{"id":"W4409598670","doi":"10.20527/fishscientiae.v14i2.236","title":"KAJIAN POTENSI LAHAN BASAH MANGROVE SEBAGAI AGEN FITOREMEDIASI LOGAM BERAT DI DESA SUNGAI MUSANG","year":2024,"lang":"id","type":"article","venue":"Fish Scientiae","topic":"Heavy Metal Pollution Remediation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mangrove; Chemistry; Environmental science; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00009633684,0.0002803387,0.0001239462,0.0002835596,0.0009451516,0.000453306,0.0001458833,0.0001362824,0.002831457],"category_scores_gemma":[0.00006971164,0.0001093993,0.0001363553,0.0002836218,0.0002105435,0.0002765268,0.0003479021,0.0001975149,0.0002784894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003403688,"about_ca_system_score_gemma":0.0007487205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006279292,"about_ca_topic_score_gemma":0.04396703,"domain_scores_codex":[0.9999593,0.000004926832,0.000002407525,0.00001104662,0.00000892831,0.00001334874],"domain_scores_gemma":[0.9999249,0.000008711917,0.00001647804,0.000006508158,0.0000229574,0.00002036859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004271253,0.0004870441,0.4363082,0.0007003269,0.0001112562,0.003441938,0.007453758,0.0007344537,0.3289876,0.00184196,0.001879215,0.2176271],"study_design_scores_gemma":[0.00001911216,0.0003280153,0.9436667,0.00005953151,0.0000760221,0.001359582,0.006743524,0.0006013634,0.01623474,0.0003156099,0.0305705,0.00002524981],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930624,0.000321284,0.0004062355,0.00008497239,0.00001134361,0.0000296655,0.00008796861,0.00002060668,0.005975513],"genre_scores_gemma":[0.9891606,0.0003808881,0.001621824,0.00004174268,0.000006578451,0.00002994183,0.0001600548,0.000005934628,0.00859254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006279292,"threshold_uncertainty_score":0.0124855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01354502821643431,"score_gpt":0.2409059400094801,"score_spread":0.2273609117930458,"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."}}