{"id":"W6963890384","doi":"10.25345/c5609s","title":"MassIVE MSV000084643 - N terminome analysis of pediatric acute leukemia patients and matched xenografts","year":2019,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Acute leukemia; Acute lymphocytic leukemia; Cancer; Leukemia; MEDLINE","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006604563,0.0007796691,0.001034295,0.001478754,0.000576512,0.0009476551,0.001216981,0.001037106,0.02319199],"category_scores_gemma":[0.002974082,0.0003129922,0.001033136,0.002472219,0.0002718745,0.0004272404,0.001410113,0.0007627454,0.01134865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006708158,"about_ca_system_score_gemma":0.001441274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01300953,"about_ca_topic_score_gemma":0.02547141,"domain_scores_codex":[0.9995564,0.00006009248,0.00004324012,0.0001827564,0.00007118907,0.00008643132],"domain_scores_gemma":[0.9991763,0.0002576333,0.0001034857,0.0002004929,0.0001501829,0.0001118335],"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":[0.001624787,0.00007210124,0.02915078,0.001671797,0.000577479,0.0004313829,0.000154174,0.00245414,0.004536207,0.001359782,0.942103,0.01586449],"study_design_scores_gemma":[0.001836297,0.0002024306,0.1107082,0.0008283626,0.0007630411,0.001488275,0.000355783,0.00326466,0.003492726,0.004659306,0.8723048,0.00009612066],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006474471,0.0003371027,0.0002299396,0.0001153195,0.00003236092,0.0000170605,0.9912136,0.0002738601,0.001306242],"genre_scores_gemma":[0.008236484,0.0001944129,0.0005915732,0.0001336899,0.00001346215,0.00008477556,0.9900018,0.00006738039,0.0006763823],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02319199,"threshold_uncertainty_score":0.07758492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01414048757210374,"score_gpt":0.2401790318319232,"score_spread":0.2260385442598195,"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."}}