{"id":"W3031053999","doi":"10.3390/ijgi9060342","title":"Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Office for Philosophy and Social Sciences; Ministry of Education of the People's Republic of China; Major Program of National Fund of Philosophy and Social Science of China; National Natural Science Foundation of China","keywords":"Population; Mobile phone; Phone; Geography; Crime prevention; Population size; Demography; Econometrics; Criminology; Computer science; Psychology; Mathematics; Sociology; Telecommunications","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":[0.001097329,0.00009165962,0.0002127401,0.0004395309,0.0002631781,0.0003270561,0.0005483354,0.00006902003,0.0004293086],"category_scores_gemma":[0.0005795754,0.00008981268,0.0001268205,0.0005022539,0.00007929174,0.003281337,0.0001022073,0.0001552457,0.00002149153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001871605,"about_ca_system_score_gemma":0.0001678,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009745852,"about_ca_topic_score_gemma":0.001977013,"domain_scores_codex":[0.9979286,0.0001399494,0.00070963,0.0001082968,0.0009839401,0.0001295792],"domain_scores_gemma":[0.9981195,0.00007864754,0.0007444727,0.0001484919,0.0007549186,0.0001539483],"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":[0.00084465,0.0003246323,0.1656401,0.00008565116,0.004540548,0.0000192822,0.1259914,0.1877697,0.0004124999,0.003461272,0.001722039,0.5091882],"study_design_scores_gemma":[0.001600911,0.0001414721,0.06609287,0.00006851757,0.00140878,0.00001097838,0.01615018,0.8558254,0.0001370596,0.000629532,0.05752346,0.000410826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7216622,0.00007362128,0.2737035,0.003452015,0.000437797,0.0001825313,0.0001458233,0.00002512277,0.0003174215],"genre_scores_gemma":[0.9974698,0.00009626145,0.0009179813,0.0006772073,0.0004455302,0.000001545977,0.0003808194,0.000003036206,0.000007859845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6680557,"threshold_uncertainty_score":0.9968483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163962255468893,"score_gpt":0.3370322963281156,"score_spread":0.3053926737734267,"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."}}