{"id":"W3034859449","doi":"10.1101/2020.06.12.135475","title":"Disentangling interactions among mercury, immunity, and infection in a Neotropical bat community","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bat Biology and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Waterloo","funders":"Defense Advanced Research Projects Agency; Natural Sciences and Engineering Research Council of Canada; Texas Christian University; Division of Environmental Biology; U.S. Department of Agriculture; Achievement Rewards for College Scientists Foundation; National Institute of Food and Agriculture; Clemson University; U.S. Department of Defense; Advanced Research Projects Agency; National Science Foundation","keywords":"Mercury (programming language); Biology; Wildlife; Bartonella; Immunity; Ecology; Habitat; Zoology; Immune system; Immunology; Virology","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":[0.0006224458,0.0002506036,0.0002763252,0.0008944703,0.0007966362,0.0005688025,0.0004401846,0.0002646034,0.001462413],"category_scores_gemma":[0.001076527,0.0002208297,0.0002709337,0.0007092742,0.0005954209,0.0005744666,0.0009971191,0.0004044167,0.000190545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007895604,"about_ca_system_score_gemma":0.0006107861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07377221,"about_ca_topic_score_gemma":0.1596685,"domain_scores_codex":[0.9996222,0.00009591402,0.00001765138,0.0001417996,0.00004648657,0.00007592433],"domain_scores_gemma":[0.9992812,0.0001267906,0.0001809136,0.00006422854,0.0001663996,0.0001804405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002854307,0.00002360239,0.9961991,0.00001061105,0.00005562382,0.00004079224,0.0008346359,0.00008983434,0.001001926,0.00003558947,0.00008891323,0.001590715],"study_design_scores_gemma":[9.02663e-7,0.00001525214,0.9986274,0.000005033267,0.000009418261,0.0000326257,0.0006957169,0.0004303363,0.00003008161,0.00003131762,0.00011976,0.000002266169],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995379,0.00003613008,0.00007632075,0.00002702949,0.000001453883,0.000004643424,0.0001453219,0.000002277135,0.0001689081],"genre_scores_gemma":[0.9995296,0.00002754242,0.000155368,0.00002330346,0.000002242247,0.000006798627,0.0001381014,0.00000181167,0.0001152039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07377221,"threshold_uncertainty_score":0.1466857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02911981552756071,"score_gpt":0.2338309464009428,"score_spread":0.2047111308733821,"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."}}