{"id":"W2104306808","doi":"10.3141/1971-17","title":"Implementation of Nationwide Public Transport Smart Card in the Netherlands: Cost-Benefit Analysis","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Public transport; Business; Smart card; Ministry of Transport; Work (physics); Government (linguistics); Competition (biology); Robustness (evolution); Cost–benefit analysis; Environmental economics; Marketing; Transport engineering; Economics; Computer science; Engineering; Computer security","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":[],"consensus_categories":[],"category_scores_codex":[0.005539618,0.0002935738,0.0005945095,0.002710477,0.0003505745,0.00009761953,0.001150094,0.0002017957,0.0003236351],"category_scores_gemma":[0.00003589934,0.0002181273,0.0006208025,0.009208133,0.0004452814,0.0007496672,0.000002227257,0.001619958,0.000005517979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002261547,"about_ca_system_score_gemma":0.0004147502,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02355626,"about_ca_topic_score_gemma":0.573223,"domain_scores_codex":[0.9921539,0.0006226351,0.002562608,0.000340354,0.003492634,0.0008279076],"domain_scores_gemma":[0.9948868,0.0008183055,0.0004232572,0.0005870864,0.003130842,0.0001537681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001404068,0.00023166,0.940773,0.0001624802,0.0003963551,0.00002632441,0.0034561,0.03954156,0.001307009,0.01030867,0.001204733,0.002451682],"study_design_scores_gemma":[0.001479261,0.0001438536,0.9807957,0.00006307268,0.0002278097,3.896591e-7,0.005478833,0.0003576991,0.001306003,0.00210353,0.007851915,0.0001919727],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883063,0.0001923217,0.006505427,0.002511292,0.0002838613,0.001478907,0.0002762719,0.00004403541,0.0004016027],"genre_scores_gemma":[0.998258,0.0003862456,0.0005660106,0.00003935976,0.0001037406,0.0002275441,0.0002681215,0.00004824372,0.0001027668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5496667,"threshold_uncertainty_score":0.982946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06832369641953971,"score_gpt":0.3731416242981651,"score_spread":0.3048179278786254,"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."}}