{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005752396,0.00006222487,0.00008211815,0.0001089108,0.0005635541,0.0001648211,0.00009561227,0.00007894803,0.0001795527],"category_scores_gemma":[0.0002815154,0.00005648352,0.00003459323,0.0005482311,0.0001131015,0.0003007817,0.000007878828,0.00005303723,0.00003124833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003580399,"about_ca_system_score_gemma":0.00009874329,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003036124,"about_ca_topic_score_gemma":0.02043623,"domain_scores_codex":[0.9992019,0.0001014576,0.000132231,0.0002300833,0.0001510209,0.0001832855],"domain_scores_gemma":[0.9993258,0.0002704923,0.0000333557,0.0001502325,0.0001283719,0.00009181717],"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":[0.0002288573,0.0006798011,0.0442991,0.0005520789,0.0002769963,0.000002194894,0.09956575,0.142426,0.005802379,0.03197686,0.02232618,0.6518638],"study_design_scores_gemma":[0.001324035,0.0005413797,0.04380278,0.00008463015,0.0002649176,7.916156e-7,0.04255977,0.7702977,0.002266259,0.07035652,0.06769056,0.0008106634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8189903,0.00003276227,0.1749801,0.002746839,0.000142976,0.0007370391,0.00001843835,0.000583978,0.001767521],"genre_scores_gemma":[0.9972157,0.00002567568,0.0005034847,0.0002158977,0.0001742788,0.00009648771,0.0000881091,0.000007443714,0.00167293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6510531,"threshold_uncertainty_score":0.9974383,"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."}}