{"id":"W6945734126","doi":"10.25446/oxford.25780536","title":"51305: Preparation for occupying poorly draining, muddy trenches during late Autumn.","year":2024,"lang":"en","type":"other","venue":"University of Oxford","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Erosion; Margin (machine learning); Ditch; Natural (archaeology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001714236,0.0003461775,0.0004726698,0.0009130354,0.0001268928,0.0000359778,0.0003858505,0.0003566501,0.00168481],"category_scores_gemma":[0.00002522388,0.0004394898,0.0003083566,0.0002783299,0.0001459785,0.0001730677,0.0001618766,0.0002069759,0.0007264133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002431793,"about_ca_system_score_gemma":0.0001265875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004340499,"about_ca_topic_score_gemma":0.001366918,"domain_scores_codex":[0.99857,0.00004030987,0.0001834164,0.0005858627,0.0002645086,0.0003558595],"domain_scores_gemma":[0.9989892,0.00002987933,0.000432473,0.0003925089,0.00006111603,0.00009481139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005038076,0.00008435739,0.0002499763,0.001333526,0.0008835388,0.00007747972,0.008259328,0.0001111178,0.001593583,0.0008768092,0.9853568,0.0006696806],"study_design_scores_gemma":[0.001106582,0.000122593,0.0001871455,0.000721515,0.000462574,0.000003826049,0.001001246,0.0005820129,0.0001993311,0.0001964486,0.9949476,0.000469121],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01636862,0.0004729271,0.0006341601,0.00005496264,0.0004296551,0.0009910194,0.001417229,0.001216293,0.9784151],"genre_scores_gemma":[0.01949147,0.00004358865,0.003796071,0.000005167892,0.0001685489,0.000001036803,0.0002955424,0.0006079655,0.9755906],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.00959081,"threshold_uncertainty_score":0.9998057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01872203435550211,"score_gpt":0.2476059104785857,"score_spread":0.2288838761230836,"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."}}