{"id":"W2745805241","doi":"","title":"海洋-由来栄養素の成り行き:貧栄養的な淡水で炭素13と窒素15を追跡しサケ死体アナログ添加後に河川生態系にリンクさせた","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","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":[0.002707392,0.0002772123,0.0002278249,0.001024171,0.003711461,0.005794701,0.0006544683,0.00107495,0.01825562],"category_scores_gemma":[0.003162147,0.0003267733,0.000433297,0.0007571111,0.007135228,0.003480234,0.001408406,0.001300752,0.001909667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007058911,"about_ca_system_score_gemma":0.009073094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04671724,"about_ca_topic_score_gemma":0.05409476,"domain_scores_codex":[0.9988084,0.000184611,0.00007426435,0.0002010662,0.0004468218,0.0002847441],"domain_scores_gemma":[0.9980621,0.0004234336,0.0003008237,0.00009737524,0.0008163991,0.0002999314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003607306,0.0002071902,0.02676779,0.0002824785,0.0000791229,0.0008353952,0.02029543,0.001280509,0.006175612,0.7892336,0.01123309,0.1432491],"study_design_scores_gemma":[0.00008249749,0.0004497263,0.1022472,0.0005850638,0.0001715839,0.0009760437,0.05241235,0.00235854,0.0242777,0.4723665,0.3438352,0.0002376277],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2913329,0.004749057,0.01639587,0.009491382,0.0005520331,0.0001928304,0.0003778015,0.00008646712,0.6768217],"genre_scores_gemma":[0.9202591,0.001062314,0.007246841,0.0007589802,0.00008054177,0.00005447967,0.0001088418,0.00003562765,0.07039326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04671724,"threshold_uncertainty_score":0.09289062,"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."}}