{"id":"W4408116727","doi":"10.22541/au.174100386.69427219/v1","title":"Metatranscriptomic Profiling of Host-Microbiome Interactions in the Telencephalon and Liver of Carollia perspicillata","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fundação para a Ciência e a Tecnologia; Universidade Federal do Pará; Fundação Amparo e Desenvolvimento da Pesquisa; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Universidade de Lisboa; Financiadora de Estudos e Projetos; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; “la Caixa” Foundation","keywords":"Biology; Microbiome; Host (biology); Profiling (computer programming); Zoology; Computational biology; Cerebrum; Evolutionary biology; Ecology; Bioinformatics; Computer science; Neuroscience","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":[0.000130909,0.0002272547,0.0001974136,0.0004969247,0.0002377869,0.0002500823,0.0001228602,0.0002289806,0.0006686043],"category_scores_gemma":[0.0001622474,0.0001278888,0.0002768745,0.0002957033,0.0001623475,0.000199808,0.00027853,0.0002649298,0.0002008341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002389371,"about_ca_system_score_gemma":0.000319032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002571475,"about_ca_topic_score_gemma":0.005490893,"domain_scores_codex":[0.9999247,0.00000562564,0.000003413151,0.00003574506,0.00001849681,0.00001201248],"domain_scores_gemma":[0.9999183,0.00002316543,0.0000165778,0.000007195093,0.00002082273,0.00001382685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001363541,0.00001061557,0.01486807,0.00007885421,0.00002446224,0.00006484614,0.0001643383,0.0003411401,0.9795008,0.00009388196,0.00008238057,0.004634334],"study_design_scores_gemma":[0.00001267091,0.0002198662,0.7359822,0.00003370272,0.0001005226,0.0005083616,0.0007048579,0.008601245,0.2493442,0.0004334163,0.004020269,0.00003868667],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919973,0.0004870644,0.002901761,0.00005189083,0.00001143222,0.0000144722,0.003678904,0.00005669186,0.0008005171],"genre_scores_gemma":[0.9818278,0.0004731348,0.008813292,0.0001098096,0.00001343262,0.0000665422,0.006691261,0.00007872011,0.001925937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002571475,"threshold_uncertainty_score":0.005113006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02234272711942731,"score_gpt":0.2724438519782359,"score_spread":0.2501011248588086,"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."}}