{"id":"W3091087283","doi":"10.14710/marj.v1i1.200","title":"KONDISI PERAIRAN BERDASARKAN BIOINDIKATOR MAKROBENTOS DI SUNGAI SEKETAK TEMBALANG KOTA SEMARANG","year":2012,"lang":"id","type":"article","venue":"Management of Aquatic Resources Journal (MAQUARES)","topic":"Marine and Coastal Ecosystems","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Physics; Forestry; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008816986,0.0009143275,0.00100705,0.00102967,0.001520162,0.004321283,0.0005594754,0.001303107,0.01092821],"category_scores_gemma":[0.0007339704,0.0004840594,0.001038307,0.0009714632,0.0007614557,0.001179053,0.001402092,0.002057886,0.003708496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347143,"about_ca_system_score_gemma":0.001964554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003689786,"about_ca_topic_score_gemma":0.007990198,"domain_scores_codex":[0.9990965,0.0001355932,0.00006066718,0.0002434554,0.0002998219,0.0001640168],"domain_scores_gemma":[0.9993187,0.0001329052,0.00009623577,0.00006509999,0.0002793148,0.0001076867],"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":[0.001468902,0.0009117471,0.02164964,0.003483152,0.0002597575,0.001076949,0.002136388,0.0009299847,0.7981002,0.004037118,0.005906361,0.1600398],"study_design_scores_gemma":[0.0001003861,0.003178177,0.09025506,0.0009594678,0.0005724482,0.002038226,0.005871851,0.00167118,0.5789192,0.002868485,0.3133959,0.000169684],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8146692,0.04805659,0.02305139,0.003872249,0.001979548,0.0007419093,0.003615003,0.0007990185,0.1032152],"genre_scores_gemma":[0.8438951,0.02119875,0.02042035,0.001615471,0.0002115581,0.0004917847,0.002470912,0.000225363,0.1094707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01092821,"threshold_uncertainty_score":0.03655851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01136941052052464,"score_gpt":0.2241309400376615,"score_spread":0.2127615295171369,"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."}}