{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":5,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":5,"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":"2bd3c62a01c7","filters":{"venue":"Foundations and Trends® in Information Retrieval"}},"results":[{"id":"W4297970707","doi":"10.1561/1500000006","title":"Email Spam Filtering: A Systematic Review","year":2008,"lang":"en","type":"review","venue":"Foundations and Trends® in Information Retrieval","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":254,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Information retrieval; World Wide Web","authors":[{"name":"Gordon V. Cormack","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03921906353638043,"gpt":0.3134118227029408,"spread":0.2741927591665603,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004781854,0.0003259739,0.001233436,0.001129063,0.0002321598,0.0004897544,0.0005173609,0.000194648,0.00005490247],"category_scores_gemma":[0.0003115973,0.0002706239,0.0002607798,0.002394579,0.00003779288,0.002396157,0.0001564144,0.0003443213,0.0002522736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001633808,"about_ca_system_score_gemma":0.0001267709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002013141,"about_ca_topic_score_gemma":0.000005942741,"domain_scores_codex":[0.997569,0.0001642124,0.001410457,0.000252622,0.0003777921,0.0002259237],"domain_scores_gemma":[0.998179,0.0001792253,0.000803387,0.0006425363,0.000108137,0.00008773342],"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":[0.000001293651,0.00002070868,4.364541e-7,0.4653804,0.00005777563,0.000004071238,0.0003252055,0.000001752445,5.273898e-9,0.004090767,0.00149059,0.528627],"study_design_scores_gemma":[0.0001599498,0.00004588216,0.000006135213,0.1756382,0.0001894193,0.0004180299,0.000005625997,0.001099384,8.918805e-8,0.0000641753,0.8220124,0.0003606954],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[3.16363e-7,0.9834206,0.01272268,0.00008445173,0.0005280856,0.0007439305,0.00002145523,0.0001394751,0.002339046],"genre_scores_gemma":[0.000009256618,0.9980909,0.0009754623,0.000129839,0.00004209483,0.0001198289,0.0003557096,0.00001010339,0.0002668631],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8205218,"threshold_uncertainty_score":0.9999746,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4252222626","doi":"10.1561/1500000061","title":"An Introduction to Neural Information Retrieval","year":2018,"lang":"en","type":"article","venue":"Foundations and Trends® in Information Retrieval","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Information retrieval; Computer science","authors":[{"name":"Bhaskar Mitra","is_ca":true},{"name":"Nick Craswell","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01178234844688753,"gpt":0.2774988290535456,"spread":0.2657164806066581,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003635361,0.00014817,0.0001273846,0.0008141898,0.000412323,0.000873381,0.0003548917,0.0000804122,0.0001040064],"category_scores_gemma":[0.00007417429,0.0001456196,0.0000317457,0.002607922,0.00007408555,0.01037661,0.0001072574,0.0001616023,0.0002156698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007416149,"about_ca_system_score_gemma":0.00003364346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000282633,"about_ca_topic_score_gemma":0.00002884022,"domain_scores_codex":[0.9986324,0.00003999993,0.0005612827,0.0002052242,0.0002989608,0.0002620822],"domain_scores_gemma":[0.9988274,0.00003163288,0.0001725186,0.0004872503,0.0003202091,0.0001609993],"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":[0.000176903,0.00008849434,0.0005336006,0.00001433734,0.00001312252,4.544148e-7,0.00438227,0.002295035,0.0002486079,0.2766958,0.0112242,0.7043272],"study_design_scores_gemma":[0.001067756,0.0007220422,0.06211844,0.00001100083,0.0000116879,0.00004870707,0.0002586972,0.4787235,0.0007692061,0.002403713,0.4533564,0.0005088761],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3614782,0.00001070401,0.6014392,0.02891662,0.001937154,0.0006743518,0.00004972687,0.0005086748,0.004985353],"genre_scores_gemma":[0.9898137,0.000006720243,0.008197025,0.001014563,0.0005123366,0.00001439541,0.0003595973,0.000004441115,0.00007721897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7038183,"threshold_uncertainty_score":0.8422033,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285059900","doi":"10.1561/1500000079","title":"Fairness in Information Access Systems","year":2022,"lang":"en","type":"article","venue":"Foundations and Trends® in Information Retrieval","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":118,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Micron Foundation; National Science Foundation","keywords":"Computer science; Information access; Centrality; Personalization; Intersection (aeronautics); Information system; World Wide Web; Data science; Information retrieval; Knowledge management","authors":[{"name":"Michael D. Ekstrand","is_ca":false},{"name":"Anubrata Das","is_ca":false},{"name":"Robin Burke","is_ca":false},{"name":"Fernando Díaz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04526192200601802,"gpt":0.3783950567943201,"spread":0.3331331347883021,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001770315,0.00008130672,0.0001293637,0.0009263347,0.00109019,0.001179537,0.0002175635,0.00009202756,0.0003718743],"category_scores_gemma":[0.0004260191,0.00009236104,0.00002889413,0.001855476,0.0001092092,0.009697228,0.0001125909,0.0003385678,0.00001794366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003667109,"about_ca_system_score_gemma":0.0002264922,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008458314,"about_ca_topic_score_gemma":0.002023744,"domain_scores_codex":[0.9984539,0.0001751227,0.000505682,0.00006858869,0.0005617643,0.0002349285],"domain_scores_gemma":[0.9993021,0.0001239725,0.0002164417,0.000094979,0.0001888621,0.00007366617],"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":[0.00009238679,0.00007848557,0.01408198,0.00004846266,0.00001466722,0.000001584942,0.1181457,0.005326332,6.690199e-7,0.8102998,0.002125616,0.04978438],"study_design_scores_gemma":[0.001929852,0.0001034644,0.1326799,0.00003403091,0.00001188488,0.000003996405,0.0801155,0.007738511,0.000001938253,0.01010651,0.7668396,0.0004348297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5617285,0.0001049087,0.001227121,0.01544443,0.002816294,0.0009467598,0.0002617613,0.0001801779,0.41729],"genre_scores_gemma":[0.9988706,0.00007703499,0.00002172866,0.0003336626,0.00004300302,0.00003854232,0.0003357458,0.000002810673,0.000276879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8001932,"threshold_uncertainty_score":0.9998573,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285724845","doi":"10.1561/1500000078","title":"Extracting, Mining and Predicting Users’ Interests from Social Media","year":2020,"lang":"en","type":"article","venue":"Foundations and Trends® in Information Retrieval","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Social media; Computer science; Data science; Internet privacy; Data mining; World Wide Web","authors":[{"name":"Fattane Zarrinkalam","is_ca":true},{"name":"S. Faralli","is_ca":false},{"name":"Guangyuan Piao","is_ca":false},{"name":"Ebrahim Bagheri","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03260333703049249,"gpt":0.287795823091296,"spread":0.2551924860608035,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001025669,0.00009623645,0.0001394346,0.0001597836,0.0001794375,0.0002158298,0.00005873088,0.00003497213,0.0004882913],"category_scores_gemma":[0.00004597761,0.0001022402,0.00003469674,0.0003966177,0.00003910955,0.0008865989,0.00007081318,0.0001305815,0.000004378576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001486267,"about_ca_system_score_gemma":0.00001260671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001018482,"about_ca_topic_score_gemma":0.00006737773,"domain_scores_codex":[0.999291,0.00002438887,0.0003449508,0.0001107401,0.00011499,0.0001139356],"domain_scores_gemma":[0.9995066,0.0001588933,0.000180248,0.00005485569,0.00004111073,0.00005831624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006112266,0.00003071745,0.3531644,0.00001068247,0.0001042215,6.316396e-7,0.04055019,0.00003102103,0.00004153892,0.007217494,0.001992227,0.5967958],"study_design_scores_gemma":[0.002608856,0.00007927931,0.770812,0.00009353551,0.0001789151,0.000001782696,0.02018661,0.1669497,0.0002259536,0.004089309,0.03408307,0.0006909674],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875245,0.00001371611,0.007165938,0.00053439,0.00005357552,0.0000521196,0.0000644801,0.0000627452,0.004528545],"genre_scores_gemma":[0.9974157,0.000002792839,0.001614254,0.00005442441,0.000244316,0.000004315651,0.0006515764,0.000004855102,0.000007735243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5961048,"threshold_uncertainty_score":0.5346447,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407308348","doi":"10.1561/1500000103","title":"Understanding and Mitigating Gender Bias in Information Retrieval Systems","year":2025,"lang":"en","type":"article","venue":"Foundations and Trends® in Information Retrieval","topic":"Gender and Technology in Education","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of Toronto; University of Waterloo; Toronto Metropolitan University","funders":"","keywords":"Computer science; Information retrieval; Data science; Psychology","authors":[{"name":"Shirin Seyedsalehi","is_ca":true},{"name":"Amin Bigdeli","is_ca":true},{"name":"Negar Arabzadeh","is_ca":true},{"name":"Batool AlMousawi","is_ca":true},{"name":"Zack Marshall","is_ca":true},{"name":"Morteza Zihayat","is_ca":true},{"name":"Ebrahim Bagheri","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1173251119499681,"gpt":0.3505290240789434,"spread":0.2332039121289753,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001239112,0.0001155158,0.0001517453,0.00172777,0.0005924376,0.0004888558,0.0000975074,0.0002239877,0.00004084891],"category_scores_gemma":[0.0006537928,0.0001268654,0.00002285216,0.002139116,0.000207215,0.003040251,0.00004332629,0.0002496494,0.00001028836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004720126,"about_ca_system_score_gemma":0.0001923954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006578036,"about_ca_topic_score_gemma":0.0004396977,"domain_scores_codex":[0.9986403,0.0001041453,0.0006172443,0.0001154611,0.0002658972,0.0002569723],"domain_scores_gemma":[0.9993217,0.0001904322,0.0001965735,0.0001193114,0.0001181321,0.00005387317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00005085679,0.00003484838,0.07538816,0.0001125503,0.00002721112,4.689451e-7,0.04621629,0.0002453736,0.000005163346,0.8619757,0.0006264123,0.01531692],"study_design_scores_gemma":[0.004273644,0.00009511699,0.4282077,0.0003435729,0.00007486176,0.00001622539,0.4153293,0.02107613,0.00005756742,0.06439704,0.06534173,0.000787134],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7950698,0.0002450157,0.01380947,0.004711578,0.001794633,0.0007394664,0.00003609683,0.0002186673,0.1833753],"genre_scores_gemma":[0.9991532,0.0001525316,0.0002190101,0.0001119284,0.00002546498,0.00001182671,0.0001598058,0.000002674421,0.0001635631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7975787,"threshold_uncertainty_score":0.5173419,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}