{"id":"W4210834064","doi":"10.1080/00330124.2021.2000446","title":"Drones and Geography: Who Is Using Them and Why?","year":2022,"lang":"en","type":"article","venue":"The Professional Geographer","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Drone; Popularity; Geospatial analysis; Geography; Regional science; Health geography; Connotation; Cartography; Political science; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001138658,0.000221556,0.0002861054,0.001236435,0.001227053,0.004573476,0.0004042103,0.0007905859,0.005357957],"category_scores_gemma":[0.008249975,0.0002299575,0.00022466,0.002210229,0.002278047,0.007271523,0.001487572,0.001076007,0.001071357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042202,"about_ca_system_score_gemma":0.0008046589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01951172,"about_ca_topic_score_gemma":0.03139802,"domain_scores_codex":[0.9982017,0.0009058585,0.0001220533,0.0001778513,0.0004111603,0.0001813388],"domain_scores_gemma":[0.9951189,0.001912622,0.00119083,0.0003051415,0.0007955997,0.0006769776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001151134,0.00008277436,0.5131644,0.001084932,0.0001321676,0.001102503,0.0755544,0.0004583062,0.001177237,0.02034547,0.02848062,0.3583021],"study_design_scores_gemma":[0.00000905753,0.0001570935,0.2982339,0.001886537,0.00009451206,0.003253635,0.294033,0.0007257969,0.0005979507,0.007360173,0.3935298,0.0001185596],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7738333,0.05102031,0.00667341,0.05604048,0.0006480535,0.00006613923,0.001121152,0.000107992,0.1104892],"genre_scores_gemma":[0.9572926,0.03116515,0.001683861,0.002978515,0.0002361796,0.00002517925,0.0003363891,0.00003607886,0.006246028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01951172,"threshold_uncertainty_score":0.03879631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237454905817735,"score_gpt":0.2234293651216806,"score_spread":0.2110548160635033,"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."}}