{"id":"W4399194230","doi":"10.24319/jtpk.15.161-172","title":"DETERMINASI STRUKTUR STOK IKAN KEMBUNG LELAKI MENGGUNAKAN METODE PCR-RFLP DI WPP-NRI 711, 572, DAN 573","year":2024,"lang":"id","type":"article","venue":"Jurnal Teknologi Perikanan dan Kelautan","topic":"Aquatic life and conservation","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta","keywords":"Physics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001150937,0.001223131,0.001269012,0.001243422,0.0009002562,0.002154635,0.0007140004,0.0009870736,0.005396376],"category_scores_gemma":[0.001248449,0.001016993,0.001246581,0.001292721,0.0007721606,0.0008649345,0.000709086,0.001494607,0.003783498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009398597,"about_ca_system_score_gemma":0.001362403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005445991,"about_ca_topic_score_gemma":0.009423707,"domain_scores_codex":[0.9982105,0.0001992224,0.0001514389,0.0006150669,0.0006011379,0.0002224764],"domain_scores_gemma":[0.9990601,0.0003407783,0.0001308632,0.0001001624,0.0003152666,0.00005282696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000164871,0.0000649104,0.00577386,0.0003348776,0.00003358191,0.0001795536,0.0003852806,0.0002759875,0.9777925,0.0002832954,0.0002163868,0.01449503],"study_design_scores_gemma":[0.000012722,0.0003029919,0.02945474,0.0001102214,0.0001862411,0.0005207197,0.0007928095,0.001635185,0.9476286,0.000444433,0.01885863,0.00005269298],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8380737,0.006373697,0.124974,0.0005242547,0.000342753,0.0008627991,0.006629622,0.001060715,0.02115833],"genre_scores_gemma":[0.6804674,0.007543002,0.2184957,0.0006160304,0.00006402868,0.001495015,0.01719034,0.0008292857,0.07329914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005445991,"threshold_uncertainty_score":0.01805264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03162960308667546,"score_gpt":0.2596689527817231,"score_spread":0.2280393496950477,"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."}}