{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":36,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":36,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"a2a6b55c8998","filters":{"venue":"Educational Data Mining"}},"results":[{"id":"W2405817496","doi":"","title":"Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques.","year":2011,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":87,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Automatic summarization; Toolbox; Computer science; Social network analysis; Identification (biology); Visualization; Social media; Online discussion; World Wide Web; Exploit; Data science; Multimedia; Information retrieval; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.126295305895185,"gpt":0.4262975173209416,"spread":0.3000022114257566,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005056555,0.0001161196,0.0003023838,0.0003090509,0.00009644098,0.00002467442,0.0005473373,0.00002881874,0.0005518376],"category_scores_gemma":[0.00001719838,0.0001304812,0.00008308717,0.001421544,0.0000487029,0.0002594048,0.000268249,0.00009355642,8.367981e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004011473,"about_ca_system_score_gemma":0.0001124379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001224507,"about_ca_topic_score_gemma":0.0003094096,"domain_scores_codex":[0.9987167,0.00008725849,0.0004847668,0.0002938003,0.0002111601,0.0002063748],"domain_scores_gemma":[0.9990271,0.00009141245,0.000338125,0.000405675,0.0001009692,0.0000366683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006275335,0.0004332886,0.9912235,0.00000272943,0.0004239401,1.410286e-7,0.0005577595,0.0002379403,0.00004356846,0.001342785,0.0005859433,0.005142165],"study_design_scores_gemma":[0.00009638852,0.00001129127,0.9775927,0.00005120258,0.0009449523,9.690489e-8,0.0006935367,0.01805462,0.0001684553,0.002079881,0.0001104221,0.0001964184],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782022,0.00004547531,0.02096697,0.00003547644,0.00004097612,0.00009972251,0.0001388271,0.00001870489,0.000451658],"genre_scores_gemma":[0.9176283,0.000001676817,0.07943891,0.00001152979,0.0005171566,0.00001414835,0.002360406,0.00001028214,0.00001752776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06057383,"threshold_uncertainty_score":0.6042235,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106157574","doi":"","title":"Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning","year":2012,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Session (web analytics); Relevance (law); Reading (process); Adaptation (eye); Test (biology); TRACE (psycholinguistics); Intelligent tutoring system; Cluster analysis; Artificial intelligence; Mathematics education; Psychology; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.1003744155676827,"gpt":0.3635039676163,"spread":0.2631295520486173,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002787256,0.0003512621,0.0003677355,0.0004879239,0.0005983962,0.0009729212,0.002280335,0.0001131003,0.00003329907],"category_scores_gemma":[0.0002847609,0.0003963549,0.0000560784,0.0005867701,0.00002138552,0.004271393,0.00200976,0.0007229999,0.0001503339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006090713,"about_ca_system_score_gemma":0.000152172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001546418,"about_ca_topic_score_gemma":0.000009411828,"domain_scores_codex":[0.9960722,0.000501518,0.0008437151,0.0008956565,0.0008033718,0.0008835469],"domain_scores_gemma":[0.9978698,0.0003162942,0.0004471957,0.0009308524,0.0001469626,0.0002888344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007981767,0.0003685529,0.9268969,0.0001893715,0.00006661504,0.00002061013,0.03808875,0.001626724,0.003121742,0.01383886,0.00001493806,0.01575896],"study_design_scores_gemma":[0.000537991,0.0001916156,0.8814506,0.00293678,0.0000888072,0.0002944476,0.02697235,0.06959485,0.0008234744,0.00001894743,0.01541174,0.001678433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9505645,0.0003499027,0.04459128,0.00003824359,0.003550102,0.0002621114,0.000003484407,0.0004304751,0.0002098985],"genre_scores_gemma":[0.9816559,0.00000794364,0.01553977,0.0000121344,0.001130563,0.00005536416,0.0002514036,0.00005307251,0.001293853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06796813,"threshold_uncertainty_score":0.9998488,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2805377368","doi":"","title":"Gaze-based Detection of Mind Wandering during Lecture Viewing.","year":2017,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Mind wandering and attention","field":"Neuroscience","cited_by":38,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Gaze; Mind-wandering; Computer science; Computer vision; Artificial intelligence; Human–computer interaction; Cognitive psychology; Psychology; Cognition; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.1330680554181721,"gpt":0.3464709313706021,"spread":0.21340287595243,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001834094,0.00009042313,0.00009589546,0.00007993855,0.0006214228,0.0001199824,0.0005432038,0.00003823089,0.0001459016],"category_scores_gemma":[0.001342762,0.0000917505,0.00002862852,0.0000600133,0.00007069641,0.0004418938,0.0001399442,0.00009315461,0.00001711435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002490199,"about_ca_system_score_gemma":0.0001052027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001781997,"about_ca_topic_score_gemma":0.00001349696,"domain_scores_codex":[0.9990712,0.00003105385,0.0001774114,0.0003648517,0.0002062611,0.0001491887],"domain_scores_gemma":[0.9987938,0.0001239236,0.0002278616,0.0007808161,0.00002850423,0.00004505328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002374213,0.00006033635,0.003808321,0.00006535118,0.000004495166,0.00000147296,0.0002948754,0.0001069082,0.968149,0.00001472444,0.00006109344,0.02740967],"study_design_scores_gemma":[0.0004283581,0.00002721065,0.1293894,0.0002392,0.00001893082,0.00002298997,0.00008678145,0.005663794,0.8623734,0.00006836629,0.001498856,0.0001826766],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960132,0.0000606046,0.002060019,0.0005510015,0.0006558399,0.00006465893,0.00004755651,0.00001119887,0.000535985],"genre_scores_gemma":[0.9980971,0.000006092718,0.001311835,0.00003117937,0.0002748538,0.000006584369,0.0000496499,0.00001100225,0.0002117564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1255811,"threshold_uncertainty_score":0.4779545,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1918293079","doi":"","title":"Mining Student Behavior Patterns in Reading Comprehension Tasks.","year":2012,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Reading comprehension; Computer science; Comprehension; Cognition; Program comprehension; Cluster analysis; Reading (process); Taxonomy (biology); Bloom's taxonomy; Cognitive skill; Cognitive psychology; Artificial intelligence; Natural language processing; Psychology; Software; Linguistics; Software system","retraction":null,"screen_n_in":null,"score":{"opus":0.1094509745644679,"gpt":0.3686672495986756,"spread":0.2592162750342077,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000792733,0.0001417346,0.0001524149,0.0001822761,0.0001587664,0.0001640596,0.001125896,0.00004263408,0.00005805676],"category_scores_gemma":[0.00008725573,0.000145757,0.00002342756,0.0002161458,0.00001218161,0.001455579,0.000743106,0.0001585633,0.00007958825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001182753,"about_ca_system_score_gemma":0.00008826027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001459523,"about_ca_topic_score_gemma":0.00001258761,"domain_scores_codex":[0.998362,0.0001040141,0.0003431159,0.0004218835,0.0003666041,0.0004023556],"domain_scores_gemma":[0.9986652,0.000298743,0.0001405351,0.0007318837,0.00005304973,0.0001105906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001037148,0.0002069274,0.9594682,0.00001274382,0.000009197685,0.000004525469,0.007525622,0.00004479941,0.0004100582,0.01792729,0.0009362584,0.01345336],"study_design_scores_gemma":[0.0001264944,0.00001836719,0.9699068,0.0002904082,0.000007838301,0.00003781397,0.002904967,0.002219187,0.00009992376,0.000006637239,0.02413182,0.0002497567],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9684156,0.0002898486,0.02734982,0.0002944589,0.002813877,0.0001566513,0.00001358421,0.00004938915,0.0006167503],"genre_scores_gemma":[0.9559906,0.00000473483,0.04181933,0.00009792565,0.000761472,0.00003387934,0.0001789052,0.00001319086,0.001099935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02319556,"threshold_uncertainty_score":0.5943796,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2401880028","doi":"","title":"A Framework for Capturing Distinguishing User Interaction Behaviors in Novel Interfaces.","year":2011,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; User interface; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.231727243849435,"gpt":0.4164599005711527,"spread":0.1847326567217178,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003358545,0.00009221432,0.00009025406,0.0001452339,0.00008146568,0.0002064076,0.00119115,0.00003989918,0.00006289037],"category_scores_gemma":[0.001125986,0.00009977529,0.00001610183,0.0002538427,0.00001812035,0.001565862,0.0004663755,0.0001138623,0.00001200008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007005168,"about_ca_system_score_gemma":0.0001676905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363295,"about_ca_topic_score_gemma":0.00008420509,"domain_scores_codex":[0.9990265,0.00001550059,0.0002675905,0.0003935841,0.0001302066,0.0001665881],"domain_scores_gemma":[0.998895,0.0002624191,0.0001330765,0.0005880961,0.00006818442,0.00005325919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002590323,0.001480426,0.09575304,0.0001029893,0.00005790908,0.000003962298,0.06001732,0.0001236032,0.0004459387,0.7960908,0.01121062,0.03468744],"study_design_scores_gemma":[0.001922795,0.0001777672,0.2363209,0.002816817,0.0001175773,0.00008674599,0.01752225,0.6422043,0.002874686,0.02413828,0.0694901,0.002327818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03492928,0.00002000081,0.9626509,0.000485343,0.001248207,0.0001036161,0.0001096985,0.00003858402,0.000414382],"genre_scores_gemma":[0.5060645,0.000001258872,0.4929292,0.0001771363,0.0001408958,0.0000163708,0.0005208127,0.000008423875,0.0001413884],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7719526,"threshold_uncertainty_score":0.4068718,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1548100229","doi":"","title":"Combining Unsupervised and Supervised Classification to Build User Models for Exploratory","year":2009,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Transferability; Machine learning; Unsupervised learning; Data modeling; Artificial intelligence; Supervised learning; Interface (matter); Exploratory data analysis; Labeled data; Data mining; Human–computer interaction; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.1168558754561229,"gpt":0.3453360740487654,"spread":0.2284801985926425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004154243,0.0001134782,0.0001218621,0.0001333128,0.0002034169,0.0002326525,0.000913916,0.00003995236,0.000004603767],"category_scores_gemma":[0.000240609,0.0001203728,0.00001846599,0.0002737609,0.00001600876,0.001214441,0.0001738194,0.00008178115,0.00001037766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002410639,"about_ca_system_score_gemma":0.0002421625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003583217,"about_ca_topic_score_gemma":0.000002208395,"domain_scores_codex":[0.9988459,0.00003634346,0.0002147927,0.0005149302,0.0001880299,0.0002000498],"domain_scores_gemma":[0.9986438,0.0002645382,0.00005680176,0.0007589094,0.0001338692,0.0001420784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003682253,0.0006398451,0.005761198,0.00006148552,0.00006112094,0.00000142313,0.01417867,0.004502294,0.004412538,0.6625732,0.07821855,0.2295529],"study_design_scores_gemma":[0.0003353705,0.00008747093,0.00937708,0.00005841239,0.00001206846,0.000003347511,0.0007590964,0.9712436,0.00002918161,0.01011427,0.007755801,0.0002243478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2039118,0.0002631794,0.6847878,0.1094918,0.0004798031,0.0003418457,0.0001011702,0.000158748,0.0004639811],"genre_scores_gemma":[0.6156697,0.000009006266,0.3816477,0.001650969,0.0002281274,0.00001707928,0.000489419,0.000008670336,0.0002792954],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9667413,"threshold_uncertainty_score":0.4908662,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1577370341","doi":"","title":"Identifying Successful Learners from Interaction Behaviour","year":2012,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Exploit; Computer science; Class (philosophy); Subject matter; Learning Management; Mathematics education; Multimedia; Artificial intelligence; Psychology; Pedagogy","retraction":null,"screen_n_in":null,"score":{"opus":0.1007397662631144,"gpt":0.391709635919051,"spread":0.2909698696559366,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003704275,0.00008043362,0.00007601096,0.00008867921,0.0001391161,0.0002430978,0.001002246,0.00003034363,0.0001669955],"category_scores_gemma":[0.0002765209,0.00008453431,0.0000208147,0.0001927597,0.00001686467,0.002621922,0.0004386328,0.0001647483,0.0002676406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003956031,"about_ca_system_score_gemma":0.0001107947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002418853,"about_ca_topic_score_gemma":0.00001309208,"domain_scores_codex":[0.9990517,0.00005899338,0.0001772742,0.0002833426,0.0002288854,0.0001998353],"domain_scores_gemma":[0.9988296,0.0002746969,0.000117504,0.0006433564,0.00003722071,0.00009759097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002090389,0.0002592639,0.9239451,0.000007601015,0.00004306282,0.000001197583,0.00606782,0.0000714177,0.0002327499,0.005917091,0.02879922,0.03465343],"study_design_scores_gemma":[0.0003326099,0.00002557019,0.850295,0.0002118511,0.00008839464,0.0000450694,0.01142955,0.09891538,0.0002396573,0.001923928,0.03578112,0.0007118721],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9227703,0.0003950662,0.05826701,0.0131957,0.00409034,0.00004326202,0.00005698779,0.000112576,0.001068793],"genre_scores_gemma":[0.8732266,0.000006811856,0.123801,0.0001790161,0.001135211,0.000002298306,0.001002682,0.000007144,0.0006392235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09884396,"threshold_uncertainty_score":0.3447209,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2806008675","doi":"","title":"Predicting Prospective Peer Helpers to Provide Just-In-Time Help to Users in Question and Answer Forums.","year":2017,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Internet privacy; Peer-to-peer; World Wide Web","retraction":null,"screen_n_in":null,"score":{"opus":0.04770860689949084,"gpt":0.3474207435199652,"spread":0.2997121366204744,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001106421,0.0001125568,0.0001333152,0.0002085948,0.0002612222,0.0005347443,0.001209109,0.00004374224,0.000004495297],"category_scores_gemma":[0.00171054,0.0001163361,0.000009523597,0.0001715496,0.00002375044,0.001425301,0.000669637,0.00009908171,0.00003927701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001519701,"about_ca_system_score_gemma":0.0002205241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001088886,"about_ca_topic_score_gemma":0.0006529371,"domain_scores_codex":[0.9985092,0.00004548358,0.0002200359,0.000622382,0.0003360772,0.0002668003],"domain_scores_gemma":[0.9987017,0.0001379759,0.00009180937,0.0008528972,0.00008673387,0.0001289012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001080719,0.00005695069,0.964248,0.0000147337,0.00000635189,0.0000030014,0.02008236,0.00005186292,0.0004273269,0.00187693,0.01000354,0.003218129],"study_design_scores_gemma":[0.0002980864,0.00007360076,0.9804229,0.0005531296,0.000002837935,0.00001513803,0.00246722,0.01163891,0.0001300706,0.0001657084,0.003974634,0.0002577239],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750274,0.00003990401,0.001651232,0.02078765,0.0009484458,0.0004886684,0.00004157043,0.00003526843,0.0009797994],"genre_scores_gemma":[0.9562254,0.000001373543,0.04143702,0.0002964383,0.000340781,0.00009770449,0.00005689083,0.0000104761,0.001533929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03978579,"threshold_uncertainty_score":0.5156552,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2893310092","doi":"","title":"Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach.","year":2018,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Career Development and Diversity","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.1295020425435878,"gpt":0.3157301250950478,"spread":0.18622808255146,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004928863,0.00008594733,0.00009935931,0.0001251518,0.0003025084,0.0001026701,0.0005929468,0.00004467872,0.0002052434],"category_scores_gemma":[0.0003090108,0.00009400049,0.00001324618,0.0003870381,0.0001325001,0.0004216204,0.0002453023,0.00006412467,0.00004335794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002888,"about_ca_system_score_gemma":0.0005351786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006098873,"about_ca_topic_score_gemma":0.0006233223,"domain_scores_codex":[0.9989223,0.00003597025,0.0001346519,0.0003258508,0.0003187775,0.0002624212],"domain_scores_gemma":[0.999391,0.0002106533,0.00004711804,0.0002115757,0.00005182448,0.00008782886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000935118,0.0001199209,0.8827096,0.00001790563,0.0000384777,0.000001202536,0.06144302,0.000003370088,0.00002754335,0.01839514,0.03093798,0.006296543],"study_design_scores_gemma":[0.0002683773,0.000008468788,0.9209502,0.00004794914,0.00001526661,0.000001741081,0.04812612,0.001798388,0.000005268742,0.0006430247,0.02777267,0.0003624677],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436357,0.0001008969,0.001155879,0.001991069,0.0007356335,0.0001577487,0.0000518668,0.00005078439,0.0521204],"genre_scores_gemma":[0.9753993,0.0000111304,0.02290284,0.00009326803,0.0006425827,0.00001154124,0.0002292582,0.000005871472,0.0007042126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05141618,"threshold_uncertainty_score":0.3833229,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W5590877","doi":"","title":"On the Faithfulness of Simulated Student Performance Data.","year":2010,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Replicate; Computer science; Data set; Bayesian probability; Set (abstract data type); Data modeling; Standard deviation; Experimental data; Artificial intelligence; Machine learning; Data mining; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.09294334681844635,"gpt":0.3429350373180371,"spread":0.2499916904995907,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009141121,0.00007930309,0.00008086402,0.00004689587,0.0001845858,0.0001050168,0.00373129,0.00002398448,0.00009268043],"category_scores_gemma":[0.0004087853,0.00005543325,0.00000998822,0.0001692378,0.00003263923,0.0006180233,0.0009594379,0.0002099658,0.00006441023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009410805,"about_ca_system_score_gemma":0.0001713677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003001851,"about_ca_topic_score_gemma":0.000004779939,"domain_scores_codex":[0.9989464,0.00004385478,0.0002000355,0.0003468569,0.0003328597,0.0001299682],"domain_scores_gemma":[0.9969442,0.0007438451,0.0001348036,0.002049914,0.00009699947,0.00003023929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006608629,0.0002634162,0.02341805,0.00003033318,0.00006724661,0.000001124486,0.003768592,0.002386053,0.001228186,0.9351678,0.01157893,0.02208368],"study_design_scores_gemma":[0.0003016957,0.0001380684,0.2511544,0.0004141624,0.00002064985,0.00002558391,0.001631907,0.5167579,0.002318204,0.0005591014,0.2261142,0.0005642102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847412,0.00002151492,0.01017832,0.001443087,0.002138601,0.0001071616,0.00004086998,0.00002290995,0.001306337],"genre_scores_gemma":[0.9930897,9.721801e-7,0.005007911,0.0000924193,0.0002998235,0.000003167203,0.0001635694,0.000005504146,0.001336899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9346087,"threshold_uncertainty_score":0.6933727,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2401260724","doi":"","title":"Conditions for Effectively Deriving a Q-Matrix from Data with Non-negative Matrix Factorization. Best Paper Award.","year":2011,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Matrix decomposition; Matrix (chemical analysis); Computer science; Matrix algebra; Factorization; Algebra over a field; Algorithm; Mathematics; Pure mathematics; Physics; Materials science","retraction":null,"screen_n_in":null,"score":{"opus":0.09830638039740121,"gpt":0.3556673213271799,"spread":0.2573609409297787,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003818723,0.0001705192,0.0001589068,0.0001308641,0.0004834392,0.0002440971,0.002442869,0.00004010085,0.000141103],"category_scores_gemma":[0.0009830783,0.0001553054,0.00002100439,0.0004738158,0.0001082965,0.005147813,0.001028866,0.00009511453,0.00007492025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004825175,"about_ca_system_score_gemma":0.0008214877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003939843,"about_ca_topic_score_gemma":0.0001546238,"domain_scores_codex":[0.9982107,0.00005117083,0.0002219206,0.00094312,0.0002894961,0.000283637],"domain_scores_gemma":[0.9965864,0.001497034,0.0001772123,0.001322153,0.000299795,0.0001174081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003180151,0.004977826,0.314743,0.0004661824,0.002470314,0.0000509958,0.1691188,0.0002051621,0.03132399,0.1401227,0.2105878,0.1256152],"study_design_scores_gemma":[0.002947131,0.0005655371,0.8030505,0.001257589,0.0003733614,0.00006261239,0.01320808,0.1279895,0.002336075,0.02908341,0.01693192,0.00219436],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02274575,0.00007438695,0.9704545,0.001014026,0.000730782,0.0005077,0.003620554,0.00005410569,0.0007982482],"genre_scores_gemma":[0.592823,0.000005919122,0.4017617,0.0001929658,0.0003380653,0.000104992,0.004539046,0.00001372569,0.0002205894],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5700772,"threshold_uncertainty_score":0.6333171,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2578850146","doi":"","title":"Discovering Process in Curriculum Data to Provide Recommendation.","year":2015,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Bottleneck; Curriculum; Process (computing); Process mining; Computer science; Path (computing); Business process discovery; Work in process; Data science; Event (particle physics); Data mining; Engineering; Business process modeling; Business process; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.1905392845128808,"gpt":0.3677279941472281,"spread":0.1771887096343474,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008690862,0.0001466208,0.0001632665,0.0003582252,0.0001104433,0.0004732748,0.001450774,0.00003114322,0.0001241489],"category_scores_gemma":[0.001857642,0.0001433637,0.00001089964,0.001186635,0.00001595524,0.006104026,0.001179968,0.0001013921,0.0001967714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000043342,"about_ca_system_score_gemma":0.0003119963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001494157,"about_ca_topic_score_gemma":0.0006408651,"domain_scores_codex":[0.9984633,0.000007952117,0.0003375876,0.0006699049,0.0002827043,0.0002385218],"domain_scores_gemma":[0.9986466,0.00003999753,0.0001587822,0.0008604499,0.0002612039,0.00003297809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005685693,0.001115669,0.6256539,0.0006529454,0.00008514536,0.000004995789,0.001615427,0.01634163,0.00004053209,0.001777348,0.2644398,0.08821583],"study_design_scores_gemma":[0.0007115515,0.000005492645,0.01305262,0.0004580162,0.0001133056,0.000004390503,0.01165447,0.7327389,0.000004265051,0.002174593,0.2382801,0.0008023462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9303414,0.0002022268,0.00839024,0.05384099,0.001396876,0.0003817922,0.0002185988,0.0001330778,0.005094833],"genre_scores_gemma":[0.9765521,0.000002732659,0.005805134,0.001357383,0.002060439,0.00004247684,0.01401502,0.00002508559,0.0001396767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7163973,"threshold_uncertainty_score":0.5846202,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2573742935","doi":"","title":"On Competition for Undergraduate Co-Op Placements: A Graph Mining Approach.","year":2016,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Open Education and E-Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Competition (biology); Computer science; Graph; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.07201283518887017,"gpt":0.3367316759755484,"spread":0.2647188407866783,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006252453,0.0001525281,0.0001285913,0.0001970489,0.0003808053,0.0002599026,0.001250547,0.00004011597,0.0001384361],"category_scores_gemma":[0.0005514434,0.0001266392,0.00003584109,0.0002500624,0.00004855719,0.001230326,0.0002015517,0.00007325185,0.0001684863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008832127,"about_ca_system_score_gemma":0.0003736065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006377545,"about_ca_topic_score_gemma":0.000002425203,"domain_scores_codex":[0.9983832,0.00009008036,0.0002647902,0.0006538122,0.0003153999,0.0002926676],"domain_scores_gemma":[0.9977887,0.001048204,0.0001776492,0.000761268,0.0001010989,0.0001230755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002571681,0.0003815216,0.001480006,0.00002719828,0.00005006122,2.42169e-7,0.001690084,0.0000615995,0.000246222,0.8365458,0.1416623,0.01782925],"study_design_scores_gemma":[0.01284502,0.001739172,0.05230688,0.002453572,0.0001722279,0.00018071,0.04234862,0.07685076,0.001230048,0.1378388,0.6672639,0.004770209],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04408851,0.00005777984,0.8381943,0.04918426,0.002928022,0.0007253927,0.0002733076,0.0002201272,0.06432837],"genre_scores_gemma":[0.7416806,0.00001498103,0.2500637,0.00109644,0.0004365176,0.0001332321,0.002028319,0.00002352278,0.00452269],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.698707,"threshold_uncertainty_score":0.5164196,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2893061728","doi":"","title":"Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System.","year":2018,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"","keywords":"Cluster analysis; Computer science; Literacy; Intelligent tutoring system; Human–computer interaction; Multimedia; Artificial intelligence; Psychology; Pedagogy","retraction":null,"screen_n_in":null,"score":{"opus":0.02379652341157226,"gpt":0.3039595551962821,"spread":0.2801630317847099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008840219,0.0002395244,0.0002301589,0.0001509747,0.0005088294,0.0004899992,0.001746695,0.00003762746,0.00003398317],"category_scores_gemma":[0.0002383812,0.0001611645,0.0000281941,0.000319839,0.00006061234,0.002045833,0.0006188563,0.0003282549,0.00002371997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001130677,"about_ca_system_score_gemma":0.000213976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003861661,"about_ca_topic_score_gemma":0.00006321002,"domain_scores_codex":[0.9977398,0.0002005049,0.0004822995,0.0007048227,0.0005021287,0.0003704277],"domain_scores_gemma":[0.9969935,0.0007086572,0.0005339403,0.001171115,0.0004902043,0.0001025746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004769579,0.0008653715,0.2984745,0.001939419,0.0007012777,0.00006702818,0.4051189,0.02170293,0.001446368,0.05133543,0.0001736959,0.2176981],"study_design_scores_gemma":[0.0009635239,0.002484997,0.04981348,0.03556722,0.00008487509,0.001030667,0.09090155,0.7913109,0.005903627,0.00001347078,0.02033082,0.001594845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5264767,0.00005109769,0.4716183,0.0001208129,0.001109138,0.0001900846,0.000009062875,0.00008115939,0.0003437344],"genre_scores_gemma":[0.9512629,0.000002542415,0.04652165,0.00004021304,0.001249189,0.00002294439,0.00008122762,0.00003026144,0.0007890734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.769608,"threshold_uncertainty_score":0.6572096,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2473928240","doi":"","title":"An Analysis of Peer-Submitted and Peer-Reviewed Answer Rationales, in an Asynchronous Peer Instruction Based Learning Environment.","year":2015,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; Dawson College; John Abbott College; Polytechnique Montréal","funders":"","keywords":"Peer instruction; Asynchronous communication; Peer feedback; Computer science; Reading (process); Peer tutor; Mathematics education; Peer-to-peer; Asynchronous learning; Peer learning; Peer review; Learning environment; World Wide Web; Psychology; Cooperative learning; Teaching method; Synchronous learning","retraction":null,"screen_n_in":null,"score":{"opus":0.1334305622283861,"gpt":0.431026281440596,"spread":0.2975957192122099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007968635,0.0001762179,0.0003407692,0.0006628792,0.0001344145,0.00005715633,0.0003513967,0.0001111011,0.001307953],"category_scores_gemma":[0.002453088,0.0001888772,0.00003164378,0.0007462314,0.0001196555,0.0005279137,0.00005636999,0.0004153689,0.00001641291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001168409,"about_ca_system_score_gemma":0.0002621276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003802061,"about_ca_topic_score_gemma":0.00007634893,"domain_scores_codex":[0.9961115,0.001873538,0.0005060292,0.0006263455,0.0006719501,0.0002106827],"domain_scores_gemma":[0.9979624,0.0004012184,0.0003396744,0.0007158728,0.0004605152,0.0001202775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007900053,0.0004946374,0.9380507,0.00001399245,0.0002113356,0.000001506122,0.01546055,0.006031596,0.0003937121,0.001141906,0.002878447,0.03524261],"study_design_scores_gemma":[0.0006768972,0.0001438527,0.9356873,0.00002036265,0.0001611629,0.000005511154,0.006385509,0.02704409,0.000007694596,0.00004771429,0.02961007,0.0002098081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828412,0.0001941054,0.01364632,0.001884484,0.0004104814,0.0001265047,0.0001219602,0.00002580185,0.000749167],"genre_scores_gemma":[0.8911651,0.000001588343,0.08874059,0.0001252724,0.0002533431,0.00002573057,0.01817844,0.00002267267,0.001487257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09167607,"threshold_uncertainty_score":0.999605,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2806081626","doi":"","title":"On the Influence on Learning of Student Compliance with Prompts Fostering Self-Regulated Learning.","year":2017,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Compliance (psychology); Computer science; Psychology; Human–computer interaction; Mathematics education; Social psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.2376734286065722,"gpt":0.4779893651028262,"spread":0.240315936496254,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002462577,0.0001761811,0.0001922636,0.00008831652,0.00113793,0.0001291434,0.001275956,0.00005316797,0.0002951168],"category_scores_gemma":[0.001786124,0.0001266269,0.00001911823,0.000123356,0.0001786897,0.0001783345,0.0003440991,0.0008332179,0.0001022671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004250578,"about_ca_system_score_gemma":0.0001060239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006590594,"about_ca_topic_score_gemma":0.000002493638,"domain_scores_codex":[0.9978168,0.0008327398,0.0002586822,0.0004928967,0.0003471302,0.000251733],"domain_scores_gemma":[0.9963291,0.001544885,0.0006287004,0.00129582,0.0001639769,0.00003756193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004955415,0.00110401,0.8133589,0.00008457597,0.0006976184,0.00001131411,0.06912854,0.00870434,0.00191286,0.04784499,0.004028948,0.05262835],"study_design_scores_gemma":[0.0003254033,0.0003684232,0.9925922,0.0005089305,0.00001531242,0.000010573,0.001905005,0.0004477504,0.00003523376,0.0000860472,0.003539989,0.0001651625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811494,0.00002088041,0.0002889031,0.001607998,0.0003585235,0.0001816663,0.000008055584,0.00005440455,0.01633022],"genre_scores_gemma":[0.9916889,7.300741e-7,0.005118711,0.0001517679,0.0001629191,0.00003414765,0.00009822864,0.0000291832,0.00271538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1792333,"threshold_uncertainty_score":0.8752155,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2893165852","doi":"","title":"Job Description Mining to Understand Work-Integrated Learning.","year":2018,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Information Systems Education and Curriculum Development","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Work (physics); Data science; Knowledge management; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.08956470542679441,"gpt":0.3114663546270516,"spread":0.2219016492002572,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007892895,0.0001448282,0.0001171454,0.0002725062,0.0004430175,0.0005543666,0.00119667,0.00004909046,0.0004033127],"category_scores_gemma":[0.0004361802,0.0001428924,0.00001823472,0.001128661,0.00003891228,0.001571884,0.0003408036,0.0000990833,0.001251008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002201868,"about_ca_system_score_gemma":0.0008573995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002611317,"about_ca_topic_score_gemma":0.0000186446,"domain_scores_codex":[0.9983919,0.00008261602,0.0004075297,0.0004333917,0.0004074397,0.0002771034],"domain_scores_gemma":[0.9984561,0.0001128574,0.000157753,0.0006820295,0.0004090421,0.0001822406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001157255,0.0000858883,0.01981197,0.00001635718,0.00004341166,7.384738e-7,0.07728411,0.00004311698,0.00006175569,0.01856525,0.8219929,0.06208288],"study_design_scores_gemma":[0.0004512014,0.0001261711,0.1141023,0.0004019381,0.0000125697,0.00009572458,0.04633392,0.01069512,0.0001195422,0.0001823738,0.8266317,0.0008474791],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1974,0.00007794783,0.7551453,0.01066932,0.006641288,0.0003723331,0.000018815,0.0002693621,0.02940566],"genre_scores_gemma":[0.6959701,0.000002346455,0.293709,0.001318274,0.0005861766,0.00002643906,0.0004914594,0.00001132569,0.007884938],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4985701,"threshold_uncertainty_score":0.9995266,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2572725214","doi":"","title":"Redefining \"What\" in Analyses of Who Does What in MOOCs.","year":2016,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.1005401988072912,"gpt":0.3978176205060965,"spread":0.2972774216988053,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000578754,0.00008796583,0.0001675214,0.0003151167,0.00003245661,0.0002189931,0.001085564,0.00003526571,0.00003898428],"category_scores_gemma":[0.0006686894,0.00006025109,0.00002078447,0.0005542362,0.00004451875,0.003439039,0.0003840482,0.00008648224,0.00001345622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003676886,"about_ca_system_score_gemma":0.0003178384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001026409,"about_ca_topic_score_gemma":0.0001082217,"domain_scores_codex":[0.9987901,0.00007299207,0.0003363454,0.0003907228,0.0002265396,0.0001833004],"domain_scores_gemma":[0.9983543,0.0006789082,0.0001412113,0.0007319194,0.00005155944,0.00004215569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001116044,0.0003762685,0.3740636,0.00005577817,0.00005118694,0.00001125649,0.006279539,0.0007281151,0.00261395,0.0232931,0.003030556,0.5894855],"study_design_scores_gemma":[0.00185142,0.0001313509,0.7668375,0.01053671,0.00004079724,0.00002406184,0.01511968,0.1680898,0.002098158,0.02410698,0.01000578,0.00115771],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272537,0.002128302,0.005529826,0.06337526,0.00112986,0.00007119122,0.00003373165,0.00003392629,0.0004442309],"genre_scores_gemma":[0.9463766,0.0005267751,0.05214637,0.0001550175,0.0001538581,0.000003584258,0.00007155089,0.000006862639,0.0005594239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5883278,"threshold_uncertainty_score":0.249322,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1514915342","doi":"","title":"Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009).","year":2009,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Library science; Data science; Computer science; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.09782056681921458,"gpt":0.3528224039029776,"spread":0.2550018370837631,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0007764476,0.0001928998,0.0001771765,0.000164418,0.0002358983,0.0003396281,0.006944014,0.00006093572,0.0001883005],"category_scores_gemma":[0.001440619,0.0001650653,0.00003830244,0.0004929398,0.00009364442,0.001162391,0.001163522,0.0002860996,0.00006590174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005755209,"about_ca_system_score_gemma":0.001359915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002442273,"about_ca_topic_score_gemma":0.000008796388,"domain_scores_codex":[0.9976426,0.0000287807,0.0004438244,0.0007982122,0.0008200544,0.0002665666],"domain_scores_gemma":[0.9973078,0.0003594276,0.0004178954,0.001356864,0.0004546822,0.0001033296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002387998,0.0009691076,0.02302753,0.00003006082,0.00009602854,9.878474e-7,0.00147763,0.000113012,0.0009426047,0.2407619,0.6885377,0.04401958],"study_design_scores_gemma":[0.00108981,0.0002772074,0.2803485,0.001429537,0.0001105941,0.0001533467,0.001973703,0.5251969,0.0002613134,0.02455672,0.1633704,0.001232067],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1906945,0.0004075566,0.003049273,0.7364143,0.006659849,0.0005120391,0.002801378,0.0001702223,0.05929086],"genre_scores_gemma":[0.8417251,0.00003813002,0.1453702,0.00254661,0.001799374,0.000006494656,0.002617666,0.0000170526,0.005879428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7338677,"threshold_uncertainty_score":0.9984289,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2965852225","doi":"","title":"Gender Differences in Work-Integrated Learning Assessments.","year":2019,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Higher Education and Employability","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Work (physics); Computer science; Engineering; Mechanical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1774446771178955,"gpt":0.4346953933341846,"spread":0.2572507162162891,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001101862,0.00008077043,0.0001134592,0.00009445908,0.0001930219,0.0001236368,0.0005467778,0.00005824118,0.007668913],"category_scores_gemma":[0.0006193866,0.00008034717,0.00001566153,0.00053086,0.00007998599,0.0004596059,0.00007887817,0.000194753,0.0004316368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001559321,"about_ca_system_score_gemma":0.001396443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001507487,"about_ca_topic_score_gemma":0.00053163,"domain_scores_codex":[0.9985538,0.0003210763,0.0001998966,0.0003587716,0.0003236756,0.0002427831],"domain_scores_gemma":[0.9988963,0.000542737,0.0000707053,0.0003235681,0.00007642582,0.00009028927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003588481,0.0001010364,0.9797221,0.000004115369,0.000006395077,8.530219e-8,0.008897092,0.00000411518,0.000003583817,0.002824942,0.005014915,0.003418074],"study_design_scores_gemma":[0.00007934622,0.000005425966,0.8904629,0.00002497835,0.000002970924,1.249773e-7,0.01247829,0.00002664804,3.996679e-7,0.0002735585,0.09654139,0.0001039276],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723874,0.00007160658,0.00005086831,0.002730254,0.001241364,0.0001408631,0.00001192876,0.00003161918,0.02333413],"genre_scores_gemma":[0.9806015,0.00001532601,0.005350422,0.0001405526,0.0002312895,0.00001695348,0.0005510278,0.000006988687,0.01308589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09152648,"threshold_uncertainty_score":0.9932382,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2965396085","doi":"","title":"Balancing Student Success and Inferring Personalized Effects in Dynamic Experiments.","year":2019,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.1691349651200919,"gpt":0.4997090568121423,"spread":0.3305740916920505,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006751697,0.0001115911,0.0001855804,0.0001203241,0.00004997831,0.00006206808,0.0002873266,0.00003452239,0.0002751424],"category_scores_gemma":[0.00272201,0.0001121469,0.00001060467,0.0001039391,0.00003446209,0.0002042918,0.0002273742,0.0000919148,0.00002244733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008834209,"about_ca_system_score_gemma":0.0001669547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002801819,"about_ca_topic_score_gemma":0.00001727076,"domain_scores_codex":[0.9989711,0.000126526,0.0002226086,0.0003232259,0.0001879747,0.0001685416],"domain_scores_gemma":[0.9953006,0.004141278,0.00009459381,0.0003806045,0.00003278567,0.00005015086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000252929,0.0004231831,0.8559341,0.0006602836,0.00008071084,0.000003153354,0.02665804,0.000008765712,0.002089069,0.1033463,0.005031652,0.005739443],"study_design_scores_gemma":[0.00135028,0.00003668946,0.9588903,0.0004944252,0.00003809828,0.00001495175,0.01628557,0.002889239,0.000169338,0.01798123,0.001447649,0.0004022682],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947808,0.0004001597,0.002131349,0.0003089743,0.001266624,0.0002609219,0.00004086315,0.00002093298,0.0007893968],"genre_scores_gemma":[0.787412,0.00002374126,0.2107517,0.0001172889,0.00006360373,0.00005051009,0.0002764897,0.00001693684,0.001287722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2086203,"threshold_uncertainty_score":0.4573216,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123682722","doi":"","title":"Proceedings of the International Conference on Educational Data Mining (EDM) (4th, Eindhoven, the Netherlands, July 6-8, 2011).","year":2011,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Library science; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.1696595518485775,"gpt":0.3428780201124152,"spread":0.1732184682638377,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0008720816,0.0001665473,0.0001351585,0.00009576092,0.0002700735,0.0002216393,0.008610782,0.00005296532,0.0003708068],"category_scores_gemma":[0.0006676161,0.0001119943,0.00003685846,0.0002532805,0.0001487639,0.0009473991,0.001906901,0.0002858903,0.0001030324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003196141,"about_ca_system_score_gemma":0.001037638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006492653,"about_ca_topic_score_gemma":0.00001822028,"domain_scores_codex":[0.9981047,0.00003663398,0.0003710447,0.00063813,0.0006347098,0.0002147781],"domain_scores_gemma":[0.9972591,0.0003894174,0.0004065038,0.001539077,0.000335957,0.00006992203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00002201891,0.0006568769,0.05625763,0.00002769581,0.0001727906,4.779628e-7,0.007333412,0.00002250501,0.0002185968,0.2834019,0.6405646,0.01132152],"study_design_scores_gemma":[0.001218197,0.0002679366,0.4882903,0.001139638,0.0002071162,0.0001866835,0.007176588,0.3069825,0.0005490345,0.0508599,0.1416989,0.001423247],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2608767,0.0006642003,0.00290713,0.5112658,0.01289476,0.000872985,0.002170457,0.0001907944,0.2081571],"genre_scores_gemma":[0.9138638,0.00002995181,0.07456053,0.001230589,0.001619537,0.00001701973,0.001011982,0.00002304225,0.007643511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6529871,"threshold_uncertainty_score":0.9967531,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3108491126","doi":"","title":"Toward a deep convolutional LSTM for eye gaze spatiotemporal data sequence classification.","year":2020,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Gaze; Convolutional neural network; Sequence (biology); Pattern recognition (psychology); Eye tracking; Deep learning; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.4364173309728986,"gpt":0.3966724997499474,"spread":0.03974483122295119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005068433,0.0001468197,0.0001632397,0.00007427065,0.0002253391,0.0001532876,0.004651511,0.00007433672,0.00005378562],"category_scores_gemma":[0.001583008,0.0001574838,0.00002378681,0.0003652348,0.0001366392,0.001291173,0.001167371,0.0001471727,0.00009544224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005419761,"about_ca_system_score_gemma":0.000868141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001756919,"about_ca_topic_score_gemma":0.000005343728,"domain_scores_codex":[0.9980105,0.00005554393,0.0003547381,0.001032286,0.0002842645,0.0002627201],"domain_scores_gemma":[0.9974195,0.000404109,0.0002109428,0.001665807,0.0001923374,0.0001072517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002618633,0.0003542826,0.04049923,0.000131577,0.0001400122,0.000005278438,0.001850131,0.00006383966,0.002028004,0.5330694,0.2593603,0.1624718],"study_design_scores_gemma":[0.0003995887,0.00006087166,0.09630442,0.00004114353,0.00002381523,0.00001652996,0.0004047439,0.8025419,0.00006634281,0.004511223,0.09524698,0.0003824124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003131306,0.0003081651,0.8815297,0.1126975,0.0005404998,0.0002052561,0.001150434,0.0001972119,0.0002398658],"genre_scores_gemma":[0.601217,0.000005377472,0.3901004,0.0008340552,0.0003379889,0.00003768769,0.007397166,0.000009132331,0.00006120327],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8024781,"threshold_uncertainty_score":0.8643743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2407070788","doi":"","title":"Degeneracy in Student Modeling with Dynamic Bayesian Networks in Intelligent Edu-Games","year":2013,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Bayesian network; Bayesian probability; Degeneracy (biology); Dynamic Bayesian network; Theoretical computer science; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.03279701645553337,"gpt":0.3049566379764062,"spread":0.2721596215208728,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005214713,0.0001638792,0.0001710112,0.0002470145,0.00008400249,0.0003055983,0.001311639,0.00004325205,0.00005649304],"category_scores_gemma":[0.00006247327,0.0001480946,0.00001660289,0.0004075735,0.00001588568,0.00118996,0.0004275044,0.0002516272,0.00003980963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001873766,"about_ca_system_score_gemma":0.0002390306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008588682,"about_ca_topic_score_gemma":0.0004111537,"domain_scores_codex":[0.9982513,0.00009181227,0.0004111523,0.0005965607,0.0002920879,0.0003570844],"domain_scores_gemma":[0.9988605,0.0001816858,0.000102056,0.0006924981,0.00008578247,0.00007742397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000487979,0.0002896292,0.09542775,0.00002111469,0.00003033301,0.00001081192,0.007301178,0.8111156,0.00004086273,0.03489693,0.0004314999,0.0504294],"study_design_scores_gemma":[0.0000900829,0.00002135205,0.02754612,0.0002811486,0.000001696016,0.00001007193,0.001646229,0.9696361,0.000004187287,0.0001493381,0.0004233226,0.0001903521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.259203,0.0004683245,0.7384124,0.0008410443,0.000522228,0.000221419,0.000001511568,0.00002406822,0.000305996],"genre_scores_gemma":[0.934044,0.00002125675,0.06473361,0.0001048219,0.0001905389,0.00007275731,0.00007987367,0.00001465543,0.0007384567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.674841,"threshold_uncertainty_score":0.6039122,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3109207671","doi":"","title":"Social Media Mining to Understand the Impact of Co-operative Education on Mental Health.","year":2020,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Mental Health via Writing","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mental health; Social media; Computer science; Business; Psychology; Internet privacy; Public relations; Political science; World Wide Web; Psychiatry","retraction":null,"screen_n_in":null,"score":{"opus":0.2947674036560933,"gpt":0.5466153739308459,"spread":0.2518479702747526,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005226698,0.0001416784,0.0001931839,0.00007612752,0.0004226954,0.00003366601,0.000474537,0.00004226386,0.001742167],"category_scores_gemma":[0.0003533962,0.000118448,0.00003665958,0.0002737407,0.00007250254,0.0001536391,0.0001019941,0.0001579884,0.0001622181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003497037,"about_ca_system_score_gemma":0.001702179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002477902,"about_ca_topic_score_gemma":0.00003661972,"domain_scores_codex":[0.9983354,0.0002541665,0.0004198772,0.0004069585,0.0002973457,0.0002862331],"domain_scores_gemma":[0.9983484,0.0008127607,0.0002360099,0.0003188396,0.00005070217,0.0002333346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001357261,0.0002390519,0.003404975,0.0000261776,0.00005294349,1.672743e-7,0.2867758,0.000004196852,0.0000629422,0.004243163,0.6834989,0.02155596],"study_design_scores_gemma":[0.0009859859,0.0007509277,0.3005933,0.0002986441,0.00001854961,0.00001956305,0.6880775,0.0002207684,0.00005842781,0.0001781953,0.008424981,0.0003731734],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8821977,0.0003373726,0.00003852035,0.1024108,0.001065707,0.0006345458,0.001634303,0.00001887614,0.01166213],"genre_scores_gemma":[0.9871666,0.00000363317,0.001680407,0.006073268,0.001532558,0.00003326454,0.003364786,0.00002313162,0.0001223872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6750739,"threshold_uncertainty_score":0.9991704,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2573938660","doi":"","title":"A Partition Tree Approach to Combine Techniques to Refine Item to Skills Q-Matrices.","year":2015,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Partition (number theory); Tree (set theory); Artificial intelligence; Data mining; Machine learning; Mathematics; Combinatorics","retraction":null,"screen_n_in":null,"score":{"opus":0.1020711713467125,"gpt":0.3366786190286713,"spread":0.2346074476819588,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000986403,0.0001407543,0.0001610344,0.0002769578,0.0001049083,0.0002517532,0.001372999,0.00003684383,0.000008934018],"category_scores_gemma":[0.0009587378,0.0001383141,0.00001950291,0.0007670411,0.000006830754,0.0006111324,0.0007385031,0.00009391681,0.0004459878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001498524,"about_ca_system_score_gemma":0.0002305523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001323964,"about_ca_topic_score_gemma":0.00001026895,"domain_scores_codex":[0.9983235,0.0000675683,0.0002997603,0.000613381,0.0004231687,0.0002726106],"domain_scores_gemma":[0.9982409,0.0001593604,0.00008290604,0.0009077885,0.000254334,0.0003546522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001897669,0.0006917875,0.003606233,0.00004180651,0.00003641547,0.000003138093,0.01562417,0.001097952,0.001117984,0.2128963,0.6821072,0.08275808],"study_design_scores_gemma":[0.0001034211,0.000213494,0.006241933,0.0002475754,0.000006430783,0.00002549503,0.0006415545,0.002439294,0.0009548303,0.0002523987,0.98849,0.0003835973],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02197742,0.00008657426,0.9479975,0.01193845,0.001289445,0.0006311173,0.00005388457,0.0002474054,0.01577826],"genre_scores_gemma":[0.153161,0.000001131251,0.8302818,0.001835271,0.001152343,0.0001550836,0.0003478117,0.00002073221,0.01304487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3063828,"threshold_uncertainty_score":0.5732419,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2571583649","doi":"","title":"An Analysis of Peer-submitted and Peer-reviewed Answer Rationales in a Web-based Peer Instruction Based Learning Environment.","year":2015,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; Dawson College; Vanier College; John Abbott College; Polytechnique Montréal","funders":"","keywords":"Computer science; Peer instruction; Peer-to-peer; Peer review; World Wide Web; Computer aided instruction; Peer feedback; Multimedia; Mathematics education; Psychology; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.1289815930826969,"gpt":0.4246660141449498,"spread":0.2956844210622529,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008437966,0.000173904,0.0003394184,0.0008083809,0.0001195158,0.0000466687,0.0003103015,0.0001037944,0.001319261],"category_scores_gemma":[0.003990571,0.0001823225,0.00004106123,0.0009113892,0.0001272199,0.000295836,0.00004551414,0.0003907197,0.00001688883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000946642,"about_ca_system_score_gemma":0.0003671879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002221812,"about_ca_topic_score_gemma":0.0000561257,"domain_scores_codex":[0.9962383,0.001770668,0.0005022844,0.0005690369,0.0007206052,0.0001990853],"domain_scores_gemma":[0.9978025,0.0006880946,0.0003485316,0.0006146999,0.0004510623,0.00009513153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001113618,0.0004216949,0.9576291,0.00001993703,0.0002264827,0.000001374764,0.007861168,0.009530446,0.0006221653,0.0008380298,0.006827147,0.01591112],"study_design_scores_gemma":[0.001158362,0.00008895388,0.8576458,0.00003496408,0.0001822815,0.000002667149,0.004159731,0.0701199,0.00001470965,0.00003288899,0.06634969,0.0002100597],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827729,0.0001842065,0.01102788,0.004485427,0.0003653916,0.0001317327,0.0001523964,0.00002828557,0.000851727],"genre_scores_gemma":[0.9007561,0.000001127572,0.08385999,0.0001963382,0.0001698567,0.00003521001,0.01297253,0.00002104883,0.001987845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09998327,"threshold_uncertainty_score":0.9995937,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2406609927","doi":"","title":"Mining User's Behaviors in Intelligent Educational Games: Prime Climb a Case Study.","year":2013,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Climb; Computer science; Prime (order theory); Human–computer interaction; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.06904220657417487,"gpt":0.3595855794393796,"spread":0.2905433728652047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000666344,0.0002430994,0.0002136892,0.000343856,0.0002825692,0.0005820217,0.002448211,0.00005971684,0.0004936184],"category_scores_gemma":[0.0003126081,0.0002565311,0.00003273971,0.000856695,0.00006315013,0.002269804,0.001207993,0.0002126878,0.0003664462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001397242,"about_ca_system_score_gemma":0.0009253034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00248234,"about_ca_topic_score_gemma":0.0004136569,"domain_scores_codex":[0.9973363,0.0001000363,0.0006542977,0.001075297,0.0004151413,0.000418916],"domain_scores_gemma":[0.9966159,0.0006947528,0.0002120825,0.002082596,0.0001752753,0.0002194036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000004164139,0.007497722,0.3047062,0.00003091113,0.0001112321,0.0001489344,0.04374173,0.00006540758,0.0001553009,0.007098273,0.1943865,0.4420536],"study_design_scores_gemma":[0.001714443,0.0004551699,0.6927103,0.0004391566,0.0001276033,0.004773575,0.1063991,0.1374799,0.0001254175,0.001557633,0.05159295,0.002624818],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803373,0.0001654459,0.009940337,0.00716894,0.0008431143,0.0008102565,0.0001371532,0.00007124215,0.0005261651],"genre_scores_gemma":[0.6455854,0.000008258166,0.3501698,0.0003782478,0.0004229093,0.0008433531,0.001231917,0.00002632756,0.001333806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4394288,"threshold_uncertainty_score":0.9999887,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2729139296","doi":"","title":"Proceedings of the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, June 26-29, 2015).","year":2015,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Educational data mining; Library science; Data science; Engineering; Computer science; Engineering physics","retraction":null,"screen_n_in":null,"score":{"opus":0.140083259068256,"gpt":0.3705966424051829,"spread":0.2305133833369269,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001227491,0.0001955744,0.000185326,0.0001715469,0.0001725978,0.0003469128,0.007281557,0.00006400636,0.0001385285],"category_scores_gemma":[0.002343706,0.0001661561,0.00003473491,0.0004718418,0.0001315528,0.001439525,0.002547899,0.0002793858,0.0001016686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008834852,"about_ca_system_score_gemma":0.00227718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003622975,"about_ca_topic_score_gemma":0.00002364709,"domain_scores_codex":[0.9974456,0.00003883924,0.0004459037,0.0008068148,0.0009981912,0.0002646536],"domain_scores_gemma":[0.9968256,0.0003630375,0.0004311815,0.001488627,0.0007249906,0.0001665473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001323786,0.0004462744,0.02852192,0.00002481086,0.00007898297,5.56973e-7,0.002033648,0.0001018893,0.0001932429,0.1223717,0.8410746,0.005139111],"study_design_scores_gemma":[0.001527258,0.0001756268,0.06392409,0.001252195,0.0001239298,0.0001857636,0.007757348,0.6293262,0.0002477434,0.02082775,0.2733709,0.001281194],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1202021,0.0003596725,0.002493539,0.830285,0.01250642,0.0004120151,0.001228388,0.000181159,0.03233179],"genre_scores_gemma":[0.7144229,0.00003294716,0.2564533,0.001813778,0.003556254,0.00002113704,0.003325002,0.00004821724,0.02032643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8284711,"threshold_uncertainty_score":0.9980896,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2965314695","doi":"","title":"Anatomy of mobile learners: Using learning analytics to unveil learning in presence of mobile devices.","year":2019,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Learning analytics; Analytics; Human–computer interaction; Data science; Multimedia; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03986845201711653,"gpt":0.3563553505440226,"spread":0.3164868985269061,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001447453,0.0001952074,0.0003737256,0.0006137692,0.00008918282,0.00009028056,0.002029302,0.00008847367,0.0002092486],"category_scores_gemma":[0.001196956,0.0002270096,0.00004538154,0.001752945,0.00006650298,0.00126374,0.0009383479,0.0004336313,0.00005895675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001510396,"about_ca_system_score_gemma":0.00117789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004416676,"about_ca_topic_score_gemma":0.00003494128,"domain_scores_codex":[0.997219,0.000277918,0.0007341595,0.0007966397,0.0006018372,0.0003705002],"domain_scores_gemma":[0.9966843,0.001093417,0.0005840507,0.001238637,0.0002795832,0.0001200517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005781063,0.0001759629,0.4981458,0.0001153912,0.00002125328,5.649019e-7,0.007000594,0.480373,0.002020265,0.0006840239,0.0002173317,0.01124005],"study_design_scores_gemma":[0.0004606929,0.0003876082,0.1303069,0.0008605328,0.00002854562,0.00001560459,0.01741028,0.8337989,0.0008104132,0.0001188182,0.01520354,0.00059811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988802,0.0002916521,0.008903361,0.0001920595,0.0005822469,0.0004445481,0.000008476938,0.00003960209,0.0007361149],"genre_scores_gemma":[0.9105886,0.00001574435,0.08846866,0.00004192816,0.00009902482,0.00004527007,0.0001467359,0.00002305896,0.0005710222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3678389,"threshold_uncertainty_score":0.9257181,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3106584815","doi":"","title":"Educational Data Mining and Personalized Support in Online Introductory Physics Courses.","year":2020,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Data science; Educational data mining; Physics education; Computer aided instruction; Mathematics education; World Wide Web; Multimedia; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.1051515045351045,"gpt":0.3664483783456825,"spread":0.261296873810578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004561848,0.0001714483,0.0002129331,0.00008537676,0.0001278621,0.0001859586,0.002274016,0.00004295553,0.0001506033],"category_scores_gemma":[0.001245367,0.0001887494,0.0000174153,0.0004767148,0.000109566,0.001333408,0.001441667,0.0002321807,0.00002876722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003632486,"about_ca_system_score_gemma":0.001779624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002515812,"about_ca_topic_score_gemma":0.00002080796,"domain_scores_codex":[0.9979689,0.00007768887,0.0003316814,0.0009752572,0.0003859299,0.0002605215],"domain_scores_gemma":[0.9978991,0.0004816003,0.0001580455,0.001180113,0.0000958818,0.0001852328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002868413,0.00137028,0.2148716,0.0001775815,0.0001567833,0.00001562119,0.01845594,0.0002850201,0.0003432773,0.02761749,0.6153911,0.1212866],"study_design_scores_gemma":[0.001014279,0.00008849966,0.08023011,0.0001429638,0.00006212824,0.00006087932,0.004106856,0.8022615,0.000006744805,0.001128786,0.1101545,0.0007427621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.3235954,0.001630971,0.01143525,0.6580288,0.00184106,0.0002536743,0.002400257,0.0001375511,0.0006769742],"genre_scores_gemma":[0.3581767,0.0001111316,0.5953431,0.00512359,0.007058369,0.000007792418,0.03266138,0.00003954107,0.001478413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8019764,"threshold_uncertainty_score":0.7696979,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3007006485","doi":"","title":"Proceedings of the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, July 2-5, 2019).","year":2019,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Library science; Computer science; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.05317215898791266,"gpt":0.3117296446957465,"spread":0.2585574857078339,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0004512325,0.0001646932,0.0001648439,0.0001015013,0.0001481048,0.0002224689,0.006618692,0.00004494991,0.0002805152],"category_scores_gemma":[0.0006507752,0.0001394984,0.00002485922,0.0003194799,0.00005484296,0.0009716322,0.001695285,0.0002296607,0.00005816935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001036498,"about_ca_system_score_gemma":0.004379112,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01566664,"about_ca_topic_score_gemma":0.009716787,"domain_scores_codex":[0.997804,0.00001868912,0.0003654295,0.0007283253,0.0008495839,0.0002339651],"domain_scores_gemma":[0.9973632,0.0004069658,0.0003648429,0.001402419,0.000371385,0.00009126183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000007807137,0.0002175899,0.05587587,0.00002848265,0.00007937121,3.330078e-7,0.0006302287,0.0001194812,0.0002919231,0.03917197,0.8987951,0.00478189],"study_design_scores_gemma":[0.000768584,0.00008633936,0.4774905,0.0008978292,0.00006538313,0.00006664546,0.001680738,0.4044855,0.000198477,0.003734077,0.1096805,0.000845391],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5746018,0.0002222816,0.000228241,0.3507437,0.008623702,0.0004254094,0.003015127,0.00006161745,0.06207808],"genre_scores_gemma":[0.9478743,0.00001858071,0.0284793,0.001130845,0.000894011,0.000006832931,0.002599388,0.00001826288,0.01897844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7891145,"threshold_uncertainty_score":0.998756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2724478195","doi":"","title":"Supporting the Encouragement of Forum Participation","year":2017,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Social Media and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"CUSUM; Quarter (Canadian coin); Point (geometry); Computer science; Control (management); Statistics; Mathematics; Artificial intelligence; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.1920892650586802,"gpt":0.5091286967951045,"spread":0.3170394317364243,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000620536,0.00002919409,0.00004530642,0.0000116991,0.00112656,0.00008273325,0.0006860195,0.00001993669,0.0002795991],"category_scores_gemma":[0.004992254,0.00002430422,0.00001258167,0.00002770499,0.0002272243,0.0003092939,0.0001227602,0.00003408337,0.00001306927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002107445,"about_ca_system_score_gemma":0.0004446227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001785603,"about_ca_topic_score_gemma":0.0009568668,"domain_scores_codex":[0.9993215,0.00005422782,0.000136419,0.00008405009,0.0002343331,0.0001694994],"domain_scores_gemma":[0.9987773,0.0004659926,0.0002300276,0.0004178843,0.00006623522,0.0000425471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002065106,0.00005957914,0.6772681,0.0000078702,0.00002204394,1.997395e-7,0.07964694,0.00000104034,0.00004224714,0.1377728,0.07330063,0.03187644],"study_design_scores_gemma":[0.0001241742,0.00001352436,0.4183199,0.000042393,0.00004918925,2.030636e-7,0.2087023,0.0001651979,0.0001898613,0.01083871,0.3614326,0.0001219856],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.901287,0.00005252597,0.00006349576,0.06858484,0.002106674,0.0001423608,0.00006948755,0.000007644515,0.02768597],"genre_scores_gemma":[0.9972187,0.000009808705,0.0007645733,0.0001764168,0.0009479738,0.0000128588,0.00008745938,0.000002647239,0.0007795401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2881319,"threshold_uncertainty_score":0.8664703,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2561395547","doi":"10.5281/zenodo.3554599","title":"Editorial Acknowledgements and Introduction to the Special Issue on EDM Journal Track","year":2016,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Track (disk drive); Library science; Computer science; Operations research; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01631803235690641,"gpt":0.2644920147379812,"spread":0.2481739823810748,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002167034,0.00006860383,0.00004756194,0.00005272017,0.0001303447,0.0000986335,0.0002114573,0.00002590744,0.00175306],"category_scores_gemma":[0.0004120681,0.00004333425,0.000006242815,0.0000540517,0.00001122512,0.0003149975,0.00004701775,0.00006966434,0.0002821757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004279518,"about_ca_system_score_gemma":0.00003404269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.032599e-7,"about_ca_topic_score_gemma":0.000004389633,"domain_scores_codex":[0.999469,0.00001131936,0.0001156989,0.0001455728,0.0001587685,0.00009965229],"domain_scores_gemma":[0.9995785,0.0000663773,0.00002414239,0.000198893,0.00007973976,0.00005235906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000796954,0.000009862631,0.00003858768,0.000004975976,0.000008926396,2.764532e-8,0.0002804948,0.00105901,0.00001831276,0.00002438074,0.9219277,0.07661979],"study_design_scores_gemma":[0.0001204248,0.00001597882,0.001529865,0.00002487604,0.000006480995,0.000002475395,0.00005636906,0.0002402085,0.0001514163,0.00006605825,0.9977188,0.00006702738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.02991431,0.0001709342,0.008110459,0.04907095,0.9063668,0.0003442172,0.0001926377,0.00009138237,0.005738336],"genre_scores_gemma":[0.006246574,0.00006738537,0.002495345,0.00005521058,0.9893612,0.000007764104,0.0000861014,0.00001379541,0.001666617],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.08299444,"threshold_uncertainty_score":0.9991595,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2980029776","doi":"10.5281/zenodo.3554673","title":"Editorial Acknowledgments and Introduction to the Special Issue for the EDM Journal Track","year":2019,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Track (disk drive); Library science; Editorial board; Publication; Computer science; Operations research; Engineering; Political science; Law","retraction":null,"screen_n_in":null,"score":{"opus":0.01591495320947732,"gpt":0.2988800628710742,"spread":0.2829651096615969,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008378778,0.00007229357,0.00005548578,0.00001066346,0.0003663129,0.0001151111,0.0004511127,0.0000258626,0.00651532],"category_scores_gemma":[0.000475764,0.00004390245,0.00001655341,0.00006953789,0.00008648146,0.0004673864,0.0003065829,0.0001100392,0.0004833515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000168002,"about_ca_system_score_gemma":0.00003705081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001870323,"about_ca_topic_score_gemma":0.00002611199,"domain_scores_codex":[0.9991953,0.00003472971,0.0001277242,0.0002313533,0.0002528832,0.0001579805],"domain_scores_gemma":[0.9992605,0.0002196237,0.00004870819,0.0003908676,0.00001054916,0.00006974776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002816434,0.00004114843,0.01537247,0.000002144714,0.000007069873,1.547744e-8,0.00120812,0.00009183606,0.0001065557,0.00001635045,0.9653976,0.01772853],"study_design_scores_gemma":[0.000127964,0.00004091664,0.1009139,0.000001942028,0.00001147058,0.000005483854,0.002023404,0.0001231019,0.00001766201,0.000177702,0.8965007,0.00005570634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"editorial","genre_scores_codex":[0.7083927,0.00009077488,0.000221752,0.09641159,0.1910961,0.001254786,0.000136831,0.000007292037,0.002388094],"genre_scores_gemma":[0.08301938,0.00002620388,0.003012983,0.0006329136,0.901466,0.00004200523,0.0001848575,0.00001619414,0.01159949],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.7103698,"threshold_uncertainty_score":0.9943929,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2400027546","doi":"","title":"Identifying Experts from Interaction Behaviour.","year":2012,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Team Dynamics and Performance","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.1985282843221672,"gpt":0.4581361215210042,"spread":0.2596078371988371,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001712959,0.00007244578,0.00006125172,0.00005602442,0.00009674817,0.00004375313,0.0002960274,0.00004175722,0.00766008],"category_scores_gemma":[0.00003453473,0.00007716859,0.00001474979,0.00006210164,0.00001566933,0.0008247288,0.0001150209,0.00008497712,0.0006043112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003616692,"about_ca_system_score_gemma":0.00002993119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005890941,"about_ca_topic_score_gemma":0.00003540282,"domain_scores_codex":[0.9993182,0.00002877282,0.0001542515,0.0002086965,0.0001034412,0.0001865817],"domain_scores_gemma":[0.9991636,0.0001440792,0.00007926219,0.0005258664,0.00001843331,0.00006873579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001159916,0.0002399406,0.8457908,0.000002284921,0.00003644228,4.474071e-7,0.01776828,4.948766e-7,0.00016964,0.001828747,0.1116651,0.02248621],"study_design_scores_gemma":[0.0001018402,0.000005019675,0.9524392,0.00002466353,0.00001711288,0.00001565813,0.007566413,0.0001992675,0.00001040983,0.00006111855,0.03943646,0.0001228537],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983771,0.0006737994,0.000425127,0.00046806,0.00881493,0.00004229487,0.0002528814,0.00001982717,0.005532081],"genre_scores_gemma":[0.9858139,0.000006308378,0.00528836,0.0001857338,0.002021095,0.00002405332,0.004896373,0.00001343211,0.001750803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1066484,"threshold_uncertainty_score":0.993247,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}