{"id":"W3118576105","doi":"10.1109/iv47402.2020.9304776","title":"PSDet: Efficient and Universal Parking Slot Detection","year":2020,"lang":"en","type":"article","venue":"","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Benchmark (surveying); Computer science; Generalization; Parking lot; Architecture; Artificial intelligence; State (computer science); Real-time computing; Machine learning; Data mining; Algorithm; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004956387,0.002149919,0.001334379,0.001649097,0.0006434752,0.001334958,0.003287139,0.001850334,0.006497663],"category_scores_gemma":[0.001842874,0.0005808586,0.001208216,0.001453565,0.0004754589,0.002021628,0.001980941,0.001387778,0.005416427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009912315,"about_ca_system_score_gemma":0.001350328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415571,"about_ca_topic_score_gemma":0.03152785,"domain_scores_codex":[0.9994106,0.00005183844,0.00002773199,0.00024829,0.0001425868,0.0001188727],"domain_scores_gemma":[0.9996693,0.00006098477,0.00002417271,0.0001055956,0.000105201,0.00003470585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001108287,0.0008492265,0.01181367,0.001044415,0.0003473926,0.0006196488,0.0001221455,0.1366581,0.009779194,0.004386222,0.4629571,0.3703147],"study_design_scores_gemma":[0.0001668703,0.0001765628,0.004124261,0.00006432786,0.00004610103,0.0004897999,0.0001517536,0.9456568,0.01171099,0.00635048,0.03099714,0.00006489598],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3015048,0.003723362,0.3067881,0.001402686,0.002285245,0.001094805,0.190551,0.1597495,0.03290059],"genre_scores_gemma":[0.4860371,0.0005908245,0.1777732,0.0006124716,0.0001569689,0.0005593286,0.3173323,0.001469914,0.01546794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01415571,"threshold_uncertainty_score":0.02814662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584460990961326,"score_gpt":0.2012533688078092,"score_spread":0.1854087588981959,"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."}}