{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004613846,0.000249643,0.0001117006,0.0009121074,0.005142159,0.006409958,0.0003130969,0.0006040035,0.009566572],"category_scores_gemma":[0.001198787,0.0001157283,0.00009105472,0.001609936,0.006293482,0.002634366,0.0008354397,0.001139009,0.001919056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005537294,"about_ca_system_score_gemma":0.00450183,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1965167,"about_ca_topic_score_gemma":0.4055019,"domain_scores_codex":[0.9996139,0.00009228988,0.00001158605,0.0000394898,0.0001757832,0.00006698263],"domain_scores_gemma":[0.999635,0.0001257106,0.00003008354,0.00003476631,0.0001097346,0.00006476459],"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":[0.00006140785,0.00002579032,0.002845898,0.0001551933,0.000008518836,0.000360878,0.07827172,0.0001685394,0.001534004,0.5658333,0.2474731,0.1032616],"study_design_scores_gemma":[0.000001127194,0.000004303767,0.001007163,0.00005200177,0.00000173487,0.0001251609,0.01083846,0.00004119256,0.0004699803,0.002785828,0.9846656,0.000007477574],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04639668,0.008349746,0.007001444,0.01647535,0.001222545,0.00003314291,0.0004473631,0.000258803,0.919815],"genre_scores_gemma":[0.569407,0.01043875,0.006588073,0.002752091,0.0002788166,0.00003296669,0.0004001888,0.0002947528,0.4098074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8034833,"threshold_uncertainty_score":0.3907457,"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."}}