{"id":"W2411894186","doi":"10.1007/978-1-61779-231-1_17","title":"In Vivo Nuclear Magnetic Resonance Metabolite Profiling in Plant Seeds","year":2011,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"GABA and Rice Research","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Steacie Institute for Molecular Sciences; National Research Council Canada","funders":"","keywords":"Metabolite profiling; Metabolite; In vivo; Nuclear magnetic resonance; Profiling (computer programming); Chemistry; Computational biology; Biology; Physics; Biochemistry; Computer science; Biotechnology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001631857,0.0001298497,0.0002755983,0.00008801904,0.00002808086,0.000009002056,0.0003392197,0.0002025033,0.0003532335],"category_scores_gemma":[0.0001873603,0.00005468421,0.00005341943,0.0007247913,0.0001047384,0.00003624442,0.0001450051,0.0003102504,0.00001106732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002491609,"about_ca_system_score_gemma":0.000009390637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00130985,"about_ca_topic_score_gemma":0.0004699598,"domain_scores_codex":[0.9973163,0.001427593,0.0002900973,0.000383657,0.00007702191,0.0005053419],"domain_scores_gemma":[0.9996245,0.0001832835,0.00003563565,0.00008229509,0.00001858432,0.00005571582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004641288,0.0000740477,0.0200256,0.000004504038,0.000001392469,0.0001002959,0.0001614517,5.945545e-7,0.9383428,0.004680404,0.000003087239,0.03655942],"study_design_scores_gemma":[0.0007587226,0.0007740048,0.5276788,0.00005253491,0.000006748456,0.00004122655,0.0006478414,0.0007175755,0.4158372,0.02448919,0.02845048,0.0005456858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9922802,0.003865006,0.0002362329,0.0002543048,0.0001092268,0.0002940045,0.00001649876,0.00001787867,0.002926686],"genre_scores_gemma":[0.3884707,0.00121147,0.6087584,0.0009507043,0.000103908,0.0001253014,0.00002004907,0.000007351828,0.000352034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6085222,"threshold_uncertainty_score":0.386766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05081941007551236,"score_gpt":0.3493707275727828,"score_spread":0.2985513174972704,"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."}}