{"id":"W4318484555","doi":"10.18609/cgti.2022.210","title":"Process development optimization for GMP manufacturing: a CAR-T case study","year":2022,"lang":"en","type":"article","venue":"Cell and Gene Therapy Insights","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Manufacturing engineering; Process (computing); Computer science; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004288672,0.0009603821,0.0007028489,0.001429255,0.002452406,0.003631415,0.001701258,0.002499146,0.006646557],"category_scores_gemma":[0.005190284,0.0004619837,0.001414814,0.001648028,0.001371873,0.001405497,0.001480071,0.002080671,0.002628806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002948513,"about_ca_system_score_gemma":0.003227899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006371242,"about_ca_topic_score_gemma":0.009249533,"domain_scores_codex":[0.9960428,0.0007426139,0.0002082457,0.0003916152,0.002088757,0.0005259502],"domain_scores_gemma":[0.9966125,0.001394515,0.0003361356,0.0005976863,0.0007406374,0.0003185992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"case_report","study_design_scores_codex":[0.002337849,0.004113816,0.0163311,0.004039895,0.0001710001,0.03854964,0.004974282,0.1766696,0.1506913,0.05116082,0.02177574,0.529185],"study_design_scores_gemma":[0.0003952652,0.007834583,0.0119552,0.0006984252,0.0003350499,0.02871444,0.006668518,0.1740487,0.3492365,0.01734319,0.402364,0.0004061694],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.597601,0.01151763,0.2433014,0.005294367,0.0006212519,0.003724584,0.001461085,0.001929942,0.1345486],"genre_scores_gemma":[0.8160597,0.004726419,0.1532424,0.0004590228,0.0001034798,0.0004259761,0.0006646765,0.0004890062,0.02382928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006646557,"threshold_uncertainty_score":0.02268094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611449580240797,"score_gpt":0.2185807969203052,"score_spread":0.2024663011178972,"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."}}