{"id":"W7001045765","doi":"","title":"Highway Public-Private Partnerships: More Rigorous Up-Front Analysis Could Better Secure Potential Benefits and Protect the Public Interest","year":2008,"lang":"en","type":"article","venue":"University of North Texas Digital Library (University of North Texas)","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public interest; Accountability; Work (physics); Government (linguistics); State (computer science); Private sector","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00004444854,0.0003529807,0.0005459628,0.0006524229,0.0004366265,0.0001023055,0.001258715,0.0001913331,0.0002197245],"category_scores_gemma":[0.00001785506,0.000371527,0.0003378493,0.001202275,0.001091659,0.002984575,0.0009369618,0.0004413776,0.00003027688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005123688,"about_ca_system_score_gemma":0.00007526239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000774441,"about_ca_topic_score_gemma":0.00206424,"domain_scores_codex":[0.9984745,0.00003954623,0.000213901,0.0004387107,0.0003454867,0.0004878253],"domain_scores_gemma":[0.9987808,0.00007091134,0.0002028707,0.0006191471,0.0001196902,0.0002065544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006423659,0.00007225618,0.9871819,0.00007404161,0.001016888,0.0001888248,0.0001265071,0.0005520055,1.974862e-7,0.0006064937,0.004225102,0.005891566],"study_design_scores_gemma":[0.0006303294,0.0001148789,0.9401821,0.00002373721,0.0002282229,0.00002552679,0.0002917819,0.001404821,0.00001482291,0.00003655057,0.05663848,0.0004087817],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938315,0.000224805,0.0008496988,0.002401055,0.00008301601,0.0003110641,0.001019984,0.0005895054,0.0006893956],"genre_scores_gemma":[0.9978129,0.0004889867,0.0003735008,0.00004054152,0.00002813122,9.954079e-8,0.0004306117,0.00002929312,0.0007959334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05241338,"threshold_uncertainty_score":0.9998736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02280224961744011,"score_gpt":0.1620101639323982,"score_spread":0.1392079143149581,"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."}}