{"id":"W2117582795","doi":"10.1093/jxb/erj157","title":"Relative and absolute quantitative shotgun proteomics: targeting low-abundance proteins in Arabidopsis thaliana","year":2006,"lang":"en","type":"article","venue":"Journal of Experimental Botany","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Theoretical Astrophysics","keywords":"Shotgun proteomics; Proteomics; Shotgun; Quantitative proteomics; Arabidopsis thaliana; Mass spectrometry; Chemistry; Label-free quantification; Proteome; Computational biology; Biochemistry; Isobaric labeling; Biology; Chromatography; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000625131,0.0005004985,0.0003933305,0.0007099677,0.0002437475,0.0005915324,0.000607567,0.0005361312,0.0006022893],"category_scores_gemma":[0.0003826268,0.0002641258,0.0003155443,0.000513611,0.0003525848,0.0004539404,0.0001867204,0.0005509781,0.000637804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006626542,"about_ca_system_score_gemma":0.0004140579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001076289,"about_ca_topic_score_gemma":0.001440919,"domain_scores_codex":[0.9996408,0.00005133387,0.00002881789,0.0001173492,0.0001342048,0.00002740962],"domain_scores_gemma":[0.9998598,0.00003405558,0.00003148161,0.000017688,0.00002919308,0.00002776324],"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":[0.00005670001,0.000005598609,0.00006969699,0.00007086995,0.000003324921,0.00003322438,0.00000639252,0.00007412855,0.9967519,0.0001248326,0.00006979823,0.00273358],"study_design_scores_gemma":[0.00002870774,0.0003737697,0.006005525,0.00001965952,0.00004316411,0.001116397,0.00004002733,0.004657872,0.9795178,0.0007376841,0.007418412,0.00004090752],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5953003,0.02277773,0.3618889,0.0009050296,0.0003533132,0.0004222766,0.008842299,0.004289434,0.00522072],"genre_scores_gemma":[0.6307732,0.007917691,0.3435439,0.000493201,0.00008905386,0.0004296233,0.009318329,0.0003719743,0.007062947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001076289,"threshold_uncertainty_score":0.004807889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178937393041648,"score_gpt":0.2836694060038731,"score_spread":0.2718800320734567,"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."}}