{"id":"W2754266722","doi":"10.1093/biosci/bix100","title":"Global Rush to Harness Drones Yields Ups and Downs","year":2017,"lang":"en","type":"article","venue":"BioScience","topic":"Global Energy and Sustainability Research","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"CARE Canada","funders":"","keywords":"Drone; Business; Aeronautics; International trade; Engineering; Biology","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":[0.0003079913,0.0000961211,0.0001080315,0.00002909531,0.000776745,0.0003446983,0.0007546867,0.00007410266,0.00005591985],"category_scores_gemma":[0.000924058,0.00008016401,0.00002693482,0.000182196,0.0004998161,0.0002717353,0.0004920763,0.00005332292,0.00005329845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007144777,"about_ca_system_score_gemma":0.00009719861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007720827,"about_ca_topic_score_gemma":0.002588548,"domain_scores_codex":[0.9987901,0.00003051037,0.00009580994,0.0003756811,0.0002858744,0.0004219968],"domain_scores_gemma":[0.998993,0.00002253643,0.00002835325,0.0006094576,0.00009534995,0.000251287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00009235667,0.0001150944,0.1560176,0.00004256096,0.000008784682,0.00008922525,0.000312031,0.0005603556,0.002367721,0.69254,0.001176416,0.1466777],"study_design_scores_gemma":[0.0002478789,0.0001061059,0.8870598,0.00001691471,0.000003592363,0.00002159393,0.0002861819,0.0005281027,0.0006330191,0.02538605,0.08546195,0.0002488184],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9445492,0.0000476503,0.0002069756,0.004632439,0.000280852,0.00008886625,0.00000873166,0.00004200459,0.05014328],"genre_scores_gemma":[0.9963682,0.00001229763,0.0002351151,0.0002752157,0.00005015062,0.00001049995,4.897223e-7,0.000002933624,0.003045114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7310421,"threshold_uncertainty_score":0.9988868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377350677958283,"score_gpt":0.3198701255724137,"score_spread":0.2960966187928309,"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."}}