{"id":"W4312930243","doi":"10.56588/iabcd.v1i1.26","title":"COMPUTATIONAL META-ANALYSIS OF CERVICAL CANCER USING AVAILABLE 16S RRNA NGS DATA","year":2022,"lang":"en","type":"article","venue":"International Association of Biologicals and Computational Digest","topic":"Cervical Cancer and HPV Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Metagenomics; Biology; Microbiome; Cervical cancer; 16S ribosomal RNA; Computational biology; Gene; Cancer; Ribosomal RNA; Human Microbiome Project; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007806775,0.0001183159,0.0006885545,0.0002912784,0.0001322746,0.00002326521,0.0003003458,0.00006474727,0.02154244],"category_scores_gemma":[0.0002113486,0.00009698606,0.0003373637,0.0006029644,0.00007999985,0.00009330369,0.0004687597,0.0001907666,0.000003789631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002524271,"about_ca_system_score_gemma":0.0002012266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004799252,"about_ca_topic_score_gemma":0.00002474987,"domain_scores_codex":[0.9975346,0.000148433,0.0005936114,0.0003753612,0.001197084,0.0001509064],"domain_scores_gemma":[0.9975399,0.0008370841,0.0004658789,0.0001417218,0.0009362845,0.0000791437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005201289,0.001077002,0.4606668,0.00005707907,0.07468367,0.000006935283,0.0001222742,0.4473606,0.0003769621,0.01072973,0.002821177,0.001577575],"study_design_scores_gemma":[0.0008917901,0.0001771435,0.620507,0.00000257017,0.01319302,0.000006126401,0.00006179029,0.3569073,0.00002562442,0.002987846,0.005098752,0.0001410443],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9235201,0.005169611,0.005260291,0.02670477,0.0004838498,0.0009960701,0.0226386,0.00008024905,0.01514648],"genre_scores_gemma":[0.9938256,0.00007096949,0.001654342,0.000765414,0.0000625667,0.00002986012,0.002956804,0.000006845411,0.0006276173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1598402,"threshold_uncertainty_score":0.979352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2168037562029694,"score_gpt":0.4144189727465549,"score_spread":0.1976152165435855,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}