{"id":"W3195558931","doi":"10.1002/jms.4783","title":"Applications of nDATA for screening, quantitation, and identification of pesticide residues in fruits and vegetables using UHPLC/ESI Q‐Orbitrap all ion fragmentation and data independent acquisition","year":2021,"lang":"en","type":"article","venue":"Journal of Mass Spectrometry","topic":"Pesticide Residue Analysis and Safety","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency","funders":"","keywords":"Orbitrap; Chemistry; Chromatography; Pesticide residue; Mass spectrometry; Pesticide; Fragmentation (computing); Repeatability; Computer science","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":[0.0008512135,0.00006911747,0.0002302261,0.0001030627,0.00006046947,0.00004758357,0.0001156489,0.00005262917,0.00001520618],"category_scores_gemma":[0.0001928409,0.00003930774,0.00002953579,0.0003402173,0.00004357046,0.0003741498,0.00005534578,0.00006876535,4.089678e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001514731,"about_ca_system_score_gemma":0.00001836866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009771338,"about_ca_topic_score_gemma":0.0002566683,"domain_scores_codex":[0.9988754,0.00007722347,0.0005605776,0.0001833044,0.0002162766,0.00008719097],"domain_scores_gemma":[0.9987375,0.0003155858,0.0006025173,0.00007550915,0.0002259264,0.00004299277],"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":[0.00004813575,0.00006560479,0.1028053,0.00006719748,0.00005389752,0.000001927265,0.00004794346,0.00006397425,0.8899936,0.000556774,0.000009689113,0.006285993],"study_design_scores_gemma":[0.0003578402,0.0001224794,0.9504398,0.00009364017,0.0001670666,0.00003179051,0.000801381,0.001997075,0.04149692,0.004386723,0.00003489938,0.00007045878],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975953,0.003436776,0.02001402,0.0003702366,0.00001297158,0.0001142747,0.00009128398,0.000001635833,0.000005817885],"genre_scores_gemma":[0.986618,0.001583217,0.01154627,0.00001484833,0.00005902495,0.000001664885,0.0001720826,0.000001062292,0.00000383564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8484967,"threshold_uncertainty_score":0.1602923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04893642682303064,"score_gpt":0.3222517923028443,"score_spread":0.2733153654798137,"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."}}