{"id":"W2746614457","doi":"","title":"環境要因を統合した季節的サイズ構成評価モデルを用いたタラバエビ属の資源評価の改善 その2:モデルの評価とシミュレーション","year":2017,"lang":"ja","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Biology; Ecology","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":[0.001686357,0.0002208031,0.000191579,0.0009418004,0.003270947,0.004563223,0.0006097134,0.001053153,0.0157773],"category_scores_gemma":[0.003385802,0.0001938479,0.0002203421,0.000777035,0.008106097,0.002619168,0.001044675,0.001131927,0.001895501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006366514,"about_ca_system_score_gemma":0.00741034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08217803,"about_ca_topic_score_gemma":0.08667117,"domain_scores_codex":[0.9991186,0.0001064106,0.00004203079,0.0001723404,0.0004199359,0.0001406216],"domain_scores_gemma":[0.9982283,0.0003693428,0.0001769348,0.0000984432,0.0008663828,0.0002606211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001012234,0.00008030744,0.01109023,0.0002029895,0.00002592904,0.0004633187,0.01197514,0.0007194632,0.002085,0.7728132,0.02095266,0.1794906],"study_design_scores_gemma":[0.00003216245,0.0001218962,0.04514091,0.0004056387,0.00008272438,0.0007068671,0.02456978,0.001439847,0.005127663,0.3919525,0.5303072,0.0001127884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1094155,0.007042833,0.01516941,0.02372083,0.001111341,0.0001199407,0.0002647297,0.00007609644,0.8430793],"genre_scores_gemma":[0.8653976,0.003221643,0.006156758,0.001457613,0.0003445312,0.00003623505,0.00008660117,0.00002757377,0.1232715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08217803,"threshold_uncertainty_score":0.1633994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02336408149617876,"score_gpt":0.2220710389523009,"score_spread":0.1987069574561222,"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."}}