{"id":"W3107925559","doi":"10.1002/etc.4941","title":"Diagnostic Fragmentation Filtering for Cyanopeptolin Detection","year":2020,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Agriculture and Agri-Food Canada; Carleton University","funders":"","keywords":"Cyanobacteria; Microcystis; Fragmentation (computing); Bloom; Algal bloom; Chemistry; Tandem mass spectrometry; Metabolomics; Mass spectrometry; Environmental chemistry; Chromatography; Biology; Computational biology; Ecology; Genetics; Organic chemistry; Phytoplankton","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001248917,0.0013791,0.0005943743,0.00219635,0.0007349706,0.0007924133,0.0008179258,0.0008068655,0.002486438],"category_scores_gemma":[0.002212337,0.0003617447,0.0007338819,0.001246452,0.0005175582,0.0008479341,0.0006767591,0.0008686876,0.0009234872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009614034,"about_ca_system_score_gemma":0.0009959646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006047545,"about_ca_topic_score_gemma":0.007069503,"domain_scores_codex":[0.9992402,0.00008026347,0.00005726765,0.000224505,0.000286261,0.0001116034],"domain_scores_gemma":[0.9988841,0.0002844229,0.0002329742,0.00007734966,0.0004365343,0.00008450836],"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":[0.0006963761,0.00005604038,0.01254793,0.0003797912,0.0001385321,0.0003495263,0.000158746,0.0003580028,0.9409987,0.0001973463,0.001032901,0.04308608],"study_design_scores_gemma":[0.00005063791,0.0004636002,0.05962182,0.00005942855,0.0001886218,0.00245541,0.0002379959,0.0203749,0.9037876,0.0004989425,0.01214035,0.0001205691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8115515,0.004615713,0.1683623,0.000469135,0.0001319459,0.0005712618,0.006075373,0.004490187,0.003732701],"genre_scores_gemma":[0.7022964,0.002146537,0.2822941,0.0008820374,0.00005954181,0.0004614884,0.008640971,0.0006601925,0.002558693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006047545,"threshold_uncertainty_score":0.0120247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006497011953564217,"score_gpt":0.1901484442936919,"score_spread":0.1836514323401277,"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."}}