{"id":"W2584304697","doi":"10.4067/s0718-33052017000100180","title":"Modelo de aproximación lineal para la medición de resiliencia en cadenas de suministro","year":2017,"lang":"es","type":"article","venue":"Ingeniare. Revista chilena de ingeniería","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cegep de Sept Iles","funders":"","keywords":"Humanities; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004989618,0.001161215,0.001171688,0.0006545836,0.002648576,0.005357516,0.003660851,0.0008547383,0.0002997758],"category_scores_gemma":[0.002632784,0.001158368,0.0005992787,0.0005008625,0.0008996732,0.001525389,0.001701742,0.001499792,0.0003032135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038513,"about_ca_system_score_gemma":0.0008810349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003252158,"about_ca_topic_score_gemma":0.0001830969,"domain_scores_codex":[0.9928884,0.0003902977,0.001324996,0.001484976,0.001137535,0.00277382],"domain_scores_gemma":[0.9951963,0.0003541736,0.001443932,0.002331101,0.0003086985,0.0003658356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008875593,0.001080063,0.8066961,0.006994589,0.001272957,0.004374523,0.008263705,0.00253184,0.007411091,0.02873387,0.03727079,0.09448294],"study_design_scores_gemma":[0.00307288,0.0001471323,0.2201325,0.003650842,0.001870501,0.0002348743,0.001837423,0.08917898,0.001612606,0.00367757,0.6716666,0.002918098],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9609553,0.006279822,0.007665561,0.006632295,0.0005485609,0.001330913,0.00008060606,0.0004020656,0.01610493],"genre_scores_gemma":[0.9808288,0.004191519,0.003698891,0.00258974,0.006448323,0.0002305813,0.00007192641,0.0002464296,0.00169379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6343958,"threshold_uncertainty_score":0.9990866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01545429950934822,"score_gpt":0.2943953579848327,"score_spread":0.2789410584754845,"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."}}