{"id":"W3175172519","doi":"10.1016/j.neucom.2021.06.055","title":"A smartly simple way for joint crowd counting and localization","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Code (set theory); Simple (philosophy); Artificial intelligence; Task (project management); Pattern recognition (psychology); Interval (graph theory); Computer vision; Mathematics; Set (abstract data type)","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":[0.0006312787,0.0001076772,0.0001667587,0.00004547981,0.0002994969,0.000315082,0.0001563995,0.00003839615,0.000001771569],"category_scores_gemma":[0.0003543824,0.0001135369,0.000050794,0.0002930662,0.00001653551,0.0001978571,0.0002279156,0.0000902158,0.000002216686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001136047,"about_ca_system_score_gemma":0.00004394482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007367908,"about_ca_topic_score_gemma":0.000006797891,"domain_scores_codex":[0.9988012,0.0001149076,0.0002381352,0.0004360002,0.0001355284,0.0002742501],"domain_scores_gemma":[0.9990478,0.0003842951,0.00009950082,0.0002469059,0.0001687601,0.00005274713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006922675,0.00008005952,0.03690991,0.0003397476,0.00003129352,0.0001034284,0.001077158,0.01179852,0.02830835,0.02228476,0.001455802,0.897604],"study_design_scores_gemma":[0.0004062725,0.00004019872,0.01650928,0.00003723358,0.000004830482,0.00007431603,0.00001872215,0.947586,0.01340188,0.004172877,0.01756244,0.0001859207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06626031,0.00008349891,0.9324236,0.0003319462,0.0002933125,0.0001120652,7.867505e-7,0.0001615006,0.0003329639],"genre_scores_gemma":[0.8227332,0.000006076813,0.1757891,0.001249623,0.0001728084,0.000005631546,0.000003994086,0.00001449653,0.00002514653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9357875,"threshold_uncertainty_score":0.4629901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03722787786192351,"score_gpt":0.2862476532103509,"score_spread":0.2490197753484274,"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."}}