002358871,0.00048808238,0.000012523769,0.00026305666,0.0025128762],"genre_scores_gemma":[0.95282125,0.00004321376,0.045958467,0.00006658696,0.0005772361,0.000035515168,0.0000020471584,0.000017441136,0.0004782521],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568576,0.000032661344,0.0006569643,0.0006868811,0.00226242,0.0006753307],"domain_scores_gemma":[0.99607027,0.00041413162,0.0002797954,0.00031514876,0.0027999762,0.00012065947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034558466,0.0001938136,0.00020531831,0.0002582893,0.0006139872,0.0008061012,0.004125287,0.0001270171,0.000065711196],"category_scores_gemma":[0.0014881457,0.00016782925,0.00025852403,0.0018499854,0.0003065518,0.0011385625,0.0024391003,0.0006988443,0.0000664264],"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":[0.000013756132,0.000041505016,0.0007072125,0.000056602155,0.00006638886,0.0000010011299,0.003320102,0.0053131836,0.15885322,0.8031308,0.0005694106,0.027926832],"study_design_scores_gemma":[0.000024654884,0.000014320899,0.00007469109,0.00010476477,0.0000038720805,0.000004232504,0.001965705,0.81478786,0.058354836,0.124382794,0.00014442373,0.00013781637],"about_ca_topic_score_codex":0.00027455625,"about_ca_topic_score_gemma":0.0000064558235,"teacher_disagreement_score":0.8094747,"about_ca_system_score_codex":0.00036367317,"about_ca_system_score_gemma":0.00050776557,"threshold_uncertainty_score":0.7773253},"labels":[],"label_agreement":null},{"id":"W4377018773","doi":"10.32473/flairs.36.133317","title":"Evaluation of Techniques for Sim2Real Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Reinforcement Learning in Robotics","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":"Ontario Tech University","funders":"","keywords":"Reinforcement learning; Computer science; Bridging (networking); Bridge (graph theory); Generalization; Noise (video); Domain (mathematical analysis); Human–computer interaction; Transfer of learning; Process (computing); Artificial intelligence; Mathematics","score_opus":0.2601722272290781,"score_gpt":0.42715826990483485,"score_spread":0.16698604267575673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377018773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03162128,0.000019722973,0.9435728,0.0049760113,0.0010135456,0.0020120423,0.000005650845,0.00026680328,0.016512118],"genre_scores_gemma":[0.9914294,0.00013209102,0.007120717,0.000021305412,0.00017697908,0.00022322996,0.000006317567,0.000013629883,0.0008763621],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99443245,0.000055357083,0.0007108808,0.0004081477,0.003930742,0.0004624391],"domain_scores_gemma":[0.9883778,0.0005529157,0.00042155787,0.00023972282,0.010343828,0.000064160056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011987141,0.00015316704,0.00020062871,0.00023520006,0.00033843823,0.0002606954,0.0029358594,0.00010784516,0.00004510781],"category_scores_gemma":[0.0043647294,0.00012917204,0.00026027588,0.0013126167,0.0003557846,0.0005899029,0.0011698594,0.0004708591,0.000019030567],"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":[0.000038173952,0.0000420232,0.0003727943,0.0001391192,0.00016460875,8.629897e-8,0.0044088243,0.19836816,0.087836646,0.6414411,0.0026339053,0.064554535],"study_design_scores_gemma":[0.000033546145,0.000121191384,0.00004775445,0.00011525465,0.000011819212,4.4747406e-7,0.0014416452,0.66042787,0.29397556,0.043137304,0.0006055086,0.000082131635],"about_ca_topic_score_codex":0.00005510717,"about_ca_topic_score_gemma":0.0000024311548,"teacher_disagreement_score":0.9598081,"about_ca_system_score_codex":0.00030861006,"about_ca_system_score_gemma":0.0004924182,"threshold_uncertainty_score":0.5455606},"labels":[],"label_agreement":null},{"id":"W4400280941","doi":"10.32473/flairs.37.1.135597","title":"Exploration of Word Embeddings with Graph-Based Context Adaptation for Enhanced Word Vectors","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Word embedding; Word (group theory); Natural language understanding; Embedding; Context (archaeology); Natural language; Graph; Representation (politics); Semantic similarity; Linguistics; Theoretical computer science","score_opus":0.19174769746374895,"score_gpt":0.3730239915487573,"score_spread":0.18127629408500837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400280941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10696344,0.000058520636,0.8866716,0.0044277534,0.00072031847,0.00065166655,0.000012053626,0.000080511185,0.0004141606],"genre_scores_gemma":[0.9680585,0.00004410308,0.031277455,0.000041908403,0.00018077303,0.00020754182,0.0000038037774,0.000016470283,0.00016938975],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702346,0.000021739745,0.00059334567,0.00058584625,0.0014094464,0.0003661497],"domain_scores_gemma":[0.9952078,0.0005898859,0.0002237621,0.00021661496,0.0036883696,0.00007359424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016674477,0.00018207215,0.00021724615,0.00022558738,0.00020222708,0.0005042288,0.001910309,0.000095587115,0.00002176321],"category_scores_gemma":[0.0005619655,0.00013949847,0.0002441439,0.001149911,0.00037911345,0.0014710063,0.0002570636,0.00038434035,0.0000054643847],"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":[0.00015210254,0.000072538816,0.00004805827,0.00026633902,0.00010903729,2.429194e-7,0.011296029,0.0041212384,0.11436623,0.7641556,0.00032960897,0.10508295],"study_design_scores_gemma":[0.000037972273,0.00009467425,0.0000076019787,0.00043511277,0.0000067398782,4.7847817e-7,0.0036455481,0.47918305,0.43053994,0.08575032,0.00020024729,0.00009829547],"about_ca_topic_score_codex":0.00013207625,"about_ca_topic_score_gemma":0.000052975945,"teacher_disagreement_score":0.86109513,"about_ca_system_score_codex":0.00017840404,"about_ca_system_score_gemma":0.0005127429,"threshold_uncertainty_score":0.56885827},"labels":[],"label_agreement":null},{"id":"W4400281151","doi":"10.32473/flairs.37.1.135275","title":"Ethics of AI Explained","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Information ethics; Applied ethics; Meta-ethics; Engineering ethics; Ethics of technology; Normative ethics; Psychology; Sociology; Epistemology; Philosophy; Engineering","score_opus":0.31417746115583306,"score_gpt":0.5028181561763003,"score_spread":0.18864069502046726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400281151","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2569106,0.0005031054,0.0028330777,0.5084128,0.0060430383,0.0012784335,0.00011366286,0.00023012012,0.22367518],"genre_scores_gemma":[0.9949499,0.0013987883,0.0004469838,0.00027058995,0.00082016014,0.00002395759,0.0000016587619,0.000017004235,0.00207096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952731,0.000101285965,0.0005809503,0.00036540633,0.0031569402,0.0005223029],"domain_scores_gemma":[0.9896577,0.0017284685,0.00014513063,0.0001361697,0.008178348,0.0001541659],"candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.010535178,0.00014491113,0.00022107789,0.00014268934,0.00085606053,0.00080149056,0.0020956036,0.0003603727,0.000379076],"category_scores_gemma":[0.0097721,0.000118194104,0.0003775574,0.0011978969,0.0027732183,0.0008902274,0.0004739759,0.0022746096,0.000031548585],"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":[0.000022373524,0.000048245158,0.00018076085,0.00011703733,0.00008308178,3.760227e-7,0.067800865,0.000007073907,0.022413876,0.9015433,0.004539761,0.0032432263],"study_design_scores_gemma":[0.000018491482,0.000066313034,0.000044407065,0.00062132615,0.00001473265,6.086516e-7,0.067619,0.0038799006,0.12433521,0.7867009,0.016542934,0.00015619019],"about_ca_topic_score_codex":0.0026907853,"about_ca_topic_score_gemma":0.00045822273,"teacher_disagreement_score":0.7380393,"about_ca_system_score_codex":0.00029279117,"about_ca_system_score_gemma":0.0021532397,"threshold_uncertainty_score":0.99994063},"labels":[],"label_agreement":null},{"id":"W4400281194","doi":"10.32473/flairs.37.1.135320","title":"Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness.","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); Degradation (telecommunications); Multivariate statistics; Long short term memory; Term (time); Corrosion; Computer science; Multivariate analysis; Artificial intelligence; Data mining; Machine learning; Materials science; Metallurgy; Artificial neural network; Telecommunications","score_opus":0.22392692594114252,"score_gpt":0.4080404702413655,"score_spread":0.184113544300223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400281194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5300804,0.000028458115,0.46884945,0.00017945115,0.00016684303,0.00027628973,0.000013777635,0.0001136997,0.00029165953],"genre_scores_gemma":[0.8508206,0.000079989724,0.14889693,0.000009225691,0.00008341395,0.000062322746,0.0000050713725,0.00002653351,0.000015938893],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764574,0.000025016861,0.0005671269,0.0004907142,0.0008946731,0.00037675933],"domain_scores_gemma":[0.9979534,0.00039220808,0.00005969294,0.00011695956,0.0013941395,0.000083594234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020120172,0.00021560115,0.00026508395,0.00054737646,0.0001588203,0.0004009174,0.0007302464,0.00013163748,0.00000902665],"category_scores_gemma":[0.000808786,0.00019484803,0.00012543038,0.001931518,0.00022218694,0.00063058326,0.0005339247,0.00063308014,0.0000012689402],"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":[0.00006601617,0.000034615572,0.019977238,0.0003833774,0.00023676804,0.000002766876,0.008392128,0.030195292,0.8810175,0.050808758,0.00005400988,0.008831551],"study_design_scores_gemma":[0.000012631097,0.000014715113,0.0015108732,0.00081647513,0.000037975482,0.0000037613577,0.0014696713,0.7735584,0.1809029,0.04152027,4.5149295e-7,0.000151905],"about_ca_topic_score_codex":0.00016854415,"about_ca_topic_score_gemma":0.000096778975,"teacher_disagreement_score":0.7433631,"about_ca_system_score_codex":0.00074631727,"about_ca_system_score_gemma":0.00021525793,"threshold_uncertainty_score":0.7945672},"labels":[],"label_agreement":null},{"id":"W4400281282","doi":"10.32473/flairs.37.1.135043","title":"Latent Beta-Liouville Probabilistic Modeling for Bursty Topic Discovery in Textual Data","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","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":"Concordia University","funders":"","keywords":"Latent Dirichlet allocation; Burstiness; Perplexity; Computer science; Topic model; Natural language processing; Language model; Word (group theory); Probabilistic logic; Dirichlet distribution; Artificial intelligence; Range (aeronautics); Mathematics","score_opus":0.312632176089768,"score_gpt":0.4053127273422721,"score_spread":0.09268055125250407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400281282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13139385,0.0002585897,0.84828687,0.0154765565,0.0017453809,0.0011030616,0.00008164008,0.00010832739,0.0015457162],"genre_scores_gemma":[0.9875429,0.0001400643,0.011294596,0.000043325566,0.00042251253,0.00011481547,0.000009648156,0.0000141651935,0.00041796276],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654716,0.000022055136,0.0006833407,0.00094478886,0.0012720381,0.0005306039],"domain_scores_gemma":[0.9976671,0.00045939197,0.0000836039,0.0005144729,0.0012007456,0.00007470678],"candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.003142123,0.00018290189,0.0002204546,0.00016607328,0.00019497084,0.0012250956,0.005549003,0.00010766474,0.000015963333],"category_scores_gemma":[0.00095651246,0.00014193855,0.00018854605,0.0007648363,0.00022725026,0.0017760909,0.0025786783,0.00063869875,0.000010342332],"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":[0.000019074945,0.000066425244,0.00017230446,0.00019408988,0.000053039417,7.7601067e-7,0.0020088488,0.005353256,0.005118276,0.9666145,0.00051446544,0.019884942],"study_design_scores_gemma":[0.000024891297,0.000030158659,0.000012690384,0.00030871073,0.0000053515037,0.0000022578063,0.0010202721,0.7982248,0.007001175,0.19293065,0.0003196842,0.000119331875],"about_ca_topic_score_codex":0.00031857865,"about_ca_topic_score_gemma":0.000078407,"teacher_disagreement_score":0.8561491,"about_ca_system_score_codex":0.00031280544,"about_ca_system_score_gemma":0.0005189169,"threshold_uncertainty_score":0.99983144},"labels":[],"label_agreement":null},{"id":"W4400281600","doi":"10.32473/flairs.37.1.135596","title":"Decoding Complexity: A Mathematical Framework for Enhanced Translation Comprehension","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Decoding methods; Translation (biology); Computer science; Comprehension; Natural language processing; Theoretical computer science; Artificial intelligence; Cognitive science; Programming language; Psychology; Algorithm; Biology; Genetics","score_opus":0.27049055119903,"score_gpt":0.4406814160020632,"score_spread":0.1701908648030332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400281600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005175868,0.0003578137,0.9803994,0.011435023,0.0007031218,0.0006363724,0.000012898107,0.000299841,0.0009796264],"genre_scores_gemma":[0.56712127,0.000058993446,0.43240684,0.00004369886,0.00021825511,0.00007919828,0.0000017969588,0.000011831886,0.00005812044],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970865,0.000022545646,0.00054977275,0.00058774016,0.0013156926,0.0004377047],"domain_scores_gemma":[0.9963104,0.0012445307,0.00012709273,0.00022122115,0.00201465,0.00008207007],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0016564842,0.00018891868,0.0002232907,0.00015825115,0.00035316162,0.0011337607,0.0028451819,0.0001679262,0.000054388427],"category_scores_gemma":[0.0010499621,0.00014350124,0.00031245817,0.0009575826,0.0004730049,0.0009015316,0.00063221337,0.0007665457,0.000019529847],"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":[0.000020082472,0.000032472595,0.000002771858,0.00021860527,0.00003437128,2.7939723e-7,0.002680149,0.0000042280813,0.13621923,0.8019593,0.00036269453,0.058465816],"study_design_scores_gemma":[0.000008702438,0.000025274541,6.853369e-7,0.00050441106,0.0000037516947,0.0000029323587,0.00036151495,0.217899,0.3095219,0.47144166,0.00015575679,0.00007440745],"about_ca_topic_score_codex":0.000014529595,"about_ca_topic_score_gemma":0.000002796582,"teacher_disagreement_score":0.5619454,"about_ca_system_score_codex":0.00019406849,"about_ca_system_score_gemma":0.00020485942,"threshold_uncertainty_score":0.99990314},"labels":[],"label_agreement":null},{"id":"W4400282035","doi":"10.32473/flairs.37.1.135283","title":"Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks 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":"Université Laval","funders":"","keywords":"Encoder; Transformer; Computer science; Artificial intelligence; Sequence (biology); Sequence learning; Speech recognition; Pattern recognition (psychology); Engineering; Electrical engineering; Biology; Voltage","score_opus":0.374587593761272,"score_gpt":0.47993995937539824,"score_spread":0.10535236561412625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400282035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16201976,0.00014068255,0.80765545,0.019072708,0.0006578078,0.0013583574,0.000039236365,0.00012508602,0.008930911],"genre_scores_gemma":[0.99669987,0.000266736,0.002390859,0.000029728099,0.00022404232,0.0001752175,0.0000034262023,0.00001357556,0.00019652539],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760944,0.00003188832,0.00049335376,0.00047871066,0.0010155673,0.00037101386],"domain_scores_gemma":[0.99675065,0.00095404504,0.00016877934,0.0002180656,0.0018466073,0.00006187438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020855502,0.00016295756,0.00016085758,0.000093036964,0.00047460914,0.00096784794,0.0024529018,0.00008772501,0.000017671362],"category_scores_gemma":[0.00026255494,0.00009757494,0.00041818628,0.00093728415,0.00044570246,0.0012375539,0.00019174775,0.0007461051,0.0000064418687],"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":[0.000013899879,0.000051660667,0.000037852613,0.000054722503,0.00007143321,6.7828886e-8,0.0022108369,0.011025977,0.13068317,0.81167406,0.00083706324,0.043339264],"study_design_scores_gemma":[0.000013231133,0.00008213828,0.000058023354,0.00018188781,0.000005376303,0.0000018298065,0.0012221765,0.7430351,0.06830662,0.1867673,0.00024784665,0.00007853064],"about_ca_topic_score_codex":0.00010609575,"about_ca_topic_score_gemma":0.0000026727207,"teacher_disagreement_score":0.83468014,"about_ca_system_score_codex":0.00020990064,"about_ca_system_score_gemma":0.0003939722,"threshold_uncertainty_score":0.933298},"labels":[],"label_agreement":null},{"id":"W4400282308","doi":"10.32473/flairs.37.1.135277","title":"Embedding Ethics Into Artificial Intelligence: Understanding What Can Be Done, What Can't, and What Is Done","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Embedding; Engineering ethics; Ethics of technology; Computer science; Ethical issues; Ethical decision; Management science; Sociology; Artificial intelligence; Information ethics; Engineering; Meta-ethics","score_opus":0.3507927044911351,"score_gpt":0.47804983450773036,"score_spread":0.12725713001659528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400282308","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20653592,0.0058387266,0.006758669,0.7623395,0.012012323,0.0018078773,0.00006295666,0.0002964419,0.0043476066],"genre_scores_gemma":[0.8938046,0.102017686,0.0005429793,0.001504142,0.0014129668,0.000053242315,0.000007172032,0.00004851753,0.0006086636],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9920825,0.00020771293,0.0010321897,0.0010551214,0.004412143,0.0012103764],"domain_scores_gemma":[0.9911359,0.0028895324,0.00029492096,0.0002665085,0.004918869,0.000494268],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.012151051,0.0004261018,0.0004700627,0.0003472331,0.0031688455,0.023425674,0.002347853,0.00077417167,0.00033380822],"category_scores_gemma":[0.004386782,0.0003805735,0.0004466683,0.0017841072,0.0047185374,0.009303156,0.0010876127,0.003515783,0.000021245307],"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":[0.00004117005,0.00005235848,0.00002486189,0.00017793979,0.00016080255,0.0000024725139,0.2914402,0.000021629243,0.005548189,0.6681601,0.0007323507,0.033637963],"study_design_scores_gemma":[0.00001276379,0.00005805468,0.0000021505402,0.0017517243,0.000024293333,0.000002002437,0.4805503,0.0068755676,0.031610403,0.47713247,0.0017231895,0.00025707926],"about_ca_topic_score_codex":0.0057144426,"about_ca_topic_score_gemma":0.0052424925,"teacher_disagreement_score":0.76083535,"about_ca_system_score_codex":0.0017003075,"about_ca_system_score_gemma":0.0023484905,"threshold_uncertainty_score":0.99986464},"labels":[],"label_agreement":null},{"id":"W4400282938","doi":"10.32473/flairs.37.1.135537","title":"Fluid Path Detection Model for Lab on a Chip Images Using Deep Learning-Based Segmentation Approach","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Artificial intelligence; Computer science; Path (computing); Chip; Deep learning; Lab-on-a-chip; Computer vision; Pattern recognition (psychology); Machine learning; Materials science; Nanotechnology; Microfluidics; Telecommunications","score_opus":0.11882508507711297,"score_gpt":0.370022375763588,"score_spread":0.251197290686475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400282938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012326246,0.000047019665,0.9846647,0.000888064,0.00045862433,0.00064784824,0.000010317012,0.00024011059,0.0007170891],"genre_scores_gemma":[0.94673824,0.00006281193,0.052389175,0.000077001765,0.00023521177,0.00022462157,0.0000033305637,0.000021195421,0.00024839584],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737257,0.00003638411,0.0004168548,0.0006101958,0.0011749308,0.00038903797],"domain_scores_gemma":[0.99738926,0.00026702887,0.0001332222,0.00016544748,0.0019799543,0.000065092776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002061486,0.00019200076,0.00015745318,0.0002582668,0.00051848,0.0010118369,0.0014257946,0.00012000541,0.000009892499],"category_scores_gemma":[0.0005037791,0.00015734034,0.0002995117,0.00087814516,0.00023161492,0.0008472433,0.00030161717,0.0006490982,0.000007644645],"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":[0.00012391599,0.00015130508,0.000013740435,0.0003201595,0.00008660529,4.589389e-7,0.0040092,0.016428838,0.7619864,0.082183585,0.0004711413,0.13422465],"study_design_scores_gemma":[0.000016936187,0.00008668847,0.0000018320096,0.00007956258,0.0000045065417,0.0000017112258,0.00049074856,0.50206345,0.47162536,0.025497751,0.00005188674,0.00007957833],"about_ca_topic_score_codex":0.00007035665,"about_ca_topic_score_gemma":0.0000031240663,"teacher_disagreement_score":0.934412,"about_ca_system_score_codex":0.0004283835,"about_ca_system_score_gemma":0.00026155973,"threshold_uncertainty_score":0.97571665},"labels":[],"label_agreement":null},{"id":"W4400348335","doi":"10.32473/flairs.37.1.135561","title":"Abstractive Text Summarization Based on Neural Fusion","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","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":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Lethbridge","keywords":"Automatic summarization; Computer science; Natural language processing; Artificial intelligence; Text graph; Graph; Segmentation; Information retrieval; Baseline (sea); Selection (genetic algorithm); Multi-document summarization; Theoretical computer science","score_opus":0.13458505484915448,"score_gpt":0.3708120438842319,"score_spread":0.2362269890350774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400348335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1519111,0.000108327324,0.7529386,0.048841722,0.005973094,0.0012023604,0.00003419119,0.0004479056,0.03854268],"genre_scores_gemma":[0.99450165,0.00004769429,0.0044343635,0.00015290115,0.00037811318,0.00004168205,0.000002716314,0.000013646455,0.00042721312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965495,0.00002865161,0.0004543728,0.0006541598,0.0019174869,0.00039585886],"domain_scores_gemma":[0.99719,0.0005375834,0.00011091947,0.00026107213,0.0018102133,0.000090239075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017550087,0.00017495986,0.0001433613,0.00019868882,0.00029849345,0.0010111921,0.0026841275,0.00011395946,0.00009619408],"category_scores_gemma":[0.0006836159,0.00013489256,0.00023206255,0.0009846733,0.0002596336,0.0008857129,0.0006884683,0.00082328275,0.00006611689],"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":[0.000035094956,0.00008605559,0.00016384113,0.000077908815,0.00003542038,0.0000014525215,0.0019913395,0.004230486,0.053814884,0.8563767,0.0013518535,0.08183498],"study_design_scores_gemma":[0.000016424363,0.000057105488,0.0001003477,0.00023749779,0.0000030411777,0.0000016565775,0.00052388175,0.8141717,0.12076221,0.06336026,0.00065812556,0.00010775807],"about_ca_topic_score_codex":0.00010153227,"about_ca_topic_score_gemma":0.000006376764,"teacher_disagreement_score":0.8425906,"about_ca_system_score_codex":0.0002912778,"about_ca_system_score_gemma":0.00034134075,"threshold_uncertainty_score":0.9750949},"labels":[],"label_agreement":null},{"id":"W4410397730","doi":"10.32473/flairs.38.1.138888","title":"Leveraging Faithfulness in Abstractive Text Summarization with Elementary Discourse Units","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Automatic summarization; Natural language processing; Computer science; Linguistics; Artificial intelligence; Psychology; Philosophy","score_opus":0.11124415059323012,"score_gpt":0.3668951031233309,"score_spread":0.2556509525301008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410397730","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58636194,0.000026076044,0.38936883,0.01367913,0.0006383571,0.0006066224,0.0000061224573,0.0000515512,0.009261389],"genre_scores_gemma":[0.9941651,0.000041759213,0.0051214737,0.000106554246,0.000084905594,0.00006936634,0.0000027738695,0.000007637285,0.00040041198],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730116,0.00003095003,0.0004938237,0.0005473628,0.0012028468,0.0004238685],"domain_scores_gemma":[0.9967806,0.00027462636,0.00016844513,0.00023381037,0.0024874585,0.00005507415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016066816,0.00016486525,0.00017684884,0.00024021087,0.00026640258,0.0004361379,0.0027196947,0.0000758537,0.0000288632],"category_scores_gemma":[0.0004187314,0.00012922598,0.000071857925,0.0018588762,0.00034613526,0.0010266763,0.0009708255,0.00069185864,0.000005251298],"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":[0.00007150171,0.00013436134,0.010713778,0.0000749874,0.000092132854,0.000001350128,0.008271636,0.0022780222,0.015045849,0.9103408,0.00037823434,0.052597374],"study_design_scores_gemma":[0.0001254759,0.000055386696,0.0023182624,0.00080905494,0.000008907963,0.0000027529181,0.032953586,0.4922805,0.29993248,0.17099944,0.0002581671,0.0002559582],"about_ca_topic_score_codex":0.00058150117,"about_ca_topic_score_gemma":0.000097202006,"teacher_disagreement_score":0.7393413,"about_ca_system_score_codex":0.00036994877,"about_ca_system_score_gemma":0.00061540015,"threshold_uncertainty_score":0.5269683},"labels":[],"label_agreement":null},{"id":"W4410397769","doi":"10.32473/flairs.38.1.139141","title":"Online Community Modeling and Moderation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Social Media and Politics","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":"Université Laval","funders":"","keywords":"Moderation; Psychology; Computer science; Social psychology","score_opus":0.2771773974238197,"score_gpt":0.4584976435238617,"score_spread":0.181320246100042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410397769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95578146,0.000054805907,0.0030372376,0.020080848,0.0008847783,0.00036153753,0.000020073603,0.000039310347,0.019739937],"genre_scores_gemma":[0.9973945,0.0005826451,0.0006426833,0.00019567159,0.00039053525,0.000025714296,0.0000032314704,0.0000061669875,0.0007588684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99797595,0.00010840276,0.00036063459,0.00019578723,0.0009959944,0.00036325597],"domain_scores_gemma":[0.9961746,0.00062707224,0.000085672,0.00010499597,0.0029170017,0.000090681075],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0022675944,0.00010288592,0.00014912622,0.00009237752,0.0015737374,0.0003327544,0.0011373713,0.00014626537,0.00004039201],"category_scores_gemma":[0.0034656767,0.000091375034,0.000113882845,0.0005749448,0.0011927903,0.0003309709,0.00045902192,0.0008753469,0.0000034481218],"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":[0.00002241433,0.00011438712,0.0015111332,0.00004214473,0.000045741628,4.1529933e-8,0.03739844,0.00007823544,0.0075475927,0.94500005,0.00065287057,0.0075869467],"study_design_scores_gemma":[0.00003314582,0.00002323941,0.00009757467,0.00020317482,0.000012993942,2.1966311e-7,0.22961545,0.13298011,0.023560537,0.6119435,0.0014228215,0.00010724025],"about_ca_topic_score_codex":0.006222969,"about_ca_topic_score_gemma":0.0008896319,"teacher_disagreement_score":0.33305657,"about_ca_system_score_codex":0.000246627,"about_ca_system_score_gemma":0.00055201334,"threshold_uncertainty_score":0.99972606},"labels":[],"label_agreement":null},{"id":"W4410397895","doi":"10.32473/flairs.38.1.138971","title":"RQPool: A Novel Multi-Branch Graph-Level Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Anomaly detection; Computer science; Graph; Anomaly (physics); Artificial intelligence; Theoretical computer science; Physics","score_opus":0.15943033505508222,"score_gpt":0.36086864632026355,"score_spread":0.20143831126518133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410397895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13032363,0.000061331644,0.8578454,0.005013309,0.0032005375,0.0005486458,0.0000137575,0.00012611006,0.002867225],"genre_scores_gemma":[0.99038863,0.00021243103,0.00795364,0.00018707463,0.00026385274,0.00008741393,8.598809e-7,0.000010063903,0.00089604635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967509,0.00003391614,0.00064843823,0.00068205077,0.0013592085,0.00052551215],"domain_scores_gemma":[0.99553156,0.000277569,0.00023161576,0.00031911576,0.0035440917,0.00009603176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021754687,0.00021783262,0.00022620095,0.00030803363,0.00070712133,0.00066351146,0.0035646812,0.00019663431,0.00003632758],"category_scores_gemma":[0.0007304618,0.00018489982,0.00035208353,0.0024065808,0.0005220319,0.0010164315,0.0011219952,0.00094109453,0.000023713093],"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":[0.000071156974,0.00025069417,0.00024544683,0.00007034846,0.00011722132,1.9649609e-7,0.0015309023,0.00015623978,0.43513635,0.48211658,0.0013918714,0.07891301],"study_design_scores_gemma":[0.000073898096,0.00006226601,0.0006271079,0.00018994207,0.000006644265,0.000004246798,0.00060285995,0.26816523,0.63518757,0.0935308,0.0013952662,0.00015418947],"about_ca_topic_score_codex":0.0004074993,"about_ca_topic_score_gemma":0.00016323705,"teacher_disagreement_score":0.860065,"about_ca_system_score_codex":0.00025687937,"about_ca_system_score_gemma":0.0002848523,"threshold_uncertainty_score":0.7539996},"labels":[],"label_agreement":null},{"id":"W4410398250","doi":"10.32473/flairs.38.1.138756","title":"Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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":"Breast cancer; Medicine; Radiology; Magnetic resonance imaging; Breast MRI; Cancer; Mammography; Medical physics; Internal medicine","score_opus":0.1759041074243973,"score_gpt":0.4583605814808804,"score_spread":0.2824564740564831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410398250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18291332,0.00042384592,0.6547824,0.14235008,0.0012683506,0.00531679,0.0007054361,0.00026707986,0.01197266],"genre_scores_gemma":[0.9580874,0.0004812013,0.039033797,0.00037691905,0.0004004148,0.0010766981,0.0000055007977,0.000020904607,0.00051720487],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803644,0.000007583356,0.00050651527,0.00041959353,0.0006657731,0.00036406086],"domain_scores_gemma":[0.99494284,0.00036500813,0.00021381318,0.00016335523,0.0042356066,0.000079402074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009461621,0.00015869456,0.00023009043,0.00012737313,0.00045715403,0.0001449211,0.00065645855,0.00008336147,0.00008551037],"category_scores_gemma":[0.0005955307,0.0001291953,0.00026591885,0.0007894636,0.000512762,0.0002638446,0.00045111918,0.00046311377,0.0000017687739],"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":[0.00020645851,0.00025620998,0.014868632,0.00032981628,0.00013351435,4.5908814e-7,0.0005547637,0.00024843897,0.24523497,0.60194844,0.009118062,0.12710021],"study_design_scores_gemma":[0.00009593255,0.000031153457,0.0006827652,0.0013001212,0.00005845351,0.000009852768,0.0037239534,0.22185819,0.56706405,0.20194589,0.003060049,0.00016959042],"about_ca_topic_score_codex":0.00025882496,"about_ca_topic_score_gemma":0.000012446964,"teacher_disagreement_score":0.775174,"about_ca_system_score_codex":0.00044896032,"about_ca_system_score_gemma":0.00046153375,"threshold_uncertainty_score":0.52684313},"labels":[],"label_agreement":null},{"id":"W4410398281","doi":"10.32473/flairs.38.1.138970","title":"Flexible Dirichlet Mixture Model for Multi-modal data Clustering","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Modal; Cluster analysis; Latent Dirichlet allocation; Mixture model; Computer science; Dirichlet distribution; Data mining; Mathematics; Artificial intelligence; Topic model; Materials science","score_opus":0.3601967099388323,"score_gpt":0.46848168253118033,"score_spread":0.10828497259234804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410398281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015104585,0.00007421227,0.98502517,0.009911838,0.00088402315,0.00087774725,0.00009887792,0.0001169069,0.0015007964],"genre_scores_gemma":[0.5616374,0.0002532886,0.42975354,0.00024254563,0.00032349167,0.00029065952,0.000016696566,0.000031645115,0.0074507003],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99566674,0.000027950924,0.000676033,0.0011043546,0.0016852437,0.0008396778],"domain_scores_gemma":[0.9939089,0.0005311086,0.00019579368,0.000901266,0.0043289815,0.00013395671],"candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0031512985,0.00025931833,0.00029597455,0.00023740708,0.00065914553,0.0008450337,0.012231864,0.00017237541,0.000012420605],"category_scores_gemma":[0.0023248943,0.00021790045,0.00023105853,0.0013653807,0.000582255,0.0014313832,0.008736842,0.00091347174,0.000010675614],"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":[0.00020710207,0.0003752105,0.00019083392,0.0005111713,0.00031002075,8.1816e-7,0.0039007154,0.016169045,0.10418768,0.74418706,0.0117922425,0.11816808],"study_design_scores_gemma":[0.000082355815,0.000031622574,0.000018761302,0.00020010359,0.000005706021,0.0000018570305,0.0007792484,0.8340309,0.08647263,0.07717104,0.0010454896,0.00016028289],"about_ca_topic_score_codex":0.00007856959,"about_ca_topic_score_gemma":0.000036097794,"teacher_disagreement_score":0.81786186,"about_ca_system_score_codex":0.00036949367,"about_ca_system_score_gemma":0.00074315444,"threshold_uncertainty_score":0.99928033},"labels":[],"label_agreement":null},{"id":"W4410398730","doi":"10.32473/flairs.38.1.139110","title":"Preface","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Historical Geography and Geographical Thought","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Philosophy","score_opus":0.13027231724660346,"score_gpt":0.418289214212581,"score_spread":0.28801689696597754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410398730","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09881697,0.00045210888,0.003348448,0.0724717,0.004046714,0.0011981238,0.000029329682,0.00017502785,0.8194616],"genre_scores_gemma":[0.98783654,0.0009840337,0.0007153693,0.0001880871,0.00037754315,0.00006357128,9.4412763e-7,0.0000067974415,0.0098271305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966572,0.00006888291,0.00046701505,0.00041736223,0.0018195868,0.0005699314],"domain_scores_gemma":[0.99553996,0.0005348765,0.00013016767,0.00016443658,0.0034947477,0.00013581883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036071397,0.00013754565,0.00019149182,0.00018719064,0.0012504593,0.00031213308,0.0025916232,0.00019486876,0.00034296906],"category_scores_gemma":[0.0026174067,0.000111428664,0.0004241246,0.0037874766,0.0022483324,0.000339043,0.0005318556,0.0007893419,0.000032068063],"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":[0.000039102528,0.000098884455,0.002232812,0.00002757335,0.00007189707,1.1644048e-7,0.0042525623,0.000006054641,0.0044372887,0.9630659,0.009131743,0.01663602],"study_design_scores_gemma":[0.00003643855,0.000037675523,0.00055802375,0.00023454784,0.00001681156,2.1477472e-7,0.020663856,0.0008221494,0.04178981,0.64381677,0.2918432,0.00018051795],"about_ca_topic_score_codex":0.0011113897,"about_ca_topic_score_gemma":0.0001862135,"teacher_disagreement_score":0.88901955,"about_ca_system_score_codex":0.0002408546,"about_ca_system_score_gemma":0.00046369332,"threshold_uncertainty_score":0.96176493},"labels":[],"label_agreement":null},{"id":"W4410432471","doi":"10.32473/flairs.38.1.138855","title":"AI Governance in Academia: Guidelines for Generative AI","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université du Québec à Trois-Rivières","keywords":"Generative grammar; Corporate governance; Engineering ethics; Artificial intelligence; Computer science; Psychology; Engineering; Management; Economics","score_opus":0.32864949325457654,"score_gpt":0.46368494767640184,"score_spread":0.1350354544218253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410432471","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0927541,0.0006360677,0.067625254,0.78926843,0.007043088,0.0035831453,0.00018700623,0.00017952625,0.038723357],"genre_scores_gemma":[0.9829962,0.0004391311,0.0015984034,0.010457884,0.0018513886,0.00030081815,0.000016383789,0.000023326622,0.0023164193],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99677634,0.00000737357,0.0009033216,0.00061561173,0.0011426624,0.00055469054],"domain_scores_gemma":[0.98906106,0.00024327457,0.0003082135,0.00020578089,0.010161861,0.000019795692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00249802,0.00024705895,0.00030110133,0.0002249098,0.00036588198,0.00071095274,0.0027200596,0.0003793542,0.00024018271],"category_scores_gemma":[0.005069472,0.0001959439,0.0002487803,0.0018323667,0.00057519303,0.0017576162,0.0011820538,0.0015190389,0.00003863804],"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":[0.00012518,0.00008099663,0.0026566982,0.00022095449,0.000047220823,1.7051046e-7,0.0001443433,0.00016652267,0.015323407,0.8406019,0.12354429,0.017088309],"study_design_scores_gemma":[0.00010491641,0.0000151674,0.00069846,0.0009033917,0.000021467924,7.153719e-7,0.0021853838,0.17334367,0.14003845,0.56476134,0.11763509,0.00029193092],"about_ca_topic_score_codex":0.0006891386,"about_ca_topic_score_gemma":0.00021072333,"teacher_disagreement_score":0.89024216,"about_ca_system_score_codex":0.00022733772,"about_ca_system_score_gemma":0.00030667495,"threshold_uncertainty_score":0.799036},"labels":[],"label_agreement":null},{"id":"W4410432646","doi":"10.32473/flairs.38.1.138913","title":"Creating Domain-Specific Datasets for Intelligent Environmental Feature Comparison","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced Computational Techniques and Applications","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 Waterloo; University of Windsor","funders":"","keywords":"Domain (mathematical analysis); Feature (linguistics); Computer science; Artificial intelligence; Data mining; Pattern recognition (psychology); Information retrieval; Mathematics","score_opus":0.10599425016704206,"score_gpt":0.4030994945546468,"score_spread":0.2971052443876047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410432646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005900546,0.00012221237,0.97720915,0.01236621,0.00040687507,0.0009094731,0.00012473868,0.000077326615,0.0028834522],"genre_scores_gemma":[0.77215767,0.00024594538,0.22628602,0.00019751044,0.0001928429,0.00035439944,0.00004441152,0.00001210383,0.0005090891],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997427,0.000018030292,0.00056004693,0.00062883977,0.00096192176,0.00040417383],"domain_scores_gemma":[0.9979302,0.0005327617,0.00024000263,0.00032880524,0.0008918,0.00007639679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010954124,0.0001917349,0.00021598059,0.00012784201,0.00067429134,0.0004440063,0.003636539,0.00010770039,0.000022114009],"category_scores_gemma":[0.00013920787,0.00016253286,0.0002469761,0.0007415857,0.00043475247,0.0004705934,0.0013629636,0.000531006,0.000010939689],"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":[0.000024586432,0.00012709585,0.00013897844,0.000027857008,0.000045596527,7.40694e-8,0.0008003162,0.00023025718,0.03228854,0.9268279,0.01339401,0.026094772],"study_design_scores_gemma":[0.000041871008,0.00005248084,0.000099203,0.00014717522,0.0000047960975,0.0000018409356,0.002591807,0.094440445,0.2509519,0.5582559,0.09325191,0.00016067285],"about_ca_topic_score_codex":0.000011097277,"about_ca_topic_score_gemma":0.0000023149994,"teacher_disagreement_score":0.7662571,"about_ca_system_score_codex":0.00032955137,"about_ca_system_score_gemma":0.00016072522,"threshold_uncertainty_score":0.67576545},"labels":[],"label_agreement":null}]}