{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001271429,0.0005476677,0.0003847356,0.0006816556,0.008542917,0.009309854,0.0006766707,0.002569231,0.05579603],"category_scores_gemma":[0.004598821,0.0002670204,0.0004022424,0.0008282997,0.003102836,0.005205443,0.004568581,0.002216485,0.05288484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383017,"about_ca_system_score_gemma":0.003360692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01476224,"about_ca_topic_score_gemma":0.03053138,"domain_scores_codex":[0.9986894,0.0003465727,0.00003283098,0.0001839258,0.000359178,0.0003881539],"domain_scores_gemma":[0.9975747,0.0003474518,0.0001438722,0.0002988872,0.0005321248,0.001102822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006744319,0.0001993935,0.008319215,0.0003249723,0.00005831613,0.001420969,0.01611318,0.000145931,0.002840201,0.02852838,0.5867099,0.3552721],"study_design_scores_gemma":[0.000007659585,0.00007218888,0.003163618,0.0001974007,0.00002188751,0.0005584784,0.01026014,0.00003461794,0.0002100879,0.002160397,0.9832924,0.00002120188],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02196828,0.007625843,0.004611884,0.130776,0.005875886,0.0001055631,0.0002367163,0.001054635,0.8277452],"genre_scores_gemma":[0.2129,0.009791717,0.004338456,0.03564233,0.001677595,0.000130485,0.0001904391,0.0003892794,0.7349398],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05579603,"threshold_uncertainty_score":0.1866563,"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."}}