{"meta":{"query_hash":"a2a6b55c8998","filters":{"venue":"Educational Data Mining"},"cohort_total":36,"direct_labels_cover":0,"predictions_cover":36,"exported":36,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a2a6b55c8998","api":"https://metacan.xera.ac/api/v1/cohort?venue=Educational+Data+Mining"},"results":[{"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Library science; Data science; Computer science; Engineering","score_opus":0.09782056681921458,"score_gpt":0.35282240390297764,"score_spread":0.25500183708376306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514915342","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04538741,0.17198886,0.5410935,0.04659661,0.05129719,0.0013652277,0.029222168,0.014767061,0.09828196],"genre_scores_gemma":[0.14971639,0.08213349,0.39892423,0.004084144,0.010127289,0.0007669548,0.062042423,0.003008502,0.28919655],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970246,0.00093737955,0.00025713167,0.0004989746,0.0010747863,0.00020721275],"domain_scores_gemma":[0.9925047,0.0023831073,0.00024397628,0.0015513838,0.0022743358,0.001042468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072597284,0.0012657215,0.0018971142,0.0039342917,0.0011895666,0.0057586604,0.0019515918,0.0016136926,0.034731396],"category_scores_gemma":[0.012719096,0.00078669155,0.0010089172,0.00271382,0.0011093899,0.0043979324,0.0028203134,0.0029259403,0.015722692],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034533034,0.00036193518,0.004413512,0.00038356005,0.0001845701,0.00016699529,0.0002538706,0.00084345916,0.0024762612,0.002925218,0.4624342,0.5252111],"study_design_scores_gemma":[0.00006867675,0.00018862807,0.01481498,0.0007681861,0.00028774803,0.0008529832,0.00064900203,0.015285106,0.008197499,0.013161463,0.94563305,0.00009266408],"about_ca_topic_score_codex":0.011145969,"about_ca_topic_score_gemma":0.021796258,"teacher_disagreement_score":0.034731396,"about_ca_system_score_codex":0.0015649727,"about_ca_system_score_gemma":0.0038579905,"threshold_uncertainty_score":0.11618805},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Transferability; Machine learning; Unsupervised learning; Data modeling; Artificial intelligence; Supervised learning; Interface (matter); Exploratory data analysis; Labeled data; Data mining; Human–computer interaction; Database","score_opus":0.11685587545612286,"score_gpt":0.3453360740487654,"score_spread":0.2284801985926425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548100229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02899082,0.00013157689,0.9671094,0.00021954332,0.000024568992,0.0002340729,0.0006761466,0.0018900494,0.0007237799],"genre_scores_gemma":[0.4664037,0.00012601695,0.52752787,0.00015805704,0.000064384396,0.00078691455,0.0033128741,0.000266267,0.0013539768],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951272,0.002216893,0.0003108033,0.0011282596,0.0009539131,0.0002629199],"domain_scores_gemma":[0.9843789,0.009766381,0.0009795508,0.00217494,0.0023264873,0.00037375756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064692423,0.0019583325,0.0015335053,0.003999995,0.000780739,0.0017694759,0.0023217925,0.0013852002,0.0010570715],"category_scores_gemma":[0.016169606,0.000656542,0.002462118,0.0017703631,0.0008502744,0.0029418028,0.0014996621,0.0024380786,0.001388516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006324808,0.0021147868,0.08828756,0.0003969972,0.0009378347,0.00025025825,0.0018785516,0.2778268,0.008818918,0.012327639,0.010844273,0.59568393],"study_design_scores_gemma":[0.0000093404915,0.00006418215,0.0030586657,0.00002683657,0.000035577166,0.00006561332,0.000085704225,0.9857012,0.0021199177,0.0078109493,0.000983695,0.0000383703],"about_ca_topic_score_codex":0.007745236,"about_ca_topic_score_gemma":0.013737378,"teacher_disagreement_score":0.007745236,"about_ca_system_score_codex":0.0011665284,"about_ca_system_score_gemma":0.0017918429,"threshold_uncertainty_score":0.034213006},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.10073976626311443,"score_gpt":0.391709635919051,"score_spread":0.2909698696559366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1577370341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98770195,0.00010279371,0.00863392,0.00006686082,0.0000058253427,0.00015191779,0.0004990239,0.00016110975,0.0026766518],"genre_scores_gemma":[0.99082935,0.00010561937,0.0068201413,0.000019902835,0.000005612246,0.000109961125,0.00082323665,0.000024645653,0.0012615073],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984779,0.00043182267,0.00017053349,0.00024335043,0.0005435376,0.00013289384],"domain_scores_gemma":[0.9909923,0.00443845,0.0013535817,0.0006254888,0.0018468418,0.0007433823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015143205,0.0005840423,0.0005199792,0.0033106569,0.00039200872,0.0015082195,0.00043671703,0.0006867439,0.0012968341],"category_scores_gemma":[0.013024032,0.00015861096,0.00042352526,0.00085726415,0.00025309843,0.0013247606,0.0010934513,0.0006015776,0.0011111393],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023983141,0.000457403,0.8730452,0.00013973363,0.00007294105,0.00016916044,0.0021898642,0.0015354248,0.0051170285,0.00025704113,0.00057983387,0.11619652],"study_design_scores_gemma":[0.000023176466,0.0009536107,0.9307201,0.00009180747,0.00010193046,0.00077947695,0.0044150716,0.048037395,0.009168359,0.0017727283,0.00384959,0.00008684865],"about_ca_topic_score_codex":0.0016348487,"about_ca_topic_score_gemma":0.0026098262,"teacher_disagreement_score":0.0033106569,"about_ca_system_score_codex":0.00027936616,"about_ca_system_score_gemma":0.00032073157,"threshold_uncertainty_score":0.00800854},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.10945097456446792,"score_gpt":0.36866724959867564,"score_spread":0.25921627503420774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1918293079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97060555,0.0004500004,0.019696018,0.00032272312,0.000027960152,0.00016322744,0.006574764,0.00046295364,0.0016968437],"genre_scores_gemma":[0.97059286,0.00018294022,0.01915068,0.00004695194,0.000015692442,0.00015530332,0.008839606,0.000030285524,0.0009855939],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989303,0.00030193364,0.00015451411,0.00026867268,0.00026415088,0.0000804298],"domain_scores_gemma":[0.9926884,0.0036398065,0.0012999487,0.00063577475,0.0013742939,0.00036179382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012345957,0.00052793184,0.0004797241,0.004628413,0.00030693904,0.00081978174,0.00056384003,0.00070561335,0.00076330535],"category_scores_gemma":[0.009314451,0.00014508881,0.00056661805,0.0031920613,0.00016456474,0.0007216082,0.0004589483,0.0005032507,0.000887511],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022608611,0.0007257161,0.80371386,0.00032149896,0.00025724553,0.00016617369,0.001131917,0.0024730146,0.006243941,0.00027702772,0.0031435003,0.18131995],"study_design_scores_gemma":[0.000016864988,0.00032948997,0.94106644,0.000076437114,0.00012975832,0.00040000942,0.001780408,0.045012295,0.0061356653,0.0018775448,0.0031363235,0.000038811802],"about_ca_topic_score_codex":0.0036831745,"about_ca_topic_score_gemma":0.007926523,"teacher_disagreement_score":0.004628413,"about_ca_system_score_codex":0.00033546652,"about_ca_system_score_gemma":0.00047049197,"threshold_uncertainty_score":0.0073235035},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.10037441556768274,"score_gpt":0.3635039676163,"score_spread":0.26312955204861727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106157574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994585,0.0000035324588,0.00046270643,0.0000048941242,3.738391e-7,0.000007199504,0.000006339117,0.000005697433,0.00005077883],"genre_scores_gemma":[0.99917185,0.0000062259014,0.0006816651,0.000003996656,5.2288954e-7,0.000009966643,0.000030384852,0.000001599331,0.000093826435],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990972,0.00038721357,0.000069384914,0.0001826972,0.00018965208,0.00007386204],"domain_scores_gemma":[0.99483585,0.002843779,0.0010756127,0.00034933325,0.00053842756,0.0003570342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012263715,0.00018225935,0.00027576956,0.0007083454,0.00022888042,0.00070307974,0.00032278683,0.00034540432,0.00046108608],"category_scores_gemma":[0.0097965505,0.0001490781,0.00015478375,0.00038100997,0.00033252404,0.0003735402,0.0005176387,0.00031268082,0.00013039102],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076470576,0.0012399664,0.89784086,0.00007343941,0.00009356477,0.0001933218,0.0046169916,0.0022439833,0.026134524,0.00015678073,0.00011608532,0.066525705],"study_design_scores_gemma":[0.000031682106,0.0017629613,0.9611423,0.000013716201,0.000060064416,0.0002346071,0.00253369,0.019931436,0.013472538,0.00034190697,0.0004478082,0.000027287055],"about_ca_topic_score_codex":0.0010851827,"about_ca_topic_score_gemma":0.0016415039,"teacher_disagreement_score":0.0012263715,"about_ca_system_score_codex":0.00030669422,"about_ca_system_score_gemma":0.0002907845,"threshold_uncertainty_score":0.0064857006},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Library science; Data science","score_opus":0.16965955184857748,"score_gpt":0.3428780201124152,"score_spread":0.17321846826383772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123682722","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030241422,0.14198497,0.6357857,0.03668114,0.04035578,0.001263509,0.026074817,0.013874311,0.07373833],"genre_scores_gemma":[0.1234286,0.08127341,0.485048,0.004435647,0.010621074,0.00095843186,0.07603771,0.0033932857,0.21480389],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99551046,0.0015800548,0.0004248213,0.0006614484,0.0015556911,0.00026749333],"domain_scores_gemma":[0.9890131,0.0040771794,0.00035660126,0.0025366256,0.0027455022,0.0012709936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009304886,0.001421152,0.002264698,0.004330129,0.0011369035,0.006953275,0.0023460323,0.0020565572,0.03817329],"category_scores_gemma":[0.01586493,0.001049894,0.0013796168,0.00326899,0.0013003981,0.0057717166,0.0032410473,0.0033898575,0.020408668],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037334865,0.00043546225,0.0036488357,0.0005316594,0.00023337586,0.00019239091,0.00028273766,0.001019096,0.00289767,0.003475949,0.44688985,0.5400196],"study_design_scores_gemma":[0.00007231377,0.0002315312,0.0123776775,0.0008098206,0.00033713403,0.0010352203,0.0006596632,0.017327035,0.00879876,0.016555188,0.94170076,0.00009489108],"about_ca_topic_score_codex":0.006361331,"about_ca_topic_score_gemma":0.011471468,"teacher_disagreement_score":0.03817329,"about_ca_system_score_codex":0.0013315235,"about_ca_system_score_gemma":0.0035540625,"threshold_uncertainty_score":0.12770236},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science","score_opus":0.19852828432216715,"score_gpt":0.4581361215210042,"score_spread":0.25960783719883707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400027546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7193152,0.0016149529,0.24635091,0.0015734639,0.00016346111,0.0006546512,0.012237957,0.002099552,0.015989978],"genre_scores_gemma":[0.9483294,0.00029389595,0.03987767,0.00009754593,0.000053586475,0.00021522361,0.008332633,0.000054480715,0.002745635],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973928,0.00074218493,0.00023556431,0.0007606321,0.00061165035,0.00025718616],"domain_scores_gemma":[0.9889008,0.006715344,0.001134032,0.001106509,0.0014708469,0.00067248126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028472955,0.0008092499,0.0007143765,0.0061192797,0.00053330406,0.0012547192,0.001180083,0.0011604285,0.002711534],"category_scores_gemma":[0.020793172,0.00024927672,0.0006664043,0.0022239508,0.00030476725,0.001545426,0.001144233,0.0010336658,0.0024919072],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009181089,0.001437365,0.5250075,0.000815882,0.00040275656,0.0004826644,0.002076394,0.015970927,0.007896933,0.0037824893,0.024605986,0.41660312],"study_design_scores_gemma":[0.00008308203,0.00054239796,0.3257386,0.00044348737,0.00029651407,0.0010648316,0.004545214,0.6059307,0.012377814,0.024059584,0.02482551,0.000092249604],"about_ca_topic_score_codex":0.004251667,"about_ca_topic_score_gemma":0.007618015,"teacher_disagreement_score":0.0061192797,"about_ca_system_score_codex":0.000517214,"about_ca_system_score_gemma":0.00088936754,"threshold_uncertainty_score":0.01505816},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.09830638039740121,"score_gpt":0.35566732132717993,"score_spread":0.25736094092977874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401260724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011237353,0.0005223754,0.97836226,0.0019322308,0.0003055573,0.00038227276,0.0015154708,0.00069112814,0.00505133],"genre_scores_gemma":[0.14452519,0.00069849595,0.8434286,0.00072517747,0.0007829938,0.0011640727,0.005239097,0.00029692036,0.0031394393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9916066,0.0032791554,0.0010118494,0.0015212029,0.0019764006,0.0006047572],"domain_scores_gemma":[0.8628937,0.11046086,0.005343963,0.008229102,0.010326543,0.0027459273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011979878,0.0012146774,0.0016510008,0.001884025,0.0017376115,0.004167857,0.0018322695,0.0033153505,0.013041593],"category_scores_gemma":[0.14438055,0.0012450988,0.0016166259,0.002312281,0.0031833458,0.009649607,0.004810835,0.0057619973,0.00702162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018688769,0.0008667556,0.008828665,0.0019165744,0.0002754973,0.001717635,0.0014219325,0.047079366,0.029562708,0.40701798,0.09910331,0.40034065],"study_design_scores_gemma":[0.00032228883,0.0005040102,0.0038973214,0.00042028297,0.000082831655,0.0014039071,0.0009546021,0.3940998,0.021838361,0.5579899,0.018280013,0.00020664712],"about_ca_topic_score_codex":0.00200202,"about_ca_topic_score_gemma":0.0033440127,"teacher_disagreement_score":0.013041593,"about_ca_system_score_codex":0.00079818553,"about_ca_system_score_gemma":0.0048207436,"threshold_uncertainty_score":0.0633564},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; User interface; Operating system","score_opus":0.23172724384943497,"score_gpt":0.41645990057115273,"score_spread":0.18473265672171776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401880028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017239248,0.00071547506,0.9672226,0.0004896641,0.000078300865,0.00066923304,0.0018167264,0.006881339,0.004887522],"genre_scores_gemma":[0.18045786,0.00034849413,0.81350935,0.00022029002,0.000046245856,0.0009631761,0.0016606842,0.00022061968,0.002573329],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763083,0.00060862425,0.00023775449,0.00053474004,0.0008123186,0.00017580995],"domain_scores_gemma":[0.99382305,0.002108765,0.00086175033,0.0010329295,0.001728475,0.00044492923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027230396,0.0013343005,0.0005608487,0.004602515,0.0010769792,0.0036666286,0.0014675788,0.0015821485,0.003146012],"category_scores_gemma":[0.014290327,0.0006149735,0.001108793,0.0021414561,0.0012911673,0.00425818,0.002080951,0.0018805454,0.0016045957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009637135,0.001191307,0.03366049,0.0016241671,0.00039355783,0.0005571528,0.008815954,0.010276218,0.09316141,0.12794909,0.034207527,0.6871995],"study_design_scores_gemma":[0.00014269327,0.0011350529,0.049157005,0.0009859855,0.00047052174,0.0020625098,0.004403534,0.5783465,0.047050226,0.2094694,0.106302686,0.00047380754],"about_ca_topic_score_codex":0.007723513,"about_ca_topic_score_gemma":0.010458476,"teacher_disagreement_score":0.007723513,"about_ca_system_score_codex":0.00079911476,"about_ca_system_score_gemma":0.0015950372,"threshold_uncertainty_score":0.015357137},"labels":[],"label_agreement":null},{"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.126295305895185,"score_gpt":0.4262975173209416,"score_spread":0.3000022114257566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405817496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7477109,0.0006223443,0.23642433,0.00089106255,0.0001155597,0.0005371468,0.0052237045,0.0012207859,0.007254263],"genre_scores_gemma":[0.90814203,0.00027971406,0.08550491,0.000045684323,0.00005176223,0.00031580406,0.0033988238,0.00007164733,0.0021895613],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985911,0.0006188545,0.00008407984,0.00025070456,0.00038137936,0.00007381005],"domain_scores_gemma":[0.99312806,0.0042619803,0.0012228561,0.00048533082,0.00052623585,0.00037549945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016923154,0.00054100953,0.00026045996,0.0057707755,0.0005090362,0.0006899652,0.0004597345,0.0004913604,0.0012704838],"category_scores_gemma":[0.0074858684,0.00014324539,0.00052198896,0.0031686,0.000299437,0.0012576147,0.000809526,0.00050995866,0.00046088875],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041486084,0.000978674,0.34299472,0.00081203703,0.0006383043,0.00064138643,0.007091888,0.030789096,0.025827393,0.010067078,0.011232444,0.5685121],"study_design_scores_gemma":[0.000040694977,0.0005167183,0.40106124,0.00017691831,0.00021831812,0.00091431197,0.005121052,0.51333207,0.016495602,0.023382317,0.038601257,0.00013954435],"about_ca_topic_score_codex":0.0023969042,"about_ca_topic_score_gemma":0.004047483,"teacher_disagreement_score":0.0057707755,"about_ca_system_score_codex":0.00044476838,"about_ca_system_score_gemma":0.0003176093,"threshold_uncertainty_score":0.008949876},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Climb; Computer science; Prime (order theory); Human–computer interaction; Engineering; Mathematics","score_opus":0.06904220657417487,"score_gpt":0.3595855794393796,"score_spread":0.2905433728652047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406609927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895425,0.00023844068,0.00408357,0.0006488361,0.00002215865,0.00031676647,0.0013083022,0.00019741335,0.0036420089],"genre_scores_gemma":[0.981059,0.00021111434,0.013332962,0.00017284551,0.00001013982,0.00016437667,0.0016546497,0.000034156263,0.0033605897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99922836,0.00033481972,0.00005031493,0.00013346778,0.00016963502,0.00008347096],"domain_scores_gemma":[0.996415,0.002099699,0.00028484355,0.00024744854,0.00044697244,0.0005059623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010644916,0.00040254797,0.00037105966,0.0013992877,0.000995432,0.0010800497,0.00087513204,0.000924608,0.0012570155],"category_scores_gemma":[0.006929438,0.00021109892,0.00031168852,0.001437665,0.0003838895,0.0007805999,0.0008428121,0.0007362387,0.00061452936],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014830764,0.005081356,0.6962558,0.0007303916,0.00034489995,0.008294164,0.016481385,0.0077360636,0.0044418653,0.0025112554,0.028902026,0.22773771],"study_design_scores_gemma":[0.0002915229,0.0025709355,0.77904254,0.00046378217,0.00030732577,0.006274581,0.03740763,0.111431666,0.0074843294,0.0057978244,0.048754755,0.0001731212],"about_ca_topic_score_codex":0.018581457,"about_ca_topic_score_gemma":0.041935302,"teacher_disagreement_score":0.018581457,"about_ca_system_score_codex":0.00056202355,"about_ca_system_score_gemma":0.0007987931,"threshold_uncertainty_score":0.036946595},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Bayesian network; Bayesian probability; Degeneracy (biology); Dynamic Bayesian network; Theoretical computer science; Artificial intelligence; Machine learning","score_opus":0.03279701645553337,"score_gpt":0.30495663797640615,"score_spread":0.2721596215208728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407070788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19081464,0.0006480369,0.7931254,0.004469502,0.00006372709,0.0001308346,0.00038154537,0.00020768857,0.010158691],"genre_scores_gemma":[0.9521476,0.000281652,0.039453506,0.00029839104,0.00004552696,0.00014388682,0.00018371368,0.000072822186,0.007372861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99328506,0.004325588,0.00024100445,0.00095889595,0.00061312126,0.00057635986],"domain_scores_gemma":[0.9397161,0.05415662,0.0022153473,0.0015966481,0.0010705831,0.0012446957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01645701,0.0009702025,0.0036463353,0.0015623708,0.0017471404,0.0038112607,0.0045813886,0.0043948935,0.0051464476],"category_scores_gemma":[0.07348604,0.0022778974,0.0013756119,0.0013546157,0.0050110333,0.009826473,0.005495176,0.005834573,0.0004360768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025425435,0.00011218362,0.003032335,0.000083657134,0.00007684926,0.00009276068,0.00041916777,0.6396135,0.00010753062,0.34376493,0.0012620838,0.011180702],"study_design_scores_gemma":[0.000025313979,0.00001147224,0.00019903659,0.000016165866,0.00001070711,0.000011553289,0.000030442929,0.80758923,0.000035169658,0.19189873,0.00015936296,0.000012807768],"about_ca_topic_score_codex":0.018174725,"about_ca_topic_score_gemma":0.016258117,"teacher_disagreement_score":0.018174725,"about_ca_system_score_codex":0.004685441,"about_ca_system_score_gemma":0.002665296,"threshold_uncertainty_score":0.08703399},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.13343056222838606,"score_gpt":0.43102628144059596,"score_spread":0.2975957192122099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473928240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98873705,0.00014532091,0.0060385945,0.00010612312,0.00003500504,0.0003098604,0.0018944357,0.0002814329,0.0024522461],"genre_scores_gemma":[0.9818771,0.000069887734,0.0109412,0.000039203765,0.000023053784,0.00033510284,0.0038489138,0.000091418406,0.002774206],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98013556,0.0082294345,0.0016984787,0.0017758736,0.0076250155,0.0005355781],"domain_scores_gemma":[0.8015792,0.14222279,0.017287577,0.012659983,0.023655877,0.002594592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0105283605,0.0003932836,0.00056645926,0.003483507,0.0007313807,0.002113728,0.00086642173,0.0007913939,0.0014253318],"category_scores_gemma":[0.10143744,0.0002422213,0.0004562113,0.0030914447,0.0006711515,0.0014641309,0.0013831003,0.001068832,0.0010730114],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00352526,0.003649246,0.60776883,0.0012920338,0.000443502,0.0007482562,0.015292396,0.0045398585,0.024656836,0.0022205892,0.0053216238,0.3305416],"study_design_scores_gemma":[0.000112434696,0.0026742094,0.9113512,0.00013454143,0.00015465185,0.0006616194,0.0062476834,0.034180973,0.022415664,0.0025083532,0.019373572,0.00018511538],"about_ca_topic_score_codex":0.0015018772,"about_ca_topic_score_gemma":0.002570121,"teacher_disagreement_score":0.0105283605,"about_ca_system_score_codex":0.00072569045,"about_ca_system_score_gemma":0.0011004275,"threshold_uncertainty_score":0.055679917},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Track (disk drive); Library science; Computer science; Operations research; Engineering","score_opus":0.01631803235690641,"score_gpt":0.26449201473798123,"score_spread":0.24817398238107483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561395547","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000038255916,0.0008523363,0.00017680181,0.0133767035,0.9841293,0.000028899394,0.00008731523,0.00006949,0.0012409909],"genre_scores_gemma":[0.00049015804,0.0014082921,0.00029602982,0.010390787,0.97088575,0.000053391654,0.0001958979,0.00015984802,0.016119977],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9908832,0.000994066,0.0013467778,0.001169078,0.004887219,0.00071958214],"domain_scores_gemma":[0.9041086,0.0115187885,0.0062010414,0.0030497152,0.06165584,0.013465961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010987176,0.002944701,0.0027335538,0.0066892575,0.003975269,0.011682649,0.0028329848,0.008567779,0.07981867],"category_scores_gemma":[0.05473311,0.0010562671,0.0028706056,0.0028445323,0.0014960542,0.0052282885,0.0031481094,0.01000602,0.052410834],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040400293,0.00001622308,0.000045990586,0.00013593993,0.0000054769375,0.000049697428,0.000010003732,0.000016240121,0.000096929965,0.00015111965,0.9924489,0.0069829486],"study_design_scores_gemma":[0.00003985898,0.0000528296,0.000431122,0.0003080893,0.000018366429,0.00015439124,0.000059750622,0.000119199896,0.00018620313,0.00074211153,0.9978655,0.000022685135],"about_ca_topic_score_codex":0.0009843572,"about_ca_topic_score_gemma":0.0032663997,"teacher_disagreement_score":0.07981867,"about_ca_system_score_codex":0.0025990694,"about_ca_system_score_gemma":0.0048520477,"threshold_uncertainty_score":0.26702005},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.1289815930826969,"score_gpt":0.4246660141449498,"score_spread":0.2956844210622529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571583649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96386546,0.0010027884,0.022186847,0.00062241347,0.00015163874,0.0012141626,0.004583539,0.0011764544,0.0051966864],"genre_scores_gemma":[0.94843435,0.00024394102,0.041048545,0.00008288626,0.000055329954,0.00048342795,0.0067192316,0.0001615085,0.0027708376],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9708962,0.010317074,0.0027388176,0.0020407536,0.013365299,0.0006418436],"domain_scores_gemma":[0.65649647,0.26593998,0.016424427,0.0106962705,0.047459878,0.0029829445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021153225,0.000429426,0.0006743769,0.006461359,0.0010261735,0.0030820186,0.0016646064,0.0012279722,0.0018193914],"category_scores_gemma":[0.17389405,0.00028204644,0.0006842398,0.0040511084,0.0006577799,0.0027841057,0.0014078136,0.0010928904,0.000991615],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003270423,0.0033109311,0.42521504,0.0019008844,0.0006356466,0.0008932616,0.008150927,0.004836457,0.010872285,0.003950495,0.011649707,0.52531403],"study_design_scores_gemma":[0.00043009248,0.0031605775,0.65340346,0.0009008916,0.0012810627,0.001702413,0.014161004,0.23608601,0.02983005,0.01276001,0.045830335,0.0004541088],"about_ca_topic_score_codex":0.0036101684,"about_ca_topic_score_gemma":0.008043903,"teacher_disagreement_score":0.021153225,"about_ca_system_score_codex":0.0011679338,"about_ca_system_score_gemma":0.0041152374,"threshold_uncertainty_score":0.11187029},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Data science","score_opus":0.10054019880729122,"score_gpt":0.3978176205060965,"score_spread":0.2972774216988053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572725214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45612943,0.019083997,0.35420087,0.088482864,0.0020659806,0.0005135842,0.0041807336,0.0010939465,0.074248664],"genre_scores_gemma":[0.9258702,0.0015048253,0.06354618,0.0049145417,0.00031141934,0.00030427612,0.0008450628,0.0002599189,0.0024435301],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98258024,0.012573343,0.00069224485,0.0021156312,0.0014112652,0.00062724634],"domain_scores_gemma":[0.84386253,0.13122328,0.0075235553,0.009920889,0.0051171863,0.002352457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03258809,0.000700107,0.00070049317,0.004455563,0.0025947455,0.007667333,0.0016792237,0.0016623448,0.0034012045],"category_scores_gemma":[0.1276535,0.00041000912,0.0010234987,0.0054923887,0.005479085,0.01198846,0.0053419257,0.004894634,0.00059554016],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006035748,0.00029744388,0.19619769,0.0021385201,0.0008939027,0.00026441104,0.09375095,0.0016371213,0.0034001262,0.31987634,0.022734502,0.3582055],"study_design_scores_gemma":[0.000055835953,0.0001269807,0.11634366,0.003931085,0.0005961552,0.00026566893,0.08498889,0.011621895,0.006392509,0.670479,0.1050654,0.00013293409],"about_ca_topic_score_codex":0.013900385,"about_ca_topic_score_gemma":0.029271184,"teacher_disagreement_score":0.03258809,"about_ca_system_score_codex":0.0026242174,"about_ca_system_score_gemma":0.00594673,"threshold_uncertainty_score":0.17234439},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Competition (biology); Computer science; Graph; Theoretical computer science","score_opus":0.07201283518887017,"score_gpt":0.33673167597554843,"score_spread":0.2647188407866783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573742935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8537158,0.0019315211,0.05359625,0.004351098,0.00036958678,0.0005858955,0.051277485,0.00097273476,0.03319962],"genre_scores_gemma":[0.9524541,0.00031381275,0.021086162,0.00017509756,0.000085587904,0.00014690864,0.020242473,0.0000949711,0.005400913],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998319,0.0003597965,0.000083924046,0.000423704,0.0004948859,0.000318814],"domain_scores_gemma":[0.99192,0.004048169,0.0011845054,0.00053233746,0.0010917629,0.0012232892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014580236,0.0006873777,0.0008483754,0.0080540255,0.0012187817,0.0023363794,0.0024913233,0.0012684314,0.009806172],"category_scores_gemma":[0.014027632,0.0002554895,0.0010110254,0.007917207,0.0004823552,0.0024400405,0.0013102675,0.001453406,0.0019352344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008672549,0.0022238407,0.6355593,0.0005350827,0.000622824,0.00060312136,0.0006513273,0.04794251,0.000980522,0.023840053,0.08451561,0.2016585],"study_design_scores_gemma":[0.00014277523,0.0004634166,0.3258864,0.0002881324,0.00032163592,0.0006612925,0.005333612,0.54696834,0.0017190007,0.07882689,0.039276365,0.00011217452],"about_ca_topic_score_codex":0.028782303,"about_ca_topic_score_gemma":0.060262706,"teacher_disagreement_score":0.028782303,"about_ca_system_score_codex":0.0013280306,"about_ca_system_score_gemma":0.0025226779,"threshold_uncertainty_score":0.05722958},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.10207117134671254,"score_gpt":0.3366786190286713,"score_spread":0.23460744768195876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573938660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036796292,0.00023448563,0.9928806,0.00013366783,0.00005264666,0.00014066181,0.00067780825,0.001258568,0.00094192167],"genre_scores_gemma":[0.062498704,0.00013790805,0.9320246,0.000104272134,0.00006908577,0.00030312483,0.00289863,0.00030488585,0.00165879],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969518,0.0015062998,0.00019792575,0.00047624946,0.0006634493,0.00020428338],"domain_scores_gemma":[0.98719645,0.0076639955,0.00046826084,0.0014122477,0.0029418897,0.0003172027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004601019,0.0010231116,0.0013241097,0.004840528,0.0011567103,0.00198809,0.0020089084,0.0012202907,0.006851785],"category_scores_gemma":[0.032582756,0.000728496,0.0016732416,0.0060155443,0.00052366464,0.0022204057,0.002110261,0.0023526887,0.004246149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048085878,0.00042016752,0.012632428,0.0003543389,0.00070561573,0.00019337246,0.0009042166,0.04646135,0.005153531,0.026579564,0.028027141,0.8780874],"study_design_scores_gemma":[0.00012821319,0.00028654828,0.007232001,0.00016335609,0.0004286259,0.00032043073,0.00047982336,0.8274382,0.004611658,0.13054562,0.02827754,0.00008811729],"about_ca_topic_score_codex":0.012473061,"about_ca_topic_score_gemma":0.021481203,"teacher_disagreement_score":0.012473061,"about_ca_system_score_codex":0.000660512,"about_ca_system_score_gemma":0.002157809,"threshold_uncertainty_score":0.024800897},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.19053928451288077,"score_gpt":0.3677279941472281,"score_spread":0.17718870963434735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2578850146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13123254,0.0027987806,0.7318118,0.0039315894,0.00047112594,0.0016161468,0.09836705,0.013158178,0.016612744],"genre_scores_gemma":[0.4154199,0.0022810567,0.47444537,0.00039955633,0.0001999378,0.00083543954,0.10018114,0.00026531797,0.0059722946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796414,0.00033026488,0.0002526129,0.0005347718,0.0007497607,0.00016852442],"domain_scores_gemma":[0.995129,0.0021093162,0.00048537363,0.001032963,0.0010658021,0.00017752139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020151264,0.0008572013,0.00072128576,0.0069454955,0.000522817,0.0022355097,0.0008871014,0.0013133485,0.004287184],"category_scores_gemma":[0.013744828,0.00043013372,0.0014486043,0.0075422423,0.00020446266,0.0021585403,0.0008049261,0.0013259989,0.004871313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003112416,0.0011228868,0.14847732,0.0013103986,0.0004564204,0.00051736523,0.00062455994,0.030623097,0.0070569925,0.0110070305,0.040894073,0.7575986],"study_design_scores_gemma":[0.00009589665,0.00043623385,0.079769656,0.0005881956,0.00037803926,0.00068518537,0.0012373481,0.7291706,0.018542662,0.03742606,0.13153994,0.00013012289],"about_ca_topic_score_codex":0.0145075,"about_ca_topic_score_gemma":0.024420545,"teacher_disagreement_score":0.0145075,"about_ca_system_score_codex":0.0009878352,"about_ca_system_score_gemma":0.0017929929,"threshold_uncertainty_score":0.028846145},"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,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"CUSUM; Quarter (Canadian coin); Point (geometry); Computer science; Control (management); Statistics; Mathematics; Artificial intelligence; Geography","score_opus":0.1920892650586802,"score_gpt":0.5091286967951045,"score_spread":0.31703943173642435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724478195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930708,0.00010065317,0.0029337436,0.00025947517,0.000025485391,0.000026416034,0.0011407951,0.00012721581,0.0023154458],"genre_scores_gemma":[0.9975587,0.000032563195,0.0013051588,0.00001313909,0.00002568856,0.000022249878,0.00061704806,0.000012384017,0.0004129786],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99828315,0.0006611189,0.000120578625,0.00031867917,0.00041598614,0.00020044442],"domain_scores_gemma":[0.95765775,0.024364484,0.01059135,0.0024202964,0.0036129782,0.0013530875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004201111,0.00026245994,0.00031674205,0.003044665,0.0005923536,0.0015902633,0.0005727777,0.0005534917,0.0015753778],"category_scores_gemma":[0.035415657,0.00026167862,0.00021470239,0.0022357458,0.00044488642,0.0012304761,0.00082990906,0.000986206,0.00060757075],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031389797,0.00015998464,0.9240977,0.00009127402,0.00006892084,0.00012152327,0.0037604407,0.0031295628,0.0024625254,0.0019591122,0.0021755742,0.061659493],"study_design_scores_gemma":[0.000011780989,0.00017748306,0.9549335,0.00004308468,0.000029688776,0.00012357344,0.0022777,0.03213155,0.002084226,0.0017369405,0.006396615,0.000053896187],"about_ca_topic_score_codex":0.004572393,"about_ca_topic_score_gemma":0.007565052,"teacher_disagreement_score":0.004572393,"about_ca_system_score_codex":0.0006136023,"about_ca_system_score_gemma":0.00039413708,"threshold_uncertainty_score":0.02221787},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Educational data mining; Library science; Data science; Engineering; Computer science; Engineering physics","score_opus":0.14008325906825597,"score_gpt":0.37059664240518286,"score_spread":0.2305133833369269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729139296","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03606335,0.16835111,0.5191714,0.051317554,0.066534206,0.0014426847,0.042980816,0.017425684,0.09671322],"genre_scores_gemma":[0.14738157,0.081003666,0.3860776,0.0049568117,0.013380074,0.0009607962,0.107706435,0.0034878203,0.25504524],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99574465,0.0013999586,0.00038286264,0.0006604365,0.0015356998,0.00027646145],"domain_scores_gemma":[0.98898894,0.0030728064,0.0003682158,0.0023555364,0.003743609,0.0014709528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010261927,0.0012611366,0.0019070322,0.004535731,0.0011212758,0.006414364,0.002308323,0.0016139746,0.0360216],"category_scores_gemma":[0.015849303,0.000796807,0.0011213773,0.0029736354,0.0011401018,0.0049330415,0.0038664385,0.0030209336,0.02038362],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026816936,0.00032696783,0.003663022,0.0004108498,0.00015366543,0.0001280803,0.00023098744,0.0007578324,0.0019018839,0.003334498,0.5521263,0.43669787],"study_design_scores_gemma":[0.000049655926,0.00016685802,0.012638549,0.000908021,0.00019967322,0.00059413223,0.0007119836,0.012429483,0.006331318,0.0146355685,0.9512483,0.00008639285],"about_ca_topic_score_codex":0.0070962403,"about_ca_topic_score_gemma":0.012814842,"teacher_disagreement_score":0.0360216,"about_ca_system_score_codex":0.0016101075,"about_ca_system_score_gemma":0.0041996706,"threshold_uncertainty_score":0.1205042},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.13306805541817213,"score_gpt":0.34647093137060214,"score_spread":0.21340287595243002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805377368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9708201,0.002721469,0.01731794,0.00029275822,0.000091850976,0.000106839085,0.0036431835,0.00062533375,0.004380432],"genre_scores_gemma":[0.9898482,0.00059428276,0.0069790413,0.00006299914,0.000059058122,0.000058517948,0.00131915,0.000030612842,0.0010482423],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982566,0.000046317844,0.000010842858,0.000046609057,0.00004492132,0.00002562573],"domain_scores_gemma":[0.9989108,0.00049392396,0.00016912613,0.00008308171,0.00025776558,0.000085282925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036072155,0.00021724097,0.00023187602,0.0012646382,0.00018203478,0.00033381648,0.00022294547,0.00036801942,0.0014993207],"category_scores_gemma":[0.0033592514,0.00010596679,0.00019296371,0.0007615352,0.00009119149,0.00033467717,0.0003358989,0.0003449372,0.00056364393],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029388338,0.00036098476,0.37692183,0.0007663043,0.00044178296,0.00057965564,0.0021616505,0.0015521802,0.23804943,0.0007836583,0.014860448,0.3605832],"study_design_scores_gemma":[0.000056560217,0.0003918423,0.9593787,0.000088853,0.00016983827,0.001147643,0.0006848756,0.014856574,0.018986566,0.0008240203,0.0033717814,0.000042749994],"about_ca_topic_score_codex":0.0043392824,"about_ca_topic_score_gemma":0.010053256,"teacher_disagreement_score":0.0043392824,"about_ca_system_score_codex":0.00018509864,"about_ca_system_score_gemma":0.00021624695,"threshold_uncertainty_score":0.00862807},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Internet privacy; Peer-to-peer; World Wide Web","score_opus":0.04770860689949084,"score_gpt":0.3474207435199652,"score_spread":0.29971213662047436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806008675","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921679,0.00037479994,0.0028753371,0.0006988082,0.00006823317,0.0001336626,0.0007811718,0.00017942309,0.0027207038],"genre_scores_gemma":[0.9937723,0.00009540148,0.0029652347,0.00006905606,0.000046412082,0.000056518442,0.00079021853,0.000015716141,0.0021891205],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986381,0.0006874146,0.00006501248,0.00020448747,0.0002160737,0.00018894981],"domain_scores_gemma":[0.9786548,0.012361589,0.0023092076,0.00074274925,0.0022585455,0.0036730957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002782176,0.0003136326,0.00032218607,0.0011931952,0.0006907469,0.0008339448,0.0006617892,0.0011524941,0.0054532737],"category_scores_gemma":[0.021220107,0.00017035886,0.0002626845,0.00047821295,0.00022953341,0.0015913509,0.0008332696,0.00073087413,0.002402356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018062843,0.0022540626,0.8625409,0.0003039807,0.00011490277,0.00035589514,0.0040370915,0.0012438641,0.003664858,0.0009889518,0.0147547275,0.10793439],"study_design_scores_gemma":[0.00046871562,0.003647941,0.80825865,0.00024598322,0.00035569546,0.001186603,0.019021962,0.13281395,0.0063783578,0.006547019,0.02096927,0.00010573747],"about_ca_topic_score_codex":0.0035108961,"about_ca_topic_score_gemma":0.0064998954,"teacher_disagreement_score":0.0054532737,"about_ca_system_score_codex":0.0003516102,"about_ca_system_score_gemma":0.00072448567,"threshold_uncertainty_score":0.018242955},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Compliance (psychology); Computer science; Psychology; Human–computer interaction; Mathematics education; Social psychology","score_opus":0.23767342860657217,"score_gpt":0.4779893651028262,"score_spread":0.24031593649625402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806081626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974974,0.000078906036,0.0009247349,0.00029153877,0.000020763166,0.000026754338,0.00006617205,0.000027816184,0.0010658993],"genre_scores_gemma":[0.99903333,0.000031315205,0.000561394,0.000038020047,0.000007378216,0.000023508735,0.00003484812,0.000011904675,0.00025829233],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9902954,0.0068435078,0.00034087012,0.00086716335,0.0012782606,0.00037483647],"domain_scores_gemma":[0.6113385,0.35132897,0.020154044,0.0072068274,0.0045125787,0.005459062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014599512,0.0001996861,0.00035545861,0.0004603686,0.00039247965,0.0015622542,0.000600869,0.0007609164,0.002299397],"category_scores_gemma":[0.15455346,0.00018926029,0.00045994797,0.00045872707,0.0006927305,0.0007374774,0.0008154697,0.0016760197,0.0003444778],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009817727,0.0036402165,0.865494,0.00015463906,0.00043233403,0.0001663474,0.0025762373,0.003017814,0.004357521,0.0013710933,0.0014463606,0.107525535],"study_design_scores_gemma":[0.0002680583,0.003819361,0.96803904,0.000078904355,0.0005867207,0.00010208101,0.00107278,0.01713415,0.0057689752,0.0020543279,0.0010274217,0.0000481736],"about_ca_topic_score_codex":0.002626895,"about_ca_topic_score_gemma":0.0026395016,"teacher_disagreement_score":0.014599512,"about_ca_system_score_codex":0.00075416197,"about_ca_system_score_gemma":0.002036643,"threshold_uncertainty_score":0.077210546},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.023796523411572255,"score_gpt":0.3039595551962821,"score_spread":0.28016303178470986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893061728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958103,0.0002103154,0.001967036,0.00016084278,0.000009049792,0.000043178632,0.0012603991,0.000076823315,0.0004621651],"genre_scores_gemma":[0.9957117,0.00007072985,0.0020664723,0.0000309812,0.0000045392662,0.00002976746,0.0017654635,0.0000065587415,0.00031382096],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999585,0.000110428366,0.00006502449,0.00011721422,0.00006731201,0.00005496183],"domain_scores_gemma":[0.99719363,0.0014149869,0.000438345,0.00015885549,0.000519228,0.000274992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045130914,0.00022515708,0.0002690186,0.0015914426,0.0001818176,0.0005644753,0.00042230837,0.00064132316,0.0009207539],"category_scores_gemma":[0.006956028,0.0000736633,0.00026909893,0.00088578,0.0001081576,0.0004937704,0.00044265666,0.0003058929,0.00043892962],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005403309,0.00041426512,0.9090241,0.0001325051,0.00011556859,0.0002695906,0.0010561569,0.0011572173,0.0024290762,0.00012076629,0.0022581306,0.08248235],"study_design_scores_gemma":[0.000026254207,0.0004482269,0.95437634,0.000076027696,0.00013815316,0.0010136911,0.0034232654,0.03402067,0.0032118757,0.0011391476,0.0020966043,0.00002991638],"about_ca_topic_score_codex":0.00439577,"about_ca_topic_score_gemma":0.007030349,"teacher_disagreement_score":0.00439577,"about_ca_system_score_codex":0.00021727807,"about_ca_system_score_gemma":0.00023121064,"threshold_uncertainty_score":0.0087403655},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Work (physics); Data science; Knowledge management; Engineering","score_opus":0.08956470542679441,"score_gpt":0.3114663546270516,"score_spread":0.22190164920025718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893165852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16651827,0.002418311,0.7562772,0.0026776039,0.00026363466,0.001007634,0.03254946,0.0066382247,0.031649668],"genre_scores_gemma":[0.62723804,0.0009364884,0.3304458,0.00033121006,0.000077444376,0.0005610941,0.030950498,0.0003730184,0.009086344],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992132,0.00020555184,0.000086282496,0.0001742238,0.0002208996,0.00009989548],"domain_scores_gemma":[0.9970536,0.0014927326,0.0003374079,0.00043796023,0.0004947274,0.0001834999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001197271,0.0005960015,0.00033925424,0.0033213072,0.00051884225,0.0015985462,0.00127081,0.0005834203,0.0058420734],"category_scores_gemma":[0.0067478907,0.00022198373,0.00078420585,0.0027300774,0.0003556942,0.0022992482,0.0011601212,0.0007685957,0.0022726809],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005558915,0.0012768926,0.10541615,0.0019258412,0.00025614243,0.00062710483,0.003880206,0.027931273,0.00873118,0.049536914,0.04811368,0.75174874],"study_design_scores_gemma":[0.00007646037,0.00038285498,0.07792598,0.0010269777,0.0001707858,0.00076505856,0.008703105,0.6147997,0.01791638,0.15555091,0.122559205,0.00012267793],"about_ca_topic_score_codex":0.008813324,"about_ca_topic_score_gemma":0.012896212,"teacher_disagreement_score":0.008813324,"about_ca_system_score_codex":0.0009030944,"about_ca_system_score_gemma":0.001608334,"threshold_uncertainty_score":0.019543648},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Data science","score_opus":0.1295020425435878,"score_gpt":0.31573012509504783,"score_spread":0.18622808255146003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893310092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9892391,0.00033849635,0.0010239132,0.0007327129,0.000057549674,0.00007261895,0.0062697814,0.00002095944,0.0022448916],"genre_scores_gemma":[0.9939744,0.00014942924,0.00123281,0.00012304285,0.000058268237,0.00010947531,0.0031482542,0.000009072182,0.0011952195],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990663,0.0002734249,0.000113537484,0.00016465063,0.00023067872,0.00015144995],"domain_scores_gemma":[0.9908092,0.0058350903,0.0014467497,0.00028504035,0.0009459998,0.00067792274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002226532,0.00018215059,0.00032781766,0.0026271471,0.00057761837,0.0009079147,0.00047665785,0.0004129156,0.0039401595],"category_scores_gemma":[0.015560854,0.00008129436,0.00039898767,0.0031523276,0.00019196082,0.00063176977,0.0006858936,0.0006263335,0.0009517509],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032944826,0.0003906315,0.9235532,0.00014284284,0.00009217387,0.00023173989,0.0021188045,0.00018028136,0.0007857219,0.0005593836,0.0076728864,0.06394287],"study_design_scores_gemma":[0.00001573537,0.00013860871,0.98547935,0.000109315144,0.00007426708,0.00019661382,0.0058530066,0.0021247412,0.00052490184,0.0010911314,0.0043764417,0.000015970685],"about_ca_topic_score_codex":0.004433283,"about_ca_topic_score_gemma":0.009015064,"teacher_disagreement_score":0.004433283,"about_ca_system_score_codex":0.00031326982,"about_ca_system_score_gemma":0.0009241421,"threshold_uncertainty_score":0.01318115},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Learning analytics; Analytics; Human–computer interaction; Data science; Multimedia; Artificial intelligence","score_opus":0.03986845201711653,"score_gpt":0.3563553505440226,"score_spread":0.3164868985269061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965314695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04345479,0.02617725,0.6083626,0.072633624,0.0018933319,0.00035039426,0.0008144587,0.0023132493,0.24400032],"genre_scores_gemma":[0.5083719,0.025627319,0.38047698,0.0059241997,0.0017004694,0.00048365167,0.00051027565,0.00040827808,0.07649702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992772,0.0002900701,0.000037241454,0.00013766607,0.00020068361,0.000057066518],"domain_scores_gemma":[0.998184,0.0010042107,0.00009901835,0.0002506903,0.0002594871,0.00020251438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001182878,0.00049010536,0.00019640986,0.0015399709,0.00075385446,0.0040228353,0.0010864473,0.0017184542,0.0042022457],"category_scores_gemma":[0.005069011,0.0002803128,0.00033809175,0.0008687257,0.0049310266,0.0075559965,0.002825633,0.0026096273,0.0019217894],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010781481,0.000101989834,0.0069295997,0.00052295765,0.000030917196,0.0005506503,0.0095985215,0.0015873936,0.0051886644,0.5375103,0.032597546,0.40527365],"study_design_scores_gemma":[0.000014514861,0.0001139409,0.0058755265,0.00087319576,0.000023365277,0.0022720573,0.0042166943,0.009072712,0.004119934,0.5680632,0.40529957,0.00005524892],"about_ca_topic_score_codex":0.0009801862,"about_ca_topic_score_gemma":0.0012334278,"teacher_disagreement_score":0.0042022457,"about_ca_system_score_codex":0.000673272,"about_ca_system_score_gemma":0.0012909367,"threshold_uncertainty_score":0.014057934},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.16913496512009185,"score_gpt":0.49970905681214234,"score_spread":0.3305740916920505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965396085","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71189374,0.001923678,0.27162892,0.0026890885,0.00035702554,0.00070098275,0.003777267,0.0017099334,0.005319338],"genre_scores_gemma":[0.968982,0.00015434173,0.026900431,0.00048446085,0.00018555182,0.0004395577,0.0013208481,0.00011108806,0.0014218749],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98302126,0.012729552,0.00033671153,0.0027740092,0.000638376,0.0005000882],"domain_scores_gemma":[0.7817712,0.19084659,0.006059621,0.017611638,0.0013460714,0.0023648837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028604254,0.0010860949,0.001966215,0.0014220021,0.0006516948,0.0021274125,0.0023470973,0.0024138005,0.0037058177],"category_scores_gemma":[0.12363612,0.00069962547,0.0011550174,0.001536065,0.0015845296,0.0033421365,0.0015120374,0.003158962,0.0010099482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.019728959,0.0053837304,0.35679826,0.0011448244,0.004887772,0.00046253638,0.0010732762,0.25190794,0.00951383,0.028253486,0.01367252,0.30717292],"study_design_scores_gemma":[0.000817189,0.0027436903,0.08351966,0.00011735015,0.0018443837,0.00018467863,0.0002900418,0.78734106,0.0062660296,0.11210399,0.004648418,0.00012346418],"about_ca_topic_score_codex":0.0021745523,"about_ca_topic_score_gemma":0.0036266048,"teacher_disagreement_score":0.028604254,"about_ca_system_score_codex":0.0010615046,"about_ca_system_score_gemma":0.0014354569,"threshold_uncertainty_score":0.15127558},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Work (physics); Computer science; Engineering; Mechanical engineering","score_opus":0.17744467711789552,"score_gpt":0.4346953933341846,"score_spread":0.2572507162162891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965852225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9915257,0.0003693691,0.00035808916,0.00029017066,0.000052055846,0.000015826161,0.00096972013,0.000013657688,0.006405459],"genre_scores_gemma":[0.9971607,0.000055209937,0.00008969982,0.00003020749,0.000008342332,0.000010041243,0.00029902536,0.0000059928016,0.0023409799],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99874735,0.00026221396,0.00010722926,0.00022188158,0.00042203497,0.00023918724],"domain_scores_gemma":[0.98944294,0.004740124,0.0020746614,0.00066915696,0.0016482159,0.0014249348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021320283,0.00017594545,0.00023144619,0.00095925137,0.0003410275,0.0012682038,0.00041868372,0.00038919755,0.008870408],"category_scores_gemma":[0.023048598,0.00010983938,0.0002926691,0.00081334374,0.00027541615,0.00096550205,0.0009968455,0.0006011388,0.0016699551],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048533382,0.00032296826,0.9599897,0.000026208436,0.00007328716,0.000113932445,0.0022022391,0.00012386596,0.00054226303,0.00057006226,0.0015872908,0.03396284],"study_design_scores_gemma":[0.000005682522,0.00008560708,0.9974105,0.000020162895,0.000011193813,0.00004669581,0.00094505574,0.00016064283,0.00013446822,0.0002678485,0.0009067024,0.000005407083],"about_ca_topic_score_codex":0.007925499,"about_ca_topic_score_gemma":0.014755618,"teacher_disagreement_score":0.008870408,"about_ca_system_score_codex":0.00036869472,"about_ca_system_score_gemma":0.00061531854,"threshold_uncertainty_score":0.02967441},"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,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Library science; Editorial board; Publication; Computer science; Operations research; Engineering; Political science; Law","score_opus":0.015914953209477323,"score_gpt":0.2988800628710742,"score_spread":0.2829651096615969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980029776","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000080070575,0.0010034317,0.0003679746,0.018915212,0.97695607,0.00004671313,0.00013507486,0.00010412125,0.0023913346],"genre_scores_gemma":[0.0010828834,0.0020673855,0.0007331101,0.011000282,0.9502441,0.000082758845,0.00034231102,0.0002657705,0.034181274],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933782,0.00080993574,0.0008980604,0.0007345998,0.003649798,0.0005293536],"domain_scores_gemma":[0.9307524,0.0072388686,0.00411743,0.002043032,0.045482963,0.010365325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009392004,0.0019796377,0.0017595282,0.0046747196,0.0034713652,0.009071988,0.0020785737,0.0049911346,0.08281295],"category_scores_gemma":[0.04512609,0.0007842391,0.00201864,0.0019443035,0.0011577173,0.0039491826,0.0026060394,0.0069583277,0.05256037],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033562443,0.00001447027,0.00006333556,0.000118552205,0.000004491753,0.000049191338,0.000014218566,0.000014956626,0.000113454764,0.00022108285,0.99110126,0.0082514845],"study_design_scores_gemma":[0.000022522021,0.000040054118,0.00034773163,0.00022082942,0.0000125968945,0.00013682923,0.000060135404,0.00008895094,0.00019311444,0.00064509566,0.9982158,0.000016406979],"about_ca_topic_score_codex":0.0008522353,"about_ca_topic_score_gemma":0.0027364618,"teacher_disagreement_score":0.08281295,"about_ca_system_score_codex":0.002299734,"about_ca_system_score_gemma":0.004516732,"threshold_uncertainty_score":0.2770369},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Library science; Computer science; Data science","score_opus":0.05317215898791266,"score_gpt":0.31172964469574654,"score_spread":0.2585574857078339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007006485","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026062254,0.1889363,0.32724375,0.0806876,0.08261504,0.0012415464,0.08450031,0.016761703,0.19195153],"genre_scores_gemma":[0.0917446,0.08818383,0.19975892,0.006974962,0.0131051475,0.00061743753,0.12910327,0.0032678724,0.46724394],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971263,0.0006870538,0.00022426396,0.00043414335,0.0012817726,0.00024647545],"domain_scores_gemma":[0.9905702,0.002110678,0.00029342176,0.001365987,0.004124294,0.0015355123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062381458,0.001134671,0.0013924994,0.004435546,0.0012337228,0.0058489344,0.0018939468,0.0013499472,0.06285701],"category_scores_gemma":[0.010819521,0.0005895644,0.0008789753,0.003481796,0.0011810204,0.0035300562,0.0030386802,0.0025259664,0.029405396],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013204398,0.00012141004,0.002551745,0.0003265207,0.00008681626,0.00008825064,0.00012568785,0.00038297952,0.0014947569,0.003428735,0.70196027,0.2893007],"study_design_scores_gemma":[0.000015902559,0.000051824456,0.0066018924,0.00042504986,0.00008122817,0.00027485634,0.00029669836,0.0042739096,0.0026671423,0.0062504057,0.9790181,0.000042989846],"about_ca_topic_score_codex":0.0412708,"about_ca_topic_score_gemma":0.06843454,"teacher_disagreement_score":0.9587292,"about_ca_system_score_codex":0.0027199246,"about_ca_system_score_gemma":0.0066710487,"threshold_uncertainty_score":0.21027762},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.10515150453510448,"score_gpt":0.3664483783456825,"score_spread":0.26129687381057803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106584815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.807534,0.004540845,0.1287804,0.021528933,0.0005469934,0.0007794941,0.012210747,0.0051956484,0.018883027],"genre_scores_gemma":[0.94861495,0.00068050466,0.042213622,0.0005803831,0.00019439917,0.00017425828,0.0037923662,0.000080751255,0.0036687446],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982218,0.0006858515,0.00012352705,0.0003541123,0.00045546,0.00015927122],"domain_scores_gemma":[0.99045897,0.0059222407,0.00082572916,0.0011111352,0.000826625,0.00085536967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025518117,0.00030209255,0.0004123745,0.0017996398,0.00066536834,0.0022531375,0.0011342684,0.001002087,0.002857996],"category_scores_gemma":[0.022579145,0.00021978482,0.00038266196,0.002860069,0.0002733477,0.0029654824,0.0016053956,0.0015082663,0.00156274],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001250999,0.0024728146,0.17611313,0.00039877,0.00017702258,0.00038592325,0.000829293,0.011059285,0.0025474834,0.005869085,0.04363511,0.7552611],"study_design_scores_gemma":[0.00022898514,0.00091077347,0.1983088,0.0005242922,0.00032198688,0.00083250366,0.005376832,0.6164569,0.02355218,0.087877914,0.065502554,0.00010622],"about_ca_topic_score_codex":0.004419144,"about_ca_topic_score_gemma":0.0078195175,"teacher_disagreement_score":0.004419144,"about_ca_system_score_codex":0.00071288843,"about_ca_system_score_gemma":0.0017884218,"threshold_uncertainty_score":0.013495445},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.43641733097289864,"score_gpt":0.39667249974994745,"score_spread":0.039744831222951194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108491126","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057859525,0.0026334845,0.92340094,0.0011530876,0.0003913702,0.000080040125,0.0021478005,0.008514282,0.003819399],"genre_scores_gemma":[0.64146644,0.0013073601,0.3349986,0.0007690577,0.00018550453,0.0001488247,0.004339145,0.00023023595,0.016554791],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981254,0.000029680485,0.000012591572,0.00006914529,0.00003635967,0.00003968356],"domain_scores_gemma":[0.99963486,0.0001270433,0.00003087011,0.000044486456,0.00013314314,0.00002966553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005190161,0.00071145105,0.00045055622,0.0006498769,0.00025030342,0.000554151,0.0011004469,0.0007878991,0.0024192687],"category_scores_gemma":[0.0015062641,0.00037907367,0.0005605539,0.0007231675,0.00020994953,0.0010774584,0.00082897797,0.0013513332,0.0016086464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035884167,0.00033849868,0.0032668156,0.00018701893,0.00021587481,0.00014571148,0.00012953213,0.08877752,0.041982267,0.005061466,0.022717787,0.83681864],"study_design_scores_gemma":[0.0000063376324,0.00004277374,0.00056487654,0.000016213697,0.000018897936,0.000027745335,0.000020225705,0.9897451,0.005401529,0.0027099168,0.0014409007,0.000005534929],"about_ca_topic_score_codex":0.0195879,"about_ca_topic_score_gemma":0.032345086,"teacher_disagreement_score":0.0195879,"about_ca_system_score_codex":0.0007925489,"about_ca_system_score_gemma":0.0012033089,"threshold_uncertainty_score":0.03894776},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.29476740365609333,"score_gpt":0.5466153739308459,"score_spread":0.2518479702747526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109207671","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7987788,0.0076514217,0.024681045,0.017788295,0.0011337547,0.0006930699,0.12035147,0.0011878432,0.027734334],"genre_scores_gemma":[0.954032,0.0017597378,0.012445936,0.00073589385,0.0005074661,0.0003548162,0.026175573,0.00006782152,0.003920742],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987778,0.00052983296,0.00013284125,0.00019585481,0.00024420128,0.00011936852],"domain_scores_gemma":[0.980316,0.014444396,0.0021718466,0.00095573603,0.0014074721,0.00070447364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020410644,0.00063879375,0.0004921978,0.0061731827,0.0005917721,0.0017969657,0.00072172267,0.0006375712,0.004281458],"category_scores_gemma":[0.018042564,0.00017429769,0.00085470226,0.0048587476,0.00024550382,0.0015120757,0.0010466225,0.0012264246,0.001478116],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050447974,0.0010856931,0.725617,0.0012877134,0.0007694643,0.0004039673,0.0018069879,0.0022191387,0.0009830253,0.0030675484,0.041611347,0.22064364],"study_design_scores_gemma":[0.00008962988,0.00048588947,0.819079,0.0016882317,0.0010354872,0.0007053538,0.012277598,0.0719603,0.003938072,0.020954804,0.067701034,0.00008457039],"about_ca_topic_score_codex":0.00877013,"about_ca_topic_score_gemma":0.0148733575,"teacher_disagreement_score":0.00877013,"about_ca_system_score_codex":0.0005864844,"about_ca_system_score_gemma":0.0011512564,"threshold_uncertainty_score":0.017438173},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.09294334681844635,"score_gpt":0.3429350373180371,"score_spread":0.24999169049959075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5590877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8915287,0.00028273388,0.10274367,0.0006809002,0.000088428525,0.00037807898,0.0018723578,0.0006982146,0.0017268703],"genre_scores_gemma":[0.9840279,0.000050191775,0.013608429,0.00009299224,0.000011516112,0.000120037614,0.0017539582,0.00004739679,0.00028752498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9831251,0.012932494,0.00069622375,0.0015765727,0.0013811832,0.0002883844],"domain_scores_gemma":[0.6718745,0.26538485,0.014255752,0.036610737,0.010057434,0.0018167637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052241858,0.0008860522,0.0008312648,0.0018987537,0.0006385627,0.002003133,0.0025440436,0.0029796965,0.0012931998],"category_scores_gemma":[0.21900737,0.00067728874,0.0013664457,0.0013484502,0.00282226,0.0027179436,0.0017840994,0.0025574288,0.00048273054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096917676,0.00037425576,0.06292617,0.00019766821,0.0003683609,0.00017325977,0.0008652908,0.9004869,0.0011434775,0.014068724,0.0017659863,0.016660655],"study_design_scores_gemma":[0.00005810828,0.00022364133,0.01067639,0.00007034737,0.00002302855,0.00011289286,0.00011724836,0.97969925,0.0016026234,0.00692161,0.0004574887,0.00003749356],"about_ca_topic_score_codex":0.008230931,"about_ca_topic_score_gemma":0.0048650503,"teacher_disagreement_score":0.052241858,"about_ca_system_score_codex":0.0022385889,"about_ca_system_score_gemma":0.0010218988,"threshold_uncertainty_score":0.2762847},"labels":[],"label_agreement":null}]}