{"id":"W4407097255","doi":"10.1371/journal.ppat.1012876","title":"Decoding Cryptococcus: From African biodiversity to worldwide prevalence","year":2025,"lang":"en","type":"article","venue":"PLoS Pathogens","topic":"Fungal Infections and Studies","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health; Ernest Oppenheimer Memorial Trust; Canadian Institute for Advanced Research; Division of Intramural Research, National Institute of Allergy and Infectious Diseases","keywords":"Biodiversity; Decoding methods; Cryptococcus; Biology; Geography; Environmental health; Medicine; Microbiology; Ecology; Computer science; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00310882,0.0004657866,0.0003931528,0.003629282,0.0005775375,0.002057428,0.0004685466,0.0007558661,0.003913671],"category_scores_gemma":[0.01980842,0.0002029754,0.0001554215,0.004459452,0.0018545,0.004592662,0.002721965,0.001442461,0.0004171311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000775595,"about_ca_system_score_gemma":0.001538477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007808071,"about_ca_topic_score_gemma":0.01020703,"domain_scores_codex":[0.9987279,0.0005277841,0.0001063308,0.0001599058,0.0002423875,0.000235717],"domain_scores_gemma":[0.9940299,0.002223034,0.001593454,0.0003962283,0.001176351,0.0005810914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004621174,0.00005194058,0.467647,0.002443558,0.0002978892,0.0006899207,0.01182768,0.0005453846,0.002919828,0.0303982,0.01616391,0.4665524],"study_design_scores_gemma":[0.00003890519,0.0003015369,0.7392265,0.01152744,0.0003650274,0.003062105,0.04943936,0.001576118,0.002882552,0.06867916,0.1228141,0.00008717346],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7000303,0.1676274,0.008098174,0.09263965,0.00216338,0.00004757098,0.003339697,0.00006931703,0.0259846],"genre_scores_gemma":[0.9730484,0.02233828,0.001554874,0.001873373,0.0004954261,0.00001639109,0.0002298484,0.00002026919,0.0004230812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007808071,"threshold_uncertainty_score":0.01644117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540385345459876,"score_gpt":0.2708886741522709,"score_spread":0.2454848206976721,"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."}}