{"id":"W4312990968","doi":"10.46427/gold2022.12668","title":"Stability of selenide in reducing waters - insights from chromatographic analyses","year":2022,"lang":"en","type":"article","venue":"Goldschmidt2022 abstracts","topic":"Selenium in Biological Systems","field":"Nursing","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Selenide; Stability (learning theory); Chromatography; Chemistry; Computer science; Machine learning; Selenium; Organic chemistry","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":[0.0004036312,0.0003725009,0.0002198167,0.0008161635,0.0003500616,0.0006930834,0.0003190177,0.0004642121,0.001823273],"category_scores_gemma":[0.001000125,0.0001876837,0.0002084289,0.0004883887,0.0004958641,0.0004181851,0.0003865551,0.0004880713,0.0009469457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139177,"about_ca_system_score_gemma":0.0004036716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002683814,"about_ca_topic_score_gemma":0.001996104,"domain_scores_codex":[0.9997032,0.000041135,0.00001369299,0.0000785161,0.0001203452,0.00004300998],"domain_scores_gemma":[0.9997346,0.0000671747,0.00003742317,0.00001576824,0.0001151276,0.00002986989],"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":[0.0001054759,0.00003398815,0.003468425,0.00008931401,0.00003551622,0.000114937,0.0001644705,0.0001613161,0.9833879,0.0003928758,0.0002149535,0.01183081],"study_design_scores_gemma":[0.000009499778,0.0002700533,0.03480835,0.00004046007,0.0000467926,0.0007859243,0.0006514488,0.002904129,0.9480886,0.001134926,0.01120151,0.00005831608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9611902,0.008672217,0.01756533,0.0004465694,0.00007887292,0.00007536262,0.001270683,0.0003129129,0.01038789],"genre_scores_gemma":[0.972841,0.006487932,0.01195878,0.0002778182,0.00005395165,0.00005948422,0.001395962,0.00009958437,0.006825456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002683814,"threshold_uncertainty_score":0.006099463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05202706660134118,"score_gpt":0.2943350644006579,"score_spread":0.2423079977993167,"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."}}