{"id":"W1995671308","doi":"10.1074/mcp.m300110-mcp200","title":"Depth of Proteome Issues","year":2004,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"National Institute of General Medical Sciences","keywords":"Proteome; Computational biology; Chemistry; Computer science; Biology; Biochemistry","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":[0.02345053,0.001128644,0.0009723452,0.002518392,0.002397133,0.007671946,0.002339592,0.002769634,0.01072162],"category_scores_gemma":[0.02640856,0.0007281702,0.001154023,0.002088446,0.003830085,0.01515203,0.00901581,0.00648276,0.003318688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003059862,"about_ca_system_score_gemma":0.00355438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006012431,"about_ca_topic_score_gemma":0.0005729858,"domain_scores_codex":[0.9921702,0.003150547,0.0005294366,0.00116605,0.002506695,0.0004771754],"domain_scores_gemma":[0.9848946,0.005751277,0.001074663,0.003260757,0.003838756,0.001180004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004383683,0.0001710166,0.004056031,0.002093998,0.0001381802,0.001077202,0.005289262,0.001766241,0.02604415,0.5051993,0.07571698,0.3780093],"study_design_scores_gemma":[0.00003305616,0.0001195866,0.002327387,0.0007458745,0.00004080039,0.001575176,0.002726631,0.002362764,0.007099802,0.2831153,0.6997912,0.00006240237],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06065033,0.07717603,0.3480883,0.3214955,0.01827646,0.0007126681,0.001912461,0.00194742,0.1697408],"genre_scores_gemma":[0.4438727,0.04873983,0.3398731,0.0770876,0.01046031,0.001071361,0.002400918,0.002063279,0.07443095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02345053,"threshold_uncertainty_score":0.1240197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00889946133140648,"score_gpt":0.2556932934019867,"score_spread":0.2467938320705802,"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."}}