{"id":"W3203543862","doi":"10.1101/2021.09.27.461748","title":"Global Metabolome Analysis of <i>Dunaliella tertiolecta, Phaeobacter italicus R11</i> Co-cultures using Thermal Desoprtion - Comprehensive Two-dimensional Gas Chromatography - Time-of-Flight Mass Spectrometry (TD-GC×GC-TOFMS)","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Algal biology and biofuel production","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Genome Canada","keywords":"Metabolomics; Dunaliella; Metabolome; Biofuel; Mass spectrometry; Biomass (ecology); Biology; Chemistry; Biochemical engineering; Chromatography; Algae; Botany; Biotechnology; Ecology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002773569,0.0008360513,0.000411681,0.0007254428,0.0002718035,0.0005925066,0.0002751429,0.0003419174,0.004119424],"category_scores_gemma":[0.0002030648,0.0001455068,0.0006093276,0.0007627644,0.0001376163,0.0002857252,0.0005582048,0.00046598,0.001131591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002383769,"about_ca_system_score_gemma":0.0003353927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001427831,"about_ca_topic_score_gemma":0.00260934,"domain_scores_codex":[0.9997446,0.00001736422,0.00001135443,0.0001113317,0.00007955788,0.0000357689],"domain_scores_gemma":[0.9999157,0.0000172052,0.00001641311,0.00001250263,0.00002279949,0.00001539256],"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":[0.0003129586,0.00002860928,0.002787734,0.0003064539,0.00005932523,0.0001179462,0.00004352588,0.0005016441,0.9795641,0.0001290104,0.001485393,0.01466335],"study_design_scores_gemma":[0.00002796124,0.0003544852,0.04573359,0.00005691692,0.0001281325,0.0003783632,0.0001966511,0.006364037,0.9324834,0.0003051127,0.01389752,0.00007366594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.825637,0.003045868,0.04946437,0.0008727481,0.0002309125,0.0002237522,0.1109564,0.003547488,0.006021327],"genre_scores_gemma":[0.824589,0.003012269,0.08626686,0.000410793,0.00006033453,0.0004302235,0.07295415,0.0005401755,0.01173622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004119424,"threshold_uncertainty_score":0.01378083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095917146688061,"score_gpt":0.238417044652246,"score_spread":0.2274578731853654,"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."}}