{"id":"W4313008009","doi":"10.1039/d2gc02345h","title":"An eco-friendly, low-cost, and automated strategy for phosphoproteome profiling","year":2022,"lang":"en","type":"article","venue":"Green Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; York University","funders":"Special Project for Research and Development in Key areas of Guangdong Province; Guangdong Academy of Agricultural Sciences; Science and Technology Planning Project of Guangdong Province; Natural Sciences and Engineering Research Council of Canada; Guangzhou Municipal Science and Technology Project; China Scholarship Council","keywords":"Profiling (computer programming); Environmentally friendly; Computer science; Biochemical engineering; Chemistry; Engineering; Biology","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":[0.0002166043,0.0001889138,0.0001935899,0.0000197651,0.000320892,0.00003111319,0.0002218834,0.00008757694,0.00008772312],"category_scores_gemma":[0.00002774088,0.0002011969,0.00006882984,0.00009433485,0.0000603185,0.000004615452,0.0002228863,0.0001252869,6.703917e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002396019,"about_ca_system_score_gemma":0.0000763026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000224818,"about_ca_topic_score_gemma":0.000004287097,"domain_scores_codex":[0.9988256,0.00001937868,0.0001941517,0.000506408,0.0001205264,0.0003339009],"domain_scores_gemma":[0.9993921,0.000007969133,0.00009763546,0.0003362568,0.00005744743,0.0001085706],"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":[0.000114373,0.00007351922,0.0007166464,0.00009338931,0.00006838649,0.000003370786,0.00001718565,0.0001108011,0.9963104,0.00004746637,0.0009251034,0.001519423],"study_design_scores_gemma":[0.0009070708,0.0002747131,0.0003123853,0.000002918168,0.0000284444,0.00003642162,0.0004451475,0.002323332,0.9774235,0.0001393978,0.01778147,0.000325133],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967188,0.0008798642,0.0003489565,0.00008533744,0.00007050316,0.0004274402,0.0003904735,0.00007544817,0.001003125],"genre_scores_gemma":[0.9957539,0.00007100677,0.001619556,0.00008506951,0.0002077488,0.0004578508,0.0006939076,0.00003395849,0.001077018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01888677,"threshold_uncertainty_score":0.8204572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115196241099922,"score_gpt":0.2672533582179599,"score_spread":0.2557337341079677,"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."}}