{"id":"W2753103283","doi":"","title":"カナダ海洋資源に関する統合データベースに向けて:便益,現状及び研究ギャップ","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":"Ecology; Biology; Environmental 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":[0.002862412,0.0002538416,0.0002163715,0.0009943504,0.002809766,0.005557704,0.0005642258,0.001450727,0.01136561],"category_scores_gemma":[0.0044743,0.0002044163,0.000230356,0.0008354345,0.009008518,0.003739745,0.001065972,0.001324436,0.001564001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00453757,"about_ca_system_score_gemma":0.005735065,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02389832,"about_ca_topic_score_gemma":0.02140957,"domain_scores_codex":[0.9988272,0.0002143612,0.00006639911,0.0002364475,0.0004637207,0.0001918328],"domain_scores_gemma":[0.9979663,0.0004915643,0.0002925539,0.0001282989,0.000863678,0.0002576783],"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":[0.00009712811,0.00008159685,0.01212614,0.0002168847,0.00002864759,0.0005464256,0.009717101,0.0004901929,0.001804078,0.8475561,0.01564365,0.1116919],"study_design_scores_gemma":[0.00003698064,0.0001328935,0.04224248,0.0003607528,0.0000814833,0.001145597,0.02460689,0.001347866,0.004063731,0.5647744,0.3610964,0.0001104522],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1902862,0.01456357,0.0181049,0.05979756,0.001815693,0.0001162592,0.0003538014,0.00008821198,0.7148737],"genre_scores_gemma":[0.9288542,0.00354761,0.004559542,0.001663996,0.0004103572,0.00004039946,0.00006932932,0.00002336171,0.06083116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9761017,"threshold_uncertainty_score":0.04751843,"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."}}