{"id":"W3190296772","doi":"","title":"P1 多理論モデルに基づく局所食品の若年成人の認識への定性的アプローチ【JST・京大機械翻訳】","year":2019,"lang":"ja","type":"article","venue":"Journal of Nutrition Education and Behavior","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003966013,0.0002794895,0.0002179401,0.0004282579,0.0009270572,0.001297713,0.0003905679,0.0006425073,0.04424636],"category_scores_gemma":[0.001796466,0.0001851472,0.0002338373,0.0003106688,0.0007394602,0.0009694074,0.0004255794,0.001132473,0.007088594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000588422,"about_ca_system_score_gemma":0.0008603836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00329194,"about_ca_topic_score_gemma":0.002343385,"domain_scores_codex":[0.9997713,0.00002836499,0.00001291376,0.00006949306,0.00007876647,0.00003914809],"domain_scores_gemma":[0.9994335,0.0001468527,0.0000717622,0.00003969159,0.000223091,0.00008516106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.002258355,0.0009485291,0.03413987,0.0007845964,0.0001427017,0.003049724,0.003075326,0.000758319,0.2493043,0.05113277,0.03219703,0.6222084],"study_design_scores_gemma":[0.0002601344,0.002381188,0.2110636,0.0003835045,0.0002846413,0.01052799,0.005150125,0.004312786,0.3778998,0.0955609,0.2920055,0.0001698322],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5129052,0.003140069,0.01839403,0.005797431,0.001164789,0.0003281363,0.001404209,0.0003356855,0.4565305],"genre_scores_gemma":[0.8851584,0.001508267,0.005736173,0.0005169885,0.0002843313,0.000131696,0.0004520013,0.00007798088,0.1061341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04424636,"threshold_uncertainty_score":0.1480189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021966590140139,"score_gpt":0.2610370213728911,"score_spread":0.2508173554714898,"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."}}