{"id":"W7002293628","doi":"","title":"Mobile Phone Data Analytics to Support Disaster and Disease Outbreak Response","year":2024,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Geospatial analysis; Analytics; Population; Mobile phone; Emergency management; Natural disaster; Data processing; Event (particle physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002016131,0.0001905449,0.0002667345,0.001043157,0.0004041958,0.0002608143,0.001300248,0.0001603799,0.0006254628],"category_scores_gemma":[0.004592882,0.0001887756,0.00006249873,0.002223665,0.001091406,0.001081596,0.0008369049,0.0002444552,0.000191144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233427,"about_ca_system_score_gemma":0.001133938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00111141,"about_ca_topic_score_gemma":0.002177373,"domain_scores_codex":[0.9976838,0.0001166611,0.0005182602,0.000855485,0.0004753292,0.0003504414],"domain_scores_gemma":[0.9968805,0.0001155872,0.0001223266,0.002078583,0.0003494732,0.0004535731],"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":[0.0007321481,0.001668391,0.01917637,0.0007492425,0.0006142075,0.0001124334,0.005947204,0.0007400735,0.0009746503,0.2092641,0.4487139,0.3113073],"study_design_scores_gemma":[0.0001806238,0.0000672979,0.0007964082,0.00008278876,0.0001804187,7.960886e-7,0.001213802,0.007206235,0.00009179881,0.0004735962,0.9894537,0.0002525494],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6591333,0.002998885,0.1043775,0.1885875,0.001788971,0.004253909,0.02374011,0.003244597,0.01187514],"genre_scores_gemma":[0.9883255,0.0001209672,0.002616731,0.0004950681,0.0001150581,0.0001715911,0.00367884,0.00001835234,0.004457845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5407397,"threshold_uncertainty_score":0.7698045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04779233093614711,"score_gpt":0.3448665604778013,"score_spread":0.2970742295416542,"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."}}