{"id":"W580805382","doi":"","title":"MOBILE TECHNOLOGY WORKS ... EVEN IN A SMALL TOWN","year":2004,"lang":"en","type":"article","venue":"Parking Today","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Revenue; Enforcement; Staffing; Business; Mobile phone; Population; Mobile technology; Finance; Transport engineering; Telecommunications; Engineering; Mobile computing; Economics; Political science","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":[0.0003706659,0.0001662273,0.0002347803,0.0004721647,0.00004311305,0.00004887041,0.0003494578,0.0002253815,0.0000443776],"category_scores_gemma":[0.00007035344,0.0001763618,0.00004423843,0.0008358789,0.00004371346,0.0000693894,0.00008418912,0.0004714049,0.0001910387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003310875,"about_ca_system_score_gemma":0.00003823643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001114234,"about_ca_topic_score_gemma":0.000275885,"domain_scores_codex":[0.9987312,0.00002737148,0.0002784724,0.0002518155,0.0001476336,0.0005635331],"domain_scores_gemma":[0.9994277,0.00005745187,0.00002209857,0.0004044014,0.0000244106,0.00006395745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000512716,0.000260413,0.103617,0.0004321486,0.0002027453,0.0007239324,0.006194295,0.5819361,0.05341423,0.002249536,0.002385186,0.2485331],"study_design_scores_gemma":[0.0105696,0.0006396897,0.02720274,0.008205955,0.0000509805,0.0005862527,0.002482805,0.04120454,0.1119449,0.02652852,0.7662983,0.004285729],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750091,0.002872032,0.004732125,0.0001083807,0.0006010213,0.0004798424,0.000001653111,0.0009174192,0.01527845],"genre_scores_gemma":[0.997416,0.00007765612,0.001781808,0.00001213558,0.0001213335,0.0003225424,0.000002429982,0.0000511262,0.0002149198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7639132,"threshold_uncertainty_score":0.7191824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467757555431625,"score_gpt":0.2471668886119573,"score_spread":0.232489313057641,"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."}}