{"meta":{"query_hash":"1713cc715d34","filters":{"venue":"ScienceBank"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"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/1713cc715d34","api":"https://metacan.xera.ac/api/v1/cohort?venue=ScienceBank"},"results":[{"id":"W4412570869","doi":"10.61340/fbdtpm","title":"From big data to personalized medicine: bioinformatics perspectives and challenges","year":2025,"lang":"en","type":"article","venue":"ScienceBank","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","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":"Humber Polytechnic","funders":"","keywords":"Personalized medicine; Big data; Data science; Translational bioinformatics; Computer science; Bioinformatics; Medicine; Data mining; Genomics; Biology; Genetics","score_opus":0.089784149992143,"score_gpt":0.3464414346519495,"score_spread":0.2566572846598065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412570869","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009967636,0.110423386,0.013539194,0.8674995,0.00324467,0.000029446519,0.00022798027,0.00006954776,0.0039694826],"genre_scores_gemma":[0.091140196,0.49859658,0.06671327,0.26830348,0.07048066,0.0005454134,0.000797152,0.00024483452,0.003178422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9742356,0.016391683,0.00094400696,0.002012357,0.0053380975,0.0010783303],"domain_scores_gemma":[0.8303819,0.14312768,0.0025640896,0.004617116,0.011427031,0.007882226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06132177,0.0018110952,0.00394115,0.0045350078,0.004494924,0.020037256,0.0063737333,0.016759107,0.008331404],"category_scores_gemma":[0.06860367,0.0014392307,0.002237734,0.0058013382,0.024384735,0.044033643,0.011311413,0.03127635,0.002906713],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002166068,0.00028054335,0.0022275886,0.0031046392,0.0003086794,0.00044022858,0.001446734,0.0044487934,0.0003667893,0.6791278,0.17752045,0.13051116],"study_design_scores_gemma":[0.00004120723,0.000044370463,0.00044497804,0.0015130104,0.000035499317,0.00022519173,0.0017478984,0.0030843914,0.000111033856,0.9112169,0.0814668,0.00006877765],"about_ca_topic_score_codex":0.005051498,"about_ca_topic_score_gemma":0.004027368,"teacher_disagreement_score":0.06132177,"about_ca_system_score_codex":0.0067034764,"about_ca_system_score_gemma":0.01382661,"threshold_uncertainty_score":0.32430434},"labels":[],"label_agreement":null}]}