{"id":"W2994440022","doi":"10.1016/j.chroma.2019.460775","title":"Chemotyping and identification of protected Dalbergiatimber using gas chromatography quadrupole time of flight mass spectrometry","year":2019,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Canadian Forest Service; Natural Resources Canada; Environment and Climate Change Canada","funders":"Canadian Forest Service; Natural Resources Canada; Environment and Climate Change Canada; University of British Columbia; FPInnovations; Australian National University","keywords":"Dalbergia; Chemistry; Species identification; Identification (biology); Chromatography; Mass spectrometry; Gas chromatography; Botany; Biology; Zoology","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":[0.0005694664,0.00021907,0.0006592908,0.0009459226,0.0000603265,0.00005095148,0.0003393186,0.0001756493,0.0005766119],"category_scores_gemma":[0.00006006611,0.0001905726,0.0004445296,0.001224498,0.0001845517,0.0003038525,0.00005945455,0.0003649911,0.000006106664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002793653,"about_ca_system_score_gemma":0.0001119089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000019449,"about_ca_topic_score_gemma":5.139393e-7,"domain_scores_codex":[0.9975241,0.00006072043,0.001133279,0.0002312152,0.0007381237,0.0003125472],"domain_scores_gemma":[0.9974917,0.0001085898,0.001509587,0.0003674777,0.0003824458,0.0001401859],"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":[0.00008846277,0.0001733043,0.02485016,0.001065511,0.0004233627,0.000007593655,0.0002360743,0.00001422829,0.972927,0.00004495679,0.00006796603,0.0001013341],"study_design_scores_gemma":[0.00122844,0.0001286191,0.01079533,0.0008585193,0.0001050839,0.0001747439,0.0002540232,0.0009209521,0.9848487,0.0004220039,0.00006331292,0.0002002864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968626,0.00104319,0.0001966422,0.00005317806,0.00005913764,0.0001597945,0.00001645939,0.00001738199,0.001591563],"genre_scores_gemma":[0.9963435,0.00009635204,0.003353909,0.000004280829,0.00009595286,0.000001938125,0.000004389718,0.00003421833,0.00006546167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01405482,"threshold_uncertainty_score":0.7771325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007183431727244352,"score_gpt":0.2388455883377366,"score_spread":0.2316621566104923,"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."}}