{"id":"W2161797935","doi":"10.1101/gr.5488207","title":"Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines","year":2006,"lang":"en","type":"article","venue":"Genome Research","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency","funders":"Michael Smith Health Research BC; Canadian Institutes of Health Research; Canada's Michael Smith Genome Sciences Centre; National Cancer Institute; Genome Canada","keywords":"Biology; Serial analysis of gene expression; SAGE; Computational biology; Gene expression profiling; Genetics; Gene; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002445393,0.000104494,0.0001239386,0.00007174497,0.0001333835,0.00002306318,0.0001721876,0.0001817105,0.00003890288],"category_scores_gemma":[0.00001635133,0.00009986376,0.00003641325,0.0001926345,0.0001970606,0.000003459143,0.0002050818,0.0001341857,0.000006322457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006030616,"about_ca_system_score_gemma":0.00004898872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001868118,"about_ca_topic_score_gemma":0.00009970237,"domain_scores_codex":[0.9989042,0.0001089383,0.0001859441,0.000415041,0.0001170908,0.0002687995],"domain_scores_gemma":[0.999344,0.00001138345,0.00004626978,0.0004075782,0.0001424813,0.0000483196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003169524,0.0001210169,0.0006383157,0.00002294706,0.00001101681,0.000001159054,0.00004234974,0.000006737615,0.9964744,0.00003415397,0.002159338,0.0004568779],"study_design_scores_gemma":[0.0001454529,0.00009467241,0.003968718,0.00000587386,0.000006456757,0.000001866704,0.0000625728,0.00003187801,0.8940465,0.0004210399,0.1011199,0.00009500016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881382,0.006732326,0.003645081,0.0002057854,0.00002903925,0.0003725113,0.00020832,0.00002404062,0.0006447072],"genre_scores_gemma":[0.9781418,0.001400094,0.01427288,0.00001728288,0.00030814,0.00006995801,0.001735776,0.00002661247,0.004027476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1024278,"threshold_uncertainty_score":0.4072326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01245477755945856,"score_gpt":0.2774571461193647,"score_spread":0.2650023685599062,"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."}}