{"id":"W3035160372","doi":"10.1007/978-3-030-45570-5_13","title":"The Impact of Artificial Intelligence on the Military Profession","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Canada; Department of National Defence","funders":"","keywords":"Military intelligence; Construct (python library); Affect (linguistics); Engineering ethics; Military science; Military theory; Political science; Engineering; Management science; Psychology; Computer science; Law","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":[0.00088727,0.000381019,0.0002221156,0.001112255,0.002593233,0.005638042,0.0004145002,0.001793706,0.01659406],"category_scores_gemma":[0.001643755,0.0001791526,0.0001605343,0.001433132,0.007638157,0.002886198,0.001463991,0.003054477,0.003105497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004193808,"about_ca_system_score_gemma":0.003892162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006413334,"about_ca_topic_score_gemma":0.01423412,"domain_scores_codex":[0.9991651,0.0004218533,0.00001053218,0.00004032381,0.0002936866,0.00006853532],"domain_scores_gemma":[0.9987738,0.0009247437,0.00003771639,0.00004388801,0.0001384676,0.00008134031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00000286417,0.00001207189,0.00004278288,0.00003382294,0.000001081266,0.00002572913,0.0007501877,0.0001927378,0.00005745613,0.9366413,0.04132822,0.02091188],"study_design_scores_gemma":[0.00000152646,0.000006067727,0.0002868959,0.0001836769,0.000001245553,0.00005422613,0.0009207245,0.0003090463,0.00008176758,0.3817094,0.6164407,0.000004769627],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.000959943,0.01144204,0.0009220064,0.007274179,0.0005494495,0.000006624346,0.00001318726,0.00000931941,0.9788232],"genre_scores_gemma":[0.0998998,0.03212902,0.002467739,0.007391186,0.00200977,0.00006392616,0.00005466081,0.00008444445,0.8558993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01659406,"threshold_uncertainty_score":0.05551261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1448006872521067,"score_gpt":0.4219990429494032,"score_spread":0.2771983556972965,"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."}}