{"id":"W4400162063","doi":"10.25071/2561-5467.1215","title":"Ken W. Sayers, U.S. Navy Minecraft. A History and Directory from World War I to Today by Rob Dienesch","year":2024,"lang":"en","type":"article","venue":"The Northern Mariner / Le marin du nord","topic":"Military History and Strategy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Directory; Navy; World War II; History; Political science; Computer science; Operating system; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0003490196,0.000696463,0.0003319688,0.002057423,0.002184019,0.001947582,0.0004090232,0.0009932119,0.1304632],"category_scores_gemma":[0.0008575411,0.0003064823,0.0001550274,0.002558593,0.0004019605,0.001901787,0.0009763179,0.001393686,0.06266075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008308597,"about_ca_system_score_gemma":0.001982049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03010137,"about_ca_topic_score_gemma":0.1109184,"domain_scores_codex":[0.9998178,0.00002368849,0.00001225074,0.00003240742,0.0000864747,0.00002730421],"domain_scores_gemma":[0.9991842,0.0001138825,0.00007994638,0.0000246617,0.0003358607,0.0002613894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000006126851,0.000004642775,0.0002968716,0.0000486326,7.043892e-7,0.0000174097,0.0001093775,0.000008164192,0.00003449295,0.0003938991,0.9656285,0.03345117],"study_design_scores_gemma":[0.00000154942,0.000004174131,0.001381278,0.0001397242,0.000001056211,0.00004161323,0.0004804754,0.000007350189,0.00004567126,0.0001393643,0.9977549,0.000002774299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.003589527,0.4011698,0.000637166,0.1388981,0.03291204,0.00009253181,0.007420329,0.001037209,0.4142433],"genre_scores_gemma":[0.007517538,0.08803787,0.0003965531,0.004667085,0.001338696,0.00003953423,0.00132393,0.0001835318,0.8964953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1304632,"threshold_uncertainty_score":0.4364429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444400230998895,"score_gpt":0.2315904632696928,"score_spread":0.2171464609597039,"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."}}