{"id":"W4312863930","doi":"10.1007/698_2022_892","title":"Chromatography High-Resolution Mass Spectrometry in Food and Environmental Chemistry","year":2022,"lang":"en","type":"book-chapter","venue":"The handbook of environmental chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Canada","funders":"","keywords":"Orbitrap; Mass spectrometry; Mass; Chemistry; Environmental analysis; Resolution (logic); Chromatography; Analytical Chemistry (journal); Mass spectrum; Computer science; Artificial intelligence","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":[0.0007331326,0.001625826,0.001864359,0.002477978,0.0008303492,0.002300837,0.002236537,0.001663447,0.03355514],"category_scores_gemma":[0.0005840834,0.0009834901,0.0007800099,0.004406677,0.0007351481,0.003323474,0.001317602,0.003134924,0.06228102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007469192,"about_ca_system_score_gemma":0.001330249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001243614,"about_ca_topic_score_gemma":0.003619049,"domain_scores_codex":[0.9993178,0.00006115362,0.00002735199,0.0001155591,0.0004376692,0.00004050976],"domain_scores_gemma":[0.9996455,0.0001281135,0.00001827067,0.00003881596,0.0001426809,0.00002666917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003472598,0.0001228674,0.00009493787,0.001241914,0.00002819185,0.0001261684,0.00008027698,0.0005188378,0.01959899,0.0216292,0.3215666,0.6349573],"study_design_scores_gemma":[0.000003038609,0.00002181569,0.000150327,0.0001533542,0.00001049086,0.0003735115,0.0000188881,0.0003176613,0.005718173,0.006349955,0.9868658,0.00001687443],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.001129518,0.579143,0.1036305,0.002594631,0.0109757,0.0001889665,0.0009834139,0.002317892,0.2990364],"genre_scores_gemma":[0.00431689,0.2672988,0.05891674,0.002980547,0.002810354,0.0001933984,0.001404489,0.0008686918,0.6612102],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03355514,"threshold_uncertainty_score":0.1122531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004802423241492684,"score_gpt":0.1751697092601062,"score_spread":0.1703672860186135,"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."}}