{"id":"W2954014300","doi":"","title":"一目でわかるクリニカルレシピ：「糖尿病の食事（合併症予防の食事）」","year":2018,"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":[0.0004397496,0.0004071951,0.0004175588,0.0002070121,0.0002521899,0.00003005473,0.0007002625,0.0004637805,0.01703164],"category_scores_gemma":[0.0001267373,0.0004055619,0.000127139,0.0003416223,0.0009529612,0.0002419315,0.0001296576,0.0009015829,0.006739374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000678535,"about_ca_system_score_gemma":0.00009288455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004945288,"about_ca_topic_score_gemma":0.00003459078,"domain_scores_codex":[0.9977676,0.00007081983,0.0004863383,0.0004550126,0.0003827193,0.0008375237],"domain_scores_gemma":[0.9988086,0.000110115,0.00005782021,0.0006218828,0.00008530215,0.0003162296],"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":[0.0002454395,0.0004757493,0.001140722,0.001218883,0.001745743,0.0009004713,0.01074461,0.00009578196,0.02385135,0.08150502,0.7377733,0.1403029],"study_design_scores_gemma":[0.003015148,0.000712816,0.001698713,0.0004020063,0.000419368,0.0002566444,0.002018879,0.03262229,0.02256149,0.02114108,0.913603,0.00154854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1772354,0.02251774,0.002358613,0.005849232,0.0108494,0.0005318547,0.0001026214,0.002562302,0.7779929],"genre_scores_gemma":[0.9923789,0.002552653,0.0007943286,0.0005340218,0.002311844,0.00002789196,0.00001986726,0.0000632399,0.001317265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8151435,"threshold_uncertainty_score":0.9998396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01772030216248074,"score_gpt":0.2674092177864826,"score_spread":0.2496889156240019,"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."}}