{"id":"W2752328034","doi":"","title":"テングハギ(Naso unicornis)の年齢と成長:ハワイ諸島における新しい核実験起源の放射性炭素年代の測定法","year":2016,"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; Geography","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":[0.0004554253,0.0003413505,0.0001914901,0.0008090475,0.001701497,0.0006733185,0.0003067369,0.0004590744,0.005011663],"category_scores_gemma":[0.0006744701,0.0002088672,0.0001602179,0.0007564382,0.001081435,0.0006023246,0.0007507667,0.0003082123,0.0008650883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009873083,"about_ca_system_score_gemma":0.0006966522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02515364,"about_ca_topic_score_gemma":0.06222929,"domain_scores_codex":[0.9997861,0.00003122645,0.00001864254,0.00005929809,0.00006557351,0.00003923411],"domain_scores_gemma":[0.999567,0.00007338011,0.0001310252,0.00002911492,0.0001148211,0.00008461834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001528843,0.0002888684,0.3518524,0.001011369,0.0001330291,0.004770124,0.006409623,0.0009998627,0.223194,0.03214304,0.008751651,0.3689172],"study_design_scores_gemma":[0.00006830485,0.001585706,0.6910217,0.0005459978,0.0005169769,0.01064209,0.01433114,0.00215251,0.07037592,0.01141861,0.1972076,0.0001334409],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8531926,0.005336525,0.001618171,0.001094914,0.0002493668,0.00006040786,0.0004091131,0.00006306099,0.1379759],"genre_scores_gemma":[0.965284,0.002486185,0.002870518,0.0004050922,0.00007395571,0.00004071629,0.0002803202,0.00001130573,0.02854781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02515364,"threshold_uncertainty_score":0.05001444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701915083153964,"score_gpt":0.200570821060513,"score_spread":0.1835516702289734,"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."}}