{"id":"W4240585663","doi":"10.1149/ma2006-01/34/1178","title":"Statistical Analysis Applied to the Fitting of an EIS Model to Copper Deposition","year":2006,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Copper; Deposition (geology); Statistical analysis; Computer science; Materials science; Metallurgy; Statistics; Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00921075,0.001067908,0.0009267459,0.001890064,0.0006849014,0.001074853,0.001112714,0.0008387771,0.01070408],"category_scores_gemma":[0.04400551,0.0003550613,0.00140245,0.002904624,0.0006610794,0.0006828565,0.0007134948,0.001568667,0.002066831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000501741,"about_ca_system_score_gemma":0.001204689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003862505,"about_ca_topic_score_gemma":0.002611117,"domain_scores_codex":[0.9951693,0.002780998,0.0003091713,0.000558994,0.0009650437,0.0002164079],"domain_scores_gemma":[0.9788963,0.01497502,0.0009338385,0.002605321,0.002467951,0.0001216028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004119041,0.001205201,0.04000268,0.001407623,0.001641903,0.001149701,0.0007280192,0.4210936,0.0506089,0.01513685,0.01928722,0.4436193],"study_design_scores_gemma":[0.00005631322,0.0009184018,0.01670342,0.00004125452,0.0001330813,0.000274618,0.0001910834,0.9586771,0.0160751,0.003505446,0.003365876,0.00005830817],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1138745,0.0001567141,0.8760271,0.0002190252,0.0002130221,0.0004534369,0.00172725,0.004937464,0.002391452],"genre_scores_gemma":[0.8364086,0.0002009335,0.1540699,0.0001170311,0.00008655931,0.001415732,0.003494373,0.001199156,0.003007652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01070408,"threshold_uncertainty_score":0.04871166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008133191804923789,"score_gpt":0.2193630006145312,"score_spread":0.2112298088096074,"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."}}