{"meta":{"query_hash":"4d42310e734c","filters":{"venue":"American Journal of Machine Learning"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/4d42310e734c","api":"https://metacan.xera.ac/api/v1/cohort?venue=American+Journal+of+Machine+Learning"},"results":[{"id":"W7143771036","doi":"10.71465/ajml3023","title":"Machine Learning in Healthcare: Forecasting Patient Outcomes with Predictive Models","year":2022,"lang":"","type":"article","venue":"American Journal of Machine Learning","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Key (lock); Predictive modelling; Health care; Precision medicine; Predictive analytics; Patient care","score_opus":0.020866316449385466,"score_gpt":0.26736633729489395,"score_spread":0.24650002084550848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143771036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7328324,0.0136030605,0.18821648,0.060278494,0.002168109,0.0016580529,0.00008166189,0.0003943227,0.00076743413],"genre_scores_gemma":[0.9794301,0.00039873068,0.01740167,0.0020341144,0.00020446876,0.00005836844,0.000023059214,0.0002257002,0.00022380539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9796868,0.009836158,0.0033558656,0.0014327624,0.003604753,0.0020836513],"domain_scores_gemma":[0.9870379,0.002158685,0.008178741,0.00081052777,0.00085332285,0.00096081675],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006089167,0.0011694451,0.0026402427,0.0021090072,0.0023749326,0.0003350791,0.002436113,0.00012206954,0.00015624767],"category_scores_gemma":[0.0015071937,0.001067546,0.0005695502,0.004076663,0.00047618258,0.0013665062,0.0021594511,0.016711993,0.000005334623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007109428,0.0001893937,0.33052227,0.000062782805,0.00013696238,0.0007783836,0.0108488705,0.4572694,0.0000021923286,0.0002382765,0.00000600062,0.19923453],"study_design_scores_gemma":[0.0024350598,0.031856906,0.01466636,0.00050483237,0.00008431533,0.00351617,0.004275053,0.93807876,0.0000029067478,0.00037953624,0.0032595235,0.0009405766],"about_ca_topic_score_codex":0.016398488,"about_ca_topic_score_gemma":0.00042048297,"teacher_disagreement_score":0.48080936,"about_ca_system_score_codex":0.0018184914,"about_ca_system_score_gemma":0.0018036652,"threshold_uncertainty_score":0.99917746},"labels":[],"label_agreement":null},{"id":"W7143815617","doi":"10.71465/ajml3081","title":"Predicting Social Trends Using Machine Learning Models","year":2025,"lang":"","type":"article","venue":"American Journal of Machine Learning","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Social media; Time series; Social learning; Support vector machine; Predictive modelling","score_opus":0.027590196106625567,"score_gpt":0.3022395184861487,"score_spread":0.27464932237952316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143815617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41432253,0.006897437,0.5709312,0.0032827652,0.0014500533,0.000086724394,0.00000464482,0.000109769135,0.0029148778],"genre_scores_gemma":[0.97646445,0.0004881396,0.019577393,0.0003225374,0.001006689,0.0000010451929,0.00000903171,0.000063283784,0.0020674218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9924349,0.0020050704,0.0023559758,0.00079777633,0.0013473522,0.0010589026],"domain_scores_gemma":[0.9933558,0.00054031843,0.0048361863,0.00034453755,0.0006042756,0.00031884896],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0035724845,0.00070632104,0.0018062853,0.0024482943,0.0020798868,0.0008743264,0.001570318,0.00014623313,0.00023236425],"category_scores_gemma":[0.00039749368,0.0006956754,0.0011830342,0.0045401086,0.00041195363,0.0014269621,0.00085077924,0.0037285984,0.0000064128935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015271957,0.00013779092,0.13790931,0.00003139977,0.0010363972,0.0000726158,0.0056057707,0.4003122,0.000616781,0.0007158197,0.000034118777,0.4533751],"study_design_scores_gemma":[0.0013984445,0.0009284019,0.0020361368,0.00054006116,0.00064947,0.00014448032,0.0021339627,0.98838186,0.00009571979,0.00013326571,0.0030175364,0.00054064515],"about_ca_topic_score_codex":0.0010465545,"about_ca_topic_score_gemma":0.000024157931,"teacher_disagreement_score":0.5880697,"about_ca_system_score_codex":0.0003403076,"about_ca_system_score_gemma":0.00045130725,"threshold_uncertainty_score":0.99954945},"labels":[],"label_agreement":null},{"id":"W7144341289","doi":"10.71465/ajml3046","title":"Enhancing Social Media Analytics with Machine Learning Techniques","year":2024,"lang":"","type":"article","venue":"American Journal of Machine Learning","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Social media; Social media analytics; Sentiment analysis; Analytics; Predictive analytics; Supervised learning; Big data","score_opus":0.011825259445127375,"score_gpt":0.2739776808386315,"score_spread":0.26215242139350414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7144341289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114420645,0.016824124,0.8606848,0.0051009785,0.0013063073,0.00016907357,0.0000066759626,0.00041419762,0.0010731713],"genre_scores_gemma":[0.9510656,0.0016732543,0.044593357,0.00016947609,0.0018319958,0.0000018808058,0.000012930749,0.000110165354,0.00054134405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936234,0.0011800532,0.0017875998,0.00077692297,0.001732565,0.0008994981],"domain_scores_gemma":[0.99505764,0.0012895019,0.002434034,0.00029010922,0.00053328817,0.0003954254],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0038264901,0.000697211,0.0015614877,0.0016765051,0.0008746767,0.0014830874,0.0011956774,0.00011937819,0.0003717365],"category_scores_gemma":[0.00053674675,0.0005611862,0.00078697986,0.0037345244,0.0005030784,0.0010995212,0.0004071505,0.0042267786,0.000039903578],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019742534,0.00013320487,0.021803217,0.0001256553,0.0018614121,0.0011083945,0.02320079,0.007420447,0.002946886,0.0008035846,0.000112123096,0.9402869],"study_design_scores_gemma":[0.00088187645,0.0046898425,0.0015274871,0.0027255274,0.0015322712,0.001401274,0.0065750517,0.92233264,0.004768233,0.00011117184,0.051911026,0.0015435648],"about_ca_topic_score_codex":0.00016306421,"about_ca_topic_score_gemma":0.0000690298,"teacher_disagreement_score":0.9387433,"about_ca_system_score_codex":0.00025072275,"about_ca_system_score_gemma":0.0005170028,"threshold_uncertainty_score":0.999684},"labels":[],"label_agreement":null}]}