{"id":"W330683372","doi":"10.1016/j.ecolind.2015.04.011","title":"Using plane tree leaves for biomonitoring of dust borne heavy metals: A case study from Isfahan, Central Iran","year":2015,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Lichen and fungal ecology","field":"Agricultural and Biological Sciences","cited_by":120,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Isfahan University of Technology; University of Alberta","keywords":"Biomonitoring; Bioindicator; Environmental science; Pollution; Environmental chemistry; Atmospheric dust; Air pollution; Atmospheric pollution; Sampling (signal processing); Heavy metals; Chemistry; Aerosol; Ecology; Biology; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004967768,0.0002183101,0.0005060838,0.00004284232,0.0002156354,0.00004069674,0.0003057471,0.000268308,0.0002743017],"category_scores_gemma":[0.0002966687,0.00008754637,0.0001467295,0.0004186028,0.0001549024,0.0001100679,0.0001458964,0.0001735379,0.00001163368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008819362,"about_ca_system_score_gemma":0.00002682871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002299527,"about_ca_topic_score_gemma":0.00316327,"domain_scores_codex":[0.9981161,0.0002236244,0.0004730589,0.0004540353,0.0001969757,0.0005361494],"domain_scores_gemma":[0.9985926,0.0006673322,0.0002417686,0.00008671522,0.0000481337,0.0003634378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002774612,0.002327221,0.9670966,0.000007061038,0.0002034721,0.0006435753,0.001297891,0.00001641447,0.02047023,0.00009505345,0.0004780867,0.007086899],"study_design_scores_gemma":[0.0012476,0.004123573,0.9661772,0.000007607326,0.0001642204,0.0001062364,0.01807656,0.0001667145,0.007601024,0.0003343487,0.001604735,0.0003902236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979841,0.0001386572,0.000008464547,0.000224459,0.0005539587,0.0006903964,0.0001790453,0.00006807952,0.0001528619],"genre_scores_gemma":[0.99879,0.000003700685,0.0004413059,0.00008238432,0.0005629291,0.00004474384,0.00003798037,0.000002257587,0.00003467483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01677867,"threshold_uncertainty_score":0.3570037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1502585332426616,"score_gpt":0.3210712437962411,"score_spread":0.1708127105535796,"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."}}