{"id":"W2775860235","doi":"","title":"“Lessons learned” in WWI: The German Army, Vimy Ridge and the Elastic Defence in Depth in 1917","year":2017,"lang":"en","type":"article","venue":"Journal of military and strategic studies","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Offensive; German; Battle; Front (military); Law; Doctrine; Political science; Ridge; Counterattack; Engineering; Operations research; History; Ancient history; Geography; Archaeology; Cartography; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002093239,0.00008441733,0.000224332,0.00006991747,0.0004056708,0.00002026038,0.0002185343,0.0000342786,0.00001691663],"category_scores_gemma":[0.0006160991,0.00003716385,0.00003061862,0.00007344069,0.001047704,0.0001910628,0.00004881685,0.0005278587,0.000001630907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004909959,"about_ca_system_score_gemma":0.00003809499,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00286451,"about_ca_topic_score_gemma":0.0669347,"domain_scores_codex":[0.9989058,0.0003444805,0.0002512637,0.0001143798,0.0001706478,0.0002134412],"domain_scores_gemma":[0.998742,0.001001714,0.0000861922,0.0000947105,0.00002396312,0.00005142522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001575279,0.00002022092,0.9350456,0.00005446588,0.00004930267,0.000346931,0.007507478,0.000734524,0.000009158664,0.001453407,0.00002556725,0.05317808],"study_design_scores_gemma":[0.0006732248,0.0002391447,0.8142309,0.00006284082,0.000004329605,0.00004349798,0.004443168,0.001113751,0.000001328868,0.1791107,0.00003625174,0.00004087881],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9468858,0.04503321,0.000004103148,0.00670752,0.00006091628,0.0001197011,0.000003827391,0.000001023439,0.001183933],"genre_scores_gemma":[0.9524896,0.04734976,0.00004207044,0.00005247727,0.00004530503,0.00000161088,3.024113e-7,7.067898e-7,0.00001819963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1776573,"threshold_uncertainty_score":0.9500913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1302280392778477,"score_gpt":0.3589457139019912,"score_spread":0.2287176746241435,"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."}}