{"id":"W1940531479","doi":"10.1080/00330124.2015.1033668","title":"Mapping Human Terrain in the<i>Joint Army–Navy Intelligence Study of Korea</i>(1945)","year":2015,"lang":"en","type":"article","venue":"The Professional Geographer","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Geospatial-Intelligence Agency","keywords":"Navy; Terrain; Human intelligence; Geographer; Empire; Geospatial analysis; Geography; Population; Military intelligence; Adversary; Cartography; History; Operations research; Archaeology; Sociology; Engineering; Artificial intelligence; Demography; Computer science; Computer security","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":[0.0030167,0.0001043839,0.0001344215,0.0001140118,0.0002541536,0.00001436133,0.0005226437,0.00004867753,0.0006909934],"category_scores_gemma":[0.0001503655,0.00004614565,0.00005135364,0.0005915678,0.0003126746,0.00008277366,0.00008070544,0.0004584091,0.00004935276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002596088,"about_ca_system_score_gemma":0.00003872314,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00749556,"about_ca_topic_score_gemma":0.006581098,"domain_scores_codex":[0.9976708,0.0009332676,0.0002717852,0.0001859574,0.000636812,0.0003013573],"domain_scores_gemma":[0.9992637,0.0002750189,0.0000853738,0.000231643,0.00005685592,0.00008740303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002365443,0.0002584837,0.8850283,0.00001799616,0.00002800727,0.00002077873,0.03857361,0.0006920918,0.0001101435,0.0002556938,0.0004198418,0.07435848],"study_design_scores_gemma":[0.0002359646,0.000757664,0.8807759,0.00003948528,0.000003111695,0.000005052098,0.04344206,0.000390647,0.00003912109,0.07370614,0.0005153122,0.0000896052],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940367,0.0002399173,0.0002126365,0.000944671,0.0001375854,0.0007758501,0.000006299188,0.00001490452,0.003631427],"genre_scores_gemma":[0.9993089,0.00001118691,0.000135579,0.000207305,0.00006042835,0.00002243876,0.00001104592,0.00000195458,0.0002411633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07426888,"threshold_uncertainty_score":0.9991136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1082587750180375,"score_gpt":0.329884974767969,"score_spread":0.2216261997499315,"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."}}