{"id":"W4396853544","doi":"10.1109/fnwf58287.2023.10520391","title":"An Accurate, Low-Parameter and Deployable ML Architecture for Next Location Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Hyperparameter; Enhanced Data Rates for GSM Evolution; Factor (programming language); Architecture; Graphics processing unit; Quality of service; Efficient energy use; Real-time computing; Artificial intelligence; Parallel computing; Computer network; 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":[0.0005546393,0.0008574361,0.0004997193,0.0004464364,0.0004486801,0.0008593291,0.002073207,0.0007327477,0.003858672],"category_scores_gemma":[0.002747207,0.0004751717,0.0005330335,0.0004301585,0.0003975427,0.002347216,0.0009382747,0.00150647,0.001818798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003688,"about_ca_system_score_gemma":0.001397875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01059335,"about_ca_topic_score_gemma":0.01195104,"domain_scores_codex":[0.9996351,0.00005500129,0.0000268603,0.0001280424,0.00009197412,0.00006307913],"domain_scores_gemma":[0.9992566,0.0002300784,0.00005572498,0.0001558882,0.0002574383,0.00004427112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003672209,0.0002859538,0.008773555,0.0001361783,0.0001570249,0.0001656597,0.0001906819,0.7034465,0.02358227,0.004478058,0.0079481,0.2504688],"study_design_scores_gemma":[0.00001250267,0.00006293927,0.0007564449,0.000007938303,0.00002040375,0.00002709376,0.00002630148,0.9894395,0.006510014,0.001624212,0.001498903,0.00001369283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1000556,0.0003823178,0.8830736,0.000664425,0.0001540247,0.0001152848,0.0004300956,0.01130058,0.0038241],"genre_scores_gemma":[0.7769353,0.0002199785,0.2170395,0.0003330404,0.00005668561,0.0002033051,0.001058755,0.000194063,0.003959435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01059335,"threshold_uncertainty_score":0.02106339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0317477230379796,"score_gpt":0.3227988871973937,"score_spread":0.2910511641594141,"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."}}