{"id":"W1976335329","doi":"10.1182/blood-2008-03-144063","title":"Targeting LSCs: powering an old tool","year":2008,"lang":"en","type":"letter","venue":"Blood","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Institute of Allergy and Infectious Diseases","keywords":"In silico; Computational biology; Medicine; Biology; Bioinformatics; Cancer research; Gene; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003167766,0.0006308011,0.0009616874,0.0006005996,0.001724449,0.003137904,0.001312505,0.01748865,0.009296964],"category_scores_gemma":[0.01353615,0.0003136439,0.0008150078,0.0003323003,0.004302956,0.004285872,0.001384787,0.02465491,0.005872502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001721742,"about_ca_system_score_gemma":0.001163502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007378809,"about_ca_topic_score_gemma":0.00156674,"domain_scores_codex":[0.9975847,0.0008859519,0.0002310596,0.0002572771,0.0008038784,0.0002371195],"domain_scores_gemma":[0.9928024,0.005113338,0.0002200407,0.0003530391,0.0008569537,0.0006541649],"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":[0.0002177105,0.00008604117,0.0007124845,0.0002414403,0.00003802284,0.002177567,0.0001308977,0.0002765222,0.001391806,0.02699343,0.864058,0.1036761],"study_design_scores_gemma":[0.0001850898,0.0001896153,0.0004184744,0.000270133,0.00004150897,0.002822185,0.0002779104,0.0005772954,0.00128014,0.04508167,0.9488081,0.00004795484],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0007773726,0.01149182,0.00197827,0.9640202,0.01313461,0.00001966778,0.00004211981,0.00007966218,0.00845637],"genre_scores_gemma":[0.02333398,0.01661387,0.00339698,0.871979,0.06542867,0.0001003205,0.00005031786,0.00007021062,0.01902668],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01748865,"threshold_uncertainty_score":0.03110141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02165078520912755,"score_gpt":0.2777029850017009,"score_spread":0.2560521997925734,"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."}}