{"id":"W4388768867","doi":"10.1101/2023.11.16.567369","title":"New sensitive tools to characterize meta-metabolome response to short- and long-term cobalt exposure in dynamic river biofilm communities","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Université de Pau et des Pays de l'Adour; Muséum National d'Histoire Naturelle; Total; Centre National de la Recherche Scientifique; Agence Nationale de la Recherche; Institut national de la recherche scientifique","keywords":"Metabolome; Metabolomics; Metabolite; Context (archaeology); Biology; Bioaccumulation; Biomass (ecology); Environmental chemistry; Chemistry; Ecology; Biochemistry; Bioinformatics","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":[0.0006653502,0.0007101358,0.0006139419,0.001717737,0.0003205781,0.001174982,0.0004016043,0.0008856707,0.001295953],"category_scores_gemma":[0.0008587345,0.0004361408,0.0005292005,0.001114676,0.0003470252,0.0006991959,0.0009568995,0.001059876,0.0006709785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003346405,"about_ca_system_score_gemma":0.0002986658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009218429,"about_ca_topic_score_gemma":0.001962253,"domain_scores_codex":[0.999409,0.0000783766,0.00003463994,0.000220929,0.0001974914,0.00005955178],"domain_scores_gemma":[0.9992759,0.0002097925,0.0002212345,0.00006832624,0.0001528714,0.00007198515],"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":[0.0001374792,0.00004968554,0.007317437,0.0002633436,0.0000677936,0.00005360302,0.00007705142,0.0003775001,0.9782915,0.0001642496,0.0002363374,0.01296411],"study_design_scores_gemma":[0.00002973745,0.0004988906,0.1147301,0.00007885493,0.0001811028,0.0007030127,0.0004805365,0.02440205,0.8486276,0.001246439,0.00890034,0.0001213472],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5833351,0.008128711,0.3848243,0.0007020856,0.0002290347,0.0003413848,0.01340849,0.003857231,0.005173615],"genre_scores_gemma":[0.7039974,0.004065491,0.2787428,0.0008789184,0.0001160603,0.001205028,0.005507742,0.0003453514,0.005141205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001717737,"threshold_uncertainty_score":0.004335403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03865819327271577,"score_gpt":0.2480479077665573,"score_spread":0.2093897144938415,"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."}}