{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":29,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":29,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"b0f5578f786e","filters":{"venue":"World Wide Web"}},"results":[{"id":"W2011562771","doi":"10.1007/s11280-007-0033-x","title":"Bringing Semantics to Web Services with OWL-S","year":2007,"lang":"en","type":"article","venue":"World Wide Web","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":502,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Defense Advanced Research Projects Agency","keywords":"Computer science; OWL-S; Web service; World Wide Web; Web standards; Semantic Web; WS-Policy; Web modeling; Social Semantic Web; WS-I Basic Profile; Semantic Web Stack; Ontology; Semantic Web Rule Language; Software engineering; Web development; Semantic analytics; Web intelligence; Web application security","authors":[{"name":"David Martín","is_ca":false},{"name":"Mark Burstein","is_ca":false},{"name":"Drew McDermott","is_ca":false},{"name":"Sheila A. McIlraith","is_ca":true},{"name":"Massimo Paolucci","is_ca":false},{"name":"Katia Sycara","is_ca":false},{"name":"Deborah L. McGuinness","is_ca":false},{"name":"Evren Sirin","is_ca":false},{"name":"Naveen Srinivasan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.004156329108519309,"gpt":0.2137402983946391,"spread":0.2095839692861198,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004341123,0.0002889132,0.0002504692,0.0005799942,0.0002209375,0.0002657816,0.001500178,0.00004504629,0.00002840191],"category_scores_gemma":[0.000003297606,0.0002375706,0.0000624519,0.002237333,0.0000231441,0.0004056139,0.0005150502,0.0002367887,0.0002467577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000390728,"about_ca_system_score_gemma":0.0000756718,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001953552,"about_ca_topic_score_gemma":0.02804836,"domain_scores_codex":[0.9977725,0.00003073457,0.0003112059,0.0006216521,0.0005160445,0.000747881],"domain_scores_gemma":[0.9983269,0.0002142594,0.0001133652,0.000898372,0.0001208395,0.0003262606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006784378,0.00095979,0.5481755,0.002172256,0.0007604103,0.002539405,0.0526052,0.01035797,0.07114998,0.1653137,0.007887743,0.1373996],"study_design_scores_gemma":[0.001651353,0.0003484604,0.05587191,0.001095916,0.0000716641,0.0001449332,0.0009470588,0.04942362,0.02401334,0.001571069,0.8631099,0.001750795],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8403519,0.0003008971,0.09695017,0.01063371,0.000928843,0.0004714701,0.000004686303,0.001077107,0.04928126],"genre_scores_gemma":[0.9419162,0.000004459057,0.04291684,0.01400081,0.0002433707,0.000006727059,0.000003710306,0.00003036864,0.0008774847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8552221,"threshold_uncertainty_score":0.9896872,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137580272","doi":"10.1007/s11280-005-1544-y","title":"Studying the XML Web: Gathering Statistics from an XML Sample","year":2005,"lang":"en","type":"article","venue":"World Wide Web","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Computer science; XML validation; Efficient XML Interchange; XML Schema Editor; XML Base; Streaming XML; Document Structure Description; XML Signature; XML Encryption; Information retrieval; World Wide Web; XML database; cXML; XML; Database","authors":[{"name":"Denilson Barbosa","is_ca":true},{"name":"Laurent Mignet","is_ca":false},{"name":"Pierangelo Veltri","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02279868175895083,"gpt":0.2698407601171572,"spread":0.2470420783582064,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002765235,0.0001642383,0.0001779912,0.00005789367,0.0003241576,0.0001415888,0.0006087224,0.00001831619,0.0000877421],"category_scores_gemma":[0.0001008999,0.000120982,0.00003266999,0.0002767713,0.00005746041,0.0007541244,0.0003049779,0.0001661396,0.0001024962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005061784,"about_ca_system_score_gemma":0.00006895511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004973633,"about_ca_topic_score_gemma":0.01127283,"domain_scores_codex":[0.9986674,0.0000923418,0.0002769695,0.0003747762,0.0002740943,0.0003144622],"domain_scores_gemma":[0.9982024,0.0006047207,0.0001125691,0.0009379047,0.00004422102,0.00009817519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002357697,0.000191882,0.01674887,0.00002934468,0.00009911246,0.00006017298,0.007051683,0.004902028,0.004503118,0.6974509,0.02345475,0.2454845],"study_design_scores_gemma":[0.0002498568,0.00002479608,0.00255271,0.00004118465,0.000007442942,0.00000279766,0.0002292666,0.07355724,0.0003069788,0.002143076,0.920643,0.0002416896],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01930918,0.0003404383,0.9772409,0.001095631,0.0004664154,0.0001772598,0.000347041,0.000224432,0.0007987552],"genre_scores_gemma":[0.4280125,0.00002355577,0.5693811,0.00120318,0.000507595,0.00002636696,0.00003662204,0.00002327834,0.0007858186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8971882,"threshold_uncertainty_score":0.6290505,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2990135320","doi":"10.1007/s11280-019-00746-1","title":"A survey on data provenance in IoT","year":2019,"lang":"en","type":"article","venue":"World Wide Web","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Francis Xavier University","funders":"National Postdoctoral Program for Innovative Talents; China Electronics Technology Group Corporation; Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; Aalto-Yliopisto; Ministry of Public Security of the People's Republic of China; Academy of Finland; National Natural Science Foundation of China","keywords":"Computer science; Internet of Things; Data science; Provenance; Big data; Data mining; Computer security","authors":[{"name":"Rui Hu","is_ca":false},{"name":"Zheng Yan","is_ca":false},{"name":"Wenxiu Ding","is_ca":false},{"name":"Laurence T. Yang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2385431741418509,"gpt":0.4059295051544295,"spread":0.1673863310125786,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01156358,0.00009776508,0.0001933985,0.0004673954,0.00004754981,0.0003518021,0.002693243,0.00001580468,0.0005634761],"category_scores_gemma":[0.004251306,0.00007199513,0.00002419732,0.002133032,0.00003571437,0.0001756593,0.001264866,0.0001287154,0.006650039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003006762,"about_ca_system_score_gemma":0.00006504141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003482241,"about_ca_topic_score_gemma":0.01121742,"domain_scores_codex":[0.9969155,0.0002489263,0.000429308,0.001088281,0.001049882,0.0002680627],"domain_scores_gemma":[0.9937202,0.002240772,0.0001308923,0.003796621,0.0000550048,0.00005655934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00002135287,0.00005621139,0.3427425,0.000001761349,0.000002211013,0.000005808472,0.00002817583,0.0004794934,0.00001301345,0.0007404498,0.6173936,0.03851541],"study_design_scores_gemma":[0.0002102485,0.00001325297,0.5282978,0.00003102003,6.469601e-7,1.15223e-7,0.00002071444,0.02783802,0.000006598686,0.0008202202,0.4426767,0.00008468336],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8924169,0.0001083644,0.0002799863,0.00327959,0.004170974,0.0005954819,0.0003481863,0.00008424276,0.09871628],"genre_scores_gemma":[0.9300696,0.000001022821,0.000297664,0.0007198604,0.00003079185,0.000001945333,0.00006381437,0.000005748409,0.06880952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1855552,"threshold_uncertainty_score":0.9941234,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3007466464","doi":"10.1007/s11280-020-00785-z","title":"A comprehensive analysis of adverb types for mining user sentiments on amazon product reviews","year":2020,"lang":"en","type":"article","venue":"World Wide Web","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Adverb; Sentiment analysis; Superlative; Computer science; Natural language processing; Degree (music); Artificial intelligence; Product (mathematics); Meaning (existential); Linguistics; Information retrieval; Noun; Mathematics; Psychology","authors":[{"name":"Ummara Ahmed Chauhan","is_ca":false},{"name":"Muhammad Tanvir Afzal","is_ca":false},{"name":"Abdul Shahid","is_ca":false},{"name":"Moloud Abdar","is_ca":true},{"name":"Mohammad Ehsan Basiri","is_ca":false},{"name":"Xujuan Zhou","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05783494863255713,"gpt":0.3031553894291836,"spread":0.2453204407966265,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002118403,0.0001633871,0.0005873991,0.0004045961,0.00007170025,0.00005907844,0.000461898,0.00001737162,0.00008968357],"category_scores_gemma":[0.00008120945,0.0001413358,0.000401046,0.002430926,0.00002132209,0.0001572097,0.0001310529,0.0000608703,0.00004982774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001774376,"about_ca_system_score_gemma":0.0000236879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004522838,"about_ca_topic_score_gemma":0.00001399969,"domain_scores_codex":[0.998452,0.00008463809,0.0004735829,0.0005208353,0.00026409,0.0002048245],"domain_scores_gemma":[0.998818,0.0001976808,0.0003288488,0.000455812,0.0001109091,0.00008878682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004541471,0.001004949,0.2072635,0.0009999037,0.01660171,0.00003254744,0.01035338,0.02543321,0.0587179,0.02393887,0.5672143,0.08798558],"study_design_scores_gemma":[0.0009157639,0.0002033492,0.01019184,0.0002134079,0.001486572,2.116216e-7,0.00008571021,0.3576406,0.01779044,0.00002518198,0.6109428,0.0005040652],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7183322,0.01929382,0.1908088,0.05419442,0.002690954,0.005140246,0.00009962601,0.0007276146,0.008712334],"genre_scores_gemma":[0.9464176,0.000149127,0.0453047,0.004865368,0.0001913288,0.00004678666,0.00005486883,0.00002145451,0.002948712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3322074,"threshold_uncertainty_score":0.5763506,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153910905","doi":"10.1007/s11280-013-0240-6","title":"Neighborhood randomization for link privacy in social network analysis","year":2013,"lang":"en","type":"article","venue":"World Wide Web","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Renmin University of China","keywords":"Computer science; Graph; Social network (sociolinguistics); Link (geometry); Theoretical computer science; Randomization; Node (physics); Computer network","authors":[{"name":"Amin Milani Fard","is_ca":true},{"name":"Ke Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01784440482386519,"gpt":0.2628639492616792,"spread":0.245019544437814,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005904449,0.0001619236,0.0003695013,0.0005428931,0.0001669814,0.0002479788,0.01123962,0.0001021385,0.00005452347],"category_scores_gemma":[0.006644049,0.0001575249,0.0001559797,0.003886017,0.00004867075,0.0007506232,0.01454488,0.000199931,0.00004627245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009029711,"about_ca_system_score_gemma":0.00005617228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003955287,"about_ca_topic_score_gemma":0.0002426047,"domain_scores_codex":[0.9983123,0.00009784522,0.0004026596,0.0004993316,0.0002064209,0.0004814599],"domain_scores_gemma":[0.9959088,0.0006299109,0.0001781595,0.00315048,0.0000880199,0.00004465566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003566296,0.00005140056,0.05816038,0.00001516558,0.0002033466,0.000003542624,0.0000837108,0.001228184,0.00005489026,0.0190175,0.8858182,0.03532805],"study_design_scores_gemma":[0.001759278,0.00001161997,0.01279109,0.0000103486,0.00004362404,3.478242e-7,0.000002490058,0.5739756,0.00004766166,0.4014998,0.009661273,0.0001969135],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00302605,0.0001388906,0.8846192,0.1090659,0.000304051,0.0007947438,0.00000714186,0.0006494724,0.00139458],"genre_scores_gemma":[0.7308007,0.0000189981,0.2674544,0.0008862003,0.0002782469,0.0002739979,0.00004232562,0.00001538033,0.0002297806],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8761569,"threshold_uncertainty_score":0.99411,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057871674","doi":"10.1007/s11280-013-0250-4","title":"Malicious URL detection by dynamically mining patterns without pre-defined elements","year":2013,"lang":"en","type":"article","venue":"World Wide Web","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Flexibility (engineering); Task (project management); Data mining; Feature selection; Selection (genetic algorithm); Feature (linguistics); Artificial intelligence","authors":[{"name":"Da Huang","is_ca":true},{"name":"Kai Xu","is_ca":false},{"name":"Jian Pei","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.005457942593613912,"gpt":0.2096292484384356,"spread":0.2041713058448217,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002016445,0.0001794447,0.0001553903,0.0001683867,0.00016534,0.0003324712,0.0005079274,0.00005868813,0.0001089621],"category_scores_gemma":[0.00005767127,0.0001759282,0.0000602455,0.0003942728,0.00001906217,0.0005675023,0.0001432387,0.0001808899,0.0002172251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001012453,"about_ca_system_score_gemma":0.00002058442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006476418,"about_ca_topic_score_gemma":0.00211896,"domain_scores_codex":[0.9985188,0.00008116687,0.0003047031,0.0004336837,0.0003019991,0.0003597034],"domain_scores_gemma":[0.9991081,0.00009814212,0.0001465249,0.0004633159,0.0000636468,0.0001202558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004634219,0.0002394982,0.4329687,0.00005525151,0.0001256178,0.00001209384,0.001429057,0.0001971569,0.1354963,0.0003234193,0.04575123,0.3833553],"study_design_scores_gemma":[0.001391652,0.0003942263,0.2296914,0.0001345948,0.00003093724,0.00003082965,0.00003686386,0.7125524,0.02092963,0.001107527,0.03277304,0.0009269608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7725047,0.00003090657,0.2231784,0.0008788658,0.001262078,0.0002870185,0.000003010358,0.0004520965,0.001402939],"genre_scores_gemma":[0.9900786,0.000003485671,0.004809718,0.0008177547,0.00009427212,0.00008147791,0.00000641366,0.0000213598,0.004086932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7123552,"threshold_uncertainty_score":0.7174143,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046598510","doi":"10.1007/s11280-013-0204-x","title":"Mining most frequently changing component in evolving graphs","year":2013,"lang":"en","type":"article","venue":"World Wide Web","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":45,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Intuition; Component (thermodynamics); Graph; Data mining; Machine learning; Artificial intelligence; Theoretical computer science","authors":[{"name":"Yajun Yang","is_ca":false},{"name":"Jeffrey Xu Yu","is_ca":false},{"name":"Hong Gao","is_ca":false},{"name":"Jian Pei","is_ca":true},{"name":"Jianzhong Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00964463561293763,"gpt":0.2315516708931424,"spread":0.2219070352802048,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001861175,0.0001800066,0.0002653144,0.0006048768,0.0001115729,0.0001038372,0.0002245814,0.00001713147,0.002166534],"category_scores_gemma":[0.000004685612,0.0001870367,0.0001157011,0.00111094,0.00003256344,0.0001966113,0.00013552,0.0001764173,0.00007999842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005301808,"about_ca_system_score_gemma":0.00002053071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413311,"about_ca_topic_score_gemma":0.0005729881,"domain_scores_codex":[0.9987418,0.00005227269,0.0003215618,0.0002729703,0.0001523003,0.0004590635],"domain_scores_gemma":[0.9993355,0.0001285366,0.0001123451,0.0002998608,0.00004694972,0.00007676143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002003425,0.00009044631,0.9355883,0.000006029129,0.00007086086,0.000003944217,0.0003670462,0.0002235046,0.004162723,0.02396165,0.02248345,0.01304002],"study_design_scores_gemma":[0.002828802,0.00009525428,0.4089892,0.001760089,0.0002292109,0.000003027269,0.003838922,0.346142,0.009842522,0.1335505,0.08930179,0.003418732],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94991,0.0002011559,0.001830152,0.0005147342,0.00009479174,0.0003308577,0.000003119865,0.0001582864,0.04695687],"genre_scores_gemma":[0.9947869,0.000001767015,0.003769947,0.0001755756,0.0001404173,0.0001059751,0.00003235706,0.00002543023,0.0009616748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5265992,"threshold_uncertainty_score":0.9987456,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1480033117","doi":"10.1023/a:1015185802583","title":"Integrating Web Prefetching and Caching Using Prediction Models","year":2001,"lang":"en","type":"article","venue":"World Wide Web","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","authors":[{"name":"Qiang Yang","is_ca":false},{"name":"Henry Hanning Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03065098892257655,"gpt":0.259374212231979,"spread":0.2287232233094024,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002821195,0.0001720212,0.0001660894,0.0003178787,0.0002942875,0.0001929231,0.0005594926,0.00005114493,0.000003324132],"category_scores_gemma":[0.0001609209,0.0001628211,0.00002851645,0.0006132541,0.00006201194,0.002503658,0.0006401492,0.0003597054,0.000003277017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001259758,"about_ca_system_score_gemma":0.00004980126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007165765,"about_ca_topic_score_gemma":0.0003975505,"domain_scores_codex":[0.9987255,0.00004405662,0.0002540062,0.0004625623,0.0001975499,0.0003163683],"domain_scores_gemma":[0.999064,0.0001450654,0.0001235958,0.0005766603,0.00003311686,0.0000576019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002929329,0.0001013087,0.03080932,0.00007375921,0.00007146502,0.0002390725,0.002374328,0.2278337,0.06578244,0.2711029,0.00158836,0.399994],"study_design_scores_gemma":[0.0001666868,0.00001632411,0.0001213595,0.0001090623,0.000005864516,0.00006348025,0.00009118988,0.9707122,0.0003267243,0.02489436,0.003327805,0.0001649172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1767364,0.0003094827,0.8192689,0.0003813558,0.0002064676,0.0001226549,0.000007311048,0.0009579622,0.002009445],"genre_scores_gemma":[0.7455701,0.00004245704,0.2540923,0.0001242297,0.00004018531,0.000006352263,0.000002967363,0.00001205937,0.0001093999],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7428785,"threshold_uncertainty_score":0.6639652,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2999796443","doi":"10.1007/s11280-019-00755-0","title":"A multiview learning method for malware threat hunting: windows, IoT and android as case studies","year":2020,"lang":"en","type":"article","venue":"World Wide Web","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Malware; Android (operating system); Android malware; Computer security; Artificial intelligence; Internet of Things; Machine learning; Operating system","authors":[{"name":"Hamid Darabian","is_ca":false},{"name":"Ali Dehghantanha","is_ca":true},{"name":"Sattar Hashemi","is_ca":false},{"name":"Mohammad Taheri","is_ca":false},{"name":"Amin Azmoodeh","is_ca":true},{"name":"Sajad Homayoun","is_ca":false},{"name":"Kim‐Kwang Raymond Choo","is_ca":false},{"name":"Reza M. Parizi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04135592852039305,"gpt":0.3494341114699402,"spread":0.3080781829495472,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000375802,0.0002178189,0.0003637744,0.0001312573,0.0003302854,0.00009933035,0.0002536595,0.0000404151,0.000006987973],"category_scores_gemma":[0.001077338,0.0002066313,0.00008351606,0.0005333938,0.00005463645,0.0003004975,0.0004256653,0.0002579342,0.000009728448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004186327,"about_ca_system_score_gemma":0.00003083975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002362022,"about_ca_topic_score_gemma":0.0001491689,"domain_scores_codex":[0.9985656,0.0001210842,0.0002638416,0.0006128558,0.0001435624,0.0002930665],"domain_scores_gemma":[0.9984128,0.0009048242,0.0001547354,0.0002555229,0.000139058,0.0001330709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001186363,0.00005891946,0.003664626,0.000774534,0.0002315197,0.002089134,0.005467421,0.001031409,0.008045051,0.01163313,0.003418687,0.9634669],"study_design_scores_gemma":[0.003251698,0.001663212,0.000269871,0.0005606046,0.0001614204,0.003826017,0.001957062,0.2379366,0.1098657,0.0231804,0.6154058,0.00192153],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006290925,0.002919181,0.9841799,0.004180636,0.0001195848,0.0007027029,0.000004152109,0.001235837,0.000367062],"genre_scores_gemma":[0.3702901,0.0001307003,0.6268811,0.001702288,0.00008358365,0.0001500721,9.776002e-7,0.00002770475,0.0007335398],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9615454,"threshold_uncertainty_score":0.8426179,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017082499","doi":"10.1007/s11280-012-0168-2","title":"Finding email correspondents in online social networks","year":2012,"lang":"en","type":"article","venue":"World Wide Web","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Similarity (geometry); Social network (sociolinguistics); World Wide Web; Data science; Graph; Information retrieval; Social media; Artificial intelligence; Theoretical computer science","authors":[{"name":"Yi Cui","is_ca":true},{"name":"Jian Pei","is_ca":true},{"name":"Guanting Tang","is_ca":true},{"name":"Wo-Shun Luk","is_ca":true},{"name":"Daxin Jiang","is_ca":false},{"name":"Ming Hua","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0216689767445914,"gpt":0.2994256717349167,"spread":0.2777566949903253,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002829568,0.0001593295,0.0002577943,0.0002281183,0.0001108609,0.00003637409,0.0002029728,0.00003104877,0.001242085],"category_scores_gemma":[0.000004907914,0.0001674482,0.0001365394,0.0007124202,0.00003092282,0.0001462999,0.0001307338,0.0002943915,0.00004417595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006487955,"about_ca_system_score_gemma":0.00002050778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001395234,"about_ca_topic_score_gemma":0.0006884428,"domain_scores_codex":[0.9988074,0.00008574466,0.0002871946,0.0001740579,0.0001489723,0.0004966085],"domain_scores_gemma":[0.9994813,0.0001268686,0.0001143253,0.0001801944,0.00001931984,0.00007799721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001451496,0.0001866577,0.9538961,0.000001517798,0.00003410359,0.000001094401,0.0001249642,0.0002025767,0.00004618385,0.005642105,0.02872195,0.01112826],"study_design_scores_gemma":[0.00110955,0.00001896939,0.7844618,0.0001189571,0.0001133363,6.765442e-7,0.0003449761,0.03416093,0.0001475448,0.004153999,0.1745195,0.0008497785],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745469,0.0001913579,0.006195564,0.0003043911,0.0003400492,0.0001949534,0.00001634647,0.0001275872,0.01808283],"genre_scores_gemma":[0.9939765,0.000001614684,0.0005830359,0.0001613684,0.001600714,0.00002001012,0.00009206223,0.00002392332,0.003540781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1694343,"threshold_uncertainty_score":0.9996709,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2609579487","doi":"10.1007/s11280-017-0460-2","title":"Collaborative text categorization via exploiting sparse coefficients","year":2017,"lang":"en","type":"article","venue":"World Wide Web","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Sparse approximation; Pattern recognition (psychology); Categorization; Artificial intelligence; Classifier (UML); Sparse matrix; Representation (politics); Information retrieval; Data mining","authors":[{"name":"Lina Yao","is_ca":false},{"name":"Quan Z. Sheng","is_ca":false},{"name":"Xianzhi Wang","is_ca":false},{"name":"Shengrui Wang","is_ca":true},{"name":"Xue Li","is_ca":false},{"name":"Sen Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01904446666712901,"gpt":0.2666869600097453,"spread":0.2476424933426163,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000180381,0.0001310135,0.0001331556,0.0001945851,0.0007185917,0.0007188632,0.001409987,0.00004425931,0.00003299072],"category_scores_gemma":[0.0002713512,0.0001241996,0.00003082716,0.0004714935,0.0001313225,0.001019404,0.0003947787,0.0001075505,0.0003174217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006153612,"about_ca_system_score_gemma":0.0000763657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001744491,"about_ca_topic_score_gemma":0.0001076875,"domain_scores_codex":[0.998906,0.00003104236,0.0002125517,0.0003666638,0.0002404985,0.0002432525],"domain_scores_gemma":[0.9983078,0.00007460759,0.0003229534,0.001093861,0.0001461854,0.00005460116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001251943,0.0001837013,0.04214465,0.00001737512,0.00003314676,0.00002358771,0.001110046,0.0002077654,0.01168314,0.651364,0.02749866,0.2657214],"study_design_scores_gemma":[0.002493059,0.0001740699,0.09498329,0.0001616374,0.00003188891,0.000007934374,0.0009252041,0.1570261,0.134213,0.05507298,0.5532948,0.001616104],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02720687,0.0001291291,0.9164581,0.01326913,0.001245641,0.0004592788,0.000005538569,0.001342671,0.03988361],"genre_scores_gemma":[0.987865,0.00001582687,0.007503513,0.0001875354,0.00003861269,0.00003819998,0.000005017593,0.000009036043,0.004337231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9606581,"threshold_uncertainty_score":0.6932015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2126514643","doi":"10.1023/a:1019697023170","title":"Evaluation of Strong Consistency Web Caching Techniques","year":2002,"lang":"en","type":"article","venue":"World Wide Web","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; University of Wisconsin-Madison","keywords":"Computer science; Cache; Cache invalidation; Cache algorithms; Smart Cache; Distributed computing; Consistency (knowledge bases); Eventual consistency; Cache coherence; Causal consistency; Consistency model; Computer network; CPU cache; Data consistency; Sequential consistency","authors":[{"name":"Longbing Cao","is_ca":true},{"name":"M. TAMER ÖZSU","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05286046055294654,"gpt":0.2679706923057275,"spread":0.215110231752781,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012258,0.0001033637,0.000149487,0.0002269908,0.00009554984,0.00005921367,0.0004079959,0.00002733442,0.00008721525],"category_scores_gemma":[0.0001422072,0.0001008707,0.0000839697,0.0003224373,0.00003786323,0.0003030818,0.0001073135,0.0001408626,0.00003116625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008017695,"about_ca_system_score_gemma":0.00007849206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007610298,"about_ca_topic_score_gemma":0.0002767232,"domain_scores_codex":[0.9984878,0.0001977347,0.0002537444,0.0002471511,0.0006383262,0.0001752977],"domain_scores_gemma":[0.9990214,0.0001206613,0.0001206252,0.0004697341,0.0002215729,0.00004605601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008129497,0.0003431495,0.01435031,0.0000493799,0.0001396361,0.00002296462,0.0008923965,0.001109765,0.04240035,0.0749274,0.03629207,0.8294644],"study_design_scores_gemma":[0.0004793648,0.00006130052,0.0009790052,0.0001623127,0.00007824534,0.00001127446,0.00003624119,0.9857638,0.002739985,0.00167717,0.007744513,0.0002667473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4116932,0.006195048,0.03994232,0.005522096,0.001432944,0.001006904,0.0000150877,0.001412274,0.5327802],"genre_scores_gemma":[0.9963525,0.00002003025,0.002268035,0.0002000094,0.00004352716,0.00001494993,9.112143e-7,0.000006695031,0.001093358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9846541,"threshold_uncertainty_score":0.4113388,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2146492580","doi":"10.1007/s11280-011-0127-3","title":"Opinion helpfulness prediction in the presence of “words of few mouths”","year":2011,"lang":"en","type":"article","venue":"World Wide Web","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Probabilistic logic; Computer science; Phenomenon; Support vector machine; Logistic regression; Artificial intelligence; Machine learning; Metric (unit); Context (archaeology); Recommender system; Psychology; Social psychology","authors":[{"name":"Richong Zhang","is_ca":true},{"name":"Thomas Tran","is_ca":true},{"name":"Yongyi Mao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03919549582236045,"gpt":0.2552999584863777,"spread":0.2161044626640172,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006089524,0.00007351421,0.0001503912,0.0002348501,0.00003158758,0.00001740774,0.0007126767,0.00002192102,0.00007201507],"category_scores_gemma":[0.00003878951,0.00005356778,0.00006943969,0.0009955514,0.0000480928,0.0002433023,0.0001137463,0.00007790232,0.000007349898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008726954,"about_ca_system_score_gemma":0.00003045216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001225911,"about_ca_topic_score_gemma":0.0001084518,"domain_scores_codex":[0.9989473,0.000114485,0.0003305199,0.0001861507,0.0002934707,0.0001280437],"domain_scores_gemma":[0.9991767,0.0001354171,0.0001771953,0.0004421601,0.00004726815,0.00002128073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008434697,0.0008335088,0.7475804,0.0001642515,0.0001318044,0.000008903785,0.04235031,0.0008907675,0.003040628,0.1456299,0.01890307,0.0403821],"study_design_scores_gemma":[0.001728974,0.0003449822,0.675756,0.0008930248,0.00005692435,0.000007155313,0.0029041,0.2441484,0.04977736,0.008494213,0.01533825,0.0005505563],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7452068,0.00168206,0.1759293,0.003125357,0.00365877,0.001007098,0.00001881519,0.0001708108,0.06920098],"genre_scores_gemma":[0.9971233,0.00001537049,0.002327625,0.00005183604,0.00003409257,0.00001002507,0.000002288651,0.000003054732,0.0004324055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2519165,"threshold_uncertainty_score":0.2184431,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2038680758","doi":"10.1007/s11280-012-0179-z","title":"Riding the tide of sentiment change: sentiment analysis with evolving online reviews","year":2012,"lang":"en","type":"article","venue":"World Wide Web","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Sentiment analysis; Revenue; Data science; Work (physics); Data mining; Information retrieval; Artificial intelligence","authors":[{"name":"Yang Liu","is_ca":false},{"name":"Xiaohui Yu","is_ca":true},{"name":"Aijun An","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05033064498506178,"gpt":0.2948579997154636,"spread":0.2445273547304018,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001336435,0.0002240403,0.0005174287,0.0005191431,0.0001838822,0.0001096377,0.0006764909,0.00002211112,0.0002758826],"category_scores_gemma":[0.0000273233,0.0001379108,0.0003549742,0.003105426,0.00004722106,0.0005572183,0.0003177446,0.0001341567,0.00005743424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006578828,"about_ca_system_score_gemma":0.000022732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000755302,"about_ca_topic_score_gemma":0.0002555302,"domain_scores_codex":[0.997896,0.0001968703,0.0005622358,0.0003497712,0.0005448886,0.0004503023],"domain_scores_gemma":[0.9982007,0.0001712396,0.0005017595,0.0009135511,0.00008361002,0.0001291267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001605352,0.0007310081,0.9351023,0.00008708627,0.003199937,0.000008042741,0.005164132,0.0007465005,0.00236467,0.005551256,0.02387305,0.02315592],"study_design_scores_gemma":[0.001353518,0.0001376616,0.2158445,0.000896988,0.004891,0.00001202738,0.0007860921,0.376835,0.01229439,0.000073422,0.3854279,0.001447456],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5600815,0.09554771,0.2937964,0.02961352,0.003364245,0.004237898,0.00004806908,0.0006558832,0.01265475],"genre_scores_gemma":[0.9725785,0.0002542612,0.02366329,0.0008417093,0.0002833462,0.00003793624,0.00002334809,0.00001390228,0.002303676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7192578,"threshold_uncertainty_score":0.5623841,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2843323114","doi":"10.1007/s11280-018-0616-8","title":"MPE: a mobility pattern embedding model for predicting next locations","year":2018,"lang":"en","type":"article","venue":"World Wide Web","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University; York University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Trajectory; Embedding; Salient; Object (grammar); Range (aeronautics); Variety (cybernetics); Location data","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.04345428430531642,"gpt":0.3420575332160506,"spread":0.2986032489107342,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001356658,0.0001029357,0.0001528446,0.0001341057,0.001429314,0.0001368434,0.0002702383,0.00005645774,0.0005558071],"category_scores_gemma":[0.0007297619,0.0001100586,0.0001293149,0.0005026835,0.0003934366,0.0002674116,0.00003173567,0.0001055076,0.00007106417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001886652,"about_ca_system_score_gemma":0.0004138208,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003654915,"about_ca_topic_score_gemma":0.267464,"domain_scores_codex":[0.9986395,0.0001215363,0.000297093,0.0003435692,0.0002568698,0.0003414528],"domain_scores_gemma":[0.9985919,0.0005341635,0.0001032296,0.0003385258,0.0003014799,0.0001306966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001009949,0.001395812,0.4593854,0.0003243465,0.0003776206,0.000001620786,0.1688483,0.06840302,0.0009157715,0.03084088,0.04270637,0.2266998],"study_design_scores_gemma":[0.000156474,0.00001673884,0.00128708,0.00003320927,0.0000561673,3.704573e-8,0.002668045,0.975912,0.000044141,0.004080848,0.01560127,0.0001440282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4179098,0.00009239323,0.5554844,0.00670506,0.0003475006,0.001077663,0.00005973308,0.0003857423,0.0179377],"genre_scores_gemma":[0.9918669,0.000005210867,0.0009104208,0.0007044062,0.0005814303,0.0001598026,0.00001966946,0.00001205829,0.005740117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9075089,"threshold_uncertainty_score":0.9998707,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1493789029","doi":"10.1023/a:1012408428703","title":"A Conceptual Model and Rule-Based Query Language for HTML","year":2001,"lang":"en","type":"article","venue":"World Wide Web","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Query language; Information retrieval; Simple (philosophy); Semantics (computer science); Conceptual graph; Programming language; Knowledge representation and reasoning; Artificial intelligence","authors":[{"name":"Mengchi Liu","is_ca":true},{"name":"Tok Wang Ling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02028049143640919,"gpt":0.2545614389812293,"spread":0.2342809475448201,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001862046,0.0001016291,0.0001487113,0.000139719,0.00009179644,0.0001079747,0.0003496876,0.00002386741,0.00001449261],"category_scores_gemma":[0.00006032427,0.00009261964,0.00006162015,0.000300598,0.00006197121,0.0002042861,0.00008282305,0.00006433199,0.00001689748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001490743,"about_ca_system_score_gemma":0.00007860686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004144382,"about_ca_topic_score_gemma":0.000396387,"domain_scores_codex":[0.9991975,0.00002311059,0.0001328829,0.0003048635,0.0001205814,0.0002210105],"domain_scores_gemma":[0.9992478,0.000203952,0.00004700164,0.0003869346,0.00002771763,0.0000865232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002286061,0.0006443863,0.0488997,0.000127746,0.0003666219,0.0003780999,0.01000634,0.03794679,0.02227403,0.3074173,0.3355173,0.2361931],"study_design_scores_gemma":[0.0005909597,0.00002163878,0.0002378602,0.00002379851,0.00001926482,0.00000284436,0.0000906222,0.9716007,0.0003240294,0.0005955894,0.02631734,0.0001753585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2645397,0.0005333106,0.7257128,0.004536161,0.00009957942,0.0001414805,0.00005895873,0.0002718707,0.00410615],"genre_scores_gemma":[0.9269509,0.000005888748,0.06557959,0.001901702,0.00005856623,0.00002011302,0.00002237337,0.000008548905,0.005452299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9336539,"threshold_uncertainty_score":0.3776919,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2042613727","doi":"10.1007/s11280-008-0049-x","title":"An Operable Email Based Intelligent Personal Assistant","year":2008,"lang":"en","type":"article","venue":"World Wide Web","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Computer science; World Wide Web; sort; Ontology; Key (lock); Email authentication; Function (biology); Software; Information retrieval; Computer security","authors":[{"name":"Wenbin Li","is_ca":false},{"name":"Ning Zhong","is_ca":false},{"name":"Yiyu Yao","is_ca":true},{"name":"Jiming Liu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02495571437703318,"gpt":0.2386349339253795,"spread":0.2136792195483464,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002512503,0.0001152242,0.0001145856,0.0001529135,0.0002868428,0.0001323451,0.0004915059,0.00003068653,0.0002138733],"category_scores_gemma":[0.00002636907,0.000106608,0.00006528836,0.0005080969,0.00004133386,0.0004010302,0.00004275393,0.0001566008,0.0001692893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008125581,"about_ca_system_score_gemma":0.0001496863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001354609,"about_ca_topic_score_gemma":0.0004807944,"domain_scores_codex":[0.9989166,0.00007380861,0.000152604,0.0003369443,0.0002829352,0.0002370743],"domain_scores_gemma":[0.9992877,0.00007610023,0.00004238857,0.0004054891,0.00005202858,0.0001363492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000496523,0.003441753,0.2500784,0.0001834508,0.0002486353,0.001931921,0.02775031,0.01469023,0.09176011,0.04546918,0.3793915,0.184558],"study_design_scores_gemma":[0.0005111316,0.0002588624,0.02138327,0.00005108778,0.000008598512,0.00005688353,0.00004762383,0.6694435,0.02052302,0.0004275852,0.2867834,0.0005050516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4972991,0.0003922482,0.4779046,0.004506403,0.002306796,0.0002632591,0.000006015819,0.0008655302,0.01645607],"genre_scores_gemma":[0.984304,0.000007293206,0.01136244,0.001495854,0.0001210289,0.00001036023,0.000002874443,0.000009621477,0.002686515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6547532,"threshold_uncertainty_score":0.4347347,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1493874060","doi":"10.1023/a:1019217210183","title":"Digital payment systems for Internet commerce: The state of the art","year":2000,"lang":"en","type":"article","venue":"World Wide Web","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Payment; The Internet; Context (archaeology); Principal (computer security); Protocol (science); Taxonomy (biology); World Wide Web; Computer security","authors":[{"name":"Octavian Ureche","is_ca":true},{"name":"Réjean Plamondon","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01061774757899923,"gpt":0.2196965376633797,"spread":0.2090787900843805,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000215029,0.000085787,0.0001059272,0.0000331476,0.00007345454,0.000213793,0.001061683,0.00001008606,0.00002576993],"category_scores_gemma":[0.00001056569,0.00004603739,0.0001101605,0.0003161583,0.00008829857,0.0002334536,0.0001735863,0.00008549453,0.00003210645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001133169,"about_ca_system_score_gemma":0.00002163979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003282441,"about_ca_topic_score_gemma":0.0001637882,"domain_scores_codex":[0.9992415,0.00004259542,0.0002037597,0.0001664206,0.0001786345,0.0001671245],"domain_scores_gemma":[0.9990066,0.0002203368,0.00007010332,0.000642972,0.00002576644,0.00003421772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006252243,0.0002311919,0.009334625,0.00006927547,0.00009674805,0.000002275606,0.001918166,0.0004399916,0.00003341641,0.1930535,0.6804501,0.1143082],"study_design_scores_gemma":[0.0001969234,0.00002943323,0.00323059,0.00004587009,0.000004986552,0.000002781104,0.00001629213,0.01332816,0.00008987673,0.002985556,0.9799904,0.00007913144],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4333119,0.004346256,0.3645272,0.0335882,0.005800617,0.005767366,0.002351209,0.0006224787,0.1496848],"genre_scores_gemma":[0.9945192,0.0000213094,0.0002085693,0.0006118244,0.00003069025,0.00003039316,0.000009488332,0.000004847355,0.004563637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5612073,"threshold_uncertainty_score":0.2061611,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2125999281","doi":"10.1007/s11280-010-0094-0","title":"Mining discriminative items in multiple data streams","year":2010,"lang":"en","type":"article","venue":"World Wide Web","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Discriminative model; Data stream; Data stream mining; Heuristic; Information retrieval; Volume (thermodynamics); Space (punctuation); Data mining; Reading (process); Streaming data; Raw data; STREAMS; Artificial intelligence","authors":[{"name":"Zhenhua Lin","is_ca":true},{"name":"Bin Jiang","is_ca":true},{"name":"Jian Pei","is_ca":true},{"name":"Daxin Jiang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04568083920431813,"gpt":0.2889493389425767,"spread":0.2432684997382585,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003059063,0.0001067424,0.0001163539,0.000147581,0.00009718798,0.0001640197,0.001992456,0.00002562948,0.00002023996],"category_scores_gemma":[0.0001938356,0.00009903967,0.00001630492,0.0006144959,0.00006151202,0.0007397921,0.0009012338,0.0002309866,0.00006525376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001255768,"about_ca_system_score_gemma":0.00005969258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002613039,"about_ca_topic_score_gemma":0.0113237,"domain_scores_codex":[0.9988921,0.00002107277,0.000193272,0.0005071072,0.0001499132,0.0002365568],"domain_scores_gemma":[0.9979067,0.0003649742,0.00006717978,0.00155885,0.00002408288,0.00007819558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002862775,0.0002817482,0.0478109,0.000009904034,0.00001391814,0.00002883744,0.001750937,0.00001945285,0.004072876,0.0353619,0.04370196,0.8669447],"study_design_scores_gemma":[0.0003798126,0.00001244261,0.0255427,0.00003492746,0.000004202563,0.000004561027,0.0001692868,0.7583588,0.0006002543,0.0007087748,0.2139377,0.0002465287],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7221877,0.0001813567,0.2112149,0.01756886,0.002973625,0.001036314,0.0008386122,0.0008881204,0.04311048],"genre_scores_gemma":[0.787156,0.000002907276,0.2113175,0.0001872818,0.00008465022,0.00003053297,0.0001333642,0.000008851807,0.00107891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8666981,"threshold_uncertainty_score":0.6318892,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2270926629","doi":"10.1007/s11280-015-0370-0","title":"Identifying semantic blocks in Web pages using Gestalt laws of grouping","year":2015,"lang":"en","type":"article","venue":"World Wide Web","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Computer science; Gestalt psychology; Web page; Information retrieval; Merge (version control); Artificial intelligence; Natural language processing; Data mining; Theoretical computer science; Algorithm; Perception; World Wide Web","authors":[{"name":"Zhen Xu","is_ca":true},{"name":"James Miller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07740306236981702,"gpt":0.3017733189573059,"spread":0.2243702565874888,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006748162,0.0001199022,0.0002090796,0.0003922072,0.00004801417,0.0001067773,0.0005603893,0.00004051585,0.000007337286],"category_scores_gemma":[0.0001018013,0.0001178005,0.0000549702,0.001135926,0.00006158129,0.0004942593,0.000243772,0.0001540376,0.00001155188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009034257,"about_ca_system_score_gemma":0.0001464127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006823931,"about_ca_topic_score_gemma":0.0002471105,"domain_scores_codex":[0.9987015,0.00008159311,0.0003730753,0.000282489,0.0003171213,0.0002442011],"domain_scores_gemma":[0.999129,0.00008936747,0.0001758574,0.0004180467,0.0001094842,0.00007823019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004324002,0.0005083383,0.1339924,0.0004498383,0.00006470909,0.0002472108,0.00459113,0.0001723091,0.7224828,0.05558565,0.005328736,0.07653362],"study_design_scores_gemma":[0.00153291,0.0001103456,0.01759162,0.001427545,0.00003792983,0.00006423735,0.0005402811,0.471089,0.4695987,0.02264616,0.01428216,0.001079076],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4083763,0.001366492,0.5776315,0.002314426,0.000613255,0.0004804511,0.000003098526,0.0006775855,0.00853695],"genre_scores_gemma":[0.9710889,0.00003603356,0.02808052,0.0001235386,0.00003175541,0.000005284526,8.902621e-7,0.00001010547,0.0006229503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5627126,"threshold_uncertainty_score":0.4803765,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226101728","doi":"10.1007/s11280-021-00972-6","title":"HOPLoP: multi-hop link prediction over knowledge graph embeddings","year":2021,"lang":"en","type":"article","venue":"World Wide Web","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Traverse; Embedding; Knowledge graph; Theoretical computer science; Graph; Margin (machine learning); Generalization; Machine learning; Artificial intelligence; Mathematics","authors":[{"name":"Varun Ranganathan","is_ca":true},{"name":"Denilson Barbosa","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01586810132797616,"gpt":0.2643288682557699,"spread":0.2484607669277937,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001587425,0.0002514092,0.0002454749,0.0002784712,0.000207906,0.0001583856,0.0006341705,0.00008770088,0.0000622337],"category_scores_gemma":[0.00009441192,0.0002565257,0.0001842774,0.002394042,0.00007624952,0.0007829491,0.0004623604,0.0004397826,0.0001349928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006408888,"about_ca_system_score_gemma":0.00009152884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003487648,"about_ca_topic_score_gemma":0.000372425,"domain_scores_codex":[0.9980286,0.00009932342,0.0003425821,0.0007606125,0.0002680918,0.0005008038],"domain_scores_gemma":[0.9984113,0.0002591497,0.0001176832,0.0008431546,0.0001751742,0.0001935604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008431669,0.001278644,0.1153989,0.0002201145,0.0003871383,0.00119349,0.003559451,0.01406598,0.0408207,0.1323252,0.366287,0.3243791],"study_design_scores_gemma":[0.002172655,0.0000840804,0.04782672,0.0003015683,0.0000394713,0.00006947674,0.00002643955,0.3796588,0.006955142,0.01298189,0.5489263,0.0009574395],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04055155,0.006442018,0.9202047,0.00619009,0.008400173,0.0006389827,0.00002805366,0.002748535,0.01479593],"genre_scores_gemma":[0.8740268,0.0003452853,0.1020115,0.002661254,0.0007823559,0.00006276667,0.00002615084,0.00006875821,0.02001508],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8334753,"threshold_uncertainty_score":0.9999887,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3032897077","doi":"10.1007/s11280-021-00874-7","title":"Distributed attribute-based access control system using permissioned blockchain","year":2021,"lang":"en","type":"preprint","venue":"World Wide Web","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Linux Foundation","keywords":"Access control; Computer science; Blockchain; Computer security; Transparency (behavior); Discretionary access control; Audit; Context (archaeology); Process (computing); Overhead (engineering); Role-based access control; Operating system; Business","authors":[{"name":"Sara Rouhani","is_ca":true},{"name":"Rafael Belchior","is_ca":false},{"name":"Rui Santos Cruz","is_ca":false},{"name":"Ralph Deters","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02616940292259946,"gpt":0.2754544998696013,"spread":0.2492850969470018,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007444741,0.0005855738,0.0009602826,0.0005549913,0.0005400896,0.0007793468,0.004008883,0.0006381674,0.00003513988],"category_scores_gemma":[0.0001429648,0.000600024,0.0003687329,0.0016894,0.0001582444,0.0001029301,0.002861188,0.001391724,0.00001311463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005724198,"about_ca_system_score_gemma":0.001169197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001775118,"about_ca_topic_score_gemma":0.0001974926,"domain_scores_codex":[0.9960747,0.0003419626,0.0008270108,0.001542983,0.0005160784,0.0006972281],"domain_scores_gemma":[0.9951395,0.000389421,0.0006325583,0.003103107,0.0004690988,0.00026636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002566467,0.004375478,0.1069261,0.007227351,0.002704056,0.003673566,0.0009285379,0.4864588,0.01315863,0.3140887,0.04242318,0.01777888],"study_design_scores_gemma":[0.0009869543,0.00001123216,0.001136574,0.0006651236,0.00009536685,0.00001785872,0.00003936523,0.9898826,0.00226958,0.001078188,0.003116838,0.0007003071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08813477,0.0009471802,0.8982303,0.008687251,0.0008514367,0.001028564,0.0003381556,0.001660757,0.0001215804],"genre_scores_gemma":[0.9820157,0.000005242172,0.0164496,0.0007733955,0.0001213835,0.0003284689,0.0001980326,0.00003846843,0.00006974395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8938809,"threshold_uncertainty_score":0.9996451,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2141289279","doi":"10.1007/s11280-006-8563-1","title":"Alternative Architectures and Protocols for Providing Strong Consistency in Dynamic Web Applications","year":2006,"lang":"en","type":"article","venue":"World Wide Web","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Consistency (knowledge bases); Overhead (engineering); Distributed computing; Latency (audio); Data consistency; Scheme (mathematics); Consistency model; Computer network; Operating system","authors":[{"name":"M. Hossein Sheikh Attar","is_ca":true},{"name":"M. TAMER ÖZSU","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01475095680386853,"gpt":0.2715443938185558,"spread":0.2567934370146873,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001650982,0.0001100797,0.0001333591,0.0002133125,0.000117306,0.0001257803,0.0002987189,0.00001818734,9.656295e-7],"category_scores_gemma":[0.00001986931,0.0001039464,0.00004363345,0.000219341,0.00004721545,0.00009694715,0.00009298872,0.0001152794,0.000002290057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000534903,"about_ca_system_score_gemma":0.00007861559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001625948,"about_ca_topic_score_gemma":0.003338771,"domain_scores_codex":[0.9991068,0.00003632374,0.0002028983,0.0003341947,0.0001088148,0.000210962],"domain_scores_gemma":[0.9993743,0.0002308625,0.00007865716,0.0002474134,0.00003400792,0.00003470447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001360496,0.000568351,0.0711867,0.000409962,0.00007740105,0.00003667862,0.0008091363,0.008894687,0.04596047,0.7198169,0.00226268,0.149841],"study_design_scores_gemma":[0.001874582,0.0001011406,0.007858029,0.0002630351,0.0000114796,0.00001518165,0.00004304988,0.9144773,0.0005509993,0.05455925,0.0197912,0.0004546877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2549722,0.0007054685,0.6862654,0.004691762,0.0001937192,0.04091343,0.00007150159,0.0005435054,0.01164299],"genre_scores_gemma":[0.9820088,0.00000102099,0.006010153,0.0001285142,0.00004173546,0.01102151,0.000003749845,0.000008090659,0.0007764161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9055827,"threshold_uncertainty_score":0.4238811,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2943003953","doi":"10.1007/s11280-019-00686-w","title":"Mining top-k sequential patterns in transaction database graphs","year":2019,"lang":"en","type":"article","venue":"World Wide Web","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China; Ministry of Science and Technology","keywords":"Computer science; Database transaction; Graph database; Database; Graph; Sequence (biology); Sequence database; Vertex (graph theory); Transaction data; Theoretical computer science; Algorithm","authors":[{"name":"Mingtao Lei","is_ca":false},{"name":"Lingyang Chu","is_ca":true},{"name":"Zhefeng Wang","is_ca":false},{"name":"Jian Pei","is_ca":true},{"name":"Caifeng He","is_ca":false},{"name":"Xi Zhang","is_ca":false},{"name":"Binxing Fang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01612727833813928,"gpt":0.2525454430266931,"spread":0.2364181646885539,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001924064,0.00009384951,0.0001054629,0.0002201095,0.0000457811,0.0001049572,0.0004972105,0.00002095279,0.0001124621],"category_scores_gemma":[0.000004958838,0.00009686079,0.0000372632,0.0005870633,0.00001211353,0.0005693063,0.0000763598,0.0001230623,0.0001413063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002679083,"about_ca_system_score_gemma":0.00004057371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001981208,"about_ca_topic_score_gemma":0.0008828432,"domain_scores_codex":[0.999057,0.00002778874,0.0001824291,0.0003663945,0.0001541922,0.0002122163],"domain_scores_gemma":[0.9992357,0.00006708804,0.00004796668,0.0005787971,0.0000145244,0.00005591559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002732228,0.0005666827,0.3289539,0.0001460186,0.00006461881,0.0001113997,0.002941799,0.0007104072,0.02753424,0.07948624,0.01513527,0.5443221],"study_design_scores_gemma":[0.00311107,0.0001342395,0.09916561,0.0005332067,0.0000315985,0.00004380727,0.0003853336,0.6445836,0.01162738,0.001937377,0.2370472,0.0013996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6942971,0.00004292812,0.2996425,0.001689195,0.0007410665,0.0002691456,0.00007639737,0.000188094,0.003053528],"genre_scores_gemma":[0.9648704,0.00001052654,0.03359148,0.000359199,0.00003426703,0.00002942338,0.00005673173,0.000008528306,0.00103947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6438732,"threshold_uncertainty_score":0.3949868,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2053259448","doi":"10.1007/s11280-007-0029-6","title":"Clustered Chain Path Index for XML Document: Efficiently Processing Branch Queries","year":2007,"lang":"en","type":"article","venue":"World Wide Web","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Waterloo; National University of Singapore","keywords":"Computer science; Cardinality (data modeling); XML; Path (computing); Scalability; Index (typography); Data mining; Path expression; Inverted index; XPath; Tree (set theory); Stream processing; Search engine indexing; XML database; Algorithm; Information retrieval; Database; Query language; Parallel computing; Programming language","authors":[{"name":"Hongqiang Wang","is_ca":false},{"name":"Jianzhong Li","is_ca":false},{"name":"Hongzhi Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009802303416945914,"gpt":0.2593626724000832,"spread":0.2495603689831373,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008370365,0.0002308358,0.000271428,0.0002344358,0.0003549815,0.0001563257,0.0003985151,0.00004670708,0.000009382751],"category_scores_gemma":[0.00009220564,0.0002053894,0.00008181071,0.0007024625,0.00008995419,0.0009541175,0.0002350141,0.0001301609,0.00001689335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007943987,"about_ca_system_score_gemma":0.0001227868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003280606,"about_ca_topic_score_gemma":0.0009631747,"domain_scores_codex":[0.9980428,0.00003361958,0.0004582478,0.0005307149,0.0003276163,0.0006070559],"domain_scores_gemma":[0.9987463,0.0002131529,0.0002200382,0.0005621523,0.0001207646,0.0001375676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003621071,0.0002404349,0.009356876,0.000987816,0.00006131429,0.00008746613,0.00583345,0.00118716,0.004888154,0.6028149,0.0108782,0.3633021],"study_design_scores_gemma":[0.001539321,0.0001076084,0.002673781,0.0004224122,0.000008821017,0.00002184006,0.000232332,0.01987686,0.004343525,0.003398897,0.9667112,0.0006633826],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006748549,0.0006485297,0.9883742,0.0006274523,0.000620612,0.0005294367,0.00002019029,0.0002751765,0.00215589],"genre_scores_gemma":[0.914048,0.000005329524,0.08064592,0.0008524208,0.0002353185,0.00006723295,0.0000185899,0.00002654293,0.004100629],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.955833,"threshold_uncertainty_score":0.8375538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2508222813","doi":"10.1007/s11280-016-0410-4","title":"Crawling ranked deep Web data sources","year":2016,"lang":"en","type":"article","venue":"World Wide Web","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Crawling; Ranking (information retrieval); Web crawler; Information retrieval; Deep Web; Hyperlink; Web search query; Variety (cybernetics); Web page; Data mining; Learning to rank; World Wide Web; Search engine; Artificial intelligence; The Internet","authors":[{"name":"Yan Wang","is_ca":false},{"name":"Jianguo Lü","is_ca":true},{"name":"Jessica Chen","is_ca":true},{"name":"Yaxin Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02944221598943287,"gpt":0.2526845032144845,"spread":0.2232422872250516,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005952448,0.0001529565,0.0002141862,0.0002484134,0.0001698485,0.0002449452,0.003080039,0.00003010939,0.0001575488],"category_scores_gemma":[0.0002427961,0.0001029445,0.00006597288,0.000702623,0.00006810174,0.0009391605,0.001209084,0.00009257252,0.0006961977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000214397,"about_ca_system_score_gemma":0.00008746487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000277888,"about_ca_topic_score_gemma":0.0007837945,"domain_scores_codex":[0.9983179,0.00008109201,0.000264363,0.0006740842,0.0003012345,0.0003613367],"domain_scores_gemma":[0.9969209,0.0004557066,0.0001073936,0.0023494,0.00003872043,0.000127859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003597839,0.0002505816,0.1184923,0.00005119202,0.0006854907,0.0002176713,0.0009381491,0.0001030287,0.05685702,0.01437997,0.2061457,0.6018429],"study_design_scores_gemma":[0.001065985,0.00002438004,0.002395343,0.0001982029,0.00007733131,0.00001080258,0.00004279646,0.1348247,0.002455202,0.0011588,0.8571342,0.0006122898],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1007089,0.001589725,0.8451302,0.03590384,0.001215721,0.0001831593,0.0001652464,0.001360727,0.01374246],"genre_scores_gemma":[0.9706023,0.00004721089,0.02269148,0.0007288522,0.0002204379,0.000004009802,0.00002352325,0.00001357397,0.005668646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8698933,"threshold_uncertainty_score":0.8948445,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2034320955","doi":"10.1007/s11280-015-0323-7","title":"MUBaaS: mobile ubiquitous brokerage as a service","year":2015,"lang":"en","type":"article","venue":"World Wide Web","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Scalability; Cloud computing; Mobile device; Computer network; Synchronization (alternating current); Middleware (distributed applications); Latency (audio); Ubiquitous computing; Wireless network; Mobile computing; Distributed computing; Wireless; World Wide Web; Telecommunications; Channel (broadcasting); Operating system","authors":[{"name":"Richard K. Lomotey","is_ca":true},{"name":"Ralph Deters","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02015686498433985,"gpt":0.2526832242808479,"spread":0.232526359296508,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004328756,0.0001970785,0.0002111952,0.0001898053,0.0001402936,0.0002697356,0.001164524,0.00005007083,0.00001452085],"category_scores_gemma":[0.00005285174,0.000191084,0.00006882403,0.001120179,0.00002281956,0.0004219004,0.0006631387,0.000224233,0.00211117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009186257,"about_ca_system_score_gemma":0.0002702418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002165092,"about_ca_topic_score_gemma":0.0001495142,"domain_scores_codex":[0.9983278,0.00007315965,0.0002615588,0.0004557227,0.0003698436,0.0005119523],"domain_scores_gemma":[0.9985933,0.0001496744,0.00009110562,0.0007425191,0.0001353605,0.0002880205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003498676,0.000280568,0.004639665,0.00008510116,0.00006381789,0.0005376845,0.01012183,0.001384241,0.001114197,0.008437188,0.8747604,0.09854031],"study_design_scores_gemma":[0.0006199818,0.0001068221,0.0003589994,0.00005792144,0.000007064622,0.00005917231,0.00004365476,0.04725264,0.0009849863,0.004317581,0.9458129,0.0003783037],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6771604,0.001561316,0.03244551,0.01085928,0.03796991,0.0008566036,9.1959e-7,0.002441681,0.2367043],"genre_scores_gemma":[0.9501727,0.00000592004,0.01774988,0.01057874,0.004091795,0.0000512246,0.000005657363,0.00004583693,0.01729821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2730123,"threshold_uncertainty_score":0.9986658,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385607465","doi":"10.1007/s11280-023-01201-y","title":"A large-scale holistic measurement of crowdsourced edge cloud platform","year":2023,"lang":"en","type":"article","venue":"World Wide Web","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Server; Enhanced Data Rates for GSM Evolution; Cloud computing; Quality of service; Edge device; Edge computing; Service (business); Computer network; Software deployment; Distributed computing; Operating system; Telecommunications","authors":[{"name":"Yicheng Feng","is_ca":false},{"name":"Shihao Shen","is_ca":false},{"name":"Mengwei Xu","is_ca":false},{"name":"Cheng Zhang","is_ca":false},{"name":"Xin Wang","is_ca":false},{"name":"Xiaofei Wang","is_ca":false},{"name":"Wenyu Wang","is_ca":false},{"name":"Victor C. M. Leung","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04959657868932868,"gpt":0.2611838857615039,"spread":0.2115873070721752,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001072702,0.0001633465,0.0002507101,0.000327386,0.0001787718,0.00008405917,0.0007934512,0.00004033186,0.000009458786],"category_scores_gemma":[0.0001063861,0.0001569872,0.000118933,0.001482958,0.00003737647,0.0001569055,0.0005091384,0.0001685035,0.0003618122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008491748,"about_ca_system_score_gemma":0.0001179366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000154533,"about_ca_topic_score_gemma":0.00007334299,"domain_scores_codex":[0.9981232,0.00003491376,0.0003559389,0.0003511099,0.00057469,0.0005601641],"domain_scores_gemma":[0.9989206,0.0001464568,0.0001249092,0.0005830924,0.0001055969,0.0001193172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004665187,0.0003506297,0.01889384,0.0003566541,0.0001500904,0.0001395826,0.01102713,0.001115849,0.00850615,0.02161224,0.9121489,0.02565231],"study_design_scores_gemma":[0.001734914,0.0001190284,0.02441557,0.000382057,0.00003375892,0.00001155302,0.0001238688,0.2797898,0.007383168,0.007864709,0.6773945,0.0007470502],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6606522,0.0004934197,0.2532532,0.003518456,0.03663575,0.0006955732,0.000004576501,0.002132311,0.04261451],"genre_scores_gemma":[0.993206,0.000003611511,0.002886205,0.0002580614,0.001325442,0.000009697116,0.000003783313,0.0000203905,0.002286847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3325537,"threshold_uncertainty_score":0.6401753,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1605784496","doi":"10.1023/a:1020912027991","title":"Editorial: Introduction to Web Media Information Systems","year":2002,"lang":"en","type":"article","venue":"World Wide Web","topic":"Web Applications and Data Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","authors":[{"name":"Qing Li","is_ca":false},{"name":"M. TAMER ÖZSU","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009203799279004997,"gpt":0.2040411249779905,"spread":0.1948373256989855,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003122216,0.00009144571,0.00009370974,0.0003175398,0.00009652704,0.0004230017,0.0005922429,0.0000249825,0.00004863051],"category_scores_gemma":[0.00007575283,0.00008790763,0.00002182501,0.0009421433,0.00001103638,0.001467421,0.0002272839,0.00007747106,0.003111254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000768695,"about_ca_system_score_gemma":0.0000145351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003027854,"about_ca_topic_score_gemma":0.00005411261,"domain_scores_codex":[0.9989585,0.00002406597,0.0002607438,0.0002227209,0.0003539532,0.0001800257],"domain_scores_gemma":[0.9988769,0.00004551277,0.00008051864,0.0008063861,0.00009501702,0.00009563541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[7.48318e-7,0.00001302267,0.00002560749,0.000006757055,0.000003313924,1.335046e-7,0.00009766977,0.0003073972,0.00004099973,0.0813685,0.9107929,0.007342959],"study_design_scores_gemma":[0.0001182591,0.00001028156,0.0001434845,0.000006064285,0.000003217959,7.6699e-7,0.00001739678,0.03197518,0.0000188058,0.0001129776,0.9674913,0.0001022854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008032499,0.0001943204,0.5270323,0.07291725,0.3359781,0.001822986,0.00005670735,0.001519318,0.05967584],"genre_scores_gemma":[0.6888403,0.0001271902,0.04113775,0.003508891,0.257599,0.000797026,0.0001834115,0.00003186676,0.007774609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.688037,"threshold_uncertainty_score":0.9976649,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}