{"id":"W2183046939","doi":"10.1080/16258312.2002.11517098","title":"Evaluating Logistical Efficiency Using Data Envelopment Analysis: The Case of Trois-Rivières’ Harbour Elevators","year":2002,"lang":"en","type":"article","venue":"Supply Chain Forum an International Journal","topic":"Elevator Systems and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Elevator; Data envelopment analysis; Harbour; Process (computing); Product (mathematics); Operations research; Computer science; Decomposition; Engineering; Statistics; Mathematics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001213431,0.0001792893,0.0002616666,0.0002972909,0.0002168072,0.0001826073,0.001043391,0.00006181827,0.0004273151],"category_scores_gemma":[0.00017548,0.0001319132,0.0001465318,0.000274351,0.0000683518,0.000341626,0.0001280519,0.0003202174,0.000009130652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001398241,"about_ca_system_score_gemma":0.00003761951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000221123,"about_ca_topic_score_gemma":0.0002189869,"domain_scores_codex":[0.9979594,0.0001251721,0.0007038694,0.000221529,0.0006433973,0.0003466036],"domain_scores_gemma":[0.9988638,0.000121648,0.0002001266,0.0004644356,0.0002152143,0.0001348333],"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":[0.0001069457,0.0006509097,0.09642486,0.00006093807,0.01032245,0.003451802,0.005675678,0.7667306,0.0096688,0.01313495,0.002082232,0.09168985],"study_design_scores_gemma":[0.0005094991,0.00005629476,0.001113282,0.00003462668,0.0002345228,0.00159538,0.0009859997,0.9948128,0.0001259367,0.0001129403,0.0002635032,0.000155191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9365328,0.0006634474,0.06081893,0.0003917302,0.001083983,0.0001476037,0.0001482545,0.00004155506,0.0001717005],"genre_scores_gemma":[0.9979779,0.00003808961,0.001360956,0.00005914568,0.000481039,0.000005650561,0.0000220947,0.00002335354,0.00003175454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2280822,"threshold_uncertainty_score":0.5379263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0635810705806344,"score_gpt":0.3264034292803005,"score_spread":0.2628223586996661,"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."}}