{"id":"W2901553425","doi":"10.1080/23249935.2018.1546780","title":"Penalization and augmented Lagrangian for O-D demand matrix estimation from transit segment counts","year":2018,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Augmented Lagrangian method; Matrix (chemical analysis); Quadratic equation; Mathematical optimization; Transit (satellite); Applied mathematics; Convergence (economics); Computer science; Mathematics; Public transport; Geometry; Engineering; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.0007564682,0.0001380396,0.0001682014,0.0003545849,0.0008418955,0.00006299804,0.0002091453,0.00009434097,0.0003259067],"category_scores_gemma":[0.0000319586,0.0001407442,0.00005518354,0.001672539,0.0007233325,0.0004937748,0.000001030017,0.00005842618,0.00001779846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006012133,"about_ca_system_score_gemma":0.0002526052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004651215,"about_ca_topic_score_gemma":0.0005212261,"domain_scores_codex":[0.9982774,0.00002154554,0.0003150772,0.0004211984,0.0006294512,0.0003353296],"domain_scores_gemma":[0.999226,0.00007173526,0.00009635213,0.0001299597,0.0002788179,0.0001971029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002024694,0.001481588,0.4004351,0.0005981476,0.000400164,0.0000706013,0.2449282,0.03105105,0.01204661,0.1659496,0.002706352,0.1383078],"study_design_scores_gemma":[0.005310984,0.0006341556,0.8000711,0.0003159131,0.0009206496,0.000003679782,0.002408435,0.06142547,0.005224713,0.004511917,0.1174014,0.001771653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1330005,0.0002063908,0.8607027,0.0004553202,0.0003728209,0.0007048441,0.0002299429,0.0002130466,0.004114408],"genre_scores_gemma":[0.9786193,0.0001433323,0.02029265,0.00009277237,0.00009200806,0.00002401902,0.0002845579,0.00001419564,0.0004371655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8456188,"threshold_uncertainty_score":0.6475265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792189381049445,"score_gpt":0.314050242466595,"score_spread":0.2961283486561005,"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."}}