{"id":"W2244513926","doi":"","title":"Performance industrielle - Alfa Laval monte en lean","year":2011,"lang":"es","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008946095,0.0001830856,0.0001417182,0.00007263263,0.00006974878,0.0000393158,0.0001663861,0.0001901716,0.001867265],"category_scores_gemma":[0.000009344213,0.0001709119,0.00003553763,0.00009567893,0.00001811311,0.000239426,0.00005243544,0.0002387389,0.0002156113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002717059,"about_ca_system_score_gemma":0.00002013908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002686644,"about_ca_topic_score_gemma":0.000005586445,"domain_scores_codex":[0.9992598,0.000007427776,0.0001941326,0.0001702573,0.0001126758,0.0002557121],"domain_scores_gemma":[0.9996583,0.00001195889,0.0000334277,0.0001910417,0.00002670694,0.00007858304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001507329,0.0003216181,0.1087454,0.002051774,0.0003867233,0.00003034655,0.01345723,0.579512,0.0002097913,0.008509341,0.0139519,0.2726731],"study_design_scores_gemma":[0.0008054101,0.0002411276,0.07929732,0.0002514109,0.00008868894,0.000009174367,0.0002692295,0.7224906,0.1462326,0.00006675369,0.04928783,0.0009598411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6934744,0.0002098214,0.004058214,0.00004513453,0.0003810897,0.0001381294,0.000004511608,0.0002978903,0.3013908],"genre_scores_gemma":[0.9880263,0.001186765,0.002137601,0.00002677342,0.0001398668,0.000006347852,0.000005657222,0.00003801331,0.008432735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2945518,"threshold_uncertainty_score":0.9990451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01887533711540055,"score_gpt":0.1862904029601014,"score_spread":0.1674150658447008,"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."}}