{"id":"W2300716503","doi":"10.1017/s0069005800010869","title":"Distinguishing Friend from Foe: Law and Policy in the Age of Battlefield Biometrics","year":2013,"lang":"en","type":"article","venue":"Canadian Yearbook of international Law/Annuaire canadien de droit international","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Ottawa; Global Affairs Canada","funders":"","keywords":"Biometrics; Context (archaeology); Computer security; Internet privacy; National security; Law; Political science; International law; Computer science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.01800773,0.000299284,0.0005514296,0.001476117,0.01725159,0.01734523,0.002940997,0.02399841,0.009742396],"category_scores_gemma":[0.04080594,0.0004971213,0.0004058637,0.001585779,0.03428132,0.01389188,0.007147433,0.02146937,0.001250244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02755892,"about_ca_system_score_gemma":0.03014952,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2107954,"about_ca_topic_score_gemma":0.153093,"domain_scores_codex":[0.9868866,0.004168095,0.0005362236,0.001678607,0.002676591,0.004053961],"domain_scores_gemma":[0.9606954,0.02781556,0.002169761,0.001046048,0.004524368,0.003748856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002190985,0.00006271105,0.002766133,0.00004324424,0.000006149339,0.0005460478,0.01992687,0.0003012997,0.0002069716,0.9062176,0.04981171,0.02008936],"study_design_scores_gemma":[0.00002154394,0.00007597589,0.004519168,0.001343151,0.00002386353,0.0004862608,0.04256466,0.00116468,0.0004358194,0.3088878,0.6403325,0.0001445907],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03327676,0.008182075,0.002261159,0.7760506,0.001139016,0.0000404086,0.0000830716,0.00004377512,0.1789231],"genre_scores_gemma":[0.6560637,0.007377458,0.001345223,0.3074786,0.00142902,0.00008006157,0.00007078452,0.00006892714,0.0260863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7892045,"threshold_uncertainty_score":0.419137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099926542991151,"score_gpt":0.2335382265643113,"score_spread":0.2225389611343998,"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."}}