{"id":"W7070628778","doi":"","title":"Predicting pharmaceutical manufacturing changes using molecular laser-induced breakdown spectroscopy and chemometrics","year":2008,"lang":"en","type":"article","venue":"NPARC","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chemometrics; Laser-induced breakdown spectroscopy; Process analytical technology; Pharmaceutical manufacturing; Quality by Design; Manufacturing process; Process (computing); Quality (philosophy)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0009149933,0.0006091517,0.0005147848,0.001201232,0.0001769472,0.0007747706,0.0003471571,0.000797208,0.0006168666],"category_scores_gemma":[0.002946048,0.0002046503,0.0004936189,0.001202104,0.0002430981,0.0006526215,0.0002210039,0.0006782774,0.0003174973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000473281,"about_ca_system_score_gemma":0.0003745451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001666121,"about_ca_topic_score_gemma":0.002076596,"domain_scores_codex":[0.9994342,0.000118498,0.00003514264,0.0001172962,0.000270142,0.00002476324],"domain_scores_gemma":[0.9987665,0.0006694576,0.0003127931,0.00007534333,0.0001512027,0.00002469109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005382356,0.0008348027,0.07854462,0.0007815468,0.0003232119,0.0002964477,0.000105096,0.1888606,0.4547191,0.001263174,0.001099617,0.2726336],"study_design_scores_gemma":[0.00002081506,0.0005411618,0.03394379,0.00002105964,0.00008810194,0.0002438628,0.00005518508,0.7423366,0.2196473,0.001401921,0.001641718,0.00005843025],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6868008,0.004364976,0.3023748,0.0004760268,0.00005014133,0.0001301504,0.001670749,0.00125802,0.002874397],"genre_scores_gemma":[0.8956792,0.00140092,0.1013991,0.0000944684,0.00001748066,0.00005837172,0.0007046805,0.00003619923,0.0006095698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001666121,"threshold_uncertainty_score":0.004839003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02862424940764172,"score_gpt":0.2588467578645651,"score_spread":0.2302225084569234,"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."}}