{"id":"W6968814483","doi":"10.5281/zenodo.4067798","title":"DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BASED TRAFFIC PREDICTION","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Regional Economic Development and Innovation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial neural network; Deep learning; Key (lock); Regression; Land use; Regression analysis; Land-use planning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004083905,0.000137141,0.0001280213,0.0002393654,0.001273488,0.001402677,0.0003832648,0.00004933697,0.001506747],"category_scores_gemma":[0.0002652431,0.0001466733,0.00004586942,0.001130433,0.00004614362,0.000999519,0.0003459653,0.0002113516,0.002216859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003726702,"about_ca_system_score_gemma":0.000002083733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008668411,"about_ca_topic_score_gemma":6.50382e-7,"domain_scores_codex":[0.9988272,0.00004889166,0.0002627923,0.000362168,0.0002155956,0.0002833657],"domain_scores_gemma":[0.999484,0.0000131837,0.0001352529,0.0001224543,0.0001958483,0.00004926584],"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":[0.0005119184,0.0001244576,0.009881682,0.000212348,0.00008850734,0.0000108542,0.0005262132,0.3801567,0.0001975167,0.01093826,0.4709621,0.1263895],"study_design_scores_gemma":[0.0003320185,0.00004285772,0.01101683,0.00001160799,0.000008298396,0.000002119085,0.00006896642,0.2902223,0.000001929051,0.00003147289,0.6981292,0.0001324408],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8809602,0.00004582885,0.03783624,0.02184446,0.0004948938,0.001543435,0.00002127716,0.003907869,0.05334586],"genre_scores_gemma":[0.9937572,0.00000331114,0.0003878605,0.002078711,0.0009895168,1.283176e-7,0.002166123,0.0005098502,0.0001073238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2271671,"threshold_uncertainty_score":0.999634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018007037429878,"score_gpt":0.1975881529687312,"score_spread":0.09578744922574339,"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."}}