{"id":"W2940563241","doi":"10.1002/rcm.8470","title":"<sup>17</sup> O‐excess as a detector for co‐extracted organics in vapor analyses of plant isotope signatures","year":2019,"lang":"en","type":"article","venue":"Rapid Communications in Mass Spectrometry","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Environmental chemistry; Isotope; Detector; Analytical Chemistry (journal); Radiochemistry; Chemistry; Environmental science; Physics; Nuclear physics; Optics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008425236,0.0002268357,0.0005654446,0.0006892359,0.00009752205,0.00002822986,0.002157601,0.0002315444,0.005440866],"category_scores_gemma":[0.0003603507,0.0002284034,0.0001464142,0.002249768,0.0002494977,0.0002197521,0.0004459369,0.0005661857,0.0001917646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004716032,"about_ca_system_score_gemma":0.0000556753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004561408,"about_ca_topic_score_gemma":0.001232077,"domain_scores_codex":[0.9977781,0.0003019461,0.0007446775,0.0004271777,0.0003052156,0.0004429457],"domain_scores_gemma":[0.9963333,0.001090839,0.0003339583,0.002150441,0.00003085937,0.00006060446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002284733,0.001724864,0.5547611,0.00006913373,0.0003182391,0.00001025917,0.001184312,0.04835756,0.3881956,0.002123052,0.001449997,0.001577487],"study_design_scores_gemma":[0.004478564,0.001202224,0.6092277,0.000119258,0.0002695579,0.00002911401,0.002556194,0.289676,0.06581677,0.008779455,0.01644479,0.001400429],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872034,0.0007303504,0.0005175932,0.0004312666,0.0000266397,0.0009697794,0.00007341345,0.00004043733,0.01000712],"genre_scores_gemma":[0.9770966,0.0005404623,0.02159625,0.0001687519,0.00001221062,0.0001260723,0.0001277655,0.00003026895,0.0003016605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3223788,"threshold_uncertainty_score":0.9954683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222245113586127,"score_gpt":0.3096524764828785,"score_spread":0.2874300253470173,"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."}}