{"id":"W4396853536","doi":"10.1109/fnwf58287.2023.10520414","title":"Modeling Local Demand for Mobile Spectrum using Large Crowdsourced Datasets","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Carleton University; Communications Research Centre Canada","funders":"","keywords":"Computer science; Scarcity; Proxy (statistics); Population; Software deployment; Intuition; Mobile device; Machine learning; Economics; Microeconomics","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":[0.002195169,0.0008186743,0.0006634441,0.001489095,0.0006556651,0.001078809,0.001638692,0.001446844,0.002142266],"category_scores_gemma":[0.007675079,0.0003141836,0.00096785,0.002586024,0.0005818384,0.00162863,0.001317994,0.0009952181,0.000915383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001559372,"about_ca_system_score_gemma":0.0007391741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02338415,"about_ca_topic_score_gemma":0.03487345,"domain_scores_codex":[0.9988236,0.0005435885,0.00006418095,0.0003085937,0.0001670934,0.00009301467],"domain_scores_gemma":[0.9956806,0.002836742,0.0003258489,0.000659256,0.0003594241,0.0001381274],"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":[0.0004949386,0.0007987736,0.09249416,0.0005518622,0.0003984793,0.0007015289,0.0008086835,0.7992797,0.001641528,0.01352502,0.03020379,0.05910142],"study_design_scores_gemma":[0.00004673047,0.00007200353,0.01442547,0.00005241586,0.00002962378,0.00007907166,0.0006240378,0.9635378,0.0007186118,0.01140891,0.008964719,0.00004057687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8132751,0.0008442583,0.1061241,0.00368083,0.0003149675,0.0005321315,0.06078966,0.001146155,0.01329281],"genre_scores_gemma":[0.9357191,0.0002276217,0.03098422,0.0002980113,0.00008299139,0.0003595589,0.029749,0.00006863908,0.002510788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02338415,"threshold_uncertainty_score":0.04649609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04535291406692403,"score_gpt":0.3599344505420194,"score_spread":0.3145815364750954,"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."}}