{"id":"W6968408985","doi":"10.5281/zenodo.15615538","title":"Biosimilars in the Era of Artificial Intelligence: Canada's Health Regulations and Their Role in the Approval Process","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biosimilar; Process (computing); Product (mathematics); Quality (philosophy); Key (lock); Health care; Healthcare system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001396114,0.00007197481,0.0001209547,0.00009224257,0.0005693191,0.00006560068,0.0005339825,0.00005630447,0.0003547258],"category_scores_gemma":[0.0003165831,0.0000435101,0.00001806603,0.0006878158,0.000258108,0.00003096207,0.0001508267,0.0003524517,0.000008162064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004122321,"about_ca_system_score_gemma":0.0000289094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002790907,"about_ca_topic_score_gemma":0.001793178,"domain_scores_codex":[0.9985023,0.0008326313,0.0002471557,0.0001722729,0.000053158,0.0001924937],"domain_scores_gemma":[0.9995289,0.0001057642,0.00005732724,0.0002016674,0.00009415393,0.00001213926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002077047,0.0005039924,0.0001384825,0.000156144,0.00007353687,0.000003387571,0.01392806,0.00006757893,0.006147172,0.06079593,0.04318077,0.8747972],"study_design_scores_gemma":[0.0005817263,0.0005175702,0.03254692,0.0002049404,0.00002698757,0.00009943837,0.05141536,0.00127263,0.01179272,0.01973853,0.881502,0.0003011553],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5631914,0.008404655,0.01105459,0.3289117,0.0005528467,0.005202298,0.001117745,0.0002837384,0.08128104],"genre_scores_gemma":[0.9986356,0.00005900874,0.0000493851,0.001081138,0.000008723163,9.47182e-8,0.00008097872,0.00004348746,0.00004158022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8744961,"threshold_uncertainty_score":0.4378799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03221565830325024,"score_gpt":0.290833726371683,"score_spread":0.2586180680684327,"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."}}