{"id":"W232431361","doi":"","title":"カナダ、コンフェデレーション詩人とマリタイムの神話--『エヴァンジェリン』との間テクスト性から","year":2009,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","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"],"consensus_categories":[],"category_scores_codex":[0.0003965035,0.000872417,0.0007208301,0.0003727981,0.001216466,0.0002581067,0.000946049,0.0007611556,0.0003124663],"category_scores_gemma":[0.0004382812,0.0009345335,0.0002940135,0.0008511631,0.0009272941,0.002024787,0.000214813,0.001401167,0.0005637147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005160038,"about_ca_system_score_gemma":0.0006058016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003635022,"about_ca_topic_score_gemma":0.0001080409,"domain_scores_codex":[0.9957939,0.00009738475,0.001123676,0.001044086,0.0008795314,0.001061356],"domain_scores_gemma":[0.9973747,0.0001739871,0.000165064,0.001630267,0.0002915908,0.0003643562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002465809,0.0004352689,0.0006839054,0.0003281918,0.0003968175,0.002283476,0.0004969661,0.005123865,0.01375166,0.9385902,0.03482592,0.002837148],"study_design_scores_gemma":[0.00384085,0.00140378,0.02384053,0.002402089,0.0008272778,0.003861348,0.001785571,0.008906606,0.03496115,0.04679288,0.8662655,0.005112355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2801922,0.05808228,0.01780024,0.005433241,0.03013531,0.001856075,0.005130513,0.005606435,0.5957637],"genre_scores_gemma":[0.9875339,0.001604486,0.005088823,0.0002995931,0.002595592,0.00004017169,0.001460912,0.0000484554,0.001328027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8917973,"threshold_uncertainty_score":0.9993105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218965169538471,"score_gpt":0.2342423099605933,"score_spread":0.2220526582652086,"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."}}