{"id":"W4405251849","doi":"10.1038/s41587-024-02493-9","title":"Leveraging machine learning and big data techniques to map the global patent landscape of phage therapy","year":2024,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Bacteriophages and microbial interactions","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute for Work & Health; Artificial Intelligence in Medicine (Canada); University of Toronto; York University","funders":"","keywords":"Phage therapy; Focus (optics); Field (mathematics); Data science; Computational biology; Geography; Computer science; Biology; Genetics; Physics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001361936,0.00009512073,0.00009279925,0.00004200055,0.00007573578,0.00003380749,0.0003581317,0.0002664635,0.0002022437],"category_scores_gemma":[0.00002045962,0.00006028478,0.00002134206,0.0002649915,0.0001084026,0.00006272063,0.0006475294,0.000590731,0.00002499616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003768041,"about_ca_system_score_gemma":0.000005094229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002447548,"about_ca_topic_score_gemma":0.000127086,"domain_scores_codex":[0.9993744,0.0000222604,0.0001012708,0.0003007445,0.00006204956,0.0001392175],"domain_scores_gemma":[0.999629,0.00002258773,0.00002723628,0.0002961565,0.000003622052,0.00002143252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001408114,0.00001839637,0.001093974,0.000007972467,0.00002125459,0.00000934635,0.0000691425,0.000001115913,0.5611492,0.0001732427,0.003102883,0.4343394],"study_design_scores_gemma":[0.00005987532,0.00008407413,0.002398135,0.00003355009,0.000008187707,0.00007237445,0.00005393457,0.0002473193,0.09747847,0.0001336094,0.8993453,0.00008514657],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9303744,0.01472035,0.002414866,0.04804154,0.001143201,0.000582869,0.0003058397,0.0006866995,0.0017302],"genre_scores_gemma":[0.9975017,0.0007666969,0.001080892,0.000410781,0.00004986525,0.000004677962,0.00002342093,0.00000847039,0.0001534879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8962424,"threshold_uncertainty_score":0.2566465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02309829691608416,"score_gpt":0.275594780318455,"score_spread":0.2524964834023709,"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."}}