{"id":"W2745373312","doi":"","title":"早期大腸癌の内視鏡治療(EMR~ESD：内視鏡的粘膜下層剥離術)","year":2005,"lang":"ja","type":"article","venue":"Pharma Medica","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000423534,0.000444097,0.0004575319,0.0002277333,0.0001820464,0.00002998653,0.0006837395,0.0004825798,0.0111],"category_scores_gemma":[0.0001017012,0.0004564727,0.0001685969,0.0003586834,0.0003552656,0.0003565743,0.0001112952,0.001220082,0.003260506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001102802,"about_ca_system_score_gemma":0.00008469089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003100168,"about_ca_topic_score_gemma":0.00003748145,"domain_scores_codex":[0.9976336,0.00006647409,0.0005687948,0.0004501259,0.0004167417,0.0008641992],"domain_scores_gemma":[0.9988521,0.000132488,0.00005999843,0.0005733839,0.00004302367,0.0003389805],"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":[0.0001457238,0.0006447412,0.0008320603,0.001071324,0.001538513,0.0006840589,0.007036,0.003507689,0.01366233,0.04502337,0.581009,0.3448452],"study_design_scores_gemma":[0.002082064,0.000134973,0.0006027018,0.0002074412,0.0002563905,0.0001540215,0.0008895687,0.04079607,0.009340496,0.002926047,0.9416632,0.0009470329],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1954727,0.1104576,0.001543892,0.02351694,0.005559588,0.0007724768,0.0001512353,0.003699343,0.6588262],"genre_scores_gemma":[0.9862084,0.007981305,0.001207105,0.0007066918,0.001598887,0.00003978415,0.00002746299,0.00006595762,0.002164436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7907356,"threshold_uncertainty_score":0.9997887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509723361502533,"score_gpt":0.2592082271930458,"score_spread":0.2441109935780205,"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."}}